Background
Long non-coding RNAs (lncRNAs) play major roles in the regulation of chromatin architecture, transcription, nuclear domains, and nuclear bodies [
1]. LncRNAs are involved in epigenetic regulation, and in response to environmental cues can adopt different structures and interact with different partners to organize chromatin organization and transcription. Involvement of lncRNAs in cellular phenomena of genome imprinting, dosage compensation, pluripotency, differentiation commitment, tissue-specificity of gene expression, and their aberrant functionality in different diseases underscores their importance in different pathogenesis pathways including cancer pathogenesis [
2,
3].
LncRNAs are classified into three categories of mRNA-like intergenic transcripts (lincRNAs), antisense transcripts, and RNA polymerase II derived unconventional lncRNAs [
1]. Natural antisense transcripts (NATs) are transcribed from the opposite strands of either protein-coding or noncoding genes, and are believed to make R-loops and tether the chromatin modifiers and remodelers to the locus, in order to regulate the genes in the opposite strand [
1,
4]. Aside from the regulation of opposite genes, these antisense transcripts are also known to be involved in microRNA sponging.
The adenoma-carcinoma pathway accounts for approximately 75% of colorectal cancers [
2]. In this pathway, the epigenetic deregulation of genome function, aberrant and variable gene expression, alteration of cell fate and identity, development of mutations in coding genes, cellular transformation, cellular heterogeneity, and other phenomena drive the cells to a pathologic state of adenocarcinoma [
5]. Mounting evidence indicates the involvement of different categories of lncRNAs in cancer pathogenesis. For example, the colon cancer-associated transcript 1 (CCAT1) is an mRNA-like lincRNA that is upregulated in various human malignancies and is involved in different pathologic cellular processes of CRC [
6‐
12]. And, the HOXA transcript antisense RNA, myeloid-specific 1 (HOTAIRM1) is a NAT that can directly regulate the spatial organization of chromatin and is involved in colorectal cancers [
3,
13,
14].
Colorectal polyps are a combination of heterogeneous cells that can develop to multiclonal tumors, containing an indeterminate number of distinct clones of cells greater than one [
15]. In fact, the relative frequency of different cell clones, affected by their environment and influenced by their own epigenome and genetic risk factors, may determine the progress of different population of cells (in polyps) to different carcinomas. Hence, identification of molecular players in the early non-advanced and advanced adenomas may prove to be very important for prognosis prediction and treatment strategies of advanced adenocarcinomas. In this study, we profile the expression of two NATs, protein kinase C theta antisense RNA 1 (PRKCQ-AS1), and special AT-rich sequence binding protein 1 antisense RNA 1 (SATB1-AS1) in colorectal low-grade adenomas, advanced adenomas, and adenocarcinomas. Furthermore using different bioinformatics analyses and webservers, we provide evidence and emphasize on the importance of PRKCQ-AS1 expression profiling for the pathogenesis and prognosis prediction of colorectal cancer.
Methods
Polyp and cancer biopsies
Before sampling, an informed consent questionnaire succinctly describing the research outline was described for each patient and was filled. Colorectal biopsy samples and polypectomy specimens were acquired from patients at the Gastroenterology Ward, Imam Reza Hospital, Mashhad University of Medical Sciences, Mashhad, Iran. The samples were immediately placed in pre-numbered cryovials containing RNA Shield (DENAzist Asia Co., Iran) and were transferred to liquid nitrogen within 30 min. Biopsy samples were also placed in containers with the same identification numbers and were sent to Mashhad Pathology Laboratory, Mashhad, for histopathological analysis. Samples from adjacent mucosa were prepared and processed as described above to be used as control. Frozen samples were transferred in portable containers of liquid nitrogen to Research Institute of Biotechnology, Ferdowsi University of Mashhad, and stored in − 80 freezer for further processing. A total of 12 low-grade adenoma, 14 advanced (high-grade) adenoma, and nine cancer tissue samples along with their adjacent mucosa were used in this study.
