INTRODUCTION
Hepatocellular carcinoma (HCC) accounts for >80% of liver cancer, which ranked sixth in morbidity and third in mortality among all cancers in 2020.[ 1 , 2 ] More than half of HCC patients are already at an intermediate or advanced stage at the time of diagnosis.[ 2 ] Despite recent progress in treatments for intermediate or advanced HCC, such as new antiangiogenic drugs (sorafenib, lenvatinib, bevacizumab, and Apatinib), programmed death-1/programmed death ligands-1 (atezolizumab, pembrolizumab, and nivolumab), cytotoxic T lymphocyte-associated antigen-4 (ipilimumab), ablation (microwave ablation), and intravascular interventional technique (transarterial chemoembolization), said treatment strategies are relatively limited.[ 3–6 ] Therefore, there is an urgent need to find more novel targets to further improve treatments against advanced HCC.
Under normal circumstances, the progression of HCC is not only related to neovascularization and immune abnormalities but also to uncontrolled HCC proliferation. Cyclin-dependent kinases (CDKs), a family of 20 cellular cycle protein kinases (CDK1 ~20) regulates cell proliferation.[ 7 ] Therefore, CDKs could be involved in the progression of HCC. In fact, cumulative evidence has suggested that CDK overexpression can affect HCC tumorigenesis, progression, therapeutic effects, and outcomes.[ 8 ] However, the identification of suitable CDKs as prognostic biomarkers for HCC is still worthy of further discussion. Besides, the comprehensive and meaningful analysis of the role of CDKs in HCC has been possible based on the establishment of various strong databases formed by second-generation gene sequencing method. Therefore, to reveal the relationship between CDKs and HCC progression and prognosis and explore the possible mechanism of CDKs causing differences in HCC efficacy, we conducted a comprehensive bioinformatics analysis of CDKs in HCC based on several public databases, thus providing additional therapeutic targets for HCC.
SUBJECTS AND METHODS
ONCOMINE dataset
The ONCOMINE (www.oncomine.org [ 9 ] It has been cited in >1,100 peer-reviewed articles. The Oncomine Platform has several unique features (i.e., scalability, high quality, consistency, standardized analysis) and is thus regarded as a foundation for breakthrough discoveries. This study sets 0.0001, 2, and the top 10% as P value, fold change, and gene rank thresholds, respectively. A Student’s t -test was used for analyzing differences in CDK expression in HCC.
GEPIA dataset
GEPIA (http://gepia.cancer-pku.cn/detail.php [ 10 ] It provides multiple online analysis functions such as differential expression analysis, survival analysis, detection of similar genes, correlation analysis, and others.
Human protein atlas analysis
The human protein atlas (https://www.proteinatlas.org/ [ 11 ]
cBioPortal
The cBioPortal, a web resource (cBioPortal for Cancer Genomics), can explore, visualize, and analyze multidimensional cancer genomics data.[ 12 ] We analyzed 348 HCC samples (TCGA, PanCancer Atlas) having complete data.
Kaplan Meier plotter
The Kaplan-Meier plotter, an online survival analysis resource [Kaplan-Meier plotter [Liver RNAseq] (kmplot.com)], whose databases include GEO, EGA, and TCGA for discovery and validation of biomarkers related to survival, can assess the correlation between the expression of 30 k genes and survival in >25 k samples from 21 cancer types, including liver cancer.[ 13 ]
GeneMANIA and David 6.8
GeneMANIA (https://genemania.org/ [ 14 ] DAVID 6.8[ 15 ] (https://david.ncifcrf.gov/home.jsp) is used for enrichment analysis of Kyoto Encyclopedia of Genes and Genomes pathways of CDKs and the interactive closely neighbor genes acquired by GeneMANIA.
TRRUST
TRRUST (https://www.grnpedia.org/trrust/ [ 16 ] The TRRUST database can also predict upstream TFs of CDK-related pathways.
