Implication of Long Non-coding RNA HOTTIP Haplotype on Liver Cancer Metastasis
NCT ID: NCT06544005
Last Updated: 2025-04-24
Study Results
Results pending
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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Recruitment Status
COMPLETED
Total Enrollment
198 participants
Study Classification
OBSERVATIONAL
Study Start Date
2022-03-16
Study Completion Date
2024-12-31
Brief Summary
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Hepatocellular carcinoma (HCC) represents the fourth common cancer and the most common cause of mortality-caused and morbidity-related cancer. Different reports implied several lncRNAs role in the progression and metastasis of HCC such as Homeobox A (HOXA) transcript at the distal tip (HOTTIP). Dysregulation of HOTTIP is associated with various malignancies including HCC, affecting survival and prognosis of cancer patients. HOTTIP promoted HCC cell proliferation/metastasis and might act as an oncogenic-lncRNA in HCC.
Genetic variations such as single-nucleotide polymorphisms (SNPs), when inherited together, as a group, known as haplotypes. Haplotypes can alter the expression of coding genes and the protein non-coding genes like lncRNAs, therefore, affecting the disease course, including liver, a hypothesis to be addressed.
Only few studies have focused on the polymorphisms of the onco-lncRNA HOTTIP gene. A study found that specific HOTTIP SNPs have the potential to be biomarkers for HCC risk and prognosis, where one haplotype of HOTTIP "rs17501292-rs2067087-rs17427960" showed a 1.91-fold increased risk of HCC.
Genetic variations such as single-nucleotide polymorphisms (SNPs), when inherited together, as a group, known as haplotypes. Haplotypes can alter the expression of coding genes and the protein non-coding genes like lncRNAs, therefore, affecting the disease course, including liver, a hypothesis to be addressed.
Only few studies have focused on the polymorphisms of the onco-lncRNA HOTTIP gene. A study found that specific HOTTIP SNPs have the potential to be biomarkers for HCC risk and prognosis, where one haplotype of HOTTIP "rs17501292-rs2067087-rs17427960" showed a 1.91-fold increased risk of HCC.
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Detailed Description
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1. Introduction Hepatocellular carcinoma HCC is a complex disease, which could be caused by viruses as hepatitis B and C infection, drug abuse, and chemicals such as aflatoxin. However, treatments for HCC are limited, and most of them are only effective at an early stage. At an advanced stage, this cancer is associated with a poor prognosis, due to frequent cancer metastasis, tumor recurrence and a lack of curative treatment. Different reports implied several lncRNAs role in the progression and metastasis of HCC such as HOXA transcript at the distal tip (HOTTIP). HOTTIP might act as an oncogenic-lncRNA in HCC.
Genetic variations such as single-nucleotide polymorphisms (SNPs), when inherited together, as a group, because of high Linkage Disequilibrium (LD), there tends to be redundant information. The regions of the genome with high LD, that harbor a specific set of SNPs, are inherited together, known as haplotypes. Haplotypes can alter the expression of coding genes and the protein non-coding genes like lncRNAs.
Only few studies have focused on the polymorphisms of the onco-lncRNA HOTTIP gene. A recent study found that specific HOTTIP SNPs have the potential to be biomarkers for HCC risk and prognosis, where one haplotype of HOTTIP gene "rs17501292-rs2067087-rs17427960" showed a 1.91-fold increased risk of HCC (P = 0.006) in Chinese patients' samples.
..............
2. Objectives 2.1. Genotype different haplotype SNPs of lncRNA HOTTIP in whole blood samples (Liquid biopsy) from metastatic HCC patients, and to be compared with sex and age-matched non-metastatic patients.
2.2. Correlate HCC clinicopathological characteristics (tumor stage and grade, tumor progression (TNM) and different clinical presentations as presence/absence of metastasis, and other classical clinico-pathological prognostic biomarkers such as α-fetoprotein (AFP), carcinoembryonic antigen (CEA), alkaline phosphatase (ALP), alanine transaminase (ALT), aspartate transaminase (AST), International normalized ratio (INR), prothrombin concentration (PC), total bilirubin, Gamma-glutamyltransferase (GGT), and complete blood count (CBC), blood pressure, blood glucose level, serum insulin, and body mass index (BMI), c-reactive protein (CRP), albumin, blood urea nitrogen (BUN) and prognostic marker(s)/outcome to HOTTIP genotypes or different haplotype SNPs.
2.3. Correlate HOTTIP lncRNA influence on some HCC hallmarks (tumor growth, proliferation, and/or metastasis).
2.4. Molecular Docking for exploring potential drugs that could be re-purposed to inhibit our target oncogenic lncRNA HOTTIP.
...................
3. Subjects - 198 HCC patients attending the Faculty of Medicine, Ain Shams University Hospital, with 1:1 or 1:2 ratio for the HCC groups metastatic vs non metastatic HCC groups (according to clinical evidence/relevance and/or availability).
Group 1; non-metastatic HCC patients, Group 2; metastatic HCC patients, diagnosed with primary HCC receiving any type of therapy (neoadjuvant or radiotherapy).
Eligibility criterion are adult age and male/female 1:1. Criteria for HCC diagnosis following the Ain Shams University (ASU) hospital role relying on AFP level and CT scan or the fine needle biopsy.
Groups to be matched socioeconomically, in age range, sex Male/Female 1:1, residence (case-controlled study).
\- Exclusion criteria. HCC patients who have history of liver transplantation, have other cancer types at the time of selection, presented by renal insufficiency, and thyroid dysfunction will be excluded from the study. Additionally, patients with incomplete data or histopathology diagnosis.
* Data to be collected HCC patients' Clinico-pathological Criteria. Clinical data will be obtained from medical records and the original pathology reports. These data to be compiled in an Excel sheet.
Clinical data to be recorded and assessed:
* Cancer family history,
* Individual cancer history and the tumor clinical assessment done using the tumor-node-metastasis (TNM) classification of the American Joint Committee on Cancer (AJCC).
* HCC histological grading made in accordance to Edmondson-Steiner (ES).
* The characteristics of the HCC patients with regards to Ultrasound (US) findings (cirrhosis, ascites, and splenomegaly), Total and direct bilirubin, ALP, Prothrombin time (PT), PC, INR, hemoglobin (HB), CBC, White Blood Cells (WBCs), Platelets and ALT to Platelet Ratio index (APRI) score\[The formula for the APRI score is (AST/upper limit of the normal AST range) X 100/Platelet Count\], serum insulin, blood glucose level, BMI, BP, albumin, BUN.
* Tumor size, as well as clinico-pathological biomarkers AFP, CEA, GGT data will be collected from patient files for further correlations and statistical analysis.
* For both groups treatment type and for group 2 the site of metastatic destination, disease-free survival (DFS), overall survival (OS), the duration of patient survival from the time of treatment initiation will be considered as a universally accepted direct measure of clinical benefit.
* Methodology 4.1. Bioinformatic Analysis: Selected Polymorphic Sites The investigators selected polymorphisms using 1000 Genome data (http://www.internationalgenome.org/home), as reported previously. The tag SNPs were selected separately using the following criteria: (1) Haploview with the Tagger function was used; (2) the population of the HapMap selected Yoruba in Ibadan, Nigeria (YRI) population; (3) those for which pairwise tagging had r2 of ≥ 0.8; and (4) those with a minor allele frequency of ≥ 5%. The selection area was enlarged by 10 kb both upstream and downstream for HOTTIP lncRNA gene. Fast SNP and f SNP searches were used to predict the potential SNP function (http://compbio.cs.queensu.ca/F-SNP/).
After block identification, many SNPs were detected, among them, 2 SNPs with high LD (\< 0.8) were selected: rs17501292-rs2067087.
4.2. Blood Samples. Genomic DNA will be extracted from whole ethylenediaminetetraacetic acid (EDTA) blood samples from all subjects using the DNA Mini Kit (Qiagen, Valencia, CA) according to the manufacturer's instructions. The yield will be measured by Nano Drop 2000 (Thermo Fisher Scientific, UK) and will be aliquoted into 5 clean Eppendorf tubes and stored at -80°C, until biochemical assessment at the Faculty of Pharmacy, Ain-Shams University, Advanced Biochemistry Research Lab (ABRL).
