AI-driven network biology identifies SRC as a therapeutic target in metastatic pancreatic adenocarcinoma

Pancreatic adenocarcinoma (PAAD) is notorious for its limited treatment options and dismal prognosis, underscoring an urgent need for innovative therapeutic strategies. This study leverages advanced bioinformatics and a novel ChatGPT-4o evaluation framework to identify and validate SRC kinase as a significant drug target for PAAD. Initial bioinformatics screening illustrated SRC’s critical involvement in essential signaling pathways associated with the disease. Subsequently, the ChatGPT-4o framework provided an unbiased, efficient evaluation, emphasizing SRC’s role in disease mechanisms. Our findings reveal substantial overexpression of SRC in PAAD compared to normal pancreatic tissue, with expression levels escalating in line with tumor grade and stage. Protein expression analysis using the Human Protein Atlas dataset showed a statistically significant difference between PAAD samples and normal samples for SRC ( P < 1e-15). Survival analysis using the Kaplan-Meier Plotter (KMP) dataset further demonstrates a strong association between high SRC expression and reduced survival, with hazard ratios (HR) of 1.45 ( P < 0.00769) for overall survival and 2.15 ( P < 9.19e-5) for disease free survival in advanced disease stages. SRC's involvement in key oncogenic pathways suggests its potential as part of combination therapies, enhancing the efficacy of existing treatments by targeting complementary mechanisms. This study shows the therapeutic relevance of SRC and demonstrates the effectiveness of combining innovative AI technologies with traditional bioinformatics to accelerate the discovery and validation of critical drug targets in oncology.