scholarly journals Low ARID1A Expression is Associated with Poor Prognosis in Hepatocellular Carcinoma

Cells ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 2002
Author(s):  
Sun Yim ◽  
Sang Kang ◽  
Ji-Hyun Shin ◽  
Yun Jeong ◽  
Bo Sohn ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Clinical Significance ◽  
Ingenuity Pathway Analysis ◽  
Gene Expression Data ◽  
Pathway Analysis ◽  
Poor Prognosis ◽  
Specific Gene ◽  
Expression Data ◽  
Immune Activity

AT-rich interactive domain 1A (ARID1A) is one of the most frequently mutated genes in hepatocellular carcinoma (HCC), but its clinical significance is not clarified. We aimed to evaluate the clinical significance of low ARID1A expression in HCC. By analyzing the gene expression data of liver from Arid1a-knockout mice, hepatic Arid1a-specific gene expression signature was identified (p < 0.05 and 0.5-fold difference). From this signature, a prediction model was developed to identify tissues lacking Arid1a activity and was applied to gene expression data from three independent cohorts of HCC patients to stratify patients according to ARID1A activity. The molecular features associated with loss of ARID1A were analyzed using The Cancer Genome Atlas (TCGA) multi-platform data, and Ingenuity Pathway Analysis (IPA) was done to uncover potential signaling pathways associated with ARID1A loss. ARID1A inactivation was clinically associated with poor prognosis in all three independent cohorts and was consistently related to poor prognosis subtypes of previously reported gene signatures (highly proliferative, hepatic stem cell, silence of Hippo pathway, and high recurrence signatures). Immune activity, indicated by significantly lower IFNG6 and cytolytic activity scores and enrichment of regulatory T-cell composition, was lower in the ARID1A-low subtype than ARID1A-high subtype. Ingenuity pathway analysis revealed that direct upstream transcription regulators of the ARID1A signature were genes associated with cell cycle, including E2F group, CCND1, and MYC, while tumor suppressors such as TP53, SMAD3, and CTNNB1 were significantly inhibited. ARID1A plays an important role in immune activity and regulating multiple genes involved in HCC development. Low-ARID1A subtype was associated with poor clinical outcome and suggests the possibility of ARID1A as a prognostic biomarker in HCC patients.

scholarly journals HisCoM-PAGE: Hierarchical Structural Component Models for Pathway Analysis of Gene Expression Data

Genes ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 931 ◽  
Author(s):  
Mok ◽  
Kim ◽  
Lee ◽  
Choi ◽  
Lee ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Gene Expression Data ◽  
Pathway Analysis ◽  
Structural Component ◽  
Biological Data ◽  
Gene Set Enrichment Analysis ◽  
Expression Data ◽  
Global Test ◽  
Causal Pathways

Although there have been several analyses for identifying cancer-associated pathways, based on gene expression data, most of these are based on single pathway analyses, and thus do not consider correlations between pathways. In this paper, we propose a hierarchical structural component model for pathway analysis of gene expression data (HisCoM-PAGE), which accounts for the hierarchical structure of genes and pathways, as well as the correlations among pathways. Specifically, HisCoM-PAGE focuses on the survival phenotype and identifies its associated pathways. Moreover, its application to real biological data analysis of pancreatic cancer data demonstrated that HisCoM-PAGE could successfully identify pathways associated with pancreatic cancer prognosis. Simulation studies comparing the performance of HisCoM-PAGE with other competing methods such as Gene Set Enrichment Analysis (GSEA), Global Test, and Wald-type Test showed HisCoM-PAGE to have the highest power to detect causal pathways in most simulation scenarios.

Identification of perturbed signaling pathways from gene expression data using information divergence

2017 ◽  
Vol 13 (9) ◽  
pp. 1797-1804
Author(s):  
Xinying Hu ◽  
Hang Wei ◽  
Haoran Zheng
Keyword(s):  
Gene Expression ◽  
Probability Distribution ◽  
Signaling Pathways ◽  
Gene Expression Data ◽  
Pathway Analysis ◽  
Expression Data ◽  
Analysis Method ◽  
Information Divergence

We propose a pathway analysis method based on information divergence and the probability distribution of the regulation capacity.

scholarly journals Pathway analysis of transcriptomic data shows immunometabolic effects of vitamin D

2018 ◽  
Vol 60 (2) ◽  
pp. 95-108 ◽  
Author(s):  
Amadeo Muñoz Garcia ◽  
Martina Kutmon ◽  
Lars Eijssen ◽  
Martin Hewison ◽  
Chris T Evelo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Vitamin D ◽  
Cell Line ◽  
Gene Expression Data ◽  
Pathway Analysis ◽  
Immune Cell ◽  
Cell Model ◽  
Active Form ◽  
Expression Data ◽  
Regulatory Pathways

Unbiased genomic screening analyses have highlighted novel immunomodulatory properties of the active form of vitamin D, 1,25-dihydroxyvitamin D (1,25(OH)2D). However, clearer interpretation of the resulting gene expression data is limited by cell model specificity. The aim of the current study was to provide a broader perspective on common gene regulatory pathways associated with innate immune responses to 1,25(OH)2D, through systematic re-interrogation of existing gene expression databases from multiple related monocyte models (the THP-1 monocytic cell line (THP-1), monocyte-derived dendritic cells (DCs) and monocytes). Vitamin D receptor (VDR) expression is common to multiple immune cell types, and thus, pathway analysis of gene expression using data from multiple related models provides an inclusive perspective on the immunomodulatory impact of vitamin D. A bioinformatic workflow incorporating pathway analysis using PathVisio and WikiPathways was utilized to compare each set of gene expression data based on pathway-level context. Using this strategy, pathways related to the TCA cycle, oxidative phosphorylation and ATP synthesis and metabolism were shown to be significantly regulated by 1,25(OH)2D in each of the repository models (Z-scores 3.52–8.22). Common regulation by 1,25(OH)2D was also observed for pathways associated with apoptosis and the regulation of apoptosis (Z-scores 2.49–3.81). In contrast to the primary culture DC and monocyte models, the THP-1 myelomonocytic cell line showed strong regulation of pathways associated with cell proliferation and DNA replication (Z-scores 6.1–12.6). In short, data presented here support a fundamental role for active 1,25(OH)2D as a pivotal regulator of immunometabolism.

scholarly journals dynDeepDRIM: a dynamic deep learning model to infer direct regulatory interactions using single cell time-course gene expression data

2021 ◽  
Author(s):  
Yu Xu ◽  
Jiaxing Chen ◽  
Aiping Lyu ◽  
William K Cheung ◽  
Lu Zhang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Data ◽  
Time Course ◽  
Target Genes ◽  
Neighborhood Context ◽  
Specific Gene ◽  
Expression Data ◽  
Regulatory Interactions ◽  
Gene Functions

Time-course single-cell RNA sequencing (scRNA-seq) data have been widely applied to reconstruct the cell-type-specific gene regulatory networks by exploring the dynamic changes of gene expression between transcription factors (TFs) and their target genes. The existing algorithms were commonly designed to analyze bulk gene expression data and could not deal with the dropouts and cell heterogeneity in scRNA-seq data. In this paper, we developed dynDeepDRIM that represents gene pair joint expression as images and considers the neighborhood context to eliminate the transitive interactions. dynDeepDRIM integrated the primary image, neighbor images with time-course into a four-dimensional tensor and trained a convolutional neural network to predict the direct regulatory interactions between TFs and genes. We evaluated the performance of dynDeepDRIM on five time-course gene expression datasets. dynDeepDRIM outperformed the state-of-the-art methods for predicting TF-gene direct interactions and gene functions. We also observed gene functions could be better performed if more neighbor images were involved.

BIOMARKER DISCOVERY AND VISUALIZATION IN GENE EXPRESSION DATA WITH EFFICIENT GENERALIZED MATRIX APPROXIMATIONS

2007 ◽  
Vol 05 (02a) ◽  
pp. 251-279 ◽  
Author(s):  
WENYUAN LI ◽  
YANXIONG PENG ◽  
HUNG-CHUNG HUANG ◽  
YING LIU
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Biomarker Discovery ◽  
Attribute Selection ◽  
Specific Gene ◽  
Expression Data ◽  
Selection Methods ◽  
Data Set ◽  
Generalized Matrix ◽  
Matrix Approximations

In most real-world gene expression data sets, there are often multiple sample classes with ordinals, which are categorized into the normal or diseased type. The traditional feature or attribute selection methods consider multiple classes equally without paying attention to the up/down regulation across the normal and diseased types of classes, while the specific gene selection methods particularly consider the differential expressions across the normal and diseased, but ignore the existence of multiple classes. In this paper, to improve the biomarker discovery, we propose to make the best use of these two aspects: the differential expressions (that can be viewed as the domain knowledge of gene expression data) and the multiple classes (that can be viewed as a kind of data set characteristic). Therefore, we simultaneously take into account these two aspects by employing the 1-rank generalized matrix approximations (GMA). Our results show that GMA cannot only improve the accuracy of classifying the samples, but also provide a visualization method to effectively analyze the gene expression data on both genes and samples. Based on the mechanism of matrix approximation, we further propose an algorithm, CBiomarker, to discover compact biomarker by reducing the redundancy.

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