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

Comparing the influence of big data resources on medical knowledge recall for staff with and without medical collaboration platform

BACKGROUND

This study aims to examine how big data resources affect the recall of prior medical knowledge by healthcare professionals, and how this differs in environments with and without remote consultation platforms.

METHOD

This study investigated two distinct categories of medical institutions, namely 132 medical institutions with platforms, and 176 medical institutions without the platforms. Big data resources are categorized into two levels-medical institutional level and public level-and three types, namely data, technology, and services. The data are analyzed using SmartPLS2.

RESULTS

(1) In both scenarios, shared big data resources at the public level have a significant direct impact on the recall of prior medical knowledge. However, there is a significant difference in the direct impact of big data resources at the institutional level in both scenarios. (2) In institutions with platforms, for the three big data resources (the medical big data assets and big data deployment technical capacity at the medical institutional level, and policies of medical big data at the public level) without direct impacts, there exist three indirect pathways. (3) In institutions without platforms, for the two big data resources (the service capability and big data technical capacity at the medical institutional level) without direct impacts, there exist three indirect pathways.

CONCLUSIONS

The different interactions between big data, technology, and services, as well as between different levels of big data resources, affect the way clinical doctors recall relevant medical knowledge. These interaction patterns vary between institutions with and without platforms. This study provides a reference for governments and institutions to design big data environments for improving clinical capabilities.

Global Transcriptional Profiling of Granulosa Cells from Polycystic Ovary Syndrome Patients: Comparative Analyses of Patients with or without History of Ovarian Hyperstimulation Syndrome Reveals Distinct Biomarkers and Pathways

Ovarian hyperstimulation syndrome (OHSS) is often a complication of polycystic ovarian syndrome (PCOS), the most frequent disorder of the endocrine system, which affects women in their reproductive years. The etiology of OHSS is multifactorial, though the factors involved are not apparent. In an attempt to unveil the molecular basis of OHSS, we conducted transcriptome analysis of total RNA extracted from granulosa cells from PCOS patients with a history of OHSS (n = 6) and compared them to those with no history of OHSS (n = 18). We identified 59 significantly dysregulated genes (48 down-regulated, 11 up-regulated) in the PCOS with OHSS group compared to the PCOS without OHSS group (p-value < 0.01, fold change >1.5). Functional, pathway and network analyses revealed genes involved in cellular development, inflammatory and immune response, cellular growth and proliferation (including DCN, VIM, LIFR, GRN, IL33, INSR, KLF2, FOXO1, VEGF, RDX, PLCL1, PAPPA, and ZFP36), and significant alterations in the PPAR, IL6, IL10, JAK/STAT and NF-κB signaling pathways. Array findings were validated using quantitative RT-PCR. To the best of our knowledge, this is the largest cohort of Saudi PCOS cases (with or without OHSS) to date that was analyzed using a transcriptomic approach. Our data demonstrate alterations in various gene networks and pathways that may be involved in the pathophysiology of OHSS. Further studies are warranted to confirm the findings.

Impact of big data resources on clinicians’ activation of prior medical knowledge

Background

Activating prior medical knowledge in diagnosis and treatment is an important basis for clinicians to improve their care ability. However, it has not been systematically explained whether and how various big data resources affect the activation of prior knowledge in the big data environment faced by clinicians.

Objective

The aim of this study is to contribute to a better understanding on how the activation of prior knowledge of clinicians is affected by a wide range of shared and private big data resources, to reveal the impact of big data resources on clinical competence and professional development of clinicians.

Method

Through the comprehensive analysis of extant research results, big data resources are classified as big data itself, big data technology and big data services at the public and institutional levels. A survey was conducted on clinicians and IT personnel in Chinese hospitals. A total of 616 surveys are completed, involving 308 medical institutions. Each medical institution includes a clinician and an IT personnel. SmartPLS version 2.0 software package was used to test the direct impact of big data resources on the activation of prior knowledge. We further analyze their indirect impact of those big data resources without direct impact.

Results

(1) Big data quality environment at the institutional level and the big data sharing environment at the public level directly affect activation of prior medical knowledge; (2) Big data service environment at the institutional level directly affects activation of prior medical knowledge; (3) Big data deployment environment at the institutional level and big data service environment at the public level have no direct impact on activation of prior knowledge of clinicians, but they have an indirect impact through big data quality environment and service environment at the institutional level and the big data sharing environment at the public level.

