Skip to main content
Journal of the Egyptian National Cancer Institute
Download PDF

Noninvasive identification of molecular biomarkers of hepatocellular carcinoma in HCV-Egyptian patients

Journal of the Egyptian National Cancer Institute volume 35, Article number: 11 (2023) Cite this article

Abstract

Background

This study was performed to investigate the expression of different biomarkers in patients with hepatocellular carcinoma and its connection with detective biomarkers. To achieve this objective, seventy subjects were examined in this study, sub-grouped to forty HCC patients and thirty HCV-affected patients with matched thirty healthy individuals. The study involved several groups of participants who were matched based on their age and gender.

Methods

The expression pattern of biomarkers was monitored by quantitative polymerase chain reaction (qRT-PCR). Finally, we utilized a ROC curve to investigate the predictive accurateness of those distinct biomarkers as well as a traditional tumor marker, AFP, in detecting HCC cases.

Results

The baseline biomarker expression levels were markedly greater in HCC patients than in those affected by HCV or healthy subjects. We stated that the sensitivity and the specificity of the different biomarkers alone did not improve than that of AFP alone. When comparing AFP with different biomarkers, the diagnostic validity improves only when combining with CK-1.

Conclusions

Overall, our results indicate that CK-1 mRNA expression could help as a noninvasive tumor biomarker for HCC prognosis and diagnosis when combining with AFP.

Background

Hepatocellular carcinoma is the sixth most widespread malignancies in the globe and the third most lethal [1], which accounts for 70–85% of cases [2]. HCV, HBV, alcoholic and nonalcoholic liver disease, and fatty liver disease consider the major etiological HCC risk factors. These dangerous factors contribute to the development and cirrhosis progression, which is manifested in approximately 80–90% of HCC-affected patients. Outstanding to the high infection incidence of HCV and HBV in Egypt, the HCC occurrence rate has increased in the last 10 years [3]. The situation in Egypt is severe with the world’s highest prevalence due to an epidemic outbreak of HCV. The 5-year aggregate risk of HCC development in people with cirrhosis varies between 5 and 30%, depending on the cause (high in HCV), ethnicity or area (more higher in Asians), and degree of cirrhosis (high in decompensated illness) [4]. Although CT and MRI are more effective at detecting HCC than ultrasonography, they are correlated with a higher proportion of false positives [5]. Although modern treatments such as liver transplantation, surgical resection, and ablation therapy are now accessible [6], and despite the improvement of molecular-targeted medicines like sorafenib, patients with advanced HCC have a low 5-year overall survival rate [7]. The high frequency of metastasis and recurrence following local handling is the fundamental explanation for advanced HCC’s poor survival rate [8]. Because it is low cost and straightforward to carry out, and easily obtainable, AFP quantification is widely used for surveillance of HCC. AFP level alone (without liver ultrasonography) is not advisable as an HCC monitoring test due to its poor specificity and sensitivity for HCC detected. AFP has a low sensitivity for diagnosing HCC at 20 ng/ml a serum cut-off level, ranging from 25 to 65% [9]. The lack of a distinct biomarker for HCC with greater specificity and sensitivity than AFP highlights the need for further biomarkers responsible for human hepatocarcinogenesis to be developed.

Biomarkers provide a wide range of potential applications in cancer, including differential diagnosis, screening, prognosis, and disease progression and monitoring [10]. Diagnostic biomarkers can be used to optimize disease diagnosis. Different feature selection strategies are often used to tackle diagnostic biomarker discovery, which is the process of identifying key traits that can differentiate tumors from healthy samples [11]. Many researches have focused on finding investigative biomarkers by looking for differentially expressed genes (DEGs), which are the most useful genes from a huge number of inappropriate ones. Yin et al., for example, identified biomarkers for hepatocellular carcinoma by combining DEG screening with the weighted genomic co-expression network analysis technique.

Contemporary gene profiling analysis enables the identification of particular genetic patterns as well as the molecular mechanisms implicated in HCC [12, 13]. This method may aid in the identification of good screening indicators for HCC. Multiple reports that used this technique outlined numerous genes and their proteins that were considerably overexpressed in tumor tissues and showed promise as novel HCC biomarkers [14, 15].

New insights for HCC diagnosis may come from multi-omics, which includes transcriptomics, genomics, epigenomics, glycoproteomics/glycomics, proteomics, and metabolomics. More data has emerged that circulating tumor DNAs (ctDNAs) and their epigenetic modifications can be employed as credible biomarkers in the fields of genomics and epigenomics [16,17,18]. In terms of transcriptomics, noncoding RNAs and mRNAs showed major alterations (lncRNAs, circRNAs, miRNAs) [19]. Potential protein biomarkers for the identification of HCC have been found in proteomics, including Golgi protein-73 (GP73), heat shock protein 90 (Hsp90), alpha-1-fucosidase (AFU), Dikkopf-1 (DKK1), midkine (MDK), osteopontin, and des-gamma-carboxy prothrombin (DCP) [20]. Glycosylation, phosphorylation, acetylation, and ubiquitination are examples of posttranslational modifications (PTMs) that might be taken into consideration when looking for new biomarkers [21].

In this study, we looked at gene expression levels for six currently proposed HCC biomarkers, including AFP and glypican 3 (GPC3), Midkine (MDK), a disintegrin and metalloproteinase 8 (ADAM8), hepatic growth factor (HGF), and cytokeratin-1 (CK-1) in individuals with hepatocellular carcinoma, compared to serum AFP levels, to assess whether they can be used for predictive indicators or for screening in HCC Egyptian patients.

Methods

The present study involved 70 chronic liver disease (CLD) subjects (36 men and 34 women), distributed into 2 groups: group 1 involved 40 HCC patients; group 2 involved 30 HCV subjects but no evidence of HCC. They ranged in age from 44 to 58 years. They were chosen from people who were referred to Menoufia University, Egypt’s National Liver Institute Hospital. Thirty apparently healthy subjects were divided into 15 (50.0%) males and 15 (50.0%) females, with 44 to 55 age range. This research was performed according to the national and international ethical guidelines (good clinical practice, Declaration of Helsinki), and the procedures were agreed according to the National Liver Institute Hospital Local Ethics Committee, NLI (IRB00003413) Menoufia University. All HCC and HCV subjects were positive for serum HCV, which was validated by qualitative RT-PCR to detect RNA of HCV. Ultrasonography and computed tomography (CT) scans revealed that HCC patients had a localized lesion. All of the individuals tested negative for hepatitis B surface antigen in their blood (ELISA). Only patients who granted informed consent had their blood samples taken. All patients and controls were given a thorough medical history. Patients undergoing infection with HBV or any other hepatitis diagnosable disorder rather than HCV, antiviral treatment, previous HCC, and any concomitant tumors rather than HCC were all excluded. All individuals had peripheral blood samples taken for normal assessment, which included a complete blood count (CBC), liver functions, and commercial assays for anti-HCV titer, prothrombin time, AFP, HBc-Ab, and HBsAg. EDTA-peripheral blood was taken for qRT-PCR and promptly frozen at − 80 °C until analysis.

