LncRNA-miRNA crosstalk in HCC cases on top of HCV infection

doi:10.21203/rs.3.rs-1534465/v1

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LncRNA-miRNA crosstalk in HCC cases on top of HCV infection

https://doi.org/10.21203/rs.3.rs-1534465/v1

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Hepatocellular carcinoma (HCC) continues to be a serious issue because of its high morbidity and mortality in the absence of early identification and proper treatment. Increasing evidence reveals that long non-coding RNAs (lncRNAs) and micro RNAS (miRNAs) interact to form a gene regulatory network. Their interactions lead to a variety of complex diseases, such as HCC. So, the current study aims to assess whether the interaction between "HOTAIR, HOTTIP, and H19 as lncRNAs and miRNA-152 as a miRNA" has a significant role in the progression of HCV-genotype (4) patients to hepatocellular carcinoma, in order to employ them as noninvasive prognostic and diagnostic epigenetic biomarkers. By using the online computational tools, we predict the group of miRNAs that may interact with the group of lncRNAs under this investigation. 133 subjects were included in this study. A total of 103 chronic hepatitis C patients were divided into two groups: Group I (chronic liver disease “CLD” without HCC, n = 65) was classified into two subgroups: non-cirrhotic (30) and cirrhotic (35); Group II (CLD with HCC, n = 38). A control (healthy volunteer, n = 30). The expressions of the studied genes were analysed by real-time PCR. The study of lncRNAs has shown a consistent reverse correlation with miRNA-152 in line with the progression of the disease, which leads us to suggest that they should be used as noninvasive prognostic biomarkers for HCV-induced liver cirrhosis and HCC in HCV Genotype 4 patients. MiRNA-152 is not the only one that can interact with the lncRNAs that have been investigated, but there are others, such as miRNA-19a and miRNA-106a, that should be considered.

LncRNAs

HOTAIR

HOTTIP

H19

miRNA-152

HCV-genotype 4

Cirrhosis

HCC

Biomarkers

Hepatocellular carcinoma (HCC) is a type of cancer that affects the liver and is the second highest cause of cancer death in the world [1]. HCCs most commonly develop in the context of cirrhosis and are linked to chronic hepatitis B virus (HBV) or hepatitis C virus (HCV) infection, alcoholic liver disease, obesity, or metabolic problems [2]. Despite improvements in overall survival rates among HCC patients due to advances in surgical procedures, 5-year overall survival remains low at 18% [3]. Due to late diagnosis, most patients cannot be treated with surgical resections or liver transplants. Furthermore, HCC is linked with a significant recurrence rate, which approaches 50% 5 years following surgery [4]. Novel treatment targets may improve the terrible outcomes of individuals with late-stage HCC [2].

HCC, like many other cancers, is distinguished by the involvement of numerous gene networks and an imbalance of signalling pathways [5, 6].These genetic dysregulations are caused by protein-coding genes and non-coding RNA (ncRNA) genes [7].

Noncoding transcripts encode up to 98% of the human genome, the majority of which are processed to produce small noncoding RNA such as long non-coding RNA (lncRNA) and microRNA (miRNA) [8]. The capacity of these RNAs to exercise their biological roles at a high level is a common feature of these RNAs [9]. With the advancement of high-throughput sequencing technology, several ncRNAs have recently been identified as functional molecules that play key roles in a variety of biological processes and pathological situations [10, 11]. In the realm of hepatocellular carcinoma, certain significant ncRNAs have been discovered as players in the disease's pathogenesis [12, 13]. For example, LncRNAs and miRNAs have demonstrated great evolutionary conservation, which supports their functional involvement indirectly, for example, by influencing numerous biological and pathological processes, such as tumour incidence [14].

LncRNAs and miRNAs interact with one another to build a massive and complicated regulatory network that controls gene expression at the transcriptional, post-transcriptional, and post-translational levels. These two large families of noncoding RNAs are involved in practically all aspects of cell cycle control, including cell division, senescence, differentiation, stress response, immunological activation, and apoptosis, via multi-level regulation [15].

In light of this, the interactions of noncoding RNAs on the regulatory network have attracted a lot of interest in medical research [16]. A thorough knowledge of the molecular and cellular implications of such noncoding interactions can provide significant insight into disease mechanisms at the molecular level. Noncoding RNAs in newly identified relationships related to early diagnosis of HCC might be regarded as possible diagnostic indicators and so have high utility in treatment efforts [8].

