Identification of an Inflammatory Response-Related Gene Signature to Predict Survival and Immune Status in Glioma Patients.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35647198", "kind": "science-record", "revision": 4, "content_hash": "sha256:6daf2a4d6013757202d6e63a17b3e0fe1b60cf3e3b67bec16da01afb8abf8a70", "created_at": "2026-11-01T18:14:34Z", "updated_at": "2026-11-01T22:06:39Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of an Inflammatory Response-Related Gene Signature to Predict Survival and Immune Status in Glioma Patients.", "doi": "10.1155/2022/8972730", "pmid": "35647198", "authors": ["Zhaoyue Yan", "Yushuai Gao", "Jinliang Yu", "Zhiyuan Shen", "Xingyao Bu"], "journal": "Journal of immunology research", "year": 2022, "abstract": "Background: Glioma is the most common primary brain tumor with high mortality and poor outcomes. As a hallmark of cancers, inflammatory responses are crucial for their progression. The present study is aimed at exploring the prognostic value of inflammatory response-related genes (IRRGs) and constructing a prognostic IRRG signature for gliomas.\nMaterials and Methods: We investigated the relationship between IRRGs and gliomas by integrating the transcriptomic data for gliomas from public databases. Differentially expressed IRRGs (DE-IRRGs) were identified in the GSE4290 cohort. Further, univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses were conducted to construct an IRRG signature using The Cancer Genome Atlas (TCGA) cohort. Gliomas from the Chinese Glioma Genome Atlas (CGGA) cohort were employed for independent validation. The performance of gene signature was assessed by survival and receiver operating characteristic curve analyses. The differences in clinical correlations, immune infiltrate types, immunotherapeutic response predictions, and pathway enrichment among subgroups were investigated via bioinformatic algorithms.\nResults: In total, 37 DE-IRRGs were determined, of which 31 were found to be associated with survival. Ultimately, eight genes were retained to construct an IRRG signature that further classified glioma patients into two groups; the high-risk group suffered a poorer outcome as compared to the low-risk group. Furthermore, the high-risk group was significantly correlated with several risk factors, including older age, higher tumor grade, IDH wild type, 1p19q noncodel, and MGMT unmethylation. The nomogram was constructed by integrating the risk scores and other independent clinical characteristics. Moreover, the high-risk group had a greater immune infiltration and was most likely to benefit from immunotherapy. Gene set enrichment analysis suggested that immune and oncogenic pathways were enriched in high-risk glioma patients.\nConclusion: We constructed a signature composed of eight IRRGs for gliomas, which could effectively predict survival and guide decision-making for treatment.", "claims": [{"id": "claim-method", "statement": "First, the univariate Cox regression analysis was performed to detect the prognostic DE-IRRGs based on TCGA data. Only the overall survival- (OS-) associated genes (P < 0.05) were further analyzed. Subsequently, the least absolute shrinkage and selection operator (LASSO) regression analysis was used to obtain an optimal predictive model by minimizing the risk of overfitting [28]. Finally, a prognostic signature was constructed using the coefficients derived from multivariate Cox regression analysis", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35647198", "locator": "sections[4] \"2.2. Construction and Validation of a Prognostic IRRG Signature\" paragraph 2", "quote": "First, the univariate Cox regression analysis was performed to detect the prognostic DE-IRRGs based on TCGA data. Only the overall survival- (OS-) associated genes (P < 0.05) were further analyzed. Subsequently, the least absolute shrinkage and selection operator (LASSO) regression analysis was used to obtain an optimal predictive model by minimizing the risk of overfitting [28]. Finally, a prognostic signature was constructed using the coefficients derived from multivariate Cox regression analysis"}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data of this study are available in GEO database (https://www.ncbi.nlm.nih.gov/), TCGA database (https://portal.gdc.cancer.gov/), and CGGA database (http://www.cgga.org.cn/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35647198", "locator": "sections[22] \"Data Availability\" paragraph 2", "quote": "The data of this study are available in GEO database (https://www.ncbi.nlm.nih.gov/), TCGA database (https://portal.gdc.cancer.gov/), and CGGA database (http://www.cgga.org.cn/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We constructed a signature composed of eight IRRGs for gliomas, which could effectively predict survival and guide decision-making for treatment.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35647198", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "We constructed a signature composed of eight IRRGs for gliomas, which could effectively predict survival and guide decision-making for treatment."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We constructed a signature composed of eight IRRGs for gliomas, which could effectively predict survival and guide decision-making for treatment.", "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:35647198"], "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:GSE4290"], "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:GSE4290"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35647198", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2022/8972730", "sha256": "sha256:cb69730082f719ae6d5caf32a90b42b4dcaa1d4688f218d9c7b73cd826cf8a51", "bytes": 58683, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1155/2022/8972730", "version": null, "note": "Full article fetched from PMC; 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