Functional enrichment analysis of LYSET and identification of related hub gene signatures as novel biomarkers to predict prognosis and immune infiltration status of clear cell renal cell carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37740762", "kind": "science-record", "revision": 3, "content_hash": "sha256:9cbb734d69b9adb7d849291ff181b958d12b5a9414f839bd7a2bd2f1b3bde880", "created_at": "2026-11-01T19:02:35Z", "updated_at": "2026-11-01T22:10:20Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Functional enrichment analysis of LYSET and identification of related hub gene signatures as novel biomarkers to predict prognosis and immune infiltration status of clear cell renal cell carcinoma.", "doi": "10.1007/s00432-023-05280-2", "pmid": "37740762", "authors": ["Yuxing Chen", "Jinhang He", "Tian Jin", "Ye Zhang", "Yunsheng Ou"], "journal": "Journal of cancer research and clinical oncology", "year": 2023, "abstract": "PURPOSE: The latest research shows that the lysosomal enzyme trafficking factor (LYSET) encoded by TMEM251 is a key regulator of the amino acid metabolism reprogramming (AAMR) and related pathways significantly correlate with the progression of some tumors. The purpose of this study was to explore the potential pathways of the TMEM251 in clear cell renal cell carcinoma (ccRCC) and establish related predictive models based on the hub genes in these pathways for prognosis and tumor immune microenvironment (TIME).\nMETHODS: We obtained mRNA expression data and clinical information of ccRCC samples from The Cancer Genome Atlas (TCGA), E-MATE-1980, and immunotherapy cohorts. Single-cell sequencing data (GSE152938) were downloaded from the Gene Expression Omnibus (GEO) database. We explored biological pathways of the LYSET by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of TMEM251-coexpression genes. The correlation of LYSET-related pathways with the prognosis was conducted by Gene Set Variation Analysis (GSVA) and unsupervised cluster analysis. The least absolute shrinkage and selection operator (LASSO) and Cox regression were used to identify hub prognostic genes and construct the risk score. Immune infiltration analysis was conducted by CIBERSORTx and Tumor Immune Estimation Resource (TIMER) databases. The predictive value of the risk score and hub prognostic genes on immunotherapy responsiveness was analyzed through the tumor mutation burden (TMB) score, immune checkpoint expression, and survival analysis. Immunohistochemistry (IHC) was finally used to verify the expressions of hub prognostic genes.\nRESULTS: The TMEM251 was found to be significantly correlated with some AAMR pathways. AAGAB, ENTR1, SCYL2, and WDR72 in LYSET-related pathways were finally identified to construct a risk score model. Immune infiltration analysis showed that LYSET-related gene signatures significantly influenced the infiltration of some vital immune cells such as CD4 + cells, NK cells, M2 macrophages, and so on. In addition, the constructed risk score was found to be positively correlated with TMB and some common immune checkpoint expressions. Different predictive values of these signatures for Nivolumab therapy responsiveness were also uncovered in immunotherapy cohorts. Finally, based on single-cell sequencing analysis, the TMEM251 and the hub gene signatures were found to be expressed in tumor cells and some immune cells. Interestingly, IHC verification showed a potential dual role of four hub genes in ccRCC progression.\nCONCLUSION: The novel predictive biomarkers we built may benefit clinical decision-making for ccRCC. Our study may provide some evidence that LYSET-related gene signatures could be novel potential targets for treating ccRCC and improving immunotherapy efficacy. Our nomogram might be beneficial to clinical choices, but the results need more experimental verifications in the future.", "claims": [{"id": "claim-method", "statement": "The least absolute shrinkage and selection operator (LASSO) and Cox regression were used to identify hub prognostic genes and construct the risk score.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37740762", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The least absolute shrinkage and selection operator (LASSO) and Cox regression were used to identify hub prognostic genes and construct the risk score."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets for this study can be found in TCGA cohort (https://portal.gdc.cancer.gov) and the ArrayExpress cohort (https://www.ebi.ac.uk/biostudies/arrayexpress).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37740762", "locator": "sections[38] \"notes\" paragraph 1", "quote": "The datasets for this study can be found in TCGA cohort (https://portal.gdc.cancer.gov) and the ArrayExpress cohort (https://www.ebi.ac.uk/biostudies/arrayexpress)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Four hub genes, AAGAB, ENTR1, SCYL2, and WDR72, were identified to construct a ccRCC risk score model.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37740762", "locator": "sections[22] \"Construction of a risk model and identification of clinical relevance\" paragraph 2", "quote": "Finally, we obtained four hub genes including AAGAB, ENTR1, SCYL2, and WDR72 that had independent prognostic values to construct a risk model."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Four hub genes, AAGAB, ENTR1, SCYL2, and WDR72, were identified to construct a ccRCC risk score model.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:37740762"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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