Progressions of the correlation between lipid metabolism and immune infiltration characteristics in gastric cancer and identification of BCHE as a potential biomarker.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38357546", "kind": "science-record", "revision": 3, "content_hash": "sha256:54d041adb74d6ff52d9bca12f36ebccc238b10233de370badbc5ad185526093e", "created_at": "2026-11-01T19:20:18Z", "updated_at": "2026-11-01T22:11:16Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Progressions of the correlation between lipid metabolism and immune infiltration characteristics in gastric cancer and identification of BCHE as a potential biomarker.", "doi": "10.3389/fimmu.2024.1327565", "pmid": "38357546", "authors": ["Shibo Wang", "Xiaojuan Huang", "Shufen Zhao", "Jing Lv", "Yi Li", "Shasha Wang", "Jing Guo", "Yan Wang", "Rui Wang", "Mengqi Zhang", "Wensheng Qiu"], "journal": "Frontiers in immunology", "year": 2024, "abstract": "Background: Globally, gastric cancer (GC) is a category of prevalent malignant tumors. Its high occurrence and fatality rates represent a severe threat to public health. According to recent research, lipid metabolism (LM) reprogramming impacts immune cells' ordinary function and is critical for the onset and development of cancer. Consequently, the article conducted a sophisticated bioinformatics analysis to explore the potential connection between LM and GC.\nMethods: We first undertook a differential analysis of the TCGA queue to recognize lipid metabolism-related genes (LRGs) that are differentially expressed. Subsequently, we utilized the LASSO and Cox regression analyses to create a predictive signature and validated it with the GSE15459 cohort. Furthermore, we examined somatic mutations, immune checkpoints, tumor immune dysfunction and exclusion (TIDE), and drug sensitivity analyses to forecast the signature's immunotherapy responses.\nResults: Kaplan-Meier (K-M) curves exhibited considerably longer OS and PFS (p<0.001) of the low-risk (LR) group. PCA analysis and ROC curves evaluated the model's predictive efficacy. Additionally, GSEA analysis demonstrated that a multitude of carcinogenic and matrix-related pathways were much in the high-risk (HR) group. We then developed a nomogram to enhance its clinical practicality, and we quantitatively analyzed tumor-infiltrating immune cells (TIICs) using the CIBERSORT and ssGSEA algorithms. The low-risk group has a lower likelihood of immune escape and more effective in chemotherapy and immunotherapy. Eventually, we selected BCHE as a potential biomarker for further research and validated its expression. Next, we conducted a series of cell experiments (including CCK-8 assay, Colony formation assay, wound healing assay and Transwell assays) to prove the impact of BCHE on gastric cancer biological behavior.\nDiscussion: Our research illustrated the possible consequences of lipid metabolism in GC, and we identified BCHE as a potential therapeutic target for GC. The LRG-based signature could independently forecast the outcome of GC patients and guide personalized therapy.", "claims": [{"id": "claim-method", "statement": "According to Figure 3A , we performed Lasso Cox analysis and cross-validation to avoid the signature from overfitting. Ultimately, we conducted a multivariate Cox regression analysis to ensure the stability and optimality of the signature.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38357546", "locator": "sections[20] \"Establish a lipid metabolism-related prognostic signature\" paragraph 3", "quote": "According to Figure 3A , we performed Lasso Cox analysis and cross-validation to avoid the signature from overfitting. Ultimately, we conducted a multivariate Cox regression analysis to ensure the stability and optimality of the 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:38357546", "locator": "sections[31] \"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": "A lipid metabolism-related five-gene prognostic signature independently predicts gastric cancer prognosis and may help guide personalized therapy.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38357546", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Our research illustrated the possible consequences of lipid metabolism in GC, and we identified BCHE as a potential therapeutic target for GC. The LRG-based signature could independently forecast the outcome of GC patients and guide personalized therapy."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A lipid metabolism-related five-gene prognostic signature independently predicts gastric cancer prognosis and may help guide personalized therapy.", "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:38357546"], "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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