Identification of driver genes based on gene mutational effects and network centrality

Abstract Background As one of the deadliest diseases in the world, cancer is driven by a few somatic mutations that disrupt the normal growth of cells, and leads to abnormal proliferation and tumor development. The vast majority of somatic mutations did not affect the occurrence and development of cancer; thus, identifying the mutations responsible for tumor occurrence and development is one of the main targets of current cancer treatments. Results To effectively identify driver genes, we adopted a semi-local centrality measure and gene mutation effect function to assess the effect of gene mutations on changes in gene expression patterns. Firstly, we calculated the mutation score for each gene. Secondly, we identified differentially expressed genes (DEGs) in the cohort by comparing the expression profiles of tumor samples and normal samples, and then constructed a local network for each mutation gene using DEGs and mutant genes according to the protein–protein interaction network. Finally, we calculated the score of each mutant gene according to the objective function. The top-ranking mutant genes were selected as driver genes. We name the proposed method as mutations effect and network centrality. Conclusions Four types of cancer data in The Cancer Genome Atlas were tested. The experimental data proved that our method was superior to the existing network-centric method, as it was able to quickly and easily identify driver genes and rare driver factors.

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Integrating Genetic and Transcriptomic Data to Reveal Pathogenesis and Prognostic Markers of Pancreatic Adenocarcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.747270 ◽

2021 ◽

Vol 12 ◽

Author(s):

Kaisong Bai ◽

Tong Zhao ◽

Yilong Li ◽

Xinjian Li ◽

Zhantian Zhang ◽

...

Keyword(s):

Gene Expression ◽

Pancreatic Adenocarcinoma ◽

Somatic Mutation ◽

Somatic Mutations ◽

Prognostic Markers ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Genomic Variation ◽

The Cancer Genome Atlas ◽

Driver Genes

Pancreatic adenocarcinoma (PAAD) is one of the deadliest malignancies and mortality for PAAD have remained increasing under the conditions of substantial improvements in mortality for other major cancers. Although multiple of studies exists on PAAD, few studies have dissected the oncogenic mechanisms of PAAD based on genomic variation. In this study, we integrated somatic mutation data and gene expression profiles obtained by high-throughput sequencing to characterize the pathogenesis of PAAD. The mutation profile containing 182 samples with 25,470 somatic mutations was obtained from The Cancer Genome Atlas (TCGA). The mutation landscape was generated and somatic mutations in PAAD were found to have preference for mutation location. The combination of mutation matrix and gene expression profiles identified 31 driver genes that were closely associated with tumor cell invasion and apoptosis. Co-expression networks were constructed based on 461 genes significantly associated with driver genes and the hub gene FAM133A in the network was identified to be associated with tumor metastasis. Further, the cascade relationship of somatic mutation-Long non-coding RNA (lncRNA)-microRNA (miRNA) was constructed to reveal a new mechanism for the involvement of mutations in post-transcriptional regulation. We have also identified prognostic markers that are significantly associated with overall survival (OS) of PAAD patients and constructed a risk score model to identify patients’ survival risk. In summary, our study revealed the pathogenic mechanisms and prognostic markers of PAAD providing theoretical support for the development of precision medicine.

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Cooperative driver pathway discovery via fusion of multi-relational data of genes, miRNAs and pathways

Briefings in Bioinformatics ◽

10.1093/bib/bbz167 ◽

2020 ◽

Author(s):

Jun Wang ◽

Ziying Yang ◽

Carlotta Domeniconi ◽

Xiangliang Zhang ◽

Guoxian Yu

Keyword(s):

