Identification and Assessment of Necroptosis-Related Genes in Clinical Prognosis and Immune Cells in Diffuse Large B-Cell Lymphoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35814424", "kind": "science-record", "revision": 4, "content_hash": "sha256:03fa30d56e5659ce9222944862b43f489f8d12ad525d148dc1785f2188e64101", "created_at": "2026-11-01T18:14:52Z", "updated_at": "2026-11-01T22:07:00Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and Assessment of Necroptosis-Related Genes in Clinical Prognosis and Immune Cells in Diffuse Large B-Cell Lymphoma.", "doi": "10.3389/fonc.2022.904614", "pmid": "35814424", "authors": ["Qikai Zhang", "Zongsi Zhu", "Jiaqiang Guan", "Cuiping Zheng"], "journal": "Frontiers in oncology", "year": 2022, "abstract": "Background: With the unveiling of new mechanisms and the advent of new drugs, the prognosis of diffuse large B-cell lymphoma (DLBCL) becomes promising, but some patients still progress to the relapse or refractory stage. Necroptosis, as a relatively novel programmed cell death, is involved in the development of multiple tumors. There are no relevant studies on the prognostic significance of necroptosis in DLBCL to date.\nMethods: We identified the differential necroptosis-related genes (NRGs) by comparing the DLBCL and normal control in GSE12195 and GSE56315 datasets. TCGA DLBC and GSE10846 containing clinical information and microarray expression profiling were merged as the entire cohort. We performed consensus clusters based on NRGs and two clusters were obtained. Kaplan-Meier (K-M) survival analysis, GSVA, GO, KEGG, and ssGSEA were used to analyze the survival, function, and immune microenvironment between two clusters. With LASSO and proportional hazard model construction, we identified differentially expressed genes (DEGs) between NRG clusters, calculated the risk score, established a prognostic model, and validated its value by calibration and ROC curves. The entire cohort was divided into the training and test cohort, and GSE87371 was included as an external validation cohort. K-M, copy number variation, tumor mutation burden, and drug sensitivity were also analyzed.\nResults: We found significant differences in prognosis between the two NRG clusters. Cluster A with a poor prognosis had a decreased expression of NRGs and a relatively suppressed immune microenvironment. GSVA analysis indicated that cluster A was related to the downregulation of the TGF-β signaling pathway and the activation of the Notch signaling pathway. The risk score had an accurate predictive ability. The nomogram could help predict the survival probability of DLBCL patients in the entire cohort and the external validation cohort. The area under the curve (AUC) of the nomogram, risk score, and International Prognostic Index was 0.723, 0.712, and 0.537, respectively. γ/δ T cells and Macrophage 1 cells decreased while Macrophage 2 cells and Natural Killer resting cells increased in the high-risk group. In addition, the high-risk group was more sensitive to the PI3K inhibitor and the PDK inhibitor.\nConclusion: We explored the potential role of necroptosis in DLBCL from multiple perspectives and provided a prognostic nomogram for the survival prediction of DLBCL. Necroptosis was downregulated and was correlated with an immunosuppressed tumor microenvironment and poor prognosis in DLBCL. Our study may deepen the understanding and facilitate the development of new therapy targets for DLBCL.", "claims": [{"id": "claim-method", "statement": "K-M and Cox regression were used to identify the prognosis-related NRGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35814424", "locator": "sections[8] \"Statistical Analysis\" paragraph 2", "quote": "K-M and Cox regression were used to identify the prognosis-related NRGs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35814424", "locator": "sections[17] \"Data Availability Statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The area under the curve (AUC) of the nomogram, risk score, and International Prognostic Index was 0.723, 0.712, and 0.537, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35814424", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The area under the curve (AUC) of the nomogram, risk score, and International Prognostic Index was 0.723, 0.712, and 0.537, respectively."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The area under the curve (AUC) of the nomogram, risk score, and International Prognostic Index was 0.723, 0.712, and 0.537, respectively.", "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:35814424"], "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:GSE10846"], "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:GSE10846"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35814424", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fonc.2022.904614", "sha256": "sha256:5a3546d4e2dfefd8714cd061e60677da39c8dc81e43a8a4835ef13a4a2f3711c", "bytes": 51018, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fonc.2022.904614", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9257018", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE10846", "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=GSE10846", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE10846", "version": null, "note": "The original contributions presented in the study are included in the article/ Supplementary Material . 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