Variations in the <i>AURKA</i> Gene: Biomarkers for the Development and Progression of Hepatocellular Carcinoma

Wang, Bin; Hsu, Chin‐Jung; Chou, Chia-Hsuan; Lee, Hsiang-Lin; Chiang, Whei-Ling; Su, Chen-Ming; Tsai, Hsiao‐Chi; Yang, Shun‐Fa; Tang, Chih-Hsin

doi:10.7150/ijms.22513

Cited by 39 publications

(36 citation statements)

References 39 publications

(43 reference statements)

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“…Alisertib, an inhibitor of AURKA, could inhibit cell viability and induce apoptosis in HCC cells [47]. Wang et al showed genetic variations of AURKA may be a reliable biomarker for the development of HCC [48]. Our study also indicated that increased expression levels of AURKA were relative to unfavorable OS and DFS in HCC patients.…”

Section: Discussionsupporting

confidence: 64%

Identification of hub genes and potential drugs in hepatocellular carcinoma through integrated bioinformatics analysis

Chen

Xia

Wan

et al. 2020

Preprint

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BackgroundHepatocellular carcinoma (HCC) is the third cancer-related cause of death in the world. Until now, the involved mechanisms during the development of HCC are largely unknown. This study aims to explore the driven-genes and potential drugs in HCC. MethodsThree mRNA expression datasets were used to analyze the differentially expressed genes (DEGs) in HCC. The bioinformatics approaches include identification of DEGs and hub genes, GO terms analysis and KEGG enrichment analysis, construction of protein–protein interaction network. The expression levels of hub genes were validated based on TCGA, GEPIA and the Human Protein Atlas. Moreover, overall survival and disease-free survival analysis of the hub genes were further conducted by Kaplan-Meier plotter and the GEPIA. DGIdb database was performed to search the candidate drugs for HCC. ResultsFinally, 197 DEGs were identified. The PPI network was constructed using STRING software. Then ten genes were selected and considered as the hub genes. The ten genes were all closely related to the survival of HCC patients. DGIdb database predicted 39 small molecules as the possible drugs for treating HCC. ConclusionsOur study provides some new insights into HCC pathogenesis and treatments. The candidate drugs may improve the efficiency of HCC therapy in future.

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Section: Discussionsupporting

confidence: 64%

Identification of hub genes and potential drugs in hepatocellular carcinoma through integrated bioinformatics analysis

Chen

Xia

Wan

et al. 2020

Preprint

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“…Many researchers have found that four key genes were involved in cell cycle, participating in tumorigenesis and tumor proliferation. AURKA has been studied in a wide range of human malignancies and is associated with poor prognosis in several malignancies, including cervical squamous cell carcinoma, hepatocellular carcinoma, and nonsmall cell lung carcinoma (Ma et al, ; Wang et al, ; Zheng et al, ). Besides, Yang et al, () reported that AURKA as a transactivating co‐factor in the induction of the c‐Myc oncoprotein in breast cancer stem cells (BCSCs).…”

Section: Discussionmentioning

confidence: 99%

Identification of novel biomarkers and small molecule drugs in human colorectal cancer by microarray and bioinformatics analysis

Chen¹,

Wang

Shen

et al. 2019

Molec Gen & Gen Med

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Background Colorectal cancer (CRC) is one of the most common malignant tumors. In the present study, the expression profile of human multistage colorectal mucosa tissues, including healthy, adenoma, and adenocarcinoma samples was downloaded to identify critical genes and potential drugs in CRC. Methods Expression profiles, GSE33113 and GSE44076, were integrated using bioinformatics methods. Differentially expressed genes (DEGs) were analyzed by R language. Functional enrichment analyses of the DEGs were performed using the Database for Annotation, visualization, and integrated discovery (DAVID) database. Then, the search tool for the retrieval of interacting genes (STRING) database and Cytoscape were used to construct a protein–protein interaction (PPI) network and identify hub genes. Subsequently, survival analysis was performed among the key genes using Gene Expression Profiling Interactive Analysis (GEPIA). Connectivity Map (CMap) was used to query potential drugs for CRC. Results A total of 428 upregulated genes and 751 downregulated genes in CRC were identified. The functional changes of these DEGs were mainly associated with cell cycle, oocyte meiosis, DNA replication, p53 signaling pathway, and progesterone‐mediated oocyte maturation. A PPI network was identified by STRING with 482 nodes and 2,368 edges. Survival analysis revealed that high mRNA expression of AURKA, CCNB1, CCNF, and EXO1 was significantly associated with longer overall survival. Moreover, CMap predicted a panel of small molecules as possible adjuvant drugs to treat CRC. Conclusion Our study found key dysregulated genes involved in CRC and potential drugs to combat it, which may provide novel insights and potential biomarkers for prognosis, as well as providing new CRC treatments.

