Circulating small extracellular vesicles microRNAs plus CA-125 for treatment stratification in advanced ovarian cancer

A total of 221 AOC patients (HGSOC with FIGO stage III or IV) who were treated with PDS or NACT-IDS from January 2018 to June 2022 were recruited in this study (R0, n = 99; non-R0, n = 122). In addition, we obtained plasma samples from benign pelvic diseases (n = 21), early-stage ovarian cancer with FIGO stage I or II (n = 20), and advanced colorectal cancer (n = 22) patients. We also collected primary tumor tissues (n = 58) and other site tissues (n = 18) from these AOC patients after PDS. None of the patients involved had infectious diseases. AOC samples with the following characteristics were removed: (1) treatment with surgical operation or chemotherapy before plasma collection; (2) hemolysis. Each participant signed a written informed consent form. The study obtained ethical approval (No. KYJJ-2019-043) from the Hunan Cancer Hospital Institutional Review Board. The CA-125 level of each AOC patient was measured by chemiluminescence assay (Beckman, DXI800, CA, USA) before treatment at four clinical centers. Blood samples (8 mL) from every individual in a fasting state were collected with an EDTA-K2 anticoagulant tube in the early morning, stored at 4 °C, and then processed within 30 min. The samples were extracted via centrifugation at 2000 ×g for 10 min, and 13,000 ×g for 10 min at 4 °C to exclude effects from platelet-derived vesicles as described in refs. [15, 16]. Then the isolated plasma was aliquoted into 2 mL tubes for storage at − 80 °C. Samples from other centers were also processed as described above and then transported via dry ice.

sEVs were purified from 2 mL of plasma from AOC patients by differential ultracentrifugation [16]. After the plasma sample was thawed on ice, centrifuged at 3000 ×g for 15 min. The supernatant was carefully pipetted into a new tube and diluted with PBS to 23 mL, and centrifuged at 13,000 ×g for 30 min (Beckman Coulter, Brea, CA, USA). Through a 0.22 μm filter, the supernatant was ultracentrifuged at 100,000 ×g for 2 h at 4 °C to collect a pellet of sEVs. The pellet was dissolved with PBS, transferred to a new ultracentrifuge tube and centrifuged again at 100,000 ×g 4 °C for 2 h to eliminate any contaminants from the protein aggregates. Finally, the enriched pellet of sEVs was resuspended twice with 100 µL PBS and then collected into a new tube. The isolation method of plasma sEVs was performed strictly according to the MISEV2018 guidelines [17].

Primary tumor tissue, matched adjacent tissue, metastatic tumor tissue, and distant normal tissue were collected from AOC patients undergoing PDS. After collecting the living tissue, residual blood was washed with PBS, cut into 500 mg per block, and then placed in a frozen tube. The tissue was quickly transferred into liquid nitrogen flash-frozen for 1 h, and then stored at − 80 °C. Tissue sEVs were separated based on the protocol previously established by Vella et al. [18]. with some modifications [19]. Tissue dissociation was performed using the Miltenyi Human Tumor Dissociation Kit (Miltenyi Biotec, No. 130-095-929, Germany). Before extraction, it was resuspended by enzymes H, R, and A, according to the instructions, and the dissociated mixture containing 2.2 mL RPMI, 100 μL enzyme H, 50 μL enzyme R, and 12.5 μL enzyme A was prepared. A small piece of tissue (~ 200 mg) was weighed, and ultrathin sections were taken using a frozen slicer (to minimize foreign contamination caused by tissue cell rupturing, thereby enlarging the surface area). Then, the dissociation mixture prepared above was added and incubated at 37 °C for 15 min to enzymatic dissociation and permeabilize the tissue. Halt protease and phosphatase inhibitor single-use cocktail, EDTA-free (100X) (Thermo Scientific, No. 78443, USA) was added into dissociation solution and gently filtered twice through a 70 μm filter to remove residual tissue.

The mixed suspension was centrifuged at 300 ×g for 10 min at 4 °C. The supernatant was transferred to a new centrifuge tube and centrifuged at 2000 ×g for 10 min at 4 °C. The cell-free supernatant was centrifuged at 10,000 ×g for 20 min at 4 °C and then gently passed through a 0.22 μm filter to remove remaining cell debris. The supernatant underwent additional ultracentrifugation at 150,000 ×g for 2 h at 4 °C. The precipitates were collected and resuspended in 1 mL PBS, and further purified by an Exosupur column (Echobiotech, China). Finally, the sEVs-containing fraction was condensed to 200 μL via a 100 kDa Amicon Ultra ultrafiltration centrifuge tube (Merck, Germany).

