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15 pages, 3467 KiB

Open AccessEditor’s ChoiceArticle

The Potential Regulation of A-to-I RNA Editing on Genes in Parkinson’s Disease

by Sijia Wu, Qiuping Xue, Xinyu Qin, Xiaoming Wu, Pora Kim, Jacqueline Chyr, Xiaobo Zhou and Liyu Huang

Genes 2023, 14(4), 919; https://doi.org/10.3390/genes14040919 - 15 Apr 2023

Cited by 1 | Viewed by 2434

Abstract

Parkinson’s disease (PD) is characterized by dopaminergic neurodegeneration and an abnormal accumulation of α-synuclein aggregates. A number of genetic factors have been shown to increase the risk of PD. Exploring the underlying molecular mechanisms that mediate PD’s transcriptomic diversity can help us understand [...] Read more.

Parkinson’s disease (PD) is characterized by dopaminergic neurodegeneration and an abnormal accumulation of α-synuclein aggregates. A number of genetic factors have been shown to increase the risk of PD. Exploring the underlying molecular mechanisms that mediate PD’s transcriptomic diversity can help us understand neurodegenerative pathogenesis. In this study, we identified 9897 A-to-I RNA editing events associated with 6286 genes across 372 PD patients. Of them, 72 RNA editing events altered miRNA binding sites and this may directly affect miRNA regulations of their host genes. However, RNA editing effects on the miRNA regulation of genes are more complex. They can (1) abolish existing miRNA binding sites, which allows miRNAs to regulate other genes; (2) create new miRNA binding sites that may sequester miRNAs from regulating other genes; or (3) occur in the miRNA seed regions and change their targets. The first two processes are also referred to as miRNA competitive binding. In our study, we found 8 RNA editing events that may alter the expression of 1146 other genes via miRNA competition. We also found one RNA editing event that modified a miRNA seed region, which was predicted to disturb the regulation of four genes. Considering the PD-related functions of the affected genes, 25 A-to-I RNA editing biomarkers for PD are proposed, including the 3 editing events in the EIF2AK2, APOL6, and miR-4477b seed regions. These biomarkers may alter the miRNA regulation of 133 PD-related genes. All these analyses reveal the potential mechanisms and regulations of RNA editing in PD pathogenesis. Full article

(This article belongs to the Special Issue Transcriptomics and Bioinformatics in Precision Medicine)

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18 pages, 2255 KiB

Open AccessArticle

Network-Based and Machine-Learning Approaches Identify Diagnostic and Prognostic Models for EMT-Type Gastric Tumors

by Mehdi Sadeghi, Mohammad Reza Karimi, Amir Hossein Karimi, Nafiseh Ghorbanpour Farshbaf, Abolfazl Barzegar and Ulf Schmitz

Genes 2023, 14(3), 750; https://doi.org/10.3390/genes14030750 - 19 Mar 2023

Cited by 3 | Viewed by 2716

Abstract

The microsatellite stable/epithelial-mesenchymal transition (MSS/EMT) subtype of gastric cancer represents a highly aggressive class of tumors associated with low rates of survival and considerably high probabilities of recurrence. In the era of precision medicine, the accurate and prompt diagnosis of tumors of this [...] Read more.

The microsatellite stable/epithelial-mesenchymal transition (MSS/EMT) subtype of gastric cancer represents a highly aggressive class of tumors associated with low rates of survival and considerably high probabilities of recurrence. In the era of precision medicine, the accurate and prompt diagnosis of tumors of this subtype is of vital importance. In this study, we used Weighted Gene Co-expression Network Analysis (WGCNA) to identify a differentially expressed co-expression module of mRNAs in EMT-type gastric tumors. Using network analysis and linear discriminant analysis, we identified mRNA motifs and microRNA-based models with strong prognostic and diagnostic relevance: three models comprised of (i) the microRNAs miR-199a-5p and miR-141-3p, (ii) EVC/EVC2/GLI3, and (iii) PDE2A/GUCY1A1/GUCY1B1 gene expression profiles distinguish EMT-type tumors from other gastric tumors with high accuracy (Area Under the Receiver Operating Characteristic Curve (AUC) = 0.995, AUC = 0.9742, and AUC = 0.9717; respectively). Additionally, the DMD/ITGA1/CAV1 motif was identified as the top motif with consistent relevance to prognosis (hazard ratio > 3). Molecular functions of the members of the identified models highlight the central roles of MAPK, Hh, and cGMP/cAMP signaling in the pathology of the EMT subtype of gastric cancer and underscore their potential utility in precision therapeutic approaches. Full article

