{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T06:18:23Z","timestamp":1787897903975,"version":"build-2784847793"},"reference-count":61,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T00:00:00Z","timestamp":1787875200000},"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>Pancreatic cancer (PC) is a fatal malignancy, with glycolysis and T cells playing crucial roles in its pathogenesis. This study explored prognosis-related genes in glycolysis and T cells in PC using bioinformatics methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We first quantified distinct T-cell subsets via immune infiltration analysis of the TCGA-PAAD cohort and extracted T cell-related genes (T-RGs). Differentially expressed genes (DEGs) were obtained from GSE28735 and TCGA-PAAD. Intersecting these DEGs with T-RGs and glycolysis-related genes (G-RGs) yielded candidate genes. We screened prognostic genes using univariate Cox and LASSO regression, built a prognostic model in TCGA-PAAD and validated it in GSE57495. A nomogram was constructed for survival prediction, its reliability verified by calibration and ROC curves. We compared immune microenvironment, pathway enrichment, mutation landscape and drug sensitivity between high- and low-risk subgroups, and built lncRNA-miRNA-mRNA regulatory networks.\n                      <jats:italic>In vitro<\/jats:italic>\n                      and\n                      <jats:italic>in vivo<\/jats:italic>\n                      mouse tumorigenesis assays explored GPR87\u2019s potential involvement in glycolytic activity and CD8\n                      <jats:sup>+<\/jats:sup>\n                      T cell infiltration. Seahorse analysis, glucose uptake detection and immunohistochemistry further explored roles in glucose metabolism and anti-tumor immunity.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      <jats:italic>MET<\/jats:italic>\n                      ,\n                      <jats:italic>KDELR3<\/jats:italic>\n                      ,\n                      <jats:italic>AK4<\/jats:italic>\n                      , and\n                      <jats:italic>GPR87<\/jats:italic>\n                      were determined as prognostic genes. In the training set, this exploratory prognostic model showed moderate predictive performance, with area under the curve values of 0.72, 0.71, and 0.71 at 1, 2, and 3 years, respectively. The risk score and N-stage were identified as independent prognosis predictors, and the developed nomogram demonstrated moderate predictive performance. Functional pathways revealed enrichment in 44 pathways. There were 17 differential immune cells. In addition, risk scores were correlated with 28 immune checkpoints. The regulatory network comprised 26 miRNAs and 50 lncRNAs. Furthermore, computationally predicted correlations between prognostic genes and 60 drugs were identified in different risk groups.\n                      <jats:italic>In vitro<\/jats:italic>\n                      and\n                      <jats:italic>in vivo<\/jats:italic>\n                      experiments demonstrated that\n                      <jats:italic>GPR87<\/jats:italic>\n                      knockdown inhibits the proliferation, migration, and clonogenic ability of PC cells while promoting their apoptosis. Furthermore,\n                      <jats:italic>GPR87<\/jats:italic>\n                      expression was negatively correlated with CD8\n                      <jats:sup>+<\/jats:sup>\n                      T cell, and\n                      <jats:italic>GPR87<\/jats:italic>\n                      knockdown inhibited glycolysis in pancreatic cancer cells.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      <jats:italic>MET<\/jats:italic>\n                      ,\n                      <jats:italic>KDELR3<\/jats:italic>\n                      ,\n                      <jats:italic>AK4<\/jats:italic>\n                      , and\n                      <jats:italic>GPR87<\/jats:italic>\n                      were identified as candidate prognostic genes in PC, providing preliminary hypothesis-generating insights that require validation in independent institutional or prospective clinical cohorts before any clinical application.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1918556","type":"journal-article","created":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T05:35:52Z","timestamp":1787895352000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Glycolysis and T cell-associated gene signature predicts prognosis and therapeutic responses in pancreatic cancer"],"prefix":"10.3389","volume":"17","author":[{"given":"Wancheng","family":"Li","sequence":"first","affiliation":[{"name":"The Second School of Clinical Medicine, Lanzhou University","place":["Lanzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongao","family":"Fan","sequence":"additional","affiliation":[{"name":"The Second School of Clinical Medicine, Lanzhou University","place":["Lanzhou, 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