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“…The only gene within the other proliferation signaling pathway that showed prognostic potential was AURKA, which promotes centrosome duplication and cytokinesis at the G2/M phase of the cell cycle. AURKA is a biomarker for HCC development and progression and is a potential target for therapy [83,84]. AURKA expression is dramatically high in TCGA-LIHC dataset, with a significantly increased expression as the disease progresses, and displays a strong predictive ability for disease outcome (Figure 4B, 4C, Supplementary Figure S4B, Tables 1 and 2).…”

Section: Other Growth Factors/proliferation Signaling Pathways and Tementioning

confidence: 99%

Prognostic significance of SOCS1 and SOCS3 tumor suppressors in hepatocellular carcinoma and its correlation to key oncogenic signaling pathways

Khan

Ghosh

Variya

et al. 2020

Preprint

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Suppressor of cytokine signaling (SOCS) proteins SOCS1 and SOCS3 are considered tumor suppressors in liver hepatocellular carcinoma (LIHC). To gain insight into the underlying molecular mechanisms, the expression of SOCS1/ SOCS3 was evaluated in The Cancer Genome Atlas LIHC dataset along with key oncogenic signaling pathway genes. SOCS1 expression was not significantly reduced in HCC yet higher expression predicted favorable prognosis, whereas SOCS3 lacked predictive potential despite lower expression. Only a small proportion of the cell cycle, receptor tyrosine kinase, growth factor and RAS-RAF-MEK-MAPK signaling genes negatively correlated with SOCS1 or SOCS3, of which even fewer showed elevated expression in HCC and predicted survival. However, many PI3K-AKT-MTOR pathway genes showed mutual exclusivity with SOCS1/SOCS3 and displayed independent predictive ability. Among genes that negatively correlated with SOCS1/SOCS3, CDK2, MLST8, AURKA, MAP3K4 and RPTOR showed corresponding modulations in the livers of mice lacking Socs1 or Socs3 during liver regeneration and in experimental HCC, and in Hepa1-6 murine HCC cells overexpressing SOCS1/SOCS3. However, Cox proportional hazards model identified CXCL8, DAB2 and PIK3R1 as highly predictive in combination with SOCS1 or SOCS3. These data suggest that developing prognostic biomarkers and precision treatment strategies based on SOCS1/SOCS3 expression need careful testing in different patient cohorts.Prognostic significance of SOCS1 and SOCS3 in HCC Khan et al.,3 genes that regulate hepatocyte proliferation and survival. Our findings show that the expression of SOCS1 and SOCS3 negatively correlates with several genes in a similar fashion, but also show distinct regulation of a number of genes in several oncogenic signaling pathways. The latter could explain, at least partly, the inability of SOCS3 to compensate for the loss of SOCS1 and vice versa in animal models of HCC. We identify SOCS1 but not SOCS3 as an independent prognostic factor, whereas both display improved predictive potential when combined with certain key genes of the oncogenic signaling pathways. Besides, our findings reveal important differences between published works on putative HCC biomarkers and the TCGA data, highlighting the need for further studies on prognostic biomarkers for precision HCC therapy.

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Variations in the AURKA Gene: Biomarkers for the Development and Progression of Hepatocellular Carcinoma

Cited by 39 publications

References 39 publications

Identification of hub genes and potential drugs in hepatocellular carcinoma through integrated bioinformatics analysis

Identification of hub genes and potential drugs in hepatocellular carcinoma through integrated bioinformatics analysis

Identification of novel biomarkers and small molecule drugs in human colorectal cancer by microarray and bioinformatics analysis

Prognostic significance of SOCS1 and SOCS3 tumor suppressors in hepatocellular carcinoma and its correlation to key oncogenic signaling pathways

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