scholarly journals A Bayesian network approach for modeling mixed features in TCGA ovarian cancer data

2015 ◽  
Author(s):  
Qingyang Zhang ◽  
Ji-Ping Wang ◽  
Keyword(s):  
Ovarian Cancer ◽  
Bayesian Network ◽  
Molecular Mechanisms ◽  
Gene Clusters ◽  
The Cancer Genome Atlas ◽  
Cellular Functions ◽  
Driver Genes ◽  
Cancer Data ◽  
Integrative Framework ◽  
Mixed Features

AbstractWe propose an integrative framework to select important genetic and epigenetic features related to ovarian cancer and to quantify the causal relationships among these features using a logistic Bayesian network model based on The Cancer Genome Atlas data. The constructed Bayesian network has identified four gene clusters of distinct cellular functions, 13 driver genes, as well as some new biological pathways which may shed new light into the molecular mechanisms of ovarian cancer.

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

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.

scholarly journals Personalized characterization of diseases using sample-specific networks

2016 ◽  
Author(s):  
Xiaoping Liu ◽  
Yuetong Wang ◽  
Hongbin Ji ◽  
Kazuyuki Aihara ◽  
Luonan Chen
Keyword(s):  
Drug Resistance ◽  
Molecular Mechanisms ◽  
Complex Disease ◽  
The Cancer Genome Atlas ◽  
Single Sample ◽  
System Level ◽  
Driver Genes ◽  
Network Information ◽  
Cancer Genome Atlas ◽  
Network Patterns

ABSTRACTA complex disease generally results not from malfunction of individual molecules but from dysfunction of the relevant system or network, which dynamically changes with time and conditions. Thus, estimating a condition-specific network from a sample is crucial to elucidating the molecular mechanisms of complex diseases at the system level. However, there is currently no effective way to construct such an individual-specific network by expression profiling of a single sample because of the requirement of multiple samples for computing correlations. We developed here with a statistical method, i.e., a sample-specific network method, which allows us to construct individual-specific networks based on molecular expression of a single sample. Using this method, we can characterize various human diseases at a network level. In particular, such sample-specific networks can lead to the identification of individual-specific disease modules as well as driver genes, even without gene sequencing information. Extensive analysis by using the Cancer Genome Atlas data not only demonstrated the effectiveness of the method, but also found new individual-specific driver genes and network patterns for various cancers. Biological experiments on drug resistance further validated one important advantage of our method over the traditional methods, i.e., we even identified those drug resistance genes that actually have no clearly differential expression between samples with and without the resistance, due to the additional network information.

scholarly journals Identifying cancer type specific oncogenes and tumor suppressors using limited size data

2016 ◽  
Vol 14 (06) ◽  
pp. 1650031 ◽  
Author(s):  
Ana B. Pavel ◽  
Cristian I. Vasile
Keyword(s):  
Tumor Suppressors ◽  
Molecular Mechanisms ◽  
Lung Squamous Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Driver Mutations ◽  
Cancer Type ◽  
Multiple Cancer ◽  
Driver Genes ◽  
Cancer Subtypes ◽  
Cancer Types

Cancer is a complex and heterogeneous genetic disease. Different mutations and dysregulated molecular mechanisms alter the pathways that lead to cell proliferation. In this paper, we explore a method which classifies genes into oncogenes (ONGs) and tumor suppressors. We optimize this method to identify specific (ONGs) and tumor suppressors for breast cancer, lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) and colon adenocarcinoma (COAD), using data from the cancer genome atlas (TCGA). A set of genes were previously classified as ONGs and tumor suppressors across multiple cancer types (Science 2013). Each gene was assigned an ONG score and a tumor suppressor score based on the frequency of its driver mutations across all variants from the catalogue of somatic mutations in cancer (COSMIC). We evaluate and optimize this approach within different cancer types from TCGA. We are able to determine known driver genes for each of the four cancer types. After establishing the baseline parameters for each cancer type, we identify new driver genes for each cancer type, and the molecular pathways that are highly affected by them. Our methodology is general and can be applied to different cancer subtypes to identify specific driver genes and improve personalized therapy.

scholarly journals Integrated multi-omics analysis of ovarian cancer using variational autoencoders

