Skip to main content
Egyptian Journal of Medical Human Genetics
Download PDF

Long non-coding RNA HOTAIR and HOTTIP as potential biomarkers for hepatitis C virus genotype 4-induced hepatocellular carcinoma

Egyptian Journal of Medical Human Genetics volume 21, Article number: 7 (2020) Cite this article

Abstract

Background

Long non-coding RNAs (lncRNAs) homeobox (Hox) transcript antisense intergenic RNA (HOTAIR) and HOXA transcript at the distal tip (HOTTIP) have been suggested to be implicated in liver cancer tumorigenesis and progression; however, little is known about the role of the plasma HOTAIR and HOTTIP in liver cancer diagnosis and prognosis. The current study aimed at measuring the plasma levels of long non-coding RNAs (HOTAIR and HOTTIP) expression in chronic liver disease (CLD) due to HCV genotype 4 infection with/without cirrhosis and HCC patients in an attempt to evaluate the potential benefits of these new circulating as non-invasive diagnostic biomarkers and a novel therapeutic strategy for liver cirrhosis and carcinogenesis of Egyptian patients. Hundred subjects were included in this study, divided into two groups; group I (50 patients) were classified into subgroup Ia (CLD without cirrhosis, n = 25) and subgroup Ib (CLD with cirrhosis, n = 25), group II (CLD patients with HCC, n = 25), and control (healthy volunteer, n = 25). The expression of lncRNAs (HOTAIR and HOTTIP) genes was analyzed by real-time PCR.

Results

LncRNAs (HOTAIR and HOTTIP) showed upregulation in all diseased groups, which was in consistent with the progression of the disease toward the HCC stage. In addition, HOTAIR and HOTTIP showed a diagnostic ability to discriminate between cases of cirrhosis and HCC compared with healthy control (p < 0.001), while HOTAIR and HOTTIP did not show a discrimination significant differences between cirrhotic cases and non-cirrhotic cases. By using receiver operating characteristic curve (ROC) analysis, it was found that LncRNAs (HOTAIR and HOTTIP) could diagnose liver cancer with 64.0% sensitivity and 86.0% specificity and 48.0% sensitivity and 88.0% specificity. Furthermore, both genes can be considered as the predictor and prognostic parameters for cirrhosis (OR = 1.111, p = 0.05) and (OR = 1.07, p = 0.05) respectively, and HCC (OR = 1.047, p = 0.01) and (OR = 1.05, p = 0.003). The increased HOTAIR and HOTTIP expression were associated with advanced tumor stages and higher grades.

Conclusion

These results strongly prompt us that HOTAIR and HOTTIP genes can be used as non-invasive prognostic biomarkers and new therapeutic targets for HCV genotype 4-induced HCC.

Background

Egypt has a very high prevalence of the hepatitis C virus (HCV) worldwide, about 22% of Egyptian blood donors suffer from HCV with 170,000 to 200,000 new cases yearly and 40,000 dying from the disease every year “(http://egyptianstreets.com/2015/05/04/egypt-has-highest-prevalence-of-hepatitis-c-in-the world-who)”. HCV infection is a major cause of CLD, which can progress to liver fibrosis, cirrhosis and even HCC [1]. HCV increases the risk of HCC development by nearly 17-fold compared with healthy individuals [2]. Although the overall prevalence of chronic hepatitis C is declining, the complications of the disease are increasing because of the aging of the infected population and the progression of liver fibrosis [3].

LncRNAs account for a large portion of the non-coding transcripts. They comprise of in excess of 200 nucleotides and have no or restricted protein-coding potential. Previously, they were believed to be transcriptional noise [4]. But recently, expression analyses of lncRNAs in a wide range of tissues have revealed their importance for different homeostatic and physiological functions, such as gene imprinting, cell differentiation, and organogenesis [5, 6]. A solid affiliation has been found between deregulated lncRNA articulation and the improvement of maladies.

Through the modulation of oncogenic and tumor-suppressing pathways, it was noted that the aberrant expression of lncRNAs contributed to the development and progression of cancer [7,8,9,10]. LncRNA has attracted attention as a new participant in carcinogenesis and emerged as a new source of non-invasive cancer biomarkers [3, 11,12,13]. Most of these lncRNAs are upregulated in HCCs, but less expressed or undetectable in normal individuals [14].

The HOX family qualities are realized interpretation factors with a key job in embryogenesis and carcinogenesis [15, 16]. Their expression is deregulated in many cancers [17]. In humans, HOX genes are organized into 4 clusters (A, B, C, and D), which are situated on various chromosomes [18]. Interestingly, several lncRNAs associated with HOX genomic regions can partake in the regulation of HOX genes and work together in their functions [19]. HOTAIR lncRNA is transcribed from the antisense strand of the HoxC gene, which is located on chromosome 12q13.13 between the HoxC11 and HoxC12 genes [20] and one of the most significant administrative RNAs in human cells. It was first described by Rinn et al. [21] as a spliced and polyadenylated RNA with six exons and 2.2 kb nucleotides. HOTTIP gene is located at the homeobox A (HOXA) locus (chromosomal locus 7p15.2) which encodes the 3764 bp transcript. Therefore, the lncRNA is termed ‘HOXA transcript at the distal tip’ (HOTTIP) [22, 23].

