Quantitative analysis of the mitochondrial proteome in human ovarian carcinomas

in Endocrine-Related Cancer

Mitochondria play important roles in growth, signal transduction, division, tumorigenesis and energy metabolism in epithelial ovarian carcinomas (EOCs) without an effective biomarker. To investigate the proteomic profile of EOC mitochondrial proteins, a 6-plex isobaric tag for relative and absolute quantification (iTRAQ) proteomics was used to identify mitochondrial expressed proteins (mtEPs) in EOCs relative to controls, followed by an integrative analysis of the identified mtEPs and the Cancer Genome Atlas (TCGA) data from 419 patients. A total of 5115 quantified proteins were identified from purified mitochondrial samples, and 262 proteins were significantly related to overall survival in EOC patients. Furthermore, 63 proteins were identified as potential biomarkers for the development of an EOC, and our findings were consistent with previous reports on a certain extent. Pathway network analysis identified 70 signaling pathways. Interestingly, the results demonstrated that cancer cells exhibited an increased dependence on mitophagy, such as peroxisome, phagosome, lysosome, valine, leucine and isoleucine degradation and fatty acid degradation pathways, which might play an important role in EOC invasion and metastasis. Five proteins (GLDC, PCK2, IDH2, CPT2 and HMGCS2) located in the mitochondrion and enriched pathways were selected for further analysis in an EOC cell line and tissues, and the results confirmed reliability of iTRAQ proteomics. These findings provide a large-scale mitochondrial proteomic profiling with quantitative information, a certain number of potential protein biomarkers and a novel vision in the mitophagy bio-mechanism of a human ovarian carcinoma.

Abstract

Mitochondria play important roles in growth, signal transduction, division, tumorigenesis and energy metabolism in epithelial ovarian carcinomas (EOCs) without an effective biomarker. To investigate the proteomic profile of EOC mitochondrial proteins, a 6-plex isobaric tag for relative and absolute quantification (iTRAQ) proteomics was used to identify mitochondrial expressed proteins (mtEPs) in EOCs relative to controls, followed by an integrative analysis of the identified mtEPs and the Cancer Genome Atlas (TCGA) data from 419 patients. A total of 5115 quantified proteins were identified from purified mitochondrial samples, and 262 proteins were significantly related to overall survival in EOC patients. Furthermore, 63 proteins were identified as potential biomarkers for the development of an EOC, and our findings were consistent with previous reports on a certain extent. Pathway network analysis identified 70 signaling pathways. Interestingly, the results demonstrated that cancer cells exhibited an increased dependence on mitophagy, such as peroxisome, phagosome, lysosome, valine, leucine and isoleucine degradation and fatty acid degradation pathways, which might play an important role in EOC invasion and metastasis. Five proteins (GLDC, PCK2, IDH2, CPT2 and HMGCS2) located in the mitochondrion and enriched pathways were selected for further analysis in an EOC cell line and tissues, and the results confirmed reliability of iTRAQ proteomics. These findings provide a large-scale mitochondrial proteomic profiling with quantitative information, a certain number of potential protein biomarkers and a novel vision in the mitophagy bio-mechanism of a human ovarian carcinoma.

Introduction

Epithelial ovarian carcinoma (EOC) is the cause of more deaths than any other female genital tract cancers, and nearly accounts for 6% of all cancers among women (Sakhuja et al. 2017). Detection of early-stage EOCs is challenging since this disease is clinically asymptomatic in early stage, with being progressing toward the development of metastasis (Gadducci et al. 2007). In China, an increasing trend in mortality was observed for three of the ten most common cancers (breast, cervix and ovary), with stable trends for colorectum, lung, uterine and thyroid cancers (Chen et al. 2016). Even through tumor biomarkers CA125 and HE4 were widely used in clinical practice (Cymbaluk-Ploska et al. 2018), and more combined determinations would result in significantly improved sensitivity and efficiency, which might lead successfully to achieve a significant reduction in mortality.

Mitochondria are involved in various cellular processes, from regulation of metabolic flux to apoptosis. Mitochondrial dysfunctions have been proposed as a cause of cancer, are a biomarker for the early-stage detection of a cancer and are a therapeutic target for a cancer (Kim et al. 2017). Moreover, those mitochondrial ribosomal protein-encoding genes could be anti-oncogenes, which can even be new therapeutic targets or prognostic biomarkers. MRPL41, known as bcl-2-interacting mitochondrial ribosomal protein L41, indicated that the differential expression of MRPL41 in carcinomas is reflected by the various epigenetic states together with different responses and promoter methylation through the estrogen receptor (Kim et al. 2013). In addition, mitochondria could be 'fuel’ to a cancer metabolism. Higher expression of MRPS15 in epithelial breast cells was revealed in an analysis of paired adjacent stromal tissues and neoplasm tissues (Sotgia et al. 2012). Those studies showed that the neoplasm-relevant communication pathways were linked with mitochondrial proteins. The role of mitochondrial ribosomal protein S23 (MRPS23) in carcinoma cell proliferation could be a potential therapeutic target, in case of interference of hepatocellular cancer proliferation, oncogenesis and metastasis (Pu et al. 2017). The expression of COX1 was involved in endometrial cancers (Ksiezakowska-Lakoma et al. 2017), esophageal adenocarcinoma (Huhta et al. 2017) and lung cancer (Michalak et al. 2016). Taken all together, these examples support the notion that mitochondria contribute to the angiogenetic, tumorigenic, proliferation, invasive and metastatic features of cancer cells. However, no large-scale quantitative reference map of a human EOC mitochondrial proteome was reported previously. Moreover, only focusing on single molecule biomarker is a narrowest form for cancer prediction, prevention and treatment (Cheng & Zhan 2017, Zhan et al. 2017a). Cancer, by definition, is a kind of gene disease or protein disease, and results in a series of molecular alterations (Gonzalez-Angulo et al. 2012). Multiple biomarkers could provide a novel approach to predict, prevent and personalize the treatment of an EOC. Furthermore, newly discovered proteins could be used as potential targets or biomarkers (Wang et al. 2015). However, the proteomic profile of EOC mitochondrial proteins has not been elucidated. It is necessary to find a series of mitochondrial biomarkers on EOC in terms of detection technology. Proteomics has developed as a powerful approach to investigate novel biomarkers and drug targets (Ray et al. 2011), and it has mainly applied in the identification and quantification of proteomic components including post-translational modifications (Zhan et al. 2017b,c). Due to great improvement in mass spectrometry (MS) analysis, peptide identification and protein sequence coverage showed a preferable consistency in complex samples (Riley & Coon 2018). The commonly used quantitative proteomic methods include gel-based such as 2D gel electrophoresis (2DGE) and 2D difference in-gel electrophoresis (2D DIGE) and gel-free-based (Hu et al. 2013), to allow highly sensitive and high-throughput identification of proteins/peptides and post-translational modifications (van der Wal et al. 2018). In general, gel-free methods are able to break through restrictions of gel-based methods that are inefficient in resolving proteins that are insoluble, lowly abundant or large proteins (>200 kDa) (Pasing et al. 2017). Multi-dimensional liquid chromatography-tandem mass spectrometry (MDLC–MS/MS)-based proteomics techniques were developed rapidly, and isobaric tag for relative and absolute quantification (iTRAQ) was featured with the advantages of strong separation ability and analysis range. A few of the advantages of the use of iTRAQ to reveal biomarkers or molecular mechanisms of different cancers have been reported (Zhou et al. 2017). Even 2DGE-MS with isotopic labeling and the application of high-sensitivity MS enables the quantification of a much larger part (estimated up to at least 500,000 protein species) of the human proteome as assumed before (Zhan et al. 2018).

This study used iTRAQ proteomics to identify and quantify mitochondrial expressed proteins (mtEPs) in EOCs. A total of 5115 proteins was identified and quantified. Moreover, Kyoto encyclopedia of genes and genomes (KEGG) analysis found a variety of signaling pathways such as peroxisome, phagosome, lysosome, valine, leucine and isoleucine degradation, and fatty acid degradation pathway, and they might play an important role in EOC mitophagy. The Cancer Genome Atlas (TCGA) database and mtEPs data were integrated and analyzed to investigate the gene ontology (GO) functional enrichments, protein–protein interactions and gene coexpression. Thus, 63 proteins as potential biomarkers for the development of EOCs were identified; and ERBB2, PTBP1 and H2AFX as biomarkers confirmed with previous reports were significantly related to overall survival in EOCs. These findings provide a certain number of proteins with the potential biomarkers and a novel vision in the mitophagy bio-mechanism of human EOCs. More importantly, we propose the use of the multi-biomarkers strategy to predict, prevent and personalize the treatment of an EOC.

Materials and methods

EOC tissue specimen

Ovarian tissues provided by Department of Gynecology, Xiangya Hospital (Changsha, China) were obtained from 18 female patients (EOC: n = 7 and control: n = 11) and were approved by the Xiangya Hospital Medical Ethics Committee of Central South University, and informed consent was obtained from the participation. Seven EOC patients were diagnosed as high-grade, poorly or moderately differentiated carcinoma cells. Eleven control ovaries were with benign gynecologic diseases, such as fibroids, adenomyosis, ovary serous cystadenoma, cervical intraepithelial neoplasia, atypical hyperplasia of endometrium and pelvic organ prolapse. Each obtained tissue was quickly put into liquid nitrogen and then stored in −80°C.

