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基因表达特征预测大肠癌患者的预后和辅助化疗的敏感性
Authors Li J, Zhang J, Hu H, Cai Y, Ling J, Wu Z, Deng Y
Received 25 December 2019
Accepted for publication 16 April 2020
Published 11 May 2020 Volume 2020:12 Pages 3301—3310
DOI https://doi.org/10.2147/CMAR.S243490
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 2
Editor who approved publication: Professor Bilikere Dwarakanath
Purpose: Molecular characteristics using gene-expression profiling can undoubtedly improve the prediction of treatment responses, and ultimately, the clinical outcome of cancer patients. We aimed at developing a genetic signature to improve the prediction of chemosensitivity and prognosis of patients with colorectal cancer (CRC).
Patients and Methods: We analyzed microarray data of 32 CRC patients to explore the potential functions and pathways involved in the disease relapse in CRC. Gene expression profiles and clinical follow-up information of GSE39582, GSE17536, and GSE103479 were downloaded from the Gene Expression Omnibus database (GEO) to identify prognostic genes. Eventually, a model of 15-mRNA signature was established, in which its efficacy for predicting chemosensitivity and prognosis was examined.
Results: Based on the proposed model of 15-mRNA signature, the test series patients could be classified into high-risk or low-risk subgroup with significantly different overall survival (OS) rate (hazard ratio [HR]=1.48, 95% confidence interval [CI]=1.30– 1.70, P≤ 0.001). The prognostic value of this 15-mRNA signature was confirmed in another validation series. Further analysis revealed that the prognostic value of this signature was independent of the TNM stage and can predict adjuvant chemosensitivity of patients with early-stage CRC.
Conclusion: We identified a novel 15-mRNA signature in patients with CRC, which could be clinically helpful in the prognosis evaluation and the process of selection of patients with early-stage CRC for undergoing adjuvant chemotherapy.
Keywords: colorectal cancer (CRC), chemosensitivity, survival, a 15-gene signature, prognosis, risk score
