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ORIGINAL ARTICLE
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GNB1, a novel diagnostic and prognostic potential biomarker of head and neck and liver hepatocellular carcinoma


 Department of Biochemistry and Biotechnology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan

Date of Submission30-Dec-2020
Date of Acceptance09-Sep-2021
Date of Web Publication27-Apr-2022

Correspondence Address:
Yasir Hameed,
Department of Biochemistry and Biotechnology, The Islamia University of Bahawalpur, Bahawalpur
Pakistan
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jcrt.jcrt_1901_20

 > Abstract 


Background: Nucleotide-binding protein beta 1 (GNB1) encodes for heterotrimeric G-protein subunit beta (β) that is involved in various transmembrane signaling systems. A little has been reported earlier regarding GNB1 dysregulation in human cancers. Therefore, we carried out this study to investigate GNB1 expression levels and explore its prognostic values in distinct human cancers through a multi-layered bioinformatics approach.
Methods: GNB1 expression and promoter methylation levels across distinct cancers were analyzed using UALCAN, GENT2, and MEXPRESS databases, whereas its potential prognostic values were evaluated through Kaplan–Meier plotter. Then, cBioPortal was utilized to analyze the GNB1-related genetic mutations and copy number variations (CNVs). However, GNB1-related pathways were explored using DAVID. Moreover, the correlational analysis between GNB1 expression and CD8+ T immune cell infiltration and a gene–drug interaction network analysis were performed using TIMER, CDT, and Cytoscape.
Results: GNB1 was found commonly upregulated in 23 major subtypes of human cancers and its upregulation was significantly correlated with the reduced overall survival of only head and neck squamous cell carcinoma (HNSC) and liver hepatocellular carcinoma (LIHC). This implies that GNB1 plays a significant role in the development and progression of these cancers. The GNB1 was also found to be upregulated in HNSC and LIHC patients of different clinicopathological features. Pathways enrichment analysis revealed the involvement of GNB1-enriched genes in several diverse pathways, whereas a few interesting correlations were also documented between GNB1 expression and promoter methylation level, CD8+ T immune cell infiltration, and CNVs. In addition, we have also predicted few drugs that could regulate the GNB1 expression.
Conclusions: Our findings suggested GNB1 as a potential diagnostic and prognostic biomarker of HNSC and LIHC.

Keywords: Biomarker, diagnostic, GNB1, head and neck carcinoma, liver hepatocellular carcinoma, prognostic



How to cite this URL:
Usman M, Hameed Y. GNB1, a novel diagnostic and prognostic potential biomarker of head and neck and liver hepatocellular carcinoma. J Can Res Ther [Epub ahead of print] [cited 2022 Dec 8]. Available from: https://www.cancerjournal.net/preprintarticle.asp?id=344237




 > Introduction Top


Cancer is a major public health threat that causes substantial morbidity and mortality worldwide.[1] Tens of millions of people worldwide are diagnosing with cancer each year and more than half of them eventually die from this disease.[1] In many countries, after cardiovascular diseases, cancer is ranked second as the leading cause of death and there are quite chances that in the near future cancer will become the number one killer disease worldwide.[2],[3] People worldwide are now really starting to worry because of significant increases in the number of diagnosed, living with, and dying of cancers patients.[4] Lung cancer, particularly, is the most common cancer and the leading cause of cancer-related deaths among males, whereas, in women, breast cancer is the most commonly diagnosed and the leading cause of cancer-related deaths.[5] Despite the recent advances in cancer diagnosis and treatment, cancer is still rising significantly and results in tremendous social and economic burdens worldwide. Hence, there is an urgent need to explore the underlying biological mechanisms of carcinogenesis and investigate the possible potential diagnostic and prognostic biomarkers of cancer.

Nucleotide-binding protein beta 1 (GNB1) encodes for a heterotrimeric G-protein subunit beta (β),[6] which further associates with two other subunits Gα and Gγ to regulate the signaling function of G-protein-coupled receptors (GPCRs). A link between Gα and Gβγ covers the interaction sites on both α subunit and the Gβγ dimer, thus preventing the effector interactions and inactivation of G-protein. The binding of a ligand to GPCR promotes its activation where Gα disassociates from the Gβγ dimer and GPCR. This disassociation allows Gβγ to freely regulate numerous effector proteins and signaling cascades, including interactions with a variety of enzymes and ion channels. In addition, few effectors such as calcium channel inhibition, potassium channel activation, phospholipase C-β2 activation, and phosphoinositide IB class 3-kinase activation are directly regulated by Gβγ.[7] Best to our knowledge, not enough has been reported in the medical literature regarding GNB1 dysregulation and its diagnostic and prognostic role in human cancers. However, in a recent study, Yoda et al.[8] conducted the proteomic analysis to show that the cells expressing mutant GNB1 had the increased activation of various oncogenic pathways including AKT, mTOR, and ERK pathways.