TISIDB
The TISIDB (http://cis.hku.hk/TISIDB/index.php [ 17 ] Here, we used TISIDB to explore relationships between CDK gene expression and the abundance of tumor-infiltrating lymphocytes, three kinds of immunomodulators, chemokines (or receptors), and clinical features in HCC.
CDKs and drug response
The correlation analysis between CDKs expression and sensitive drug response was conducted using CellMiner.[ 18 ]
RESULTS
Differential expression of CDK genes in HCC
The expression levels of 21 CDK genes in HCC and normal tissues were first explored using the ONCOMINE database [Figure 1 ]. The expression levels of CDK1, CDK4, and CDK7 were significantly higher in HCC than in normal tissue but that of CDK5 was significantly lower. Moreover, the expression levels of other CDKs (CDK2, CDK3, CDK6–10, CDK11A, CDK11B, CDK12–20) were similar in HCC and normal tissue. Next, we investigated these differences using GEPIA. As expected, the expression levels of CDK1 and CDK4 in HCC were significantly higher than in normal tissue [Figure 2a ,c ,f and g ]. However, unlike in the ONCOMINE database, the expression level of CDK5 was also significantly higher in HCC [Figure 2d and h ], as were the expression levels of CDK3 and CDK16 [Figure 2b and e ]; in contrast, the expression level of CDK11A was significantly lower in patients with HCC [Figure 2i ]. The differential expression analysis of other CDKs (CDK2, CDK6–10, CDK11B, CDK12–15, CDK17–20) was consistent with that in the ONCOMINE database. We also found that the relative expression between HCC and normal tissue of CDK1 was the highest among all CDKs evaluated [Figures 1 and Supplementary 1 ]. To identify CDKs associated with clinical outcomes in HCC, we excluded CDK2, CDK6–10, CDK11B, CDK12–15, and CDK17–20 from further analysis because both ONCOMINE and GEPIA databases showed no significant differences between HCC and normal tissue. We also excluded CDK3, CDK5, and CDK11A due to the conflicting results between the GEPIA and ONCOMINE databases.
Figure 1: Expression levels of 20 different CDKs (CDK1 ~ 20) in different cancers (ONCOMINE). The number of significantly over-expressed and under-expressed datasets among 20 different CDKs is shown in red and blue, respectively. CDK, cyclin-dependent kinase
Figure 2: CDKs with significant differences in expression between HCC tissue and normal tissue (GEPIA). HCC, hepatocellular carcinoma; CDK, cyclin-dependent kinase. Green represents tumor (T) and purple represents normal tissue (N). LIHC, Liver hepatocellular carcinoma. (a-e) represent differential expression analysis of CDK1, CDK3, CDK4, CDK5, CDK16 between HCC tissue and normal tissue using match TCGA normal data, respectively. (f-i) represent differential expression analysis of CDK1, CDK4, CDK5, CDK11A between HCC tissue and normal tissue using match TCGA normal and GTEx data, respectively
Supplementary 1: Relative expression level of CDKs in HCC. HCC, hepatocellular carcinoma; CDK, cyclin-dependent kinase
We further explored the protein expression of CDK1 and CDK4 in HCC and normal tissue with the human protein atlas. CDK1 and CDK4 were not detected in normal tissues; conversely, medium CDK1 expression and low or medium CDK4 expression were observed in HCC tissues [Supplementary 2 ].
Supplementary 2: Representative CDK immunohistochemistry results in HCC and normal tissues (human protein atlas). HCC, hepatocellular carcinoma; CDK, cyclin-dependent kinase
Correlation of CDK1 and CDK4 with prognosis of HCC
We assessed the relationship between the differential CDK1 and CDK4 expression and the pathological stage of HCC patients and found a significantly positive correlation between CDK1 (P = 5.76e − 5) and CDK4 (P = 7.13e − 3) expression and pathological stage [Supplementary 3 ]. Moreover, we found a significantly positive correlation between CDK1 (P = 1.00e − 10) and CDK4 (P = 4.42e − 6) expression and tumor grade [Supplementary 4a and b ] in TISIDB. These data suggested that CDK1 and CDK4 play important roles in HCC progression. Furthermore, we investigated the effect of CDK1 and CDK4 on the prognosis of patients with HCC using the Kaplan-Meier plotter. High CDK1 (P =0.00012) and CDK4 (P =0.00089) expression was significantly associated to shorter recurrence-free survival [Figure 3a and c ]. In addition, high CDK1 (P = 1.2e − 5) and CDK4 (P = 6.2e − 7) expression was significantly related to shorter overall survival [Figure 3b and d ]. Similar results were obtained in the GEPIA dataset [Supplementary 5 ].