Preparation of mitochondria

Ovarian tissue samples were divided into EOC (n = 7) and control (n = 11) groups. The mitochondrial isolation buffer was prepared with 210 mM mannitol, 70 mM sucrose, 100 mM potassium chloride (KCl), 1 mM diamine tetraacetic acid (EDTA), 50 mM Tris–HCl, 0.1 mM ethylene glycol bis(2-aminoethyl ether)tetraacetic acid (EGTA), 1 mM phenylmethanesulfonyl fluoride (PMSF) protease inhibitor, 2 mM sodium orthovanadate (V), 0.2% bovine serum albumin (BSA) and pH 7.4. (i) The EOC tissues (1.5 g) were fully minced (1 mm3 pieces) and homogenized (2 min; 4°C) in 13.5 mL mitochondrial isolation buffer that contained 0.2 mg/mL Nagarse. The tissue homogenates were well mixed with another 3 mL mitochondrial isolation buffer, followed by centrifugation (1300 g, 10 min and 4°C) to remove crude nuclear fraction. The supernatant was re-centrifuged (10,000 g, 10 min and 4°C) to remove microsomes in the supernatant. The debris was re-suspended in 2 mL mitochondrial isolation buffer, followed by centrifugation (7000 g, 10 min and 4°C) to obtain crude mitochondria in the debris (Jiang et al. 2004). The extracted crude mitochondria were dissolved in 12 mL 25% Nycodenz (Sigma), and then a discontinuous Nycodenz gradient was made through filling with 5 mL of 34%, 8 mL of 30%, 12 mL of 25% (contained crude mitochondria), 8 mL of 23%, and 3 mL of 20% Nycodenz from bottom to top in a tube, followed by centrifugation (52,000 g, 90 min, 4°C) (Schonenberger & Kovacs 2017). After centrifugation, the purified mitochondria were contained at the interface of 25–30%, which was collected in a clean tube. The collected mitochondria were diluted with mitochondrial isolation buffer to three-fold volume, and centrifuged (15,000 g, 20 min, 4°C). The debris was re-suspended in 2 mL mitochondrial isolation buffer, followed by centrifugation (15,000 g, 20 min, 4°C). The final debris was the purified mitochondria. (ii) For the mitochondria purified from the control tissues, the above procedure (i) was modified as the following: prior to homogenization, 8 mL of 0.05% trypsin/20 mM EDTA in PBS solution was added to the minced control tissues and digested for 30 min at room temperature, followed by centrifugation (200 g, 5 min). The discontinuous Nycodenz gradient was made through filling with 8 mL of 38%, 5 mL of 34%, 8 mL of 30%, 12 mL of 25% (contained crude mitochondria), 8 mL of 23% and 3 mL of 20% Nycodenz from bottom to top in a tube, followed by centrifugation (52,000 g, 90 min, 4°C). After centrifugation, the purified mitochondria were contained at the interface of 25–30% to the interface of 34–38%. The other procedure is the same as the procedure (i).

All purified mitochondria from EOC and control tissues were put together, respectively. Then, proteins were extracted from purified EOC and control mitochondria, respectively.

iTRAQ quantitative proteomics and bioinformatics

The purified EOC and control mitochondria were digested with trypsin. (i) iTRAQ labeling: The tryptic peptides were labeled with a 6-plex iTRAQ Multiplex Kit according to the manufacturer’s instructions (Applied Biosystems iTRAQ Reagents–Chemistry Reference Guide, P/N 4351918A). Briefly, the peptides were dissolved in 100 mM tetraethyl ammonium bromide solution (pH 8.5) before the labeling reagent was added. After 2-h incubation, the reaction was quenched by adding an equal volume of water. Six labeled peptide samples were mixed equally and dried with a speed-vac (Qi et al. 2016). (ii) Strong cation exchange (SCX) fractionation: The labeled peptide mixture was fractionated with SCX chromatography. (iii) LC–MS/MS: Each fractionation was subjected to LC–MS/MS analysis on a Q Exactive mass spectrometer (Thermo Scientific) that was coupled to Easy nLC (Proxeon Biosystems, now Thermo Fisher Scientific) for 60 min. MS/MS spectra were acquired and used to search protein database with MASCOT engine (Matrix Science, London, UK; version 2.2) embedded into Proteome Discoverer 1.4. The identified proteins were used for KEGG pathway analysis with Cytoscape (two-sided hypergeometric test, Kappa score >0.9, adjusted P value <0.01 corrected with Benjamini-Hochberg), and further for KEGG pathway enrichment with DAVID Bioinformatics Resources 6.7 (https://david.ncifcrf.gov/home.jsp). Heat map was plotted by Multiple Experiment Viewer (https://sourceforge.net/projects/mev-tm4/files/mev-tm4/). GO biological process (BP), molecular function (MF) and cellular component (CC) were analyzed with Cytoscape ClueGO (two-sided hypergeometric test, adjusted P value <0.05 corrected with Benjamini-Hochberg). GO CC was further enriched with PANTHER (http://www.pantherdb.org/). Biomarkers that had been reported were checked by CooLGeN (http://ci.smu.edu.cn/CooLGeN/Home.php).

TCGA data of EOC patients

TCGA data portal provides a platform for researchers to search, download and analyze datasets generated from TCGA database (http://cancergenome.nih.gov/) (Zhang et al. 2016). Level 3 RNA-seq V2 and clinical data were obtained from the TCGA data of 419 EOC patients. The gene expression missing value (expression = 0) more than 20% was excluded after the pretreatment. Overall survival analysis of genes in EOCs was calculated by the Kaplan–Meier method, and compared to the log-rank test with R 3.4.2 version (https://www.r-project.org/). The P value less than 0.01 was considered as statistically significant.

Prediction of protein–protein interaction

The overlapped proteins of iTRAQ-identified proteins and TCGA overall related genes were analyzed by STRING 10.0 (http://string-db.org/cgi/input.pl) with a high confidence of parameter (>0.700).

Construction of mRNA–mRNA network of the overlapped proteins

The Pearson correlation test was used to determine whether the expression levels of overlapped proteins of iTRAQ identified proteins and TCGA overall related genes were correlated with each other, respectively. The mRNA–mRNA correlations of those overlapped proteins with P values <0.05 were corrected with Benjamini-Hochberg. Extracted data with negative correlation (r < −0.5) or positive correlation (r > 0.7) from were used to construct circus chart by Package RCircos. Each line denoted one mRNA–mRNA pair. Each region along the circle represented one of the 24 chromosomes (Zhang et al. 2013). Chromosomal location was obtained by Ensembl (http://asia.ensembl.org/index.html).

Cell lines and cell culture

EOC cell line TOV-21G cells and control cell line IOSE80 cells were purchased from Keibai Academy of Science (Nanjing, China). TOV-21G cells were cultured in RPMI-1640 medium, and IOSE80 cells were cultured in DMEM medium (Corning, NY, USA) supplemented with 10% fetal bovine serum (FBS, Gibco). All these cells were maintained with 5% CO2 atmosphere at 37°C.

RNA extraction and quantitative real-time PCR (qRT-PCR) analyses

Total RNAs were extracted from cell lines with TRizol Reagent (Invitrogen) according to the manufacturer’s instructions. For the detection of SNHG3 and target genes, total RNAs were reversely transcribed into cDNAs and then used to perform qRT-PCR with SYBR Premix Ex Taq (TaKaRa). β-actin was used as an internal control for mRNA quantification.

Western blotting

Equal amounts of proteins were separated by 10% SDS-PAGE gels and blotted onto nitrocellulose membranes. The blotted proteins on the membrane were incubated with primary antibodies against GLDC, PCK2, IDH2, CPT2 and HMGCS2 (1:1000; Abcam) and β-actin (1:2000; Santa Cruz Biotechnology) at 4°C overnight. The membranes were incubated for 2 h with horseradish peroxidase-conjugated goat anti-rat secondary antibody (1:5000; Santa Cruz Biotechnology) at room temperature.

Statistical analysis

Data were expressed as the mean ± s.d. of triplicates. Each experiment was repeated at least three times. Statistical analyses were performed using SPSS 13.0 (SPSS Inc.). The Student’s t-test was used to assess the between-group differences of in vitro studies with a statistical significance level of P < 0.05. Some cases were corrected with Benjamini-Hochberg (FDR) for multiple testing.

