Identification of anoikis-related lncRNAs and analysis of immune infiltration in patients with breast cancer

Background Breast cancer is a significant worldwide health concern with a complex etiology and multiple treatment approaches. Anoikis, a programmed cell death mechanism, has emerged as a critical regulatory pathway in biological processes like proliferation and migration in breast cancer. Long non-coding RNAs (lncRNAs), specifically those related to anoikis (arLncRNAs), have been linked to breast cancer development. Therefore, investigating the role of arLncRNAs in breast cancer is of utmost importance. Methods In this study, we collected RNA sequencing data from The Cancer Genome Atlas (TCGA) database and identified anoikis-related genes from GeneCards, which we then compared with the TCGA dataset. Using differential analysis, functional enrichment analysis, and single-factor Cox regression, we identified arLncRNAs that are significantly associated with breast cancer prognosis and constructed a reliable risk model. Results We identified eight arLncRNAs that are significantly associated with breast cancer prognosis: LINC01235, AL136531.1, AL122010.1, LINC01871, MAPT-AS1, SEMA3B-AS1, ST7-AS1, and AC091182.2. Among these, LINC01235 and AC091182.2 were identified as unfavorable prognostic factors, while the remaining six (AL136531.1, AL122010.1, LINC01871, MAPT-AS1, SEMA3B-AS1, ST7-AS1) were identified as favorable prognostic factors. Furthermore, we analyzed the relationship between cancer-related immune infiltration and the arLncRNAs risk model. Our results showed that the degree of immune infiltration was higher in the low-risk group. Conclusions We have identified eight arLncRNAs, namely LINC01235, AL136531.1, AL122010.1, LINC01871, MAPT-AS1, SEMA3B-AS1, ST7-AS1, and AC091182.2, which possess significant predictive value for breast cancer prognosis and response to immunotherapy. These arLncRNAs could be potentially exploited as clinical targets for the treatment of breast cancer associated with anoikis.