Interdisciplinary Approaches to Identify Genetic Biomarkers Impacting Lung Cancer Progration

Lung cancer is the leading cause of cancer deaths worldwide. It is the most common form of cancer globally, accounting for 12.3% of all cancer cases, with approximately 1.2 million new diagnoses recorded in the year 2000. While smoking is a primary cause of most lung cancers, it is important to note that nonsmokers can also be susceptible to this deadly cancer. Cancer occurs when cells mutate and proliferate uncontrollably. It is essential to monitor lung cancer, as patients face a significant risk of developing comorbidities, including stroke, colorectal cancer, encephalopathy, endometrial cancer, glioblastoma, and Parkinson's disease. To accurately diagnose and develop cancer treatments, it is essential to identify a specific set of influential genes. In this study, we utilized the Cancer Genome Atlas (TCGA) data set to detect significant genes tied to lung cancer and their relationship to comorbidities. Consequently, we conducted preprocessing of the data set and carried out the Kruskal-Wallis test along with Bonferroni correction methods to identify significant genes. A total of 14 significant abnormal genes have been identified from a pool of 18,784 genes. To guess what these important genes do, we looked at functional pathways, gene ontology, protein-protein interactions, protein-chemical interactions, and protein-drug interactions. We also employed the Cox proportional hazard model and the product limit (P.L.) estimator to investigate the significant impact of clinical and genetic factors on survival in lung cancer patients. This investigation lays the groundwork for pinpointing genes that influence cancer and comprehending the effects of lung cancer on the progression of comorbid conditions.