Ignite Creation Date:
2024-10-26 @ 3:36 PM
Last Modification Date:
2024-10-26 @ 3:36 PM
Study NCT ID:
NCT06534242
Status:
COMPLETED
Last Update Posted:
None
First Post:
2024-07-30
Brief Title:
Implication of Genetic Variations in Long Intergenic Non-coding RNA 00511 LINC00511 in Colorectal Cancer
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Implication of Genetic Variations in Long Intergenic Non-coding RNA 00511 LINC00511 in Colorectal Cancer
Status:
COMPLETED
Status Verified Date:
2024-08
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
As there is a lack of information about the association between LINC00511 SNPs variants and CRC susceptibility so this study was undertaken to address whether these SNPs would increase CRC risk or could predict its prognosis The aim of this study was to investigate the association between LINC00511 SNPs rs17780195 or rs9906859 and rs1558535 and CRC susceptibility andor pathogenesis in addition to finding out the interaction between these SNPs and clinicopathological factors such as histopathological type tumor size lymph node metastasis and tumor grade
Detailed Description:
1 Introduction
11 Background Colorectal cancer CRC represents the 7th most common cancer in Egypt The global burden of CRC is expected to increase 60 by 2030 in terms of new cases and deaths
Traditional treatments including radio- and chemo-therapies are associated with various undesirable side-effects Meanwhile the 5-year survival rate for CRC is 64 but drops to 12 for metastatic CRC Therefore development of reliable and accurate prognostic markers is necessary Long non-coding RNAs lncRNAs play an important role in different types of cancer through regulation of gene expression protein synthesis being epigenetic signatures
Long intergenic ncRNA 00511 LINC00511 is a 2265 bp ncRNA that exerts an oncogenic function in many cancers such as glioma ovarian cancer and CRC Single nucleotide polymorphisms SNPs in lncRNAs have been found to be associated with cancer Such genetic variants may increase or reduce the risk of cancer depending on the location of these SNPs Recently LINC00511 SNPs were associated with breast cancer BC risk in Chinese population and currently the chief supervisor is studying LINC00511 SNPs in Egyptian BC Moreover to address implication of LINC00511 SNPs in CRC being not studied yet might be useful for understanding CRC pathogenesis linking LINC00511 SNPs to disease severity as well as for discovering new target for CRC prevention andor treatment
12 Aim of the work Investigation of the association between LINC00511 SNPs rs17780195 or rs9906859 and rs1558535 and CRC susceptibility andor pathogenesis in addition to finding out the interaction between these SNPs and clinicopathological factors such as histopathological type tumor size lymph node metastasis and tumor grade
2 Subjects Study Participants will be Classified into two main groups
21 Group I CRC Cases 200 CRC patients were recruited from Mansoura University Hospitals previously signed an informed consent to participate in the study after obtaining the required institutional research ethics committee REC review board approval Sample size was calculated according to the same SNPs studied in BC in a Chinese study according to the relevant statistical methods Exclusion Criteria patients with HBV schistosomiasis HIV alcohol intake thyroid dysfunction inflammatory diseases diabetes mellitus and cardiovascular disorders Subjects receiving any chemotherapy or radiotherapy or had undergone a GIT surgical operation patients with blood disorder diseases any cancer other than CRC patients with neuronal diseases respiratory diseases uterine diseases kidney diseases cirrhosis of the liver prolonged use of corticosteroids or sex hormones Additionally patients with incomplete data or histopathology diagnosis
Clinico-pathological Criteria Clinical data will be obtained from medical records and the original pathology reports These data to be compiled in an excel file The following clinical data to be recorded and assessed as in the attached excel file
Full family history will be recorded for all participants -Individual cancer history and the tumor clinical assessment done using the tumor-node- metastasis TNM classification of American Joint Committee on Cancer AJCC
The characteristics of the participants with regards to body mass index BMI inflammation markers platelets count CBC and liver function tests -Cancer histopathological type to be determined tumor size as well as clinico- pathological biomarkers carcinoembryonic antigen CEA and carcinoma antigen 199 CA199 Ki-67 or PCNA if any data will be collected from patient files for further correlations and statistical analysis