Results

Quantitative proteome map of EOC mitochondria

The iTRAQ-SCX-LC–MS/MS quantitative analysis of isolated mitochondrial samples identified a total of 5115 proteins that were present in EOC and control tissues (Supplementary Table 1, see section on supplementary data given at the end of this article). Each protein was identified with at least one peptide sequence matches (PSMs). Those proteins identified in EOC and control tissues were distributed within a range of molecular weight (MW) 2.6–1158.2 kDa and pI 3.81–12.25, which was consistent with the pI distribution pattern of the previously identified proteins in the mitochondrial fraction reported by Rezaul et al. (Rezaul et al. 2005). In addition, iTRAQ proteomic analysis also obtained protein quantitative information, including 2565 (50.14%) upregulated proteins and 2550 (49.86%) downregulated proteins in EOC relative to control tissues. The top 10 upregulated proteins were androgen-induced 1/Golgi SNAP receptor complex member 1 variant 1 fusion protein (AIG1), pituitary tumor-transforming gene 1 protein-interacting protein (PTTG1IP), protein S100-A14 (S100A14), agmatinase mitochondrial (AGMAT), normal mucosa of esophagus-specific gene 1 protein (NMES1), tetraspanin-1 (TSPAN1), protein-glutamine gamma-glutamyltransferase K (Fragment) (TGM1), MHC class II antigen (Fragment) (HLA-DPA1), uncharacterized protein C1orf53 (fragment) (C1orf53) and MHC class I antigen (Fragment) (HLA-A). The top 10 downregulated proteins were collagen alpha-1(II) chain (COL2A1), nanospan (NSPN), SUN domain-containing protein 2 (fragment) (SUN2), pumilio homolog 1 (Fragment) (PUM1), ubiquitin C (UbC), Xin actin-binding repeat-containing protein 1 (XIRP1), cytochrome c oxidase subunit 4 isoform 2 (COX4I2), histone H2A type 2-B (HIST2H2AB), collagen, type V, alpha 2, isoform CRA_b (COL5A2) and collagen alpha-1 (X) chain (COL10A1). Comparative proteomics analysis focuses on those statistically significantly differentially expressed proteins (DEPs) that might be the real potential biomarkers. However, one could not ignore those no-expression-level-changed proteins, which might have experienced post-translational modifications, such as phosphorylation, glycosylation, acetylation, methylation, nitration, ubiquitylation, sumoylation, succinylation, sulfation, myristoylation, palmitoylation, deamidation, prenylation and hydroxylation (Cleland 2018, Khan et al. 2018, Sloutsky & Naegle 2018) to result in different proteoforms or they might be hub-molecules in a molecular network with less changes relative to the boundary molecules in a given condition (Zhan et al. 2017b).

GO enrichment analysis

To ascertain how the identified proteins promoted phenotype in an EOC tissue, GO analysis was performed using PANTHER. A total of 5115 proteins were classified according to the CCs. Figure 1A showed variable CCs: cell part (42.7%), organelle (28.2%) and macromolecular complex (17.8%). Moreover, 1108 overall survival-related genes were obtained by TCGA database (Supplementary Tables 2, 3 and 4). There were 262 significant proteins (Supplementary Table 5) obtained when overlapping analysis was performed between 5115 identified proteins and TCGA database (Fig. 1C). Moreover, 262 overlapped proteins were classified according to the BP, CC and MF. As shown in Fig. 1D, E and F, the less P value and more significant enrichment were shown with the greater node size. The same color indicated the same function group. Among the groups, a representative was chosen of the most significant term and lag highlighted. The overlapped proteins were mainly distributed in endoplasmic reticulum, cellular amino acid metabolic process, viral process, regulation of cellular response to heat and response to heat according to BP (Supplementary Table 6). The localization of overlapped proteins was also differentially distributed in peroxisome, COP9 signalosome, intracellular ribonucleoprotein complex, cell-substrate adherens junction and mitochondrial matrix according to CC (Supplementary Table 7). A series of MFs were involved in 262 overlapped proteins, including GTP binding, unfolded protein binding, oxidoreductase activity (acting on CH-OH group of donors), intramolecular oxidoreductase activity and aminoacyl-tRNA ligase activity (Supplementary Table 8). Compared to previous studies, 992 proteins of 5115 proteins have been reported to relate with ovary (Supplementary Table 9). For example, VTCN1 was overexpressed in early-stage EOCs and was independent of CA125 expression (Simon et al. 2007, Fortner et al. 2018) and CDKN2A was the similar biomarker for early-stage EOCs (Jiang et al. 2017). CD44, PLAT and PTBP1 showed prognostic value in EOC patients (Borgfeldt et al. 2003, Zhang et al. 2010, Bartakova et al. 2018). Correlation between tumor mesothelin (MSLN) expression and serum MSLN in EOC patients was fine (Hanaoka et al. 2017). In our findings, 63 proteins were identified as novel biomarkers in EOCs (the fold change ≥1.5) (Table 1). Our findings were consistent with previous reports on a certain extent such that ERBB2, PTBP1 and H2AFX were not only biomarkers but also significantly related to overall survival (Fig. 2D, E and F).

Figure 1
Figure 1

Network analysis of identified proteins by iTRAQ. (A) A total of 5115 proteins was classified according to the cell components with PANTHER. (B) KEGG pathway analysis mapped the identified proteins to 70 signaling pathways. (C). The overlapped proteins were obtained between 5115 identified proteins and TCGA overall related survival genes. (D, E and F) The overlapped proteins were classified according to the biological process (BP), cellular component (CC) and molecular function (MF). The less P value and more significant enrichment were shown with the greater node size. The same color indicated the same function group. Among the groups, we chose a representative of the most significant term and lag highlighted. A full colour version of this figure is available at https://doi.org/10.1530/ERC-18-0243.

Citation: Endocrine-Related Cancer 25, 10; 10.1530/ERC-18-0243

Figure 2
Figure 2

The interaction networks of identified proteins. (A) The protein–protein interactions (PPIs) network of 262 overlapped proteins. (B) The mRNA-mRNA pair analysis of 262 overlapped proteins constructed circus chart by R package. Upregulated proteins by iTRAQ in red letters and downregulated proteins in green letters. Red line represents positive correlation and green line represents negative correlation. (C) Cancer cells exhibit an increased dependence on mitophagy, such as peroxisome, phagosome, valine, leucine and isoleucine degradation, fatty acid degradation pathway. D-F. Kaplan-Meier (KM) survival curve of ERBB2, PTBP1 and H2AFX in an epithelial ovarian carcinoma (EOC). A full colour version of this figure is available at https://doi.org/10.1530/ERC-18-0243.

Citation: Endocrine-Related Cancer 25, 10; 10.1530/ERC-18-0243

Table 1

63 proteins identified as biomarkers in ovarian cancers.

Swiss-Prot accessionDescriptionCoverage (%)Unique peptidesPSMsMW (kDa)Calc. pIRatio (T/N)t-test P value
P0C0S5Histone H2A31.322213.5410.60.250.00081850
P16104Histone H2AX21.713515.1410.70.330.00011480
P55083Microfibril-associated glycoprotein 414.5 42328.63 5.6 0.36 0.00056068
Q53TN4Cytochrome b reductase 17.7 2331.62 8.8 0.44 0.00246457
Q13884Beta-1-syntrophin5.4 1358.03 8.6 0.45 0.00355956
P84157Matrix-remodeling-associated protein 727.0 3621.45 4.3 0.45 0.00258059
A7YME7IASPP short isoform5.7 1144.08 4.9 0.47 0.00426621
Q5VY30Retinol binding protein 4, plasma, isoform CRA_b5.01222.96 6.1 0.48 0.00014748
Q9NQG1Protein MANBAL23.5249.46 9.2 0.49 0.00204730
Q4KMQ2Anoctamin-67.8 46106.10 7.8 0.50 0.00026427
A6NLN1Polypyrimidine tract binding protein 1, isoform CRA_b12.7 51456.48 9.4 0.50 0.00094192
P22626Heterogeneous nuclear ribonucleoproteins A2/B119.3 62037.41 9.0 0.51 0.00126949
O75954Tetraspanin-94.6 1126.76 7.7 0.52 0.00345367
E9PIY1Protein kinase C and casein kinase substrate in neurons protein 313.8 4537.19 6.9 0.53 0.00852630
A0A0S2Z4B5Serine/threonine-protein phosphatase (Fragment)1.8 1157.62 6.3 0.54 0.00095765
P3005060S ribosomal protein L1244.9 51217.81 9.4 0.57 0.00161750
F8W150Ankyrin repeat domain-containing protein 13A (Fragment)14.5 119.37 6.8 0.58 0.00239321
H0YMA2Tropomodulin-2 (Fragment)12.7 2712.38 9.4 0.59 0.00074392
O43491Band 4.1-like protein 230.1 2038112.52 5.4 0.60 0.00039823
P07355Annexin A261.7 2111038.58 7.8 0.61 0.00087133
F6RFD5Destrin44.4 62015.39 8.6 0.62 0.00045171
B7ZLW0LPP protein10.6 4765.73 7.4 0.63 0.00184847
P04004Vitronectin21.3 92854.27 5.8 0.63 0.00052603
Q16186Proteasomal ubiquitin receptor ADRM16.1 2442.13 5.1 0.63 0.00219031
J3KTF8Rho GDP-dissociation inhibitor 1 (Fragment)25.4 4721.50 5.5 0.64 0.00621335
Q86SR1Polypeptide N-acetylgalactosaminyltransferase 1012.4 5768.95 8.6 0.64 0.00045129
P2539840S ribosomal protein S1253.8 72114.51 7.2 0.65 0.00153508
K7ES31Eukaryotic translation initiation factor 3 subunit K20.4 2415.86 6.7 0.65 0.00562823
F8W8H5Ras-related protein Rab-245.2 1119.66 5.4 0.66 0.00776930
O75369Filamin-B26.6 4495277.99 5.7 0.66 0.00535785
E9PKG6Nucleobindin-241.4 112640.34 5.2 1.51 0.00014496
Q14554Protein disulfide-isomerase A526.6 122559.56 7.9 1.51 0.01206170
Q9BZF19.5 810101.13 7.0 1.52 0.00131055
Q6ZNC8Lysophospholipid acyltransferase 12.8 1156.52 9.2 1.53 0.04319160
Q04837Single-stranded DNA-binding protein, mitochondrial55.4 77817.25 9.6 1.53 0.00187904
Q9Y5M8Signal recognition particle receptor subunit beta44.3 113729.68 9.0 1.55 0.01881880
A0A087WVA1Selenoprotein T10.3 2222.33 8.8 1.59 0.01394870
Q9NPJ3Acyl-coenzyme A thioesterase 1325.0 3814.95 9.1 1.62 0.00447179
I3L3P5Protein disulfide-isomerase (Fragment)57.7 13416.87 4.8 1.67 0.00135696
C5HTZ1Amiloride-sensitive sodium channel subunit alpha4.5 1128.38 6.7 1.68 0.00519917
Q14696LDLR chaperone MESD34.6 81326.06 7.8 1.68 0.00845485
O75396Vesicle trafficking protein SEC22b29.3 61924.58 6.9 1.69 0.00132399
Q8IUS5Epoxide hydrolase 413.0 4442.30 8.4 1.72 0.00500565
Q9HD23Magnesium transporter MRS2 homolog, mitochondrial14.0 5750.29 6.9 1.72 0.00200604
K7EIE9Tyrosine-protein phosphatase non-receptor type 2 (Fragment)7.3 1117.74 9.7 1.75 0.04562050
H0YDQ1Probable dolichyl pyrophosphate Glc1Man9GlcNAc2 alpha-1,3-glucosyltransferase (Fragment)10.3 129.78 10.1 1.77 0.00484252
O43295SLIT-R0.8 11124.43 6.7 1.79 0.01316100
Q8N4H5Mitochondrial import receptor subunit T27.5 246.03 9.7 1.79 0.00695095
P13667Protein disulfide-isomerase A461.7 4226872.89 5.1 1.81 0.00452458
Q96RP9Elongation factor G, mitochondrial36.2 255683.42 7.0 1.88 0.00092116
Q4G148Glucoside xylosyltransferase 18.2 3550.53 8.6 1.92 0.00044414
P54687Branched-chain amino acid aminotransferase, cytosolic5.4 2242.94 5.3 1.92 0.00195883
Q15084Protein disulfide-isomerase A658.2 189148.09 5.1 1.96 0.00742499
G3V5T0Maleylacetoacetate isomerase28.7 1722.60 7.7 2.07 0.00081986
Q16822Phosphoenolpyruvate carboxykinase [GTP], mitochondrial35.0 13170.68 7.6 2.18 0.00448436
P50591Tumor necrosis factor ligand superfamily member 104.3 1132.49 7.4 2.38 0.00972657
Q168802-hydroxyacylsphingosine 1-beta-galactosyltransferase5.9 3361.40 9.5 2.38 0.01062680
P09758Tumor-associated calcium signal transducer 226.6 71035.69 8.9 2.41 0.00111629
P55145Mesencephalic astrocyte-derived neurotrophic factor42.3 82320.69 8.7 2.48 0.00286699
Q6P1M0Long-chain fatty acid transport protein 411.7 51972.02 8.5 2.51 0.00846281
Q9UBX3Mitochondrial dicarboxylate carrier34.5 11231.26 9.5 2.54 0.01059370
Q96CP7Calfacilitin3.2 1128.53 9.5 2.57 0.00684684
Q9BSE5Agmatinase, mitochondrial11.4 2337.64 7.6 3.40 0.00074157

MW, Molecular weight; PSMs, Peptide sequence matches; Ratio (T/N), Ratio of tumors to normal controls.

The protein–protein and mRNA–mRNA interactions of 262 overlapped proteins

The 262 overlapped proteins were uploaded to STRING for protein–protein interaction analysis. The combined scores of nodes ranged from 0.700 to 0.999. Some key proteins were identified in the part of the common proteins between 5115 identified proteins and TCGA database’s RNAs, such as VCP (fold change = 1.04, P = 0.05), RHOA (fold change = 0.88, P = 0.04), RPL7A (fold change = 1.12, P = 0.03), HSPA8 (fold change = 0.90, P = 0.08), HSPD1 (fold change = 1.42, P = 0.03) and PRPF19 (fold change = 0.93, P = 0.31) (Fig. 2A; Supplementary Table 10). The 262 overlapped proteins were also used to construct circus chart by R package. Examination of the genomic location of the correlated mRNAs within each mRNA–mRNA pair revealed that most of these genes resided on different chromosomes. However, there were some gene pairs sited in different chromosomal location, including CTSS-GBP2, CTSS-GBP4, IFT74-PLAA, GRAMD4-SBF1, ALG8-NARS2, RPL12-RPL7A, GBP2-GBP4, GBF1-HIF1AN, PLXNB2-SBF1, RARS2-SNX14 and PTPN2-SEH1L (Fig. 2B). These results indicated whether genes affected by each other through neighboring genes or not (Blinka et al. 2016). The mRNA–mRNA correlations were revealed (Supplementary Table 11), and chromosomal locations were obtained by Ensembl (Supplementary Table 12).

KEGG pathway enrichment analysis indicated mitophagy

KEGG pathway analysis mapped the identified proteins to 70 signaling pathways (Fig. 1B; Supplementary Table 13). Interestingly, the results demonstrated that cancer cells exhibited an increased dependence on mitophagy, such as peroxisome, phagosome, lysosome, valine, leucine and isoleucine degradation and fatty acid degradation pathway, which might play an important role in EOC invasion and metastasis (Fig. 2C). Mitophagy involves the engulfment of any material in a double-membrane enclosed autophagosome, which subsequently fuses with lysosomes. In autophagosomes, mitochondrion, proteins or peroxisome were usually observed. Autophagosomes fuse with lysosomes and emit high-energy substances, including fatty acid and amino acid (Zimmermann & Reichert 2017). In this progress, mitophagy is dependent on the general autophagy machinery and relies on a growing cadre of ‘mitophagy adaptors’ and regulatory molecules, such as FUNDC1, BNIP3L(NIX), PGAM5, CK, OPA1, prohibitin2, OPTN, TBK1, p62 and Bcl2-L13 (Drake et al. 2017). This current study identified those proteins too, even though few of them appeared to have changing expression patterns (Table 2). The results were consistent with previous studies, for those proteins activated downstream mitophagy by post-translational modifications (Zimmermann & Reichert 2017).

Table 2

Mitophagy adaptors and regulatory molecules involved the identified proteins in ovarian cancer biological system.

Accession numberProtein nameGene nameCoverage (%)Unique peptidesPSMsMW (kDa)Calc. pIRatio (T/N)t-test P value
Q8IVP5FUN14 domain-containing protein 1FUNDC110.971117.168.621.160.048224
H0YBC7BCL2/adenovirus E1B 19 kDa protein-interacting protein 3-like (Fragment)BNIP3L(NIX)9.191220.085.710.770.002312
Q96HS1Serine/threonine-protein phosphatase PGAM5, mitochondrialPGAM532.53103731.988.681.490.003318
E7EU96Casein kinase II subunit alphaCSNK2A1 (CK)25.456845.287.940.840.011967
O60313Dynamin-like 120 kDa protein, mitochondrialOPA151.1544130111.567.871.190.000372
Q99623Prohibitin-2PHB281.612422033.289.831.260.000444
A0A0S2Z5I6Optineurin isoform 3 OPTN7.942228.305.290.620.010129
B4E164cDNA FLJ56613, highly similar to Serine/threonine-protein kinase TBK1 (EC 2.7.11.1)TBK12.421156.966.771.250.011201
B4E3V2cDNA FLJ52854, highly similar to Sequestosome-1p6210.471131.815.411.100.19687
B7Z737cDNA FLJ52784, highly similar to Bcl-2-like 13 proteinBcl2-L1313.172221.624.390.810.039927

MW, Molecular weight; PSMs, Peptide sequence matches; Ratio (T/N), Ratio of tumors to normal controls.

A peroxisome is a kind of organelle in nearly all eukaryotic cells. It is involved in metabolism of fatty acids, amino acids and polyamines, and reduction of reactive oxygen, and also contains two enzymes in the pentose phosphate pathway, so it is important for energy metabolism (Wanders & Waterham 2006). The peroxisome-related proteins were notably increased in EOCs relative to controls (fold change >1.5), including NUDT19 (fold change = 1.54, P = 0.006), PECR (fold change = 1.56, P = 0.0004), PEX13 (fold change = 1.61, P = 0.003), ABCD3 (fold change = 1.72, P = 0.003), PEX14 (fold change = 1.98, P = 0.0006), IDH2 (fold change = 2.01, P = 0.002), GSTK1 (fold change = 2.13, P = 0.0000004) and ACAA1 (fold change = 2.70, P = 0.01) (Fig. 3 and Table 3), which indicated peroxisome was metabolically active in EOC tissues. Mitophagy is the selective degradation of mitochondria by autophagy. Defective mitochondria following stress or damage were swallowed by phagosomes (Lemasters 2005). The phagosome-related proteins were significantly increased in EOCs relative to controls (fold change >1.5), including TAP (fold change = 1.60, P = 0.008), MSR1 (fold change = 1.61, P = 0.02), FcyR (fold change = 1.64, P = 0.005), SEC22 (fold change = 1.69, P = 0.01) and p22phox (fold change = 2.10, P = 0.0002) (Fig. 4 and Table 4), which indicated phagosome was metabolically active in EOC tissues.

