scholarly journals Genetic links between endometriosis and cancers in women

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8135 ◽  
Author(s):  
Salma Begum Bhyan ◽  
Li Zhao ◽  
YongKiat Wee ◽  
Yining Liu ◽  
Min Zhao
Keyword(s):  
Gene Expression ◽  
Cancer Progression ◽  
Large Scale ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Genetic Relationships ◽  
Meta Analysis ◽  
Gene Expression Profiles ◽  
Expression Of Genes ◽  
Genetic Mechanisms

Endometriosis is a chronic disease occurring during the reproductive stage of women. Although there is only limited association between endometriosis and gynecological cancers with regard to clinical features, the molecular basis of the relationship between these diseases is unexplored. We conducted a systematic study by integrating literature-based evidence, gene expression and large-scale cancer genomics data in order to reveal any genetic relationships between endometriosis and cancers in women. We curated 984 endometriosis-related genes from 3270 PubMed articles and then conducted a meta-analysis of the two public gene expression profiles related to endometriosis which identified Differential Expression of Genes (DEGs). Following an overlapping analysis, we identified 39 key endometriosis-related genes common in both literature and DEG analysis. Finally, the functional analysis confirmed that all the 39 genes were associated with the vital processes of tumour formation and cancer progression and that two genes (PGR and ESR1) were common to four cancers of women. From network analysis, we identified a novel linker gene, C3AR1, which had not been implicated previously in endometriosis. The shared genetic mechanisms of endometriosis and cancers in women identified in this study provided possible new avenues of multiple disease management and treatments through early diagnosis.

scholarly journals Discovering novel driver mutations from pan-cancer analysis of mutational and gene expression profiles

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242780
Author(s):  
Houriiyah Tegally ◽  
Kevin H. Kensler ◽  
Zahra Mungloo-Dilmohamud ◽  
Anisah W. Ghoorah ◽  
Timothy R. Rebbeck ◽  
...  
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Driver Mutations ◽  
Literature Mining ◽  
Cancer Genes ◽  
Multiple Cancer ◽  
Driver Genes

As the genomic profile across cancers varies from person to person, patient prognosis and treatment may differ based on the mutational signature of each tumour. Thus, it is critical to understand genomic drivers of cancer and identify potential mutational commonalities across tumors originating at diverse anatomical sites. Large-scale cancer genomics initiatives, such as TCGA, ICGC and GENIE have enabled the analysis of thousands of tumour genomes. Our goal was to identify new cancer-causing mutations that may be common across tumour sites using mutational and gene expression profiles. Genomic and transcriptomic data from breast, ovarian, and prostate cancers were aggregated and analysed using differential gene expression methods to identify the effect of specific mutations on the expression of multiple genes. Mutated genes associated with the most differentially expressed genes were considered to be novel candidates for driver mutations, and were validated through literature mining, pathway analysis and clinical data investigation. Our driver selection method successfully identified 116 probable novel cancer-causing genes, with 4 discovered in patients having no alterations in any known driver genes: MXRA5, OBSCN, RYR1, and TG. The candidate genes previously not officially classified as cancer-causing showed enrichment in cancer pathways and in cancer diseases. They also matched expectations pertaining to properties of cancer genes, for instance, showing larger gene and protein lengths, and having mutation patterns suggesting oncogenic or tumor suppressor properties. Our approach allows for the identification of novel putative driver genes that are common across cancer sites using an unbiased approach without any a priori knowledge on pathways or gene interactions and is therefore an agnostic approach to the identification of putative common driver genes acting at multiple cancer sites.

scholarly journals Gene Expression Correlates of the Cortical Network Underlying Sentence Processing

2020 ◽  
Vol 1 (1) ◽  
pp. 77-103 ◽  
Author(s):  
Xiang-Zhen Kong ◽  
Nathalie Tzourio-Mazoyer ◽  
Marc Joliot ◽  
Evelina Fedorenko ◽  
Jia Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Sentence Processing ◽  
Neural Development ◽  
Large Scale ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Gene Expression Profiles ◽  
Functional Network ◽  
Integrative Approach ◽  
Processing Network

A pivotal question in modern neuroscience is which genes regulate brain circuits that underlie cognitive functions. However, the field is still in its infancy. Here we report an integrated investigation of the high-level language network (i.e., sentence-processing network) in the human cerebral cortex, combining regional gene expression profiles, task fMRI, large-scale neuroimaging meta-analysis, and resting-state functional network approaches. We revealed reliable gene expression–functional network correlations using three different network definition strategies, and identified a consensus set of genes related to connectivity within the sentence-processing network. The genes involved showed enrichment for neural development and actin-related functions, as well as association signals with autism, which can involve disrupted language functioning. Our findings help elucidate the molecular basis of the brain’s infrastructure for language. The integrative approach described here will be useful for studying other complex cognitive traits.

