Expression of yhwaz and gene regulation network in hepatocellular carcinoma
Erratum in: /10.3892/ol.2020.11996
- Authors:
- Published online on: March 27, 2020 https://doi.org/10.3892/ol.2020.11481
- Pages: 3971-3981
-
Copyright: © Lin et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated mortality, with >700,000 new cases being diagnosed annually throughout the world (1). HCC is the result of complex interactions between genetic and non-genetic host factors, including exposure to environmental chemicals and viruses. In >90% of cases, a primitive chronic liver disease, e.g., cirrhosis, usually develops and creates a procarcinogenic environment in its final stage (2).
The yhwaz gene encodes 14-3-3ζ, which belongs to a family of highly conserved dimeric proteins that are considered master regulators of intracellular signaling (3–5). 14-3-3ζ has been shown to interact with numerous protein kinases, enzymes, receptor proteins, structural and cytoskeletal proteins, proteins associated with cell cycle and transcriptional control and proteins involved in apoptosis (6). Upregulated expression of yhwaz is frequently observed in several types of cancer (including lung and liver cancer) (7–9). For example, yhwaz is upregulated in >40% of cases of breast cancer and is able to activate transforming growth factor-β/SMAD signaling during the epithelial-to-mesenchymal-transition, thus promoting tumor progression (10). 14-3-3ζ interacts with β-catenin, GLI family zinc finger 2, Yes-associated protein 1 and the downstream effectors of the Wnt, Hedgehog and Hippo signaling pathways, all of which are involved to varying degrees in a number of different types of cancer (such as breast cancer or lymphocytic leukemia) (11–14). Therefore, 14-3-3ζ may potentially be used as a prognostic biomarker for the diagnosis of certain types of cancer.
The upregulation of yhwaz in patients with HCC has been frequently examined (15) and 14-3-3ζ protein expression levels have been found to be significantly upregulated in hepatoma cell lines. In HCC cell lines with upregulated 14-3-3ζ expression, knockdown of 14-3-3ζ using RNA interference was reported to inhibit cell proliferation by activating the c-jun N-terminal kinase and p38/mitogen-activated protein kinase (MAPK) (16). Additionally, knockdown of 14-3-3ζ was observed to enhance radio-induced apoptosis in liver cancer stem-like cells (17). Wang et al (18) demonstrated that 14-3-3ζ was upregulated in the hepatocarcinomatous environment, attenuated the anti-tumor function of tumor-infiltrating T cells and may have partially been transferred from liver cancer cells to T cells via exosomes. A recent study revealed that 14-3-3ζ regulated the stability of heme oxygenase 1, which in turn promoted cancer cell proliferation via activation of STAT3 signaling in HCC (19). Nonetheless, the prognostic roles of yhwaz at the mRNA level in HCC remain to be clearly elucidated. In the present study, bioinformatics tools were used to analyze the functional effects of yhwaz expression in liver cancer. The expression of yhwaz was shown to be elevated in liver cancer tissues and cell lines and this upregulation was associated with poor prognosis. Additionally, it was demonstrated that mutations in yhwaz may have important implications in the survival rates of patients with liver cancer. By contrast, upregulated expression levels of microRNAs (miRNAs/miRs) that targeted yhwaz were associated with improved prognosis. The results demonstrated that the regulatory network of 14-3-3ζ and its interacting proteins serve an important role in the development and prognosis of HCC, suggesting the potential application of 14-3-3ζ as a therapeutic target for liver cancer. Additionally, the results of the present study support additional in vitro and in vivo studies on the functional effects of 14-3-3ζ expression in HCC.
Materials and methods
Bioinformatics tools and databases
Several bioinformatics tools were used to analyze yhwaz expression in patients with HCC. The specific tools that were used are listed in Table I.
Integrative molecular database of hepatocellular carcinoma (HCCDB)
The HCCDB is a database that contains information on HCC expression (20). In the current database release, HCCDB archived 15 public HCC gene expression datasets (HCCDB1, 3, 4, 6–9, 11–18) containing a total of 3917 samples from The Cancer Genome Atlas (TCGA) (21).
Genotype-tissue expression (GTEx)
Among the 15 datasets from GTEx (https://www.gtexportal.org) (22), 12 datasets contain both tumor and the adjacent normal samples, whereas only tumor samples are available for the three remaining datasets.