HOTAIR promotes the carcinogenic activity of HCC cells via multiple mechanisms, such as the suppression of RNA-binding motif protein, triggering of epithelial-mesenchymal transition, and interaction with miRNAs [24]. The expression of HOTAIR was found to be stimulated by c-Myc in gallbladder cancer cells [25]. c-Myc also may have a pivotal role in hepatocarcinogenesis [25, 26]. HOTAIR was also noticed to be regulated by IκB kinase [27]. Suppression of HOTAIR was noticed to promote the inhibition of cell proliferation and invasion [28]. HOTTIP is overexpressed in numerous cancers [23, 29, 30] and in HCC [31]. The knockdown of HOTTIP in pancreatic cancer was found to lead to cell proliferation arrest and impaired cell invasion via inhibiting epithelial-mesenchymal transition [32].

Previous research has shown that circulating non-coding RNAs (nRNA) could be used as a non-invasive biomarker for the early detection of cancers [33,34,35], such as lncRNAs, can be detected in body fluids and may have diagnostic and prognostic roles in cancer. Recent studies have investigated the clinical implication of cell-free lncRNAs in cancer patients [36, 37].

Aim of the work

The aim of the study is to develop a non-invasive diagnostic tool based on measuring the plasma levels of different lncRNAs markers namely HOTAIR and HOTTIP in order to detect HCV genotype 4-induced HCC at the early stages of the disease.

Methods

Study design

We are planning a study of subjects in which we will regress their values of the patient’s against control. Prior data indicate that the standard deviation of control is 0.6 and the standard deviation of the regression errors will be 1.9. If the true slope of the line obtained by regressing patients against control is 1.7, we will need to study 25 subjects for each group to be able to reject the null hypothesis that this slope equals zero with probability (power) 90%. The Type I error probability associated with this test of this null hypothesis is 0.05.

Patient’s criteria

Patients enrolled in this study were admitted to the Gastroenterology and Hepatology Department in affiliated hospital of our institution from November 2016 to August 2018. Diagnosis of patients was based on full medical history, thorough clinical examination, abdominal ultrasonography and laboratory assessment including CBC, and liver function tests, serological and HCV genotyping by HybProbe probes with the light cycler carousel-based system.

This study was approved by our institution Ethics Review Board and informed consent was obtained from all the subjects included in this study according to the roles of the Declaration of Helsinki 1975. Approval of local ethical committee (REC number 01/19).

Inclusion criteria

All included patients were suffering from chronic hepatitis C genotype (4), persisting more than 6 months (HCV RNA positive). They did not receive any specific treatment for HCV during the last 6 months.

All chronic liver disease (CLD) who developed malignancy on top of previous HCV infection. Diagnosis of cirrhosis was depending upon ultrasonographic criteria (surface irregularity, coarse echo pattern, portal vein diameter, splenic size, presence or absence of ascites), laboratory findings of hypoalbuminemia and hypoprothrombinemia, in addition to APRI score (APRI score calculated regarding AST to Platelet Ratio Index (APRI) = [AST Level (IU/L)/AST (upper limit of normal) (IU/L)]/platelet count (109/L)×100, (normal < 0.05, CLD without cirrhosis 0.5–1.5 and cirrhosis ≤ 1.5) [38] and esophageal or gastric varices diagnosed by endoscopy as a sign of portal hypertension in indicated patients. Diagnosis of HCC was depending upon the presence of focal hepatic lesions diagnosed by abdominal ultrasound and confirmed by triphasic spiral computed tomography (CT) and/or magnetic resonance imaging according to the American Association for the Study of Liver Diseases (AASLD) 2011 guidelines [39].

Exclusion criteria

The exclusion criteria included any concomitant cause CLD such as patients with history of schistosomiasis, chronic viral diseases other than HCV, dual HBV and HCV infection, non-alcoholic steatohepatitis (NASH), autoimmune hepatitis, biliary disorders, malignancies other than HCC, regular intake of hepatotoxic drugs, alcohol abuse, diabetes, and HCV-infected patients receiving direct-acting antiviral or immunomodulatory interferon-α therapy.

Based on the inclusion and exclusion criteria, 100 patients were included in this study. Seventy-five patients with chronic hepatitis C were classified into two major groups: group I, CLD without HCC (n = 50), and group II, CLD with HCC (n = 25). Group I was further subdivided into two subgroups: (Ia) (CLD without cirrhosis) (n = 25) and (Ib) (CLD with cirrhosis) (n = 25). In addition, twenty-five age- and sex-matched healthy adults served as a control group.

Specimen collection and handling

About 7 ml peripheral venous blood were collected under strict aseptic conditions by clean venipuncture using vacuum blood collection tubes and distributed as 2.5 ml in EDTA tubes for complete blood picture, 2.5 ml in another sterile EDTA tube (stored at −80°C) for viral RNA extraction for HCV genotyping, miRNA, and mRNA extraction. In addition, 2 ml in a plane tube were allowed to clot at 37 °C, and then centrifuged at 3000 rpm for 10 min and the collected serum was stored at −80°C to be used for performing liver and kidney functions, and other specific serological tests.

