Cyclin Dependent Kinase 19 Upregulation Correlates With Unfavorable Prognosis in Hepatocellular Carcinoma

doi:10.21203/rs.3.rs-445045/v1

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Research Article

Cyclin Dependent Kinase 19 Upregulation Correlates With Unfavorable Prognosis in Hepatocellular Carcinoma

https://doi.org/10.21203/rs.3.rs-445045/v1

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Objectives: Cyclin dependent kinase 19 (CDK19) is a component of the Mediator co-activator complex, which is required for transcriptional activation. In this study, we will utilize the public data and combine it with wet-bench experiments in hepatic cell lines to elucidate the potential roles of CDK19 in hepatocellular cancer (HCC).

Materials and Methods: We studied the relationships between CDK19 expression and several clinical features related with HCC by consulting Oncomine and UALCAN. The prognostic value of CDK19 was tested using the Kaplan‐Meier Plotter database. We presented the mutations of CDK19 and addressed its relations with immune cells with the use of cBioPortal, and COSMIC and TIMER database. Hub genes were obtained and further analysed using the STRING database. To test the in silico findings, we knocked down CDK19 with short hairpin RNA (shRNA) technology in two hepatic cell lines, and then several functional characterization experiments were conducted.

Results: A remarkably higher level of CDK19 expression was found in HCC tissues than normal liver tissues, and CDK19 mRNA expression has high diagnostic value in HCC patients. Subgroup analysis showed that CDK19 overexpression were associated with gender, tumor stage and TP53 mutant. Prognostic values of CDK19 upregulation for overall survival (OS) were significant in patients with stage 2-3, stage 3-4, grade 2 and etc. 1% of the patients have mutations at CDK19, and we did not observe a potential relationship between CDK19 mutation and prognosis. CDK19 showed positive correlations with the abundances of CD4+ T cells, macrophages and dendritic cells. We identified 10 genes that correlated with CDK19, 8 of which presented excellent prognostic value in HCC. Besides, these hub genes were directly involved in cell division and regulation of G2/M transition of mitotic cell cycle. PPI and pathway predictions indicated that CDK19 should have a high possibility to be involved with several cellular functions, such as proliferation, migration, and invasion. These functions were strongly interfered in two independent hepatic cell lines, after knocking down CDK19.

Conclusions: CDK19 could serve as a prognostic marker in HCC and it deserves further work to test its therapeutic potential to HCC.

Gastroenterology & Hepatology

Cyclin dependent kinase 19

hepatocellular carcinoma

prognosis

knocking down

proliferation

migration

invasion

Hepatocellular carcinoma (HCC), accounting for 75%~85% of primary liver cancer, ranked 6th most commonly diagnosed and ranked 3th in cancer-related death globally [1]. In recent years, the incidence of HCC increased dramatically and will continue to rise over the next 10–20 years [2]. The major challenges are metastasis and recurrence after resection, which contributes to the dismal prognosis of HCC. Nowadays, several novel therapeutic options for HCC are emerging and have shown to improve the survival rates, but the overall prognosis is still unsatisfactory [3]. Thus, to identify promising prognostic biomarkers is still urgent and necessary.

Cyclin dependent kinase 19 (CDK19) is a cyclin-dependent transcription-regulating kinase. CDK19 and its homolog CDK8, being termed as ‘Mediator Kinase’, have been shown to have crucial roles in cellular homeostasis and developmental programming. In a mutually exclusive manner, CDK19 or CDK8 can form a Mediator co-activator complex with another three proteins, CCNC, MED12 and MED13[4]. CDK19 or CDK8 reversibly regulate RNA polymerase II to control transcriptional activity. CDK8 has been reported to involve in the development of malignancies, including cancers of colon, breast and pancreas [5–7]. In contrast, the role of CDK19 in carcinogenesis is rarely studied and only sporadically reported in prostatic cancer, colorectal cancer, breast cancer and etc [8–10]. Meanwhile, some small-molecule CDK8/19 inhibitors have been found to possess beneficial effects on tumor treatment, and a clinical trial (ClinicalTrials.gov Identifier: NCT03065010) with estrogen receptor-positive breast cancer has been on its way [11]. Studies have shown that Sorafenib is a molecular inhibitor of CDK19 [12]. Here, we analyzed the HCC patients treated with Sorafenib by bioinformatics, and found that they had a better prognosis (Fig. 3I).

