A N7-Methylguanine-Related Gene Signature Applicable for the Prognosis and Microenvironment of Prostate Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35602299", "kind": "science-record", "revision": 4, "content_hash": "sha256:89bc3fbe13d41b5cd39d522a4e84571adad073fb89bfa3bfe9410983bc673ede", "created_at": "2026-11-01T18:14:30Z", "updated_at": "2026-11-01T22:06:34Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A N7-Methylguanine-Related Gene Signature Applicable for the Prognosis and Microenvironment of Prostate Cancer.", "doi": "10.1155/2022/8604216", "pmid": "35602299", "authors": ["Wangli Mei", "Xuyang Jia", "Shiyong Xin", "Xiang Liu", "Liang Jin", "Xianchao Sun", "Jia-Xin Zhang", "Bihui Zhang", "Guosheng Yang", "Ping Chen", "Lin Ye"], "journal": "Journal of oncology", "year": 2022, "abstract": "Background: Despite the constant iteration of small-molecule inhibitors and immune checkpoint inhibitors, PRAD (prostate adenocarcinoma) patients with distant metastases and biochemical recurrence maintain a poor survival outcome along with an increasing morbidity in recent years. N7-Methylguanine, a new-found type of RNA modification, has demonstrated an essential role in tumor progression but has hardly been studied for its effect on prostate carcinoma. The current study aimed to seek m7G (N7-methylguanosine) related prognostic biomarkers and potential targets for PRAD treatment.\nMethods: 42 genes related to m7G were collected from former literatures and GSEA (Gene Set Enrichment Analysis) website. Then, RNA-seq (RNA sequencing) and clinical data from TCGA-PRAD (The Cancer Genome Atlas-Prostate) cohort were retrieved to screen the differentially expressed m7G genes to further construct a multivariate Cox prognostic model for PRAD. Next, GSE116918, a prostate cancer cohort acquired from GEO (Gene Expression Omnibus) database, was analyzed for the external validation group to assess the ability to predict BFFS (biochemical failure-free survival) of our m7G prognostic signature. Kaplan-Meier, ROC (receiver operator characteristic), AUC (areas under ROC curve), and calibration curves were adopted to display the performance of this prognostic signature. In addition, immune infiltration analysis was implemented to evaluate the effect of these m7G genes on immunoinfiltrating cells. Correlation with drug susceptibility of the m7G signature was also analyzed by matching drug information in CellMiner database.\nResults: The m7G-related prognostic signature, including three genes (EIF3D, EIF4A1, LARP1) illustrated superior prognostic ability for PRAD in both training and validation cohorts. The 5-year AUC were 0.768 for TCGA-PRAD and 0.608 for GSE116918. It can well distinguish patients into different risk groups of biochemical recurrence (p =1e-04 for TCGA-PRAD and p =0.0186 for GSE116918). Immune infiltration analysis suggested potential regulation of m7G genes on neutrophils and dendritic cells in PRAD.\nConclusions: A m7G-related prognostic signature was constructed and validated in the current study, giving new sights of m7G methylation in predicting the prognostic and improving the treatment of PRAD.", "claims": [{"id": "claim-method", "statement": "Then, lasso (least absolute shrinkage and selection operator) [23] Cox regression analysis was adopted for this prognostic signature construction by using R package “glmnet.”", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35602299", "locator": "sections[4] \"2.2. Analysis and Validation of Our Prognostic Signature\" paragraph 2", "quote": "Then, lasso (least absolute shrinkage and selection operator) [23] Cox regression analysis was adopted for this prognostic signature construction by using R package “glmnet.”"}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data could be acquired from TCGA dataset (https://portal.gdc.cancer.gov/) and GEO dataset (https://www.ncbi.nlm.nih.gov/geo/). The data of drug sensitivity could be acquired from the CellMiner database (https://discover.nci.nih.gov/cellminer). The code used in the study is available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35602299", "locator": "sections[23] \"Data Availability\" paragraph 2", "quote": "The data could be acquired from TCGA dataset (https://portal.gdc.cancer.gov/) and GEO dataset (https://www.ncbi.nlm.nih.gov/geo/). The data of drug sensitivity could be acquired from the CellMiner database (https://discover.nci.nih.gov/cellminer). The code used in the study is available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The m7G-related prognostic signature, including three genes (EIF3D, EIF4A1, LARP1) illustrated superior prognostic ability for PRAD in both training and validation cohorts.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35602299", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The m7G-related prognostic signature, including three genes (EIF3D, EIF4A1, LARP1) illustrated superior prognostic ability for PRAD in both training and validation cohorts."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The m7G-related prognostic signature, including three genes (EIF3D, EIF4A1, LARP1) illustrated superior prognostic ability for PRAD in both training and validation cohorts.", "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:35602299"], "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:GSE116918"], "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:GSE116918"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35602299", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2022/8604216", "sha256": "sha256:7dd6f8d8bedb3ca6b833eeb4e426335d78bc831972492f1e563b1c38fa7fa0ab", "bytes": 39846, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1155/2022/8604216", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9122703", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE116918", "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=GSE116918", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE116918", "version": null, "note": "The data could be acquired from TCGA dataset (https://portal.gdc.cancer.gov/) and GEO dataset (https://www.ncbi.nlm.nih.gov/geo/). The data of drug sensitivity could be acquired from the CellMiner database (https://discover.nci.nih.gov/cellminer). The code used in the study is available from the corresponding author on reasonable request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The data could be acquired from TCGA dataset (https://portal.gdc.cancer.gov/) and GEO dataset (https://www.ncbi.nlm.nih.gov/geo/). The data of drug sensitivity could be acquired from the CellMiner database (https://discover.nci.nih.gov/cellminer). 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Then, RNA-seq (RNA sequencing) and clinical data from TCGA", "Enrichment Analysis) website. Then, RNA-seq (RNA sequencing) and clinical data from TCGA-PRAD (The Cance"], "order_basis": "paper_text_order:783", "id": "assay:1"}, {"role": "data_generation", "definition_id": "ihc", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["signature and clinical features of PRAD. (g) Immunohistochemistry figures from The Human Protein Atlas databas"], "order_basis": "paper_text_order:3500", "id": "assay:2"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Then, lasso (least absolute shrinkage and selection operator) [23] Cox regression analysis was adopted for this prognostic signature construction by using R package “glmnet.”"], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}