Evidence map›Paper›PMID 41239481›Full record

ArticleBMC medical genomics2025

Machine learning-based screening and validation of pyroptosis-associated prognostic genes and potential drugs in cervical cancer.

Zongchen Hou, Guiju Tang, Hang Chu, Zhengxi Wang, Lufang Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical genomics, 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

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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

2 citing papers in PubMed.

  1. Article
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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

5 authors.

Zongchen Hou *Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei, 430022, P.R. China.
Guiju Tang *Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei, 430022, P.R. China.
Hang ChuThe First Clinical College of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China.
Zhengxi WangSchool of Nuclear Technology and Chemistry & Biology, Hubei Key Laboratory of Radiation Chemistry and Functional Materials, Hubei University of Science and Technology, Xianning, 437100, PR China. Wangzhengxi@hbust.edu.cn.
Lufang WangDepartment of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei, 430022, P.R. China. 2018xh0110@hust.edu.cn.

Funding

Key Laboratory of Anesthesiology and Resuscitation (Huazhong University of Science and Technology), Ministry of Education No.2024MZFS010the Open Research Fund of Hubei Province Key Laboratory of Precision Radiation Oncology No.2024ZLJZFL003
6 · The paper itself

Abstract

Pyroptosis is a newly discovered form of programmed cell death, but its mechanism in the development of cervical cancer has not been elucidated. Cervical cancer differentially expressed pyroptosis-related genes were identified via bioinformatic analysis Gene Expression Omnibus (GEO) dataset GSE7803, GSE9750, GSE63514 and GSE67522. The correlation between the expression of pyroptosis-related genes in normal cervical tissue and cervical cancer tissue was analyzed through the TCGA database. Using LASSO regression algorithm to establish a prediction model for the obtained genes related to pyroptosis. Exploring the functions of differentially expressed genes through GO and KEGG pathway analysis. Using PPI network analysis to screen hub genes, using CIBERSORT method for immune infiltration analysis of prognostic genes, and finally predicting drug-gene interactions in DGIdb database. A total of 19 pyroptosis-related genes were screened from the GEO dataset of cervical cancer tissues, revealing their regulation of endopeptidase activity, inflammation response, positive regulation of cytokine production and cellular response to environmental stimuli. LASSO regression algorithm was used to establish prediction models for 7 of these genes, and 3 pyroptosis-related genes (SPP1, VEGFA, and CXCL8) closely associated with the prognosis of cervical cancer were identified. qRT-PCR confirmed that compared with normal cervical tissue, the expression of SPP1, VEGFA, and CXCL8 was increased in cervical cancer (P<0.05). SPP1, VEGFA, and CXCL8 are most closely related to macrophages, Th2, and neutrophils, respectively. 148 potential targeted drugs targeting key genes were predicted, providing a possible basis for predicting the prognosis and treatment of cervical cancer. Knocking down SPP1 can inhibit cell proliferation and migration in cervical cancer cells in vitro. In conclusion, our study has identified key genes related to pyroptosis in cervical cancer, which potentially become effective clinical prognostic biomarkers, and further research is needed to explore their underlying mechanisms.

Indexed as

Antineoplastic AgentsMachine LearningPyroptosisUterine Cervical NeoplasmsBiomarkers, TumorComputational BiologyDatabases, GeneticFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansPrognosisProtein Interaction MapsAntineoplastic AgentsBiomarkers, TumorCervical cancerDrug gene predictionEnrichment analysisImmune infiltrationLasso regressionPyroptosis

Identifiers

PMID41239481
PMCPMC12619504

What Socratic holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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 Socratic graph.