Figure 3
Figure 3

Peroxisome pathway altered in an ovarian cancer. Green rectangle with red mark means the identified proteins. Green rectangle without red mark means species-specific enzymes. White rectangle means reference pathway. The solid line means molecular interaction. The dot line means indirect effect. The circle means mostly chemical complex. The pathway node in the right panel corresponds to the red marked node in the left diagram. ID number is the Swiss-Prot accession number. Ratio (T/N) = Ratio of tumors to controls. A full colour version of this figure is available at https://doi.org/10.1530/ERC-18-0243.

Citation: Endocrine-Related Cancer 25, 10; 10.1530/ERC-18-0243

Figure 4
Figure 4

Phagosome pathway altered in an ovarian cancer. Green rectangle with red mark means the identified proteins. Green rectangle without red mark means species-specific enzymes. White rectangle means reference pathway. The solid line means molecular interaction. The dot line means indirect effect. The circle means mostly chemical complex. The pathway node in the right panel corresponds to the red marked node in the left diagram. ID number is the Swiss-Prot accession number. Ratio (T/N) = Ratio of tumors to controls. A full colour version of this figure is available at https://doi.org/10.1530/ERC-18-0243.

Citation: Endocrine-Related Cancer 25, 10; 10.1530/ERC-18-0243

Table 3

Peroxisome pathway involved the identified proteins in ovarian cancer biological system.

Accession numberProtein nameGene nameCoverage (%)Unique peptidesPSMsMW (kDa)Calc. pIRatio (T/N)t-test P value
Q9Y2Q3Glutathione S-transferase kappa 1GSTK166.371312925.488.412.134.24E-07
P091103-ketoacyl-CoA thiolase, peroxisomalACAA146.2345044.268.442.700.010033
H7C1313-ketoacyl-CoA thiolase, peroxisomal (Fragment) ACAA156.2113830.318.630.920.363631
P04040CatalaseCAT42.13195859.727.381.330.002223
P43155Carnitine O-acetyltransferase CRAT21.09132270.818.440.870.008569
Q99489D-aspartate oxidaseDDO6.161137.517.910.980.801381
Q9BTZ2Dehydrogenase/reductase SDR family member 4DHRS429.571929.528.561.340.001585
O95822Malonyl-CoA decarboxylase, mitochondrialMLYCD27.7992354.978.951.170.007631
A8MXV4Nucleoside diphosphate-linked moiety X motif 19NUDT1917.8751142.217.641.550.006403
Q9BY49Peroxisomal trans-2-enoyl-CoA reductasePECR26.471032.528.811.570.000414
Q92968Peroxisomal membrane protein PEX13PEX132.481244.108.051.610.003575
O75381Peroxisomal membrane protein PEX14PEX1414.855741.214.941.990.000651
P56589Peroxisomal biogenesis factor 3PEX38.853442.118.151.220.013231
Q06830Peroxiredoxin-1PRDX156.2886622.108.130.990.687097
Q7Z7M4Superoxide dismutase (Fragment)SOD257.28109723.667.311.490.000523
P48735Isocitrate dehydrogenase [NADP], mitochondrialIDH256.642735550.888.692.020.002074
P00441Superoxide dismutase [Cu-Zn]SOD129.8731115.936.130.760.002799
E7ETN3Uncharacterized protein NUDT1217.581525123.946.801.270.023697
Q9ULC5Long-chain-fatty-acid--CoA ligase 5ACSL57.473575.946.921.200.033585
Q9HC35Echinoderm microtubule-associated protein-like 4EML46.3257108.846.400.860.040138
P28288ATP-binding cassette sub-family D member 3ABCD319.88124175.439.361.730.003973
Q969G5Protein kinase C delta-binding proteinPRKCDBP18.775827.686.430.600.002111
Q99424Peroxisomal acyl-coenzyme A oxidase 2ACOX223.05141976.787.561.330.001417
O96011Peroxisomal membrane protein 11BPEX11B35.1481928.419.851.420.003051
Q96CP5PMPCB protein (Fragment)PMPCB24.17112253.486.711.410.003341

MW, Molecular weight; PSMs, Peptide sequence matches; Ratio (T/N), Ratio of tumors to normal controls.

Table 4

Phagosome pathways involved identified proteins operated in ovarian cancer biological system.

Pathway codeAccession numberProtein nameGene nameCoverage (%)Unique peptidesPSMsMW (kDa)Calc. pIRatio (T/N)t-test P value
ATPaseP27449V-type proton ATPase 16 kDa proteolipid subunitATP6V0C11.6111815.738.441.630.074595
ATPaseP38606V-type proton ATPase catalytic subunit A ATP6V1A23.5131968.265.521.120.948809
ATPaseP21281V-type proton ATPase subunit B, brain isoformATP6V1B211.552656.465.810.990.037427
ATPaseP21283V-type proton ATPase subunit C 1ATP6V1C115.186743.917.461.1860.000291
ATPaseP36543V-type proton ATPase subunit E 1ATP6V1E110.183626.138.001.410.494573
ATPaseQ16864V-type proton ATPase subunit FATP6V1F7.561113.365.521.090.00215
C3bP01024Complement C3C344.0259144187.036.400.890.001276
CANXP27824CalnexinCANX38.511516767.534.601.410.011842
calnixinD6RB85Calnexin (Fragment) CANX50.6915815.994.741.380.007655
collectinsQ9BWP8Collectin-11COLEC114.061128.655.410.530.018448
coroninP31146Coronin-1ACORO1A29.0781150.996.681.160.561446
cathepsinQ9HBQ7Cathepsin L, isoform CRA_bCTSL9.271116.834.581.090.000904
p22phoxP13498Cytochrome b-245 light chainCYBA11.282321.009.542.100.000256
gp91P04839Cytochrome b-245 heavy chainCYBB12.9881565.298.631.410.031874
DyneinQ9Y6G9Cytoplasmic dynein 1 light intermediate chain 1DYNC1LI110.525756.546.420.830.006861
DyneinO43237Cytoplasmic dynein 1 light intermediate chain 2DYNC1LI210.164754.076.380.800.009882
EEA1Q15075Early endosome antigen 1EEA16.95810162.375.680.810.005954
FcyRP12318Low affinity immunoglobulin gamma Fc region receptor II-aFCGR2A3.151134.986.681.640.005709
αvβ3P05556Integrin beta-1ITGB134.46236788.365.390.850.009677
αvβ5P18084Integrin beta-5ITGB59.145788.006.061.460.222117
M6PRP20645Cation-dependent mannose-6-phosphate receptorM6PR20.9451030.975.830.930.01956
MPOP05164MyeloperoxidaseMPO26.17163383.818.991.400.002417
MRC2Q9UBG0C-type mannose receptor 2MRC29.061216166.575.830.610.00081
MSR1P21757Macrophage scavenger receptor types I and IIMSR11.551149.735.911.610.020868
Rab5P20339Ras-related protein Rab-5ªRAB5A4031423.648.151.270.005865
Rab7P51149Ras-related protein Rab-7aRAB7A76.33132923.476.701.200.001324
SEC22O75396Vesicle trafficking protein SEC22bSEC22B29.361924.586.911.690.012313
SEC22I1VE18SEC22 vesicle trafficking protein B (Fragment)SEC22B37.74236.124.941.370.026863
SEC61A1P61619Protein transport protein Sec61 subunit alpha isoform 1SEC61A115.5571952.238.061.400.333448
STX7O15400Syntaxin-7STX715.713329.805.550.970.001201
TAPX5CMH5Antigen peptide transporter 2TAP228.31173677.657.851.600.008156
TfRP02786Transferrin receptor protein 1TFRC27.63183984.826.611.450.244793
α2β1F5H4Z8Thrombospondin-3THBS38.1357103.194.641.060.019908
TUBBP07437Tubulin beta chainTUBB70.95421549.644.891.300.029027
TUBBQ13885Tubulin beta-2A chainTUBB2A62.47315149.874.890.860.879846
TUBBQ9BUF5Tubulin beta-6 chain TUBB646.1956649.834.880.990.012313

MW, Molecular weight; PSMs, Peptide sequence matches; Ratio (T/N), Ratio of tumors to normal controls.

