Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT06432413 |
| Other study ID # |
RHDIRB2020110301 REC #15 |
| Secondary ID |
|
| Status |
Completed |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
June 1, 2021 |
| Est. completion date |
December 30, 2022 |
Study information
| Verified date |
May 2024 |
| Source |
Ain Shams University |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
This work aims to Investigate the role of circulating notch associated lncRNAs SNHG3 and
LUNAR1 as possible non invasive prognostic biomarkers for colorectal cancer (CRC) monitoring
via measuring the gene expression level of lncRNAs SNHG3 and LUNAR1 in serum of CRC patients
compared with control subjects. Also, to investigate the correlation between SNHG3 and LUNAR1
expression levels and CRC clinicopathological features and their relevance for CRC patients'
clinico-pathological features outcomes assessment
Description:
1. INTRODUCTION Background: Colorectal cancer (CRC) has been identified as a major public
health concern given its high incidence and mortality rates (1). In 2020, CRC was the
third-most prevalent malignancy after breast and lung cancer, accounting for 10% of new cases
and ranking second in terms of mortality with 9.4% of deaths (2). Unfortunately, by year
2030, more than 2.2 million new cases and 1.1 million fatalities from CRC are globally
anticipated (3).
Problem: Despite significant improvements in CRC surgical or radiological interventional
treatment approaches with neo-adjuvant therapies, patients' prognosis remains bleak (4,5).
Metastases and post-surgical tumor recurrence are prevalent, particularly, in more advanced
cancer cases (6), which may account for the increased number of cases and fatalities
prediction, being against the national efforts for achieving Egypt Vision 2030 implementing
SDGs.
Problem statement: Conventionally used CRC prognostic markers for monitoring treatment
outcome and pointing to cancer recurrence are carbohydrate antigen 19.9 (CA19.9) and/or
carcinoembryonic antigen (CEA) that are not that sensitive (7) per patients subclassification
or stratification related to CRC pathological characteristics for more efficient and earlier
prognosis. Thence, a growing demand for sensitive and precise bio-molecular marker(s) better
correlating with CRC prognostic markers and/or CRC clinical outcome (8), that is if
successful would constitute the bases for "Better Health" SDG#3 and less cancer recurrence
and, therefore, less mortality.
Liquid biopsies are used for cancer diagnosis or prognosis, like breast cancer, leukemia,
liver cancer or CRC, via measurement of tumor-derived bio-molecular markers including
circulating ncRNAs including long non-coding RNAs (lncRNAs), microRNA, exosomal ncRNAs,
oncogenes or tumor suppressor genes, and their mutations, tumor-related-cytokines, and
down-stream target proteins (9-22).
After extensive literature search and mining to mind-the-research-gap(s) for better "Cancer
Epigenetics Study; a Step-toward ncRNA-Precision" we have chosen, in the current work, two
notch-related lncRNAs to study in relation-to-CRC prognosis.
Notch-signaling pathway is an ubiquitous cascade within species to control a broad variety of
biological events, including cell division, proliferation, and cell death (23,24). Recent
investigations have revealed the fundamental role of Notch-cascade in CRC evolution (25).
Intestinal epithelial cells' homeostatic self-renewal and tumor-promoting transformation can
both be managed by Notch signaling (26). Stimulation of Notch-cascade can be epigenetically
triggered by dysregulated non-protein coding RNAs' (ncRNAs) expressions (27); a hot area of
research nowadays to figure out the impact of Notch-related ncRNAs on CRC risk and/or
progression.
The significant utility of tumor-expressed Notch-associated lncRNAs as prognostic malignancy
indicators proves how they are connected to carcinogenesis or metastasis and therefore,
reflect the outcome(s) in different cancer types including CRC (28-31).
Small Nucleolar RNA Host Gene3 (SNHG3) is 4950bp lncRNA situated on chromosome 1p35.3 (32).
In breast cancer, upregulated SNHG3 triggers Notch system activation as a result of its
competitive binding to human homo sapiens (hsa) micro-RNA (miR) hsa-miR-154-3p exacerbating
proliferation and metastasis of cancer cells (33). Moreover, SNHG3 positively regulates
Notch1 expression in ovarian cancer through hsa-miR-139-5p suppression, accelerating tumor
cells' proliferation and migration (34). SNHG3 exerted a carcinogenic role in prostate
cancer, osteosarcoma, glioma, gastric cancer, laryngeal cancer, bladder cancer, and CRC (32).
Huang et al. reported SNHG3 elevated expression in CRC cells and tissues, stimulating cancer
progression through sponging hsa-miR-182-5p (35). Therefore, SNHG3 is considered as a
malignancy enhancer that regulates Notch system in various cancer types.
Leukemia-associated nc-insulin-like growth factor1 receptor (IGF1R)-Activator RNA1 (LUNAR1)
serves as a downstream target of Notch-signaling, in the same time LUNAR1 acts through
Notch-signaling stimulation. LUNAR1 is a transcript of 491 nucleotides (nt) gene on
chromosome 15q26.3 with four exons and poly (A) tail (36). LUNAR1 was identified to be
elevated in CRC tissues, triggered by Notch1 stimulation, accelerating CRC progression via
retaining IGF1R expression (37), being a positive regulator of cell division Aim of the
study: Per, few research publications on both notch-related lncRNAs, SNHG3 and LUNAR1, in CRC
prognosis or CRC risk assessment as well as patients' stratification based on
clinico-pathological characteristics, therefore, assessment of the clinical utility of SNHG3
and LUNAR1 lncRNAs fold change expressions in CRC patients' peripheral blood liquid biopsy is
alarming (as step toward implementing ncRNA precision) Study objective(s) to assess, first,
the expression level and pattern of Notch-associated lncRNAs SNHG3 and LUNAR1 in peripheral
blood liquid biopsy, polled from treatment-naïve Egyptian CRC patients' cohort, compared to
age-matched and sex-matched apparently healthy volunteer subjects as controls. Second, to
evaluate lncRNAs SNHG3 and LUNAR1 expression usefulness as sensitive, non-invasive prognostic
bio-molecular marker(s) for CRC monitoring. Third, to investigate the correlation between
SNHG3 and LUNAR1 expression with CRC clinic-pathological features. Finally, to explore the
relevance of the investigated lncRNAs for CRC patients' clinical features outcomes
assessment. All these objectives to be confirmed or ruled out by in silico analysis and
bioinformatics databases search SUBJECTS 2.1. Sample Size and Power of the Study. In
accordance with prior reference studies (36,38) the sample size was estimated utilizing
sample size online calculator https://riskcalc.org/samplesize/# for comparison of the area
under the curve (AUC) with a null hypothesis value (done January, 2021) through using
two-sided significance level of 0.05 and the power (1-beta) of 0.95 as the two-sided
confidence level of 95%. Groups will be 45 samples for CRC patients and 17 controls for SNHG3
and 48 CRC patients' vs 16 controls for LUNAR1.
2.2. Study Design. Case-controlled, retrospective observational study. The study was carried
out from June, 2021 to October, 2022.
2.3. Institutional Review Board (IRB) Statement: This study was ethically approved by the
review board Research Ethical Committee (REC) of Faculty of Pharmacy, Ain Shams University
(REC ID 15, 2021) that was approved by the Faculty of Medicine, Ain Shams University
Hospitals, Ain Shams University REC. This research investigation was conducted out in
compliance with Declaration of Helsinki principles World Medical Association Declaration of
Helsinki: Ethical principles for medical research involving human subjects, 2013. Every
volunteering participant in the study, whether apparently healthy controls or CRC patients,
were fully aware of the study's objective and signed a written comprehensive informed consent
(I.C) form (ethically-approved) were considered in.