Conclusions

Big data technology, big data itself and big data service at the public level and institutional level interact and influence each other to activate prior medical knowledge. This study highlights the implications of big data resources on improvement of clinicians’ diagnosis and treatment ability.

Mechanism of Impact of Big Data Resources on Medical Collaborative Networks From the Perspective of Transaction Efficiency of Medical Services: Survey Study

Background

The application of big data resources and the development of medical collaborative networks (MCNs) boost each other. However, MCNs are often assumed to be exogenous. How big data resources affect the emergence, development, and evolution of endogenous MCNs has not been well explained.

Objective

This study aimed to explore and understand the influence of the mechanism of a wide range of shared and private big data resources on the transaction efficiency of medical services to reveal the impact of big data resources on the emergence and development of endogenous MCNs.

Methods

This study was conducted by administering a survey questionnaire to information technology staff and medical staff from 132 medical institutions in China. Data from information technology staff and medical staff were integrated. Structural equation modeling was used to test the direct impact of big data resources on transaction efficiency of medical services. For those big data resources that had no direct impact, we analyzed their indirect impact.

Results

Sharing of diagnosis and treatment data (β=.222; P=.03) and sharing of medical research data (β=.289; P=.04) at the network level (as big data itself) positively directly affected the transaction efficiency of medical services. Network protection of the external link systems (β=.271; P=.008) at the level of medical institutions (as big data technology) positively directly affected the transaction efficiency of medical services. Encryption security of web-based data (as big data technology) at the level of medical institutions, medical service capacity available for external use, real-time data of diagnosis and treatment services (as big data itself) at the level of medical institutions, and policies and regulations at the network level indirectly affected the transaction efficiency through network protection of the external link systems at the level of medical institutions.

Conclusions

This study found that big data technology, big data itself, and policy at the network and organizational levels interact with, and influence, each other to form the transaction efficiency of medical services. On the basis of the theory of neoclassical economics, the study highlighted the implications of big data resources for the emergence and development of endogenous MCNs.

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

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.

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

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.

Bi-allelic variants in HOPS complex subunit VPS41 cause cerebellar ataxia and abnormal membrane trafficking

Membrane trafficking is a complex, essential process in eukaryotic cells responsible for protein transport and processing. Deficiencies in vacuolar protein sorting (VPS) proteins, key regulators of trafficking, cause abnormal intracellular segregation of macromolecules and organelles and are linked to human disease. VPS proteins function as part of complexes such as the homotypic fusion and vacuole protein sorting (HOPS) tethering complex, composed of VPS11, VPS16, VPS18, VPS33A, VPS39 and VPS41. The HOPS-specific subunit VPS41 has been reported to promote viability of dopaminergic neurons in Parkinson’s disease but to date has not been linked to human disease. Here, we describe five unrelated families with nine affected individuals, all carrying homozygous variants in VPS41 that we show impact protein function. All affected individuals presented with a progressive neurodevelopmental disorder consisting of cognitive impairment, cerebellar atrophy/hypoplasia, motor dysfunction with ataxia and dystonia, and nystagmus. Zebrafish disease modelling supports the involvement of VPS41 dysfunction in the disorder, indicating lysosomal dysregulation throughout the brain and providing support for cerebellar and microglial abnormalities when vps41 was mutated. This provides the first example of human disease linked to the HOPS-specific subunit VPS41 and suggests the importance of HOPS complex activity for cerebellar function.

Identification of Novel Key Targets and Candidate Drugs in Oral Squamous Cell Carcinoma

Background:

Oral Squamous Cell Carcinoma (OSCC) is the most common malignant epithelial neoplasm. It is located within the top 10 ranking incidence of cancers with a poor prognosis and low survival rates. New breakthroughs of therapeutic strategies are therefore needed to improve the survival rate of OSCC harboring patients.

Objective:

Since targeted therapy is considered as the most promising therapeutic strategies in cancer, it is of great significance to identify novel targets and drugs for the treatment of OSCC.

Methods:

A series of bioinformatics approaches were launched to identify the hub proteins and their potential agents. Microarray analysis and several online functional activity network analysis were firstly utilized to recognize drug targets in OSCC. Subsequently, molecular docking was used to screen their potential drugs from the specs chemistry database. At the same time, the assessment of ligand-based virtual screening model was also evaluated.

