1. Introduction
Colorectal cancer (CRC) is currently ranked as the third most frequent cancer and as second in cancer-related mortality worldwide [1,2]. The annual incidence rate in 2020 was estimated to be 2 million new cases, and 3.2 million cases are expected in 2040, which represents a predicted 63% increase in the next 20 years [1]. As with most solid cancers, metastasis is the prevalent cause of CRC mortality [3,4]. According to Ciardiello et al. [5], around 20% of CRC patients present with metastatic disease at initial diagnosis, and up to 50% of patients with non-metastatic CRC develop metastatic disease over time. The most common site of metastatic disease in CRC patients is the liver, followed by the lungs [6,7]. The current standard treatment for liver metastasis is liver resection, although there is a 60% to 70% recurrence rate [8,9]. However, the majority of patients with liver metastases are ineligible for surgery because of an insufficient future liver remnant (FLR) or extensive tumour dissemination [6]. A small FLR with a low hepatic functional reserve can increase the risk of posthepatectomy liver failure [10].
Metastasis is a complex and multistep process that involves a myriad of epigenetic and transcriptomic changes [11,12,13,14]. Multiple genes have been identified with changes in methylation and expression in CRC liver metastasis when compared with primary CRC tumours [15,16,17,18,19,20,21,22,23]. Although studies have identified methylation alterations between unpaired CRC primary and liver metastasis samples [16,17], identifying these changes by comparing matched primary tumours and metastases from the same patient provides a more direct measurement of epigenetic changes in tumour progression [24]. The analysis of paired samples also reduces inter-individual variations and increases statistical power [25,26]. However, it is challenging to obtain paired primary cancer and liver metastasis samples because metastasectomies are infrequently carried out [25]. Further, paired samples are not often retained as a part of routine clinical practice. Most studies on paired CRC liver metastasis and DNA methylation have focused on analysing the methylation patterns of specific genes or miR-200 family members [18,19,20,21,22,23]. However, there is very limited data available on genome-wide methylation patterns for paired CRC primary and liver metastasis. Two studies have used microarray-based platforms to analyse genome-wide methylation patterns, but neither included genome-wide transcriptome analysis on the paired samples [20,22]. We have recently performed whole-genome-scale sequence-based methylation and combined transcriptome studies paired primary CRC and matched liver metastasis samples to identify epigenetic markers for CRC liver metastasis [27]. This approach provides the opportunity to detect functional epigenetic signatures and yield insights into molecular mechanisms of cancer progression and the development of metastases. Furthermore, it is important to acknowledge the value of transcriptome analyses, specifically in CRC liver metastasis. For instance, the application of machine learning coupled with bioinformatic analysis allowed insights into gene expression patterns, functional significance and prognostic relevance in CRC liver metastasis patients [28]. This investigation utilised microarray-based gene expression data obtained from hepatic resections of liver metastases from primary colorectal tumours [28]. However, relying solely on transcriptome data from hepatic resections of liver metastases overlooks crucial information about changes in primary tumours. This approach provides a limited view of tumour evolution and interactions beyond gene expression and lacks the capacity for multi-omics analysis. Utilising paired samples in conjunction with multi-omics data is preferable for revealing the epigenetic and transcriptomic alterations in CRC liver metastasis.
To our knowledge, here we present new whole-genome-scale sequence-based methylation and combined transcriptome data of clinically well-annotated paired primary CRC and matched liver metastasis samples. We have generated DNA methylation using Reduced Representation Bisulfite Sequencing (RRBS) and transcriptome (RNA-Seq) maps of paired primary cancers and liver metastases from 10 CRC patients. The samples were obtained from both male (n = 7) and female (n = 3) patients aged between 57 and 75. Robust clinical data relevant to this study were obtained to conduct a broad range of analyses. Here, we present a detailed description of the tissue samples, experimental design, methods and bioinformatic pipeline, which include all the tools and packages to analyse the RRBS and transcriptome data (Figure 1).
