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©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
Establishment of a prognosis predictive model for liver cancer based on expression of genes involved in the ubiquitin-proteasome pathway
Hua Li, Yi-Po Ma, Hai-Long Wang, Cai-Juan Tian, Yi-Xian Guo, Hong-Bo Zhang, Xiao-Min Liu, Peng-Fei Liu
Hua Li, Department of Endoscopy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin’s Clinical Research Center for Cancer, Tianjin 300060, China
Yi-Po Ma, Department of Critical Care Medicine, Dingzhou City People’s Hospital, Dingzhou 073000, Hebei Province, China
Hai-Long Wang, Peng-Fei Liu, Department of Oncology, Tianjin Academy of Traditional Chinese Medicine Affiliated Hospital, Tianjin 300120, China
Cai-Juan Tian, Hong-Bo Zhang, Tianjin Marvel Medical Laboratory, Tianjin Marvelbio Technology Co., Ltd, Tianjin 300180, China
Yi-Xian Guo, Department of Intelligent Technology, Tianjin Yunquan Intelligent Technology Co., Ltd, Tianjin 300381, China
Xiao-Min Liu, Department of Oncology, Tianjin Huanhu Hospital, Tianjin 300350, China
Co-first authors: Hua Li and Yi-Po Ma.
Co-corresponding authors: Xiao-Min Liu and Peng-Fei Liu.
Author contributions: Liu XM and Liu PF conceptualized and designed the research; Li H and Ma YP collected the data and wrote the manuscript; Wang HL conducted the data mining and prepared the figures; Tian CJ, Guo YX, and Zhang HB conducted the bioinformatics analysis; all authors were involved in the critical review of the results and have contributed to, read, and approved the final manuscript. Li H and Ma YP contributed equally to this work and are the co-first authors. Liu XM and Liu PF contributed equally to this study and are the co-corresponding authors. There are two primary reasons behind appointing Li H and Ma YP as co-first authors, and Liu XM and Liu PF as co-corresponding authors. First, our research was conducted through a collaborative effort, and the selection of first and corresponding authors aptly mirrors the distribution of responsibilities and the shared commitment of time and effort needed to carry out the study and produce the resulting paper. This approach ensures effective communication and facilitates the management of post-submission matters, ultimately enhancing the paper's overall quality and reliability. Second, each of these researchers made substantial and equal contributions throughout the entire research process. Designating them as co-first authors or co-corresponding authors not only acknowledges and respects their equivalent input but also highlights the spirit of teamwork and collaboration that characterized this study. In summary, the choice to designate Li H and Ma YP as co-first authors, and Liu XM and Liu PF as co-corresponding authors is appropriate for our manuscript as it accurately reflects our team's collaborative ethos and equal contributions.
Supported by the Tianjin Municipal Natural Science Foundation, No. 21JCYBJC01110.
Institutional review board statement: TCGA is a public database. The patients involved in the database have obtained ethical approval. Users can download relevant data for free for research and publish relevant articles. Our study was based on open-source data, so there are no statements on ethics approval and consent.
Informed consent statement: Our study is based on open-source data, so there are no statements on informed consent.
Conflict-of-interest statement: All authors declare that they have no competing interests to disclose.
Data sharing statement: Publicly available datasets were analyzed in this study, and these can be found in the TCGA database (
http://portal.gdc.cancer.gov/).
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: Peng-Fei Liu, MD, Chief Doctor, Surgical Oncologist, Department of Oncology, Tianjin Academy of Traditional Chinese Medicine Affiliated Hospital, No. 354 North Road, Hongqiao District, Tianjin 300120, China.
liupengfeitj@163.com
Received: October 11, 2023
Peer-review started: October 11, 2023
First decision: December 7, 2023
Revised: December 27, 2023
Accepted: February 5, 2024
Article in press: February 5, 2024
Published online: March 24, 2024
Processing time: 163 Days and 5.6 Hours
BACKGROUND
The ubiquitin-proteasome pathway (UPP) has been proven to play important roles in cancer.
AIM
To investigate the prognostic significance of genes involved in the UPP and develop a predictive model for liver cancer based on the expression of these genes.
METHODS
In this study, UPP-related E1, E2, E3, deubiquitylating enzyme, and proteasome gene sets were obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, aiming to screen the prognostic genes using univariate and multivariate regression analysis and develop a prognosis predictive model based on the Cancer Genome Atlas liver cancer cases.
RESULTS
Five genes (including autophagy related 10, proteasome 20S subunit alpha 8, proteasome 20S subunit beta 2, ubiquitin specific peptidase 17 like family member 2, and ubiquitin specific peptidase 8) were proven significantly correlated with prognosis and used to develop a prognosis predictive model for liver cancer. Among training, validation, and Gene Expression Omnibus sets, the overall survival differed significantly between the high-risk and low-risk groups. The expression of the five genes was significantly associated with immunocyte infiltration, tumor stage, and postoperative recurrence. A total of 111 differentially expressed genes (DEGs) were identified between the high-risk and low-risk groups and they were enriched in 20 and 5 gene ontology and KEGG pathways. Cell division cycle 20, Kelch repeat and BTB domain containing 11, and DDB1 and CUL4 associated factor 4 like 2 were the DEGs in the E3 gene set that correlated with survival.
CONCLUSION
We have constructed a prognosis predictive model in patients with liver cancer, which contains five genes that associate with immunocyte infiltration, tumor stage, and postoperative recurrence.
Core Tip: This study unveils the crucial role of the ubiquitin-proteasome pathway (UPP) in liver cancer prognosis. Five key genes (autophagy related 10, proteasome 20S subunit alpha 8, proteasome 20S subunit beta 2, ubiquitin specific peptidase 17 like family member 2, and ubiquitin specific peptidase 8) identified from The Cancer Genome Atlas datasets constitute a robust prognostic model, accurately predicting liver cancer outcomes. Immunocyte infiltration analysis highlights associations of these genes with immune cell abundance, while clinical correlations link them to tumor stage and recurrence. Differential gene expression and pathway enrichment elucidate underlying biological processes. E3 analysis identifies specific ligases (cell division cycle 20, Kelch repeat and BTB domain containing 11, and DCAF4L2) with significant expression differences, further emphasizing the integral role of the UPP in liver cancer development and providing valuable insights for precision medicine and prognosis prediction.
