extfieldsofstudy: Medicine Biology

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Mitochondrial DNA somatic mutation burden and heteroplasmy are associated with chronological age, smoking, and HIV infection

Abstract The gradual accumulation of mitochondrial DNA (mtDNA) mutations is implicated in aging and may contribute to the accelerated aging phenotype seen with tobacco smoking and HIV infection. mtDNA mutations are thought to arise from oxidative damage; however, recent reports implicate polymerase γ errors during mtDNA replication. Investigations of somatic mtDNA mutations have been hampered by technical challenges in measuring low‐frequency mutations. We use primer ID‐based next‐generation sequencing to quantify both somatic and heteroplasmic blood mtDNA point mutations within the D‐loop, in 164 women and girls aged 2–72 years, of whom 35% were smokers and 56% were HIV‐positive. Somatic mutations and the occurrence of heteroplasmic mutations increased with age. While transitions are theorized to result from polymerase γ errors, transversions are believed to arise from DNA oxidative damage. In our study, both transition and transversion mutations were associated with age. However, transition somatic mutations were more prevalent than transversions, and no heteroplasmic transversions were observed. We also measured elevated somatic mutations, but not heteroplasmy, in association with high peak HIV viremia. Conversely, heteroplasmy was higher among smokers, but somatic mutations were not, suggesting that smoking promotes the expansion of preexisting mutations rather than de novo mutations. Taken together, our results are consistent with blood mtDNA mutations increasing with age, inferring a greater contribution of polymerase γ errors in mtDNA mutagenesis. We further suggest that smoking and HIV infection both contribute to the accumulation of mtDNA mutations, though in different ways.

| INTRODUC TI ON
Mitochondria contain their own DNA, which code for 22 tRNAs, 2 rRNAs, and 13 proteins that are essential components of the electron transport chain. Mitochondrial DNA (mtDNA) mutations, whether inherited or acquired, have been linked to several degenerative diseases, cancers, and aging (Polyak et al., 1998;Wallace, 1999). The free radical theory of aging proposes that mtDNA, given its close physical proximity to the electron transport chain, is susceptible to oxidative damage by reactive oxygen species (ROS), leading to mtDNA damage as well as other cellular injury (Gerschman, Gilbert, Nye, Dwyer, & Fenn, 1954;Harman, 1956;Miquel, Economos, Fleming, & Johnson, 1980). Therefore, it was often assumed that ROS would increase newly acquired (somatic) mtDNA mutations, as opposed to inherited ones. However, more recent studies suggest that somatic mtDNA mutations may be predominantly introduced through replication errors by mitochondrial polymerase γ during mtDNA replication (Kennedy, Salk, Schmitt, & Loeb, 2013;Trifunovic et al., 2004). Over time, these low-frequency mutations may undergo clonal expansion and reach the pathologic levels that contribute to aging (Payne et al., 2011). When more than one mtDNA species is observed, it is referred to as heteroplasmy.
Although the association between mtDNA mutations and aging is well accepted, few studies have been able to examine the dynamics of somatic and heteroplasmic mtDNA mutations in a large cohort, in part because detecting and quantifying de novo somatic mtDNA mutations is technically challenging.
A previous study has proposed that people acquire mtDNA mutations at a rate of 6 per 10 8 bp per year (Marcelino & Thilly, 1999).
If this is true, an assay with an extremely low background error rate would be required to measure the random accumulation of such rare mutations. Currently, only primer ID (PID)-based sequencing methods (Hiatt, Patwardhan, Turner, Lee, & Shendure, 2010;Jabara, Jones, Roach, Anderson, & Swanstrom, 2011) have achieved sufficiently low background rates (e.g., ≤10 -5 mutations per bp) for detection of somatic mtDNA mutations. Above this threshold, detected mutations are more likely to be inherited or heteroplasmic, having arisen from clonal expansion of previously existing mutations.
Given this, the definition of somatic versus heteroplasmic mtDNA mutations in the current literature may be inconsistent. Within the current study, mtDNA mutations refer to mutations present on the single strand analyzed.
People living with HIV (HIV-positive) appear to experience premature aging (Bhatia, Ryscavage, & Taiwo, 2012). Most studies report a decrease in lifespan of ≥10 years among HIV-positive individuals compared to HIV-negative controls (Antiretroviral Therapy Cohort Collaboration, 2008), as well as earlier onset and higher prevalence of age-related comorbidities (Guaraldi et al., 2011), such as cardiovascular disease (Hsue et al., 2004), some non-AIDS-defining cancers (Shiels, Pfeiffer, & Engels, 2010), neurocognitive decline (Ances et al., 2010), and osteoporosis (Serrano et al., 1995). Previous studies on mtDNA mutation burden in HIV have had limited sample size and focused on HIV and/or HIV therapy (Martin et al., 2003;Payne et al., 2011) without considering other potential confounders.
Larger studies have either concentrated on heteroplasmic mtDNA mutations (Li et al., 2017) or used assays not sensitive enough to detect low-frequency somatic mutations (Jitratkosol et al., 2012).
Tobacco smoking is associated with accelerated aging (Bernhard, Moser, Backovic, & Wick, 2007). Smoking increases oxidative damage, which is in turn hypothesized to cause acceleration of aging (Kiyosawa et al., 1990;Loft et al., 1992). However, no next-generation sequencing (NGS) studies have examined the association between mtDNA mutations and smoking to date. Using other methods, increased mtDNA heteroplasmy has been linked to smoking (Lewis, Fradley, Griffiths, Baxter, & Parry, 2002;Tan et al., 2008), but evidence for mtDNA somatic point mutations is generally lacking (Coller et al., 1998). Again, these studies focused on smoking and were not powered to consider other factors that may be associated with mtDNA mutations.
The goal of the present study was to use a low background NGS assay to measure both somatic and heteroplasmic mtDNA mutation burden in relation to aging, taking into consideration important confounders of the aging process. We hypothesized that somatic mtDNA mutations increase with age, smoking, and HIV infection.  Figure S1 in our Supporting Information. The assay's background error rate was calculated based on the repeated inclusion (n = 12) of a single cloned plasmid DNA control that functioned as a "no mutation" negative control for the assay. The assay's background somatic mtDNA substitution frequency was estimated at 0.063 [0.00-0.24] or 0.01 mutations per mtDNA genome, which was significantly lower than clinical samples (p < .0001; Figure S2). The assay's coefficient of variation was estimated at 6.3%, based on the inclusion of the same clinical sample in both runs (total n = 3).

| Blood somatic mtDNA substitution frequencies
The primary finding of our study is that these somatic mtDNA substitution mutations were significantly associated with age (p < .0001), whereby older individuals showed higher mutation frequencies ( Figure 1). This association with age was seen among all participants (n = 138, r = .37, p < .001; Figure 1) and adult participants (n = 113, r = .27, p = .025; Figure 1). Furthermore, transition mutations, namely A ↔ G and C ↔ T mutations, were significantly more frequent than transversion mutations (A ↔ C, G ↔ T, T ↔ A, and C ↔ G) (p < .0001; Figure   S3b). Similar frequencies and types of mutations were seen in both HIV groups ( Figure S3d). This remained true upon trichotomizing the groups according to their HIV status and peak HIV pVL ( Figure S3e). The background substitutions, among the 12 cloned plasmids, also consisted primarily of transition mutations ( Figure S3a).
Among participants who had nonzero somatic mtDNA substitution frequencies (n = 160), plotting each type of mutation against age individually reveals that both transition (n = 159, ρ = .38, p < .0001) and transversion (n = 31, ρ = .42, p = .020) Note: Data presented as median [IQR] (range) or n (%); ethnicity is unknown for all pediatric HIV-negative participants; cART, combination antiretroviral therapy; PI, protease inhibitor; NNRTI, non-nucleoside reverse transcriptase inhibitor; BMI, body mass index is not presented for groups that include pediatric participants. BMI was unknown for 5 adults, current CD4+ cell count was unknown for 1 adult. pVL, HIV plasma viral load < 50 copies/ml is "undetectable." Current pVL was unknown for 2 adults. Mann-Whitney test was used for age, alcohol use, and BMI. Chi-square test was used for smoking status, drug use, and ethnicity. somatic mutation burden were positively associated with age ( Figure 2b,d), after removing participants with a corresponding mutation (transition or transversion) frequency equal to zero, respectively.
Having established the association between somatic mtDNA substitution frequency and age, we then investigated whether HIV, a chronic infection, was associated with increased mtDNA mutation frequencies. HIV-positive status did not show any association with somatic mtDNA substitution frequencies univariately (Table S1). However, HIV-positive participants with a peak pVL ≥ 100,000 copies/ml had a higher somatic mtDNA substitution frequency than HIV-negative participants (p = .048; Figure 1d and Table S1). Other variables investigated for possible inclusion in these models were ethnicity and BMI. Neither showed an association (p ≥ .1) with the somatic mtDNA substitution frequency (Table S1). Of note, there was also no evidence of an association between somatic mtDNA substitution frequency and smoking.
Similarly, among HIV + adults, only HIV peak pVL and CD4 nadir showed some association (Table S1).

| Multivariable correlates of somatic mtDNA substitution frequency
In a linear regression model of adult participants (n = 139, R 2 = .107) that included age, smoking status, and trichotomized HIV status, both older age and having HIV with pVL ≥ 100,000 copies/ml (vs. HIV−negative) remained independently associated with somatic mtDNA mutation burden, although the latter exhibited a large confidence interval ( Figure 1h). The same model among all participants (n = 164, R 2 = .159; Figure 1g) only showed age as being associated with somatic mtDNA substitution frequencies. Our models describe 11% and 16% of the variance in somatic mtDNA substitutions among adult and all participants, respectively.

| Blood mtDNA heteroplasmic substitution frequencies
Heteroplasmy was detected in 39 of the 164 participants, and information regarding heteroplasmic positions is provided in Table   S2. All heteroplasmic variants were transition mutations, and the pattern of the heteroplasmic positions was different for most participants. HIV-specific variables were also explored and again showed no association (Table S3). Only smoking status was associated with the occurrence of heteroplasmy among all (p = .007; Figure 3a), but not adult-only (p = .055; Figure 3d) participants in univariate analysis (Table S2). An interaction was noted between age and smoking status with respect to the presence of mtDNA heteroplasmy, whereby the frequency of heteroplasmy increased with age among nonsmokers, but decreased with age among smokers ( Figure 4a). Binary logistic regression models were built that included age, trichotomized HIV and peak pVL status, smoking status, and an age* smoking interaction term, for all participants (n = 164, R 2 = .339; Figure 3g) and adult-only participants (n = 139, R 2 = .31; Figure 3h). In both models, smoking F I G U R E 1 Older chronological age and high peak HIV viral load are associated with increased blood somatic mtDNA substitutions. The measured mtDNA mutation frequencies (expressed as mutations per 10,000 bp) are presented (a-f) and p-values for both raw and transformed (ln [x + 1]) data are shown. (a, b) Blood somatic mtDNA substitutions are positively correlated with chronological age among all participants (a) and adult participants (b). (c, d) With respect to HIV, only adult participants with a peak HIV plasma viral load ≥ 100,000 copies/ml showed a marginally higher somatic mtDNA substitution frequency compared to HIV-negative controls. (e, f) Tobacco smoking was not associated with somatic mtDNA mutation substitutions. (g, h) Forest plot showing the estimated size of the effect (β value) and the 95% confidence interval on that estimate, based on an analysis of covariance for all (R 2 = .159) and adult (R 2 = .107) participants. The models suggest that older age remains independently associated with increased somatic mtDNA mutations, after adjusting for HIV and smoking. Furthermore, among adults, having a peak pVL ≥ 100,000 copies/ml is also associated with a higher mtDNA mutation frequency although the 95% confidence interval is wide. All univariate tests on raw data were nonparametric and used ( independently associated with heteroplasmy. Older age was also independently associated with heteroplasmy (p = .008) among all participants ( Figure 3g). These models describe 31% and 34% of the variance in mtDNA heteroplasmy among adult and all participants, respectively. In subanalyses within smoker groups, nonsmokers showed a significant increase in heteroplasmy with increasing age (p = .004; Figure 4a,c), while current smokers showed the reverse, namely a decrease in heteroplasmy with age (p = .025, Figure 4a,b).
F I G U R E 3 Older chronological age and tobacco smoking are associated with increased blood mtDNA heteroplasmy. Heteroplasmy was categorized as a yes/no variable. (a, b) The occurrence of heteroplasmy is not univariately associated with chronological age. (c, d) No association is observed between HIV and the occurrence of heteroplasmy. (e, f) Among all participants, current smokers show an increased occurrence of heteroplasmy (e); however, this difference is not seen among adult participants (p = .055) (f). (g, h) Forest plot showing the estimated size of the effect (β value) and the 95% confidence interval on that estimate, based on binary logistic regressions of all (R 2 = .339) and adult (R 2 = .308) participants. The model among all participants suggests that older age and smoking are associated with the occurrence of heteroplasmy (g); however among adults, smoking showed an association with heteroplasmy (h). Furthermore, a significant interaction between smoking and age is observed in both models In an effort to independently validate this observation, data were obtained from an independent cohort at Yale New Haven Hospital (Li et al., 2017). After removing past smokers and participants with unknown smoking status, the narrow age range 53 [50-56] (30-66), and smaller sample size (n = 40) of the remaining participants made repeating our models within this separate cohort difficult ( Figure   S4). When the two cohorts were combined (results presented in Supporting Information), heteroplasmy was independently associated with older age (p < .001), smoking (p < .001), and the same age*smoking interaction term described above ( Figure S5g,h) (p < .001), among all participants (R 2 = .24, n = 204; Figure S5g) and all adults (R 2 = .18, n = 159; Figure S5h). Further exploring the interaction term ( Figure S6a), participants who currently smoked showed a nonsignificant trend toward lower heteroplasmy while they aged (p = .098; Figure S6b), and participants who never smoked had more heteroplasmy as they aged (p < .001, Figure S6c). While HIV status was univariably associated with heteroplasmy, we were unable to include it in the final multivariable models, due to the lack of peak pVL data for half of the HIV + participants in the Yale New Haven Hospital cohort.

| Somatic mutations
We quantified somatic mtDNA substitution burden in human blood for the first time and show that somatic mtDNA substitutions increase with age. Our results therefore support the general theory that mtDNA mutations accumulate with age (Harman, 1956;Michikawa, Mazzucchelli, Bresolin, Scarlato, & Attardi, 1999). Age alone explained 14% of the variance in somatic mtDNA mutation frequency and remained the strongest independent predictor after adjusting for covariates. Our study builds on the work of a dual primer ID-based study showing higher mtDNA somatic mutation frequencies in the brain of five elderly individuals compared to five infants (Kennedy et al., 2013). Of note, that study suggested that mutations accumulate asymmetrically on the two strands of mtDNA and that they increase with age on both strands (Kennedy et al., 2013). As our single PID method measured putative somatic mtDNA mutations on one mtDNA strand, we cannot ascertain whether these variants are present in the other strand. It is possible that some of our signal F I G U R E 4 Current tobacco smokers experience a decrease in heteroplasmy with older age, while never smokers show the opposite. Heteroplasmy was categorized as a yes/no variable. (a) Percentage of study participants with heteroplasmy binned by age and smoking status, with total number of participants for each bin (above bar), and percentage of those participants (bar height). (b, c) Binary logistic regression models among current smokers (R 2 = .225) and never smokers (R 2 = .155) to further understand the effect of age on heteroplasmy. Older age is associated with a decrease in the occurrence of heteroplasmy among current smokers (b), while the reverse is seen for never smokers (c). This indicates an interaction whereby the effect of age on heteroplasmy is modulated by smoking status is due to chemical lesions that could be repaired or become a double-stranded mutation. Nevertheless, our observation that these increase with age is consistent with the dual PID study (Kennedy et al., 2013).
We did not observe this age effect in our previous study restricted to mtDNA transversion (A ↔ C & T ↔ G) mutations in the blood of HIV-positive and HIV-negative mothers and their infants (Jitratkosol et al., 2012). This is likely related to the low sample size, the young age of the mothers, and the relative scarcity of transversion mutations seen to increase with age in our own study.
Indeed, with respect to the nature of the substitutions, transition mutations (A ↔ G, C ↔ T) were the most frequently observed, while transversion mutations (A ↔ C, A ↔ T, C ↔ G, G ↔ T) were rarely seen in our study, echoing the results seen in the above brain mtDNA study (Kennedy et al., 2013). Transition mutations are generally believed to arise from polymerase γ errors (Longley, Nguyen, Kunkel, & Copeland, 2001;Spelbrink et al., 2000), while transversion mutations are considered the signature of DNA oxidative damage.
The 7,8-dihydro-8-oxo-deoxyguanosine (8-oxodG) lesion (Yasui et al., 2014), one of the most commonly studied DNA oxidative lesions, has been shown to increase with age (Fraga, Shigenaga, Park, Degan, & Ames, 1990). However, several other lesions may also lead to transition mutations (Basu, Loechler, Leadon, & Essigmann, 1989;Kreutzer & Essigmann, 1998), and 8-oxodG is only one of the 37 reported major products of oxidative damage to DNA (Evans, Dizdaroglu, & Cooke, 2004). Therefore, although our results do support the growing body of literature suggesting that polymerase γ errors are the major source of age-related mtDNA mutations and variants (Kennedy et al., 2013;Trifunovic et al., 2004), we cannot rule out the potential contribution of oxidative damage as a source of mtDNA transition mutations in our study (Fraga et al., 1990;Harman, 1956;Schriner et al., 2005). In fact, among the subset of participants (19%) with detectable somatic transversion mutations, these mutations showed a weak but significant increase with age.
In addition to the effect of chronological age on somatic mtDNA substitutions, we sought to explore whether these were influenced by other factors including tobacco smoking and chronic immune activation/inflammation as seen among people living with HIV. In our sample, we failed to detect any association between somatic mtDNA substitutions and smoking. To date, no study of smoking and somatic mtDNA mutations has been conducted in blood; however, a small (n = 4 vs. 4) study of bronchial epithelial tissue also failed to detect any association with smoking, although the study was not sequencing-based (Coller et al., 1998).
With respect to HIV, we only detected a significantly increased somatic mtDNA substitution burden among participants who had a HIV-negative controls (n = 4) (Payne et al., 2011). Our study builds on these by suggesting that HIV status alone is not necessarily associated with somatic mtDNA mutation frequency, as those with a peak pVL < 100,000 copies/µl had comparable mutation frequencies to HIV-negative individuals. Higher peak pVL may reflect poorer host control of the virus or delayed initiation of cART, both of which would increase exposure to HIV viremia and immune activation, and could potentially lead to mtDNA mutations.
Given that mtDNA mutations are estimated to accumulate at a frequency of one per 6 × 10 8 bp per year (Marcelino & Thilly, 1999),

| Heteroplasmy
The majority of studies to date have measured mtDNA heteroplasmy, defined herein as a given mutation present at >2% frequency for a given position. We report a positive relationship between older age and the presence of heteroplasmic point mutations, consistent with the literature (Michikawa et al., 1999;Munscher, Muller-Hocker, & Kadenbach, 1993).
Furthermore, unlike somatic mutations, mtDNA heteroplasmy was significantly more prevalent among participants who were current smokers as opposed to never smokers. We also failed to detect any heteroplasmy among pediatric participants. There is an extensive body of literature linking smoking and oxidative DNA damage (Kiyosawa et al., 1990;Loft et al., 1992). Two non-sequencing-based studies suggest that mtDNA heteroplasmy is increased in the parotid gland (Lewis et al., 2002) and buccal cells (Tan et al., 2008) of tobacco smokers. This association of smoking with mtDNA heteroplasmy, along with the lack of association with somatic mtDNA substitutions, suggests that smoking promotes the clonal expansion of mtDNA mutations rather than the generation of de novo somatic mutations. In support of this, both increased leukocyte count (Petitti & Kipp, 1986) and mtDNA content fluctuations (Lee, Lu, Fahn, & Wei, 1998) have been associated with tobacco smoking.
The observed interaction between age and smoking with respect to mtDNA heteroplasmy, whereby the occurrence of heteroplasmy increases with age among nonsmokers but decreases among smokers has not been reported. Although our study cannot ascertain the mechanism behind this, we speculate that the higher heteroplasmic mtDNA mutation burden, or related factors in smokers, may more readily lead to the elimination of damaged cells carrying heteroplasmic mtDNA mutations. This highlights the importance of considering lifestyle confounders that may influence the frequency of detected mtDNA mutations. Unlike smoking, we did not find evidence that stressors associated with living with HIV lead to increased heteroplasmy. However, larger studies are needed to confirm this, especially since the vast majority of our participants living with HIV were treated with antiretroviral therapy and had well-controlled HIV viremia.
Although we were unable to replicate the observed interaction between smoking and age in the Yale New Haven Hospital cohort alone, this was attributed to the narrow age range and lower sample size. When the CARMA cohort was combined with the Yale New Haven Hospital cohort, we were able to fully replicate the observations seen in the CARMA cohort alone. Large cohorts with a wide age range, information on smoking status, and next-generation mtDNA sequencing data are very uncommon. In context, our study builds upon current knowledge on smoking and mtDNA mutation by utilizing primer ID NGS and a large cohort of individuals with a wide age range; however, further studies will be needed to confirm this interaction in an independent cohort.
To date, two small studies (Martin et al., 2003;Payne et al., 2011) and one larger study (Li et al., 2017)  In our study, we found no association between HIV status and mtDNA heteroplasmy. Although the presence of heteroplasmy was somewhat more frequent among persons with a high HIV peak pVL, this did not reach significance in any model. A longitudinal study is required to confirm whether somatic mtDNA mutations, which we found more frequently among those with high peak pVL, eventually lead to heteroplasmy over time. Our study was not designed or powered to investigate the effect of cART.

| Strengths and limitations
Our study has several strengths: (a) The use of a novel mtDNA substitution burden assay allows the quantification of very low-frequency mutations in blood cells; (b) a large sample size facilitated multivariable modeling; (c) the broad age range of our participants, the similar behavioral characteristics between HIV groups, and the exclusion of other chronic infections (HCV and HBV) enhanced our ability to detect independent associations with age, smoking, and/or HIV.
However, the study has some limitations. As this is an observational cross-sectional study, we cannot infer causality. Furthermore, we cannot tease apart the possible effect of HIV versus that of cART. Moreover, our analysis was restricted to mtDNA substitutions; hence, we cannot comment on mtDNA insertions and deletions. Since our analysis was restricted to the D-loop region, we cannot generalize the observed mutation rates to the rest of the mitochondrial genome. Because we studied whole blood, we cannot address the distribution of mutations among blood cell subsets or platelets, and these could change with age, smoking, or HIV. Finally, because our study only included female participants, it may not be generalizable to males.

| CON CLUS IONS
Low-frequency mtDNA somatic substitution mutations can be quantified in blood cells, a tissue with fast turnover and lower mutation rates (Li, Schröder, Ni, Madea, & Stoneking, 2015). Our data suggest that the blood somatic mtDNA mutation burden increases with age, consistent with current thinking on biological aging, and may be increased among persons living with uncontrolled HIV. Our findings with respect to mtDNA heteroplasmy are consistent with the hypothesis that smoking promotes clonal expansion of mtDNA mutations, which may play a role in accelerating biological aging among smokers. In this study, HIV status was trichotomized: HIV-negative,

| Study sample
HIV-positive with peak pVL < 100,000 copies/ml, and HIV-positive with peak pVL ≥ 100,000 copies/ml. This categorization was chosen based on our previous observation that having a peak HIV pVL > 100,000 copies/ml was associated with shorter leukocyte telomere length (Zanet et al., 2014), another marker of aging.

| DNA extraction and mtDNA quantification
All blood specimens were stored at −80°C until DNA extraction. Total DNA was extracted from 0.1 ml whole blood using the (see Table S4) at a final concentration of 1 µM. The qPCR conditions were as follows: one cycle of 95°C/10 min, followed by 45 cycles of 95°C/5s; 60°C/10 s; and 72°C/5 s, with acquisition at the end of the 72°C step. Temperature ramping was 4.4°C/s for all steps except the 95 to 60°C step which ramped at 2.2°C/s. Each extract was quantified in duplicate, and the mtDNA copy numbers were determined based on a standard curve consisting of a plasmid containing the cloned amplicon, serially diluted 1:10 with a linear range extending from 3.34 × 10 7 to 3.34 × 10 1 copies/μl.

| Labeling with Primer IDs via single-cycle primer extension
The 25 Table S5) according to the revised Cambridge reference sequence (https ://www.mitom ap.org/foswi ki/bin/view/MITOM AP/ Human MitoSeq). The D-loop region was chosen as it was less likely F I G U R E 5 Study participants and the inclusion/exclusion criteria applied to samples analyzed. Of 1,119 CARMA cohort participants, 724 children and 395 adults were available for inclusion. As the cohort was predominantly female (75%), only females were included. Among the adults, past smokers were excluded, as were participants with a history of hepatitis C virus (HCV) or hepatitis B virus (HBV) infection, to focus on the effect of HIV. Among the 323 pediatric participants, due to restricted space in the sequencing runs, 12 HIV-positive and 13 HIV-negative participants were randomly selected for inclusion, all of whom were assumed HCV and HBV uninfected to be deleted compared to the rest of mitochondrial genome or have pseudogenes. A negative control designed to estimate assay background consisted of 5x10 5 copies of a clonal plasmid DNA containing the mtDNA region of interest. Both sequencing experiments also included one or two internal controls consisting of whole blood DNA extracted from the same individual(s). Amplification conditions were 1 cycle of 95°C/140s, 60°C/20s, and 72°C/195s. The reactions were then stored at −80°C.
For every 12 participant DNA extracts assayed, three negative controls were generated by replacing the mtDNA, the extension primer, or the DNA polymerase with water. These controls were used to estimate the background incorporation of the extension primers and were taken through the subsequent steps of the assay. To prevent cross-contamination, DNA extract labeled with the same multiplex identifier (MID) was never manipulated at the same time, and surfaces/instruments were thoroughly cleaned between handlings.

| AMPure Purification
To remove unincorporated extension primer, each reaction was combined with 50 μl of UltraPure distilled water (Invitrogen) and 50 μl of AMPure beads (Beckman Coulter) for a 10-min incubation at room temperature, then placed into the DynaMag-2 Magnet (Life Technologies) for 5 min, as per the manufacturer's protocol. In brief, the supernatant was removed and 200 μl of 70% ethanol was added before lightly vortexing for 5 s and placing the sample on the magnetic particle collector for 1 min. This step was repeated once more, after which the remaining supernatant was removed and the tube was placed in a 37°C heating block for 5 min. The purified product was eluted by addition of 20 μl of 10 mM Tris and 2 mM EDTA pH 7.5 for a 2-min incubation on the magnetic particle collector. The supernatant contains the purified mtDNA, which was stored at −80°C.

| qPCR quantification of PID-labeled mtDNA templates
The undiluted AMPure-purified and PID-labeled mtDNA templates were quantified via qPCR as described above using the short KSF and MT48F primers (Table S4), both at a final concentration of 1 μM. The standard curve consisted of serial 1:10 dilutions of plasmid containing the cloned amplicon, with a linear dynamic range of 1.45 × 10 7 to 1.45 × 10 1 copies/μl. The controls lacking DNA template, enzyme, and primer were assayed together with the respective study participant DNA extracts. The highest copy number observed among these controls (typically ≤ 10% of the participant DNA extracts) was subtracted from the copy number of the same amplified DNA sample to estimate the PID-labeled mtDNA concentration.
A 2% agarose gel with a 0.5× final concentration of GelRed Nucleic Acid Gel Stain 10,000× (Biotium) was run until clear separation of the 606 bp amplicon from the lower molecular weight primer-dimer-like material was observed. The desired band was rapidly excised under low-intensity UV illuminator, and DNA was extracted from the gel using the QIAquick Gel Extraction Kit (QIAGEN), according to the manufacturer's protocol. Columns were wiped dry prior to elution, and incubation with elution buffer was extended to 10 min prior to the final spin. To avoid cross-contamination within and between gel extractions, no two participants' DNA extracts with the same MID were run in the same gel chamber, and new scalpels were used to excise each band. Between gels, running buffer was changed and all combs, gel trays, and chambers were thoroughly washed.
Each gel-extracted product was diluted 1:40,000 in QIAGEN AE buffer and quantified by qPCR as described above, using primers MT325F and MT474R (Table S4)

| Single read GS FLX Sequencing
The and output in FNA file format. All data for the present study were obtained from two full GS FLX runs, each run containing specimens from 82 study participants and 16 assay-specific controls.
The number of adults and children, HIV-positive and HIV-negative, and smokers/never smokers were not significantly different between the two runs. For each run, samples were selected such that the median age, and the number of smokers and participants living with HIV were similar in each run although the second run tended to have fewer children.  (Table S5) within the first 100 bp were removed from the analysis.

| Data analysis with primer IDs
Mafft sequence aligner within Unipro UGENE v1.24.1 (http://ugene. net/) was used, with the opening penalty set at 0.05, the extension penalty at 0.05, and the max iterations at 10. PID consensus sequences were built from ≥5 distinct sequences or reads. Each participant's consensus sequence was built from ≥100 PID consensus sequences. A mutation was defined as any difference between the participant DNA extract's consensus sequence and the PID's consensus sequence, if the mutation was present in ≥75% of the ≥5 sequences used to build the PID consensus sequence. These thresholds were established a priori, based on pilot work.
Only substitution mutations between bp 16,560 and 279 (see Figure S7 Both code and data used in this manuscript are available upon request. Our main outcome of interest was somatic mtDNA substitution mutation burden, reported as substitutions per 10,000 bp, extrapolated from the 289-bp interrogated region in the mitochondrial D-loop. Only PID groups with ≥5 sequences were considered for analysis. As such, the somatic mtDNA substitution frequency = (number of somatic mtDNA substitutions × 10,000) divided by (number of distinct PID groups with 5 or more sequences × 289 bp). mtDNA heteroplasmy was reported as a binary variable (yes/ no). Figure 6 summarizes the principle of the assay, which exploits the fact that true mutations present in the original mtDNA template will be present in a majority of sequences sharing the same PID (established as ≥75% here). Mutations caused by PCR errors after the single-cycle primer extension step or sequencing errors will result in a scattered pattern within PID groups.

