Discovering Potential Taxonomic Biomarkers of Gastrointestinal Cancers from Various Human Microbiota via G-S-M Machine Learning Approach

Beyza Canakcimaksutoglu, Nur Sebnem Ersoz, Burcu Bakir-Gungor, Malik Yousef

Analysis of microbial abundance profiles offers significant potential for improving cancer prediction and candidate biomarker discovery. This study aimed to identify cancer-associated microbial biomarkers across five gastrointestinal (GI) cancers: head and neck, esophagus, stomach, colon, and colorectal cancers by analyzing tissue and blood samples from the TCMA dataset in parallel. A novel machine learning model, MicrobiomeGSM, was developed to enhance biological interpretability and reduce computational complexity through a taxonomic grouping strategy. Classification performance of MicrobiomeGSM was rigorously evaluated using a Random Forest Classifier with 100-fold Monte Carlo Cross-Validation. MicrobiomeGSM model effectively identified colon adenocarcinoma (COAD) using a set of 30 genus-level species, achieving a 97% AUC and 97% specificity. Comparative analysis was also performed with six traditional feature selection (TFS) algorithms; CMIM, mRMR, FCBF, IG, XGB, and SKB. Comparison of MicrobiomeGSM with TFS methods showed that while TFS methods capture statistical patterns, MicrobiomeGSM effectively leverages biological structures to identify clinically relevant candidate biomarkers. Also, MicrobiomeGSM competes with TFS methods in the analysis of high-dimensional datasets. In conclusion, these findings demonstrate that incorporating microbial abundance profiles with their taxonomic information into machine learning improve the interpretability and effectiveness of microbiome-based candidate biomarker discovery and may support future precision oncology application.