Epigenetic aberrations of gene expression in a rat model of hepatocellular carcinoma

Boycott, Cayla; Beetch, Megan; Yang, T.Y.; Lubecka, Katarzyna; Ma, Yuexi; Zhang, Jiaxi; Kendall, Lucinda Kurzava; Ullmer, Melissa; Ramsey, Benjamin; Torregrosa-Allen, Sandra; Elzey, Bennett D.; Cox, Abigail; Atallah, Nadia M.; Hui, Alisa; Villanueva, Nathaniel L.; Conti, Aline de; Huan, Tao; Pogribny, Igor P.; Stefańska, Barbara

doi:10.1080/15592294.2022.2069386

Cited by 6 publications

(5 citation statements)

References 84 publications

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“…Recent studies have con rmed that MMP12 can inhibit the differentiation of macrophages into M2 type in the intestinal tumor microenvironment and play a role in immune activation [39]. MMP12 is closely related to tumor-related signaling pathways such as (Wnt)/β-catenin, which can lead to malignant proliferation of tumor cells [40][41][42].In summary, KRT20, MMP12, AGR2, CXCL5 and MAGEA8 are associated with malignant development of HCC, and high expression of KRT20, MMP12, AGR2, CXCL5 and MAGEA8 lead to worse prognosis, while high expression of CYP3A4 is associated with good prognosis. The results of previous studies are consistent with those of our risk model.…”

Section: Discussionmentioning

confidence: 99%

A model based on Immunogenic cell death-related genes can predict prognosis and drug sensitivity in patients with hepatocellular carcinoma

Shi,

Zhang,

Han

et al. 2024

Preprint

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Background: Immunogenic cell death (ICD) can activate the immune system against cancer in host with immunocompetent. However, the prognostic role of ICD-related genes (IRGs) in hepatocellular carcinoma (HCC) is unknown.We aimed to establish a prognostic model based on IRGs, and to verify the relationship between this model and the immune microenvironment of HCC, and whether this model can predict the prognosis of patients with HCC. Methods: The Cancer Genome Atlas-Liver Hepatocellular Carcinoma(TCGA-LIHC), and The Liver Cancer, Riken Japan (LIRI-JP) were downloaded via public databases, and 34 IRGs were extracted from the literature. First, consensus clustering analysis was applied in TCGA-LIHC to classified LIHC samples into different clusters based on IRGs. Differentially expressed genes (DEGs) between LIHC and normal samples in TCGA-LIHC, and DEGs among clusters were respectively sifted out through differential expression analysis, and they were overlapped to obtain IRGs-DEGs. Next, univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses were implemented on IRGs-DEGs for identifing prognosis related genes, and HCC samples were divided into high/low risk groups via risk score. Moreover, independent prognostic factors were yielded via univariate and multivariate Cox analyses, and enrichment analysis was applied for exploring biological pathways related to the prognosis model. Finally, the role of prognostic model in immune microenvironment and chemotherapy of HCC were analyzed. Results: HCC samples were classified into two clusters in the light of IRGs, and cluster2 had a poorer survival state than that of cluster1. Totally 2197 DEGs in TCGA-LIHC and 112 DEGs between clusters were yielded, and they were intersected to get 72 IRGs-DEGs. Six prognosis-related genes, namely KRT20, MMP12, AGR2, CXCL5, CYP3A4, and MAGEA8 were finally identified via univariate Cox and LASSO analyses. Besides, the risk score and grade were found to be correlated with LIHC prognosis. Obviously, the prognostis model was related to immune, and metabolism related pathways like nitrogen metabolism, and adaptive immune response. Moreover, tumor immune dysfunction and exclusion (TIDE) score was sensibly lowly expressed in low risk group, suggesting that low risk group patients were more susceptible to immunotherapy. Ultimatly, high risk group was more sensitive to Camptothecin, Sorafenib and others, while low risk group was more responsive to Veliparib and Dabrafenib. Conclusion: Through bioinformatic analysis, a prognosis model (contained KRT20, MMP12, AGR2, CXCL5, CYP3A4, and MAGEA8) was constructed in HCC, contributing to studies related to prognosis and treatment of HCC.

