Eleven inflammation-related genes risk signature model predicts prognosis of patients with breast cancer.

BACKGROUND

Changes in gene expression are associated with malignancy. Analysis of gene expression data could be used to reveal cancer subtypes, key molecular drivers, and prognostic characteristics and to predict cancer susceptibility, treatment response, and mortality. It has been reported that inflammation plays an important role in the occurrence and development of tumors. Our aim was to establish a risk signature model of breast cancer with inflammation-related genes (IRGs) to evaluate their survival prognosis.

METHODS

We downloaded 200 IRGs from the Molecular Signatures Database (MSigDB). The data of breast cancer were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Differential gene expression analysis, the least absolute shrinkage and selection operator (LASSO), Cox regression analysis, and overall survival (OS) analysis were used to construct a multiple-IRG risk signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were carried out to annotate functions of the differentially expressed IRGs (DEIRGs) The predictive accuracy of the prognostic model was evaluated by time-dependent receiver operating characteristic (ROC) curves. Subsequently, nomograms were constructed to guide clinical application according to the univariate and multivariate Cox proportional hazards regression analyses. Eventually, we applied gene set variation analysis (GSVA), mutation analysis, immune infiltration analysis, and drug response analysis to compare the differences between high- and low-risk patients.

RESULTS

Totally, 65 DEIRGs were obtained after comparing 1,092 breast cancer tissues with 113 paracancerous tissues in TCGA. Among them, 11 IRGs (, , , , , , , , , , ) were screened with nonzero coefficient by LASSO regression analysis to construct the prognostic model, which was validated in GSE96058.The 11-gene IRGs risk signature model stratified patients into high- or low-risk groups, with those in the low-risk group having longer survival time and less deaths. Multivariate Cox analysis manifested that risk score, age, and stage were the three independent prognostic factors for breast cancer patients. There were 12 pathways with higher activities and 24 pathways with lower activities in the high-risk group compared with the low-risk group, yet no difference of gene mutation load was observed between the two groups. In immune infiltration analysis, we noted that the proportion of T cells showed a decreased trend according to the increase of risk score and most of the immune cells were enriched in the low-risk group. Inversely, macrophages M2 were more highly distributed in the high-risk group. We identified 67 approved drugs that showed a different effect between the high- and low-risk patients and the top 2 gene-drug pairs were -sunitinib and -ruxolitinib.

CONCLUSIONS

The 11-IRG risk signature model is a promising tool to predict the survival of breast cancer patients and the expressions of and may serve as potential targets for therapy of breast cancer.