2. Data Description
Patient data, which included sex, age and tumour site, were obtained from patient medical records (Table 1). Additional information regarding patient data can be accessed through the supplementary Table 1 in [27]. All the RRBS and RNA-Seq data reported in this article have been deposited in the GEO database under accession number GSE213402 (see the GEO website at
3. Results
3.1. DNA and RNA Quality
We have obtained DNA and RNA from the fresh frozen clinical samples for epigenomic and transcriptomic analysis. The purity of the DNA was determined by measuring the 260/280 and 260/230 ratios. A 260/280 ratio within the range of 1.8–2.0 and a 260/230 ratio within 2.0–2.2 shows high purity. The data from the current study shows a mean 260/280 ratio of 1.90 and a mean 260/230 ratio of 2.02, indicating the high quality of the DNA. The RNA purity was also evaluated by using absorbance ratios at 260/280 and 260/230 nm values. The acceptable absorbance ratio for RNA at 260/280 is ~2.0, and 260/230 is between 2.0 and 2.2 [29]. Our purified RNA from the fresh frozen samples had a mean 260/280 ratio of 2.07 and a mean 260/230 ratio of 1.74.
3.2. RRBS and RNA-Seq Data Quality
As a first quality evaluation, we analysed the raw RRBS reads. Adaptor sequences were trimmed, and low-quality reads were filtered out based on quality scores from the raw reads. Subsequently, the quality of trimmed reads was reassessed using FastQC with MultiQC packages, which showed that the quality was high with Phred quality scores > 30 throughout the 100 bp sequence cycle (Figure 2a). The per sequence GC content, which is the GC distribution of each sample compared to normal GC distribution, showed a shift in distribution due to the bisulphite conversion of Cs to Ts, therefore demonstrating the excellent quality of the methylation data (Figure 2b).
Following the initial quality assessment of the raw RNA reads, reads with a Phred score < 23 were filtered, and adaptor sequences were trimmed. After trimming the reads, only high-quality (Phred score > 24) reads were retained (Figure 2c). The distribution of duplicated sequences in the libraries was skewed to the left, with a high number of duplicates (>20%) at sequence duplication levels < 2 and >5k (Figure 2d). This is expected in RNA-Seq data, which contains highly expressed genes that will be over-sequenced [30].
3.3. RRBS Alignment Quality and Characteristics of Methylome
RRBS reads were aligned to human genome hg19 with Bismark alignment with default parameters [31]. We have obtained a total of 1398 million sequenced reads for RRBS experiments, which was an average of 69 million reads per sample, and 36 million quality-filtered reads were mapped uniquely to the human genome per sample. We obtained a unique mapping efficiency between 25.40% and 62.80% (Table 2). Other studies have reported low mapping efficiency in fresh frozen samples, reaching as low as 16.7% [32,33]. We observed an average CpG methylation rate of 49.35% and a low non-CpG methylation rate (ranging from 0.31% to 0.54%) (Table 2), indicating that the bisulphite conversion process was highly efficient. There were a total of 5,470,738 CpGs that were in common to at least eight samples per group and ≥5-fold coverage, and the majority of these were situated within intronic, intergenic regions and promoters. Specifically, approximately 38.69% were found in introns, 35.80% in intergenic regions and 10.75% in promoters (Figure 3a). Further analysis showed that 28.19% of the CpGs were located in various subregions of CpG islands, including CpG island core, coreshore, shelf and shore, while 71.81% were situated in open sea regions (Figure 3b).
3.4. RRBS Technical Replicates
To examine the technical reproducibility, we included a technical replicate for a CRC primary sample (CRC316) and also for the paired CRC liver metastasis sample (LM316). The libraries were prepared using the same DNA extract, kits and platform. Spearman’s correlation analysis was used to assess the concordance between the replicates within the common CpG sites (with a coverage of 10 or more reads). The results showed strong correlations of DNA methylation levels between the two CRC316 replicates (Pearson R = 0.93, p < 2.2 × 10−16) and between the two LM316 replicates (Pearson R = 0.94, p < 2.2 × 10−16), indicating high reproducibility of RRBS results for fresh frozen clinical samples. This is consistent with our previous description of RRBS results and reproducibility [34,35]. For our differential methylation analysis, we have combined the technical replicates to increase CpG coverage [27].
