Identification of Survival-Associated Hub Genes in Pancreatic Adenocarcinoma Based on WGCNA

Pancreatic adenocarcinoma is one of the leading causes of cancer-related death worldwide. Since little clinical symptoms were shown in the early period of pancreatic adenocarcinoma, most patients were found to carry metastases when diagnosis. The lack of effective diagnosis biomarkers and therapeutic targets makes pancreatic adenocarcinoma difficult to screen and cure. The fundamental problem is we know very little about the regulatory mechanisms during carcinogenesis. Here, we employed weighted gene co-expression network analysis (WGCNA) to build gene interaction network using expression profile of pancreatic adenocarcinoma from The Cancer Genome Atlas (TCGA). STRING was used for the construction and visualization of biological networks. A total of 22 modules were detected in the network, among which yellow and pink modules showed the most significant associations with pancreatic adenocarcinoma. Dozens of new genes including PKMYT1, WDHD1, ASF1B, and RAD18 were identified. Further survival analysis yielded their valuable effects on the diagnosis and treatment of pancreatic adenocarcinoma. Our study pioneered network-based algorithm in the application of tumor etiology and discovered several promising regulators for pancreatic adenocarcinoma detection and therapy.

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Community Detection in Large-Scale Bipartite Biological Networks

Frontiers in Genetics ◽

10.3389/fgene.2021.649440 ◽

2021 ◽

Vol 12 ◽

Author(s):

Genís Calderer ◽

Marieke L. Kuijjer

Keyword(s):

Biological Networks ◽

Large Scale ◽

Interaction Network ◽

Gene Interaction ◽

Community Structures ◽

Gene Interaction Network ◽

Structure Detection ◽

Wide Range ◽

Disease Associations ◽

Or Gene

Networks are useful tools to represent and analyze interactions on a large, or genome-wide scale and have therefore been widely used in biology. Many biological networks—such as those that represent regulatory interactions, drug-gene, or gene-disease associations—are of a bipartite nature, meaning they consist of two different types of nodes, with connections only forming between the different node sets. Analysis of such networks requires methodologies that are specifically designed to handle their bipartite nature. Community structure detection is a method used to identify clusters of nodes in a network. This approach is especially helpful in large-scale biological network analysis, as it can find structure in networks that often resemble a “hairball” of interactions in visualizations. Often, the communities identified in biological networks are enriched for specific biological processes and thus allow one to assign drugs, regulatory molecules, or diseases to such processes. In addition, comparison of community structures between different biological conditions can help to identify how network rewiring may lead to tissue development or disease, for example. In this mini review, we give a theoretical basis of different methods that can be applied to detect communities in bipartite biological networks. We introduce and discuss different scores that can be used to assess the quality of these community structures. We then apply a wide range of methods to a drug-gene interaction network to highlight the strengths and weaknesses of these methods in their application to large-scale, bipartite biological networks.

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Rapid evolution of gained essential developmental functions of a young gene via interactions with other essential genes

10.1101/226936 ◽

2017 ◽

Author(s):

Yuh Chwen G. Lee ◽

Iuri M. Ventura ◽

Gavin R. Rice ◽

Don-Yuan Chen ◽

Manyuan Long

Keyword(s):

Developmental Stages ◽

Rapid Evolution ◽

Interaction Network ◽

Gene Interaction ◽

Gene Interaction Network ◽

Evolutionary Trajectory ◽

Functional Sites ◽

New Genes ◽

Expression Of Genes ◽

Parental Gene

AbstractNew genes originated relatively recently and are only present in a subset of species in a phylogeny. Accumulated evidence suggests that new genes, like old genes that are conserved across species, can also take on important functions and be essential for the survival and reproductive success of organisms. While there are detailed analyses of the mechanisms underlying gained fertility functions by new genes, how new genes rapidly became essential for viability remains unclear. We focused on a young retro-duplicated gene (CG7804, which we named Cocoon) in Drosophila that originated three million years ago. We found that, unlike its evolutionarily conserved and broadly expressed parental gene, Cocoon has evolved rapidly under positive selection since its birth and accumulates many amino acid divergences at functional sites from the parental gene. Despite its young age, Cocoon is essential for the survival of D. melanogaster at multiple developmental stages, including the critical embryonic stage, and its expression is essential in different tissues from its parental gene. Functional genomic analyses found that Cocoon gained multiple DNA binding targets, which regulates the expression of genes that have other essential functions and/or have multiple gene-gene interactions. Our observations suggest that Cocoon acquired essential function to survival through forming interactions that have large impacts on the gene interaction network. Our study is an important step towards deciphering the evolutionary trajectory by which new genes functionally diverge from the parental gene and become essential.

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Elevated expression of LRRC15 Shows Putative Tumor-promoting Characteristics in tumor microenvironment of breast cancer

10.21203/rs.3.rs-871638/v1 ◽

2021 ◽

Author(s):

Lei Li ◽

Yin-Jiao Fei ◽

Ming-Xing Liang ◽

Hong-Lei Zhou ◽

Guan-Qun Wo ◽

...

Keyword(s):

Breast Cancer ◽

Cell Proliferation ◽

Interaction Network ◽

Gene Interaction ◽

The Cancer Genome Atlas ◽

Clinicopathological Parameters ◽

Receptor Interaction ◽

Gene Interaction Network ◽

Kaplan Meier ◽

Elevated Expression

Abstract Background Leucine-rich repeat containing 15 (LRRC15), belongs to the LRR superfamily and has emerged as a marker of cancer-associated fibroblasts. It was found to be particularly upregulated in breast cancer (BCa). This study aimed to investigate the correlation between LRRC15 expression and immune microenviroment and visualize its prognostic landscape in BCa. Methods The mRNA expression level, prognostic value, correlation of immunity, gene-gene interaction network of LRRC15 in BCa were analyzed utilizing the Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, Kaplan-Meier plotter, and TIMER database. We next analyzed the biological functions of LRRC15 and pathways of its co-expressed genes, and its correlation with immune system responses via the Metascape and GeneMANIA database, respectively. We validated the expression of LRRC15 in BCa via western blot and IHC assays and analyzed its correlation with clinicopathological parameters. Results We explored LRRC15 expression in multiple types of cancer based on the Cancer Genome Atlas (TCGA) database, with the effect being particularly pronounced in BCa. Both mRNA and protein abundance of LRRC15 were significantly elevated in BCa as compared to its non-tumor counterparts. Overexpression of LRRC15 significantly associated with reduced overall survival. LRRC15 knockdown significantly inhibited cell proliferation and cell cycle in BCa cells. There were significant positive correlations between LRRC15 expression and tumor-infiltrating immune cells (TIICs), with a particularly strong effect on macrophage infiltration. Moreover, markers of TIICs exhibited different LRRC15-related immune infiltration patterns. GSEA analysis showed that upregulated expression of LRRC15 was related to ECM receptor interaction, focal adhesion, regulation of actin cytoskeleton, and TGF Beta signaling pathway. Conclusions These findings revealed that LRRC15 served as a novel prognostic biomarker and putative oncogene for BCa by promoting cell proliferation, giving a novel hint for therapeutics of BCa.

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Incorporating K-mers Highly Correlated to Epigenetic Modifications for Bayesian Inference of Gene Interactions

Current Bioinformatics ◽

10.2174/1574893615999200728193621 ◽

2020 ◽

Vol 15 ◽

Author(s):

Dariush Salimi ◽

Ali Moeini

Keyword(s):

Bayesian Inference ◽

Biological Networks ◽

Interaction Network ◽

Gene Interaction ◽

Epigenetic Modifications ◽

Physical Interaction ◽

Gene Interaction Network ◽

Human Genes ◽

Highly Correlated ◽

Specific Pathway

Objective: A gene interaction network, along with its related biological features, has an important role in computational biology. Bayesian network, as an efficient model, based on probabilistic concepts is able to exploit known and novel biological casual relationships between genes. Success of Bayesian networks in predicting the relationships greatly depends on selecting priors Methods: K-mers have been applied as the prominent features to uncover similarity between genes in a specific pathway, suggesting that this feature can be applied to study genes dependencies. In this study, we propose k-mer (4,5 and 6-mers) highly correlated with epigenetic modifications, including 17 modifications, as a new prior for Bayesian inference in gene interaction network Result: Employing this model on a network of 23 human genes and on a network based on 27 genes related to yeast resulted in F-measure improvements in different biological networks Conclusion: The improvements in the best case are 12%, 36% and 10% in pathway, co-expression, and physical interaction, respectively.

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Large-Scale Gastric Cancer Susceptibility Gene Identification Based on Gradient Boosting Decision Tree

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.815243 ◽

2022 ◽

Vol 8 ◽

Author(s):

Qing Chen ◽

Ji Zhang ◽

Banghe Bao ◽

Fan Zhang ◽

Jie Zhou

Keyword(s):

Gastric Cancer ◽

Decision Tree ◽

Large Scale ◽

Clinical Symptoms ◽

Cancer Susceptibility ◽

Susceptibility Gene ◽

Interaction Network ◽

Gene Interaction ◽

Gradient Boosting ◽

Gene Interaction Network

The early clinical symptoms of gastric cancer are not obvious, and metastasis may have occurred at the time of treatment. Poor prognosis is one of the important reasons for the high mortality of gastric cancer. Therefore, the identification of gastric cancer-related genes can be used as relevant markers for diagnosis and treatment to improve diagnosis precision and guide personalized treatment. In order to further reveal the pathogenesis of gastric cancer at the gene level, we proposed a method based on Gradient Boosting Decision Tree (GBDT) to identify the susceptible genes of gastric cancer through gene interaction network. Based on the known genes related to gastric cancer, we collected more genes which can interact with them and constructed a gene interaction network. Random Walk was used to extract network association of each gene and we used GBDT to identify the gastric cancer-related genes. To verify the AUC and AUPR of our algorithm, we implemented 10-fold cross-validation. GBDT achieved AUC as 0.89 and AUPR as 0.81. We selected four other methods to compare with GBDT and found GBDT performed best.

