The exploration of disease-specific gene regulatory networks in esophageal carcinoma and stomach adenocarcinoma

Qin, Guimin; Yang, Luqiong; Ma, Yan; Liu, Jiayan; Huo, Qiuyan

doi:10.1186/s12859-019-3230-6

Cited by 20 publications

(11 citation statements)

References 49 publications

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“…Gene regulatory networks (GRNs) are key drivers of embryogenesis, and their importance for guiding cell behavior and physiology persists through all stages of life ( Alvarez-Buylla et al., 2008 ; Huang et al., 2005 ). Understanding the dynamics of GRNs is of high priority not only for the study of developmental biology ( Davidson, 2010 ; Peter and Davidson, 2011 ) but also for the prediction and management of numerous disease states ( Fazilaty et al., 2019 ; Qin et al., 2019 ; Singh et al., 2018 ). Much work has gone into the computational inference of GRN models ( De Jong, 2002 ; Delgado and Gómez-Vela, 2019 ), and the development of algorithms for predicting their dynamics over time ( Schlitt and Brazma, 2007 ).…”

Section: Introductionmentioning

confidence: 99%

Gene regulatory networks exhibit several kinds of memory: Quantification of memory in biological and random transcriptional networks

et al. 2021

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Section: Introductionmentioning

confidence: 99%

Gene regulatory networks exhibit several kinds of memory: Quantification of memory in biological and random transcriptional networks

et al. 2021

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“…Considering the achieved results on the identification of the clusters, scAEspy can be used at the basis of methods that aim at automatically identifying the cell-types composing the scRNA-Seq datasets under analysis [ 68 ]. As a matter of fact, scAEspy coupled with BBKNN was successfully applied to integrate 15 different foetal human samples, enabling the identification of rare blood progenitor cells [ 69 ].…”

Section: Discussionmentioning

confidence: 99%

Analysis of single-cell RNA sequencing data based on autoencoders

et al. 2021

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Background Single-cell RNA sequencing (scRNA-Seq) experiments are gaining ground to study the molecular processes that drive normal development as well as the onset of different pathologies. Finding an effective and efficient low-dimensional representation of the data is one of the most important steps in the downstream analysis of scRNA-Seq data, as it could provide a better identification of known or putatively novel cell-types. Another step that still poses a challenge is the integration of different scRNA-Seq datasets. Though standard computational pipelines to gain knowledge from scRNA-Seq data exist, a further improvement could be achieved by means of machine learning approaches. Results Autoencoders (AEs) have been effectively used to capture the non-linearities among gene interactions of scRNA-Seq data, so that the deployment of AE-based tools might represent the way forward in this context. We introduce here scAEspy, a unifying tool that embodies: (1) four of the most advanced AEs, (2) two novel AEs that we developed on purpose, (3) different loss functions. We show that scAEspy can be coupled with various batch-effect removal tools to integrate data by different scRNA-Seq platforms, in order to better identify the cell-types. We benchmarked scAEspy against the most used batch-effect removal tools, showing that our AE-based strategies outperform the existing solutions. Conclusions scAEspy is a user-friendly tool that enables using the most recent and promising AEs to analyse scRNA-Seq data by only setting up two user-defined parameters. Thanks to its modularity, scAEspy can be easily extended to accommodate new AEs to further improve the downstream analysis of scRNA-Seq data. Considering the relevant results we achieved, scAEspy can be considered as a starting point to build a more comprehensive toolkit designed to integrate multi single-cell omics.

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“…Gene Regulatory Network database (GRNdb) (Fang et al, 2020) is a gene regulatory network database, which includes a large number of human and mouse transcription factor and target gene pairs. We download the TF-target gene pairs from the GRNdb, and filter out the pairs in which the target genes are differentially expressed genes and hypermethylated/hypomethylated genes (Qin et al, 2019). Then we calculate the Pearson Correlation Coefficient (PCC) for each TF-target gene pair based on their expression level, and the cut-off is set as 0.5 and construct stage-specific gene regulatory networks.…”

Section: Stage-specific Gene Regulatory Network Constructionmentioning

confidence: 99%

Prediction of Disease Genes Based on Stage-Specific Gene Regulatory Networks in Breast Cancer

Fan¹,

Hou²,

Qin³

2021

Front. Genet.

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Breast cancer is one of the most common malignant tumors in women, which seriously endangers women’s health. Great advances have been made over the last decades, however, most studies predict driver genes of breast cancer using biological experiments and/or computational methods, regardless of stage information. In this study, we propose a computational framework to predict the disease genes of breast cancer based on stage-specific gene regulatory networks. Firstly, we screen out differentially expressed genes and hypomethylated/hypermethylated genes by comparing tumor samples with corresponding normal samples. Secondly, we construct three stage-specific gene regulatory networks by integrating RNA-seq profiles and TF-target pairs, and apply WGCNA to detect modules from these networks. Subsequently, we perform network topological analysis and gene set enrichment analysis. Finally, the key genes of specific modules for each stage are screened as candidate disease genes. We obtain seven stage-specific modules, and identify 20, 12, and 22 key genes for three stages, respectively. Furthermore, 55%, 83%, and 64% of the genes are associated with breast cancer, for example E2F2, E2F8, TPX2, BUB1, and CKAP2L. So it may be of great importance for further verification by cancer experts.

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The exploration of disease-specific gene regulatory networks in esophageal carcinoma and stomach adenocarcinoma

Cited by 20 publications

References 49 publications

Gene regulatory networks exhibit several kinds of memory: Quantification of memory in biological and random transcriptional networks

Gene regulatory networks exhibit several kinds of memory: Quantification of memory in biological and random transcriptional networks

Analysis of single-cell RNA sequencing data based on autoencoders

Prediction of Disease Genes Based on Stage-Specific Gene Regulatory Networks in Breast Cancer

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