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Constructing a global transcriptional regulatory landscape for early non-small cell lung cancer to identify hub genes and key pathways.

ABSTRACT: This study aimed to investigate the potential pathogenesis of early non-small cell lung cancer (NSCLC), including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), by constructing a global transcriptional regulatory landscape to identify hub genes and key pathways. A total of 1,206 differentially expressed genes (DEGs) in early NSCLC were identified compared to normal lung tissue samples in GSE33532 and GSE29013. DEGs-related protein-protein interaction networks (PPIs) were constructed based on the STRING database and were then modularly analyzed using the ClusterOne tool. The enrichment analysis revealed that multiple modules were significantly involved in pathways such as the TNF signaling pathway, PPAR signaling pathway and PI3K/AKt signaling pathway. Ten genes were identified as hub genes in the PPIs and also found up-regulated at protein level. The prognostic value of the hub genes and the ten hub gene set variation score varied according to the different pathological types of NSCLC, which suggested the ten hub gene expression patterns can reflect the heterogeneity of two types of NSCLC. In conclusion, by carrying out a series of in-depth analyses, hub genes and key pathways associated with early NSCLC were identified by a global transcriptional regulatory landscape.

SUBMITTER: Bu J

PROVIDER: S-EPMC7585098 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Constructing a global transcriptional regulatory landscape for early non-small cell lung cancer to identify hub genes and key pathways.

Bu Jianlong J Zhang Pinyi P Zhu Kaibin K Yan Yubo Y Shi Bowen B Wang Junfeng J Xu Shidong S

Aging 20200914 18

This study aimed to investigate the potential pathogenesis of early non-small cell lung cancer (NSCLC), including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), by constructing a global transcriptional regulatory landscape to identify hub genes and key pathways. A total of 1,206 differentially expressed genes (DEGs) in early NSCLC were identified compared to normal lung tissue samples in GSE33532 and GSE29013. DEGs-related protein-protein interaction networks (PPIs) were con ...[more]

PMID: 32931455

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Project description:The proteotranscriptomic landscape depends on the transcription, mRNA-turnover, translation, and regulated-destruction of proteins. Gene-specific mRNA-to-protein correlation is the consequence of the dynamic interplays of the different regulatory processes of proteotranscriptomic landscape. So far, the critical impact of mRNA and protein stability on their subsequent correlation on a global scale remained unresolved. Whether the mRNA-to-protein correlations are constrained by their stability and conserved across mammalian species including human is unknown. Moreover, whether the stability-dependent correlation pattern is altered in the tumor has not been explored. To establish the quantitative relationship between stability and correlation between mRNA and protein levels, we performed a multi-omics data integration study across mammalian systems including diverse types of human tissues and cell lines in a genome-wide manner. The current study illuminated an important aspect of the mammalian proteotranscriptomic landscape by providing evidence that stability-constrained mRNA-to-protein correlation follows a hierarchical pattern that remains conserved across different tissues and mammalian species. By analyzing the tumor and non-tumor tissues, we further illustrated that mRNA-to-protein correlations deviate in tumor tissues. By gene-centric analysis, we harnessed the hierarchical correlation patterns to identify altered mRNA-to-protein correlation in tumors and characterized the tumor correlation-enhancing and -repressing genes. We elucidated the transcriptional regulatory circuits controlling the correlation-enhancing and -repressing genes that are associated with metabolic reprogramming and cancer-associated pathways in tumor tissue. By tightly controlling the mRNA-to-protein correlation of specific genes, the transcriptional regulatory circuits may enable the tumor cells to evolve in varying tumor microenvironment. The mRNA-to-protein correlation analysis thus can serve as a unique approach to identify the pathways prioritized by the tumor cells at different clinical stages. The component of transcriptional regulatory circuits identified by the current study can serve as potential candidates for stage-dependent anticancer therapy.

