Jing Wen1,2, Geng Wang2,3, Xuan Xie4, Guangrong Lin5, Hong Yang2,6, Kongjia Luo2,6, Qianwen Liu2,6, Yihong Ling7, Xiuying Xie1,2, Peng Lin2,6, Yuping Chen2,3, Huizhong Zhang4, Tiehua Rong2,6, Jianhua Fu1,2,6. 1. State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China. 2. Guangdong Esophageal Cancer Research Institute, Guangzhou, China. 3. Department of Thoracic Surgery, Cancer Hospital of Shantou University Medical College, Shantou, China. 4. Department of Thoracic Surgery, Sun Yat-sen University Memorial Hospital, Guangzhou, China. 5. Guangzhou Haige Communications Group Incorporated Company, Guangzhou, China. 6. Department of Thoracic Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China. 7. Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, China.
Abstract
OBJECTIVE: This study was intended to identify prognostic biomarkers for lymph node (LN)-positive locoregional esophageal squamous cell carcinoma (ESCC) patients. SUMMARY OF BACKGROUND DATA: Surgery is a major treatment for LN-positive locoregional ESCC patients in China. However, patient outcomes are poor and heterogeneous. METHODS: ESCC-associated miRNAs were identified by microarray and validated by quantitative real-time polymerase chain reaction analyses in ESCC and normal esophageal epithelial samples. A multi-miRNA based classifier was established using a least absolute shrinkage and selection operator model in a training set of 145 LN-positive locoregional ESCCs, and further assessed in internal testing and independent validation sets of 145 and 243 patients, respectively. RESULTS: Twenty ESCC-associated miRNAs were identified and validated. A 4-miRNA based classifier (miR-135b-5p, miR-139-5p, miR-29c-5p, and miR-338-3p) was generated to classify LN-positive locoregional ESCC patients into high and low-risk groups. Patients with high-risk scores in the training set had a lower 5-year overall survival rate [8.7%, 95% confidence interval (CI): 0-20.3] than those with low-risk scores (50.3%, 95% CI: 40.0-60.7; P < 0.0001). The prognostic accuracy of the classifier was validated in the internal testing (P < 0.0001) and independent validation sets (P = 0.00073). Multivariate survival analyses showed that the 4-miRNA based classifier was an independent prognostic factor, and the combination of the 4-miRNA based classifier and clinicopathological prognostic factors significantly improved the prognostic accuracy of clinicopathological prognostic factors alone. CONCLUSION: Our 4-miRNA based classifier is a reliable prognostic prediction tool for overall survival in LN-positive locoregional ESCC patients and might offer a novel probability of ESCC treatment individualization.
OBJECTIVE: This study was intended to identify prognostic biomarkers for lymph node (LN)-positive locoregional esophageal squamous cell carcinoma (ESCC) patients. SUMMARY OF BACKGROUND DATA: Surgery is a major treatment for LN-positive locoregional ESCC patients in China. However, patient outcomes are poor and heterogeneous. METHODS: ESCC-associated miRNAs were identified by microarray and validated by quantitative real-time polymerase chain reaction analyses in ESCC and normal esophageal epithelial samples. A multi-miRNA based classifier was established using a least absolute shrinkage and selection operator model in a training set of 145 LN-positive locoregional ESCCs, and further assessed in internal testing and independent validation sets of 145 and 243 patients, respectively. RESULTS: Twenty ESCC-associated miRNAs were identified and validated. A 4-miRNA based classifier (miR-135b-5p, miR-139-5p, miR-29c-5p, and miR-338-3p) was generated to classify LN-positive locoregional ESCC patients into high and low-risk groups. Patients with high-risk scores in the training set had a lower 5-year overall survival rate [8.7%, 95% confidence interval (CI): 0-20.3] than those with low-risk scores (50.3%, 95% CI: 40.0-60.7; P < 0.0001). The prognostic accuracy of the classifier was validated in the internal testing (P < 0.0001) and independent validation sets (P = 0.00073). Multivariate survival analyses showed that the 4-miRNA based classifier was an independent prognostic factor, and the combination of the 4-miRNA based classifier and clinicopathological prognostic factors significantly improved the prognostic accuracy of clinicopathological prognostic factors alone. CONCLUSION: Our 4-miRNA based classifier is a reliable prognostic prediction tool for overall survival in LN-positive locoregional ESCC patients and might offer a novel probability of ESCC treatment individualization.