miR-142-3p inhibits LPS-induced activation of NF-κB by targeting IRAK1 in colorectal cancer

Uncovering driver genes is crucial for understanding heterogeneity in cancer. L 1-type regularization approaches have been widely used for uncovering cancer driver genes based on genome-scale data. Although the existing methods have been widely applied in the field of bioinformatics, they possess several drawbacks: subset size limitations, erroneous estimation results, multicollinearity, and heavy time consumption. We introduce a novel statistical strategy, called a Recursive Random Lasso (RRLasso), for high dimensional genomic data analysis and investigation of driver genes. For time-effective analysis, we consider a recursive bootstrap procedure in line with the random lasso. Furthermore, we introduce a parametric statistical test for driver gene selection based on bootstrap regression modeling results. The proposed RRLasso is not only rapid but performs well for high dimensional genomic data analysis. Monte Carlo simulations and analysis of the “Sanger Genomics of Drug Sensitivity in Cancer dataset from the Cancer Genome Project” show that the proposed RRLasso is an effective tool for high dimensional genomic data analysis. The proposed methods provide reliable and biologically relevant results for cancer driver gene selection.

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miR-142-3p inhibits LPS-induced activation of NF-κB by targeting IRAK1 in colorectal cancer

Abstract: inflammatory signals originating from colorectal cancer cells have a vital role in the development of colorectal cancer (CRC). microRnas (miRnas) have been demonstrated to be involved in the

Cited by 3 publications

References 22 publications

miR-142-3p simultaneously targets HMGA1, HMGA2, HMGB1, and HMGB3 and inhibits tumorigenic properties and in-vivo metastatic potential of human cervical cancer cells

miR-142-3p simultaneously targets HMGA1, HMGA2, HMGB1, and HMGB3 and inhibits tumorigenic properties and in-vivo metastatic potential of human cervical cancer cells

Prominent roles of microRNA-142 in cancer

Recursive Random Lasso (RRLasso) for Identifying Anti-Cancer Drug Targets

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