A systematic survey of HOX and TALE expression profiling in human cancers

HOX and TALE genes encode homeodomain (HD)-containing transcription factors that act in concert in different tissues to coordinate cell fates and morphogenesis throughout embryonic development. These two evolutionary conserved families contain several members that form different types of protein complexes on DNA. Mutations affecting the expression of HOX or TALE genes have been reported in a number of cancers, but whether and how the two gene families could be perturbed together has never been explored systematically. As a consequence, the putative collaborative role between HOX and TALE members for promoting or inhibiting oncogenesis remains to be established in most cancer contexts. Here, we address this issue by considering HOX and TALE expression profiling in normal and cancer adult tissues, using normalized RNA-sequencing expression data deriving from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) research projects. Information was extracted from 28 cancer types originating from 21 different tissues, constituting a unique comparative analysis of HOX and TALE expression profiles between normal and cancer contexts in human. We present the general and specific rules that could be deduced from this large-scale comparative analysis. Overall this work provides a precious annotated support to better understand the role of specific HOX/TALE combinatorial codes in human cancers.

Download Full-text

Development and Validation of a Prognostic Nomogram Based on DNA Methylation-Driven Genes for Patients With Ovarian Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.675197 ◽

2021 ◽

Vol 12 ◽

Author(s):

Min Zhou ◽

Shasha Hong ◽

Bingshu Li ◽

Cheng Liu ◽

Ming Hu ◽

...

Keyword(s):

Ovarian Cancer ◽

Dna Methylation ◽

Mrna Expression ◽

Risk Score ◽

Cox Regression ◽

Expression Profiles ◽

Figo Stage ◽

Prognostic Indicator ◽

Tissue Expression ◽

The Cancer Genome Atlas

Background: DNA methylation affects the development, progression, and prognosis of various cancers. This study aimed to identify DNA methylated-differentially expressed genes (DEGs) and develop a methylation-driven gene model to evaluate the prognosis of ovarian cancer (OC).Methods: DNA methylation and mRNA expression profiles of OC patients were downloaded from The Cancer Genome Atlas, Genotype-Tissue Expression, and Gene Expression Omnibus databases. We used the R package MethylMix to identify DNA methylation-regulated DEGs and built a prognostic signature using LASSO Cox regression. A quantitative nomogram was then drawn based on the risk score and clinicopathological features.Results: We identified 56 methylation-related DEGs and constructed a prognostic risk signature with four genes according to the LASSO Cox regression algorithm. A higher risk score not only predicted poor prognosis, but also was an independent poor prognostic indicator, which was validated by receiver operating characteristic (ROC) curves and the validation cohort. A nomogram consisting of the risk score, age, FIGO stage, and tumor status was generated to predict 3- and 5-year overall survival (OS) in the training cohort. The joint survival analysis of DNA methylation and mRNA expression demonstrated that the two genes may serve as independent prognostic biomarkers for OS in OC.Conclusion: The established qualitative risk score model was found to be robust for evaluating individualized prognosis of OC and in guiding therapy.

Download Full-text

Comprehensive In Silico Characterization and Expression Profiling of Nine Gene Families Associated with Calcium Transport in Soybean

Agronomy ◽

10.3390/agronomy10101539 ◽

2020 ◽

Vol 10 (10) ◽

pp. 1539 ◽

Cited By ~ 1

Author(s):

Houqing Zeng ◽

Bingqian Zhao ◽

Haicheng Wu ◽

Yiyong Zhu ◽

Huatao Chen

Keyword(s):

