Evidence map›Paper›PMID 42336904›Full record

ArticleScientific reports2026

Integrated bioinformatics, machine learning, and experimental validation identify a four-gene diagnostic signature for cervical cancer associated with PI3K/AKT signaling.

Hailong Zhang, Luhong Xie, Yameng Liu, Hanlin Yang, Shaoju Min, Yurong Zhu, Jie Ren, Xuehui Li, Yujie Tan

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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

9 authors.

Hailong Zhang *Center for Clinical Laboratories, The Affiliated Hospital of Guizhou Medical University, Guiyang, China.
Luhong Xie *Center for Clinical Laboratories, The Affiliated Hospital of Guizhou Medical University, Guiyang, China.
Yameng Liu *Center for Clinical Laboratories, The Affiliated Hospital of Guizhou Medical University, Guiyang, China.
Hanlin YangCenter for Clinical Laboratories, The Affiliated Hospital of Guizhou Medical University, Guiyang, China.
Shaoju MinCenter for Clinical Laboratories, The Affiliated Hospital of Guizhou Medical University, Guiyang, China.
Yurong ZhuCenter for Clinical Laboratories, The Affiliated Hospital of Guizhou Medical University, Guiyang, China.
Jie RenDepartment of Obstetrics and Gynecology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China. rj624@163.com.
Xuehui LiCenter for Clinical Laboratories, The Affiliated Hospital of Guizhou Medical University, Guiyang, China. 18275281480@163.com.
Yujie TanCenter for Clinical Laboratories, The Affiliated Hospital of Guizhou Medical University, Guiyang, China. tanyujie@gmc.edu.cn.

Funding

he Guizhou Provincial Postgraduate Research Fund Project Grant No. 2024YJSKYJJ308Key Laboratory for Chronic Disease Biomarkers of Guizhou Medical University Grant No. 2024fy004National Natural Science Foundation of China Grant No. NSFC82360588Science and Technology Fund Project of Guizhou Health Commission Grant No. gzwkj2024-184the General Program of Guizhou Provincial Department of Science and Technology Grant No. Qiankehe Basic Research MS(2026)843the Science and Technology Foundation of Health Commission of Guizhou Province Grant No. gzwkj2026-073
6 · The paper itself

Abstract

Early and accurate diagnosis remains a major challenge in cervical cancer management. This study aimed to identify reliable diagnostic biomarkers for cervical cancer by integrating bioinformatics and machine learning approaches and to further validate their biological relevance experimentally. Transcriptomic data from the Gene Expression Omnibus and The Cancer Genome Atlas were analyzed using differential expression analysis, weighted gene co-expression network analysis, and three machine learning algorithms to identify core genes. Diagnostic performance was evaluated using receiver operating characteristic curves and a nomogram model. Functional relevance was explored by drug sensitivity analysis, ssGSEA, immune infiltration analysis, and single-cell RNA sequencing. RT-qPCR validation was performed in 10 paired cervical cancer and adjacent normal tissues, while Western blotting was performed in three paired tissue samples. In vitro validation was conducted using SiHa and HeLa cells. Four genes, CCND1, TRIP13, MYBL2, and GNB4, were identified as potential diagnostic biomarkers, and the combined model showed superior diagnostic performance compared with any single gene (AUC = 0.989). Treatment with 3-methyladenine altered the expression of these genes, suggesting their potential association with PI3K/AKT-related pathway activity. Moreover, siRNA-mediated GNB4 knockdown suppressed cervical cancer cell proliferation and reduced PI3K and AKT phosphorylation, providing preliminary evidence for the functional involvement of GNB4 in PI3K/AKT pathway activation. CCND1, TRIP13, MYBL2, and GNB4 may serve as promising diagnostic biomarkers for cervical cancer. Their dysregulation was associated with PI3K/AKT pathway activity and may reflect molecular alterations involved in cervical cancer progression. In particular, GNB4 showed potential diagnostic relevance and preliminary functional significance, suggesting that it may represent a candidate biomarker and molecular target for further investigation.

Indexed as

Computational BiologyMachine LearningPhosphatidylinositol 3-KinasesProto-Oncogene Proteins c-aktSignal TransductionUterine Cervical NeoplasmsBiomarkers, TumorCell Line, TumorCell ProliferationFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHeLa CellsHumansBiomarkers, TumorPhosphatidylinositol 3-KinasesProto-Oncogene Proteins c-aktBioinformatics analysisCervical cancerDiagnostic biomarkersMachine learningPI3K/AKT signaling pathway

Identifiers

PMID42336904
PMCPMC13294357

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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 OpenQuestion graph.