Laboratory investigations

All individuals were subjected to general investigations including; haemogram, using an automated cell counter (Celltac 5, Nihon Kohden, Tokyo, Japan). A battery of liver function tests was performed using standard methods. Alpha-fetoprotein (AFP) was measured using an autoanalyzer (Hitachi 736, Hitachi Japan). Coagulation tests were measured using Stago Compact Max, USA. Serological diagnosis of HCV infection was done using Murex anti-HCV, version IV, Murex Diagnostics limited, Dartford, England. HCV-RNA by PCR. Hepatitis B surface antigen and HBV core antibody were examined by enzyme-linked immunosorbent assay (ELISA) system assay. Autoimmune hepatitis was investigated by detecting anti-nuclear antibodies (ANA) using the immunospec ANA screen ELISA test system. A serological examination of schistosomiasis (Schistosoma mansoni Ab) was done using antibody detection, an in-house ELISA.

HCV genotyping: Viral RNA Extraction was done using a high pure viral RNA kit (version 18, 2011), cat. no: (11858882001) “(https://www.roche.com)”. cDNA synthesis (transcriptor first strand) was done according to the cDNA synthesis kit (transcriptor first strand) (version 6.0, 2010), cat. no: (04379012001) “(https://www.roche.com)”. HCV genotyping detection was done using hot start reaction mix detection for PCR using HybProbe probes with the light cycler carousel-based system (version 15, 2011), cat. no: (03003248001) “(https://www.roche.com)”.

Target gene expression: Total RNA extraction was done according to a high pure RNA isolation kit (version 12, 2011), cat. no: (11828665001) “(https://www.roche.com)”. Gene expression detections were performed using light cycler EvoScript RNA SYBR green I master (version 2, 2017), easy to use reaction mix for one-step RT-qPCR cat. no: (07800134001) “(https://www.roche.com)”. The primer sequences are illustrated in Table 1.

Table 1 Primers of genes included in the study
Full size table

Analysis of results depending on the SYBR green I filter combination (465–510) on light cycler EvoScript RNA SYBR green I master and comparative CT methods were applied to analyze data. Housekeeping gene Β-actin was used as an endogenous control to normalize the amount of total mRNA in each sample of HOTAIR and HOTTIP between different samples. Genes expression was calculated relative to the control samples (used as the calibrator sample) using the formula 2_ΔΔCT and were expressed as fold change “(https://bitesizebio.com)”.

Statistical analysis

The data were analyzed using Microsoft Excel 2010 and statistical package for social science (SPSS version 24.0) for Windows (SPSS IBM., Chicago, IL). Continuous normally distributed variables were represented as mean±SD with a 95% confidence interval, while non-normal variables were summarized as median with 25 and 75 percentile, and using the frequencies and percentage for categorical variables; a p value < 0.05 was considered statistically significant. To compare the means of normally distributed variables between groups, the Student’s t test was performed, and the Mann-Whitney test was used in non-normal variables. χ2 test or Fisher’s exact test was used to determine the distribution of categorical variables between groups. The diagnostic performance of HOTAIR and HOTTIP was assessed by ROC curves. The area under the ROC (AUROC) was used as an index to compare the accuracy of tests. The cut-off for the diagnosis of the group of the study was taken from the point of maximum combined sensitivity and specificity. The sensitivity and specificity for relevant cut-offs were also displayed. Spearman’s rank correlation coefficient (r) was done to show the correlation between non-parametric parameters, while Pearson’s correlation for parametric parameters. Effect modification was evaluated by stratification, statistical interaction and was assessed by including main effect variables and their product terms in the logistic regression model.

Results

Individual demographic and routine laboratory characteristics of the studied groups are shown in Table 2.

Table 2 Socio-demographic characteristics, laboratory investigations, and ultrasound finding among patients of the studied groups
Full size table

Depending on the fold change low, the fold change results showed that the gene expressions of HOTAIR and HOTTIP constantly upregulated in the studied groups (Table 3).

Table 3 Biomarkers fold change in the studied groups
Full size table

Gene expression of HOTAIR in all studied diseased groups was highly significantly (p < 0.001) upregulated compared with the control group. There was no significant upregulation in subgroup Ia when compared with subgroup Ib, while group II showed a significant upregulation when compared with subgroup Ia and subgroup Ib (p < 0.001 and p < 0.01) respectively (Fig. 1) (Table 4).

Fig. 1
figure 1

Box plot of HOTAIR gene expression in studied groups

Full size image
Table 4 Biomarkers gene expression in the studied groups
Full size table

In contrast, HOTTIP expression, a highly significantly (p value < 0.001) upregulated in all diseased groups when compared with the control group. But there was no significant difference in subgroup Ia when compared with subgroup Ib. While it was obvious, the expression of HOTTIP in group II was significantly (p value < 0.01) upregulated compared with the subgroup Ia, subgroup Ib, and the group I (Fig. 2) (Table 4).

Fig. 2
figure 2

Box plot of HOTTIP gene expression in studied groups

Full size image

The correlation study revealed a significant direct moderate correlation of HOTAIR with HOTTIP (r = 0.652 and p value = 0.001 (Fig. 3) and fair correlation between HOTAIR with AFP (r = 0.481 and p value = 0.001) (Fig. 4). While there were inverse fair correlations with albumin (r = −0.304 and p value = 0.007) (Table 5).

