Constructing a Risk Prediction Model for Lung Cancer Recurrence by Using Gene Function Clustering and Machine Learning

Author(s): Jing Zhong, Jian-Ming Chen, Song-Lin Chen, Yun-Feng Yi*.

Journal Name: Combinatorial Chemistry & High Throughput Screening
Accelerated Technologies for Biotechnology, Bioassays, Medicinal Chemistry and Natural Products Research

Volume 22 , Issue 4 , 2019

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Abstract:

Objective: A significant proportion of patients with early non-small cell lung cancer (NSCLC) can be cured by surgery. The distant metastasis of tumors is the most common cause of treatment failure. Precisely predicting the likelihood that a patient develops distant metastatic risk will help identify patients who can further intervene, such as conventional adjuvant chemotherapy or experimental drugs.

Methods: Current molecular biology techniques enable the whole genome screening of differentially expressed genes, and rapid development of a large number of bioinformatics methods to improve prognosis.

Results: The genes associated with metastasis do not necessarily play a role in the pathogenesis of the disease, but rather reflect the activation of specific signal transduction pathways associated with enhanced migration and invasiveness.

Conclusion: In this study, we discovered several genes related to lung cancer resistance and established a risk model to predict high-risk patients.

Keywords: Functional cluster, machine learning, recurrent, predictive model, gene expression, lung cancer.

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Article Details

VOLUME: 22
ISSUE: 4
Year: 2019
Page: [266 - 275]
Pages: 10
DOI: 10.2174/1386207322666190129111749
Price: $65

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