In the present study, we comprehensively analyzed the GNB1 expression and its association with prognostic values of distinct cancer patients through a multilayered bioinformatics approach. The findings of our study have provided some useful information regarding the correlation between GNB1 expression and its prognostic values in head and neck squamous cell carcinoma (HNSC) and liver hepatocellular carcinoma (LIHC), as well as suggested the role of GNB1 as a potential diagnostic and prognostic biomarker in these cancers.


 > Methods Top


The UALCAN database

The UALCAN (http://ualcan.path.uab.edu/index.html) is an online available TCGA cancer dataset analyzing resource.[9] We used this tool in our study for the pan-cancer expression analysis of the GNB1 to document its differential expression across multiple cancer subtypes. We also utilized this database to analyze the clinicopathological attribute-specific expression of GNB1 in different cancers. In UALCAN, the transcriptomics data are represented as transcript per million, and for statistics purpose, a t-test of unequal variance was applied using a PERL script with a Comprehensive Perl Archive Network module.

Kaplan–Meier plotter

The Kaplan–Meier (KM) plotter (http://www.kmplot.com/) is a web port dedicated to cancer survival biomarkers identification and validation.[10] In our study, we used this web port for predicting the prognostic effect of GNB1 expression on the overall survival of cancer patients in distinct cancer subtypes. Hazard ratio (HR), 95% confidence interval (CI), and log-rank P value were determined and displayed.

GENT2 database

The GENT2 database (http://gent2.appex.kr/) offers a reliable multiomics analysis of the cancer-related TCGA data.[15] In this study, to validate the transcription expression level of GNB1 in different cancer subtypes, we employed this tool to analyze the GNB1 differential expression patterns in independent cancer cohorts' expression data that were acquired by the Affymetrix U133Plus2.0 platform.

MEXPRESS database

The MEXPRESS database[11] was utilized to assess the Pearson's correlation between GNB1 transcription expression and its promoter methylation levels in different cancer subtypes. P < 0.05 was considered statistically significant.

cBioportal database

The cBioPortal (https://www.cbioportal.org/) is an online web port that facilitates researchers in cancer-related multiomics data analysis.[12] In the current study, we used cBioPortal to evaluate the GNB1 genetic alterations and copy number variations (CNVs) in TCGA datasets of different cancers.

PPI network construction, visualization, and pathway enrichment analysis

In the present work, we utilized the STRING web port (version 11.0) to find out the PPI of GNB1 neighboring proteins.[13] A confidence (combined score) >7 was used as a threshold for drawing PPI in the analysis. Then, the PPI network was visualized using Cytoscape software.[14] The KEGG pathway analysis of the GNB1 enriched genes was performed through an online tool, DAVID (http://david.ncifcrf.gov/summary.jsp).[15] P < 0.05 was considered statistically significant.

CD8+ T immune cell infiltration and GNB1 expression in cancer patients

TIMER (http://cistrome. org/TIMER/) is a dedicated web port for tumor immunity analysis.[16] We utilized this web port to acquire a correlation (Pearson) between CD8+ T immune cell infiltration and GNB1 expression in the defined cancer subtypes. P < 0.05 was considered statistically significant.

GNB1 gene–drug interaction network analysis

The comparative toxicogenomics database (CTD) was used to retrieve the information of chemotherapeutic drugs that could reduce or enhance the mRNA or protein expression levels of the genes of interest.[17] In our study, the GNB1 was searched in the CTD database and a gene–drug interaction network was constructed and visualized using Cytoscape software. This analysis has helped us to identify the potential expression regulatory drugs of GNB1.


