Development and validation of a CIMP-associated prognostic model for hepatocellular carcinoma

Li, Ganxun; Xu, Weiqi; Zhang, Lu; Liu, Tongtong; Jin, Guan-Nan; Song, Jia; Wu, Jingjing; Wang, Yuwei; Chen, Weixun; Zhang, Chuanhan; Chen, Xiaoping; Ding, Zeyang; Zhu, Peng; Zhang, Bixiang

doi:10.1016/j.ebiom.2019.08.064

Cited by 59 publications

(63 citation statements)

References 69 publications

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“…A previous study of the GSE6764 gene expression dataset in the Gene Expression Omnibus database revealed that the expression levels of GNA14 were reduced in HCC tissues compared with normal liver tissue (25). In addition, a CpG island methylation phenotype-associated prognostic model involved in the expression of PLEKHB1, ESR1, SLCO2A1, and GNA14 was trained and validated in HCC, serving as an independent prognostic factor for HCC (26). However, the role of GNA14 expression levels in diagnosis and prognosis of hepatocellular carcinoma is still unclear.…”

Section: Discussionmentioning

confidence: 99%

Downregulation of GNA14 in hepatocellular carcinoma indicates an unfavorable prognosis

Tao

Wenjing

2020

Oncol Lett

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Guanine nucleotide-binding protein subunit α14 (GNA14) knockdown was demonstrated to inhibit the proliferation of endometrial carcinoma cells in a recent study; however, its role in hepatocellular carcinoma (HCC) is unknown. In the present study, the clinical significance of GNA14 in HCC was assessed using a dataset of patients with HCC from The Cancer Genome Atlas database. The Integrative Molecular Database of Hepatocellular Carcinoma and Oncomine databases were also used to identify the expression levels of GNA14 in HCC tissues. The association between GNA14 expression levels and clinicopathological features was assessed using the Wilcoxon signed-rank test and logistic regression analysis. Kaplan-Meier curves and Cox regression analysis were applied to evaluate the independent risk factors for clinical outcomes. The present study determined GNA14 DNA methylation levels and tumor-infiltrating immune cells, as well as used Gene Set Enrichment Analysis (GSEA) in HCC. GNA14 mRNA expression levels were lower in HCC compared with normal tissues. Downregulation of GNA14 in HCC was significantly associated with tumor grade, clinical stage and T stage. Furthermore, low expression of GNA14 was an independent predictor for survival outcomes. GNA14 expression levels were partially correlated with the infiltration of B cells and macrophages. Additionally, GSEA analysis revealed that the expression levels of GNA14 were associated with multiple signaling pathways, such as translation, DNA replication, and homologous recombination.In conclusion, low GNA14 expression may be a novel biomarker for diagnosis and prognosis prediction for patients with HCC.

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Section: Discussionmentioning

confidence: 99%

Downregulation of GNA14 in hepatocellular carcinoma indicates an unfavorable prognosis

Tao

Wenjing

2020

Oncol Lett

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“…WES data provide a spectrum of somatic mutations in patients with prostate cancer. Tumor mutational burden (TMB), a new biomarker of immunotherapy response, is de ned as the number of nonsynonymous mutations per trillion bases [14]. The difference in total mutational burden in different subtypes was obtained by calculating the number of nonsynonymous mutations in the whole genome and analyzing them by the Kruskal-Wallis method.…”

Section: Data Acquisition and Data Analysismentioning

confidence: 99%

Prediction of Prognosis in Patients with Prostate Cancer by DNA Methylation Classification

Chen

Pan

et al. 2020

Preprint

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Abstract Background: Prostate cancer is the second most frequently diagnosed cancer and the fifth leading cause of cancer-related death. It is estimated that the incidence of prostate cancer is on the rise worldwide. Epigenetic changes in tumors play an important role in the occurrence and development of prostate cancer. DNA methylation is one of the mechanisms of tumor epigenetic regulation and may be a new biomarker that has great potential in early tumor screening, treatment guidance and prognosis prediction. The purpose of this study was to explore a classification method from the perspective of DNA methylation.Methods: The least absolute shrinkage and selection operator (LASSO) method was used to analyze DNA methylation and RNA-seq data from the Cancer Genome Atlas (TCGA). The methylation sites with small differences were eliminated, and the 21 methylation sites with the most significant differences were retained for analysis. Using their corresponding gene expression levels, a recurrence prediction model for prostate cancer patients was constructed to distinguish high-risk, medium-risk, and low-risk cases. Immune cell abundance analysis, gene enrichment analysis, Tumor burden mutation analysis and gene copy number variation analysis were then used to analyze the differences among these three subtypes and their underlying mechanisms. Results: We observed the difference in disease-free survival (DFS) of the three methylated subtypes in the test set, which was verified in the validation set. We found three subtypes have different proportions of immune cells, especially in memory B cells, M2 macrophages, Treg cells. GSVA and GSEA analysis revealed that the relevant metastasis gene sets of prostate cancer were enriched in high-risk cases. In addition, the mutation frequencies of TP53, TTN and KMT2D were the highest, and gradually increased in the three genotypes according to Tumor burden mutation (TMB) analysis. Gene copy number variation (CNV) showed that AR, LAPTM4B, and MTDH were significantly amplified, while ATP1B2 and FAM92B were significantly deleted. Finally, univariate and multivariate analysis showed that there were statistical differences among the three methylation subtypes, which can be used as an index to predict prostate cancer recurrence.Conclusions: Our study suggests that classification based on DNA methylation is an independent factor for predicting tumor recurrence in patients with prostate cancer.

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“…However, the SMAD family's expression and function in some cancers are still unclear, and there are fewer reports about prognosis, especially in lung cancer. At present, the use of large databases and regulatory networks has been widely accepted in biology, such as the prognostic characteristics of melanoma by transcriptome analysis [12], and the development of new prognostic models in liver cancer [13]. In tihs study, a meta-analysis of the expression and prognostic value of SMAD family members in cancer using multiple databases.…”

Section: Introductionmentioning

confidence: 99%

Integrated TCGA and GEO analysis showed that SMAD7 is an independent prognostic factor for lung adenocarcinoma.

Dai

Wang

Xiang

et al. 2020

Preprint

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The lack of effective markers leads to missed optimal treatment times, resulting in poorer prognosis In most cancers. SMAD family members are important cytokines in the transforming growth factor-beta (TGF-β) family. They jointly regulate the processes of cell growth, differentiation, and apoptosis. However, the expression of SMAD family genes in pan-cancers and their impact on prognosis have not been elucidated. Perl software and R software were used to perform expression analysis and survival curve analysis on the data collected by TCGA, GTEx and GEO, and the potential regulatory pathways were determined through GO enrichment and KEGG enrichment analysis. It was found that SMAD7 and SMAD9 expression decreased in Lung adenocarcinoma (LUAD), and their expression was positively correlated with survival time. Additionally, SMAD7 could be used as an independent prognostic factor for LUAD. In general, SMAD7 and SMAD9 can be used as prognostic markers of LUAD, further, SMAD7 is expected to become a therapeutic target for LUAD.

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Development and validation of a CIMP-associated prognostic model for hepatocellular carcinoma

Cited by 59 publications

References 69 publications

Downregulation of GNA14 in hepatocellular carcinoma indicates an unfavorable prognosis

Downregulation of GNA14 in hepatocellular carcinoma indicates an unfavorable prognosis

Prediction of Prognosis in Patients with Prostate Cancer by DNA Methylation Classification

Integrated TCGA and GEO analysis showed that SMAD7 is an independent prognostic factor for lung adenocarcinoma.

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