A precise prognostic signature in CTNNB1-mutant hepatocellular carcinoma: Prognosis prediction and precision treatment exploration

Wang, Junying; Zhu, Guangyu

doi:10.1016/j.heliyon.2023.e22382

Cited by 3 publications

(1 citation statement)

References 66 publications

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“…The largest cluster had half of the liver cancer samples and only 20% of the CTNNB1 gene mutations that were present in the largest cluster for resolution of 1. CTNNB1 is an important driver gene for liver cancer ( Wang and Zhu, 2023 ) and hence their removal highlights the disease subtype.…”

Section: Resultsmentioning

confidence: 99%

Network analysis of driver genes in human cancers

Patil,

Roberts,

Gebremedhin

2024

Front. Bioinform.

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Cancer is a heterogeneous disease that results from genetic alteration of cell cycle and proliferation controls. Identifying mutations that drive cancer, understanding cancer type specificities, and delineating how driver mutations interact with each other to establish disease is vital for identifying therapeutic vulnerabilities. Such cancer specific patterns and gene co-occurrences can be identified by studying tumor genome sequences, and networks have proven effective in uncovering relationships between sequences. We present two network-based approaches to identify driver gene patterns among tumor samples. The first approach relies on analysis using the Directed Weighted All Nearest Neighbors (DiWANN) model, which is a variant of sequence similarity network, and the second approach uses bipartite network analysis. A data reduction framework was implemented to extract the minimal relevant information for the sequence similarity network analysis, where a transformed reference sequence is generated for constructing the driver gene network. This data reduction process combined with the efficiency of the DiWANN network model, greatly lowered the computational cost (in terms of execution time and memory usage) of generating the networks enabling us to work at a much larger scale than previously possible. The DiWANN network helped us identify cancer types in which samples were more closely connected to each other suggesting they are less heterogeneous and potentially susceptible to a common drug. The bipartite network analysis provided insight into gene associations and co-occurrences. We identified genes that were broadly mutated in multiple cancer types and mutations exclusive to only a few. Additionally, weighted one-mode gene projections of the bipartite networks revealed a pattern of occurrence of driver genes in different cancers. Our study demonstrates that network-based approaches can be an effective tool in cancer genomics. The analysis identifies co-occurring and exclusive driver genes and mutations for specific cancer types, providing a better understanding of the driver genes that lead to tumor initiation and evolution.

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Section: Resultsmentioning

confidence: 99%

Network analysis of driver genes in human cancers

Patil,

Roberts,

Gebremedhin

2024

Front. Bioinform.

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Recent developments in molecular targeted therapies for hepatocellular carcinoma in the genomic era

Testa

2024

Expert Review of Molecular Diagnostics

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Pancancer analysis of the interactions between CTNNB1 and infiltrating immune cell populations

Xu,

Yang,

Han

et al. 2024

Medicine

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Recently, evidence has indicated that CTNNB1 is important in a variety of malignancies. However, how CTNNB1 interacts with immune cell infiltration remains to be further investigated. In this study, we focused on the correlations between CTNNB1 and tumorigenesis, tumor progression, mutation, phosphorylation, and prognosis via gene expression profiling interaction analysis; TIMER 2.0, cBioPortal, GTEx, CPTAC, and GEPIA2 database analyses; and R software. CTNNB1 mutations are most found in uterine endometrioid carcinoma and hepatocellular carcinoma. However, no CTNNB1 mutations were found to be associated with a poor prognosis. In addition, CTNNB1 DNA methylation levels were higher in normal tissues than in tumor tissues in cancer except for breast invasive carcinoma, which had higher methylation levels in tumor tissues. The phosphorylation level of the S675 and S191 sites of CTNNB1 was greater in the primary tumor tissues in the clear cell renal cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, pancreatic adenocarcinoma, and breast cancer datasets but not in the glioblastoma multiform dataset. As for, with respect to immune infiltration, CD8 + T-cell infiltration was negatively correlated with the expression of CTNNB1 in thymoma and uterine corpus endometrial carcinoma. The CTNNB1 level was found to be positively associated with the infiltration index of the corresponding fibroblasts in the TCGA tumors of colon adenocarcinoma, human papillomavirus-negative head and neck squamous cell carcinoma, mesothelioma, testicular germ cell tumor, and thymoma. We also identified the top CTNNB1-correlated genes in the TCGA projects and analyzed the expression correlation between CTNNB1 and selected target genes, including PPP4R2, RHOA, and SPRED1. Additionally, pathway enrichment suggested that NUMB is involved in the Wnt pathway. This study highlights the predictive role of CTNNB1 across cancers, suggesting that CTNNB1 might serve as a potential biomarker for the diagnosis and prognosis evaluation of various malignant tumors.

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A precise prognostic signature in CTNNB1-mutant hepatocellular carcinoma: Prognosis prediction and precision treatment exploration

Cited by 3 publications

References 66 publications

Network analysis of driver genes in human cancers

Network analysis of driver genes in human cancers

Recent developments in molecular targeted therapies for hepatocellular carcinoma in the genomic era

Pancancer analysis of the interactions between CTNNB1 and infiltrating immune cell populations

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