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Updated: May 28, 2026

Multiomics Analysis of TMEM200A as a Pan-Cancer Biomarker

Multiomics Analysis of TMEM200A as a Pan-Cancer Biomarker

Published on: September 15, 2023

Leveraging Multi-Model Machine Learning Algorithms for Tumor-Normal Classification and Discovery of Biomarkers in

Duaa Mohammad Alawad¹, Mark Fertel¹, Chindo Hicks¹

¹Department of Genetics and the Bioinformatics and Computational Medicine Program, School of Medicine, Louisiana State University Health Sciences Center, 533 Bolivar Street, New Orleans, LA 70112, USA.

|May 27, 2026

Summary

This summary is machine-generated.

Machine learning models integrating gene expression and mutation data accurately classify colorectal cancer (CRC) tumors. This approach identifies potential diagnostic biomarkers and therapeutic targets, aiding in the fight against rising CRC incidence and mortality.

Keywords:

classification colorectal cancer biomarkers gene expression machine learning somatic mutations

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Last Updated: May 28, 2026

Multiomics Analysis of TMEM200A as a Pan-Cancer Biomarker

Multiomics Analysis of TMEM200A as a Pan-Cancer Biomarker

Published on: September 15, 2023

Area of Science:

Oncology
Bioinformatics
Machine Learning

Background:

Colorectal cancer (CRC) remains a leading cause of cancer mortality, with increasing incidence in younger populations.
Current diagnostic and therapeutic strategies require enhancement due to rising CRC rates.
There is a critical need for novel biomarkers and therapeutic targets for CRC.

Purpose of the Study:

To develop and validate machine learning (ML) algorithms for classifying colorectal cancer (CRC) tumor-normal samples.
To identify potential diagnostic biomarkers and therapeutic targets for CRC using integrated genomic data.
To explore the utility of multi-model integrative ML in cancer research.

Main Methods:

Utilized RNA sequencing (RNA-Seq) and somatic mutation data from The Cancer Genome Atlas (TCGA).
Developed and applied multi-model integrative Machine Learning (ML) algorithms for sample classification and biomarker discovery.
Validated ML models using two independent external datasets.

Main Results:

Achieved accurate classification of tumor-normal samples using ML algorithms.
Identified a 58-gene signature with potential as diagnostic biomarkers for colorectal cancer.
Functional analysis revealed enrichment of Wnt and GPCR signaling pathways in somatic mutations.

Conclusions:

Multi-model integrative ML algorithms effectively classify tumor samples when combining gene expression and somatic mutation data.
This integrated approach holds promise for discovering novel biomarkers and therapeutic targets in colorectal cancer.
The identified gene signature and pathways offer potential avenues for future diagnostic and therapeutic development in CRC.