Development and Validation of a Gene Expression‐Based Prognostic Model for Patients With Small Cell Lung Cancer Treated With Chemotherapy

ABSTRACT Small cell lung cancer (SCLC) remains one of the most aggressive malignancies, with limited tools for predicting outcomes and guiding treatment. We developed an SCLC‐Predictive Risk Score (PRS) model for patients treated with chemotherapy using least absolute shrinkage and selection operator regression. The model comprised five genes and was developed in a training cohort of 50 patients using an innovative 795‐gene panel of tumor‐ and immune‐related genes analyzed on the NanoString nCounter platform. Gene expression was measured in routine clinical formalin‐fixed, paraffin‐embedded SCLC specimens, predominantly puncture specimens (36/50). The SCLC‐PRS model stratified patients into high‐risk (PRS‐H) and low‐risk (PRS‐L) groups; patients in the PRS‐H group had significantly worse overall survival (hazard ratio [HR] = 0.35, 95% confidence interval [CI]: 0.18–0.69; p = 0.002) and progression‐free survival (HR = 0.44, 95% CI: 0.23–0.84; p = 0.008). The prognostic performance of the model was externally validated in two independent cohorts (GSE60052, n = 38; George cohort, n = 47). Multivariate Cox analysis confirmed SCLC‐PRS as an independent prognostic factor. PRS‐L tumors exhibit a favorable tumor immune microenvironment, with increased CD45 + immune infiltration, reduced Tregs or exhausted CD8 + T cells, and upregulated antigen presentation (MHC‐I/II). Overall, the SCLC‐PRS model is an innovative, cross‐platform‐compatible, and clinically applicable prognostic tool with potential utility for risk stratification and personalized treatment.