Integrated analysis of immune-related long noncoding RNAs as diagnostic biomarkers in psoriasis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33732554", "kind": "science-record", "revision": 7, "content_hash": "sha256:e88d6bb5acbf1962865d620e0de5286ce093d6b74077a3279e288dac35231151", "created_at": "2026-11-01T15:41:29Z", "updated_at": "2026-11-01T22:03:21Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrated analysis of immune-related long noncoding RNAs as diagnostic biomarkers in psoriasis.", "doi": "10.7717/peerj.11018", "pmid": "33732554", "authors": ["Feixiang Fan", "Zhen Huang", "Yongfeng Chen"], "journal": "PeerJ", "year": 2021, "abstract": "BACKGROUND: Psoriasis is a chronic immune-mediated inflammatory dermatosis. Long noncoding RNAs (lncRNAs) play an important role in immune-related diseases. This study aimed to identify potential immune-related lncRNA biomarkers for psoriasis.\nMETHODS: We screened differentially expressed immune-related lncRNAs biomarkers using GSE13355 (skin biopsy samples of 180 cases) from Gene Expression Omnibus (GEO). Moreover, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and Gene Set Enrichment Analysis (GSEA) were performed to explore biological mechanisms in psoriasis. In addition, we performed LASSO logistic regression to identify potential diagnostic lncRNAs and further verify the diagnostic value and relationship with drug response using two validation sets: GSE30999 (skin biopsy samples of 170 cases) and GSE106992 (skin biopsy samples of 192 cases). Furthermore, we estimated the degree of infiltrated immune cells and investigated the correlation between infiltrated immune cells and diagnostic lncRNA biomarkers.\nRESULTS: A total of 394 differentially expressed genes (DEGs) were extracted from gene expression profile. GO and KEGG analysis of target genes found that immune-related lncRNAs were primarily associated with epidermis development, skin development, collagen-containing extracellular matrix, and glycosaminoglycan binding and mainly enriched in cytokine-cytokine receptor interaction and influenza A and chemokine signaling pathway. We found that LINC01137, LINC01215, MAPKAPK5-AS1, TPT1-AS1, CARMN, CCDC18-AS1, EPB41L4A-AS, and LINC01214 exhibited well diagnostic efficacy. The ROC and ROC CI were 0.944 (0.907-0.982), 0.953 (0.919-0.987), 0.822 (0.758-0.887), 0.854 (0.797-0.911), 0.957(0.929-0.985), 0.894 (0.846-0.942), and 0.964 (0.937-0.991) for LINC01137, LINC01215, MAPKAPK5-AS1, TPT1-AS1,CARMN, CCDC18-AS1, EPB41L4A-AS1, and LINC01214. LINC01137, LINC01215, and LINC01214 were correlated with drug response. LINC01137, CCDC18-AS1, and CARMN were positively correlated with activated memory CD4 T cell, activated myeloid dendritic cell (DC), neutrophils, macrophage M1, and T follicular helper (Tfh) cells, while negatively correlated with T regulatory cell (Treg). LINC01215, MAPKAPK5-AS1, TPT1-AS1, EPB41L4A-AS, and LINC01214 were negatively correlated with activated memory CD4 T cell, activated myeloid DC, neutrophils, macrophage M1, and Tfh, while positively correlated with Treg.\nCONCLUSIONS: These findings indicated that these immune-related lncRNAs may be used as potential diagnostic and predictive biomarkers for psoriasis.", "claims": [{"id": "claim-method", "statement": "Biomarkers of psoriasis were screened using LASSO logistic regression feature selection algorithm (Tibshirani, 1996) based on immune-related lncRNA expression matrices. The psoriasis gene expression profile datasets GSE13355 (Nair et al., 2009), GSE30999 (Correa da Rosa et al., 2017; Suarez-Farinas et al., 2012), and GSE106992 (Brodmerkel et al., 2019) were downloaded from Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) using the GEOquery (Davis & Meltzer, 2007) package of R software (version 3.6.5, http://r-project.org/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33732554", "locator": "sections[8] \"Screening and validation of immune-related lncRNA biomarkers\" paragraph 2", "quote": "Biomarkers of psoriasis were screened using LASSO logistic regression feature selection algorithm (Tibshirani, 1996) based on immune-related lncRNA expression matrices."