Heterogeneous Network ◽

Characteristic Curve ◽

Interaction Network ◽

The Cancer Genome Atlas ◽

Driver Genes ◽

Cancer Data ◽

Pathway Interaction ◽

Target Disease ◽

Driver Pathway ◽

Pathway Interaction Network

Abstract Discovering driver pathways is an essential step to uncover the molecular mechanism underlying cancer and to explore precise treatments for cancer patients. However, due to the difficulties of mapping genes to pathways and the limited knowledge about pathway interactions, most previous work focus on identifying individual pathways. In practice, two (or even more) pathways interplay and often cooperatively trigger cancer. In this study, we proposed a new approach called CDPathway to discover cooperative driver pathways. First, CDPathway introduces a driver impact quantification function to quantify the driver weight of each gene. CDPathway assumes that genes with larger weights contribute more to the occurrence of the target disease and identifies them as candidate driver genes. Next, it constructs a heterogeneous network composed of genes, miRNAs and pathways nodes based on the known intra(inter)-relations between them and assigns the quantified driver weights to gene–pathway and gene–miRNA relational edges. To transfer driver impacts of genes to pathway interaction pairs, CDPathway collaboratively factorizes the weighted adjacency matrices of the heterogeneous network to explore the latent relations between genes, miRNAs and pathways. After this, it reconstructs the pathway interaction network and identifies the pathway pairs with maximal interactive and driver weights as cooperative driver pathways. Experimental results on the breast, uterine corpus endometrial carcinoma and ovarian cancer data from The Cancer Genome Atlas show that CDPathway can effectively identify candidate driver genes [area under the receiver operating characteristic curve (AUROC) of $\geq $0.9] and reconstruct the pathway interaction network (AUROC of>0.9), and it uncovers much more known (potential) driver genes than other competitive methods. In addition, CDPathway identifies 150% more driver pathways and 60% more potential cooperative driver pathways than the competing methods. The code of CDPathway is available at http://mlda.swu.edu.cn/codes.php?name=CDPathway.

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Modularity of the metabolic gene network as a prognostic biomarker for hepatocellular carcinoma

10.1101/224998 ◽

2017 ◽

Author(s):

Fengdan Ye ◽

Dongya Jia ◽

Mingyang Lu ◽

Herbert Levine ◽

Michael W Deem

Keyword(s):

Hepatocellular Carcinoma ◽

Prognostic Biomarker ◽

Malignant Tumors ◽

Aerobic Glycolysis ◽

Expression Patterns ◽

Cancer Recurrence ◽

Metabolic Reprogramming ◽

The Cancer Genome Atlas ◽

Driver Genes ◽

Tumor Stages

AbstractAbnormal metabolism is an emerging hallmark of cancer. Cancer cells utilize both aerobic glycolysis and oxidative phosphorylation (OXPHOS) for energy production and biomass synthesis. Understanding the metabolic reprogramming in cancer can help design therapies to target metabolism and thereby to improve prognosis. We have previously argued that more malignant tumors are usually characterized by a more modular expression pattern of cancer-associated genes. In this work, we analyzed the expression patterns of metabolism genes in terms of modularity for 371 hepatocellular carcinoma (HCC) samples from the Cancer Genome Atlas (TCGA). We found that higher modularity significantly correlated with glycolytic phenotype, later tumor stages, higher metastatic potential, and cancer recurrence, all of which contributed to poorer prognosis. Among patients with recurred tumor, we found the correlation of higher modularity with worse prognosis during early to mid-progression. Furthermore, we developed metrics to calculate individual modularity, which was shown to be predictive of cancer recurrence and patients’ survival and therefore may serve as a prognostic biomarker. Our overall conclusion is that more aggressive HCC tumors, as judged by decreased host survival probability, had more modular expression patterns of metabolic genes. These results may be used to identify cancer driver genes and for drug design.

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Comprehensive Analysis of the TIFY Gene Family and Its Expression Profiles under Phytohormone Treatment and Abiotic Stresses in Roots of Populus trichocarpa

Forests ◽

10.3390/f11030315 ◽

2020 ◽

Vol 11 (3) ◽

pp. 315

Author(s):

Hanzeng Wang ◽

Xue Leng ◽

Xuemei Xu ◽

Chenghao Li

Keyword(s):

