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Decoding Cervical Cancer Biomarkers: An Integrated Framework of Bioinformatics, Machine Learning, and Experimental

Pradnya Kamble1, Kajal Dubey1, Abhiyanta Mukherjee2

  • 1Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India.

Cancer Investigation
|October 22, 2025
PubMed
Summary
This summary is machine-generated.

This study identifies seven key genes as potential biomarkers for early cervical cancer detection. These findings, validated by RT-PCR, could improve diagnostic accuracy for this common female cancer.

Keywords:
Cervical cancerDiagnostic biomarkerMachine learningSurvival analysisTranscriptomics

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Area of Science:

  • Oncology
  • Bioinformatics
  • Genomics

Background:

  • Cervical cancer is a leading cause of mortality in women worldwide.
  • Persistent high-risk human papillomavirus (HPV) infection is a primary driver of cervical cancer.
  • Late diagnosis limits treatment efficacy, highlighting the need for precise biomarkers.

Purpose of the Study:

  • To identify significant diagnostic biomarker genes for cervical cancer using bioinformatics and machine learning.
  • To validate potential biomarkers through experimental methods.

Main Methods:

  • Utilized transcriptomics datasets and high-throughput sequencing data.
  • Applied bioinformatics and machine learning (ML) approaches to analyze gene expression.
  • Validated identified biomarker genes using real-time polymerase chain reaction (RT-PCR).

Main Results:

  • Identified seven dysregulated genes (APOD, SPARCL1, AR, MCM2, NUSAP1, PLK1, STIL) with significant diagnostic value.
  • Developed ML prediction models for cervical cancer diagnosis based on differentially expressed genes.
  • Validated the diagnostic potential of the identified genes via RT-PCR.

Conclusions:

  • The identified genes show promise as diagnostic biomarkers for cervical cancer.
  • Bioinformatics and ML approaches are effective in discovering novel cancer biomarkers.
  • These findings may contribute to earlier and more accurate diagnosis of cervical cancer.