Identification of RN7SK LncRNA as a novel biomarker in Alzheimer's disease using bioinformatics and expression analysis

Abstract

Alzheimer's disease (AD) is a degenerative illness that accounts for the common type of dementia among adults over the age of 65. Despite extensive studies on the pathogenesis of the disease, early diagnosis of AD is still debatable. In this research, we performed bioinformatics approaches on the AD-related E-MTAB 6094 dataset to uncover new potential biomarkers for AD diagnosis. To achieve this, we performed in-depth in silico assays, including differentially expressed genes analysis, weighted gene co-expression network analyses, module-trait association analyses, gene ontology and pathway enrichment analyses, and hub genes network analyses. Finally, the expression of the identified candidate genes was evaluated in AD patients PBMC samples by qRT-PCR. Through computational analyses, we found that RN7SK LncRNA and its co-expressed genes of TNF, TNFAIP3, CCLT3, and FLT3 are from key genes in AD development that are associated with inflammatory responses. Our experimental validation revealed that RN7SK LncRNA and TNF were substantially up-regulated in AD samples (P = 0.006 and P = 0.023, respectively). Whereas, TNFAIP3 expression was significantly decreased (P = 0.016). However, the expression of CCL3 and FLT3 did not differ significantly between two groups (P = 0.396 and P = 0.521, respectively). In conclusion, in this study a novel LncRNA associated with AD pathogenesis were identified, which may provide new diagnostic biomarker for AD.

Keywords: Alzheimer’s disease; Bioinformatics; Biomarker; LncRNA; PBMC; RN7SK.

Fig. 1

A flow chart outlining the study plan, data preparation, and analysis.

Fig. 2

The boxplot displays the distributions of the selected sample’s values prior to (A) and after (B) normalization, which were appropriate for differential expression analyses.

Fig. 3

(A) Volcano plot displaying differentially expressed mRNAs between AD and normal samples. The cut-off criteria for DEmRNAs analysis were set as |logFC| ≥ 1, adj. P-value < 0.001. Blue dots indicate down-regulated DEmRNAs and red dots indicate up-regulated DEmRNAs. (B) Heatmap illustrating the DELncRNA between AD and healthy control samples.

Fig. 4

Analysis of the E-MTAB-6094 dataset using weighted gene co-expression networks (WGCNA) method. (A) The soft-thresholding power and the scale-free fit index. (B) Dynamic dendrogram showing clustered genes according to a dissimilarity measure. (C) Heatmap illustrating the correlation between module eigengenes and clinical phenotype of Alzheimer’s disease. (D) The number of genes in each module.

Fig. 5

Shared genes between DEGs and interested blue module.

Fig. 6

Gene ontology analysis of shared mRNA genes in terms of: (A) Biological Process, (B) Cellular Component, and (C) Molecular Function.

Fig. 7

KEGG pathway analysis of the mRNA genes of LncRNA-mRNA co-expression network. Permission has been obtained from Kanehisa laboratories for using KEGG pathway database.

Fig. 8

Vital nodes of mRNA-LncRNA co-expression sub-network visualized by Cytoscape.

Fig. 9

qRT-PCR results of candidated transcripts. (A) Relative expression level of the target genes in AD patients and healthy controls measured by qRT-PCR analysis. The results are expressed as mean ± SEM. (B) 2% Agarose gel electrophoresis of the PCR products showing, the specificity of the amplified transcripts.

Fig. 10

ROC curve analysis of RN7SK, TNF, and TNFAIP3 as biomarker for differentiating between AD patients and controls. The sensitivity and specificity of RN7SK, TNF, and TNFAIP3 transcription were 50% and 84% (AUC = 0.658, P = 0. 0063), 58% and 88% (AUC = 0.632, P = 0.0229), and 32% and 94% (AUC = 0.64, P = 0.0158), respectively.