A Novel Inflammatory Response-Related Gene Signature Predicts Immune Status and Prognosis of Breast Cancer

Abstract

Purpose. Breast cancer is the most common type of cancer and the leading cause of cancer-related death in women worldwide. In this study, we aimed to construct an inflammatory response-related gene model for predicting the immune status and prognosis of breast cancer patients. Methods. We obtained the inflammatory response-related genes from the Molecular Signatures Database. Furthermore, we used univariate Cox regression analysis, the least absolute shrinkage and selection operator (LASSO) regression analysis, and multivariate Cox regression to construct an inflammatory response-related gene signature (IRGS) model based on dataset obtained from The Cancer Genome Atlas (TCGA). Patients were consequently categorized into high-risk and low-risk groups. Kaplan–Meier analysis was used to compare the overall survival (OS) of high-risk and low-risk groups. Following that, we validated the model using a dataset (GSE96058) acquired from Gene Expression Omnibus (GEO) database. Univariate and multivariate Cox analyses were used to determine the independent prognostic value of the IRGS in the TCGA and GSE96058 cohorts. A nomogram was constructed to predict the OS in the TCGA cohort. Further, we used Gene Set Enrichment Analysis (GSEA), CIBERSORT, and single-sample Gene Set Enrichment Analysis (ssGSEA) to evaluate the associations of IRGS with immune-associated pathways and immune infiltration. Finally, the relationship between the expression of the signature genes and drug sensitivity was conducted using Pearson correlation analysis. Results. We established an IRGS to stratify breast cancer patients into the low-risk and high-risk groups. In both the training and validation sets, patients in the high-risk group had significantly shorter OS than those in the low-risk group. The risk score was significantly correlated with the clinical characteristics and could be used as a tool to predict the prognosis of breast cancer. Moreover, we found that the IRGS risk score was an independent predictor of OS in breast cancer patients, and a nomogram model based on IRGS risk score and other clinical factors could effectively predict the prognosis of breast cancer patients. Furthermore, the IRGS risk score was correlated with immune characteristics and was inversely associated with the abundance of immune cell infiltration. Patients with a low IRGS risk score had higher expression levels of immune checkpoint genes, suggesting that IRGS can be used as a potential indicator for immunotherapy. Finally, we found that the expression levels of prognostic genes were significantly correlated with tumor cell sensitivity to chemotherapeutic drugs. Conclusion. Overall, these findings suggest that the IRGS can be used to predict the prognosis and immune status of breast cancer patients and provide new therapeutic targets for the treatment of these patients.

Development and validation of an inflammatory response-related prognostic model and immune infiltration analysis in glioblastoma

“…Increased inflammation and immune infiltration in the tumor microenvironment have been reported to associate with shorter OS in patients with high-grade gliomas (11). Inflammatory response-related models have been reported for prognostic prediction in hepatocellular carcinoma, lung adenocarcinoma, breast cancer and clear cell renal cell carcinoma (12)(13)(14)(15). These findings prompted the role of inflammatory response in cancer prognosis with a promising therapeutic target for cancers.…”

Identification of a novel inflammatory-related gene signature to evaluate the prognosis of gastric cancer patients

“…In addition to blood parameters, several inflammatory response-related genes have also been used to assess cancer recurrence and metastasis. These studies suggest that the IRGS can be used to predict the prognosis and immune status of breast cancer patients, HCC patients, bladder ancer patients, and pancreatic cancer patients[ 21 - 24 ]. Polymorphisms in inflammatory response-related genes are involved in the modulation of the inflammatory response in the pathogenesis of GC[ 25 ].…”