Biological Networks for Cancer Candidate Biomarkers Discovery

Gene co-expression network construction and analysis for identification of genetic biomarkers associated with glioblastoma multiforme using topological findings

BACKGROUND

Glioblastoma multiforme (GBM) is one of the most malignant types of central nervous system tumors. GBM patients usually have a poor prognosis. Identification of genes associated with the progression of the disease is essential to explain the mechanisms or improve the prognosis of GBM by catering to targeted therapy. It is crucial to develop a methodology for constructing a biological network and analyze it to identify potential biomarkers associated with disease progression.

METHODS

Gene expression datasets are obtained from TCGA data repository to carry out this study. A survival analysis is performed to identify survival associated genes of GBM patient. A gene co-expression network is constructed based on Pearson correlation between the gene's expressions. Various topological measures along with set operations from graph theory are applied to identify most influential genes linked with the progression of the GBM.

RESULTS

Ten key genes are identified as a potential biomarkers associated with GBM based on centrality measures applied to the disease network. These genes are SEMA3B, APS, SLC44A2, MARK2, PITPNM2, SFRP1, PRLH, DIP2C, CTSZ, and KRTAP4.2. Higher expression values of two genes, SLC44A2 and KRTAP4.2 are found to be associated with progression and lower expression values of seven gens SEMA3B, APS, MARK2, PITPNM2, SFRP1, PRLH, DIP2C, and CTSZ are linked with the progression of the GBM.

CONCLUSIONS

The proposed methodology employing a network topological approach to identify genetic biomarkers associated with cancer.

GP, Kumar SU, El Allali A, Alsamman AM, Zayed H. Integrated gene network analysis sheds light on understanding the progression of Osteosarcoma

Introduction

Osteosarcoma is a rare disorder among cancer, but the most frequently occurring among sarcomas in children and adolescents. It has been reported to possess the relapsing capability as well as accompanying collateral adverse effects which hinder the development process of an effective treatment plan. Using networks of omics data to identify cancer biomarkers could revolutionize the field in understanding the cancer. Cancer biomarkers and the molecular mechanisms behind it can both be understood by studying the biological networks underpinning the etiology of the disease.

Methods

In our study, we aimed to highlight the hub genes involved in gene-gene interaction network to understand their interaction and how they affect the various biological processes and signaling pathways involved in Osteosarcoma. Gene interaction network provides a comprehensive overview of functional gene analysis by providing insight into how genes cooperatively interact to elicit a response. Because gene interaction networks serve as a nexus to many biological problems, their employment of it to identify the hub genes that can serve as potential biomarkers remain widely unexplored. A dynamic framework provides a clear understanding of biological complexity and a pathway from the gene level to interaction networks.

Results

Our study revealed various hub genes viz. TP53, CCND1, CDK4, STAT3, and VEGFA by analyzing various topological parameters of the network, such as highest number of interactions, average shortest path length, high cluster density, etc. Their involvement in key signaling pathways, such as the FOXM1 transcription factor network, FAK-mediated signaling events, and the ATM pathway, makes them significant candidates for studying the disease. The study also highlighted significant enrichment in GO terms (Biological Processes, Molecular Function, and Cellular Processes), such as cell cycle signal transduction, cell communication, kinase binding, transcription factor activity, nucleoplasm, PML body, nuclear body, etc.

Conclusion

To develop better therapeutics, a specific approach toward the disease targeting the hub genes involved in various signaling pathways must have opted to unravel the complexity of the disease. Our study has highlighted the candidate hub genes viz. TP53, CCND1 CDK4, STAT3, VEGFA. Their involvement in the major signaling pathways of Osteosarcoma makes them potential candidates to be targeted for drug development. The highly enriched signaling pathways include FOXM1 transcription pathway, ATM signal-ling pathway, FAK mediated signaling events, Arf6 signaling events, mTOR signaling pathway, and Integrin family cell surface interactions. Targeting the hub genes and their associated functional partners which we have reported in our studies may be efficacious in developing novel therapeutic targets.

