Identification of Overexpressed Genes in Malignant Pleural Mesothelioma

Malignant pleural mesothelioma (MPM) is a fatal tumor lacking effective therapies. The characterization of overexpressed genes could constitute a strategy for identifying drivers of tumor progression as targets for novel therapies. Thus, we performed an integrated gene-expression analysis on RNAseq data of 85 MPM patients from TCGA dataset and reference samples from the GEO. The gene list was further refined by using published studies, a functional enrichment analysis, and the correlation between expression and patients’ overall survival. Three molecular signatures defined by 15 genes were detected. Seven genes were involved in cell adhesion and extracellular matrix organization, with the others in control of the mitotic cell division or apoptosis inhibition. Using Western blot analyses, we found that ADAMTS1, PODXL, CIT, KIF23, MAD2L1, TNNT1, and TRAF2 were overexpressed in a limited number of cell lines. On the other hand, interestingly, CTHRC1, E-selectin, SPARC, UHRF1, PRSS23, BAG2, and MDK were abundantly expressed in over 50% of the six MPM cell lines analyzed. Thus, these proteins are candidates as drivers for sustaining the tumorigenic process. More studies with small-molecule inhibitors or silencing RNAs are fully justified and need to be undertaken to better evaluate the cancer-driving role of the targets herewith identified.

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gprofiler2 -- an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler

F1000Research ◽

10.12688/f1000research.24956.2 ◽

2020 ◽

Vol 9 ◽

pp. 709 ◽

Cited By ~ 1

Author(s):

Liis Kolberg ◽

Uku Raudvere ◽

Ivan Kuzmin ◽

Jaak Vilo ◽

Hedi Peterson

Keyword(s):

Gene List ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Automated Analysis ◽

R Package ◽

Biological Data ◽

Functional Enrichment ◽

Link Type ◽

Functional Profiling ◽

Rest Api

g:Profiler (https://biit.cs.ut.ee/gprofiler) is a widely used gene list functional profiling and namespace conversion toolset that has been contributing to reproducible biological data analysis already since 2007. Here we introduce the accompanying R package, gprofiler2, developed to facilitate programmatic access to g:Profiler computations and databases via REST API. The gprofiler2 package provides an easy-to-use functionality that enables researchers to incorporate functional enrichment analysis into automated analysis pipelines written in R. The package also implements interactive visualisation methods to help to interpret the enrichment results and to illustrate them for publications. In addition, gprofiler2 gives access to the versatile gene/protein identifier conversion functionality in g:Profiler enabling to map between hundreds of different identifier types or orthologous species. The gprofiler2 package is freely available at the CRAN repository.

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Identification of Latent Diagnostic Biomarkers and Biological Pathways in Dermatomyositis Based on WGCNA

Journal of Oncology ◽

10.1155/2021/1920111 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Shaxi Ouyang ◽

Yifang Liu ◽

Changjuan Xiao ◽

Qinghua Zeng ◽

Xun Luo ◽

...

Keyword(s):

Cell Lines ◽

Differentially Expressed Gene ◽

Interaction Network ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Receptor Interaction ◽

Interferon Α ◽

Pathway Enrichment Analysis ◽

Oligoadenylate Synthetase

Introduction. Dermatomyositis (DM) is a chronic autoimmune disease of predominantly lymphocytic infiltration mainly involving the transverse muscle. Its pathogenesis is remaining unknown. This research is designed to probe the latent pathogenesis of dermatomyositis, identify potential biomarkers, and reveal the pathogenesis of dermatomyositis through information biology analysis of gene chips. Methods. In this study, we utilised the GSE14287 and GSE11971 datasets rooted in the Gene Expression Omnibus (GEO) databank, which included a total of 62 DM samples and 9 normal samples. The datasets were combined, and the differentially expressed gene sets were subjected to weighted gene coexpression network analysis, and the hub gene was screened using a protein interaction network from genes in modules highly correlated with dermatomyositis progression. Results. A total of 3 key genes—myxovirus resistance-2 (MX2), oligoadenylate synthetase 1 (OAS1), and oligoadenylate synthetase 2 (OAS2)—were identified in combination with cell line samples, and the expressions of the 3 genes were verified separately. The results showed that MX2, OAS1, and OAS2 were highly expressed in LPS-treated cell lines compared to normal cell lines. The results of pathway enrichment analysis of the genes indicated that all 3 genes were enriched in the cytosolic DNA signalling and cytokine and cytokine receptor interaction signalling pathways; the results of functional enrichment analysis showed that all 3 were enriched in interferon-α response and interferon-γ response functions. Conclusions. This is important for the study of the pathogenesis and objective treatment of dermatomyositis and provides important reference information for the targeted therapy of dermatomyositis.