Supplementary 3: Correlation between CDK1 (a) and CDK4 (b) and clinical pathological stage (I-IV) of patients with HCC (GEPIA). HCC, hepatocellular carcinoma; CDK, cyclin-dependent kinase
Supplementary 4: Correlation between CDK1 (a) and CDK4 (b) and tumor grade (1-4) in patients with HCC (TISIDB). HCC, hepatocellular carcinoma; CDK, cyclin-dependent kinase
Figure 3: Correlation between CDK1 and CDK4 and RFS and OS of patients with liver cancer using the Kaplan-Meier plotter. HCC, hepatocellular carcinoma; CDK, cyclin-dependent kinase; RFS, recurrence-free survival; OS, overall survival. a and c represent the relationship between CDK1 and CDK4 and RFS, respectively. b and d represent the relationship between CDK1 and CDK4 and OS, respectively. HR, hazard ratio
Supplementary 5: Correlation between CDK1 and CDK4 and DFS and OS of patients with HCC using the GEPIA. HCC, hepatocellular carcinoma; CDK, cyclin-dependent kinase; OS, overall survival; DFS, disease-free survival. a and c represent the relationship between CDK1 and CDK4 and DFS, respectively. b and d represent the relationship between CDK1 and CDK4 and OS, respectively. HR, hazard ratio
Pathways enrichment analysis and TFs of CDK1 and CDK4
Genemania was used for showing the 50 most neighboring physical interaction genes of CDK1 and CDK4 by the automatic weighting method [Figure 4a ]. Genemania suggested that CCND1, CKS2, CCND2, CCNA2, CCND3, CCNB2, CKS1B, RGCC, CCNB1, CDKN2B, CDKN2A, CDK6, CDKN2C, PKMYT1, CDKN2D, WEE1, CDKN1B, CDKN1A, AMPH, E2F4, CCNA1, GADD45B, CDC25C, CCNE1, ZRANB2, BCL2, HDAC1, YBX3, FBXO5, EGFR, CSNK2B, CDC6, TP53BP1, FOXM1, CDKN3, ERCC6L, PSMD10, VCP, RAB1A, BLM, HIF1A, TFDP1, RB1, CDK2, CDKN1C, BRCA2, PSMC3IP, EIF4EBP2, CDC25B, and RAD51C were associated with CDK1 and CDK4 function. Using the DAVID 6.8 for pathway enrichment analysis on these 52 genes, we found 50 enriched pathways, of which 36 pathways had an FDR < 0.01 and P <.05 [Figure 4b ]. As expected, multiple signaling pathways were significantly related to various cancer types including HCC, especially hepatitis virus-related HCC [Figure 4b ]. Due to the significant differential expression of CDK1 and CDK4 in HCC versus normal tissue, we further investigated the TFs of the differentially expressed CDK1 and CDK4 gene based on TRRUST databases. TFs with multiple known regulatory modes of CDK1 and CDK4 were separately identified [Figure 5a ]; among them, one TF, interferon regulatory factor 1 (IRF1), co-represses CDK1 and CDK4 [Figure 5b ]. However, using GEPIA, we found only four TFs (i.e, E2F1, PTTG1, RELA, SP1) significantly associated with the prognosis of HCC patients.