Quantitative real-time-PCR (qRT-PCR) for ADAM8

PureLink RNA Mini Kit (Thermos Scientific, USA) was used to isolate total RNA from peripheral blood mononuclear cells (PBMCs) according to the protocol guidelines. The RNA content and purity were defined using a NanoDrop spectrophotometer (Thermo Fisher Scientific, MA, USA). The reverse transcription was done on the isolated RNA using the HiSenScriptRH( −) cDNA synthesis kit (iNtRon, Korea) utilizing 1-μg RNA. Then, qPCR amplification was achieved in a 10-μl mixture having 1 × Universal SYBR Green RealMOD Real-Time PCR Master Mix (iNtRon, Korea), 25 ng of cDNA, and 2 pmol of each specific primer pair (Table 1) in StepOne™ Real-Time PCR Detection System (Applied Biosystem, CA, USA). Thermocycling was set to 95 °C for 10 min and then 40 cycles of 15 s at 95 °C and 1 min at 60 °C. The relative mRNA levels of the examined genes were determined using qRT-PCR, with GAPDH as an internal reference and the different biomarkers mRNA expressed as fold change. The differential expression level of various genes was normalized to the housekeeping marker (GAPDH); the cycle threshold (CT) was determined using the relative CT technique and expressed as 2−ΔΔCT [22].

Table 1 Demographic data and biochemical and hematological characteristics of the study
Full size table

Statistical analysis

The Statistical Package for the Social Sciences was utilized to examine the data (SPSS version 20.0) (Armonk, NY, USA: IBM Corp). The following assessments were utilized to test significance of variations in qualitative data (percentage and number) and quantitative statistics (standard deviation and mean). The chi-square was applied to compare differences in qualitative differences and percentages among groups. ANOVA was managed to test the variations between quantitative parametric multiple groups while nonparametric tested by Kruskal–Wallis tests. The cut-off value was determined by ROC curve. Spearman’s correlation identifies nonparametric correlation. The P-value cutoff for significance was determined at < 0.05.

Results

Demographic, hematological, biochemical, and characteristics data of the studied groups

Table 1 summarizes the hematological, demographic, and biochemical data of the groups under investigation. Healthy controls, HCV patients, and HCC patients had median ages of 45.48, 51.4, and 58.2, respectively. The HCV, HCC, and control groups did not differ significantly in terms of gender. HCC patients had considerably greater ALT levels, SGOT, AST, total bilirubin, and INR than the other studied groups, but albumin, hemoglobin, and platelet number were lower with significant value. There were no statistical significant differences among the subjects under study for creatinine or WBCs.

Expression of the biomarkers and AFP level in the studied group

Table 2 shows the ranges of gene expression levels identified by qRT-PCR in studied groups, standardized to the housekeeping GAPDH gene, and compared to the level of AFP in serum. The median serum AFP level demonstrated a significant rise in HCC subjects versus HCV and normal control groups (P = 0.001). The expression level of CK1, ADAM8, GPC3, HGF, and MDK was found significantly higher in HCV and HCC groups compared to a group of healthy controls (Fig. 1).

Table 2 Parameters among the studied groups
Full size table
Fig. 1
figure 1

The ROC curve analysis of AFP and different biomarkers as markers for hepatocellular carcinoma. ROC, receiver operating characteristic; AFP, alpha-fetoprotein

Full size image

Combination of AFP and different biomarkers for potential diagnosis of HCC

The accuracy of the noninvasive HCC biomarkers together with AFP serum expression was compared using ROC curve analysis. Table 3 shows the diagnostic ability and appropriate values of cutoff for the examined genes expression and AFP for HCC detection. ROC curve evaluation demonstrated that the AFP area under the curve (AUC), as well as its sensitivity and specificity, were higher than those of the other genes (Fig. 2), with the exception of ADAM-8, which could detect HCC at a level of 0.57-fold with 85.7% sensitivity and 60.3% specificity, whereas AFP could detect HCC at a level of 90 ng/dl with 78.7% sensitivity and 60.3%. specificity. When compared to the standard tumor marker AFP, these findings show that only ADAM8 is more sensitive but less specific in predicting HCC, implying that it will have better consequences on HCC prediction compared to HCV and the healthy control group.

