In Silico RNA-Seq Analysis Reveals Key Transcriptomic Signatures in Pancreatic Cancer

Pancreatic cancer is one of the most lethal malignancies, with limited therapeutic options and a five-year survival rate below 10%. This study aimed to elucidate the molecular mechanisms underlying pancreatic cancer progression and identify potential biomarkers and therapeutic targets through in silico analysis of RNA sequencing (RNA-Seq) data. Using the publicly available GSE119224 dataset, we identified 693 differentially expressed genes (DEGs) with a fold change ≥2.0 and adjusted p-value <0.05. Hierarchical clustering and principal component analysis revealed distinct gene expression profiles between cancerous and normal pancreatic tissues. Gene Ontology (GO) and KEGG pathway analyses indicated significant enrichment in pathways such as chronic myeloid leukemia, ErbB signaling pathway, thyroid hormone signaling pathway, human papillomavirus infection, prostate cancer, AGE-RAGE signaling pathway in diabetic complications, hepatitis C, breast cancer, neurotrophin signaling pathway, colorectal cancer. glioma, endocrine resistance, renal cell carcinoma, pancreatic cancer which are implicated in tumorigenesis and cancer progression. Protein-protein interaction (PPI) network analysis identified hub genes, including UBC, HNF4A, APP, CFTR, ALB, PLK1, TOP2A, INSR, CLU, SUMO2, CDC20, ELAV1, ERBB4, CDC20, APLP1, SUMO1, GATA4, SP1, EGR1 with key roles in cellular processes associated with cancer. Additionally, miRNA-target gene network analysis highlighted microRNAs such as hsa-mir-16-5p and hsa-let-7b-5p as critical regulators. Prognostic assessment using survival analysis and receiver operating characteristic (ROC) curves demonstrated that genes such as CFTR and PLK1 have potential as diagnostic and prognostic biomarkers. Our findings provide a comprehensive transcriptomic profile of pancreatic cancer, offering insights into the molecular pathways and regulatory networks involved in its progression. Although experimental validation is necessary, these results highlight promising biomarkers and therapeutic targets that warrant further investigation. This study underscores the utility of RNA- Seq and bioinformatics tools in advancing pancreatic cancer research and improving patient outcomes.