Abstract 6217: Differential genome-wide gene regulatory networks for lung cancer

Abstract Background: Lung cancer remains the leading cause of cancer death worldwide due to the fact that most cases are diagnosed at distant stages, and treatment is less effective. During the past two decades, much effort has been devoted to understanding tumor biology and developing targeted therapeutics, which have significantly improved patient survival. With the increasingly available multi-omics data from healthy controls and lung cancer patients, we constructed gene regulatory networks for the two main subtypes of non-small-cell lung cancer (NSCLC), the most common type of lung cancer. In addition, differential networks were constructed by integrative analysis of data from lung cancer tissue and tissues from healthy lungs. Methods: Transcriptomics and genomic data were obtained from the patients with lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) tissue available in The Cancer Genome Atlas (TCGA) project, and both types of omics data of normal lung tissue were downloaded from The Genotype-Tissue Expression (GTEx) consortium. The networks for each subtype and the differential networks were constructed by utilizing our newly developed machine learning tools, two-stage penalized least square (2SPLS) approach, and analysis of variance of directed networks (NetANOVA), respectively. Results: For the top three identified subnetworks of LUAD, we identified some well-known genes associated with lung cancer, such as EGFR, IRF1, KRAS, ERBB2, PIK3CA, MYC, at bootstrap frequency 90%. Based on the top three subnetworks with a bootstrap frequency of 100%, results from the Ingenuity Pathway Analysis (IPA) indicated several significant pathways, primary immunodeficiency signaling and communication between innate and adaptive immune cells. GO enrichment analysis showed statistically significant biological processes, including immune response, extracellular matrix organization, and significant KEGG pathways such as Hematopoietic cell lineage. The differential networks between LUAD, LUSC, and healthy lungs showed the same regulations across all three groups, such as the regulation between EEF2 and RACK1, the perturbations between LUAD and healthy (two-way regulations between HIGD2A and ARL10), and LUSC and healthy (GRB14 regulates EIF3E) respectively. In addition, we identified the differences between LUAD and LUSC, such as bi-directional regulations between CLTB and ARL10. Conclusion: Although all groups share most regulations, we identified differential gene regulations between healthy lung tissue and lung cancer tissue and between LUAD and LUSC of NSCLC. Using multi-omics data generated from lung tissues, coupled with advanced machine learning methods, the causal relationships between genome-wide genes from the analysis will help us understand the molecular mechanism of lung cancer and facilitate the development of personalized treatment strategies. Citation Format: Min Zhang, Zhongli Jiang, Dabao Zhang. Differential genome-wide gene regulatory networks for lung cancer abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 6217.