Hyperpolarization-activated cyclic nucleotide-gated gene signatures and poor clinical outcome of cancer patient
Original Article

Hyperpolarization-activated cyclic nucleotide-gated gene signatures and poor clinical outcome of cancer patient

Nam Nhut Phan1, Tung Thanh Huynh1,2, Yen-Chang Lin1

1Graduate Institute of Biotechnology, Chinese Culture University, Taipei, Taiwan; 2NTT Institute of Hi-Technology, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam

Contributions: (I) Conception and design: NN Phan, TT Huynh; (II) Administrative support: None; (III) Provision of study materials or patients: All authors; (IV) Collection and assembly of data: NN Phan; (V) Data analysis and interpretation: All authors; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

Correspondence to: Dr. Yen-Chang Lin, PhD. Graduate Institute of Biotechnology, Chinese Culture University, Taipei, Taiwan. Email: lyc10@ulive.pccu.edu.tw; lycnthu@gmail.com.

Background: We investigated the mRNA expression of hyperpolarization-activated cyclic nucleotide-gated genes (HCN1-4) in multiple types and subtypes of cancers.

Methods: We performed a meta-analysis of public microarray data from Oncomine and NextBio Research databases to discover the mRNA expression level of HCN1-4 in cancers. Survival analysis was also used to investigate the correlation between overexpression of HCN gene family with overall survival rate of cancer patients using Kaplan-Meier Plotter database and PROGgene V2 database.

Results: HCN genes (HCN1-4) over-expression and under-expression in multiples types of cancers such as CNS and brain cancer, breast cancer, colorectal cancer, melanoma, and lymphoma were found. HCN1 was significantly correlated with low overall survival of breast cancer [hazard ratio (HR) =7.42, P=0.0019] and colorectal cancer (HR =1.66, P=0.0071) patients. The lower survival rates of lung cancer (HR =2.5, P=0.0107), kidney cancer (HR =1.1, P=0.004) and gastric cancer (HR =1.33, P=0.0037) patients were significantly correlated with the expression of HCN2. HCN3 was significantly correlated to lower survival rates of breast cancer (HR =1.65, P=0.0016), and kidney cancer (HR =1.17, P=0.0049). HCN4 was highly correlated with the lower survival rates of breast cancer of gastric cancer (HR =1.25, P=0.022), lung cancer (HR =5.37, P=0.0433) and ovarian cancer (HR =13.58, P=0.0426).

Conclusions: These data suggested that HCN genes (HCN1-4) are likely to be potential candidates for cancer diagnosis and prognosis.

Keywords: Hyperpolarization-activated cyclic nucleotide-gated channel (HCN channel); cancers; Oncomine; overall survival; diagnosis; prognosis

Submitted Mar 30, 2017. Accepted for publication Jul 10, 2017.

doi: 10.21037/tcr.2017.07.22

Introduction

Cancer is the leading cause of death all over the globe in recent decades. According to WHO, there were 8.2 million people who died from cancer in the year 2012, and in the next two decades this figure will grow to around 22 million (1). Until now, only 30% of cancers could be prevented (1). Commonly, cancer patients would undergo surgery synergistically with chemotherapy and/or radiotherapy, which is painful, and has high mortality rate. According to the U.S. National Cancer Institute, around 200 cancer drugs are commercialized in the market. Nevertheless, researchers in cancer field are still looking to develop new anti-cancer drugs with more specificity and high efficiency (2).

Hyperpolarization-activated cyclic nucleotide-gated (HCN) channel is one of hundreds of intra-membrane ion channels involved in ion transport. HCN channels are encoded by four genes, namely HCN1, HCN2, HCN3 and HCN4 (3). These four genes are predominantly localized and expressed in the heart and the central nervous system (3,4). HCN channels are activated by hyperpolarization, and which permit Na+ and K+ to flow inward to the cell (5). HCN channels’ main physiological functions are in the heart (6) and the nervous system (4). HCN genes were found to play a role in arrhythmogenic disease and neurological disease (7). The pharmacological properties of these ion channels in cancer are relatively unknown.

Oncomine is a web-based database, which contains more than 700 independent datasets with an estimated 90,000 microarray trials (8,9). The use of Oncomine in several publications confirmed it is a reliable source of clinical datasets (10-16). Oncomine standardizes and organizes the datasets of public cancer microarray data into different cancer types and subtypes (8,9).

NextBio Research database (Illumina INC.) is a web-based platform containing microarray data of more than 20,000 published studies. This online database was introduced by Giovanni Coppola in his book in 2013 (17) and has been used in previous studies (18,19).

