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Development of Compendium for Esophageal Squamous Cell Carcinoma
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Identification of Potential Biomarkers Using Integrative Approach: A Case Study of ESCC.

Manaswita Saikia1, Dhruba K Bhattacharyya1, Jugal K Kalita2

  • 1Department of Computer Science and Engineering, Tezpur University, Napaam, Tezpur, Assam 784028 India.

SN Computer Science
|December 27, 2022
PubMed
Summary

This study introduces a consensus method to identify potential disease biomarkers by integrating microarray and RNA-Seq data. The approach successfully identified 25 validated differentially expressed genes (DEGs) for esophageal squamous cell carcinoma (ESCC).

Keywords:
Biomarker identificationDifferential expression analysisDifferentially expressed geneEsophageal squamous cell carcinomaMicroarrayRNA-sequencing

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Area of Science:

  • Bioinformatics
  • Genomics
  • Cancer Research

Background:

  • Accurate identification of differentially expressed genes (DEGs) is crucial for biomarker discovery in critical diseases.
  • Integrating data from multiple platforms like microarray and RNA-Seq presents challenges but offers a more robust approach.

Purpose of the Study:

  • To develop an unbiased and integrative framework for identifying potential biomarkers using a consensus-based approach.
  • To validate identified biomarkers for esophageal squamous cell carcinoma (ESCC) through biological analysis and literature evidence.

Main Methods:

  • A consensus-based method integrating three microarray and three RNA-Seq analysis methods was developed.
  • The framework identifies DEGs independently per dataset and applies a consensus function to prioritize common genes.
  • Differential co-expression (DCE) and preservation analysis were used to study gene interaction changes.

Main Results:

  • The method performed satisfactorily on validated datasets for esophageal squamous cell carcinoma (ESCC).
  • Identified 25 DEGs with strong biological relevance and existing literature support as potential ESCC biomarkers.
  • Discovered an additional 8 probable DEGs requiring further investigation.

Conclusions:

  • The consensus-based approach provides a robust strategy for unbiased biomarker identification from integrated transcriptomic data.
  • The identified DEGs represent promising candidates for further validation as diagnostic or prognostic biomarkers for ESCC.