In this study, we first investigated the expression of CDK19 and the prognostic value of CDK19 in HCC patients. Next, we evaluated the relationship between CDK19 and immune infiltrates, identified a 10-gene hub correlated with CDK19 strongly, and explored the underlying roles of CDK19 by PPI and pathway analysis. Then, we conducted CDK19-knockdown in two HCC cell lines and conformed its relevant functions from in vitro assays.

mRNA/protein expression and survival analysis

We search ‘CDK19’ as the gene symbol in the Oncomine database (http://www.oncomine.org ; accessed from 2 February, 2021). CDK19 expression values from four GEO datasets (including Roessler liver, Wurmbach liver, Roessler liver2 and Chen liver) were obtained and the dot plots were generated using GraphPad Prism 7.0 software. Then, we collected and collated the results about CDK19 expression levels in different subgroups using UALCAN database (http://ualcan.path.uab.edu ; accessed from 2 February, 2021) [13]. Moreover, we investigated the protein expression of CDK19 in the Human Protein Atlas database (www.proteinatlas.org; accessed from 2 February, 2021) [14, 15]. Next, CKD19 expression and overall survival in HCC patients were evaluated using Kaplan‐Meier Plotter database (http://kmplot.com; accessed from 2 February, 2021) [16].

Mutant and immune infiltrates analysis

We evaluated the mutant frequenc of CDK19 in HCC patients using the cBioPortal database (http://www.cbioportal.org/; accessed from 2 February, 2021) [17, 18]. We selected all listed 1000 HCC samples of 5 studies as study object. Then, we validated the mutation of CDK19 in HCC in the Catalogue of Somatic Mutations in Cancer (COSMIC) database (http://cancer.sanger.ac.uk; accessed from 2 February, 2021) [19, 20]. Next, we further explored the associations between CDK19 and immune cells using the Tumor Immune Estimation Resource (TIMER) database (https://cistrome.shinyapps.io/timer/; accessed from 2 February, 2021) [21].

Differential expression genes and hub genes analysis

We investigated the differential expression genes (DEGs) using LinkedOmics (http://www.linkedomics.org; accessed from 2 February, 2021) database, where contains 32 cancer types’ multi-omics data[22]. 371 TCGA-HCC RNAseq samples were analysed by Spearman test. Then the hub genes were determined using cytoscope software (https://cytoscape.org/; accessed from 2 February, 2021) based on the top 200 significantly correlated DEGs. The prognostic significance of hub genes and the association between CDK19 and hub genes were investigated using the Kaplan‐Meier Plotter database (http://kmplot.com; accessed from 2 February, 2021) [16] and GEPIA (http://gepia.cancer-pku.cn/; accessed from 2 February, 2021)[23].

Protein-Protein Interaction network and GO/KEGG analysis

Protein-Protein Interaction (PPI) analysis for CDK19 were performed using STRING database (https://string-db.org/; accessed from 2 February, 2021) [24]. We picked out the top 200 significantly related genes to establish the cluster of the network with default criteria. Next, we conducted Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) biological process enrichment analysis, and the results were visualized with the bioinformatics online tool (http://www.bioinformatics.com.cn; accessed from 2 February, 2021).

Reagents and cell culture

HCC cell lines (Hep.G2 and SK-Hep-1) were purchased from Chinese Academy of Sciences. Both cell lines have been cell line certified and are free of mycoplasma infection. These cells were cultured in DMEM medium with 10% fetal bovine serum and maintained in an environment suitable for cell growth. For CDK19 knockdown, we used the short hairpin RNA (shRNA) delivered by lentivirus (purchased from Genomeditech, shanghai, China), which targeting the sequence 5’-GCATGACTTGTGGCATATTAT-3’ (Gene ID: 23097).

qPCR analysis

After 48h of lentiviral transduction, the mRNA expression of CDK19 were evaluated. Total cellular RNA were extracted and relative mRNA expression was determined. The primers for CDK19 were: forward primer 5ʹ-GTTTCACCGTGCATCAAAAGC-3ʹ; reverse primer 5ʹ-ACCCAATTTGCATGGAGGTAATG-3ʹ. GAPDH was set as an internal reference gene.

Cell proliferation assay

After 48h of viral transduction, the cells of sh-CDK19-NC and sh-CDK19 were seeded evenly into the 96‐well plates with 10000 cells/well. Then, we detected cell viability using Cell Counting Kit-8 at 0h, 24h, 48h, 72h. These cells were incubated with 10 ul CCK-8 for 2h and 450 nm absorption value was recorded using VICTOR Nivo Multimode Plate Reader (PerkinElmer Inc., Massachusetts, USA).

