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pmid:35280360
Establishment and validation of a novel immune-related prognostic signature in malignant pleural mesothelioma.
Xiangxin Zhang · Xiangdong Huang · Zhexin Wang · Kejian Zhang
Annals of translational medicine · 2022 doi:10.21037/atm-22-527
Paper
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Experiment
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Data
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Analysis
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Complete file
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This study aimed to establish a prognostic signature for malignant pleural mesothelioma (MPM) patients.\nMethods: We obtained the relevant data of MPM patients in The Cancer Genome Atlas (TCGA), and univariate and multivariate Cox regression were used to construct the prediction signature and verify it with the external validation dataset GSE2549. A nomogram was then constructed, and its predictive ability was evaluated and analyzed the level of immune cell infiltration in different groups in the signature.\nResults: An IRG-related prognostic signature composed of INHBA, CAT, SORT1, TNFSF13B, and BIRC5 was constructed, with patients divided into high-risk and low-risk groups according to the risk score. The survival time of overall survival (OS), progression-free survival (PFS), disease-free interval (DFI), and relapse-free survival (RFS) in low-risk groups was longer than in high-risk groups. Furthermore, the signature had high predictive performance, and the receiver operating characteristic (ROC) of 1, 2, and 3 years could reach 0.853, 0.881, and 0.914, respectively. The predictive accuracy of the signature was verified by using the independent GSE2549 dataset. The levels of activated CD4 T cells, immature dendritic cells, and type 2 T helper cells were higher in high-risk patients. The gene set enrichment analysis (GSEA) analysis showed that a high concentration and P53 signal pathways were found in high-risk groups.\nConclusions: This research developed and verified a new type of immune prognostic signature based on five IRGs, which can predict the prognosis of tumor patients and provide new ideas for individualized treatment.", "claims": [{"id": "claim-method", "statement": "Firstly, the “survival” R package was used to perform univariate Cox analysis to screen out genes that were significantly relevant to the OS (P<0.05), and multivariate Cox regression analysis was then performed to obtain the coefficient.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35280360", "locator": "sections[5] \"Establishment of DEIRGs prognostic signature\" paragraph 2", "quote": "Firstly, the “survival” R package was used to perform univariate Cox analysis to screen out genes that were significantly relevant to the OS (P<0.05), and multivariate Cox regression analysis was then performed to obtain the coefficient."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Furthermore, the signature had high predictive performance, and the receiver operating characteristic (ROC) of 1, 2, and 3 years could reach 0.853, 0.881, and 0.914, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35280360", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Furthermore, the signature had high predictive performance, and the receiver operating characteristic (ROC) of 1, 2, and 3 years could reach 0.853, 0.881, and 0.914, respectively."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Furthermore, the signature had high predictive performance, and the receiver operating characteristic (ROC) of 1, 2, and 3 years could reach 0.853, 0.881, and 0.914, 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:35280360"], "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:35280360", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/atm-22-527", "sha256": "sha256:0678599a45ee1a0ae101c21ee6fcc10e795379c4aef6e8f56c8bb2b1d294d544", "bytes": 28665, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.21037/atm-22-527", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8908124", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}], "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": "verbatim", "quote": "Firstly, the “survival” R package was used to perform univariate Cox analysis to screen out genes that were significantly relevant to the OS (P<0.05), and multivariate Cox regression analysis was then performed to obtain the coefficient."}}], "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": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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