Therefore, early diagnosis of HCC is critical for achieving curative and effective therapies and improving long-term survival rates. A better knowledge of the molecular processes underlying the onset and development of HCC is essential for establishing early diagnoses and creating innovative treatment methods.

Accordingly, the purpose of this work is to investigate the association between HOTAIR, HOTTIP, and H19 as lncRNAs and miRNA-152 as a miRNA in HCC patients on top HCV genotype (4) in order to employ them as noninvasive prognostic and diagnostic biomarkers. In addition, predicting the group of miRNAs that may interact with the group of lncRNAs (HOTAIR, HOTTIP and H19) under investigation using the online computational tools.

The Research Ethics Committee at Theodor Bilharz Research Institute (TBRI-REC) (FWA00010609), approved this study and an informed consent was obtained from all patients according to the rules of the Declaration of Helsinki 1975. Approval of local ethical committee number is (TBRI-REC number PT (673)).

Patients in this research were collected from the TBRI's Gastroenterology and Hepatology Department in Giza, Egypt. Patients' diagnoses were based on a complete medical history, clinical examination, abdominal ultrasonography, and laboratory evaluation. In addition to HCV genotyping using HybProbe probes.

All of the patients included in the study had chronic hepatitis C genotype (4) that lasted more than 6 months (HCV RNA Positive). During the previous six months, they did not get any HCV-specific therapy. While Patients with schistosomiasis, chronic viral diseases other than HCV, dual HBV and HCV infection, nonalcoholic steatohepatitis (NASH), autoimmune hepatitis, biliary disorders, malignancies other than HCC, regular hepatotoxic drugs, alcohol abuse, diabetes, and HCV-infected patients receiving DAAS or immunomodulatory interferon-therapy were all excluded.

Based on the inclusion and exclusion criteria, this study included 133 patients. A total of 103 chronic hepatitis C patients were separated into two groups: CLD without HCC (n = 65) and CLD with HCC (n = 38). Group I (CLD without HCC) was divided into two subgroups: non-cirrhotic (30) and cirrhotic (35). A control group of thirty healthy volunteers of the same age and gender was also used.

Molecular analysis:

HCV genotyping was performed using hot start reaction mix detection for PCR with HybProbe probes on a lightcycler carousel-based system (version 15, 2011), cat. No: (03003248001), (https://www.roche.com).

Lightcycler 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 was used to detect gene expression. The primer sequences are shown in (Table 1). Analysis of the results based on the SYBR green I filter combination (465–510), on lightcycler EvoScript RNA SYBR green I master. The data in this master's thesis was analysed using comparative CT methodologies. Housekeeping gene U6 serves as an endogenous reference to normalise for any differences in the amount and quality of the examined ncRNAs between samples. Using the formula 2 − ΔΔCT, gene expression was estimated relative to the control samples (used as the calibrator sample) and represented as a fold-change (https://www.biostars.org/).

Bioinformatics analysis:

HOTAIR, HOTTIP, and H19 target miRNAs with miRNA-152 were predicted using miRNet version 2.0, which serves as a miRNA-centric network visual analytics platform (https://www.mirnet.ca), as well as predicting lncRNA target genes and is based on four well-annotated databases: miRTarBase v8.0, TarBase v8.0, and miRecords.

Statistical analysis:

Microsoft Excel 2016 and the social science statistical tool "IBM SPSS Statistics for Windows, version 26" were used to analyse the data (IBM Corp., Armonk, N.Y., USA). Non-normal variables were summarised as the median with the 25th and 75th percentiles; a p value of 0.05 was considered statistically significant. In non-normal variables, the Mann-Whitney U test will be employed. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of HOTAIR, HOTTIP, H19, and miRNA152. As an accuracy metric for the prognostic performance of chosen tests, the area under the ROC (AUC) was determined. The threshold for a study group's diagnosis was determined by the point of greatest combined sensitivity and specificity. The interaction between the examined markers was evaluated using bivariate Pearson correlation (r). The binary logistic regression analysis was used to examine prognostic performance and/or risk assessment.

Gene expression:

According to the rule of fold change, HOTAIR, HOTTIP, and H19 gene expressions were consistently upregulated, whereas miRNA-152 was downregulated in the groups investigated (Table 2) (Fig. 1).

