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The presence of autoantibodies is closely associated with the occurrence and development of cancer. Autoantibodies can be used as biomarkers for early breast cancer diagnosis. However, the relationship between autoantibodies and the prognosis of breast cancer patients remains elusive. This retrospective study aimed to investigate the correlation between the presence of autoantibodies and outcomes in breast cancer patients. This study included a total of 155 patients from People's Hospital of Henan University (Zhengzhou, China) who were diagnosed with breast cancer from January 2017 to December 2021. The enrolled patients' clinicopathological features were collected, and 88 patients were ultimately involved in the analysis. Univariate and multivariate Cox regression analyses were performed to search for the risk factors related to the poor prognosis of breast cancer patients. The association between the presence of autoantibodies and patients' survival was analysed using Kaplan–Meier curves. After screening, there were 38 autoantibody‐positive cases and 50 autoantibody‐negative cases. Breast cancer patients with autoantibody‐positive had a 57% lower risk of death compared with autoantibody‐negative patients. Multivariate Cox regression analysis indicated that the presence of autoantibody could be a potential prognostic predictor for breast cancer, independent of age, histological grade, pathological classification, clinical stage, and the expression levels of hormonal receptors. In addition, autoantibody‐positive breast cancer patients had longer progression‐free survival and overall survival compared with autoantibody‐negative cases. Positive autoantibody was found as an independent biomarker of better prognosis in breast cancer patients, providing a new strategy for the prognostic assessment of breast cancer patients. [ABSTRACT FROM AUTHOR]

Background. Currently, immunotherapy is widely used for breast cancer (BC) patients, and tumor mutation burden (TMB) is regarded as a valuable independent predictor of response to immunotherapy. However, specific gene mutations and their relationship with TMB and tumor-infiltrating immune cells in BC are not fully understood. Methods. Comprehensive bioinformatic analyses were performed using data from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets. Survival curves were analyzed via Kaplan-Meier analysis. Univariate and multivariate Cox regression analyses were used for prognosis analysis. Gene set enrichment analysis (GSEA) was performed to explore regulatory mechanisms and functions. The CIBERSORT algorithm was used to calculate the tumor-infiltrating immune cell fractions. Results. We analyzed somatic mutation data of BC from TCGA and ICGC datasets and found that 19 frequently mutated genes were reported in both cohorts, namely, SPTA1, TTN, MUC17, MAP3K1, CDH1, FAT3, SYNE1, FLG, HMCN1, RYR2 (ryanodine receptor 2), GATA3, MUC4, PIK3CA, KMT2C, TP53, PTEN, ZFHX4, MUC16, and USH2A. Among them, we observed that RYR2 mutation was significantly associated with higher TMB and better clinical prognosis. Moreover, GSEA revealed that RYR2 mutation-enriched signaling pathways were related to immune-associated pathways. Furthermore, based on the CIBERSORT algorithm, we found that RYR2 mutation enhanced the antitumor immune response by enriching CD8+ T cells, activated memory CD4+ T cells, and M1 macrophages. Conclusion. RYR2 is frequently mutated in BC, and its mutation is related to increased TMB and promotes antitumor immunity; thus, RYR2 may serve as a valuable biomarker to predict the immune response. [ABSTRACT FROM AUTHOR]