Reverse transcription quantitative PCR
Total RNA was isolated from sample biopsies using Column RNA Isolation Kit (DENAzist Asia Co., Iran). The quality and quantity of extracted RNA were evaluated by gel electrophoresis and a Biotek Epoch 2 microplate spectrophotometer (Biotek, USA). Total RNA (5 μg) was reverse transcribed using random hexamer oligos and MMLV reverse transcriptase (Thermo Fisher Scientific, USA). Transcripts of five genes of CCAT1, cMYC, PRKCQ-AS1, SATB1-AS1, and beta-actin were quantified by specific dual-labeled hybridization probes (Table
1) in quantitative RT-PCR reactions containing Premix Ex Taq (Probe qPCR) master mix (Takara, Japan), cDNA from each sample, and specific primers, carried out in a Rotor-Gene Q real-time PCR cycler (Qiagen, USA).
Table 1
Oligonucleotides used in this study
CCAT1 (NR_108049.1) | F: CTGACAACATCGACTTTG R: CTCACAGTTTTCAAGGGA Probe: FAM-CTTAGCCATACAGAGCCAACCTG-BHQ1 | 108 | qPCR |
ACTB | F: TGCAGAAGGAGATCACTG R: CTTGCTGATCCACATCTG Probe: CY5-AAGATCAAGATCATTGCTCCTCCTGA-BHQ2 | 141 | qPCR |
MYC | F: TCCACAGAAACAACATCG R: CTCGGATTCTCTGCTCTC Probe: HEX-TTCTTCCTCATCTTCTTGTTCCTCCTC-BHQ1 | 147 | qPCR |
PRKCCQ-AS1 (NR_036501) | F: ACTGCTTTCAACTTTACTG R: AGTCCTCAGCATTATTCC Probe: FAM-AACCATCTTCTAGGCACAGTAGC-BHQ1 | 138 | qPCR |
SATB1-AS1 (NR_125803.1) | F: AAGGGTGGAAGAGTAAAC R: GTTGGATGAGAAAGTTCAG Probe: FAM-CCATCTTGACAGGAAGCAGAAGTTC-BHQ1 | 188 | qPCR |
QPCR reactions were repeated to adjust the reaction temperature, the concentration of primers, and to acquire the best amplification curves (Additional file
1: Figure S1). The identity of PCR products was confirmed by Sanger sequencing (Macrogen Inc., South Korea). Amplified fragments were sub-cloned in pTZ57R or pGATA plasmids, or extracted from gels, and their serial dilution (indicating a specific copy number) was used to make standard curves. Each dilution was subjected to three PCR reactions, and real-time readings were performed in triplicate. Then, the log10 of copy numbers for each gene was plotted against the cycle threshold (Ct) numbers to make a standard curve (Additional file
2: Figure S2). The slope of standard curves was used in the following equation: E = (10
−1/slope − 1) × 100% to calculate reaction efficiency. All standard curves were linear in the analyzed range with an acceptable correlation coefficient (R
2) (Additional file
2: Figure S2). Gene expression ratios for each of the four genes of CCAT1, cMYC, PRKCQ-AS1, and SATB1-AS1 over beta-actin were calculated at each stage of low-grade adenoma, advanced adenoma, and adenocarcinoma using Pfaffl method of relative quantification [
16]. The statistical differences between the expression ratios in different stages were analyzed by Mann–Whitney
U test.
Analyses of differential expression
Differentially expressed genes in our two datasets of CCAT1-ko versus CCAT1-wt HT-29 cells, and CRC cancer biopsy versus adjacent mucosa have been shown in volcano plots. Also, we used different bioinformatics tools and databases, including GEPIA 2 [
17], a webserver that extracts data from the Cancer Genome Atlas (TCGA;
https://portal.gdc.cancer.gov/) data portal and the GTEx database of normal tissues, to show differential expression of genes in adenocarcinoma samples.