Migration and invasion assay

Wound healing assay and transwell assay were proceeded to evaluate the migration and invasion ability of HCC cells. 48h post transfection, cells were trypsinized and seeded into culture insert (80209, IBIDI Co., Germany) and migration transwell chambers (3422, Corning Co., USA). After 48 hours, the migration and invasion ability were detected as previously detailed protocols[25].

Statistical analysis

We used GraphPad Prism 7.0 software to analyse partial results. Data were summarized as the means ± SEM. Differences between 2 groups were evaluated using Student’s t-test.

High expression of CDK19 in HCC

CDK19 is a cyclin-dependent transcription-regulating kinase. Both CDK19 and its homolog CDK8, being termed as ‘Mediator Kinase’, have been shown to play crucial roles in cellular homeostasis and to be related with several diseases. Based on Oncomine, we evaluated the expression of CDK19 in HCC from 4 GEO datasets (Roessler liver, Roessler liver 2, Chen liver, and Wurmbach liver) [26-28]. As a result, the expression of CDK19 in HCC tissues was significantly upregulated. For example, CDK19 showed 1.71-fold increases in the Roessler liver datasets (Figure 1A). The difference of CDK19 expression across these four studies was significant, indicating that there may be to some extent inter-patient variations existed. (P＜0.05) (Figure 1B). We next investigated the protein expression of CDK19 in HCC by using HPA database. As shown in Figure 1C, CDK19 was hardly detected in a normal liver tissue (Patient ID 2556), but from a liver tumor tissue (Patient ID 2177) it showed quite strong signal (Figure 1C).

To further explore the inter-patient variations of CDK19, we studied the expression patterns of CDK19 in TCGA-LIHC by using UALCAN, according to several different clinical features. As shown in Figure 2A, CDK19 had much higher expression in LIHC patients (n=371) than in normal control group (n=50) (Figure 2A). While clustering the patients into different subgroups based on their age, gender, race and weight, we can observe differential expression profiles (Figure 2B-2E). For example, the expression difference would not be significant between the control and patients at the age of 81-100 Yrs. Besides, there was seemingly a correlation between CDK19 expression and different tumor severity (Figure 2F-2H). Among it, the difference of patients between stage 1 and stage 3 was quite obvious (P=0.0021). Considering that TP53 is one of the most commonly mutated genes in HCC, we found that there may be a correlation link between TP53 and CDK19. Intriguingly, CDK19 was accumulated much more in patients with TP53 mutation than others, p=0.00012 (Figure 2I).

Survival results and multivariate analysis in HCC

We evaluated the prognostic significance of CDK19 in 364 patients using the Kaplan‐Meier Plotter database. We found that the higher the expression of CDK19, the worse the overall survival (OS, HR = 1.55, log‐rank P = 1.8E-2) in HCC patients (Figure 3A). The prognostic value of CDK19 in different clinical subgroups of HCC was investigated. The results indicated that OS was relatively poor in patients with high CDK19 mRNA expression, under the conditions of stage 2-3, stage 3-4, grade 2, male sex, Asian race, alcohol consumed and non‐hepatitis virus infected (Figure 3B-3H). Collectively, CDK19 expression level can serve as a valuable prognostic biomarker in HCC patients and the prognostic significance varies depending on different clinical subgroups, which can guide our clinical practice in a personized pattern.

Mutations of CDK19 in HCC

Next, we investigated mutation landscape of CDK19 in a large number of HCC patients by using cBioPortal software. Overall, 1000 samples from 998 patients allocated in five studies (AMC, INSERM, RIKEN, MSK and TCGA‐PanCancer Atlas) were selected for analysis [29-33]. From our datamining, 8 alterations of CDK19 were found, with one missense mutation which appeared in early HCC (Figure 4A). And the somatic mutant frequency of CDK19 was around 1% (Figure 4B). However, CDK19 somatic status can not be used to distinguish the OS in HCC patients (Figure 4C). In addition, we used COSMIC database to verify the mutation of CDK19 in HCC. There were only 9 mutations from 951 tissues (somatic mutation frequency: 0.95%) and the only type of mutations was missense substitution (Figure 4D). The substitution mutations solely occurred at C＞A (100%) (Figure 4E).

The relationship between CDK19 and immune infiltrates in HCC

To investigate the correlation between CDK19 and immune infiltrates, we used TIMER online tool. The relationships between 6 immune cell types (B cell, CD8+T cells, CD4+ T cells, macrophage, neutrophil and myeloid dendritic cell) and CDK19 expression were determined by Spearman tests (tumor purity adjusted, Figure 5A). It revealed that CDK19 was significantly in positive correlation with these 6 immune infiltrates, especially macrophage (R=0.48) and myeloid dendritic cell (R=0.458) (Figure 5B-5G). Then it inspired us to further research if there may be a potential association between CDK19 and immune cell gene markers. Similar to the findings above, CDK19 had positive correlations with the respective gene markers of those 6 immune cells (Table 1). Among the listed gene markers, QRSL1 (R=0.700), IRF5 (R= 0.483), STAT1 (R= 0.469), NRP1 (R=0.469) and PTGS2 (R=0.467) are most relevant ones (Table 1).