4.3. SNPs Genotyping rs17501292 (NC\_000007.14:27201853: T:C, NC\_000007.14:27201853: T: G ;T \> C, G), rs2067087 (NC\_000007.14:27202040:G:C,NC\_000007.14:27202040:G:T ; G\> C,T),will be genotyped using real-time polymerase chain reaction (RT-PCR) with the TaqMan allelic discrimination assay on a 7900 system using predesigned primer/probe (Applied Biosystems Inc).
............................
* STATISTICAL ANALYSIS 5.1. SPSS v.25 USA (SPSS, Chicago, IL) or Stat or Graph Pad will be used for statistical analysis.
5.2. Data will be collected, excel tabulated and tested for normality by Kolmogorov-Smirnov test.
5.3. Between-group differences in sex variability will be compared by the χ2 test and by analysis of variance for age variability. Multivariate logistic regression with adjustments for age and sex will be used to show the association between selected lncRNA polymorphisms and HCC risk.
5.4. Normally distributed variables will be expressed as mean+ (S.E.M) and analyzed using two samples independent t-test. Median (interquartile range) will be used to express nonparametric data, and subsequently analyzed using Mann Whitney U test.
5.5. The two-way pairwise interactions of lncRNA SNP-SNP will be calculated using multivariate logistic regression. Univariate then multivariate survival analyses will be carried out by the log-rank test and the Cox proportional hazards model.
5.6. Pearson's Chi-square analysis or Fisher's exact test will be employed to compare the difference of categorical variables.
5.7. Receiver Operating Characteristics (ROC) curves will be drawn using Medcalc to get the sensitivity and specificity of some markers together with the SNPs.
5.8. Survival curves will be drawn using Kaplan-meier curve. 5.9. Hardy-Weinberg test will be used to assure equilibrium of our study participants with the population or not.
5.10. For all analyses, a two-tailed P value of 0.05 or less will be considered as statistically significant
Genetic variations such as single-nucleotide polymorphisms (SNPs), when inherited together, as a group, because of high Linkage Disequilibrium (LD), there tends to be redundant information. The regions of the genome with high LD, that harbor a specific set of SNPs, are inherited together, known as haplotypes. Haplotypes can alter the expression of coding genes and the protein non-coding genes like lncRNAs.
Only few studies have focused on the polymorphisms of the onco-lncRNA HOTTIP gene. A recent study found that specific HOTTIP SNPs have the potential to be biomarkers for HCC risk and prognosis, where one haplotype of HOTTIP gene "rs17501292-rs2067087-rs17427960" showed a 1.91-fold increased risk of HCC (P = 0.006) in Chinese patients' samples.
..............
2. Objectives 2.1. Genotype different haplotype SNPs of lncRNA HOTTIP in whole blood samples (Liquid biopsy) from metastatic HCC patients, and to be compared with sex and age-matched non-metastatic patients.
2.2. Correlate HCC clinicopathological characteristics (tumor stage and grade, tumor progression (TNM) and different clinical presentations as presence/absence of metastasis, and other classical clinico-pathological prognostic biomarkers such as α-fetoprotein (AFP), carcinoembryonic antigen (CEA), alkaline phosphatase (ALP), alanine transaminase (ALT), aspartate transaminase (AST), International normalized ratio (INR), prothrombin concentration (PC), total bilirubin, Gamma-glutamyltransferase (GGT), and complete blood count (CBC), blood pressure, blood glucose level, serum insulin, and body mass index (BMI), c-reactive protein (CRP), albumin, blood urea nitrogen (BUN) and prognostic marker(s)/outcome to HOTTIP genotypes or different haplotype SNPs.
2.3. Correlate HOTTIP lncRNA influence on some HCC hallmarks (tumor growth, proliferation, and/or metastasis).
2.4. Molecular Docking for exploring potential drugs that could be re-purposed to inhibit our target oncogenic lncRNA HOTTIP.
...................
3. Subjects - 198 HCC patients attending the Faculty of Medicine, Ain Shams University Hospital, with 1:1 or 1:2 ratio for the HCC groups metastatic vs non metastatic HCC groups (according to clinical evidence/relevance and/or availability).
Group 1; non-metastatic HCC patients, Group 2; metastatic HCC patients, diagnosed with primary HCC receiving any type of therapy (neoadjuvant or radiotherapy).
Eligibility criterion are adult age and male/female 1:1. Criteria for HCC diagnosis following the Ain Shams University (ASU) hospital role relying on AFP level and CT scan or the fine needle biopsy.