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Muta Tah Hira ◽  
M. A. Razzaque ◽  
Claudio Angione ◽  
James Scrivens ◽  
Saladin Sawan ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Survival Analysis ◽  
Dimensionality Reduction ◽  
Cancer Prognosis ◽  
High Dimensional ◽  
Omics Data ◽  
Cellular Functions ◽  
Cancer Data ◽  
Molecular Features ◽  
Omics Analysis

AbstractCancer is a complex disease that deregulates cellular functions at various molecular levels (e.g., DNA, RNA, and proteins). Integrated multi-omics analysis of data from these levels is necessary to understand the aberrant cellular functions accountable for cancer and its development. In recent years, Deep Learning (DL) approaches have become a useful tool in integrated multi-omics analysis of cancer data. However, high dimensional multi-omics data are generally imbalanced with too many molecular features and relatively few patient samples. This imbalance makes a DL based integrated multi-omics analysis difficult. DL-based dimensionality reduction technique, including variational autoencoder (VAE), is a potential solution to balance high dimensional multi-omics data. However, there are few VAE-based integrated multi-omics analyses, and they are limited to pancancer. In this work, we did an integrated multi-omics analysis of ovarian cancer using the compressed features learned through VAE and an improved version of VAE, namely Maximum Mean Discrepancy VAE (MMD-VAE). First, we designed and developed a DL architecture for VAE and MMD-VAE. Then we used the architecture for mono-omics, integrated di-omics and tri-omics data analysis of ovarian cancer through cancer samples identification, molecular subtypes clustering and classification, and survival analysis. The results show that MMD-VAE and VAE-based compressed features can respectively classify the transcriptional subtypes of the TCGA datasets with an accuracy in the range of 93.2-95.5% and 87.1-95.7%. Also, survival analysis results show that VAE and MMD-VAE based compressed representation of omics data can be used in cancer prognosis. Based on the results, we can conclude that (i) VAE and MMD-VAE outperform existing dimensionality reduction techniques, (ii) integrated multi-omics analyses perform better or similar compared to their mono-omics counterparts, and (iii) MMD-VAE performs better than VAE in most omics dataset.

Enrichment of Up-regulated and Down-regulated Gene Clusters Using Gene Ontology, miRNAs and lncRNAs in Colorectal Cancer

2019 ◽  
Vol 22 (8) ◽  
pp. 534-545
Author(s):  
Fahimeh Fattahi ◽  
Jafar Kiani ◽  
Mohsen Khosravi ◽  
Somayeh Vafaei ◽  
Asghar Mohammadi ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Gene Ontology ◽  
Gene Networks ◽  
Molecular Mechanisms ◽  
Gene Clusters ◽  
Bioinformatics Analyses ◽  
Cancer Data ◽  
Peptide Receptor ◽  
Non Coding Rnas ◽  
Gene Data

Aim and Objective: It is interesting to find the gene signatures of cancer stages based on the omics data. The aim of study was to evaluate and to enrich the array data using gene ontology and ncRNA databases in colorectal cancer. Methods: The human colorectal cancer data were obtained from the GEO databank. The downregulated and up-regulated genes were identified after scoring, weighing and merging of the gene data. The clusters with high-score edges were determined from gene networks. The miRNAs related to the gene clusters were identified and enriched. Furthermore, the long non-coding RNA (lncRNA) networks were predicted with a central core for miRNAs. Results: Based on cluster enrichment, genes related to peptide receptor activity (1.26E-08), LBD domain binding (3.71E-07), rRNA processing (2.61E-34), chemokine (4.58E-19), peptide receptor (1.16E-19) and ECM organization (3.82E-16) were found. Furthermore, the clusters related to the non-coding RNAs, including hsa-miR-27b-5p, hsa-miR-155-5p, hsa-miR-125b-5p, hsa-miR-21-5p, hsa-miR-30e-5p, hsa-miR-588, hsa-miR-29-3p, LINC01234, LINC01029, LINC00917, LINC00668 and CASC11 were found. Conclusion: The comprehensive bioinformatics analyses provided the gene networks related to some non-coding RNAs that might help in understanding the molecular mechanisms in CRC.

scholarly journals Long non-coding RNA ZFAS1 promotes pancreatic cancer proliferation and metastasis by sponging miR-497-5p to regulate HMGA2 expression

