{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T09:45:48Z","timestamp":1776246348638,"version":"3.50.1"},"reference-count":61,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T00:00:00Z","timestamp":1776211200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation for Young Scientists of China","award":["81702908"],"award-info":[{"award-number":["81702908"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>SUMOylation, a type of posttranslational protein modification, is linked to numerous biological processes, including tumorigenesis, the immunological response, and DNA repair. Immune-related genes are crucial for immune monitoring and the formation of the tumor immunological microenvironment. The aim of this work was to create a survival prediction model that combines SUMOylation and immune-related gene expression to guide personalized treatment strategies for triple-negative breast cancer (TNBC) patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>RNA sequencing (RNA-seq) data and clinical information from TNBC patients were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). An unsupervised clustering method was applied to identify different SUMOylation-related subclusters in TNBC. Using immune cell infiltration levels and immunological scores, we performed weighted gene coexpression network analysis (WGCNA) to identify SUMOylated and immune-related genes (SIRGs). Three machine learning methods, namely, least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF), were utilized to construct a risk prognosis model from the SIRGs. The model genes were also analyzed through single-cell sequencing. Furthermore, we explored the correlations between risk score and patient prognosis, immune cell infiltration, and immunotherapy. The expression of characteristic genes was ultimately validated by quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The expression levels of genes (MITD1, IL12B, and ZP1) identified by machine learning were used to develop a prognostic model and nomogram. The low-risk patients had significantly better survival rates than the high-risk patients in both the training and validation cohorts. Specifically, patients in the low-risk group had increased immune cell infiltration, immune checkpoint gene expression, and sensitivity to immunotherapy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The constructed SIRG-related prognostic model can accurately predict prognosis and treatment efficacy for patients with TNBC.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.7717\/peerj.21139","type":"journal-article","created":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T08:45:29Z","timestamp":1776242729000},"page":"e21139","source":"Crossref","is-referenced-by-count":0,"title":["A SUMOylation\/immune-related gene signature predicts the prognosis and immunotherapy efficacy of patients with triple-negative breast 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