Xue-Jia Lin1, Yutian Chong2, Zhi-Wei Guo3, Chen Xie2, Xiao-Jing Yang3, Qi Zhang2, Sheng-Ping Li4, Yujuan Xiong5, Yunfei Yuan4, Jun Min6, Wei-Hua Jia7, Yusheng Jie2, Min-Shan Chen4, Mei-Xian Chen4, Jian-Hong Fang3, Chunxian Zeng3, Yaojun Zhang4, Rong-Ping Guo4, Yuankai Wu2, Guoli Lin2, Limin Zheng3, Shi-Mei Zhuang8. 1. Key Laboratory of Liver Disease of Guangdong Province and Department of Infectious Diseases, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China; Key Laboratory of Gene Engineering of the Ministry of Education, Collaborative Innovation Center for Cancer Medicine, School of Life Sciences, Sun Yat-sen University, Guangzhou, China. 2. Key Laboratory of Liver Disease of Guangdong Province and Department of Infectious Diseases, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China. 3. Key Laboratory of Gene Engineering of the Ministry of Education, Collaborative Innovation Center for Cancer Medicine, School of Life Sciences, Sun Yat-sen University, Guangzhou, China. 4. Department of Hepatobiliary Oncology, Cancer Center, Sun Yat-sen University, Guangzhou, China. 5. Department of Laboratory Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China. 6. Department of Hepatobiliary Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China. 7. Bank of Tumor Resources, Cancer Center, Sun Yat-sen University, Guangzhou, China. 8. Key Laboratory of Liver Disease of Guangdong Province and Department of Infectious Diseases, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China; Key Laboratory of Gene Engineering of the Ministry of Education, Collaborative Innovation Center for Cancer Medicine, School of Life Sciences, Sun Yat-sen University, Guangzhou, China. Electronic address: zhuangshimei@163.com.
Abstract
BACKGROUND: The ability of circulating microRNAs (miRNAs) to detect preclinical hepatocellular carcinoma has not yet been reported. We aimed to identify and assess a serum miRNA combination that could detect the presence of clinical and preclinical hepatocellular carcinoma in at-risk patients. METHODS: We did a three-stage study that included healthy controls, inactive HBsAg carriers, individuals with chronic hepatitis B, individuals with hepatitis B-induced liver cirrhosis, and patients with diagnosed hepatocellular carcinoma from four hospitals in China. We used array analysis and quantitative PCR to identify 19 candidate serum miRNAs that were increased in six patients with hepatocellular carcinoma compared with eight control patients with chronic hepatitis B. Using a training cohort of patients with hepatocellular carcinoma and controls, we built a serum miRNA classifier to detect hepatocellular carcinoma. We then validated the classifiers' ability in two independent cohorts of patients and controls. We also established the classifiers' ability to predict preclinical hepatocellular carcinoma in a nested case-control study with sera prospectively collected from patients with hepatocellular carcinoma before clinical diagnosis and from matched individuals with hepatitis B who did not develop cancer from the same surveillance programme. We used the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) to evaluate diagnostic performance, and compared the miRNA classifier with α-fetoprotein at a cutoff of 20 ng/mL (AFP20). FINDINGS: Between Aug 1, 2009, and Aug 31, 2013, we recruited 257 participants to the training cohort, and 352 and 139 participants to the two independent validation cohorts. In the third validation cohort, 27 patients with hepatocellular carcinoma and 135 matched controls were included in the nested case-control study, which ran from Aug 1, 2009, to Aug 31, 2014. We identified a miRNA classifier (Cmi) containing seven differentially expressed miRNAs (miR-29a, miR-29c, miR-133a, miR-143, miR-145, miR-192, and miR-505) that could detect hepatocellular carcinoma. Cmi showed higher accuracy than AFP20 to distinguish individuals with hepatocellular carcinoma from controls in the validation cohorts, but not in the training cohort (AUC 0·826 [95% CI 0·771-0·880] vs 0·814 [0·756-0·872], p=0·72 in the training cohort; 0·817 [0·769-0·865] vs 0·709 [0·653-0·765], p=0·00076 in validation cohort 1; and 0·884 [0·818-0·951] vs 0·796 [0·706-0·886], p=0·042 for validation cohort 2). In all four cohorts, Cmi had higher sensitivity (range 70·4-85·7%) than did AFP20 (40·7-69·4%) to detect hepatocellular carcinoma at the time of diagnosis, whereas its specificity (80·0-91·1%) was similar to that of AFP20 (84·9-100%). In the nested case-control study, sensitivity of Cmi to detect hepatocellular carcinoma was 29·6% (eight of 27 cases) 12 months before clinical diagnosis, 48·1% (n=13) 9 months before clinical diagnosis, 48·1% (n=13) 6 months before clinical diagnosis, and 55·6% (n=15) 3 months before clinical diagnosis, whereas sensitivity of AFP20 was only 7·4% (n=2), 11·1% (n=3), 18·5% (n=5), and 22·2% (n=6) at the corresponding timepoints (p=0·036, p=0·0030, p=0·021, p=0·012, respectively). Cmi had a larger AUC than did AFP20 to identify small-size (AUC 0·833 [0·782-0·883] vs 0·727 [0·664-0·792], p=0·0018) and early-stage (AUC 0·824 [0·781-0·868] vs 0·754 [0·702-0·806], p=0·015) hepatocellular carcinoma and could also detect α-fetoprotein-negative (AUC 0·825 [0·779-0·871]) hepatocellular carcinoma. INTERPRETATION: Cmi is a potential biomarker for hepatocellular carcinoma, and can identify small-size, early-stage, and α-fetoprotein-negative hepatocellular carcinoma in patients at risk. The miRNA classifier could be valuable to detect preclinical hepatocellular carcinoma, providing patients with a chance of curative resection and longer survival. FUNDING: National Key Basic Research Program, National Science and Technology Major Project, National Natural Science Foundation of China.
