CDCA5 regulates proliferation in hepatocellular carcinoma and has potential as a negative prognostic marker

Shen, Zhiqing; Yu, Xueping; Zheng, Yijuan; Lai, Xueping; Li, Julan; Hong, Yongzhe; Zhang, Huatang; Chen, Chunlin; Su, Zhijun; Guo, Rui

doi:10.2147/ott.s154754

Cited by 47 publications

(50 citation statements)

References 31 publications

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“…The results showed that the OS probability of patients with high expression of CDCA5 and CDCA8 in tumor tissues was significantly decreased. This was consistent with the effect of the two on the survival time of patients in other types of tumors [30][31][32]. Secondly, we analyzed the correlation between the two and all genes involved in the p53 signaling pathway.…”

Section: Discussionsupporting

confidence: 77%

Construct ion of co - expression modules related to survival by WGCNA and identif ication of potential prognostic biomarkers in glioblastoma

Zhou

Guo

Liu

et al. 2020

Preprint

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Background: Glioblastoma (GBM) is a malignant brain tumor with poor prognosis. However, the potential pathogenesis and therapeutic target s still needs to be explored.Methods: Herein, The Cancer Genome Atlas (TCGA) expression profile data and clinical information were downloaded, and the Weighted Gene Co-expression Network Analysis (WGCNA) was conducted. Subsequently, hub genes which closely related to the poor prognosis of GBM were obtained. Further, the relationship between the genes of interest and prognosis of GBM patients, and immune microenvironment were analysed. Patients from TCGA were divided into high- and low-risk groups.Results: Top 10 hub genes (CDC20, NCAPH, CDCA5, BUB1, CDCA8, PBK, KIF2C, TPX2, TTK and TOP2A) were obtained. Then, we performed single-gene analysis on CDCA5 and CDCA8 as genes of interest. We found that their expression levels were closely related to overall survival (OS). They had good correlations with the genes that regulate cell cycle in p53 signaling pathway. Moreover, it revealed that high amplication of CDCA5 was correlated with CD8 + T cells while CDCA8 with CD4 + T cells, and the expression of these two genes showed a significant difference in OS.Conclusions: These results might provide new molecular targets and intervention strategy for GBM.

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Section: Discussionsupporting

confidence: 77%

Construct ion of co - expression modules related to survival by WGCNA and identif ication of potential prognostic biomarkers in glioblastoma

Zhou

Guo

Liu

et al. 2020

Preprint

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“…Interestingly, USP22 levels are significantly upregulated in human brain glioma cells, and it is associated with cell regulatory protein Cyclin B1 expression [22]. Cyclin B1 plays an important role in cell cycle arrest at the G2/M phase and serves a crucial role in the expansion of tumor cells in HCC [23]. Our results demonstrated that Cyclin B1 expression was decreased in the combined group.…”

Section: Discussionmentioning

confidence: 60%

Ubiquitin-Specific Protease 22/Silent Information Regulator 1 Axis Plays a Pivotal Role in the Prognosis and 5-Fluorouracil Resistance in Hepatocellular Carcinoma

Wang

Ling

et al. 2019

Dig Dis Sci

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Background Ubiquitin-specific protease 22 (USP22) is described as a key subunit of the Spt-Ada-Gcn5 acetyl transferase complex, which plays an important role in the prognosis and resistance to chemotherapy drugs in hepatocellular carcinoma (HCC). Silent information regulator 1 (SIRT1) is a member of the sirtuin family that is deubiquitinated by USP22. However, it is still unknown whether USP22 and SIRT1 co-expression is associated with disease progression and 5-Fluorouracil (5-FU) resistance in HCC. Methods 141 patients who received hepatectomy at our hospital from January 2010 to December 2014 were enrolled in this study. The expression of USP22 and SIRT1 was detected by immunohistochemical staining. Clinicopathological features, including age, gender, tumor number, tumor size, tumor differentiation, tumor stage, alpha-fetoprotein and microscopic vascular invasion, were assessed. Further experiments confirmed the role of SIRT1 in 5-FU drug resistance in vivo. Results Immunohistochemical staining showed that the high expression of USP22 and SIRT1 was frequently observed in HCC tissues relative to normal liver tissues. Overexpression of USP22 is associated with microscopic vascular invasion (MVI). Further analysis showed that the co-expression of USP22 and SIRT1 was more effective in predicting the prognosis of HCC. The SIRT1 inhibitor EX-527 dramatically inhibited the expression of Cyclin B1 and resistance-associated protein 3 (MRP3) to reduce 5-FU drug resistance in vivo. Conclusion These findings suggest that the co-expression of USP22 and SIRT1 is significantly associated with unfavorable HCC progression. The inhibition of SIRT1 in vivo could be valuable in improving 5-FU drug sensitivity and inhibiting tumor cell proliferation and inducing apoptosis.

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“…Two of the genes in our biomarker panel i.e. CDCA5 and RAMP3 already identified as prognostic markers in HCC in literature [58,59]. In recent past, role of pseudogenes has been revealed in pathogenesis of cancer [60–62] including HCC (25391452, 28823960).…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Classification of early and late stage Liver Hepatocellular Carcinoma patients from their genomics and epigenomics profiles

Kaur

Bhalla

Raghava

2018

Preprint

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BackgroundLiver Hepatocellular Carcinoma (LIHC) is the second major cancer worldwide, responsible for millions of premature deaths every year. Prediction of clinical staging is vital to implement optimal therapeutic strategy and prognostic prediction in cancer patients. However, to date, no method has been developed for predicting stage of LIHC from genomic profile of samples.ResultsIn current study, in silico models have been developed for classifying LIHC patients in early and late stage using RNA expression and DNA methylation data. The Cancer Genome Atlas (TCGA) dataset contains 173 early and 177 late stage samples of LIHC, was extensively analysed to identify differentially expressed RNA transcripts and methylated CpG sites that can discriminate early and late stages of LIHC samples with high precision. Naive Bayes model developed using 51 features that combine 21 CpG methylation sites and 30 RNA transcripts achieved maximum MCC 0.58 with accuracy 78.87% on validation dataset. Further, we also analysed genomics and epigenomics profiles of normal and LIHC samples and developed model to classify LIHC samples with AUROC 0.99. In addition, multiclass models developed for classifying samples in normal, early and late stage of cancer and achieved accuracy of 76.54% and AUROC of 0.86.ConclusionOur study reveals stage prediction of LIHC samples with high accuracy based on genomics and epigenomics profiling is a challenging task in comparison to classification of LIHC and normal samples. Comprehensive analysis, differentially expressed RNA transcripts, methylated CpG sites in LIHC samples and prediction models are available from CancerLSP ( http://webs.iiitd.edu.in/raghava/cancerlsp/).

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CDCA5 regulates proliferation in hepatocellular carcinoma and has potential as a negative prognostic marker

Cited by 47 publications

References 31 publications

Construct ion of co - expression modules related to survival by WGCNA and identif ication of potential prognostic biomarkers in glioblastoma

Construct ion of co - expression modules related to survival by WGCNA and identif ication of potential prognostic biomarkers in glioblastoma

Ubiquitin-Specific Protease 22/Silent Information Regulator 1 Axis Plays a Pivotal Role in the Prognosis and 5-Fluorouracil Resistance in Hepatocellular Carcinoma

Classification of early and late stage Liver Hepatocellular Carcinoma patients from their genomics and epigenomics profiles

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