Overall survival analysis was performed on the basis of gene expression. For the test of the hypothesis, the log-rank test is used in GEPIA 2. The hazards ratio (HR) was selected based on the Cox PH model and the 95% confidence interval (CI) was selected to be displayed as the dotted line. The gene expression threshold of 50% (median value) was determined to split the high-expression and low-expression cohorts.
Expression correlation analysis
Correlation analysis between sense and antisense genes was performed by pair-wise gene expression correlation analysis with the expression data of TCGA and GTEx, using the method of the Pearson correlation coefficient using the GEPIA 2 webserver. As previously suggested [
18], we considered the following correlation coefficients: 0.00–0.19 as very weak, 0.20–0.39 as weak, 0.40–0.59 as moderate, 0.60–0.79 as strong, and 0.80–1.0 as very strong.
Analysis of gene expression in relation to mutations in driver genes
Six driver genes were selected in colon cancer using CancerMine webserver [
19]. Based on the number of citations in CancerMine, these six driver genes were APC, BRAF, TP53, PIK3CA, SMAD4, and EGFR. In TCGA portal 361 cases with mutations in these driver genes versus 11 cases without any mutation in these genes were selected and the expression profiling files were extracted in order to analyze the expression levels of PRKCQ-AS1 and SATB1-AS1 and to compare using
t-test.
Discussion
In this study, we profile the expression of two antisense transcripts of PRKCQ-AS1 and SATB1-AS1, along with CCAT1, and cMyc in colorectal low-grade adenomas, advanced adenomas, and adenocarcinomas. In addition, by analyzing TCGA datasets using GEPIA 2 webserver, we analyze the differential signature score and prognostic impact of these genes. We also identified the correlation between the signature scores of PRKCQ-AS1 and SATB1-AS1 and signature scores of their sense genes, PRKCQ and SATB1, respectively. The overall findings suggest that PRKCQ-AS1 may have a prognostic value for CRC and early adenomatous stages.
Antisense transcripts can have prognostic value for many diseases. HOX transcript antisense RNA (HOTAIR) for example, is significantly overexpressed in multiple tumors including breast cancer [
21], colorectal cancer [
22], thyroid cancer [
23], pancreatic cancer [
24] and gastric cancer [
25]. HOTAIR’s up-regulation is associated with poor prognosis. The over-expression of a lncRNA in cancer tissue is identified by analysis of total RNA of the biopsied sample. Thus, given the heterogeneous nature of colorectal cancer, detection of a higher level of a given lncRNA (including antisense RNA) transcript could be due to very high expression of the gene in some cells (and not all cells) of the tumor (and tumor biopsy). A significant correlation between upregulation of an antisense transcript and survival in a patient, therefore, could be related to clonal proliferation of high-expressing cells compared to other cells. Our findings show that while PRKCQ-AS1 demonstrates a high impact for prognosis prediction (Fig.
2b); it has different levels of expression (ranging from well above the median to well below median) in different patient samples (Figs.
1c and
2b). This can be attributed to the clonal proliferation of high-expressing cells in some patient samples. This explanation emphasizes the importance of knowing the molecular mechanisms of transcriptional control for a given lncRNA and identification of factors that affect its transcription. It is interesting to note that CCAT1 and cMYC that show significantly higher expression levels in colorectal low-grade adenomas, advanced adenomas, and adenocarcinomas, do not provide a significant prognosis prediction impact (Figs.
1 and
2). Thus, the expression level of a given gene may be high or low in the majority of patient samples, without a prognosis prediction impact. In contrast, the expression level of a gene may be widely diverged in different patient samples with a significant prognosis prediction impact.
