{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T23:40:28Z","timestamp":1767138028729,"version":"build-2238731810"},"reference-count":35,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T00:00:00Z","timestamp":1704672000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Environmental Toxicology"],"published-print":{"date-parts":[[2024,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    The high mortality rate and postoperative recurrence of hepatocellular carcinoma (HCC) contribute to the burden on society and healthcare. The prognostic value and underlying mechanisms of cellular senescence and tumor microenvironment (TME) in HCC remain unclear. Bulk transcriptomic data were obtained from 368 HCC samples in The Cancer Genome Atlas\u2010liver hepatocellular carcinoma cohort and 64 samples from the GSE116174 dataset. Single\u2010cell RNA sequencing (scRNA\u2010seq) data of HCC were obtained from the GSE149614 dataset, including 18 tumor samples from 10 patients. Prognosis\u2010related cellular senescence genes and immune cells were identified through univariate analysis. Least absolute shrinkage and selection operator regression analysis was performed to construct the CellAge score and TME score, both of which were identified as independent prognostic factors for HCC based on multivariate Cox analysis. The combined CellAge and TME scores showed improved prognostic stratification for HCC patients, as confirmed by multivariate Cox analysis (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u2009.001). The gene set enrichment analysis (GSEA) revealed enrichment of the extracellular matrix receptor interaction signaling pathway in the group with high CellAge scores and low TME scores, which exhibited a worse prognosis. Single\u2010cell sequencing results revealed higher expression activity of the cAMP response element modulator (CREM) extended transcription factor in HCC cells and most immune cells, indicating its involvement in TME remodeling. Finally, the tumor immune dysfunction and exclusion (TIDE) analysis demonstrated that the combined scores could predict the outcomes of immune therapy in patients with HCC. In conclusion, cellular senescence contributes to TME remodeling in HCC, and the developed CellAge and TME scores serve as independent prognostic factors and predictors of immune therapy in HCC.\n                  <\/jats:p>","DOI":"10.1002\/tox.24121","type":"journal-article","created":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T05:34:20Z","timestamp":1704692060000},"page":"2512-2527","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["RETRACTED: Prognostic value and potential mechanism of cellular senescence and tumor microenvironment in hepatocellular carcinoma: Insights from bulk transcriptomics and single\u2010cell sequencing analysis"],"prefix":"10.1002","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9710-3158","authenticated-orcid":false,"given":"Chang","family":"Qu","sequence":"first","affiliation":[{"name":"Department of General Surgery, Xuanwu Hospital Capital Medical University  Beijing China"},{"name":"Clinical Center for Acute Pancreatitis Capital Medical University  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5413-8924","authenticated-orcid":false,"given":"Qian","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Thyroid and Breast Surgery Peking University First Hospital  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiongdi","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of General Surgery, Xuanwu Hospital Capital Medical University  Beijing China"},{"name":"Clinical Center for Acute Pancreatitis Capital Medical University  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Li","sequence":"additional","affiliation":[{"name":"Department of General Surgery, Xuanwu Hospital Capital Medical University  Beijing China"},{"name":"Clinical Center for Acute Pancreatitis Capital Medical University  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,1,8]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21708"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhep.2022.08.021"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1245\/s10434-023-13338-1"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21161"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12072-023-10488-y"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41422-023-00820-4"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.3390\/biom13040686"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41392-023-01451-2"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41556-023-01145-5"},{"key":"e_1_2_11_11_1","first-page":"1374\u20101389","article-title":"Longevity\u2010associated transcription factor ATF7 promotes healthspan by suppressing cellular senescence and systematic inflammation","author":"Huang Y","year":"2023","journal-title":"Aging Dis"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/path.4554"},{"issue":"1","key":"e_1_2_11_13_1","article-title":"Cellular senescence in hepatocellular carcinoma: the passenger or the driver?","volume":"12","author":"Cai X","year":"2022","journal-title":"Cell"},{"key":"e_1_2_11_14_1","volume-title":"A Package for Survival Analysis in R","author":"Therneau TM","year":"2019"},{"key":"e_1_2_11_15_1","unstructured":"KassambaraA KosinskiM BiecekP.Survminer: Drawing Survival Curves using \u2018ggplot2\u2019. 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