}, {"asset": "source:pmid:33732554", "locator": "sections[3] \"Gene expression data processing\" paragraph 2", "quote": "The psoriasis gene expression profile datasets GSE13355 (Nair et al., 2009), GSE30999 (Correa da Rosa et al., 2017; Suarez-Farinas et al., 2012), and GSE106992 (Brodmerkel et al., 2019) were downloaded from Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) using the GEOquery (Davis & Meltzer, 2007) package of R software (version 3.6.5, http://r-project.org/)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Raw data are available in Supplemental Files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33732554", "locator": "sections[26] \"Data Availability\" paragraph 2", "quote": "Raw data are available in Supplemental Files."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:33732554"], "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:GSE106992"], "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:GSE106992"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33732554", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7717/peerj.11018", "sha256": 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"uri": null, "sha256": "sha256:1c84a539e6948a3d47666269efd20b13048ede85fdbd96a91dec3713d5411b81", "bytes": 840, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[11]/table-wrap[1]#table-1", "version": null, "note": "The top 10 differentially expressed genes (upregulated).The logFC and p values of these top 10 upregulated differentially expressed genes are presented.", "structure": {"pmcid": "PMC7950217", "section_index": 11, "section_title": "Gene expression data preprocessing and DEGs identification", "row_count": 11, "column_count": 4}, "table": {"label": "Table 1", "caption": "The top 10 differentially expressed genes (upregulated).The logFC and p values of these top 10 upregulated differentially expressed genes are presented.", "rows": [["Gene symbol", "logFC", "P-value", "adj P-value"], ["SERPINB4", "6.553829", "1.98E−82", "3.44E−79"], ["PI3", "5.404915", "8.91E−107", "1.86E−102"], ["TCN1", "4.999575", "6.35E−94", "2.65E−90"], ["SPRR2C", "4.709046", "4.24E−93", "1.26E−89"], ["S100A12", "4.677867", "4.94E−105", "5.15E−101"], ["AKR1B10", "4.568268", "5.91E−94", "2.65E−90"], ["SERPINB3", "4.389195", "3.26E−79", "4.53E−76"], ["S100A9", "4.388357", "1.97E−71", "1.18E−68"], ["IL36G", "4.072299", "1.21E−88", "3.16E−85"], ["C10orf99", "3.962806", "4.10E−75", "3.88E−72"]], "row_count": 11, "column_count": 4, "numeric_cells": 35, "header_rows": 1, "locator": "section[11]/table-wrap[1]#table-1"}}, {"id": "asset:table:pmid:33732554:2", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:606763364bd155984ca6fa80990deaf227424b03fb33027264ae132ac30df23a", "bytes": 847, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[11]/table-wrap[2]#table-2", "version": null, "note": "The top 10 differentially expressed genes (down regulated).The logFC and p value of these top 10 down regulated differentially expressed genes are presented.", "structure": {"pmcid": "PMC7950217", "section_index": 11, "section_title": "Gene expression data preprocessing and DEGs identification", "row_count": 11, "column_count": 4}, "table": {"label": "Table 2", "caption": "The top 10 differentially expressed genes (down regulated).The logFC and p value of these