 > Results Top


Expression level analysis of GNB1 in distinct types of human cancers

To find the differences in GNB1 expression in tumor and normal tissues, the TCGA expression profile across tumor samples and their paired normal tissues were utilized through the UALCAN platform. Results demonstrated that GNB1 was significantly (P < 0.05) downregulated in Kidney Chromophobe (KICH) samples, whereas overexpressed in 23 distinct other types of the human cancer samples as compared to the normal controls including HNSC and LIHC [Figure 1].
Figure 1: GNB1 expression profile in distinct types of human cancers. (a) Only in cancer samples and (b) in cancer samples relative to normal controls. P < 0.05 was considered to indicate a statistically significant result

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GNB1 prognostic potential in various types of cancers

The KM plotter online tool was applied to investigate the effect of GNB1 higher expression on the OS duration of the different cancer patients. This analysis will help us to understand the role of GNB1 expression in reducing the lifespan of cancer patients. Results of this analysis revealed that overexpression of GNB1 was significantly (P < 0.05) associated with decreased OS duration of the HNSC (HR = 1.44, 95% CI: 1.1–1.89, P = 0.0075) and LIHC (HR = 2.25, 95% CI: 1.57–3.24, P = 6.4e–06) patients [Figure 2]. Taken together, these results indicate that the higher expression level of GNB1 is vital in the tumorigenesis of the HNSC and LIHC. Therefore, the next part of our study mainly focuses on the unique role of GNB1 in these two types of human cancers.
Figure 2: Overall survival analysis of the GNB1 in different cancers. (a) In head and neck squamous cell carcinoma and (b) in liver hepatocellular carcinoma. P < 0.05 was considered to indicate a statistically significant result

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Reanalysis of GNB1 expression in distinct cancers showing its significant negative prognostic values

The distinct cancer subtypes (HNSC and LIHC) in which GNB1 overexpression has shown the significant (P < 0.05) negative correlations with the OS duration were reanalyzed to verify the significance of GNB1 expression in HNSC and LIHC patients of different clinicopathological features including different cancer stages, patients' races, and genders. Results have shown that GNB1 was also significantly (P < 0.05) overexpressed in HNSC and LIHC patients of different clinicopathological features as compared to the normal controls [Figure 3].
Figure 3: Expression analysis of GNB1 in head and neck squamous cell carcinoma and liver hepatocellular carcinoma patients of different clinicopathological parameters. (a) Individual cancer stages based expression analysis of GNB1 in head and neck squamous cell carcinoma, (b) Patients race based expression analysis of GNB1 in head and neck squamous cell carcinoma, (c) Patients gender based expression analysis of GNB1 in head and neck squamous cell carcinoma, (d) Individual cancer stages based expression analysis of GNB1 in liver hepatocellular carcinoma, (e) Patients race based expression analysis of GNB1 in liver hepatocellular carcinoma, and (f) Patients gender based expression analysis of GNB1 in liver hepatocellular carcinoma. P <0.05 was considered to indicate a statistically significant result

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Validation of GNB1 higher expression using independent cohorts of cancers showing its significant negative prognostic values

Based on GENT2, we further validated the GNB1 expression using independent cohorts of HNSC and LIHC. As per the expectations, our results were in agreement with the results of UALCAN, indicating the robustness of the evidence. This analysis revealed the significant (P > 0.05) higher expression of GNB1 in HNSC and LIHC [Figure 4].
Figure 4: Validation of GNB1 expression levels in head and neck squamous cell carcinoma and liver hepatocellular carcinoma using independent cohorts via GENT2. Blue color represents the normal samples while red color indicates the cancer samples. P < 0.05 was selected as cutoff criterion

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Promoter methylation analysis of the GNB1 in cancers with its significant overexpression and prognostic values

Variation in the promoter methylation level can result in many lethal diseases including cancer.[18] To assess the correlation between GBN1 promoter methylation and its overexpression in defined cancer subtypes, we utilized the MEXPRESS database. As per the analysis, we found a significant (P > 0.05) negative correlation between the promoter methylation and upregulation of GNB1 in HNSC and LIHC [Figure 5]. Collectively, these data suggested that GNB1 promoter hypomethylation is significantly involved in its upregulation in HNSC and LIHC.
Figure 5: A MRXPRESS based correlation analysis between GNB1 expression and its promoter methylation in head and neck squamous cell carcinoma and liver hepatocellular carcinoma. (a) In head and neck squamous cell carcinoma and (b) in liver hepatocellular carcinoma. A negative sign indicates the negative correlation between SHMT2 expression and its promoter methylation using a specific probe at a specific CpG island. P < 0.05 was considered to indicate a statistically significant result

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Copy number variations and mutational analysis of GNB1 in cancers showing its significant overexpression and negative prognostic values

