Author:
Song Yuting,Wang Ying,Geng Xin,Wang Xianming,He Huisi,Qian Youwen,Dong Yaping,Fan Zhecai,Chen Shuzhen,Wen Wen,Wang Hongyang
Abstract
Abstract
Background
The incidence and prevalence of nonalcoholic fatty liver disease related hepatocellular carcinoma (NAFLD-HCC) are rapidly increasing worldwide. This study aimed to identify biomarker genes for prognostic prediction model of NAFLD-HCC hepatectomy by integrating text-mining, clinical follow-up information, transcriptomic data and experimental validation.
Methods
The tumor and adjacent normal liver samples collected from 13 NAFLD-HCC and 12 HBV-HCC patients were sequenced using RNA-Seq. A novel text-mining strategy, explainable gene ontology fingerprint approach, was utilized to screen NAFLD-HCC featured gene sets and cell types, and the results were validated through a series of lab experiments. A risk score calculated by the multivariate Cox regression model using discovered key genes was established and evaluated based on 47 patients’ follow-up information.
Results
Differentially expressed genes associated with NAFLD-HCC specific tumor microenvironment were screened, of which FABP4 and VWF were featured by previous reports. A risk prediction model consisting of FABP4, VWF, gender and TNM stage were then established based on 47 samples. The model showed that overall survival in the high-risk score group was lower compared with that in the low-risk score group (p = 0.0095).
Conclusions
This study provided the landscape of NAFLD-HCC transcriptome, and elucidated that our model could predict hepatectomy prognosis with high accuracy.
Funder
Shanghai Municipal Health Commission
Science and Technology Commission of Shanghai Municipality
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Cancer Research,Genetics,Oncology