HOTAIR, HOTTIP, and H19 expression levels were considerably higher in all studied groups compared to controls (p values < 0.01, < 0.001, and < 0.01 respectively). When cirrhotic and HCC patients were compared to non-cirrhotic patients, HOTAIR, HOTTIP, and H19 expression levels were considerably higher (p values = 0.03, 0.04, and < 0.001 respectively) for cirrhotic patients and (p values < 0.001, < 0.001, and < 0.001 respectively) for HCC patients. It also revealed a substantial increase in HOTAIR, HOTTIP, and H19 expression in HCC patients when compared to cirrhotic patients (p values < 0.001, 0.013, and 0.005 respectively) (Table 2, Fig. 1a, b, c).

On the other hand, miRNA-152 expression, decreased considerably in all studied groups as compared to the control group (p value = 0.01). When cirrhotic and HCC patients were compared to non-cirrhotic patients, miRNA-152 expression levels were significantly lower in cirrhotic patients (p values = 0.007) and HCC patients (p values 0.001). It also found a significant decrease in miRNA-152 expression in HCC patients compared to cirrhotic individuals (p values 0.001). (Table 2, Fig. 1d).

Diagnostic performance:

The diagnostic performances of the HOTAIR, HOTTIP, H19, and miRNA-152 genes in the examined groups were demonstrated using Receiver Operating Curves (ROC).

It was found that serum H19 and miRNA-152 at cut-off values of (> 3.758 and 0.165 respectively), had sensitivity of (48.6% and 51.4%) and specificity of (93.3% and 70.0%) with (p values 0.0001 and 0.007) for discriminating the cirrhotic group from the non-cirrhotic group. While the HOTAIR and HOTTIP genes have no role in distinguishing between cirrhotic and non-cirrhotic patients (Table 3, Fig. 2a).

For discrimination of the HCC group from the cirrhotic group, it was found that serum HOTAIR, HOTTIP, H19 and miRNA-152 at cut-off values of (> 2.000, > 14.929, > 9.318 and < 0.055), had sensitivity of (100.0%, 39.5%, 47.4% and 65.7%) and specificity of (40.0%, 97.1%, 94.3% and 68.4%) with (p values < 0.0001, 0.007, 0.003 and 0.001) respectively (Table 3, Fig. 2b).

For discrimination of the HCC group from CLD patients, it was found that serum HOTAIR, HOTTIP, H19 and miRNA-152 at cut-off values of (> 2.000, > 17.268, > 8.574, and < 0.055, had sensitivity of (100.0%, 39.5%, 57.9%, and 68.4%) and specificity of (47.7%,98.5%,89.2%, and 70.8%,) with (p values < 0.0001, < 0.0001, < 0.0001and < 0.0001) respectively (Table 3, Fig. 2c).

Prognostic performance:

Cirrhosis risk based on gene expression: H19 and miRNA-152 were deemed predictive and/or prognostic characteristics for cirrhotic progression in univariate logistic regression analysis. A one-degree rise in H19 and miRNA-152 enhanced the likelihood of having cirrhosis by factors of (1.36 and 2.32) with (p values = 0.02 and 0.01) respectively. The HOTAIR and HOTTIP genes play no function in distinguishing cirrhotic from non-cirrhotic patients.

HCC risk based on gene expression: HOTAIR, HOTTIP, H19, and miRNA-152 were studied as potential predictors and/or prognostic factors for HCC advancement. A one-degree rise in HOTAIR, HOTTIP, H19, or miRNA-152 increased the probabilities of having HCC by factors of (1.12, 1.08, 1.07, and 2.54) with p values of (0.01, 0.01, 0.03, and 0.001) respectively.

Concerning CLD in general (group I: non-cirrhotic and cirrhotic), there was a 1 degree rise in HOTAIR, HOTTIP, H19, and miRNA-152, which increased the likelihood of developing HCC by factors of (1.15, 1.10, 1.08, and 3.27) with p values of (0.001, 0.001, 0.02, and 0.001) respectively (Table 4).

Correlation study:

Overall, the correlation analysis found that miRNA152 has a significant inverse correlation with HOTAIR, HOTTIP, and H19; (r = -0.259 and p value = 0.01), (r = -0.206 and p value = 0.05), and (r = -0.218 and p value = 0.03) respectively (Fig. 3).

