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Research Article

Characteristics of Oxidative Phosphorylation-Related Subtypes and Construction of a Prognostic Signature in Ovarian Cancer

Author(s): Jiaojiao Lu*, Shuai Zhen and Xu Li

Volume 25, Issue 3, 2025

Published on: 16 September, 2024

Page: [327 - 344] Pages: 18

DOI: 10.2174/0115665232323373240905104033

Price: $65

TIMBC 2026
Abstract

Background: Ovarian cancer is associated with a high mortality rate. Oxidative Phosphorylation (OXPHOS) is an active metabolic pathway in cancer; nevertheless, its role in ovarian cancer continues to be ambiguous. Therefore, the objective of this study was to identify the prognostic value of OXPHOS-related genes and the immune landscape in ovarian cancer.

Methods: We obtained public ovarian cancer-related datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and recognized OXPHOS-related genes from the GeneCards database and literature. Cox regression analyses were conducted to identify prognostic OXPHOS-related genes and develop a prognostic nomogram based on the OXPHOS score and clinicopathological features of patients. Functional enrichment analyses were employed to identify related processes.

Results: A 12-gene signature was identified to classify the ovarian cancer patients into high- and low-risk groups. The Immunophenoscore (IPS) was higher in the OXPHOS score-high group than in the OXPHOS score-low group, suggesting a better response to immune checkpoint inhibitors. Functional enrichment analyses unveiled that OXPHOS-related genes were considerably abundant in a series of immune processes. The calibration curves of the constructed prognostic nomograms at 1, 2, and 3 years exhibited strong concordance between the anticipated and observed survival probabilities of ovarian cancer patients.

Conclusion: We have constructed a prognostic model containing 12 OXPHOS-related genes and demonstrated its strong predictive value in ovarian cancer patients. OXPHOS has been found to be closely linked to immune infiltration and the reaction to immunotherapy, which may contribute to improving individualized treatment and prognostic evaluation in ovarian cancer.

Keywords: Ovarian cancer, oxidative phosphorylation-related gene, overall survival, The Cancer Genome Atlas, prognosis, immune infiltration.