For the metastatic patients sub-group if any or if follow up will be done treatment type and 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
22 Control Group apparently healthy age and sex matched 200 volunteers not suffering from any disease or taking any medication Control subjects with normal kidney functions and liver enzyme levels absence of any clinical or laboratory evidence of CRC were recruited during routine checkup examinations for themselves or their relatives or from the Chronic Diseases Screening National Presidential Program
3 Methodology
31 In silico databases access
The HUGO Gene Nomenclature Committee HGNC supported by the National Human Genome Research Institute NHGRI provided a report httpswwwgenenamesorg accessed on April 2022 and identification of LINC00511 gene using gene card identification accessed on April 2022 In addition LINC00511 gene expression was obtained from RNA-seq using data unit TPM from 53 human tissue samples from the Genotype-Tissue Expression GTEx Project from Expression Atlas Gene expression across species and biological conditions httpswwwebiacukgxahome accessed Jan 3rd 2022
SNP Selection Based on the previous study by Han et al who studied several LINC00511 SNPs in breast cancer and after screening for a minor allele frequency MAF above 005 5 MAF was obtained from the International Genome Sample Resource IGSR Supporting open human variation data httpswwwinternationalgenomeorg in 1000 genomes data httpswwwensemblorg 1000GENOMESphase_3
SNPinfo Web Server httpsnpinfoniehsnihgovcgi- binsnpinfosnpfunccgi was accessed to obtain SNP info in DNA sequence and to predict one SNP effect on potential neighbors
32 Blood sampling 5 ml collected from controls and CRC patients on EDTA anticoagulant vacutainers and stored at -80º C until biochemical assessment at the Advanced Biochemistry Research Lab ABRL Faculty of pharmacy Ain Shams University research setting
33 DNA Extraction from blood using a QIAmp DNA Blood Mini extraction kit from 200 μL whole blood according to the manufacturers instructions DNA Quantification was done by NanoDrop 2000 Thermo Fisher Scientific UK
34 SNPs Genotyping using TaqMan SNP genotyping assay will be used to perform the genotyping for LINC00511 SNPs rs17780195 rs9906859 and rs1558535 through using the TaqMan Universal Master Mix No UNG Thermo Fisher Scientific USA and StepOnePlus Thermal Cycler Applied Biosystems USA
35 Statistical analysis The statistical analysis was performed in R software version 420 First genotype frequencies for each studied SNP were checked to be in concordance with the Hardy-Weinberg Equilibrium HWE using Pearsons χ 2 Chi-squared tests
Students t-test and the χ2 test were used to compare quantitative and qualitative variables between the CRC and control groups respectively After adjusting for demographic factors age sex BMI family history of cancer in first degree relatives an unconditional logistic regression applied to explore the independent risk factors for CRC among the examined LINC00511 SNPs using the co-dominant dominant over-dominant and recessive genotypic models then being compared using measures of model fit and prediction the Akaike Information Criterion AIC Bayesian Information Criterion BIC Deviance Information Criterion DIC Pseudo R2 McFaddens Cox and Snells and Nagelkerkes and the area-under-receiver operating characteristics AUC curve The additiveco-dominant model was superior to all models based on these criteria Sensitivity SN specificity SP positive predictive value PPV and negative predicted value NPV were reported for the model with the best fit and predictive power not for mere disease diagnosis The optimal cut-offs for age CA19-9 and CEA categorization of LINC00511 SNPs were determined using ROC analysis BMI was categorized based on the standard definitions of overweight and obese individuals subjects with BMI 25 kgm2 were considered overweight or obese SNPs genotypes were analyzed using the snpReady library in R Granato et al 2018 missing genotypes were imputed using Wrights method based on Wrights equilibrium and missing baseline demographic and clinical data were imputed using the predictive mean matching method implemented in R van Buuren Groothuis-Oudshoorn 2011
Multiple logistic regression analysis was done to examine the association between each SNP alleles genotypes haplotypes and CRC prevalence stage and grade while adjusting for baseline covariates age BMI and additional risk factors for tumor stage and tumor grade patients cancer family history tumor site history of IBD classical tumor markers CEA and CA199 and the presence or absence of vascular infiltration