Plasma and tissue sEVs were isolated via the above methods. Then, 20 μL of magnetic microbeads with antibodies (EpCAM, FAP, CD31, CD235a, CD45; Miltenyi Biotec) was added to 100 μL of sEVs suspension. After incubation for 60 min at 4 °C, the magnetic immune mixture was resuspended with 1 mL of PBS. The LD column (Miltenyi Biotec) was placed in a magnetic rack, and the column was washed three times with 2 mL PBS. The magnetic immune mixture was added to the LD column and then washed three times with 1 mL PBS to clean unlabeled sEVs. The LD column was removed and placed in a 15 mL collection tube. PBS (3 mL) was added to the column, and the magnetic mixture containing labeled sEVs was pushed into the tube by a plunger. The mixture was centrifuged at 100,000 ×g for 70 min, and the precipitates were resuspended in 100 μL of PBS. Then, 100 μL of elution buffer (0.1 M glycine pH 2.8) was used to dilute the labeled sEV suspension, which was vortexed for 30 s, and combined with 10 μL of renaturation buffer (1 M Tris–HCl, pH 7.4). The mixture was centrifuged at 10,000 ×g for 30 min to separate the sEVs from magnetic beads. The supernatant containing specific sEVs was stored at − 80 °C for RNA extraction.

sEVs fixed in 1% paraformaldehyde for 10 min and washed with deionized water. The sEV suspension (10 μL) was placed over a Formvar-carbon-coated 300-mesh copper net and incubated for 10 min at room temperature. After washing with deionized water, the sEVs were negatively stained with 2% uranyl oxalate solution for 1 min at room temperature. The grids were dried for 5 min under incandescent light. Images of sEVs were acquired with an FEI Tecnai G2 Spirit Transmission Electron Microscope (TEM) (FEI; Houston, TX, USA).

Based on the characteristics of the Brownian motion and light scattering, the hydrodynamic diameter and concentration of sEVs were measured by NTA (ZetaView PWX 110, Particle Metrix, Germany). SEVs were diluted to 1 mL with PBS and injected into the cuvette. The hydrodynamic diameter and concentration of particles were calculated from the diffusion coefficient by the Stokes–Einstein equation. Five videos of approximately 10 s duration each were recorded for every sample, and analysis of the data regarding particle movement was by the nanoparticle tracking analysis (NTA) software.

Protein quantification of sEVs samples was conducted using the BCA Protein Assay Kit (Thermo Fisher Scientific, No. 23,225, USA). The primary antibodies used were CD9 (Abcam, ab92726), CD63 (Abcam, ab216130), CD81 (Abcam, ab109201), TSG101 (Abcam, ab125011), HSP70 (Abcam, ab2787), calnexin (Abcam, ab22595), EpCAM (Abcam, ab32392), FAP (Abcam, ab207178), CD31 (Abcam, ab9498), CD45 (Abcam, ab40763), and β-actin (Proteintech, 66,009–-1-lg). For secondary antibodies, goat anti-rabbit (A0208, Beyotime) and goat anti-mouse (A0216, Beyotime) IgG horseradish peroxidase were used.

Total RNA extracted from sEVs of 2 mL plasma was used for miRNA library preparation (Ribobio, China). The clean reads (17–45 nt) were contrasted with human genome databases (Silva, GtRNAdb, Rfam, and Repbase) using the Bowtie software [20]. The clean reads were further compared with mature miRNAs in the miRDeep2 and miRBase databases. Next, the miRNA expression levels were normalized by the RPM. The significant differential expression analysis of miRNAs was conducted using the edgeR and limma packages.

The plasma from the same patient was divided into 3 equal parts. One of which extracted sEV fractions was treated with RNase A (10 μg/ML, Tiangen, No. RT405, China), which is used to eliminate the free RNA carried by non-vesicles, for 15 min at 37 °C. The remaining two plasma samples were not digested with RNase, one of which was directly used to extract plasma RNA, and the other was used to extract RNA from sEVs.