(This article belongs to the Special Issue Transcriptomics and Bioinformatics in Precision Medicine)

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14 pages, 4052 KiB

Open AccessArticle

Involvement of MicroRNA-27a-3p in the Licorice-Induced Alteration of Cd28 Expression in Mice

by Gang Feng, Guozheng Liang, Yaqian Zhang, Jicong Hu, Chuandong Zhou, Jiawen Li, Wenfeng Zhang, Han Shen, Fenglin Wu, Changli Tao, Yan Liu and Hongwei Shao

Genes 2022, 13(7), 1143; https://doi.org/10.3390/genes13071143 - 25 Jun 2022

Cited by 2 | Viewed by 2378

Abstract

Licorice has previously been shown to affect gene expression in cells; however, the underlying mechanisms remain to be clarified. We analyzed the microRNA expression profile of serum from mice treated by gavage with licorice decoction, and obtained 11 differentially expressed microRNAs (DEmiRNAs). We [...] Read more.

Licorice has previously been shown to affect gene expression in cells; however, the underlying mechanisms remain to be clarified. We analyzed the microRNA expression profile of serum from mice treated by gavage with licorice decoction, and obtained 11 differentially expressed microRNAs (DEmiRNAs). We also screened differentially expressed genes (DEgenes) based on RNA-Seq data, and 271 common genes were identified by intersection analysis of the predicted target genes of 11 DEmiRNAs and the DEgenes. The miRNA–gene network showed that most of the hub genes were immune-related. KEGG enrichment analysis of the 271 genes identified three significant pathways, and the 21 genes involved in these three pathways, and the 11 DEmiRNAs, were constructed into a miRNA pathway–target gene network, in which mmu-miR-27a-3p stood out. Compared to ImmPort, there were 13 immune genes within the above group of 21 genes, and three intersected with the mmu-miR-27a-3p predicted target genes, Cd28, Grap2 and Cxcl12, of which the expression of Cd28 changed most significantly. We confirmed the regulation of Cd28 by mmu-miR-27a-3p using a dual-luciferase assay, and further confirmed that overexpression of mmu-miR-27a-3p could significantly downregulate the expression of Cd28 in lymphocytes. These results indicate that mmu-miR-27a-3p could be involved in the licorice-mediated regulation of the expression of Cd28 in mice. Full article

(This article belongs to the Special Issue Transcriptomics and Bioinformatics in Precision Medicine)

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14 pages, 3817 KiB

Open AccessArticle

A Novel Strategy to Identify Prognosis-Relevant Gene Sets in Cancers

by Junyi Pu, Hui Yu and Yan Guo

Genes 2022, 13(5), 862; https://doi.org/10.3390/genes13050862 - 12 May 2022

Cited by 3 | Viewed by 2483

Abstract

Molecular prognosis markers hold promise for improved prediction of patient survival, and a pathway or gene set may add mechanistic interpretation to their prognostic prediction power. In this study, we demonstrated a novel strategy to identify prognosis-relevant gene sets in cancers. Our study [...] Read more.

Molecular prognosis markers hold promise for improved prediction of patient survival, and a pathway or gene set may add mechanistic interpretation to their prognostic prediction power. In this study, we demonstrated a novel strategy to identify prognosis-relevant gene sets in cancers. Our study consists of a first round of gene-level analyses and a second round of gene-set-level analyses, in which the Composite Gene Expression Score critically summarizes a surrogate expression value at gene set level and a permutation procedure is exerted to assess prognostic significance of gene sets. An optional differential coexpression module is appended to the two phases of survival analyses to corroborate and refine prognostic gene sets. Our strategy was demonstrated in 33 cancer types across 32,234 gene sets. We found oncogenic gene sets accounted for an increased proportion among the final gene sets, and genes involved in DNA replication and DNA repair have ubiquitous prognositic value for multiple cancer types. In summary, we carried out the largest gene set based prognosis study to date. Compared to previous similar studies, our approach offered multiple improvements in design and methodology implementation. Functionally relevant gene sets of ubiquitous prognostic significance in multiple cancer types were identified. Full article

(This article belongs to the Special Issue Transcriptomics and Bioinformatics in Precision Medicine)