Autophagosomes fuse with lysosomes to form autolysosome, and ‘goods’ in autophagy has also been degraded into products (fatty acids and amino acids). Those kind of high-energy substances were transported to the cytoplasm of cells for reuse (Lemasters 2005). The lysosome pathway-related proteins were significantly increased in EOCs relative to controls (fold change >1.5), including CD63 (fold change = 1.76, P = 0.007948), ATP6V0C (fold change = 1.63, P = 0.000424), GNPTG (fold change = 1.57, P = 0.018055) and ACP2 (fold change = 1.64, P = 0.006068), AP1M2 (fold change = 1.603138, P = 0.003299), CTSG AP1M2 (fold change = 1.62, P = 0.029695) (Fig. 5 and Table 5), which indicated lysosome pathway was metabolically active in EOC tissues. The fatty acid degradation-related proteins were significantly increased in EOCs relative to controls (fold change >1.5), including ECHS1 (fold change = 1.52, P = 0.003), EHHADH (fold change = 1.62, P = 0.002), ECI1 (fold change = 1.64, P = 0.001) and CPT2 (fold change = 2.05, P = 0.019) (Fig. 6 and Table 6), which indicated fatty acid degradation pathway was metabolically active in EOC tissues.

Figure 5
Figure 5

Lysosome pathway altered in an ovarian cancer. Green rectangle with red mark means the identified proteins. Green rectangle without red mark means species-specific enzymes. White rectangle means reference pathway. The solid line means molecular interaction. The dot line means indirect effect. The circle means mostly chemical complex. The pathway node in the right panel corresponds to the red marked node in the left diagram. ID number is the Swiss-Prot accession number. Ratio (T/N) = Ratio of tumors to controls. A full colour version of this figure is available at https://doi.org/10.1530/ERC-18-0243.

Citation: Endocrine-Related Cancer 25, 10; 10.1530/ERC-18-0243

Figure 6
Figure 6

Fatty acid degradation pathway altered in an ovarian cancer. Green rectangle with red mark means the identified proteins. Green rectangle without red mark means species-specific enzymes. White rectangle means reference pathway. The solid line means molecular interaction. The dot line means indirect effect. The circle means mostly chemical complex. The pathway node in the upper panel corresponds to the red marked node in the lower diagram. ID number is the Swiss-Prot accession number. Ratio (T/N) = Ratio of tumors to controls. A full colour version of this figure is available at https://doi.org/10.1530/ERC-18-0243.

Citation: Endocrine-Related Cancer 25, 10; 10.1530/ERC-18-0243

Table 5

Lysosome pathways involved identified proteins operated in ovarian cancer biological system.

Accession numberProtein nameGene nameCoverage (%)Unique peptidesPSMsMW (kDa)Calc. pIRatio (T/N)t-test P value
F8VV56CD63 antigenCD6320.6931016.007.881.760.007948
P27449V-type proton ATPase 16 kDa proteolipid subunitATP6V0C11.6111815.728.441.630.000424
Q8TBM3V-type proton ATPase subunit aATP6V0A26.182243.145.821.400.002528
P17900Ganglioside GM2 activatorGM2A13.473320.825.311.230.00379
A2VDJ4GNPTG protein (Fragment)GNPTG3.421132.936.791.570.018055
A0A1B0GTP7Acid ceramidaseASAH130.2111632.557.620.770.007232
I3NI22N-sulphoglucosamine sulphohydrolase (Fragment)SGSH6.71121.696.190.680.00013
P11117Lysosomal acid phosphataseACP214.6661248.316.741.640.006068
K7EJJ1AP-1 complex subunit mu-2 (Fragment)AP1M26.941127.875.581.600.003299
Q13367AP-3 complex subunit beta-2AP3B23.5115118.985.591.280.01856
Q6PK82AP3D1 protein (Fragment)AP3D17.755698.479.130.800.009833
Q9BR63FARSB protein (Fragment)FARSB28.72151965.656.840.700.004887
A0A1B0GW44Cathepsin DCTSD39.01133243.666.540.780.004092
P08311Cathepsin GCTSG35.6971328.8111.191.620.029695
Q00610Clathrin heavy chain 1CLTC40.1257128191.495.690.810.000287
P09496Clathrin light chain ACLTA14.925727.064.510.870.050676
P16278Beta-galactosidaseGLB114.3381276.026.571.240.003044
A0A024R8Q1Glucosidase, alpha acid (Pompe disease, glycogen storage disease type II), isoform CRA_aGAA11.3428105.275.991.410.003841
P08236Beta-glucuronidaseGUSB13.5291674.687.021.390.036837
E5RH11Heparan-alpha-glucosaminide N-acetyltransferaseHGSNAT8.251110.658.940.420.004235
Q9BVJ8HEXA protein (Fragment) HEXA11.985947.065.001.420.009748
P07686Beta-hexosaminidase subunit betaHEXB20.68101963.076.761.370.008104
P11717Cation-independent mannose-6-phosphate receptorIGF2R10.361824274.205.941.240.014874
M0QXC5Napsin-A (Fragment)NAPSA2.611129.096.391.840.000808
P07602ProsaposinPSAP34.35175858.075.171.400.013245
Q14108Lysosome membrane protein 2 SCARB223.43103054.265.141.350.012884
B3KY44Solute carrier family 11 (Proton-coupled divalent metal ion transporters), member 2, isoform CRA_c SLC11A22.681144.867.652.670.048925
E9LUE7Sphingomyelin phosphodiesterase 1 isoform 5SMPD15.283367.217.201.240.023889
Q8NBK3Sulfatase-modifying factor 1SUMF112.34540.536.651.940.002598

MW, Molecular weight; PSMs, Peptide sequence matches; Ratio (T/N); Ratio of tumors to normal controls.

Table 6

Fatty acid degradation pathway involved the identified proteins in ovarian cancer biological system.

Pathway codeAccession numberProtein nameGene nameCoverage (%)Unique peptidesPSMsMW (kDa)Calc. pIRatio (T/N)t-test P value
1.3.99D4QEZ8Short-chain acyl-CoA dehydrogenaseACADS27.6781944.337.721.336.42809E-06
1.3.8.9P49748Very long-chain specific acyl-CoA dehydrogenase, mitochondrialACADVL49.621215470.358.751.200.0057736
2.3.1.9P24752Acetyl-CoA acetyltransferase, mitochondrialACAT142.86198145.178.850.900.0055291
6.2.1.3O60488Long-chain-fatty-acid--CoA ligase 4ACSL418.7171479.148.381.100.0571799
6.2.1.3Q9ULC5Long-chain-fatty-acid--CoA ligase 5ACSL57.473575.946.921.200.0335853
1.1.1.1P11766Alcohol dehydrogenase class-3ADH530.75112839.707.490.520.000616293
1.2.1.3P30837Aldehyde dehydrogenase X, mitochondrialALDH1B143.52187657.176.801.110.0450192
1.2.1.3P05091Aldehyde dehydrogenase, mitochondrialALDH255.9209056.357.050.760.00533149
1.2.1.3P491894-trimethylaminobutyraldehyde dehydrogenaseALDH9A137.04184853.775.870.990.75121
CPT2P23786Carnitine O-palmitoyltransferase 2, mitochondrialCPT242.457773.738.182.050.0199416
4.2.1.17P30084Enoyl-CoA hydratase, mitochondrialECHS175.861915631.378.071.520.00356432
5.3.3.8P42126Enoyl-CoA delta isomerase 1, mitochondrialECI134.4485032.808.541.640.00199072
4.2.1.17Q08426Peroxisomal bifunctional enzymeEHHADH33.47223479.449.141.620.00225176
1.3.8.6Q92947Glutaryl-CoA dehydrogenase, mitochondrialGCDH23.2981648.108.061.210.0654308
1.1.1.35Q16836Hydroxyacyl-coenzyme A dehydrogenase, mitochondrialHADH64.01166934.278.851.340.0210962
1.1.1.211P40939Trifunctional enzyme subunit alpha, mitochondrialHADHA69.074237982.949.041.180.0130629
2.3.1.16P55084Trifunctional enzyme subunit beta, mitochondrialHADHB64.77525151.269.401.070.0654946

MW, Molecular weight; PSMs, Peptide sequence matches; Ratio (T/N); Ratio of tumors to normal controls.

The valine, leucine and isoleucine degradation-related proteins were significantly increased in EOCs relative to controls (fold change >1.5), including ECHS1 (fold change = 1.52, P = 0.003), HIBCH (fold change = 1.58, P = 0.002), EHHADH (fold change = 1.61, P = 0.002), IL4I1 (fold change = 1.82, P = 0.008), HMGCS2 (fold change = 2.17, P = 0.002) and ACAA1 (fold change = 2.70, P = 0.0100) (Fig. 7 and Table 7), which indicated valine, leucine and isoleucine degradation pathway was metabolically active in EOC tissues.

Figure 7
Figure 7

Valine, leucine and isoleucine degradation pathway altered in an ovarian cancer. Green rectangle with red mark means the identified proteins. Green rectangle without red mark means species-specific enzymes. White rectangle means reference pathway. The solid line means molecular interaction. The dot line means indirect effect. The circle means mostly chemical complex. The pathway node in the right panel corresponds to the red marked node in the left diagram. ID number is the Swiss-Prot accession number. Ratio (T/N) = Ratio of tumors to controls. A full colour version of this figure is available at https://doi.org/10.1530/ERC-18-0243.

Citation: Endocrine-Related Cancer 25, 10; 10.1530/ERC-18-0243

Table 7

Valine, leucine and isoleucine degradation pathways involved the identified proteins in ovarian cancer biological system.

