Skip to main content

The role of BCL9 genetic variation as a biomarker for hepatitis C-related hepatocellular carcinoma in Egyptian patients

Abstract

Background

Hepatocellular carcinoma (HCC) is considered one of the most common cancers related to mortality around the world, and susceptibility is related with genetic, lifestyle, and environmental factors. Copy number variation of the Bcell CLL/lymphoma 9 (BCL9) gene is a type of structural variation which can influence gene expression and can be related with specific phenotypes and diseases and has a role in hepatocarcinogenesis. Our aims were to assess the copy number variation (CNV) in the BCL9 gene and explore its role in HCV-related HCC Egyptian patients. A total of 50 HCV-related HCC patients were enrolled in the study (including 25 early HCC and 25 late HCC cases); the copy number of the BCL9 gene was detected using quantitative polymerase reaction.

Results

There was a highly statistically significant difference between the two groups (early and late HCC patients) in gender, bilharziasis, performance status, child score class, child grade, focal lesion size, portal vein, and ascites. CNV was detected and represented by the gain in the BCL9 gene in 14% of patients, and all of them were males. Also, it was noticed that the ratio of gain in BCL9 copy number in late individuals was about 1.5 times than that in early HCC individuals. Moreover, our results showed that the distribution of performance status > 1, average and enlarged liver, focal lesion size, thrombosed portal vein, and AFP was higher in patients with BCL9 copy number gain.

Conclusion

We detected about 14% gain in BCL9 copy number in Egyptian HCC patients. But the variation in copy number of the BCL9 gene did not affect HCC development in our patients’ cohort.

Background

Hepatocellular carcinoma (HCC) is one of the most universal problems worldwide, it is the 6th most common cancer and the 4th cause of cancer-related death all over the world. There is variation in the predominance of HCC in the epidemiological data from one place to another [1, 2]. In Egypt, HCC ranks the 4th common cancer, and it is considered the most reason for mortality- and morbidity-associated cancer [3, 4].

There are many risk factors in HCC development, which include [1] environmental factors such as hepatitis B virus (HBV) and hepatitis C virus (HCV) infection, chemical compounds, alcohol, and smoking [2]; host-related risk factors such as gender, ethnicity, obesity, autoimmune hepatitis, diabetes, and non-alcoholic fatty liver disease (NAFLD) [3]; genetic-related factors such as the history of HCC, aflatoxins, and genetic alterations [5]. Both HBV and HCV increase HCC risk by 20-fold [6,7,8]. In Egypt, HCV is the most critical risk factor in liver cancer including HCC; about 21.4% of HCC individuals have a family history of HCC [9, 10].

Although the survival rate is approximately 5 years, this rate is estimated to be 18%, and the median survival is only 7 months, if untreated [11,12,13,14]. HCC patients are often diagnosed at the advanced phase when treatment is restricted to the systemic therapy and multi-kinase inhibitors which are combined with side effects [15, 16]. So, we are in a critical need for characterization of the molecular mechanistic targets in HCC and tumor-specific signaling pathways, which are still a major goal of HCC research, to diagnose and treat it at the early stage.

Also, it was shown that human genetic variations such as genetic mutation, single nucleotide polymorphism (SNP), copy number variation (CNV) affect the susceptibility to liver cancer and carcinogenesis [17, 18].

Copy number variations have been discovered as intra-species alterations, which occurs as a repeating of a sequence of nucleotides in tandem multiple times in an individual’s genome, including insertion (gain) or deletion (loss) of genetic material with a highly significant biological role; they range in the human genome from 10 kb to 1 Mb in size and cover about 12% of the human genome [19, 20]. These CNVs can change the gene structure and gene expression and lead to significant phenotypic variation [21]. Many studies have reported a critical role for CNVs in important molecular mechanisms of pathogenesis in autoimmune diseases and infectious diseases [22,23,24,25].

The previous studies identified that several genes involved in many diseases exhibit CNV such as CYP2D6, MMP-6, BCL9, and BCL9L; these CNVs can be considered as prognostic biomarkers for many diseases [26,27,28,29,30,31]. CN gains on 1q, 8q, and 20q and losses on 1p, 8p, 4q, 13q, 16q, and 17p have been previously recognized in HCC [32,33,34].

The human Bcell CLL/lymphoma 9 (BCL9) gene, located at chromosome 1q21, was discovered as an overexpressed gene in a precursor B cell acute lymphoblastic leukemia cell line with t(1;14)(q21;q32) translocation [35]. It was found that BCL9 is an essential co-activator in the Wnt/β-catenin signaling pathway, enhances β-catenin-mediated transcription activity, and increases cell proliferation, invasion, migration, and metastatic potential of tumor cell [36, 37].

BCL9 has an oncogenic function as it is overexpressed in many solid tumors including HCC and colorectal cancer; this is combined with a poor prognosis of HCC cases [38,39,40]. Previous studies have examined the role of BCL9 in cancer, including HCC [27, 30, 41]. A copy number gain on 1q is frequently observed in HCC; the BCL9 regional chromosomal gain represents a primary mechanism in the activation of proto-oncogenes during HCC progression [27, 28, 33]. The prognostic significance of BCL9 in HCC remains uncertain. However, in hepatocellular tumors, the deletion of BCL9 increased survival rates, reduced liver size and cell proliferation, decreased expression of Wnt target genes, and inhibited the HCC migration and invasion [30, 41, 42]. So, BCL9 can be the center stage in the HCC treatment.