2.4. Study Participants 2.4.1. Patients group. 70 CRC Egyptian patients, admitted to the
Oncology Center of the Faculty of Medicine, Ain Shams University Hospitals (ASUH) or the
Oncological Surgery Department of Dar-El-Shafa Hospital, before surgical treatment or CRC
neo-adjuvant treatment, if they were eligible and agreed to participate (signed the I.C) were
recruited in the research. Male-to-female was 30:40 with an age-range; minimum-maximum 24 -79
years.
2.4.2. Apparently healthy control group. 26 randomly-chosen, age-matched, and sex-matched
healthy subjects served as the control group. Male-to-female was 9:17 and age range;
minimum-maximum 35 -78 years. Control group subjects were chosen from those who came to visit
hospital workers in the hospital or from blood donors at the ASUH Blood Donation Unit. None
of the control group did take any medications or have any diseases upon questioner and blood
samples was taken for CBC.
2.4.3. Inclusion and Exclusion Criteria Patients, came to the Oncology Center of ASUH or the
Oncological Surgery Department of Dar-El-Shafa Hospital, who experienced a range of colonic
symptoms, such as constipation, abdominal discomfort, rectal bleeding, and abrupt weight loss
were included in the study when diagnosis of CRC was clinically confirmed by abdominal
radiography, colonoscopy, and histopathology.
Exclusion criteria patients who received chemotherapy, radiotherapy or undergone surgery.
Patients with other types of cancer were also excluded. Individuals with missing data were
not included.
2.4.4. Patients Pathological and Clinical Data For each CRC participant colonoscopy outcomes,
abdominal radiographic imaging, pathological evaluations were used to define CRC staging.
Tumor lymph-node metastasis (TNM) staging criteria was based on the American Joint Committee
on Cancer (AJCC) (39) criterion.
CRC study participants' family history, hypertension (HTN) or diabetes mellitus (DM) were
recorded as non-communicable diseases (NCD), to correlate them to notch-related lncRNAs
studied.
Inflammatory conditions such as ulcerative colitis, tumor size, tumor location if rectal,
colonic or rectosigmoid, being mucinous tumor or not, tumor invasion depth, lymph-node
metastasis (LNM), presence of vascular invasion or not, tumor differentiation status;
adenocarcinoma, moderately differentiated adenocarcinoma or poorly differentiated
adenocarcinoma as well as the presence of the sub histologic features, signet ring cell, or
not, were collected from eligible volunteering CRC patients' files.
3. Methods 3.1. In Silico Analysis 3.1.1. Notch-related lncRNA Bioinformatics from various
databases via (accessed January, 2021 and revised July, 2023) Gene Set Enrichment Analysis
(GSEA) (40) with ClusterProfiler utilizing the Kyoto Encyclopedia of Genes and Genomes (KEGG)
(41,42) from Genome net https://www.genome.jp/ was used to analyze the functional enrichment
of genes, diseases, networks, drugs, and pathways related to Notch-signaling. Ensemble
database search (43) https://www.ensembl.org/index.html for the potential probable
Notch-related lncRNAs SNHG3 and LUNAR1 genes National Center for Biotechnology Information
(NCBI) https://www.ncbi.nlm.nih.gov/ search for characterization of the investigated hsa
lncRNAs SNHG3 gene and transcripts (2) and hsa lncRNAs LUNAR1 gene and transcript (1). HUGO
Gene Nomenclature Committee (HGNC) (44) https://www.genenames.org/ hsa lncRNAs genes SNHG3
and LUNAR1.
3.1.2. LncRNADisease v3.0 Expression LncRNA and Disease Database (version 3.0) (45) to
explore the Notch-related lncRNAs expression CRC retrieved from validated experimental
results in publications or predicted http://www.rnanut.net/lncrnadisease/index.php/home
3.1.3. Expression via ENCORI Project Pan-Cancer Analysis Platform (46)
https://rnasysu.com/encori/panGeneDiffExp.php of lncRNA or genes across 32 types of Cancers.
The expression box-plot values of genes from RNA-seq data were scaled with log2(FPKM + 0.01),
while the ones from miRNA-seq data were scaled with log2(RPM + 0.01). Differential expression
Analysis for SNHG3, LUNAR1 and its transcript IGF1R expression levels in CRC tumor samples vs
control samples.