| Validation in an independent cohort
Furthermore, we validated our results using data from an independent cohort enrolled at Yale New Haven Hospital, New Haven, Connecticut, USA (Li et al., 2017) (Li et al., 2017). We included in the current analysis data from study participants living with HIV who were either current smokers or never smokers. Although the original study included HIV-negative controls, smoking status was not available for those individuals. Only substitution mutations within the same 289-bp region of the mitochondrial D-loop were considered. F I G U R E 6 Schematic of the mtDNA mutation assay. Sequences belonging to a given individual are grouped according to their multiplex identifier (MID) and according to their mtDNA primer identifier (PID). A consensus sequence is generated for each individual, based on all sequence groups containing at least five reads. Each group's consensus sequence is then compared to the individuals' consensus sequence. If a mutation (i.e., not present in the individual' consensus sequence) is present in ≥75% of all reads belonging to a given group, it is categorized as a "true" mutation which is presumed to have been present in the original mtDNA molecule, at the time of primer labeling. All other mutations are deemed to represent PCR errors that occurred after the initial single-cycle primer extension step or sequencing errors "true" mutation PCR or sequencing MID PID MID PID consensus sequences

| Statistics for somatic mtDNA substitutions
Given the non-normal distribution of the raw data, somatic mtDNA substitution frequencies were ln(x + 1)-transformed, where x is the somatic mtDNA substitution frequency per 10,000 bp. Comparisons between the HIV-positive and HIV-negative groups were done using the Mann-Whitney U and Fisher's exact tests as appropriate.
Univariate associations between (transformed) somatic mtDNA substitution mutation burdens and possible explanatory variables were investigated with linear regressions (age, log-transformed BMI, current CD4 count), Spearman's correlation (CD4 nadir), two-sample t or Mann-Whitney U tests (smoking status, current HIV pVL, peak pVL), one-way ANOVA, or Kruskal-Wallis (trichotomized HIV status, HIV treatment status, and ethnicity), as appropriate. An a priori decision was made to include age, smoking status, and HIV status in all models. Other variables were included in the ANCOVA multivariable model if they were important univariately (p < .1), and improved the fit of the multivariable model.

| Statistics for heteroplasmic mtDNA substitutions
Mann-Whitney U or Fisher's exact tests were used to compare groups and assess univariate associations in the presence or absence of heteroplasmy. For the multivariable modes, variables were selected as above for inclusion in the binary logistic regression model.

| Statistics used to analyze specific mtDNA substitution types
Mann-Whitney U tests or Kruskal-Wallis tests with a Dunn's correction for multiple comparisons were used to compare the somatic mutation frequency of specific types of mutations. Spearman's correlation was used to assess their relationship with age.

| Statistical software
Statistical analyses were performed with XLSTAT Version 2013.1.01 (Addinsoft) and JMP software, v. 12.2.0 (SAS Institute), with the exception of Dunn's and Tukey's corrections, which were done with GraphPad Prism version 7.01 (GraphPad Software).

ACK N OWLED G EM ENTS
Funding for the study was from the Canadian Institutes of Health

CO N FLI C T O F I NTE R E S T
None declared.


s2fieldsofstudy: Biology Medicine

text:
Alpha lipoic acid inhibits oxidative stress‐induced apoptosis by modulating of Nrf2 signalling pathway after traumatic brain injury

Abstract Alpha lipoic acid (ALA) is a powerful antioxidant which has been widely used in the treatment of different system diseases, such as cardiovascular and cerebrovascular diseases. But, there are few studies that refer to protective effects and potential mechanisms on traumatic brain injury (TBI). This study was carried out to investigate the neuroprotective effect following TBI and illuminate the underlying mechanism. Weight drop‐injured model in rats was induced by weight‐drop. ALA was administrated via intraperitoneal injection after TBI. Neurologic scores were examined following several tests. Neurological score was performed to measure behavioural outcomes. Nissl staining and TUNEL were performed to evaluate the neuronal apoptosis. Western blotting was engaged to analyse the protein content of the Nuclear factor erythroid 2‐related factor 2 (Nrf2) and its downstream protein factors, including hemeoxygenase‐1 (HO‐1) and quinine oxidoreductase‐1 (NQO1). ALA treatment alleviated TBI‐induced neuron cell apoptosis and improved neurobehavioural function by up‐regulation of Nrf2 expression and its downstream protein factors after TBI. This study presents new perspective of the mechanisms responsible for the neuronal apoptosis of ALA, with possible involvement of Nrf2 pathway.

| INTRODUC TI ON
Traumatic brain injury (TBI) is a public health problem in modern society. 1 TBI causes primary mechanical injury of neuron cells and initiates secondary damage that occur after the primary damage. Many investigations indicated that secondary damage result in neuronal apoptosis is the main reason of poor outcome after TBI which is progressive from hours to days. 2 Many pathological process were reported to be related to the neuronal apoptosis in the secondary damage. 3,4 So, relieve of neuronal apoptosis was the fundamental therapy purpose of TBI. However, despite long-sustained efforts have been made to expound its mechanisms, its effective treatment is still not raised.
Alpha-lipoic acid (ALA) is a kind of naturally occurring compound which is reported to have antioxidant activities. 5 It is also a powerful antioxidant inhibiting ROS-induced damaging effects. Previous study has shown that ALA exerts neuroprotective effects against TBI by suppressing the mitochondrial apoptotic pathway. 6,7 In addition, ALA was found to have the function to regulating the level of anti-oxidative enzymes as well as preventing hepatic inflammatory responses and oxidative stress after trauma. 8 However, the effects of ALA to TBI have not been investigated. 9 However, the mechanisms of the protective function of ALA and its effects on apoptotic mechanisms after TBI remain unknown.
Many researches have proved that nuclear factor erythroid 2related factor 2 (Nrf2), is a key leucine zipper redox sensitive transcription factor which has been reported to be an regulator in cell antioxidant mechanism and considered to be a protector for many organs in different models. 10,11 Under normal conditions, Nrf2 is sequestered in the cytoplasm where it is interacted with the Kelchlike ECH associating protein (Keap1). 12 The enzymes regarded as antioxidants including superoxide dismutase (SOD) and glutathione peroxidase (GPx). All of these enzymes form a powerful antioxidant defense mechanism. In conditions of stimulations, Nrf2 translocates from the cytoplasm to the nucleus, and binds to the antioxidant response element (ARE) which is a regulatory element, and resulting in protective response including many antioxidant enzymes up-regulation, such as quinine oxidoreductase-1 (NQO1) and heme oxygenase-1(HO-1). 13,14 Except that, it has been reported that it is an important regulator in protect TBI-induced second brain injury. 15 It is reasonably believed that the activation of Nrf2 could be involved in the post-TBI neuroprotective effect of ALA administration. In this study, it is evaluated the influence of ALA on the cerebral up-regulation of Nrf2 activity after TBI.

| Experimental animals
Male Sprague-Dawley rats (weight: 250-280 g) were gained from experimental animal center of the Second Affiliated Hospital of Nanjing University of Chinese Medicine. Animal experiments were approved by the Animal Care and Use Committee in according to the rules for animal research. The rats were raised under controlled temperature (24 ± 0.5℃) and free access to forage and drinking water was allowed for 7 days.

| Model of TBI
Rats were placed in a stereotaxic frame after intraperitoneal anaesthetized by 10% chloral hydrate (400 mg/kg). About 2-cm midline scalp incision was made to expose the skull. Then, a 6-mm hole was performed over the left parietal cortex; the centre of the hole was 2.5 mm lateral to the midline on the mid-coronal plane. During the operation, the dura should not be broken. After that, a 40-g weight was released onto the dura from 25 cm high. 16 The scalp wound was sutured after that. Sham animals received the same procedures except weight injured.

| Tissue preparation
Rats were intraperitoneally anaesthetized by sodium pentobarbital (50 mg/kg; i.p) and perfused intracardially with cold (4°C) 0.9% saline by thoracotomy towards the cannula. The left cortex peri-contusion was rapidly removed and ipsilateral cerebral cortex tissue 3 mm from the margin of the contusion site was frozen in liquid nitrogen, and then preserved at −80°C freezer. The tissues of remaining rats were immersed in 4% paraformaldehyde, kept at −80°C overnight.

| Neurobehavioural assessment
Severity of neurological deficit was evaluated at 24 hours by the neurological severity score (NSS) system. It is graded on a scale of 0-18(0 = minimum deficit, and 18 = maximum deficit). NSS scoring roots on motor, sense reflex, and balance functions. Thus, the higher score means the greater injury. All the animals were recorded at 1, 2, 3 and 7 days after TBI. The behavioural test was carried out by two independent investigators who were blind to the experimental groups.

| Real-time quantitative polymerase chain reaction
Total RNA was extracted from harvested cortex using RNAiso Plus

| Immunohistochemical staining
Brain tissue samples were cut at 5 μm after fixing in formalin for 3 days.
Sections were washed for 15 minutes in phosphate-buffered saline (PBS), and were immersed in 10% goat serum containing PBS-Triton (0.3%) for 1 hour at room temperature. After that, the sections were incubated overnight at 4°C with primary antibody against Nrf2 (1:400, Santa Cruz, CA, USA). After three washes with PBS, counterstain was done by diaminobenzidine and haematoxylin. Of course, control tissue was performed without the primary antibody step.

| Terminal deoxynucleotidyl transferasemediated dUTP nick-end labelling analysis
The apoptotic cells were conducted by using a TUNEL detection kit

| Malondialdehyde content, Superoxide Dismutase and glutathione peroxidase activity
The MDA content, SOD and GPx activity were measured with a spectrophotometer using the appropriate kits (Nanjing Jiancheng Biochemistry Co, Nanjing, China) following the manufacturer's instructions. Total protein concentration was determined by the Bradford method. The MDA level, SOD and GPx activity was expressed as nmol/mg protein and U/mg protein, respectively.

| Statistical analysis
All statistical analyses were performed by SPSS, version 17.0 (SPSS, Inc, Chicago, IL, USA). Data are expressed as the mean ± SD and the differences were analysed using ANOVA and Tukey tests. Statistical significance was set at value of P < 0.05.

| ALA improved neurological function after TBI
Animals were trained through the test for 3 days before experiment.
F I G U R E 1 Administration of ALA promotes neurobehaviour after TBI. Higher neurological severity scores (NSS) reflect more severe injury. Data are expressed as the mean ± SD. ***P < 0.001, versus sham group; # P < 0.05, ## P < 0.01, versus TB + vehicle group NSS score was higher in the TBI and TBI + vehicle group than the sham and sham + ALA group after 1 day after TBI. However, at 2, 3 and 7 days following TBI, NSS score declined in the TBI + ALA group compared with the TBI and TBI + vehicle group.

