Colorectal adenoma subtypes exhibit signature molecular profiles: unique insights into the microenvironment of advanced precancerous lesions for early detection applications

Academic Editors: Lisardo Bosca and Serge Roche Received: 17 December 2024 Revised: 24 January 2025 Accepted: 8 February 2025 Published: 14 February 2025 Citation: Mancuso, F.M.; Higareda-Almaraz, J.C.; Canal-Noguer, P.; Bertossi, A.; Perera-Lluna, A.; Roehrl, M.H.A.; Kruusmaa, K. Colorectal Adenoma Subtypes Exhibit Signature Molecular Profiles: Unique Insights into the Microenvironment of Advanced Precancerous Lesions for Early Detection Applications. Cancers 2025, 17, 654. https://doi.org/10.3390/ cancers17040654 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Article Colorectal Adenoma Subtypes Exhibit Signature Molecular Profiles: Unique Insights into the Microenvironment of Advanced Precancerous Lesions for Early Detection Applications Francesco Mattia Mancuso 1, Juan Carlos Higareda-Almaraz 1, Pol Canal-Noguer 1, Arianna Bertossi 2, Alexandre Perera-Lluna 3,4 , Michael Herbert Alexander Roehrl 5and Kristi Kruusmaa 2,* 1Universal Diagnostics S.A., 41013 Seville, Spain; [email protected] (F.M.M.); [email protected] (J.C.H.-A.); [email protected] (P.C.-N.) 2Research & Development, Universal Diagnostics d.o.o., 1000 Ljubljana, Slovenia; [email protected] 3B2SLab, Institute for Research and Innovation in Health (IRIS), Universitat Politècnica de Catalunya—BarcelonaTech, 08028 Barcelona, Spain; alexandre.per[email protected] 4Networking Biomedical Research Centre in the Subject Area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 28029 Madrid, Spain 5Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA; [email protected]d.edu *Correspondence: kristi.kr[email protected] Simple Summary: Colorectal cancer originates from benign growths in the colon known as adenomas. Understanding how these adenomas become malignant is crucial to the early detection and prevention of colorectal cancer. This study examines DNA methylation and genetic differences among various types of advanced adenomas to identify potential markers that indicate progression toward cancer. By analyzing DNA methylation patterns, gene copy-number changes, and mutations in these lesions, particular signals and pathways associated with each subtype were found. These discoveries can help refine screening methods to detect distinct adenomas lesions at an early stage. By providing new insights into the initial changes leading to colorectal cancer, these findings may significantly impact approaches to prevention and early intervention strategies. Abstract: Background: Colorectal cancer (CRC) is characterized by the uncontrolled growth of malignant colonic or rectal crypt epithelium. About 85% of CRCs evolve through a stepwise progression from advanced precancerous adenoma lesions. A better understanding of the evolution from adenoma to carcinoma can provide a window of opportunity not only for early detection and therapeutic intervention but potentially also for cancer prevention strategies. Methods: This study investigates the heterogeneous methylation, copy-number alteration (CNA), and mutation signals of histological adenoma subtypes in the context of progression from normal colon to advanced precancerous lesions (APLs) and early-stage CRC. Results: Differential methylation analysis revealed 2321 significantly altered regions among APLs: 137 hypermethylated regions in serrated vs. tubular, 2093 in serrated vs. tubulovillous, and 91 in tubular vs. tubulovillous adenoma subtypes. The most differentiating pathways for serrated adenomas belonged to cAMP signaling and the regulation of pluripotency of stem cells, while regions separating tubular and tubulovillous subtypes were enriched for WNT signaling. CNA events were mostly present in tubular or tubulovillous adenomas, with the most frequent signals being seen in chromosomes 7, 12, 19, and 20. In contrast, early-stage CRC exhibited signals in chromosomes 7, 8, and 20, indicating different processes between APL and early-stage CRC. Mutations reinforce subtype-level differences, showing specific alterations in each subtype. Conclusions: These Cancers 2025,17, 654 https://doi.org/10.3390/cancers17040654 Cancers 2025,17, 654 2 of 19 findings are especially important for developing early detection or cancer prevention tests trying to capture adenoma signatures. Keywords: colorectal cancer; adenoma; advanced precancerous lesion; methylation; copynumber alterations; pathway enrichment 1. Introduction Colorectal cancer (CRC) is a heterogeneous malignancy originating from the epithelial cells lining the colon and rectum, posing a significant global health burden with over 1.9 million new cases and 935,000 deaths in 2020 [ 1 ]. It is the third most diagnosed cancer and the second leading cause of cancer-related mortality worldwide. Despite advancements in screening and treatment, CRC continues to exhibit high mortality rates, emphasizing the need for deeper molecular insights to enhance early detection and develop targeted therapeutic interventions [2]. CRC arises through a complex interplay of genetic and environmental factors. While hereditary syndromes such as familial adenomatous polyposis (FAP) and Lynch syndrome contribute to a subset of cases [ 3 ], most CRCs occur sporadically and are influenced by lifestyle factors like diet, physical inactivity, and smoking. However, in many instances, the exact causes remain unknown, highlighting the need for further research into additional