Figure 4: Fifty genes with highest neighboring physical interaction between CDK1 and CDK4 (a) and pathway enrichment analysis of CDK1 and CDK4 and their 50 most neighboring physical interaction genes (b). CDK, cyclin-dependent kinase
Figure 5: Key transcription factors of CDK1 and CDK4 (a) and Venn diagram of CDK1 and CDK4 transcription factors (b). CDK, cyclin-dependent kinase
Genetic alteration of CDKs, survival analysis, differential protein expression analysis, mutual exclusivity, and co-occurrence analysis
A comprehensive online analysis of genetic alteration of CDKs in HCC was performed using TCGA datasets from cBioPortal. A total of 348 HCC samples (TCGA, PanCancer Atlas) with complete data were analyzed. As a result, CDK1–10, CDK11A, CDK11B, and CDK12–20 were altered in 6%, 9%, 8%, 10%, 13%, 9%, 9%, 4%, 5%, 3%, 8%, 9%, 9%, 9%, 9%, 2.6%, 8%, 5%, 18%, 6%, and 5% of HCC samples, respectively [Supplementary 6a ]. The most common genetic alteration was high mRNA expression [Supplementary 6b ]. Moreover, we performed a survival analysis based on the genetic alterations in HCC, finding that the genetic alteration status of CDKs was significantly correlated with disease-free survival (DFS) (P = 6.287e-3) and progression-free survival (PFS) (P = 3.736e-3) of patients with HCC, but not with disease-specific survival (DSS) (P =0.171) and overall survival (OS) [P =0.250; Supplementary 6c -f ]. We further explored the differential protein expression resulting from genetic alteration of CDKs; only progesterone receptor (PGR) protein expression was significantly reduced among all proteins expressed by the 202 genes [P = 2.150e-4; Supplementary 7a ]. Furthermore, we investigated the effect of PGR on prognosis of patients with HCC using GEPIA; PGR protein expression was significantly correlated with DFS (P =.027) of patients with HCC but not with OS [P =.56; Supplementary 7b and c ]. Next, a mutual exclusivity and co-occurrence analysis on CDK genes in patients with HCC showed that these 21 genes were not significantly mutually exclusive and that CDK1 had significant co-occurrence with CDK4 (Log2 Odds Ratio >3, P <.001) and CDK20 (Log2 Odds Ratio >3, P =.001) while CDK4 had significant co-occurrence with CDK10 (Log2 Odds Ratio >3, P <.001), CDK13 (Log2 Odds Ratio = 2.329, P <.001), and CDK16 (Log2 Odds Ratio >3, P <.001). Furthermore, CDK6 had significant co-occurrence with CDK13 (Log2 Odds Ratio >3, P <.001) and CDK14 (Log2 Odds Ratio = 2.675, P <.001) and CDK11A had significant co-occurrence with CDK11B (Log2 Odds Ratio >3, P <.001).
Supplementary 6: Genetic alterations (a and b) in CDKs and their relationship with DFS (c), PFS (d), DSS (e), and OS (f) in patients with HCC (cBioPortal). HCC, hepatocellular carcinoma; CDK, cyclin-dependent kinase; RFS, recurrence-free survival; OS, overall survival; DFS, disease-free survival; PFS, progression-free survival; DSS, disease-specific survival
Supplementary 7: Differential protein expression between genetic unaltered and altered groups (a) and relationship between PGR and DFS (b) and OS (c) in patients with HCC (cBioPortal). HCC, hepatocellular carcinoma; OS, overall survival; DFS, disease-free survival; PGR, progesterone receptor
Immune-related analysis of CDK1 and CDK4 in patients with HCC
We conducted a comprehensive analysis of the relationship between the differentially expressed CDK1 and CDK4 and immune status in patients with HCC using the TISIDB and GEPIA2 databases. First, we found that CDK1 expression was significantly associated with the abundance of tumor-infiltrating activated CD4+ T cells (rho = 0.624, P < 2.2e − 16), Type 2 T helper cells (Th2) (rho = 0.339, P = 2.43e − 11), monocytes (rho = -0.327, P = 1.17e − 10), and eosinophils [rho = -0.318, P = 4.36e − 10; Supplementary 8a -e ]. CDK4 expression was also significantly associated with the abundance of tumor-infiltrating activated CD4+ T cells (rho = 0.326, P = 1.38e − 10) and eosinophils [rho = -0.308, P = 1.47e − 09; Supplementary 8f -h ]. Second, using the GEPIA2 database we found that CDK1 expression was significantly associated with an exhausted T cell-related signature (HAVCR2, TIGIT, LAG3, PDCD1, CXCL13, LAYN; R = 0.32, P = 2e − 10) [Supplementary 9a ] as was CDK4 expression [R = 0.33, P = 6.2e − 11; Supplementary 9b ].
