Bioinformatics and Genetic Correlation Studies of Functional Gene Partners of Tp53 Gene Associated With Hepatocellular Carcinoma and Liver Cirrhosis Among Patients in Ucth, Calabar

doi:10.21203/rs.3.rs-4530115/v1

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Bioinformatics and Genetic Correlation Studies of Functional Gene Partners of Tp53 Gene Associated With Hepatocellular Carcinoma and Liver Cirrhosis Among Patients in Ucth, Calabar

https://doi.org/10.21203/rs.3.rs-4530115/v1

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The present study investigated the rate of change (mutation) in TP53 and the associated functional partner genes and revealed that they play very significant role in hepatocellular carcinoma and liver cancer disease conditions in humans. Genetic correlation analysis has shown that there is very high association, strong relationship with significant impact between TP53 and the functional partner genes. The strength of association between TP53 gene and other functional partners gene was found to be high (> 0.5) in normal liver but low than in cancerous liver. The proteomic parameters of TP53 and other functional partner genes such as the molecular weights, number of amino acids, theoretical pl, total number of atoms, total number of positive and negative amino acids residues, extinction coefficients, estimated half-life, instability index, aliphatic index and hydropathicity were revealed in the study and viewed using the circos visualizer which showed bigger strands for genes with high molecular weights. The expasy.org prosites analysis of the TP53 and associated functional partner genes revealed the functional domain protein site reaction hotspots with the corresponding amino acids. The main prosites reaction hotspots were the protein kinase II phosphorylation site (PKC) which was similar for all the associated genes. Other domain reaction hotspots for TP53 and associated genes includes N-glycosylation sites, N-myristylation sites, N-Amidation sites, Tyrosine II kinase phosphorylation sites I and II, the casein II protein kinase phosphorylation sites (CK2) and the cAMP and cGMP phosphorylation sites. Two principal components were used to explain the variations in dimensionality of the TP53 and associated genes. ATM gene showed the highest loading value in PC1 while EP300 gene exerts the highest impact in terms of dimensionality in PC2. The principal component axes delineated the genes into two major cluster sets. Major cluster one had four genes which major two had 6 genes. The intensity of interactions among the genes to induce hepatocellular carcinoma and liver cancer was demonstrated using interactive heatmap with red colour depicting intense interactions, black colour depicting moderate interactions and green colour depicting slow interactions.

TP53 GENE

MDM2 Functional partner gene correlation

gene expression

gene structure

gene characteristics

hepatic carcinoma

mutations

bioinformatics

Hepatocellular carcinoma (HCC) is a global health problem, with an increasing incidence worldwide [1]. This disease can be associated with risk factors identified by epidemiological studies which include chronic infection with hepatitis B and C viruses (HBV, HCV), non-alcoholic fatty liver disease (NAFLD), and ethanol consumption [2]. Studies by some oncologists and molecular biologists have reported on the contributions of TP53 gene mutation in the molecular etiology of Hepatocellular Carcinoma [3, 4 &5]. In HCC, TP53 mutational frequency has been reported to range between 22 and 33% [6, 7, 8, 9 &10]. The frequency of TP53 mutations varies within geographic regions, etiological factors and carcinogen exposure, with higher frequencies in regions where hepatitis B virus (HBV) infection is endemic [6, 11]. Liver cancer shows no symptoms in the early stage, but symptoms are evident in the advanced stage, which leads to third most lethal cancer type. Early diagnosis of HCC and treatment are likely to prolong the lifespan of liver cancer patients. There is need for the identification of high sensitivity and specificity markers for HCC [12].

Hepatocellular carcinoma is the deadly complication of liver diseases and usually develops from liver cirrhosis in most cases. In Asia and Africa, hepatitis B viral infection (HBV), with or without exposure to aflatoxin B1, is the most frequent etiology, whereas hepatitis C viral infection (HCV), severe alcohol abuse is most frequently related to HCC in Western countries [13, 14 & 15]. HCV infection is a global health problem, as well as been the major cause of end-stage liver disease such as liver cirrhosis (LC) and HCC. Most people that are infected with HCV become chronic hepatitis sufferers, while some of them may develop liver cirrhosis after years of follow up, which will be followed by HCC [16]. Aside from the known risk factors of HCC, which include dietary Aflatoxin B1 consumption, alcohol consumption; several genetic disorders such as hemochromatosis (an iron overload disease) that are associated with an increased risk of HCC [17]. Regardless of its etiology, cirrhosis alone is also an independent risk factor for HCC. Polymorphisms of TP53 gene have been associated with HCC, but their impacts on the progression of liver disease are receiving constant attention by scientists [18, 19]. Tumour suppressor gene (TP53) codes for a protein that regulates the cell cycle and hence functions as tumour suppressant [19]. It is located on chromosome 17p and encodes a 53-KDa nuclear phosphoprotein, which plays a major role in safeguarding the human genome [20]. When tumor suppressor genes do not function properly, cells can grow out of control, which will lead to cancer. According to [21] it is important for cells in multicellular organisms to suppress cancer. TP53 has been described as the guardian of the genome, which refers to its role in conserving stability by preventing genome mutation. The development of HCC is a multistage process and related to viral infection and chemical carcinogens. [22] reported that in the molecular aspects, receptors, deregulation of growth factors and their downstream signaling pathway components represents a central pro-tumorigenic principle in human hepatocarcinogenesis.

