Emerging Role of NOTCH-related Long Non-coding RNA(s) as Biomarkers in Liquid Biopsy From Colorectal Cancer Patients

Overview

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

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 (\*).

Conditions

Eligibility

Locations (1)

Outcomes