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

Become EABM
Become Reviewer
Call for Editor


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.

Menis, J.; Besse, B.; Lacombe, D. Methodology of clinical trials in lung cancer. Chin. Clin. Oncol., 2015, 4(4), 44.
Hudson, A.M.; Wirth, C.; Stephenson, N.L.; Fawdar, S.; Brognard, J.; Miller, C.J. Using large-scale genomics data to identify driver mutations in lung cancer: Methods and challenges. Pharmacogenomics, 2015, 16(10), 1149-1160.
Burotto, M.; Thomas, A.; Subramaniam, D.; Giaccone, G.; Rajan, A. Biomarkers in early-stage non-small-cell lung cancer: Current concepts and future directions. J. Thorac. Oncol., 2014, 9(11), 1609-1617.
Kourou, K.; Exarchos, T.P.; Exarchos, K.P.; Karamouzis, M.V.; Fotiadis, D.I. Machine learning applications in cancer prognosis and prediction. Comput. Struct. Biotechnol. J., 2015, 13, 8-17.
San Segundo, E.; Tsanas, A.; Gómez-Vilda, P. Euclidean distances as measures of speaker similarity including identical twin pairs: A forensic investigation using source and filter voice characteristics. Forensic Sci. Int., 2017, 270, 25-38.
Sõber, S.; Rull, K.; Reiman, M.; Ilisson, P.; Mattila, P.; Laan, M. RNA sequencing of chorionic villi from recurrent pregnancy loss patients reveals impaired function of basic nuclear and cellular machinery. Sci. Rep., 2016, 6, 38439.
Liao, Y.Y.; Lee, T.S.; Lin, Y.M. A Fisher exact test will be more proper. Radiology, 2006, 239(1), 300-301. author reply 301.
Nam, H.; Lee, J.; Lee, D. Computational identification of altered metabolism using gene expression and metabolic pathways. Biotechnol. Bioeng., 2009, 103(4), 835-843.
Ferstl, F.; Kanzler, M.; Rautenhaus, M.; Westermann, R. Time-Hierarchical clustering and visualization of weather forecast ensembles. IEEE Trans. Vis. Comput. Graph., 2017, 23(1), 831-840.
Liu, A.N.; Wang, L.L.; Li, H.P.; Gong, J.; Liu, X.H. Correlation between posttraumatic growth and posttraumatic stress disorder symptoms based on pearson correlation coefficient: A meta-analysis. J. Nerv. Ment. Dis., 2017, 205(5), 380-389.

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2019
Published on: 23 July, 2019
Page: [266 - 275]
Pages: 10
DOI: 10.2174/1386207322666190129111749
Price: $65

Article Metrics

PDF: 35
PRC: 1