A specific TP53 gene mutation at codon 249 in exon 7 was associated with Aflatoxin B1 (AFB₁) inducing HCC in certain population with high AFB1 contamination. The wild-type TP53 gene exhibits a polymorphism at codon 72 in exon 4, with a single nucleotide change that causes a substitution of proline for arginine (Arg72Pro). The two polymorphism variants of TP53 are functionally distinct, and these differences may influence cancer risk. The polymorphism consists of a single base pair change of either arginine or proline which creates 3 distinct genotypes: Homozygous for Arginine (Arg/Arg), Homozygous for proline (Pro/Pro) and Heterozygous (Pro/Arg). TP53 codon 72 have been reported to be associated with cancer of the lung, esophagus, stomach, colorectal, breast, bladder and cervix [22]. TP53 is the most frequently mutated gene in human cancer. According to [23, 24], mutations in the TP53 gene may lead to three types of cellular outcome like formation of many mutant TP53 isoforms (can exert dominant-negative effects over wild type TP53 expressed from the remaining wildtype allele), Some mutant TP53 isoforms carry new pro-oncogenic activities referred to as ‘gain of function’ and lastly TP53 gene mutation can result in loss of wildtype activities (activation of TP53 target genes) and loss of tumor suppressor function as published [24]. [25], reported that mutations of TP53 gene or positive immunostaining for mutated TP53 protein can be used as a significant indicator of poor prognosis in patients with HCC.

Researchers in different populations have revealed the association of candidate genes to the molecular etiology of hepatocellular carcinoma [26, 27, 28, 29]. Results in some studied populations highlighted the association of TP53 gene polymorphism in the pathogenesis of the disease, but few others observed no association in the populations investigated [30, 31, 32, 33]. There is paucity of documented molecular research on the genetic polymorphism of TP53 gene and the association with hepatocellular carcinoma in Nigeria. Thus, this research seeks to investigate the genetic polymorphism of TP53 gene and association with liver cirrhosis and hepatocellular carcinoma in Calabar, Nigeria.

2.1 The study location

The study was carried out at the Department of Internal Medicine, Gastroenterology and Hepatology Unit, University of Calabar Teaching Hospital, Calabar, Nigeria from September 2020 to September 2023.

2.2 Ethical approval

Ethical approval was obtained from the University of Calabar Teaching Hospital Ethical Review Committee before the commencement of this study.

2.3 Subject enrolment

A total of 55 participants from different ethnic groups were included in this study. The participants were grouped based on their ethnicity, age, marital status, sex. 35 were patients with hepatocellular carcinoma & 10 were patients with liver cirrhosis.

2.4 Inclusion criteria

All patients were individuals suffering from liver disease.

2.5 Exclusion criteria

The individuals with a history of any liver disease (familial or personal) were excluded from the study.

2.6 Instrument for data and sample collection

Informed consent was obtained from the patients, guardian or parent before data collection and sample collection was initiated. Detailed medical history, age, race or ethnicity, social history/risk factors like alcohol intake, multiple sex partners/unprotected sex, dietary habits and family history of liver cancer case(s) was collected from the subjects recruited for this study. A 2-3ml of whole blood was collected from both patients and controls, put into EDTA bottle and labeled properly. Blood samples was stored in deep freezer (-20⁰c) before onward transportation to the virology and molecular diagnostic units, International Institute for Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria for molecular analysis.

2.7 Genomic DNA extraction

DNA was extracted from all the blood samples. The DNA extraction, DNA quantification, Polymerase Chain Reaction (PCR), restriction fragment length polymorphism (RFLP) of the targeted genes under consideration (TP53, genes) were carried out at virology and molecular diagnostic units, International Institute for Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria. DNA extraction was carried out according to Dellaporta et al., (1983) protocol with some modifications as reported by [34]. About 150μl of whole blood was transferred into 1.5ml Eppendorf tube placed on a rack and 350μl of extraction buffer was added to the tube. 40µl of 20% Sodium dodecyl sulphate (SDS) was added and the tubes were inverted 3-4 times before incubation using water bath at 65 ⁰C for 10 minutes. The tubes were brought out of the incubator and allow to cool at room temperature. Potassium acetate (160μl of the 5M solution) was added to the tubes. The tubes were inverted 2-3 times and centrifuged at 10,000 rcf for 10 minutes. Four hundred micro liters of supernatant was carefully transferred into new Eppendorf tubes and 200μl of cold isopropanol was added. The tubes were inverted gently 5-6 times to precipitate DNA. The tubes were stored in the fridge at 4°C for 15-20 minutes.

The tubes were then centrifuged at 10,000 rcf for 10 minutes to sediment the DNA. The supernatant was decanted gently to ensure the pellets are not disturbed, 500μl of cold ethanol was added to the pellet to wash the DNA. It was centrifuged again at 10,000 rcf for 5-10 minutes. The ethanol was removed, and the DNA was allowed to air-dry at room temperature until no trace of alcohol was observed in the tubes.

The DNA was re-suspended in 50μl of T.E buffer and stored in the freezer as stock solution. An aliquot of the DNA stock solution was run on agarose gel electrophoresis to check the quality of DNA. Each sample of genomic DNA (5μl) was mix with 2ul of loading dye and transfer into the wells of the gel. A voltage of 110V was applied for about 45 minutes. The samples were score as faint, +, 2+, 3+ depending on the intensity of the bands to indicate the amount of DNA extracted after mounting and viewing in the automated gel documentation system.

2.8 Primer for the TP53 gene isolation and sequencing

The forward primer used to amplify the TP53 gene on exon 7 is: 5¹-CTTGCCACAGGTCTCCCCAA-3¹ and reverse primer 5¹–AGGGGTCACCGGCAAGCAGA-3¹[36,37].

2.9 Constitution of PCR cocktail

The PCR cocktail was a total volume of 25µl reaction, which comprises of 18.3µl of distilled water (H20), 2.5µl of 10× buffer, 1µl of dNTPs, 0.5µl of forward primers, 0.5µl of reverse primers, 0.2µl of Taq polymerase and 2µl of genomic DNA. The cycling conditions for PCR reaction was denaturation for 5mins at 94⁰c, annealing was carried out at 55⁰c for 60seconds and final extension at 72⁰c for 10 mins The amplicons of 254 base-pair were visualized by staining the gel with ethidium bromide, after electrophoresis on 2% agarose gel [37].