Table 3 The diagnostic validity and the optimal cut-off values of different gene expressions and AFP as markers for HCC
Full size table
Fig. 2
figure 2

The ROC curve analysis of AFP combination with different biomarkers as markers for hepatocellular carcinoma. ROC, receiver operating characteristic

Full size image

Combination of the different genes with the AFP as a potential tumor marker for HCC patients

The combined application of the different genes under this study and AFP-based biomarkers has been tested to address the problem of markers being suboptimum due to poor sensitivity and specificity (Tables 4, 5). The combination of AFP and CK-1 markers used in study achieved 92.9% sensitivity and 72.8% specificity. No other combination for HCC detection showed improved sensitivity more than AFP sensitivity alone. On the other hand, when we combine the different markers without AFP, the result showed no increase of sensitivity or specificity than using AFP alone. The three combinations of AFP with two markers did not mark increase of the diagnostic ability for HCC as it raises the sensitivity in most combination from 78.6% for AFP alone to nearly 86% sensitivity, while the specificity did not improve (Fig. 2).

Table 4 The diagnostic validity and the optimal cut-off values of AFP and HCC marker combination
Full size table
Table 5 The diagnostic validity and the optimal cut-off values of different HCC markers combination
Full size table

Discussion

HCC is a frequent and ambitious malignancy with a poor prognosis all over the world. Approximately 70 to 90% of individuals with HCC had history of cirrhosis or another chronic liver illness carried on by infection with + HBV or HCV [23]. HCV is the most common underlying etiology of liver cirrhosis, accounting for 91.32% of HCC cases, according to a major epidemiological study conducted on Egyptian HCC patients, while chronic infection of HVB was found only in 2.51% of HCC cases [24]. HCC patients with early identification and resection may have a better chance of long-term survival. Unfortunately, just 10 to 20% of patients with HCC are candidates for resection using current diagnostic methods [25]. In clinical practice, AFP is currently the only serological biomolecule for HCC. Because of its low sensitivity and nonspecific rise in noncancerous hepatic disorders, other diagnostic biomarkers are urgently needed to aid HCC early identification, particularly in AFP-normal cases and tumors of smaller sizes.

HCC pathogenesis is a multistage and multistep process that involves both genetic and environmental elements. The establishment of diagnostic biomarkers with predictive value can be aided by molecular identification of genetic abnormalities in the number and cellular content of tumor cells, as well as the microenvironment of tumor cell [26]. Multiple studies have indicated that biomarkers including MDK, ADAM-8, HGF, GPC-3, and CK1 can influence transcription via regulating contacts and signalling events in cell membrane focal adhesions, which appear to be crucial to transcriptional control [27, 28].

In many forms of solid tumors, including HCC, MDK plays an important role in tumorigenesis-related processes such as anti-apoptosis, migration, mitogenesis, angiogenesis, proliferation, and transformation [29, 30]. In this study, the diagnosis rate of both MDK and AFP was considerably greater in HCC subjects rather than subjects with healthy control individuals and HCV subjects (P < 0.004), but the diagnostic ability of MDK alone is still lower than AFP detection (78.7%, 72.8 vs 50%, 68%) sensitivity and specificity respectively. These data were in line with those of Shaheen et al. [31] who stated that the median MDK expression level was considerably higher in HCC patients when compared to cirrhotic subjects as well as unaffected controls.

ADAM-8 has been found to involved in multiple cellular processes. It has been linked to allergies, carcinogenesis, abnormal neural cell signalling, and arthritis, and it has been shown to be upregulated in a variety of malignancies [32, 33]. In the present investigation, the mRNA expression level of ADAM-8 and AFP levels was substantially greater in HCC patients when compared to HCV subjects and healthy individuals (P < 0.02), but the diagnostic validity of ADAM-8 alone is more sensitive and less specific than AFP (78.7%, 72.8 vs 85.7%, 59.1%) sensitivity and specificity respectively. These findings are consistent with Fitzmorris and Singal’s [34] study, which found that ADAM8 expression was linked to serum AFP increase, tumor size, histological differentiation, tumor recurrence, tumor metastasis, and tumor stage.