| ALA suppressed neuronal apoptosis after TBI
As shown in Figure 2, the cell number counting showed that few positive cells were found in the sham group. Furthermore, the apoptotic index was raised after TBI insult. There was no difference between the TBI and the vehicle-treated group; however, lower level of apoptotic cells was found in the TBI + ALA group.
As shown in Figure 2A, expression of cleaved caspase-3 significantly increased in the TBI group and TBI + vehicle group compared with the sham and sham + ALA group, and it was notably suppressed after ALA administration. Except that, the level of cleaved caspase-3 was significantly rose following TBI and notably down-regulated after ALA administration.

| ALA reduced the expression of apoptotic factors
The protective effects of ALA against TBI-induced neural apoptosis were examined by Western blot analysis (Figure 2). These effects were reversed in TBI rat treated with ALA that the expression of Bax was inhibited. The expression of the pro-apoptotic factor Bax increased following TBI when compared with the Sham group ( Figure 2A), whereas the expression of the anti-apoptotic factor Bcl-2 decreased when compared with the Sham group (Figure 2A,   B). However, treatment with 100 mg/kg of ALA reversed the expression levels of Bax and Bcl-2 relative to the TBI + vehicle group.

| ALA promoted the expression of Nrf2
Western blot analysis and immunohistochemistry were applied to detect the distribution and level of Nrf2. Subsequently, Nrf2 was mainly located in the cytoplasm in the sham and sham + ALA ( Figure 3A, C). Meanwhile, the expression bosting of Nrf2 was ob-

| ALA promoted the expression of Nrf2 downstream factors
The expression of NQO-1 and HO-1 was also measured by Western blot analysis. As shown in Figure 4, NQO-1 and HO-1 increased significantly following TBI ( Figure 5). ALA treatment further enhanced the level of protein compared with the TBI + vehicle group. These results implied that ALA induced Nrf2 downstream factors expression ( Figure 5A,B). Moreover, the result of F I G U R E 2 The effect of ALA on cleaved caspase-3, Bax, and Bcl-2 expression in cortical neural cells in rat model of TBI was assessed by Western blot analysis. Expression was normalized to the level of β-actin. Data are presented as mean ± SD. *P < 0.05, **P < 0.01, ***P < 0.001 versus sham group; # P < 0.05, ## P < 0.01 versus TBI + vehicle group F I G U R E 3 Alpha lipoic acid promoted translocation of Nrf2 from cytoplasm to nucleus and enhanced Nrf2 binding. A, The representative photomicrographs showing Nrf2 immunohistochemistry of tissue from different group after TBI. (B, C) The total and nuclear Nrf2 expression after ALA treatment in rat with TBI was measured by Western blot. Bars represent the mean ± SD. *P < 0.05, ***P < 0.001 compared with the sham group; # P < 0.05, ## P < 0.01 versus TBI + vehicle group. Black arrows: Nrf2 positive neuron cell HO-1 and NQO-1 in Western blot analysis was consistent with the mRNA level ( Figure 5C).

| ALA reduced oxidative stress following TBI
To evaluate the effect of ALA on oxidative stress induced by TBI, MDA level, SOD and GPx activity, indicators of lipid peroxidation and antioxidant levels, respectively, were assessed.
MDA level was increased in the TBI + vehicle group compared with the Sham group. This effect was mitigated by the administration of ALA ( Figure 6). GPx and SOD activity were both decreased after TBI, while ALA treatment increased their activity ( Figure 6).

| D ISCUSS I ON
In this study, ALA was elected to treat TBI and found improved neurological function. Meanwhile, neuronal apoptosis was decreased along with the up-regulation of bcl-2 by ALA administration. ALA is a favourable safety profile in many animal experiment and clinical trials which has the effect of antioxidant and anti-inflammatory. 17,18 F I G U R E 4 Apoptotic index was determined using TUNEL assays after TBI. ALA treatment significantly decreased the percentage of apoptotic cells after TBI. Data are presented as mean ± SD; ***P < 0.001 versus sham group; # P < 0.05 versus TBI + vehicle group. Black arrows: Nrf2 positive neuron cell This finding showed that ALA significantly abated the percentage of apoptotic cells as well as the level of active caspase-3, which were enhanced by brain injury. Previous study has found that ALA has protective effect against ischaemia/reperfusion injury via regulation of the expression of the bcl-2. 19 More important is that ALA administered alleviated the mitochondrial apoptotic pathway following TBI and made the prognosis improve significantly. 20 Therefore, it was reasonably believed that ALA can alleviate oxidative stress-induced apoptosis following TBI. 21 However, the mechanism of it on brain injury has not yet been undefined. In this study, ALA significantly Similarly, NQO1 mRNA was elevated after TBI and was further raised with administration of ALA. β-actin was used as a loading control. Data are presented as mean ± SD, n = 6 per group; *P < 0.05, **P < 0.01 and ***P < 0.001 versus sham group; # P < 0.05 versus TBI + vehicle group F I G U R E 6 Alpha lipoic acid attenuated oxidative stress caused by TBI. A, Measurements of MDA levels. (B, C) The activity of GPx and SOD. Data represent the mean ± SD. *P < 0.05, **P < 0.01 and ***P < 0.001 versus sham group; # P < 0.05, ## P < 0.01, ### P < 0.001 versus TBI + vehicle the mitochondrial outer membrane which leading to degradation of DNA, terminates in cell apoptosis. Here it was indicated that the level of Bax increased after TBI, imply that the up-regulation of cleaved caspase-3, leading to neuronal apoptosis. These results illustrated that ALA could effectively reduce neuron apoptosis.
This beneficial role of ALA led us to consider the neuroprotective effects and the mechanisms underlying oxidative insult. 22,23 The ability of ALA to stimulate the activity of antioxidant enzymes plays a critical role in its antioxidative characteristics. Lipid peroxidation, which refers to the oxidative degradation of lipids, increases membrane permeability, leading to cell damage. MDA has been utilized as an index of lipid peroxidation. In addition, both of SOD and GPx are antioxidant enzymes, which catalyse the reduction in glutathione. 24 The conversion of MDA and the activity of SOD and GPx in the cortex of mice with TBI indicate that oxidative stress occurs following TBI. 25,26 Apart from that, the results of this study also showed that treatment with ALA partly relieves this impact, suggesting that ALA could attenuate TBI-induced oxidative stress.
To confirm the Nrf2 signal pathway is involved in the neuroprotective role of ALA, the expression of Nrf2 protein after ALA administration was investigated. Previous evidence indicated it plays key role in cellular adaptation to oxidative stress, by elevating phase Ⅱ enzymes such as NQO1 and HO-1 which were activated after TBI. 27,28 This study demonstrated that the treatment of ALA resulted in a significant increase in Nrf2 expression, as well as in the level of NQO1 and HO-1. Additional, the changes of Nrf2 signalling pathway after ALA administration was investigated. This study showed that the migration of Nrf2 from the cytoplasm to the nucleus following TBI insult, and ALA promoted this process. Except that, in this study, total Nrf2 protein was significantly up-regulated following TBI and was much higher after ALA administration. These findings expound that the ALA influence of Nrf2 not only on its protein expression but also its translocation. All these results suggest that Nrf2 plays an important role in ALA treating craniocerebral injury.
In conclusion, this study demonstrated that ALA may have a protective effect against oxidative reaction induced neural apoptosis following TBI and this effect was likely to associate with the activation of Nrf2 signalling pathway. This study is contributed to illuminate the neuroprotective effect of ALA after TBI and its relation molecular mechanism. All results provide experimental evidence for ALA in the treatment of neurotrauma. There are two limitations in this study. First, ALA was used to elucidate the mechanism of anti-oxidative stress-induced apoptosis after TBI. Further studies are needed to confirm the results with primary microglia. Second, this study is an in vitro model, and the optimum concentrations of ALA in vivo remains unknown. Therefore, further investigations are warranted for the elucidation of anti-apoptosis after TBI mechanism of ALA in vivo.

ACK N OWLED G EM ENTS
This work was supported by grants from the Natural Science Research Project in Higher Education of Anhui Province (No. KJ2018A0253).

CO N FLI C T O F I NTE R E S T
The authors have declared that no potential conflicts of interests exist.


extfieldsofstudy: Chemistry Medicine

text:
Curcumin inhibits the proliferation and invasion of MG-63 cells through inactivation of the p-JAK2/p-STAT3 pathway

Purpose The aims of this study were to determine the effect of curcumin on osteosarcoma (OS) cells due to inactivation of the p-JAK2/p-STAT3 pathway and evaluate the prognostic value of this pathway in OS. Materials and methods We exposed a human OS cell line (MG-63) to different concentrations of curcumin. Then, we characterized the effects on MG-63 cells using assays (cell viability, colony formation, cell cycle, wound healing, invasion), flow cytometry, Western blot, immunohistochemical analyses, and tumor xenograft. Results The half-maximal inhibitory of curcumin for MG-63 cells at 24 hours was 27.6 µM. The number of colonies of MG-63 cells was decreased obviously upon curcumin (10 and 20 µM) treatment. We also found increased accumulation of MG-63 cells in the G2/M phase upon curcumin (10 and 20 µM) treatment. Apoptosis was increased in 10 and 20 µM curcumin-treated MG-63 cells. After incubation of physically wounded cells for 24 hours, the percentage wound width increased upon curcumin exposure. Curcumin obviously decreased the expression of pJAK-2 and pSTAT-3 in MG-63 cells in a dose-dependent manner. Curcumin dose-dependently inhibited the proliferation, migration, and invasion of MG-63 cells and induced arrest of the G0/G1 phase and apoptosis by inhibiting the p-JAK2/p-STAT3 pathway. The linear correlativity between expression of p-JAK2 and STAT3 was very prominent, and both were closely associated with lung metastasis. In vivo study suggested that curcumin suppressed tumor growth through JAK2/STAT3 signaling. Conclusion Curcumin-mediated inhibition of the proliferation and migration of MG-63 cells was associated with inactivation of JAK/STAT signaling.