Fig. 3
figure 3

Correlation between HOTAIR and HOTTIP

Full size image
Fig. 4
figure 4

Correlation between HOTAIR and AFP

Full size image
Table 5 Correlation study of the studied genes
Full size table

HOTTIP expression showed a significant direct fair correlation with AFP (r = 0.400 and p value = 0.001) (Fig. 5), while there were inverse fair correlations with albumin (r = −0.392 and p value = 0.001) (Table 5).

Fig. 5
figure 5

Correlation between HOTTIP and AFP

Full size image

Receiver operating curves (ROC) were established to show the diagnostic performances of the HOTAIR and HOTTIP genes in the studied groups (Table 6).

Table 6 Diagnostic performances of HOTAIR and HOTTIP to discriminate (cirrhotic and HCC) patients from CLD patients
Full size table

ROC curve

In HOTAIR gene, for discrimination of cirrhotic group from without cirrhosis grou, it was found that plasma HOTAIR at the cut-off value of 4.7, with sensitivity of 48.0% and specificity of 72.0% with areas under curve (AUC) of 52.0 (p value = 0.8, 95% confidence interval C.I 35.6%–68.4%) and accuracy of 60.0% (Fig. 6a), for discrimination of HCC group from cirrhotic group, it was found that plasma HOTAIR at the cut-off value of > 7.0, with sensitivity of 64.0% and specificity of 76.0% with areas under curve (AUC) of 69.8 (p value = 0.007, 95% confidence interval C.I 55.5%–84.1%) and accuracy of 60.0% (Fig. 6c), and for discrimination of HCC group from CLD patients, it was found plasma HOTAIR at the cut-off value of > 9.2, with sensitivity of 64.0% and specificity of 86.0% with areas under curve (AUC) of 74.2 (p value = 0.001, 95% confidence interval C.I 61.9%–86.5%) and accuracy of 60.0% (Fig. 6d) (Table 6).

Fig. 6
figure 6

ROC curve of HOTAIR in the studied groups

Full size image

In HOTTIP gene, for discrimination of cirrhotic group from without cirrhosis group, it was found plasma HOTTIP at the cut-off value of 13.4, with sensitivity of 32.0% and specificity of 100.0% with areas under curve (AUC) of 53.9 (p value = 0.640, 95% confidence interval C.I 37.1%–70.8%) and accuracy of 66.0% (Fig. 7a), for discrimination of HCC group from cirrhotic group, it was found plasma HOTTIP at the cut-off value of > 34.8, with sensitivity of 36.0% and specificity of 92.0% with areas under curve (AUC) of 65.8 (p value = 0.033, 95% confidence interval C.I 51.3%–80.4%) and accuracy of 64.0% (Fig. 7c), and for discrimination of HCC group from CLD patients, it was found plasma HOTTIP at the cut-off value of > 16.6, with sensitivity of 48.0% and specificity of 88.0% with areas under curve (AUC) of 68.3 (p value = 0.007, 95% confidence interval C.I 55.0%–81.7%) and accuracy of 74.7% (Fig. 7d) (Table 6).

Fig. 7
figure 7

ROC curve of HOTTIP in the studied groups

Full size image

Univariate logistic regression analysis performed to characterize the two markers (HOTAIR and HOTTIP) as a predictor and/or prognostic parameter are shown in Table 7.

  • Cirrhosis risk according to gene expression

Table 7 Univariate analysis showing the predictive power of different biomarkers for cirrhosis and HCC diagnosis
Full size table

Regarding univariate logistic regression analysis, the HOTAIR and HOTTIP were considered predictor and/or prognostic parameters for cirrhotic progression. An increase in 1 ° of HOTAIR increased the odds of being cirrhosis by a factor of 1.111 with p value = 0.05, but an increase in 1 ° of HOTTIP increased the odds of being cirrhosis by a factor of 1.07 with p value = 0.05.

  • HCC risk according to gene expression

The HOTTIP was considered a predictor and/or prognostic parameter for HCC progression. An increase in 1 ° of its expression level increased the odds of being HCC by a factor of 1.029 with p value = 0.05. But there is no significance for HOTAIR in the characterization of HCC progression (OR = 1.027, p value = 0.1).

Concerning CLD as a general group (subgroups Ia and Ib) had an increase in 1 ° of HOTAIR which increased the odds of being HCC by a factor of 1.047 with p value = 0.01. For HOTTIP, an increase in 1 ° of HOTTIP which increased the odds of being HCC by a factor of 1.05 with p value = 0.003.

Discussion

LncRNAs play a central role in the regulation of differentiation, cell development, and proliferation [40]. Moreover, specific expression of lncRNA in the development of tumors has been revealed and can be used as a promising biomarker to diagnose and monitor tumors, and lncRNAs also could be collected easily from body fluids and tumor tissues [41]. The aim of our study is to develop a non-invasive diagnostic tool based on measuring the plasma levels of different long non-coding RNAs in order to detect HCV-induced HCC at the early stages of the disease.

Our results revealed a significant upregulation of HOTAIR gene in cirrhotic and HCC patients when compared with controls (p < 0.001). Similarly, a significant increase was noticed on comparing HCC with chronic HCV without and with cirrhosis patients (p < 0.001, p < 0.01) respectively. No significant difference was found between chronic HCV with cirrhosis and chronic HCV without cirrhosis. Our results are in agreement with Eman et al. [42] and Nande et al. [43] who found a similar increase in HOTAIR expression in patients with gastric cancer and non-small cell cancer patients. Gupta et al. reported that HOTAIR was overexpressed in breast cancer and its high expression in primary breast tumors was a significant predictor of subsequent metastasis and mortality [44]. HOTAIR expression was found to be higher in HCC tissues than in adjacent non-cancerous tissues [13, 45,46,47].