According to the standard protocol, total RNA from plasma or sEVs was extracted and purified by the miRNeasy Serum/Plasma Advanced Kit (Qiagen, No. 217,204, USA). Total RNA from tissue was extracted via a miRNeasy Mini Kit (Qiagen, No. 217004, USA). RNA degradation and contamination were detected by gel electrophoresis. The integrity of RNA was assessed on an Agilent 2200 TapeStation (Agilent Technologies, CA, USA). Additionally, RNA samples were quantified by Qubit^®2.0 (Life Technologies, USA).

C. elegans cel-miR-39 was used as an external calibration, and U6 was used as an internal reference for tissue and tissue sEV samples. miRNA quantification was performed by a Light Cycler 480 (Roche, Germany) using specific miRNA TaqMan gene expression probes (Synbio Technologies, China) mixed with cDNA templates (Takara, RR037A, Japan). The relative expression levels of candidate miRNAs were normalized to the control group via the Eq. 2-ΔΔCt [21]. The primer and probe sequences of the miRNAs were listed in Additional file 1: Table S3.

Target genes of miRNAs were screened via TargetScan and miRWalk. The Tothill dataset (GSE61568) [22] was used to establish the miRNA-gene regulatory network and perform pathway analysis. Tumor cases with low malignant potential, non-serous histology, or low grade from the Tothill dataset were excluded. Samples that received NACT or did not provide residual disease status were excluded. Accordingly, miRNA target genes were matched using only data from primary tumors of patients with HGSOC undergoing surgery in the Tothill dataset. The DAVID website (https://​david.​ncifcrf.​gov.) was used to conduct functional clustering and enrichment pathway analysis. We classified the GO and KEGG pathways using the two-sided Fisher's exact test and calculated FDR to correct P values. A corrected P value < 0.05 was considered to be significantly enriched in the GO/KEGG pathway. GSEA was performed by GSEA software (http://​software.​broadinstitute.​org/​gsea/​).

LASSO analysis was performed to select the most relevant predictors via the "glmnet" (strictly set alpha = 1) package. Based on the R0 group as a control, we calculated the relative expression abundance of selected miRNAs in the non-R0 and differential diagnostic groups (BPD, ESOC, ACC). Log2 transformation of 4-miRNA qRT-PCR expression values of all samples was completed using the log (x + 1, 2) formula [11, 23]. Serum CA-125 expression values were converted via log10. We preliminarily fitted the model using logistic regression, as described in refs. [11, 24]. Based on this multivariate logistic regression model, we calculated the risk probability of residual disease for each subject through leave-one-out cross-validation. Moreover, the index score of differential diagnostic patients was also calculated by this risk model. We assessed the optimal cutoff value of the above prediction probability (4-miRNA combined CA-125) by receiver operating characteristic (ROC) curves and calculated sensitivity, specificity, AUC, PPV, and NPV in the discovery set [23]. While using the same cutoff value of risk prediction, the corresponding sensitivity, specificity, AUC, PPV, and NPV were calculated by the ROC curve in the validation set. The NRI and IDI were calculated by R software to quantify the improvement of diagnostic separation [25]. Furthermore, DCA was conducted by the “rmda” package in R. Gpower software was used to calculate the post hoc power of the prediction model. Multivariate and univariate logistic regression were employed to analyze the influence of various clinicopathological variables on residual disease status, including age, stage, lymph node metastasis, TP53 mutation, serum CA-125 levels, and 4-miRNA panel.

For all cohorts, MedCalc statistical software, version 19.1.0 (Medcalc Software, Ostend, Belgium) was used for the calculation and visualization of ROC curve relative indicators [24]. Statistical analysis was carried out using SPSS, GraphPad Prism 8, and R software (version 3.6.3). Comparisons between groups were performed using an unpaired t test or Mann–Whitney U test. The limit of significance was defined as a P value < 0.05. The results were visualized via the R packages VennDiagram, pheatmap, GOplot, and ggplot2.

RNA sequence data of all samples were publicly stored in the Gene Expression Omnibus of NCBI, GSE223126 (https://​www.​ncbi.​nlm.​nih.​gov/​geo/​query/​acc.​cgi?​acc=​GSE223126). The accession numbers of previously published datasets involved in our study were GSE113486 [26], GSE94533 [27], and GSE61568 [22].