Supplementary 8: Relationship between CDK1 (a–e) and CDK4 (f–h) expression and tumor-infiltrating lymphocytes in HCC tissue (TISIDB). HCC, hepatocellular carcinoma; CDK, cyclin-dependent kinase
Supplementary 9: Relationship between CDK1 (a) and CDK4 (b) expression and exhausted T cell-related signature in HCC tissue (GEPIA2). HCC, hepatocellular carcinoma; CDK, cyclin-dependent kinase
Potentially effective drugs
The correlation between CDK1 and CDK4 expression and predicted potentially sensitive drugs is displayed in Figure 6 .
Figure 6: Relationship between CDK1 and CDK4 expression and predicted potentially sensitive drug (CellMiner). CDK, cyclin-dependent kinase
DISCUSSION
At present, the treatment of advanced HCC is still very difficult. Therefore, it is urgent to find more clinically significant targets. CDKs have been reported to be involved in cell growth and division, real-time regulation of cell cycle progression, and initiation of cell cycle transition.[ 19–21 ] There are currently 20 different CDKs reported in mammalian cells.[ 22 ] Abnormalities in these kinases have been reported in various human cancers, and their amplification, overexpression, or mutation can lead to worse prognosis .[ 23–25 ] In fact, there have been some reports that CDKs are closely related to the prognosis of HCC.[ 26–30 ] However, the identification of meaningful CDKs still deserves further discussion. In this comprehensive bioinformatics analysis of 20 different CDKs in patients with HCC, CDK1 and CDK4 were the most reliable and meaningful biomarkers associated with HCC prognosis . They are significantly elevated in HCC tissues and significantly associated with higher pathological grades, clinical stages, and worse prognosis .
CDK1, one of Ser/Thr protein kinases, plays an important role in cellular G1/S and G2/M phase transitions and is a crucial determinant of mitotic progression.[ 31–33 ] CDK1 overexpression in HCC has been previously confirmed and is significantly associated with worse prognosis ,[ 34–36 ] which is consistent with our findings. However, reducing or blocking CDK1 overexpression could improve the poor prognosis .[ 34–36 ] CDK4 is another cell cycle regulator involved in retinoblastoma phosphorylation and promoting release of E2F factors, thereby regulating G1/S phase progression of the cell cycle.[ 37–39 ] Likewise, CDK4 overexpression in HCC has also been significantly associated with worse prognosis ,[ 40 , 41 ] as confirmed by our research. Interestingly, CDK1-related and CDK4-related signaling pathways were closely related to virus-related malignancies, especially hepatitis virus-related HCC, consistent with previous studies.[ 42–46 ] Moreover, we found that the TF IRF1 could co-repress CDK1 and CDK4. IRF-1 can regulate the transcription of its target genes playing crucial roles in pathological and physiological phenomena such as viral infection, carcinogenesis, proinflammatory injury, and immune system development.[ 47 ] Thus, IRF1 may be a potentially clinically meaningful target for improving HCC prognosis , even if GEPIA indicated that IRF1 was not significantly associated with the prognosis of HCC patients but four other TFs (E2F1, PTTG1, RELA, and SP1) were.