The genes correlated with CDK19 in HCC

We investigated the genes correlated with CDK19 using LinkedOmics software. As the volcano map showed (Figure 6A), the negatively and positively related genes were located to the left and right areas, respectively. The top 50 positively and negatively related genes were identified based on Spearman test, and were shown in heatmap separately (Figure 6B-6C). To address whether there is some hub genes existed, we input the top 200 positively related genes with CDK19 into the STRING online database and cytoscape software. Based on gene degree, the 10 most relevant hub genes were obtained, including CEP135, CEP162, CEP192, CEP290, CNTRL, HAUS6, IQCB1, NEDD1, TCTN2 and WDHD1 (Figure 6D). We were surprised to find that almost all Hub genes are directly involved in cell division and regulation of G2/M transition of mitotic cell cycle. The correlations between CDK19 and the 10 hub genes were validated by using GEPIA web-tool (Supplementary Figure 1). Lastly, we found that 8 of the 10 top hub genes presented excellent prognostic value in HCC (Figure 6E), especially IQCB1 (HR=2.05) and NEDD1 (HR=1.93) (Figure 6E).

PPI and KEGG/GO enrichment of CDK19 in HCC

By utilizing the STRING software, we constructed a protein-protein interaction (PPI) network based on the top 200 significantly related genes (Figure 7A). As shown in the network, CDK19 can directly interact with MED23 and CNOT2, through which further interact with other proteins. As we all know, MED23 and CNOT2 are both involved in regulation of gene expression and transcription. Meanwhile, we also found a lot of proteins in the network took part in tumor development, including PHIP driving glioblastoma motility and invasion[34], HDAC2 regulating breast cancer progression and proliferation[35] and ZFN292 participating hepatoma proliferation and vasculogenic mimicry[36].

Then, we investigated the GO/KEGG enrichment signaling pathways, which CDK19 may be involved in. GO biological process analysis of CDK19 showed that binding to transcription cofactors, regulations of transcription factors and mRNA were significantly affected and enriched (Figure 7B). KEGG pathway enrichment showed that CDK19 is mainly involved in mitosis through different ways (Figure 7C).

CDK19 involved in several cellular functions of hepatic tumor cell lines

To validate the findings related with CDK19 from bioinformatics analysis, we chose two independent hepatic cell lines, Hep.G2 and SK-Hep-1, as our in-vitro models. Here, small hairpin RNA (shRNA) based method was utilized to knock down CDK19, and the lentivirus containing sh-CDK19 was employed as the transgene delivery tool. Firstly, after 48h of lentivirus infection, we isolated mRNA from the cells, and performed qPCR to check the CDK19 level. As shown in Figure 8A, CDK19 was knocked down successfully in both cell lines, comparing with non-targeting control (NC). To address if CDK19 is involved in cell growth, we next conducted cell viability assay. In comparison to the control, knocking-down of CDK19 clearly inhibited the proliferation of both SK-Hep-1 and Hep.G2 cells (Figure 8B).

As suggested from the bioinformatics analysis, CDK19 may have relations with migration and invasion abilities, directly or indirectly through interaction with other invasion-relevant proteins. Aiming to validate its involvement in migration, wound healing assay was performed in the two cell lines. As seen from Figure 8C, much less tumor cells can migrate while comparing with the control. In order to conform CDK19 has contributions to invasion ability, we did transwell invasion assay to the cell lines. Similar as above, knocking-down of CDK19 can significantly decrease the amount of invaded tumor cells (Figure 8D).

Taken together, knocking-down of CDK19 can decrease the proliferation, migration and invasion abilities of hepatic tumor cells, indicating that CDK19 may serve as a promising therapeutic target in HCC.

HCC is one of the most aggressive cancers with poor survival [37]. Globally, about 750,000 new cases and 780,000 death cases of HCC are recorded every year [38]. Because of late diagnosis, metastasis and quick progression, HCC has become a major killer in cancers. Currently, effective therapeutic strategies for HCC patients is still in a hard dilemma. However, with the development of next-generation sequencing and multi-omics, the cellular and molecular mechanisms responsible for HCC tumorigenesis becomes gradually clear[39]. Meanwhile, many new biomarkers for HCC have been discovered, which provide valuable information for diagnosis and/or prognosis of HCC.

