Identification of the key immune-related genes in aneurysmal subarachnoid hemorrhage.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36172261", "kind": "science-record", "revision": 4, "content_hash": "sha256:f6497e12a09346dfd0539508202fb63637c336adcb3b70484cf2ea32516ddf32", "created_at": "2026-11-01T18:15:27Z", "updated_at": "2026-11-01T22:07:40Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of the key immune-related genes in aneurysmal subarachnoid hemorrhage.", "doi": "10.3389/fnmol.2022.931753", "pmid": "36172261", "authors": ["Xing Wang", "Dingke Wen", "Chao You", "Lu Ma"], "journal": "Frontiers in molecular neuroscience", "year": 2022, "abstract": "Subarachnoid hemorrhage (SAH) is a major cause of death and morbidity worldwide, often due to rupture of intracranial aneurysms (IAs). Immune infiltration and inflammatory activation play key roles in the process of aneurysmal SAH (aSAH). This study aimed to elaborate the immune infiltration and identify related biomarkers both in blood and tissue samples from patients with aSAH. Expression data of aSAH and healthy control samples were obtained from gene expression omnibus (GEO) database. Overall, a blood sample dataset GSE36791 and a tissue sample dataset GSE122897 were included. Differentially expressed genes (DEGs) between aSAH and healthy samples were explored. We applied GO biological and Gene Set Enrichment Analyses (GSEA) processes to access the functional enrichment. Then feature elimination algorithms based on random forest were used to screen and verify the biomarkers of aSAH. We performed three computational algorithms including Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), Microenvironment Cell Populations-counter (MCPcounter), and xcell to evaluate the immune cell infiltration landscape to identify the unique infiltration characteristics associated with rupturing. We found 2,220 DEGs (856 upregulated and 1,364 downregulated) in the original dataset. Functional analysis revealed most of these genes are enriched in immunological process, especially related with neutrophil response. Similar signaling pathway enrichment patterns were observed in tissue sample dataset and ClueGo. Analysis of immune microenvironment infiltration suggested neutrophils were abnormally upregulated in aSAH compared with those in the control group. Key gene SRPK1 was then filtered based on feature elimination algorithms, and transcription factor (TF) ZNF281 is assumed to participate in immunomodulation by regulating expression of SRPK1. Several immunomodulators such as CXCR1 and CXCR2 also appear to be involved in the progression of aSAH. In the present study, we performed a comprehensive stratification and quantification of the immune infiltration status of aSAH. By exploring the potential mechanism for aSAH based on several computational algorithms, key genes including SRPK1 and ZNF281 were filtered. This study may be of benefit to patients who are at high risk of suffering aSAH which allows for early diagnosis and potential therapy.", "claims": [{"id": "claim-method", "statement": "Candidate genes were used as the input of random forest to construct prediction models. This procedure was applied using the R package “randomForest” with 2,000 trees. The aim was to identify the top 15 key genes. ROC and the area under the curve (AUC) value were then performed to compare the predictive sensitivity and specificity of concerned genes in differentiating between aSAH samples and control samples. These analyses were derived using the “pROC” package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36172261", "locator": "sections[8] \"Random forest and receiver operating characteristic (ROC)\" paragraphs 1-3", "quote": "Candidate genes were used as the input of random forest to construct prediction models. This procedure was applied using the R package “randomForest” with 2,000 trees. The aim was to identify the top 15 key genes. ROC and the area under the curve (AUC) value were then performed to compare the predictive sensitivity and specificity of concerned genes in differentiating between aSAH samples and control samples. These analyses were derived using the “pROC” package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36172261", "locator": "sections[20] \"Data Availability Statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "SRPK1 distinguished aSAH from healthy blood samples with an AUC of 0.88, and its tissue-sample model achieved an AUC of 0.82.", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "The AUC of the model was 0.88, with 95% confidence intervals ranging from 0.77 to 0.98, demonstrating the robustness of SRPK1 in distinguishing between aSAH and healthy blood samples (Figures 4B,C)."}, "evidence": [{"asset": "source:pmid:36172261", "locator": "sections[15] \"Predictive model construction and evaluation of the key genes\" paragraph 2", "quote": "The AUC of the model was 0.88, with 95% confidence intervals ranging from 0.77 to 0.98, demonstrating the robustness of SRPK1 in distinguishing between aSAH and healthy blood samples (Figures 4B,C)."}, {"asset": "source:pmid:36172261", "locator": "sections[15] \"Predictive model construction and evaluation of the key genes\" paragraph 2", "quote": "Similarly, the AUC was 0.82, with 95% confidence intervals ranging from 0.68 to 0.95 in the model of tissue samples (Figures 4D,E)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "SRPK1 distinguished aSAH from healthy blood samples with an AUC of 0.88, and its tissue-sample model achieved an AUC of 0.82.", "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:36172261"], "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:GSE122897"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:machine_learning_random_forest", "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:GSE122897"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:machine_learning_random_forest"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36172261", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fnmol.2022.931753", "sha256": "sha256:ac07cc2fb6cb7c3e3cc5a0355b30a5ac8cbf750dec202a2e723dc7fd0cd512e4", "bytes": 38275, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fnmol.2022.931753", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9511034", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE122897", "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=GSE122897", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE122897", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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This procedure was applied using the R package “randomForest” with 2,000 trees. The aim was to identify the top 15 key genes. ROC and the area under the curve (AUC) value were then performed to compare the predictive sensitivity and specificity of concerned genes in differentiating between aSAH samples and control samples. These analyses were derived using the “pROC” package."], "order_basis": "analysis_only", "id": "assay:1"}]}