Simultaneously, the HOTAIR gene has a direct correlation with the HOTTIP and H19 genes (r = 0.813, p value 0.001) and (r = 0.349, p value = 0.001) respectively. Furthermore, HOTTIP and H19 have a substantial direct link (r = 0.315, p value = 0.001).

Perspectives of bioinformatics analysis:

According to results of gene networks analysis using miRNet webserver database many cascade of regulatory interactions involving the HOTAIR, HOTTIP, and H19 as lncRNA, miRNA-152, and all possible functionally target miRNAs are shown in (Fig. 4) (https://www.mirnet.ca), which reviled the target miRNAs such as miRNA-19a and miRNA-106a, some of which are intimately connected with HOTAIR, HOTTIP, and H19, whose expression variations are involved in HCC formation. Furthermore, this might reveal novel links between them as well as prospective correlations with HCC disease.

Hepatocellular carcinoma remains a major problem because to its high morbidity and mortality in the absence of early detection and adequate therapy. A few molecular biomarkers have been employed successfully in clinical diagnostics, most notably as prognostic or diagnostic tools, and even as therapeutic targets for HCC. LncRNAs were once thought to be transcription noise; however, extensive researches, as well as a large number of clinical observations and experimental studies, have revealed that lncRNAs play an important role in tumorigenesis and cancer development by interacting with miRNAs, mRNAs, and even proteins [17].

It is well recognised that the interaction between miRNA and lncRNA is critical for gene regulation. However, the number of known lncRNA-miRNA interactions is still quite small, and computational techniques for predicting new ones are restricted. Given that lncRNAs and miRNAs share intrinsic patterns in their interactions, the underlying lncRNA-miRNA interactions might be predicted using the known ones, which could be viewed as a semi-supervised learning issue [8]. So, the purpose of this study was to look into the relationship between HOTAIR, HOTTIP, and H19 as lncRNAs and miRNA-152 as a miRNA in HCC patients on-top HCV genotype (4) in order to use them as a noninvasive predictive and diagnostic biomarkers. Furthermore, using online computational methods, anticipate the group of miRNAs that may interact with the group of lncRNAs under research (HOTAIR, HOTTIP, and H19).

This study revealed a significant upregulation of HOTAIR, HOTTIP, and H19 with the progression of disease. These results in agreements with [18], who reported that the gene expressions of HOTAIR and HOTTIP constantly upregulated in the HCC patients on-top of HCV [19], also employed four different patient cohorts to show that H19 expression is lower in human HCC tissue than in non-tumorous tissue. Interestingly, despite the fact that H19 was downregulated in HCC, elevated H19 expression was identified in a small patient subgroup in each of the examined patient cohorts. Furthermore, increased expression of H19 has been identified in various malignancies, including gastric cancer [20] and bladder cancer [21], where H19 levels were considerably greater in patients than in controls.

In this mechanistic study, the revealed inverse correlation between (HOTAIR, HOTTIP, and H19) and miRNA-152 expressions (r = -0.259 and p value = 0.01), (r = -0.206 and p value = 0.05), and (r = -0.218 and p value = 0.03) respectively indicate that (HOTAIR, HOTTIP, and H19) upregulation induces the downregulation of miRNA-152, resulting in down production at mRNA and expectedly at the protein levels in the studied patients, which is directly proportional to disease progression. The preceding relationship comes mostly in harmony with similar study the same context using a different case study and strategy [22], reported that LncRNAs and miRNAs influence gene expression involved in epithelial-mesenchymal plasticity. These noncoding RNAs influence cancer spread and prognosis by their complex and multi-layered influence on protein expression patterns.

A lot of researches have begun to discover the relationships between miRNA and lncRNA, and more information regarding miRNA's effect on lncRNA function is increasingly becoming available [8]. MiRNA can cause lncRNA degradation in some instances. In other circumstances, lncRNA serves as miRNA sponges/decoys, competes with miRNA for binding mRNAs, or produces miRNAs. The competitive endogenous RNA (ceRNA) hypothesis has newly been presented and has become a popular position for understanding the interaction between lncRNA and miRNA [23]. Through matching the miRNA response elements (MREs), lncRNA competes with pseudogenes, circular RNAs, and mRNAs for binding or sequestering miRNAs from the same pool. Given that both lncRNA and miRNA are important regulators of gene expression and interact with one another, it is not surprising that their connection in the interaction network is tightly controlled. Understanding the lncRNA-miRNA interaction networks that drive the onset and progression of many diseases is critical, but needs clarification [24]. In addition, this novel lncRNA-miRNA (HOTAIR, HOTTIP, and H19-miRNA-152) regulatory circuit may provide a new epigenetic therapeutic target in HCC patients on-top HCV.

