Machine Learning–Driven Computational Identification of Prognostic Gene Signatures In Human Cancers With Experimental Validation

Abstract

Background: Cancer is a major cause of both morbidity and mortality in global contexts, and hence, there exists a need to have valid prognostic biomarkers that could be used to guide individualized treatment modalities. The development of computational biology and machine learning has facilitated the discovery of prognostic gene signatures, but their clinical application is mostly subject to experimental validation. It is thus imperative to combine computational analysis with experimental validation in coming up with clinically meaningful prognostic models.

Objective: This paper aimed to examine how computational and machine learning methods can be used to discover prognostic gene signatures in human cancer and to assess the relevance of experimental validation in making them more useful in clinical practice.

Methodology: A structured questionnaire based on a quantitative, cross-sectional study design, where 222 respondents with expertise in bioinformatics, computational biology, and cancer research could complete the questionnaire. The data were analyzed through descriptive statistics, machine learning–assisted computational analysis normality test, reliability and validity test, and inferential statistics, which included: independent samples t-test, one-way ANOVA, Kruskal-Wallis test, Chi-Square test of independence, Pearson correlation test, and multiple regression analysis. The statistical tests were conducted on SPSS, and the level of significance was set at p 0.05.

Results: Findings showed that the data were normally distributed and, as such, showed high reliability and construct validity. The results of inferential tests showed that demographic variables and the key constructs of the study had significant differences and relationships. Pearson correlation machine learning–assisted computational analysis revealed that there were strong positive relationships between computational understanding, confidence in computational methods, experimental validation, and clinical usefulness. Regression analysis revealed that the predictors of clinical usefulness were found to be significantly positive and were as follows: computational understanding, confidence in computational methods, and experimental validation, explaining a significant percentage of variance in the model.

Conclusion: The results of this paper support the idea that computational detection of prognostic gene signatures, when used with experimental validation, substantially increases their clinical usefulness. The paper discusses the significance of integrative machine learning–driven computational and experimental strategies in the future of cancer prognostics studies and as a tool in the personalized management of cancer.