2.10 Purification of the amplicons and sequencing of PCR products

Amplicons were purified at the Virology and Molecular Diagnostics Unit, International Institute for Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria using the alcohol-based method [38]. 21μl of amplicons were put into newly neatly PCR tubes and 52.5μl of 95% ethanol (21μl x 2.5μl) was added to precipitate the amplicons. It was then centrifuged at 12,000 rcf for 10 minutes and gently decanted; remaining the pellets attached to the wall of the tube. Approximately, 500μl of 70% alcohol were added and then centrifuged at 12,000 rcf for five minutes. It was then carefully decanted and the amplicon pellets were air dry at room temperature (25⁰C- 28⁰C) [38,39]. Finally, 22μl of distilled water was added to each PCR tube pellets of purified amplicons and stored in deep freezer for further usage. The suspended purified amplicons tubes were wrapped in foil and packed in ice containers for shipment to Inquaba Biotech. Laboratory, South Africa [37,40] for new generation sequencing.

All methods described in this section were performed in accordance with the relevant guidelines and regulations and has been cited accordingly.

2.11. Bioinformatics analysis, identification of functional TP53 gene partners and genetic correlations studies

The DNA sequences for TP53 gene sequenced from Inquaba biotech were received in chromatogram peaks. The chromas software was used to extract the DNA or nucleotide sequences and translated to protein sequences using the Expasy.org. sequence translator tool. The protein sequences for other functional partner genes were obtained from the major genes (coloured circles in the protein – protein interaction network in figure 3). The sequence TP53 gene isolated from the liver cancer and hepatocellular carcinoma patients were analyzed for sequence homology using the basic local alignment search tool (BLAST) of the National center for biological information (NCBI). Sequence similarity and identity search of the sequence revealed 100% similarity and identity with TP53. The TP53 was analyzed for gene oncology using the STRING OPTION of the expasy.org online interactive programme of the SWISS institute of bioinformatics (SIB). The associated network revealed the associated functional gene partners, their oncology which included the molecular characteristics, biological processes, and cellular functions. The PROTPARAM option of expasy.org was used to unveil the properties of each of the functional partner genes with TP53 for the normal and disease conditions. The genetic correlation analyzer R online programme was used to establish the association and line of best fit for each of the functional gene partners and TP53 gene in normal and disease conditions.

2.12 Statistical analysis

The proteomic characteristics for the functional gene partners of TP53 in hepatocellular carcinoma and Liver cancer conditions were analyzed and displayed using a circos molecular data visualizer. The Genetic Correlation Analyzer R package was used to estimate the determination coefficients R and degree of association in terms of slopes and intercepts for the TP53 gene and associated functional gene partners in both normal and diseased conditions. The clustvis R online programme was used to reveal the clustering pattern, dimensionality and the interactive heatmap intensity among the TP53 and associated genes. Significance levels of comparison were determined at 0.05 level of significance.

3.1.1 Figure 1 and Table 1 shows the results of the Proteomic characteristics of TP53 and functional partners genes associated with Hepato carcinogenesis and liver cancer patients. The circos visualization of the results revealed a great diversity in proteomic properties among TP53 gene and the other functional partner genes. The dimensions of each strand linkage is proportional to the weight and measure of the features under consideration. The molecular weight (MW) for some of the functional partners genes in green colour were very high and consequently represented and linked with very large strands to ATM, CREBBP and EP300. When compared to other genes with small strands, it implies that the molecular weights for these genes were high and least in the gens with small strands. The extinction coefficient of the gene when exposed to UVR property and the hydropathicity attributes of the functional partner genes presented the least measurements and consequently represented with the smallest black-coloured strands. Hence the size of the strand per linkage is proportional to size and impact on the subject.

Table 1 presents the results of proteomic properties and figure 2 presents the results of interactive intensity of TP53 gene and associated functional partner genes in Hepato carcinoma and liver cancer patients. The number of amino acids varied from 3065 aa in ATM to 248 aa in SFN. The molecular weight of the genes was proportional to the number of amino acids present in the gene. ATM showed the highest molecular weight of 350,687 mg/mol while SFN had the least molecular weight of 27774 mg/mol. The isoelectric and electrode potentials of the genes varied from moderately acidic (4.64) to low alkalinity (8.83). The total number of negatively charged amino acid residues (Asp + Glu) ranges from 48 in SFN gene to 389 in ATM functional gene partner. The total number of positively charged amino acids residues (Arg + Lys) varied from 30 to 366 in SFN. The number of atoms in the gene molecules range from only 3864 in SFN to 49436 in ATM. Extinction coefficients among the genes also varied from 0.315 g/L in DAXX to 1.02 g/L in ATM. The estimated half-life of the TP53 and associated genes were 30 hrs when estimated using the human recticulyte cell. All TP53 and associated genes showed genetic and thermal instability except for RPA1 gene which was thermally and genetically stable with instability index of 35.83%. All the evaluated genes showed high aliphatic indices and ranged from 59.74% in HSP90AA1 to 94.61% in SFN gene. The solvent affinity index (hydropathicity) results revealed that TP53 and all the associated genes were hydrophilic with positive values of GRAVY ranging from 0.362 in RPA1 gene to 0.786 in TP53 gene. As revealed in the interactive heatmap, the intensity of the proteomic properties among the genes was more in ATM (deep brown colour) gene and least intense in DAXX gene (Deep blue colour) as shown in Figure 2.

Table 1: Proteomic characteristics of TP53 gene and functional partner genes associated with hepatocellular carcinoma and liver cancer patients.

