Construction and validation of an eight pyroptosis-related lncRNA risk model for breast cancer

We developed a risk model based on pyroptosis-related long non-coding RNAs (lncRNAs) and assessed its prognostic value and clinical significance in breast cancer (BRCA).

BRCA RNA sequencing data with corresponding clinical information were retrieved from The Cancer Genome Atlas (TCGA) database. Univariate Cox regression analysis was used to examine correlations between prognosis of BRCA patients and the expression levels of pyroptosis-related lncRNAs. A prognostic model was developed and validated by identifying the correlation of risk scores with tumor immune infiltration and immune cell function through immune response analysis. Functional analyses of focal dysfunction-related lncRNA were also carried out. Lastly, single sample gene set enrichment analysis (ssGSEA) was conducted to determine the differences in immune responses between the low- and high-risk groups.

We divided the TCGA-BRCA dataset into 3 clusters by consensus clustering, and identified 11 pyroptosis-related lncRNAs that are differentially expressed between tumors and normal tissues. In addition, we determined if PD-L1 expression is associated with clustering and gene expression. The list was further narrowed down to eight pyroptosis-related lncRNAs and their regression coefficients were obtained through LASSO regression analysis. The relative proportion of 22 different immune cells in the BRCA microenvironment was determined using the CIBERSORT algorithm to explore the indicative effects of risk score on the tumor microenvironment (TME). We found that the resting mast cells, M0, and M2 macrophages were positively correlated with the risk scores.

The potential role of pyroptosis-related lncRNAs in BRCA prognosis may be exploited as a treatment target for patients with BRCA.