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pmid:35663937

Comprehensive Profiling Reveals Prognostic and Immunogenic Characteristics of Necroptosis in Soft Tissue Sarcomas.

Lin Qi · Ruiling Xu · Xiaolei Ren · Wenchao Zhang · Zhimin Yang · Chao Tu · Zhihong Li

Frontiers in immunology · 2022 doi:10.3389/fimmu.2022.877815

Paper

complete

Experiment

complete

Data

partial

Analysis

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Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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title
Comprehensive Profiling Reveals Prognostic and Immunogenic Characteristics of Necroptosis in Soft Tissue Sarcomas.
doi
10.3389/fimmu.2022.877815
pmid
35663937
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[0]
Lin Qi
[1]
Ruiling Xu
[2]
Xiaolei Ren
[3]
Wenchao Zhang
[4]
Zhimin Yang
[5]
Chao Tu
[6]
Zhihong Li
journal
Frontiers in immunology
year
2022
abstract
Soft tissue sarcomas (STSs) are heterogeneous malignancies derived from mesenchymal cells. Due to its rarity, heterogeneity, and limited overall response to chemotherapy, STSs represent a therapeutic challenge. Necroptosis is a novel therapeutic strategy for enhancing immunotherapy of cancer. Nevertheless, no research has explored the relationship between necroptosis-related genes (NRGs) and STSs. In this study, differentially expressed NRGs were identified using The Cancer Genome Atlas (TCGA) and The Cancer Genotype-Tissue Expression (GTEx) project. The expression levels of 34 NRGs were significantly different. Several key NRGs were validated using RT-qPCR and our own sequencing data. Patients with STSs were divided into two clusters using consensus cluster analysis, and significant differences were observed in their survival (p=0.002). We found the differentially expressed genes (DEGs) between the two clusters and carried out subsequent analysis. The necroptosis-related gene signatures with 10 key DEGs were identified with a risk score constructed. The prognosis of TCGA-SARC cohort with low necroptosis-related risk score was better (p<0.001). Meanwhile, the low-risk group had a significantly increased immune infiltration. Using the data of GSE17118 and another immunotherapy cohort as external validations, we observed significant survival differences between the two risk groups (p=0.019). The necroptosis-related risk score proved to be an independent prognostic factor, and a nomogram was further established and integrated with other clinical features. Notably, the necroptosis-related gene signature could also act as the prognostic indicator in other malignancies based on pan-cancer analysis. In summary, the study outlines NRGs in STSs and their potential role in prognosis and will be one of the important directions for future research.
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DEGs with significant effect on survival rate were screened (p < 0.01), which were further entered into the least absolute shrinkage and selection operator (LASSO) Cox regression analysis to narrow down the scope of gene selection using “glmnet” R package (Version 4.1-2).
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DEGs with significant effect on survival rate were screened (p < 0.01), which were further entered into the least absolute shrinkage and selection operator (LASSO) Cox regression analysis to narrow down the scope of gene selection using “glmnet” R package (Version 4.1-2).
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Patients with STSs were divided into two clusters using consensus cluster analysis, and significant differences were observed in their survival (p=0.002).
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Patients with STSs were divided into two clusters using consensus cluster analysis, and significant differences were observed in their survival (p=0.002).
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TCEAL7 Inhibition of c-Myc Activity in Alternative Lengthening of Telomeres Regulates hTERT Expression
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Full-length transcriptome sequencing reveals gene expression signatures in human soft tissue sarcomas
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erent. Several key NRGs were validated using RT-qPCR and our own sequencing data. Patients with S
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n of NRGs. SW-982, SW-872, and hSS-005R were cultured in Dulbecco’s modified Eagle medium (DMEM) (Gib
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DEGs with significant effect on survival rate were screened (p < 0.01), which were further entered into the least absolute shrinkage and selection operator (LASSO) Cox regression analysis to narrow down the scope of gene selection using “glmnet” R package (Version 4.1-2).