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Section: Discussionmentioning

confidence: 99%

A model based on Immunogenic cell death-related genes can predict prognosis and drug sensitivity in patients with hepatocellular carcinoma

Shi,

Zhang,

Han

et al. 2024

Preprint

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“…As an oncogenic factor, CDD can contribute to transforming non‐alcoholic fatty liver disease (NAFLD) into HCC in transgenic mice 24 . However, it is worth noting that this method requires a long induction time of at least 52 weeks or more 25,26 . CDD combined with other oncogenic agents constructing HCC animal models could shorten the induction time and improve the success rate.…”

Section: Induced Animal Modelsmentioning

confidence: 99%

“…24 However, it is worth noting that this method requires a long induction time of at least 52 weeks or more. 25,26 CDD combined with other oncogenic agents constructing HCC animal models could shorten the induction time and improve the success rate. There was a marked elevation in the content of alpha1-fetoprotein in both the liver and plasma of rats fed with CDD plus DEN, accelerating tumor formation.…”

Section: Diet-induced Animal Modelsmentioning

confidence: 99%

Advances in experimental animal models of hepatocellular carcinoma

Li,

Wang,

Ren

et al. 2023

Cancer Medicine

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Hepatocellular carcinoma (HCC) is a common malignant tumor with insidious early symptoms, easy metastasis, postoperative recurrence, poor drug efficacy, and a high drug resistance rate when surgery is missed, leading to a low 5‐year survival rate. Research on the pathogenesis and drugs is particularly important for clinical treatment. Animal models are crucial for basic research, which is conducive to studying pathogenesis and drug screening more conveniently and effectively. An appropriate animal model can better reflect disease occurrence and development, and the process of anti‐tumor immune response in the human body. This review summarizes the classification, characteristics, and advances in experimental animal models of HCC to provide a reference for researchers on model selection.

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“…Gene expression and methylation changes have also emerged as cancer signatures and can be used for the classification of cancers [18,19]. Liver chances have also been shown to be characterized by unique changes in gene expression and chromosomal rearrangements [20,21].…”

Section: Introductionmentioning

confidence: 99%

Multi-Omics Integration for Liver Cancer Using Regression Analysis

Raj,

Petreaca,

Mirzaei

2024

CIMB

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Genetic biomarkers have played a pivotal role in the classification, prognostication, and guidance of clinical cancer therapies. Large-scale and multi-dimensional analyses of entire cancer genomes, as exemplified by projects like The Cancer Genome Atlas (TCGA), have yielded an extensive repository of data that holds the potential to unveil the underlying biology of these malignancies. Mutations stand out as the principal catalysts of cellular transformation. Nonetheless, other global genomic processes, such as alterations in gene expression and chromosomal re-arrangements, also play crucial roles in conferring cellular immortality. The incorporation of multi-omics data specific to cancer has demonstrated the capacity to enhance our comprehension of the molecular mechanisms underpinning carcinogenesis. This report elucidates how the integration of comprehensive data on methylation, gene expression, and copy number variations can effectively facilitate the unsupervised clustering of cancer samples. We have identified regressors that can effectively classify tumor and normal samples with an optimal integration of RNA sequencing, DNA methylation, and copy number variation while also achieving significant p-values. Further, these regressors were trained using linear and logistic regression with k-means clustering. For comparison, we employed autoencoder- and stacking-based omics integration and computed silhouette scores to evaluate the clusters. The proof of concept is illustrated using liver cancer data. Our analysis serves to underscore the feasibility of unsupervised cancer classification by considering genetic markers beyond mutations, thereby emphasizing the clinical relevance of additional global cellular parameters that contribute to the transformative process in cells. This work is clinically relevant because changes in gene expression and genomic re-arrangements have been shown to be signatures of cellular transformation across cancers, as well as in liver cancers.

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Epigenetic aberrations of gene expression in a rat model of hepatocellular carcinoma

Cited by 6 publications

References 84 publications

A model based on Immunogenic cell death-related genes can predict prognosis and drug sensitivity in patients with hepatocellular carcinoma

A model based on Immunogenic cell death-related genes can predict prognosis and drug sensitivity in patients with hepatocellular carcinoma

Advances in experimental animal models of hepatocellular carcinoma

Multi-Omics Integration for Liver Cancer Using Regression Analysis

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