Stress Responses ◽

Expression Profiling ◽

In Silico ◽

Growth Regulation ◽

Expression Profiles ◽

Phosphorus Deficiency ◽

Critical Role ◽

Functional Characterization ◽

Gene Families ◽

Transport Proteins

Calcium (Ca2+) plays a critical role in the regulation of growth and development and environmental stress responses in plants. The membrane-associated Ca2+ transport proteins are required to mediate Ca2+ signaling and maintain Ca2+ homeostasis. Ca2+ channels, pumps (ATPases), and antiporters are three major classes of Ca2+ transporters. Although the genome-wide analysis of Ca2+ transporters in model plants Arabidopsis and rice have been well documented, the identification, classification, phylogenesis, expression profiles, and physiological functions of Ca2+ transport proteins in soybean are largely unknown. In this study, a comprehensive in silico analysis of gene families associated with Ca2+ transport was conducted, and a total of 207 putative Ca2+ transporter genes have been identified in soybean. These genes belong to nine different families, such as Ca2+-ATPase, Ca2+/cation antiporter, cyclic nucleotide-gated ion channel (CNGC), and hyperosmolality induced cytosolic Ca2+ concentration channel (OSCA). Detailed analysis of these identified genes was performed, including their classification, phylogenesis, protein domains, chromosomal distribution, and gene duplication. Expression profiling of these genes was conducted in different tissues and developmental stages, as well as under stresses using publicly available RNA-seq data. Some genes were found to be predominantly expressed in specific tissues like flowers and nodules, and some genes were found to be expressed strongly during seed development. Seventy-four genes were found to be significantly and differentially expressed under abiotic and biotic stresses, such as salt, phosphorus deficiency, and fungal pathogen inoculation. In addition, hormonal signaling- and stress response-related cis-elements and potential microRNA target sites were analyzed. This study suggests the potential roles of soybean Ca2+ transporters in stress responses and growth regulation, and provides a basis for further functional characterization of putative Ca2+ transporters in soybean.

Download Full-text

GEDS: A Gene Expression Display Server for mRNAs, miRNAs and Proteins

Cells ◽

10.3390/cells8070675 ◽

2019 ◽

Vol 8 (7) ◽

pp. 675 ◽

Cited By ~ 5

Author(s):

Xia ◽

Liu ◽

Zhang ◽

Guo

Keyword(s):

Gene Expression ◽

Cell Lines ◽

Gene Expression Data ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Cancer Cell Line ◽

Tissue Expression ◽

The Cancer Genome Atlas ◽

Expression Data ◽

Protein Levels

High-throughput technologies generate a tremendous amount of expression data on mRNA, miRNA and protein levels. Mining and visualizing the large amount of expression data requires sophisticated computational skills. An easy to use and user-friendly web-server for the visualization of gene expression profiles could greatly facilitate data exploration and hypothesis generation for biologists. Here, we curated and normalized the gene expression data on mRNA, miRNA and protein levels in 23315, 9009 and 9244 samples, respectively, from 40 tissues (The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GETx)) and 1594 cell lines (Cancer Cell Line Encyclopedia (CCLE) and MD Anderson Cell Lines Project (MCLP)). Then, we constructed the Gene Expression Display Server (GEDS), a web-based tool for quantification, comparison and visualization of gene expression data. GEDS integrates multiscale expression data and provides multiple types of figures and tables to satisfy several kinds of user requirements. The comprehensive expression profiles plotted in the one-stop GEDS platform greatly facilitate experimental biologists utilizing big data for better experimental design and analysis. GEDS is freely available on http://bioinfo.life.hust.edu.cn/web/GEDS/.

Download Full-text

Unraveling patterns of disrupted gene expression across a complex tissue

10.1101/2021.07.08.451646 ◽

2021 ◽

Author(s):

Kelsie E Hunnicutt ◽

Jeffrey M Good ◽

Erica L Larson

Keyword(s):