BACKGROUND: The ability of circulating microRNAs (miRNAs) to detect preclinical hepatocellular carcinoma has not yet been reported. We aimed to identify and assess a serum miRNA combination that could detect the presence of clinical and preclinical hepatocellular carcinoma in at-risk patients. METHODS: We did a three-stage study that included healthy controls, inactive HBsAg carriers, individuals with chronic hepatitis B, individuals with hepatitis B-induced liver cirrhosis, and patients with diagnosed hepatocellular carcinoma from four hospitals in China. We used array analysis and quantitative PCR to identify 19 candidate serum miRNAs that were increased in six patients with hepatocellular carcinoma compared with eight control patients with chronic hepatitis B. Using a training cohort of patients with hepatocellular carcinoma and controls, we built a serum miRNA classifier to detect hepatocellular carcinoma. We then validated the classifiers' ability in two independent cohorts of patients and controls. We also established the classifiers' ability to predict preclinical hepatocellular carcinoma in a nested case-control study with sera prospectively collected from patients with hepatocellular carcinoma before clinical diagnosis and from matched individuals with hepatitis B who did not develop cancer from the same surveillance programme. We used the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) to evaluate diagnostic performance, and compared the miRNA classifier with α-fetoprotein at a cutoff of 20 ng/mL (AFP20). FINDINGS: Between Aug 1, 2009, and Aug 31, 2013, we recruited 257 participants to the training cohort, and 352 and 139 participants to the two independent validation cohorts. In the third validation cohort, 27 patients with hepatocellular carcinoma and 135 matched controls were included in the nested case-control study, which ran from Aug 1, 2009, to Aug 31, 2014. We identified a miRNA classifier (Cmi) containing seven differentially expressed miRNAs (miR-29a, miR-29c, miR-133a, miR-143, miR-145, miR-192, and miR-505) that could detect hepatocellular carcinoma. Cmi showed higher accuracy than AFP20 to distinguish individuals with hepatocellular carcinoma from controls in the validation cohorts, but not in the training cohort (AUC 0·826 [95% CI 0·771-0·880] vs 0·814 [0·756-0·872], p=0·72 in the training cohort; 0·817 [0·769-0·865] vs 0·709 [0·653-0·765], p=0·00076 in validation cohort 1; and 0·884 [0·818-0·951] vs 0·796 [0·706-0·886], p=0·042 for validation cohort 2). In all four cohorts, Cmi had higher sensitivity (range 70·4-85·7%) than did AFP20 (40·7-69·4%) to detect hepatocellular carcinoma at the time of diagnosis, whereas its specificity (80·0-91·1%) was similar to that of AFP20 (84·9-100%). In the nested case-control study, sensitivity of Cmi to detect hepatocellular carcinoma was 29·6% (eight of 27 cases) 12 months before clinical diagnosis, 48·1% (n=13) 9 months before clinical diagnosis, 48·1% (n=13) 6 months before clinical diagnosis, and 55·6% (n=15) 3 months before clinical diagnosis, whereas sensitivity of AFP20 was only 7·4% (n=2), 11·1% (n=3), 18·5% (n=5), and 22·2% (n=6) at the corresponding timepoints (p=0·036, p=0·0030, p=0·021, p=0·012, respectively). Cmi had a larger AUC than did AFP20 to identify small-size (AUC 0·833 [0·782-0·883] vs 0·727 [0·664-0·792], p=0·0018) and early-stage (AUC 0·824 [0·781-0·868] vs 0·754 [0·702-0·806], p=0·015) hepatocellular carcinoma and could also detect α-fetoprotein-negative (AUC 0·825 [0·779-0·871]) hepatocellular carcinoma. INTERPRETATION: Cmi is a potential biomarker for hepatocellular carcinoma, and can identify small-size, early-stage, and α-fetoprotein-negative hepatocellular carcinoma in patients at risk. The miRNA classifier could be valuable to detect preclinical hepatocellular carcinoma, providing patients with a chance of curative resection and longer survival. FUNDING: National Key Basic Research Program, National Science and Technology Major Project, National Natural Science Foundation of China.
Authors: Ismail Labgaa; Carlos Villacorta-Martin; Delia D'Avola; Amanda J Craig; Johann von Felden; Sebastiao N Martins-Filho; Daniela Sia; Ashley Stueck; Stephen C Ward; M Isabel Fiel; Milind Mahajan; Parissa Tabrizian; Swan N Thung; Celina Ang; Scott L Friedman; Josep M Llovet; Myron Schwartz; Augusto Villanueva Journal: Oncogene Date: 2018-04-09 Impact factor: 9.867