Metabolic Health Together with a Lipid Genetic Risk Score Predicts Survival of Small Cell Lung Cancer Patients

Simple Summary Despite the progress in surgery and therapies, small cell lung cancer (SCLC) is still one of the most lethal types of cancer. The disease control remains heterogeneous and consequently, the ability to predict patient survival would be of great clinical value. Here, we propose for the first time, a metabolic precision approach for SCLC patients. We found that a healthy metabolic status contributes to increasing SCLC survival. Moreover, we discovered that two lipid metabolism-related genes, racemase and perilipin 1, and a genetic risk score of both genes, predict better SCLC survival. Our results show that a metabolic scenario characterized by metabolic health, lipid gene expression and environmental factors, is crucial for increase SCLC survival. Small cell lung cancer (SCLC) prognosis is the poorest of all types of lung cancer. Its clinical management remains heterogeneous and therefore, the capability to predict survival would be of great clinical value. Metabolic health (MH) status and lipid metabolism are two relevant factors in cancer prevention and prognosis. Nevertheless, their contributions in SCLC outcome have not yet been analyzed. We analyzed MH status and a transcriptomic panel of lipid metabolism genes in SCLC patients, and we developed a predictive genetic risk score (GRS). MH and two lipid metabolism genes, racemase and perilipin 1, are biomarkers of SCLC survival (HR = 1.99 (CI95%: 1.11-3.61) p = 0.02, HR = 0.36 (CI95%: 0.19-0.67), p = 0.03 and HR = 0.21 (CI95%: 0.09-0.47), respectively). Importantly, a lipid GRS of these genes predict better survival (c-index = 0.691). Finally, in a Cox multivariate regression model, MH, lipid GRS and smoking history are the main predictors of SCLC survival (c-index = 0.702). Our results indicate that the control of MH, lipid gene expression and environmental factors associated with lifestyle is crucial for increased SCLC survival. Here, we propose for the first time, a metabolic precision approach for SCLC patients.