Groups to be matched socioeconomically, in age range, sex Male/Female 1:1, residence (case-controlled study).
\- Exclusion criteria. HCC patients who have history of liver transplantation, have other cancer types at the time of selection, presented by renal insufficiency, and thyroid dysfunction will be excluded from the study. Additionally, patients with incomplete data or histopathology diagnosis.
* Data to be collected HCC patients' Clinico-pathological Criteria. Clinical data will be obtained from medical records and the original pathology reports. These data to be compiled in an Excel sheet.
Clinical data to be recorded and assessed:
* Cancer family history,
* Individual cancer history and the tumor clinical assessment done using the tumor-node-metastasis (TNM) classification of the American Joint Committee on Cancer (AJCC).
* HCC histological grading made in accordance to Edmondson-Steiner (ES).
* The characteristics of the HCC patients with regards to Ultrasound (US) findings (cirrhosis, ascites, and splenomegaly), Total and direct bilirubin, ALP, Prothrombin time (PT), PC, INR, hemoglobin (HB), CBC, White Blood Cells (WBCs), Platelets and ALT to Platelet Ratio index (APRI) score\[The formula for the APRI score is (AST/upper limit of the normal AST range) X 100/Platelet Count\], serum insulin, blood glucose level, BMI, BP, albumin, BUN.
* Tumor size, as well as clinico-pathological biomarkers AFP, CEA, GGT data will be collected from patient files for further correlations and statistical analysis.
* For both groups treatment type and for group 2 the site of metastatic destination, disease-free survival (DFS), overall survival (OS), the duration of patient survival from the time of treatment initiation will be considered as a universally accepted direct measure of clinical benefit.
* Methodology 4.1. Bioinformatic Analysis: Selected Polymorphic Sites The investigators selected polymorphisms using 1000 Genome data (http://www.internationalgenome.org/home), as reported previously. The tag SNPs were selected separately using the following criteria: (1) Haploview with the Tagger function was used; (2) the population of the HapMap selected Yoruba in Ibadan, Nigeria (YRI) population; (3) those for which pairwise tagging had r2 of ≥ 0.8; and (4) those with a minor allele frequency of ≥ 5%. The selection area was enlarged by 10 kb both upstream and downstream for HOTTIP lncRNA gene. Fast SNP and f SNP searches were used to predict the potential SNP function (http://compbio.cs.queensu.ca/F-SNP/).
After block identification, many SNPs were detected, among them, 2 SNPs with high LD (\< 0.8) were selected: rs17501292-rs2067087.
4.2. Blood Samples. Genomic DNA will be extracted from whole ethylenediaminetetraacetic acid (EDTA) blood samples from all subjects using the DNA Mini Kit (Qiagen, Valencia, CA) according to the manufacturer's instructions. The yield will be measured by Nano Drop 2000 (Thermo Fisher Scientific, UK) and will be aliquoted into 5 clean Eppendorf tubes and stored at -80°C, until biochemical assessment at the Faculty of Pharmacy, Ain-Shams University, Advanced Biochemistry Research Lab (ABRL).
4.3. SNPs Genotyping rs17501292 (NC\_000007.14:27201853: T:C, NC\_000007.14:27201853: T: G ;T \> C, G), rs2067087 (NC\_000007.14:27202040:G:C,NC\_000007.14:27202040:G:T ; G\> C,T),will be genotyped using real-time polymerase chain reaction (RT-PCR) with the TaqMan allelic discrimination assay on a 7900 system using predesigned primer/probe (Applied Biosystems Inc).
............................
* STATISTICAL ANALYSIS 5.1. SPSS v.25 USA (SPSS, Chicago, IL) or Stat or Graph Pad will be used for statistical analysis.
5.2. Data will be collected, excel tabulated and tested for normality by Kolmogorov-Smirnov test.
5.3. Between-group differences in sex variability will be compared by the χ2 test and by analysis of variance for age variability. Multivariate logistic regression with adjustments for age and sex will be used to show the association between selected lncRNA polymorphisms and HCC risk.
5.4. Normally distributed variables will be expressed as mean+ (S.E.M) and analyzed using two samples independent t-test. Median (interquartile range) will be used to express nonparametric data, and subsequently analyzed using Mann Whitney U test.