UALCAN
UALCAN is an interactive web resource for studying cancer transcriptome data (23). In particular, 50 healthy individuals and 371 patients with primary HCC were used to analyze the association among yhwaz levels and their clinical characteristics.
Oncomine
Oncomine database is a bioinformatics initiative aimed at collecting, standardizing, analyzing, and delivering cancer transcriptome data to the biomedical research community (24). Two microarray datasets Roessler Liver (43 samples) and Roessler Liver 2 (445 samples) were used for analysis in this study. Based on these open source bioinformatics platforms, yhwaz expression profiles were obtained for both human HCC tissues and cell lines.
Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis
KEGG (25) is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies.
Kaplan-Meier plotter
The Kaplan-Meier plotter (26) with the log-rank test was used to determine disease prognosis, including overall survival (OS) time and post-progression survival (PPS) time. Clinical data from TCGA were used for Kaplan-Meier plotter analysis in order to evaluate the clinical relevance of yhwaz mRNA expression levels in HCC. In this project 364 (male, 250; female, 121) HCC RNA-seq samples were used.
CBioPortal
CBioPortal (27,28) can be used to download and analyze large-scale cancer genomics datasets and was used in the present study to analyze mutations in yhwaz. In this project 1065 HCC samples were used for analysis.
TargetScan (version 7.1)
TargetScan (29) was used to predict the biological targets of miRNAs by searching for the presence of conserved 8mer and 7mer sites that matched the seed region of each miRNA.
Gene set enrichment analysis (GSEA)
GSEA (30,31) is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states.
GeneMANIA
GeneMANIA (32) is a flexible, user-friendly web interface for constructing protein-protein interaction (PPI) network, generating hypotheses about gene function, analyzing gene lists, and prioritizing genes for functional assays.
Gene expression profiling interactive analysis (GEPIA)
GEPIA (33) is a newly developed interactive web server for analyzing the RNA sequencing expression data of 9,736 tumors and 8,587 normal samples from the TCGA and the GTEx projects, using a standard processing pipeline.
Tumor grade and tumor stages
The tumor grade is divided into four subtypes: Grade 1, well differentiated (low grade); grade 2, moderately differentiated (intermediate grade); grade 3, poorly differentiated (high grade); and grade 4, undifferentiated (high grade). The tumor stage class depends on the tumor-node-metastasis staging system (AJCC 8th edition) (34).
Statistical analysis
Data are expressed as mean ± SD and analyzed for significance using GraphPad Prism 6.00 software (IBM Corp.). Difference between two-groups was assessed using Student's t-test. One-way ANOVA followed by Newman-Keuls post hoc testing (95% confidence) was used to determine difference among more than two groups. The survival analysis was illustrated by Kaplan-Meier curves with log-rank test. Univariate and multivariate survival analyses were performed using the likelihood ratio test of the stratified Cox proportional hazards model of SPSS 17.0 (IBM Corp.). P<0.05 was considered to indicate a statistically significant difference.
Results
Yhwaz is upregulated in HCC cell lines and tissues
The results from the analysis of the data in the GTEx portal revealed that ywhaz is expressed in multiple organs and tissues (Fig. S1). As yhwaz has been reported to affect a number of different types of cancer, such as liver, lung and breast (3), HCCDB, an analytical tool for gene expression profiling, was used to determine yhwaz expression in tumor and other tissues. As presented in Fig. 1A and Table II, yhwaz expression was significantly higher in tumor tissues compared with tissue adjacent to HCC tissues. Additionally, in patients with cirrhosis, yhwaz expression was higher compared with healthy human liver tissues. Liver HCC tissues exhibited upregulated expression levels of yhwaz compared with healthy liver tissue (Fig. 1B). In order to investigate the changes in yhwaz expression between HCC and adjacent non-tumor tissues, yhwaz expression profiles were used from different independent bioinformatics databases. Based on the gene expression profiles obtained from the UALCAN database, yhwaz was determined to be upregulated in the majority of HCC tissues (Fig. 1C). To confirm this finding, two microarray datasets from the Oncomine database (24) were analyzed (Fig. 1D and E). yhwaz expression levels were significantly increased in HCC tissues compared with in healthy liver tissues. Taken together, these data demonstrate that increased mRNA expression of yhwaz contributed to HCC, indicating its potential role in liver cancer.