In this study, data mining of Oncomine and NextBio Research database was performed to conduct a meta-analysis of HCN gene expression across multiple types and subtypes of cancer. In addition, analysis of survival rate of cancer patients and HCN gene expression was conducted to investigate how these expressions affect the overall survival of cancer patients in the 3 and 5 years’ period.

Methods

Data mining

A meta-analysis was performed to analyze the mRNA expression level of HCN gene family in clinical cancer specimens following PRISMA guidelines (20,21) (Figure 1, Tables S1-S9).

Figure 1 PRISMA 2009 flow diagram. The flow diagram shows screening process and selection criteria for suitable studies of the meta-analysis (21).

HCN gene (HCN1-4) expression within 17 cancer types was investigated. The mRNA expression of HCN genes in cancerous tissues was grouped by origin of tissue and then compared to normal tissue. Oncomine (www.oncomine.org) and NextBio Research database (https://www.nextbio.com) were used to analyze the mRNA expression of HCN gene family in clinical cancer tissues (22).

Database search strategy

In this study, the cancer vs. normal filter was chosen, which only displayed datasets examining HCN gene mRNA expression in the same origin of tissue. In order to be included in the study, all the data from Oncomine and NextBio research database must satisfy the following threshold: P<0.05, a fold change >1.5 and a gene rank percentile <10% (only applicable to data from Oncomine) (9) (Figure 1). Statistical analyses were conducted with Oncomine and NextBio Research default algorithms such as P values, two-tailed Student’s t-test, and multiple testing corrections. In total, there were 120 studies with 8,471 samples included in this study. All the searches were performed from December 2015 to December 2016.

Survival analysis

The correlation between HCN gene family and overall survival rate was analyzed using Kaplan-Meier plotter (http://kmplot.com/) (23) and PROGgeneV2 (24). Two groups of patients were used for the comparison on survival rates with high and low expression levels of HCN1, HCN2, HCN3 and HCN4 gene.

All the searches were performed from December 2015 to December 2016.

Results

Expression of HCN1 in multiple types and subtypes of cancer

In general, HCN1 gene over-expressed in diverse types of cancer such as colorectal cancer, leukemia, lung cancer, melanoma, and prostate cancer whereas the mRNA expression of HCN1 was under-expressed in breast cancer and bladder cancer. In addition, HCN1 gene also over and under expressed in both lymphoma and pancreatic cancer (Figure 2).

Figure 2 Expression of hyperpolarization-activated cyclic nucleotide-gated genes in multiple types of cancer. Expression of HCN genes in 17 types of cancers compared to normal matched type tissue controls. The color correlates with over and under expression of genes in specific cancer. Red color represents for over expression and blue color represents for under expression. The search criteria threshold was set at P<0.05 with fold change >1.5 and gene rank percentile <10% for screening microarray datasets of cancer versus normal cases. HCN, hyperpolarization-activated cyclic nucleotide-gated.

The highest expression fold change of HCN1 in cancer and normal matched type tissue was displayed in Figure 3. HCN1 expression in brain cancer particular in glioblastoma, glioma was extremely low with the lowest fold change of −151-fold relative to normal brain tissue. However, HCN1 expression was up-regulated in hepatocellular carcinoma and lung cancer with the fold change of 18.8 and 19.9-fold respectively.

Figure 3 Expression of HCN1 genes in multiple subtypes of 17 cancers. Only two datasets shown over expression of HCN1 in cancer while the rest of cancer subtype had under expression of HCN1.

Survival analysis of HCN1 expression using Kaplan-Meier plotter and PROGgeneV2 showed that HCN1 had significant correlation with mortality in breast cancer [hazard ratio (HR) =7.42, P=0.0019] and colorectal cancer (HR =1.66, P=0.0071) (Figure 4).

Figure 4 HCN1 mRNA expression and overall survival patient with breast cancer and colorectal cancer. High expression of HCN1 results in poor survival rate of patient in 3 and 5 years’ period. P<0.05 means statistically significant difference.

Expression of HCN2 in multiple types and subtypes of cancer

In our study, we found that HCN2 gene overexpressed in colorectal cancer, kidney cancer, lung cancer, lymphoma, melanoma, and prostate cancer whereas HCN2 expression level showed both up and down regulation in breast cancer, leukemia, pancreatic cancer, sarcoma, and kidney cancer (Figure 2).

HCN2 fold change was calculated and displayed in Figure 5. HCN2 expression was down-regulated in esophageal squamous cell carcinoma with fold change of −37.3 and −21.6-fold relative to normal matched type tissue. In contrast, HCN2 was up-regulated in lung cancer, breast cancer, liver cancer, and thyroid cancer with 38.1, 21.4, 13.3, 10.9-fold respectively higher than normal control tissue.

Figure 5 Expression of HCN2 genes in multiple subtypes of 17 cancers. High expression of HCN2 was recorded in multiple subtypes of cancer.

To further investigate the expression of HCN2 and overall survival rate of cancer patients, we used Kaplan-Meier plotter analysis and PROGgeneV2 and found that the lower survival rates of lung cancer (HR =2.5, P=0.0107), kidney cancer (HR =1.1, P=0.004) and gastric cancer (HR =1.33, P=0.0037) had significant correlation with the expression of HCN2 (Figure 6). The current data suggested that overexpression of HCN2 may be involved in the particular process of lung cancer. This observation may make HCN2 a potential biomarker for esophageal squamous cell carcinoma, lung cancer, kidney cancer and gastric cancer, breast cancer, liver cancer, and thyroid cancer diagnosis and prognosis.

Figure 6 HCN2 mRNA expression and overall survival patient with kidney cancer, lung cancer and gastric cancer. High expression of HCN2 results in poor survival rate of patient in 3 and 5 years’ period. P<0.05 means statistically significant difference.

Expression of HCN3 in multiple types and subtypes of cancer

Our data showed that HCN3 gene over-expressed in breast cancer, kidney cancer, lung cancer, liver cancer, gastric cancer, ovarian cancer, bladder cancer, kidney cancer whereas HCN3 was under-expressed in prostate cancer (Figure 2).

HCN3 was under expressed in many subtypes of brain cancer such as primary tumor dermal neurofibroma, cultured plexiform neurofibroma-derived Schwann cell, pediatic tumor tissue ependymoma, pediatic tumor tissue anaplastic astrocytoma with fold change of −4.5, −3.5, −3.2, −3-fold respectively compared to normal matched type tissue (Figure 7). In contrast, HCN3 over-expressed in liver and lung cancer tissue with fold change of 3.9 and 3.6 respectively, relatively to normal matched type sample (Figure 7).

Figure 7 Expression of HCN3 genes in multiple subtypes of 17 cancers. High and low expression of HCN3 was recorded in multiple subtypes of cancer.

In addition, Kaplan-Meier plotter and PROGgeneV2 analysis showed overexpression of HCN3 in breast cancer was significantly correlated with lower survival rates and poor prognosis value of breast cancer (HR =1.65, P=0.0016), kidney cancer (HR =1.17, P=0.0049) but higher survival rate and good prognosis value in lung cancer (HR =0.33, P=0.0272) and ovarian cancer (HR =0.53, P=0.0386) patients (Figure 8). This result may indicate HCN3 as a potential biomarker for diagnosis and prognosis of brain cancer, breast cancer, kidney cancer, lung cancer and ovarian cancer.

Figure 8 HCN3 mRNA expression and overall survival patient with breast cancer, kidney cancer, lung cancer and ovarian cancer. High expression of HCN3 results in poor survival rate of patient in 3 and 5 years’ period. P<0.05 means statistically significant difference.

Expression of HCN4 in multiple types and subtypes of cancer

HCN4 gene was found over-expression in kidney cancer, leukemia, lung cancer, sarcoma, ovarian cancer, and thyroid cancer whereas it under expressed in breast cancer. In addition, both over and under expression of HCN4 were found in bladder cancer, kidney cancer, and esophageal cancer (Figure 2).

HCN4 was under-expressed in stage I and II endometrial carcinoma with the fold change extremely low (−157 and −135-fold). In contrast, HCN4 was over expressed in Thyroid carcinomas, Thyroid tissues-papillary thyroid carcinoma, liver cancer, and prostate cancer with fold change of 30.4, 10, 11.2, and 7.8-fold respectively (Figure 9).

Figure 9 Expression of HCN4 genes in multiple subtypes of 17 cancers. High and low expression of HCN4 was recorded in multiple subtypes of cancer. HCN4 shown extremely low expression level in stage I endometrioid carcinoma and high expression in thyroid carcinoma.

Kaplan-Meier plotter analysis showed that upregulation of HCN4 was highly correlated with the lower survival rates of patients with gastric cancer (HR =1.25, P=0.022), lung cancer (HR =5.37, P=0.0433) and ovarian cancer (HR =13.58, P=0.0426) but higher survival rate in patient with breast cancer (HR =0.8, P=0.00016) (Figure 10). From these results, HCN4 can be considered as the potential marker in breast cancer, gastric cancer, lung cancer and ovarian cancer diagnosis, thyroid carcinomas.

Figure 10 HCN4 mRNA expression and overall survival patient with breast cancer, gastric cancer, lung cancer and ovarian cancer. High expression of HCN4 dramatically declined the survival rate of patient in 3 and 5 years’ period. P<0.05 means statistically significant difference.

