Construction of an immune infiltration landscape based on immune-related genes in cervical cancer

Abstract

Clinical trials demonstrated that immunotherapy improved the prognosis of patients with cervical cancer (CC), which is strongly associated with immune infiltration landscape. We aimed to comprehensively analyze the immune infiltration landscape and provide directions for immunotherapy of CC. The study was based on the Cancer Genome Atlas (TCGA) database and utilized immune-related genes (IRGs) to identify heterogeneous immune subtypes. The ESTIMATE and CIBERSORT algorithms were performed to unravel the landscape of the tumor immune microenvironment. The IRGs score was constructed by principal component analysis. Then, we analyzed the differences in immune-related characteristics and prognosis between high and low IRGs score groups. An independent immunotherapy cohort (IMvigor210) was used to verify the reliability and stability of the IRGs score. Herein, a total of 272 TCGA-CC samples were divided into high (n = 199) and low (n = 73) IRGs score groups. The infiltration of CD8 T cells, memory resting CD4 T cells, and memory activated CD4 T cells, as well as better prognostic outcomes, mainly exhibited in the low IRGs score group and gene cluster A. GSEA analysis showed that JAK/STAT and VEGF signaling pathways were activated in the low IRGs score group. In contrast, the high IRGs score group with least lymphocyte infiltration may contribute to the poor prognosis. The prognosis of the IMvigor210 cohort was still significantly different between high and low IRGs score groups (P < 0.001). This study demonstrated that the IRGs score could be an independent prognostic biomarker and provide direction for tailoring immunotherapy strategies in the future clinical treatment.