Gene Family ◽

Abiotic Stresses ◽

Expression Profiles ◽

Populus Trichocarpa ◽

Expression Patterns ◽

Interaction Network ◽

Pcr Analysis ◽

Phylogenetic Tree Analysis ◽

Element Analysis ◽

Cis Element

The TIFY gene family is specific to land plants, exerting immense influence on plant growth and development as well as responses to biotic and abiotic stresses. Here, we identify 25 TIFY genes in the poplar (Populus trichocarpa) genome. Phylogenetic tree analysis revealed these PtrTIFY genes were divided into four subfamilies within two groups. Promoter cis-element analysis indicated most PtrTIFY genes possess stress- and phytohormone-related cis-elements. Quantitative real-time reverse transcription polymerase chain reaction (qRT–PCR) analysis showed that PtrTIFY genes displayed different expression patterns in roots under abscisic acid, methyl jasmonate, and salicylic acid treatments, and drought, heat, and cold stresses. The protein interaction network indicated that members of the PtrTIFY family may interact with COI1, MYC2/3, and NINJA. Our results provide important information and new insights into the evolution and functions of TIFY genes in P. trichocarpa.

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In Silico Discovery of Candidate Drugs against Covid-19

Viruses ◽

10.3390/v12040404 ◽

2020 ◽

Vol 12 (4) ◽

pp. 404 ◽

Cited By ~ 42

Author(s):

Claudia Cava ◽

Gloria Bertoli ◽

Isabella Castiglioni

Keyword(s):

Gene Expression ◽

In Silico ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Interaction Network ◽

Enrichment Analysis ◽

Basic Mechanism ◽

Cell Receptor ◽

The Cancer Genome Atlas ◽

Pathway Enrichment Analysis

Previous studies reported that Angiotensin converting enzyme 2 (ACE2) is the main cell receptor of SARS-CoV and SARS-CoV-2. It plays a key role in the access of the virus into the cell to produce the final infection. In the present study we investigated in silico the basic mechanism of ACE2 in the lung and provided evidences for new potentially effective drugs for Covid-19. Specifically, we used the gene expression profiles from public datasets including The Cancer Genome Atlas, Gene Expression Omnibus and Genotype-Tissue Expression, Gene Ontology and pathway enrichment analysis to investigate the main functions of ACE2-correlated genes. We constructed a protein-protein interaction network containing the genes co-expressed with ACE2. Finally, we focused on the genes in the network that are already associated with known drugs and evaluated their role for a potential treatment of Covid-19. Our results demonstrate that the genes correlated with ACE2 are mainly enriched in the sterol biosynthetic process, Aryldialkylphosphatase activity, adenosylhomocysteinase activity, trialkylsulfonium hydrolase activity, acetate-CoA and CoA ligase activity. We identified a network of 193 genes, 222 interactions and 36 potential drugs that could have a crucial role. Among possible interesting drugs for Covid-19 treatment, we found Nimesulide, Fluticasone Propionate, Thiabendazole, Photofrin, Didanosine and Flutamide.

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Data Fusion Techniques for the Integration of Multi-Domain Genomic Data from Uveal Melanoma

Cancers ◽

10.3390/cancers11101434 ◽

2019 ◽

Vol 11 (10) ◽

pp. 1434 ◽

Cited By ~ 1

Author(s):

Max Pfeffer ◽

André Uschmajew ◽

Adriana Amaro ◽

Ulrich Pfeffer

Keyword(s):

Gene Expression ◽

Data Fusion ◽

Uveal Melanoma ◽

Copy Number ◽

Somatic Mutations ◽

Genomic Data ◽

The Cancer Genome Atlas ◽

Driver Genes ◽

Discrimination Power ◽

Value Decomposition

Uveal melanoma (UM) is a rare cancer that is well characterized at the molecular level. Two to four classes have been identified by the analyses of gene expression (mRNA, ncRNA), DNA copy number, DNA-methylation and somatic mutations yet no factual integration of these data has been reported. We therefore applied novel algorithms for data fusion, joint Singular Value Decomposition (jSVD) and joint Constrained Matrix Factorization (jCMF), as well as similarity network fusion (SNF), for the integration of gene expression, methylation and copy number data that we applied to the Cancer Genome Atlas (TCGA) UM dataset. Variant features that most strongly impact on definition of classes were extracted for biological interpretation of the classes. Data fusion allows for the identification of the two to four classes previously described. Not all of these classes are evident at all levels indicating that integrative analyses add to genomic discrimination power. The classes are also characterized by different frequencies of somatic mutations in putative driver genes (GNAQ, GNA11, SF3B1, BAP1). Innovative data fusion techniques confirm, as expected, the existence of two main types of uveal melanoma mainly characterized by copy number alterations. Subtypes were also confirmed but are somewhat less defined. Data fusion allows for real integration of multi-domain genomic data.