S/N	Proteomic Characteristics	DAXX	PRA1	SFN	ATM	HS90AA1	EP300	CREBBP	SIRT1	MDM2	TP53
1	No. of Amino acid	740	616	248	3065	865	2414	2442	747	479	1134
2	Molecular weight	81372	68138	27774	350687	102343	201454	232655	82197	56328	132654
3	Theoretical pl.	4.79	6.92	4.68	6.39	5.07	8.81	8.83	4.55	4.64	5.88
4	Negative charged amino acid residue	135	71	48	389	160	178	183	134	52	146
5	Positively charged amino acid residue	87	71	30	366	122	208	214	75	54	132
6	Total no. of atoms	11264	9536	3864	49436	13769	36568	36792	11314	7682	17652
7	Extinction coefficient	0.315	0.931	0.937	1.02	0.824	0.561	0.543	0.575	0.765	0.655
8	Estimated half life	30	30	30	30	30	30	30	30	30	30
9	Instability index	73.37	35.83	48.59	45.21	47.71	67.04	65.91	53.88	58.71	58.22
10	Aliphatic index	69.14	78.96	94.61	78.09	59.74	60.95	78.41	65.68	70.75	78.17
11	Hydropathicity	0.848	0.362	0.599	0.247	0.699	0.725	0.69	0.551	0.786	0.76

Biological functions of Network functional partner genes of TP53 Associated With Hepatocellular Carcinoma and Liver Cancer

TP53

TP53 (Fig. 3 and Fig. 3b) is cellular tumor antigen p53; the gene acts as a tumor suppressor in many tumor types including hepato cellular carcinoma and liver cirrhosis. TP53 gene induces growth arrest or apoptosis depending on the physiological circumstances and cell type. The gene is also involved in cell cycle regulation as a trans-activator that acts to negatively regulate cell division by controlling a set of functional partner genes required for this process. One of the activated genes is an inhibitor of cyclin-dependent kinases. Apoptosis induction seems to be mediated either by stimulation of BAX and FAS antigen expression, or by repression of Bcl-2 expression. It has 393 amino acid residues.

SIRT1

SIRT 1 (Fig.3) is NAD-dependent protein deacetylase sirtuin-1 gene. It is a NAD-dependent protein deacetylase that links transcriptional regulation directly to intracellular energetics and participates in the coordination of several separated cellular functions such as cell cycle, response to DNA damage, metabolism, apoptosis and autophagy. It modulates chromatin function through deacetylation of histones and promote alterations in the methylation of histones and DNA, leading to transcriptional repression. This gene deacetylates a broad range of transcription factors and coregulators, thereby regulating target gene expression in cancer cells.

RPA1

RPA 1 (Fig.3) is replication protein A with 70 kDa DNA-binding subunit and an N-terminally processed amino acid. As part of the heterotrimeric replication protein A complex (RPA/RP-A), it binds and stabilizes single-stranded DNA intermediates, that form during DNA replication or upon DNA stress. It prevents their reannealing and in parallel, recruits and activates different proteins and complexes involved in DNA metabolism. Thereby plays an essential role both in DNA replication and the cellular response to DNA damage. In the cellular response to DNA damage, the RPA complex controls DNA repair and DNA damage checkpoint activation.

MDM2

MDM2 is (Fig.3) E3 ubiquitin-protein ligase Mdm2; E3 ubiquitin-protein ligase that mediates ubiquitination of p53/TP53, leading to its degradation by the proteasome. It inhibits p53/TP53- and p73/TP73-mediated cell cycle arrest and apoptosis by binding its transcriptional activation domain. MDM2 also acts as ubiquitin ligase E3 toward itself and ARRB1. It further permits the nuclear export of p53/TP53. The gene promotes proteasome-dependent ubiquitin-independent degradation of retinoblastoma RB1 protein and also inhibits DAXX-mediated apoptosis by inducing its ubiquitination and degradation.

CREBBP

CREBBP is (Fig.3) CREB-binding protein. This gene acetylates histones by giving a specific tag for transcriptional activation. It also acetylates non- histone proteins, like DDX21, FBL, IRF2, MAFG, NCOA3, POLR1E/PAF53 and FOXO1. It binds specifically to phosphorylated CREB and enhances its transcriptional activity toward cAMP-responsive genes. The gene acts as a coactivator of ALX1 and as a circadian transcriptional coactivator which enhances the activity of the circadian transcriptional activators: NPAS2-ARNTL/BMAL1 and CLOCK-ARNTL/BMAL1 heterodimers.

EP300

EP300 (Fig.3) is Histone acetyltransferase p300 gene. It functions as histone acetyltransferase and regulates transcription via chromatin remodeling. It also acetylates all four core histones in nucleosomes. Histone acetylation gives an epigenetic tag for transcriptional activation. EP300 mediates cAMP-gene regulation by binding specifically to phosphorylated CREB protein. It further mediates acetylation of histone H3 at 'Lys-122' (H3K122ac), a modification that localizes at the surface of the histone octamer and stimulates transcription, possibly by promoting nucleosome instability.

DAXX

DAXX (Fig. 3) is Death domain-associated protein 6 gene. The gene is a transcription corepressor known to repress transcriptional potential of several sumoylated transcription factors. The gene also down-regulates basal and activated transcription. Its transcription repressor activity is modulated by recruiting it to subnuclear compartments like the nucleolus or PML/POD/ND10 nuclear bodies through interactions with MCSR1 and PML, respectively. DAXX helps to regulate transcription in PML/POD/ND10 nuclear bodies together with PML and may influence TNFRSF6-dependent apoptosis and thereby inhibits transcriptional activation of PAX3 and ETS1.

ATM

ATM (Fig.3) is Serine-protein kinase. The Serine/threonine protein kinase activates checkpoint signaling upon double strand breaks (DSBs), apoptosis and genotoxic stresses such as ionizing ultraviolet A light (UVA), thereby acting as a DNA damage sensor. ATM gene recognizes the substrate consensus sequence [ST]-Q .It phosphorylates 'Ser-139' of histone variant H2AX at double strand breaks (DSBs), thereby regulating DNA damage response mechanism. It also plays a role in pre-B cell allelic exclusion, a process leading to expression of a single immunoglobulin heavy chain allele to enforce clonality and monospecific reactions.

HSP90AA1

HSP90AA1 (Fig. 3) is Heat shock protein HSP 90-alpha gene. It is a molecular chaperone that promotes the maturation, structural maintenance and proper regulation of specific target proteins involved for instance in cell cycle control and signal transduction. The gene undergoes a functional cycle that is linked to its ATPase activity which is essential for its chaperone activity. This cycle probably induces conformational changes in the client proteins, thereby causing their activation. The gene interacts dynamically with various co-chaperones that modulate its substrate recognition, ATPase cycle and chaperone function.

SFN

SFN (Fig.3) is 14-3-3 protein sigma gene. The adapter protein is implicated in the regulation of a large spectrum of both general and specialized signaling pathways. It binds to a large number of functional partners, usually by recognition of a phosphoserine or phosphothreonine motif. It’s binding generally results in the modulation of the activity of the binding partner. When bound to SIRT17, SFN regulates protein synthesis and epithelial cell growth by stimulating Akt/mTOR pathway. SFN also regulate MDM2 autoubiquitination and degradation and thereby activating p53/TP53 tumour protein.

TP53BP2

TP53BP2 (Fig.3) is Apoptosis-stimulating of p53 protein 2 gene. It is a regulator that plays a central role in regulation of apoptosis and cell growth via its interactions with proteins such as TP53. It regulates TP53 by enhancing the DNA binding and transactivation function of TP53 on the promoters of proapoptotic genes in vivo. It also inhibits the ability of APPBP1 to conjugate NEDD8 to CUL1, and thereby decreases APPBP1 ability to induce apoptosis. The gene impedes cell cycle progression at G2/M. Its apoptosis-stimulating activity is inhibited by its interaction with DDX42.

Genetic correlation analysis of TP53 and functional partner genes associated with hepatocellular carcinoma and liver cancer in normal and disease conditions.

Results of analysis of genetic correlation for TP53 and SIRT1 genes (Fig. 4a and Table 2) using the Genetic Correlation R Analyzer programme showed positive slope values (rate of mutation in genes), positive intercept values, positive determination coefficients R and weak, non-significant strength of associations (<0.5) in both cancerous and normal liver conditions. However, the degree of association was higher (37%) in liver cancer than in normal liver conditions (25%).

Results of analysis of genetic correlation for TP53 and RPA1 genes (figure 4b) using the Genetic Correlation R Analyzer programme showed very small negative slope values (rate of mutation in genes), positive intercept values, positive determination coefficients R and very strong, significant strength of associations (>0.5) in both cancerous and normal liver conditions. The degree of association was however higher (93%) in liver cancer than in normal liver conditions (87%).

Genetic correlation analysis for TP53 and MDM2 genes (Fig. 4c and Table 2) using the Genetic Correlation R Analyzer programme showed very small positive slope values (rate of mutation in genes), positive intercept values, positive determination coefficients R and very weak, non-significant strength of associations (>0.5) in both cancerous and normal liver conditions. The degree of association was very low (4%) in liver cancer and a bit higher in normal liver conditions (16%).

Genetic correlation analysis for TP53 and CREBBP genes (Fig. 4d and Table 2) using the Genetic Correlation R Analyzer programme showed very small positive slope values (rate of mutation of genes), positive intercept values, positive determination coefficients R and very weak, non-significant strength of association (>0.5) in cancerous condition but strong, significant strength of association (>0.5) in normal liver conditions. The degree of association was very low (5.9%) in liver cancer but very high in normal liver conditions (82%).

Results of analysis of genetic correlation for TP53 and EP300 genes (Fig. 4e and Table 2) using the Genetic Correlation R Analyzer programme showed very small positive slope values (rate of mutation of genes), positive intercept values, positive determination coefficients R and a weak, non-significant strength of association (>0.5) in cancerous liver and a strong, significant strength of association (>0.5) in normal liver conditions. The degree of association was however low (24%) in liver cancer but high in normal liver conditions (75%).

Results of analysis of genetic correlation for TP53 and DAXX genes (Fig. 4f and Table 2) using the Genetic Correlation R Analyzer programme showed very small negative slope values (rate of mutation of genes), positive intercept values, positive determination coefficients R and strong, significant strength of associations (>0.5) in both cancerous and normal liver conditions. The degree of association was however higher (89%) in liver cancer than in normal liver conditions (88%).

Results of analysis of genetic correlation for TP53 and ATM genes (Fig. 4g and Table 2) using the Genetic Correlation R Analyzer programme showed very small positive slope values (rate of mutation of genes), positive intercept values, positive determination coefficients R and very weak, non-significant strength of associations (>0.5) in both cancerous and normal liver conditions. The degree of association was (10%) in liver cancer and 12% in normal liver conditions.

Results of analysis of genetic correlation for TP53 and HSP90AA1 genes (Fig. 4h and Table 2) using the Genetic Correlation R Analyzer programme showed very small positive slope value (rate of mutation in genes) in liver cancer and a very small negative slope value (rate of mutation of genes) in normal liver conditions, positive intercept values, positive determination coefficients R and strong, significant strength of associations (>0.5) in both cancerous and normal liver conditions. The degree of association was however higher (78%) in liver cancer than in normal liver conditions (50%).

Results of analysis of genetic correlation for TP53 and SFN genes (Fig. 4i and Table 2) using the Genetic Correlation R Analyzer programme showed very small negative slope value (rate of mutation in genes) in liver cancer and very small positive slope value (rate of mutation of genes) in normal liver condition, positive intercept values, positive determination coefficients R and a strong, significant strength of associations (>0.5) in both cancerous and normal liver cases. The degree of association was however higher (77%) in normal liver than in liver cancer conditions (73%).
Results of analysis of genetic correlation for TP53 and TP53BP2 genes (Fig. 4j and Table) using the Genetic Correlation R Analyzer programme showed very small positive slope values (rate of mutation of genes), positive intercept values, positive determination coefficients R and very weak, non-significant strength of association (<0.5) in liver cancer and a strong, significant strength of association (>0.5) in normal liver condition. The degree of association was higher (83%) in normal liver than in liver cancer conditions (5.2%).

