Identification of Cancer Dysfunctional Subpathways by Integrating DNA Methylation, Copy Number Variation, and Gene-Expression Data

Liu, Siyao; Zheng, Buhong; Sheng, Yuqi; Kong, Qingfei; Jiang, Ying; Yang, Yang; Han, Xudong; Liang, Cheng; Zhang, Yunpeng; Han, Junwei

doi:10.3389/fgene.2019.00441

Cited by 15 publications

(11 citation statements)

References 48 publications

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“…Interactions among proteins or genes are meant to be specific and biologically meaningful ( Wiredja and Bebek, 2017 ; Sanchez and Mackenzie, 2020 ). It has been reported that network-based approaches have high efficacy in identifying biomarkers for numerous complex diseases, including cancers ( Wang et al, 2017 ; Chen et al, 2019 ; Liu et al, 2019 ; Uddin et al, 2019 ; Khan et al, 2020 ; Van et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

A Network-Based Methodology to Identify Subnetwork Markers for Diagnosis and Prognosis of Colorectal Cancer

et al. 2021

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The development of reliable methods for identification of robust biomarkers for complex diseases is critical for disease diagnosis and prognosis efforts. Integrating multi-omics data with protein-protein interaction (PPI) networks to investigate diseases may help better understand disease characteristics at the molecular level. In this study, we developed and tested a novel network-based method to detect subnetwork markers for patients with colorectal cancer (CRC). We performed an integrated omics analysis using whole-genome gene expression profiling and copy number alterations (CNAs) datasets followed by building a gene interaction network for the significantly altered genes. We then clustered the constructed gene network into subnetworks and assigned a score for each significant subnetwork. We developed a support vector machine (SVM) classifier using these scores as feature values and tested the methodology in independent CRC transcriptomic datasets. The network analysis resulted in 15 subnetwork markers that revealed several hub genes that may play a significant role in colorectal cancer, including PTP4A3, FGFR2, PTX3, AURKA, FEN1, INHBA, and YES1. The 15-subnetwork classifier displayed over 98 percent accuracy in detecting patients with CRC. In comparison to individual gene biomarkers, subnetwork markers based on integrated multi-omics and network analyses may lead to better disease classification, diagnosis, and prognosis.

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Section: Introductionmentioning

confidence: 99%

A Network-Based Methodology to Identify Subnetwork Markers for Diagnosis and Prognosis of Colorectal Cancer

et al. 2021

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“…Several widely used and convenient bio-sequence feature representation tools have been developed (Mrozek et al, 2013;Liu et al, 2015aLiu et al, , 2019cYu et al, 2015Cheng and Hu, 2018;Hu et al, 2019;Muhammod et al, 2019). The two main tools used in this work were iLearn (Hu et al, 2019) and PyFeat (Muhammod et al, 2019).…”

Section: Feature Representationmentioning

confidence: 99%

RF-PseU: A Random Forest Predictor for RNA Pseudouridine Sites

Zhang

Ding

et al. 2020

Front. Bioeng. Biotechnol.

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One of the ubiquitous chemical modifications in RNA, pseudouridine modification is crucial for various cellular biological and physiological processes. To gain more insight into the functional mechanisms involved, it is of fundamental importance to precisely identify pseudouridine sites in RNA. Several useful machine learning approaches have become available recently, with the increasing progress of next-generation sequencing technology; however, existing methods cannot predict sites with high accuracy. Thus, a more accurate predictor is required. In this study, a random forest-based predictor named RF-PseU is proposed for prediction of pseudouridylation sites. To optimize feature representation and obtain a better model, the light gradient boosting machine algorithm and incremental feature selection strategy were used to select the optimum feature space vector for training the random forest model RF-PseU. Compared with previous state-of-the-art predictors, the results on the same benchmark data sets of three species demonstrate that RF-PseU performs better overall. The integrated average leave-one-out cross-validation and independent testing accuracy scores were 71.4% and 74.7%, respectively, representing increments of 3.63% and 4.77% versus the best existing predictor. Moreover, the final RF-PseU model for prediction was built on leave-one-out cross-validation and provides a reliable and robust tool for identifying pseudouridine sites. A web server with a user-friendly interface is accessible at http://148.70.81.170:10228/rfpseu.

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“…A large number of genetic components within entire pathways presents a challenge for precise medical analyses (9)(10)(11). Subpathways are defined as local gene subregions within canonical biological pathways, and their dysfunction has been reported to be associated with the occurrence, development, and prognosis of cancer (12)(13)(14). Subpathways are also used as signature, biomarkers, and drug recognition of cancer (13,15,16).…”

Section: Introductionmentioning

confidence: 99%

Inference of Subpathway Activity Profiles Reveals Metabolism Abnormal Subpathway Regions in Glioblastoma Multiforme

et al. 2020

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Glioblastoma, also known as glioblastoma multiforme (GBM), is the most malignant form of glioma and represents 81% of malignant brain and central nervous system (CNS) tumors. Like most cancers, GBM causes metabolic recombination to promote cell survival, proliferation, and invasion of cancer cells. In this study, we propose a method for constructing the metabolic subpathway activity score matrix to accurately identify abnormal targets of GBM metabolism. By integrating gene expression data from different sequencing methods, our method identified 25 metabolic subpathways that were significantly abnormal in the GBM patient population, and most of these subpathways have been reported to have an effect on GBM. Through the analysis of 25 GBM-related metabolic subpathways, we found that (S)-2,3-Epoxysqualene, which was at the central region of the sterol biosynthesis subpathway, may have a greater impact on the entire pathway, suggesting a potential high association with GBM. Analysis of CCK8 cell activity indicated that (S)-2,3-Epoxysqualene can indeed inhibit the activity of U87-MG cells. By flow cytometry, we demonstrated that (S)-2,3-Epoxysqualene not only arrested the U87-MG cell cycle in the G0/G1 phase but also induced cell apoptosis. These results confirm the reliability of our proposed metabolic subpathway identification method and suggest that (S)-2,3-Epoxysqualene has potential therapeutic value for GBM. In order to make the method more broadly applicable, we have developed an R system package crmSubpathway to perform disease-related metabolic subpathway identification and it is freely available on the GitHub ( https://github.com/hanjunwei-lab/crmSubpathway ).

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Identification of Cancer Dysfunctional Subpathways by Integrating DNA Methylation, Copy Number Variation, and Gene-Expression Data

Cited by 15 publications

References 48 publications

A Network-Based Methodology to Identify Subnetwork Markers for Diagnosis and Prognosis of Colorectal Cancer

A Network-Based Methodology to Identify Subnetwork Markers for Diagnosis and Prognosis of Colorectal Cancer

RF-PseU: A Random Forest Predictor for RNA Pseudouridine Sites

Inference of Subpathway Activity Profiles Reveals Metabolism Abnormal Subpathway Regions in Glioblastoma Multiforme

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