The prognostic index of m7G-related genes in CRC correlates with immune infiltration.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36482181", "kind": "science-record", "revision": 2, "content_hash": "sha256:23817e9b760013b8b3c8a4661b600a00cd2e75c582310d29b95ee2ebb54ae99e", "created_at": "2026-11-01T19:00:58Z", "updated_at": "2026-11-01T21:29:37Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The prognostic index of m7G-related genes in CRC correlates with immune infiltration.", "doi": "10.1038/s41598-022-25823-w", "pmid": "36482181", "authors": ["Xinkun Huang", "Bin Zhu", "Chenyu Qian", "Ying Feng"], "journal": "Scientific reports", "year": 2022, "abstract": "N7-methyladenosine (m7G) modifications have been the subject of growing research interest with respect to their relationship with the progression and treatment of various cancers. This analysis was designed to examine the association between m7G-related gene expression and colorectal cancer (CRC) patient outcomes. Initial training analyses were performed using the TCGA dataset, with the GSE28722 dataset then being used to validate these results. Univariate Cox analyses were initially conducted to screen out prognostic m7G-related genes, after which a LASSO approach was used to construct an m7G risk score (MRS) model. Kaplan-Meier curves, ROC curves, and Cox analyses were subsequently used to validate the prognostic utility of this model in CRC patients. The R maftools package was further employed to assess mutational characteristics in CRC patients in different MRS subgroups, while the ESTIMATE, CIBERSORT, and ssGSEA tools were used to conduct immune infiltration analyses. A WGCNA was then performed to identify key immune-associated hub genes. The EIF4E3, GEMIN5, and NCBP2 genes were used to establish the MRS model. Patients with high MRS scores exhibited worse overall survival than patients with low scores. In Cox analyses, MRS scores were independently associated with CRC patient prognosis. Patients with low MRS scores exhibited a higher tumor mutational burden and higher levels of microsatellite instability. In immune infiltration analyses, higher immune checkpoint expression and greater immune cell infiltration were also observed in patients with low MRS scores. WGCNA analyses further identified 25 CD8+ T cell infiltration-associated genes. These findings suggest that MRS values represent a useful biomarker capable of differentiating among CRC patients with different immunological features and prognostic outcomes, offering an opportunity to better determine which patients are likely to benefit from immune checkpoint inhibitor treatment.", "claims": [{"id": "claim-method", "statement": "Univariate Cox analyses were initially conducted to screen out prognostic m7G-related genes, after which a LASSO approach was used to construct an m7G risk score (MRS) model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36482181", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Univariate Cox analyses were initially conducted to screen out prognostic m7G-related genes, after which a LASSO approach was used to construct an m7G risk score (MRS) model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "CRC expression matrix data were obtained from TCGA database (https://portal.gdc.cancer.gov/) and GSE28722 dataset in GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?a cc = GSE28722). Mutation spectrum data from TCGA database (https://portal.gdc.cancer.gov/). The above data sets are publicly available and can be downloaded from the Internet or obtained by contacting the authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36482181", "locator": "sections[27] \"Data availability\" paragraph 2", "quote": "CRC expression matrix data were obtained from TCGA database (https://portal.gdc.cancer.gov/) and GSE28722 dataset in GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?a cc = GSE28722). Mutation spectrum data from TCGA database (https://portal.gdc.cancer.gov/). 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