Table 2: Genetic correlation and P- values of TP53 and related functional partner genes associated with Hepatic carcinoma and Liver cancer among patients under consideration.

S/N	Gene name	Description	TP53 Correlation in Liver Cancer	TP53 Correlation in Normal Liver	Rate of change (mutation of genes)	P-Value
1	TP53	Tumour protein 53
2	SIRT 1	Sirtuin 1	Y=0.028 +0.34x; R= 0.37	Y=0.018 +0.14x; R= 0.25	Small and positive for both conditions	0.00124
3	ATM	ATM serine/Threonine kinase	Y=0.043 +0.13x; R= 0.1	Y=0.083 +0.11x; R= 0.12	Small and positive for both conditions	0.001687
4	TP53BP2	Tumour protein P53 binding protein 2	Y=0.045 +0.047x; R= 0.052	Y=0.016 + 0.66x; R= 0.83	Small and positive for both conditions	0.001792
5	MDM2	MDM2 proto-oncogene	Y=0.032 +0.05x; R= 0.44	Y=0.04 +0.11x; R= 0.16	Small and positive for both conditions	0.002179
6	RPA 1	Replication protein A1	Y=-0.0059 +1.3x; R= 0.93	Y=-0.022 +0.95x; R= 0.87	Small and negative for both conditions	0.000001465
7	DAXX	Death domain associated protein	Y=-0.0097 +1.2x; R= 0.89	Y=-0.0042 +0.91x; R= 0.88	Small and negative for both conditions	0.000002221
8	HSP90AA1	Heat shock protein 90 alpha family class A member 1	Y=0.014 +0.77x; R= 0.78	Y=-0.08 +0.52x; R= 0.5	Small and positive in cancer but negative in normal condition	0.00001064
9	SFN	Stratifin	Y=-0.018 +0.82x; R= 0.72	Y=0.0066 +0.63x; R= 0.77	Small and negative for cancer but positive for normal condition	0.0000265
10	CREBBP	CREB binding protein	Y=0.041 +0.049x; R= 0.059	Y=0.051 +0.069x; R= 0.82	Small and positive for both conditions	0.00002827
11	EP300	E1A binding protein P300	Y=0.033 +0.22x; R= 0.24	Y=0.061 +0.68x; R= 0.75	Small and positive for both conditions	0.00005194

The results of determination coefficients R for TP53 and functional partner genes is presented in figure 5. The coefficient depicts the strength of association between TP53 and each partner gene in normal liver and liver cancer conditions. The degree or strength of association was higher in normal liver for 6 of the 10 evaluations while 4 showed high strength of associations in liver cancer conditions.

The result of functional partner gene reaction hotspots of TP53 and other genes are presented in Table 3. The prosites in silico tool of expasy.org suite was used to obtain the sequence for each functional partner gene from the protein – protein interaction (Figure 2) network and used for the scanning, analysis, and identification of the reaction prosites of these genes in liver cancer cells. The results revealed that the protein kinase II phosphorylation site II was the most functional protein reactional site for the TP53 and associated genes in liver cancer initiation and progression. Other functional protein domain hotspot sites for TP53 and associated genes included the N-glycosylation protein site, N -Myristylation protein site, N-Amidation protein sites, Tyrosine kinase 1 and II phosphorylation sites, cAMP and cGMP phosphorylation sites as well as the casein kinase II phosphorylation sites (Table 3).

Table 3: Functional Domain Reaction Hotspots for TP53 and functional gene partners in hepatocellular carcinoma and Liver Cancer conditions

Gene Name	Description	Molecular protein Family signature	PKC	cAMP/cGMP site	N-Glycosylation site	N-Myristylation site	Tyrosine Phosphorylation site 1	Tyrosine phosphorylation site 2	CK2	N-Amidation site	Rich region
TP53	Tumour protein 53	P53 family signature (237 – 249 bp)	99 – 101 bp	-	239 – 242 bp	117 – 122 bp	-	320 – 327 bp	18 -21 bp	-	Proline (58 -92 bp
SIRT 1	Sirtuin 1	Sirtuin Catalytic domain signature 236 – 496 bp	177- 179	-	342 – 345 bp	12 – 17 bp	635 – 642 bp	516 – 523 bp	47 – 50 bp	-	Alanine (54 -–98 bp)
ATM	ATM serine/Threonine kinase	FAT domain family signature (1940 – 2566 KDa)	21 – 23 TeR	1920 - 1923 KRpS	81 - 84 NVSA	134 - 139 GAIYGA	116 – 123 bp	2160 – 2167 KRSLFSVY	127 - 130 TVKD		Leucine (1217 – 1238 KDa)
TP53BP2	Tumour protein P53 binding protein 2	P53 protein family signature	7 -9 TaR	9-12 RKsT 116 – 119 KRvT							Proline (76 – 79) VaTR
MDM2	MDM2 proto-oncogene	Zinc finger domain family signature 309 – 328 KDa	55 – 57 TmK	-	11-14 NMSV	18 – 23 GAVtTS			28 – 31 SeqE		-
RPA 1	Replication protein A1	Replication factor protein family signature	43 -45 SrK	AsTR 129 -132							-
DAXX	Death domain associated protein	Protein signature	138 – 140 SaK	590 -593 RKqS	364 – 367 NRSL	8-13 GAiaAI	595 -602 KPvDyreY	327 – 334 RrhlDliY	40 -43 SssE	54 – 57 gGKK	Glutamic acid 434 – 572 KDa
HSP90AA1	Heat shock protein 90 alpha family class A member 1	Heat shock protein family signature 160 – 169 KDa	16 -18 SIR	330 – 333 KHhS	173 -176 NSSD	6 -11 GGdqsT	275 – 282 KhnDdegT	635 – 642 KhglEviY	5 – 8 SqqD		Glutamic acid 345 – 390 KDa
SFN	Stratifin	Protein signature 1 41 – 51 KDa	146 – 148 SaR		175 -178 NFSV	53 – 58 GGqrAA	77 – 84 kqPEVreY		37 – 40 SceE		Proline 213 – 232 KDa
CREBBP	CREB binding protein	ABC Transporter family signature 123 – 137 bp	387 – 389 bp	668 – 671 bp	25 – 28 bp	48 -53 bp	634 – 641 bp		23 – 26 bp	1200 – 1203 bp	Glutamine (683 -960 bp)
EP300	E1A binding protein P300	Bromodomain family signature 1067 – 1139 KDa	12 – 14 SaK	647 – 650 KRrT	49 – 52 NSTE	30 -35 GTdfGS	613 – 620 RKVEgdmY		26 – 29 SasD	1164 – 1167 cGRK	Proline 666 – 943 KDa

Single Linkage genetic cluster and principal component analysis of TP53 and functional partner genes in hepatocellular carcinoma and liver cancer patient results are presented in Table 4. Two major principal components were used to explain the dimensionality of variations in gene action in liver cancer conditions. The Serine/Threonine kinase ATM gene showed the highest impact (0.46) on the first principal component while the heat shock protein 90 alpha family class (HSP90AA1) gene impacted the least (-0.0822) effect on PC1. The contributions of the different genes to PC2 showed all negative dimensionality with SFN gene contributing the highest loading value of -0.0958 to the dimensionality observed in PC2 while the least loading value of -0.434 to the overall variation was contributed by RPA1 gene. The TP53 and associated gens were delineated into two major cluster groups based on their dimensionality. Cluster one had TP53, RPA1, DAXX and SFN genes. Cluster 2 has ATM, MDM2, EP300, SIRT 1, CREBBP and HSP90AA1 genes as sub cluster members (Table 4). The red-coloured site (Fig. 6) revealed axis with high reaction intensity among the TP53 and associated genes. The green-coloured axis portray interactions that are very weak and less intense while the black-coloured axis reveals areas of moderate to average interactions among TP53 and associated functional partner genes (Fig. 6)

Table 4: Single linkage genetic cluster and principal component analysis of TP53 and other functional partner genes in hepatocellular carcinoma and liver cancer patients

The high proportion of liver cirrhosis and hepatocellular carcinoma among married women may have resulted in high incidence of the disease presentation in men due to transfer of hepatitis virus infection to their spouses who have not received the viral vaccination. The diverse distribution of hepatocellular carcinoma and liver cirrhosis in different ethnic groups have been documented in several population [41,42,43,44,45]. These documented findings are similar to our present results in Calabar where the disease was distributed among the ethnic groups in Northern/central Cross River State, Efiks and Ibibios.

Also, similar genotypes with varying mutations were published in Egyptian populations [46,47,48,49,50,51,52,53,54]. In Morocco population, the same genotypes were reported [55,56,57,58,59,60], Kenyan population [61,62,63,64,65] and in Ibadan, Nigeria [66,67,68,69,70] among HCC and liver cirrhosis patients although in different proportions. The detection of strong and positive association between TP53 and associated genes in liver cancer patients and in normal liver conditions point to the fact that TP53 gene was associated with the molecular etiology of the disease in a sample population of Calabar. This finding is like the documented research in Egypt [71,72,73,74] where the TP53 gene was highly implicate in liver disease conditions and normal healthy conditions as well. The TP53 gene mutation produced the causal alleles in the studied populations and are associated in the molecular etiology of liver cirrhosis in Calabar, Nigeria, like other published results in Ibadan, Nigeria [75 - 79], Egyptians populations [80 – 90]. The TP53 gene polymorphisms were detected in more males than female cases in our present study in Calabar, agreeing to other documented reports [91 – 94]. The mutation of TP53 gene may lead to expression of a mutant TP53 protein that has lost wild type function (controlling of cell cycles) and may deploy a dominant negative regulation over the rest of wild type TP53 that suppress tumour through hetero-oligomerizing with wild type TP53. Therefore, acting as a competitive inhibitor of wild type TP53 gene, supported by other published research [95 -100]

Specifically, TP53 gene mutations variants detected in this study among HCC and liver cirrhosis patients were associated with the disease etiology. [101-104] reported no association of TP53 gene to the molecular etiology of cancers in Iran, disagreeing with this present finding in Calabar among HCC and liver cirrhosis patients. The high prevalence of mutant alleles of the TP53 gene polymorphism among HCC and liver cirrhosis patient in Calabar may be associated with consumption of food heavily contaminated by aflatoxin B1 and HBV/HCV co-infections endemic city in Northern and Central Cross River State. Groundnut has been reported to be the most heavily contaminated food item by aflatoxin [105, 106]. Groundnut consumption and groundnut soup are some of the local delicacies of the natives of North/Central Cross River State, and these may be attributed to the high prevalence of HCC among the people of the region. Researchers have associated the amount of groundnut ingested daily with significant levels of serum aflatoxin and the presence of this genetic mutation [107], supporting our findings in Calabar for high prevalence of this TP53 gene polymorphism.

The mutant alleles of the TP53 gene polymorphism were also detected in liver cirrhosis patients in our present findings in Calabar. The alteration of the TP53 gene in mutant alleles may have altered critical cell-cycle arrest, apoptosis after DNA damage, thereby promoting the progression from chronic liver diseases to hepatocellular carcinoma. This have been supported by previous studies conducted [108]. The mutations of TP53 gene individuals are also fast track by chronic hepatitis infections, Aflatoxin-B1, alcoholism/smoking, family history of cancer and other environmental factors. These factors are previously reported in different studies [108, 109]. The case-control research has associated gene polymorphism to the etiology of liver cirrhosis and hepatocellular carcinoma in Calabar, Nigeria.