Discussion

In this study, we showed that HCN family members (HCN1, HCN2, HCN3, HCN4) overexpressed in numerous cancerous tissue relative to normal matched tissue. The increased expression of these four genes in multiple types and subtypes of cancer was also significantly correlated with low and high survival rates of cancer patients. This correlation suggests that HCN genes might play a key role in cancer particularly in brain cancer, lung cancer, liver cancer, esophageal cancer, thyroid cancer, ovarian cancer which had notably high fold change compared to normal matched type tissue. However, further study is required to confirm the mechanism of how HCN genes play a role in cancer.

In neuropathic pain, lacking of HCN1 gene expression by genetic deletion showed mitigation in neuronal damage (25). Another research showed that HCN1 deficiency caused epilepsy, ataxia and learning compromise (7). In a recent study, HCN1 showed under-expression with fold change of 0.65 in breast cancer cells after Maitake D-Fraction treatment (26). Single nucleotide polymorphism of HCN1 was found association with shorter survival of breast cancer patient (27). Moreover, inhibiting of HCN channel functions in embryonic stem cells by ZD7288, a HCN channels blocker, and cesium revealed that cell proliferation was decreased under the effects of these two drugs (28). HCN3 gene was also implied as the potential target for tumor suppression (28). In our findings, HCN3 also showed overexpression in multiple types of cancers such as breast cancer, and liver cancer. Therefore, we speculate that HCN3 is likely to be a candidate to study cancer cell proliferation and cancer cell cycle.

The HCN gene is commonly located in ventricular myocytes and neuron cells. Previous studies on the roles of the HCN channels were primarily focused on neurological diseases such as epilepsies and neuropathic pain disorders and cardiac related diseases (7,29,30). HCN channel functions are largely unknown in cancer. HCN channels (HCN1-4) have been known to allow the flow of Na+ and K+ ions (1:4 ration) inward and outward of the cell, which creates a hyperpolarization activated current named Ih. This current was showed to participate in regulating the heart rate and the firing of neurons. Moreover, HCN channels also play a role in the determination of resting membrane potential, dendritic integration, synaptic transmission and learning (7). HCN2 roles in inflammatory and neuropathic pain have been uncovered recently (31). Intriguingly, apart from the permeability of Na+ and K+ inward cell, HCN2 and HCN4 also allow Ca2+ ion into the cell (32). This happened due to the dephosphorylation of Thr549 within the regulatory region of HCN2; and calcium ion influx causes cell apoptosis due to cytotoxicity (33). cAMP was acknowledged to modulate HCN2 in gating activity (34,35). Moreover, in non-small cell lung carcinomas, HCN2 has also been triggered by PKC inhibitors such as staurosporine (STS) or PKC412 and under expression of HCN2 can prevent cell apoptosis (36). As a consequence, if HCN2 is mutated or overexpressed in cancer cells, it can lead to cancer cell not going through apoptosis. Thus, HCN2 expression is crucial and likely to be a potential target for cancer treatment via inhibiting of HCN2 expression.

The current study is the pioneer meta-analysis research about HCN gene expression in multiple types and subtypes of cancer. HCN1-4 could have potency as biomarker for cancer disease diagnosis and prognosis. Further study on HCN genes and specific types of cancer as suggested in the present study may help to reveal the underlying molecular mechanism of these genes in cancer.

Table S.1
Table S1 HCN1 expression in multiple types of cancer from NextBio Research database
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Table S.2
Table S2 HCN1 expression in multiple types of cancer from Oncomine database
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Table S.3
Table S3 HCN2 expression in multiple types of cancer from NextBio Research database
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Table S.4
Table S4 HCN2 expression in multiple types of cancer from Oncomine database
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Table S.5
Table S5 HCN3 expression in multiple types of cancer from NextBio Research database
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Table S.6
Table S6 HCN3 expression in multiple types of cancer from Oncomine database
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Table S.7
Table S7 HCN4 expression in multiple types and subtype of cancer from NextBio Research database
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Table S.8
Table S8 HCN4 expression in multiple type and subtype of cancer from Oncomine database
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Table S.9
Table S9 Preferred reporting items for systematic review and meta-analysis protocols checklist (140)
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Acknowledgments

Funding: This project is supported by National Science Council of Yuan (NSC 104-2320-B-034-003; NSC 105-2320-B-034-001 to YC Lin).

Footnote

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/tcr.2017.07.22). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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Cite this article as: Phan NN, Huynh TT, Lin YC. Hyperpolarization-activated cyclic nucleotide-gated gene signatures and poor clinical outcome of cancer patient. Transl Cancer Res 2017;6(4):698-708. doi: 10.21037/tcr.2017.07.22

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