Construction of a prognostic model for small-cell lung cancer based on lipid metabolism related proteins

Small cell lung cancer (SCLC) is a highly invasive tumor with poor prognosis. There is an urgent need to develop novel biomarkers to identify patients with high-risk SCLC and optimize individual treatment strategies. We conducted a comprehensive, mass spectrometry-based proteomic analysis of 120 tumors (90 primary, 15 lymph node, 15 brain) from 105 SCLC patients. Metabolic transformation has become a hallmark of cancer. Additionally, we have found a correlation between lipid metabolism and the recurrence and metastasis of SCLC in our previous study, based on this deep, unbiased proteomic profiling. This analysis aimed to construct a prognostic risk score for SCLC using lipid metabolism related proteins and explore the underlying molecular biological mechanisms. We first identified 940 proteins related to lipid metabolism, of which 51 were associated with the prognosis of patients. The unsupervised consensus clustering results showed two different lipid metabolism patterns for SCLC, which was associated with patient prognosis and immune cell infiltration. Eight survival related lipid metabolism proteins were further identified through LASSO regression to construct a prognostic risk score, which is an independent prognostic factor for SCLC patients and validated in an independent cohort. The correlation of risk score with neuronal characteristics and anti-tumor immune response was revealed through pathway enrichment analysis of differentially expressed proteins between low-risk and high-risk groups. Finally, low-risk group showed significantly higher sensitivity to almost all chemotherapy and targeted therapy drugs compared to the high-risk group.