Correlation Between Immune Cell Infiltration and PD-L1 Expression and Immune-Related lncRNA Determination in...

As a key element of the tumor microenvironment (TME), immune cell infiltration (ICI) is a frequently observed histologic finding in people with triple-negative breast cancer (TNBC), and it is linked to immunotherapy sensitivity. Nonetheless, the ICI in TNBC, to the best of our knowledge, has not been comprehensively characterized. In our current work, computational algorithms based on biological data from next-generation sequencing were employed to characterize ICI in a large cohort of TNBC patients. We defined various ICI patterns by unsupervised clustering and constructed the ICI scores using the principal component analysis (PCA). We observed patients with different clustering patterns had distinct ICI profiles and different signatures of differentially expressed genes. Patients with a high ICI score tended to have an increased PD-L1 expression and improved outcomes, and these patients were associated with decreased tumor mutational burden (TMB). Interestingly, it was showed that patients with high TMB exhibited an ameliorated overall survival (OS) than patients with low TMB. Furthermore, TMB scores only affected the prognosis of TNBC patients in the low-ICI score group but not in the high group. Finally, we identified a new immune-related lncRNA (irlncRNA) signature and established a risk model for the TNBC prognosis prediction. In addition, the high-risk group was related to poor prognosis, a high infiltration level of plasma B cells, monocytes, M2 macrophages, and neutrophils and a low PD-L1 expression. Therefore, the characterization and systematic evaluation of ICI patterns might potentially predict the prognosis and immunotherapy response in TNBC patients.