Project description:Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, accounting for ~80% of all lung cancer cases. The aim of the present study was to identify key genes and pathways in NSCLC, in order to improve understanding of the mechanism of lung cancer. The GSE33532 gene expression dataset, containing 20 normal and 80 NSCLC samples, was used. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to obtain the enrichment data of differently expressed genes (DEGs). Disease modules within NSCLC were constructed by Cytoscape, using protein-protein interaction (PPI) from the Search Tool for the Retrieval of Interacting Genes database. In addition, the Kaplan Meier plotter KMplot was used to assess the top hub genes in the PPI network. As a result, 1,795 genes were identified in NSCLC; 729 were upregulated and 1,066 were downregulated. The results of the GO analysis indicated that the upregulated DEGs were significantly enriched in 'biological processes' (BP), including 'cell cycle and nuclear division'; the downregulated DEGs were also significantly enriched in BP, including 'response to wounding', 'anatomical structure morphogenesis' and 'response to stimulus'. Upregulated DEGs were also enriched in 'cell cycle', 'DNA replication' and the 'tumor protein 53 signaling pathway', while the downregulated DEGs were also enriched in 'complement and coagulation cascades', 'malaria' and 'cell adhesion molecules'. The top 9 hub genes were cyclin-dependent kinase 9 (CDK1), polo-like kinase 1, aurora kinase B, cell division cycle 20, baculoviral initiator of apoptosis repeat containing 5, mitotic checkpoint serine/threonine kinase B, proliferating cell nuclear antigen (PCNA), centromere protein A and MAD2 mitotic arrest deficient-like 1, and the KMplot results revealed that the high expression levels of these genes resulted in significantly low survival rates, compared with low expression samples (P<0.05), with the exception of PCNA and CDK1. In the pathway crosstalk analysis, 26 nodes and 41 interactions were divided into two groups: One module of the two groups primarily included 'metabolism of amino acid' and the other primarily contained 'tumor necrosis signaling' pathways. In conclusion, the present study assisted in improving the understanding of the molecular mechanisms underlying NSCLC development, and the results may help the understanding of the biological mechanism of NSCLC.

Project description:Abstract AbstractBackground: Type II diabetes mellitus (T2DM), characterized by fasting hyperglycemia and impaired insulin secretion and action, is a global health burden. Despite the advances in this field, the mechanism underlying T2DM is far from clear. Objective: The present study sheds light upon a systematic evaluation of the genes, pathways, and interaction networks underlying T2DM with the aid of bioinformatics. Methods: Two Gene Expression Omnibus microarray datasets: GSE148961 and GSE26168 were selected for this study. The common differentially expressed genes (DEG) were sorted by the cutoff |logFC|≥1.0 for the first dataset and |logFC|≥0.263 for the second. Gene Ontology (GO), functional enrichment, and protein-protein interaction (PPI) network were analyzed in Search Tool for the Retrieval of Interacting Genes (STRING). The MCODE and CytoHubba plugins in Cytoscape (v3.7.2) were used to identify gene clusters and top hub genes, respectively. Top 10 nodes were ranked in CytoHubba according to MCC, DMNC, MNC, Degree, and EPC methods, and genes common in at least 3 methods were selected as top nodes. Results: 88 common DEGs were identified by Venn diagram (http://bioinformatics.psb.ugent.be/cgi-bin/liste/Venn/calculate_venn.htpl). GO analysis had 91 significantly enriched biological processes, including regulated exocytosis, secretion, vesicle mediated transport, antibacterial humoral response, and neutrophil degranulation. 4 molecular functions- fibrinogen binding, fibronectin binding, lipopolysaccharide binding, and Extracellular matrix binding; and 38 cellular components, including secretory vesicle, endomembrane system, and adherens junction were significant. The PPI network was highly significant (p-value < 0.001) at medium confidence (0.400) with 88 nodes, 140 edges, and an average node degree of 3.18. The MCODE plugin revealed two clusters, the former with 14 nodes, 73 edges, and a score of 9.733, and the latter with 6 nodes, 14 edges, and a score of 4.000. 9 candidate genes: ELANE, DEFA4, BPI, MPO, LTF, CAMP, OLFM4, LCN2, and VCL were identified, amongst which ELANE, LCN2, and MPO are associated with T2DM pathogenesis, while BPI and LTF have protective effects. OLFM4 deletion has been observed to improve glucose tolerance in mice models. Conclusion: This study provides a comprehensive analysis of genes, pathways, and functions which may be pivotal in T2DM pathogenesis and may represent potential therapeutic targets.