To understand the relationship between CDK family gene alterations and prognosis , we investigated 348 patients with HCC using the cBioPortal database. We found that the most common genetic alteration was high mRNA expression; compared with HCC patients without CDK gene mutations, patients with mutations had significantly shorter DFS and PFS but similar DSS and OS. To further understand the reasons for this phenomenon, we found that only PGR, an estrogen-inducible protein and down-stream effector of estrogen-receptor signaling, was significantly down-regulated among 202 proteins in the altered CDK gene group.[ 48 ] Notably, low PGR expression was associated with significantly worse DFS but not OS. This suggests that the significantly poorer prognosis caused by CDKs genetic alteration may be related to the significant downregulation of PGR expression.
To the best of our knowledge, limited research has investigated the relationship between CDK1 and CDK4 expression and the immune system in patients with HCC. Here, we found that CDK1 and CDK4 expression was significantly associated with the abundance of tumor-infiltrating activated CD4+ T cells. To combat tumors, activated CD4+ T cells can differentiate CD8+ T cells into cytotoxic T lymphocytes through multiple mechanisms, while maintaining and enhancing the antitumor response of cytotoxic T lymphocytes. Moreover, even in the absence of CD8+ T cells, CD4+ T cells can directly kill tumor cells via the interferon-g mechanism.[ 49 ] Therefore, CDK1 and CDK4 may play an important role in antitumor immunity. Interestingly, CDK1 and CDK4 expression was significantly associated with an exhausted T cell-related signature. Therefore, effective immunosuppressive agents may achieve better results in the treatment of HCC patients with high expression of CDK1 and CDK4.
Despite our interesting findings, there are some limitations to our research. First, this study is based on the analysis of online databases, which requires experimental verification. Nevertheless, our study is the first to comprehensively use multiple tumor-related online databases to explore the prognostic value of CDKs in HCC. It is worth noting that more and more studies have noted CDK1 and CDK4 as potential targets for HCC[ 34–36 , 40 , 41 ] and investigate the potential of the CDK gene family for HCC treatment. In particular, we found four TFs (E2F1, PTTG1, RELA, and SP1) significantly associated with the prognosis of HCC patients, which may serve as future treatment targets. In addition, CDK1 and CDK4 could be used as potential biomarkers to predict immunosuppressant efficacy. Therefore, antiTF (E2F1, PTTG1, RELA, and SP1) therapy combined with immunotherapy may be a new therapeutic strategy for patients with HCC and high CDK1 and CDK4 expression.
Abbreviations: HCC, hepatocellular carcinoma; CDK, cyclin-dependent kinase; IRF1, interferon regulatory factor 1; KEGG, Kyoto Encyclopedia of Genes and Genomes; RFS, recurrence-free survival; OS, overall survival; DFS, disease-free survival; PFS, progression-free survival; DSS, disease-specific survival; PGR, progesterone receptor.
Authorship
This study was conceived and designed by Shuanggang Chen, Weijun Fan, and Guoping Zhang. Shuanggang Chen, Binyan Shen, Ying Wu, Lunjun Shen, Han Qi, Fei Cao, Tao Huang, Hongtong Tan, Guoping Zhang, and Weijun Fan participated in data analysis. The manuscript was mainly drafted by Shuanggang Chen, and all authors contributed to the draft. All authors critically reviewed or revised the manuscript. The manuscript has been read and approved by all the authors, that the requirements for authorship as stated earlier in this document have been met, and that each author believes that the manuscript represents honest work.
Statement of ethics
Ethical review approval and written informed consent for participation was not required because of the publicly available online nature of this study.
Financial support and sponsorship
This study was supported by the National Natural Science Foundation of China (No. 81771954) and Guangdong Province Key Field Research and Development Project (2019B110233001).
Conflicts of interest
There are no conflicts of interest.
Acknowledgments
We thank all members who created and maintained the online database used for analysis in this study.
Data availability: Publicly available online datasets were analyzed in our study; therefore, all of datasets can be found in the corresponding web resources.
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