A total of 284 pretreatment plasma samples and 76 surgical tissue samples were collected in this study (Fig. 1A). We sought to find a reliable panel for residual disease risk detection in AOC patients. Our design of the three-phase study is shown in Fig. 1B, Additional file 1 Figure S1. Concretely, a total of 284 patients from 4 clinical centers were included in the initial analysis, including 99 AOC patients without any residual disease (R0), 122 AOC patients with any residual disease (non-R0), 20 early-stage OC patients, 21 benign pelvic disease patients, and 22 advanced colorectal cancer patients. Using these samples, we constructed three cohorts: a screening cohort (n = 37), a discovery cohort (n = 30), and a validation cohort (n = 154).

To focus on the sEVs released from AOC patients, we first determined the presence of vesicles within the plasma of AOC patients using electron microscopy. Transmission electron microscopy (TEM) images showed that large and small vesicles isolated from AOC patients' plasma had completely different sizes (Fig. 2A). There were also obvious differences in the diameter ranges of large and small vesicles via nanoparticle tracking analysis (NTA) (Fig. 2B). Furthermore, we found that the large vesicles harbored visible peaks for the 28S ribosomal RNA and a narrower peak for small-RNAs, while small vesicles had a broader small-RNA peak as determined by high-resolution electrophoresis (Agilent 2100 Bioanalyzer) (Fig. 2C). The typical sEV marker proteins CD9, CD63, CD81, TSG101, and HSP70 were also detected in our isolated vesicles, whereas the negative marker protein, calnexin, was absent (Additional file 1: Figure S2). These data proved that the extracted vesicles were extracellular and that miRNAs were mostly derived from small extracellular vesicles (sEVs).

To identify a global expression profile of circulating sEVs-derived miRNAs between non-R0 and R0 patients, we tested 37 AOC patient samples in the screening set (R0, n = 17; non-R0, n = 20) using small RNA sequencing. In all samples, we detected 1832 known miRNAs, and 151 differentially expressed miRNAs (DEMs) between the two groups (|log2(FC)|> 1 and P < 0.05) (Fig. 3A). To avoid the bias in the differential analysis caused by miRNAs with low expression, 51 DEMs (42 upregulated; 9 downregulated) with an average RPM > 50 was chosen from 151 DEMs as candidate miRNAs (Fig. 3B). The log (FC) value and the P value of these 51 selected DEMs are included in Additional file 1: Table S1. Unsupervised cluster heatmap and principal component analysis (PCA) further identified that these 51 DEMs could distinguish non-R0 subjects from R0 subjects (Fig. 3B, C). Moreover, the driver genes of HGSOC (TP53, PTEN, BRAC, RB1, etc.) have been recognized and published in Nature [28, 29]. 15 miRNAs targeting these driver genes were further filtered from 51 DEMs.

In the independent discovery set consisting of 30 AOC patients (R0, n = 15; non-R0, n = 15), we examined the diagnostic robustness of the above 15 selected miRNAs by TaqMan qRT-PCR for further screening of miRNAs with expressed commonality in order to exclude the influence of individual differences. The qRT-PCR data illustrated that 15 miRNAs had a potential co-regulation (r > 0.6) (Fig. 4A). The fold change, P value, and area under the receiver-operator characteristic (ROC) curve (AUC) of 15 miRNAs are shown in Additional file 1: Table S2. Then, we screened 8 miRNAs with significant differences from 15 miRNAs in the discovery set (Additional file 1: Table S2). Since the diagnostic performance of a single miRNA was relatively low (AUC: 0.58 ~ 0.78), we planned to use multiple miRNAs combined with clinical serum biomarkers to develop the prediction model.

