Exploring Kawasaki disease-specific hub genes revealing a striking similarity of expression profile to bacterial infections using weighted gene co-expression network analysis (WGCNA) and co-expression modules identification tool (CEMiTool): An integrated bioinformatics and experimental study

Esmaeili, Sajjad; Mehrgou, Amir; Kakavandi, Naser; Rahmati, Yazdan

doi:10.1016/j.imbio.2020.151980

Cited by 8 publications

(8 citation statements)

References 31 publications

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“…To further reveal the potential roles that AS may play in L. chinense , we performed WGCNA of the AS genes. As a powerful tool with which to reveal the relationships between different gene sets or between genes and phenotypes, WGCNA has been extensively applied in different fields, such as medicine and plant science ( Wang J.-L. et al., 2019 ; Zhu et al., 2019 ; Esmaeili et al., 2020 ; Jia et al., 2020 ). We found that pistils and leaves may have huge differences in transcription and post-transcriptional regulation, because most blue-module genes that were related to transcription and post-transcriptional regulation were strongly expressed in pistils but weakly expressed in leaves ( Figures 6A , S3A , and S4A ).…”

Section: Discussionmentioning

confidence: 99%

“…Genes from the same coexpression module usually have similar expression patterns and closely related functions ( Jia et al., 2020 ). Researchers can associate coexpression modules with different traits to reveal the relationships between gene sets and phenotypes, and this method had been widely applied in the fields of medicine and plant science ( Wang J.-L. et al., 2019 ; Zhu et al., 2019 ; Esmaeili et al., 2020 ; Jia et al., 2020 ). However, using WGCNA to reveal the AS gene coexpression module in L. chinense had not been reported.…”

Section: Introductionmentioning

confidence: 99%

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Alternative Splicing Enhances the Transcriptome Complexity of Liriodendron chinense

Tu¹,

Shen²,

Wen³

et al. 2020

Front. Plant Sci.

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Alternative splicing (AS) plays pivotal roles in regulating plant growth and development, flowering, biological rhythms, signal transduction, and stress responses. However, no studies on AS have been performed in Liriodendron chinense, a deciduous tree species that has high economic and ecological value. In this study, we used multiple tools and algorithms to analyze transcriptome data derived from seven tissues via hybrid sequencing. Although only 17.56% (8,503/48,408) of genes in L. chinense were alternatively spliced, these AS genes occurred in 37,844 AS events. Among these events, intron retention was the most frequent AS event, producing 1,656 PTCcontaining and 3,310 non-PTC-containing transcripts. Moreover, 183 long noncoding RNAs (lncRNAs) also underwent AS events. Furthermore, weighted gene coexpression network analysis (WGCNA) revealed that there were great differences in the activities of transcription and post-transcriptional regulation between pistils and leaves, and AS had an impact on many physiological and biochemical processes in L. chinense, such as photosynthesis, sphingolipid metabolism, fatty acid biosynthesis and metabolism. Moreover, our analysis showed that the features of genes may affect AS, as AS genes and non-AS genes had differences in the exon/intron length, transcript length, and number of exons/introns. In addition, the structure of AS genes may impact the frequencies and types of AS because AS genes with more exons or introns tended to exhibit more AS events, and shorter introns tended to be retained, whereas shorter exons tended to be skipped. Furthermore, eight AS genes were verified, and the results were consistent with our analysis. Overall, this study reveals that AS and gene interaction are mutual-on one hand, AS can affect gene expression and translation, while on the other hand, the structural characteristics of the gene can also affect AS. This work is the first to comprehensively report on AS in L. chinense, and it can provide a reference for further research on AS in L. chinense.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Alternative Splicing Enhances the Transcriptome Complexity of Liriodendron chinense

Tu¹,

Shen²,

Wen³

et al. 2020

Front. Plant Sci.

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“…Finally, standardized mean rank (SMR) was generated to assist decision making. In this study, datasets with SMR values > 5 were excluded from analysis (Esmaeili et al, 2020).…”

Section: Microarray Gene Expression Meta-analysismentioning

confidence: 99%

Identification of Diagnostic Markers for Major Depressive Disorder Using Machine Learning Methods

