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Published on: December 9, 2016
Identifying the crucial oncogenic mechanisms of DDX56 based on a machine learning-based integration model of
Hui Jiang1,2, Haotian Zheng1,2, Xinjie Zhao1,2
1Department of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Abstract:
RNA-binding proteins (RBPs) play a fundamental role in cellular metabolism, with their disturbance leading to large-scale transcriptomic dysregulation. RBP dysregulation is highly prevalent in human cancers; however, its role in lung adenocarcinoma (LUAD) has not been systematically investigated. To establish a more effective and robust risk model, a machine learning integration program was used to screen hub prognostic RBPs. Our risk model C-index performed extremely well among 103 published signatures. The high-risk group had a lower immune score and worse immunotherapy effects. As one of the members of the RNA helicase family, DDX56 can interact with certain transcription factors, thereby regulating the expression of its downstream targets. DDX56 exerts an anti-apoptotic effect and reduces the sensitivity to carboplatin treatment by promoting Bcl-2 transcription in LUAD cells. Additionally, DDX56 activates NF-kB signaling pathways, which may be related to DDX56-mediated promotion of Bcl-2 transcription, proliferation, migration, and invasion in LUAD patients.
Insights
This study identifies key RNA-binding proteins (RBPs) in lung adenocarcinoma (LUAD) using machine learning. A novel risk model highlights DDX56 as a potential therapeutic target, impacting patient prognosis and treatment response.
Area of Science:
- Oncology
- Molecular Biology
- Bioinformatics
Background:
- RNA-binding proteins (RBPs) are crucial for cellular processes, and their dysregulation is common in cancers.
- The specific role of RBPs in lung adenocarcinoma (LUAD) remains under-investigated.
- Developing robust prognostic models is essential for improving LUAD patient outcomes.
Purpose of the Study:
- To systematically investigate the role of prognostic RNA-binding proteins (RBPs) in lung adenocarcinoma (LUAD).
- To develop and validate a machine learning-based risk model for predicting LUAD patient prognosis.
- To identify specific RBPs, like DDX56, and elucidate their functional mechanisms in LUAD progression.
Main Methods:
- Utilized a machine learning integration program to screen for hub prognostic RBPs in LUAD.
- Developed a risk model based on identified RBPs and evaluated its performance against existing signatures.
- Investigated the functional role of DDX56 in LUAD cells, including its effects on apoptosis, drug sensitivity, and signaling pathways.
Main Results:
- The developed machine learning risk model demonstrated superior performance (high C-index) compared to 103 published signatures.
- Patients in the high-risk group exhibited lower immune scores and poorer responses to immunotherapy.
- DDX56, an RNA helicase, was identified as a key prognostic RBP that promotes proliferation, migration, and invasion by upregulating Bcl-2 and activating NF-kB signaling in LUAD.
Conclusions:
- A novel machine learning-derived risk model effectively predicts LUAD prognosis and immunotherapy response.
- DDX56 plays a significant role in LUAD pathogenesis by promoting cell survival, proliferation, and metastasis.
- DDX56 represents a potential therapeutic target for improving treatment strategies in lung adenocarcinoma.
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