| Autor: |
Yuting Song, Ying Wang, Xin Geng, Xianming Wang, Huisi He, Youwen Qian, Yaping Dong, Zhecai Fan, Shuzhen Chen, Wen Wen, Hongyang Wang |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Cancer Cell International, Vol 23, Iss 1, Pp 1-11 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1475-2867 |
| DOI: |
10.1186/s12935-023-03106-2 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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