In the studies of HCC, the role of BCL9 expression has not been characterized especially in Egypt. Therefore, in the present study, we evaluated BCL9 CNV by qPCR in order to elucidate the prognostic significance of BCL9 in HCC-infected patients with clinopathological characteristics.

Methods

Study population

A total of 50 patients were enrolled in the present study including (25 HCC-infected patients in the early stage and 25 HCC infected patients in the last stage). All cases were gathered from the National Hepatology and Tropical Medicine Research Institute, Cairo, Egypt, between December 2019 and April 2020. Subjects who had co-infection with HBV, HIV, EBV, CMV, bilharziasis, diabetes mellitus, autoimmune diseases, and other cancers and also had a history of alcohol consumption were excluded from the study.

This study was approved by the Ethics committee of the Medical Research, registration number (17122), National Research Centre, Dokki, Cairo, Egypt, and all participants signed the patient’s concept form for this research. Fibrosis was assessed by FIB4 score. Some patients had a biopsy report assessed by Metavir. This study was approved by the local research ethics committee, and written informed consent was obtained from all participants in this study.

Blood collection and plasma separation

Collect about 10 ml peripheral blood in EDTA tubes, then centrifuge the tubes immediately at 3000 rpm for 10 min, at room temperature, then at 14,000 rpm for 15 min at 4 °C. Finally, transfer the supernatant plasma into 2-ml tubes and preserve them at once at − 80°C until further use.

Circulating-free DNA extraction

Circulating-free DNA (cfDNA) was isolated from the frozen plasma using the QIAamp Circulating Nucleic Acid Kit (Qiagen#55114) following the manufacturer’s instructions and store at − 20 °C for genetic determination. The concentration of the purified cfDNA was determined by NanoDrop™ One/OneC Microvolume UV-Vis Spectrophotometer (Catalog number: 701-058108, Thermo Fisher Scientific, Invitrogen).

Measurement of BCL9 copy number

The copy number variations were determined by using Real-Time Quantitative PCR [43, 44]. The copy number of the BCL9 gene was measured in each cfDNA sample using the Mx3000p comparative quantitative PCR system (Agilent Technologies, Germany) and TaqMan copy number assay. TaqMan CN assay is a duplex experiment using The FAM™ dye-labeled TaqMan® Copy Number Target Assay for BCL9 (Assay id:Hs00964453-cn, Cat. No. 4400291, Applied Biosystem, USA), with the VIC® dye-labeled TaqMan® Copy Number Reference Assay for RNase P (RPPH1) (Assay id:4403326, Applied Biosystem, USA) in the same well to quantity small fold changes. The quantitative polymerase chain reaction (qPCR) was carried out in 20 μl volume using 10 μl (2×) TaqMan genotyping master mix (Applied Biosystem, USA), 1 μl (20×) TaqMan copy number reference assay RNase P (RPPH1) as an internal control (Assay id:4403326, Applied Biosystem, USA), 1 μl (20×) Target BCL9 copy number assay (Assay id:Hs00964453-cn, Cat. No. 4400291, Applied Biosystem, USA), 4 μl cfDNA sample (5 ng/μl concentration), and complete the reaction volume with DNAse-free water (Promega, USA). TaqMan target CN assay and TaqMan® Copy Number Reference Assay are in the same well to quantity small fold changes. All assays were performed in triplicates of each sample and a calibrator sample (healthy sample) and negative control (with water instead of cfDNA sample) in 96-well PCR plate for amplification, using the following program: 95 °C for 10 min as the initial melting, followed by 40 cycles at 95 °C for 15 s and 60 °C for 1 min.

The copy number was calculated by the ΔΔCt method [45] to obtain the relative quantity (RQ) then multiply it by the copy number of the calibrator, which in this case are 2.

Statistical analysis

The statistical analysis was performed using the Social Sciences (SPSS) version 26 (IBM Corp., Armonk, NY, USA). Data were summarized using a median and interquartile range in normally quantitative data, using mean ± standard deviation in non-normally distributed data, and using frequency (count) and relative frequency (percentage) for categorical data. Comparison between the distributions of categorical variables was performed using the chi-square (χ2) method, and comparisons between quantitative variables were done using the non-parametric Kruskal-Wallis test. Moreover, variables were described as odds ratio (OR) with 95% confidence interval (95% CI) by using crosstabs and binary logistic regression analysis. All P-values were 2-sided, and P-values less than 0.05 were considered as statistically significant and less than 0.01 were considered highly significant.

Results

Demographic data and clinical investigation of the study population and Association of HCC characteristics and baseline parameters with HCC development

The demographic, biochemical, virological, and histopathological data of all 50 HCV-related HCC-infected patients are summarized in Table 1. Patients have a mean age of 63.14 with 78% of males, about 84% of patients have higher AFP, and all patients were cirrhotic. Fifty percent of patients were at the early stage, and the other 50% were at the late stage, the majority of patients have child score class B, patent portal vein, and no ascites.

Table 1 Demographic and clinical parameters of the early versus late HCC patients

Univariate analysis of our data in Table 1 showed that there was a highly statistically significant difference between the two groups (early and late HCC patients) in gender, BCLC, bilharziasis, performance status, child score class, child grade, focal lesion size, portal vein and ascites, and creatinine. Ninety-six percent of late HCC patients were males, about half of late HCC patients (56%) had enlarged liver in size; on the contrary, half of early HCC patients (56%) had an average liver. Also, it was found that all early HCC patients had no thrombosed portal vein (0.00%) and 92% of them had no ascites.

