scholarly journals Network Properties of Cancer Prognostic Gene Signatures in the Human Protein Interactome

Genes ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 247
Author(s):  
Jifeng Zhang ◽  
Shoubao Yan ◽  
Cheng Jiang ◽  
Zhicheng Ji ◽  
Chenrun Wang ◽  
...  
Keyword(s):  
Clustering Coefficient ◽  
Human Protein ◽  
Random Sets ◽  
P Value ◽  
Gene Signatures ◽  
Prognostic Gene ◽  
Gene Sets ◽  
Protein Interactome ◽  
Network Properties ◽  
Set Distance

Prognostic gene signatures are critical in cancer prognosis assessments and their pinpoint treatments. However, their network properties remain unclear. Here, we obtained nine prognostic gene sets including 1439 prognostic genes of different cancers from related publications. Four network centralities were used to examine the network properties of prognostic genes (PG) compared with other gene sets based on the Human Protein Reference Database (HPRD) and String networks. We also proposed three novel network measures for further investigating the network properties of prognostic gene sets (PGS) besides clustering coefficient. The results showed that PG did not occupy key positions in the human protein interaction network and were more similar to essential genes rather than cancer genes. However, PGS had significantly smaller intra-set distance (IAD) and inter-set distance (IED) in comparison with random sets (p-value < 0.001). Moreover, we also found that PGS tended to be distributed within network modules rather than between modules (p-value < 0.01), and the functional intersection of the modules enriched with PGS was closely related to cancer development and progression. Our research reveals the common network properties of cancer prognostic gene signatures in the human protein interactome. We argue that these are biologically meaningful and useful for understanding their molecular mechanism.

scholarly journals Network properties of cancer prognostic genes and their gene sets in the human protein interactome

2019 ◽  
Author(s):  
Jifeng Zhang ◽  
Cheng Jiang ◽  
Zhicheng Ji ◽  
Chenrun Wang
Keyword(s):  
Essential Gene ◽  
Housekeeping Gene ◽  
Human Protein ◽  
Random Sets ◽  
Gene Set ◽  
Gene Sets ◽  
Interactome Network ◽  
Protein Interactome ◽  
Network Modules ◽  
String Networks

Abstract Background Identifying prognostic genes (PG) is crucial for estimating survival time and providing pinpoint treatments for patients with cancer. However, prognostic genes sets (PGS) reported in most existing research have low reproducibility and overlap ever between the same cancers or their subtypes. Their common characteristic as well as the molecular mechanism of action is still elusive. Methods Here, we obtained nine prognostic gene sets (including 1,439 prognostic genes) of different types of cancer from 23 high quality literatures, and systemically investigated eight network topological properties for PG and PGS compared with background and four other gene sets (cancer gene set CA, essential gene set ES, housekeeping gene set HK, and metastasis-angiogenesis gene set MA) based on the HPRD and String networks. Results The results showed that PG did not occupy key positions in the human protein interactome network, and were more similar to ES rather than CA. Also, PGS had significantly small intraset distance (IAD) and interset distance (IED) in comparison with random sets. Further, we also found that PGS tended to have be distributed within network modules rather than between modules, the functional intersection of the modules enriched with PGS was closely related to cancer. Conclusions Our research reveals the common properties of cancer PG and PGS in the human protein interactome network, and can help us understand and discover cancer prognostic biomarkers.

scholarly journals Mapping the human protein interactome

Cell Research ◽  
2008 ◽  
Vol 18 (7) ◽  
pp. 716-724 ◽  
Author(s):  
Daniel Figeys
Keyword(s):  
Human Protein ◽  
Protein Interactome

scholarly journals 1000 Genomes-based meta-analysis identifies 10 novel loci for kidney function

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Mathias Gorski ◽  
Peter J. van der Most ◽  
Alexander Teumer ◽  
Audrey Y. Chu ◽  
Man Li ◽  
...  
Keyword(s):  
Kidney Function ◽  
Kidney Development ◽  
Association Studies ◽  
Meta Analysis ◽  
European Ancestry ◽  
P Value ◽  
Genome Wide Association Studies ◽  
1000 Genomes ◽  
Gene Sets ◽  
Project Promise

Abstract HapMap imputed genome-wide association studies (GWAS) have revealed >50 loci at which common variants with minor allele frequency >5% are associated with kidney function. GWAS using more complete reference sets for imputation, such as those from The 1000 Genomes project, promise to identify novel loci that have been missed by previous efforts. To investigate the value of such a more complete variant catalog, we conducted a GWAS meta-analysis of kidney function based on the estimated glomerular filtration rate (eGFR) in 110,517 European ancestry participants using 1000 Genomes imputed data. We identified 10 novel loci with p-value < 5 × 10−8 previously missed by HapMap-based GWAS. Six of these loci (HOXD8, ARL15, PIK3R1, EYA4, ASTN2, and EPB41L3) are tagged by common SNPs unique to the 1000 Genomes reference panel. Using pathway analysis, we identified 39 significant (FDR < 0.05) genes and 127 significantly (FDR < 0.05) enriched gene sets, which were missed by our previous analyses. Among those, the 10 identified novel genes are part of pathways of kidney development, carbohydrate metabolism, cardiac septum development and glucose metabolism. These results highlight the utility of re-imputing from denser reference panels, until whole-genome sequencing becomes feasible in large samples.