Yhwaz expression may be a prognostic factor in HCC
Although yhwaz has been demonstrated to participate in the development of multiple types of cancer, there are no clear reports on the association between its expression and clinical prognosis in HCC. The prognostic efficacy of the mRNA expression of ywhaz in patients with HCC was analyzed using the Kaplan-Meier plotter (26). As presented in Fig. 2A-C and Table II, increased expression of yhwaz was found to increase the risk of death and decrease survival probability in female (Fig. 2A) and male (Fig. 2B) patients with HCC alone or all both sex combined (Fig. 2C; Table III). Therefore, yhwaz expression analysis was performed with regard to the clinicopathological characteristics of patients. As presented in Fig. 2D, yhwaz expression in African-American patients with HCC was significantly higher compared with Caucasian patients. The expression levels of yhwaz were shown to increase with tumor grade (Fig. 2E). Based on tumor stages (34), the ywhaz expression level was significant higher in patients of all tumor stages compared with healthy individuals, but yhwaz expression levels in stage 3 cancer were significantly higher compared with stage 1 (P<0.01; Fig. 2F), and there were no other significant difference amongst the other stages. The expression levels of yhwaz decreased with age in patients with HCC (Fig. 2G). The weight of patients seemed to have less influence on the expression of yhwaz, with the only significant difference observed between normal weight patients and obese patients with HCC (Fig. 2H), and Together, these data suggest that high expression levels of yhwaz may predict tumors with increased malignancy.
Yhwaz mutations affect the outcome of patients with HCC
Mutations in a gene may not always affect the function of the coded proteins. In the cBioPortal data sets, based on the TCGA, there were 8 studies (35–40) on HCC, and mutations, amplification and deep deletions were observed in the yhwaz sequence in these datasets (Fig. 3A). yhwaz was mutated or otherwise altered in 98 (7.00%) of the 1,487 patients. These alterations were amplified in 90 cases (6.00%), deep deletion in 2 cases (0.13%) and mutations in 6 cases (0.40%). Therefore, amplification was the most common type of yhwaz alteration in HCC. The 6 mutations identified are shown in Table IV, and the single nucleotide polymorphisms seen were S207I, N108S, R127G and C94S. The biological interaction network of yhwaz was next identified in HCC. The tab Network in cBioPortal was used to show yhwaz neighboring genes that were altered with a frequency >15% (Fig. 3B; Table V). The neighbor genes of yhwaz with highest frequency of alterations were CRTC2 (38.1%), SDC2 (34.4%) and F11R (29.2%). OS and disease-free survival were significantly decreased in patients with yhwaz alterations compared with individuals without alterations (Fig. 3D), suggesting that alterations in yhwaz may affected the survival of patients with HCC.
 | Table V.Type and frequency of yhwaz neighbor gene alterations in patients with hepatocellular carcinoma. |
Increased expression of miRNAs that target yhwaz are associated with improved survival time in patients with HCC
miR-22 has been reported to target yhwaz and inhibit its expression in HCC cells (41). miRNAs that targeted yhwaz were predicted using TargetScan. As presented in Fig. 4A, miR-22 and miR-1/miR-206 were predicted to exhibit a higher probability of binding with yhwaz. miR-451 negatively regulated the expression of yhwaz through binding with the 3′untranslated region of yhwaz (42). miR-155 and miR-140 were also predicted to exhibit a high probability of binding with yhwaz. For reasons not yet known, the survival curves for miR-140-3p (Fig. 4G) exhibited a late stage crossover. The most mentioned miRNAs that were predicted to bind with yhwaz, were associated with improved survival time in patients with HCC when expression was upregulated (Fig. 4B-G; Table VI). Together, these data show that miRNAs that were predicted to target yhwaz were associated with improved survival time in patients with HCC.
