Single-cell transcriptomics reveals heterogeneity in esophageal squamous epithelial cells and constructs models for predicting patient prognosis and immunotherapy.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38098487", "kind": "science-record", "revision": 3, "content_hash": "sha256:f7f65d1753fe8fba04e457b341327bba8e4f9a4b5d7e92b87d0c3525fe9411c8", "created_at": "2026-11-01T19:03:02Z", "updated_at": "2026-11-01T22:10:53Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Single-cell transcriptomics reveals heterogeneity in esophageal squamous epithelial cells and constructs models for predicting patient prognosis and immunotherapy.", "doi": "10.3389/fimmu.2023.1322147", "pmid": "38098487", "authors": ["Chenglin Li", "Wei Song", "Jialing Zhang", "Yonggang Luo"], "journal": "Frontiers in immunology", "year": 2023, "abstract": "Background: Esophageal squamous cell carcinoma (ESCC), characterized by its high invasiveness and malignant potential, has long been a formidable challenge in terms of treatment.\nMethods: A variety of advanced analytical techniques are employed, including single-cell RNA sequencing (scRNA-seq), cell trajectory inference, transcription factor regulatory network analysis, GSVA enrichment analysis, mutation profile construction, and the inference of potential immunotherapeutic drugs. The purpose is to conduct a more comprehensive exploration of the heterogeneity among malignant squamous epithelial cell subgroups within the ESCC microenvironment and establish a model for predicting the prognosis and immunotherapy outcomes of ESCC patients.\nResults: An analysis was conducted through scRNA-seq, and three Cluster of malignant epithelial cells were identified using the infer CNV method. Cluster 0 was found to exhibit high invasiveness, whereas Cluster 1 displayed prominent characteristics associated with epithelial-mesenchymal transition. Confirmation of these findings was provided through cell trajectory analysis, which positioned Cluster 0 at the initiation stage of development and Cluster 1 at the final developmental stage. The abundance of Cluster 0-2 groups in TCGA-LUAD samples was assessed using ssGSEA and subsequently categorized into high and low-expression groups. Notably, it was observed that Cluster 0-1 had a significant impact on survival (p<0.05). Furthermore, GSVA enrichment analysis demonstrated heightened activity in hallmark pathways for Cluster 0, whereas Cluster 1 exhibited notable enrichment in pathways related to cell proliferation. It is noteworthy that a prognostic model was established utilizing feature genes from Cluster 0-1, employing the Lasso and stepwise regression methods. The results revealed that in TCGA and GSE53624 cohorts, the low-risk group demonstrated significantly higher overall survival and increased levels of immune infiltration. An examination of four external immunotherapy cohorts unveiled that the low-risk group exhibited improved immunotherapeutic efficacy. Additionally, more meaningful treatment options were identified for the low-risk group.\nConclusion: The findings revealed distinct interactions between malignant epithelial cells of ESCC and subgroups within the tumor microenvironment. Two cell clusters, strongly linked to survival, were pinpointed, and a signature was formulated. This signature is expected to play a crucial role in identifying and advancing precision medicine approaches for the treatment of ESCC.", "claims": [{"id": "claim-method", "statement": "Univariate Cox regression analysis was utilized to evaluate the influence of these genes on the survival status of ESCC. To minimize the risk of overlooking significant factors, we set the cutoff P-value to 0.05. Following this, we applied the LASSO Cox regression method (21) to reduce the number of candidate genes, ultimately creating the most optimal survival signature.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38098487", "locator": "sections[9] \"Building the high-performance epithelial-associated signature (EAS) of ESCC\" paragraph 2", "quote": "Univariate Cox regression analysis was utilized to evaluate the influence of these genes on the survival status of ESCC. To minimize the risk of overlooking significant factors, we set the cutoff P-value to 0.05. Following this, we applied the LASSO Cox regression method (21) to reduce the number of candidate genes, ultimately creating the most optimal survival signature."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material .", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38098487", "locator": "sections[27] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. 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": "Cluster 0-1 epithelial-cell clusters had a significant impact on survival in ESCC (p<0.05).", "kind": "quantitative", "target": {"value": 0.05, "unit": "p-value", "tolerance": null, "source": "Notably, it was observed that Cluster 0-1 had a significant impact on survival (p<0.05)."}, "evidence": [{"asset": "source:pmid:38098487", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Notably, it was observed that Cluster 0-1 had a significant impact on survival (p<0.05)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Cluster 0-1 epithelial-cell clusters had a significant impact on survival in ESCC (p<0.05).", "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:38098487"], "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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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE188900", "GSE53624", "GSE91061", "GSE100797", "GSE126044", "GSE35640"], "resolved_accessions": ["GSE188900", "GSE53624", "GSE91061", "GSE100797", "GSE126044", "GSE35640"], "statements": ["The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material ."], "basis": "The paper names GSE188900, GSE53624, GSE91061, GSE100797, GSE126044, GSE35640 in Gene Expression Omnibus (GEO) database and the accession resolves in the repository. 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Furthermore, we used siRNA to inhibit the expression of APLP2 in KYSE30"], "order_basis": "paper_text_order:5288", "id": "assay:4"}, {"role": "data_generation", "definition_id": "apoptosis_assay", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["expression of APLP2 in KYSE30 and TE1 cells. CCK-8 and colony formation assays revealed that th", "01; ns, P > 0.05. 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