Identification of key long non-coding RNA-associated competing endogenous RNA axes in Brodmann Area 10 brain region of schizophrenia patients.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36405929", "kind": "science-record", "revision": 4, "content_hash": "sha256:1c4239f1bf4260bb511f3de23fb82e8f3c97d8293248465ccaa305025dbd25b8", "created_at": "2026-11-01T19:00:52Z", "updated_at": "2026-11-01T22:50:38Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of key long non-coding RNA-associated competing endogenous RNA axes in Brodmann Area 10 brain region of schizophrenia patients.", "doi": "10.3389/fpsyt.2022.1010977", "pmid": "36405929", "authors": ["Hani Sabaie", "Mahdi Gholipour", "Mohammad Reza Asadi", "Samin Abed", "Mirmohsen Sharifi-Bonab", "Mohammad Taheri", "Bashdar Mahmud Hussen", "Serge Brand", "Seyedeh Morvarid Neishabouri", "Maryam Rezazadeh"], "journal": "Frontiers in psychiatry", "year": 2022, "abstract": "Schizophrenia (SCZ) is a serious mental condition with an unknown cause. According to the reports, Brodmann Area 10 (BA10) is linked to the pathology and cortical dysfunction of SCZ, which demonstrates a number of replicated findings related to research on SCZ and the dysfunction in tasks requiring cognitive control in particular. Genetics' role in the pathophysiology of SCZ is still unclear. Therefore, it may be helpful to understand the effects of these changes on the onset and progression of SCZ to find novel mechanisms involved in the regulation of gene transcription. In order to determine the molecular regulatory mechanisms affecting the SCZ, the long non-coding RNA (lncRNA)-associated competing endogenous RNAs (ceRNAs) axes in the BA10 area were determined using a bioinformatics approach in the present work. A microarray dataset (GSE17612) consisted of brain post-mortem tissues of the BA10 area from SCZ patients and matched healthy subjects was downloaded from the Gene Expression Omnibus (GEO) database. This dataset included probes for both lncRNAs and mRNAs. Using the R software's limma package, the differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) were found. The RNA interactions were also discovered using the DIANA-LncBase and miRTarBase databases. In the ceRNA network, positive correlations between DEmRNAs and DElncRNAs were evaluated using the Pearson correlation coefficient. Finally, lncRNA-associated ceRNA axes were built by using the co-expression and DElncRNA-miRNA-DEmRNA connections. We identified the DElncRNA-miRNA-DEmRNA axes, which included two key lncRNAs (PEG3-AS1, MIR570HG), seven key miRNAs (hsa-miR-124-3p, hsa-miR-17-5p, hsa-miR-181a-5p, hsa-miR-191-5p, hsa-miR-26a-5p, hsa-miR-29a-3p, hsa-miR-29b-3p), and eight key mRNAs (EGR1, ETV1, DUSP6, PLOD2, CD93, SERPINB9, ANGPTL4, TGFB2). Furthermore, DEmRNAs were found to be enriched in the \"AGE-RAGE signaling pathway in diabetic complications\", \"Amoebiasis\", \"Transcriptional misregulation in cancer\", \"Human T-cell leukemia virus 1 infection\", and \"MAPK signaling pathway\". This study offers research targets for examining significant molecular pathways connected to the pathogenesis of SCZ, even though the function of these ceRNA axes still needs to be investigated.", "claims": [{"id": "claim-method", "statement": "We applied linear models for microarray data (limma) package (version 3.52.2) of R (version 4.2.1) for differential gene expression analysis between patients with SCZ and control subjects (21) in Bioconductor (https://www.bioconductor.org/) (22). Student t-test was applied to evaluate the statistical significance of differential expression, and P-value adjustment was carried out using Benjamini-Hochberg method. We investigated the positive correlation between DElncRNAs and DEmRNAs in ceRNA networks using Pearson correlation analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36405929", "locator": "sections[4] \"Data preprocessing and identification of DEmRNAs and DElncRNAs\" paragraph 2", "quote": "We applied linear models for microarray data (limma) package (version 3.52.2) of R (version 4.2.1) for differential gene expression analysis between patients with SCZ and control subjects (21) in Bioconductor (https://www.bioconductor.org/) (22)."}, {"asset": "source:pmid:36405929", "locator": "sections[4] \"Data preprocessing and identification of DEmRNAs and DElncRNAs\" paragraph 2", "quote": "Student t-test was applied to evaluate the statistical significance of differential expression, and P-value adjustment was carried out using Benjamini-Hochberg method."}, {"asset": "source:pmid:36405929", "locator": "sections[6] \"Analysis of correlation between DEmRNAs and DElncRNAs, protein–protein interaction (PPI) network analysis, and lncRNA-associated ceRNA axes construction\" paragraph 2", "quote": "We investigated the positive correlation between DElncRNAs and DEmRNAs in ceRNA networks using Pearson correlation analysis."}], "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/query/acc.cgi?acc=GSE17612.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36405929", "locator": "sections[15] \"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/query/acc.cgi?acc=GSE17612."}], "state": "complete"}, {"id": "claim-target-result", "statement": "According to cut-off values for identification of DEGs and DElncRNAs (|log2FC | ≥ 0.5, and adjusted P < 0.001), a total of 183 DEmRNAs and six DElncRNAs were observed between SCZ and control samples.", "kind": "quantitative", "target": {"value": 0.001, "unit": "p-value", "tolerance": null, "source": "According to cut-off values for identification of DEGs and DElncRNAs (|log2FC | ≥ 0.5, and adjusted P < 0.001), a total of 183 DEmRNAs and six DElncRNAs were observed between SCZ and control samples."}, "evidence": [{"asset": "source:pmid:36405929", "locator": "sections[9] \"Identification of DEmRNAs and DElncRNAs\" paragraph 3", "quote": "According to cut-off values for identification of DEGs and DElncRNAs (|log2FC | ≥ 0.5, and adjusted P < 0.001), a total of 183 DEmRNAs and six DElncRNAs were observed between SCZ and control samples."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "According to cut-off values for identification of DEGs and DElncRNAs (|log2FC | ≥ 0.5, and adjusted P < 0.001), a total of 183 DEmRNAs and six DElncRNAs were observed between SCZ and control samples.", "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:36405929"], "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:GSE17612"], "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:student_t", "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:GSE17612"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:student_t"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36405929", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fpsyt.2022.1010977", "sha256": "sha256:85cf8a325a25e455ff4f28630d8fc52c5b2ca7a38cd684927ed133a7f1827e50", "bytes": 52606, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fpsyt.2022.1010977", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9671706", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE17612", "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=GSE17612", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE17612", "version": null, "note": "Publicly available datasets were analyzed in this study. 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A microarray dataset (GSE17612) consisted of brain post-m", "Methods In the current study, a microarray dataset (GSE17612) consisting of brain post-"], "order_basis": "paper_text_order:926", "id": "assay:1"}, {"role": "analysis", "definition_id": "group_comparison", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["We applied linear models for microarray data (limma) package (version 3.52.2) of R (version 4.2.1) for differential gene expression analysis between patients with SCZ and control subjects (21) in Bioconductor (https://www.bioconductor.org/) (22). Student t-test was applied to evaluate the statistical significance of differential expression, and P-value adjustment was carried out using Benjamini-Hochberg method. We investigated the positive correlation between DElncRNAs and DEmRNAs in ceRNA networks using Pearson correlation analysis."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}