Regarding HOTTIP gene, a significant upregulation was revealed in cirrhotic and HCC patients when compared with controls (p < 0.001). In addition, there is a significant increase in HCC patients when compared with chronic HCV without and with cirrhosis patients (p < 0.01). The contribution of HOTTIP as having oncogenic roles has been revealed in tumorigenesis; recent advances have confirmed that HOTTIP is frequently overexpressed in many tumors to play oncogenic roles in cancer development and progression, including HCC [31]. HOTTIP expression was noticed to increase the HCC cell proliferation [48] and was associated with HCC progression and poor clinical outcome [31, 49].

The diagnostic performance of HOTAIR and HOTTIP gene expressions as markers in cirrhotic patients at different cut-off points using ROC curve showed that no significant difference between cirrhotic and non-cirrhotic patients regarding HOTAIR at the cut-off value of 4.7, with sensitivity of 48.0% and specificity of 72.0% with areas under curve (AUC) of 52.0 (p value = 0.8, 95% confidence interval C.I 35.6%–68.4%) and accuracy of 60.0%, also no significant difference between cirrhotic and non-cirrhotic patients regarding HOTTIP at the cut-off value of 13.4, with sensitivity of 32.0% and specificity of 100.0% with areas under curve (AUC) of 53.9 (p value = 0.640, 95% confidence interval C.I 37.1%–70.8%) and accuracy of 66.0%. The results indicated that HOTAIR and HOTTIP could not be used for discrimination between cirrhotic and non-cirrhotic patients.

In case of HCC, for discrimination of HCC group from cirrhotic group, it was found that a highly significant difference between HCC and cirrhotic patients regarding HOTAIR at the cut-off value of > 7.0, with sensitivity of 64.0% and specificity of 76.0% with areas under curve (AUC) of 69.8 (p value = 0.007, 95% confidence interval C.I 55.5%–84.1%) and accuracy of 60.0%, also a significant difference between HCC and cirrhotic patients regarding HOTTIP at the cut-off value of > 34.8, with sensitivity of 36.0% and specificity of 92.0% with areas under curve (AUC) of 65.8 (p value = 0.033, 95% confidence interval C.I 51.3%–80.4%) and accuracy of 64.0%. The results indicated that HOTAIR and HOTTIP can be used to discriminate between HCC patients than cirrhotic patients.

Concerning for discrimination of HCC patients from CLD (cirrhotic and non-cirrhotic) patients, it was found that a highly significant difference between HCC and CLD patients regarding HOTAIR at the cut-off value of > 9.2, with sensitivity of 64.0% and specificity of 86.0% with areas under curve (AUC) of 74.2 (p value = 0.001, 95% confidence interval C.I 61.9%–86.5%) and accuracy of 60.0%, also a highly significant difference between HCC and CLD patients regarding HOTTIP at the cut-off value of > 16.6, with sensitivity of 48.0% and specificity of 88.0% with areas under curve (AUC) of 68.3 (p value = 0.007, 95% confidence interval C.I 55.0%–81.7%) and accuracy of 74.7%. These results indicated that HOTAIR and HOTTIP can be used for discrimination between HCC and CLD without HCC.

Interestingly, according to regression analysis, the expression levels of HOTAIR and HOTTIP were considered as significant predictors associated with the changes of the cirrhotic progression and cirrhosis risk assessment (p value = 0.05 and p value = 0.05) respectively. Ge et al. suggested that the miR-192/-204-HOTTIP axis was a significant molecular pathway during tumorigenesis of HCC and demonstrated the prognostic and potential therapeutic roles of HOTTIP [50], in contrast, Quagliata et al. reported that the patients with higher lncRNAs HOTTIP/HOXA13 expression had poorer prognosis in liver cancer [32]. Also, overexpression of HOTAIR was found to be strongly associated with unfavorable prognosis for patients with HCC [51]. Evidently, the expression levels of HOTTIP increased the odds of being cirrhosis when selected as significant predictors associated with the chances of diagnosis HCC versus cirrhosis patients (p value = 0.05) and concerning CLD as a general group (non-cirrhotic and cirrhotic) had the expression levels of HOTAIR and HOTTIP were considered as predictor parameters associated with the chances of diagnosis for HCC progression and HCC risk assessment (p value = 0.01and p value = 0.003) respectively.

Correlation analysis revealed a significant correlation between HOTAIR and HOTTIP (r = 0.652 and p = 0.001). A significant direct correlation of HOTAIR and HOTTIP with AFP (r = 0.481 and p value = 0.001 and r = 0.400 and p value = 0.001) respectively. A significant inverse correlation of HOTAIR and HOTTIP with albumin (r = −0.304 and p value = 0.001and r = −0.392 and p value = 0.001) respectively.

Conclusions

Our results suggested that the gradual overexpression of HOTAIR and HOTTIP gene expressions with the progression of disease prompts us to use it as a non-invasive diagnostic and prognostic biomarkers for HCV-induced HCC in HCV genotype 4 patients.