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[1]
Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin 2023; 73(1): 17-48.
[http://dx.doi.org/10.3322/caac.21763] [PMID: 36633525]
[2]
Torre LA, Trabert B, DeSantis CE, et al. Ovarian cancer statistics, 2018. CA Cancer J Clin 2018; 68(4): 284-96.
[http://dx.doi.org/10.3322/caac.21456] [PMID: 29809280]
[3]
Borley J, Wilhelm-Benartzi C, Brown R, Ghaem-Maghami S. Does tumour biology determine surgical success in the treatment of epithelial ovarian cancer? A systematic literature review. Br J Cancer 2012; 107(7): 1069-74.
[http://dx.doi.org/10.1038/bjc.2012.376] [PMID: 22935582]
[4]
Ahmed N, Escalona R, Leung D, Chan E, Kannourakis G. Tumour microenvironment and metabolic plasticity in cancer and cancer stem cells: Perspectives on metabolic and immune regulatory signatures in chemoresistant ovarian cancer stem cells. Semin Cancer Biol 2018; 53: 265-81.
[http://dx.doi.org/10.1016/j.semcancer.2018.10.002] [PMID: 30317036]
[5]
Li SS, Ma J, Wong AST. Chemoresistance in ovarian cancer: exploiting cancer stem cell metabolism. J Gynecol Oncol 2018; 29(2): e32.
[http://dx.doi.org/10.3802/jgo.2018.29.e32] [PMID: 29468856]
[6]
Ashton TM, McKenna WG, Kunz-Schughart LA, Higgins GS. Oxidative phosphorylation as an emerging target in cancer therapy. Clin Cancer Res 2018; 24(11): 2482-90.
[http://dx.doi.org/10.1158/1078-0432.CCR-17-3070] [PMID: 29420223]
[7]
Nayak AP, Kapur A, Barroilhet L, Patankar MS. Oxidative phosphorylation: A target for novel therapeutic strategies against ovarian cancer. Cancers (Basel) 2018; 10(9): 337.
[http://dx.doi.org/10.3390/cancers10090337] [PMID: 30231564]
[8]
Larman TC, DePalma SR, Hadjipanayis AG, et al. Spectrum of somatic mitochondrial mutations in five cancers. Proc Natl Acad Sci USA 2012; 109(35): 14087-91.
[http://dx.doi.org/10.1073/pnas.1211502109] [PMID: 22891333]
[9]
Yu M. Generation, function and diagnostic value of mitochondrial DNA copy number alterations in human cancers. Life Sci 2011; 89(3-4): 65-71.
[http://dx.doi.org/10.1016/j.lfs.2011.05.010] [PMID: 21683715]
[10]
Dar S, Chhina J, Mert I, et al. Bioenergetic adaptations in chemoresistant ovarian cancer cells. Sci Rep 2017; 7(1): 8760.
[http://dx.doi.org/10.1038/s41598-017-09206-0] [PMID: 28821788]
[11]
Sriramkumar S, Sood R, Huntington TD, et al. Platinum-induced mitochondrial OXPHOS contributes to cancer stem cell enrichment in ovarian cancer. J Transl Med 2022; 20(1): 246.
[http://dx.doi.org/10.1186/s12967-022-03447-y] [PMID: 35641987]
[12]
Guo Y, Hu B, Fu B, Zhu H. Atovaquone at clinically relevant concentration overcomes chemoresistance in ovarian cancer via inhibiting mitochondrial respiration. Pathol Res Pract 2021; 224: 153529.
[http://dx.doi.org/10.1016/j.prp.2021.153529] [PMID: 34174549]
[13]
Ghilardi C, Moreira-Barbosa C, Brunelli L, et al. PGC1α/β expression predicts therapeutic response to oxidative phosphorylation inhibition in ovarian cancer. Cancer Res 2022; 82(7): 1423-34.
[http://dx.doi.org/10.1158/0008-5472.CAN-21-1223] [PMID: 35131872]
[14]
Xi S, Jing L, Lili W, et al. Magnetic controlled capsule endoscope (MCCE)‘s diagnostic performance for H. pylori infection status based on the Kyoto classification of gastritis. BMC Gastroenterol 2022; 22(1): 502.
[http://dx.doi.org/10.1186/s12876-022-02589-z] [PMID: 36474169]
[15]
Colaprico A, Silva TC, Olsen C, et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res 2016; 44(8): e71.
[http://dx.doi.org/10.1093/nar/gkv1507] [PMID: 26704973]
[16]
Barrett T, Troup DB, Wilhite SE, et al. NCBI GEO: mining tens of millions of expression profiles--database and tools update. Nucleic Acids Res 2007; 35(Database): D760-5.
[http://dx.doi.org/10.1093/nar/gkl887] [PMID: 17099226]
[17]
Mok SC, Bonome T, Vathipadiekal V, et al. A gene signature predictive for outcome in advanced ovarian cancer identifies a survival factor: microfibril-associated glycoprotein 2. Cancer Cell 2009; 16(6): 521-32.
[http://dx.doi.org/10.1016/j.ccr.2009.10.018] [PMID: 19962670]
[18]
Brodsky AS, Fischer A, Miller DH, et al. Expression profiling of primary and metastatic ovarian tumors reveals differences indicative of aggressive disease. PLoS One 2014; 9(4): e94476.
[http://dx.doi.org/10.1371/journal.pone.0094476] [PMID: 24732363]
[19]