Gene Expression ◽

Evolutionary Biology ◽

Large Scale ◽

Expression Profiles ◽

Cell Types ◽

Tissue Expression ◽

Network Evolution ◽

F1 Hybrids ◽

Cellular Composition ◽

Mus Musculus Musculus

Whole tissue RNASeq is the standard approach for studying gene expression divergence in evolutionary biology and provides a snapshot of the comprehensive transcriptome for a given tissue. However, whole tissues consist of diverse cell types differing in expression profiles, and the cellular composition of these tissues can evolve across species. Here, we investigate the effects of different cellular composition on whole tissue expression profiles. We compared gene expression from whole testes and enriched spermatogenesis populations in two species of house mice, Mus musculus musculus and M. m. domesticus, and their sterile and fertile F1 hybrids, which differ in both cellular composition and regulatory dynamics. We found that cellular composition differences skewed expression profiles and differential gene expression in whole testes samples. Importantly, both approaches were able to detect large-scale patterns such as disrupted X chromosome expression although whole testes sampling resulted in decreased power to detect differentially expressed genes. We encourage researchers to account for histology in RNASeq and consider methods that reduce sample complexity whenever feasible. Ultimately, we show that differences in cellular composition between tissues can modify expression profiles, potentially altering inferred gene ontological processes, insights into gene network evolution, and processes governing gene expression evolution.

Download Full-text

Identification of five Hub Genes Associated with Development and Prognosis of Lung Adenocarcinoma by Co-Expression Analysis

Current Bioinformatics ◽

10.2174/1574893616666210624141520 ◽

2021 ◽

Vol 16 ◽

Author(s):

Sangsang Chen ◽

Xuqing Zhu ◽

Jing Zheng ◽

Tingting Xu ◽

Yinmin Xu ◽

...

Keyword(s):

Lung Cancer ◽

Lung Adenocarcinoma ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Interaction Network ◽

Tissue Expression ◽

The Cancer Genome Atlas ◽

Hub Genes ◽

Network Analyses ◽

Study Gene Expression

Objective: Non-small cell lung cancer (NSCLC) is one of the most common types of lung cancer, while lung adenocarcinoma (LUAD) is the most common subtype of NSCLC. In this study, we aimed to identify potential markers that are associated with the prognosis and development of LUAD. Methods and results: In this study, gene expression profiles from 594 LUAD samples were downloaded from The Cancer Genome Atlas (TCGA) database, and 2,503 differentially expressed genes (DEGs) were obtained. Secondly, weighted gene co-expression network analysis (WGCNA) was used to construct a co-expression network for these DEGs, and 16 modules were obtained. Among these, the genes related to the most significant module (turquoise) were found to be closely associated with the stage of LUAD. Then, functional annotation revealed that the genes in the turquoise module were mainly enriched in the pathways involved in the cell cycle and meiosis. Seven candidate hub genes were further screened by using WGCNA and protein-protein interaction network analyses. Expression data of the 7 candidate hub genes in different pathological stages in TCGA-LUAD were taken as the training set, while those in the GSE41271 dataset were used as the validation set. As a result, 5 hub genes (KIF11, KIF23, KIF4A, NUSPA1, RRM2) closely related to the pathological stage of LUAD were screened. Finally, survival and tissue expression analyses were performed on the five hub genes. The results suggested that the five hub genes were closely related to the occurrence and prognosis of LUAD. Conclusion: The study identified five hub genes that could be used as important predictors for the prognosis and development of LUAD.

Download Full-text

Mining influential genes based on deep learning

BMC Bioinformatics ◽

10.1186/s12859-021-03972-5 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Lingpeng Kong ◽

Yuanyuan Chen ◽

Fengjiao Xu ◽

Mingmin Xu ◽

Zutan Li ◽

...

Keyword(s):