2021 ◽  
Vol 12 (10) ◽  
Author(s):  
Min Rao ◽  
Song Xu ◽  
Yong Zhang ◽  
Yifan Liu ◽  
Wenkang Luan ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Cancer Cells ◽  
Molecular Mechanisms ◽  
Biological Effects ◽  
The Cancer Genome Atlas ◽  
Luciferase Reporter ◽  
Cancer Data ◽  
Pancreatic Cancer Cells ◽  
Cancer Tissues

AbstractThe lncRNA ZFAS1 plays a carcinogenic regulatory role in many human tumours, but it is rarely reported in pancreatic cancer. We identify the role and molecular mechanisms of ZFAS1 in pancreatic cancer. The expression of ZFAS1, miR-497-5p and HMGA2 in pancreatic cancer tissues was detected by qRT-PCR. Pancreatic cancer data in The Cancer Genome Atlas were also included in this study. CCK8, EdU, transwell and scratch wound assays were used to investigate the biological effects of ZFAS1 in pancreatic cancer cells. MS2-RIP, RNA pull-down, RNA-ChIP and luciferase reporter assays were used to clarify the molecular biological mechanisms of ZFAS1 in pancreatic cancer. The role of ZFAS1 in vivo was also confirmed via xenograft experiments. ZFAS1 was overexpressed in pancreatic cancer tissues. ZFAS1 promoted the growth and metastasis of pancreatic cancer cells, and miR-497-5p acted as a tumour suppressor gene in pancreatic cancer by targeting HMGA2. We also demonstrated that ZFAS1 exerts its effects by promoting HMGA2 expression through decoying miR-497-5p. We also found that ZFAS1 promoted the progression of pancreatic cancer in vivo by modulating the miR-497-5p/HMGA2 axis. In conclusion, this study revealed a new role for and the molecular mechanisms of ZFAS1 in pancreatic cancer, identifying ZFAS1 as a novel target for the diagnosis and treatment of pancreatic cancer.

scholarly journals Identification of Molecular Markers Associated With Ovarian Cancer Prognosis Using Bioinformatics Analysis

2020 ◽  
Author(s):  
Li Chuang ◽  
Yuan Lyu ◽  
Caixia Liu
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Ovarian Cancer ◽  
Overall Survival ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Brca1 Expression ◽  
Hub Genes

Abstract Background Ovarian cancer is associated with a high mortality rate worldwide. However, the pathogenesis, clinicopathological features, and genetic mechanisms of ovarian cancer are still unclear, and it is essential to identify prognostic markers for its clinical diagnosis and treatment. Here, we utilized bioinformatic analysis to identify potential genes involved in mediating BRCA1 expression to elucidate the potential mechanisms underlying ovarian cancer. Methods Gene expression profiling (GSE14407) was performed to identify differentially expressed genes (DEGs) and analyze the weighted gene co-expression network. We selected the key module that was significantly associated with BRCA1 expression and performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for genes in the hub module. We then screened the hub genes utilizing the Search Tool for the Retrieval of Interacting Genes Database (STRING) and Molecular Complex Detection (MCODE) plug-in in Cytoscape. We validated gene expression levels through The Cancer Genome Atlas and GTEx databases for hub genes and screened genes that were related to overall survival in patients with ovarian cancer using the Kaplan-Meier plotter database. Results In total, 3124 DEGs were detected, including 433 upregulated genes and 2691 downregulated genes. We selected the brown module, which was the most significantly associated with BRCA1 expression. GO analysis showed that the hub module genes were significantly enriched in biological processes, including the mitotic cell cycle process, chromosome segregation, and cell division. KEGG analysis showed that the hub module genes were particularly enriched in the cell cycle, p53 signaling pathway, and small cell lung cancer. We selected 30 hub genes from the protein-protein interaction network, which had 88 nodes and 721 edges. Further analyses identified PBK as a prognosis-associated hub gene. Notably, PBK expression was significantly increased in ovarian cancer tissues, as demonstrated by immunohistochemistry analysis using samples from the Human Protein Atlas database.Conclusion PBK was found to be associated with overall survival in patients with ovarian cancer. Our results provide insights into our understanding of the molecular mechanisms and molecular diagnosis of ovarian cancer.