In contrast to our findings in CCAT1 knockout cells, the transcription of PRKCQ-AS1 did not follow CCAT1 transcription in different stages of colorectal adenoma to adenocarcinoma. This can be explained by a possibility that in the cell line hosting the knockout gene (HT-29) in comparison with the colorectal tissue, different regulatory mechanisms may exist for CCAT1 and PRKCQ-AS1 transcription. Another explanation could be that while the lack of expression of CCAT1 in knockout cells may have a profound effect on a network of genes including PRKCQ-AS1, the zero level of expression of CCAT1 is never achieved and repeated in colorectal tissues. And thus, the increased transcription of PRKCQ-AS1 in cells with increased expression of CCAT1 should not be expected.
Since colorectal polyps and tumors are heterogeneous and multiclonal [
15], and the Darwinian evolution of colorectal cancer starts from early adenomas [
26], they are expected to show varying levels of expression for a specific gene. This justifies the very wide levels of expression of different genes in different biopsies; in our study more than 30 folds for PRKCQ-AS1 and more than 60 folds for SATB1-AS1 (Figs.
1 and
4). However, it is important to identify the role of the overexpressed antisense transcript during cancer pathogenesis. Different functions have been defined for antisense lncRNA transcripts including chromatin remodeling of the target gene, regulation of the alternative splicing of the sense transcript, miRNA sponging, and masking of miRNA binding site on sense RNA [
27]. The antisense transcripts can either repress or promote the expression of their sense gene. In our analysis, we found that in CRC the expression of PRKCQ-AS1 and SATB1-AS1 is significantly correlated with the expression of PRKCQ and SATB1, respectively (Figs.
3 and
5). It has been postulated that significantly concordant (same direction) or discordant (opposite direction) expression levels for a pair of sense and antisense genes may indicate common mechanisms regulating the expression of two genes. However, a recent report shows that sense-antisense pairs whose expression is strongly—positively or negatively—correlated can be regulated independently [
28]. This suggests the requirement for functional studies to reveal the mechanism of expression and the nature of concordant or discordant expression correlations. Further experiments will be required to identify the role of PRKCQ-AS1 and SATB1-AS1 in CRC pathogenesis and the importance of the level of expression of the sense-antisense pairs in colorectal cancer.
Our analysis of antisense gene expression in relation to mutations in sense genes and driver genes shows that the expression of PRKCQ-AS1 is not affected and deregulated when the sense gene (PRKCQ) or driver genes (APC, BRAF, TP53, PIK3CA, SMAD4, and EGFR) are mutated. By multi-region whole-exome sequencing, Mimori and colleagues [
26] report that early tumors accumulate a higher proportion of subclonal driver mutations than the advanced tumors, and that the variant allele frequencies of subclonal mutations tend to be higher in early tumors. These authors suggest that the subclonal mutations are subject to selective sweep in early tumorigenesis. Again, the heterogeneous nature of cells in the early or advanced adenomas and adenocarcinomas may explain the lack of correlation (Fig.
3c, d). The expression of SATB1-AS1 however, is significantly decreased in CRC samples that comprise mutations in the sense gene of SATB1 (Fig.
5b). This could be an indication of the fact that SATB1 is involved in the regulation of transcription of SATB1-AS1. We did not find any changes in the expression of SATB1-AS1, where driver genes were mutated, indicating the heterogeneous nature of cells in CRC, and emphasizing on this point that individual cell analysis for the expression of lncRNAs and analysis of mutations might be necessary to perform.
Conclusions
In conclusion, we have provided the expression profile analysis of two antisense transcripts of PRKCQ-AS1 and SATB1-AS1, CCAT1 lncRNA, and cMYC in colorectal low-grade adenomas, advanced adenomas, and adenocarcinomas. Although PRKCQ-AS1 expression is not high in all colorectal biopsies, it demonstrates a high impact for prognosis prediction of colorectal cancer and survival. In this report, we argue that in early stages of colorectal adenomas (even in low-grade non-advanced polyps), the expression of a gene and its ratio to a reference gene could have a prognostic impact, and this expression may not be affected by mutations in driver genes or mutations in the sense gene.
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