Robust Biomarker Screening Using Spares Learning Approach for Liver Cancer Prognosis

LncRNAs, miRNAs, mRNAs, methylation, and proteins exert profound biological functions and are widely applied as prognostic features in liver cancer. This study aims to identify prognostic biomarkers' signature for liver cancer. Samples with inadequate tumor purity were filtered out and the expression data from different resources were retrieved. The Spares learning approach was applied to select lncRNAs, miRNAs, mRNAs, methylation, and proteins' features based on their differentially expressed groups. The LASSO boosting technique was employed for the predictive model construction. A total of 200 lncRNAs, 200 miRNAs, 371 mRNAs, 371 methylations, and 184 proteins were observed to be differentially expressed. Five lncRNAs, 11 miRNAs, 30 mRNAs, 4 methylations, and 3 proteins were selected for further evaluation using the feature elimination technique. The highest accuracy of 89.32% is achieved as a result of training and learning by Spares learning methodology. Final outcomes revealed that 5 lncRNA, 11 miRNA, 30 mRNA, 4 methylation, and 3 protein signatures could be potential biomarkers for the prognosis of liver cancer patients.