scholarly journals Epiphycan Predicts Poor Outcomes and Promotes Metastasis in Ovarian Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Lu Deng ◽  
Dandan Wang ◽  
Shouzhen Chen ◽  
Weiguo Hu ◽  
Ru Zhang
Keyword(s):  
Ovarian Cancer ◽  
Molecular Mechanisms ◽  
Ovarian Cancer Cell Line ◽  
Cancer Cell Line ◽  
Scratch Test ◽  
The Cancer Genome Atlas ◽  
Primary Ovarian Cancer ◽  
Skov3 Cells ◽  
Cancer Genome Atlas ◽  
Poor Outcomes

The small leucine-rich proteoglycan (SLRP) family is widely expressed in extracellular matrix and aggravates tumor progression. However, epiphycan (EPYC), as a member of the SLRPs family, its biological function in cancer has not been confirmed. Thus, we aimed to clarify the role of EPYC in progression of ovarian cancer (OC), and further analyze the molecular mechanisms implicated in tumorigenesis. Here, we analyzed the differential expression genes of GSE38734, including 4 matched primary OC and metastatic tissues. We obtained OC RNAseqs data from the Cancer Genome Atlas (TCGA) and analyzed the correlation between EPYC expression and OC staging, pathological grading, etc. The expression of EPYC in OC and normal ovarian tissues was compared in Oncomine website. We used siRNAs to interfere the expression of EPYC in ovarian cancer cell line SKOV3. Scratch test, transwell-matrigel chamber, CCK8 assay were used to detect the changes of SKOV3 migration, invasion and proliferation ability after EPYC was interfered. We used R software to make GO and KEGG analysis of related genes of EPYC. We used the Hitpredict website to predict interacting proteins. The results showed that the expression of EPYC in metastatic ovarian cancer was higher than primary ovarian cancer, and that in primary cancer was higher than normal ovaries. After siRNA interferes with EPYC expression, the migration, invasion and proliferation of SKOV3 cells were weakened. EPYC mainly played a role in ECM organization, and involved in PI3K/Akt, focal adhesion signaling pathways. EPYC might interact with PLCG2 and CRK, and be involved in signal transduction.

scholarly journals OPCML Methylation and the Risk of Ovarian Cancer: A Meta and Bioinformatics Analysis

2021 ◽  
Vol 9 ◽  
Author(s):  
Yang Shao ◽  
Jing Kong ◽  
Hanzi Xu ◽  
Xiaoli Wu ◽  
YuePeng Cao ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Risk ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Clinicopathological Parameters ◽  
Control Group ◽  
Ovarian Cancer Risk ◽  
Sample Type ◽  
Cancer Data ◽  
Increased Risk

Background: The association of opioid binding protein cell adhesion molecule-like (OPCML) gene methylation with ovarian cancer risk remains unclear.Methods: We identified eligible studies by searching the PubMed, Web of Science, ScienceDirect, and Wanfang databases. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were used to determine the association of OPCML methylation with ovarian cancer risk. Meta-regression and subgroup analysis were used to assess the sources of heterogeneity. Additionally, we analyzed the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets to validate our findings.Results: Our study included 476 ovarian cancer patients and 385 controls from eight eligible studies. The pooled OR was 33.47 (95% CI = 12.43–90.16) in the cancer group vs. the control group under the random-effects model. The association was still significant in subgroups according to sample type, control type, methods, and sample sizes (all P < 0.05). Sensitivity analysis showed that the finding was robust. No publication bias was observed in Begg's (P = 0.458) and Egger's tests (P = 0.261). We further found that OPCML methylation was related to III/IV (OR = 4.20, 95% CI = 1.59–11.14) and poorly differentiated grade (OR = 4.37; 95% CI = 1.14–16.78). Based on GSE146552 and GSE155760, we validated that three CpG sites (cg16639665, cg23236270, cg15964611) in OPCML promoter region were significantly higher in cancer tissues compared to normal tissues. However, we did not observe the associations of OPCML methylation with clinicopathological parameters and overall survival based on TCGA ovarian cancer data.Conclusion: Our findings support that OPCML methylation is associated with an increased risk of ovarian cancer.

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

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Yun-Yun Tang ◽  
Pi-Jing Wei ◽  
Jian-ping Zhao ◽  
Junfeng Xia ◽  
Rui-Fen Cao ◽  
...  
Keyword(s):  
Somatic Mutations ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Interaction Network ◽  
The Cancer Genome Atlas ◽  
Local Network ◽  
Network Centrality ◽  
Driver Genes ◽  
Cancer Data ◽  
Mutant Genes

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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