Discovery of prognostic biomarkers for predicting lung cancer metastasis using microarray and survival data

NCUIR > college of Health Sciences and Technology > Department of Biomedical Sciences and Engineering > journal & Dissertation > Item 987654321/102725

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/102725

Title:	Discovery of prognostic biomarkers for predicting lung cancer metastasis using microarray and survival data
Authors:	蘇立仁;Huang, Hui-Ling;Wu, Yu-Chung;Su, Li-Jen;Huang, Yun-Ju;Charoenkwan, Phasit;Chen, Wen-Liang;Lee, Hua-Chin;Chu, William Cheng-Chung;Ho, Shinn-Ying
Contributors:	生醫理工學院生醫科學與工程學系
Keywords:	Adenocarcinoma - genetics;Adenocarcinoma - mortality;Adenocarcinoma - secondary;Aged;Algorithms;Bioinformatics;Biomarkers, Tumor - genetics;Biomedical and Life Sciences;Carcinoma, Large Cell - genetics;Carcinoma, Large Cell - mortality;Carcinoma, Large Cell - secondary;Carcinoma, Squamous Cell - genetics;Carcinoma, Squamous Cell - mortality;Carcinoma, Squamous Cell - secondary;Comparative genomics;Computational Biology/Bioinformatics;Computer Appl. in Life Sciences;Epithelial-Mesenchymal Transition;Female;Follow-Up Studies;Humans;Life Sciences;Lung Neoplasms - genetics;Lung Neoplasms - mortality;Lung Neoplasms - pathology;Male;Medical records;Methodology;Methodology Article;Microarray Analysis;Microarrays;Neoplasm Staging;Prognosis;Prospective Studies;Signal Transduction;Survival Rate
Date:	2015-02-21
Issue Date:	2026-04-23 11:15:43 (UTC+8)
Publisher:	BioMed Central Ltd.;London: BioMed Central
Abstract:	摘要： Background Few studies have investigated prognostic biomarkers of distant metastases of lung cancer. One of the central difficulties in identifying biomarkers from microarray data is the availability of only a small number of samples, which results overtraining. Recently obtained evidence reveals that epithelial–mesenchymal transition (EMT) of tumor cells causes metastasis, which is detrimental to patients’ survival. Results This work proposes a novel optimization approach to discovering EMT-related prognostic biomarkers to predict the distant metastasis of lung cancer using both microarray and survival data. This weighted objective function maximizes both the accuracy of prediction of distant metastasis and the area between the disease-free survival curves of the non-distant and distant metastases. Seventy-eight patients with lung cancer and a follow-up time of 120 months are used to identify a set of gene markers and an independent cohort of 26 patients is used to evaluate the identified biomarkers. The medical records of the 78 patients show a significant difference between the disease-free survival times of the 37 non-distant- and the 41 distant-metastasis patients. The experimental results thus obtained are as follows. 1) The use of disease-free survival curves can compensate for the shortcoming of insufficient samples and greatly increase the test accuracy by 11.10%; and 2) the support vector machine with a set of 17 transcripts, such as CCL16 and CDKN2AIP, can yield a leave-one-out cross-validation accuracy of 93.59%, a test accuracy of 76.92%, a large disease-free survival area of 74.81%, and a mean survival prediction error of 3.99 months. The identified putative biomarkers are examined using related studies and signaling pathways to reveal the potential effectiveness of the biomarkers in prospective confirmatory studies. Conclusions The proposed new optimization approach to identifying prognostic biomarkers by combining multiple sources of data (microarray and survival) can facilitate the accurate selection of biomarkers that are most relevant to the disease while solving the problem of insufficient samples. 其他題名： BMC Bioinformatics 出版者： London: BioMed Central 出版日期： 2015-02-21 出處： BMC bioinformatics, 2015-02, Vol.16 (1), p.54, Article 54 資源來源： Publicly Available Content Database 版權： Huang et al.; licensee BioMed Central. 2015 版權： COPYRIGHT 2015 BioMed Central Ltd. 識別號： ISSN: 1471-2105 識別號： EISSN: 1471-2105 識別號： DOI: 10.1186/s12859-015-0463-x 識別號： PMID: 25881029
Appears in Collections:	[Department of Biomedical Sciences and Engineering ] journal & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	16	View/Open

社群 sharing

Loading...