MicroRNAs and their target mRNAs as potential biomarkers among smokers and non-smokers with lung adenocarcinoma

The spike-and-slab quantile LASSO for robust variable selection in cancer genomics studies

Data irregularity in cancer genomics studies has been widely observed in the form of outliers and heavy-tailed distributions in the complex traits. In the past decade, robust variable selection methods have emerged as powerful alternatives to the nonrobust ones to identify important genes associated with heterogeneous disease traits and build superior predictive models. In this study, to keep the remarkable features of the quantile LASSO and fully Bayesian regularized quantile regression while overcoming their disadvantage in the analysis of high-dimensional genomics data, we propose the spike-and-slab quantile LASSO through a fully Bayesian spike-and-slab formulation under the robust likelihood by adopting the asymmetric Laplace distribution (ALD). The proposed robust method has inherited the prominent properties of selective shrinkage and self-adaptivity to the sparsity pattern from the spike-and-slab LASSO (Roc̆ková and George, J Am Stat Associat, 2018, 113(521): 431-444). Furthermore, the spike-and-slab quantile LASSO has a computational advantage to locate the posterior modes via soft-thresholding rule guided Expectation-Maximization (EM) steps in the coordinate descent framework, a phenomenon rarely observed for robust regularization with nondifferentiable loss functions. We have conducted comprehensive simulation studies with a variety of heavy-tailed errors in both homogeneous and heterogeneous model settings to demonstrate the superiority of the spike-and-slab quantile LASSO over its competing methods. The advantage of the proposed method has been further demonstrated in case studies of the lung adenocarcinomas (LUAD) and skin cutaneous melanoma (SKCM) data from The Cancer Genome Atlas (TCGA).

Identification of an 11-miRNA-regulated and surface-protein genes signature predicts the prognosis of lung adenocarcinoma based on multi-omics study

Lung adenocarcinoma (LUAD) is one of the most prevalent and lethal cancers worldwide, signifying a critical need for improved prognostic tools. A growing number of studies have highlighted the role of microRNAs (miRNAs) and their regulatory functions in tumorigenesis and cancer progression. In this context, we performed an extensive analysis of bulk RNA- and miRNA-sequencing to identify LUAD-associated prognostic genes. A risk score system based on 11 miRNA-regulated and surface-protein genes was developed, which was later validated by internally and externally using the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), respectively. Further single-cell RNA sequencing analysis revealed significant interactions between various cellular subpopulations within the tumor microenvironment, with the most pronounced differences observed between endothelial and epithelial cells. The mutational analysis highlighted TP53 as a key signaling pathway associated with the risk score. The study underscores that immune suppression, indicated by a positive association with regulatory T cells (Tregs) and an inverse correlation with M1-type macrophages, is prevalent in high-risk LUAD patients. These findings provide a promising prognostic tool for clinical outcomes of LUAD patients, facilitating future development of therapeutic strategies and enhancing our understanding of the regulatory function of miRNAs in LUAD.