{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:15:54Z","timestamp":1787026554123,"version":"build-2736575974"},"reference-count":77,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T00:00:00Z","timestamp":1767571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Prostate cancer (PCa) is biologically heterogeneous, and its molecular underpinnings remain incompletely define. In this study, we sought to identify genes shared between PCa cells and stem-like subpopulations and to develop a prognostic model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>RNA sequencing was performed on PC3 cells and side population stem-like cells (SPC). Primary prostate tumor data were obtained from GSE172301, and The Cancer Genome Atlas (TCGA) provided transcriptomes with clinical annotations. Differential expression, immune microenvironment and infiltration analyses were conducted. Single-cell spatiotemporal transcriptomics data were analyzed using Seurat and spatialLibs. To delineate the role of PLXNA4 in PCa cells, we performed CCK-8 viability assays, EdU incorporation assays, Annexin V\u2013FITC\/PI flow cytometry for apoptosis, and Matrigel-coated Transwell invasion assays.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We identified 562 upregulated and 671 downregulated genes in SPC. A total of nine genes emerged, including CPNE6, RASL10B, GCNT4, STAC2, RBPMS2, PADI3, PLXNA4, S100A14, and MMP9, as potential targets using the support vector machine (SVM) and LASSO methods, with MMP9 highly expressed in tumor cells. A three-gene prognostic signature (RASL10B, RBPMS2, ANGPTL3) stratified patients into risk groups. The high-risk group showed enrichment of Gene Ontology terms related to immune activation, antigen receptor signaling, and B-cell\u2013mediated immunity. We also cataloged seven ubiquitin-related markers and putative ubiquitination sites. Functionally, PLXNA4 depletion reduced cell viability and proliferation, increased apoptosis, and suppressed invasion in PCa cells.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We identified nine target genes and propose a three-gene prognostic model for outcome prediction in PCa. Our findings suggest that targeting PLXNA4 may offer new therapeutic opportunities for the treatment of PCa, including immunotherapy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2025.1709264","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T12:56:17Z","timestamp":1767617777000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Identification of prognostic biomarkers and development of a prediction model for prostate 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