Gene Expression ◽

Deep Learning ◽

Expression Profiling ◽

Large Scale ◽

Target Genes ◽

Expression Profiles ◽

Pearson Correlation ◽

Genomic Information ◽

Deep Mine ◽

Functional Connections

Abstract Background Currently, large-scale gene expression profiling has been successfully applied to the discovery of functional connections among diseases, genetic perturbation, and drug action. To address the cost of an ever-expanding gene expression profile, a new, low-cost, high-throughput reduced representation expression profiling method called L1000 was proposed, with which one million profiles were produced. Although a set of ~ 1000 carefully chosen landmark genes that can capture ~ 80% of information from the whole genome has been identified for use in L1000, the robustness of using these landmark genes to infer target genes is not satisfactory. Therefore, more efficient computational methods are still needed to deep mine the influential genes in the genome. Results Here, we propose a computational framework based on deep learning to mine a subset of genes that can cover more genomic information. Specifically, an AutoEncoder framework is first constructed to learn the non-linear relationship between genes, and then DeepLIFT is applied to calculate gene importance scores. Using this data-driven approach, we have re-obtained a landmark gene set. The result shows that our landmark genes can predict target genes more accurately and robustly than that of L1000 based on two metrics [mean absolute error (MAE) and Pearson correlation coefficient (PCC)]. This reveals that the landmark genes detected by our method contain more genomic information. Conclusions We believe that our proposed framework is very suitable for the analysis of biological big data to reveal the mysteries of life. Furthermore, the landmark genes inferred from this study can be used for the explosive amplification of gene expression profiles to facilitate research into functional connections.

Download Full-text

lncRNA and mRNA signature for prognosis prediction of glioblastoma

Future Oncology ◽

10.2217/fon-2019-0538 ◽

2020 ◽

Vol 16 (13) ◽

pp. 837-848 ◽

Cited By ~ 2

Author(s):

Guohong Liu ◽

Yunbao Pan ◽

Yueying Li ◽

Haibo Xu

Keyword(s):

Cox Regression ◽

Expression Profiles ◽

Tissue Expression ◽

The Cancer Genome Atlas ◽

Normal Brain ◽

Multiple Gene ◽

Cox Regression Analysis ◽

R Language ◽

Cancer Genome Atlas ◽

Novel Biomarkers

Aims: We aimed to find out potential novel biomarkers for prognosis of glioblastoma (GBM). Materials & methods: We downloaded mRNA and lncRNA expression profiles of 169 GBM and five normal samples from The Cancer Genome Atlas and 129 normal brain samples from genotype-tissue expression. We use R language to perform the following analyses: differential RNA expression analysis of GBM samples using ‘edgeR’ package, survival analysis taking count of single or multiple gene expression level using ‘survival’ package, univariate and multivariate Cox regression analysis using Cox function plugged in ‘survival’ package. Gene ontology and Kyoto encyclopedia of genes and genomes pathway analysis were performed using FunRich tool online. Results and conclusion: We obtained differentially DEmRNAs and DElncRNAs in GBM samples. Most prognostically relevant mRNAs and lncRNAs were filtered out. ‘GPCR ligand binding’ and ‘Class A/1’ are found to be of great significance. In short, our study provides novel biomarkers for prognosis of GBM.

Download Full-text

The Rhododendron Plant Genome Database (RPGD): a comprehensive online omics database for Rhododendron

BMC Genomics ◽

10.1186/s12864-021-07704-0 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Ningyawen Liu ◽

Lu Zhang ◽

Yanli Zhou ◽

Mengling Tu ◽

Zhenzhen Wu ◽

...

Keyword(s):

Large Scale ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Gene Families ◽

Plant Genome ◽

Omics Data ◽

Flanking Sequence ◽

Genome Database ◽

Functional Annotations ◽

Medicinal Value

Abstract Background The genus Rhododendron L. has been widely cultivated for hundreds of years around the world. Members of this genus are known for great ornamental and medicinal value. Owing to advances in sequencing technology, genomes and transcriptomes of members of the Rhododendron genus have been sequenced and published by various laboratories. With increasing amounts of omics data available, a centralized platform is necessary for effective storage, analysis, and integration of these large-scale datasets to ensure consistency, independence, and maintainability. Results Here, we report our development of the Rhododendron Plant Genome Database (RPGD; http://bioinfor.kib.ac.cn/RPGD/), which represents the first comprehensive database of Rhododendron genomics information. It includes large amounts of omics data, including genome sequence assemblies for R. delavayi, R. williamsianum, and R. simsii, gene expression profiles derived from public RNA-Seq data, functional annotations, gene families, transcription factor identification, gene homology, simple sequence repeats, and chloroplast genome. Additionally, many useful tools, including BLAST, JBrowse, Orthologous Groups, Genome Synteny Browser, Flanking Sequence Finder, Expression Heatmap, and Batch Download were integrated into the platform. Conclusions RPGD is designed to be a comprehensive and helpful platform for all Rhododendron researchers. Believe that RPGD will be an indispensable hub for Rhododendron studies.