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A Novel Metastasis-related Genes Based Signature for Predicting the Progression-free Interval of Patients With Papillary Thyroid Carcinoma

10.21203/rs.3.rs-1221000/v1 ◽

2022 ◽

Author(s):

Rui Liu ◽

Zhen Cao ◽

Meng-wei Wu ◽

Xiao-bin Li ◽

Hong-wei Yuan ◽

...

Keyword(s):

Papillary Thyroid Carcinoma ◽

Thyroid Carcinoma ◽

Cell Lines ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Bioinformatic Analysis ◽

Functional Enrichment ◽

Clinical Parameters ◽

Papillary Thyroid ◽

Free Interval

Abstract Background: We aimed to build a novel model with metastasis-related genes (MTGs) signature and relevant clinical parameters for predicting progression-free interval (PFI) after surgery for papillary thyroid carcinoma (PTC).Methods: We performed a bioinformatic analysis of integrated PTC datasets with the MTGs to identify differentially expressed MTGs (DE-MTGs). Then we generated PFI-related DE-MTGs and established a novel MTGs based signature. After that, we validated the signature on multiple datasets and PTC cell lines. Further, we carried out uni- and multivariate analysis to identify independent prognostic characters. Finally, we established a signature and clinical parameters-based nomogram for predicting the PFI of PTC. Results: We identified 155 DE-MTGs related to PFI in PTC. The functional enrichment analysis showed that the DE-MTGs were associated with an essential oncogenic process. Consequently, we found a novel 10-gene signature and could distinguish patients with poorer prognoses and predicted PFI accurately. The novel signature had a C-index of 0.76 and the relevant nomogram had a C-index of 0.80. Also, it was closely related to pivotal clinical characters of datasets and invasiveness of cell lines. And the signature was confirmed a significant independent prognostic factor in PTC. Finally, we built a nomogram by including the signature and relevant clinical factors. Validation analysis showed that the nomogram's efficacy was satisfying in predicting PTC’s PFI. Conclusions: The MTG signature and nomogram were closely associated with PTC prognosis and may help clinicians improve the individualized prediction of PFI, especially for high-risk patients after surgery.

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Toward comprehensive functional analysis of gene lists weighted by gene essentiality scores

10.1101/2021.04.26.441450 ◽

2021 ◽

Author(s):

Rui Fan ◽

Qinghua Cui

Keyword(s):

Functional Analysis ◽

Gene List ◽

Lung Squamous Cell Carcinoma ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Hypergeometric Test ◽

Exact Test ◽

Gene Set ◽

Gene Functional Analysis

ABSTRACTGene functional enrichment analysis represents one of the most popular bioinformatics methods for annotating the pathways and function categories of a given gene list. Current algorithms for enrichment computation such as Fisher’s exact test and hypergeometric test totally depend on the category count numbers of the gene list and one gene set. In this case, whatever the genes are, they were treated equally. However, actually genes show different scores in their essentiality in a gene list and in a gene set. It is thus hypothesized that the essentiality scores could be important and should be considered in gene functional analysis. For this purpose, here we proposed WEAT (https://www.cuilab.cn/weat/), a weighted gene set enrichment algorithm and online tool by weighting genes using essentiality scores. We confirmed the usefulness of WEAT using two case studies, the functional analysis of one aging-related gene list and one gene list involved in Lung Squamous Cell Carcinoma (LUSC). Finally, we believe that the WEAT method and tool could provide more possibilities for further exploring the functions of given gene lists.

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Integral Analyses of Competing Endogenous RNA Mechanisms and DNA Methylation Reveal Regulatory Mechanisms in Osteosarcoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.763347 ◽

2021 ◽

Vol 9 ◽

Author(s):

Tingrui Wu ◽

Bo Wei ◽

Hao Lin ◽

Boan Zhou ◽

Tao Lin ◽

...