Hepatocyte growth factor (HGF) is a pleiotropic cytokine that has been linked to the pathophysiology of many malignancies by boosting cancer cell motility and invasiveness in vivo and in vitro [28]. In the current study, HGF expression levels were noticeably greater in HCC patients when compared to HCV subjects and healthy individuals (P < 0.02), but unfortunately, the diagnostic validity of HGF alone is less sensitive and less specific than AFP (78.7%, 72.8 vs 71.4%, 54.5%) sensitivity and specificity respectively. This comes to agree with the study done by Dong et al. [35] who stated that HGF mRNA expression levels in HCC cases increased significantly as compared to both control individuals and the CH groups.

Glypican-3 (GPC3) is thought to have a vital controlling function in cell growth in mesodermal embryonic tissues. In mechanism way, GPC3 is possible to be implicated in the regulation of Wnt, bone morphogenic protein, and signalling pathways because it influences growth and death of the cell in particular types throughout development [36, 37]. In the current study, compared to patients with HCV, healthy individuals, and patients with HCC, patients with HCC had considerably greater levels of GPC-3 expression (P < 0.001), but unfortunately, the diagnostic validity of HGF alone is less sensitive and less specific than AFP (78.7%, 72.8 vs 64.3, 63.4%) sensitivity and specificity respectively. In the same way, GPC3 expression has been found in a variety of malignancies, including lung squamous cell carcinoma (SqCC), ovarian carcinoma, gastric carcinoma, and melanomas, and it is especially prominent in HCC [38].

Cytokeratin mRNA expression is likely to be modified in correlation with surges in malignancy and metastatic ability and a number of cytokeratins thought to be utilized as prognostic indicators in several epithelial cancers. Several cytokeratin subtypes have been found to be expressed in HCC [39]. In this study, the AFP (gold standard marker) sensitivity and specificity were 39% and 100% at 90 IU/ml cutoff, while these values are still higher than the sensitivity and specificity of CK-1 detection of HCC (71.4%, 59.1%). Bessa et al. [40] had comparable data (AUC 0.71) to the current results (AUC 0.79) for identifying HCC by measurement of AFP in Egyptian subjects with HCV-dependent HCC. Therefore, it seems that there was no ideal biomarker which has sensitivity and specificity more than the gold standard AFP in detection of HCC; in such case, there is a requirement to improve the HCC detection utilizing AFP. Our research looked into the idea of combining numerous markers to aid in the HCC detection by utilizing AFP. The CK-1 combination with AFP increased the sensitivity of HCC detection from 71.4% for CK-1 and 78.6% for AFP to 92%, while no improvement of specificity was achieved. The three combinations of AFP with two markers did not mark the increase of the diagnostic ability for HCC as it raises the sensitivity in most combination from 78.6% for AFP alone to nearly 86% sensitivity, while the specificity did not improve.

Conclusions

In conclusion, the use of different biomarkers overlaying such as CK1 with AFP could improve the HCC detection. The ability to identify HCC patients from unaffected normal individuals and liver cirrhosis subjects, a category at high risk, provides hope for early identification of HCC cases. In order to confirm that those markers can be used, more research is needed to verify its efficiency for various populations.

Availability of data and materials

The datasets used during the present study are available from the corresponding author upon request.

Abbreviations

HCC:

Hepatocellular carcinoma

HCV:

Hepatitis C virus

HBV:

Hepatitis B virus

AFP:

Alpha-fetoprotein

GPC3:

Glypican-3

MDK:

Midkine

ADAM8:

A disintegrin and metalloproteinase 8

HGF:

Hepatic growth factor

CK-1:

Cytokeratin-1

DEGs:

Differentially expressed genes

PBMCs:

Peripheral blood mononuclear cells

qRT-PCR:

Quantitative real-time PCR

CT:

The cycle threshold

GP73:

Golgi protein-73

Hsp90:

Heat shock protein 90

AFU:

Alpha-1-fucosidase

DKK1:

Dikkopf-1

DCP:

Des-gamma-carboxy prothrombin

References

  1. Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer. 2010;127(12):2893–917.

