Exploring Gene Signatures in Different Molecular Subtypes of Gastric Cancer (MSS/ TP۵۳+, MSS/TP۵۳-): A Network-based and Machine Learning Approach
Publish place: Journal of Genetic Resources، Vol: 6، Issue: 2
Publish Year: 1399
نوع سند: مقاله ژورنالی
زبان: English
View: 341
This Paper With 14 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
این Paper در بخشهای موضوعی زیر دسته بندی شده است:
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_SGR-6-2_010
تاریخ نمایه سازی: 6 اردیبهشت 1400
Abstract:
Gastric cancer (GC) is one of the leading causes of cancer mortality, worldwide. Molecular understanding of GC’s different subtypes is still dismal and it is necessary to develop new subtype-specific diagnostic and therapeutic approaches. Therefore developing comprehensive research in this area is demanding to have a deeper insight into molecular processes, underlying these subtypes. In this study, a three-step methodology was developed to identify important genes and subnetworks in two subtypes of GC (TP۵۳+ and TP۵۳-). First, weighted gene co-expression network analysis was performed to explore co-expressed gene modules in both subtypes. Afterward, the relationship of each module with the tumor pathological stage (as a clinical trait indicating tumor progression) was studied by decision tree machine learning algorithm and the best predicting module was selected for further analysis (modules with ۲۴۱ genes for TP۵۳+ and ۱۴۴۱ genes for TP۵۳- were identified). Subsequently, a motif exploring and motif ranking analysis was implemented to explore three-member signature gene motifs in the selected modules' biological network. These motifs may have key regulatory roles in the studied GC subtypes. Motif members of TP۵۳- mostly contain MAPK signaling pathway genes which show their key role in this subtype of GC. In the case of the TP۵۳+ subtype, our findings demonstrated that alternative splicing and SNARE proteins could prompt the initiation and advancement of the disease. These findings can be used to develop new diagnostic and therapeutic approaches based on the personalized medicine concept. This methodology could be implemented to unravel underlying mechanisms and pathways in other complex phenotypes and diseases.
Keywords:
Gastric cancer , Molecular subtypes , Weighted gene co-expression network analysis , Decision tree , Network Analysis
Authors
Mehdi Sadeghi
Department of Cell and Molecular Biology, Faculty of Science, Semnan University, Semnan, Iran
Nafiseh Ghorbanpour
Research Institute for Fundamental Sciences (RIFS), University of Tabriz, Tabriz, Iran
Abolfazl Barzegar
Research Institute for Fundamental Sciences (RIFS), University of Tabriz, Tabriz, Iran
Iliya Rafiei
Research Institute for Fundamental Sciences (RIFS), University of Tabriz, Tabriz, Iran
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :