Identification and Validation of Novel Biomarkers for Hepatocellular Carcinoma, Liver Fibrosis/Cirrhosis and Chronic Hepatitis B via Transcriptome Sequencing Technology

Abstract

Dandan Zhao,1,2 Xiaoxiao Zhang,1,2 Yuhui Tang,1,2 Peilin Guo,1,2 Rong Ai,1,2 Mengmeng Hou,1,2 Yiqi Wang,1,2 Xiwei Yuan,1,2 Luyao Cui,1,2 Yuguo Zhang,1,2 Suxian Zhao,1,2 Wencong Li,1,2 Yang Wang,1,2 Xiaoye Sun,1,2 Lingdi Liu,1,2 Shiming Dong,1,2 Lu Li,1,2 Wen Zhao,1,2 Yuemin Nan1,2 1Department of Traditional and Western Medical Hepatology, Third Hospital of Hebei Medical University, Shijiazhuang, Hebei, People’s Republic of China; 2Hebei Provincial Key Laboratory of Liver Fibrosis in Chronic Liver Diseases, Shijiazhuang, Hebei, People’s Republic of ChinaCorrespondence: Yuemin Nan, Department of Traditional and Western Medical Hepatology, Third Hospital of Hebei Medical University, No. 139 Ziqiang Road, Shijiazhuang, Hebei Province, 050051, People’s Republic of China, Tel [phone], Fax [phone], Email [email protected]: The aim of this study was to identify and validate novel biomarkers for distinguishing among hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC), liver fibrosis/liver cirrhosis (LF/LC) and chronic hepatitis B (CHB).Patients and Methods: Transcriptomic sequencing was conducted on the liver tissues of 5 patients with HCC, 5 patients with LF/LC, 5 patients with CHB, and 4 healthy controls. The expression levels of selected mRNAs and proteins were assessed by quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemical (IHC) staining, and were verified in validation set (n=200) and testing set (n=400) via enzyme-linked immunosorbent assay (ELISA).Results: A total of 9 hub mRNAs were identified by short time-series expression miner and weighted gene co-expression network analysis. Of note, the results of qRT-PCR and IHC staining demonstrated that SHC adaptor protein 1 (SHC1), SLAM family member 8 (SLAMF8), and interleukin-32 (IL-32) exhibited gradually increasing trends in the four groups. Subsequent ELISA tests on the validation cohort indicated that the plasma levels of SHC1, SLAMF8 and IL-32 also gradually increased. Furthermore, a diagnostic model APFSSI (age, PLT, ferritin, SHC1, SLAMF8 and IL-32) was established to distinguish among CHB, LF/LC and HCC. The performance of APFSSI model for discriminating CHB from healthy subjects (AUC=0.966) was much greater compared to SHC1 (AUC=0.900), SLAMF8 (AUC=0.744) and IL-32 (AUC=0.821). When distinguishing LF/LC from CHB, APFSSI was the most outstanding diagnostic parameter (AUC=0.924), which was superior to SHC1, SLAMF8 and IL-32 (AUC=0.812, 0.684 and 0.741, respectively). Likewise, APFSSI model with the greatest AUC value displayed an excellent performance for differentiating between HCC and LF/LC than other variables (SHC1, SLAMF8 and IL-32) via ROC analysis. Finally, the results in the test set were consistent with those in the validation set.Conclusion: SHC1, SLAMF8 and IL-32 can differentiate among patients with HCC, LF/LC, CHB and healthy controls. More importantly, the APFSSI model greatly improves the diagnostic accuracy of HBV-associated liver diseases.Keywords: HBV-associated liver diseases, transcriptome sequencing technology, SHC adaptor protein 1, SLAM family member 8, interleukin-32

Keywords

• hbv-associated liver diseases
• transcriptome sequencing technology
• shc adaptor protein 1
• slam family member 8
• interleukin-32