The human Mediator complex (MED) is a transcriptional regulator consisting of 30 subunits. It is designated to 4 distinct MED modules including head, middle, tail and kinase modules. By interacting with RNA-polymerase‐II and transcription factors, MED impact almost all cellular processes, such as elongation, initiation, and chromatin architecture[40]. It is not surprising that MED contributes to pathogenesis of diseases, and that the genetic variations or changes of Mediator subunits can lead to a number of pathologies including cancers. For example, CDK8 kinase module and MED29 promoted the expression of β-catenin in colorectal cancer, and they were shown to have oncogenic and tumour-suppressive functions in pancreatic cancer cells, respectively[6, 41]. Moreover, a few studies found that MED1/17 and MED19 were extremely highly expressed in prostate cancer and breast cancer. By reducing the expression of MED1/17 and MED19, the growth and proliferation of cancer cells were inhibited[42, 43]. Therefore, particular Mediator subunit was proposed as hallmarks or therapeutic targets in cancers. CDK19 and its gene paralog CDK8 are part of the Mediator’s kinase module, which reversibly associates with the Mediator core structure, and leads to gene transcription activity as signaling pathway coactivators and corepressors[44]. As previously mentioned, multiple studies found CDK19 and CDK8 involved in diverse cancer entities [5–10]. However, to our knowledge, the detailed characteristics of CDK19 in HCC are not well understood yet. Here, we tried to answer the following questions with great enthusiasm. First, what is the potential mechanism of CDK19 in HCC? Second, what mutations of CDK19 occur in HCC? Does these mutations have a relationship with the prognosis? Third, is CDK19 involved in the pathological process of HCC through immune infiltration? To answer such questions, we conducted a series informatics analysis and experiments in this study.

Based on our work, we found that CDK19 is upregulated in HCC patients. High expression of CDK19 showed tight correlations with the clinical features of HCC patients, such as OS. These results indicated that CDK19 can be used as a novel prognostic marker in HCC. As for mutational alterations of CDK19 in HCC, we found the mutation frequency was only 0.1%, mainly missense substitutions. Nowadays, immunotherapy is a most promising treatment for cancers. To explore whether there is a possibility to combine CDK19-targeting and canonical immunotherapy, we researched if there was a potential relationship between CDK19 expression and immune infiltration in HCC. Our work showed that the expression of CDK19 was in positive correlation with immune infiltrates. These results indicated that CDK19 may play an important role in HCC immune microenvironment. Hofmann et al. recently found that CDK8/19 inhibitors could enhance the killer function of NK cells and promote the lysis of primary leukemia cells[45]. Their works inspired us that CDK8/19 inhibitors may be used in treatment of HCC in the future. With datamining on PPI network and GO/KEGG analysis, we found that CDK19 took part in regulations of some critical transcription factors and it may be involved in mitosis, proliferation, invasion and migration from the mRNA level. Hence, we further explored whether CDK19 could affect the malignancies of HCC cell lines. A serial of phenotypic functional experiments demonstrated that CDK19 may participate in HCC development, by promoting the proliferation, migration and invasion. These phenotypic characteristics were similar with the ones found in prostate cancer, gastric cancer, and head and neck squamous cell carcinoma where CDK19 participated[46–48].

In conclusion, we firstly demonstrated that upregulated CDK19 expression was correlated with a poor prognosis in HCC from several clinic-related features. CDK19 is in a positive correlation with in immune infiltrates in. We addressed that CDK19 shall be involved with several cellular functions, such as proliferation, migration, and invasion. These findings highlighted the potential value of CDK19 expression as a prognostic marker, and meanwhile targeting CDK19 deserved further work to test its therapeutic use for HCC.

CDK19	Cyclin dependent kinase 19
HCC	hepatocellular cancer
DEGs	differential expression genes
GEPIA	Gene Expression Profiling Interactive Analysis
MED	Mediator complex
COSMIC	Catalogue of Somatic Mutations in Cancer
TIMER	Tumor Immune Estimation Resource
KEGG	Kyoto Encyclopedia of Genes and Genomes
GO	Gene Ontology
shRNA	short hairpin RNA

Acknowledgements

Not applicable

Authors' contributions

XPC, JWD conceived this idea and performed cells’ trials. JMZ conducted partial analysis. HQC and ZC revised the manuscript. All authors contributed to this manuscript. All authors read and approved the final manuscript.

Funding

This work was supported by National Science and Technology Major Project of China (2018ZX10302206 and 2017ZX10202203).

