Accurate screening on cancer biomarkers contributes to health assessment, drug screening, and targeted therapy for precision medicine. The rapid development of high-throughput sequencing technology has identified abundant genomic biomarkers, but most of them are limited to single-cancer analysis. Based on the combination of Fisher score, Recursive feature elimination, and Logistic regression (FRL), this paper proposes an integrative feature selection algorithm named FRL to explore potential cancer genomic biomarkers on cancer subsets. Fisher score is initially used to calculate the weights of genes to rapidly reduce the dimension. Recursive feature elimination and Logistic regression are then jointly employed to extract the optimal subset. Compared to the current differential expression analysis tool GEO2R based on the Limma algorithm, FRL has greater classification precision than Limma. Compared with five traditional feature selection algorithms, FRL exhibits excellent performance on accuracy (ACC) and F1-score and greatly improves computational efficiency. On high-noise datasets such as esophageal cancer, the ACC of FRL is 30% superior to the average ACC achieved with other traditional algorithms. As biomarkers found in multiple studies are more reliable and reproducible, and reveal stronger association on potential clinical value than single analysis, through literature review and spatial analyses of gene functional enrichment and functional pathways, we conduct cluster analysis on 10 diverse cancers with high mortality and form a potential biomarker module comprising 19 genes. All genes in this module can serve as potential biomarkers to provide more information on the overall oncogenesis mechanism for the detection of diverse early cancers and assist in targeted anticancer therapies for further developments in precision medicine.
Plentiful information is contained in the massive gene expression data obtained by DNA microarray technology. Efficient computer analysis methods are beneficial for extracting helpful information quickly and accurately and assist in understanding biological phenomena from enormous data. In this paper, through the integration of fisher-score in filter layer, recursive feature elimination and logistic regression in embedded layer, FSRL is proposed to search latent biomarkers in various cancers with high mortality rates. Compared with the currently popular online analysis tool GEO2R, FSRL has higher classification accuracy. FSRL performs better in evaluation index than five methods and dramatically enhances calculative efficiency. In prostate datasets, evaluation indicator such as accuracy of FSRL, is 40.1% higher than the average. Since biomarkers obtained through multiple cancer sets are more reliable and repeatable than single analysis, cluster analysis is conducted on six cancers with high mortality rates, and 13 genes are screened to form potential genomic biomarker modules. The genes are validated through literature review, GO analysis, and functional pathway retrieval to provide information on carcinogenic mechanisms. They can be used as a decision support system for potential biomarkers to help to narrow scope of biotic experiment scope and detect multiple cancers in targeted anti-cancer therapies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.