Bowen NJ, Walker LD, Matyunina LV, et al. Gene expression profiling supports the hypothesis that human ovarian surface epithelia are multipotent and capable of serving as ovarian cancer initiating cells. BMC Med Genomics 2009; 2(1): 71.
[http://dx.doi.org/10.1186/1755-8794-2-71] [PMID: 20040092]
[20]
Davis S, Meltzer PS. GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics 2007; 23(14): 1846-7.
[http://dx.doi.org/10.1093/bioinformatics/btm254] [PMID: 17496320]
[21]
Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 2012; 28(6): 882-3.
[http://dx.doi.org/10.1093/bioinformatics/bts034] [PMID: 22257669]
[22]
Stelzer G, Rosen N, Plaschkes I, Zimmerman S, Twik M, Fishilevich S. The genecards suite: From gene data mining to disease genome sequence analyses. Curr Protoc Bioinform 2016; 54: 30.
[23]
Liu J, Chen T, Yang M, et al. Development of an oxidative phosphorylation-related and immune microenvironment prognostic signature in uterine corpus endometrial carcinoma. Front Cell Dev Biol 2021; 9: 753004.
[http://dx.doi.org/10.3389/fcell.2021.753004] [PMID: 34901000]
[24]
Ritchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015; 43(7): e47.
[http://dx.doi.org/10.1093/nar/gkv007] [PMID: 25605792]
[25]
Yu G. Gene ontology semantic similarity analysis using GOSemSim. Methods Mol Biol 2020; 2117: 207-15.
[http://dx.doi.org/10.1007/978-1-0716-0301-7_11] [PMID: 31960380]
[26]
Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000; 28(1): 27-30.
[http://dx.doi.org/10.1093/nar/28.1.27] [PMID: 10592173]
[27]
Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 2012; 16(5): 284-7.
[http://dx.doi.org/10.1089/omi.2011.0118] [PMID: 22455463]
[28]
Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 2005; 102(43): 15545-50.
[http://dx.doi.org/10.1073/pnas.0506580102] [PMID: 16199517]
[29]
Liberzon A, Subramanian A, Pinchback R, Thorvaldsdóttir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics 2011; 27(12): 1739-40.
[http://dx.doi.org/10.1093/bioinformatics/btr260] [PMID: 21546393]
[30]
Wilkerson MD, Hayes DN. Consensus clusterplus: A class discovery tool with confidence assessments and item tracking. Bioinformatics 2010; 26(12): 1572-3.
[http://dx.doi.org/10.1093/bioinformatics/btq170] [PMID: 20427518]
[31]
Brière G, Darbo É, Thébault P, Uricaru R. Consensus clustering applied to multi-omics disease subtyping. BMC Bioinformatics 2021; 22(1): 361.
[http://dx.doi.org/10.1186/s12859-021-04279-1] [PMID: 34229612]
[32]
Shao N, Tang H, Mi Y, Zhu Y, Wan F, Ye D. A novel gene signature to predict immune infiltration and outcome in patients with prostate cancer. OncoImmunology 2020; 9(1): 1762473.
[http://dx.doi.org/10.1080/2162402X.2020.1762473] [PMID: 32923125]
[33]
Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics 2013; 14(1): 7.
[http://dx.doi.org/10.1186/1471-2105-14-7] [PMID: 23323831]
[34]
Barbie DA, Tamayo P, Boehm JS, et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 2009; 462(7269): 108-12.
[http://dx.doi.org/10.1038/nature08460] [PMID: 19847166]
[35]
Hugo W, Zaretsky JM, Sun L, et al. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell 2017; 168(3): 542.
[http://dx.doi.org/10.1016/j.cell.2017.01.010] [PMID: 28129544]
[36]
Van Allen EM, Miao D, Schilling B, et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 2015; 350(6257): 207-11.
[http://dx.doi.org/10.1126/science.aad0095] [PMID: 26359337]
[37]
Mayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res 2018; 28(11): 1747-56.
[http://dx.doi.org/10.1101/gr.239244.118] [PMID: 30341162]
[38]
Mermel CH, Schumacher SE, Hill B, Meyerson ML, Beroukhim R, Getz G. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol 2011; 12(4): R41.
[http://dx.doi.org/10.1186/gb-2011-12-4-r41] [PMID: 21527027]
[39]
Xiang J, Su R, Wu S, Zhou L. Construction of a prognostic signature for serous ovarian cancer based on lactate metabolism-related genes. Front Oncol 2022; 12: 967342.
[http://dx.doi.org/10.3389/fonc.2022.967342] [PMID: 36185201]
[40]
Ding B, Yan W, Shen S, et al. Immune-related genes’ prognostic, therapeutic and diagnostic value in ovarian cancer immune-related gene biomarker in ovarian cancer. Cancer Contr 2023; 30.
[http://dx.doi.org/10.1177/10732748231168756] [PMID: 37078136]
[41]
Cheng Z, Chen Y, Huang H. Identification and validation of a novel prognostic signature based on ferroptosis-related genes in ovarian cancer. Vaccines (Basel) 2023; 11(2): 205.
