COL25A1 and METAP1D DNA methylation are promising liquid biopsy epigenetic biomarkers of colorectal cancer using digital PCR

Abstract

Background: Colorectal cancer is a public health issue and was the third leading cause of cancer-related death worldwide in 2022. Early diagnosis can improve prognosis, making screening a central part of colorectal cancer management. Blood-based screening, diagnosis and follow-up of colorectal cancer patients are possible with the study of cell-free circulating tumor DNA. This study aimed to identify novel DNA methylation biomarkers of colorectal cancer that can be used for the follow-up of patients with colorectal cancer.

Methods: A DNA methylation profile was established in the Gene Expression Omnibus (GEO) database (n = 507) using bioinformatics analysis and subsequently confirmed using The Cancer Genome Atlas (TCGA) database (n = 348). The in silico profile was then validated on local tissue and cell-free DNA samples using methylation-specific digital PCR in colorectal cancer patients (n = 35) and healthy donors (n = 35).

Results: The DNA methylation of COL25A1 and METAP1D was predicted to be a colorectal cancer biomarker by bioinformatics analysis (ROC AUC = 1, 95% CI [0.999-1]). The two biomarkers were confirmed with tissue samples, and the combination of COL25A1 and METAP1D yielded 49% sensitivity and 100% specificity for cell-free DNA.

Conclusion: Bioinformatics analysis of public databases revealed COL25A1 and METAP1D DNA methylation as clinically applicable liquid biopsies DNA methylation biomarkers. The specificity implies an excellent positive predictive value for follow-up, and the high sensitivity and relative noninvasiveness of a blood-based test make these biomarkers compatible with colorectal cancer screening. However, the clinical impact of these biomarkers in colorectal cancer screening and follow-up needs to be established in further prospective studies.

Keywords: Biomarker; COL25A1; Circulating tumor DNA; Colorectal cancer; DNA methylation; Digital PCR; Liquid biopsy; METAP1D.

Fig. 1

Flowchart of the CpG selection from Infinium HumanMethylation450k to the panel. CpG was selected with three filters to keep the 100 most discriminating between blood, tumor and non-tumor colorectal samples. The panel was the most discriminating combination of 6 CpG among the 100 selected

Fig. 2

Maximal AUC of the panel on the GEO datasets according to the number of CpGs in the panels. The performance of panels comprising up to twenty CpGs was assessed using the GEO datasets, with evaluation conducted based on the area under the curve (AUC). The AUC of the most effective panel is depicted by the black lines, while the 95% confidence intervals computed with Delong method are indicated by the boxes. The inclusion of a second CpG enhanced the panel’s performance, but additional CpGs did not yield further improvements

Fig. 3

Heatmap of the panel’s B values on the GEO datasets. The panel’s CpG differentiated non-tumor and whole blood from tumor samples (n = 140, 192 and 175, respectively). Non-tumor and blood samples were hypomethylated (blue) unlike the hypermethylated (red) tumor samples on the panel’s CpG. However, no distinct sub-groups were identified though an unsupervised clustering process

Fig. 4

ROC curves for the panel on both the GEO and TCGA datasets. ROC curves generated and the AUC calculated from the GEO and TCGA datasets. The ROC 95% confidence intervals computed with bootstrapping are depicted by the blue shapes. The panel’s performance on the GEO dataset yielded an AUC of 0.998 and was confirmed on the TCGA dataset with an AUC of 1 and no significant statistical difference (bilateral Delong test, p = 0.527)

Fig. 5

Panel methylation profile in other tumor and non-tumor samples from the TCGA datasets. A The panel methylation means in colorectal cancer compared to other cancers from TCGA: pancreas, stomach, liver, esophagus, breast cancer, pancreatic cancer, lung cancer and melanoma. Additionally, colorectal cancer samples were compared to non-tumor tissues samples available in the TCGA datasets. In all comparisons, the colorectal cancer samples were more methylated than the other samples (with exception to non-tumor skin and non-tumor stomach samples due to the insufficient number of samples). B ROC curves and the AUC calculated from the colorectal samples compared to stomach, liver, esophagus, breast, lung, pancreas, melanoma and skin samples. ROC 95% confidence interval computed with bootstrapping are depicted by the blue shapes

Fig. 6

Transcription profile of the panel related genes between tumor and non-tumor colorectal samples from the TCGA datasets. Violin plot of the transcription counts of the genes associated with the panel. Gene expression was measured in counts and was normalized using Deseq2. COL25A1 and FGF12 had significantly lower expression in colorectal cancer samples compared to non-tumor colorectal samples (Student test, p = 0.009 and < 0.001, respectively). OPLAH did not demonstrate differential expression between the two sample types (p = 0.2513) and METAP1D had a significantly higher expression in colorectal cancer samples compared to non-tumor colorectal samples

Fig. 7

Quantitative methylation specific PCR of COL251A, METAP1D and OPLAH on tumor and non-tumor colorectal tissues samples. The methylation level of COL25A1, METAP1D and OPLAH was significantly higher in tumor compared to non-tumor tissues samples (Wilcoxon test, p < 10⁻⁴ for each biomarker)

Fig. 8

ROC curves of the combination of COL25A1 and METAP1D on plasma samples. ROC’s curves of the mean of the copy number of methylated COL25A1 and METAP1D on plasma samples. Plasma samples were collected from colorectal cancer patients (n = 35) and healthy donors (n = 35). The ROC 95% confidence interval computed with bootstrapping is shown by the blue shape

Fig. 9

COL25A1 and METAP1D methylation (copy/µL) by cellular control in plasma samples. The number of methylated copies were significantly higher in the plasma samples of patients with metastatic colorectal cancer (n = 35) compared to plasma samples of healthy donor (n = 35) for both COL25A1 and METAP1D (Wilcoxon test, p < 3.10⁻⁴ and 0.024, respectively). Specificity for both biomarkers was 100%, the sensitivity was 35% for COL25A1, 42% for METAP1D and 49% for the combination of COL25A1 and METAP1D