ArrayMAPPIT: A Screening Platform for Human Protein Interactome Analysis

2011 ◽  
pp. 283-294 ◽  
Author(s):  
Sam Lievens ◽  
Nele Vanderroost ◽  
Dieter Defever ◽  
José Van der Heyden ◽  
Jan Tavernier
Keyword(s):  
Human Protein ◽  
Protein Interactome ◽  
Screening Platform

Transcriptomics based prediction of metastasis in TNBC patients: Challenges in cross-platforms validation

2021 ◽  
Author(s):  
Naorem Leimarembi Devi ◽  
Anjali Dhall ◽  
Sumeet Patiyal ◽  
Gajendra P. S. Raghava
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Prediction Models ◽  
Machine Learning Techniques ◽  
Lymph Node Status ◽  
P Value ◽  
Gene Signatures ◽  
Node Metastasis ◽  
Microarray Datasets

Triple-negative breast cancer (TNBC) is more prone to metastasis and recurrence than other breast cancer subtypes. This study aimed to identify genes that can act as diagnostic biomarkers for predicting lymph node metastasis in TNBC patients. The transcriptomic data of TNBC with or without lymph node metastasis was acquired from TCGA, and the differentially expressed genes were identified. Further, logistic-regression method has been used to identify the top 15 genes (or 15 gene signatures) based on their ability to predict metastasis (AUC>0.65). These 15 gene signatures were used to develop machine learning techniques based prediction models; Gaussian Naive Bayes classifier outperformed other with AUC>0.80 on both training and validation datasets. The best model failed drastically on nine independent microarray datasets obtained from GEO. We investigated the reason for the failure of our best model, and it was observed that the certain genes in 15 gene signatures were showing opposite regulating trends, i.e., genes are upregulated in TCGA-TNBC patients while it is downregulated on other microarray datasets or vice-versa. In conclusion, the 15 gene signatures may act as diagnostic markers for the detection of lymph node metastatic status in TCGA dataset, but quite challenging across multiple platforms. We also identified the prognostic potential of the 15 selected genes and found that overexpression of ZNRF2, FRZB, and TCEAL4 was associated with poor survival with HR>2.3 and p-value≤0.05. In order to provide services to the scientific community, we developed a webserver named 'MTNBCPred' for the prediction of metastatic and non-metastatic lymph node status of TNBC patients (http://webs.iiitd.edu.in/raghava/mtnbcpred/ ).

Abstract 17426: Pulmonary Vein Isolation ‘Rewires’ Electrical Communications to Enhance Small-world Network Topology During Atrial Fibrillation

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Hiroshi Ashikaga ◽  
Jonathan Chrispin ◽  
Degang Wu ◽  
Joshua Garland
Keyword(s):  
Atrial Fibrillation ◽  
Communication Network ◽  
Mutual Information ◽  
Path Length ◽  
Random Network ◽  
Small World ◽  
Clustering Coefficient ◽  
Small World Network ◽  
Z Score ◽  
Network Properties

Recent evidence suggests that pulmonary vein isolation (PVI) may perturb the electrophysiological substrate for maintenance of atrial fibrillation (AF). Our previous work indicates that information theory metrics can quantify electrical communications during arrhythmia. We hypothesized that PVI ‘rewires’ the electrical communication network during AF such that the topology exhibits higher levels of small-world network properties, with higher clustering coefficient and lower path length, than would be expected by chance. Thirteen consecutive patients (n=6 with prior PVI and n=7 without) underwent AF ablation using a 64-electrode basket catheter in the left atrium. Multielectrode recording was performed during AF for 60 seconds, followed by PVI. Mutual information was calculated from the time series between each pair of electrodes using the Kraskov-Stögbauer-Grassberger estimator. The all-to-all mutual information matrix (64x64; Figure, upper panels) was thresholded by the median and standard deviations of mutual information to build a binary adjacency matrix for electrical communication networks. The properties of small-world network ( swn ; ‘small-world-ness’) were quantified by the ratio of the observed average clustering coefficient to that of a random network over the ratio of the observed average path length to that of a random network. swn was expressed in normal Z standard deviation units. As the binarizing threshold increased, the Z-score of swn decreased (Figure, lower panel). However, the Z-score at each threshold value was consistently higher with prior PVI than those without (p<0.05). In conclusion, electrical communication network during AF with prior PVI is associated with higher levels of small-world network properties than those without. This finding supports the concept that PVI perturbs the underlying substrate. In addition, swn of electrical communication network may be a promising metric to quantify substrate modification.

