Biomolecular Databases and Subnetwork Identification Approaches of Interest to Big Data Community: An Expert Review

Al‐Harazi, Olfat; Allali, Achraf El; Çolak, Dilek

doi:10.1089/omi.2018.0205

Cited by 18 publications

(15 citation statements)

References 103 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Genome-wide gene expression profiling provides valuable insight into the transcriptional changes that appear during the carcinogenic process beyond what may be obvious from studies evaluating only clinicopathologic characteristics. The investigation of human diseases using a combination of the human genome-wide molecular data and interactome may further provide an important viewpoint for understanding the molecular features of diseases ( Al-Harazi et al, 2016 ; Al-Harazi et al, 2019 ). Here, we used several genome-wide gene expression profiling datasets from human eHCCs and a rat model of early HCC to conduct an integrative analysis.…”

Section: Resultsmentioning

confidence: 99%

“…The orange color refers to eHCC samples, and blue for normal controls. molecular features of diseases (Al-Harazi et al, 2016;Al-Harazi et al, 2019). Here, we used several genome-wide gene expression profiling datasets from human eHCCs and a rat model of early HCC to conduct an integrative analysis.…”

Section: Identification Of a Blood-based Gene Signature For Early Hcc (Ehcc)mentioning

confidence: 99%

See 1 more Smart Citation

Identification of Gene Signature as Diagnostic and Prognostic Blood Biomarker for Early Hepatocellular Carcinoma Using Integrated Cross-Species Transcriptomic and Network Analyses

et al. 2021

Self Cite

View full text Add to dashboard Cite

Background: Hepatocellular carcinoma (HCC) is considered the most common type of liver cancer and the fourth leading cause of cancer-related deaths in the world. Since the disease is usually diagnosed at advanced stages, it has poor prognosis. Therefore, reliable biomarkers are urgently needed for early diagnosis and prognostic assessment.Methods: We used genome-wide gene expression profiling datasets from human and rat early HCC (eHCC) samples to perform integrated genomic and network-based analyses, and discovered gene markers that are expressed in blood and conserved in both species. We then used independent gene expression profiling datasets for peripheral blood mononuclear cells (PBMCs) for eHCC patients and from The Cancer Genome Atlas (TCGA) database to estimate the diagnostic and prognostic performance of the identified gene signature. Furthermore, we performed functional enrichment, interaction networks and pathway analyses.Results: We identified 41 significant genes that are expressed in blood and conserved across species in eHCC. We used comprehensive clinical data from over 600 patients with HCC to verify the diagnostic and prognostic value of 41-gene-signature. We developed a prognostic model and a risk score using the 41-geneset that showed that a high prognostic index is linked to a worse disease outcome. Furthermore, our 41-gene signature predicted disease outcome independently of other clinical factors in multivariate regression analysis. Our data reveals a number of cancer-related pathways and hub genes, including EIF4E, H2AFX, CREB1, GSK3B, TGFBR1, and CCNA2, that may be essential for eHCC progression and confirm our gene signature’s ability to detect the disease in its early stages in patients’ biological fluids instead of invasive procedures and its prognostic potential.Conclusion: Our findings indicate that integrated cross-species genomic and network analysis may provide reliable markers that are associated with eHCC that may lead to better diagnosis, prognosis, and treatment options.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Identification Of a Blood-based Gene Signature For Early Hcc (Ehcc)mentioning

confidence: 99%

Identification of Gene Signature as Diagnostic and Prognostic Blood Biomarker for Early Hepatocellular Carcinoma Using Integrated Cross-Species Transcriptomic and Network Analyses

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…A disease subnetwork or module consists of linked genes or proteins that share mutations, biological processes or expression variations which can be related to a specific disease ( Al-Harazi et al, 2016 ). Previous reports indicated that the development of disease-related subnetwork markers is a robust approach that can provide markers with higher accuracy in disease classification in comparison to using individual genes ( Al-Harazi et al, 2016 ; Khunlertgit and Yoon, 2016 ; Al-Harazi et al, 2019 ). Indeed, network-based analysis of gene expression profiling was performed to identify subnetworks and hub genes that are associated with different cancer, including breast cancer ( Khan et al, 2020 ), lung cancer ( Huang et al, 2015 ), ovarian cancer ( Zhang et al, 2013 ), and others and have demonstrated the significance of the method in discovering genes related to development and progression of cancer (28).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…The network-based markers have been shown to be effective in disease classification, (Zhang et al, 2013;Al-Harazi et al, 2016;Al-Harazi et al, 2019;Khan et al, 2020). Several molecular interaction databases, including DIP (Salwinski et al, 2004), BioGRID (Chatr-Aryamontri et al, 2017, HPRD (Mishra et al, 2006), IntAct (Kerrien et al, 2007), BIND (Alfarano et al, 2005), and MINT (Alfarano et al, 2005) databases have been used to construct the PPI network.…”

Section: Discussionmentioning

confidence: 99%

“…Network-based methodologies are widely used for the prediction of potential candidate genes and in the construction of gene regulatory networks for different diseases (Nair et al, 2014;Dai et al, 2020;Wang et al, 2021). It has been reported that network-based methods are more effective in discovering cancer biomarkers if integrated with omics datasets (Al-Harazi et al, 2016;Cao et al, 2017;Al-Harazi et al, 2019;List et al, 2020). Indeed, our CRC associated 15-subnetwork markers that we identified in this study achieved excellent accuracy in disease classification that was better than that of several well-known colorectal cancer prognostic gene signatures, such as ColoGuideEx (Ågesen et al, 2012), ColoPrint (Tan and Tan, 2011) and Oncotype DX (Clark-Langone et al, 2010) as well as the 24-gene DEGs.…”

Section: Discussionmentioning

confidence: 99%

Table2.XLSX

View full text Add to dashboard Cite

Biomolecular Databases and Subnetwork Identification Approaches of Interest to Big Data Community: An Expert Review

Cited by 18 publications

References 103 publications

Identification of Gene Signature as Diagnostic and Prognostic Blood Biomarker for Early Hepatocellular Carcinoma Using Integrated Cross-Species Transcriptomic and Network Analyses

Identification of Gene Signature as Diagnostic and Prognostic Blood Biomarker for Early Hepatocellular Carcinoma Using Integrated Cross-Species Transcriptomic and Network Analyses

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

Table2.XLSX

Contact Info

Product

Resources

About