The diagnostic performance of the studied gene expressions as markers in cirrhotic patients at different cut-off points using the ROC curve showed that there was a significant differences regarding H19 with an area under curve (AUC) of 0.770 (95% confidence interval C.I: 0.656–0.883) and accuracy of 69.2 (p. value 0.0001). Likewise, in miRNA-152, with an AUC of 0.704 (95% C.I: 0.563–0.825) and an accuracy of 67.8 (p. value = 0.007), while there were no significant difference between cirrhotic and non-cirrhotic patients regarding HOTAIR and HOTTIP. The results indicated that H19 and miRNA-152 could be used for discrimination between cirrhotic and non-cirrhotic patients, while HOTAIR and HOTTIP could not be used.

In the case of HCC, it was revealed that there was a significant difference between HCC and cirrhotic patients regarding HOTAIR, HOTTIP, H19, and miRNA-152: with an AUC of 0.729 (95% C.I: 0.615–0.844) and an accuracy of 71.2 (p. value < 0.0001); AUC of 0.670 (95% C.I: 0.546–0.793) and an accuracy of 67.1 (p. value = 0.007); AUC of 0.691 (95% C.I: 0.566–0.816) and an accuracy of 69.9 (p. value = 0.003); and AUC of 0.73 (95% C.I: 0.607–0.852) and an accuracy of 72.1 (p. value = 0.001) respectively. The results indicated that HOTAIR, HOTTIP, H19 and miRNA-152 could be used to discriminate between HCC patients than cirrhotic patients.

In terms of distinguishing HCC patients from CLD (cirrhotic and non-cirrhotic) patients, it was revealed that there was a significant difference between HCC and CLD patients regarding HOTAIR, HOTTIP, H19, and miRNA-152: with an AUC of 0.786 (95% C.I: 0.700–0.872) and an accuracy of 67.0 (p. value < 0.0001); AUC of 0.712 (95% C.I: 0.607–0.817) and an accuracy of 76.7 (p. value < 0.0001); AUC of 0.768 (95% C.I: 0.668–0.868) and an accuracy of 77.7 (p. value < 0.0001); and AUC of 0.779 (95% C.I: 0.692–0.866) and an accuracy of 78.1 (p. value < 0.0001); respectively. The results indicated that HOTAIR, HOTTIP, H19 and miRNA-152 could be used to discriminate between HCC patients than CLD patients.

Interestingly, regression analysis revealed that the expression levels of H19 and miRNA-152 were significant predictors of changes in cirrhosis and HCC patients vs non-cirrhotic patients, although the expression levels of HOTAIR and HOTTIP showed no significant differences. When HOTAIR, HOTTIP, H19, and miRNA-152 expression levels were chosen as significant predictors connected with the chances of diagnosing HCC vs cirrhosis patients and/or CLD patients, they clearly enhanced the risks of becoming HCC. A recent study provided a strategy for identifying a unique class of possible HCC biomarkers.

These findings suggested that elevated lncRNAs, which are downregulated by miRNAs, might be viable molecular targets for the development of particular HCC therapeutics [17]. Tissues and liquid biopsies can be utilised as beginning points for investigating the potential of both non-coding RNA groups as biomarkers, and they are currently routinely employed as biomarkers in cardiac, neurological, and cancer diseases [25–30]. In addition, miRNA and lncRNA-based tests are frequently utilised in conjunction with established protein biomarkers to increase diagnostic specificity and sensitivity [31].

As a logical follow-up for this study, we unraveled the cascade of regulatory interactions involving the lncRNA (HOTAIR, HOTTIP and H19) and miRNA-152 and all possible target another miRNAs (Fig. 4) by miRNet visual analytics platform server which proved all possible miRNAs that target the studied lncRNAs associated with HCC development. Interestingly, the target miRNAs (miRNA-19a and miRNA-106a) are well ncRNAs whose expression alterations are implicated in HCC development. This is consistent with [32] who reported that; miRNA-19a is implicated in linked to metastasis, while miRNA-106a is implicated in ovarian cancer-associated EMT.