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Rapid Evolution of Gained Essential Developmental Functions of a Young Gene via Interactions with Other Essential Genes

Molecular Biology and Evolution ◽

10.1093/molbev/msz137 ◽

2019 ◽

Vol 36 (10) ◽

pp. 2212-2226 ◽

Cited By ~ 3

Author(s):

Yuh Chwen G Lee ◽

Iuri M Ventura ◽

Gavin R Rice ◽

Dong-Yuan Chen ◽

Serafin U Colmenares ◽

...

Keyword(s):

Developmental Stages ◽

Rapid Evolution ◽

Interaction Network ◽

Gene Interaction ◽

Gene Interaction Network ◽

Evolutionary Trajectory ◽

Functional Sites ◽

New Genes ◽

Parental Gene ◽

Contrasting Effect

Abstract New genes are of recent origin and only present in a subset of species in a phylogeny. Accumulated evidence suggests that new genes, like old genes that are conserved across species, can also take on important functions and be essential for the survival and reproductive success of organisms. Although there are detailed analyses of the mechanisms underlying new genes’ gaining fertility functions, how new genes rapidly become essential for viability remains unclear. We focused on a young retro-duplicated gene (CG7804, which we named Cocoon) in Drosophila that originated between 4 and 10 Ma. We found that, unlike its evolutionarily conserved parental gene, Cocoon has evolved under positive selection and accumulated many amino acid differences at functional sites from the parental gene. Despite its young age, Cocoon is essential for the survival of Drosophila melanogaster at multiple developmental stages, including the critical embryonic stage, and its expression is essential in different tissues from those of its parental gene. Functional genomic analyses found that Cocoon acquired unique DNA-binding sites and has a contrasting effect on gene expression to that of its parental gene. Importantly, Cocoon binding predominantly locates at genes that have other essential functions and/or have multiple gene–gene interactions, suggesting that Cocoon acquired novel essential function to survival through forming interactions that have large impacts on the gene interaction network. Our study is an important step toward deciphering the evolutionary trajectory by which new genes functionally diverge from parental genes and become essential.

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DriverFinder: A Gene Length-Based Network Method to Identify Cancer Driver Genes

Complexity ◽

10.1155/2017/4826206 ◽

2017 ◽

Vol 2017 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Pi-Jing Wei ◽

Di Zhang ◽

Hai-Tao Li ◽

Junfeng Xia ◽

Chun-Hou Zheng

Keyword(s):

Cell Carcinoma ◽

Interaction Network ◽

Gene Interaction ◽

Renal Clear Cell Carcinoma ◽

Copy Number Variations ◽

The Cancer Genome Atlas ◽

Gene Length ◽

Gene Interaction Network ◽

Driver Genes ◽

Network Method

Integration of multi-omics data of cancer can help people to explore cancers comprehensively. However, with a large volume of different omics and functional data being generated, there is a major challenge to distinguish functional driver genes from a sea of inconsequential passenger genes that accrue stochastically but do not contribute to cancer development. In this paper, we present a gene length-based network method, named DriverFinder, to identify driver genes by integrating somatic mutations, copy number variations, gene-gene interaction network, tumor expression, and normal expression data. To illustrate the performance of DriverFinder, it is applied to four cancer types from The Cancer Genome Atlas including breast cancer, head and neck squamous cell carcinoma, thyroid carcinoma, and kidney renal clear cell carcinoma. Compared with some conventional methods, the results demonstrate that the proposed method is effective. Moreover, it can decrease the influence of gene length in identifying driver genes and identify some rare mutated driver genes.

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Sample-specific perturbation of gene interactions identifies breast cancer subtypes

Briefings in Bioinformatics ◽

10.1093/bib/bbaa268 ◽

2020 ◽

Author(s):

Yuanyuan Chen ◽

Yu Gu ◽

Zixi Hu ◽

Xiao Sun

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Biological Networks ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Interaction Network ◽

Gene Interaction ◽

Breast Cancer Subtypes ◽

Gene Interaction Network ◽

Cancer Subtypes

Abstract Breast cancer is a highly heterogeneous disease, and there are many forms of categorization for breast cancer based on gene expression profiles. Gene expression profiles are variables and may show differences if measured at different time points or under different conditions. In contrast, biological networks are relatively stable over time and under different conditions. In this study, we used a gene interaction network from a new point of view to explore the subtypes of breast cancer based on individual-specific edge perturbations measured by relative gene expression value. Our study reveals that there are four breast cancer subtypes based on gene interaction perturbations at the individual level. The new network-based subtypes of breast cancer show strong heterogeneity in prognosis, somatic mutations, phenotypic changes and enriched pathways. The network-based subtypes are closely related to the PAM50 subtypes and immunohistochemistry index. This work helps us to better understand the heterogeneity and mechanisms of breast cancer from a network perspective.

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