Keyword(s):

Cell Lines ◽

Binding Sites ◽

Control Cell ◽

Bone Tumour ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Biological Processes ◽

Survival Times ◽

Poor Overall Survival

Background: Osteosarcoma (OS) is the most common primary malignant bone tumour in children and adolescents, with rapid growth, frequent metastasis, and a poor prognosis, but its pathogenesis has not been fully elucidated. Exploring the pathogenesis of OS is of great significance for improving diagnoses and finding new therapeutic targets.Methods: Differentially expressed circRNAs (DECs), miRNAs (DEMs), methylated DNA sites (DMSs), and mRNAs (DEGs) were identified between OS and control cell lines. GSEA of DEGs and functional enrichment analysis of methylated DEGs were carried out to further identify potential biological processes. Online tools were used to predict the miRNA binding sites of DECs and the mRNA binding sites of DEMs, and then construct a circRNA-miRNA-mRNA network. Next, an analysis of the interaction between methylated DEGs was performed with a protein-protein interaction (PPI) network, and hub gene identification and survival analysis were carried out. The expression pattern of circRNA-miRNA-mRNA was validated by real-time PCR.Results: GSEA and functional enrichment analysis indicated that DEGs and methylated DEGs are involved in important biological processes in cancer. Hsa_circ_0001753/has_miR_760/CD74 network was constructed and validated in cell lines. Low expression levels of CD74 are associated with poor overall survival times and show good diagnostic ability.Conclusion: Methylated DEGs may be involved in the development of OS, and the hsa_circ_0001753/has_miR_760/CD74 network may serve as a target for the early diagnosis of and targeted therapy for OS.

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An evolutionary transcriptomics approach links CD36 to membrane remodeling in replicative senescence

10.1101/294512 ◽

2018 ◽

Author(s):

Marie Saitou ◽

Darleny Y. Lizardo ◽

Recep Ozgur Taskent ◽

Alec Millner ◽

Gunes Ekin Atilla-Gokcumen ◽

...

Keyword(s):

Cell Lines ◽

Cellular Senescence ◽

Replicative Senescence ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Core Gene ◽

Functional Enrichment ◽

Membrane Remodeling ◽

Lipid Metabolism Gene ◽

The Media

SummaryCellular senescence, the irreversible ceasing of cell division, has been associated with organismal aging, prevention of cancerogenesis, and developmental processes. As such, the evolutionary basis and biological features of cellular senescence remain a fascinating area of research. In this study, we conducted comparative RNAseq experiments to detect genes associated with replicative senescence in two different human cell lines and at different time points. We identified 841 and 900 genes (core senescence-associated genes) that are significantly up- and downregulated in senescent cells, respectively, in both cell lines. Our functional enrichment analysis showed that downregulated core genes are primarily involved in cell cycle processes while upregulated core gene enrichment indicated various lipid-related processes. We further demonstrated that downregulated genes are significantly more conserved than upregulated genes. Using both transcriptomics and genetic variation data, we identified one of the upregulated, lipid metabolism gene, CD36 as an outlier. We found that overexpression of CD36 induces a senescence-like phenotype and, further, the media of CD36-overexpressing cells alone can induce a senescence-like phenotype in proliferating young cells. Moreover, we used a targeted lipidomics approach and showed that phosphatidylcholines accumulate during senescence in these cells, suggesting that upregulation of CD36 could contribute to membrane remodeling during senescence. Overall, these results contribute to the understanding of evolution and biology of cellular senescence and identify several targets and questions for future studies.

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gprofiler2 -- an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler

F1000Research ◽

10.12688/f1000research.24956.1 ◽

2020 ◽

Vol 9 ◽

pp. 709 ◽

Cited By ~ 4

Author(s):

Liis Kolberg ◽

Uku Raudvere ◽

Ivan Kuzmin ◽

Jaak Vilo ◽

Hedi Peterson

Keyword(s):

Gene List ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Automated Analysis ◽

R Package ◽

Biological Data ◽

Functional Enrichment ◽

Link Type ◽

Functional Profiling ◽

Rest Api

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Guidelines for reliable and reproducible functional enrichment analysis

10.1101/2021.09.06.459114 ◽

2021 ◽

Author(s):

Kaumadi Wijesooriya ◽

Sameer A Jadaan ◽

Kaushalya L Perera ◽

Tanuveer Kaur ◽

Mark Ziemann

Keyword(s):

Contemporary Literature ◽

Gene List ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

P Value ◽

Rna Seq ◽

Gene Sets ◽

Methodological Errors ◽

Peer Reviewers

Gene set enrichment tests (a.k.a. functional enrichment analysis) are among the most frequently used methods in computational biology. Despite this popularity, there are concerns that these methods are being applied incorrectly and the results of some peer-reviewed publications are unreliable. These problems include the use of inappropriate background gene lists, lack of false discovery rate correction and lack of methodological detail. An example analysis of public RNA-seq reveals that these methodological errors alter enrichment results dramatically. To ascertain the frequency of these errors in the literature, we performed a screen of 186 open access research articles describing functional enrichment results. We find that 95% of analyses using over-representation tests did not implement an appropriate background gene list or did not describe this in the methods. Failure to perform p-value correction for multiple tests was identified in 43% of analyses. Many studies lacked detail in the methods section about the tools and gene sets used. Only 15% of studies avoided major flaws, which highlights the poor state of functional enrichment rigour and reporting in the contemporary literature. We provide a set of minimum standards that should act as a checklist for researchers and peer-reviewers.