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16 pages, 2216 KiB

Open AccessArticle

Gene Expression Meta-Analysis of Potential Shared and Unique Pathways between Autoimmune Diseases under Anti-TNFα Therapy

by Charalabos Antonatos, Mariza Panoutsopoulou, Georgios K. Georgakilas, Evangelos Evangelou and Yiannis Vasilopoulos

Genes 2022, 13(5), 776; https://doi.org/10.3390/genes13050776 - 27 Apr 2022

Cited by 3 | Viewed by 4459

Abstract

While anti-TNFα has been established as an effective therapeutic approach for several autoimmune diseases, results from clinical trials have uncovered heterogeneous patients’ response to therapy. Here, we conducted a meta-analysis on the publicly available gene expression cDNA microarray datasets that examine the differential [...] Read more.

While anti-TNFα has been established as an effective therapeutic approach for several autoimmune diseases, results from clinical trials have uncovered heterogeneous patients’ response to therapy. Here, we conducted a meta-analysis on the publicly available gene expression cDNA microarray datasets that examine the differential expression observed in response to anti-TNFα therapy with psoriasis (PsO), inflammatory bowel disease (IBD) and rheumatoid arthritis (RA). Five disease-specific meta-analyses and a single combined random-effects meta-analysis were performed through the restricted maximum likelihood method. Gene Ontology and Reactome Pathways enrichment analyses were conducted, while interactions between differentially expressed genes (DEGs) were determined with the STRING database. Four IBD, three PsO and two RA datasets were identified and included in our analyses through our search criteria. Disease-specific meta-analyses detected distinct pro-inflammatory down-regulated DEGs for each disease, while pathway analyses identified common inflammatory patterns involved in the pathogenesis of each disease. Combined meta-analyses further revealed DEGs that participate in anti-inflammatory pathways, namely IL-10 signaling. Our analyses provide the framework for a transcriptomic approach in response to anti-TNFα therapy in the above diseases. Elucidation of the complex interactions involved in such multifactorial phenotypes could identify key molecular targets implicated in the pathogenesis of IBD, PsO and RA. Full article

(This article belongs to the Special Issue Transcriptomics and Bioinformatics in Precision Medicine)

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18 pages, 2604 KiB

Open AccessArticle

Transcriptomic Analysis of Canine Osteosarcoma from a Precision Medicine Perspective Reveals Limitations of Differential Gene Expression Studies

by Rebecca L. Nance, Sara J. Cooper, Dmytro Starenki, Xu Wang, Brad Matz, Stephanie Lindley, Annette N. Smith, Ashley A. Smith, Noelle Bergman, Maninder Sandey, Jey Koehler, Payal Agarwal and Bruce F. Smith

Genes 2022, 13(4), 680; https://doi.org/10.3390/genes13040680 - 13 Apr 2022

Cited by 4 | Viewed by 3483

Abstract

Despite significant advances in cancer diagnosis and treatment, osteosarcoma (OSA), an aggressive primary bone tumor, has eluded attempts at improving patient survival for many decades. The difficulty in managing OSA lies in its extreme genetic complexity, drug resistance, and heterogeneity, making it improbable [...] Read more.

Despite significant advances in cancer diagnosis and treatment, osteosarcoma (OSA), an aggressive primary bone tumor, has eluded attempts at improving patient survival for many decades. The difficulty in managing OSA lies in its extreme genetic complexity, drug resistance, and heterogeneity, making it improbable that a single-target treatment would be beneficial for the majority of affected individuals. Precision medicine seeks to fill this gap by addressing the intra- and inter-tumoral heterogeneity to improve patient outcome and survival. The characterization of differentially expressed genes (DEGs) unique to the tumor provides insight into the phenotype and can be useful for informing appropriate therapies as well as the development of novel treatments. Traditional DEG analysis combines patient data to derive statistically inferred genes that are dysregulated in the group; however, the results from this approach are not necessarily consistent across individual patients, thus contradicting the basis of precision medicine. Spontaneously occurring OSA in the dog shares remarkably similar clinical, histological, and molecular characteristics to the human disease and therefore serves as an excellent model. In this study, we use transcriptomic sequencing of RNA isolated from primary OSA tumor and patient-matched normal bone from seven dogs prior to chemotherapy to identify DEGs in the group. We then evaluate the universality of these changes in transcript levels across patients to identify DEGs at the individual level. These results can be useful for reframing our perspective of transcriptomic analysis from a precision medicine perspective by identifying variations in DEGs among individuals. Full article

