Immune-related gene ANGPT1 is an adverse biomarker for endometrial carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35116604", "kind": "science-record", "revision": 5, "content_hash": "sha256:04ad59f40bb1505f5dca53f9e66953bf371216651805fd043d1734c6e70f5ec2", "created_at": "2026-11-01T17:36:51Z", "updated_at": "2026-11-01T22:48:22Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Immune-related gene ANGPT1 is an adverse biomarker for endometrial carcinoma.", "doi": "10.21037/tcr-21-671", "pmid": "35116604", "authors": ["Binbin Nong", "Tongye Su", "Mingyang Jin", "Jintai Huang", "Aimin Huang", "Dalang Fang", "Jie Wei"], "journal": "Translational cancer research", "year": 2021, "abstract": "BACKGROUND: Immunotherapy has made great strides in cancer treatment. Endometrial carcinoma (EC) has been 1 of the most common tumors among women. This study aimed to screen immune-related prognosis biomarkers for EC.\nMETHODS: The transcriptome profiling and clinical data of EC were downloaded from The Carcinoma Genome Atlas (TCGA) public database, and differentially expressed genes (DEGs) were obtained through the limma package in R software. An immune-related genes (IRGs) list was collected from the ImmPort database. We constructed a free-scale gene co-expression network via weighted gene co-expression network analysis (WGCNA). Then, the intersection genes of the module genes which significantly related to EC, along with IRGs and DEGs were screened as the candidate genes for further analysis. We identified the hub gene via Venn analysis of the protein-protein interaction (PPI) network genes and the prognostic genes, and verified expression of the hub gene through Human Protein Atlas (HPA) and Gene Expression Omnibus (GEO) databases which provided the GSE17025 dataset. Furthermore, we used the CIBERSORT deconvolution algorithm to explore tumor immune cells infiltration in EC, and investigated correlations between the hub gene and immune cells.\nRESULTS: The differential expression analysis demonstrated that there were 900 up-regulated genes and 1,008 down-regulated genes in TCGA-UCEC (Uterine Corpus Endometrial Carcinoma) cohort. There were 74 candidate intersection genes in blue module genes, IRGs, and DEGs. Finally, angiopoietin 1 (ANGPT1) was identified as the hub gene in EC. Low expression of ANGPT1 was associated with better overall survival (OS) in EC patients. The expression of ANGPT1 was negatively correlated with regulatory T cells (Tregs), but positively correlated with resting memory cluster of differentiation 4 (CD4) T cells, activated dendritic cells (DCs), activated natural killer (NK) cells, and activated memory CD4 T cells (P<0.05, Spearman). A high-infiltrating regulatory T cell would improve the prognosis for EC patients.\nCONCLUSIONS: The gene ANGPT1 can increase the infiltration of T cells and improve the prognosis of EC patients.", "claims": [{"id": "claim-method", "statement": "We identified the DEGs via the limma package (3.44.0 version) in R software with the thresholds of log2 Fold Change |logFC| >2.0 and false discovery rate (FDR <0.05). The survival curve was established by Kaplan-Meier method, and the difference was statistically significant (log-rank P<0.05). Wilcoxon was used for difference analysis, Spearman was used for correlation analysis, P<0.05 was considered statistically significant.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35116604", "locator": "sections[5] \"Screen of DEGs\" paragraph 2", "quote": "We identified the DEGs via the limma package (3.44.0 version) in R software with the thresholds of log2 Fold Change |logFC| >2.0 and false discovery rate (FDR <0.05)."}, {"asset": "source:pmid:35116604", "locator": "sections[9] \"Survival analysis\" paragraph 2", "quote": "The survival curve was established by Kaplan-Meier method, and the difference was statistically significant (log-rank P<0.05)."}, {"asset": "source:pmid:35116604", "locator": "sections[12] \"Statistical analysis\" paragraph 2", "quote": "Wilcoxon was used for difference analysis, Spearman was used for correlation analysis, P<0.05 was considered statistically significant."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The transcriptome profiling and clinical data of EC were downloaded from The Carcinoma Genome Atlas (TCGA) database (https://portal.gdc.carcinoma.gov/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35116604", "locator": "sections[3] \"Raw data\" paragraph 2", "quote": "The transcriptome profiling and clinical data of EC were downloaded from The Carcinoma Genome Atlas (TCGA) database (https://portal.gdc.carcinoma.gov/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "ANGPT1 was identified as the hub gene in endometrial carcinoma.