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35663937", "kind": "science-record", "revision": 4, "content_hash": "sha256:b2df470d23b72a542671504a164f5efe34999f8b9d2bf8f6fa069c579ba545cf", "created_at": "2026-11-01T18:14:36Z", "updated_at": "2026-11-01T22:06:41Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comprehensive Profiling Reveals Prognostic and Immunogenic Characteristics of Necroptosis in Soft Tissue Sarcomas.", "doi": "10.3389/fimmu.2022.877815", "pmid": "35663937", "authors": ["Lin Qi", "Ruiling Xu", "Xiaolei Ren", "Wenchao Zhang", "Zhimin Yang", "Chao Tu", "Zhihong Li"], "journal": "Frontiers in immunology", "year": 2022, "abstract": "Soft tissue sarcomas (STSs) are heterogeneous malignancies derived from mesenchymal cells. Due to its rarity, heterogeneity, and limited overall response to chemotherapy, STSs represent a therapeutic challenge. Necroptosis is a novel therapeutic strategy for enhancing immunotherapy of cancer. Nevertheless, no research has explored the relationship between necroptosis-related genes (NRGs) and STSs. In this study, differentially expressed NRGs were identified using The Cancer Genome Atlas (TCGA) and The Cancer Genotype-Tissue Expression (GTEx) project. The expression levels of 34 NRGs were significantly different. Several key NRGs were validated using RT-qPCR and our own sequencing data. Patients with STSs were divided into two clusters using consensus cluster analysis, and significant differences were observed in their survival (p=0.002). We found the differentially expressed genes (DEGs) between the two clusters and carried out subsequent analysis. The necroptosis-related gene signatures with 10 key DEGs were identified with a risk score constructed. The prognosis of TCGA-SARC cohort with low necroptosis-related risk score was better (p<0.001). Meanwhile, the low-risk group had a significantly increased immune infiltration. Using the data of GSE17118 and another immunotherapy cohort as external validations, we observed significant survival differences between the two risk groups (p=0.019). The necroptosis-related risk score proved to be an independent prognostic factor, and a nomogram was further established and integrated with other clinical features. Notably, the necroptosis-related gene signature could also act as the prognostic indicator in other malignancies based on pan-cancer analysis. In summary, the study outlines NRGs in STSs and their potential role in prognosis and will be one of the important directions for future research.", "claims": [{"id": "claim-method", "statement": "DEGs with significant effect on survival rate were screened (p < 0.01), which were further entered into the least absolute shrinkage and selection operator (LASSO) Cox regression analysis to narrow down the scope of gene selection using “glmnet” R package (Version 4.1-2).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35663937", "locator": "sections[8] \"Prognostic Model Development\" paragraph 2", "quote": "DEGs with significant effect on survival rate were screened (p < 0.01), which were further entered into the least absolute shrinkage and selection operator (LASSO) Cox regression analysis to narrow down the scope of gene selection using “glmnet” R package (Version 4.1-2)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are publicly available. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE198568.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35663937", "locator": "sections[24] \"Data Availability Statement\" paragraph 2", "quote": "The original contributions presented in the study are publicly available. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE198568."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Patients with STSs were divided into two clusters using consensus cluster analysis, and significant differences were observed in their survival (p=0.002).", "kind": "quantitative", "target": {"value": 0.002, "unit": "p-value", "tolerance": null, "source": "Patients with STSs were divided into two clusters using consensus cluster analysis, and significant differences were observed in their survival (p=0.002)."}, "evidence": [{"asset": "source:pmid:35663937", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Patients with STSs were divided into two clusters using consensus cluster analysis, and significant differences were observed in their survival (p=0.002)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Patients with STSs were divided into two clusters using consensus cluster analysis, and significant differences were observed in their survival (p=0.002).", "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:35663937"], "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:GSE17118"], "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:GSE17118"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35663937", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2022.877815", "sha256": "sha256:4738837cf827c2b780b6fc1dabe4bff396bcbe8dfef271842bea3f36dda66471", "bytes": 61201, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2022.877815", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9159500", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE17118", "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=GSE17118", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE17118", "version": null, "note": "The original contributions presented in the study are publicly available. 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