Abstract
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.
Highlights
Gastric cancer (GC), an important cancer worldwide, is responsible for over 1,000,000 new cases in 2018 and an estimated 783,000 deaths, making it the fifth most frequently diagnosed cancer and the third leading cause of cancer death [1]
We propose a systems medicine design procedure using systems biology approaches for finding essential biomarkers and recommending potential candidate drugs by the application of deep neural network of drug-target interaction (DeepDTI) and two filters considering drug regulation ability and drug sensitivity
According to the differential core signaling pathways analysis results and considering the overlap nodes with the library of integrated network-based cellular signature (LINCS) dataset, we identified PLSCR1, BCL2, BECN1, and STAT1 to be the essential biomarkers for preventing the progression from early stage to middle stage of GC
Summary
Gastric cancer (GC), an important cancer worldwide, is responsible for over 1,000,000 new cases in 2018 and an estimated 783,000 deaths, making it the fifth most frequently diagnosed cancer and the third leading cause of cancer death [1]. Recent evidence shows that the microenvironments of GC are associated with lymphatic invasion, vascular invasion, lymph node metastasis and the survival of GC patients [8] Both microenvironments of GC and the molecular mechanisms triggered by the corresponding cascade signaling pathways are important for improving the management of GC and discovering potential therapeutic targets. We propose a systems medicine design procedure using systems biology approaches for finding essential biomarkers and recommending potential candidate drugs by the application of DeepDTI and two filters considering drug regulation ability and drug sensitivity. Few studies propose systematic design procedure from identifying potential biomarkers for one disease to recommending their potential candidate drugs from the viewpoint of systems engineering. An interdisciplinary approach to understand disease progression mechanisms by interpreting heterogeneous data, gives us an alternative way for drug discovery to overcome GC
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