Identification of a potential sialylation-related pattern for the prediction of prognosis and immunotherapy response in small cell lung cancer.

ABSTRACT: 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). 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. 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. Our study identified 2 distinct clust

SUBMITTER: Yu Y 

PROVIDER: S-EPMC11479454 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature