Identification of immune-related genes and development of a prognostic model in mantle cell lymphoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36660618", "kind": "science-record", "revision": 4, "content_hash": "sha256:dfc695c7ea2393ef3687e7a85ac0bb73cdf14ea08ec1ea7ae98ba49593e5e17e", "created_at": "2026-11-01T19:01:12Z", "updated_at": "2026-11-01T22:51:03Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of immune-related genes and development of a prognostic model in mantle cell lymphoma.", "doi": "10.21037/atm-22-5815", "pmid": "36660618", "authors": ["Wei Zhang", "Jin-Ning Shi", "Hai-Ning Wang", "Ting Zhang", "Xuan Zhou", "Hong-Mei Zhang", "Feng Zhu"], "journal": "Annals of translational medicine", "year": 2022, "abstract": "Background: The immune landscape, prognostic model, and molecular variations of mantle cell lymphoma (MCL) remain unclear. Hence, an integrated bioinformatics analysis of MCL datasets is required for the development of immunotherapy and the optimization of targeted therapies.\nMethods: Data were obtained from the Gene Expression Omnibus (GEO) database (GSE32018, GSE45717 and GSE93291). The differentially expressed immune-related genes were selected, and the hub genes were screened by three machine learning algorithms, followed by enrichment and correlation analyses. Next, MCL molecular clusters based on the hub genes were identified by K-Means clustering, the probably approximately correct (PAC) algorithm, and principal component analysis (PCA). The landscape of immune cell infiltration and immune checkpoint molecules in distinct clusters was explored by single-sample gene-set enrichment analysis (ssGSEA) as well as the CIBERSORT and xCell algorithms. The prognostic genes and prognostic risk score model for MCL clusters were identified by least absolute shrinkage and selection operator (LASSO)-Cox analysis and cross-validation for lambda. Correlation analysis was performed to explore the correlation between the screened prognostic genes and immune cells or immune checkpoint molecules.\nResults: Four immune-related hub genes (CD247, CD3E, CD4, and GATA3) were screened in MCL, mainly enriched in the T-cell receptor signaling pathway. Based on the hub genes, two MCL molecular clusters were recognized. The cluster 2 group had a significantly worse overall survival (OS), with down-regulated hub genes, and a variety of activated immune effector cells declined. The majority of immune checkpoint molecules had also decreased. An efficient prognostic model was established, including six prognostic genes (LGALS2, LAMP3, ICOS, FCAMR, IGFBP4, and C1QA) differentially expressed between two MCL clusters. Patients with a higher risk score in the prognostic model had a poor prognosis. Furthermore, most types of immune cells and a range of immune checkpoint molecules were positively correlated with the prognostic genes.\nConclusions: Our study identified distinct molecular clusters based on the immune-related hub genes, and showed that the prognostic model affected the prognosis of MCL patients. These hub genes, modulated immune cells, and immune checkpoint molecules might be involved in oncogenesis and could be potential prognostic biomarkers in MCL.", "claims": [{"id": "claim-method", "statement": "The Wilcoxon test was utilized to compare two independent non-parametric samples. Survival analysis was performed using the log-rank test. 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The screened genes correlated with the relative cells or molecules were evaluated using Pearson’s correlation test."}], "state": "complete"}, {"id": "claim-data-access", "statement": "There were 24 tumor samples and seven normal controls in GSE32018, five tumor samples and eight normal controls in GSE45717, and 123 tumor samples in GSE93291.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36660618", "locator": "sections[4] \"Data selection and acquisition\" paragraph 2", "quote": "There were 24 tumor samples and seven normal controls in GSE32018, five tumor samples and eight normal controls in GSE45717, and 123 tumor samples in GSE93291."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study established a prognostic risk score model based on six prognostic genes, with higher-risk patients having a poorer prognosis.", "kind": "quantitative", "target": {"value": 0.0001, "unit": "p-value", "tolerance": null, "source": "MCL patients who had a higher prognostic model risk score had a poorer prognosis, as verified by Kaplan-Meier survival analysis (P<0.0001) (Figure 7D)."}, "evidence": [{"asset": "source:pmid:36660618", "locator": "sections[17] \"Identification of the prognostic genes and development of the prognostic risk score model for distinct MCL molecular clusters\" paragraph 2", "quote": "MCL patients who had a higher prognostic model risk score had a poorer prognosis, as verified by Kaplan-Meier survival analysis (P<0.0001) (Figure 7D)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The study established a prognostic risk score model based on six prognostic genes, with higher-risk patients having a poorer prognosis.", "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:36660618"], "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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