Identifying gastric cancer related genes using the shortest path algorithm and protein-protein interaction network

Systems Medicine Design based on Systems Biology Approaches and Deep Neural Network for Gastric Cancer

Gastric cancer (GC) is the third leading cause of cancer death in the world. It is associated with the stimulation of microenvironment, aberrant epigenetic modification, and chronic inflammation. However, few researches discuss the GC molecular progression mechanisms from the perspective of the system level. In this study, we proposed a systems medicine design procedure to identify essential biomarkers and find corresponding drugs for GC. At first, we did big database mining to construct candidate protein-protein interaction network (PPIN) and candidate gene regulation network (GRN). Second, by leveraging the next-generation sequencing (NGS) data, we performed system modeling and applied system identification and model selection to obtain real genome-wide genetic and epigenetic networks (GWGENs). To make the real GWGENs easy to analyze, the principal network projection method was used to extract the core signaling pathways denoted by KEGG pathways. Subsequently, based on the identified biomarkers, we trained a deep neural network of drug-target interaction (DeepDTI) with supervised learning and filtered our candidate drugs considering drug regulation ability and drug sensitivity. With the proposed systematic strategy, we not only shed the light on the progression of GC but also suggested potential multiple-molecule drugs efficiently.

Systems Medicine Design for Triple-Negative Breast Cancer and Non-Triple-Negative Breast Cancer Based on Systems Identification and Carcinogenic Mechanisms

Triple-negative breast cancer (TNBC) is a heterogeneous subtype of breast cancers with poor prognosis. The etiology of triple-negative breast cancer (TNBC) is involved in various biological signal cascades and multifactorial aberrations of genetic, epigenetic and microenvironment. New therapeutic for TNBC is urgently needed because surgery and chemotherapy are the only available modalities nowadays. A better understanding of the molecular mechanisms would be a great challenge because they are triggered by cascade signaling pathways, genetic and epigenetic regulations, and drug–target interactions. This would allow the design of multi-molecule drugs for the TNBC and non-TNBC. In this study, in terms of systems biology approaches, we proposed a systematic procedure for systems medicine design toward TNBC and non-TNBC. For systems biology approaches, we constructed a candidate genome-wide genetic and epigenetic network (GWGEN) by big databases mining and identified real GWGENs of TNBC and non-TNBC assisting with corresponding microarray data by system identification and model order selection methods. After that, we applied the principal network projection (PNP) approach to obtain the core signaling pathways denoted by KEGG pathway of TNBC and non-TNBC. Comparing core signaling pathways of TNBC and non-TNBC, essential carcinogenic biomarkers resulting in multiple cellular dysfunctions including cell proliferation, autophagy, immune response, apoptosis, metastasis, angiogenesis, epithelial-mesenchymal transition (EMT), and cell differentiation could be found. In order to propose potential candidate drugs for the selected biomarkers, we designed filters considering toxicity and regulation ability. With the proposed systematic procedure, we not only shed a light on the differences between carcinogenetic molecular mechanisms of TNBC and non-TNBC but also efficiently proposed candidate multi-molecule drugs including resveratrol, sirolimus, and prednisolone for TNBC and resveratrol, sirolimus, carbamazepine, and verapamil for non-TNBC.

Matrix metalloproteinase expression and molecular interaction network analysis in gastric cancer

Gastric cancer (GC) is one of the most common types of cancer of the digestive tract. Invasion of tumor cells into surrounding tissue and metastasis are among the most significant checkpoints in tumor progression. It is known that matrix metalloproteinases (MMPs) are involved in these processes; however, knowledge of their molecular interaction networks is still limited. Investigation of these networks could provide a more comprehensive picture of the function of MMPs in tumorigenesis. Furthermore, it could be used to develop new approaches to targeted anticancer therapy. In this study, we performed microarray analysis, and 1666 genes that were aberrantly expressed in GC tissues were identified (fold change >2, P<0.05). In addition, quantitative polymerase chain reaction analysis has confirmed that MMP1, MMP3, MMP7, MMP10, MMP11 and MMP12 expression is upregulated in GC. In addition, the MMP3 expression level was negatively correlated with GC differentiation (P<0.05). By integrating the microarray information and BioGRID and STRING databases, we constructed an MMP-related molecular interaction network and observed that 18 genes (including MMPs) were highly expressed in GC tissues. The most enriched of these 18 genes in the Gene Oncology (GO) and pathway analysis were in extracellular matrix disassembly (GO biological process) and extracellular matrix-receptor interaction (KEGG pathway), which are closely correlated with cancer invasion and metastasis. Collectively, our results suggest that the MMP-related interaction network has a role in GC progression, and therefore further studies are required in order to investigate these network interactions in tumorigenesis.

