Analysis of C-X-C motif chemokine receptors in breast cancer: potential value in immunotherapy and prognostic prediction.

Abstract

The concept of individualized therapy has advanced the development of prognostic biomarkers to manage patients with breast cancer (BRCA). Immunotherapy has shown great potential in treating BRCA, and the C-X-C motif chemokine receptor (CXCR) has generated interest in regulating tumor progression through the immune microenvironment. Although CXCRs were utilized for prognosis prediction in glioma with favourable capability, the prognostic and therapeutic role of CXCR in BRCA is unclear. We used The Cancer Genome Atlas (TCGA) database to analyze 1,095 BRCA patients' transcription, mutation, survival time and survival status. Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE), Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), quanTIseq, and Estimating the Proportion of Immune and Cancer cells (EPIC) algorithms were performed to infer the association of CXCR genes with immune cells. We randomly divided the TCGA dataset into a training set and a validation set according to 1:1, constructed a LASSO Cox regression model based on CXCR family genes using the glmnet R package in the training set, assembled clinical variables to draw a visual Nomogram using the R package rms, and validated the model by receiver operating characteristic (ROC) curves, calibration curves with clinical decision curves in the validation set efficacy. Compared to normal samples, CXCR3/4/5 messenger RNA (mRNA) expression levels were upregulated in BRCA samples, whereas CXCR1/2 mRNA expression levels were downregulated. High CXCR3/5/6 expression was associated with a good prognosis. Subsequently, we divided the CXCRs into 2 molecular subgroups based on their expression patterns and explored prognosis, immune infiltration, functional enrichment, hallmarks, and immune response differentiation between the two subgroups. After LASSO Cox regression modeling, a CXCR score predicting overall survival (OS) was constructed, and the predictive accuracy was assessed. By pooling clinical variables, a nomogram individual risk assessment method was established to measure the identification of genuinely high-risk patients who should receive interventions. In summary, CXCR genes were associated with immune infiltration and survival in BRCA patients, and our CXCR-based prognostic model could better predict the prognosis of BRCA patients and provide potential immunotherapy targets for clinical purposes.