The role of PRDM1 gene polymorphism in the progression of hepatocellular carcinoma in Egyptian patients
Abstract
In Egypt, hepatocellular carcinoma (HCC) ranks as the second largest cause of cancer mortality. PRDM1 is a tumor suppressor gene essential for the differentiation and regulation activity of plasma cells and T cells. It plays a vital role in T cell exhaustion of chronic viral infection and HCC. We aimed to study the role of PRDM1 gene polymorphism in HCV and HCC-related to hepatitis C virus (HCV) progress in Egyptians. The case-control study included 300 Egyptian patients divided into 100 HCC,100 cirrhosis, and 100 control. Laboratory investigations were done for some clinicopathological biomarkers, including liver function tests, complete blood picture, serum alpha-fetoprotein, and hepatitis markers (HBsAg, anti-HCV-Ab). TaqMan allelic discrimination assay technique was used to genotype PRDM1 gene polymorphism. Multivariant analysis (logistic regression) assessed the association between the polymorphisms with HCC progression and designed the suggested model for HCC prediction. The frequencies of the G allele and GG phenotype in the control group were significantly more than that of the HCC and cirrhosis group. However, GA genotypes and A allele frequencies significantly increased in the HCC patients than in cirrhosis and controls. In addition, by comparing the HCC group and the non-HCC group (controls and cirrhotic patients), the subjects carrying AA or GA have 2 times more risk to develop HCC than those carrying GG genotypes (odd ratio = 2.045% and 95% confidence interval are (1.123−3.722) p = 0.019). Multivariate analysis results suggested a model of Aspartate transaminase (AST), Albumin, and PRDM1 polymorphism to predict the risk of HCC in Egyptians. In addition, PRDM1 polymorphism has an association with HCC prognosis (tumor size). For PRDM1 polymorphism, the A allele and AA might be considered as HCC-related to the HCV risk factor. In addition, AST, Albumin, and PRDM1 polymorphism predict the risk of HCC in Egyptians Therefore, the polymorphism might help in identifying the susceptible Egyptians to HCC. In addition, polymorphism might have a role in HCC prognosis.
1 INTRODUCTION
Hepatocellular carcinoma (HCC) is a primary malignancy in the hepatocyte and is the most widespread liver cancer.1 In 2018, HCC was classified as the fifth most diagnosed cancer and the third reason for cancer-related death globally, with about 841 000 new cases and 782 000 deaths annually2, 3 In Egypt, the HCC incidence rate has increased markedly in the last decade.3, 4 It has significant morbidity and high mortality rates.3, 5 It is usually asymptomatic in the early stages, and most patients present with an incurable disease at the time of detection.6 Alpha-fetoprotein (AFP), a specific glycoprotein produced primarily by the fetal liver, is the most practical serum biomarker for HCC diagnosis. Although, its sensitivity and specificity vary significantly from 40%–65% and 76%–96%, respectively.7 Therefore, there is a continuous search for new biomarkers for the early detection of HCC.8 Surgical resection remains the treatment of choice for these tumors, but unfortunately, only 10%−20% of primary HCCs are respectable at the diagnosis. Moreover, it has a high recurrent rate after treatment. So early HCC diagnosis is vital for a good prognosis.6 The most common cause of HCC is chronic hepatitis or liver cirrhosis caused by hepatitis C virus (HCV) and hepatitis B virus (HBV) infections. HCV is an epidemic in Egypt.7 However, not all HCV patients are susceptible to developing HCC. Both the environmental and genetic risk factors participate in the multistage process of this complex disease development.9, 10 Continuous research is ongoing worldwide to identify the patients more suspectable to HCC and evaluate sensitive and specific new markers for the diagnosis of HCC.6 Therefore, identifying those susceptible patients will improve the clinical outcomes including, delayed HCC development, early detection, and improved response to treatment.11, 12 Emerging research has indicated close interaction between various immune cells and tumor cells.13 Total B cells, memory B cells, T follicular helper cells, and M1 macrophages were strongly infiltrated into HCC.14 In addition, T cell Exhaustion occurs in HCC and is also associated with poor clinical outcomes.15 PR domain zinc finger protein 1 (PRDM1), also known as B lymphocyte-induced maturation protein 1 (BLIMP1), is a transcriptional repressor encoded by the PRDM1 gene. It is involved in regulating B cell and T cell differentiation and function.16 In most natural killer cell line studies, the promoter of PRDM1 is methylated. So PRDM1 is believed to be a tumor suppressor gene.9 PRDM1 also plays an essential role in regulating T cell exhaustion in chronic viral infection and developed many types of cancer, including HCC.17 Accordingly, this study assesses the effect of gene PRDM1 rs1010273 polymorphisms in Egyptian patients with chronic HCV and development HCC.
