Identification of an immune cell infiltration-related gene signature for prognosis prediction in triple-negative breast cancer

TIL-related biomarker identification in TNBC

Abstract

Triple-negative breast cancer TNBC with higher immunogenicity and tumor-infiltrating lymphocyte (TIL) enrichment can benefit from immunotherapy relative to other breast cancer subtypes. Our work was designed to identify the TIL-related hub genes in TNBC and construct a prognostic signature for TNBC. TNBC gene expression files were obtained from the TCGA database. CIBERSORT algorithm and random forest risk model were used for immune infiltration group division. The TIL-related differentially expressed genes (DEGs) were then selected and subject to GO, KEGG analyses and GSEA. Next, Lasso cox regression analyses were adopted for constructing a prognostic risk model, followed by evaluation using time-dependent ROC curves. The copy number variation between the two risk groups was also analyzed, and major genomic mutation types were identified. Additionally, the nomogram was constructed with calibration curve for clinical prognosis analysis. Our results showed that totally 113 TNBC samples were allocated into the high or low-immune risk groups. We identified 243 DEGs between groups, namely TIL-related DEGs, with 128 upregulated and 115 downregulated genes. Among the TIL-related DEGs, 6 hub genes (SLITRK3, PCDHGB3, NELL2, SRRM4, ASIC2 and B4GALNT2) were screened out and constructed a prognostic risk signature, which had good performance for long-term prognosis prediction. Analysis of genomic mutation showed that the TP53, PIK3CA, TTH, etc. showed high mutation frequency in the two prognostic risk groups. Moreover, the higher risk score of the prognostic risk model predicted poor overall survival in TNBC patients, and nomogram and calibration curve confirmed the potent prediction ability of this model. To sum up, six TIL-related biomarkers (SLITRK3, PCDHGB3, NELL2, SRRM4, ASIC2 and B4GALNT2) were identified and used for the construction of the prognostic risk model, which might provide novel insight for the clinical decisions.