Download Full-text

Comparative transcriptomic analysis reveals conserved transcriptional programs underpinning organogenesis and reproduction in land plants

10.1101/2020.10.29.361501 ◽

2020 ◽

Author(s):

Irene Julca ◽

Camilla Ferrari ◽

María Flores-Tornero ◽

Sebastian Proost ◽

Ann-Cathrin Lindner ◽

...

Keyword(s):

Phylogenetic Trees ◽

Large Scale ◽

Expression Profiles ◽

Gene Families ◽

Male Reproduction ◽

Land Plants ◽

Easy Access ◽

Specific Gene ◽

Male Gametes ◽

Organ Specific

AbstractThe evolution of plant organs, including leaves, stems, roots, and flowers, mediated the explosive radiation of land plants, which shaped the biosphere and allowed the establishment of terrestrial animal life. Furthermore, the fertilization products of angiosperms, seeds serve as the basis for most of our food. The evolution of organs and immobile gametes required the coordinated acquisition of novel gene functions, the co-option of existing genes, and the development of novel regulatory programs. However, our knowledge of these events is limited, as no large-scale analyses of genomic and transcriptomic data have been performed for land plants. To remedy this, we have generated gene expression atlases for various organs and gametes of 10 plant species comprising bryophytes, vascular plants, gymnosperms, and flowering plants. Comparative analysis of the atlases identified hundreds of organ- and gamete-specific gene families and revealed that most of the specific transcriptomes are significantly conserved. Interestingly, the appearance of organ-specific gene families does not coincide with the corresponding organ’s appearance, suggesting that co-option of existing genes is the main mechanism for evolving new organs. In contrast to female gametes, male gametes showed a high number and conservation of specific genes, suggesting that male reproduction is highly specialized. The expression atlas capturing pollen development revealed numerous transcription factors and kinases essential for pollen biogenesis and function. To provide easy access to the expression atlases and these comparative analyses, we provide an online database, www.evorepro.plant.tools, that allows the exploration of expression profiles, organ-specific genes, phylogenetic trees, co-expression networks, and others.

Download Full-text

Differential expression of COVID-19-related genes in European Americans and African Americans

10.1101/2020.06.09.143271 ◽

2020 ◽

Cited By ~ 1

Author(s):

Urminder Singh ◽

Eve Syrkin Wurtele

Keyword(s):

Gene Expression ◽

African Americans ◽

Large Scale ◽

Tissue Expression ◽

The Cancer Genome Atlas ◽

Postal Code ◽

Socioeconomic Condition ◽

European Americans ◽

Expression Of Genes ◽

Sequencing Studies

ABSTRACTThe Coronavirus disease 2019 (COVID-19) pandemic has affected African American populations disproportionately in regards to both morbidity and mortality. A multitude of factors likely account for this discrepancy. Gene expression represents the interaction of genetics and environment. To elucidate whether levels of expression of genes implicated in COVID-19 vary in African Americans as compared to European Americans, we re-mine The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) RNA-Seq data. Multiple genes integral to infection, inflammation and immunity are differentially regulated across the two populations. Most notably, F8A2 and F8A3, which encode the HAP40 protein that mediates early endosome movement in Huntington’s Disease, are more highly expressed by up to 24-fold in African Americans. Such differences in gene expression can establish prognostic signatures and have critical implications for precision treatment of diseases such as COVID-19. We advocate routine inclusion of information such as postal code, education level, and profession (as a proxies for socioeconomic condition) and race in the metadata about each individual sampled for sequencing studies. This relatively simple change would enable large-scale data-driven approaches to dissect relationships among race, socio-economic factors, and disease.

Download Full-text