System Maintenance Notice

Please note that maintenance work is scheduled between 4 AM and 5 AM - CET. During this time, you may experience reduced performance. Thanks for your patience.

Publications

Journal Article Epublish

PubMed: 40694220 DOI: 10.1007/s12672-025-02892-7

Construction of mitochondrial signature (MS) for the prognosis of ovarian cancer.

Discov Oncol 16(1):1388

Authors: Miao Ao, You Wu, Kunyu Wang, Haixia Luo, Wei Mao, Anqi Zhao, Xiaomeng Su, Yan Song, Bin Li

Affiliations

  • A Department of Gynecology Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Obstetrics and Gynecology Department, Beijing Huairou Maternal and Child Health Care Hospital, Beijing, China.
  • A Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • A Department of Gynecology Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Abstract

BACKGROUND

Ovarian cancer (OV) continues to be the most lethal type of gynecological cancer with a poor prognosis. During tumorigenesis and cancer advancement, mitochondria are key players in energy metabolism. This study focuses on exploring the mitochondria-related genes for the prognosis of OV.

METHODS

RNA expression profiles and single-cell data were acquired from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus databases for screening and validating mitochondria-related differentially expressed genes (DEGs). After univariate Cox analysis, prognostic genes were carried out for modeling mitochondria signature (MS) based on 101 combinations of 10 machine learning algorithms. Functional enrichment analysis was performed on this prognostic gene set. Immune infiltration analysis was performed between MS groups. Validation for the prognostic model gene OAT was performed to identify the prognostic significance, combined with in vitro experiments to explore its expressions in OV cells. qRT-PCR assay was performed to examine the expression of OAT in human ovarian cancer cell samples and normal ovarian epithelial cells.

RESULTS

A total of 21 prognostic mitochondria-related DEGs were identified for reliably constructing the model MS with excellent prognostic performance in OV. GO and KEGG analysis confirmed these genes were enriched in the generation of precursor metabolites and energy. It illustrated more lymphocyte infiltration in the high MS group than low MS group. OAT served as a novel biomarker for OV patients, showing poor survival in OV patients with high expression of OAT. qPCR assays confirmed its significantly high expression in human ovary cancer cell lines.

CONCLUSIONS

The MS offers tailored risk evaluations and immunotherapy treatments for each OV patient. MS model gene OAT has been recognized as a new oncogene for OV linked to immune escape.

MeSH

Cells, Genes, Neoplasms, Mitochondria, Ovarian Neoplasms, Prognosis