{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T22:27:43Z","timestamp":1792794463228,"version":"4.5.0"},"reference-count":77,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T00:00:00Z","timestamp":1764633600000},"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>Skin cutaneous melanoma (SKCM) is a highly invasive cancer with dismal prognosis. Integrated stress response (ISR) is associated with tumorigenesis and progression, but its relationship with SKCM prognosis is unclear. This research aimed to identify relevant prognostic genes for SKCM prognosis and treatment insights.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data were obtained from public databases. Differential expression and regression analyses were used to identify prognostic genes. Based on these genes and independent prognostic factors, risk models and nomograms were constructed to assess their clinical application potential in SKCM. Then, immune microenvironment changes in SKCM were explored according to risk-group grouping, providing a basis for stratified treatment decisions for SKCM patients. Finally, RT-qPCR was used to validate the results.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      <jats:italic>DTL<\/jats:italic>\n                      ,\n                      <jats:italic>DTX3L<\/jats:italic>\n                      ,\n                      <jats:italic>KCNMB1<\/jats:italic>\n                      ,\n                      <jats:italic>NDRG1<\/jats:italic>\n                      ,\n                      <jats:italic>GPX2<\/jats:italic>\n                      ,\n                      <jats:italic>DERL3<\/jats:italic>\n                      and\n                      <jats:italic>MBTPS2<\/jats:italic>\n                      were validated as prognostic genes. A risk model was constructed to classify patients into High Risk Group (HRG) and Low Risk Group (LRG), with high-risk SKCM patients having a higher mortality rate. A nomogram integrating clinical indicators was an effective SKCM survival prediction tool. The immune microenvironment differed significantly between risk groups, and most differentially infiltrated immune cells had higher infiltration levels in the Low Risk Group (LRG). Immunotherapy analysis suggested that the Low Risk Group (LRG) might benefit little from treatment, highlighting the need for stratified treatment of SKCM patients. RT-qPCR showed that prognostic genes were up-regulated in human melanoma cells compared to fibroblasts.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The identification of ISR-featured prognostic genes and risk score stratification provide new insights into targeting SKCM and enhancing the efficacy of immunotherapy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2025.1689103","type":"journal-article","created":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T06:26:25Z","timestamp":1764656785000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":35,"title":["Identification and validation of prognostic genes and prognostic models associated with cutaneous melanoma and integrative stress 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