Decoding Prognostic Signatures in Brain Metastatic Non-Small-Cell Lung Cancer via Integrated Multi-Omics and Network Analysis

Non-small-cell lung cancer (NSCLC) constitutes approximately all lung cancers (LCs), and metastasis remains a major challenge in its treatment, thus necessitating the detection of novel molecular players involved in this process. In this study, we performed a comprehensive analysis of microarray and RNA-seq cohorts extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) and associated them with metastasis-related genes involved in brain metastasis (BM) in NSCLC. We thus identified differentially expressed metastatic genes (DEMGs) and constructed a protein-protein interaction network (PPIN) using these DEMGs. These DEMGs were further analyzed for associations with patient age, gender, and tumor stage, and the significant impact of specific genes on overall survival (OS) was assessed to determine the prognostic significance of the identified targets. We finally constructed a three-node microRNA (miRNA) feed-forward loop (FFL) involving miR-23b-3p, CD44, and five transcription factors (TFs) [EOMES, FOS, FOSL1, GLIS3, TP63] specific to NSCLC metastasis. Further mutational analysis of these FFL elements revealed that all were altered in the patient samples analyzed. Thus, our study identified potential genomic drivers that may play crucial roles in NSCLC BM. Overall, it provides valuable insights for the discovery of novel therapeutic targets in the management of NSCLC metastasis. However, further in vitro and in vivo experimentations are needed to justify the prognostic role of NSCLC biomarkers in BM pathogenesis.