Availability of data and materials

The datasets generated and analyzed were obtained from online databases including Oncomine (http://www.oncomine.org), UALCAN (http://ualcan.path.uab.edu), Human Protein Atlas database (www.proteinatlas.org), Kaplan‐Meier Plotter (http://kmplot.com), Catalogue of Somatic Mutations in Cancer (COSMIC) (http://cancer.sanger.ac.uk), cBioPortal (http://www.cbioportal.org), Tumor Immune Estimation Resource (TIMER) (https://cistrome.shinyapps.io/timer), LinkedOmics (http://www.linkedomics.org), cytoscope software (https://cytoscape.org), GEPIA (http://gepia.cancer-pku.cn), STRING (https://string-db.org), bioinformatics online tool (http://www.bioinformatics.com.cn).

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians 2021.
Rimassa L, Personeni N, Czauderna C, Foerster F, Galle P: Systemic treatment of HCC in special populations. Journal of hepatology 2021, 74(4):931–943.
Pinto Marques H, Gomes da Silva S, De Martin E, Agopian VG, Martins PN: Emerging biomarkers in HCC patients: Current status. International journal of surgery (London, England) 2020, 82s:70–76.
Dannappel MV, Sooraj D, Loh JJ, Firestein R: Molecular and in vivo Functions of the CDK8 and CDK19 Kinase Modules. Frontiers in cell and developmental biology 2018, 6:171.
Xu W, Wang Z, Zhang W, Qian K, Li H, Kong D, Li Y, Tang Y: Mutated K-ras activates CDK8 to stimulate the epithelial-to-mesenchymal transition in pancreatic cancer in part via the Wnt/β-catenin signaling pathway. Cancer Lett 2015, 356(2 Pt B):613–627.
Firestein R, Bass AJ, Kim SY, Dunn IF, Silver SJ, Guney I, Freed E, Ligon AH, Vena N, Ogino S et al: CDK8 is a colorectal cancer oncogene that regulates beta-catenin activity. Nature 2008, 455(7212):547–551.
Xu D, Li CF, Zhang X, Gong Z, Chan CH, Lee SW, Jin G, Rezaeian AH, Han F, Wang J et al: Skp2-macroH2A1-CDK8 axis orchestrates G2/M transition and tumorigenesis. Nature communications 2015, 6:6641.
Brägelmann J, Klümper N, Offermann A, von Mässenhausen A, Böhm D, Deng M, Queisser A, Sanders C, Syring I, Merseburger AS et al: Pan-Cancer Analysis of the Mediator Complex Transcriptome Identifies CDK19 and CDK8 as Therapeutic Targets in Advanced Prostate Cancer. Clin Cancer Res 2017, 23(7):1829–1840.
Humphreys KJ, Cobiac L, Le Leu RK, Van der Hoek MB, Michael MZ: Histone deacetylase inhibition in colorectal cancer cells reveals competing roles for members of the oncogenic miR-17-92 cluster. Mol Carcinog 2013, 52(6):459–474.
Zhou Y, Han C, Li D, Yu Z, Li F, Li F, An Q, Bai H, Zhang X, Duan Z et al: Cyclin-dependent kinase 11(p110) (CDK11(p110)) is crucial for human breast cancer cell proliferation and growth. Sci Rep 2015, 5:10433.
Roninson IB, Győrffy B, Mack ZT, Shtil AA, Shtutman MS, Chen M, Broude EV: Identifying Cancers Impacted by CDK8/19. Cells 2019, 8(8).
Dale T, Clarke PA, Esdar C, Waalboer D, Adeniji-Popoola O, Ortiz-Ruiz MJ, Mallinger A, Samant RS, Czodrowski P, Musil D et al: A selective chemical probe for exploring the role of CDK8 and CDK19 in human disease. Nature chemical biology 2015, 11(12):973–980.
Chandrashekar DS, Bashel B, Balasubramanya SAH, Creighton CJ, Ponce-Rodriguez I, Chakravarthi B, Varambally S: UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia (New York, NY) 2017, 19(8):649–658.
Pontén F, Jirström K, Uhlen M: The Human Protein Atlas–a tool for pathology. J Pathol 2008, 216(4):387–393.
Uhlen M, Zhang C, Lee S, Sjöstedt E, Fagerberg L, Bidkhori G, Benfeitas R, Arif M, Liu Z, Edfors F et al: A pathology atlas of the human cancer transcriptome. Science 2017, 357(6352).
Menyhárt O, Nagy Á, Győrffy B: Determining consistent prognostic biomarkers of overall survival and vascular invasion in hepatocellular carcinoma. Royal Society open science 2018, 5(12):181006.
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 2012, 2(5):401–404.
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. Science signaling 2013, 6(269):pl1.
Forbes SA, Beare D, Gunasekaran P, Leung K, Bindal N, Boutselakis H, Ding M, Bamford S, Cole C, Ward S et al: COSMIC: exploring the world's knowledge of somatic mutations in human cancer. Nucleic Acids Res 2015, 43(Database issue):D805-811.
Forbes SA, Beare D, Boutselakis H, Bamford S, Bindal N, Tate J, Cole CG, Ward S, Dawson E, Ponting L et al: COSMIC: somatic cancer genetics at high-resolution. Nucleic Acids Res 2017, 45(D1):D777-d783.
Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, Li B, Liu XS: TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res 2017, 77(21):e108-e110.
Vasaikar SV, Straub P, Wang J, Zhang B: LinkedOmics: analyzing multi-omics data within and across 32 cancer types. Nucleic acids research 2018, 46(D1):D956-d963.