Pathway codeAccession numberProtein nameGene nameCoverage (%)Unique peptidesPSMsMW (kDa)Calc. pIRatio (T/N)t-test P value
6.2.1.16Q86V21Acetoacetyl-CoA synthetaseAACS2.682275.106.241.350.00053
2.6.1.22P804044-aminobutyrate aminotransferase, mitochondrialABAT50.8182456.407.961.050.032216
2.3.1.16P091103-ketoacyl-CoA thiolase, peroxisomalACAA146.2345044.268.442.700.010033
2.3.1.16H7C1313-ketoacyl-CoA thiolase, peroxisomal (Fragment)ACAA156.2113830.318.630.920.363631
1.3.99Q9UKU7Isobutyryl-CoA dehydrogenase, mitochondrialACAD843.13142345.047.851.420.011082
1399.12P45954Short/branched-chain-specific acyl-CoA dehydrogenase, mitochondrialACADSB25.9381547.466.991.290.010581
2.3.1.9P24752Acetyl-CoA acetyltransferase, mitochondrialACAT142.86198145.178.850.900.005529
6.2.1Q4G176Acyl-CoA synthetase family member 3, mitochondrialACSF323.26111564.098.371.270.039637
1.2.1.3P30837Aldehyde dehydrogenase X, mitochondrialALDH1B143.52187657.176.801.110.045019
1.2.1.3P05091Aldehyde dehydrogenase, mitochondrialALDH255.9209056.357.050.760.005331
1.2.1.3Q02252Methylmalonate-semialdehyde dehydrogenase (acylating), mitochondrialALDH6A156.26259657.808.500.884.4E-05
1.2.1.3P491894-trimethylaminobutyraldehyde dehydrogenaseALDH9A137.04184853.775.870.990.75121
2.6.1.42B3KSI3Branched-chain amino acid aminotransferaseBCAT237.78112039.898.241.030.224773
23.1.168P11182Lipoamide acyltransferase component of branched-chain alpha-keto acid dehydrogenase complex, mitochondrialDBT38.38186753.458.511.180.001486
4.2.1.17P30084Enoyl-CoA hydratase, mitochondrialECHS175.861915631.378.071.520.003564
4.2.1.17Q08426Peroxisomal bifunctional enzymeEHHADH33.47223479.449.141.620.002252
4.2.1.17Q16836Hydroxyacyl-coenzyme A dehydrogenase, mitochondrialHADH64.01166934.278.851.340.021096
1.1.1.35P40939Trifunctional enzyme subunit alpha, mitochondrialHADHA69.074237982.959.041.180.013063
1.1.1.31P319373-hydroxyisobutyrate dehydrogenase, mitochondrialHIBADH31.8593135.318.131.120.007616
3.1.2.4Q6NVY13-hydroxyisobutyryl-CoA hydrolase, mitochondrialHIBCH34.46142943.458.191.580.002504
2.3.3.10P54868Hydroxymethylglutaryl-CoA synthase, mitochondrialHMGCS212.871156.608.162.170.002099
1.1.1.178Q997143-hydroxyacyl-CoA dehydrogenase type-2HSD17B1073.95156626.917.781.350.00185
1.4.3.2Q96RQ9L-amino-acid oxidaseIL4I113.7661362.848.681.820.008397
1.3.8.4P26440Isovaleryl-CoA dehydrogenase, mitochondrialIVD37.35155146.298.191.280.001903
6.4.1.4Q96RQ3Methylcrotonoyl-CoA carboxylase subunit alpha, mitochondrialMCCC144.55216680.427.781.250.073636
6.4.1.4Q9HCC0Methylcrotonoyl-CoA carboxylase beta chain, mitochondrialMCCC245.29216461.297.681.450.000881
5.1.99.1Q96PE7Methylmalonyl-CoA epimerase, mitochondrial OS=Homo sapiens GN= PE=1 SV=1 - [MCEE_HUMAN]MCEE38.075818.749.081.220.048685
5.4.99.2P22033Methylmalonyl-CoA mutase, mitochondrialMUT33.73194383.086.931.130.01005
6.4.1.3P05165Propionyl-CoA carboxylase alpha chain, mitochondrialPCCA34.62204880.017.521.030.226927
2.8.3.5P55809Succinyl-CoA:3-ketoacid coenzyme A transferase 1, mitochondrialOXCT132.12143456.127.460.980.379129

MW, Molecular weight; PSMs, Peptide sequence matches; Ratio (T/N); Ratio of tumors to normal controls.

qRT-PCR and Western blotting validated the consistency of iTRAQ quantitative mitochondrial proteomics

To validate the DEPs identified by iTRAQ quantitative mitochondrial proteomics, we examined the protein expressions of the identified DEPs, including GLDC, PCK2, IDH2, CPT2 and HMGCS2 in the mitochondrial protein samples prepared from EOC and control tissues, and the mRNA and protein expressions of those five DEPs in the cultured EOC cells TOV21G and control cells IOSE80. Except for HMGCS2, a significant increase in the mRNA and protein expression levels of GLDC, PCK2, IDH2 and CPT2 was observed in the cultured cells by q-PCR and Western blot, respectively (Fig. 8A and B). The results of Western blot in the prepared mitochondrial samples had a good consistency with the results of iTRAQ quantitative mitochondrial proteomics (Fig. 8C).

Figure 8
Figure 8

qRT-PCR and Western blot analyses to validate results of iTRAQ quantitative mitochondrial proteomics. (A) qTR-PCR analysis to quantify the expression levels of GLDC, PCK2, IDH2, CPT2 and HMGCS2 between EOC cells TOV21G and control cells IOSE80. (B) Protein expression levels of GLDC, PCK2, IDH2, CPT2 and HMGCS2 in EOC cells TOV21G and control cells IOSE80. (C) Mitochondrial proteins of EOC and control tissues were analyzed by Western blot using antibodies against GLDC, PCK2, IDH2, CPT2 and HMGCS2. The levels of GLDC, PCK2, IDH2, CPT2 and HMGCS2 were normalized relative to β-actin. Data represent mean ± s.d. A full colour version of this figure is available at https://doi.org/10.1530/ERC-18-0243.

Citation: Endocrine-Related Cancer 25, 10; 10.1530/ERC-18-0243

Discussion

EOC is the leading cause of death from gynecologic cancer and the concealed characteristics caused difficulty to be diagnosed in the early stage (Chan et al. 2006). The symptoms index plus CA125 screening may be the best way to identify women who may have an EOC (Karabudak et al. 2013). However, the 5-year overall survival rate for patients diagnosed with stage III−IV EOC remains still poor (about 30%) (Miller et al. 2016). Joint detection of different tumor biomarkers is necessary to increase the specificity of the immunoassay. Proteomics provides a feasible approach for large-scale screening of EOC-related proteins to enhance the understanding of EOC pathogenesis. There are few reports on proteomics analysis of EOCs (Hiramatsu et al. 2016). Until now, it is the first time to use iTRAQ-based quantitative proteomics to identify EOC-related proteins in mitochondria. Mitochondria play a central role in the regulation of cellular signals, metabolism and apoptosis in cancer cells. In this study, one aimed at identifying the potential mitochondrial biomarkers for the prediction, prevention, diagnosis and treatment of EOCs. Mitochondrial purification was progressed by Nycodenz density gradient approach, followed by an 6-plex iTRAQ proteomics to identify mtEPs in EOCs relative to controls. Among 5115 iTRAQ-identified proteins, 262 proteins were significantly related to overall survival in EOC patients. The iTRAQ and TCGA data were integrated, and GO analysis, protein–protein interaction and gene coexpression were analyzed. Moreover, 63 proteins were identified as potential markers for the development of an EOC, such that ERBB2, PTBP1 and H2AFX as reported were significantly related to overall survival. To some degree, these results indicate that our findings were consistent with previous studies and also make new discoveries. Moreover, we searched CooLGeN database with key words of ‘marker or biomarker’ and ‘cancer’ to understand the situation of our findings in the research of other cancers, not only EOCs. One found that 17 biomarkers had been reported in other cancers, including ANXA2, TACSTD2, TNFSF10, HNRNPA2B1, RBP4, PPP1R13L, UGT8, VTN, BCAT1, PTPN2, PDIA6, MFAP4, GSTZ1, NUCB2, P4HB and PTBP1, which indicated our new found biomarkers were reliable. Numerous reported EOC protein biomarkers such as MUC16, MSLN, ERBB2, CHI3L1, MUC1, CD44, VTCN1 and CRP (Simon et al. 2007, Chiang et al. 2015, Stewart et al. 2015, Jiang et al. 2017, Bartakova et al. 2018, Fortner et al. 2018) were also identified in this iTRAQ proteomics study. It demonstrated that iTRAQ proteomic strategy is a reliable tool for identity of EOC biomarker. Compared to previous studies, 992 proteins of 5115 identified proteins have been reported to associate ovary checked by CooLGeN database. Among 992 proteins, there were 33 proteins upregulated more than two-fold; and 12 of those 33 identified proteins have not been reported in previous EOC biomarker studies, including IDH2, RAB43, SYNGR2, RDH10, CYBA, HMGCS2, PCK2, RIDA, KIF23, UGT8, SC11A2 and AIG1, which indicates that those proteins need further studies to testify them as novel EOC biomarkers. In the documented study, most of them endeavor on the effect of single factor or gene mutation on the development of cancer. However, some studies found that only one single molecular event does not lead to the occurrence of cancer. A typical cancer occurrence model needs the mutation of two to eight driver genes (van der Wal et al. 2018). In this study, a recognized biomarker pattern should be put forward, and it means that the use of a set of biomarkers builds a data model to improve the accuracy and specificity of prediction, diagnosis, prognosis and therapy of an EOC. Despite its common use in cancer treatment, biomarkers have not really entered the era of precision medicine to predict, prevent and personalize the treatment of an EOC, and there have been no approaches to adjust personalized medicine based on biological differences between or within tumors. A genome-based model for adjusting radiotherapy dose (GARD), which was reported in 2017, has attracted much attention (Scott et al. 2017). One is going to use the gene expression-based drug-reaction index and the linear quadratic model to derive the drug dose, which one speculates would relate to clinical outcome. In the future, it is necessary for one to collect prospective clinical data to validate the model.