Introduction
Osteosarcoma (OS) is the most prevalent primary cancer of the bones. Standard treatment comprises multiagent neoadjuvant chemotherapy (eg, doxorubicin, cisplatin, high dose of methotrexate or ifosfamide) followed by surgery and adjuvant chemotherapy with the same agents. This widely applied treatment has improved 5-year survival from 25% in the early 1970s to ~70% in the last decade. 1,2 However, outcomes for OS remain unsatisfactory for patients with metastasis. 3 Moreover, highdose chemotherapy also induces multidrug resistance and cachexia. 4,5 Meanwhile, a high dose of currently used drugs is limited by their side effects: nephrotoxicity, cardiomyopathy, hemorrhagic cystitis, and nephrotoxicity. 6,7 Therefore, development of novel, safe, efficacious therapeutic agents for late-stage OS is especially urgent. Curcumin is a phenolic, yellow compound found in Curcuma longa. It has been reported to have a wide range of biologic and pharmacologic activities: anti-inflammatory, antidiabetes mellitus, and antioxidant. 8 Recently, the anticancer effect of curcumin has garnered considerable attention. Unlike cytotoxic drugs, curcumin has shown minimal toxicity and high safety at high doses in clinical trials. 9,10 Studies have shown curcumin's activities against cancer of the breast, 11,12 pancreas, 13 colon, 14 prostate gland, 12 as well as melanoma 15,16 and OS. [17][18][19][20] Lee et al 17 reported that curcumin caused the death of OS cells by blocking cells successively in G(1)/S and G(2)/M phases and activating the caspase-3 pathway. Leow et al 18 found that curcumin exhibited anti-invasive and antimetastatic effects in OS cells though activation of the Wnt/βcatenin pathway. Moreover, curcumin has been reported to inhibit the proliferation and invasion of OS cells by regulating miRNA-125a and miRNA-138. 19,20 However, how curcumin works against OS is not known. We explored a pathway to explain the inhibitory property of curcumin on OS cells.

Materials and methods cell culture and reagents
A human OS cell line (MG-63) was obtained from the Shanghai Cell Bank of the Chinese Academy of Science (Shanghai, People's Republic of China). Cells were grown in DMEM (Thermo Fisher Scientific, Waltham, MA, USA) supplemented with 10% FBS (Thermo Fisher Scientific) and 1% penicillin and streptomycin (100 mg/mL of each) in a humidified atmosphere of 5% CO 2 at 37°C. Curcumin (99% purity) was purchased from Sigma-Aldrich Co. (St Louis, MO, USA), and 100 mM of it was stored in 99.9% dimethyl sulfoxide (Sangon Biotech, Shanghai, People's Republic of China). Curcumin at 5,10,15,20,25,30,35,40, and 80 µM was used to treat MG-63 cells.
colony-formation assay MG-63 cells (5×10 4 /dish) were seeded in 100 mm dishes with curcumin (0, 10, and 20 µM). Two weeks later, cells were washed twice with PBS, fixed with 10% formaldehyde for 5 minutes, and then stained with 1% crystal violet for 30 seconds. Each clone with 30 cells was counted using a dissection microscope.

cell-cycle assay
MG-63 cells treated with 0, 10, or 20 µM of curcumin for 48 hours were harvested through trypsinization. Then, they were fixed in 70% (v/v) ethanol at -20°C for 24 hours. Before detection, cells were resuspended in a solution of RNase (0.2 mg) and 1 mL of propidium iodide (PI)/Triton X-100 (20 µg [PI]/0.1% Triton X-100) in the dark for 15 minutes at room temperature. After washing twice, cell-cycle distribution was analyzed using a FACSCalibur™ flow cytometer (Becton Dickinson, Franklin Lakes, NJ, USA).

Apoptosis detection by flow cytometry
Cells treated as stated earlier were trypsinized and harvested after washing with ice-cold PBS. Cells (5×10 5 ) were stained with Annexin V-fluorescein isothiocyanate/PI (Thermo Fisher Scientific) according to manufacturer's instructions and resuspended in 500 µL of binding buffer. Flow cytometry was undertaken using a flow cytometer (FACSCalibur).

Wound-healing assay
Cell mobility was assessed by a wound-healing assay. MG-63 cells (1×10 6 ) were seeded in six-well plates until 80% confluence had been reached. A sterile yellow 100 µL pipette tip was used to make a straight scratch. Detached cells were removed by washing with PBS. Then, cells were treated with curcumin (0, 10, or 20 µM) in the serum-free medium. Phase-contrast images were taken in the same field at 0 and 24 hours. Experiments were carried out in triplicate.

invasion assay in vitro
For the cell-invasion test, 24-well transwells (pore size =8 mm; Corning Incorporated, Corning, NY, USA) were coated with Matrigel (1 mg/mL) according to the manufacturer's (Becton Dickinson) instructions. Briefly, 1×10 4 cells were seeded into the upper chamber containing 0, 10, or 20 µM of curcumin. Then, 24 hours later, noninvasive cells in the upper chamber were removed with a cotton swab. After fixation with 4% paraformaldehyde for 20 minutes, the upper chamber was stained with Giemsa solution for 5 minutes. Cells in five random fields were counted at a high power to obtain the mean value. Each experiment was carried out in triplicate on separate occasions.

immunohistochemical (ih) analysis
OS tissue samples in paraffin from 80 patients who had been followed up for 3 years were collected. The baseline characteristics of patients are given in Table 1. Expression of p-JAK2 (BM4839; Boster Biological Technology, Wuhan, People's Republic of China) and STAT3 (BM3922; Boster Biological Technology) was measured using a SV0004 two-step immunohistochemistry kit according to manufacturer's instructions. The ethics committee of Shanghai Sixth People's Hospital approved the research protocol on human tissues. All patients provided written informed consent.

Tumor xenograft model
A total of six BALB/c nude mice were divided into two groups (three per group). The animal experimental operations were approved by the Animal Care and Use Committee of Shanghai Jiao Tong University Affiliated Sixth People's Hospital. MG-63 cells (1×10 6 ) were injected subcutaneously into the right thigh of each mouse. As soon as the xenografts were 0.5 cm in diameter, curcumin (Sigma-Aldrich Co.) was administered orally to them at a dose of 50 mg/kg QD for four consecutive weeks. The tumor, pJAK2, and STAT3 levels were evaluated. All of the in vivo experimental protocols were approved by the Animal Care and Use Committee of Shanghai Jiao Tong University Affiliated Sixth People's Hospital. The procedures were performed according to the guidelines and regulations of Shanghai Jiao Tong University Affiliated Sixth People's Hospital (Shanghai, People's Republic of China).

statistical analyses
Data are the mean ± SD of triplicates. Statistical analyses of data were undertaken using Prism 5.0 (GraphPad Software, Inc., La Jolla, CA, USA). Statistical significance was determined by one-way or two-way ANOVA. P0.05 was considered as significant.         Western blot. Curcumin treatment decreased expression of p-JAK2 and p-STAT3 in MG-63 cells obviously in a dosedependent manner (Figure 7).

JaK2/sTaT3 signaling is closely related to metastasis
The importance of JAK2 and STAT3 in OS is uncertain. We measured expression of p-JAK2 and STAT3 by the IH method in the tissue of OS patients with or without lung metastasis ( Figure 8). For lung-metastasis samples, median expression of p-JAK2 and STAT3 was 9.23 and 14.82, respectively, which was much higher than that in patients without metastasis (2.43 and 5.06, respectively). There was a positive correlation between expression of p-JAK2 and STAT3 (r=0.715, P=0.002).

curcumin inhibited Os cells in vivo
We established OS xenografts to evaluate the effect of curcumin on tumor growth in vivo. After curcumin treatment for 4 weeks, the tumor sizes were significantly smaller than those in the control group ( Figure 9A and B). The IH analysis also demonstrated the expression of p-JAK2 and STAT3 ( Figure 9C and D), confirming that curcumin suppressed OS tumor growth through JAK2/STAT3 signaling.

Discussion
Curcumin has been shown to possess potent activity in vitro and in vivo against cancer of the prostate gland, 21 breast, 22 colon, 23 as well as OS. 24 In the present study, curcumin elicited dramatic inhibition of proliferation and arrest of the G2⁄M cycle in MG-63 OS cells as demonstrated by the CCK-8 assay and flow cytometry, respectively. These results are consistent with data published by other research teams. [25][26][27][28] Curcumin undertakes anticancer activities through nuclear factor-kappa B, 29 AKT/mTOR, 30 Wnt, 31 and JAK/STAT 32 signaling pathways. Whether curcumin exerts an antitumor effect in OS cells via JAK/STAT signaling has yet to be determined. We found that the phosphorylation levels of STAT3 in curcumin-treated MG-63 cells were decreased. JAK/STAT signaling has a central role in tumorigenesis; p-STAT3 activation contributes to cell proliferation by regulating expression of various genes required for cell proliferation. After activation by cytokine-mediated transphosphorylation, JAK2 triggers the phosphorylation of STAT3 proteins. JAK2 is involved in epithelial-mesenchymal transition and promotes the metastasis of several types of cancer cell. JAK2 phosphorylation was decreased significantly in curcumin-treated groups, especially in the high-concentration groups (10 and 20 µM), similar to STAT3 phosphorylation. This observation suggests that curcumin exerts its effects by inhibiting JAK2 phosphorylation (and thus inhibiting STAT3 phosphorylation) without affecting expression of both proteins in MG-63 cells.
Aberrant activation of JAK/STAT signaling has been documented in cancers of the head and neck, 33 breast, 34 and prostate gland 35 and in OS. 36,37 Few reports have focused on the role of JAK/STAT signaling in OS. Gerland et al 38 found that growth hormone induced expression of the JAK/STAT negative-regulatory proteins, which was inhibited by 1-alpha, 25-dihydroxyvitamin D. Sandoval-Usme et al reported that imvastatin decreases the proliferation, migration, and invasion of OS cells in a time-and dose-dependent manner through the JAK/STAT pathway. 36 We found a positive correlation between expression of p-JAK2 and STAT3, and both were closely related to lung metastasis.