Tang Z, Kang B, Li C, Chen T, Zhang Z: GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic acids research 2019, 47(W1):W556-w560.
Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P et al: STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2019, 47(D1):D607-d613.
Liu F, Lou G, Zhang T, Chen S, Xu J, Xu L, Huang C, Liu Y, Chen Z: Anti-metastasis traditional Chinese medicine monomer screening system based on perinucleolar compartment analysis in hepatocellular carcinoma cells. American journal of translational research 2019, 11(6):3555–3566.
Chen X, Cheung ST, So S, Fan ST, Barry C, Higgins J, Lai KM, Ji J, Dudoit S, Ng IO et al: Gene expression patterns in human liver cancers. Mol Biol Cell 2002, 13(6):1929–1939.
Roessler S, Jia HL, Budhu A, Forgues M, Ye QH, Lee JS, Thorgeirsson SS, Sun Z, Tang ZY, Qin LX et al: A unique metastasis gene signature enables prediction of tumor relapse in early-stage hepatocellular carcinoma patients. Cancer Res 2010, 70(24):10202–10212.
Wurmbach E, Chen YB, Khitrov G, Zhang W, Roayaie S, Schwartz M, Fiel I, Thung S, Mazzaferro V, Bruix J et al: Genome-wide molecular profiles of HCV-induced dysplasia and hepatocellular carcinoma. Hepatology 2007, 45(4):938–947.
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 RB1 and FGF19 aberrations for patient stratification. Hepatology 2014, 60(6):1972–1982.
Fujimoto A, Totoki 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 on mutation patterns and recurrent mutations in chromatin regulators. Nat Genet 2012, 44(7):760–764.
Harding JJ, Nandakumar 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 2019, 25(7):2116–2126.
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 2018, 173(2):291–304.e296.
Schulze K, Imbeaud S, Letouzé 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 2015, 47(5):505–511.
de Semir D, Bezrookove V, Nosrati M, Scanlon KR, Singer E, Judkins J, Rieken C, Wu C, Shen J, Schmudermayer C et al: PHIP drives glioblastoma motility and invasion by regulating the focal adhesion complex. Proceedings of the National Academy of Sciences of the United States of America 2020, 117(16):9064–9073.
Darvishi N, Rahimi K, Mansouri K, Fathi F, Menbari MN, Mohammadi G, Abdi M: MiR-646 prevents proliferation and progression of human breast cancer cell lines by suppressing HDAC2 expression. Molecular and cellular probes 2020, 53:101649.
Yang W, Liu Y, Gao R, Xiu Z, Sun T: Knockdown of cZNF292 suppressed hypoxic human hepatoma SMMC7721 cell proliferation, vasculogenic mimicry, and radioresistance. Cellular signalling 2019, 60:122–135.
Villanueva A: Hepatocellular Carcinoma. N Engl J Med 2019, 380(15):1450–1462.
Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians 2018, 68(6):394–424.
Dhanasekaran R, Nault JC, Roberts LR, Zucman-Rossi J: Genomic Medicine and Implications for Hepatocellular Carcinoma Prevention and Therapy. Gastroenterology 2019, 156(2):492–509.
Soutourina J: Transcription regulation by the Mediator complex. Nat Rev Mol Cell Biol 2018, 19(4):262–274.
Kuuselo R, Savinainen K, Sandström S, Autio R, Kallioniemi A: MED29, a component of the mediator complex, possesses both oncogenic and tumor suppressive characteristics in pancreatic cancer. Int J Cancer 2011, 129(11):2553–2565.
Vijayvargia R, May MS, Fondell JD: A coregulatory role for the mediator complex in prostate cancer cell proliferation and gene expression. Cancer Res 2007, 67(9):4034–4041.
Li LH, He J, Hua D, Guo ZJ, Gao Q: Lentivirus-mediated inhibition of Med19 suppresses growth of breast cancer cells in vitro. Cancer Chemother Pharmacol 2011, 68(1):207–215.
Allen BL, Taatjes DJ: The Mediator complex: a central integrator of transcription. Nat Rev Mol Cell Biol 2015, 16(3):155–166.
Hofmann MH, Mani R, Engelhardt H, Impagnatiello MA, Carotta S, Kerenyi M, Lorenzo-Herrero S, Böttcher J, Scharn D, Arnhof H et al: Selective and Potent CDK8/19 Inhibitors Enhance NK-Cell Activity and Promote Tumor Surveillance. Mol Cancer Ther 2020, 19(4):1018–1030.
Becker F, Joerg V, Hupe MC, Roth D, Krupar R, Lubczyk V, Kuefer R, Sailer V, Duensing S, Kirfel J et al: Increased mediator complex subunit CDK19 expression associates with aggressive prostate cancer. Int J Cancer 2020, 146(2):577–588.
Paulsen FO, Idel C, Ribbat-Idel J, Kuppler P, Klapper L, Rades D, Bruchhage KL, Wollenberg B, Brägelmann J, Perner S et al: CDK19 as a Potential HPV-Independent Biomarker for Recurrent Disease in HNSCC. Int J Mol Sci 2020, 21(15).
Zhao JQ, Li XN, Fu LP, Zhang N, Cai JH: ISOC1 promotes the proliferation of gastric cancer cells by positively regulating CDK19. Eur Rev Med Pharmacol Sci 2020, 24(22):11602–11609.