    Article  CAS  PubMed  Google Scholar 

  2. Perz JF, Armstrong GL, Farrington LA, Hutin YJ, Bell BP. The contributions of hepatitis B virus and hepatitis C virus infections to cirrhosis and primary liver cancer worldwide. J Hepatol. 2006;45(4):529–38.

    Article  PubMed  Google Scholar 

  3. El-Garem H, Abdel-HafezB H, Foaud A, Al Akel W, EldienAtia M, Salah M, et al. Tissue biomarkers in the early detection of hepatocellular carcinoma among Egyptian patients with chronic hepatitis C: a possible genetic profile. J Adv Med Med Res. 2013;3(4):1858–70.

  4. Fattovich G, Stroffolini T, Zagni I, Donato F. Hepatocellular carcinoma in cirrhosis: incidence and risk factors. Gastroenterology. 2004;127(5):S35–50.

    Article  PubMed  Google Scholar 

  5. Kobayashi K, Sugimoto T, Makino H, Kumagai M, Unoura M, Tanaka N, et al. Screening methods for early detection of hepatocellular carcinoma. Hepatology. 1985;5(6):1100–5.

    Article  CAS  PubMed  Google Scholar 

  6. Belghiti J, Fuks D. Liver resection and transplantation in hepatocellular carcinoma. Liver cancer. 2012;1(2):71–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Llovet JM, Ricci S, Mazzaferro V, Hilgard P, Gane E, Blanc J-F, et al. Sorafenib in advanced hepatocellular carcinoma. N Engl J Med. 2008;359(4):378–90.

    Article  CAS  PubMed  Google Scholar 

  8. Ueno M, Uchiyama K, Ozawa S, Hayami S, Shigekawa Y, Tani M, et al. Adjuvant chemolipiodolization reduces early recurrence derived from intrahepatic metastasis of hepatocellular carcinoma after hepatectomy. Ann Surg Oncol. 2011;18(13):3624–31.

    Article  PubMed  Google Scholar 

  9. Paul SB, Gulati MS, Sreenivas V, Madan K, Gupta AK, Mukhopadhyay S, et al. Evaluating patients with cirrhosis for hepatocellular carcinoma: value of clinical symptomatology, imaging and alpha-fetoprotein. Oncology. 2007;72(Suppl. 1):117–23.

    Article  PubMed  Google Scholar 

  10. Henry NL, Hayes DF. Cancer biomarkers. Mol Oncol. 2012;6(2):140–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. ShahrjooiHaghighi A, Frigui H, Zhang X, Wei X, Shi B, McClain CJ. Ensemble feature selection for biomarker discovery in mass spectrometry-based metabolomics. In: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing. 2019.

    Google Scholar 

  12. Okabe H, Satoh S, Kato T, Kitahara O, Yanagawa R, Yamaoka Y, et al. Genome-wide analysis of gene expression in human hepatocellular carcinomas using cDNA microarray: identification of genes involved in viral carcinogenesis and tumor progression. Can Res. 2001;61(5):2129–37.

    CAS  Google Scholar 

  13. Mas VR, Maluf DG, Archer KJ, Yanek K, Kong X, Kulik L, et al. Genes involved in viral carcinogenesis and tumor initiation in hepatitis C virus-induced hepatocellular carcinoma. Mol Med. 2009;15(3):85–94.

    Article  CAS  PubMed  Google Scholar 

  14. Gehrau RC, Archer KJ, Mas VR, Maluf DG. Molecular profiles of HCV cirrhotic tissues derived in a panel of markers with clinical utility for hepatocellular carcinoma surveillance. PLoS ONE. 2012;7(7): e40275.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Xiang Z-L, Zeng Z-C, Fan J, Tang Z-Y, Zeng H-Y, Gao D-M. Gene expression profiling of fixed tissues identified hypoxia-inducible factor-1α, VEGF, and matrix metalloproteinase-2 as biomarkers of lymph node metastasis in hepatocellular carcinoma. Clin Cancer Res. 2011;17(16):5463–72.

