Evidence map›Paper›PMID 40245751›Full record

ArticleTranslational oncology2025

Knockdown TNF family prognosis index crucial gene PDE4B promoted PANoptosis of ovarian carcinoma cell:Based in vitro and in vivo experiments.

Qianqian Yu, Yunxiao Wang, Ting Fu, Dongyu Han, Linlin Wang, Lin Zhao, Yongle Xu

Abstract read
In one paragraph

Article in Translational oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Qianqian YuNational Clinical Research Center for Laboratory Medicine, Department of Laboratory Medicine, The First Hospital of China Medical University, Units of Medical Laboratory , Chinese Academy of Medical Sciences, Shenyang, 110001, PR China.
Yunxiao WangDepartment of Gynecology and Obstetrics, The Fifth People's Hospital of Shunde (Longjiang Hospital of Shunde District), Foshan, PR China.
Ting FuThe affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University; Department of Gynecology and Obstetrics, The affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University.
Dongyu HanDepartment of Obstetrics and Gynecology, Suzhou Hospital, Affiliated Hospital of Meddical School, Nanjing University, Suzhou, PR China.
Linlin WangDepartment of Obstetrics and Gynecology, Suzhou Hospital, Affiliated Hospital of Meddical School, Nanjing University, Suzhou, PR China.
Lin ZhaoDepartment of Obstetrics and Gynecology, Suzhou Hospital, Affiliated Hospital of Meddical School, Nanjing University, Suzhou, PR China. Electronic address: zhaolin5517@163.com.
Yongle XuThe affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University; Center for Reproduction and Genetics, The affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University. Electronic address: chenyanzhu2009@sina.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ovarian cancer represents a malignancy characterized by high incidence and mortality rates, necessitating further elucidation of its underlying mechanisms. We conducted an analysis using bulk transcriptomic data of ovarian cancer and normal ovarian tissues, as well as single-cell sequencing data according to publicly available databases. Through calculation of Gene Set Variation Analysis (GSVA) scores for TNF family genes, weighted gene co-expression network analysis (WGCNA) for hub genes identification, and subsequent Gene Ontology (GO) enrichment analysis, we delineated pathways crucial in ovarian cancer pathogenesis. Furthermore, differential expression gene analysis facilitated the identification of genes with pronounced expression levels in tumor tissues and their intersection with hub genes, followed by GO analyses across molecular functions (MF), cellular components (CC), and biological processes (BP). Utilizing multivariable Cox regression and LASSO analyses, we constructed a prognostic model comprising 14 genes (GFPT2, PDE4B, PODNL1, TGFBI, CSF1R, PTGIS, SFRP2, COL5A2, TRAC, SLAMF7, VCAN, GBP1P1, C2, TRBV28). Both training and validation sets demonstrated robust diagnostic and prognostic capabilities. Clinical information and immune cell infiltration analyses were further conducted based on the model. In the single-cell sequencing analysis, reducing dimensional complexity and classifying cell types were performed, followed by exploration of gene expression patterns within each subtype and investigation of temporal expression variations across cell subtypes. Biological functional exploration and drug sensitivity analyses were also conducted. Our study contributes novel insights and theoretical foundations for prognosis, treatment, and development of drugs in patients.

Indexed as

Gene ExpressionOvarian CancerPrognostic ModelSingle-Cell SequencingTranscriptomics

Identifiers

PMID40245751
PMCPMC12127490

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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.