Mapping biological networks in lung adenocarcinoma using transcriptomic analysis to identify prognostic biomarkers and therapeutic targets.

e20038 Background: Lung adenocarcinoma (LUAD) is among the most frequently diagnosed cancers worldwide and remains a leading cause of cancer-related mortality. Despite advances in targeted therapies, LUAD remains challenging to treat, partly due to its genetic heterogeneity and multifactorial progression. We performed an integrative transcriptomic analysis to better characterize the molecular underpinnings of LUAD, identify clinically relevant prognostic markers, and highlight potential therapeutic avenues. Methods: We gathered RNA sequencing (RNA-Seq) data from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC), as well as microarray data from two Gene Expression Omnibus cohorts (GSE72094 and GSE68465). All datasets underwent normalization (voom and quantile normalization) and batch correction using ComBat while preserving critical variables such as KRAS mutation status and gender. We conducted adjusted differential expression analyses comparing tumor versus normal samples, examining gender-specific changes, and pinpointing KRAS-associated genes. Survival analyses employed Cox proportional hazards regression, adjusted for age, cancer stage, gender, and KRAS mutation. We used gene set variation analysis (GSVA) to delineate pathway-level patterns and WGCNA constructed partial correlation networks to reveal coexpression among favorable or poor prognostic markers. Results: A total of 5,988 (DEGs) were identified in tumor samples, of which 270 were linked to KRAS mutation. Notably, KRAS expression emerged as a survival determinant independent of its mutation status, reinforcing the importance of KRAS signaling in LUAD biology. ADRB2 (β2-adrenergic receptor) exhibited a protective effect, suggesting that modulating β2-adrenergic signaling may offer therapeutic value. In contrast, HDGF, KRAS, and RAF1 were correlated with poorer outcomes. Pathway enrichment analyses spotlighted the PDGF and mTOR pathways as potentially actionable drivers of disease progression, whereas lower activity in the VEGF pathway signaled better patient survival. Coexpression networks demonstrated distinct clusters separating favorable prognostic markers from poor prognostic markers, indicating divergent molecular mechanisms that could inform personalized treatment strategies. Conclusions: Our integrative transcriptomic approach refines existing knowledge of LUAD pathophysiology by revealing a KRAS-independent prognostic role for KRAS, uncovering ADRB2 as a favorable biomarker, and elucidating PDGF and mTOR pathways as therapeutic targets. These findings deepen our understanding of LUAD’s molecular landscape and highlight potential directions for novel treatment strategies, although further preclinical and clinical validation is required to confirm their translational applicability.