Availability of data and materials

The datasets used and analyzed in the current study are available from the corresponding author upon reasonable request.

Abbreviations

AASLD:

American Association for the Study of Liver Diseases

AFP:

Alpha fetoprotein

ALP:

Alkaline phosphatase

ALT:

Alanine aminotrasferase

ANA:

Anti-nuclear antibodies

APRI:

AST, platelet ratio index

AST:

Aspartate aminotransferase

AUC:

Area under the ROC

CLD:

Chronic liver disease

CT:

Computed tomography

CT:

Threshold

ELISA:

Enzyme-linked immunosorbent assay

HCC:

Hepatocellular carcinoma

HCV:

Hepatitis C virus

HOTAIR:

Hox transcript antisense intergenic RNA

HOTTIP:

HOXA transcript at the distal tip

HOXC:

Homeobox gene C cluster

INR:

International normalized ratio

LNCRNAs:

Long non-coding RNAs

PC:

Prothrombine concentration

PT:

Prothrombine time

US:

Ultrasound

References

  1. Zoheiry M, Hasan S, El-Ahwany E, et al (2015): Serum markers of epithelial mesenchymal transition as predictors of HCV-induced liver fibrosis, cirrhosis and hepatocellular carcinoma. Electron Physician 20; 7(8):1626.

  2. Bartosch B, Thimme R, Blum HE, Zoulim F (2009): Hepatitis C virus-induced hepatocarcinogenesis. J Hepatol; 51: 810-820[PMID: 19545926 DOI: https://doi.org/10.1016/j.jhep.2009.05.008].

    Article  CAS  PubMed  Google Scholar 

  3. Schanzer D, Paquette D, Lix L (2014) Historical trends and projected hospital admissions for chronic hepatitis C infection in Canada: a birth cohort analysis. CMAJ 2:139

    Article  Google Scholar 

  4. Hou J, Meng F, Chan L et al (2016) Circulating plasma MicroRNAs as diagnostic markers for NSCLC. Front Genet 7:193

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Pauli A, Rinn J, Schier A (2011) Non-coding RNAs as regulators of embryogenesis. Nat Rev Genet 12:136–149

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Fatica A, Bozzoni I (2014) Long non-coding RNAs: New players in cell differentiation and development. Nat Rev Genet 15:7–21

    Article  CAS  PubMed  Google Scholar 

  7. Huarte M, Rinn J (2010) Large non-coding RNAs: Missing links in cancer? Hum Mol Genet 19:R152–R161

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Xu M, Qi P, Du X (2014) Long non-coding RNAs in colorectal cancer: Implications for pathogenesis and clinical application. Mod Pathol 27:1310–1320

    Article  CAS  PubMed  Google Scholar 

  9. Shen X, Qi P, Du X (2015) Long non-coding RNAs in cancer invasion and metastasis. Mod Pathol 28:4–13

    Article  CAS  PubMed  Google Scholar 

  10. Chang Y, Zhang K, Hu Z et al (2016) Hypoxia-regulated lncRNAs in cancer. Gene 575:1–8

    Article  CAS  PubMed  Google Scholar 

  11. Calin G, Liu C, Ferracin M et al (2007) Ultraconserved regions encoding ncRNAs are altered in human leukemias and carcinomas. Cancer Cell 12:215–229

    Article  CAS  PubMed  Google Scholar 

  12. Popov N, Gil J (2010) Epigenetic regulation of the INK4b- ARF-INK4a locus: In sickness and in health. Epigenetics 5:685–690

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Zhao W, Song M, Zhang J et al (2015) Combined identification of long non-coding RNA CCAT1 and HOTAIR in serum as an effective screening for colorectal carcinoma. Int J Clin Exp Pathol 8:14131–14140

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Ishibashi M, Kogo R, Shibata K, et al (2013): Clinical significance of the expression of long non-coding RNA HOTAIR in primary hepatocellular carcinoma. Oncol Rep; 29: 946-950 [PMID: 23292722 DOI: https://doi.org/10.3892/or.2012.2219].

    Article  PubMed  CAS  Google Scholar 

  15. Bhatlekar S, Fields JZ, Boman BM (2014) HOX genes and their role in the development of human cancers. J Mol Med 92(8):811–823

    Article  CAS  PubMed  Google Scholar 

  16. Shah N and Sukumar S (2010): The Hox genes and their roles in oncogenesis. Nat Rev Cancer.;10(5):361.

    Article  CAS  PubMed  Google Scholar 

  17. Plowright L, Harrington K, Pandha H, Morgan R (2009): HOX transcription factors are potential therapeutic targets in non-small-cell lung cancer (targeting HOX genes in lung cancer). Br J Cancer.;100(3):470.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Hoegg S, Meyer A (2005) Hox clusters as models for vertebrate genome evolution. Trends Genet 21(8):421–424

    Article  CAS  PubMed  Google Scholar 

  19. Wang Y, Dang Y, Liu J, Ouyang X (2016) The function of homeobox genes and lncRNAs in cancer. Oncol Lett 12(3):1635–1641

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Woo CJ, Kingston RE (2007) Hotair lifts noncoding rnas to new levels. Cell 129:1257–1259