Using cBioportal, the information related to GNB1 genetic alterations including amplification, deletion, mutation, and fusion in HNSC was obtained from the TCGA HNSC (TCGA, Firehose Legacy) dataset consisting of 504 cancerous samples. However, in LIHC, we utilized the TCGA LIHC (TCGA, Firehose Legacy) dataset consisting of 366 cancerous samples for the extraction of similar information. Results revealed that GNB1 harbors genetic alterations in only 2.4% (12/504) and 4% (14/366) queued cases of the HNSC and LIHC, respectively, and among the observed genetic abnormalities, deep deletions were the most frequent [Figure 6].
Figure 6: Copy number variations and genetic alterations analysis of the GNB1 in TCGA head and neck squamous cell carcinoma and liver hepatocellular carcinoma datasets. (a) In head and neck squamous cell carcinoma dataset and (b) in liver hepatocellular carcinoma dataset

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PPI network construction, visualization, and pathway enrichment analysis of GNB1

The PPI network of GNB1 was constructed using the STRING database and visualized through Cytoscape software to recognize the set of GNB1 enriched genes. In total, one set of 11 GNB1 enriched genes was identified [Figure 7]a. We further processed this GNB1-enriched set of genes for pathway enrichment analysis. Results revealed that GNB1-enriched genes were significantly involved in various diverse pathways including “GABAergic synapse”, “Morphine addiction”, “Circadian entrainment”, and “Retrograde endocannabinoid signaling” [Figure 7] and [Table 1].
Figure 7: PPI network and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis of the GNB1 enriched genes. (a) A PPI network of GNB1 enriched genes and (b) KEGG pathway analysis of the GNB1 enriched genes

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Table 1: Detail of Kyoto encyclopedia of genes and genomes pathway analysis of the GNB1 enriched genes

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CD8+ T immune cell infiltration and GNB1 expression in head and neck squamous cell carcinoma and liver hepatocellular carcinoma

The functions of, and interactions between, the innate and adaptive immune systems are vital for anticancer immunity. Cytotoxic T cells expressing cell-surface CD8+ T are the most powerful effectors in the anticancer immune response and form the backbone of the current successful cancer immunotherapies.[19] In the current study, the Spearman's correlation between the expression of GNB1 and CD8+ T cells was calculated using the TIMER database. Results revealed a significant (P > 0.05) positive correlation between the mRNA expression of GNB1 and CD8+ T immune cell infiltration in HNSC and LIHC [Figure 8].
Figure 8: TIMER-based spearman correlational analysis between the GNB1 expression and CD8+ T immune cell infiltration in head and neck squamous cell carcinoma and liver hepatocellular carcinoma. P <0.05 was considered to indicate a statistically significant result

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Gene–drug interaction network analysis of the GNB1

To explore the relationship between GNB1 and available cancer therapeutic drugs, a gene–drug interaction network was developed using the CTD database and Cytoscape. By analyzing this network, it was observed that the expression of GNB1 could potentially influence by a variety of drugs. For example, bisphenol A and diazinon could elevate the expression level of GNB1, whereas barium and chloropicrin could reduce GNB1 expression level [Figure 9].
Figure 9: Gene–drug interaction network of the GNB1 and chemotherapeutic drugs. Red arrows: Chemotherapeutic drugs increase the expression of GNB1; green arrows: Chemotherapeutic drugs decrease the expression of GNB1. The numbers of arrows between chemotherapeutic drugs and key genes in this network represent the supported numbers of literatures by previous reports

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 > Discussion Top


Despite the great advances in early detection and accurate treatment, cancer is still a major threat to human survival worldwide.[20] HNSC accounts for over 80% of head and neck malignancies and kills thousands of people every year worldwide. Although a decline in HNSC cases has been observed recently because of better treatment options, the incidence of HNSC patients under the age of 45 is still growing in a constant manner.[21]

LIHC is another most common primary liver malignancy and also one of the major causes of cancer-related death around the globe. It is the 9th leading cause of cancer-related deaths in the United States of America. Despite the availability of modern technologies for screening, diagnosis, and treatment, LIHC continues to rise as well.[22] In sum, it is urgently required to investigate the novel potential and useful biomarkers to improve the diagnosis, treatment, and prognosis of cancer patients.

GNB1 encodes for the heterotrimeric G-protein beta subunit (Gβ), which mediates the downstream signaling of GPCRs to regulate the several effector molecules.[23] So far, very few studies have been conducted to investigate the oncogenic role of GNB1. For example, Wazir et al. revealed that GNB1 is associated with the dysregulation of the mechanistic target of rapamycin-related antiapoptosis pathway in breast cancer patients.[24] Moreover, Chen et al. and colleagues have demonstrated the downregulation of GNB1 in clear cell renal cell carcinoma and associated it with worsened disease characteristics and prognosis.[25] Best to our knowledge, no other study has been yet carried out to document the expression profile of GNB1 in distinct other cancer subtypes. Therefore, the present study was initiated to uncover the possible diagnostic and prognostic role of GNB1 in certain human cancer subtypes based on data mining.

