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 YTHDC1-mediated VPS25 regulates cell cycle by targeting JAK-STAT signaling in human glioma cells

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiaolong Zhu ◽  
Hui Yang ◽  
Mengying Zhang ◽  
Xingwei Wu ◽  
Lan Jiang ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Glioma Cells ◽  
Prognostic Indicator ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Endosomal Sorting ◽  
Cancer Genome Atlas

Abstract Background Glioma is a common type of malignant brain tumor with a high mortality and relapse rate. The endosomal sorting complex required for transport (ESCRT) has been reported to be involved in tumorigenesis. However, the molecular mechanisms have not been clarified. Methods Bioinformatics was used to screen the ESCRT subunits highly expressed in glioma tissues from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The function of the ESCRT subunits in glioma cells was examined in vitro. Transcriptome sequencing analyzed the target genes and signaling pathways affected by the ESCRT subunit. Finally, the relationship between m6A (N6-methyladenosine) modification and high expression of the ESCRT subunit was studied. Results VPS25 was upregulated in glioma tissues, which was correlated with poor prognosis in glioma patients. Furthermore, VPS25 knockdown inhibited the proliferation, blocked the cell cycle, and promoted apoptosis in glioma cells. Meanwhile, VPS25 induced a G0/G1 phase arrest of the cell cycle in glioma cells by directly mediating p21, CDK2, and cyclin E expression, and JAK-signal transducer and activator of transcription (STAT) activation. Finally, YTHDC1 inhibited glioma proliferation by reducing the expression of VPS25. Conclusion These results suggest that VPS25 is a promising prognostic indicator and a potential therapeutic target for glioma.

scholarly journals Multi-omics analysis to identify driving factors in colorectal cancer

Epigenomics ◽  
2020 ◽  
Vol 12 (18) ◽  
pp. 1633-1650
Author(s):  
Xi Xu ◽  
Chaoju Gong ◽  
Yunfeng Wang ◽  
Yanyan Hu ◽  
Hong Liu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Molecular Mechanisms ◽  
Copy Number Variations ◽  
The Cancer Genome Atlas ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Omics Analysis ◽  
Competing Endogenous Rna Network ◽  
Cancer Genome Atlas

Aim: We aim to identify driving genes of colorectal cancer (CRC) through multi-omics analysis. Materials & methods: We downloaded multi-omics data of CRC from The Cancer Genome Atlas dataset. Integrative analysis of single-nucleotide variants, copy number variations, DNA methylation and differentially expressed genes identified candidate genes that carry CRC risk. Kernal genes were extracted from the weighted gene co-expression network analysis. A competing endogenous RNA network composed of CRC-related genes was constructed. Biological roles of genes were further investigated in vitro. Results: We identified LRRC26 and REP15 as novel prognosis-related driving genes for CRC. LRRC26 hindered tumorigenesis of CRC in vitro. Conclusion: Our study identified novel driving genes and may provide new insights into the molecular mechanisms of CRC.

scholarly journals Identification of tRNA-derived ncRNAs in TCGA and NCI-60 panel cell lines and development of the public database tRFexplorer

Database ◽  
2019 ◽  
Vol 2019 ◽  
Author(s):  
Alessandro La Ferlita ◽  
Salvatore Alaimo ◽  
Dario Veneziano ◽  
Giovanni Nigita ◽  
Veronica Balatti ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Expression Profile ◽  
Molecular Mechanisms ◽  
Gene Expression Regulation ◽  
The Cancer Genome Atlas ◽  
Tumor Type ◽  
Public Database ◽  
The Public ◽  
Cancer Genome Atlas ◽  
First Time