scholarly journals A simple in silico approach to generate gene-expression profiles from subsets of cancer genomics data

BioTechniques ◽  
2019 ◽  
Vol 67 (4) ◽  
pp. 172-176 ◽  
Author(s):  
Mohammed Khurshed ◽  
Remco J Molenaar ◽  
Cornelis JF van Noorden
Keyword(s):  
Gene Expression ◽  
In Silico ◽  
Large Scale ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Large Datasets ◽  
Web Interface ◽  
Simple Method ◽  
Clinical Interest

In biomedical research, large-scale profiling of gene expression has become routine and offers a valuable means to evaluate changes in onset and progression of diseases, in particular cancer. An overwhelming amount of cancer genomics data has become publicly available, and the complexity of these data makes it a challenge to perform in silico data exploration, integration and analysis, in particular for scientists lacking a background in computational programming or informatics. Many web interface tools make these large datasets accessible but are limited to process large datasets. To accelerate the translation of genomic data into new insights, we provide a simple method to explore and select data from cancer genomic datasets to generate gene-expression profiles of subsets that are of specific genetic, biological or clinical interest.

scholarly journals Gene Expression Correlates of the Cortical Network Underlying Sentence Processing

2018 ◽  
Author(s):  
Xiang-Zhen Kong ◽  
Nathalie Tzourio-Mazoyer ◽  
Marc Joliot ◽  
Evelina Fedorenko ◽  
Jia Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Sentence Processing ◽  
Neural Development ◽  
Large Scale ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Gene Expression Profiles ◽  
Functional Network ◽  
Integrative Approach ◽  
Processing Network

AbstractA pivotal question in modern neuroscience is which genes regulate brain circuits that underlie cognitive functions. However, the field is still in its infancy. Here we report an integrated investigation of the high-level language network (i.e., sentence processing network) in the human cerebral cortex, combining regional gene expression profiles, task fMRI, large-scale neuroimaging meta-analysis, and resting-state functional network approaches. We revealed reliable gene expression-functional network correlations using three different network definition strategies, and identified a consensus set of genes related to connectivity within the sentence-processing network. The genes involved showed enrichment for neural development and actin-related functions, as well as association signals with autism, which can involve disrupted language functioning. Our findings help elucidate the molecular basis of the brain’s infrastructure for language. The integrative approach described here will be useful to study other complex cognitive traits.

scholarly journals Gene Expression and Transcriptome Profiling of Changes in a Cancer Cell Line Post-Exposure to Cadmium Telluride Quantum Dots: Possible Implications in Oncogenesis

Dose-Response ◽  
2021 ◽  
Vol 19 (2) ◽  
pp. 155932582110198
Author(s):  
Mohammed S. Aldughaim ◽  
Mashael R. Al-Anazi ◽  
Marie Fe F. Bohol ◽  
Dilek Colak ◽  
Hani Alothaid ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantum Dots ◽  
Cancer Cells ◽  
Cadmium Telluride ◽  
Cancer Progression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Dependent Manner ◽  
Cdte Qds ◽  
Cadmium Telluride Quantum Dots

Cadmium telluride quantum dots (CdTe-QDs) are acquiring great interest in terms of their applications in biomedical sciences. Despite earlier sporadic studies on possible oncogenic roles and anticancer properties of CdTe-QDs, there is limited information regarding the oncogenic potential of CdTe-QDs in cancer progression. Here, we investigated the oncogenic effects of CdTe-QDs on the gene expression profiles of Chang cancer cells. Chang cancer cells were treated with 2 different doses of CdTe-QDs (10 and 25 μg/ml) at different time intervals (6, 12, and 24 h). Functional annotations helped identify the gene expression profile in terms of its biological process, canonical pathways, and gene interaction networks activated. It was found that the gene expression profiles varied in a time and dose-dependent manner. Validation of transcriptional changes of several genes through quantitative PCR showed that several genes upregulated by CdTe-QD exposure were somewhat linked with oncogenesis. CdTe-QD-triggered functional pathways that appear to associate with gene expression, cell proliferation, migration, adhesion, cell-cycle progression, signal transduction, and metabolism. Overall, CdTe-QD exposure led to changes in the gene expression profiles of the Chang cancer cells, highlighting that this nanoparticle can further drive oncogenesis and cancer progression, a finding that indicates the merit of immediate in vivo investigation.

scholarly journals LncGSEA: a versatile tool to infer lncRNA associated pathways from large-scale cancer transcriptome sequencing data

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yanan Ren ◽  
Ting-You Wang ◽  
Leah C. Anderton ◽  
Qi Cao ◽  
Rendong Yang
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Clinical Samples ◽  
Sequencing Data ◽  
Multiple Cancer ◽  
Regulatory Pathways ◽  
Cancer Transcriptome ◽  
Versatile Tool

