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Colon cancer diagnosis and staging classification based on machine learning and bioinformatics analysis.

Ying Su1, Xuecong Tian1, Rui Gao2

  • 1College of Software, Xinjiang University, Urumqi, 830046, Xinjiang, China.

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|March 27, 2022
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Identifying colon cancer biomarkers aids early diagnosis and treatment. This study utilized gene expression data to develop models for diagnosing colon cancer and its stages, identifying key prognostic genes.

Keywords:
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Area of Science:

  • Oncology
  • Bioinformatics
  • Genomics

Background:

  • Advanced colon cancer metastasis presents significant treatment challenges.
  • Early diagnosis and staging of colon cancer are crucial for improving patient prognosis.
  • Gene expression profiling offers potential for identifying diagnostic and prognostic markers.

Purpose of the Study:

  • To identify key gene expression markers for colon cancer diagnosis and staging.
  • To develop machine learning models for accurate colon cancer detection and classification.
  • To discover novel genes associated with colon cancer prognosis.

Main Methods:

  • Weighted Gene Co-expression Network Analysis (WGCNA) to identify cancer-correlated gene modules.
  • Least Absolute Shrinkage and Selection Operator (Lasso) algorithm for feature gene extraction.
  • Random Forest (RF), Support Vector Machine (SVM), and decision trees for classification; Protein-Protein Interaction (PPI) network analysis for survival analysis.

Main Results:

  • RF model achieved high accuracy (99.81%) in distinguishing colon cancer from healthy controls.
  • RF model demonstrated notable accuracy (91.5%) in diagnosing colon cancer stages I-IV.
  • Eight genes (GCNT2, GLDN, SULT1B1, UGT2B15, PTGDR2, GPR15, BMP5, CPT2) were identified as significant prognostic markers.

Conclusions:

  • Gene expression analysis combined with machine learning effectively diagnoses colon cancer and its stages.
  • The identified prognostic genes hold potential for targeted therapeutic strategies and improved patient outcomes.
  • This study provides a foundation for developing novel diagnostic and prognostic tools for colon cancer.