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Identification and validation of a metabolic-related gene risk model predicting the prognosis of lung, colon, and

Jiyauddin Khan1, Chanchal Bareja1, Kountay Dwivedi2

  • 1Dr B R Ambedkar Center for Biomedical Research, University of Delhi, Delhi, 110007, India.

Scientific Reports
|January 8, 2025
PubMed
Summary

This study identified shared metabolism-related genes (MRGs) across breast, colorectal, and lung cancers. Key MRGs show prognostic significance, and machine learning models accurately predict patient survival, highlighting potential for targeted metabolic therapies.

Keywords:
Breast cancerCancer metabolismColorectal cancerLung cancerMachine learningMetabolism-related genesOverall survivalqRT-PCR

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Area of Science:

  • Oncology
  • Metabolic Research
  • Bioinformatics

Background:

  • Cancer cells undergo metabolic reprogramming to survive in altered microenvironments.
  • Understanding shared metabolic alterations across different cancer types is crucial for developing effective therapies.
  • Metabolism-related genes (MRGs) play a vital role in cancer cell adaptation.

Purpose of the Study:

  • To identify common MRGs and their interactions across breast (BRC), colorectal (CRC), and lung (LUC) cancers.
  • To investigate the prognostic significance of key MRGs for patient survival.
  • To evaluate the utility of machine learning models in predicting patient outcomes based on MRGs.

Main Methods:

  • Utilized gene expression data from Gene Expression Omnibus (GEO).
  • Employed bioinformatics tools (MSigDB, WebGestalt, String, Cytoscape) for gene identification and network analysis.
  • Validated key MRGs using Gene Expression Profiling Interactive Analysis 2 (GEPIA2) and quantitative reverse transcription PCR (qRT-PCR).
  • Applied machine learning algorithms (KNN, SVR, XGBoost) for survival prediction.

Main Results:

  • Identified 46 overlapping MRGs and 20 key/hub MRGs common to BRC, CRC, and LUC.
  • Found 11 key MRGs to be prognostically significant.
  • Confirmed expression profiles of key MRGs via qRT-PCR, noting cell-type-specific patterns.
  • Support Vector Regressor (SVR) demonstrated high accuracy in predicting overall patient survival.

Conclusions:

  • Shared MRGs across BRC, CRC, and LUC represent potential biomarkers for metabolic therapies.
  • Key MRGs with prognostic significance offer targets for therapeutic intervention.
  • Machine learning models, particularly SVR, enhance the predictive capability for patient outcomes, suggesting clinical utility.