Identification of novel candidate drivers connecting different dysfunctional levels for lung adenocarcinoma using protein-protein interactions and a shortest path approach

Tumors are formed by the abnormal proliferation of somatic cells with disordered growth regulation under the influence of tumorigenic factors. Recently, the theory of “cancer drivers” connects tumor initiation with several specific mutations in the so-called cancer driver genes. According to the differentiation of four basic levels between tumor and adjacent normal tissues, the cancer drivers can be divided into the following: (1) Methylation level, (2) microRNA level, (3) mutation level, and (4) mRNA level. In this study, a computational method is proposed to identify novel lung adenocarcinoma drivers based on dysfunctional genes on the methylation, microRNA, mutation and mRNA levels. First, a large network was constructed using protein-protein interactions. Next, we searched all of the shortest paths connecting dysfunctional genes on different levels and extracted new candidate genes lying on these paths. Finally, the obtained candidate genes were filtered by a permutation test and an additional strict selection procedure involving a betweenness ratio and an interaction score. Several candidate genes remained, which are deemed to be related to two different levels of cancer. The analyses confirmed our assertions that some have the potential to contribute to the tumorigenesis process on multiple levels.

Mining for genes related to choroidal neovascularization based on the shortest path algorithm and protein interaction information

BACKGROUND

Choroidal neovascularization (CNV) is a serious eye disease that may cause visual loss, especially for older people. Many factors have been proven to induce this disease including age, gender, obesity, and so on. However, until now, we have had limited knowledge on CNV's pathogenic mechanism. Discovering the genes that underlie this disease and performing extensive studies on them can help us to understand how CNV occurs and design effective treatments.

METHODS

In this study, we designed a computational method to identify novel CNV-related genes in a large protein network constructed using the protein-protein interaction information in STRING. The candidate genes were first extracted from the shortest paths connecting any two known CNV-related genes and then filtered by a permutation test and using knowledge of their linkages to known CNV-related genes.

RESULTS

A list of putative CNV-related candidate genes was accessed by our method. These genes are deemed to have strong relationships with CNV.

CONCLUSIONS

Extensive analyses of several of the putative genes such as ANK1, ITGA4, CD44 and others indicate that they are related to specific biological processes involved in CNV, implying they may be novel CNV-related genes.

GENERAL SIGNIFICANCE

The newfound putative CNV-related genes may provide new insights into CNV and help design more effective treatments. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

PPI network analysis of mRNA expression profile of ezrin knockdown in esophageal squamous cell carcinoma

Ezrin, coding protein EZR which cross-links actin filaments, overexpresses and involves invasion, metastasis, and poor prognosis in various cancers including esophageal squamous cell carcinoma (ESCC). In our previous study, Ezrin was knock down and analyzed by mRNA expression profile which has not been fully mined. In this study, we applied protein-protein interactions (PPI) network knowledge and methods to explore our understanding of these differentially expressed genes (DEGs). PPI subnetworks showed that hundreds of DEGs interact with thousands of other proteins. Subcellular localization analyses found that the DEGs and their directly or indirectly interacting proteins distribute in multiple layers, which was applied to analyze the shortest paths between EZR and other DEGs. Gene ontology annotation generated a functional annotation map and found hundreds of significant terms, especially those associated with cytoskeleton organization of Ezrin protein, such as "cytoskeleton organization," "regulation of actin filament-based process," and "regulation of actin cytoskeleton organization." The algorithm of Random Walk with Restart was applied to prioritize the DEGs and identified several cancer related DEGs ranked closest to EZR. These analyses based on PPI network have greatly expanded our comprehension of the mRNA expression profile of Ezrin knockdown for future examination of the roles and mechanisms of Ezrin.