Gene regulatory Networks Reveal Sex Difference in Lung Adenocarcinoma

Abstract

Lung adenocarcinoma (LUAD) has been observed to have significant sex differences in incidence, prognosis, and response to therapy. However, the molecular mechanisms responsible for these disparities have not been investigated extensively. Sample-specific gene regulatory network methods were used to analyze RNA sequencing data from non-cancerous human lung samples from The Genotype Tissue Expression Project (GTEx) and lung adenocarcinoma primary tumor samples from The Cancer Genome Atlas (TCGA); results were validated on independent data. We observe that genes associated with key biological pathways including cell proliferation, immune response and drug metabolism are differentially regulated between males and females in both healthy lung tissue, as well as in tumor, and that these regulatory differences are further perturbed by tobacco smoking. We also uncovered significant sex bias in transcription factor targeting patterns of clinically actionable oncogenes and tumor suppressor genes, including AKT2 and KRAS. Using differentially regulated genes between healthy and tumor samples in conjunction with a drug repurposing tool, we identified several small-molecule drugs that might have sex-biased efficacy as cancer therapeutics and further validated this observation using an independent cell line database. These findings underscore the importance of including sex as a biological variable and considering gene regulatory processes in developing strategies for disease prevention and management.

Higher-order correction of persistent batch effects in correlation networks

“…There are a number of gene regulatory network (GRN) inference methods that use co-expression data as input [Glass et al, 2013,Khosravi et al, 2015,Van Der Wijst et al, 2018,Micheletti, 2023], and so we decided to test the effect of using COBRA batch-corrected co-expression on GRN inference. For network inference, we used PANDA [Glass et al, 2013] to infer a GRN for THCA following the same approach for other cancer types [Saha et al, 2023]. PANDA takes three prior networks as inputs: a protein-protein interaction network (PPI) indicating that some TFs can work coordinately to regulate their target genes, a prior network based on mapping TFs to their binding motifs in the genome and identifying likely TF-gene regulatory associations, and gene co-expression to capture the fact that genes deemed to be co-regulated would likely also be co-expressed.…”

Aging-associated Alterations in the Gene Regulatory Network Landscape Associate with Risk, Prognosis and Response to Therapy in Lung Adenocarcinoma

“…We addressed this gap in understanding by using the networkmodeling approaches, PANDA [19] and LIONESS [20], to derive person-specific gene regulatory networks for non-cancerous lung tissue samples from the Genotype Tissue Expression project (GTEx) and LUAD tumor samples from The Cancer Genome Atlas (TCGA), with a focus on evaluating age-associated genes and their regulation by TFs. This approach was motivated by multiple earlier network-modeling analyses that identified disease relevant regulatory features in both healthy tissues as well as in tumor [21,22,23].…”