3.1.4. Functional Enrichment Analysis and Targeted Pathways KEGG Targeted Pathways and STRING
Protein-Protein Interaction (PPI) Networks version 11.5 https://string-db.org/ (47,48)
(Accessed on July, 2023).
LncRNAWiki 2.0 LncRNA - LncRNAWiki - CNCB-NGDChttps://ngdc.cncb.ac.cn/lncrnawiki/ (Accessed
August 30th, 2023) 3.2. Blood Samples From CRC patients and healthy volunteers, five
milliliters of peripheral venous blood fluid biopsy were collected and stored in
clot-activating polymer gel vacutainers (Greiner Bio-One GmbH, Australia). The samples were
centrifuged in vacutainers at a speed of 4000 rpm for ten minutes at room temperature (25°C).
The obtained serum was aliquoted and stored at -80°C in RNAse-free Eppendorf tubes.
3.2.1. Total RNA Extraction Using the miRNeasy Mini kit (Cat. No. 217004; Qiagen, Hilden,
Germany), total RNA was isolated from sera in accordance with the protocol's instructions.
Aliquots of the extracted RNA were kept at -80°C after being dissolved in 30 μl of RNase-free
water.
3.2.2. Quantitation of purified RNA Using a NanoDrop®-1000 spectrophotometer (Thermo
Scientific, Wilmington, DE, USA), the isolated RNA's purity and concentration are evaluated.
RNA's quantity was determined in the sample utilizing absorbance at 260 nm (A260 = 1 → 44
ng/μl). In addition, the purity of RNA was evaluated utilizing A260/280 nm ratios. The
acceptable 260/280 ratio ranges from 1.8 to 2.1, whereas the 260/230 ratio is more than 1.7.
3.2.3. Reverse Transcription Complementary DNA (cDNA) synthesis was carried out using the
High-Capacity cDNA Reverse Transcription Kit with RNase Inhibitor (Cat. No 4374966, Applied
Biosystems, ThermoFisher Scientific, USA) according to the manufacturer's regulations. In a
20 µl reaction volume, reverse transcription was carried out at 25 °C for 10 minutes, 37 °C
for 120 minutes, and then underwent heat inactivation for 5 minutes at 85 °C. The produced
cDNA was maintained at -80°C till further investigations.
3.2.4. Expression Measurement of lncRNAs Using Quantitative Real-Time Reverse Transcription
Polymerase Chain Reaction (qRT-PCR) QRT-PCR was carried out in a 20 µl reaction using the
TaqMan® Gene Expression Master Mix (Cat. No 4370048, Applied Biosystems, ThermoFisher
Scientific, USA). In order to determine the expression levels of lncRNAs SNHG3 and LUNAR1,
TaqMan gene expression assays for human SNHG3 (Hs05055352_s1, Cat. No 4448892, ThermoFisher
Scientific, USA) and human LUNAR1 (Hs03829521_s1, Cat. No 4426961, ThermoFisher Scientific,
USA) were used and the TaqMan gene expression assay for human glyceraldehyde 3-phosphate
dehydrogenase (GAPDH) (Cat. No 4326317E, ThermoFisher Scientific, USA) was used as an
endogenous standard to normalize the values. The reaction was conducted using the StepOne™
qRT-PCR technique (Applied Biosystems, CA, USA). The thermal cycling strategy was as follows:
a stage of initial uracil-N-glycosylase (UNG) incubation at 50°C for 2 minutes, followed by
activation step lasting 10 minutes at 95 °C proceeded by 40 cycles of denaturation for 15 sec
at 95 °C, annealing and extending for 1 minute at 60 °C.
Per the manufacturer, the assay "limit of detection" (LoD) can detect cDNA template from 1pg
to 100 ng in nuclease free water.
The cycle threshold (Ct) technique as a fold change (2-ΔΔCt) was used to calculate and
normalize the lncRNAs expression levels, utilizing GAPDH as the housekeeping gene. ΔCt was
obtained by subtracting the Ct values of GAPDH from either SNHG3 or LUNAR1 Ct values (49),
where; ΔΔCt = ΔCtCRC samples - ΔCthealthy control samples 3.2.5. CEA and CA19-9 determination
by electrochemiluminescence immunoassay One portion of the obtained sera was utilized for the
purpose of determining CEA and CA19-9 tumor markers. Electrochemiluminescence immunoassay
utilizing Cobas® e 602 developed by Roche Diagnostics, GmbH, Germany was used to determine
the serum concentrations of CEA (Cat. No 11731629 322) and CA19.9 (Cat. No 11776193500),
according to the manufacturer's protocol.
3.2.6. Routine biochemical testing results record from patients' files Liver function tests;
aspartate aminotransferase (AST) and Alanine aminotransferase (ALT) as well as serum
creatinine and serum urea levels, hemoglobin (Hgb), platelet count, lymphocytes count, and
prothrombin time (PT) were all gathered from patients' files.
3.2.7. Ratios and Indices In meters, participants' heights and in kilograms their weights
were recorded, to calculate Body mass index (BMI in kg/m2) where overweight subjects have BMI
of 25-29.9 kg/m2, 30 kg/m2 or over are obese, while 18.5-24.9 kg/m2 is indicative of normal
weight, https://www.nhlbi.nih.gov/health/educational/lose_wt/BMI/bmicalc.htm The immune
response-related inflammation indicator biomarker able to predict the accompanying
inflammatory disorder severity is the platelet-to-lymphocytes ratio (PLR) (10,50).
3.3. Statistical Analysis With the aid of GraphPad Prism® version 9.01 (GraphPad Software,
San Diego, CA, USA, SPSS 23.0 (statistical package for social studies software) (IBM, Armonk,
NY), MedCalc Statistic Software version 19.1 (MedCalc Software by Ostend, Belgium), and
Microsoft Office Excel 2019, statistical analysis was carried out.
Chi-square test (χ2) was utilized to assess the associations between participants'
characteristics and the groups. The Shapiro-Wilk normality test as well as Kolmogorov-Smirnov
test were applied to determine the pattern of the normal distribution for the both groups and
subgroups of data. Data was expressed as mean ±SD when it passed the normality test.
Student's (t) test was utilized to determine any significant differences between two normally
distributed groups. Additionally, one-way ANOVA (F) followed by post hoc Tukey's multiple
comparison test was used, when required, to determine significant variations between multiple
groups. While, the data that didn't pass the normality test was expressed as median
(interquartile range) (IQR) (25th percentile-75th percentile). The Mann-Whitney (U) test was
used to pinpoint the significant changes between two sets of participants. Moreover, the
Kruskal-Wallis (H) test and consequently Dunn's multiple comparison test were used, when
needed, to determine whether there were statistically significant differences between
different groups.
The receiver operating characteristic (ROC) curve as well as AUC were implemented to evaluate
the abilities of serum lncRNAs SNHG3 and LUNAR1 for discrimination between groups and with
groups for subgrouping identification. The best cut-off, sensitivities (SNs), specificities
(SPs) as well as negative and positive predictive values NPVs and PPVs, respectively, were
all determined using the ROC curve, with AUC estimated range from 0 to 1.
Negative likelihood ratios (LRs) are employed in medical testing to evaluate the marker
discriminative efficiency. The ratio, which denotes the likelihood that a person has the
disease or condition, confirms the SNs and SPs identified by the ROC curve. SN and SP provide
a different meaning for the LR, with negative LR equal to (100-SN)/SP.
Multiple regression models were used to assess the influence of the participants demographic
and patients' clinicopathological data (as independent factors) on the expression levels of
the lncRNAs SNHG3 and LUNAR1 that we considered as the dependent variables. Correlation
between numerous variables was assessed using Spearman's correlation coefficient (r).
Statistical analysis tests are set significant when the two-tailed p-value is <0.05 (*).