In the present study, we revealed that GNB1 was downregulated in one subtype, whereas upregulated in 23 subtypes of human cancers. Moreover, we have further shown that GNB1 overexpression was significantly (P < 0.05) correlated with the decreased OS duration of HNSC and LIHC. Taken together, these findings revealed that GNB1 might play a crucial role in the initiation and progression of HNSC and LIHC; therefore, in the present study, our main focus is these two cancer subtypes. We further evaluated that GNB1 was also significantly (P < 0.05) overexpressed in HNSC and LIHC patients of different clinicopathological features including different cancer stages, patients' races, and genders.

Next, it was tried to explore the possible causes of GNB1 overexpression, and for that purpose, we carried out the correlation analysis between GNB1 overexpression and its promoter methylation level, CNVs, and genetic mutations in HNSC and LIHC patients. GNB1 was mainly enriched in the deep deletion abnormalities in insignificant proportions [2.4% (12/504), 4% (14/366)] of the HNSC and LIHC patients. Therefore, it is unlikely that genetic abnormalities participate in the expression regulation of the GNB1. Furthermore, the results of GNB1 promoter methylation revealed an expected negative correlation between GNB1 overexpression and its promoter hypomethylation in HNSC and LIHC. Therefore, in sum, our results revealed that promoter hypomethylation has a solid impact on the upregulation of GNB1 in HNSC and LIHC. To date, various HNSC-related biomarkers have been discovered, for instance, Kim et al. and collogues published a review article in which they compared the results of different studies which extensively investigated epidermal growth factor receptor, cyclin D1 (CCND1), Bcl-2 (B-cell lymphoma 2), cyclin-dependent kinase inhibitor p27 (Kip1), vascular endothelial growth factor, and p53 as potential HNSC biomarkers. They overall concluded that the diagnostic and prognostic or predictive values of these biomarkers are not consistent.[26] Moreover, best to our knowledge, none of these or any other biomarkers have been generalized so far in HNSC patients of different clinicopathological features. In the present study, we showed the significant (P < 0.05) upregulation of GNB1 expression in HNSC patients of different clinicopathological features including different cancer stages, patients' races, and genders as compared to the normal controls. Furthermore, GNB1 promoter methylation level and OS information have also proven its useful values as a novel potential biomarker of HNSC patients.

Early diagnosis is crucial to the patient's survival suffering from LIHC. For this purpose, currently, various biomarkers including alfa-fetoprotein (AFP), AFP-L3, or Des-γ-carboxyprothrombin are being used,[27] and among all of them, serum AFP is the most reliable and commonly used biomarker for LIHC patients; however, its sensitivity and precision are only around 50%.[28] Moreover, similar to HNSC, none of these or any other biomarkers have been generalized so far in LIHC patients of different clinicopathological features. In this study, we have shown the significant (P < 0.05) upregulation of GNB1 expression in LIHC patients of different clinicopathological features including different cancer stages, patients' races, and genders as compared to the normal controls. We have further revealed that GNB1 overexpression is significantly (P < 0.05) associated with decreased OS of the LIHC patients. Hence, we suggested GNB1 upregulation as a novel diagnostic and prognostic biomarker of LIHC.

CD8 + T immune cells provide anticancer immunity.[29] We have observed the negative correlations between GNB1 expression and CD8 + T immune cell infiltration in HNSC and LIHC, which may bring some new ideas for the treatment of HNSC and LIHC patients who do not benefit from the existing immune checkpoint inhibitors/regulators.

In the present study, GNB1-associated genes pathway enrichment analysis revealed their involvement in few important signaling pathways including “GABAergic synapse,” “Morphine addiction,” “Circadian entrainment,” and “Retrograde endocannabinoid signaling.” In addition, we have also identified few potential drugs that could regulate the GNB1 expression and are vital to design the more appropriate therapeutic strategy for HNSC and LIHC.


 > Conclusions Top


In our study, we utilized several bioinformatics-based online databases and tools comprised wet-lab experimental data to comprehensively analyze GNB1 in distinct types of cancers. The benefits of this in silico method are large samples cohort, very low cost, and enable genomics research and functional analyses at a large scale. Our results revealed that GNB1 can act as a promising diagnostic and prognostic biomarker of HNSC and LIHC. However, voluminous testing is required before clinical implication.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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