MicroRNA Signature Predicts Survival and Relapse in Lung Cancer

Sung Liang Yu; Hsuan Yu Chen; Gee Chen Chang; Chih Yi Chen; Huei Wen Chen; Sher Singh; Chiou Ling Cheng; Chong Jen Yu; Yung Chie Lee; Han Shiang Chen; Te Jen Su; Ching Cheng Chiang; Han Ni Li; Qi Sheng Hong; Hsin Yuan Su; Chun Chieh Chen; Wan Jiun Chen; Chun Chi Liu; Wing Kai Chan; Wei J. Chen; Ker Chau Li; Jeremy J.W. Chen; Pan Chyr Yang

doi:10.1016/j.ccr.2007.12.008

MicroRNA Signature Predicts Survival and Relapse in Lung Cancer

Sung Liang Yu
, Hsuan Yu Chen
, Gee Chen Chang
, Chih Yi Chen
, Huei Wen Chen
, Sher Singh
, Chiou Ling Cheng
, Chong Jen Yu
, Yung Chie Lee
, Han Shiang Chen
, Te Jen Su
, Ching Cheng Chiang
, Han Ni Li
, Qi Sheng Hong
, Hsin Yuan Su
, Chun Chieh Chen
, Wan Jiun Chen
, Chun Chi Liu
, Wing Kai Chan
, Wei J. Chen

Ker Chau Li, Jeremy J.W. Chen, Pan Chyr Yang^*

^*Corresponding author for this work

Department of Life Science

Research output: Contribution to journal › Article › peer-review

740 Citations (Scopus)

Abstract

We investigated whether microRNA expression profiles can predict clinical outcome of NSCLC patients. Using real-time RT-PCR, we obtained microRNA expressions in 112 NSCLC patients, which were divided into the training and testing sets. Using Cox regression and risk-score analysis, we identified a five-microRNA signature for the prediction of treatment outcome of NSCLC in the training set. This microRNA signature was validated by the testing set and an independent cohort. Patients with high-risk scores in their microRNA signatures had poor overall and disease-free survivals compared to the low-risk-score patients. This microRNA signature is an independent predictor of the cancer relapse and survival of NSCLC patients.

Original language	English
Pages (from-to)	48-57
Number of pages	10
Journal	Cancer Cell
Volume	13
Issue number	1
DOIs	https://doi.org/10.1016/j.ccr.2007.12.008
Publication status	Published - 2008 Jan 8

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

CELLCYCLE

ASJC Scopus subject areas

Oncology
Cancer Research

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10.1016/j.ccr.2007.12.008

Cite this

Yu, S. L., Chen, H. Y., Chang, G. C., Chen, C. Y., Chen, H. W., Singh, S., Cheng, C. L., Yu, C. J., Lee, Y. C., Chen, H. S., Su, T. J., Chiang, C. C., Li, H. N., Hong, Q. S., Su, H. Y., Chen, C. C., Chen, W. J., Liu, C. C., Chan, W. K., ... Yang, P. C. (2008). MicroRNA Signature Predicts Survival and Relapse in Lung Cancer. Cancer Cell, 13(1), 48-57. https://doi.org/10.1016/j.ccr.2007.12.008

Yu, SL, Chen, HY, Chang, GC, Chen, CY, Chen, HW, Singh, S, Cheng, CL, Yu, CJ, Lee, YC, Chen, HS, Su, TJ, Chiang, CC, Li, HN, Hong, QS, Su, HY, Chen, CC, Chen, WJ, Liu, CC, Chan, WK, Chen, WJ, Li, KC, Chen, JJW & Yang, PC 2008, 'MicroRNA Signature Predicts Survival and Relapse in Lung Cancer', Cancer Cell, vol. 13, no. 1, pp. 48-57. https://doi.org/10.1016/j.ccr.2007.12.008

@article{e52c282148a54e519ebd7947c7ab884f,

title = "MicroRNA Signature Predicts Survival and Relapse in Lung Cancer",

abstract = "We investigated whether microRNA expression profiles can predict clinical outcome of NSCLC patients. Using real-time RT-PCR, we obtained microRNA expressions in 112 NSCLC patients, which were divided into the training and testing sets. Using Cox regression and risk-score analysis, we identified a five-microRNA signature for the prediction of treatment outcome of NSCLC in the training set. This microRNA signature was validated by the testing set and an independent cohort. Patients with high-risk scores in their microRNA signatures had poor overall and disease-free survivals compared to the low-risk-score patients. This microRNA signature is an independent predictor of the cancer relapse and survival of NSCLC patients.",