top 10 down regulated differentially expressed genes are presented.", "rows": [["Gene symbol", "logFC", "P-value", "adj P-value"], ["WIF1", "−3.98135", "2.64E−53", "2.54E−51"], ["BTC", "−3.27259", "1.33E−50", "1.07E−48"], ["CCL27", "−3.22479", "8.40E−59", "1.33E−56"], ["KRT77", "−3.21433", "5.75E−63", "1.43E−60"], ["IL37", "−3.03789", "1.49E−57", "2.11E−55"], ["C5orf46", "−2.75129", "3.38E−61", "6.77E−59"], ["THRSP", "−2.6802", "7.85E−20", "6.04E−19"], ["MSMB", "−2.59999", "5.13E−35", "1.19E−33"], ["PM20D1", "−2.56708", "4.88E−12", "2.09E−11"], ["ELOVL3", "−2.43197", "4.72E−14", "2.35E−13"]], "row_count": 11, "column_count": 4, "numeric_cells": 35, "header_rows": 1, "locator": "section[11]/table-wrap[2]#table-2"}}, {"id": "asset:table:pmid:33732554:3", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:d0dd51fc803ba5235a4340722fbfbcaa81b75d1433406ae354e9f17b96cf4677", "bytes": 1901, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[13]/table-wrap[1]#table-3", "version": null, "note": "The details of the 16 immune-related lncRNAs.The gene symbol, gene type, description, location and phenotypes of the 16 immune-related lncRNAs.", "structure": {"pmcid": "PMC7950217", "section_index": 13, "section_title": "Identification of IRGs and immune-related lncRNAs", "row_count": 17, "column_count": 6}, "table": {"label": "Table 3", "caption": "The details of the 16 immune-related lncRNAs.The gene symbol, gene type, description, location and phenotypes of the 16 immune-related lncRNAs.", "rows": [["Gene symbol", "Gene type", "Correlation coefficients", "P value", "Location", "Phenotypes"], ["LINC01214", "LncRNA", "0.832", "2.13E−47", "Chr3:150,265,407-150,296,6 5", "No report"], ["LINC01215", "LncRNA", "0.842", "1.09E−49", "Chr3: 108,125,821-108,138,610", "No report"], ["LINC01137", "LncRNA", "0.861", "4.43E−54", "Chr1: 37,350,934-37,474,411", "Vaccinia virus infection"], ["LINC01305", "LncRNA", "0.710", "5.72E−29", "Chr2: 174,326,027-174,330,643", "Epithelial-mesenchymal transition"], ["CARMN", "LncRNA", "0.779", "5.65E−38", "Chr5: 149,406,689-149,432,835", "Vaccinia virus infection"], ["CCDC18-AS1", "LncRNA", "0.724", "1.56E−30", "Chr1: 93,262,186-93,346,025", "No report"], ["DUBR", "LncRNA", "0.754", "2.53E−34", "Chr3: 107,220,744-107,348,464", "No report"], ["EPB41L4A-AS", "LncRNA", "0.772", "7.94E−37", "Chr5: 112,160,526-112,164,818", "Metabolic reprogramming"], ["MAPKAPK5-AS1", "LncRNA", "0.795", "2.03E−40", "Chr12:111,839,764-111,842,902", "Tumorigenesis"], ["TPT1-AS1", "LncRNA", "0.766", "5.65E−36", "Chr13: 45,341,345-45,417,975", "Tumor promotion"], ["PGM5-AS1", "LncRNA", "0.854", "1.81E−52", "Chr9: 68,353,614-68,357,893", "Tumor suppression"], ["SH3PXD2A-AS1", "LncRNA", "0.909", "1.50E−69", "Chr10:103,745,966-103,755,423", "Tumor promotion"], ["LINC00173", "LncRNA", "0.745", "4.26E−33", "Chr12:116,533,422-116,536,518", "Chemoresistance"], ["LINC00518", "LncRNA", "0.731", "2.61E−31", "Chr6: 10,429,255-10,435,015", "Tumor promotion, chemoresistance"], ["LINC00526", "LncRNA", "0.755", "1.68E−34", "Chr18: 5,236,724-5,238,598", "Tumor suppression"], ["EMX2OS", "LncRNA", "0.811", "2.36E−43", "Chr2: 117484293-117545068", "No report"]], "row_count": 17, "column_count": 6, "numeric_cells": 57, "header_rows": 1, "locator": "section[13]/table-wrap[1]#table-3"}}, {"id": "asset:table:pmid:33732554:4", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:9f3e6f8cf3813412e4f58552fce44e23c5ff237cc1f3e49d2596d9838ce43cb5", "bytes": 638, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[15]/table-wrap[1]#table-4", "version": null, "note": "The gene symbol, AUC and 95% CI of AUC of the 10 immune-related lncRNAs.", "structure": {"pmcid": "PMC7950217", "section_index": 15, "section_title": "Identification