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Immune-associated biomarkers for early diagnosis of Parkinson's disease based on hematological lncRNA-mRNA co-expression.

ABSTRACT: Early stage diagnosis of Parkinson's disease (PD) is challenging without significant motor symptoms. The identification of effective molecular biomarkers as a hematological indication of PD may help improve the diagnostic timeliness and accuracy. In this paper, we analyzed and compared the blood samples of PD and control (CTR) patients to identify the disease-related changes and determine the putative biomarkers for PD diagnosis. Based on the RNA sequencing analysis, differentially expressed genes (DEGs) were identified, and the co-expression network of DEGs was constructed using the weighted correlation network analysis (WGCNA). The analysis leads to the identification of 87 genes that were exclusively regulated in the PD group, whereas 66 genes were significantly increased and 21 genes were significantly decreased in contrast to the control group. The results indicate that the core lncRNA-mRNA co-expression network greatly changes the immune response in PD patients. Specifically, the results showed that PWAR6, LINC00861, AC83843.1, IRF family, IFIT family and CaMK4 may play important roles in the immune system of PD. Based on the findings from this the present study, future research aims at identify novel therapeutic strategies for PD.

SUBMITTER: Lei K

PROVIDER: S-EPMC7753636 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Immune-associated biomarkers for early diagnosis of Parkinson's disease based on hematological lncRNA-mRNA co-expression.

Lei Kecheng K Zhang Liwen L He Yijing Y Sun Hui H Tong Weifang W Xu Yichun Y Jin Lingjing L

Bioscience reports 20201127

Early stage diagnosis of Parkinson's disease (PD) is challenging without significant motor symptoms. The identification of effective molecular biomarkers as a hematological indication of PD may help improve the diagnostic timeliness and accuracy. In this paper, we analyzed and compared the blood samples of PD and control (CTR) patients to identify the disease-related changes and determine the putative biomarkers for PD diagnosis. Based on the RNA sequencing analysis, differentially expressed gen ...[more]

PMID: 33245101

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Project description:Pediatric sepsis is a great threat to death worldwide. However, the pathogenesis has not been clearly understood until now in sepsis. This study identified differentially expressed mRNAs and lncRNAs based on Gene Expression Omnibus (GEO) database. And the weighted gene co-expression network analysis (WGCNA) was performed to explore co-expression modules associated with pediatric sepsis. Then, Gene Ontology (GO), KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway, mRNA‑lncRNA and mRNA‑lncRNA-pathway co-expression network analysis was conducted in selected significant module. A total of 1941 mRNAs and 225 lncRNAs were used to conduct WGCNA. And turquoise module was selected as a significant module that was associated with particular traits. The mRNAs functions associated with many vital processes were also shown by GO and KEGG pathway analysis in the turquoise module. Finally, 15 mRNAs (MAPK14, ITGAM, HK3, ALOX5, CR1, HCK, NCF4, PYGL, FLOT1, CARD6, NLRC4, SH3GLB1, PGS1, RAB31, LTB4R) and 4 lncRNAs (GSEC, NONHSAT160878.1, XR_926068.1 and RARA-AS1) were selected as hub genes in mRNA‑lncRNA-Pathway co-expression network. We identified 15 mRNAs and 4 lncRNAs as diagnostic markers, which have potential functions in pediatric sepsis. Our study provides more directions to study the molecular mechanism of pediatric sepsis.Abbreviations: mRNA: messenger RNA; lncRNA: long noncoding RNAs; GEO: Gene Expression Omnibus; WGCNA: weighted gene co-expression network analysis; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; SIRS: systemic inflammatory response syndrome; TOM: topological overlap measure; BP: biological process; MF: molecular function; CC: cellular component; ROC: receiver operating characteristic curve; AUC: area under curve; MAPK14: Mitogen-activated protein kinase 14; ALI: acute lung injury; ITGAM: Integrin subunit alpha M; HK3: Hexokinase 3; LPS: lipopolysaccharide; 5-LO: 5-lipoxygenase; LTs: leukotrienes; LTB4R: leukotriene B4 receptor.

Project description:BackgroundEsophageal cancer (EC) is one of the most common malignant cancers in the world. Endoplasmic reticulum (ER) stress is an adaptive response to various stress conditions and has been implicated in the development of various types of cancer. Long noncoding RNAs (lncRNAs) refer to a group of noncoding RNAs (ncRNAs), which regulate gene expression by interacting with DNA, RNA and proteins. Accumulating evidence suggests that lncRNAs are critical regulators of gene expression in development, differentiation, and human diseases, such as cancers and heart diseases. However, the prognostic model of EC based on ER stress-related mRNA and lncRNA has not been reported.MethodsFirstly, we downloaded RNA expression profiles from The Cancer Genome Atlas (TCGA) and obtained ER stress-related genes from the Molecular Signature Database (MSigDB). Next, Weighted Correlation Network Analysis (WGCNA) co-expression analysis was used to identify survival-related ER stress-related modules. Prognostic models were developed using univariate and Least absolute shrinkage and selection operator (LASSO) regression analyses on the training set and validated on the test set. Afterwards, The Receiver Operating Characteristic (ROC) curve and nomogram were used to evaluate the performance of risk prediction models. Differentially expressed gene (DEG) and enrichment analysis were performed between different groups in order to identify the biological processes correlated with the risk score. Finally, the fraction of immune cell infiltration and the difference of tumor microenvironment were identified in high-risk and low-risk groups.ResultsThe WGCNA co-expression analysis identified 49 ER genes that are highly associated with EC prognosis. Using univariate Cox regression and LASSO regression analysis, we developed prognostic risk models based on nine signature genes (four mRNAs and five lncRNAs). Both in the training and in the test sets, the overall survival (OS) of EC patients in the high-risk group was significantly lower than that in the low-risk group. The Kaplan-Meier curve and the ROC curve demonstrate the prognostic model we built can precisely predict the survival with more than 70% accuracy. The correlation analysis between the risk score and the infiltration of immune cells showed that the model can indicate the state of the immune microenvironment in EC.ConclusionIn this study, we developed a novel prognostic model for esophageal cancer based on ER stress-related mRNA-lncRNA co-expression profiles that could predict the prognosis, immune cell infiltration, and immunotherapy response in patients with EC. Our results also may provide clinicians with a quantitative tool to predict the survival time of patients and help them individualize treatment strategies for the patients with EC.

Dataset Information

Immune-associated biomarkers for early diagnosis of Parkinson's disease based on hematological lncRNA-mRNA co-expression.

Publications

Immune-associated biomarkers for early diagnosis of Parkinson's disease based on hematological lncRNA-mRNA co-expression.

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