et al. 2021

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BackgroundMajor depressive disorder (MDD) is a global health challenge that impacts the quality of patients’ lives severely. The disorder can manifest in many forms with different combinations of symptoms, which makes its clinical diagnosis difficult. Robust biomarkers are greatly needed to improve diagnosis and to understand the etiology of the disease. The main purpose of this study was to create a predictive model for MDD diagnosis based on peripheral blood transcriptomes.Materials and MethodsWe collected nine RNA expression datasets for MDD patients and healthy samples from the Gene Expression Omnibus database. After a series of quality control and heterogeneity tests, 302 samples from six studies were deemed suitable for the study. R package “MetaOmics” was applied for systematic meta-analysis of genome-wide expression data. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic effectiveness of individual genes. To obtain a better diagnostic model, we also adopted the support vector machine (SVM), random forest (RF), k-nearest neighbors (kNN), and naive Bayesian (NB) tools for modeling, with the RF method being used for feature selection.ResultsOur analysis revealed six differentially expressed genes (AKR1C3, ARG1, KLRB1, MAFG, TPST1, and WWC3) with a false discovery rate (FDR) < 0.05 between MDD patients and control subjects. We then evaluated the diagnostic ability of these genes individually. With single gene prediction, we achieved a corresponding area under the curve (AUC) value of 0.63 ± 0.04, 0.67 ± 0.07, 0.70 ± 0.11, 0.64 ± 0.08, 0.68 ± 0.07, and 0.62 ± 0.09, respectively, for these genes. Next, we constructed the classifiers of SVM, RF, kNN, and NB with an AUC of 0.84 ± 0.09, 0.81 ± 0.10, 0.73 ± 0.11, and 0.83 ± 0.09, respectively, in validation datasets, suggesting that the SVM classifier might be superior for constructing an MDD diagnostic model. The final SVM classifier including 70 feature genes was capable of distinguishing MDD samples from healthy controls and yielded an AUC of 0.78 in an independent dataset.ConclusionThis study provides new insights into potential biomarkers through meta-analysis of GEO data. Constructing different machine learning models based on these biomarkers could be a valuable approach for diagnosing MDD in clinical practice.

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“…Unlike previous screening out DEGs, WGCNA clusters highly relevant genes into one module and relates it to clinical features, which may be more conductive to identify diagnostic markers and therapeutic targets [ 13 ]. To date, WGCNA has been extensively utilized in genomic research, such as glioblastoma [ 14 ], Kawasaki disease [ 15 ], schizophrenia spectrum [ 16 ], and so on. It is speculated that identification of such co-expression patterns can shed more lights on the disease-related biological pathways.…”

Section: Introductionmentioning

confidence: 99%

Gene correlation network analysis to identify regulatory factors in sciatic nerve injury

Ling

et al. 2021

J Orthop Surg Res

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Background Sciatic nerve injury (SNI), which frequently occurs under the traumatic hip and hip fracture dislocation, induces serious complications such as motor and sensory loss, muscle atrophy, or even disabling. The present work aimed to determine the regulating factors and gene network related to the SNI pathology. Methods Sciatic nerve injury dataset GSE18803 with 24 samples was divided into adult group and neonate group. Weighted gene co-expression network analysis (WGCNA) was carried out to identify modules associated with SNI in the two groups. Moreover, differentially expressed genes (DEGs) were determined from every group, separately. Subsequently, co-expression network and protein–protein interaction (PPI) network were overlapped to identify hub genes, while functional enrichment and Reactome analysis were used for a comprehensive analysis of potential pathways. GSE30165 was used as the test set for investigating the hub gene involvement within SNI. Gene set enrichment analysis (GSEA) was performed separately using difference between samples and gene expression level as phenotype label to further prove SNI-related signaling pathways. In addition, immune infiltration analysis was accomplished by CIBERSORT. Finally, Drug–Gene Interaction database (DGIdb) was employed for predicting the possible therapeutic agents. Results 14 SNI status modules and 97 DEGs were identified in adult group, while 15 modules and 21 DEGs in neonate group. A total of 12 hub genes was overlapping from co-expression and PPI network. After the results from both test and training sets were overlapped, we verified that the ten real hub genes showed remarkably up-regulation within SNI. According to functional enrichment of hub genes, the above genes participated in the immune effector process, inflammatory responses, the antigen processing and presentation, and the phagocytosis. GSEA also supported that gene sets with the highest significance were mostly related to the cytokine–cytokine receptor interaction. Analysis of hub genes possible related signaling pathways using gene expression level as phenotype label revealed an enrichment involved in Lysosome, Chemokine signaling pathway, and Neurotrophin signaling pathway. Immune infiltration analysis showed that Macrophages M2 and Regulatory T cells may participate in the development of SNI. At last, 25 drugs were screened from DGIdb to improve SNI treatment. Conclusions The gene expression network is determined in the present work based on the related regulating factors within SNI, which sheds more light on SNI pathology and offers the possible biomarkers and therapeutic targets in subsequent research.

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Exploring Kawasaki disease-specific hub genes revealing a striking similarity of expression profile to bacterial infections using weighted gene co-expression network analysis (WGCNA) and co-expression modules identification tool (CEMiTool): An integrated bioinformatics and experimental study

Cited by 8 publications

References 31 publications

Alternative Splicing Enhances the Transcriptome Complexity of Liriodendron chinense

Alternative Splicing Enhances the Transcriptome Complexity of Liriodendron chinense

Identification of Diagnostic Markers for Major Depressive Disorder Using Machine Learning Methods

Gene correlation network analysis to identify regulatory factors in sciatic nerve injury

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