    Article  CAS  PubMed  Google Scholar 

  16. Campos-Carrillo A, Weitzel JN, Sahoo P, Rockne R, Mokhnatkin JV, Murtaza M, et al. Circulating tumor DNA as an early cancer detection tool. Pharmacol Ther. 2020;207: 107458.

    Article  CAS  PubMed  Google Scholar 

  17. Zhou J, Shi Y-H, Fan J. Circulating cell-free nucleic acids: promising biomarkers of hepatocellular carcinoma. Semin Oncol. 2012;39(4):440-8.

  18. Tang J-C, Feng Y-L, Guo T, Xie A-Y, Cai X-J. Circulating tumor DNA in hepatocellular carcinoma: trends and challenges. Cell Biosci. 2016;6(1):1–9.

    Article  CAS  Google Scholar 

  19. Supplitt S, Karpinski P, Sasiadek M, Laczmanska I. Current achievements and applications of transcriptomics in personalized cancer medicine. Int J Mol Sci. 2021;22(3):1422.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Block TM, Comunale MA, Lowman M, Steel LF, Romano PR, Fimmel C, et al. Use of targeted glycoproteomics to identify serum glycoproteins that correlate with liver cancer in woodchucks and humans. Proc Natl Acad Sci. 2005;102(3):779–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Liu W, Li J, Zhang P, Hou Q, Feng S, Liu L, et al. A novel pan-cancer biomarker plasma heat shock protein 90alpha and its diagnosis determinants in clinic. Cancer Sci. 2019;110(9):2941–59.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2− ΔΔCT method. Methods. 2001;25(4):402–8.

    Article  CAS  PubMed  Google Scholar 

  23. El–Serag HB, Rudolph KL. Hepatocellular carcinoma: epidemiology and molecular carcinogenesis. Gastroenterology. 2007;132(7):2557–76.

    Article  PubMed  Google Scholar 

  24. Shaker MK, Abdella HM, Khalifa MO, Dorry AKE. Epidemiological characteristics of hepatocellular carcinoma in Egypt: a retrospective analysis of 1313 cases. Liver Int. 2013;33(10):1601–6.

    Article  CAS  PubMed  Google Scholar 

  25. Lai E, Fan S-T, Lo C-M, Chu K-M, Liu C-L, Wong J. Hepatic resection for hepatocellular carcinoma. An audit of 343 patients. Ann Surg. 1995;221(3):291.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Liver EAFTSOT. EASL clinical practice guidelines: management of hepatocellular carcinoma. J Hepatol. 2018;69(1):182–236.

    Article  Google Scholar 

  27. Mammoto A, Mammoto T, Ingber DE. Mechanosensitive mechanisms in transcriptional regulation. J Cell Sci. 2012;125(13):3061–73.

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Wang H, Rao B, Lou J, Li J, Liu Z, Li A, et al. The function of the HGF/c-Met axis in hepatocellular carcinoma. Front Cell Dev Biol. 2020;8:55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Kato M, Shinozawa T, Kato S, Awaya A, Terada T. Increased midkine expression in hepatocellular carcinoma: immunohistochemical and in situ hybridization analyses. Arch Pathol Lab Med. 2000;124(6):848–52.

    Article  CAS  PubMed  Google Scholar 

  30. Muramatsu T. Midkine and pleiotrophin: two related proteins involved in development, survival, inflammation and tumorigenesis. J Biochem. 2002;132(3):359–71.

    Article  CAS  PubMed  Google Scholar 

  31. Shaheen KY, Abdel-Mageed AI, Safwat E, AlBreedy AM. The value of serum midkine level in diagnosis of hepatocellular carcinoma. Int J Hepatol. 2015;2015:146389.