Table 1. Correlations between CDK19 and immune cells’ gene markers in HCC

Cells Subtypes	Markers	Non-purity Adjusted		Purity Adjusted
Cells Subtypes	Markers	ρ(rho)	P	ρ(rho)	P
B cells	CD19	0.213	4.90E-04	0.245	8.60E-05
	CD79A	0.189	2.60E-03	0.26	1.90E-05
T cells (general)	CD3D	0.173	4.70E-03	0.237	1.80E-04
	CD2	0.183	2.30E-03	0.272	5.80E-06
	CD3E	0.182	2.10E-03	0.278	3.20E-06
CD8+ T cells	CD8B	0.126	5.00E-02	0.186	2.60E-03
	CD8A	0.195	1.20E-03	0.265	7.70E-06
CD4+ T cells	QRSL1	0.698	2.60E-54	0.7	5.10E-51
	STAT1	0.443	4.20E-18	0.469	5.30E-19
	STAT4	0.312	1.60E-08	0.348	3.70E-10
	STAT5A	0.404	7.60E-15	0.435	3.10E-16
	STAT6	0.339	1.10E-10	0.335	1.00E-09
	CD4	0.303	1.70E-08	0.362	5.20E-11
	TBX21	0.096	1.60E-01	0.16	9.40E-03
Tumur Associated Macrophages	CCL2	0.217	2.10E-04	0.289	4.50E-07
	CD68	0.193	1.30E-03	0.239	6.90E-05
	IL10	0.264	1.00E-05	0.34	1.80E-09
Type I Macrophages	IRF5	0.483	1.90E-21	0.483	6.40E-20
	NOS2	0.156	6.10E-03	0.172	3.30E-03
	PTGS2	0.364	8.50E-12	0.467	1.60E-18
Type II Macrophages	CD163	0.15	1.00E-02	0.216	1.70E-04
	MS4A4A	0.156	8.60E-03	0.235	5.40E-05
	VSIG4	0.173	3.40E-03	0.245	2.90E-05
Neutrophil	CCR7	0.181	2.30E-03	0.26	9.90E-06
	ITGAM	0.315	5.20E-09	0.365	2.60E-11
Dendritic Cells	CD1C	0.252	6.00E-06	0.3	1.30E-07
	HLA-DPB1	0.175	3.70E-03	0.234	7.30E-05
	HLA-DQB1	0.086	2.80E-01	0.133	4.80E-02
	HLA-DRA	0.171	4.60E-03	0.23	7.90E-05
	ITGAX	0.337	1.00E-09	0.425	6.20E-15
	NRP1	0.459	1.00E-19	0.469	2.70E-19

No competing interests reported.

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Cyclin Dependent Kinase 19 Upregulation Correlates With Unfavorable Prognosis in Hepatocellular Carcinoma

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