Variation of copy number of BCL9 in male versus female HCC patients

Among our studied population of HCC patients, CNV was detected and represented by the gain in BCL9 gene in 14% of patients and all of them were males as in Fig. 1, and CNV gain was not documented in female HCC patients. CNV frequencies in the BCL9 gene are outlined in Table 2.

Fig. 1
figure 1

Pie chart of distribution of BCL9 CNV in HCC patients. The figure shows the copy number distribution of the BCL9 gene. Seven HCC patients (14%) had gain in BCL9 CN, and 43 patients (86%) had no gain in BCL9 CN. Gain: CN gain, no. = 7, % = 14; no gain: no gain in CN, no. = 43, % = 86

Table 2 Distribution of CNV in HCC patients

Correlation of CNV in the BCL9 gene in HCC patients with HCC development

CNV in the BCL9 gene showed differential distribution within early and late HCC patients as shown in Table 3. The distribution of gain within early and late HCC individuals was 12% and 16%, respectively, so it was noticed that the ratio of gain in BCL9 copy number in late individuals was about 1.5 times than that in early HCC individuals.

Table 3 BCL9 CN gain in HCC patients according to early and late HCC stages

Correlation between variation in the BCL9 gene CN in male patients and the clinical characteristics of HCC patients

The differential alteration was seen in clinical and HCC characteristics parameters according to CNV in the BCL9 gene as outlined in Table 4. This difference did not reach a significant association in all studied parameters; our results showed that 57% of those carrying gain in CNV were at an advanced stage of HCC (BCLC stage C). The frequency of family history of HCC in males within CNV gain HCC patients was few (14.29%).

Table 4 BCL9 CN gain according to male gender and clinical and HCC characteristics parameters

Also, the frequencies of performance status > 1, average and enlarged liver, focal lesion size, and thrombosed portal vein were higher in male patients having BCL9 CN gain.

Although there was no statistical significance between gain in CN of BCL9 and AFP, the majority of patients (about 85%) who have CNV gain have higher AFP values. Additionally, AST and ALT values were higher in the CNV gain group.

Discussion

HCC is considered the most common primary liver cancer, it accounts for about 90% of cases, which develops mostly in the setting of chronic liver disease [3, 46].

There is gender variation of HCC incidence; as in this current study, 78% of HCC cases were males, and this agrees with the previous studies which showed that HCC predominantly is more common in males [11, 47]. The strongest risk factor for HCC development is cirrhosis, about 80% of HCCs develop in the cirrhotic liver [48,49,50]. Here, we found that all patients have a cirrhotic liver.

The prognosis of patients with this tumor remains poor [50]. Progression pattern may be a key prognostic parameter and important pattern for the fate of HCC, and the performance of histological biomarkers with clinical data has shown promise for surveillance and early diagnosis [1]. In this study, our results showed a highly significant correlation between stages of HCC groups (early and late HCC) and gender, bilharziasis, performance status, child score and grade, focal lesion size, portal vein, and ascites. Our results showed that most of our late HCC patients had enlarged liver, while our early HCC patients group lacks thrombosed portal vein and ascites, this is consistent with previous reports [51,52,53,54,55]. Also, these results are in line with the results of Hyeon et al., who confirmed that tumor stage, tumor recurrence, and survival were significantly associated with larger tumor size, higher Edmondson grade, microvascular invasion, major portal vein, higher BCLC grade, higher AFP, liver cirrhosis, and viral etiology [27].

Also, it is known that HCC is a heterogenous pathology, involving many somatic genetic mutations; these genetic alterations and abnormal activation of specific pathology are responsible for HCC development, tumor differentiation, and related clinical outcomes [6, 7, 56]. Copy number (CN) in genomic DNA is one of the most observed genetic alterations and chromosomal instability in HCC and can be used as an indicator of major disease [31,32,33,34, 57].

A gain in 1q chromosome is one of the most frequently detected chromosomal alterations in HCC and has been suggested as an early genomic event in the HCC development [58]. BCL9 is considered one of the important oncogenes located at 1q21.1 and encodes B cell CLL/lymphoma 9 which is considered as co-activator of β-catenin-mediated transcription in the Wnt/β-catenin signaling, and its upregulation plays a vital role in certain cancer development and progression, including colorectal cancer and HCC [37, 38]. On the other hand, it inhibits tumor growth and increases the survival outcome of multiple myeloma and colon cancer through blocking the Wnt/β-catenin pathway [30, 41]. Wang et al. credentialed BCL9 as a candidate driver gene as it plays a significant role in HCC growth and survival and may serve as a potential therapeutic target for the disease treatment, as it found a significant correlation between BCL9 somatic copy number and gene expression in primary HCCs [59]. Also, other studies have examined the role of BCL9 in cancer, including HCC [27, 30, 41, 42].

In this study, we focused on studying the alteration in CN of BCL9 in Egyptian HCC patients as a crucial risk factor in HCC development. To the best of our knowledge, this is the first study to use CN analysis of BCL9 to explore the relation between CNV and HCC development, and the role of BCL9 in HCC has not been reported previously in Egypt.

