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In Silico Transcriptomic Analysis for Identification of Potential Diagnostic and Prognostic Biomarkers and

Leila Nezamabadi Farahani1, Anoshirvan Kazemnejad1, Mahlagha Afrasiabi2

  • 1Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.

Archives of Iranian Medicine
|March 19, 2026
PubMed
Summary

This study identified key genes for cervical cancer diagnosis, treatment, and prognosis using bioinformatics. These molecular markers require further validation for clinical application.

Keywords:
BiomarkersCervix neoplasmGene expressionGenetic algorithmSupport vector machine

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Area of Science:

  • Bioinformatics
  • Genomics
  • Oncology

Background:

  • Cervical cancer is a leading global malignancy in women.
  • Significant clinical and public health challenges persist.
  • This study utilizes bioinformatics to address these challenges.

Purpose of the Study:

  • Identify potential diagnostic biomarkers for cervical cancer.
  • Discover novel therapeutic targets for cervical cancer treatment.
  • Determine prognostic markers for cervical cancer patient outcomes.

Main Methods:

  • Applied a hybrid genetic algorithm (GA) and support vector machine (SVM) machine learning approach.
  • Utilized high-dimensional gene expression data from GEO and TCGA.
  • Performed protein-protein interaction (PPI) and functional enrichment analyses.

Main Results:

  • Achieved ~99% accuracy in distinguishing cervical cancer from normal tissues.
  • Identified 8 diagnostic biomarkers, 42 therapeutic targets (e.g., CDK1, BRCA1), and 6 prognostic markers.
  • Uncovered E2F1 and TP63 as key regulators of prognostic genes.

Conclusions:

  • The identified gene signatures offer potential for hypothesis generation.
  • Provides a computational framework for prioritizing cervical cancer biomarkers and targets.
  • Emphasizes the need for experimental and clinical validation of in silico findings.