Firths logistic regression was implemented in the case of quasi-separation in one or more variables Haploview software version 20 was used to calculate r2 and D as the measurements of linkage disequilibrium extent between pairwise SNP combinations blocks of different genotypes were determined using the SHEsis software httpanalysisbio-xcnmyAnalysisphp A stratified analysis and the SHEsis plus online software httpshesisplusbio-xcnSHEsishtml applied to further evaluate the association between LINC00511 SNPs and CRC susceptibility as well as haplotypes frequency as a measurement of genetic distribution was directly calculated in CRC and healthy control groups Further Epistasis was analyzed by Multifactor Dimensionality Reduction MDR package software in R httpsritchielaborgsoftwaremdr-download for carrying out SNP-SNP or gene-gene and SNP-Environment or gene-environment interaction analysis applied to evaluate the interactive role of genetic and demographic factors false Discovery rate was controlled by adjusting the significance level using Benjamini and Hochberg Benjamini and Yekutieli Holm step-down Sidak step-down and Sidak single-step p-value adjustment procedures When comparing the predictive ability of the models using the pseudo-R-squared measures and the SN SP PPV and NPV by ROC curve in addition to the MDR part aids for LINC00511 SNPs rs prediction Prediction method LD values were calculated by a pairwise estimation between LINC00511 SNPs genotyped in the same sample and within a given window An established method was used to estimate the maximum likelihood of the proportion that each possible haplotype contributed to the double heterozygote
To confirm differences between obtained results in the patient group were not by chance a Bonferroni calculation was applied to the data
Finally the investigators analyzed the overall survival OS for 2 years in the CRC patients n200 with recording the date of death or last contact with the Clinician as the follow-up end point For disease-free survival DFS as the event free survival EFS in patients with non-metastatic CRC at the time of diagnosis date of relapse or last contact with the Clinician was the follow-up end point The non-parametric Kaplan-Meier method PROC LIFETEST were done for OS curves and EFS The survival probability calculator generates the Kaplan-Meier curve with 95 CI using log-rank test using the Chi square distribution for comparison of more than of two groups
The relative risk of disease relapse was estimated as hazard ratio HR Univariable survival analyses were done for gender MBI tumor site family history of cancer lymph node involvement TNM stage and tumor grades separately for the CRC patients group as well as for LINC00511 SNPs genotypes
Level of significance A two-sided value of P 005 was deemed as a sign of statistical significance
11 Background Colorectal cancer CRC represents the 7th most common cancer in Egypt The global burden of CRC is expected to increase 60 by 2030 in terms of new cases and deaths
Traditional treatments including radio- and chemo-therapies are associated with various undesirable side-effects Meanwhile the 5-year survival rate for CRC is 64 but drops to 12 for metastatic CRC Therefore development of reliable and accurate prognostic markers is necessary Long non-coding RNAs lncRNAs play an important role in different types of cancer through regulation of gene expression protein synthesis being epigenetic signatures
Long intergenic ncRNA 00511 LINC00511 is a 2265 bp ncRNA that exerts an oncogenic function in many cancers such as glioma ovarian cancer and CRC Single nucleotide polymorphisms SNPs in lncRNAs have been found to be associated with cancer Such genetic variants may increase or reduce the risk of cancer depending on the location of these SNPs Recently LINC00511 SNPs were associated with breast cancer BC risk in Chinese population and currently the chief supervisor is studying LINC00511 SNPs in Egyptian BC Moreover to address implication of LINC00511 SNPs in CRC being not studied yet might be useful for understanding CRC pathogenesis linking LINC00511 SNPs to disease severity as well as for discovering new target for CRC prevention andor treatment
12 Aim of the work Investigation of the association between LINC00511 SNPs rs17780195 or rs9906859 and rs1558535 and CRC susceptibility andor pathogenesis in addition to finding out the interaction between these SNPs and clinicopathological factors such as histopathological type tumor size lymph node metastasis and tumor grade
2 Subjects Study Participants will be Classified into two main groups
21 Group I CRC Cases 200 CRC patients were recruited from Mansoura University Hospitals previously signed an informed consent to participate in the study after obtaining the required institutional research ethics committee REC review board approval Sample size was calculated according to the same SNPs studied in BC in a Chinese study according to the relevant statistical methods Exclusion Criteria patients with HBV schistosomiasis HIV alcohol intake thyroid dysfunction inflammatory diseases diabetes mellitus and cardiovascular disorders Subjects receiving any chemotherapy or radiotherapy or had undergone a GIT surgical operation patients with blood disorder diseases any cancer other than CRC patients with neuronal diseases respiratory diseases uterine diseases kidney diseases cirrhosis of the liver prolonged use of corticosteroids or sex hormones Additionally patients with incomplete data or histopathology diagnosis
Clinico-pathological Criteria Clinical data will be obtained from medical records and the original pathology reports These data to be compiled in an excel file The following clinical data to be recorded and assessed as in the attached excel file
Full family history will be recorded for all participants -Individual cancer history and the tumor clinical assessment done using the tumor-node- metastasis TNM classification of American Joint Committee on Cancer AJCC
The characteristics of the participants with regards to body mass index BMI inflammation markers platelets count CBC and liver function tests -Cancer histopathological type to be determined tumor size as well as clinico- pathological biomarkers carcinoembryonic antigen CEA and carcinoma antigen 199 CA199 Ki-67 or PCNA if any data will be collected from patient files for further correlations and statistical analysis
For the metastatic patients sub-group if any or if follow up will be done treatment type and 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
22 Control Group apparently healthy age and sex matched 200 volunteers not suffering from any disease or taking any medication Control subjects with normal kidney functions and liver enzyme levels absence of any clinical or laboratory evidence of CRC were recruited during routine checkup examinations for themselves or their relatives or from the Chronic Diseases Screening National Presidential Program
3 Methodology
31 In silico databases access
The HUGO Gene Nomenclature Committee HGNC supported by the National Human Genome Research Institute NHGRI provided a report httpswwwgenenamesorg accessed on April 2022 and identification of LINC00511 gene using gene card identification accessed on April 2022 In addition LINC00511 gene expression was obtained from RNA-seq using data unit TPM from 53 human tissue samples from the Genotype-Tissue Expression GTEx Project from Expression Atlas Gene expression across species and biological conditions httpswwwebiacukgxahome accessed Jan 3rd 2022
SNP Selection Based on the previous study by Han et al who studied several LINC00511 SNPs in breast cancer and after screening for a minor allele frequency MAF above 005 5 MAF was obtained from the International Genome Sample Resource IGSR Supporting open human variation data httpswwwinternationalgenomeorg in 1000 genomes data httpswwwensemblorg 1000GENOMESphase_3
SNPinfo Web Server httpsnpinfoniehsnihgovcgi- binsnpinfosnpfunccgi was accessed to obtain SNP info in DNA sequence and to predict one SNP effect on potential neighbors
32 Blood sampling 5 ml collected from controls and CRC patients on EDTA anticoagulant vacutainers and stored at -80º C until biochemical assessment at the Advanced Biochemistry Research Lab ABRL Faculty of pharmacy Ain Shams University research setting
33 DNA Extraction from blood using a QIAmp DNA Blood Mini extraction kit from 200 μL whole blood according to the manufacturers instructions DNA Quantification was done by NanoDrop 2000 Thermo Fisher Scientific UK
34 SNPs Genotyping using TaqMan SNP genotyping assay will be used to perform the genotyping for LINC00511 SNPs rs17780195 rs9906859 and rs1558535 through using the TaqMan Universal Master Mix No UNG Thermo Fisher Scientific USA and StepOnePlus Thermal Cycler Applied Biosystems USA
35 Statistical analysis The statistical analysis was performed in R software version 420 First genotype frequencies for each studied SNP were checked to be in concordance with the Hardy-Weinberg Equilibrium HWE using Pearsons χ 2 Chi-squared tests
Students t-test and the χ2 test were used to compare quantitative and qualitative variables between the CRC and control groups respectively After adjusting for demographic factors age sex BMI family history of cancer in first degree relatives an unconditional logistic regression applied to explore the independent risk factors for CRC among the examined LINC00511 SNPs using the co-dominant dominant over-dominant and recessive genotypic models then being compared using measures of model fit and prediction the Akaike Information Criterion AIC Bayesian Information Criterion BIC Deviance Information Criterion DIC Pseudo R2 McFaddens Cox and Snells and Nagelkerkes and the area-under-receiver operating characteristics AUC curve The additiveco-dominant model was superior to all models based on these criteria Sensitivity SN specificity SP positive predictive value PPV and negative predicted value NPV were reported for the model with the best fit and predictive power not for mere disease diagnosis The optimal cut-offs for age CA19-9 and CEA categorization of LINC00511 SNPs were determined using ROC analysis BMI was categorized based on the standard definitions of overweight and obese individuals subjects with BMI 25 kgm2 were considered overweight or obese SNPs genotypes were analyzed using the snpReady library in R Granato et al 2018 missing genotypes were imputed using Wrights method based on Wrights equilibrium and missing baseline demographic and clinical data were imputed using the predictive mean matching method implemented in R van Buuren Groothuis-Oudshoorn 2011
Multiple logistic regression analysis was done to examine the association between each SNP alleles genotypes haplotypes and CRC prevalence stage and grade while adjusting for baseline covariates age BMI and additional risk factors for tumor stage and tumor grade patients cancer family history tumor site history of IBD classical tumor markers CEA and CA199 and the presence or absence of vascular infiltration
Firths logistic regression was implemented in the case of quasi-separation in one or more variables Haploview software version 20 was used to calculate r2 and D as the measurements of linkage disequilibrium extent between pairwise SNP combinations blocks of different genotypes were determined using the SHEsis software httpanalysisbio-xcnmyAnalysisphp A stratified analysis and the SHEsis plus online software httpshesisplusbio-xcnSHEsishtml applied to further evaluate the association between LINC00511 SNPs and CRC susceptibility as well as haplotypes frequency as a measurement of genetic distribution was directly calculated in CRC and healthy control groups Further Epistasis was analyzed by Multifactor Dimensionality Reduction MDR package software in R httpsritchielaborgsoftwaremdr-download for carrying out SNP-SNP or gene-gene and SNP-Environment or gene-environment interaction analysis applied to evaluate the interactive role of genetic and demographic factors false Discovery rate was controlled by adjusting the significance level using Benjamini and Hochberg Benjamini and Yekutieli Holm step-down Sidak step-down and Sidak single-step p-value adjustment procedures When comparing the predictive ability of the models using the pseudo-R-squared measures and the SN SP PPV and NPV by ROC curve in addition to the MDR part aids for LINC00511 SNPs rs prediction Prediction method LD values were calculated by a pairwise estimation between LINC00511 SNPs genotyped in the same sample and within a given window An established method was used to estimate the maximum likelihood of the proportion that each possible haplotype contributed to the double heterozygote
To confirm differences between obtained results in the patient group were not by chance a Bonferroni calculation was applied to the data
Finally the investigators analyzed the overall survival OS for 2 years in the CRC patients n200 with recording the date of death or last contact with the Clinician as the follow-up end point For disease-free survival DFS as the event free survival EFS in patients with non-metastatic CRC at the time of diagnosis date of relapse or last contact with the Clinician was the follow-up end point The non-parametric Kaplan-Meier method PROC LIFETEST were done for OS curves and EFS The survival probability calculator generates the Kaplan-Meier curve with 95 CI using log-rank test using the Chi square distribution for comparison of more than of two groups
The relative risk of disease relapse was estimated as hazard ratio HR Univariable survival analyses were done for gender MBI tumor site family history of cancer lymph node involvement TNM stage and tumor grades separately for the CRC patients group as well as for LINC00511 SNPs genotypes
Level of significance A two-sided value of P 005 was deemed as a sign of statistical significance
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None