Our results showed variation in BCl9 CN towards gain which was detected in 14% of our patients; this is in agreement with Wang et al.’s study which found that BCL9 was highly amplified in 8.7% of the HCC cohort [59]. Also, we found that all of CN gain was in male patients; this information has not been discussed before and may be one of the reasons why HCC is more common in males, but this needs more research to confirm this theory.

We further noticed that the ratio of CN gain in HCC individuals at the advanced stage was higher than the early stage, and this may reflect the extent of the BCL9 CN prognostic role in HCC development. This matches with other studies which found that somatic CN of BCL9 gene was associated with advanced HCC tumor stage [30, 59, 60].

Here, our results indicated that BCL9 CN gain was associated with the higher performance status, enlarged liver size, higher focal lesion size, and thrombosed portal vein; although this association did not reach the significance, this may lead to that BCL9 CNV might have a role in HCC development, and there was not any significant association between this CN gain and the other parameters. This is in line with the other studies which suggest the involvement of BCL9 in the HCC pathogenesis and prognosis [27, 30, 61].

An important point was discussed in our results; AFP status was higher in the majority of the BCL9 CN gain group, as AFP is the most common marker for HCC diagnosis [62]; therefore, we speculate that there is a vital role of AFP to be taken into consideration as an influencer with BCL9 CNV in HCC patients, but further work is needed to verify our hypothesis.

Conclusion

This study is the first to discuss BCL9 CNV in HCC Egyptian patients; it recorded that there is gain in BCL9 CNV in 14% of HCC patients and also showed that CN gains existed only in male patients. Our finding indicated that CNV of BCL9 did not affect HCC development in our patient cohort, but it was found that gender, bilharziasis, performance status, child score class, child grade, focal lesion size, portal vein, and ascites have correlated with HCC development and prognosis.

Limitations

One of the main limitations in our work is the lack of healthy individuals to compare BCL9 CNV in HCC patients and healthy individuals. Second is the small sample number enrolled in the current study of Egyptian HCC patients. So, future studies should focus on a large number of HCC patients to confirm our results.

Availability of data and materials

All data generated or analyzed during this study are included in this article.

Abbreviations

HCC:

Hepatocellular carcinoma

CN:

Copy number

CNV:

Copy number variation

BCL9:

Bcell CLL/lymphoma 9

HCV:

Hepatitis C virus

HBV:

Hepatitis B virus

NAFLD:

Non-alcoholic fatty liver disease

SNP:

Single nucleotide polymorphism

CYP2D6:

Cytochrome P450 family 2 subfamily D member 6

MMP-6:

Matrix metalloproteinase 6

BCL9L:

B cell CLL/lymphoma 9 like

HIV:

Human immunodeficiency virus

EBV:

Epstein-Barr virus

CMV:

Cytomegalovirus

EDTA:

Ethylenediaminetetraacetic acid

cfDNA:

Circulating-free DNA

QPCR:

Quantitative polymerase chain reaction

RQ:

Relative quantity

χ 2 :

Chi-square

OR:

Odds ratio

CI:

Confidence interval

SD:

Standard deviation

BCLC:

Barcelona Clinic Liver Cancer

References

  1. 1.

    Forner A, Reig M, Bruix J (2018) Hepatocellular carcinoma. Lancet (London, England) 391:1301–1314. https://doi.org/10.1016/S0140-6736(18)30010-2

    Article  Google Scholar 

  2. 2.

    Villanueva A (2019) Hepatocellular carcinoma. The New England journal of medicine 380:1450–1462. https://doi.org/10.1056/nejmra1713263

    Article  Google Scholar 

  3. 3.

    Akinyemiju T, Abera S, Ahmed M, Alam N, Alemayohu MA, Allen C, Al-Raddadi R, Alvis-Guzman N, Amoako Y, Artaman A, Ayele TA, Barac A, Bensenor I, Berhane A, Bhutta Z, Castillo-Rivas J, Chitheer A, Choi JY, Cowie B, Dandona L, Dandona R, Dey S, Dicker D, Phuc H, Ekwueme DU, Zaki MS, Fischer F, Fürst T, Hancock J, Hay SI, Hotez P, Jee SH, Kasaeian A, Khader Y, Khang YH, Kumar A, Kutz M, Larson H, Lopez A, Lunevicius R, Malekzadeh R, McAlinden C, Meier T, Mendoza W, Mokdad A, Moradi-Lakeh M, Nagel G, Nguyen Q, Nguyen G, Ogbo F, Patton G, Pereira DM, Pourmalek F, Qorbani M, Radfar A, Roshandel G, Salomon JA, Sanabria J, Sartorius B, Satpathy M, Sawhney M, Sepanlou S, Shackelford K, Shore H, Sun J, Mengistu DT, Topór-Mądry R, Tran B, Ukwaja KN, Vlassov V, Vollset SE, Vos T, Wakayo T, Weiderpass E, Werdecker A, Yonemoto N, Younis M, Yu C, Zaidi Z, Zhu L, Murray CJL, Naghavi M, Fitzmaurice C (2017) The burden of primary liver cancer and underlying etiologies from 1990 to 2015 at the global, regional, and national level: results from the Global Burden of Disease Study 2015. JAMA oncology 3:1683–1691. https://doi.org/10.1001/jamaoncol.2017.3055

    Article  Google Scholar 

  4. 4.