To obtain better performance parameters and avoid overfitting the model, the best risk /predictive panel containing 4 miRNAs from the above 8 miRNAs was selected by least absolute shrinkage and selection operator (LASSO) regression (penalized coefficients, λ = 0.07) (Fig. 4B, C). The LASSO coefficient of 4-miRNA is displayed in Fig. 4D. Consistent with clinical application, CA-125 is an initial and important clinical biomarker of AOC [30], as CA-125 (AUC: 0.662) showed relatively higher diagnostic performance than CA-153 (AUC: 0.567) and CA-199 (AUC: 0.533) in distinguishing non-R0 from R0 patients (Fig. 4E). Interestingly, a new combination signature of 4-miRNA and CA-125 (AUC: 0.973, 95% CI 0.838 ~ 1.000) demonstrated a higher detection accuracy than the 4-miRNA panel (AUC: 0.969) or CA-125 alone (AUC: 0.662) (Fig. 4E, F), just as serum CA19-9 could also improve the diagnostic performance of the miRNA biomarker panel for detecting gastric cancer [31] or pancreatic adenocarcinoma [32]. The indicators related to the ROC of the above predictors are shown in Table 2. The risk score model of 4-miRNA and CA-125 was established by logistic regression analysis as follows: Index mC = miR-320a-3p*(0.179) + miR-378a-3p*(0.067) + miR-1307-3p*(0.052) + let-7d-3p*(− 0.198) + CA-125*(0.122) + 0.701 (cutoff = 1.483). If the index mC of AOC patients is higher than the cutoff value, they would be defined as high-risk patients with residual disease.

Next, we evaluated the accuracy of this statistical model in the independent validation set (R0, n = 67; non-R0, n = 87). Compared to 4-miRNA, CA-125, CA-153, or CA-199 alone, the combined model had the highest diagnostic utility (AUC: 0.903, 95% CI 0.846 ~ 0.945, Fig. 5A, B).

Notably, cases in our diagnostic cohort (Fig. 1B) initially erroneously discriminated by CA-125 or 4-miRNA panel were significantly reduced after reclassification by our prediction model (Fig. 5C, D). The positive predictive value (PPV = 0.926) and negative predictive value (NPV = 0.871) of this model were significantly increased compared to CA-125 (PPV = 0.706; NPV = 0.712) (Table 1). Decision curve analysis (DCA) indicated that the decision utility of 4-miRNA combined CA-125 for defining high-risk groups was superior to CA-125 or 4-miRNA alone (Fig. 5E). The quantitative evaluation of net reclassification improvement (NRI) and integrated discrimination improvement (IDI) further showed that the additional predictive power of our combined model for the residual disease was significantly improved compared with CA-125 or 4-miRNA alone (model vs CA-125, NRI = 0.471, P < 0.001; IDI = 0.538, P < 0.001; model vs 4-miRNA panel, NRI = 0.122, P = 0.001; IDI = 0.185, P = 0.003) (Table 2). Pairwise comparison of ROC curves showed that the increased AUC value of the combined model had statistical significance compared with CA-125 (AUC: 0.914, 0.679; P < 0.0001) or 4-miRNA (AUC: 0.914, 0.853; P = 0.0007) (Table 2).

To further assess the model developed herein, we investigated the influence of patient backgrounds. Through postsurgical pathological reports, we found that non-R0 and R0 AOC cases were differed for stage, lymph node metastasis, and serum CA-125 levels, whereas age and P53 mutation did not differ significantly (Table 3, Additional file 1: Table S4–S6). Additionally, multivariate logistic regression analysis revealed that our newly developed 4-miRNA panel emerged as an independent risk feature for residual disease in both clinical cohorts (discovery set: OR = 2.415; 95% CI 1.779 ~ 3.279; P < 0.0001, validation set: OR = 1.866; 95% CI 1.649 ~ 2.110; P < 0.0001, Table 3). The post hoc power of our model was above 0.9. Circulating 4-miRNA expression was also significantly different between the OC and normal groups according to analysis of public datasets (GSE113486 and GSE94533) (Fig. 5F). These results indicated that the combining model of 4-miRNA and CA-125 represented a promising classifier for residual disease detection.

Interestingly, the expression trend of 4-miRNA in tumor tissues of AOC patients was consistent with that in circulating sEVs (Fig. 6A). In addition, for testing the differential diagnostic ability of this model, we calculated the model index score of patients with benign pelvic diseases (BPD), early-stage ovarian cancer with FIGO I or II (ESOC), and advanced colorectal cancer (ACC). Surprisingly, the model index scores of the above patients were significantly lower than AOC patients (Fig. 6B). Thus, we further investigated 4-miRNA expressions in different tissue-derived sEVs. The isolated tissue vesicles were sEVs based on characterization analysis (Additional file 1: Figures S3 and S4). The expression levels of 3 up-regulated miRNAs (miR-320a-3p, miR-378a-3p, miR-1307-3p) in primary tumor tissue (PTT) and metastatic tumor tissue (MTT)-derived tissue-sEVs were significantly higher than those in adjacent tissue (AT) and non-tumor tissue (NTT) groups, while let-7d-3p expression presented a reverse trend (Fig. 6C). There was no significant difference between PTT and MTT groups. These results demonstrated that sEVs 4-miRNA expressions were mainly affected by tumor tissue or tumor microenvironment.