| Table VI.Association between yhwaz targeting miRNAs and survival time in patients with hepatocellular carcinoma. |
KEGG pathway analysis of co-expression genes correlated with yhwaz and its networks of kinase and miRNA targets in HCC
To further examine the targets of yhwaz in HCC, the KEGG pathway analysis was used to determine the kinase and miRNA target networks of associated gene sets generated by Gene Set Enrichment Analysis (GSEA) (30,31). KEGG pathway analysis showed enrichment in tumor necrosis factor, proteoglycans in cancer and miRNAs in cancer pathways (Fig. 5A). The miRNA-target network was associated with miR-149 (GAG CCA G), miR-133A/miR-133B (GGG ACC A) and miR-296 (GGG GCC C). The most significant target networks were the kinase-target networks associated primarily with the MAPK2, Rho Associated Coiled-Coil Containing Protein Kinase 1 and Polo-like Kinase 1 (Table VII). The protein-protein interaction network constructed by GeneMANIA (32) revealed associations amongst genes for MAPKAPK2 and miRNA-149. The GSEA for MAPK2 is responsible primarily for regulating the cell cycle, kinase activity and the immune response (Fig. 5B), and the GSEA for miRNA-527 (Fig. 5C) was involved primarily in the regulation of cell activation, kinase activity and leukocyte activation.
Discussion
The 14-3-3 family of proteins serve various roles in signaling and interact with several protein partners, including their function as adaptors that stimulate protein-protein interactions (3,41). Among the 14-3-3 proteins, the 14-3-3ζ isotype is one of the most studied members of this family (42). 14-3-3ζ interacts with numerous key cellular proteins involved in tumor development and progression (43). The importance of 14-3-3ζ in the development and progression of cancer has been demonstrated in a number of different types of cancer (44). In the present study, bioinformatics analysis was used to determine the prognostic value of yhwaz in liver cancer, and the results revealed that yhwaz expression was upregulated in HCC tissues and cell lines. Upregulated levels of yhwaz were significantly associated with a poor prognosis. Mutations in yhwaz significantly affected the survival time of patients with HCC, and miRNAs that targeted yhwaz were associated with outcomes of HCC.
14-3-3ζ has been suggested to be a potential prognostic marker and therapeutic target in a number of different types of cancer (43). 14-3-3ζ overexpression increases Akt phosphorylation and further increased hypoxia-inducible factor-1α expression in HCC cells (45,46). In the present study, yhwaz expression was upregulated in the major and different sub-types of HCC. 14-3-3ζ has been hypothesized to bind with the hepatitis B virus (HBV) protein X (HBx) and maintain its protein stability in HCC cells. In cancer cells where 14-3-3ζ was silenced, the cells exhibited decreased migratory and invasive capacities, and this was accompanied by decreased expression of HBx (47). Together, these data suggest that upregulated expression of 14-3-3ζ may increase the risk of HBV infection.
14-3-3ζ targets and affects the phosphorylation of a number of proteins, a number of which are involved in tumor-promoting processes, such as regulation of autophagy and tumor suppressor pathways (48). Thus, alterations in yhwaz expression may result in cancer of the liver through regulation of these pathways and processes. The most common type of alteration observed in HCC was amplification, which was observed in 6% of cases. Multiple yhwaz mutations have been observed in patients with liver cancer, the majority of which are primarily phosphorylation sites. p-AKT is the one of the targets of 14-3-3ζ, upregulation of both 14-3-3ζ. p-Akt in patients with HCC predicts a poor prognosis, and 14-3-3ζ triggers activation of the Akt signaling pathway, thus contributing to the development of HCC (49). However, in patients with upregulated expression of miRNAs that target ywhaz, prognosis was predicted to be improved compared with patients with lower expression levels of the same miRNAs. Enrichment analysis of yhwaz co-expression genes determined that the majority of the interacting genes were involved in processes associated with cancer progression, and yhwaz target GSEA may assist in identifying important networks of target kinases and miRNAs.
The present study used the online tools to perform target gene analysis on tumor data from public databases. At the same time, the limitation is that transcriptome sequencing can only detect static mutations; it does not directly provide information on protein activity or expression levels. These issues should be addressed in subsequent research using molecular biology techniques. The other one limitation is that the LIHC samples contained a relatively small number of stage 4 patients, but the clinical reality is that most patients with HCC are diagnosed for the first time when the disease progresses, and thus, the prognosis is extremely poor. Therefore, the results of the present study should be validated in clinical samples including a full coverage of different ethnic groups and HCC stages.
The present study provided theoretical evidence of the importance of yhwaz expression in hepatocarcinogenesis and highlighted its potential as a marker in HCC. Based on the results of the present study, it may be hypothesized that targeting 14-3-3ζ in patients with liver cancer may effectively improve the survival rates and prognosis of patients.
Supplementary Material
Supporting Data
Acknowledgements
Not applicable.
