Comprehensive analysis of MGLL as a novel diagnostic and prognostic biomarker for clear cell renal cell carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40994935", "kind": "science-record", "revision": 3, "content_hash": "sha256:f11333921f9d47833571d5ae20b8b962eb9781694b5d3022f0fc0b24babf1c50", "created_at": "2026-11-01T20:31:04Z", "updated_at": "2026-11-01T22:14:44Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comprehensive analysis of MGLL as a novel diagnostic and prognostic biomarker for clear cell renal cell carcinoma.", "doi": "10.3389/fonc.2025.1594540", "pmid": "40994935", "authors": ["Chen Chen", "Zhuojing Hu", "Wei Zhao", "Yunbo Ma", "Qinghua Xia"], "journal": "Frontiers in oncology", "year": 2025, "abstract": "Background: Clear cell renal cell carcinoma (ccRCC) is a malignancy with significant morbidity and metabolic-related characteristics, necessitating the exploration of novel biomarkers and therapeutic targets. This study focuses on monoglyceride lipase (MGLL), an important molecule identified through RNA sequencing of lipid metabolism-related genes.\nMethods: We investigated MGLL expression and function in ccRCC by analyzing mRNA data, clinical information, and multiple databases. We used R packages to analyze gene co-expression, immune infiltration, and m7G methylation gene correlations. We constructed a protein-protein interaction (PPI) network and performed prognostic and diagnostic receiver operating characteristic (ROC) curve analyses to identify differentially expressed genes (DEGs). We further validated these genes by qRT-PCR and performed functional experiments by knocking down MGLL using lentiviral vectors.\nResults: Both qRT-PCR experiments and immunohistochemical data demonstrate that MGLL is upregulated in ccRCC tissues relative to normal tissues. The area under the curve (AUC) values from ROC analyses of three GEO validation datasets (GSE40435, GSE66270, and GSE213324) all exceeded 0.9. The expression of MGLL is associated with poor prognosis and correlates with gender and histological grade. Functional enrichment analysis showed that genes co-expressed with MGLL were mainly involved in proteasome-mediated protein degradation, macroautophagy, and the response to endoplasmic reticulum stress. MGLL expression is significantly positively correlated with the infiltration of neutrophils, Th17 cells, eosinophils, and dendritic cells. In contrast, it is significantly negatively correlated with cytotoxic T cells, NK CD56bright cells, and CD8 T cells. The PPI network and the correlation analysis between MGLL and m7G genes identified a total of 23 DEGs. Additionally, prognostic LASSO regression coefficients combined with ROC analysis reveal that ACLY, CALM3, NSUN2, NUDT16, NUDT4, and PKM have potential prognostic and diagnostic value. qRT-PCR experiments confirmed the expression of 13 genes from the prognostic LASSO model in ccRCC cell lines ACHN, A498, and 786-O, as well as in normal renal tubular epithelial cells HK-2. Inhibition of MGLL expression reduced ccRCC cell proliferation, colony formation, and migration.\nConclusion: This investigation elucidates the diagnostic and prognostic significance of MGLL in ccRCC, while offering mechanistic insights into its biological functions and potential therapeutic implications.", "claims": [{"id": "claim-method", "statement": "We performed prognostic Least Absolute Shrinkage and Selection Operator (LASSO) coefficient selection by analyzing the cleaned data with the ‘glmnet’ package to obtain variable lambda values and visualize the results.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40994935", "locator": "sections[6] \"Immune infiltration analysis, extraction of m7G methylation modifications genes and prognostic analysis\" paragraph 2", "quote": "We performed prognostic Least Absolute Shrinkage and Selection Operator (LASSO) coefficient selection by analyzing the cleaned data with the ‘glmnet’ package to obtain variable lambda values and visualize the results."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40994935", "locator": "sections[24] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The LASSO-derived prognostic risk score stratified ccRCC patients into high-risk and low-risk cohorts with significantly poorer outcomes in the high-risk group, and its time-dependent ROC AUC values exceeded 0.7 at 1, 3, and 5 years.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40994935", "locator": "sections[20] \"Prognostic model risk characteristics and evaluation\" paragraph 2", "quote": "The LASSO-derived prognostic risk score stratified patients into distinct high-risk and low-risk cohorts, with Kaplan-Meier survival analysis demonstrating significantly poorer outcomes in the high-risk group ( Figure 7A )."}, {"asset": "source:pmid:40994935", "locator": "sections[20] \"Prognostic model risk characteristics and evaluation\" paragraph 2", "quote": "Time-dependent ROC curve analysis yielded AUC values exceeding 0.7 at 1-, 3-, and 5-year intervals, indicating superior sensitivity and specificity ( Figure 7C )."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The LASSO-derived prognostic risk score stratified ccRCC patients into high-risk and low-risk cohorts with significantly poorer outcomes in the high-risk group, and its time-dependent ROC AUC values exceeded 0.7 at 1, 3, and 5 years.", "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:40994935"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE213324"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE213324"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40994935", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fonc.2025.1594540", "sha256": "sha256:d5e9fcd5cb1feb35777e8e819a5e33ed86cefce9971873638778e0966610c95e", "bytes": 72230, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fonc.2025.1594540", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12454099", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE213324", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE213324", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE213324", "version": null, "note": "The original contributions presented in the study are included in the article/ Supplementary Material . 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