Single-cell mapping of N6-methyladenosine in esophageal squamous cell carcinoma and exploration of the risk model for immune infiltration.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37025404", "kind": "science-record", "revision": 3, "content_hash": "sha256:d0ece2263a224fb04ddcd7a91be77c1aa451ccaff8846846b671890966129a11", "created_at": "2026-11-01T19:01:40Z", "updated_at": "2026-11-01T22:09:12Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Single-cell mapping of N6-methyladenosine in esophageal squamous cell carcinoma and exploration of the risk model for immune infiltration.", "doi": "10.3389/fendo.2023.1155009", "pmid": "37025404", "authors": ["Yuanliu Nie", "Guangyue Yao", "Xiaoying Xu", "Yi Liu", "Ke Yin", "Jingjiang Lai", "Qiang Li", "Fengge Zhou", "Zhe Yang"], "journal": "Frontiers in endocrinology", "year": 2023, "abstract": "Background: N6-methyladenosine (m6A) modification is the most common RNA modification, but its potential role in the development of esophageal cancer and its specific mechanisms still need to be further investigated.\nMethods: Bulk RNA-seq of 174 patients with esophageal squamous carcinoma from the TCGA-ESCC cohort, GSE53625, and single-cell sequencing data from patients with esophageal squamous carcinoma from GSE188900 were included in this study. Single-cell analysis of scRNA-seq data from GSE188900 of 4 esophageal squamous carcinoma samples and calculation of PROGENy scores. Demonstrate the scoring of tumor-associated pathways for different cell populations. Cell Chat was calculated for cell populations. thereafter, m6A-related differential genes were sought and risk models were constructed to analyze the relevant biological functions and impact pathways of potential m6A genes and their impact on immune infiltration and tumor treatment sensitivity in ESCC was investigated.\nResults: By umap downscaling analysis, ESCC single-cell data were labelled into clusters of seven immune cell classes. Cellchat analysis showed that the network interactions of four signaling pathways, MIF, AFF, FN1 and CD99, all showed different cell type interactions. The prognostic risk model constructed by screening for m6A-related differential genes was of significant value in the prognostic stratification of ESCC patients and had a significant impact on immune infiltration and chemotherapy sensitivity in ESCC patients.\nConclusion: In our study, we explored a blueprint for the distribution of single cells in ESCC based on m6A methylation and constructed a risk model for immune infiltration analysis and tumor efficacy stratification in ESCC on this basis. This may provide important potential guidance for revealing the role of m6A in immune escape and treatment resistance in esophageal cancer.", "claims": [{"id": "claim-method", "statement": "Using the TCGA-ESCC cohort, nine out of 29 genes were selected based on Lasso regression to build a prognostic model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37025404", "locator": "sections[14] \"Acquisition of survival-associated differential genes for m6A epigenetic inheritance and construction and validation of prognostic models\" paragraph 2", "quote": "Using the TCGA-ESCC cohort, nine out of 29 genes were selected based on Lasso regression to build a prognostic model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. 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The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material ."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using the TCGA-ESCC cohort, nine out of 29 genes were selected based on Lasso regression to build a prognostic model.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37025404", "locator": "sections[14] \"Acquisition of survival-associated differential genes for m6A epigenetic inheritance and construction and validation of prognostic models\" paragraph 2", "quote": "Using the TCGA-ESCC cohort, nine out of 29 genes were selected based on Lasso regression to build a prognostic model."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Using the TCGA-ESCC cohort, nine out of 29 genes were selected based on Lasso regression to build a prognostic 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:37025404"], "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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