Machine learning algorithms reveal potential miRNAs biomarkers in gastric cancer

Abstract

Gastric cancer is the high mortality rate cancers globally, and the current survival rate is 30% even with the use of combination therapies. Recently, mounting evidence indicates the potential role of miRNAs in the diagnosis and assessing the prognosis of cancers. In the state-of-art research in cancer, machine-learning (ML) has gained increasing attention to find clinically useful biomarkers. The present study aimed to identify potential diagnostic and prognostic miRNAs in GC with the application of ML. Using the TCGA database and ML algorithms such as Support Vector Machine (SVM), Random Forest, k-NN, etc., a panel of 29 was obtained. Among the ML algorithms, SVM was chosen (AUC:88.5%, Accuracy:93% in GC). To find common molecular mechanisms of the miRNAs, their common gene targets were predicted using online databases such as miRWalk, miRDB, and Targetscan. Functional and enrichment analyzes were performed using Gene Ontology (GO) and Kyoto Database of Genes and Genomes (KEGG), as well as identification of protein–protein interactions (PPI) using the STRING database. Pathway analysis of the target genes revealed the involvement of several cancer-related pathways including miRNA mediated inhibition of translation, regulation of gene expression by genetic imprinting, and the Wnt signaling pathway. Survival and ROC curve analysis showed that the expression levels of hsa-miR-21, hsa-miR-133a, hsa-miR-146b, and hsa-miR-29c were associated with higher mortality and potentially earlier detection of GC patients. A panel of dysregulated miRNAs that may serve as reliable biomarkers for gastric cancer were identified using machine learning, which represents a powerful tool in biomarker identification.

The leading global health challenges in the artificial intelligence era

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Classification and Diagnostic Prediction of Colorectal Cancer Mortality Based on Machine Learning Algorithms: A Multicenter National Study

“…In pursuit of this objective, researchers have increasingly turned to predictive methods, with data mining and machine learning (ML) approaches taking center stage [ 4 - 8 ]. The applicability of ML extends beyond CRC and includes the prediction of survival outcomes in various cancer types, leading to several comparative studies among a subset of these methodologies [ 9 , 10 , 1 , 11 , 12 ].…”

Down regulation of Cathepsin W is associated with poor prognosis in pancreatic cancer

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