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35116604", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Finally, angiopoietin 1 (ANGPT1) was identified as the hub gene in EC."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "ANGPT1 was identified as the hub gene in endometrial carcinoma.", "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:35116604"], "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:GSE17025"], "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:GSE17025"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35116604", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tcr-21-671", "sha256": "sha256:b74bdff32ee24e07fa530e2ba7a351dd93d0ae39451646a38b3ff3d44f0ba333", "bytes": 38072, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.21037/tcr-21-671", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8798191", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE17025", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17025", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE17025", "version": null, "note": "The transcriptome profiling and clinical data of EC were downloaded from The Carcinoma Genome Atlas (TCGA) database (https://portal.gdc.carcinoma.gov/).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The transcriptome profiling and clinical data of EC were downloaded from The Carcinoma Genome Atlas (TCGA) database (https://portal.gdc.carcinoma.gov/).", "access_statement_kind": "repository_route_statement", "sample_accessions": ["GSM425837", "GSM425838", "GSM425839", "GSM425840", "GSM425841", "GSM425842", "GSM425843", "GSM425844", "GSM425845", "GSM425846", "GSM425847", "GSM425848", "GSM425849", "GSM425850", "GSM425851", "GSM425852", "GSM425853", "GSM425854", "GSM425855", "GSM425856", "GSM425857", "GSM425858", "GSM425859", "GSM425860", "GSM425861", "GSM425862", "GSM425863", "GSM425864", "GSM425865", "GSM425866", "GSM425867", "GSM425868", "GSM425869", "GSM425870", "GSM425871", "GSM425872", "GSM425873", "GSM425874", "GSM425875", "GSM425876", "GSM425877", "GSM425878", "GSM425879", "GSM425880", "GSM425881", "GSM425882", "GSM425883", "GSM425884", "GSM425885", "GSM425886", "GSM425887", "GSM425888", "GSM425889", "GSM425890", "GSM425891", "GSM425892", "GSM425893", "GSM425894", "GSM425895", "GSM425896", "GSM425897", "GSM425898", "GSM425899", "GSM425900", "GSM425901", "GSM425902", "GSM425903", "GSM425904", "GSM425905", "GSM425906", "GSM425907", "GSM425908", "GSM425909", "GSM425910", "GSM425911", "GSM425912", "GSM425913", "GSM425914", "GSM425915", "GSM425916", "GSM425917", "GSM425918", "GSM425919", "GSM425920", "GSM425921", "GSM425922", "GSM425923", "GSM425924", "GSM425925", "GSM425926", "GSM425927", "GSM425928", "GSM425929", "GSM425930", "GSM425931", "GSM425932", "GSM425933", "GSM425934", "GSM425935", "GSM425936", "GSM425937", "GSM425938", "GSM425939"], "sample_count": 103, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Gene Expression Analysis of Stage I Endometrial Cancers", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE17025 not downloaded", "asset:data:geo:GSE17025 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "reordered", "quote": "We identified the DEGs via the limma package (3.44.0 version) in R software with the thresholds of log2 Fold Change |logFC| >2.0 and false discovery rate (FDR <0.05). The survival curve was established by Kaplan-Meier method, and the difference was statistically significant (log-rank P<0.05). Wilcoxon was used for difference analysis, Spearman was used for correlation analysis, P<0.05 was considered statistically significant."}}, {"id": "method-2", "family": "group_comparison", "specific": "wilcoxon", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["group_comparison_wilcoxon"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "Wilcoxon"}}, {"id": "method-3", "family": "survival_analysis", "specific": "log_rank_test", "label": "Survival analysis", "dsl_operations": ["logrank_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "log-rank"}}, {"id": "method-4", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 0.7, "band": "medium", "quote": "limma"}}, {"id": "method-5", "family": "network_analysis", "specific": "ppi_network", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "protein-protein interaction"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["survival_km"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: kaplan_meier."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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The survival curve was established by Kaplan-Meier method, and the difference was statistically significant (log-rank P<0.05). Wilcoxon was used for difference analysis, Spearman was used for correlation analysis, P<0.05 was considered statistically significant."], "order_basis": "analysis_only", "id": "assay:1"}]}