We then detected the enrichment of known celltype-specific markers in the tumor tissue-sEV pool by western blot. EpCAM (epithelial cell adhesion molecule) [33], a transmembrane protein often overexpressed in epithelial carcinomas, was chosen as the epithelial ovarian cancer cells (EOCC) marker; FAP (fibroblast activation protein) [34] as the cancer-associated fibroblasts (CAFs) marker; CD31 [35] as the endothelial cells (ECs) marker and CD45 [36] as the tumor-infiltrating immune cells (TICs) marker. We observed that EpCAM, FAP, and CD31 were expressed abundantly in tissue-derived sEV fractions, whereas CD45 was almost undetectable (Fig. 6D). The results showed that the tumor tissue-sEVs pool was a relatively mixed environment, where EOCCs, CAFs, and ECs-derived sEVs were the main contributors, whereas TICs-derived sEVs were not enriched. We further sorted tissue-derived EVs from EOCCs, CAFs, and ECs by an immunoaffinity magnetic bead sorting system (MACS). Surprisingly, the expression levels of the 3 upregulated miRNAs in the EOCCs group were higher than those in the CAFs, and ECs groups, whereas let-7d-3p expression in the EOCCs group was the lowest (Fig. 6E).

Meanwhile, we were more interested in understanding the source of sEV 4-miRNA directly from circulation and excluding the effects of circulating confounders. EpCAM has long been utilized to detect circulating epithelial tumor cells [37] and their derived sEVs [13]. Previous studies showed that most circulating sEVs concentrated by ultracentrifugation in platelet-depleted plasma originated from erythrocytes and leukocytes rather than platelets or megakaryocytes as commonly thought [38]. CD235a and CD45 were assessed as erythrocyte and leukocyte markers, respectively. We captured plasma sEVs by MACS with these specific proteins. 3 up-regulated miRNAs were packaged at high concentrations in EOCCs-derived plasma EpCAM + -sEVs, while these were not detected in erythrocytes and leukocyte-derived sEV samples (Fig. 6F). Let-7d-3p expression was the lowest in the EOCCs group (Fig. 6F). Collectively, the differential expression of plasma sEV-derived 4-miRNA between the R0 and non-R0 groups was mainly dominated by tumor cells.

The Agilent 2100 Bioanalyzer results showed that the RNA content derived from plasma sEVs was decreased by about 55% compared with RNA extracted directly from total plasma, whereas RNA derived from plasma sEVs treated with RNase A was slightly decreased by about 13% (Additional file 1: Figure S5). Moreover, the RNase degradation assay showed that circulating miR-320a-3p and miR-1307-3p mainly existed in a sEVs manner, while a small part of miR-378a-3p and let-7d-3p was a free-form, and 4-miRNA in sEVs was largely not degraded after treatment with RNase A (Fig. 6G). These results revealed that circulating sEV 4-miRNA was sufficient stable and excluded possible contamination with non-sEV miRNAs during the extraction procedure.

Furthermore, we found 2701 target genes of 4 miRNAs in common between the TargetScan, miWalk, and residual disease-associated tissue datasets (Tothill dataset, GSE61568) (Fig. 7A). 4-miRNA target genes (TP53TG3, RB1, CCNE1, CSMD1) closely related to HGSOC were significantly differentially expressed between the R0 and non-R0 groups in the Tothill dataset (Fig. 7B). Gene Ontology (GO) analysis showed that these mRNAs were mainly concentrated in the pathways of cell migration and DNA damage repair (Fig. 7C). In addition, we found that the TP53, mTOR, FoxO, and ErbB-related signaling pathways were strongly enriched in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway categories (Fig. 7D). Gene set enrichment analysis (GSEA) also revealed that these mRNAs were significantly enriched in pathways of cell colonization and adhesion, tumor metastasis, and positive regulation of the protein metabolic process (Fig. 7E). Different but related tumor pathways of miRNA targeting suggested that 4-miRNA expression was closely associated with HGSOC progression from local to metastasis.