Computational methods and next-generation sequencing approaches to analyze epigenetics data: Profiling of methods and applications

Arora, Itika; Tollefsbol, Trygve O.

doi:10.1016/j.ymeth.2020.09.008

Cited by 36 publications

(21 citation statements)

References 138 publications

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“…Several review papers have been conducted to show the potentials, trends, and future direction of ML and DL applications in genetics, genomics, bioinformatics, and multiomics studies [7][8][9][10][11][12][13][14][15][16]. The significant outcomes of these work mostly contributed to the cancer research field.…”

Section: Recent Reviews Of Artificial Intelligence Application In Hea...mentioning

confidence: 99%

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A Review on Recent Progress in Machine Learning and Deep Learning Methods for Cancer Classification on Gene Expression Data

Mazlan

Sahabudin²,

Remli³

et al. 2021

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Data-driven model with predictive ability are important to be used in medical and healthcare. However, the most challenging task in predictive modeling is to construct a prediction model, which can be addressed using machine learning (ML) methods. The methods are used to learn and trained the model using a gene expression dataset without being programmed explicitly. Due to the vast amount of gene expression data, this task becomes complex and time consuming. This paper provides a recent review on recent progress in ML and deep learning (DL) for cancer classification, which has received increasing attention in bioinformatics and computational biology. The development of cancer classification methods based on ML and DL is mostly focused on this review. Although many methods have been applied to the cancer classification problem, recent progress shows that most of the successful techniques are those based on supervised and DL methods. In addition, the sources of the healthcare dataset are also described. The development of many machine learning methods for insight analysis in cancer classification has brought a lot of improvement in healthcare. Currently, it seems that there is highly demanded further development of efficient classification methods to address the expansion of healthcare applications.

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Section: Recent Reviews Of Artificial Intelligence Application In Hea...mentioning

confidence: 99%

“…Other reviews and research focused on specific types of cancer, such as prostate cancer [9,17] and epigenetics [12]. The authors reviewed the potential application and machine learning algorithms to be used in prostate cancer and analyzed epigenetics data, respectively.…”

Section: Recent Reviews Of Artificial Intelligence Application In Hea...mentioning

confidence: 99%

A Review on Recent Progress in Machine Learning and Deep Learning Methods for Cancer Classification on Gene Expression Data

Mazlan

Sahabudin²,

Remli³

et al. 2021

Processes

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“…With the rapid advancement of genomic technology, bioinformatics analyses have been widely used in the analysis of microarray datasets to further study the potential molecular mechanisms of cancers and to identify tumor-specific indicators [ 5 ]. Weighed gene coexpression network analysis (WGCNA) is one of these significant algorithms that provides a better understanding of gene coexpression networks and gene functions [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Low expression of CHRDL1 and SPARCL1 predicts poor prognosis of lung adenocarcinoma based on comprehensive analysis and immunohistochemical validation

et al. 2021

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Purpose Exploring the molecular mechanisms of lung adenocarcinoma (LUAD) is beneficial for developing new therapeutic strategies and predicting prognosis. This study was performed to select core genes related to LUAD and to analyze their prognostic value. Methods Microarray datasets from the GEO (GSE75037) and TCGA-LUAD datasets were analyzed to identify differentially coexpressed genes in LUAD using weighted gene coexpression network analysis (WGCNA) and differential gene expression analysis. Functional enrichment analysis was conducted, and a protein–protein interaction (PPI) network was established. Subsequently, hub genes were identified using the CytoHubba plug-in. Overall survival (OS) analyses of hub genes were performed. The Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Human Protein Atlas (THPA) databases were used to validate our findings. Gene set enrichment analysis (GSEA) of survival-related hub genes were conducted. Immunohistochemistry (IHC) was carried out to validate our findings. Results We identified 486 differentially coexpressed genes. Functional enrichment analysis suggested these genes were primarily enriched in the regulation of epithelial cell proliferation, collagen-containing extracellular matrix, transforming growth factor beta binding, and signaling pathways regulating the pluripotency of stem cells. Ten hub genes were detected using the maximal clique centrality (MCC) algorithm, and four genes were closely associated with OS. The CPTAC and THPA databases revealed that CHRDL1 and SPARCL1 were downregulated at the mRNA and protein expression levels in LUAD, whereas SPP1 was upregulated. GSEA demonstrated that DNA-dependent DNA replication and catalytic activity acting on RNA were correlated with CHRDL1 and SPARCL1 expression, respectively. The IHC results suggested that CHRDL1 and SPARCL1 were significantly downregulated in LUAD. Conclusions Our study revealed that survival-related hub genes closely correlated with the initiation and progression of LUAD. Furthermore, CHRDL1 and SPARCL1 are potential therapeutic and prognostic indicators of LUAD.

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“…With the speedy development of genomic technology, researchers have analyzed gene expression profiles using bioinformatics approaches to explore the underlying molecular mechanisms of tumors and detect cancer-specific biomarkers ( 5 ). Weighed Gene Co-expression Network Analysis (WGCNA) is an important algorithm to understand gene co-expression networks and gene functions ( 6 ).…”

Section: Introductionmentioning

confidence: 99%

Low Expression of ADCY4 Predicts Worse Survival of Lung Squamous Cell Carcinoma Based on Integrated Analysis and Immunohistochemical Verification

Liu

2021

Front. Oncol.

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PurposeThe molecular mechanism underlying the carcinogenesis and development of lung squamous cell carcinoma (LUSC) has not been sufficiently elucidated. This analysis was performed to find pivotal genes and explore their prognostic roles in LUSC.MethodsA microarray dataset from GEO (GSE19188) and a TCGA-LUSC dataset were used to identify differentially co-expressed genes through Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis. We conducted functional enrichment analyses of differentially co-expressed genes and established a protein-protein interaction (PPI) network. Then, we identified the top 10 hub genes using the Maximal Clique Centrality (MCC) algorithm. We performed overall survival (OS) analysis of these hub genes among LUSC cases. GSEA analyses of survival-related hub genes were conducted. Ultimately, the GEO and The Human Protein Atlas (THPA) databases and immunohistochemistry (IHC) results from the real world were used to verify our findings.ResultsA list of 576 differentially co-expressed genes were selected. Functional enrichment analysis indicated that regulation of vasculature development, cell−cell junctions, actin binding and PPAR signaling pathways were mainly enriched. The top 10 hub genes were selected according to the ranking of MCC scores, and 5 genes were closely correlated with OS of LUSC. Additionally, GSEA analysis showed that spliceosome and cell adhesion molecules were associated with the expression of GNG11 and ADCY4, respectively. The GSE30219 and THPA databases and IHC results from the real world indicated that although GNG11 was not detected, ADCY4 was obviously downregulated in LUSC tissues at the mRNA and protein levels.ConclusionsThis analysis showed that survival-related hub genes are highly correlated to the tumorigenesis and development of LUSC. Additionally, ADCY4 is a candidate therapeutic and prognostic biomarker of LUSC.

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Computational methods and next-generation sequencing approaches to analyze epigenetics data: Profiling of methods and applications

Cited by 36 publications

References 138 publications

A Review on Recent Progress in Machine Learning and Deep Learning Methods for Cancer Classification on Gene Expression Data

A Review on Recent Progress in Machine Learning and Deep Learning Methods for Cancer Classification on Gene Expression Data

Low expression of CHRDL1 and SPARCL1 predicts poor prognosis of lung adenocarcinoma based on comprehensive analysis and immunohistochemical validation

Low Expression of ADCY4 Predicts Worse Survival of Lung Squamous Cell Carcinoma Based on Integrated Analysis and Immunohistochemical Verification

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