Project description:BACKGROUND:Recent advances in sequencing technologies have enabled parallel assays of chromatin accessibility and gene expression for major human cell lines. Such innovation provides a great opportunity to decode phenotypic consequences of genetic variation via the construction of predictive gene regulatory network models. However, there still lacks a computational method to systematically integrate chromatin accessibility information with gene expression data to recover complicated regulatory relationships between genes in a tissue-specific manner. RESULTS:We propose a Markov random field (MRF) model for constructing tissue-specific transcriptional regulatory networks via integrative analysis of DNase-seq and RNA-seq data. Our method, named CSNets (cell-line specific regulatory networks), first infers regulatory networks for individual cell lines using chromatin accessibility information, and then fine-tunes these networks using the MRF based on pairwise similarity between cell lines derived from gene expression data. Using this method, we constructed regulatory networks specific to 110 human cell lines and 13 major tissues with the use of ENCODE data. We demonstrated the high quality of these networks via comprehensive statistical analysis based on ChIP-seq profiles, functional annotations, taxonomic analysis, and literature surveys. We further applied these networks to analyze GWAS data of Crohn's disease and prostate cancer. Results were either consistent with the literature or provided biological insights into regulatory mechanisms of these two complex diseases. The website of CSNets is freely available at http://bioinfo.au.tsinghua.edu.cn/jianglab/CSNETS/ . CONCLUSIONS:CSNets demonstrated the power of joint analysis on epigenomic and transcriptomic data towards the accurate construction of gene regulatory network. Our work provides not only a useful resource of regulatory networks to the community, but also valuable experiences in methodology development for multi-omics data integration.

Project description:BackgroundCircadian clocks provide an adaptive advantage through anticipation of daily and seasonal environmental changes. In plants, the central clock oscillator is regulated by several interlocking feedback loops. It was shown that a substantial proportion of the Arabidopsis genome cycles with phases of peak expression covering the entire day. Synchronized transcriptome cycling is driven through an extensive network of diurnal and clock-regulated transcription factors and their target cis-regulatory elements. Study of the cycling transcriptome in other plant species could thus help elucidate the similarities and differences and identify hubs of regulation common to monocot and dicot plants.Methodology/principal findingsUsing a combination of oligonucleotide microarrays and data mining pipelines, we examined daily rhythms in gene expression in one monocotyledonous and one dicotyledonous plant, rice and poplar, respectively. Cycling transcriptomes were interrogated under different diurnal (driven) and circadian (free running) light and temperature conditions. Collectively, photocycles and thermocycles regulated about 60% of the expressed nuclear genes in rice and poplar. Depending on the condition tested, up to one third of oscillating Arabidopsis-poplar-rice orthologs were phased within three hours of each other suggesting a high degree of conservation in terms of rhythmic gene expression. We identified clusters of rhythmically co-expressed genes and searched their promoter sequences to identify phase-specific cis-elements, including elements that were conserved in the promoters of Arabidopsis, poplar, and rice.Conclusions/significanceOur results show that the cycling patterns of many circadian clock genes are highly conserved across poplar, rice, and Arabidopsis. The expression of many orthologous genes in key metabolic and regulatory pathways is diurnal and/or circadian regulated and phased to similar times of day. Our results confirm previous findings in Arabidopsis of three major classes of cis-regulatory modules within the plant circadian network: the morning (ME, GBOX), evening (EE, GATA), and midnight (PBX/TBX/SBX) modules. Identification of identical overrepresented motifs in the promoters of cycling genes from different species suggests that the core diurnal/circadian cis-regulatory network is deeply conserved between mono- and dicotyledonous species.

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Constructing a global transcriptional regulatory landscape for early non-small cell lung cancer to identify hub genes and key pathways.

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Constructing a global transcriptional regulatory landscape for early non-small cell lung cancer to identify hub genes and key pathways.

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