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Downregulation of microRNA-376a in Gastric Cancer and Association with Poor Prognosis

Cellular Physiology and Biochemistry ◽

10.1159/000495820 ◽

2018 ◽

Vol 51 (5) ◽

pp. 2010-2018 ◽

Cited By ~ 13

Author(s):

Cheng Zhang ◽

Yu Liang ◽

Ming-Hui Ma ◽

Kun-Zhe Wu ◽

Chun-Dong Zhang ◽

...

Keyword(s):

Gastric Cancer ◽

Cell Lines ◽

Target Genes ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Control Group ◽

Mesenchymal Transition ◽

Tumorigenesis And Progression ◽

The Common

Background/Aims: MicroRNAs have a significant role in the tumorigenesis and progression of cancers, including gastric cancer (GC). Our study aimed to identify a novel biomarker to predict the prognosis of patients with GC. Methods: The GC microarray dataset, GSE28700, was downloaded from the Gene Expression Omnibus (GEO) database and screened for differentially expressed miRNAs (DEMs). The downregulation of miR-376a expression was verified in GC cell lines and 82 paired GC tissues by performing RT-qPCR and the correlation between its expression and clinicopathological characteristics was also explored. The target genes of miR-376a were predicted using TargetScan7.1, miRDB, and DIANA website tools. A functional enrichment analysis was performed to explore the biological role of the common target genes. Results: Bioinformatics analysis found that miR-376a was downregulated in GC tissues. Compared with the control group, RT-qPCR results showed that the expression of miR-376a in GC cell lines and tissues were also significantly decreased. The expression of miR-376a was statistically associated with T and N stage. Survival analysis with Kaplan–Meier showed that GC patients in the low expression group had a poorer prognosis than those in the high expression group (median survival of 26.4 and 46.9 months, respectively). Univariate and multivariate analysis demonstrated that low miR-376a expression was an independent prognostic marker for poor survival. Functional enrichment analysis indicated that the common targets genes were involved in cell–cell communication, VEGF and mTOR1-mediated signaling, and epithelial-to-mesenchymal transition (EMT). Conclusion: The results suggest that miR-376a could play an important role in the tumorigenesis and progression of GC and act as a novel therapeutic target and prognostic indicator in patients with GC.

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Enrichment analysis on regulatory subspaces: a novel direction for the superior description of cellular responses to SARS-CoV-2

10.1101/2021.12.15.472466 ◽

2021 ◽

Author(s):

Pedro P. Rodrigues ◽

Rafael S. Costa ◽

Rui Henriques

Keyword(s):

Machine Learning ◽

Cell Lines ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Learning Approaches ◽

Transcriptional Responses ◽

Kegg Pathways ◽

Regulatory Processes

Statement: The enrichment analysis of discriminative cell transcriptional responses to SARS-CoV-2 infection using biclustering produces a broader set of superiorly enriched GO terms and KEGG pathways against alternative state-of-the-art machine learning approaches, unraveling novel knowledge. Motivation and methods: The comprehensive understanding of the impacts of the SARS-CoV-2 virus on infected cells is still incomplete. This work identifies and analyses the main cell regulatory processes affected and induced by SARS-CoV-2, using transcriptomic data from several infectable cell lines available in public databases and in vivo samples. We propose a new class of statistical models to handle three major challenges, namely the scarcity of observations, the high dimensionality of the data, and the complexity of the interactions between genes. Additionally, we analyse the function of these genes and their interactions within cells to compare them to ones affected by IAV (H1N1), RSV and HPIV3 in the target cell lines. Results: Gathered results show that, although clustering and predictive algorithms aid classic functional enrichment analysis, recent pattern-based biclustering algorithms significantly improve the number and quality of the detected biological processes. Additionally, a comparative analysis of these processes is performed to identify potential pathophysiological characteristics of COVID-19. These are further compared to those identified by other authors for the same virus as well as related ones such as SARS-CoV-1. This approach is particularly relevant due to a lack of other works utilizing more complex machine learning tools within this context.

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