Mitophagy involves the engulfment of any material in a double-membrane enclosed autophagosome, which subsequently fuses with lysosomes. In autophagosomes, mitochondria, proteins or peroxisomes were usually observed. Autophagosomes fuse with lysosomes and emit high-energy substances, including fatty acids and amino acids (Zimmermann & Reichert 2017). Pathway network analysis mapped the identified proteins to 70 signaling pathways. Interestingly, the results demonstrated that cancer cells exhibited an increased dependence on mitophagy, such as peroxisome, phagosome, valine, leucine and isoleucine degradation, fatty acid degradation pathway, which might play an important role in EOC invasion and metastasis. In this progress, mitophagy is dependent on the general autophagy machinery and relies on a growing cadre of ‘mitophagy adaptors’ and regulatory molecules, such as FUNDC1, BNIP3L(NIX), PGAM5, CK, OPA1, prohibitin 2, OPTN, TBK1, p62 and Bcl2-L13 (Drake et al. 2017). This study identified those proteins too, even though few of them appeared expression changing. The results were consistent with previous studies, because those proteins activated downstream mitophagy by post-translational modifications (Wu et al. 2016). Two mitophagy mechanisms in mammalian cells had been reported. Firstly, receptor-mediated mitophagy is activated by phosphorylation, increasing its binding proteins for Atg8-like and recruiting them to mitochondria. Secondly, the highly ubiquitylated mitochondrial outer membrane proteins recruit adapter proteins, which in turn recruits Atg8-like proteins. For NIX (BNIP3L), BNIP3 and BCL2L13, only the activated phosphorylation mechanism and the modified residues are known but the kinases and phosphatases have not been identified; yet, for FUNDC1, both the activated and deactivated phosphorylation mechanisms, the modified residues and participated enzymes are known (Zimmermann & Reichert 2017). In this regard, one could not ignore those identified proteins without significant change. In the future, more and more proteins experienced post-translational modifications might also be biomarker, even become more effective (Peng et al. 2015).

In conclusion, this study used iTRAQ-SCX-LC–MS/MS to identify and quantify 5115 mtEPs in EOCs relative to controls. The first quantitative reference map of a human EOC tissue mitochondrial proteome has been achieved here. These findings enriched human mitochondrial database and a series of identified biomarkers could be useful for clinical practice. These results demonstrated that iTRAQ-SCX-LC–MS/MS is a reliable method to identify and quantify mitochondrial protein profiles and could assist in exploiting potential biomarkers and the novel mechanisms of EOC carcinogenesis.

Supplementary data

This is linked to the online version of the paper at https://doi.org/10.1530/ERC-18-0243.

Declaration of interest

The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.

Funding

This work was supported by the Xiangya Hospital Funds for Talent Introduction (to X Z), the Hunan Provincial Hundred Talent Plan (to X Z), the grants from China ‘863’ Plan Project (Grant No. 2014AA020610-1 to X Z), the National Natural Science Foundation of China (Grant No. 81272798 and 81572278 to X Z) and the Hunan Provincial Natural Science Foundation of China (Grant No. 14JJ7008 to X Z).

Author contribution statement

N L analyzed data, prepared figures and tables, designed and wrote the manuscript. H L collected samples and prepared mitochondrial samples. L C collected tumor tissue samples and performed clinical diagnosis. X Z conceived the concept, designed experiments and manuscript, instructed experiments and data analysis, supervised results, coordinated, critically revised/wrote manuscript and was responsible for its financial supports and the corresponding works. All authors approved the final manuscript.

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    Network analysis of identified proteins by iTRAQ. (A) A total of 5115 proteins was classified according to the cell components with PANTHER. (B) KEGG pathway analysis mapped the identified proteins to 70 signaling pathways. (C). The overlapped proteins were obtained between 5115 identified proteins and TCGA overall related survival genes. (D, E and F) The overlapped proteins were classified according to the biological process (BP), cellular component (CC) and molecular function (MF). The less P value and more significant enrichment were shown with the greater node size. The same color indicated the same function group. Among the groups, we chose a representative of the most significant term and lag highlighted. A full colour version of this figure is available at https://doi.org/10.1530/ERC-18-0243.

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    The interaction networks of identified proteins. (A) The protein–protein interactions (PPIs) network of 262 overlapped proteins. (B) The mRNA-mRNA pair analysis of 262 overlapped proteins constructed circus chart by R package. Upregulated proteins by iTRAQ in red letters and downregulated proteins in green letters. Red line represents positive correlation and green line represents negative correlation. (C) Cancer cells exhibit an increased dependence on mitophagy, such as peroxisome, phagosome, valine, leucine and isoleucine degradation, fatty acid degradation pathway. D-F. Kaplan-Meier (KM) survival curve of ERBB2, PTBP1 and H2AFX in an epithelial ovarian carcinoma (EOC). A full colour version of this figure is available at https://doi.org/10.1530/ERC-18-0243.

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    Peroxisome pathway altered in an ovarian cancer. Green rectangle with red mark means the identified proteins. Green rectangle without red mark means species-specific enzymes. White rectangle means reference pathway. The solid line means molecular interaction. The dot line means indirect effect. The circle means mostly chemical complex. The pathway node in the right panel corresponds to the red marked node in the left diagram. ID number is the Swiss-Prot accession number. Ratio (T/N) = Ratio of tumors to controls. A full colour version of this figure is available at https://doi.org/10.1530/ERC-18-0243.

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    Phagosome pathway altered in an ovarian cancer. Green rectangle with red mark means the identified proteins. Green rectangle without red mark means species-specific enzymes. White rectangle means reference pathway. The solid line means molecular interaction. The dot line means indirect effect. The circle means mostly chemical complex. The pathway node in the right panel corresponds to the red marked node in the left diagram. ID number is the Swiss-Prot accession number. Ratio (T/N) = Ratio of tumors to controls. A full colour version of this figure is available at https://doi.org/10.1530/ERC-18-0243.

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    Lysosome pathway altered in an ovarian cancer. Green rectangle with red mark means the identified proteins. Green rectangle without red mark means species-specific enzymes. White rectangle means reference pathway. The solid line means molecular interaction. The dot line means indirect effect. The circle means mostly chemical complex. The pathway node in the right panel corresponds to the red marked node in the left diagram. ID number is the Swiss-Prot accession number. Ratio (T/N) = Ratio of tumors to controls. A full colour version of this figure is available at https://doi.org/10.1530/ERC-18-0243.

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    Fatty acid degradation pathway altered in an ovarian cancer. Green rectangle with red mark means the identified proteins. Green rectangle without red mark means species-specific enzymes. White rectangle means reference pathway. The solid line means molecular interaction. The dot line means indirect effect. The circle means mostly chemical complex. The pathway node in the upper panel corresponds to the red marked node in the lower diagram. ID number is the Swiss-Prot accession number. Ratio (T/N) = Ratio of tumors to controls. A full colour version of this figure is available at https://doi.org/10.1530/ERC-18-0243.

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    Valine, leucine and isoleucine degradation pathway altered in an ovarian cancer. Green rectangle with red mark means the identified proteins. Green rectangle without red mark means species-specific enzymes. White rectangle means reference pathway. The solid line means molecular interaction. The dot line means indirect effect. The circle means mostly chemical complex. The pathway node in the right panel corresponds to the red marked node in the left diagram. ID number is the Swiss-Prot accession number. Ratio (T/N) = Ratio of tumors to controls. A full colour version of this figure is available at https://doi.org/10.1530/ERC-18-0243.

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    qRT-PCR and Western blot analyses to validate results of iTRAQ quantitative mitochondrial proteomics. (A) qTR-PCR analysis to quantify the expression levels of GLDC, PCK2, IDH2, CPT2 and HMGCS2 between EOC cells TOV21G and control cells IOSE80. (B) Protein expression levels of GLDC, PCK2, IDH2, CPT2 and HMGCS2 in EOC cells TOV21G and control cells IOSE80. (C) Mitochondrial proteins of EOC and control tissues were analyzed by Western blot using antibodies against GLDC, PCK2, IDH2, CPT2 and HMGCS2. The levels of GLDC, PCK2, IDH2, CPT2 and HMGCS2 were normalized relative to β-actin. Data represent mean ± s.d. A full colour version of this figure is available at https://doi.org/10.1530/ERC-18-0243.

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