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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

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Explainable Machine Learning Models Using Robust Cancer Biomarkers Identification from Paired Differential Gene

Elisa Díaz de la Guardia-Bolívar¹, Juan Emilio Martínez Manjón², David Pérez-Filgueiras²

¹Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071 Granada, Spain.

International Journal of Molecular Sciences

|November 27, 2024

Summary

This study introduces a new method for finding cancer biomarkers by comparing tumor and healthy tissues. The approach identifies 27 key genes that help detect carcinoma and its origin, improving biomarker discovery.

Keywords:

carcinoma gene panels machine learning robust biomarkers

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Area of Science:

Oncology
Bioinformatics
Genomics

Background:

Clinical oncology requires reliable biomarkers for patient care.
Existing methods for biomarker discovery can be limited by patient variability and data artifacts.

Purpose of the Study:

To develop a robust and interpretable method for identifying cancer biomarkers.
To discover a gene panel for distinguishing carcinoma from healthy tissue and identifying tissue-of-origin.

Main Methods:

Paired differential gene expression analysis comparing primary tumor and healthy patient tissue.
Machine learning models for biological feature selection.
Focus on carcinoma due to prevalence and data availability.

Main Results:

Identified 27 pivotal genes distinguishing healthy from carcinoma tissue across various types.
Accurately determined tissue-of-origin for eight carcinoma types.
Successfully identified primary tissue origin in metastatic samples in a proof-of-concept study.

Conclusions:

Paired differential gene expression analysis offers a robust approach for biomarker discovery in oncology.
The identified gene panel shows potential for clinical translation in carcinoma detection and origin identification.
The method effectively accounts for patient variability, enhancing biomarker reliability.