3.5. RNA-Seq Alignment Quality, Efficiency of rRNA Depletion and Transcriptome Analysis
RNA sequencing generated a total of 437 million reads, with an average of 21 million raw reads per sample. The raw RNA-Seq reads were trimmed and aligned to the human genome hg19 using HISAT aligner [36]. The summary of alignment statistics is provided in Table 3. The sequencing analysis yielded an average of 17 million uniquely mapped reads, representing 80.26% of the total sequenced reads (Table 3). Our results are consistent with the unique mapping percentages (ranging from 76% to 87%) reported in prior studies that utilised whole transcriptome RNA-Seq library preparation and ribosomal RNA depletion on fresh frozen samples [37,38] and further support the reliability of our findings. In our library preparation, ribosomal RNA (rRNA) was depleted using the Ribo-Zero Gold kit. To assess the efficiency of rRNA depletion in the library preparation, we have calculated the rRNA mapping rate. This analysis showed that the libraries had a very low percentage of rRNA reads, with a maximum rRNA mapping rate of 0.001% (Table 3). In comparison, another study reported a higher rRNA mapping rate of 0.1% in fresh frozen samples [37], indicating the effectiveness of our rRNA depletion method in minimising the presence of rRNA. Based on the Ensembl biotype gene and transcript classification, among the ~33,000 genes examined, 58.26% were classified as protein coding, which includes a distribution of 0.62% as immunoglobulin and 0.58% T cell receptor genes. The remaining 41.74% comprised non-coding genes, which encompassed 5.43% long non-coding RNA (lncRNA), 15.93% non-coding RNA (ncRNA), 0.64% processed transcript and 19.73% pseudogene entities, as illustrated in Figure 4.
4. Methods
4.1. Tumour Samples and Clinical Data Collection
Paired fresh frozen tissue samples of primary colorectal adenocarcinomas (n = 10) and liver metastases (n = 10) were obtained from the Cancer Society Tissue Bank (University of Otago, Christchurch); all patients provided written informed consent. Ethical approval was granted for the use of these samples by the University of Otago Human Ethics Committee (approval number: H16/037). All samples used for nucleic acid extractions were stored under controlled conditions for a period ranging between 6 months and 6 years before being accessed for analysis.
4.2. Genomic DNA Extraction and RRBS Library Preparation
DNA was isolated from fresh frozen tissue samples using the QIAamp DNA mini kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. DNA concentration was quantified with a Qubit dsDNA HS Assay Kit and Qubit 2.0 Fluorometer (Thermo Fisher Scientific, Waltham, MA, USA), and DNA quality was assessed using a NanoPhotometer (Implen, München, Germany). RRBS libraries were prepared following our previously described method [39]. Genomic DNA (100 ng–500 ng) was digested overnight with MspI enzyme (New England Biolabs, Ipswich, MA), using 20 units of enzyme per μg of DNA, and end repair was performed to add A-tail to the 3′ ends, which are required for adaptor ligation. Illumina TruSeq sequencing adaptors (Illumina, San Diego, CA) were ligated to the end-repaired fragments, and fragment sizes of 40 bp to 220 bp were selected using sample purification beads (SPB) (Illumina, USA). Subsequently, bisulphite conversion was performed on the size-selected fragments using the EZ DNA methylation kit (Zymo Research, Irvine, CA, USA) according to the manufacturer’s instructions. The bisulphite-converted DNA was amplified using KAPA HiFi HotStart Uracil+ (Kapa Biosystems, Roche, Basilea, Switzerland). An Agilent 2100 Bioanalyzer was used to evaluate the library quality and size range. The RRBS library was sequenced on a HiSeq 2500 instrument (Illumina, San Diego, CA, USA) with 100 bp single-end reads.
