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Constructing a molecular subtype model of colon cancer using machine learning.

Bo Zhou1, Jiazi Yu1, Xingchen Cai2

  • 1Department of General Surgery, Ningbo Medical Center Lihuili Hospital, Ningbo University, Ningbo, China.

Frontiers in Pharmacology
|October 3, 2022
PubMed
Summary

This study identifies new molecular subtypes and prognostic markers for colon cancer (CRC) using machine learning. These findings offer potential for developing novel CRC treatments by understanding its molecular mechanisms and immune interactions.

Keywords:
colon cancermachine learningmolecular subtype modelpathogenesisprognosis

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Area of Science:

  • Oncology
  • Bioinformatics
  • Molecular Biology

Background:

  • Colon cancer (CRC) is a prevalent malignancy with increasing focus on molecular mechanisms beyond clinical research.
  • Machine learning offers a powerful approach to integrate molecular data for a deeper understanding of CRC pathogenesis.
  • Identifying molecular subtypes is crucial for advancing CRC treatment strategies.

Purpose of the Study:

  • To construct molecular subtypes of colon cancer using machine learning.
  • To identify novel prognostic genes and molecular markers associated with CRC subtypes.
  • To explore the relationship between identified genes, immune infiltration, and genomic alterations in CRC.

Main Methods:

  • Utilized R language for constructing colon cancer molecular subtypes.
  • Employed GEPIA2 for prognostic gene exploration and WebGestalt for differential gene enrichment analysis.
  • Constructed protein-protein interaction networks using STRING and Cytoscape; analyzed immune cell infiltration and genomic alterations via TIMER2.0, TISIDB, and cBioportal.

Main Results:

  • Developed a molecular prognostic model for CRC, identifying ten key genes (CLC, ZG16, LRRC26, ITLN1, B3GNT6, CLCA1, GFI1, AQP8, HEPACAM2, UGT2B15) correlated with prognosis.
  • Enrichment analysis revealed differential genes are primarily linked to immune-inflammatory pathways.
  • GFI1 and CLC demonstrated associations with immune cells, immunoinhibitors, and immunostimulators; genomic alterations were not significant.

Conclusions:

  • Successfully constructed molecular subtypes of colon cancer, revealing novel prognostic markers.
  • The identified markers provide a foundation for future research into targeted CRC therapies.
  • This study highlights the potential of integrating machine learning with molecular data for advancing cancer treatment.