    Article  CAS  PubMed  Google Scholar 

  21. Rinn J, Kertesz M, Wang J et al (2007) Functional demarcation of active and silent chromatin domains in human HOX loci by noncoding RNAs. Cell 129:1311–1323

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Wang KC, Yang YW, Liu B, et al (2011): A long noncoding RNA maintains active chromatin to coordinate homeotic gene expression. Nature. 2011; 472(7341):120.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Zhang H, Zhao L, Wang Y-X et al (2015) Long non-coding RNA HOTTIP is correlated with progression and prognosis in tongue squamous cell carcinoma. Tumor Biol 36(11):8805–8809

    Article  CAS  Google Scholar 

  24. Wu L, Zhang L, Zheng S (2017) Role of the long non-coding rna hotair in hepatocellular carcinoma. Oncol Lett 14:1233–1239

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Ma MZ, Li CX, Zhang Y et al (2014) Long non‑coding RNA HOTAIR, a c‑Myc activated driver of malignancy, negatively regulates miRNA‑130a in gallbladder cancer. Mol Cancer 13:156

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Wang L, Zhang X, Jia LT et al (2014) c‑Myc‑mediated epigenetic silencing of MicroRNA‑101 contributes to dysregulation of multiple pathways in hepatocellular carcinoma. Hepatology 59:1850–1863

    Article  CAS  PubMed  Google Scholar 

  27. An J, Wu M, Xin X et al (2016) Inflammatory related gene IKKα, IKKβ, IKKγ cooperates to determine liver cancer stem cells progression by altering telomere via heterochromatin protein 1‑HOTAIR axis. Oncotarget 7:50131–50149

    PubMed  PubMed Central  Google Scholar 

  28. Gao JZ, Li J, Du JL, Li XL (2016) Long non-coding rna hotair is a marker for hepatocellular carcinoma progression and tumor recurrence. Oncol Lett 11:1791–1798

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Ren YK, Xiao Y, Wan XB et al (2015) Association of long non-coding RNA HOTTIP with progression and prognosis in colorectal cancer. Int J Clin Exp Pathol 8(9):11458

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Ye H, Liu K, Qian K (2016) Overexpression of long noncoding RNA HOTTIP promotes tumor invasion and predicts poor prognosis in gastric cancer. OncoTargets Ther 9:2081

    CAS  Google Scholar 

  31. Quagliata L, Matter MS, Piscuoglio S et al (2014) Long noncoding RNA HOTTIP/HOXA13 expression is associated with disease progression and predicts outcome in hepatocellular carcinoma patients. Hepatology. 59(3):911–923

    Article  CAS  PubMed  Google Scholar 

  32. Li Z, Zhao X, Zhou Y et al (2015) The long noncoding RNA HOTTIP promotes progression and gemcitabine resistance by regulating HOXA13 in pancreatic cancer. J Transl Med 13:84

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Resnick K, Alder H, Hagan J et al (2009) The detection of differentially expressed microRNAs from the serum of ovarian cancer patients using a novel real-time PCR platform. Gynecol Oncol 112:55–59

    Article  CAS  PubMed  Google Scholar 

  34. Komatsu S, Ichikawa D, Takeshita H et al (2011) Circulating microRNAs in plasma of patients with oesophageal squamous cell carcinoma. Br J Cancer 105:104–111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Morimura R, Komatsu S, Ichikawa D et al (2011) Novel diagnostic value of circulating miR-18a in plasma of patients with pancreatic cancer. Br J Cancer 105:1733–1740

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Wang W, Sun Y, Huang W et al (2016) Genome-wide long noncoding RNA analysis identified circulating LncRNAs as novel non-invasive diagnostic biomarkers for gynecological disease. Sci Rep 6:23343

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Wu Y, Wang Y, Weng W, et al (2016): A serum-circulating long non-coding RNA signature can discriminate between patients with clear cell renal cell carcinoma and healthy controls. Oncogenesis 2016; 5: e192.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Lin ZH, Xin YN, Dong QJ et al (2011) Performance of the aspartate aminotransferase-to-platelet ratio index for the staging of hepatitis C-related fibrosis: an updated meta-analysis. Hepatology 53:726–736

    Article  PubMed  Google Scholar 

  39. Bruix J, Sherman M (2011) American Association for the Study of Liver Diseases (AASLD) Practice Guidelines. Management of Hepatocellular Carcinoma: An Update. Hepatology 53:1020

    Article  PubMed  Google Scholar 

  40. Wu Y, Liu H, Shi X et al (2015) The long non-coding RNA HNF1AAS1 regulates proliferation and metastasis in lung adenocarcinoma. Oncotarget 6(2015):9160–9172

    PubMed  PubMed Central  Google Scholar 

  41. Qi P, Du X (2013) The long non-coding RNAs, a new cancer diagnostic and therapeutic gold mine. Mod Pathol 26:155–165

    Article  CAS  PubMed  Google Scholar 

  42. Eman TE, Perihan ES, Azaa MD et al (2018) Plasma long non-coding RNA HOTAIR as a potential biomarker for gastric cancer. The International Journal of Biological Markers 2018. Vol. 33(4):528–533

    Google Scholar 

  43. Li N, Wang Y, Liu X et al (2017) Identification of Circulating Long Noncoding RNA HOTAIR as a Novel Biomarker for Diagnosis and Monitoring of Non–Small Cell Lung Cancer. Tecnoligy in cancer research & treatment 16(6):1060–1066

    Article  CAS  Google Scholar 

  44. Gupta RA, Shah N, Wang KC et al (2010): Long non‑coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis. Nature 464: 1071‑1076, 2010.

