{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T12:02:36Z","timestamp":1780142556592,"version":"3.54.0"},"reference-count":29,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T00:00:00Z","timestamp":1780099200000},"content-version":"vor","delay-in-days":149,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Genomics"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Cervical cancer is among the most prevalent gynecological malignancies globally, and its inherent molecular heterogeneity remains a persistent obstacle to uniform treatment efficacy and patient survival. Lysosome\u2010mediated cell death (LDCD) has recently emerged as a mechanistically distinct form of regulated cell death with growing relevance in tumor biology, influencing both oncogenic progression and responsiveness to therapy. Nevertheless, its functional significance in cervical cancer and its interactions with the tumor microenvironment (TME) at single\u2010cell resolution remain insufficiently characterized.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted an integrative analysis combining single\u2010cell RNA sequencing (scRNA\u2010seq) with 15 machine learning algorithms to examine LDCD\u2010related gene expression patterns in cervical cancer. Multicohort transcriptomic data were obtained from TCGA and GEO databases, and scRNA\u2010seq data were retrieved from the GSE138080 dataset. Core hub genes were identified through differential expression analysis, protein\u2013protein interaction network construction, and consensus clustering. Prognostic models were built using multiple machine learning frameworks and evaluated by concordance index. Immune cell infiltration was quantified via CIBERSORT, and single\u2010cell\u2010level validation was performed with the Seurat pipeline.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Six LDCD\u2010associated hub genes\u2014CTSV, FER, GGA2, LAMP3, STAB1, and STAB2\u2014were identified as significant prognostic determinants. Univariate Cox regression demonstrated that CTSV (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.048) and STAB2 (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.017) were independently associated with patient survival. Consensus clustering stratified patients into two molecularly distinct subtypes with markedly different survival trajectories (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001). CoxBoost, RFSurvival, and Lasso\u2010Cox achieved the highest predictive accuracy, with C\u2010index values exceeding 0.747. Single\u2010cell analysis confirmed differential LDCD gene expression across T cells, fibroblasts, and tumor cell populations. High\u2010risk patients exhibited diminished immune scores, enrichment of immunosuppressive populations including regulatory T cells and M2 macrophages, and inferior clinical outcomes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our findings establish LDCD as a functionally important mechanism in cervical cancer at single\u2010cell resolution and demonstrate the translational utility of integrating scRNA\u2010seq with machine learning for the discovery of novel prognostic biomarkers and therapeutic targets.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1155\/ijog\/8487184","type":"journal-article","created":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T11:37:44Z","timestamp":1780141064000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Single Cell and Machine Learning\u2013Based Analysis of Lysosome Mediated Cell Death Reveals Novel Prognostic Biomarkers and Therapeutic Targets in Cervical 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