Abstract Next-generation sequencing is increasing our understanding and knowledge of non-coding RNAs (ncRNAs), elucidating their roles in molecular mechanisms and processes such as cell growth and development. Within such a class, tRNA-derived ncRNAs have been recently associated with gene expression regulation in cancer progression. In this paper, we characterize, for the first time, tRNA-derived ncRNAs in NCI-60. Furthermore, we assess their expression profile in The Cancer Genome Atlas (TCGA). Our comprehensive analysis allowed us to report 322 distinct tRNA-derived ncRNAs in NCI-60, categorized in tRNA-derived fragments (11 tRF-5s, 55 tRF-3s), tRNA-derived small RNAs (107 tsRNAs) and tRNA 5′ leader RNAs (149 sequences identified). In TCGA, we were able to identify 232 distinct tRNA-derived ncRNAs categorized in 53 tRF-5s, 58 tRF-3s, 63 tsRNAs and 58 5′ leader RNAs. This latter group represents an additional evidence of tRNA-derived ncRNAs originating from the 5′ leader region of precursor tRNA. We developed a public database, tRFexplorer, which provides users with the expression profile of each tRNA-derived ncRNAs in every cell line in NCI-60 as well as for each TCGA tumor type. Moreover, the system allows us to perform differential expression analyses of such fragments in TCGA, as well as correlation analyses of tRNA-derived ncRNAs expression in TCGA and NCI-60 with gene and miRNA expression in TCGA samples, in association with all omics and compound activities data available on CellMiner. Hence, the tool provides an important opportunity to investigate their potential biological roles in absence of any direct experimental evidence. Database URL: https://trfexplorer.cloud/

scholarly journals MTA2 as a Potential Biomarker and Its Involvement in Metastatic Progression of Human Renal Cancer by miR-133b Targeting MMP-9

Cancers ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 1851 ◽  
Author(s):  
Yong-Syuan Chen ◽  
Tung-Wei Hung ◽  
Shih-Chi Su ◽  
Chia-Liang Lin ◽  
Shun-Fa Yang ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Survival Rates ◽  
The Cancer Genome Atlas ◽  
Metastatic Progression ◽  
Tumour Grade ◽  
Potential Biomarker ◽  
Cancer Genome Atlas ◽  
Metalloproteinase 9

Metastasis-associated protein 2 (MTA2) was previously known as a requirement to maintain malignant potentials in several human cancers. However, the role of MTA2 in the progression of renal cell carcinoma (RCC) has not yet been delineated. In this study, MTA2 expression was significantly increased in RCC tissues and cell lines. Increased MTA2 expression was significantly associated with tumour grade (p = 0.002) and was an independent prognostic factor for overall survival with a high RCC tumour grade. MTA2 knockdown inhibited the migration, invasion, and in vivo metastasis of RCC cells without effects on cell proliferation. Regarding molecular mechanisms, MTA2 knockdown reduced the activity, protein level, and mRNA expression of matrix metalloproteinase-9 (MMP-9) in RCC cells. Further analyses demonstrated that patients with lower miR-133b expression had poorer survival rates than those with higher expression from The Cancer Genome Atlas database. Moreover, miR-133b modulated the 3′untranslated region (UTR) of MMP-9 promoter activities and subsequently the migratory and invasive abilities of these dysregulated expressions of MTA2 in RCC cells. The inhibition of MTA2 could contribute to human RCC metastasis by regulating the expression of miR-133b targeting MMP-9 expression.

Disease characterization using a partial correlation-based sample-specific network

2020 ◽  
Author(s):  
Yanhong Huang ◽  
Xiao Chang ◽  
Yu Zhang ◽  
Luonan Chen ◽  
Xiaoping Liu
Keyword(s):  
Partial Correlation ◽  
Single Cells ◽  
Computational Method ◽  
The Cancer Genome Atlas ◽  
Single Sample ◽  
Reference Dataset ◽  
Network Distance ◽  
Two Samples ◽  
Sample Data ◽  
Cancer Genome Atlas

Abstract A single-sample network (SSN) is a biological molecular network constructed from single-sample data given a reference dataset and can provide insights into the mechanisms of individual diseases and aid in the development of personalized medicine. In this study, we proposed a computational method, a partial correlation-based single-sample network (P-SSN), which not only infers a network from each single-sample data given a reference dataset but also retains the direct interactions by excluding indirect interactions (https://github.com/hyhRise/P-SSN). By applying P-SSN to analyze tumor data from the Cancer Genome Atlas and single cell data, we validated the effectiveness of P-SSN in predicting driver mutation genes (DMGs), producing network distance, identifying subtypes and further classifying single cells. In particular, P-SSN is highly effective in predicting DMGs based on single-sample data. P-SSN is also efficient for subtyping complex diseases and for clustering single cells by introducing network distance between any two samples.