Abstract Background Long non-coding RNAs (lncRNAs) are a growing focus in cancer research. Deciphering pathways influenced by lncRNAs is important to understand their role in cancer. Although knock-down or overexpression of lncRNAs followed by gene expression profiling in cancer cell lines are established approaches to address this problem, these experimental data are not available for a majority of the annotated lncRNAs. Results As a surrogate, we present lncGSEA, a convenient tool to predict the lncRNA associated pathways through Gene Set Enrichment Analysis of gene expression profiles from large-scale cancer patient samples. We demonstrate that lncGSEA is able to recapitulate lncRNA associated pathways supported by literature and experimental validations in multiple cancer types. Conclusions LncGSEA allows researchers to infer lncRNA regulatory pathways directly from clinical samples in oncology. LncGSEA is written in R, and is freely accessible at https://github.com/ylab-hi/lncGSEA.

Gene Expression Profiles as Predictors of Poor Outcomes in Stage II Colorectal Cancer: A Systematic Review and Meta-analysis

2009 ◽  
Vol 8 (4) ◽  
pp. 207-214 ◽  
Author(s):  
An-Ting T. Lu ◽  
Shelley R. Salpeter ◽  
Anthony E. Reeve ◽  
Steven Eschrich ◽  
Patrick G. Johnston ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Systematic Review ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Gene Expression Profiles ◽  
Stage Ii ◽  
Stage Ii Colorectal Cancer ◽  
Poor Outcomes

scholarly journals Analysis of blood-based gene expression in idiopathic Parkinson disease

Neurology ◽  
2017 ◽  
Vol 89 (16) ◽  
pp. 1676-1683 ◽  
Author(s):  
Ron Shamir ◽  
Christine Klein ◽  
David Amar ◽  
Eva-Juliane Vollstedt ◽  
Michael Bonin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Parkinson Disease ◽  
Gene Networks ◽  
Large Scale ◽  
Expression Profiles ◽  
Area Under The Curve ◽  
Gene Expression Profiles ◽  
Gene Signature ◽  
Gene Profiles ◽  
Independent Test

Objective:To examine whether gene expression analysis of a large-scale Parkinson disease (PD) patient cohort produces a robust blood-based PD gene signature compared to previous studies that have used relatively small cohorts (≤220 samples).Methods:Whole-blood gene expression profiles were collected from a total of 523 individuals. After preprocessing, the data contained 486 gene profiles (n = 205 PD, n = 233 controls, n = 48 other neurodegenerative diseases) that were partitioned into training, validation, and independent test cohorts to identify and validate a gene signature. Batch-effect reduction and cross-validation were performed to ensure signature reliability. Finally, functional and pathway enrichment analyses were applied to the signature to identify PD-associated gene networks.Results:A gene signature of 100 probes that mapped to 87 genes, corresponding to 64 upregulated and 23 downregulated genes differentiating between patients with idiopathic PD and controls, was identified with the training cohort and successfully replicated in both an independent validation cohort (area under the curve [AUC] = 0.79, p = 7.13E–6) and a subsequent independent test cohort (AUC = 0.74, p = 4.2E–4). Network analysis of the signature revealed gene enrichment in pathways, including metabolism, oxidation, and ubiquitination/proteasomal activity, and misregulation of mitochondria-localized genes, including downregulation of COX4I1, ATP5A1, and VDAC3.Conclusions:We present a large-scale study of PD gene expression profiling. This work identifies a reliable blood-based PD signature and highlights the importance of large-scale patient cohorts in developing potential PD biomarkers.

scholarly journals Discovering Distinct Patterns in Gene Expression Profiles

2008 ◽  
Vol 5 (2) ◽  
Author(s):  
Li Teng ◽  
Laiwan Chan
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Clustering Methods ◽  
Gene Expressions ◽  
Real Gene ◽  
Large Scale Dataset ◽  
Coexpressed Genes

SummaryTraditional analysis of gene expression profiles use clustering to find groups of coexpressed genes which have similar expression patterns. However clustering is time consuming and could be diffcult for very large scale dataset. We proposed the idea of Discovering Distinct Patterns (DDP) in gene expression profiles. Since patterns showing by the gene expressions reveal their regulate mechanisms. It is significant to find all different patterns existing in the dataset when there is little prior knowledge. It is also a helpful start before taking on further analysis. We propose an algorithm for DDP by iteratively picking out pairs of gene expression patterns which have the largest dissimilarities. This method can also be used as preprocessing to initialize centers for clustering methods, like K-means. Experiments on both synthetic dataset and real gene expression datasets show our method is very effective in finding distinct patterns which have gene functional significance and is also effcient.

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