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Wang J, Guan X, Shang N, Wu D, Liu Z, Guan Z, Zhang Z, Jin Z, Wei X, Liu X, Song M, Zhu W, Dai G. Dysfunction of CCT3-associated network signals for the critical state during progression of hepatocellular carcinoma. Biochim Biophys Acta Mol Basis Dis 2024; 1870:167054. [PMID: 38360074 DOI: 10.1016/j.bbadis.2024.167054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 01/25/2024] [Accepted: 01/30/2024] [Indexed: 02/17/2024]
Abstract
Hepatocellular carcinoma (HCC) is one of the most common malignant tumors and is a serious threat to human health; thus, early diagnosis and adequate treatment are essential. However, there are still great challenges in identifying the tipping point and detecting early warning signals of early HCC. In this study, we aimed to identify the tipping point (critical state) of and key molecules involved in hepatocarcinogenesis based on time series transcriptome expression data of HCC patients. The phase from veHCC (very early HCC) to eHCC (early HCC) was identified as the critical state in HCC progression, with 143 genes identified as key candidate molecules by combining the DDRTree (dimensionality reduction via graph structure learning) and DNB (dynamic network biomarker) methods. Then, we ranked the candidate genes to verify their mRNA levels using the diethylnitrosamine (DEN)-induced HCC mouse model and identified five early warning signals, namely, CCT3, DSTYK, EIF3E, IARS2 and TXNRD1; these signals can be regarded as the potential early warning signals for the critical state of HCC. We identified CCT3 as an independent prognostic factor for HCC, and functions of CCT3 involving in the "MYCtargets_V1" and "E2F-Targets" are closely related to the progression of HCC. The predictive method combining the DDRTree and DNB methods can not only identify the key critical state before cancer but also determine candidate molecules of critical state, thus providing new insight into the early diagnosis and preemptive treatment of HCC.
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Affiliation(s)
- Jianwei Wang
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 45001, China; School of Life Sciences, Zhengzhou University, Zhengzhou 45001, China
| | - Xiaowen Guan
- School of Life Sciences, Zhengzhou University, Zhengzhou 45001, China
| | - Ning Shang
- School of Life Sciences, Zhengzhou University, Zhengzhou 45001, China
| | - Di Wu
- School of Life Sciences, Zhengzhou University, Zhengzhou 45001, China
| | - Zihan Liu
- School of Life Sciences, Zhengzhou University, Zhengzhou 45001, China
| | - Zhenzhen Guan
- School of Life Sciences, Zhengzhou University, Zhengzhou 45001, China
| | - Zhizi Zhang
- School of Life Sciences, Zhengzhou University, Zhengzhou 45001, China
| | - Zhongzhen Jin
- School of Life Sciences, Zhengzhou University, Zhengzhou 45001, China
| | - Xiaoyi Wei
- School of Life Sciences, Zhengzhou University, Zhengzhou 45001, China
| | - Xiaoran Liu
- School of Life Sciences, Zhengzhou University, Zhengzhou 45001, China
| | - Mingzhu Song
- School of Life Sciences, Zhengzhou University, Zhengzhou 45001, China
| | - Weijun Zhu
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 45001, China.
| | - Guifu Dai
- School of Life Sciences, Zhengzhou University, Zhengzhou 45001, China.
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