A Pan-Cancer Atlas of Differentially Interacting Hallmarks of Cancer Proteins

Cancer hallmark genes and proteins orchestrate and drive carcinogenesis to a large extent, therefore, it is important to study these features in different cancer types to understand the process of tumorigenesis and discover measurable indicators. We performed a pan-cancer analysis to map differentially interacting hallmarks of cancer proteins (DIHCP). The TCGA transcriptome data associated with 12 common cancers were analyzed and the differential interactome algorithm was applied to determine DIHCPs and DIHCP-centric modules (i.e., DIHCPs and their interacting partners) that exhibit significant changes in their interaction patterns between the tumor and control phenotypes. The diagnostic and prognostic capabilities of the identified modules were assessed to determine the ability of the modules to function as system biomarkers. In addition, the druggability of the prognostic and diagnostic DIHCPs was investigated. As a result, we found a total of 30 DIHCP-centric modules that showed high diagnostic or prognostic performance in any of the 12 cancer types. Furthermore, from the 16 DIHCP-centric modules examined, 29% of these were druggable. Our study presents candidate systems' biomarkers that may be valuable for understanding the process of tumorigenesis and improving personalized treatment strategies for various cancers, with a focus on their ten hallmark characteristics.

Computational approaches for network-based integrative multi-omics analysis

Advances in omics technologies allow for holistic studies into biological systems. These studies rely on integrative data analysis techniques to obtain a comprehensive view of the dynamics of cellular processes, and molecular mechanisms. Network-based integrative approaches have revolutionized multi-omics analysis by providing the framework to represent interactions between multiple different omics-layers in a graph, which may faithfully reflect the molecular wiring in a cell. Here we review network-based multi-omics/multi-modal integrative analytical approaches. We classify these approaches according to the type of omics data supported, the methods and/or algorithms implemented, their node and/or edge weighting components, and their ability to identify key nodes and subnetworks. We show how these approaches can be used to identify biomarkers, disease subtypes, crosstalk, causality, and molecular drivers of physiological and pathological mechanisms. We provide insight into the most appropriate methods and tools for research questions as showcased around the aetiology and treatment of COVID-19 that can be informed by multi-omics data integration. We conclude with an overview of challenges associated with multi-omics network-based analysis, such as reproducibility, heterogeneity, (biological) interpretability of the results, and we highlight some future directions for network-based integration.

Network Biology Approaches to Achieve Precision Medicine in Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) is a chronic immune-mediated condition arising due to complex interactions between multiple genetic and environmental factors. Despite recent advances, the pathogenesis of the condition is not fully understood and patients still experience suboptimal clinical outcomes. Over the past few years, investigators are increasingly capturing multi-omics data from patient cohorts to better characterise the disease. However, reaching clinically translatable endpoints from these complex multi-omics datasets is an arduous task. Network biology, a branch of systems biology that utilises mathematical graph theory to represent, integrate and analyse biological data through networks, will be key to addressing this challenge. In this narrative review, we provide an overview of various types of network biology approaches that have been utilised in IBD including protein-protein interaction networks, metabolic networks, gene regulatory networks and gene co-expression networks. We also include examples of multi-layered networks that have combined various network types to gain deeper insights into IBD pathogenesis. Finally, we discuss the need to incorporate other data sources including metabolomic, histopathological, and high-quality clinical meta-data. Together with more robust network data integration and analysis frameworks, such efforts have the potential to realise the key goal of precision medicine in IBD.

Identification of CCNB2 as A Potential Non-Invasive Breast Cancer Biomarker in Peripheral Blood Mononuclear Cells Using The Systems Biology Approach

OBJECTIVE

Breast cancer (BC) still remains an imperative clinical issue, despite advances in the diagnosis, prognosis and treatment modalities of this malignancy. Hence, progress has been made to identify non-invasive, high sensitive and specific biomarkers. Since immune system affects development of breast cancer, peripheral blood mononuclear cells (PBMCs) -a subpopulation of immune cells- can be considered as a promising tool in the field of BC biomarker research. In the current study, we initially attempted to use concept of the present shared biomarkers in solid tumors and systemic immune profile and then evaluate correlation of these biomarkers to clinical use in cancer research.

