Metabolic subtypes and immune landscapes in esophageal squamous cell carcinoma: prognostic implications and potential for personalized therapies.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38373930", "kind": "science-record", "revision": 4, "content_hash": "sha256:e8e79389ef0bbf2c4877c9f314d5a806597e623fcc447bac02954a170fd44605", "created_at": "2026-11-01T19:20:19Z", "updated_at": "2026-11-01T22:11:17Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Metabolic subtypes and immune landscapes in esophageal squamous cell carcinoma: prognostic implications and potential for personalized therapies.", "doi": "10.1186/s12885-024-11890-x", "pmid": "38373930", "authors": ["Xiao-Wan Yu", "Pei-Wei She", "Fang-Chuan Chen", "Ya-Yu Chen", "Shuang Zhou", "Xi-Min Wang", "Xiao-Rong Lin", "Qiao-Ling Liu", "Zhi-Jun Huang", "Yu Qiu"], "journal": "BMC cancer", "year": 2024, "abstract": "BACKGROUND: This study aimed to identify metabolic subtypes in ESCA, explore their relationship with immune landscapes, and establish a metabolic index for accurate prognosis assessment.\nMETHODS: Clinical, SNP, and RNA-seq data were collected from 80 ESCA patients from the TCGA database and RNA-seq data from the GSE19417 dataset. Metabolic genes associated with overall survival (OS) and progression-free survival (PFS) were selected, and k-means clustering was performed. Immune-related pathways, immune infiltration, and response to immunotherapy were predicted using bioinformatic algorithms. Weighted gene co-expression network analysis (WGCNA) was conducted to identify metabolic genes associated with co-expression modules. Lastly, cell culture and functional analysis were performed using patient tissue samples and ESCA cell lines to verify the identified genes and their roles.\nRESULTS: Molecular subtypes were identified based on the expression profiles of metabolic genes, and univariate survival analysis revealed 163 metabolic genes associated with ESCA prognosis. Consensus clustering analysis classified ESCA samples into three distinct subtypes, with MC1 showing the poorest prognosis and MC3 having the best prognosis. The subtypes also exhibited significant differences in immune cell infiltration, with MC3 showing the highest scores. Additionally, the MC3 subtype demonstrated the poorest response to immunotherapy, while the MC1 subtype was the most sensitive. WGCNA analysis identified gene modules associated with the metabolic index, with SLC5A1, NT5DC4, and MTHFD2 emerging as prognostic markers. Gene and protein expression analysis validated the upregulation of MTHFD2 in ESCA. MTHFD2 promotes the progression of ESCA and may be a potential therapeutic target for ESCA.\nCONCLUSION: The established metabolic index and identified metabolic genes offer potential for prognostic assessment and personalized therapeutic interventions for ESCA, underscoring the importance of targeting metabolism-immune interactions in ESCA. MTHFD2 promotes the progression of ESCA and may be a potential therapeutic target for ESCA.", "claims": [{"id": "claim-method", "statement": "These genes were then used to select metabolic genes associated with survival based on a threshold of log-rank P < 0.05. Among these, expression data from 163 metabolic genes associated with survival were used for k-means unsupervised clustering. The clustering process was performed using the \"ConsensusClusterPlus\" package [29] with 500 iterations.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38373930", "locator": "sections[5] \"Metabolic ESCA subtype identification\" paragraph 2", "quote": "These genes were then used to select metabolic genes associated with survival based on a threshold of log-rank P < 0.05. Among these, expression data from 163 metabolic genes associated with survival were used for k-means unsupervised clustering. The clustering process was performed using the \"ConsensusClusterPlus\" package [29] with 500 iterations."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The authors confirm that the data supporting the findings of this study are available within the article.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38373930", "locator": "sections[33] \"Availability of data and materials\" paragraph 2", "quote": "The authors confirm that the data supporting the findings of this study are available within the article."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Consensus clustering identified three metabolic ESCA subtypes with distinct survival outcomes, and SLC5A1, NT5DC4, and MTHFD2 emerged as prognostic markers.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38373930", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Consensus clustering analysis classified ESCA samples into three distinct subtypes, with MC1 showing the poorest prognosis and MC3 having the best prognosis."}, {"asset": "source:pmid:38373930", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "WGCNA analysis identified gene modules associated with the metabolic index, with SLC5A1, NT5DC4, and MTHFD2 emerging as prognostic markers."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Consensus clustering identified three metabolic ESCA subtypes with distinct survival outcomes, and SLC5A1, NT5DC4, and MTHFD2 emerged as prognostic markers.", "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:38373930"], "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 cells were cultured in RPMI-1640 (Wako, Osaka, Japan) containing 10"], "order_basis": "paper_text_order:3636", "id": "assay:1"}, {"role": "data_generation", "definition_id": "western_blot", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": [". Additional file 3: Figure 6B. The original Western Blot images of MTHFD2 in Figure 6B. From left to", "om left to right: Het-1A, TE-1. The original Western Blot images of GAPDH in Figure 6B. From left to r", "right: Het-1A, TE-1. Figure 7B. The original Western Blot images of MTHFD2 in Figure 7B. From left to", "left to right: siNC, si-MTHFD2. The original Western Blot images of GAPDH in Figure 7B. From left to r"], "order_basis": "paper_text_order:8059", "id": "assay:2"}, {"role": "analysis", "definition_id": "clustering", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["kmeans_fit"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["These genes were then used to select metabolic genes associated with survival based on a threshold of log-rank P < 0.05. Among these, expression data from 163 metabolic genes associated with survival were used for k-means unsupervised clustering. The clustering process was performed using the \"ConsensusClusterPlus\" package [29] with 500 iterations."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}