Retrospective Study
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Sep 26, 2023; 11(27): 6383-6397
Published online Sep 26, 2023. doi: 10.12998/wjcc.v11.i27.6383
Prognostic model of hepatocellular carcinoma based on cancer grade
Guo-Xin Zhang, Xiao-Sheng Ding, You-Li Wang
Guo-Xin Zhang, You-Li Wang, Department of General Surgery, Aviation General Hospital, Beijing 100010, China
Xiao-Sheng Ding, Department of Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
Author contributions: Zhang GX contributed to research design, data collection, interpretation, and analysis, and manuscript drafting; Ding XS contributed to data analysis and interpretation; Wang YL contributed to result interpretation and critical revision of the manuscript; all authors approved the final version and agree to be accountable for all aspects of the work.
Institutional review board statement: All data in this article are sourced from databases such as TCGA, KEGG, GO, etc., and do not involve humans or animals, nor ethical issues.
Informed consent statement: The study does not involve humans or animals, nor ethical issues and informed consent statement.
Conflict-of-interest statement: The authors declare that there is no conflict of interest related to the manuscript. If any potential conflict of interest exists, the authors certify that it is fully disclosed.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: You-Li Wang, DSc, FRCS (Gen Surg), MD, Chief Physician, Instructor, Staff Physician, Surgeon, Department of General Surgery, Aviation General Hospital, No. 3 Anwai Beiyuan Courtyard, Anli Road, Chaoyang District, Beijing 100010, China. stage9999@sina.com
Received: June 22, 2023
Peer-review started: June 22, 2023
First decision: July 17, 2023
Revised: August 2, 2023
Accepted: August 23, 2023
Article in press: August 23, 2023
Published online: September 26, 2023
Processing time: 90 Days and 4.1 Hours
Abstract
BACKGROUND

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer. With highly invasive biological characteristics and a lack of obvious clinical manifestations, HCC usually has a poor prognosis and ranks fourth in cancer mortality. The aetiology and exact molecular mechanism of primary HCC are still unclear.

AIM

To select the characteristic genes that are significantly associated with the prognosis of HCC patients and construct a prognosis model of this malignancy.

METHODS

By comparing the gene expression levels of patients with different cancer grades of HCC, we screened out differentially expressed genes associated with tumour grade. By protein-protein interaction (PPI) network analysis, we obtained the top 2 PPI networks and hub genes from these differentially expressed genes. By using least absolute shrinkage and selection operator Cox regression, 13 prognostic genes were selected for feature extraction, and a prognostic risk model of HCC was established.

RESULTS

The model had significant prognostic ability in HCC. We also analysed the biological functions of these prognostic genes.

CONCLUSION

By comparing the gene profiles of patients with different stages of HCC, We have constructed a prognosis model consisting of 13 genes that have important prognostic value. This model has good application value and can be explained clinically.

Keywords: Hepatocellular carcinoma; Prognostic model; Bioinformatics; Alpha-fetoprotein

Core Tip: By comparing the gene expression levels of hepatocellular carcinoma patients with different grades, we investigated the biological function of genes and selected 13 genes to construct a prognostic model. The results show that the model has effective predictive ability for liver cancer prognosis and appreciated clinical interpretation value.