4.3. RRBS Data Analysis
The quality of RRBS reads was evaluated using the most commonly used quality control programs: FastQC (
4.4. RNA Extraction and Sequencing
Fresh frozen tissue samples were weighed, and <20 mg of tissue was homogenised with a Retsch Mixer Mill (Retsch, Germany). Total RNA was isolated with the RNEasy Plus Mini Kit (Qiagen, Hilden, Germany), and RNA purity was measured using a NanoPhotometer. RNA-seq libraries were prepared using TruSeq RNA Library Prep Kit v2 (Illumina, San Diego, CA, USA), and ribosomal RNA was depleted using Ribo-Zero Gold (Illumina, San Diego, CA) following the manufacturer’s protocol. The quality of ribosomal RNA-depleted libraries was determined on a Bioanalyzer and then sequenced on an Illumina HiSeq 2500 V4 platform, and 125 bp paired-end reads were generated.
4.5. RNA-Sequencing Data Analysis
The quality of raw reads was evaluated with FastQC (
5. Summary
The RRBS and RNA-Seq data (both raw and unprocessed) are available from the GEO database via the accession number GSE213402. The GitHub respiratory contains all the tools and codes to analyse this data using the established RRBS and RNA-Seq pipeline. This study represents the first attempt to investigate CRC liver metastasis epigenetic markers by combining RRBS and RNA-Seq data in paired CRC primary and liver metastases. Even though there is a growing interest in studying CRC liver metastasis, there is a gap in the literature regarding the application of high-throughput sequencing technology in paired CRC liver metastasis samples. This gap restricts our understanding of the epigenetic and transcriptome changes associated with CRC liver metastasis. Our data are critical to understanding these changes and regulatory mechanisms involved in the development of CRC liver metastases; they can be used as an independent cohort for future studies and validation. Further, deep learning methods could be applied in future work on these datasets to reveal novel patterns of tumours [47]. The integration of multi-omics data sets in paired samples provides a great opportunity to understand the complex dynamics of tumour progression, treatment response and the identification of novel biomarkers for early detection and diagnosis of CRC metastasis in the future. It is also valuable for the identification of potential drivers of metastasis and developing targeted therapies for patients, which could improve prognosis and survival.
Conceptualisation, E.J.R. and A.C.; formal analysis, P.A., E.J.R., G.G., P.A.S. and S.A.; investigation, E.J.R., S.A., S.A.B., A.L.L., A.A., S.S., S.P., R.V.P. and A.C.; resources, M.R.E., F.A.F., R.V.P. and A.C.; data curation, P.A., E.J.R., G.G., P.A.S. and S.S.; writing—original draft preparation, P.A.; writing—review and editing, All authors.; visualisation, P.A.; supervision, M.R.E., R.V.P., E.J.R. and A.C.; project administration, E.J.R., R.V.P. and A.C.; funding acquisition, R.V.P. and A.C. All authors have read and agreed to the published version of the manuscript.
The study was conducted in accordance with the Declaration of Helsinki and approved by the Human Ethics Committee of the University of Otago (Approval number: H16/037).
Informed consent was obtained from all subjects involved in the study.
The data have been deposited in the GEO database under accession number GSE213402.
The authors are grateful to the patients and their families who contributed to this study, the Cancer Society Tissue Bank (CSTB) and the Otago Genomics Facility at the University of Otago for providing the sequencing service and assistance.
Author Sebastian Schmeier was employed by the company Evotec SE. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Footnotes
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Enlarge this image.Figure 1. Schematic view of the RRBS and RNA-Seq experiment and sequencing analysis workflow. DNA and RNA were extracted from 10 paired primary CRC and liver metastasis tissues. For RRBS (left), MspI digested libraries were bisulfite-converted and sequenced. The quality of raw RRBS data was checked, and adaptor sequences were removed. Reads were then mapped to the reference genome, and downstream data analyses were performed. For RNA-Seq (right), ribo-depleted libraries were constructed and sequenced, followed by quality assessment, quality trimming, mapping, generation of read counts and transcript per million (TPM). The figure was prepared using BioRender (www.biorender.com, accessed on 20 December 2023).