Results:

In this study, two microarray data (GSE31056, GSE23558) were firstly selected and analyzed to get consensus candidate genes including 681 candidate genes. Additionally, we selected 33 candidate genes based on whether they belong to the kinases and transcription factors and further clustered candidate hub targets based on functions and signaling pathways with significant enrichment analysis by using DAVID and STRING online databases. Then, core PPI network was then identified and we manually selected GRB2 and IGF1 as the key drug targets according to the network analysis and previous references. Lastly, virtual screening was performed to identify potential small molecules which could target these two targets, and such small molecules can serve as the promising candidate agents for future drug development.

Conclusion:

In summary, our study might provide novel insights for understanding of the underlying molecular events of OSCC, and our discovered candidate targets and candidate agents could be used as the promising therapeutic strategies for the treatment of OSCC.

Rett Syndrome, a Neurodevelopmental Disorder, Whole-Transcriptome, and Mitochondrial Genome Multiomics Analyses Identify Novel Variations and Disease Pathways

Rett syndrome (RTT) is a severe neurodevelopmental disorder reported worldwide in diverse populations. RTT is diagnosed primarily in females, with clinical findings manifesting early in life. Despite the variable rates across populations, RTT has an estimated prevalence of ∼1 in 10,000 live female births. Among 215 Saudi Arabian patients with neurodevelopmental and autism spectrum disorders, we identified 33 patients with RTT who were subsequently examined by genome-wide transcriptome and mitochondrial genome variations. To the best of our knowledge, this is the first in-depth molecular and multiomics analyses of a large cohort of Saudi RTT cases with a view to informing the underlying mechanisms of this disease that impact many patients and families worldwide. The patients were unrelated, except for 2 affected sisters, and comprised of 25 classic and eight atypical RTT cases. The cases were screened for methyl-CpG binding protein 2 (MECP2), CDKL5, FOXG1, NTNG1, and mitochondrial DNA (mtDNA) variants, as well as copy number variations (CNVs) using a genome-wide experimental strategy. We found that 15 patients (13 classic and two atypical RTT) have MECP2 mutations, 2 of which were novel variants. Two patients had novel FOXG1 and CDKL5 variants (both atypical RTT). Whole mtDNA sequencing of the patients who were MECP2 negative revealed two novel mtDNA variants in two classic RTT patients. Importantly, the whole-transcriptome analysis of our RTT patients' blood and further comparison with previous expression profiling of brain tissue from patients with RTT revealed 77 significantly dysregulated genes. The gene ontology and interaction network analysis indicated potentially critical roles of MAPK9, NDUFA5, ATR, SMARCA5, RPL23, SRSF3, and mitochondrial dysfunction, oxidative stress response and MAPK signaling pathways in the pathogenesis of RTT genes. This study expands our knowledge on RTT disease networks and pathways as well as presents novel mutations and mtDNA alterations in RTT in a population sample that was not previously studied.

New Insights into the Impact of Genome-Wide Copy Number Variations on Complex Congenital Heart Disease in Saudi Arabia

Congenital heart diseases (CHDs) are complex traits that manifest in diverse clinical phenotypes such as the Tetralogy of Fallot (TOF), valvular and ventricular/atrial septal defects. Genetic mechanisms of CHDs have remained largely unclear to date. Copy number variations (CNVs) have been implicated in many complex diseases but their impact has not been examined extensively in various forms of CHD lesions. We report in this study, to the best of our knowledge, the largest cohort of Saudi Arab CHD patients to date who were evaluated using genome-wide CNV analysis. In a sample of 134 Saudi Arab patients with CHD, 66 exhibited pathogenic or likely pathogenic CNVs. Notably, 21 copy number gains and 11 copy number losses were detected that encompassed 141 genes and 146 genes, respectively. The most frequent gains were on 17q21.31, 8p11.21, and 22q11.23, whereas the losses were primarily localized to 16p11.2. Interestingly, all lesions have had gains at 17q21.31. Septal defects had also gains at 8p11.21 and 22q11.23, valvular lesions at 8p11.21, 22q11.23, and 2q13, and TOF at 16p11.2. Functional and network analyses demonstrated that cardiovascular and nervous system development and function as well as cell death/survival were most significantly associated with CNVs, thus highlighting the potentially important genes likely to be involved in CHD, including NPHP1, PLCB1, KANSL1, and NR3C1. In conclusion, this genome-wide analysis identifies a high frequency of CNVs mostly in patients with septal defects, primarily influencing cardiovascular developmental and functional pathways, thereby offering a deeper insight into the complex networks involved in CHD pathogenesis.