Funding
This work was supported by Zhejiang Provincial Natural Science Foundation of China (grant no: LQ18C070001), the Project Grant from Wenzhou Science and Technology Bureau (grant no: Y20180722) and the Program of Science and Technology Development in Yancheng (grant no: YK2016053).
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Authors' contributions
YL, LS and XY designed and conducted the experiments. YL and LS wrote and revised the manuscript.
Ethics approval and consent to participate
Not applicable.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
References
|
Forner A, Llovet JM and Bruix J: Hepatocellular carcinoma. Lancet. 379:1245–1255. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
European Association For The Study Of The Liver1; European Organisation For Research And Treatment Of Cancer, . EASL-EORTC clinical practice guidelines: Management of hepatocellular carcinoma. J Hepatol. 56:908–943. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Fan X, Cui L, Zeng Y, Song W, Gaur U and Yang M: 14-3-3 proteins are on the crossroads of cancer, aging, and age-related neurodegenerative disease. Int J Mol Sci. 20(pii): E35182019. View Article : Google Scholar : PubMed/NCBI | |
|
Sluchanko NN and Gusev NB: Moonlighting chaperone-like activity of the universal regulatory 14-3-3 proteins. FEBS J. 284:1279–1295. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
van Heusden GP: 14-3-3 proteins: Regulators of numerous eukaryotic proteins. IUBMB Life. 57:623–629. 2005. View Article : Google Scholar : PubMed/NCBI | |
|
Yaffe MB: How do 14-3-3 proteins work?-Gatekeeper phosphorylation and the molecular anvil hypothesis. FEBS Lett. 513:53–57. 2002. View Article : Google Scholar : PubMed/NCBI | |
|
Lin M, Morrison CD, Jones S, Mohamed N, Bacher J and Plass C: Copy number gain and oncogenic activity of YWHAZ/14-3-3zeta in head and neck squamous cell carcinoma. Int J Cancer. 125:603–611. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Bajpai U, Sharma R, Kausar T, Dattagupta S, Chattopadhayay TK and Ralhan R: Clinical significance of 14-3-3 zeta in human esophageal cancer. Int J Biol Markers. 23:231–237. 2008. View Article : Google Scholar : PubMed/NCBI | |
|
Matta A, Siu KW and Ralhan R: 14-3-3 zeta as novel molecular target for cancer therapy. Expert Opin Ther Targets. 16:515–523. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Lu J, Guo H, Treekitkarnmongkol W, Li P, Zhang J, Shi B, Ling C, Zhou X, Chen T, Chiao PJ, et al: 14-3-3zeta Cooperates with ErbB2 to promote ductal carcinoma in situ progression to invasive breast cancer by inducing epithelial-mesenchymal transition. Cancer Cell. 16:195–207. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Xu J, Acharya S, Sahin O, Zhang Q, Saito Y, Yao J, Wang H, Li P, Zhang L, Lowery FJ, et al: 14-3-3ζ turns TGF-β's function from tumor suppressor to metastasis promoter in breast cancer by contextual changes of Smad partners from p53 to Gli2. Cancer Cell. 27:177–192. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Cornell B and Toyo-Oka K: Deficiency of 14-3-3ε and 14-3-3ζ by the Wnt1 promoter-driven Cre recombinase results in pigmentation defects. BMC Res Notes. 9:1802016. View Article : Google Scholar : PubMed/NCBI | |
|
Yu J, Chen L, Chen Y, Hasan MK, Ghia EM, Zhang L, Wu R, Rassenti LZ, Widhopf GF, Shen Z, et al: Wnt5a induces ROR1 to associate with 14-3-3ζ for enhanced chemotaxis and proliferation of chronic lymphocytic leukemia cells. Leukemia. 31:2608–2614. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Zhang B, Shi Y, Gong A, Pan Z, Shi H, Yang H, Fu H, Yan Y, Zhang X, Wang M, et al: HucMSC Exosome-delivered 14-3-3ζ orchestrates self-control of the Wnt response via modulation of YAP during cutaneous regeneration. Stem Cells. 34:2485–2500. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Choi JE, Hur W, Jung CK, Piao LS, Lyoo K, Hong SW, Kim SW, Yoon HY and Yoon SK: Silencing of 14-3-3ζ over-expression in hepatocellular carcinoma inhibits tumor growth and enhances chemosensitivity to Cis-diammined dichloridoplatium. Cancer Lett. 303:99–107. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Davidson CE, Reese BE, Billingsley ML and Yun JK: The protein stannin binds 14-3-3zeta and modulates mitogen-activated protein kinase signaling. Brain Res Mol Brain Res. 138:256–263. 2005. View Article : Google Scholar : PubMed/NCBI | |