keywords = "CELLCYCLE",

author = "Yu, \{Sung Liang\} and Chen, \{Hsuan Yu\} and Chang, \{Gee Chen\} and Chen, \{Chih Yi\} and Chen, \{Huei Wen\} and Sher Singh and Cheng, \{Chiou Ling\} and Yu, \{Chong Jen\} and Lee, \{Yung Chie\} and Chen, \{Han Shiang\} and Su, \{Te Jen\} and Chiang, \{Ching Cheng\} and Li, \{Han Ni\} and Hong, \{Qi Sheng\} and Su, \{Hsin Yuan\} and Chen, \{Chun Chieh\} and Chen, \{Wan Jiun\} and Liu, \{Chun Chi\} and Chan, \{Wing Kai\} and Chen, \{Wei J.\} and Li, \{Ker Chau\} and Chen, \{Jeremy J.W.\} and Yang, \{Pan Chyr\}",

note = "Funding Information: This work was supported by the National Science Council of the Republic of China through the National Research Program for Genomic Medicine (grants NSC94-3112-B002-022 and DOH96-TD-G-111-011) and also by a computational biology grant (NSC95-3114-P-002-005-Y). K.-C.L.'s work was supported in part by NSC95-3114-P-002-005-Y and a startup funding for Mathematics in Biology from Academia Sinica and in part by NSF grants DMS0406091 and DMS0201005. The authors thank Applied Biosystems for technical support in microRNA quantification. H.Y.C. is the leading coauthor responsible for the enabling statistical analysis and computation. Drs. K.-C.L., J.J.W.C., and P.-C.Y. codirected the project and contributed equally to this work. ",

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doi = "10.1016/j.ccr.2007.12.008",

language = "English",

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journal = "Cancer Cell",

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T1 - MicroRNA Signature Predicts Survival and Relapse in Lung Cancer

AU - Yu, Sung Liang

AU - Chen, Hsuan Yu

AU - Chang, Gee Chen

AU - Chen, Chih Yi

AU - Chen, Huei Wen

AU - Singh, Sher

AU - Cheng, Chiou Ling

AU - Yu, Chong Jen

AU - Lee, Yung Chie

AU - Chen, Han Shiang

AU - Su, Te Jen

AU - Chiang, Ching Cheng

AU - Li, Han Ni

AU - Hong, Qi Sheng

AU - Su, Hsin Yuan

AU - Chen, Chun Chieh

AU - Chen, Wan Jiun

AU - Liu, Chun Chi

AU - Chan, Wing Kai

AU - Chen, Wei J.

AU - Li, Ker Chau

AU - Chen, Jeremy J.W.

AU - Yang, Pan Chyr

N1 - Funding Information: This work was supported by the National Science Council of the Republic of China through the National Research Program for Genomic Medicine (grants NSC94-3112-B002-022 and DOH96-TD-G-111-011) and also by a computational biology grant (NSC95-3114-P-002-005-Y). K.-C.L.'s work was supported in part by NSC95-3114-P-002-005-Y and a startup funding for Mathematics in Biology from Academia Sinica and in part by NSF grants DMS0406091 and DMS0201005. The authors thank Applied Biosystems for technical support in microRNA quantification. H.Y.C. is the leading coauthor responsible for the enabling statistical analysis and computation. Drs. K.-C.L., J.J.W.C., and P.-C.Y. codirected the project and contributed equally to this work.

PY - 2008/1/8

Y1 - 2008/1/8

N2 - We investigated whether microRNA expression profiles can predict clinical outcome of NSCLC patients. Using real-time RT-PCR, we obtained microRNA expressions in 112 NSCLC patients, which were divided into the training and testing sets. Using Cox regression and risk-score analysis, we identified a five-microRNA signature for the prediction of treatment outcome of NSCLC in the training set. This microRNA signature was validated by the testing set and an independent cohort. Patients with high-risk scores in their microRNA signatures had poor overall and disease-free survivals compared to the low-risk-score patients. This microRNA signature is an independent predictor of the cancer relapse and survival of NSCLC patients.

AB - We investigated whether microRNA expression profiles can predict clinical outcome of NSCLC patients. Using real-time RT-PCR, we obtained microRNA expressions in 112 NSCLC patients, which were divided into the training and testing sets. Using Cox regression and risk-score analysis, we identified a five-microRNA signature for the prediction of treatment outcome of NSCLC in the training set. This microRNA signature was validated by the testing set and an independent cohort. Patients with high-risk scores in their microRNA signatures had poor overall and disease-free survivals compared to the low-risk-score patients. This microRNA signature is an independent predictor of the cancer relapse and survival of NSCLC patients.

KW - CELLCYCLE

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UR - https://www.scopus.com/pages/publications/37349096992#tab=citedBy

U2 - 10.1016/j.ccr.2007.12.008

DO - 10.1016/j.ccr.2007.12.008

M3 - Article

C2 - 18167339

AN - SCOPUS:37349096992

SN - 1535-6108

VL - 13

SP - 48

EP - 57

JO - Cancer Cell

JF - Cancer Cell

IS - 1

ER -

MicroRNA Signature Predicts Survival and Relapse in Lung Cancer

Abstract

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Keywords

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