5.5. The two-way pairwise interactions of lncRNA SNP-SNP will be calculated using multivariate logistic regression. Univariate then multivariate survival analyses will be carried out by the log-rank test and the Cox proportional hazards model.
5.6. Pearson's Chi-square analysis or Fisher's exact test will be employed to compare the difference of categorical variables.
5.7. Receiver Operating Characteristics (ROC) curves will be drawn using Medcalc to get the sensitivity and specificity of some markers together with the SNPs.
5.8. Survival curves will be drawn using Kaplan-meier curve. 5.9. Hardy-Weinberg test will be used to assure equilibrium of our study participants with the population or not.
5.10. For all analyses, a two-tailed P value of 0.05 or less will be considered as statistically significant
Conditions
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Hepatocellular Carcinoma
Study Design
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Observational Model Type
CASE_CONTROL
Study Time Perspective
RETROSPECTIVE
Study Groups
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Non-metastatic HCC group
129 HCC patients attending the Faculty of Medicine, Ain Shams University Hospital, males and females (according to availability), diagnosed with primary HCC receiving any type of therapy (neoadjuvant or radiotherapy).
Criteria for HCC diagnosis following the ASU hospital role relying on AFP level and CT scan or the fine needle biopsy.
Groups to be matched socioeconomically, in age range, residence.
No interventions assigned to this group
Metastatic HCC group
69 HCC patients attending the Faculty of Medicine, Ain Shams University Hospital, males and females (according to availability), diagnosed with primary HCC receiving any type of therapy (neoadjuvant or radiotherapy).
Criteria for HCC diagnosis following the ASU hospital role relying on AFP level and CT scan or the fine needle biopsy.
Groups to be matched socioeconomically, in age range, residence.
No interventions assigned to this group
Eligibility Criteria
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Inclusion Criteria
* HCC patients attending the Faculty of Medicine, Ain Shams University Hospital, with 1:1 or 1:2 ratio for the HCC groups metastatic vs non metastatic HCC groups (according to clinical evidence/relevance and/or availability).
Group 1; non-metastatic HCC patients, Group 2; metastatic HCC patients, diagnosed with primary HCC receiving any type of therapy (neoadjuvant or radiotherapy).
Eligibility criterion are adult age and male/female 1:1 according to availability.
Criteria for HCC diagnosis following the ASU hospital role relying on AFP level and CT scan or the fine needle biopsy.
Groups to be matched socioeconomically, in age range, residence (case-controlled study).
Group 1; non-metastatic HCC patients, Group 2; metastatic HCC patients, diagnosed with primary HCC receiving any type of therapy (neoadjuvant or radiotherapy).
Eligibility criterion are adult age and male/female 1:1 according to availability.
Criteria for HCC diagnosis following the ASU hospital role relying on AFP level and CT scan or the fine needle biopsy.
Groups to be matched socioeconomically, in age range, residence (case-controlled study).
Exclusion Criteria
* HCC patients who have history of liver transplantation, have other cancer types at the time of selection, presented by renal insufficiency, and thyroid dysfunction will be excluded from the study. Additionally, patients with incomplete data or histopathology diagnosis.
Minimum Eligible Age
35 Years
Eligible Sex
ALL
Accepts Healthy Volunteers
No
Sponsors
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Ain Shams University
OTHER
Sponsor Role
lead
Responsible Party
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Prof. Nadia M. Hamdy, Ph.D.
Professor of Biochemistry and Molecular Biology at Biochemistry department, Faculty of Pharmacy
Responsibility Role
PRINCIPAL_INVESTIGATOR
Principal Investigators
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Nadia Hamdy, PhD
Role: PRINCIPAL_INVESTIGATOR
Faculty of Pharmacy, Ain Shams University
Locations
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Faculty of Pharmacy, Ain Shams Univeristy, Advanced Biochemistry Research Lab.
Cairo, , Egypt
Site Status
Countries
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Egypt
References
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Rashed WM, Kandeil MAM, Mahmoud MO, Ezzat S. Hepatocellular Carcinoma (HCC) in Egypt: A comprehensive overview. J Egypt Natl Canc Inst. 2020 Jan 16;32(1):5. doi: 10.1186/s43046-020-0016-x.