1.1 Study population
A case-control study was carried out in the Department of Tropical Medicine and Infectious Diseases, Faculty of Medicine, Tanta University, Tanta. The Ethics Committee of the Faculty of Pharmacy, Al-Azhar University, approved the study. Three hundred Egyptian individuals contributed to the study into three studied groups. Group (A); 100 patients with HCC-related HCV diagnosed by ultrasonography and contrast-enhanced triphasic CT, Group (B); includes 100 patients with liver cirrhosis-related HCV. All subjects were positive for HCV-Ab and negative for HBsAg. Group (C); 100 healthy Egyptian blood donors enrolled as a healthy control group. All control individuals were negative for HCV and HBV antibodies. The study excluded patients with positive HIV or hepatocellular injury including any history of alcoholism, primary biliary cirrhosis, autoimmune hepatitis, and severe nonalcoholic liver diseases with metabolic syndrome). The diagnosis is based on clinical examination, laboratory tests, ultrasound, and computed tomography.
1.2 Sample collection
10 ml of venous blood was collected from all participants by venipuncture from the cubital vein and obtained as follows: 4 ml was collected into EDTA-containing tubes for complete blood count (CBC) and genotyping of the PRDM1 gene. A 2 ml was dispensed into a citrated tube for an International normalized ratio (INR) and the remaining 4 ml was stored in another vacutainer tube (with no additives). It was lifted to clot for 15 min and then centrifuged at 3000 rpm for 10 min. Then, the sera were separated into aliquots to measure liver function tests, viral markers (HBsAg and anti-HCV-Ab), and AFP. All study participants performed the following investigations: Full assessment of history, complete clinical examination, Abdominal ultrasonography, and/or computed tomography. Laboratory investigations performed including the following: CBC, Serum Aspartate transaminase (AST), alanine transaminase (ALT) (IU/L),11 serum bilirubin (μmol/L), serum albumin (g/L), serum alkaline phosphatase (ALP), Creatinine and blood glucose did use automated Biochemistry analyzer. INR determined by Tcoag's KC1 Delta automated coagulation analyser equipment. Viral markers were also assessed (HBsAg and HCV-Ab) and serum AFP using ELISA according to the manufacturer's instructions18, 19 and PRDM1 A/G SNP (rs1010273) gene polymorphism genotyping.
1.3 DNA extraction and genotyping
- 1.
Homozygotes (with allele 1 or allele 2).
- 2.
Heterozygotes (with both allele 1 and allele 2).
The premixed PCR master mix containing Taq DNA polymerase, MgCl2, dNTPs, and reaction buffers at optimum concentrations. Five microliters of extracted DNA was added to 10 µl of master mix, 4 µl nuclease-free water, and 1 µl of each dissolved primer. Complementary DNA amplification carried out on step-one plus Real-time PCR system (Applied Biosystems): It was held at 60°C for 30 s with an initial step of enzyme activation at 95°C for 10 min, 40 cycles of denaturation at 95°C for 15 s, annealing/collection for 1 min at 60°C, and a final extension step for 30 s at 60°C.
1.4 Statistical methods
Data were analyzed using IBM SPSS advanced statistics (Statistical Package for Social Sciences), version 24 (SPSS Inc.). Hardy-The Weinberg equilibrium was evaluated using the exact test in each group. Data were analyzed using IBM SPSS advanced statistics (Statistical Package for Social Sciences), version 24 (SPSS Inc.). Numerical data were described as median and range or mean and standard deviation as appropriate, while qualitative data were described as number and percentage. Chi-square (Fisher's exact) test was used to examine the relation between qualitative variables as appropriate. Multivariate analysis was done for variables statistically significant on the univariate level to indicate independent predictive factors and to obviate the effect of confounders using the logistic regression model. A p value less than or equal to 0.05 was considered statistically significant. All tests were two-tailed.
2 RESULTS
A total of 300 Egyptian participants were involved in this study 100 were healthy controls, 100 were cirrhotic patients and 100 were HCC patients with confirmed HCV genotype 4. Baseline characteristics were recorded including age, gender, body mass index (BMI), history of smoking, and alcohol intake. The demographic data of the studied groups are represented in Table 1. In addition, this table contains a comparison between the HCC group and the non-HCC group, which includes both control and cirrhotic groups in demographic data (1). There was a statistically significant difference between the HCC and controls, cirrhotic group and non-HCC group regarding the age and BMI, smoking (p < 0.001). In contrast, The significant difference is absent in sex (p = 0.662). Allelic and Genotypes frequencies of PRDM1 rs1010273 polymorphism are also represented in the previous table. The most common G allele and GG genotype and considered the references to compare the HCC group and other groups. Both the frequencies of the G allele and GG phenotype were statistically significantly higher in the control group than that of the HCC and cirrhosis group (90%, 73.3%, and 82%, respectively, and p < 0.001) and (80%, 52%, and 65%, respectively, with p < 0.001). While the frequencies of the A allele and both GA and AA genotypes were statistically significantly higher in the HCC patients compared to the cirrhosis and controls (53%, 36%, and 20%, respectively, and p < 0.001) and (48%, 35%, and 20% respectively with p < 0.001). The biochemical parameters of the non-HCC and HCC groups are represented in Table 2. A statistically significant increase in measured biomarkers included ALT, AST, total bilirubin, INR, albumin, AFP, HCV antibodies, serum creatinine, fasting blood glucose (FBG), and platelet in the HCC group compared to the non-HCC group. Allelic and Genotypes frequencies of PRDM1 rs1010273 polymorphism in the studied groups are represented in Table 3. In the current study, the most common G allele and GG genotype and considered the references to compare the HCC group and other groups. Both the frequencies of G allele and GG phenotype were statistically significantly higher in the control group than that of the HCC and cirrhosis group (90%, 73.3%, and 82%, respectively, and p < 0.001) and (80%, 52%, and 65%, respectively, with p < 0.001). While the frequencies of the A allele and both GA and AA genotypes were statistically significantly higher in the HCC patients compared to the cirrhosis and controls (53%, 36%, and 20%, respectively, and p < 0.001) and (48%, 35%, and 20%, respectively, with p < 0.001). In addition, by comparing between the non-HCC group and the HCC group the subjects carrying AA or GA have 2 times more risk to develop HCC than those carrying GG genotypes (odd ratio [OR] = 2.045% and 95% confidence interval [CI] are (1.123–3.722) p = 0.019). Multivariate analysis (logistic regression model) to estimate the risk of being HCC versus non-HCC regarding laboratory data suggested that AST (OR = 6.33, 95% CI (2.939−13.633) and p < 0.001) and Albumin (OR = 6.33, 95% CI (2.939−13.633) and p < 0.004), were predictive risk factors for HCC. The relation between the clinicopathological data of HCC and PRDM1 gene polymorphism is presented in Table 6. There was an association between PRDM1 gene polymorphism and the diameter of the tumor or tumor size (p = 0.0001). Multivariate analysis (logistic regression model) to estimate the association between the prognosis of HCC and PRDM1 gene polymorphism is represented in Table 6. There was an association between PRDM1 gene polymorphism and the diameter of the tumor or tumor size (p = 0.0001) with OR = 11.212 95% CI (4.414−28.48).
| Parameters | Studied groups | p Value | ||
|---|---|---|---|---|
| Control | Cirrhosis | HCC | ||
| 100 | 100 | 100 | ||
| Age (years) | <0.001* | |||
| ≤57 No(%) | 48 (48%)b | 21 (21%)a | 20 (20%)a | |
| >57 No(%) | 52 (52%)b | 79 (79%)a | 80 (80%)a | |
| Gender | 0.662 | |||
| Male No (%) | 66 (66%) | 62(62%) | 68 (68%) | |
| Female No (%) | 34 (34%) | 38 (38%) | 32 (32%) | |
| BMI (kg/m2) | <0.001* | |||
| ≤25 | 46 (46%) | 18 (18%) | 15 (15%) | |
| >25 | 54 (54%) | 82 (82%) | 85 (85%) | |
| Alcohol intake | >0.05 | |||
| Yes N (%) | 4 (4%) | 5 (5%) | 2 (2%) | |
| No N (%) | 96 (96%) | 95 (95%) | 98 (95.5%) | |
| Smoking | <0.05* | |||
| Yes N (%) | 17 (17%) | 18 (18%) | 60 (60%) | |
| No N (%) | 83 (83%) | 82 (82%) | 40 (40%) | |
| PRDM1 gene polymorphism rs1010273 | ||||
| AA | 0 (0.0%) | 1 (1%) | 5 (5%) | <0.001* |
| GA | 20 (20%) | 34 (34%) | 43 (43%) | |
| GG | 80 (80%) | 65 (65%) | 52 (52%) | |
| Allelic frequency | ||||
| A allele N (%) | 20 (10%) | 36 (18%) | 53 (26.5%) | <0.001* |
| G allele N (%) | 180 (90%) | 164 (82%) | 147 (73.3%) | |
- Note: p > 0.05 is nonsignificant. Superscript letter a and b indicate cells that are sharing same small letters aren't having statistically significant difference.
- Abbreviations: BMI, body mass index; HCC, Hepatocellular carcinoma.
- * Indicate that there is significant statistical difference.
| Variables | Studied groups | p Value | |
|---|---|---|---|
| Non-HCC 200 N (%) | HCC 100 N (%) | ||
| ALT (IU/ml) | <0.001* | ||
| Normal ≤ 50 | 179 (89.5%) | 54 (54%) | |
| Abnormal > 50 | 21 (10.5%) | 46 (46%) | |
| AST (IU/ml) | <0.001* | ||
| Normal ≤ 59 | 137 (68.5%) | 12 (12%) | |
| Abnormal > 59 | 63 (31.5%) | 88 (88%) | |
| Bilirubin (mg/dl) | <0.001* | ||
| Normal 0.2−1.3 | 136 (68%) | 15 (15%) | |
| Abnormal > 1.3 | 64 (32.0%) | 85 (85%) | |
| INR | |||
| Normal | 115 (57.5%) | 12 (12%) | <0.001* |
| Abnormal | 85 (42.5%) | 88 (888%) | |
| ALP (U/l) | 0.086 | ||
| Normal 38−126 | 51 (25.5%) | 35 (35%) | |
| Abnormal > 126 | 149 (74.5%) | 65 (65%) | |
| Albumin (g/dl) | <0.001* | ||
| Normal | 104 (52.0) | 4 (4.0) | |
| Abnormal | 96 (48.0) | 96 (96.0) | |
| AFP (ng/ml) | <0.001* | ||
| Normal ≤ 10 | 131 (65.5%) | 0 | |
| Abnormal > 10 | 69 (34.5%) | 100 (100%) | |
| HCVAb | <0.001* | ||
| Negative | 100 (50.0%) | 0 (0%) | |
| Positive | 100 (50.0%) | 100 (100%) | |
| S. Creatinine (mg/dl) | <0.001* | ||
| Normal | 156 (78. %) | 19 (19%) | |
| Abnormal | 44 (22.0%) | 81 (81%) | |
| FBS (mg/dl) 90−100 mg/dl | <0.001* | ||
| Normal | 73 (36.5%) | 11 (11%) | |
| Abnormal | 127 (63.5%) | 89 (89%) | |
| Platelets (103/ml) | 0.004* | ||
| Normal | 184 (92%) | 100 (100%) | |
| Abnormal | 16 (8%) | 0 | |
| Hb (g/dl) | 0.617 | ||
| Normal 11.5−16.5 | 82 (41.0%) | 38 (38%) | |
| Abnormal | 118 (59.0%) | 62 (62%) | |
- Note: All data are represented as numbers and frequencies, p < 0.05 significant.
- Abbreviations: AFP alpha-fetoprotein; ALP, alkaline phosphatase; ALT, alanine transaminase; AST, aspartate transaminase; INR, The international normalized ratio; BMI, Body mass index; FBS, Fasting Blood serum; Hb, Hemoglobin.
- * Indicate highly significance.
| Gene polymorphism PRDM1 | Group N (%) | p Value | OR (CI) for HCC and controls | p Value | OR (CI) for HCC and cirrhosis | p Value | OR (CI) for HCC and non-HCC | p Value | ||
|---|---|---|---|---|---|---|---|---|---|---|
| Control (N = 100) N (%) | Cirrhosis (N = 100) N (%) | HCC (N = 100) N (%) | ||||||||
| AA + GA | 20 (20%) | 35 (35%) | 48 (48%) | <0.001* | 3.692 (1.97−6.92) | <0.001 | 1.714 (0.97−3.026) | 0.063 | 2.045 (1.123−3.722) | 0.019* |
| GG | 80 (80%) | 65 (65%) | 52 (52%) | Reference | Reference | Reference | ||||
| Allelic frequency | Control (N = 200) N(%) | Cirrhosis (N = 200) N(%) | HCC (N = 200) N(%) |
p Value | OR (CI) for HCC and controls | p Value | OR (CI) for HCC and cirrhosis | p Value | OR (CI) for HCC and non-HCC | pValue |
|---|---|---|---|---|---|---|---|---|---|---|
| A allele | 20 (10%) | 36 (18%) | 53 (26.5%) | <0.001* | 3.245 (1.86−5.67) | <0.001* | 1.642 (1.018−2.65) | <0.04* | 2.215 (1.452−3.379) | <0.001* |
| G allele | 180 (90%) | 164 (82%) | 147 (73.3%) | Reference | Reference | Reference |
- Note: The most common G allele and GG genotype in the healthy control was considered the reference. p < 0.05 was considered statistically significant. The results presented as OR; Odds ratio with a 95% CI confidence interval.
- Abbreviations: PRDM1, PR domain zinc finger protein 1; HCC hepatocellular carcinoma, non- HCC control, and cirrhotic groups.
- * Indicate highly significance.
3 DISCUSSION
HCC ranks as the fifth most commonly occurring malignant tumor, and the third most prevalent cause of death worldwide.2 The interaction between genetic variables and environmental stimuli decides cancers' oncogenic capability.21 Genetic polymorphisms appear as the most frequent hereditary variations and are related to various diseases' susceptibility.22
PRDM1 plays an essential role in regulating T cell exhaustion in chronic viral infection and HCC.20, 23 It acts as a transcription factor and a large family of regulators that control several critical biological processes in mammals. These functions include B lymphocyte-induced maturation protein 1 (BLIMP1) and a homolog of BLIMP1 in T cells (Hobit). Therefore, PRDM1 has a vital role in regulating T and B cell function.24 Therefore, this case-control study was performed to investigate the potential role of PRDM1 polymorphism in developed HCC-related HCV in Egyptians. In addition, design a suggested model to predict the Egyptians at risk to develop HCC.
In this study, the demographic data of the HCC group are significantly different in age, BMI, and smoking habits from controls, cirrhotic, and non-HCC groups. The Ages of most HCC patients in this study were almost > 50 years. This result agreed with the most recent World Health Report (WHO). In addition, 85% of HCC patients have BMI > 25. This agrees with Nishikawa et al. article review who concludes that obesity plays a role in hematogenesis.25 The study results come following a systematic review by Abdel-Rahman et al. who confirmed smoking is an HCC risk factor.26 There wasn't any significant difference in sex or alcohol drinking among the studied groups. Previous results agree with the result found by Ren et al. and Paranaguá-Vezozzo.26, 27 Although, in this study 68% of the population were males and 32% were females. These results come to agree with Petruzziello,28 Madihi,29 and Sepanlou.30 They found the incidence and mortality rate of HCC is higher in males than in females, with an incidence ratio of 2:1 to 6:1. Benkerroum31 explained that by the higher susceptibility of males to environmental carcinogens and greater exposure to environmental risk factors, for example, pesticides and aflatoxin. Sex hormones and other x-linked genetic factors may also be an important Marrero.32 Testosterone is an enhancer for hepatocyte cell-cycle regulators, so it accelerates HCC. In contrast, estradiol suppresses cell-cycle regulators thus suppressing the progress of HCC.
The biochemical parameters of the studied groups showed a statistically significant elevation in liver profile and some hematological biomarkers including ALT, AST, total bilirubin, ALP, INR, platelet between, and albumin in the studied groups. There is also a statistical variation between the studied groups in AFP, HCV antibodies, serum creatinine, and FBG. These results are in accordance with Ren et al.27 who find a significant difference between HCC and cirrhotic patients in albumin, INR, and AFP. Paranaguá et al. and Mohamed et al. also find a significant difference between HCC and cirrhotic HCV patients in AST, ALT, and AFP.33, 34 Mohamed et al.35 had similar results, they found a significant difference in HCC patients in AST, ALT, and T bilirubin, INR, serum AFP, serum creatinine and platelet. Mohamed et al. found a significant difference in FBG and platelet in HCC.35 However a significant difference is absent between the studied groups in hemoglobin level and clinicopathological parameters. This result agrees with Ren et al.27, 36
PRDM1 or B lymphocyte-induced maturation protein 1 (BLIMP1) is a transcriptional repressor encoded by the PRDM1 gene that is involved in the control of B cell and T cell differentiation and the fate of the effector B and T cells. Therefore, PRDM1 plays a vital role in regulating T cell exhaustion in chronic viral infection and cancer.37 T cell exhaustion occurred in viral hepatitis and HCC, but the phenotypes and mechanisms are different Wang et al.38
The current study examined the relationship between PRDM1 polymorphisms and HCC associated with HCV and cirrhotic HCV patients. The frequencies of the G allele and GG phenotype in the control group were significantly more than in the cirrhosis and HCC patients. Though the frequencies of the GA genotype and A allele were significantly more common in the HCC patients than in the cirrhosis and controls. Furthermore, comparing the non-HCC and HCC groups the subjects carrying AA or GA have 2 times more risk of developing HCC than those carrying GG genotypes (OR = 2.045% and 95%CI are (1.123−3.722) p = 0.019). This result can explain that HCC tissues expressed high levels of exhaustion markers such as TIM-3, PD-1, CTLA-4, and LAG-3 with decreasing T cell proliferation, activity, and cytokine expression.38
In the current study, there is an association between PRDM1 gene polymorphism and tumor size which suggested the polymorphism might have a role in HCC prognosis. Li et al.20 showed that PRDM1 rs1010273 SNP affected the survival of HCC-related to HBV patients. We acknowledge some limitations in our study like the relatively small sample size and noninclusion of other types of chronic liver diseases. We recommend studying the utility of the three–marker index (PRDM1 genotyping, albumin, and AST) in HCC patients secondary to other etiologies and correlating PRDM1 gene polymorphism with the diameter of the tumor or tumor size in such etiologies.
4 CONCLUSION
Individuals carrying the GA + AA genotype and the A allele of PRDM1 rs1010273 have an increased risk of developing and maybe a poor prognosis of HCC. In addition, this study suggests a model that predicts patients with HCC risk using albumin, AST, and PRDM1 rs1010273 polymorphism. Moreover, the polymorphism has an association with HCC tumor size Table 4, 5.
| Variable | Beta coefficient | Standard error | p Value | Odds ratio | 95% CI for OR | |
|---|---|---|---|---|---|---|
| AST | 1.521 | 0.450 | 0.001* | 4.575 | 1.893 | 11.062 |
| Albumin | 1.763 | 0.620 | 0.004* | 5.828 | 1.729 | 19.643 |
| Gene pleomorphism | 0.700 | 0.308 | 0.023* | 2.014 | 1.102 | 3.682 |
- * Indicate highly significance.
| Variables | Gene polymorphism | p Value | |
|---|---|---|---|
| GG (n = 52) N (%) | AA + GA (n = 48) N (%) | ||
| Grade | 0.080 | ||
| 1 | 5 (9.6%) | 11 (22.9%) | |
| 2 | 6 (11.5%) | 9 (18.8%) | |
| 3 | 41 (78.8%) | 28 (58.3%) | |
| Score | 0.240 | ||
| ≤Median value | 31 (59.6%) | 34 (70.8%) | |
| >Median value | 21 (40.4%) | 14 (29.2%) | |
| MELD | 0.733 | ||
| ≤Median value | 31 (59.6%) | 27 (56.3%) | |
| >Median value | 21 (40.4%) | 21 (43.8%) | |
| Heterogenous | 0.541 | ||
| 1 | 35 (67.3%) | 35 (72.9%) | |
| 2 | 17 (32.7%) | 13 (27.1%) | |
| PV | 0.369 | ||
| ≤1.2 | 29 (55.8%) | 31 (64.6%) | |
| >1.2 | 23 (44.2%) | 17 (35.4%) | |
| Multiplicity of tumor | 0.823 | ||
| Single | 5 (9.6%) | 4 (8.3%) | |
| Multiple | 47 (90.4%) | 44 (91.7%) | |
| Focality | 0.607 | ||
| LF | 24 (46.2%) | 18 (37.5%) | |
| RL | 14 (26.9%) | 13 (27.1%) | |
| RL, LF | 14 (26.9%) | 17 (35.4%) | |
| Okuda score | 0.103 | ||
| 0 | 4 (7.7%) | 9 (18.8%) | |
| 1 | 7 (13.5%) | 11 (22.9% | |
| 2 | 10 (19.2%) | 12 (25%) | |
| 3 | 22 (42.3%) | 12 (25%) | |
| 4 | 9 (17.3%) | 4 (8.3%) | |
| Tokyo score | 0.061 | ||
| ≤median value | 31 (59.6%) | 37 (77.1%) | |
| >Median value | 21 (40.4%) | 11 (22.9%) | |
| Maximum tumor diameter (cm) | <0.001* | ||
| ≤3.5 | 40 (76.9%) | 11 (22.9%) | |
| >3.5 | 12 (23.1%) | 37 (77.1%) | |
- Note: All data represented as numbers and frequencies, p < 0.05 statistically significant. Multivariate analysis (logistic regression model) to estimate the association between the prognosis of HCC and PRDM1 gene polymorphism is represented in Table 6. There was an association between PRDM1 gene polymorphism and diameter of tumor or tumor size (p = 0.0001) with OR = 11.212 95% CI (4.414−28.48).
- Abbreviations: CI, confidence interval; HCC, hepatocellular carcinoma; MELD, Model For End-Stage Liver Disease; OD, odd ratio; PV, portal vein.
- * Indicate highly significance.
| Variable | Beta coefficient | Standard error | p Value | Odds ratio | 95% CI for OR | |
|---|---|---|---|---|---|---|
| Lower | Upper | |||||
| Maximum tumor diameter (cm) | 2.417 | 0.476 | 0.000 | 11.212 | 4.414 | 28.483 |
- Abbreviation: CI, confidence interval.
AUTHOR CONTRIBUTIONS
Amal Ahmed Mohamed proposed the study concept and supervised the project, Amal Ahmed Mohamed, Omnia Ezzat Esmail, Aya Mohamed Ahmed Ibrahim, Sahar Makled, Eman Al-Hussain, Ali Elsaid, and Rehab R. El-Awady. Collected data of the study participants, Amal Ahmed Mohamed, Omnia Ezzat Esmail, and Aya Mohamed Ahmed Ibrahim did laboratory work, Mohamed Alboraie did statistical analysis, Amal Ahmed Mohamed and Mohamed Alboraie drafted the manuscript, Mohamed Alboraie reviewed the manuscript for intellectual content, all authors reviewed and approved the final version of the manuscript.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
ETHICS STATEMENT
This study was approved by the Ethics Committee of the Faculty of Pharmacy, Al-Azhar University.
Open Research
DATA AVAILABILITY STATEMENT
Data are available upon reasonable request.
