Single-Cell Transcriptomics Reveals STAT1 Drives LHPP Downregulation in Esophageal Squamous Cell Carcinoma Progression.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39497541", "kind": "science-record", "revision": 3, "content_hash": "sha256:da1fcd37b561a6dcf4b242c8dcee31da0c7766ec79c76e73e85b165d8338eb9a", "created_at": "2026-11-01T20:29:45Z", "updated_at": "2026-11-01T22:13:04Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Single-Cell Transcriptomics Reveals STAT1 Drives LHPP Downregulation in Esophageal Squamous Cell Carcinoma Progression.", "doi": "10.1177/15330338241293485", "pmid": "39497541", "authors": ["Xia Chen", "Zhiyun Xu", "Wubi Zhou", "Lili Zhang", "Jian Huang", "Dawei Bao"], "journal": "Technology in cancer research & treatment", "year": 2024, "abstract": "BACKGROUND: Esophageal Squamous Cell Carcinoma (ESCC) poses a significant health challenge in China due to its high incidence and mortality. Single-cell transcriptomics has transformed the approach to cancer research, allowing detailed analysis of cellular and molecular heterogeneity. The interaction between the transcription factor STAT1 and the tumor suppressor LHPP is crucial, potentially influencing cancer progression and therapeutic outcomes.\nOBJECTIVE: This study aims to explore the molecular mechanisms and cellular dynamics underlying ESCC, with a focus on the role of transcription factors, particularly STAT1, and its regulatory relationship with LHPP, in the pathogenesis of ESCC.\nMETHODS: Utilizing single-cell transcriptomics data sourced from the public database GEO (GSE160269), we identified major cell types and their transcriptomic changes in ESCC patients. Differential gene expression profiles were examined to understand the dynamics of the tumor microenvironment (TME). A cohort of 21 ESCC patients was recruited to validate the findings. Furthermore, in ESCC cell lines, we validated the transcriptional regulatory relationship between STAT1 and LHPP.\nRESULTS: Our analysis identified six major cell types within the ESCC microenvironment, revealing significant changes in cellular composition and gene expression profiles associated with tumorigenesis. Notably, a novel association between STAT1's regulatory role over LHPP was observed, suggesting a complex regulatory loop in ESCC pathogenesis. Elevated STAT1 and reduced LHPP expression were confirmed in patient samples with STAT1 negatively regulates LHPP expression at the promoter level; when the promoter binding motif regions were mutated, the transcriptional repression ability on LHPP was weakened.\nCONCLUSION: The study highlights STAT1 as a core regulator in ESCC, directly influencing LHPP expression. The findings offer novel insights into the molecular mechanisms driving ESCC, shedding light on the cellular alterations and gene regulation dynamics within the ESCC microenvironment. This research provides a foundation for developing targeted therapeutic strategies, potentially utilizing the STAT1-LHPP axis as a focal point in ESCC treatment and prognosis.", "claims": [{"id": "claim-method", "statement": "The ‘FindMarkers’ function in Seurat was used to identify differentially expressed genes in tumor versus group healthy (tumor/healthy) of each cell type, which were based on normalized data and the Wilcoxon test.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39497541", "locator": "sections[4] \"Identification of DEGs Between Healthy and Tumor Group Across Tissues and Cell Compartments\" paragraph 2", "quote": "The ‘FindMarkers’ function in Seurat was used to identify differentially expressed genes in tumor versus group healthy (tumor/healthy) of each cell type, which were based on normalized data and the Wilcoxon test."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Availability of Data and Materials: The datasets used and/or analyzed in the current study are available from the corresponding author upon reasonable request. The raw data of healthy and tumor human tissues were from GSE160269 in GEO database. The analysis code related to this study has been uploaded to the GitHub repository and is accessible at https://github.com/ChenXia133/Single-cell-RNA-seq-data-analysis-pipline/tree/main.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39497541", "locator": "sections[26] \"Footnotes\" paragraph 2", "quote": "Availability of Data and Materials: The datasets used and/or analyzed in the current study are available from the corresponding author upon reasonable request. The raw data of healthy and tumor human tissues were from GSE160269 in GEO database. The analysis code related to this study has been uploaded to the GitHub repository and is accessible at https://github.com/ChenXia133/Single-cell-RNA-seq-data-analysis-pipline/tree/main."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study highlights STAT1 as a core regulator in ESCC, directly influencing LHPP expression.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39497541", "locator": "sections[0] \"Abstract\" paragraph 5", "quote": "The study highlights STAT1 as a core regulator in ESCC, directly influencing LHPP expression."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The study highlights STAT1 as a core regulator in ESCC, directly influencing LHPP expression.", "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:39497541"], "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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Single-cell transcriptomics has transformed the approach to cancer resea", "the pathogenesis of ESCC. METHODS: Utilizing single-cell transcriptomics data sourced from the public database GEO (G", "lations. Initially, we prepared a normalized single-cell RNA sequencing (scRNA-seq) dataset, ensuring it included an"], "order_basis": "paper_text_order:0", "id": "assay:1"}, {"role": "data_generation", "definition_id": "transfection", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.85, "evidence": ["ligation kit (beyotime, D7001M). Cells were transfected at 37 °C with silencing RNA STAT1 (si-STAT1)"], "order_basis": "paper_text_order:5898", "id": "assay:2"}, {"role": "analysis", "definition_id": "group_comparison", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["The ‘FindMarkers’ function in Seurat was used to identify differentially expressed genes in tumor versus group healthy (tumor/healthy) of each cell type, which were based on normalized data and the Wilcoxon test."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}