Enlarge this image.Figure 2. MultiQC quality plots of RRBS and RNA-Seq data after trimming. (a) Mean quality values across each base position for all the RRBS samples, showing the read position on the X-axis and Phred score on the Y-axis. The y-axis with distinctions among high-quality calls (green), moderately acceptable calls (orange), and low-quality calls (red). (b) Per sequence GC content plot for all the RRBS samples—the X-axis is the percentage of GC, and the Y-axis is the percentage of reads. (c) Per base sequence quality plot for all RNA samples showing the read position on the X-axis and Phred score on the Y-axis. The Y-axis is categorised as high-quality calls (green), moderately acceptable calls (orange), and low-quality calls (red). (d) Sequence duplication levels for all RNA samples—the X-axis is the sequence duplication level, and the Y-axis is the percentage of library.
Enlarge this image.Figure 3. The proportion of CpGs covered in genomic regions and CpG islands. (a) The percentage of common CpGs overlapping genomic regions. (b) The percentage of common CpG sites in relation to various CpG island features.
Enlarge this image.Figure 4. Pie donut chart shows the percentage of genes distributed among the protein-coding and non-coding genes. The coding genes were further divided into protein coding, T cell receptor (TR) gene and Immunoglobulin (IG) genes. Non-coding genes are further classified into long non-coding RNA (lncRNA), non-coding RNA (ncRNA), processed transcript and pseudogene.
CRC patient details with RRBS and RNA-Seq accession number.
| Patient No. | Clinical Details | CRC Primary Tumour Accession Number | Liver Metastasis Accession Number | ||||
|---|---|---|---|---|---|---|---|
| Sex | Age | Primary | RRBS Data | RNA-Seq Data | RRBS Data | RNA-Seq Data | |
| 8 | M | 74 | Colon | GSM6585685 | GSM6585705 | GSM6585695 | GSM6585715 |
| 87 | M | 74 | Colon | GSM6585686 | GSM6585706 | GSM6585696 | GSM6585716 |
| 208 | M | 57 | Colon | GSM6585687 | GSM6585707 | GSM6585697 | GSM6585717 |
| 241 | F | 60 | Colon | GSM6585688 | GSM6585708 | GSM6585698 | GSM6585718 |
| 269 | F | 61 | Colon | GSM6585689 | GSM6585709 | GSM6585699 | GSM6585719 |
| 270 | M | 64 | Colon | GSM6585690 | GSM6585710 | GSM6585700 | GSM6585720 |
| 275 | F | 60 | Rectum | GSM6585691 | GSM6585711 | GSM6585701 | GSM6585721 |
| 311 | M | 57 | Colon | GSM6585692 | GSM6585712 | GSM6585702 | GSM6585722 |
| 314 | M | 65 | Colon | GSM6585693 | GSM6585713 | GSM6585703 | GSM6585723 |
| 316 | M | 75 | Colon | GSM6585694 | GSM6585714 | GSM6585704 | GSM6585724 |
Summary of RRBS alignment results for each sample.
| Sample ID | Sequenced Reads | Unique Reads | Mapping Efficiency (%) | Total CpG Meth (%) | Total Non-CpG Meth (%) |
|---|---|---|---|---|---|
| CRC8 | 67,293,600 | 342,740,70 | 50.90 | 37.00 | 0.35 |
| CRC87 | 79,942,689 | 42,651,395 | 53.40 | 56.20 | 0.49 |
| CRC208 | 15,865,582 | 9,962,519 | 62.80 | 56.60 | 0.40 |
| CRC241 | 49,656,769 | 30,745,536 | 61.90 | 50.40 | 0.42 |
| CRC269 | 54,605,569 | 27,044,502 | 49.50 | 47.90 | 0.37 |
| CRC270 | 30,066,202 | 16,094,322 | 53.50 | 42.50 | 0.38 |
| CRC275 | 90,499,303 | 55,671,910 | 61.50 | 42.20 | 0.54 |
| CRC311 | 85,268,731 | 46,371,408 | 54.40 | 48.50 | 0.45 |
| CRC314 | 94,196,062 | 51,990,759 | 55.20 | 41.10 | 0.50 |
| CRC316 | 89,855,788 | 50,133,332 | 55.80 | 48.40 | 0.53 |
| LM8 | 73,033,866 | 44,328,416 | 60.70 | 61.60 | 0.33 |
| LM87 | 98,621,906 | 44,354,592 | 45.00 | 39.70 | 0.50 |
| LM208 | 74,482,754 | 18,899,940 | 25.40 | 50.50 | 0.39 |
| LM241 | 90,013,168 | 51,489,691 | 57.20 | 47.60 | 0.48 |
| LM269 | 78,414,515 | 41,592,069 | 53.00 | 54.30 | 0.36 |
| LM270 | 89,824,235 | 38,806,877 | 43.20 | 45.30 | 0.46 |
| LM275 | 71,135,244 | 41,994,300 | 59.00 | 54.20 | 0.36 |
| LM311 | 62,045,345 | 31,262,905 | 50.40 | 56.00 | 0.31 |
| LM314 | 69,843,997 | 38,398,803 | 55.00 | 47.60 | 0.34 |
| LM316 | 33,679,596 | 20,021,531 | 55.75 | 59.40 | 0.52 |
| Average | 69,917,246.1 | 36,804,443.9 | 53.18 | 49.35 | 0.424 |
Summary of RNA-Seq alignment results for each sample.
