Immune landscape of advanced gastric cancer tumor microenvironment identifies immunotherapeutic relevant gene signature.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34893046", "kind": "science-record", "revision": 4, "content_hash": "sha256:f5df7fbe3233894cf198d99811910d1254a3d4edbb03b04ea44c7f80b8807984", "created_at": "2026-11-01T17:36:36Z", "updated_at": "2026-11-01T22:05:20Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Immune landscape of advanced gastric cancer tumor microenvironment identifies immunotherapeutic relevant gene signature.", "doi": "10.1186/s12885-021-09065-z", "pmid": "34893046", "authors": ["Simeng Zhang", "Mengzhu Lv", "Yu Cheng", "Shuo Wang", "Ce Li", "Xiujuan Qu"], "journal": "BMC cancer", "year": 2021, "abstract": "BACKGROUND: Advanced gastric cancer (AGC) is a disease with poor prognosis due to the current lack of effective therapeutic strategies. Immune checkpoint blockade treatments have shown effective responses in patient subgroups but biomarkers remain challenging. Traditional classification of gastric cancer (GC) is based on genomic profiling and molecular features. Therefore, it is critical to identify the immune-related subtypes and predictive markers by immuno-genomic profiling.\nMETHODS: Single-sample gene-set enrichment analysis (ssGSEA) and ESTIMATE algorithm were used to identify the immue-related subtypes of AGC in two independent GEO datasets. Weighted gene co-expression network analysis (WGCNA) and Molecular Complex Detection (MCODE) algorithm were applied to identify hub-network of immune-related subtypes. Hub genes were confirmed by prognostic data of KMplotter and GEO datasets. The value of hub-gene in predicting immunotherapeutic response was analyzed by IMvigor210 datasets. MTT assay, Transwell migration assay and Western blotting were performed to confirm the cellular function of hub gene in vitro.\nRESULTS: Three immune-related subtypes (Immunity_H, Immunity_M and Immunity_L) of AGC were identified in two independent GEO datasets. Compared to Immunity_L, the Immuntiy_H subtype showed higher immune cell infiltration and immune activities with favorable prognosis. A weighted gene co-expression network was constructed based on GSE62254 dataset and identified one gene module which was significantly correlated with the Immunity_H subtype. A Hub-network which represented high immune activities was extracted based on topological features and Molecular Complex Detection (MCODE) algorithm. Furthermore, ADAM like decysin 1 (ADAMDEC1) was identified as a seed gene among hub-network genes which is highly associated with favorable prognosis in both GSE62254 and external validation datasets. In addition, high expression of ADAMDEC1 correlated with immunotherapeutic response in IMvigor210 datasets. In vitro, ADAMDEC1 was confirmed as a potential protein in regulating proliferation and migration of gastric cancer cell. Deficiency of ADAMDEC1 of gastric cancer cell also associated with high expression of PD-L1 and Jurkat T cell apoptosis.\nCONCLUSIONS: We identified immune-related subtypes and key tumor microenvironment marker in AGC which might facilitate the development of novel immune therapeutic targets.", "claims": [{"id": "claim-method", "statement": "Single-sample gene-set enrichment analysis (ssGSEA) was used to evaluate the enrichment levels of the 29 immune signatures and hierarchical clustering was performed according to the ssGSEA score [18, 19]. ESTIMATE algorithm which integrated in “estimate” R package in R version 3.6.2. was applied to measure immune microenvironment infiltration based on gene expression data [14].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34893046", "locator": "sections[4] \"Data clustering and evaluation of immune and stromal scores\" paragraph 2", "quote": "Single-sample gene-set enrichment analysis (ssGSEA) was used to evaluate the enrichment levels of the 29 immune signatures and hierarchical clustering was performed according to the ssGSEA score [18, 19]. ESTIMATE algorithm which integrated in “estimate” R package in R version 3.6.2. was applied to measure immune microenvironment infiltration based on gene expression data [14]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used and/or analysed during the current study are available in TCGA (https://portal.gdc.cancer.gov) and GEO datasets (GSE62254: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62254; GSE29272: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29272) and IMvigor210 datasets: http://research-pub.gene.com/IMvigor210CoreBiologies.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34893046", "locator": "sections[34] \"Availability of data and materials\" paragraph 2", "quote": "The datasets used and/or analysed during the current study are available in TCGA (https://portal.gdc.cancer.gov) and GEO datasets (GSE62254: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62254; GSE29272: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29272) and IMvigor210 datasets: http://research-pub.gene.com/IMvigor210CoreBiologies."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Three immune-related subtypes (Immunity_H, Immunity_M and Immunity_L) of AGC were identified in two independent GEO datasets.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34893046", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Three immune-related subtypes (Immunity_H, Immunity_M and Immunity_L) of AGC were identified in two independent GEO datasets."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Three immune-related subtypes (Immunity_H, Immunity_M and Immunity_L) of AGC were identified in two independent GEO datasets.", "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:34893046"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE29272"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE29272"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34893046", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12885-021-09065-z", "sha256": 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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": ["GSE62254", "GSE29272"], "resolved_accessions": ["GSE62254", "GSE29272"], "statements": ["The datasets used and/or analysed during the current study are available in TCGA (https://portal.gdc.cancer.gov) and GEO datasets (GSE62254: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62254; GSE29272: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29272) and IMvigor210 datasets: http://research-pub.gene.com/IMvigor210CoreBiologies."], "basis": "The paper names GSE62254, GSE29272 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. 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CONCLUSIONS: We identified immune-related s", "esponse was analyzed by IMvigor210 datasets. MTT assay, Transwell migration assay and Western", "1 in MGC803 cell line (Fig. 6D, E). Besides, apoptosis in Jurkat T cells was enhanced significantly", "1-KD MGC803 cell for 48 h, respectively. The apoptosis in Jurkat T cells was measured by flow cytom"], "order_basis": "paper_text_order:1075", "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": ["ts. MTT assay, Transwell migration assay and Western blotting were performed to confirm the cellular funct", "C803 cell was knockdown of ADAMDEC1 gene and western blot was applied to detect the expression level o", "D-L1 in silencing ADAMDEC1 of MGC803 cell. E Western blot was used to detect the change of PD-L1 expre"], "order_basis": "paper_text_order:1116", "id": "assay:2"}, {"role": "data_generation", "definition_id": "transfection", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["n by gastric cancer cells. A MGC803 cell was knockdown of ADAMDEC1 gene and western blot was applie", "xpression level in MGC803 cell with ADAMDEC1 knockdown. F Jurkat T cells were co-incubated with ADA", "ll line, silencing of ADAMDEC1 expression by siRNA was conducted (Fig. 6A). The results demonst"], "order_basis": "paper_text_order:3425", "id": "assay:3"}, {"role": "data_generation", "definition_id": "flow_cytometry", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["apoptosis in Jurkat T cells was measured by flow cytometry analysis Availability of data and materials"], "order_basis": "paper_text_order:4833", "id": "assay:4"}, {"role": "analysis", "definition_id": "clustering", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["hierarchical_cluster"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Single-sample gene-set enrichment analysis (ssGSEA) was used to evaluate the enrichment levels of the 29 immune signatures and hierarchical clustering was performed according to the ssGSEA score [18, 19]. 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