Increasing data suggests that lncRNA and miRNA interact together to build a gene control network. Interactions between lncRNA and miRNA therefore give important insights into the molecular mechanisms behind the onset and progression of numerous types of complicated illnesses. The fundamental difficulty is from the minimal number of known interactions between lncRNA and miRNA (i.e. the sparsity of the lncRNA-miRNA interaction network) and the lack of knowledge of the underlying pattern of lncRNA-miRNA interaction [8]. This validates our theory in this work and raises additional research issues about the involvement of the remaining target miRNAs in tumorigenesis. More extensive research is needed to understand their involvement in HCC development.

We can conclude from the above that the interaction status of lncRNA (HOTAIR, HOTTIP, and H19) and miRNA-152 altered the process of gene expression in patients with cirrhosis and HCC cases, where disruption of miRNA-152 rhythm led to disruption and carcinogenesis in HCC instances. Furthermore, statistical evaluations of gene expression levels of the aforementioned genes can be reliable as non-invasive prognostic biomarkers for HCC in these patients, as well as novel therapeutic targets for cirrhosis caused by viral hepatitis and HCC caused by HCV genotype-4 infection.

Using the computational tools more thoroughly, it was discovered that there are more interaction partners who are substantially involved. Furthermore, other genes can be predicted to be within the target domains of lncRNA (HOTAIR, HOTTIP, and H19) and miRNA-152.

Hence, we advocate launching a larger, more complete pilot investigation in HCV-induced liver cirrhosis and HCC patients, based on the findings of bioinformatics-based evaluations and genome-wide transcriptional profiling.

95% C.I	95% Confidence Interval
AUC	Area under the ROC
CLD	Chronic liver disease
HBV	Hepatitis B virus
HCC	Hepatocellular carcinoma
HCV	Hepatitis C virus
lncRNAs	Long non-coding RNAs
miRNAs	Micro RNAs
MREs	MiRNA response elements
NASH	Nonalcoholic steatohepatitis
ncRNA	Non-coding RNA
r	Correlation
ROC	Receiver operating characteristic
Sn	Sensitivity
Sp	Specificity

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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0000845.http://www.mirbase.org
https://pubmed.ncbi.nlm.nih.gov/16728577/
https://www.biostars.org/
https://www.mirnet.ca
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4637691/.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4774541/.
https://www.roche.com

Table 1. Oligo nucleotides seq. used in RT-PCR

Gene	Sequence	Tm	Reference
HOTAIR
Forward	5‑GCA GTA GAA AAA TAG ACA TAG GAGA‑3	58.0 ^oC	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4774541/.
Reverse	5-AAT GAT AGG GAC ACA TCG GGG AAC T-3	58.0 ^oC	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4774541/.
HOTTIP
Forward	5-GTG GGG CCC AGA CCC GC-3	58.0 ^oC	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4637691/.
Reverse	5-AAT GAT AGG GAC ACA TCG GGG AAC T-3	58.0 ^oC	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4637691/.
H19
Forward	5- TGC TGC ACT TTA CAA CCA CTG -3	58.0 ^oC	http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0000845.
Reverse	5-ATG GTG TCT TTG ATG TTG GGC -3	58.0 ^oC
miRNA-152
Forward	5-CCCAGGTTCTGTGATACACTCC-3	58.0 ^oC	http://www.mirbase.org
Reverse	5-CTTCCGGGCCCAAGTTCTG-3	58.0 ^oC	http://www.mirbase.org
U6 RNA
Forward	5-CTCGCTTCGGCAGCACA-3	58.0 ^oC	https://pubmed.ncbi.nlm.nih.gov/16728577/
Reverse	5-AACGCTTCACGAATTTGCGT-3	58.0 ^oC	https://pubmed.ncbi.nlm.nih.gov/16728577/