[http://dx.doi.org/10.3390/vaccines11020205] [PMID: 36851083]
[42]
Liu Q, Yang X, Yin Y, et al. Identifying the role of oxidative stress-related genes as prognostic biomarkers and predicting the response of immunotherapy and chemotherapy in ovarian cancer. Oxid Med Cell Longev 2022; 2022: 1-50.
[http://dx.doi.org/10.1155/2022/6575534] [PMID: 36561981]
[43]
Wang Y, Xie H, Chang X, et al. Single-cell dissection of the multiomic landscape of high-grade serous ovarian cancer. Cancer Res 2022; 82(21): 3903-16.
[http://dx.doi.org/10.1158/0008-5472.CAN-21-3819] [PMID: 35969151]
[44]
Gentric G, Kieffer Y, Mieulet V, et al. PML-regulated mitochondrial metabolism enhances chemosensitivity in human ovarian cancers. Cell Metab 2019; 29(1): 156-173.e10.
[http://dx.doi.org/10.1016/j.cmet.2018.09.002] [PMID: 30244973]
[45]
Sun Q, Li J, Dong H, et al. UQCRFS1 serves as a prognostic biomarker and promotes the progression of ovarian cancer. Sci Rep 2023; 13(1): 8335.
[http://dx.doi.org/10.1038/s41598-023-35572-z] [PMID: 37221238]
[46]
Zhang HY, Sun H. Up-regulation of Foxp3 inhibits cell proliferation, migration and invasion in epithelial ovarian cancer. Cancer Lett 2010; 287(1): 91-7.
[http://dx.doi.org/10.1016/j.canlet.2009.06.001] [PMID: 19628330]
[47]
Wang J, Qian Y, Gao M. Overexpression of PDK4 is associated with cell proliferation, drug resistance and poor prognosis in ovarian cancer. Cancer Manag Res 2018; 11: 251-62.
[http://dx.doi.org/10.2147/CMAR.S185015] [PMID: 30636897]
[48]
Quan Q, Xiong X, Wu S, Yu M. Identification of immune-related key genes in ovarian cancer based on WGCNA. Front Genet 2021; 12: 760225.
[http://dx.doi.org/10.3389/fgene.2021.760225] [PMID: 34868239]
[49]
Sun F, Wei Y, Liu Z, et al. Acylglycerol kinase promotes ovarian cancer progression and regulates mitochondria function by interacting with ribosomal protein L39. J Exp Clin Cancer Res 2022; 41(1): 238.
[http://dx.doi.org/10.1186/s13046-022-02448-5] [PMID: 35934718]
[50]
Torres-Roman JS, Prado A, Saravia CH, Usquiano L, Zavaleta J, Bravo L. Evaluation of RNA Polymerase I Subunit C gene (POLR1C) as prognostic biomarker in cancer. J Clin Oncol 2018; 36(15): e24325.
[51]
Han F, Cao D, Zhu X, et al. Construction and validation of a prognostic model for hepatocellular carcinoma: Inflammatory ferroptosis and mitochondrial metabolism indicate a poor prognosis. Front Oncol 2023; 12: 972434.
[http://dx.doi.org/10.3389/fonc.2022.972434] [PMID: 36686830]
[52]
Zhou X, Gao W, Hua H, Ji Z. LncRNA-BLACAT1 facilitates proliferation, migration and aerobic glycolysis of pancreatic cancer cells by repressing CDKN1C via EZH2-induced H3K27me3. Front Oncol 2020; 10: 539805.
[http://dx.doi.org/10.3389/fonc.2020.539805] [PMID: 33072570]
[53]
Wang Z, Guan D, Huo J, et al. IL-10 enhances human natural killer cell effector functions via metabolic reprogramming regulated by mTORC1 signaling. Front Immunol 2021; 12: 619195.
[http://dx.doi.org/10.3389/fimmu.2021.619195] [PMID: 33708210]
[54]
Vacaflores A, Chapman NM, Harty JT, Richer MJ, Houtman JCD. Exposure of human CD4 T cells to IL-12 results in enhanced TCR-induced cytokine production, altered TCR signaling, and increased oxidative metabolism. PLoS One 2016; 11(6): e0157175.
[http://dx.doi.org/10.1371/journal.pone.0157175] [PMID: 27280403]
[55]
Wang Y, Wang Y, Liu W, et al. TIM-4 orchestrates mitochondrial homeostasis to promote lung cancer progression via ANXA2/PI3K/AKT/OPA1 axis. Cell Death Dis 2023; 14(2): 141.
[http://dx.doi.org/10.1038/s41419-023-05678-3] [PMID: 36806050]
[56]
Zhu QW, Yu Y, Zhang Y, Wang XH. VLCAD inhibits the proliferation and invasion of hepatocellular cancer cells through regulating PI3K/AKT axis. Clin Transl Oncol 2022; 24(5): 864-74.
[http://dx.doi.org/10.1007/s12094-021-02733-3] [PMID: 35001339]
[57]
Jones CL, Stevens BM, D’Alessandro A, et al. Inhibition of amino acid metabolism selectively targets human leukemia stem cells. Cancer Cell 2018; 34(5): 724-740.e4.
[http://dx.doi.org/10.1016/j.ccell.2018.10.005] [PMID: 30423294]
[58]
Charoentong P, Finotello F, Angelova M, et al. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep 2017; 18(1): 248-62.
[http://dx.doi.org/10.1016/j.celrep.2016.12.019] [PMID: 28052254]

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