scholarly journals Peer Review #3 of "Integrating multiple microarray dataset analysis and machine learning methods to reveal the key genes and regulatory mechanisms underlying human intervertebral disc degeneration (v0.2)"

2020 ◽  
Author(s):  
L Zhang
Keyword(s):  
Machine Learning ◽  
Intervertebral Disc ◽  
Peer Review ◽  
Disc Degeneration ◽  
Intervertebral Disc Degeneration ◽  
Microarray Dataset ◽  
Regulatory Mechanisms ◽  
Machine Learning Methods ◽  
Dataset Analysis ◽  
Key Genes

scholarly journals Integrating multiple microarray dataset analysis and machine learning methods to reveal the key genes and regulatory mechanisms underlying human intervertebral disc degeneration

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10120
Author(s):  
Hongze Chang ◽  
Xiaolong Yang ◽  
Kemin You ◽  
Mingwei Jiang ◽  
Feng Cai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intervertebral Disc ◽  
Disc Degeneration ◽  
Intervertebral Disc Degeneration ◽  
Expression Profiles ◽  
Microarray Dataset ◽  
Machine Learning Methods ◽  
Dataset Analysis ◽  
Key Genes ◽  
Optimal Feature

Intervertebral disc degeneration (IDD), a major cause of lower back pain, has multiple contributing factors including genetics, environment, age, and loading history. Bioinformatics analysis has been extensively used to identify diagnostic biomarkers and therapeutic targets for IDD diagnosis and treatment. However, multiple microarray dataset analysis and machine learning methods have not been integrated. In this study, we downloaded the mRNA, microRNA (miRNA), long noncoding RNA (lncRNA), and circular RNA (circRNA) expression profiles (GSE34095, GSE15227, GSE63492 GSE116726, GSE56081 and GSE67566) associated with IDD from the GEO database. Using differential expression analysis and recursive feature elimination, we extracted four optimal feature genes. We then used the support vector machine (SVM) to make a classification model with the four optimal feature genes. The ROC curve was used to evaluate the model’s performance, and the expression profiles (GSE63492, GSE116726, GSE56081, and GSE67566) were used to construct a competitive endogenous RNA (ceRNA) regulatory network and explore the underlying mechanisms of the feature genes. We found that three miRNAs (hsa-miR-4728-5p, hsa-miR-5196-5p, and hsa-miR-185-5p) and three circRNAs (hsa_circRNA_100723, hsa_circRNA_104471, and hsa_circRNA_100750) were important regulators with more interactions than the other RNAs across the whole network. The expression level analysis of the three datasets revealed that BCAS4 and SCRG1 were key genes involved in IDD development. Ultimately, our study proposes a novel approach to determining reliable and effective targets in IDD diagnosis and treatment.

scholarly journals Identification of Characteristic Genes in Whole Blood of Intervertebral Disc Degeneration Patients by Weighted Gene Coexpression Network Analysis (WGCNA)

2022 ◽  
Vol 2022 ◽  
pp. 1-17
Author(s):  
Xiaobo Ma ◽  
Junqiang Su ◽  
Bo Wang ◽  
Xiasheng Jin
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Intervertebral Disc ◽  
Disc Degeneration ◽  
Intervertebral Disc Degeneration ◽  
Regulatory Mechanisms ◽  
Gene Coexpression Network ◽  
Cerna Network ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

Intervertebral disc degeneration (IDD) is a major cause of lower back pain. However, to date, the molecular mechanism of the IDD remains unclear. Gene expression profiles and clinical traits were downloaded from the Gene Expression Omnibus (GEO) database. Firstly, weighted gene coexpression network analysis (WGCNA) was used to screen IDD-related genes. Moreover, least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine (SVM) algorithms were used to identify characteristic genes. Furthermore, we further investigated the immune landscape by the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm and the correlations between key characteristic genes and infiltrating immune cells. Finally, a competing endogenous RNA (ceRNA) network was established to show the regulatory mechanisms of characteristic genes. A total of 2458 genes were identified by WGCNA, and 48 of them were disordered. After overlapping the genes obtained by LASSO and SVM-RFE algorithms, genes including LINC01347, ASAP1-IT1, lnc-SEPT7L-1, B3GNT8, CHRNB3, CLEC4F, LOC102724000, SERINC2, and LOC102723649 were identified as characteristic genes of IDD. Moreover, differential analysis further identified ASAP1-IT1 and SERINC2 as key characteristic genes. Furthermore, we found that the expression of both ASAP1-IT1 and SERINC2 was related to the proportions of T cells gamma delta and Neutrophils. Finally, a ceRNA network was established to show the regulatory mechanisms of ASAP1-IT1 and SERINC2. In conclusion, the present study identified ASAP1-IT1 and SERINC2 as the key characteristic genes of IDD through integrative bioinformatic analyses, which may contribute to the diagnosis and treatment of IDD.

scholarly journals Protective effect of estrogen against intervertebral disc degeneration is attenuated by miR-221 through targeting estrogen receptor

2017 ◽  
Author(s):  
Sheng Bin ◽  
Yuan Youchao ◽  
Liu Xiangyang ◽  
Zhang Yi ◽  
Liu Hongzhe ◽  
...  
Keyword(s):  
Intervertebral Disc ◽  
Protective Effect ◽  
Disc Degeneration ◽  
Intervertebral Disc Degeneration ◽  
Regulatory Mechanisms ◽  
Luciferase Assay ◽  
Protective Effects ◽  
Cycle Arrest ◽  
Tnf Α ◽  
Cartilage Cells

AbstractIntervertebral disc degeneration (IDD) is a multifactorial disease that associates apoptosis, senescence and calcification of cartilage cells, inflammatory response and alterations in the extracellular matrix. Previous documents imply that estrogen and miR-221 may be involved in IDD. This study further investigated their regulatory mechanisms underlying IDD. Normal and degenerated cartilaginous endplates (CEP) tissues were isolated surgically from juvenile patients with idiopathic scoliosis and adult patients with IDD, respectively. PCR and western blot assays showed decreased aggrecan, Col2A1, TGF-β and estrogen receptorα (ERα) levels in CEP, but increased MMP-3, adamts-5, IL-1β, TNF-α, IL-6 and miR-221 levels. CEP cells were harvested from degenerated CEP tissues and treated with doses of 17β-E2. 17β-E2 increased expression of aggrecan and Col2A1 levels in endplate chondrocytes and secretion of TGF-β, but decreased IL-6 secretion. Moreover, 17β-E2 inhibited the apoptosis and cell-cycle arrest in G0/G1, improving the cell viability. These data indicated estrogen confers protective effect against IDD. However, we found that ERα was a target of miR-221 via luciferase assay. miR-221 up-regulation via the mimics or ERα knockdown attenuated these protective effects conferred by estrogen, while intervention of miR-221 via the inhibitors promoted the protective effects. This study provided novel evidence that estrogen confers protective effects of CEP against IDD, however, up-regulated miR-221 in degenerated CEP decreased the protective effects via targeting ERα, thus it may be an important cause for IDD.

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