    Rashed WM, Kandeil MAM, Mahmoud MO, Ezzat S (2020) Hepatocellular carcinoma (HCC) in Egypt: a comprehensive overview. Journal of the Egyptian National Cancer Institute 32:1–11. https://doi.org/10.1186/s43046-020-0016-x

    Article  Google Scholar 

  5. 5.

    Friemel J, Rechsteiner M, Frick L, Böhm F, Struckmann K, Egger M, Moch H, Heikenwalder M, Weber A (2015) Intratumor heterogeneity in hepatocellular carcinoma. Clinical Cancer Research 21:1951–1961. https://doi.org/10.1158/1078-0432.ccr-14-0122

    Article  Google Scholar 

  6. 6.

    Lee JM, Yang J, Newell P, Singh S, Parwani A, Friedman SL, Nejak-Bowen KN, Monga SP (2014) β-Catenin signaling in hepatocellular cancer: implications in inflammation, fibrosis, and proliferation. Cancer letters 343:90–97. https://doi.org/10.1016/j.canlet.2013.09.020

    Article  Google Scholar 

  7. 7.

    Wang Z, Sheng YY, Gao XM, Wang CQ, Wang XY, Lu X, Wei JW, Zhang KL, Dong QZ, Qin LX (2015) β-catenin mutation is correlated with a favorable prognosis in patients with hepatocellular carcinoma. Molecular and clinical oncology 3:936–940. https://doi.org/10.3892/mco.2015.569

    Article  Google Scholar 

  8. 8.

    Zhao C, Nguyen MH (2016) Hepatocellular carcinoma screening and surveillance. Journal of clinical gastroenterology 50:120–133. https://doi.org/10.1097/mcg.0000000000000446

    Article  Google Scholar 

  9. 9.

    Ibrahim AS, Khaled HM, Mikhail NN, Baraka H, Kamel H (2014) Cancer incidence in Egypt: results of the national population-based cancer registry program. Journal of cancer epidemiology 2014. https://doi.org/10.1155/2014/437971

  10. 10.

    Ziada DH, El Sadany S, Soliman H, Abd-Elsalam S, Salama M, Hawash N, Selim A, Hamisa M, Elsabagh HM (2016) Prevalence of hepatocellular carcinoma in chronic hepatitis C patients in Mid Delta, Egypt: a single center study. Journal of the Egyptian National Cancer Institute 28:257–262. https://doi.org/10.1016/j.jnci.2016.06.001

    Article  Google Scholar 

  11. 11.

    Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D, Bray F (2015) Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. International journal of cancer 136:E359–E386. https://doi.org/10.1002/ijc.29210

    Article  Google Scholar 

  12. 12.

    Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D (2011) Global cancer statistics. CA: a cancer journal for clinicians 61:69–90. https://doi.org/10.3322/caac.20107

    Article  Google Scholar 

  13. 13.

    Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A (2015) Global cancer statistics, 2012. CA: a cancer journal for clinicians 65:87–108. https://doi.org/10.3322/caac.21262

    Article  Google Scholar 

  14. 14.

    Yarchoan M, Agarwal P, Villanueva A, Rao S, Dawson LA, Karasic T, Llovet JM, Finn RS, Groopman JD, El-Serag HB (2019) Recent developments and therapeutic strategies against hepatocellular carcinoma. Cancer research 79:4326–4330. https://doi.org/10.1158/0008-5472.can-19-0803

    Article  Google Scholar 

  15. 15.

    Kudo M, Finn RS, Qin S, Han K-H, Ikeda K, Piscaglia F, Baron A, Park J-W, Han G, Jassem J (2018) Lenvatinib versus sorafenib in first-line treatment of patients with unresectable hepatocellular carcinoma: a randomised phase 3 non-inferiority trial. The Lancet 391:1163–1173. https://doi.org/10.1016/s0140-6736(18)30207-1

    Article  Google Scholar 

  16. 16.

    Llovet JM, Ricci S, Mazzaferro V, Hilgard P, Gane E, Blanc J-F, De Oliveira AC, Santoro A, Raoul J-L, Forner A (2008) Sorafenib in advanced hepatocellular carcinoma. New England journal of medicine 359:378–390. https://doi.org/10.1056/nejmoa0708857

    Article  Google Scholar 

  17. 17.

    Li J-F, Zheng E-Q, Xie M (2019) Association between rs738409 polymorphism in patatin-like phospholipase domain-containing protein 3 (PNPLA3) gene and hepatocellular carcinoma susceptibility: evidence from case-control studies. Gene 685:143–148. https://doi.org/10.1016/j.gene.2018.11.012

    Article  Google Scholar 

  18. 18.

    Nahon P, Zucman-Rossi J (2012) Single nucleotide polymorphisms and risk of hepatocellular carcinoma in cirrhosis. Journal of hepatology 57:663–674. https://doi.org/10.1016/j.jhep.2012.02.035

    Article  Google Scholar 

  19. 19.

    Inaki K, Liu ET (2012) Structural mutations in cancer: mechanistic and functional insights. Trends in Genetics 28:550–559. https://doi.org/10.1016/j.tig.2012.07.002

    Article  Google Scholar 

  20. 20.

    Nowakowska B (2017) Clinical interpretation of copy number variants in the human genome. Journal of applied genetics 58:449–457. https://doi.org/10.1007/s13353-017-0407-4

    Article  Google Scholar 

  21. 21.

    Itsara A, Cooper GM, Baker C, Girirajan S, Li J, Absher D, Krauss RM, Myers RM, Ridker PM, Chasman DI (2009) Population analysis of large copy number variants and hotspots of human genetic disease. The American Journal of Human Genetics 84:148–161. https://doi.org/10.1016/j.ajhg.2008.12.014

    Article  Google Scholar 

  22. 22.

    Beeghly-Fadiel A, Lu W, Shu X-O, Long J, Cai Q, Xiang Y, Gao Y-T, Zheng W (2011) MMP9 polymorphisms and breast cancer risk: a report from the Shanghai Breast Cancer Genetics Study. Breast cancer research and treatment 126:507–513. https://doi.org/10.1007/s10549-010-1119-1

    Article  Google Scholar 

  23. 23.

    Sharma KL, Misra S, Kumar A, Mittal B (2012) Higher risk of matrix metalloproteinase (MMP-2, 7, 9) and tissue inhibitor of metalloproteinase (TIMP-2) genetic variants to gallbladder cancer. Liver International 32:1278–1286. https://doi.org/10.1111/j.1478-3231.2012.02822.x

    Article  Google Scholar 

  24. 24.

    Shrestha S, Tang J, Kaslow RA (2009) Gene copy number: learning to count past two. Nature medicine 15:1127–1129. https://doi.org/10.1038/nm1009-1127

    Article  Google Scholar 

  25. 25.

    Xue Q, Cao L, Chen XY, Zhao J, Gao L, Li SZ, Fei Z (2017) High expression of MMP9 in glioma affects cell proliferation and is associated with patient survival rates. Oncology letters 13:1325–1330. https://doi.org/10.3892/ol.2017.5567

    Article  Google Scholar 

  26. 26.

    Bradford LD (2002) CYP2D6 allele frequency in European Caucasians, Asians, Africans and their descendants. Pharmacogenomics 3:229–243. https://doi.org/10.1517/14622416.3.2.229

    Article  Google Scholar 

  27. 27.

    Hyeon J, Ahn S, Lee JJ, Song DH, Park C-K (2013) Prognostic significance of BCL9 expression in hepatocellular carcinoma. Korean journal of pathology 47:130. https://doi.org/10.4132/koreanjpathol.2013.47.2.130

    Article  Google Scholar 

  28. 28.

    Kim TM, Yim SH, Shin SH, Xu HD, Jung YC, Park CK, Choi JY, Park WS, Kwon MS, Fiegler H (2008) Clinical implication of recurrent copy number alterations in hepatocellular carcinoma and putative oncogenes in recurrent gains on 1q. International journal of cancer 123:2808–2815. https://doi.org/10.1002/ijc.23901

    Article  Google Scholar 

  29. 29.

    Sistonen J, Sajantila A, Lao O, Corander J, Barbujani G, Fuselli S (2007) CYP2D6 worldwide genetic variation shows high frequency of altered activity variants and no continental structure. Pharmacogenetics and genomics 17:93–101. https://doi.org/10.1038/srep40446

    Article  Google Scholar 

  30. 30.

    Xu W, Zhou W, Cheng M, Wang J, Liu Z, He S, Luo X, Huang W, Chen T, Yan W (2017) Hypoxia activates Wnt/β-catenin signaling by regulating the expression of BCL9 in human hepatocellular carcinoma. Scientific reports 7:1–13. https://doi.org/10.1038/srep40446

    Article  Google Scholar 

  31. 31.

    Yu, X., Huang, J., Wu, S., Huang, Y., Shan, Y., and Lu, C. (2020) Copy number variations of MMP-9 are prognostic biomarkers for hepatocellular carcinoma, Translational Cancer Research 9, 698-706. 10.21037/tcr.2019.11.52

  32. 32.

    Hashimoto K, Mori N, Tamesa T, Okada T, Kawauchi S, Oga A, Furuya T, Tangoku A, Oka M, Sasaki K (2004) Analysis of DNA copy number aberrations in hepatitis C virus-associated hepatocellular carcinomas by conventional CGH and array CGH. Modern Pathology 17:617–622. https://doi.org/10.1038/modpathol.3800107

    Article  Google Scholar 

  33. 33.

    Moinzadeh P, Breuhahn K, Stützer H, Schirmacher P (2005) Chromosome alterations in human hepatocellular carcinomas correlate with aetiology and histological grade–results of an explorative CGH meta-analysis. British journal of cancer 92:935–941. https://doi.org/10.1038/sj.bjc.6602448

    Article  Google Scholar 

  34. 34.

    Zimonjic DB, Keck CL, Thorgeirsson SS, Popescu NC (1999) Novel recurrent genetic imbalances in human hepatocellular carcinoma cell lines identified by comparative genomic hybridization. Hepatology 29:1208–1214. https://doi.org/10.1002/hep.510290410

    Article  Google Scholar 

  35. 35.

    Wong N, Lai P, Lee S-W, Fan S, Pang E, Liew C-T, Sheng Z, Lau JW-Y, Johnson PJ (1999) Assessment of genetic changes in hepatocellular carcinoma by comparative genomic hybridization analysis: relationship to disease stage, tumor size, and cirrhosis. The American journal of pathology 154:37–43. https://doi.org/10.1016/s0002-9440(10)65248-0

    Article  Google Scholar 

  36. 36.

    De la Roche M, Worm J, Bienz M (2008) The function of BCL9 in Wnt/β-catenin signaling and colorectal cancer cells. BMC cancer 8:1–13. https://doi.org/10.1186/1471-2407-8-199

    Article  Google Scholar 

  37. 37.

    Kramps T, Peter O, Brunner E, Nellen D, Froesch B, Chatterjee S, Murone M, Züllig S, Basler K (2002) Wnt/wingless signaling requires BCL9/legless-mediated recruitment of pygopus to the nuclear β-catenin-TCF complex. Cell 109:47–60. https://doi.org/10.1016/s0092-8674(02)00679-7

    Article  Google Scholar 

  38. 38.

    Mani M, Carrasco DE, Zhang Y, Takada K, Gatt ME, Dutta-Simmons J, Ikeda H, Diaz-Griffero F, Pena-Cruz V, Bertagnolli M (2009) BCL9 promotes tumor progression by conferring enhanced proliferative, metastatic, and angiogenic properties to cancer cells. Cancer research 69:7577–7586. https://doi.org/10.1158/0008-5472.can-09-0773

    Article  Google Scholar 

  39. 39.

    Takada, K., Zhu, D., Bird, G. H., Sukhdeo, K., Zhao, J.-J., Mani, M., Lemieux, M., Carrasco, D. E., Ryan, J., and Horst, D. (2012) Targeted disruption of the BCL9/β-catenin complex inhibits oncogenic Wnt signaling, Science translational medicine 4, 148ra117-148ra117. https://doi.org/10.1126/scitranslmed.3003808

  40. 40.

    Zhao J-J, Lin J, Zhu D, Wang X, Brooks D, Chen M, Chu Z-B, Takada K, Ciccarelli B, Tao J (2014) miR-30-5p functions as a tumor suppressor and novel therapeutic tool by targeting the oncogenic Wnt/β-catenin/BCL9 pathway. Cancer research 74:1801–1813. https://doi.org/10.1158/0008-5472.can-13-3311-t

    Article  Google Scholar 

  41. 41.

    Gay DM, Ridgway RA, Müller M, Hodder MC, Hedley A, Clark W, Leach JD, Jackstadt R, Nixon C, Huels DJ (2019) Loss of BCL9/9l suppresses Wnt driven tumourigenesis in models that recapitulate human cancer. Nature communications 10:1–16. https://doi.org/10.1038/s41467-019-08586-3

    Article  Google Scholar 

  42. 42.

    Yang C, Xu Y, Cheng F, Hu Y, Yang S, Rao J, Wang X (2017) miR-1301 inhibits hepatocellular carcinoma cell migration, invasion, and angiogenesis by decreasing Wnt/β-catenin signaling through targeting BCL9. Cell death & disease 8:e2999–e2999. https://doi.org/10.1038/cddis.2017.356

    Article  Google Scholar 

  43. 43.

    Bodin L, Beaune PH, Loriot M-A (2005) Determination of cytochrome P450 2D6 (CYP2D6) gene copy number by real-time quantitative PCR. Journal of Biomedicine and Biotechnology 2005:248–253. https://doi.org/10.1155/JBB.2005.248

    Article  Google Scholar 

  44. 44.

    Nguyen DL, Staeker J, Laika B, Steimer W (2009) TaqMan real-time PCR quantification strategy of CYP2D6 gene copy number for the LightCycler 2.0. Clinica chimica acta 403:207–211. https://doi.org/10.1016/j.cca.2009.03.007

    Article  Google Scholar 

  45. 45.

    Schmittgen TD, Livak KJ (2008) Analyzing real-time PCR data by the comparative CT method. Nature protocols 3:1101–1108. https://doi.org/10.1038/nprot.2008.73

    Article  Google Scholar 

  46. 46.

    McGlynn KA, Petrick JL, El-Serag HB (2021) Epidemiology of hepatocellular carcinoma. Hepatology 73:4–13. https://doi.org/10.1002/hep.31288

    Article  Google Scholar 

  47. 47.

    Omar A, Abou-Alfa GK, Khairy A, Omar H (2013) Risk factors for developing hepatocellular carcinoma in Egypt. Chinese clinical oncology 2. https://doi.org/10.3978/j.issn.2304-3865.2013.11.07

  48. 48.

    Liver EAFTSOT (2018) EASL clinical practice guidelines: management of hepatocellular carcinoma. Journal of hepatology 69:182–236. https://doi.org/10.1016/j.jhep.2018.03.019

    Article  Google Scholar 

  49. 49.

    Llovet JM, Burroughs A, Bruix J (2003) Hepatocellular carcinoma. Lancet (London, England) 362:1907–1917. https://doi.org/10.1016/s0140-6736(03)14964-1

    Article  Google Scholar 

  50. 50.

    Martínez-Chantar ML, Avila MA, Lu SC (2020) Hepatocellular carcinoma: updates in pathogenesis, detection and treatment. Multidisciplinary Digital Publishing Institute. https://doi.org/10.3390/cancers12102729

  51. 51.

    Cillo U, Vitale A, Grigoletto F, Farinati F, Brolese A, Zanus G, Neri D, Boccagni P, Srsen N, D’Amico F (2006) Prospective validation of the Barcelona Clinic Liver Cancer staging system. Journal of hepatology 44:723–731. https://doi.org/10.1016/j.jhep.2005.12.015

    Article  Google Scholar 

  52. 52.

    Gomaa AI, Hashim MS, Waked I (2014) Comparing staging systems for predicting prognosis and survival in patients with hepatocellular carcinoma in Egypt. PloS one 9:e90929. https://doi.org/10.1371/journal.pone.0090929

    Article  Google Scholar 

  53. 53.

    Grieco A, Pompili M, Caminiti G, Miele L, Covino M, Alfei B, Rapaccini GL, Gasbarrini G (2005) Prognostic factors for survival in patients with early-intermediate hepatocellular carcinoma undergoing non-surgical therapy: comparison of Okuda. CLIP, and BCLC staging systems in a single Italian centre, Gut 54:411–418. https://doi.org/10.1136/gut.2004.048124

    Article  Google Scholar 

  54. 54.

    Lee, J. H., Han, S. Y., Jo, J. H., Kim, S. K., Go, B. S., Oh, J. Y., Choi, J. C., Lee, S. W., Jang, J. S., and Roh, M. H. (2007) Prognostic factors for survival in patients with hepatocellular carcinoma after radiofrequency ablation, The Korean journal of gastroenterology= Taehan Sohwagi Hakhoe chi 49, 17-23. https://doi.org/10.4166/kjg.2020.75.1.17

  55. 55.

    Marrero JA, Fontana RJ, Barrat A, Askari F, Conjeevaram HS, Su GL, Lok AS (2005) Prognosis of hepatocellular carcinoma: comparison of 7 staging systems in an American cohort. Hepatology 41:707–715. https://doi.org/10.1002/hep.20636

    Article  Google Scholar 

  56. 56.

    Li P, Cao Y, Li Y, Zhou L, Liu X, Geng M (2014) Expression of Wnt-5a and β-catenin in primary hepatocellular carcinoma. International journal of clinical and experimental pathology 7:3190

    Google Scholar 

  57. 57.

    Bigagli E, De Filippo C, Castagnini C, Toti S, Acquadro F, Giudici F, Fazi M, Dolara P, Messerini L, Tonelli F (2016) DNA copy number alterations, gene expression changes and disease-free survival in patients with colorectal cancer: a 10 year follow-up. Cellular Oncology 39:545–558. https://doi.org/10.1007/s13402-016-0299-z

    Article  Google Scholar 

  58. 58.

    Lau S-H, Guan X-Y (2005) Cytogenetic and molecular genetic alterations in hepatocellular carcinoma. Acta Pharmacologica Sinica 26:659–665. https://doi.org/10.1111/j.1745-7254.2005.00126.x

    Article  Google Scholar 

  59. 59.

    Wang K, Lim HY, Shi S, Lee J, Deng S, Xie T, Zhu Z, Wang Y, Pocalyko D, Yang WJ (2013) Genomic landscape of copy number aberrations enables the identification of oncogenic drivers in hepatocellular carcinoma. Hepatology 58:706–717. https://doi.org/10.1002/hep.26402

    Article  Google Scholar 

  60. 60.

    Huge N, Sandbothe M, Schröder AK, Stalke A, Eilers M, Schäffer V, Schlegelberger B, Illig T, Vajen B, Skawran B (2020) Wnt status-dependent oncogenic role of BCL9 and BCL9L in hepatocellular carcinoma. Hepatology international 14:373–384. https://doi.org/10.1007/s12072-019-09977-w

    Article  Google Scholar 

  61. 61.

    Moghe A, Monga SP (2020) BCL9/BCL9L in hepatocellular carcinoma: will it or Wnt it be the next therapeutic target? Springer. https://doi.org/10.1007/s12072-020-10059-5

  62. 62.

    Molparia B, Oliveira G, Wagner JL, Spencer EG, Torkamani A (2018) A feasibility study of colorectal cancer diagnosis via circulating tumor DNA derived CNV detection. PloS one 13:e0196826. https://doi.org/10.1371/journal.pone.0196826

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge the National Research Centre, as well as the National Hepatology and Tropical Medicine Research Institute, Cairo, Egypt, and Microbiology Department, Faculty of Science, Ain-Shams University, Cairo, Egypt, for providing laboratory space, types of equipment, and patient samples for the achievement of the current work.

Funding

This work was funded by the National Research Centre (NRC) of Egypt and also by the authors (Samar Samir Youssef, Eman Abd El Razek Abbas).

Author information

Affiliations

Authors

Contributions

S.S.Y. designed the research, shared in practical work; analyzed the data; and revised the manuscript. E.A.A. collected the patients’ samples, performed the practical work and data analysis, and drafted the manuscript. M.H.B. recruited the patients’ samples according to the criteria and did the clinical examination. A.B.B. supervised and contributed to the study and reviewed the manuscript. All authors reviewed the final manuscript.

Corresponding author

Correspondence to Samar Samir Youssef.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the Medical Research Ethics Committee, the committee’s registration number (17122), National Research Centre, El Buhouth St., Dokki, Cairo, Egypt.

All patients signed a written informed consent before inclusion in the study.

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Abbas, E.A., Barakat, A.B., Hassany, M. et al. The role of BCL9 genetic variation as a biomarker for hepatitis C-related hepatocellular carcinoma in Egyptian patients. J Genet Eng Biotechnol 20, 4 (2022). https://doi.org/10.1186/s43141-021-00282-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s43141-021-00282-4

Keywords

  • Hepatocellular carcinoma
  • BCL9
  • Copy number variation
  • Circulating free DNA
  • Biomarker