Reference Type
BACKGROUND
Tomimaru Y, Eguchi H, Nagano H, Wada H, Kobayashi S, Marubashi S, Tanemura M, Tomokuni A, Takemasa I, Umeshita K, Kanto T, Doki Y, Mori M. Circulating microRNA-21 as a novel biomarker for hepatocellular carcinoma. J Hepatol. 2012 Jan;56(1):167-75. doi: 10.1016/j.jhep.2011.04.026. Epub 2011 Jul 13.
Reference Type
BACKGROUND
George J, Patel T. Noncoding RNA as therapeutic targets for hepatocellular carcinoma. Semin Liver Dis. 2015 Feb;35(1):63-74. doi: 10.1055/s-0034-1397350. Epub 2015 Jan 29.
Reference Type
BACKGROUND
Tsang FH, Au SL, Wei L, Fan DN, Lee JM, Wong CC, Ng IO, Wong CM. Long non-coding RNA HOTTIP is frequently up-regulated in hepatocellular carcinoma and is targeted by tumour suppressive miR-125b. Liver Int. 2015 May;35(5):1597-606. doi: 10.1111/liv.12746. Epub 2015 Jan 27.
Reference Type
BACKGROUND
Lian Y, Cai Z, Gong H, Xue S, Wu D, Wang K. HOTTIP: a critical oncogenic long non-coding RNA in human cancers. Mol Biosyst. 2016 Oct 18;12(11):3247-3253. doi: 10.1039/c6mb00475j.
Reference Type
BACKGROUND
Dong SS, He WM, Ji JJ, Zhang C, Guo Y, Yang TL. LDBlockShow: a fast and convenient tool for visualizing linkage disequilibrium and haplotype blocks based on variant call format files. Brief Bioinform. 2021 Jul 20;22(4):bbaa227. doi: 10.1093/bib/bbaa227.
Reference Type
BACKGROUND
Hu Z, Chen J, Tian T, Zhou X, Gu H, Xu L, Zeng Y, Miao R, Jin G, Ma H, Chen Y, Shen H. Genetic variants of miRNA sequences and non-small cell lung cancer survival. J Clin Invest. 2008 Jul;118(7):2600-8. doi: 10.1172/JCI34934.
Reference Type
BACKGROUND
Gong WJ, Yin JY, Li XP, Fang C, Xiao D, Zhang W, Zhou HH, Li X, Liu ZQ. Association of well-characterized lung cancer lncRNA polymorphisms with lung cancer susceptibility and platinum-based chemotherapy response. Tumour Biol. 2016 Jun;37(6):8349-58. doi: 10.1007/s13277-015-4497-5. Epub 2016 Jan 5.
Reference Type
BACKGROUND
Hu P, Qiao O, Wang J, Li J, Jin H, Li Z, Jin Y. rs1859168 A > C polymorphism regulates HOTTIP expression and reduces risk of pancreatic cancer in a Chinese population. World J Surg Oncol. 2017 Aug 17;15(1):155. doi: 10.1186/s12957-017-1218-0.
Reference Type
BACKGROUND
Wang BG, Xu Q, Lv Z, Fang XX, Ding HX, Wen J, Yuan Y. Association of twelve polymorphisms in three onco-lncRNA genes with hepatocellular cancer risk and prognosis: A case-control study. World J Gastroenterol. 2018 Jun 21;24(23):2482-2490. doi: 10.3748/wjg.v24.i23.2482.
Reference Type
BACKGROUND
Quagliata L, Matter MS, Piscuoglio S, Arabi L, Ruiz C, Procino A, Kovac M, Moretti F, Makowska Z, Boldanova T, Andersen JB, Hammerle M, Tornillo L, Heim MH, Diederichs S, Cillo C, Terracciano LM. Long noncoding RNA HOTTIP/HOXA13 expression is associated with disease progression and predicts outcome in hepatocellular carcinoma patients. Hepatology. 2014 Mar;59(3):911-23. doi: 10.1002/hep.26740. Epub 2014 Jan 28.
Reference Type
BACKGROUND
Lewontin RC. On measures of gametic disequilibrium. Genetics. 1988 Nov;120(3):849-52. doi: 10.1093/genetics/120.3.849.
Reference Type
BACKGROUND
Other Identifiers
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RHDIRB2020110301REC#70
Identifier Type: -
Identifier Source: org_study_id
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