Identification of MAP3K15 as a potential prognostic biomarker and correlation with immune infiltrates in osteosarcoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34430620", "kind": "science-record", "revision": 7, "content_hash": "sha256:e8fa980362b59b9c05649e74a769411359ae5e1b7ff7707c8abf742fa987f9e5", "created_at": "2026-11-01T15:41:49Z", "updated_at": "2026-11-01T22:04:33Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of MAP3K15 as a potential prognostic biomarker and correlation with immune infiltrates in osteosarcoma.", "doi": "10.21037/atm-21-3181", "pmid": "34430620", "authors": ["Zhuning Chen", "Haoran Kong", "Zhaopeng Cai", "Keng Chen", "Boyang Wu", "Haonan Li", "Peng Wang", "Yanfeng Wu", "Huiyong Shen"], "journal": "Annals of translational medicine", "year": 2021, "abstract": "BACKGROUND: Osteosarcoma (OS) is a type of primary malignant tumor, and increasing evidence shows the clinical benefits of immunotherapy in treating OS. However, the lack of comprehensive studies on the complex OS immune microenvironment hinders the application of immunotherapy. Thus, this study aimed to systematically explore the immune characteristics of OS and identify novel biomarkers for OS treatment.\nMETHODS: We systematically studied the immune score and proportions of infiltrating immune cells in OS in the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases using the ESTIMATE and CIBERSORT algorithms. Differential expression and functional analyses were used to identify dysregulated genes and explore their functions. Survival and Cox regression analyses were applied to establish an immune-related prognostic signature. Additionally, qPCR and immunohistochemistry were performed to validate the results.\nRESULTS: A total of 103 differentially expressed immune genes (DEIGs) were found in the TARGET-OS and GSE39058 databases, and these DEIGs were mainly enriched in leukocyte proliferation, leukocyte differentiation, osteoclast differentiation, natural killer (NK) cell-mediated cytotoxicity, and the adaptive immune system. A predictive signature was constructed based on the survival analysis, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.65. Moreover, we found that mitogen-activated protein kinase kinase kinase 15 (MAP3K15) can predict the prognosis of patients with OS and is closely related to CD4+ T cells and macrophages. The OS patients with high MAP3K15 expression had a significantly poorer prognosis.\nCONCLUSIONS: Our study found that MAP3K15, whose expression level is closely related to immune activity in tumors, is a critical immune-related biomarker, and our findings may provide a basis for OS immunotherapy.", "claims": [{"id": "claim-method", "statement": "Univariate Cox regression analysis was first performed to identify survival-related immune genes via the SangerBox database (http://sangerbox.com/). Additionally, a robust likelihood-based survival analysis was used to increase the reliability and accuracy, allowing for iterative forward selection of multiple sets of genes based on the partial likelihood of the Cox model. Then, multivariate Cox regression was implemented to explore the independent prognostic predictor genes. TARGET-OS and GSE39058 were used to establish a prognostic immune gene signature, with TARGET-OS being the training set and GSE39058 being the validation set.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34430620", "locator": "sections[7] \"Construction of the prognostic immune gene signatures\" paragraph 2", "quote": "Univariate Cox regression analysis was first performed to identify survival-related immune genes via the SangerBox database (http://sangerbox.com/)."}, {"asset": "source:pmid:34430620", "locator": "sections[7] \"Construction of the prognostic immune gene signatures\" paragraph 2", "quote": "Additionally, a robust likelihood-based survival analysis was used to increase the reliability and accuracy, allowing for iterative forward selection of multiple sets of genes based on the partial likelihood of the Cox model."}, {"asset": "source:pmid:34430620", "locator": "sections[7] \"Construction of the prognostic immune gene signatures\" paragraph 3", "quote": "Then, multivariate Cox regression was implemented to explore the independent prognostic predictor genes."}, {"asset": "source:pmid:34430620", "locator": "sections[7] \"Construction of the prognostic immune gene signatures\" paragraph 2", "quote": "TARGET-OS and GSE39058 were used to establish a prognostic immune gene signature, with TARGET-OS being the training set and GSE39058 being the validation set."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data Sharing Statement: Available at https://dx.doi.org/10.21037/atm-21-3181", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34430620", "locator": "sections[22] \"fn-group\" paragraph 1", "quote": "Data Sharing Statement: Available at https://dx.doi.org/10.21037/atm-21-3181"}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": null, "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:34430620"], "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": "blocked", "blocked_on": "data:accession_absent", "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": [], "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": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34430620", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/atm-21-3181", "sha256": "sha256:355f8f81cc31d855fc6644df7c21cdbb5bdd6d4fa081eec303913909c35c12db", "bytes": 31138, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.21037/atm-21-3181", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8350644", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "asset:table:pmid:34430620:1", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:a69c7e649f6e10826193b023e6c7cff383d038c017dc6bc710bbfedefa8040cb", "bytes": 383, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[18]/table-wrap[1]#t1", "version": null, "note": "Primer sequence references for MAP3K15 and GAPDH", "structure": {"pmcid": "PMC8350644", "section_index": 18, "section_title": "Validation", "row_count": 3, "column_count": 3}, "table": {"label": "Table 1", "caption": "Primer sequence references for MAP3K15 and GAPDH", "rows": [["Gene", "Forward primer", "Reverse primer"], ["MAP3K15", "5'-CCTTCTACGACGCAGATGTTG-3'", "5'-GCATCGGTGTCATGGTACAAGA-3'"], ["GAPDH", "5'-CAACTCCCTCAAGATTGTCAGCAA-3'", "5'-GGCATGGACTGTGGTCATGA-3'"]], "row_count": 3, "column_count": 3, "numeric_cells": 5, "header_rows": 1, "locator": "section[18]/table-wrap[1]#t1"}}], "gaps": []}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "reordered", "quote": "Univariate Cox regression analysis was first performed to identify survival-related immune genes via the SangerBox database (http://sangerbox.com/). 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