|
Lee YK, Hur W, Lee SW, Hong SW, Kim SW, Choi JE and Yoon SK: Knockdown of 14-3-3ζ enhances radiosensitivity and radio-induced apoptosis in CD133(+) liver cancer stem cells. Exp Mol Med. 46:e772014. View Article : Google Scholar : PubMed/NCBI | |
|
Wang X, Shen H, Zhangyuan G, Huang R, Zhang W, He Q, Jin K, Zhuo H, Zhang Z, Wang J, et al: 14-3-3ζ delivered by hepatocellular carcinoma-derived exosomes impaired anti-tumor function of tumor-infiltrating T lymphocytes. Cell Death Dis. 9:1592018. View Article : Google Scholar : PubMed/NCBI | |
|
Song J, Zhang X, Liao Z, Liang H, Chu L, Dong W, Zhang X, Ge Q, Liu Q, Fan P, et al: 14-3-3ζ inhibits heme oxygenase-1 (HO-1) degradation and promotes hepatocellular carcinoma proliferation: Involvement of STAT3 signaling. J Exp Clin Cancer Res. 38:32019. View Article : Google Scholar : PubMed/NCBI | |
|
Lian Q, Wang S, Zhang G, Wang D, Luo G, Tang J, Chen L and Gu J: HCCDB: A database of hepatocellular carcinoma expression atlas. Genomics Proteomics Bioinformatics. 16:269–275. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Hutter C and Zenklusen JC: The cancer genome atlas: Creating lasting value beyond its data. Cell. 173:283–285. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
GTEx Consortium: The genotype-tissue expression (GTEx) project. Nat Genet. 45:580–585. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Chandrashekar DS, Bashel B, Balasubramanya SAH, Creighton CJ, Ponce-Rodriguez I, Chakravarthi BVSK and Varambally S: UALCAN: A portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia. 19:649–658. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Rhodes DR, Kalyana-Sundaram S, Mahavisno V, Varambally R, Yu J, Briggs BB, Barrette TR, Anstet MJ, Kincead-Beal C, Kulkarni P, et al: Oncomine 3.0: Genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles. Neoplasia. 9:166–180. 2007. View Article : Google Scholar : PubMed/NCBI | |
|
Altermann E and Klaenhammer TR: PathwayVoyager: Pathway mapping using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. BMC Genomics. 6:602005. View Article : Google Scholar : PubMed/NCBI | |
|
Lánczky A, Nagy Á, Bottai G, Munkácsy G, Szabó A, Santarpia L and Győrffy B: miRpower: A web-tool to validate survival-associated miRNAs utilizing expression data from 2178 breast cancer patients. Breast Cancer Res Treat. 160:439–446. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, Sun Y, Jacobsen A, Sinha R, Larsson E, et al: Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 6:pl12013. View Article : Google Scholar : PubMed/NCBI | |
|
Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, Jacobsen A, Byrne CJ, Heuer ML, Larsson E, et al: The cBio cancer genomics portal: An open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2:401–404. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Agarwal V, Bell GW, Nam JW and Bartel DP: Predicting effective microRNA target sites in mammalian mRNAs. Elife. 42015.doi: 10.7554/eLife.05005. | |
|
Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstråle M, Laurila E, et al: PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 34:267–273. 2003. View Article : Google Scholar : PubMed/NCBI | |
|
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES and Mesirov JP: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 102:15545–15550. 2005. View Article : Google Scholar : PubMed/NCBI | |
|
Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, Chao P, Franz M, Grouios C, Kazi F, Lopes CT, et al: The GeneMANIA prediction server: Biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 38:W214–W220. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Tang Z, Li C, Kang B, Gao G, Li C and Zhang Z: GEPIA: A web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 45:W98–W102. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Amin MB, Edge S, Greene F, Byrd DR, Brookland RK, Washington MK, Gershenwald JE, Compton CC, Hess KR, Sullivan DC, et al: AJCC cancer staging manual. 8th. New York: Springer; 2017, View Article : Google Scholar | |
