{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T22:17:44Z","timestamp":1788214664172,"version":"build-2803163510"},"reference-count":0,"publisher":"Association of Basic Medical Sciences of FBIH","license":[{"start":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T00:00:00Z","timestamp":1783296000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomol Biomed"],"abstract":"<jats:p>Breast cancer is characterized by marked prognostic and immune heterogeneity, and tumor immune evasion may contribute to variable responses to immunotherapy. This study aimed to characterize immune-evasion-associated transcriptomic patterns in breast cancer and to develop an exploratory prognostic score linked to immune microenvironment features. A curated set of 182 immune-evasion-associated genes (IEGs) was analyzed in The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) cohort. Fifteen differentially expressed IEGs distinguished two molecular clusters with different overall survival (OS), pathway-enrichment profiles, and inferred immune-cell characteristics. To construct an immune-evasion-associated prognostic score (IEPS), all 182 IEGs were screened by univariate Cox regression, and 44 OS-associated candidates were further analyzed using least absolute shrinkage and selection operator (LASSO) Cox regression, yielding a 12-gene score. Using the TCGA-derived median cutoff, high IEPS was associated with poorer OS in TCGA-BRCA and remained associated with OS in the external Gene Expression Omnibus (GEO) GSE20685 cohort; however, discrimination was modest, with external 3-, 5-, and 10-year area under the curve (AUC) values of 0.560, 0.572, and 0.581, respectively. Adding IEPS to available clinicopathological variables produced only modest performance improvement. Exploratory prediction analysis of microarray 50-gene (PAM50) subtype analysis showed that IEPS differed across molecular subtypes, indicating that the score may partly capture subtype-related transcriptomic heterogeneity. Low-IEPS tumors showed more immune-active pathway and immune-cell signatures, whereas high-IEPS tumors were associated with relatively less active antitumor immune features. Protein disulfide isomerase family A member 3 (PDIA3), one IEPS gene, was selected for preliminary functional validation; small interfering RNA (siRNA)-mediated PDIA3 knockdown reduced proliferation, clonogenic growth, and migration of breast cancer cells in vitro. Overall, IEPS may represent an exploratory transcriptome-based indicator associated with prognosis and immune-related features in breast cancer, but it should not be interpreted as a clinically validated standalone or molecular subtype-independent prognostic tool. Further independent validation and immune-relevant mechanistic studies are required, particularly to clarify the role of PDIA3 in breast cancer immune evasion.<\/jats:p>","DOI":"10.17305\/bb.2026.14152","type":"journal-article","created":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T07:32:07Z","timestamp":1783323127000},"source":"Crossref","is-referenced-by-count":0,"title":["Immune-evasion-associated prognostic gene expression score stratifies survival and immune microenvironment features in breast cancer"],"prefix":"10.17305","author":[{"given":"Bozhi","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qunyu","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shu","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1675-3678","authenticated-orcid":false,"given":"Qihui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixin","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuansong","family":"Qu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kexin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongwei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"6922","published-online":{"date-parts":[[2026,7,6]]},"container-title":["Biomolecules and Biomedicine"],"original-title":[],"link":[{"URL":"https:\/\/bjbms.org\/ojs\/index.php\/bjbms\/article\/download\/14152\/4245","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/bjbms.org\/ojs\/index.php\/bjbms\/article\/download\/14152\/4245","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T22:00:41Z","timestamp":1788213641000},"score":1,"resource":{"primary":{"URL":"https:\/\/bjbms.org\/ojs\/index.php\/bjbms\/article\/view\/14152"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,6]]},"references-count":0,"URL":"https:\/\/doi.org\/10.17305\/bb.2026.14152","relation":{},"ISSN":["2831-090X","2831-0896"],"issn-type":[{"value":"2831-090X","type":"electronic"},{"value":"2831-0896","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7,6]]}}}