Conclusion
We showed curcumin to be an antitumor agent. Curcuminmediated inhibition of the proliferation and migration of MG-63 cells was associated with inactivation of JAK/STAT signaling. Expression of JAK2 and STAT3 was much higher in OS patients with lung metastasis than in OS patients without metastasis. Downregulation of JAK/STAT signaling by curcumin could be a new strategy for OS treatment.


extfieldsofstudy: Biology Medicine

text:
A 19-miRNA Support Vector Machine classifier and a 6-miRNA risk score system designed for ovarian cancer patients

Ovarian cancer (OC) is the most common gynecologic malignancy with high incidence and mortality. The present study aimed to develop approaches for determining the recurrence type and identify potential miRNA markers for OC prognosis. The miRNA expression profile of OC (the training set, including 390 samples with recurrence information) was downloaded from The Cancer Genome Atlas database. The validation sets GSE25204 and GSE27290 were obtained from the Gene Expression Omnibus database. Prescreening of clinical factors was conducted using the survival package, and the differentially expressed miRNAs (DE-miRNAs) were identified using the limma package. Using the Caret package, the optimal miRNA set was selected to build a Support Vector Machine (SVM) classifier. The miRNAs and clinical factors independently related to prognosis were analyzed using the survival package, and the risk score system was constructed. Finally, the miRNA-target regulatory network was built by Cytoscape software, and enrichment analysis was performed. There were 46 DE-miRNAs between the recurrent and non-recurrent samples. After the optimal 19-miRNA set was selected for constructing the SVM classifier, 6 DE-miRNAs (miR-193b, miR-211, miR-218, miR-505, miR-508 and miR-514) independently related to prognosis were further extracted to build the risk score system. The neoplasm cancer status was independently correlated with the prognosis and conducted with stratified analysis. Additionally, the target genes in the regulatory network were enriched in the regulation of actin cytoskeleton and the TGF-β signaling pathway. The 6-miRNA signature may serve as a potential biomarker for OC prognosis, particularlyfor recurrence.

Introduction
Ovarian cancer (OC) is the most lethal gynecologic malignancy with a 5-year overall survival (OS) of ~47%, which has almost never changed over the past 20 years (1). In 2015, 1.2 million women suffered from OC, and the disease led to 161,100 deaths worldwide (2). The symptoms of OC are inconspicuous and non-specific thus most cases are diagnosed at a later stage (3). Therefore, early diagnosis and treatment of OC are critical for improving the outcomes of the disease, and prognosis mainly depends on the disease degree, tumor subtypes and medical conditions (2,4). Understanding the underlying mechanisms of OC could facilitate the development of advanced treatment approaches.
MicroRNAs (miRNAs) play important roles in OC pathogenesis and progression. By comparison of the transcriptome data from different tissues with genome-scale biomolecular networks, miR-124-3p was identified as a potential biomarker for OC (5). miR-27a is considered as an oncogene which inhibits forkhead box O1 (FOXO1) in OC (6), while miR-34a serves as a suppressor by downregulating histone deacetylase 1 (HDAC1) (7). miR-409-3p was found to enhance the cisplatin-sensitivity of OC cells by inhibiting autophagy controlled by Fip200 (8).
In addition, a growing number of studies have shown that the dysregulation of miRNAs is associated with the prognosis of OC. The miR-200 family members have been identified as prognostic indicators for the disease stage, tumor histology and survival of OC (9). For example, miR-200b-429 may be a promising marker for OC survival, and the low expression of miR-200 indicates a poor prognosis and plays a regulatory role in the tumor (10). An upregulated serum miR-221 expression level is correlated with tumor stage and grade of epithelial ovarian cancer (EOC), which serves as an independent factor for a poor prognostic in EOC (11). The serum level of miR-141 and miR-200c can distinguish OC patients from healthy controls, and they may be utilized as markers for predicting the prognosis of OC (12). A high expression level of miR-203 has been reported as a candidate marker that predicts the progression and adverse outcome of patients with EOC (13,14). Serum miR-21 expression was found to be increased in EOC patients, and it may function as a novel marker for the diagnosis and prognosis of EOC (15). The expression of miR-150 is higher in primary serous OC than in omental metastases, and its lower expression is associated with shorter progression free-survival in metastatic tissues (16). Nevertheless, the miRNAs related to the recurrence of OC have not been fully revealed.
Thus, exploring the correlation between miRNAs and the development and recurrence of OC is critical for improving the curative effects and prognosis of OC patients. Based on the miRNA expression profile of OC in the public database, the miRNAs correlated with the recurrence of OC were screened, and then a classifier was constructed to recognize the recurrence of OC. Combined with the prognostic information of the samples, a risk score system was constructed based on the expression levels of significant miRNAs. The present study may provide a theoretical basis for the prognostic prediction and targeted therapy of OC, particularlyrecurrent OC.

Materials and methods
Data source and prescreening of clinical factors. The miRNA expression profile of OC (the training set) was downloaded from The Cancer Genome Atlas (TCGA, https://portal.gdc. cancer.gov/) database (September 10, 2018), based on the Illumina HiSeq 2000 RNA Sequencing platform. The data in the '0a07b199-d93d-4202-a63a-b38e39dc5ca4.mirbase21. mirnas.quantification.txt' file that is level 3 was downloaded and used. Then we used the encoding information to obtain the sample information. There were 415 OC samples with available clinical information in the training set, of which 390 had information regarding recurrence: 170 were non-recurrent OC samples and 220 were recurrent. The human reference genome hg38/GRCh38 was used to annotate the expression information.
Meanwhile, other relevant datasets were searched from the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih. gov/geo/) database with the keywords 'ovarian cancer' and 'Homo sapiens'. The inclusive criteria were: i) the samples in the dataset had recurrent information; ii) the samples had prognostic information; iii) the total number of samples was no <50; and iv) the dataset was an miRNA expression profile. Based on these criteria, only two datasets, GSE25204 (17) and GSE27290 (18), were selected and used as validation datasets. The GSE25204 dataset was based on the Illumina Human v2 MicroRNA expression beadchip platform and included 85 OC samples with recurrent information (validation set 1). The GSE27290 dataset was based on the Agilent-015508 Human miRNA Microarray platform (pre-commercial version 6.0) and contained 58 OC samples with recurrent information (the validation set 2).
The clinical information of all the samples in the training set was statistically analyzed. In order to determine the basis for grouping, the univariate and multivariate Cox regression analysis in the R survival package (19) (version 2.41-1, http://bioconductor.org/packages/survivalr/) was used to screen the clinical factors significantly associated with prognosis. P<0.05 was set as the significance threshold.

Data standardization and differential expression analysis.
The miRNA expression profile matrixes of the three datasets were stacked, and the matrix for each dataset was scaled according to the expression level. The unit specification was scaled as follows to provide a sample vector: where is the 2-norm of the vector (l 2 norm).
Using the sqrt [sum(data^2)] function in R (20), the square root of the eigenvalue of matrix B = A*A T was obtained. The purpose of this normalization was to obtain the sample values scaled to 1. Using median scaling, the expression level of each miRNA was centralized and normalized according to the median and median absolute deviation (MAD). Specifically, an eigenvector x = (x 1 ,..., x n ): was assigned. The median scale normalization was defined as For the training set, the samples were grouped according to the recurrence condition of the samples. Using the R package limma (21) (version 3.34.7, https://bioconductor. org/packages/release/bioc/html/limma.html), the differentially expressed miRNAs (DE-miRNAs) between the recurrent and non-recurrent OC samples were selected. The false discovery rate (FDR) <0.05 and |log 2 fold change (FC)| >0.263 were set as the thresholds for the significant differences. According to the expression levels of the DE-miRNAs in the training datasets, the bidirectional hierarchical clustering for expression levels of these DE-miRNAs was performed based on the centered Pearson correlation algorithm, using the pheatmap package (22) (version 1.0.8, https://cran.r-project. org/web/packages/pheatmap/index.html) in R.

Construction of Support Vector Machine (SVM) classifier.
In the training set, the Cox regression analysis in the R survival package (19) was used to select the DE-miRNAs significantly related to prognosis, with the threshold of log-rank P<0.05. The DE-miRNAs significantly related to recurrent prognosis were further selected to perform the follow-up analyses.
Recursive feature elimination (RFE) is an integrated machine learning method, which considers the selection of subset as an optimization problem (23). Using the RFE algorithm in the R package Caret (24) (version 6.0-76, https://cran.r-project.org/web/packages/caret), the optimal miRNA set was filtered from the training dataset. In the 100-fold cross validation, the miRNA with the highest accuracy was selected as the signature miRNA.
SVM is a supervised classification algorithm of machine learning, which discriminates sample types by estimating the probability that a sample belongs to a certain category (25). For the training set, the SVM classifier was constructed based on the optimal miRNA set using the SVM method (Core: Sigmoid Kernel; Cross: 100-fold cross validation) in the R package e1071 (26) (version 1.6-8, https://cran.r-project. org/web/packages/e1071).
The performance of the SVM classifier was separately evaluated in the training set and the validation sets using 4 valuation indicators [Concordance index, C-index; Brier score; Log-rank P-value of Cox-proportional hazard (PH) regression; and area under the receiver operating characteristic (ROC) curve, AUC]. The C-index and Brier score were calculated using the R package survcomp (27) (version 1.30.0, http://www. bioconductor.org/packages/release/bioc/html/survcomp.html). Using the R package survival (19), the Kaplan-Meier (KM) curves for the two groups classified by the SVM classifier were generated, and the log-rank P-value of the difference between the two groups was calculated. Furthermore, the indicators of ROC curves (sensitivity, Sen; specificity, Spe; positive prediction value, PPV; negative prediction value, NPV) were calculated using the R package pROC (28) (version 1.12.1, https://cran.r-project.org/web/packages/pROC/index.html).
Construction of the risk score system. Based on the multivariate Cox regression analysis in the R survival package (19), the prognosis-associated miRNAs were further analyzed to identify the DE-miRNAs independently related to prognosis. The log-rank P<0.05 was set as the threshold.
Based on the regression coefficients of the independent prognostic miRNAs, the risk score system was constructed, and the risk score of each sample was obtained according to the following formula: Risk score = ∑Coef DE-miRNAs x Exp DE-miRNAs where Coef DE-miRNA represents the regression coefficient, and Exp DE-miRNA indicates the expression level of the corresponding miRNA.
For the training set, the samples were divided into high-and low-risk groups with the median of risk scores as the cut-off point. Using the KM curve analysis in the R survival package (19), the correlation between the risk score system and prognosis was evaluated. Meanwhile, the risk score system was confirmed in the validation sets.

