Title:Constructing a Risk Prediction Model for Lung Cancer Recurrence by Using Gene Function Clustering and Machine Learning
Volume: 22
Issue: 4
Author(s): Jing Zhong, Jian-Ming Chen, Song-Lin Chen and Yun-Feng Yi*
Affiliation:
- Department of Cardiothoracic Surgery, The Affiliated Dongnan hospital of Xiamen University, Zhangzhou 363000,China
Keywords:
Functional cluster, machine learning, recurrent, predictive model, gene expression, lung cancer.
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.