| Sample ID | Total Number of Sequenced Reads | Total Number of Uniquely Mapped Reads (GRCh37) | Uniquely Mapped Reads (%) | Percentage of Reads Aligned to rRNA Regions (%) |
|---|---|---|---|---|
| CRC8 | 23,384,046 | 19,026,422 | 81.36 | 0.0003 |
| CRC87 | 24,339,816 | 22,145,940 | 90.99 | 0.0003 |
| CRC208 | 11,577,334 | 7,738,152 | 66.83 | 0.0002 |
| CRC241 | 28,268,750 | 23,299,700 | 82.42 | 0.0002 |
| CRC269 | 21,423,020 | 18,013,129 | 84.08 | 0.0002 |
| CRC270 | 19,305,816 | 14,088,264 | 72.98 | 0.0002 |
| CRC275 | 27,242,458 | 23,347,216 | 85.70 | 0.0002 |
| CRC311 | 14,002,078 | 7,888,497 | 56.34 | 0.0001 |
| CRC314 | 20,987,192 | 18,011,052 | 85.82 | 0.0004 |
| CRC316 | 20,464,570 | 16,317,115 | 79.73 | 0.0005 |
| LM8 | 31,508,190 | 26,257,882 | 83.34 | 0.0001 |
| LM87 | 20,470,182 | 14,708,258 | 71.85 | 0.0003 |
| LM208 | 18,987,032 | 15,298,388 | 80.57 | 0.0002 |
| LM241 | 23,978,748 | 20,965,130 | 87.43 | 0.0003 |
| LM269 | 20,095,084 | 14,373,695 | 71.53 | 0.0004 |
| LM270 | 20,743,994 | 18,185,861 | 87.67 | 0.0002 |
| LM275 | 19,603,228 | 16,678,527 | 85.08 | 0.0005 |
| LM311 | 27,400,080 | 22,939,465 | 83.72 | 0.0002 |
| LM314 | 21,182,554 | 17,709,592 | 83.60 | 0.0002 |
| LM316 | 22,810,830 | 19,212,321 | 84.22 | 0.0003 |
| Average | 21,888,750.1 | 17,810,230.3 | 80.26 | 0.0005 |
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Details
; Almomani, Suzan 1 ; Bowden, Sarah A 1 ; Leichter, Anna L 1
; Ahn, Antonio 2 ; Pattison, Sharon 3
; Schmeier, Sebastian 4 ; Frizelle, Frank A 5 ; Eccles, Michael R 1
; Purcell, Rachel V 5 ; Rodger, Euan J 1
; Chatterjee, Aniruddha 6
1 Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin 9016, New Zealand;
2 Peter MacCallum Cancer Centre, Melbourne, VIC 3052, Australia; The Sir Peter MacCallum Department of Oncology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC 3052, Australia
3 Department of Medicine, Dunedin School of Medicine, University of Otago, Dunedin 9016, New Zealand
4 Evotec SE, 22419 Hamburg, Germany
5 Department of Surgery, University of Otago, Christchurch 8011, New Zealand
6 Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin 9016, New Zealand;