  45. Hajjari M, Salavaty A (2015) HOTAIR: An oncogenic long non‑coding RNA in different cancers. Cancer Biol Med 12:1–9

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Yang F, Zhang L, Huo XS et al (2011) Long noncoding RNA high expression in hepatocellular carcinoma facilitates tumor growth through enhancer of zeste homolog 2 in humans. Hepatology 54:1679–1689

    Article  CAS  PubMed  Google Scholar 

  47. Yang Z, Zhou L, Wu LM, et al (2011): Overexpression of long non-coding rna hotair predicts tumor recurrence in hepatocellular carcinoma patients following liver transplantation. Ann Surg Oncol 2011;18:1243-1250.

    Article  PubMed  Google Scholar 

  48. Tsang FH, Au SL, Wei L et al (2015) Long non-coding RNA HOTTIP is frequently up-regulated in hepatocellular carcinoma and is targeted by tumour suppressive miR-125b. Liver Int 35:1597–1606

    Article  CAS  PubMed  Google Scholar 

  49. Jin N, Yang LY, Xu ZP (2017) Long non-coding RNA HOTTIP is able to predict poor prognosis in various neoplasms: a meta-analysis. Mol Clin Oncol 7(2):263–266

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Ge Y, Yan X, Jin Y et al (2015) MiRNA-192 [corrected] and miRNA-204 directly suppress lncRNA HOTTIP and interrupt GLS1-mediated glutaminolysis in hepatocellular carcinoma. PLoS Genet 11:e1005726

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Geng YJ, Xie SL, Li Q, et al (2011): Large intervening non-coding RNA HOTAIR is associated with hepatocellular carcinoma progression. J Int Med Res; 39: 2119-2128 [PMID: 22289527].

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We wish to thank Dr. Rady E. El-Araby (Central Lab, Theodor Bilharz Research Institute (TBRI), Ministry of Scientific Research, Egypt) for statistical analysis.

Funding

This work was funded by Theodor Bilharz Research Institute (Internal project NO: 23 K) to submit chemicals but no external fund was received.

Author information

Authors and Affiliations

  1. Molecular Biology, Central Lab, Theodor Bilharz Research Institute (TBRI), GIZA city, Ministry of Scientific Research, Cairo, Egypt

    Fawzy Roshdy

  2. Virology and Immunology, Botany and Microbiology Department, Faculty of Science, Al-Azhar University, Nasr city, Cairo, Egypt

    Mohamed M. S. Farag

  3. Hematology, Clinical Laboratory, Research Department, Theodor Bilharz Research Institute (TBRI), Giza city, Ministry of Scientific Research, Cairo, Egypt

    Eman El-Ahwany

  4. Hematology, Clinical Laboratory, Research Department, Theodor Bilharz Research Institute (TBRI), Giza city, Ministry of Scientific Research, Cairo, Egypt

    Ola Mahmode & Faiza Essawy

  5. Virology, Botany and Microbiology Department, Faculty of Science, Al-Azhar University, Nasr city, Cairo, Egypt

    Adel A. Mousa

  6. Hepatoastroenterology, Hepatoastroenterology Department, Theodor Bilharz Research Institute (TBRI), Giza city, Ministry of Scientific Research, Cairo, Egypt

    Mohamed El Talkawy

Authors
  1. Fawzy Roshdy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohamed M. S. Farag
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Eman El-Ahwany
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ola Mahmode
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Adel A. Mousa
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mohamed El Talkawy
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Faiza Essawy
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

FRF made the work design, performance of lab tests, finalized the manuscript and submission. MSF participated in the molecular genetic testing. EGE consultant and drafted the manuscript. OMM patients selection and categorization, integrated the clinical and laboratory data. AAM revised the manuscript, collection of references and consultant. MDE participated in the study design, followed the patients clinically, and supplied the clinical data. FME participated in the study design, supervised the clinical part and finalized the clinical data. All authors approved the manuscript.

Corresponding author

Correspondence to Fawzy Roshdy.

Ethics declarations

Ethics approval and consent to participate

This study was approved by Theodor Bilharz Research Institute Ethics Review Board and an informed consent was obtained written from all the subjects included in this study according to the roles of the Declaration of Helsinki 1975. Approval of local ethical committee (TBRI-REC number 01/19). Patients enrolled in this study were admitted to Gastroenterology and Hepatology Department, Theodor Bilharz Research Institute, Giza, Egypt from November 2016 to August 2018.

Consent for publication

Not applicable.

Competing interests

The authors declare no conflict of interest.

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 distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Roshdy, F., Farag, M.M.S., El-Ahwany, E. et al. Long non-coding RNA HOTAIR and HOTTIP as potential biomarkers for hepatitis C virus genotype 4-induced hepatocellular carcinoma. Egypt J Med Hum Genet 21, 7 (2020). https://doi.org/10.1186/s43042-020-0048-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s43042-020-0048-8

Keywords

  • Biomarker
  • HOTAIR
  • HOTTIP
  • HCV
  • Cirrhosis
  • HCC