Concurrent analysis of copy number variation and gene expression: Application in paired non-smoking female lung cancer patients

Jung Chih Chang; Liang Chuan Lai; Tzu Pin Lu; Mong Hsun Tsai; Eric Y. Chuang; Chuhsing Kate Hsiao; Pei Chun Chen

doi:10.1109/BIBM.2010.5706636

Concurrent analysis of copy number variation and gene expression: Application in paired non-smoking female lung cancer patients

Jung Chih Chang^*
, Liang Chuan Lai
, Tzu Pin Lu
, Mong Hsun Tsai
, Eric Y. Chuang
, Chuhsing Kate Hsiao
, Pei Chun Chen

^*Corresponding author for this work

National Taiwan University
Providence University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This study developed a method to identify disease-correlated pathways by integrating copy numbers (CN) and gene expression (GE). To evaluate the correlation between CN and GE, a suitable window size was assessed by simulation. Gene Set Enrichment Analysis (GSEA) was utilized to identify the possible pathways by CN, GE, and their correlations, respectively. Each of those enriched pathways was further assigned a score to incorporate the information from CN, GE, and their correlations. A dataset of 44 female non-smoking lung cancer patients with both normal and tumor tissues was used to evaluate the performance of this method. To further appraise the predicting abilities of those pathways, patients were classified by support vector machines using the pathways identified by only copy number, only gene expression and incorporating CN, GE, and their correlations. The results showed that the proposed method earned higher accuracy, sensitivity and specificity than traditional methods.

Original language	English
Title of host publication	Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010
Pages	599-602
Number of pages	4
DOIs	https://doi.org/10.1109/BIBM.2010.5706636
State	Published - 2010
Externally published	Yes
Event	2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010 - Hong Kong, China Duration: 18 12 2010 → 21 12 2010

Publication series

Name	Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010

Conference

Conference	2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010
Country/Territory	China
City	Hong Kong
Period	18/12/10 → 21/12/10

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Access to Document

10.1109/BIBM.2010.5706636

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Chang, J. C., Lai, L. C., Lu, T. P., Tsai, M. H., Chuang, E. Y., Hsiao, C. K., & Chen, P. C. (2010). Concurrent analysis of copy number variation and gene expression: Application in paired non-smoking female lung cancer patients. In Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010 (pp. 599-602). Article 5706636 (Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010). https://doi.org/10.1109/BIBM.2010.5706636

Chang, Jung Chih ; Lai, Liang Chuan ; Lu, Tzu Pin et al. / Concurrent analysis of copy number variation and gene expression : Application in paired non-smoking female lung cancer patients. Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010. 2010. pp. 599-602 (Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010).

@inproceedings{90a5e9b67bed42d38b78489718ac9c44,

title = "Concurrent analysis of copy number variation and gene expression: Application in paired non-smoking female lung cancer patients",

abstract = "This study developed a method to identify disease-correlated pathways by integrating copy numbers (CN) and gene expression (GE). To evaluate the correlation between CN and GE, a suitable window size was assessed by simulation. Gene Set Enrichment Analysis (GSEA) was utilized to identify the possible pathways by CN, GE, and their correlations, respectively. Each of those enriched pathways was further assigned a score to incorporate the information from CN, GE, and their correlations. A dataset of 44 female non-smoking lung cancer patients with both normal and tumor tissues was used to evaluate the performance of this method. To further appraise the predicting abilities of those pathways, patients were classified by support vector machines using the pathways identified by only copy number, only gene expression and incorporating CN, GE, and their correlations. The results showed that the proposed method earned higher accuracy, sensitivity and specificity than traditional methods.",

author = "Chang, \{Jung Chih\} and Lai, \{Liang Chuan\} and Lu, \{Tzu Pin\} and Tsai, \{Mong Hsun\} and Chuang, \{Eric Y.\} and Hsiao, \{Chuhsing Kate\} and Chen, \{Pei Chun\}",

year = "2010",

doi = "10.1109/BIBM.2010.5706636",

language = "英语",

isbn = "9781424483075",

series = "Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010",

pages = "599--602",

booktitle = "Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010",

note = "2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010 ; Conference date: 18-12-2010 Through 21-12-2010",

}

Chang, JC, Lai, LC, Lu, TP, Tsai, MH, Chuang, EY, Hsiao, CK & Chen, PC 2010, Concurrent analysis of copy number variation and gene expression: Application in paired non-smoking female lung cancer patients. in Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010., 5706636, Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010, pp. 599-602, 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010, Hong Kong, China, 18/12/10. https://doi.org/10.1109/BIBM.2010.5706636

Concurrent analysis of copy number variation and gene expression: Application in paired non-smoking female lung cancer patients. / Chang, Jung Chih; Lai, Liang Chuan; Lu, Tzu Pin et al.
Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010. 2010. p. 599-602 5706636 (Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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T2 - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010

AU - Chang, Jung Chih

AU - Lai, Liang Chuan

AU - Lu, Tzu Pin

AU - Tsai, Mong Hsun

AU - Chuang, Eric Y.

AU - Hsiao, Chuhsing Kate

AU - Chen, Pei Chun

PY - 2010

Y1 - 2010

N2 - This study developed a method to identify disease-correlated pathways by integrating copy numbers (CN) and gene expression (GE). To evaluate the correlation between CN and GE, a suitable window size was assessed by simulation. Gene Set Enrichment Analysis (GSEA) was utilized to identify the possible pathways by CN, GE, and their correlations, respectively. Each of those enriched pathways was further assigned a score to incorporate the information from CN, GE, and their correlations. A dataset of 44 female non-smoking lung cancer patients with both normal and tumor tissues was used to evaluate the performance of this method. To further appraise the predicting abilities of those pathways, patients were classified by support vector machines using the pathways identified by only copy number, only gene expression and incorporating CN, GE, and their correlations. The results showed that the proposed method earned higher accuracy, sensitivity and specificity than traditional methods.

AB - This study developed a method to identify disease-correlated pathways by integrating copy numbers (CN) and gene expression (GE). To evaluate the correlation between CN and GE, a suitable window size was assessed by simulation. Gene Set Enrichment Analysis (GSEA) was utilized to identify the possible pathways by CN, GE, and their correlations, respectively. Each of those enriched pathways was further assigned a score to incorporate the information from CN, GE, and their correlations. A dataset of 44 female non-smoking lung cancer patients with both normal and tumor tissues was used to evaluate the performance of this method. To further appraise the predicting abilities of those pathways, patients were classified by support vector machines using the pathways identified by only copy number, only gene expression and incorporating CN, GE, and their correlations. The results showed that the proposed method earned higher accuracy, sensitivity and specificity than traditional methods.

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ER -

Chang JC, Lai LC, Lu TP, Tsai MH, Chuang EY, Hsiao CK et al. Concurrent analysis of copy number variation and gene expression: Application in paired non-smoking female lung cancer patients. In Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010. 2010. p. 599-602. 5706636. (Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010). doi: 10.1109/BIBM.2010.5706636

Concurrent analysis of copy number variation and gene expression: Application in paired non-smoking female lung cancer patients

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