  32. Valkovskaya N, Kayed H, Felix K, Hartmann D, Giese N, Osinsky S, et al. ADAM8 expression is associated with increased invasiveness and reduced patient survival in pancreatic cancer. J Cell Mol Med. 2007;11(5):1162–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Qi B, Liu H, Dong Y, Shi X, Zhou Q, Zeng F, et al. The nine ADAMs family members serve as potential biomarkers for immune infiltration in pancreatic adenocarcinoma. PeerJ. 2020;8: e9736.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Fitzmorris P, Singal AK. Surveillance and diagnosis of hepatocellular carcinoma. Gastroenterol Hepatol. 2015;11(1):38.

    Google Scholar 

  35. Dong N, Shi X, Wang S, Gao Y, Kuang Z, Xie Q, et al. M2 macrophages mediate sorafenib resistance by secreting HGF in a feed-forward manner in hepatocellular carcinoma. Br J Cancer. 2019;121(1):22–33.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Nishida T, Kataoka H. Glypican 3-targeted therapy in hepatocellular carcinoma. Cancers. 2019;11(9):1339.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Iglesias BV, Centeno G, Pascuccelli H, Ward F, Peters MG, Puricelli L, et al. Expression pattern of glypican-3-GPC3-during human embryonic and fetal development. Histol Histopathol. 2008.

  38. Nakatsura T, Nishimura Y. Usefulness of the novel oncofetal antigen glypican-3 for diagnosis of hepatocellular carcinoma and melanoma. BioDrugs. 2005;19(2):71–7.

    Article  CAS  PubMed  Google Scholar 

  39. Yang X-R, Xu Y, Shi G-M, Fan J, Zhou J, Ji Y, et al. Cytokeratin 10 and cytokeratin 19: predictive markers for poor prognosis in hepatocellular carcinoma patients after curative resection. Clin Cancer Res. 2008;14(12):3850–9.

    Article  CAS  PubMed  Google Scholar 

  40. Bessa SSE-D, Elwan NM, Suliman GAM, El-Shourbagy SH. Clinical significance of plasma osteopontin level in Egyptian patients with hepatitis C virus-related hepatocellular carcinoma. Arch Med Res. 2010;41(7):541–7.

    Article  Google Scholar 

Download references

Acknowledgements

The authors of this work are appreciative to the members of Biology Center at the University of Sadat City for facilitating the molecular experiments.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Molecular Diagnostics and Therapeutics Department, Genetic Engineering and Biotechnology Research Institute (GEBRI), University of Sadat City, Sadat City, Egypt

    Ahmed Daif, Moustafa A. Sakr, Hisham A. Ismail & Mahmoud Gadallah

  2. Department of Forensic Science, College of Science, AlKarkh University of Science, Baghdad, Iraq

    Mahmood A. Al-Azzawi

Authors
  1. Ahmed Daif
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mahmood A. Al-Azzawi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Moustafa A. Sakr
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hisham A. Ismail
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mahmoud Gadallah
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MS, MA, AD, HI, and MG designed the study; MS, collected the samples; MS and MG, experiments conducted and the data analysis; MS, MA, AD, HI, and MG have contributed to the writing of the manuscript; and all authors share and approved the final version.

Corresponding author

Correspondence to Moustafa A. Sakr.

Ethics declarations

Ethics approval and consent to participate

Ethical approval for this study was obtained from the ethical review committee of the National Liver Institute Hospital, Menoufia University (IRB00003413). Informed written consent to participate in the study was provided by all participants.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Daif, A., Al-Azzawi, M.A., Sakr, M.A. et al. Noninvasive identification of molecular biomarkers of hepatocellular carcinoma in HCV-Egyptian patients. J Egypt Natl Canc Inst 35, 11 (2023). https://doi.org/10.1186/s43046-023-00170-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s43046-023-00170-7

Keywords