Development and validation of an inflammatory response-related signature in triple negative breast cancer for predicting prognosis and immunotherapy

BackgroundInflammation is one of the most important characteristics of tumor tissue. Signatures based on inflammatory response-related genes (IRGs) can predict prognosis and treatment response in a variety of tumors. However, the clear function of IRGs in the triple negative breast cancer (TNBC) still needs to be explored. MethodsIRGs clusters were discovered via consensus clustering, and the prognostic differentially expressed genes (DEGs) across clusters were utilized to develop a signature using a least absolute shrinkage and selection operator (LASSO). Verification analyses were conducted to show the robustness of the signature. The expression of risk genes was identified by RT-qPCR. Lastly, we formulated a nomogram to improve the clinical efficacy of our predictive tool. ResultsThe IRGs signature, comprised of four genes, was developed and was shown to be highly correlated with the prognoses of TNBC patients. In contrast with the performance of the other individual predictors, we discovered that the IRGs signature was remarkably superior. Also, the ImmuneScores were elevated in the low-risk group. The immune cell infiltration showed significant difference between the two groups, as did the expression of immune checkpoints. ConclusionThe IRGs signature could act as a biomarker and provide a momentous reference for individual therapy of TNBC.

Automated summary

In this study, we discovered IRGs clusters and developed a signature consisting of four genes using LASSO method, which was highly correlated with the prognoses of TNBC patients. Compared to other individual predictors, the IRGs signature showed remarkable superiority. Additionally, the low-risk group had elevated ImmuneScores and significant differences in immune cell infiltration and expression of immune checkpoints compared to the high-risk group.