Dataset Information

A Diagnostic Panel for Acquired Immune-Mediated Polyneuropathies Based on the Expression of lncRNAs.

ABSTRACT: Long non-coding RNAs (lncRNAs) have been shown to alter immune responses, thus contributing to the pathobiology of autoimmune conditions. We investigated the expression levels of ANRIL, PICART1, MALAT1, CCAT1, CCAT2, and CCHE1 lncRNAs in acute and chronic inflammatory demyelinating polyneuropathy (AIDP and CIDP). ANRIL, PICART1, MALAT1, CCAT1, CCAT2, and CCHE1 lncRNAs were significantly downregulated in individuals with both AIDP and CIDP compared with unaffected individuals. Gender-based comparisons also verified such downregulations in both male and female subjects compared with sex-matched unaffected controls for all lncRNAs. There was no significant difference in the expression of any of the lncRNAs between cases with AIDP and cases with CIDP. While the expression levels of ANRIL and PICART1 were significantly correlated in healthy subjects (r = 0.86, p = 8.5E-16), similar analysis in cases with AIDP and CIDP revealed no significant correlation. The most robust correlation among patients was detected between ANRIL and MALAT1 lncRNAs (r = 0.59, p = 3.52E-6). ANRIL, MALAT1, and PICART1 had the diagnostic power of 0.96, 0.94, and 0.92 in distinguishing between cases with CIDP and controls, respectively. A combination of all lncRNAs resulted in 0.95 diagnostic power with a sensitivity of 0.85 and specificity of 0.96 for this purpose. Diagnostic power values of these lncRNAs in differentiation between cases with AIDP and controls were 0.98, 0.95, and 0.93, respectively. The combinatorial diagnostic power reached 0.98 for differentiation between cases with AIDP and controls. The six-lncRNA panel could differentiate combined cases with AIDP and CIDP from controls with area under the curve (AUC), sensitivity, and specificity values of 0.97, 0.90, and 0.96, respectively. Collectively, the lncRNA panel is suggested as a sensitive and specific diagnostic panel for acquired immune-mediated polyneuropathies.

SUBMITTER: Hussen BM

PROVIDER: S-EPMC7940672 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Diagnostic Panel for Acquired Immune-Mediated Polyneuropathies Based on the Expression of lncRNAs.

Hussen Bashdar Mahmud BM Nicknafs Fwad F Hidayat Hazha Jamal HJ Sayad Arezou A Ghafouri-Fard Soudeh S Taheri Mohammad M

Frontiers in immunology 20210223

Long non-coding RNAs (lncRNAs) have been shown to alter immune responses, thus contributing to the pathobiology of autoimmune conditions. We investigated the expression levels of ANRIL, PICART1, MALAT1, CCAT1, CCAT2, and CCHE1 lncRNAs in acute and chronic inflammatory demyelinating polyneuropathy (AIDP and CIDP). ANRIL, PICART1, MALAT1, CCAT1, CCAT2, and CCHE1 lncRNAs were significantly downregulated in individuals with both AIDP and CIDP compared with unaffected individuals. Gender-based compar ...[more]

PMID: 33708228

Similar Datasets

Project description:BackgroundColorectal cancer (CRC) is one of the most common malignant tumors with a poor prognosis. At present, the pathogenesis is not completely clear. Therefore, finding reliable prognostic indicators for CRC is of important clinical significance. In this study, bioinformatics methods were used to screen the prognostic immune-related lncRNAs of CRC, and a prognostic risk scoring model based on immune-related lncRNAs signatures were constructed to provide a basis for prognostic evaluation and immunotherapy of CRC patients.MethodsThe clinical information and RNA-seq data of CRC patients were obtained from The Cancer Genome Atlas (TCGA) database. The information of immune-related lncRNA was downloaded from the immunology database and analysis portal. The differentially expressed immune-related lncRNAs (IRLs) were screened by the edgeR package of R software. The prognostic value of IRLs was studied. Based on Cox regression analysis, a prognostic index (IRLPI) based on IRLs was established, and the relationship between the risk score and the clinicopathological characteristics of CRC was analyzed to determine the effectiveness of the risk score model as an independent prognostic factor.ResultsA total of 240 differentially expressed IRLs were identified between normal colorectal cancer tissues and normal colorectal cancer tissues, in which 8 were significantly associated with the survival of CRC patients (P < 0.05), including LINC00461, LINC01055, ELFN1-AS1, LMO7-AS1, CYP4A22-AS1, AC079612.1, LINC01351, and MIR31HG. And most of the lncRNAs related to survival were risk factors for the prognosis of CRC. The index established based on the 7 survival-related IRLs found to be highly accurate in monitoring CRC prognosis. Besides, IRLPI was significantly correlated with a variety of pathological factors and immune cell infiltration.ConclusionEight immune-related lncRNAs closely related to the prognosis of CRC patients were identified from the TCGA database. At the same time, an independent IRLPI was constructed, which may be helpful for clinicians to assess the prognosis of patients with CRC and to formulate individualized treatment plans.

Project description:BackgroundStomach adenocarcinoma (STAD), is one of the most lethal malignancies around the world. The aim of this study was to find the long noncoding RNAs (lncRNAs) acting as diagnostic and prognostic biomarker of STAD.MethodsBase on TCGA dataset, the differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) were identified between STAD and normal tissue. The machine learning and survival analysis were performed to evaluate the potential diagnostic and prognostic value of lncRNAs for STAD. We also build the co-expression network and functional annotation. The expression of selected candidate mRNAs and lncRNAs were validated by Quantitative real-time polymerase chain reaction (qRT-PCR) and GSE27342 dataset. GSE27342 dataset were also to perform gene set enrichment analysis.ResultsA total of 814 DEmRNAs and 106 DElncRNAs between STAD and normal tissue were obtained. FOXD2-AS1, LINC01235, and RP11-598F7.5 were defined as optimal diagnostic lncRNA biomarkers for STAD. The area under curve (AUC) of the decision tree model, random forests model, and support vector machine (SVM) model were 0.797, 0.981, and 0.983, and the specificity and sensitivity of the three model were 75.0% and 97.1%, 96.9% and 96%, and 96.9% and 97.1%, respectively. Among them, LINC01235 was not only an optimal diagnostic lncRNA biomarkers, but also related to survival time. The expression of three DEmRNAs (ESM1, WNT2, and COL10A1) and three optimal diagnostic lncRNAs biomarkers (FOXD2-AS1, RP11-598F7.5, and LINC01235) in qRT-PCR validation was were consistent with our integrated analysis. Except for FOXD2-AS1, ESM1, WNT2, COL10A1, and LINC01235 were upregulated in STAD, which was consistent with our integration results. Gene set enrichment analysis results indicated that DNA replication, Cell cycle, ECM-receptor interaction, and P53 signaling pathway were four significantly enriched pathways in STAD.ConclusionOur study identified three DElncRNAs as potential diagnostic biomarkers of STAD. Among them, LINC01235 also was a prognostic lncRNA biomarkers.

Dataset Information

A Diagnostic Panel for Acquired Immune-Mediated Polyneuropathies Based on the Expression of lncRNAs.

Publications

A Diagnostic Panel for Acquired Immune-Mediated Polyneuropathies Based on the Expression of lncRNAs.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets