A Predictive Model and Mechanistic Study of Treatment Effectiveness in Patients Newly Diagnosed with Small Cell Lung Cancer

Tianyun WangQiuyang luXiaodie LuoChunfang TaoJiaqin LiuHongbo zouQichao XieRui Kong^*

• The Third Affiliated Hospital of Chongqing Medical University, Chongqing, China

Extensive-stage Small cell lung cancer (ES-SCLC) represents a highly aggressive form of malignancy characterized by a poor prognosis. The identification of reliable predictive and prognostic biomarkers is essential for the optimization of treatment strategies. This study aimed to explore the potential of clinical factors and laboratory indicators as predictive and prognostic markers in patients with ES-SCLC, as well as to validate the differential expression of key protein molecules within ES-SCLC tissues. A retrospective analysis was conducted on data from 101 ES-SCLC patients receiving first-line treatment, employing logistic regression and Cox regression analyses to identify independent factors influencing treatment efficacy. To address potential confounding effects of immunotherapy on white blood cell counts, we performed subgroup analysis comparing WBC changes between patients receiving chemotherapy alone versus chemotherapy plus immunotherapy. The mean WBC change in the chemotherapy-alone group was -2.30±2.47, while in the chemotherapy plus immunotherapy group it was -2.08 ± 2.45 (p=0.659), indicating no significant difference in WBC changes between treatment regimens. Predictive models were established based on the identified factors and evaluated using cross-validation, ROC curves, and calibration curves. The expression levels of neuron-specific enolase (NSE), fibrinogen (FIB), and gastrin-releasing peptide precursor were validated using gene expression data from the Gene Expression Omnibus (GEO) database. The results revealed that pre-chemotherapy tumor size and post-cycle 2 FIB levels were independent predictors of treatment efficacy, while pre-chemotherapy white blood cell (WBC) count, pre-chemotherapy D-dimer, and post-cycle 2 gastrin-releasing peptide precursor were independent risk factors for OS. The predictive models exhibited strong performance in forecasting treatment efficacy and survival outcomes. Furthermore, the GEO analysis validated the differential expression of FIB and gastrin-releasing peptide precursor in ES-SCLC tissues relative to normal tissues. These findings offer significant insights into the prognostic significance of clinical factors and laboratory indicators in ES-SCLC, potentially informing the development of personalized treatment strategies.