Bioinformatic profiling identifies prognosis-related genes in the immune microenvironment of endometrial carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34131259", "kind": "science-record", "revision": 4, "content_hash": "sha256:7b1dc9e03b1b2b968019de29e701e5d8a3d7d97e916209fe2c487440a90c962c", "created_at": "2026-11-01T15:41:39Z", "updated_at": "2026-11-01T21:25:32Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Bioinformatic profiling identifies prognosis-related genes in the immune microenvironment of endometrial carcinoma.", "doi": "10.1038/s41598-021-92091-5", "pmid": "34131259", "authors": ["Pu Cheng", "Jiong Ma", "Xia Zheng", "Chunxia Zhou", "Xuejun Chen"], "journal": "Scientific reports", "year": 2021, "abstract": "Endometrial carcinoma (EC) is a common malignancy of female genital system which exhibits a unique immune profile. It is a promising strategy to quantify immune patterns of EC for predicting prognosis and therapeutic efficiency. Here, we attempted to identify the possible immune microenvironment-related prognostic markers of EC. We obtained the RNA sequencing and corresponding clinical data of EC from TCGA database. Then, 3 immune scores based on the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm were computed. Correlation between above ESTIMATE scores and other immune-related scores, molecular subtypes, prognosis, and gene mutation status (including BRCA and TP53) were further analyzed. Afterwards, gene modules associated with the ESTIMATE scores were screened out through hierarchical clustering analysis and weighted gene co-expression network analysis (WGCNA). Differentially expressed analysis was performed and genes shared by the most relevant modules were found out. KEGG pathway enrichment analysis was conducted to explore the biological functions of those genes. Survival analysis was carried out to identify prognostic immune-related genes and GSE17025 database was further used to confirm the correlation between immune-related genes and the ImmuneScore. The immune-related scores based on ESTIMATE algorithm was closely related to the immune microenvironment of EC. 3 gene modules that had the closest correlations with 3 ESTIMATE scores were obtained. 109 immune-related genes were preliminarily found out and 29 pathways were significantly enriched, most of which were associated with immune response. Univariate survival analysis revealed that there were 14 genes positively associated with both OS and PFS. Among which, 11 genes showed marked correlations with ImmuneScore values in GSE17025 database. Our current study profiled the immune status and identified 14 novel immune-related prognostic biomarkers for EC. Our findings may help to investigate the complicated tumor microenvironment and develop novel individualized therapeutic targets for EC.", "claims": [{"id": "claim-data-access", "statement": "The data generated are included in the manuscript and supplementary data. All the data we used in our study are publicly accessible at TCGA and NCBI GEO database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34131259", "locator": "sections[21] \"Data availability\" paragraph 2", "quote": "The data generated are included in the manuscript and supplementary data. 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the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8206132", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "asset:table:pmid:34131259:1", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:265eccf4de8f1d56fcfd444b7badbfbc8ad11ec6e36448727611fea1debeb6a3", "bytes": 1206, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[12]/table-wrap[1]#Tab1", "version": null, "note": "14 immune-related genes positively correlated with both OS and PFS of EC.", "structure": {"pmcid": "PMC8206132", "section_index": 12, "section_title": "Identification of immune microenvironment-related prognostic genes", "row_count": 15, "column_count": 4}, "table": {"label": "Table 1", "caption": "14 immune-related genes positively correlated with both OS and PFS of EC.", "rows": [["Gene symbol", "Gene name", "p value (OS)", "p value (PFS)"], ["WAS", "Wiskott-Aldrich snydrome", "0.005103711", "0.033465715"], ["GZMH", "Granzyme H", "0.008033252", "0.006877271"], ["CD7", "CD7 molecule", "0.010859632", "0.006642321"], ["NKG7", "Natural killer cell granule protein 7", "0.000412089", "0.030792653"], ["LINC01871", "Long intergenic non-protein coding RNA 1871", "0.000140799", "0.007739673"], ["TRAC", "T cell receptor alpha constant", "4.40E-05", "0.009042146"], ["CD8A", "CD8a molecule", "0.003341004", "0.012404753"], ["TRBC2", "T cell receptor beta constant", "1.03E-05", "0.001855756"], ["CD3E", "CD3e molecule", "0.000106431", "0.002676012"], ["IGSF6", "Immunoglobulin superfamily menmer 6", "0.00173721", "0.043427798"], ["RASAL3", "RAS protein activator like 3", "0.006739461", "0.039618006"], ["ITGAL", "Inergrin subunit alpha L", "0.004923864", "0.041623366"], ["S1PR4", "Sphingsine-1-phosphate receptor 4", "0.007261307", "0.029160931"], ["CCL4", "C–C motif chemokine ligand 4", "0.001976119", "0.034895726"]], "row_count": 15, "column_count": 4, "numeric_cells": 35, "header_rows": 1, "locator": "section[12]/table-wrap[1]#Tab1"}}], "gaps": []}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "clustering", "specific": "hierarchical", "label": "Clustering", "dsl_operations": ["hierarchical_cluster"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["feature_matrix"], "evidence": {"score": 0.7, "band": "medium", "quote": "hierarchical clustering"}}, {"id": "method-2", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "pathway enrichment"}}], "targets": [], "gaps": []}, "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": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["hierarchical_cluster"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: hierarchical."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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