scholarly journals Altered structural brain network resulting from white matter injury in obstructive sleep apnea

SLEEP ◽  
2019 ◽  
Vol 42 (9) ◽  
Author(s):  
Min-Hee Lee ◽  
Chang-Ho Yun ◽  
Areum Min ◽  
Yoon Ho Hwang ◽  
Seung Ku Lee ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
White Matter ◽  
Tissue Injury ◽  
Brain Network ◽  
Global Network ◽  
P Value ◽  
Obstructive Sleep ◽  
Network Properties ◽  
Structural Brain

Abstract Study Objectives To assess, using fractional anisotropy (FA) analysis, alterations of brain network connectivity in adults with obstructive sleep apnea (OSA). Abnormal networks could mediate clinical functional deficits and reflect brain tissue injury. Methods Structural brain networks were constructed using diffusion tensor imaging (DTI) from 165 healthy (age 57.99 ± 6.02 years, male 27.9%) and 135 OSA participants (age 59.01 ± 5.91 years, male 28.9%) and global network properties (strength, global efficiency, and local efficiency) and regional efficiency were compared between groups. We examined MRI biomarkers of brain tissue injury using FA analysis and its effect on the network properties. Results Differences between groups of interest were noted in global network properties (p-value < 0.05, corrected), and regional efficiency (p-value < 0.05, corrected) in the left middle cingulate and paracingulate gyri, right posterior cingulate gyrus, and amygdala. In FA analysis, OSA participants showed lower FA values in white matter (WM) of the right transverse temporal, anterior cingulate and paracingulate gyri, and left postcentral, middle frontal and medial frontal gyri, and the putamen. After culling fiber tracts through WM which showed significant differences in FA, we observed no group difference in network properties. Conclusions Changes in WM integrity and structural connectivity are present in OSA participants. We found that the integrity of WM affected brain network properties. Brain network analysis may improve understanding of neurocognitive deficits in OSA, enable longitudinal tracking, and provides explanations for specific symptoms and recovery kinetics.

scholarly journals A Network Analysis of Multiple Myeloma Related Gene Signatures

Cancers ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1452 ◽  
Author(s):  
Yu Liu ◽  
Haocheng Yu ◽  
Seungyeul Yoo ◽  
Eunjee Lee ◽  
Alessandro Laganà ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Drug Response ◽  
Molecular Mechanisms ◽  
Proteasome Inhibitors ◽  
Risk Groups ◽  
Rank Test ◽  
P Value ◽  
Causal Network ◽  
Gene Signatures ◽  
Network Analyses

Multiple myeloma (MM) is the second most prevalent hematological cancer. MM is a complex and heterogeneous disease, and thus, it is essential to leverage omics data from large MM cohorts to understand the molecular mechanisms underlying MM tumorigenesis, progression, and drug responses, which may aid in the development of better treatments. In this study, we analyzed gene expression, copy number variation, and clinical data from the Multiple Myeloma Research Consortium (MMRC) dataset and constructed a multiple myeloma molecular causal network (M3CN). The M3CN was used to unify eight prognostic gene signatures in the literature that shared very few genes between them, resulting in a prognostic subnetwork of the M3CN, consisting of 178 genes that were enriched for genes involved in cell cycle (fold enrichment = 8.4, p value = 6.1 × 10−26). The M3CN was further used to characterize immunomodulators and proteasome inhibitors for MM, demonstrating the pleiotropic effects of these drugs, with drug-response signature genes enriched across multiple M3CN subnetworks. Network analyses indicated potential links between these drug-response subnetworks and the prognostic subnetwork. To elucidate the structure of these important MM subnetworks, we identified putative key regulators predicted to modulate the state of these subnetworks. Finally, to assess the predictive power of our network-based models, we stratified MM patients in an independent cohort, the MMRF-CoMMpass study, based on the prognostic subnetwork, and compared the performance of this subnetwork against other signatures in the literature. We show that the M3CN-derived prognostic subnetwork achieved the best separation between different risk groups in terms of log-rank test p-values and hazard ratios. In summary, this work demonstrates the power of a probabilistic causal network approach to understanding molecular mechanisms underlying the different MM signatures.

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