Integrated analysis of endoplasmic reticulum stress regulators' expression identifies distinct subtypes of autism spectrum disorder.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37139330", "kind": "science-record", "revision": 3, "content_hash": "sha256:f0f1243494e68f627c186e523c76c55f985998fa1a9bcc33d6801578bf1cf6a9", "created_at": "2026-11-01T19:01:48Z", "updated_at": "2026-11-01T22:09:23Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrated analysis of endoplasmic reticulum stress regulators' expression identifies distinct subtypes of autism spectrum disorder.", "doi": "10.3389/fpsyt.2023.1136154", "pmid": "37139330", "authors": ["Yanjun Li", "Songyin Gao", "Yuelan Meng"], "journal": "Frontiers in psychiatry", "year": 2023, "abstract": "Endoplasmic reticulum (ER) stress has been demonstrated to play important roles in a variety of human diseases. However, their relevance to autism spectrum disorder (ASD) remains largely unknown. Herein, we aimed to investigate the expression patterns and potential roles of the ER stress regulators in ASD. The ASD expression profiles GSE111176 and GSE77103 were compiled from the Gene Expression Omnibus (GEO) database. ER stress score determined by the single sample gene set enrichment analysis (ssGSEA) was significantly higher in ASD patients. Differential analysis revealed that there were 37 ER stress regulators dysregulated in ASD. Based on their expression profile, the random forest and artificial neuron network techniques were applied to build a classifier that can effectively distinguish ASD from control samples among independent datasets. Weighted gene co-expression network analysis (WGCNA) screened out the turquoise module with 774 genes was closely related to the ER stress score. Through the overlapping results of the turquoise module and differential expression ER stress genes, hub regulators were gathered. The TF/miRNA-hub gene interaction networks were created. Furthermore, the consensus clustering algorithm was performed to cluster the ASD patients, and there were two ASD subclusters. Each subcluster has unique expression profiles, biological functions, and immunological characteristics. In ASD subcluster 1, the FAS pathway was more enriched, while subcluster 2 had a higher level of plasma cell infiltration as well as the BCR signaling pathway and interleukin receptor reaction reactivity. Finally, the Connectivity map (CMap) database was used to find prospective compounds that target various ASD subclusters. A total of 136 compounds were significantly enriched. In addition to some specific drugs which can effectively reverse the differential gene expression of each subcluster, we found that the PKC inhibitor BRD-K09991945 that targets Glycogen synthase kinase 3β (GSK3B) might have a therapeutic effect on both ASD subtypes that worth of the experimental validation. Our finding proved that ER stress plays a crucial role in the diversity and complexity of ASD, which may inform both mechanistic and therapeutic assessments of the disorder.", "claims": [{"id": "claim-method", "statement": "The Wilcox test results highlighted the significant upregulation in the ER stress score between the ASD and normal samples (Figure 1A), which indicates that ER stress has an important role in the development and progression of ASD.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37139330", "locator": "sections[16] \"The landscape of ERS regulators between control and ASD samples\" paragraph 2", "quote": "The Wilcox test results highlighted the significant upregulation in the ER stress score between the ASD and normal samples (Figure 1A), which indicates that ER stress has an important role in the development and progression of ASD."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/ with accession numbers GSE111176, GSE77103 and GSE3832.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37139330", "locator": "sections[26] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/ with accession numbers GSE111176, GSE77103 and GSE3832."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Differential analysis revealed that there were 37 ER stress regulators dysregulated in ASD.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37139330", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Differential analysis revealed that there were 37 ER stress regulators dysregulated in ASD."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Differential analysis revealed that there were 37 ER stress regulators dysregulated in ASD.", "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:37139330"], "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:GSE111176"], "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:GSE111176"], "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:37139330", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fpsyt.2023.1136154", "sha256": "sha256:0f2ae691b33fd99917cad12058d0a267b1c1320ab02a38c16a972ecf1567040a", "bytes": 41174, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fpsyt.2023.1136154", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10149679", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE111176", "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=GSE111176", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE111176", "version": null, "note": "Publicly available datasets were analyzed in this study. 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