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Characterizing the heterogeneity in 5-aminolevulinic acid–induced fluorescence in glioblastoma

Journal of Neurosurgery ◽

10.3171/2019.2.jns183128 ◽

2020 ◽

Vol 132 (6) ◽

pp. 1706-1714 ◽

Cited By ~ 4

Author(s):

Damian A. Almiron Bonnin ◽

Matthew C. Havrda ◽

Myung Chang Lee ◽

Linton Evans ◽

Cong Ran ◽

...

Keyword(s):

Gene Expression ◽

Tumor Tissue ◽

Aminolevulinic Acid ◽

Expression Profiles ◽

Expression Patterns ◽

Protoporphyrin Ix ◽

Progression Free Survival ◽

The Cancer Genome Atlas ◽

Diagnostic Challenge ◽

Biological Basis

OBJECTIVE5-aminolevulinic acid (5-ALA)–induced protoporphyrin IX (PpIX) fluorescence is an effective surgical adjunct for the intraoperative identification of tumor tissue during resection of high-grade gliomas. The use of 5-ALA-induced PpIX fluorescence in glioblastoma (GBM) has been shown to double the extent of gross-total resection and 6-month progression-free survival. The heterogeneity of 5-ALA-induced PpIX fluorescence observed during surgery presents a technical and diagnostic challenge when utilizing this tool intraoperatively. While some regions show bright fluorescence after 5-ALA administration, other regions do not, despite that both regions of the tumor may be histopathologically indistinguishable. The authors examined the biological basis of this heterogeneity using computational methods.METHODSThe authors collected both fluorescent and nonfluorescent GBM specimens from a total of 14 patients undergoing surgery and examined their gene expression profiles.RESULTSIn this study, the authors found that the gene expression patterns characterizing fluorescent and nonfluorescent GBM surgical specimens were profoundly different and were associated with distinct cellular functions and different biological pathways. Nonfluorescent tumor tissue tended to resemble the neural subtype of GBM; meanwhile, fluorescent tumor tissue did not exhibit a prominent pattern corresponding to known subtypes of GBM. Consistent with this observation, neural GBM samples from The Cancer Genome Atlas database exhibited a significantly lower fluorescence score than nonneural GBM samples as determined by a fluorescence gene signature developed by the authors.CONCLUSIONSThese results provide a greater understanding regarding the biological basis of differential fluorescence observed intraoperatively and can provide a basis to identify novel strategies to maximize the effectiveness of fluorescence agents.

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Modular network inference between miRNA–mRNA expression profiles using weighted co-expression network analysis

Journal of Integrative Bioinformatics ◽

10.1515/jib-2021-0029 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Nisar Wani ◽

Debmalya Barh ◽

Khalid Raza

Keyword(s):

Network Analysis ◽

Mrna Expression ◽

Mirna Expression ◽

Regulatory Networks ◽

Network Inference ◽

Expression Profiles ◽

Interaction Network ◽

Pathway Enrichment Analysis ◽

Expression Data ◽

Cancer Data

Abstract Connecting transcriptional and post-transcriptional regulatory networks solves an important puzzle in the elucidation of gene regulatory mechanisms. To decipher the complexity of these connections, we build co-expression network modules for mRNA as well as miRNA expression profiles of breast cancer data. We construct gene and miRNA co-expression modules using the weighted gene co-expression network analysis (WGCNA) method and establish the significance of these modules (Genes/miRNAs) for cancer phenotype. This work also infers an interaction network between the genes of the turquoise module from mRNA expression data and hubs of the turquoise module from miRNA expression data. A pathway enrichment analysis using a miRsystem web tool for miRNA hubs and some of their targets, reveal their enrichment in several important pathways associated with the progression of cancer.

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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.

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