and validation of diagnostic biomarkers", "row_count": 11, "column_count": 3}, "table": {"label": "Table 4", "caption": "The gene symbol, AUC and 95% CI of AUC of the 10 immune-related lncRNAs.", "rows": [["Gene symbol", "AUC", "95% CI of AUC"], ["LINC01137", "0.944", "0.907–0.982"], ["LINC01215", "0.953", "0.919–0.987"], ["MAPKAPK5-AS1", "0.762", "0.688–0.835"], ["TPT1-AS1", "0.822", "0.758–0.887"], ["CARMN", "0.854", "0.797–0.911"], ["CCDC18-AS1", "0.957", "0.929–0.985"], ["DUBR", "0.56", "0.474–0.647"], ["EPB41L4A-AS1", "0.894", "0.846–0.942"], ["LINC01214", "0.964", "0.937–0.991"], ["LINC01305", "0.544", "0.457-0.631"]], "row_count": 11, "column_count": 3, "numeric_cells": 27, "header_rows": 1, "locator": "section[15]/table-wrap[1]#table-4"}}], "gaps": ["asset:data:geo:GSE106992 not downloaded", "asset:data:geo:GSE106992 licence not read", "asset:data:geo:GSE13355 not downloaded", "asset:data:geo:GSE13355 licence not read", "asset:data:geo:GSE30999 not downloaded", "asset:data:geo:GSE30999 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "regression_modelling", "specific": "logistic", "label": "Regression modelling", "dsl_operations": ["glm_logit_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["predictors_and_outcome"], "evidence": {"score": 1.0, "band": "reordered", "quote": "Biomarkers of psoriasis were screened using LASSO logistic regression feature selection algorithm (Tibshirani, 1996) based on immune-related lncRNA expression matrices. The psoriasis gene expression profile datasets GSE13355 (Nair et al., 2009), GSE30999 (Correa da Rosa et al., 2017; Suarez-Farinas et al., 2012), and GSE106992 (Brodmerkel et al., 2019) were downloaded from Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) using the GEOquery (Davis & Meltzer, 2007) package of R software (version 3.6.5, http://r-project.org/)."}}, {"id": "method-2", "family": "principal_component_analysis", "specific": "pca", "label": "Principal component analysis", "dsl_operations": ["pca_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["wide_numeric_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "principal component analysis"}}, {"id": "method-3", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 0.8, "band": "high", "quote": "limma"}}, {"id": "method-4", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.8, "band": "high", "quote": "pathway enrichment"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor"]}, "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": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["glm_logit_fit"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: logistic."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE13355", "GSE30999", "GSE106992"], "resolved_accessions": ["GSE13355", "GSE30999", "GSE106992"], "statements": ["Raw data are available in Supplemental Files."], "basis": "The paper names GSE13355, GSE30999, GSE106992 in Gene Expression Omnibus (GEO) database and the accession resolves in the repository. 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Affymetrix", "Affymetrix Human Genome U133 Plus 2.0 Array. Affymetrix includes 47,400 probes and represents 38,500", "ed separately. There are 1313 lncRNAs on the Affymetrix Human Genome U133 Plus 2.0. GSE13355 consist"], "order_basis": "paper_text_order:3212", "id": "assay:1"}, {"role": "analysis", "definition_id": "regression_modelling", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["glm_logit_fit"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Biomarkers of psoriasis were screened using LASSO logistic regression feature selection algorithm (Tibshirani, 1996) based on immune-related lncRNA expression matrices. 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