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

Jing       Zhong; Jian-Ming       Chen; Song-Lin       Chen; Yun-Feng       Yi

doi:10.2174/1386207322666190129111749

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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DOI https://dx.doi.org/10.2174/1386207322666190129111749	Print ISSN 1386-2073
Publisher Name Bentham Science Publisher	Online ISSN 1875-5402

Combinatorial Chemistry & High Throughput Screening

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

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