|
Pilati C, Letouze E, Nault JC, Imbeaud S, Boulai A, Calderaro J, Poussin K, Franconi A, Couchy G, Morcrette G, et al: Genomic profiling of hepatocellular adenomas reveals recurrent FRK-activating mutations and the mechanisms of malignant transformation. Cancer Cell. 25:428–441. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Harding JJ, Nadakumar S, Armenia J, Khalil DN, Albano M, Ly M, Shia J, Hechtman JF, Kundra R, El Dika I, et al: Prospective genotyping of hepatocellular carcinoma: Clinical Implications of next-generation sequencing for matching patients to targeted and immune therapies. Clin Cancer Res. 25:2116–2126. 2019. View Article : Google Scholar : PubMed/NCBI | |
|
Ahn SM, Jang SJ, Shim JH, Kim D, Hong SM, Sung CO, Baek D, Haq F, Ansari AA, Lee SY, et al: Genomic portrait of resectable hepatocellular carcinomas: Implications of RB1and FGF19 aberrations for patient stratifications. Hepatology. 60:1972–1982. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Schulze K, Imbeaud S, Letouze E, Alexandrov LB, Calderaro J, Rebouissou S, Couchy G, Meiller C, Shinde J, Soysouvanh F, et al: Exome sequencing of hepatocellular carcinomas identifies new mutational signatures and potential therapeutic targets. Nat Genet. 47:505–511. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Fujimoto A, Totoku Y, Abe T, Boroevich KA, Hosoda F, Nguyen HH, Aoki M, Hosono N, Kubo M, Miya F, et al: Whole-genome sequencing of liver cancers identifies etiological influences of mutation patterns and recurrent mutations in chromatin regulators. Nat Genet. 7:760–764. 2012. View Article : Google Scholar | |
|
Hoadley KA, Yau C, Hinoue T, Wolf DM, Lazar AJ, Drill E, Shen R, Taylor AM, Cherniack AD, Thorsson V, et al: Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer. Cell. 173:291–304. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Kaplan A, Ottmann C and Fournier AE: 14-3-3 adaptor protein-protein interactions as therapeutic targets for CNS diseases. Pharmacol Res. 125:114–121. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Woodcock JM, Goodwin KL, Sandow JJ, Coolen C, Perugini MA, Webb AI, Pitson SM, Lopez AF and Carver JA: Role of salt bridges in the dimer interface of 14-3-3ζ in dimer dynamics, N-terminal α-helical order, and molecular chaperone activity. J Biol Chem. 293:89–99. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Neal CL and Yu D: 14-3-3ζ as a prognostic marker and therapeutic target for cancer. Expert Opin Ther Targets. 14:1343–1354. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Aghazadeh Y and Papadopoulos V: The role of the 14-3-3 protein family in health, disease, and drug development. Drug Discov Today. 21:278–287. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Tang Y, Lv P, Sun Z, Han L, Luo B and Zhou W: 14-3-3ζ up-regulates hypoxia-inducible factor-1α in hepatocellular carcinoma via activation of PI3K/Akt/NF-κB signal transduction pathway. Int J Clin Exp Pathol. 8:15845–15853. 2015.PubMed/NCBI | |
|
Tang Y, Liu S, Li N, Guo W, Shi J, Yu H, Zhang L, Wang K, Liu S and Cheng S: 14-3-3ζ promotes hepatocellular carcinoma venous metastasis by modulating hypoxia-inducible factor-1α. Oncotarget. 7:15854–15867. 2016.PubMed/NCBI | |
|
Tang Y, Zhang Y, Wang C, Sun Z, Li L, Dong J and Zhou W: 14-3-3ζ binds to hepatitis B virus protein X and maintains its protein stability in hepatocellular carcinoma cells. Cancer Med. 7:5543–5553. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Tzivion G, Gupta VS, Kaplun L and Balan V: 14-3-3 proteins as potential oncogenes. Semin Cancer Biol. 16:203–213. 2006. View Article : Google Scholar : PubMed/NCBI | |
|
Tang Y, Wang R, Zhang Y, Lin S, Qiao N, Sun Z, Cheng S and Zhou W: Co-Upregulation of 14-3-3ζ and P-Akt is associated with oncogenesis and recurrence of hepatocellular carcinoma. Cell Physiol Biochem. 45:1097–1107. 2018. View Article : Google Scholar : PubMed/NCBI |
