ArticleFrontiers in oncology2025
Bioinformatics and experimental approach reveal potential prognostic and immunological roles of key mitochondrial metabolism-related genes in cervical cancer.
Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- SREBF2 enhances lipid metabolism and represses anti-tumor immune responses in cervical cancer by increasing ACAT2.Communications biology · 2026Article
- Bulk and single cell RNA sequencing data reveal lactylation-related gene signatures for prognosis and immunity in cervical cancer.Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologicas · 2026Article
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4 authors.
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Abstract
Background: Metabolic remodeling is the hallmark of cancer. In recent years, mitochondrial metabolism (MM) has been considered essential in tumorigenesis and cancer progression. Understanding the role of MM in cervical cancer (CC) can provide insights into disease progression and potential therapeutic targets. Methods: Clinical data of CC patients was downloaded from the UCSC Xena dataset, and differentially expressed genes (DEGs) were identified between tumor and normal samples. MM-related genes (MMRGs) were screened from the MSigDB database. DEGs and MMRGs were then intersected to identify differentially expressed MMRGs. A prognostic risk model was constructed based on these intersecting genes through Cox regression analysis, and its association with the tumor microenvironment and immune checkpoint-related genes was evaluated. Hub genes' expression was evaluated in cells through qRT-PCR. Additionally, drug sensitivity analysis was conducted to explore potential therapeutic drugs. Results: We identified 259 overlapping genes between DEGs and MMRGs, with 55 being prognosis-related. Two molecular clusters were revealed, with C1 exhibiting poorer prognosis. A prognostic risk model comprising five genes (BDH1, MIR210, MSMO1, POLA1, and STARD3NL) was established, showing significant associations with survival outcomes of CC patients. Functional enrichment analysis revealed that DEGs between high- and low-risk groups were tightly associated with the immune system. Analysis of the immune microenvironment showed significant differences between different risk groups, with higher immune and ESTIMATE scores observed in the low-risk group. Additionally, expression levels of immune checkpoint-related genes were significantly correlated with the risk score. Drug sensitivity analysis identified potential therapeutic agents correlated with the expression of the five prognostic genes. Conclusion: Our findings underscore the importance of MM in CC progression and provide potential therapeutic targets for CC.
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