Profiles of immune cell infiltration and immune-related genes in the tumor microenvironment of osteosarcoma cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34922489", "kind": "science-record", "revision": 4, "content_hash": "sha256:73db5cea9fca4ba85364f1c127741a2baf001c10859a2b527391955fe4c6b3cc", "created_at": "2026-11-01T17:36:38Z", "updated_at": "2026-11-01T22:05:23Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Profiles of immune cell infiltration and immune-related genes in the tumor microenvironment of osteosarcoma cancer.", "doi": "10.1186/s12885-021-09042-6", "pmid": "34922489", "authors": ["Ruixuan Liu", "Yuhang Hu", "Tianyi Liu", "Yansong Wang"], "journal": "BMC cancer", "year": 2021, "abstract": "BACKGROUNDS: Osteosarcomas are one of the most common primary malignant tumors of bone. It primarily occurs in children and adolescents, with the second highest incidence among people over 50 years old. Although there were immense improvements in the survival of patients with osteosarcoma in the past 30 years, targetable mutations and agents of osteosarcomas still have been generally not satisfactory. Therefore, it is of great importance to further explore the highly specialized immune environment of bone, genes related to macrophage infiltration and potential therapeutic biomarkers and targets.\nMETHODS: The 11 expression data sets of OS tissues and the 11 data sets of adjacent non-tumorous tissues available in the GEO database GSE126209 were used to conduct immune infiltration analysis. Then, through WGCNA analysis, we acquired the co-expression modules related to Mast cells activated and performed the GO and KEGG enrichment analysis. Next, we did the survival prognosis analysis and plotted a survival curve. Finally, we analyzed the COX multivariate regression of gene expression on clinical parameters and drew forest maps for visualization by the forest plot package.\nRESULTS: OS disease-related immune cell populations, mainly Mast cells activated, have higher cell content (p = 0.006) than the normal group. Then, we identified co-expression modules related to Mast cells activated. In sum, a total of 822 genes from the top three strongest positive correlation module MEbrown4, MEdarkslateblue and MEnavajowhite2 and the strongest negative correlation module MEdarkturquoise. From that, we identified nine genes with different levels in immune cell infiltration related to osteosarcoma, eight of which including SORBS2, BAIAP2L2, ATAD2, CYGB, PAMR1, PSIP1, SNAPC3 and ZDHHC21 in their low abundance have higher disease-free survival probability than the group in their high abundances.\nCONCLUSION: These results could assist clinicians to select targets for immunotherapies and individualize treatment strategies for patients with OS.", "claims": [{"id": "claim-method", "statement": "According to the gene expression signature of specific cells, CIBERSORT [31] can estimate the content of specific cells in the expression profile of mixed cells by the deconvolution algorithm. The statistical algorithm (Fisher’s exact test) was used to find out which specific functional items were most related to a group of genes. Taking the median of gene expression as the cutoff of grouping, the samples were divided into two groups of high expression and low expression. Survival [35] packages were performed to analyze the survival difference between the two groups of samples by a survival curve. At the same time, we performed COX multivariate regression of gene expression on clinical parameters such as pathological staging, and drew forest maps for visualization by the forest plot package (Supplemental Table 1).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34922489", "locator": "sections[4] \"Immune infiltration analysis\" paragraph 2", "quote": "According to the gene expression signature of specific cells, CIBERSORT [31] can estimate the content of specific cells in the expression profile of mixed cells by the deconvolution algorithm."}, {"asset": "source:pmid:34922489", "locator": "sections[6] \"Gene function enrichment analysis\" paragraph 2", "quote": "The statistical algorithm (Fisher’s exact test) was used to find out which specific functional items were most related to a group of genes."}, {"asset": "source:pmid:34922489", "locator": "sections[7] \"Survival prognosis analysis\" paragraph 2", "quote": "Taking the median of gene expression as the cutoff of grouping, the samples were divided into two groups of high expression and low expression."}, {"asset": "source:pmid:34922489", "locator": "sections[7] \"Survival prognosis analysis\" paragraph 2", "quote": "Survival [35] packages were performed to analyze the survival difference between the two groups of samples by a survival curve."}, {"asset": "source:pmid:34922489", "locator": "sections[7] \"Survival prognosis analysis\" paragraph 2", "quote": "At the same time, we performed COX multivariate regression of gene expression on clinical parameters such as pathological staging, and drew forest maps for visualization by the forest plot package (Supplemental Table 1)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and/or analyzed during the current study are available in the NCBI’s Gene Expression Omnibus repository, with the accession number of GSE126209.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34922489", "locator": "sections[20] \"Availability of data and materials\" paragraph 2", "quote": "The datasets generated and/or analyzed during the current study are available in the NCBI’s Gene Expression Omnibus repository, with the accession number of GSE126209."}], "state": "complete"}, {"id": "claim-target-result", "statement": "OS disease-related immune cell populations, mainly Mast cells activated, have higher cell content (p = 0.006) than the normal group.", "kind": "quantitative", "target": {"value": 0.006, "unit": "p-value", "tolerance": null, "source": "OS disease-related immune cell populations, mainly Mast cells activated, have higher cell content (p = 0.006) than the normal group."}, "evidence": [{"asset": "source:pmid:34922489", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "OS disease-related immune cell populations, mainly Mast cells activated, have higher cell content (p = 0.006) than the normal group."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "OS disease-related immune cell populations, mainly Mast cells activated, have higher cell content (p = 0.006) than the normal group.", "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:34922489"], "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:GSE126209"], "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:GSE126209"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34922489", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12885-021-09042-6", "sha256": "sha256:927371f255de1677a3d51eee7c8a74428acbd5cf6dedf03785069ef27bc925b8", "bytes": 29359, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12885-021-09042-6", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8684084", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE126209", "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=GSE126209", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE126209", "version": null, "note": "The datasets generated and/or analyzed during the current study are available in the NCBI’s Gene Expression Omnibus repository, with the accession number of GSE126209.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets generated and/or analyzed during the current study are available in the NCBI’s Gene Expression Omnibus repository, with the accession number of GSE126209.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM3593744", "GSM3593745", "GSM3593746", "GSM3593747", "GSM3593748", "GSM3593749", "GSM3593750", "GSM3593751", "GSM3593752", "GSM3593753", "GSM3593754", "GSM4272756", "GSM4272757", "GSM4272758", "GSM4272759", "GSM4272760", "GSM4272761", "GSM4272762", "GSM4272763", "GSM4955769", "GSM4955770", "GSM4955771", "GSM4955772"], "sample_count": 23, "group_sizes": null, "group_sizes_basis": null, "platform": "20301", "taxon": "Homo sapiens", "title": "Gene expression profiling in  Osteosarcomas", "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:GSE126209 not downloaded", "asset:data:geo:GSE126209 licence not read"]}, "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": "According to the gene expression signature of specific cells, CIBERSORT [31] can estimate the content of specific cells in the expression profile of mixed cells by the deconvolution algorithm. 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