Evidence map›Paper›PMID 40539060›Full record

ArticleFrontiers in immunology2025

A novel mitochondrial autophagy and aging-related gene signature for predicting ovarian cancer.

Hongyu Zhang, Qiaoying Jin, Huijuan Cao, Yunyun Wang, Yiling Zhao, Hongwen Zhu, Tiansheng Qin

Abstract read
In one paragraph

Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Identification of novel molecular subtypes in ovarian cancerThe Korean journal of physiology & pharmacology : official journal of the Korean Physiological Society and the Korean Society of Pharmacology · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Hongyu Zhang *Department of Gynecology, The Second Hospital and Clinical Medical School, Lanzhou University, Lanzhou, China.
Qiaoying Jin *Cuiying Biomedical Research Center, The Second Hospital and Clinical Medical School, Lanzhou University, Lanzhou, China.
Huijuan Cao *Department of Gynecology, The Second Hospital and Clinical Medical School, Lanzhou University, Lanzhou, China.
Yunyun WangDepartment of Gynecology, The Second Hospital and Clinical Medical School, Lanzhou University, Lanzhou, China.
Yiling ZhaoDepartment of Gynecology, The Second Hospital and Clinical Medical School, Lanzhou University, Lanzhou, China.
Hongwen ZhuCuiying Biomedical Research Center, The Second Hospital and Clinical Medical School, Lanzhou University, Lanzhou, China.
Tiansheng QinDepartment of Gynecology, The Second Hospital and Clinical Medical School, Lanzhou University, Lanzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ovarian cancer (OC) is a highly malignant gynecologic tumor with a poor prognosis. In recent years, mitochondrial autophagy and aging (MiAg) have been recognized as crucial pathophysiological mechanisms leading to tumorigenesis. However, the expression of MiAg-related genes in OC and their correlation with prognosis remain unclear. In this study, we used multiple machine learning methods to identify 52 MiAg genes that were differentially expressed between OC and normal ovarian tissues. Based on these 52 differentially expressed genes (DEGs), 375 OC patients were classified into two subtypes by consensus clustering analysis. Subsequently, we evaluated the prognostic value of MiAg-related genes in relation to survival in 375 OC patients with complete survival information, and developed a MiAg prognostic score model. By applying Cox and LASSO regression methods, a five-gene signature was constructed, and the 375 OC patients in the TCGA cohort were categorized into low-risk and high-risk group based on the median risk score. Meanwhile, we categorized 174 OC patients from the Gene Expression Omnibus (GEO) database into high- and low-risk groups using the median risk score of the TCGA cohort to validate the MiAg scoring model. Furthermore, we analyzed these data with unifactorial and multifactorial analyses, functional enrichment analysis, gene mutation analysis, immune infiltration, drug susceptibility analysis, cell line analysis, and immunohistochemistry data from the HPA database. In conclusion, the MiAgscore predicted patient survival, and lower MiAgscore values were associated with a better survival advantage. A comprehensive assessment of mitochondrial autophagy and cellular senescence alterations in OC could help advance disease target development and provide more effective personalized treatment strategies for OC patients.

Indexed as

AgingAutophagyBiomarkers, TumorMitochondriaOvarian NeoplasmsTranscriptomeFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMiddle AgedPrognosisBiomarkers, Tumorcellular senescencemachine learningmitochondrial autophagyovarian cancerprognosis

Identifiers

PMID40539060
PMCPMC12177532

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.