Table 2. Gene expression of the studied genes in the studied groups

	Control N=30	Group I (CLD without HCC) N=65		Group II (CLD with HCC) N=38
	Control N=30	Non-cirrhotic N=30	Cirrhotic N=35	Group II (CLD with HCC) N=38
HOTAIR	1	1.59(0.68- 4.07) ^a	4.53(1.21- 9.99) ^{aa, b}	9.11(3.97- 29.09) ^{aa, bb, cc, **}
HOTTIP	1	3.10(1.43- 5.62) ^aa	5.31(1.83- 10.34) ^{aa, b}	8.07(3.81- 35.44) ^{aa, bb, c, **}
H19	1	1.84(1.00- 3.25) ^a	3.68(2.14 - 6.50) ^{aa, bb}	8.69(3.01- 16.11) ^{aa, bb, cc, **}
miRNA-152	1	0.32(0.06- 0.65) ^a	0.16(0.020.30- ) ^{aa, bb}	0.03(0.01- 0.07) ^{aa, bb, cc, **}

The fold change results depend on the fold change low: Fold-Change (2^-∆∆CT) is the normalized gene expression (∆CT) in the test sample divided the normalized gene expression (∆CT) in the control sample. (Fold-change values less than one indicate a negative or down-regulation).

All parameters are represented as Median with Interquartile range (25% -75%) of the fold change of the studied groups, the data were analyzed by Mann-Whitney U test.

* p value is significantly different comparing with CLD group (I).

^ap value is significantly different comparing with control group.

^b p value is significantly different comparing with HCV group.

^c p value is significantly different comparing with Cirrhotic group.

^{1 Initial}p value <0.05 is significant, ^{2 Initial} p value <0.01 is highly significant.

Table 3

Diagnostic performances of the studied genes
	Studied genes	Cutoff	Sn.	Sp.	Accuracy	AUC	95% C.I	p value
Cirrhotic Vs Non-cirrhotic	HOTAIR	> 3.837	51.4	76.7	63.1	0.610	0.470–0.751	0.124
	HOTTIP	> 5.618	48.6	80.0	63.1	0.598	0.458–0.738	0.171
	H19	> 3.758	48.6	93.3	69.2	0.770	0.656–0.883	< 0.0001
	miRNA-152	< 0.165	51.4	70.0	67.8	0.704	0.563–0.825	0.007
HCC Vs Cirrhotic	HOTAIR	> 2.000	100.0	40.0	71.2	0.729	0.615–0.844	< 0.0001
	HOTTIP	> 14.929	39.5	97.1	67.1	0.670	0.546–0.793	0.007
	H19	> 9.318	47.4	94.3	69.9	0.691	0.566–0.816	0.003
	miRNA-152	< 0.055	65.7	68.4	72.1	0.73	0.607–0.852	0.001
HCC Vs CLD	HOTAIR	> 2.000	100.0	47.7	67.0	0.786	0.700–0.872	< 0.0001
	HOTTIP	> 17.268	39.5	98.5	76.7	0.712	0.607–0.817	< 0.0001
	H19	> 8.574	57.9	89.2	77.7	0.768	0.668–0.868	< 0.0001
	miRNA-152	< 0.055	68.4	70.8	78.1	0.779	0.692–0.866	< 0.0001
Sn: Sensitivity, Sp: Specificity, AUC Area under curve and C.I: 95% Confidence Interval.
* p value < 0.05 is significant, ** p value < 0.01 is highly significant.

Table 4

Prognostic performances of the studied genes
	Studied genes	OR	95% C.I	p value
Cirrhotic Vs Non-cirrhotic	HOTAIR	1.12	0.99–1.27	0.07
	HOTTIP	1.09	0.98–1.22	0.1
	H19	1.36	1.06–1.74	0.02*
	miRNA-152	2.32	1.23–3.87	0.01*
HCC Vs Cirrhotic	HOTAIR	1.12	1.03–1.21	0.01*
	HOTTIP	1.08	1.02–1.14	0.01*
	H19	1.07	1.01–1.23	0.03*
	miRNA-152	2.54	1.35–4.54	0.001**
HCC Vs CLD	HOTAIR	1.15	1.06–1.24	0.001**
	HOTTIP	1.10	1.04–1.16	0.001**
	H19	1.08	1.03–1.21	0.02*
	miRNA-152	3.27	1.28–6.87	0.001**
OR; Odd Ratio, C.I; Confidence Interval and P value of Prognostic viability were calculated depending on logistic regression analysis.
* P. value < 0.05 is significant, ** P. value < 0.01 is highly significant.

No competing interests reported.

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LncRNA-miRNA crosstalk in HCC cases on top of HCV infection

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