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 miR-149-5p inhibits cell growth by regulating TWEAK/Fn14/PI3K/AKT pathway and predicts favorable survival in human osteosarcoma

2018 ◽  
Vol 32 ◽  
pp. 205873841878665 ◽  
Author(s):  
Rui-Da Xu ◽  
Fan Feng ◽  
Xiao-Sheng Yu ◽  
Zu-De Liu ◽  
Li-Feng Lao
Keyword(s):  
Cell Growth ◽  
Molecular Mechanisms ◽  
Clinicopathological Characteristics ◽  
The Cancer Genome Atlas ◽  
Osteosarcoma Cell ◽  
Sequencing Data ◽  
Serine Threonine Kinase ◽  
Tnf Superfamily ◽  
Cancer Genome Atlas ◽  
Tumor Growth And Metastasis

MicroRNAs (miRNAs) as small non-coding RNAs act as either tumor suppressors or oncogenes in human cancers, of which miR-149-5p (miR-149) is involved in tumor growth and metastasis, but its role and molecular mechanisms underlying osteosarcoma growth are poorly understood. The correlation of miR-149 expression with clinicopathological characteristics and prognosis in patients with sarcoma was analyzed by The Cancer Genome Atlas (TCGA) RNA-sequencing data. Osteosarcoma cell growth affected by miR-149 was evaluated by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and colony formation assays. As a result, we found that the expression level of miR-149 was markedly downregulated in human sarcoma samples and were negatively associated with tumor size, acting as an independent prognostic factor for overall survival of the sarcoma patients. Restoration of miR-149 expression suppressed osteosarcoma cell growth, while its knockdown reversed these effects. Furthermore, we identified TNFRSF12A (TNF receptor superfamily member 12A), also called fibroblast growth factor–inducible 14 (Fn14) as a direct target of miR-149, and TNFRSF12A and its ligand TNFSF12 (TNF superfamily member 12), also called tumor necrosis factor–related weak inducer of apoptosis (TWEAK), were both negatively correlated with miR-149 expression in sarcoma samples. Knockdown of TNFRSF12A suppressed cell growth, but its overexpression weakened the antiproliferative effects of miR-149 via the PI3K/AKT (AKT serine/threonine kinase) signaling pathway. Altogether, our findings show that miR-149 functions as a tumor suppressor in osteosarcoma via inhibition of the TWEAK–Fn14 axis and represents a potential therapeutic target in patients with osteosarcoma.

scholarly journals Inferring perturbation profiles of cancer samples

2020 ◽  
Author(s):  
Martin Pirkl ◽  
Niko Beerenwinkel
Keyword(s):  
Indirect Evidence ◽  
R Package ◽  
The Cancer Genome Atlas ◽  
Patient Specific ◽  
Driver Genes ◽  
Cancer Driver ◽  
Molecular Alterations ◽  
Incomplete Coverage ◽  
Cancer Genome Atlas ◽  
Gene Perturbations

AbstractMotivationCancer is one of the most prevalent diseases in the world. Tumors arise due to important genes changing their activity, e.g., when inhibited or over-expressed. But these gene perturbations are difficult to observe directly. Molecular profiles of tumors can provide indirect evidence of gene perturbations. However, inferring perturbation profiles from molecular alterations is challenging due to error-prone molecular measurements and incomplete coverage of all possible molecular causes of gene perturbations.ResultsWe have developed a novel mathematical method to analyze cancer driver genes and their patient-specific perturbation profiles. We combine genetic aberrations with gene expression data in a causal network derived across patients to infer unobserved perturbations. We show that our method can predict perturbations in simulations, CRISPR perturbation screens, and breast cancer samples from The Cancer Genome Atlas.AvailabilityThe method is available as the R-package nempi at https://github.com/cbg-ethz/[email protected], [email protected]

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.

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