Stratified analysis of clinical factors.
Using the univariate and multivariate Cox regression analysis in the R survival package (19), the clinical factors independently correlated with prognosis in the training set were screened out. Combined with the high-and low-risk samples determined by the risk score system, stratified analysis was further carried out.
miRNA-target regulatory network analysis and enrichment analysis. The risk scores of the mRNA-sequencing samples matched with the miRNA-sequencing samples were calculated using the risk score system. Based on the risk scores, the samples in the training set were divided into high-and low-risk groups. Using the R package limma (21), the differentially expressed genes (DEGs) between the two groups were selected, with the thresholds of FDR <0.05 and |log 2 FC| >0.263. Based on the starBase database (29) (version 3.0, http://starbase. sysu.edu.cn/), the miRNA-mRNA regulatory interactions in at least one of the five databases, targetScan, picTar, RNA22, PITA, and miRanda, were selected. Then, the correlation of the expression levels of the miRNAs and target DEGs in the matched samples were calculated, and the interactions with significant negative correlations were selected. Subsequently, the miRNA-target regulatory network was visualized using the Cytoscape software (30) (version 3.6.1, http://www.cytoscape.org/). Using the Database for Annotation, Visualization and Integrated Discovery (DAVID) tool (31) (version 6.8, https://david.ncifcrf.gov/), the functional and pathway enrichment analyses were carried out, with P<0.05 as the screening criterion.

Results
Prescreening of clinical factors and differential expression analysis. The clinical information of the 415 OC samples in the training set was performed with statistical analysis, and Table I. Clinical information of all the tumor samples in the training set and the prescreening of the clinical factors significantly associated with prognosis.

Univariables Cox
Multivariables then the clinical factors significantly associated with prognosis were screened. The age, tumor recurrence, and neoplasm cancer status were found to be the clinical factors significantly related to prognosis (Table I and Fig. 1). To identify the recurrence prognosis-associated miRNAs, the samples in this study were grouped based on the recurrence information.
For the training set, a total of 46 DE-miRNAs (18 upregulated and 28 downregulated) were identified between the recurrent and non-recurrent OC samples ( Fig. 2A). The clustering heatmap was drawn based on the expression levels of the DE-miRNAs, which indicated that the samples were clearly divided into two types (Fig. 2B).
Construction of the SVM classifier. Using the Cox regression analysis, 24 prognosis-associated miRNAs were selected in the training set. Using RFE algorithm, the optimal miRNA set involving 19 miRNAs (including miR-135b, miR-139, miR-151, miR-187, miR-193b, miR-210, miR-211, miR-218, miR-219, miR-30b, miR-30d, miR-365, miR-505, miR-506, miR-508, miR-509, miR-513c, miR-514 and miR-760) was selected (Fig. 3). Based on the optimal 19-miRNA set, the SVM classifier was constructed. Then, the performance of the SVM classifier in the training set and the validation sets was assessed using the 4 valuation indicators aforementioned. The results showed that the C-index values were >0.80, and Brier score values <0.1 in both the training and validation sets (Table II). As shown in the confusion table diagrams that indicated the sample classification based on the SVM   . Accuracy curve for screening the optimal miRNA set. The horizontal axis represents the number of miRNA variables, and the vertical axis represents the cross-validation accuracy. The marked content is the number of miRNAs corresponding to the optimal miRNA set. classifier, the 19-miRNA set could distinguish well the recurrent samples from the non-recurrent (Fig. 4). The AUC curves showed that the AUC values of the training set and the validation sets were >0.9 ( Fig. 4 and Table II). The KM curves suggested that the predictive results of the SVM classifier were significantly related to prognosis (P<0.05; Fig. 4). These results indicated that the 19-miRNA-based classifier could accurately determine the recurrence type of the OC samples. Table II. Evaluation indicators for the Support Vector Machine (SVM) classifier in the training set and the validation sets.  Construction of risk score system. Combining the optimal 19-miRNA set with the recurrence prognosis information of the samples, 6 independent prognosis-related DE-miRNAs (miR-193b, miR-211, miR-218, miR-505, miR-508 and miR-514) were identified (Table III). Combined with the regression coefficients of the 6 independent prognostic miRNAs, the risk score system for OC was constructed. The formula for calculating the risk score of each sample was: With the median of risk scores as the cut-off point, the samples were classified into high-and low-risk groups. For the training and the validation sets, the KM curves showed that the high-and low-risk groups determined by the risk score system were significantly associated with the actual recurrence prognosis information (Fig. 5).

Stratified analysis of the clinical factors.
In the training set, although the age, tumor recurrence, and neoplasm cancer status were all identified as prognosis-associated clinical factors, only the neoplasm cancer status was considered as an independent prognostic factor relating to the recurrence, based on the multivariate Cox regression analysis (Table IV and Fig. 6A).
To analyze the correlation between the neoplasm cancer status and recurrence prognosis separately in the high-and low-risk groups, stratified analysis was further performed for the neoplasm cancer status ( Fig. 6B and C).
miRNA-target regulatory network analysis and enrichment analysis. In total, we identified 615 DEGs (400 upregulated and 215 downregulated) between the high-and low-risk groups. Based on the StarBase database, the target genes were predicted for the 6 independent prognostic miRNAs. The overlapping genes between the target genes and the DEGs were obtained after comparison, and 601 miRNA-mRNA regulatory interactions were selected. Then, 218 interactions with significant negative correlations were retained for constructing the miRNA-target regulatory network (involving miR-193b, miR-211, miR-505, miR-508, and miR-514) (Fig. 7). In addition, the target genes in the regulatory network were enriched in 25 functional terms (such as blood vessel development and vasculature development) and 6 pathways (such as regulation of actin cytoskeleton and TGF-β signaling pathway) (Table V).

Discussion
In the present study, we identified 46 DE-miRNAs between the recurrent and non-recurrent ovarian cancer (OC) samples. Nineteen prognosis-associated miRNAs were used to construct an SVM classifier, among which 6 were deregulated and independently related to prognosis. A risk score system based on the 6 miRNAs had a high accuracy for risk prediction in both the training and validation sets. The neoplasm cancer status was a clinical factor independently correlated with recurrence. miR-193b serves as a tumor suppressor in many cancer types. Its role in OC has recently been investigated. The epigenetic silencing of miR-193a-3p could promote OC progression by targeting the growth factor receptor-bound protein-7 (GRB7) (32). miR-193b-3p has an antitumor effect in OC cells by inhibiting the p21-activated kinase 3 (33). Downregulation of miR-193b could induce OC metastasis (34). These results indicate that miR-193b may be a tumor suppressor in OC. Moreover, low expression of miR-193b is associated with a poor prognosis of OC patients (35). In the present study, miR-193b was one of the 6 miRNA signatures Table IV. Cox regression analysis for screening the clinical factor independently correlated with prognosis in the training set.  that could predict recurrence of OC, suggesting that its expression also might be linked to recurrence. Currently, only a few studies have reported the correlations between miR-218 and OC. It was reported that miR-218 prevents the proliferation and invasion in OC by downregulating its target gene, runt-related transcription factor 2 (RUNX2) (36). In colon adenocarcinoma, the long noncoding RNA MNX1-AS1 could promote progression. It acts as a competing endogenous RNA (ceRNA) of miR-218-5p and upregulates SEC61A1, the downstream target gene of miR-218-5p (37). MNX1-AS1 was also found to facilitate the progression of OC (37). However, it is unclear whether MNX1-AS1 also has this competing relationship with miR-218-5p. In the present study, miR-218 was another important miRNA identified to be related to the recurrence of OC, indicating it may be a novel predictive factor for OC recurrence.
miR-30a, miR-30e and miR-505 exhibit significantly lower expression in ovarian clear cell carcinoma (OCC) compared with those in elderly advanced ovarian papillary serous carcinoma (OPSC) patients, and the activating transcription factor 3 (ATF3) is the primary gene co-targeted by them (40). Overexpression of the tumor suppressors miR-130b-3p, miR-509-3p, miR-509-5p, miR-508-3p and miR-508-5p has been association with the improved survival of OC patients, and these miRNAs may alter the physical properties of OC cells via regulating the actin cytoskeleton (41). Moreover, by downregulating gene expression levels in the MAPK1/ERK signaling pathway, miR-508 acts as an inhibitor for cell proliferation, migration and invasion in OC cells (42). Reduced miR-514 is correlated with adverse prognosis of OC patients, and miR-514 can inhibit cell proliferation and lower cisplatin chemosensitivity in OC by regulating the ATP binding cassette subfamily (43). These findings indicate that the three miRNAs, miR-505, miR-508 and miR-514 may function as suppressors in OC development and their low expression could be associated with poor prognosis. Our results demonstrated that miR-505, miR-508 and miR-514 were three miRNA signatures in recurrent OC, suggesting that they may be the predictive indicators for OC recurrence.
For the target genes in the miRNA-target regulatory network (involving miR-193b, miR-211, miR-505, miR-508 and miR-514), they were significantly enriched in the regulation of the actin cytoskeleton and the TGF-β signaling pathway. The TGF-β signaling pathway functions in various cellular processes correlated with tumorigenesis, and the genetic variants in the pathway are related to OC risk and may help to identify high-risk individuals (44). TGF-β signaling can be suppressed by the accumulation of epigenetic modifications, which contributes to the oncogenesis of OC (45). The dynamic remodeling of the actin cytoskeleton is important for multiple cellular activities, and dysfunction of cytoskeletal proteins can lead to many diseases in humans (46). Therefore, miR-193b, miR-211, miR-505, miR-508 and miR-514 may influence the prognosis of OC via the regulation of the actin cytoskeleton and the TGF-β signaling pathway.
Although we performed comprehensive bioinformatic analyses using the miRNA expression profile of OC and confirmed the classification accuracy by the validation datasets, several limitations remain. First, the sample size with available recurrence information was small. Second, this study lacks validation experiments to validate the expression of these predictive miRNAs and interplayed target genes.. Third, the accuracy of the SVM classifier and clinical value of the 6-miRNA risk score system should be further tested in OC patients. Therefore, further experiments should be prepared and conducted to support our findings.
In conclusion, the SVM classifier may be accurate in determining the recurrence status of OC patients. Moreover, the 6-miRNA risk score system may be effective in predicting the outcome of OC patients. Furthermore, miR-193b, miR-211, miR-505, miR-508 and miR-514 may affect the prognosis of OC via regulation of the actin cytoskeleton and the TGF-β signaling pathway.