(This article belongs to the Special Issue Transcriptomics and Bioinformatics in Precision Medicine)

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16 pages, 3626 KiB

Open AccessArticle

Identification of Potential Diagnostic Biomarkers and Biological Pathways in Hypertrophic Cardiomyopathy Based on Bioinformatics Analysis

by Tingyan Yu, Zhaoxu Huang and Zhaoxia Pu

Genes 2022, 13(3), 530; https://doi.org/10.3390/genes13030530 - 17 Mar 2022

Cited by 14 | Viewed by 4220

Abstract

Hypertrophic cardiomyopathy (HCM) is a genetic heterogeneous disorder and the main cause of sudden cardiac death in adolescents and young adults. This study was aimed at identifying potential diagnostic biomarkers and biological pathways to help to diagnose and treat HCM through bioinformatics analysis. [...] Read more.

Hypertrophic cardiomyopathy (HCM) is a genetic heterogeneous disorder and the main cause of sudden cardiac death in adolescents and young adults. This study was aimed at identifying potential diagnostic biomarkers and biological pathways to help to diagnose and treat HCM through bioinformatics analysis. We selected the GSE36961 dataset from the Gene Expression Omnibus (GEO) database and identified 893 differentially expressed genes (DEGs). Subsequently, 12 modules were generated through weighted gene coexpression network analysis (WGCNA), and the turquoise module showed the highest negative correlation with HCM (cor = −0.9, p-value = 4 × 10⁻⁵²). With the filtering standard gene significance (GS) < −0.7 and module membership (MM) > 0.9, 19 genes were then selected to establish the least absolute shrinkage and selection operator (LASSO) model, and LYVE1, MAFB, and MT1M were finally identified as key genes. The expression levels of these genes were additionally verified in the GSE130036 dataset. Gene Set Enrichment Analysis (GSEA) showed oxidative phosphorylation, tumor necrosis factor alpha-nuclear factor-κB (TNFα-NFκB), interferon-gamma (IFNγ) response, and inflammatory response were four pathways possibly related to HCM. In conclusion, LYVE1, MAFB, and MT1M were potential biomarkers of HCM, and oxidative stress, immune response as well as inflammatory response were likely to be associated with the pathogenesis of HCM. Full article

(This article belongs to the Special Issue Transcriptomics and Bioinformatics in Precision Medicine)

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14 pages, 4004 KiB

Open AccessArticle

Machine Learning and Bioinformatics Framework Integration to Potential Familial DCM-Related Markers Discovery

by Concetta Schiano, Monica Franzese, Filippo Geraci, Mario Zanfardino, Ciro Maiello, Vittorio Palmieri, Andrea Soricelli, Vincenzo Grimaldi, Enrico Coscioni, Marco Salvatore and Claudio Napoli

Genes 2021, 12(12), 1946; https://doi.org/10.3390/genes12121946 - 2 Dec 2021

Cited by 12 | Viewed by 3979

Abstract

Objectives: Dilated cardiomyopathy (DCM) is characterized by a specific transcriptome. Since the DCM molecular network is largely unknown, the aim was to identify specific disease-related molecular targets combining an original machine learning (ML) approach with protein-protein interaction network. Methods: The transcriptomic profiles of [...] Read more.

Objectives: Dilated cardiomyopathy (DCM) is characterized by a specific transcriptome. Since the DCM molecular network is largely unknown, the aim was to identify specific disease-related molecular targets combining an original machine learning (ML) approach with protein-protein interaction network. Methods: The transcriptomic profiles of human myocardial tissues were investigated integrating an original computational approach, based on the Custom Decision Tree algorithm, in a differential expression bioinformatic framework. Validation was performed by quantitative real-time PCR. Results: Our preliminary study, using samples from transplanted tissues, allowed the discovery of specific DCM-related genes, including MYH6, NPPA, MT-RNR1 and NEAT1, already known to be involved in cardiomyopathies Interestingly, a combination of these expression profiles with clinical characteristics showed a significant association between NEAT1 and left ventricular end-diastolic diameter (LVEDD) (Rho = 0.73, p = 0.05), according to severity classification (NYHA-class III). Conclusions: The use of the ML approach was useful to discover preliminary specific genes that could lead to a rapid selection of molecular targets correlated with DCM clinical parameters. For the first time, NEAT1 under-expression was significantly associated with LVEDD in the human heart. Full article

(This article belongs to the Special Issue Transcriptomics and Bioinformatics in Precision Medicine)

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