Prognostic model based on six PD-1 expression and immune infiltration-associated genes predicts survival in breast cancer

Abstract

Background: The prognosis of breast cancer (BC) was associated with the expression of programmed cell death-1 (PD-1).

Methods: BC-related expression and clinical data were downloaded from TCGA database. PD-1 expression with overall survival and clinical factors were investigated. Gene set variation analysis (GSVA) and weighted gene correlation network analysis were performed to investigate the PD-1 expression-associated KEGG pathways and genes, respectively. Immune infiltration was analyzed using the ssGSEA algorithm and DAVID, respectively. Univariate and multivariable Cox and LASSO regression analyses were performed to select prognostic genes for modeling.

Results: High PD-1 expression was related to prolonged survival time (P = 0.014). PD-1 expression status showed correlations with age, race, and pathological subtype. ER- and PR-negative patients exhibited high PD-1 expression. The GSVA revealed that high PD-1 expression was associated with various immune-associated pathways, such as T cell/B cell receptor signaling pathway or natural killer cell-mediated cytotoxicity. The patients in the high-immune infiltration group exhibited significantly higher PD-1 expression levels. In summary, 397 genes associated with both immune infiltration and PD-1 expression were screened. Univariate analysis and LASSO regression model identified the six most valuable prognostic genes, namely IRC3, GBP2, IGJ, KLHDC7B, KLRB1, and RAC2. The prognostic model could predict survival for BC patients.

Conclusion: High PD-1 expression was associated with high-immune infiltration in BC patients. Genes closely associated with PD-1, immune infiltration and survival prognosis were screened to predict prognosis.

Keywords: Breast cancer; Immune infiltration; Immunotherapy; Prognosis; Programmed cell death-1.