Identification of a Potential sialylation-related pattern for the Prediction of Prognosis and Immunotherapy Response in in Small Cell Lung Cancer

Background: Our study aimed to establish a novel system for quantifying sialylation patterns and comprehensively analyze their relationship with immune cell infiltration (ICI) characterization, prognosis, and therapeutic sensitivity in small cell lung cancer (SCLC). Methods: We conducted a thorough assessment of the sialylation patterns in 100 patients diagnosed with SCLC. Our primary focus was on analyzing the expression levels of 7 prognostic sialylation-related genes (SRGs). To evaluate and quantify these sialylation patterns, we devised a sialylation score (SS) using principal component analysis algorithms. Prognostic value and therapeutic sensitivities were then evaluated using multiple methods. The GSE176307 was used to verify the predictive ability of SS for immunotherapy. Results: Our study identified two distinct clusters based on sialylation patterns. Sialylation cluster B exhibited a lower level of induced ICI therapy and immune-related signaling enrichment, which was associated with a poorer prognosis. Furthermore, there were significant differences in prognosis, response to targeted inhibitors, and immunotherapy between the high and low SS groups. Patients with high SS were characterized by decreased immune cell infiltration, chemokine and immune checkpoint expression and poorer response to immunotherapy, while the low SS group was more likely to benefit from immunotherapy. Conclusion: This work showed that the evaluation of sialylation subtypes will help to gain insight into the heterogeneity of SCLC. The quantification of sialylation patterns played a non-negligible role in the prediction of ICI characterization, prognosis and individualized therapy strategies.