MATERIALS AND METHODS

In this experimental study, available microarray gene expression datasets of BC as well as the related PBMCs were retrieved and downloaded from the Gene Expression Omnibus (GEO) database, followed by analysis using GEO2R along with affylmGUI, a R-based package, to obtain differentially expressed genes (DEGs). Signature genes from 20 types of cancer were also applied to validate DEGs. Quantitative reverse-transcription polymerase chain reaction (qRT-PCR) was carried out to assess mRNA level of CCNB2 in PBMC of the BC patients and healthy subjects.

RESULTS

DEGs analysis for the transcription profile of BC cells and PBMCs showed two shared targets, CCNB2 and PGK1. Validation with systems biology using reweighted 20 types of cancer signature genes revealed that CCNB2 is the only common target in BC and its related PBMCs, which was further validated by qRT-PCR implying a significant increase in the level of CCNB2 in the BC patients.

CONCLUSION

Results of this study demonstrated that PBMCs are affected by BC cells and CCNB2 may be of value as a diagnostic biomarker for breast cancer. However, verification would require future detailed experimental plans.

Improved annotation of Lutzomyia longipalpis genome using bioinformatics analysis

Lutzomyia longipalpis, a sand fly, is a vector-spreading pathogenic protozoan in the New World. MicroRNA (miRNA) is evolutionarily-conserved non-coding RNA, which plays critical roles in various biological processes. To date, the functions of most proteins in L. longipalpis are unknown, and few studies have addressed the roles of miRNAs in this species. In the present study, we re-annotated the protein-coding genes and identified several miRNAs using a set of comparative genomics tools. A large number of L. longipalpis proteins were found to be homologous with those in the mosquito genome, indicating that they may have experienced similar selective pressures. Among these proteins, a set of 19 putative salivary proteins were identified, which could be used for studying the transmission of Leishmania. Twenty-one novel miRNAs were characterized, including two miRNAs, miR-4113-5p and miR-5101, which are unique to L. longipalpis. Many of the targets of these two genes were found to be involved in ATP hydrolysis-coupled proton transport, suggesting that they may have important roles in the physiology of energy production. Topology analysis of the miRNA-gene network indicated that miR-9388-5p and miR-3871-5p regulate several critical genes in response to disease development. In conclusion, our work provides a basis for improving the genome annotation of L. longipalpis, and opens a new door to understanding the molecular regulatory mechanisms in this species.

Computational Approaches in Theranostics: Mining and Predicting Cancer Data

The ability to understand the complexity of cancer-related data has been prompted by the applications of (1) computer and data sciences, including data mining, predictive analytics, machine learning, and artificial intelligence, and (2) advances in imaging technology and probe development. Computational modelling and simulation are systematic and cost-effective tools able to identify important temporal/spatial patterns (and relationships), characterize distinct molecular features of cancer states, and address other relevant aspects, including tumor detection and heterogeneity, progression and metastasis, and drug resistance. These approaches have provided invaluable insights for improving the experimental design of therapeutic delivery systems and for increasing the translational value of the results obtained from early and preclinical studies. The big question is: Could cancer theranostics be determined and controlled in silico? This review describes the recent progress in the development of computational models and methods used to facilitate research on the molecular basis of cancer and on the respective diagnosis and optimized treatment, with particular emphasis on the design and optimization of theranostic systems. The current role of computational approaches is providing innovative, incremental, and complementary data-driven solutions for the prediction, simplification, and characterization of cancer and intrinsic mechanisms, and to promote new data-intensive, accurate diagnostics and therapeutics.