In contrast with the genetic correlation study analysis of the present study, hypoxic stress, like DNA damage, induces p53 protein accumulation and p53-dependent apoptosis in oncogenically transformed cells. Unlike DNA damage, hypoxia does not induce p53-dependent cell cycle arrest, suggesting that p53 activity is differentially regulated by these two stresses. Hypoxia induces p53 protein accumulation, but in contrast to DNA damage, hypoxia fails to induce endogenous downstream p53 effector mRNAs and proteins, such as p21, Bax, CIP1, WAF1 [109, 110]. Hypoxia does not inhibit the induction of p53 target genes by ionizing radiation, indicating that p53-dependent transactivation requires a DNA damage-inducible signal that is lacking under hypoxic treatment alone. The phosphatidylinositol 3-OH-kinase-Akt pathway inhibits p53-mediated transcription and apoptosis. Mdm2, a ubiquitin ligase for p53, plays a central role in regulation of the stability of p53 and serves as a good substrate for Akt. Mdm-2 targets the p53 tumor suppressor for ubiquitin-dependent degradation by the proteasome, but, in addition, the p53 transcription factor induces Mdm-2, thus, establishing a feedback loop. Hypoxia or DNA damage by abrogating binding of HIF-1 with VHL and p53 with Mdm-2, respectively, leads to stabilization and accumulation transcriptionally active HIF-1 and p53. At the molecular level, DNA damage induces the interaction of p53 with the transcriptional activator p300 as well as with the transcriptional corepressor mSin3A. In contrast, hypoxia primarily induces an interaction of p53 with mSin3A, but not with EP300 as revealed in the present study.

The present study has shown that the rate of change (mutation) in TP53 and the associated functional partner genes play major significant role in hepatocellular carcinoma and liver cancer disease conditions in humans. Genetic correlation analysis and the bioinformatics study of the TP53 and functional gene partners oncology is crucial in understanding the etiology, risk factors, associated genes, management and treatment of hepatocellular carcinoma and liver cirrhosis in diseased and normal liver conditions in humans.

TP53 Tumour protein 53

RPA1 Replication protein A1

DAXX Death domain associated protein

SFN Stratifin

ATM ATM serine/Threonine Kinase

MDM2 MDM2 proto-oncogene

EP300 E1A binding protein p300

SIRT1 Sirtin 1

CREBBP CREB binding protein

HSP90AA1 Heat shock protein 90 alpha family 1

DNA Deoxyribonucleic acid

HCC Hepatocellular carcinoma

PCA Principal component analysis

KDa KiloDalton

CK2 Casein kinase II phosphorylation site

PK2 Protein Kinase II phosphorylation site

R Coefficient of Determination

T.Pl Theoretical Isoelectric point

E.Co Extinction coefficient

IINDEX Instability index

AIINDEX Aliphatic index

PAAR Positive amino acid residues

NAAR Negative amino acid residues

MW Molecular weight

PCR Polymerase chain reaction

Ethics approval and consent to participate:

Ethical approval for the research was sought and obtained from the Ministry of health, Mary Slessor Avenue, Calabar, Cross River State, Nigeria.

Consent for publication:

The consent for publication of this research was agreed by all participating and contributing authors to this research work.

Availability of data and materials:

“Yes”. The sequences obtained from the samples have not yet been deposited. We intend to deposit the data at the NCBI. Prof. (Mrs.) Mary E. Kooffreh (Prof. of Human Genetics, Department of Genetics and Biotechnology, Faculty of Biological Sciences, University of Calabar, Calabar, Cross River State, Nigeria) should be contacted if the need arises.

The chromatograms of the TP53 gene (DNA) sequences were obtained from Inquaba Biotech for the forward and reverse primers used. The Chromas pro 7 software was used to copy the DNA sequences from the chromatograms after Bioedit using Trace edit tool. The expasy.org Translator was used to translate the DNA sequences to protein sequences. The protein sequences were BLAST for sequence similarity and identity homology. The Blast results return 100% similarity with the TP53, TP63, TP73 gene paralogs and 100% similarity with PDB structures for 26 homologs such 1q2i, 2k8f, 1ycq, 3dab, 3dac, 7nmi, 2li4, 2b3q, 2qs0, 2mzd and more.

MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTEDPGPDEAPRMPEAAPPVAPAPAAPTPAAPAP
APSWPLSSSVPSQKTYQGSYGFRLGFLHSGTAKSVTCTYSPALNKMFCQLA
KTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTEVVRRCPHHERCSDSDGLAPPQHLIRVEGNLRVEYLDDRNTFR
HSVVVPYEPPEVGSDCTTIHYNYMCNSSCMGG
MNRRPILTIITLEDSSGNLLGRNSFEVRVCACPGRDRRTEEENLRKKGEPHHELPPGSTKRALPNNTSSSPQPKKKPLD
GEYFTLQIRGRERFEMFRELNEALELKDAQAGKEPGGSRAHSSHLKSKKGQSTSRHKKLMFKTEGPDSD

Competing interests:

The authors declare that there were no competing interests in this research manuscript.

Funding:

The authors declare that no funding was received for this research study.

Authors contributions.

GMU, ISE, EBB, EIO, BEE, ENA, IAE, ENE, KME, and COA all actively participated and contributed immensely in the development of this research paper. GMU, EBB and KME conceived the idea and project concept and drafted the manuscript. ISE, EBB, EIO, BEE, ENA, IAE, ENE searched the literatures and collated relevant information for the study. EBB, KME and GMU collated relevant data using relevant instruments. GMU, KME, EBB and ISE analyzed the data using appropriate software. All authors read the manuscript and criticized where necessary before approval for submission to BMC Bioinformatics.

Acknowledgement

The authors wish to acknowledge the Director of Biggmade Int’l Academy of Sciences Ltd. For encouragement and laboratory space for workflow processing and analytical software provision.

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No competing interests reported.

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Bioinformatics and Genetic Correlation Studies of Functional Gene Partners of Tp53 Gene Associated With Hepatocellular Carcinoma and Liver Cirrhosis Among Patients in Ucth, Calabar

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2.1 The study location

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