Diagnostic Value of hTERT mRNA and in Combination With AFP, AFP-L3%, Des-γ-carboxyprothrombin for Screening of Hepatocellular Carcinoma in Liver Cirrhosis Patients HBV or HCV-Related
Hoang Bac Nguyen, Xuan Thi Thanh Le, Huy Huu Nguyen, Thanh Thanh Vo, M. Le, Ngan Nguyen, Thien Minh Do-Nguyen, Cong Minh Truong-Nguyen, Bang Suong Thi Nguyen
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引用次数: 5
Abstract
Diagnosis of hepatocellular carcinoma (HCC) in early-stage, to give an effective treatment option and improve quality of life for cancer patients, is an important medical mission globally. Combination of AFP with some biomarkers may be more supportive in both diagnosis and screening of HCC, but the range value of these markers can be applied as daily markers were unclearly. In some studies, human telomerase reverse transcriptase (hTERT mRNA) was reported as an advantage marker to diagnose cancer. The present study identified serum of 340 patients that were infected chronic hepatitis B virus or hepatitis C virus and divided in 2 groups including Hepatocellular carcinoma (HCC) and liver cirrhosis (LC) to measure their values of hTERT mRNA, AFP, AFP-L3%, and DCP, as well as combination of them. As a result, the concentration of hTERT mRNA, AFP, AFP-L3%, and DCP in HCC groups were significantly higher than that in LC group (P < .01). For detecting HCC, hTERT mRNA had sensitivity of 88% and specificity of 96% (at the cutoff value of 31.5 copies/mL), AFP sensitivity of 73% and specificity of 92% (at the cutoff value of 5.1 ng/mL), AFP-L3% sensitivity of 69% and specificity of 90% (at the cutoff value of 1.05%), DCP sensitivity of 82% and specificity of 92% (at the cutoff value of 29.01 mAU/mL). The largest area under the curve (AUC) of combination hTERT mRNA with DCP was 0.932 (sensitivity of 98.2% and specificity of 88.2%). New combination of DCP with hTERT mRNA gave a useful choice for screening of HCC in chronic HBV or HCV patients associated liver cirrhosis.
期刊介绍:
The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.