contributing factors [ 4 , 5 ]. The adenoma–carcinoma sequence, first proposed by Vogelstein, outlines the stepwise progression of CRC, with genetic alterations in key oncogenes and tumor suppressors such as APC [ 6 ], KRAS [ 7 ], and TP53 [ 8 ], playing pivotal roles in tumor initiation and progression. However, growing evidence underscores the role of epigenetic modifications, particularly DNA methylation changes, in CRC pathogenesis [9,10]. Aberrant DNA methylation has been identified as a major driver of CRC development by silencing tumor suppressor genes and activating oncogenic pathways [ 11 ]. These aberrant methylated regions are now recognized as potential diagnostic and prognostic biomarkers [ 12 ], offering insights into disease heterogeneity and treatment response [ 13 ]. Recent studies have revealed that CRC tumors harbor hundreds of hypermethylated regions compared with adjacent normal mucosa, highlighting the importance of epigenetic dysregulation in colorectal carcinogenesis [14–16]. Advanced precancerous lesions (APLs), including tubular adenomas (TAs), villous/tubulovillous adenomas (VAs/TVAs), and serrated adenomas (SAs), exhibit distinct genetic and epigenetic profiles that contribute to their variable risk of malignant transformation [ 17 ]. Serrated adenomas, whose progression is an exception to the adenoma–carcinoma sequence [ 18 ], are frequently associated with the CpG island methylator phenotype (CIMP) and microsatellite instability (MSI), further underscoring the importance of epigenetic alterations in CRC progression [19]. Although histopathology remains a cornerstone in diagnosing and subtyping APLs, accurately characterizing these lesions can be challenging. Studies report variability in assessments among pathologists, resulting in inconsistencies that may influence patient management and outcomes [ 20 ]. In certain cases, such interpretive differences can lead to uncertainties regarding lesion margins, potentially hindering timely decision making [ 21 ]. Given the complexity and heterogeneity of APLs, supplementing histopathological evaluation with additional molecular or multi-omics approaches can help enhance diagnostic accuracy and improve patient care. Despite these challenges, no diagnostic tools have been specifically developed or validated for APLs, further underscoring the critical need for improved methodologies to Cancers 2025,17, 654 3 of 19 ensure accurate diagnosis and effective clinical decision making. Currently, most available tools are designed for broader CRC applications, lacking specificity for APLs. A multiomics approach, which integrates multiple layers of biological information, is essential to achieving a comprehensive and systemic understanding of the molecular landscape in CRC [ 22 ]. Such integrative analyses not only improve diagnostic accuracy but also facilitate the identification of novel, more reliable biomarkers and therapeutic targets, enabling personalized and more effective patient management [ 23 ]. Recent multi-omics studies have already provided critical insights into global DNA methylation patterns [ 24 ], gene expression, CNVs, and mutation analysis [ 25 ], as well as metabolomics [ 26 ], highlighting the potential of these advanced approaches to revolutionize the diagnosis and treatment of CRC. However, the application of these multi-omics strategies remains largely unexplored in the context of APLs, further emphasizing the need for dedicated research efforts to develop APL-specific diagnostic tools. This study aims to identify key molecular features encompassing DMRs, CNVs, and genomic mutations across subtypes of APLs by using a multi-omics approach that integrates genome-wide data. By exploring these layers of biological information, we aim to refine APL classification and potentially improve current risk stratification models. Our findings may contribute to more informed decision making, earlier detection, and better-targeted therapeutic strategies in the management of colorectal cancer. 2. Materials and Methods 2.1. Samples In this multi-center retrospective study, a total of 168 fresh frozen (FF) tissue samples were obtained from different international biobank repositories, including TCBN (Caen, France), VCB (Melbourne, VIC, Australia), Indivumed (Hamburg, Germany), IDIBAPS (Barcelona, Spain), LBIH Biobank (Liverpool, UK), and Dx Biosamples (San Diego, CA, USA). A total of 96 and 46 paired tumor tissues and normal adjacent tissues from 48 APLs and 23 stage I CRCs were designedly selected for omics detection. An independent set of APL samples were used for validation purposes. Tissue samples with different histological backgrounds (sessile serrated lesions (SSLs), tubular adenomas (TAs), or villous/tubulovillous adenomas (VAs/TVAs)) were used to obtain DNA for whole-genome bisulfite sequencing. For our analysis, we define two samples as paired when extracted from the same patient though of differing tissue or sample type. Demographic sample donor features are listed in Table 1and Supplementary Table S1. Table 1. Relevant characteristics of patients in this study. APL APL (Validation) CRC Stage I n 48 26 23 Age median (IQR), years (range) 68.5 (14.75) (39–86) 68.5 (19.75) (23–84) 68 (13) (37–81) Gender female/male, n 17/31 16/10 14/9 Histology SSL, n 7 7 TA, n 22 4 VA/TVA, n 19 15 Cancers 2025,17, 654 4 of 19 2.2. Sample Preparation and Whole-Genome Bisulfite Sequencing (WGBS) Genomic DNA (gDNA) from FF tissue was extracted by using a DNeasy Blood & Tissue kit (Qiagen, Valencia, CA, USA) according to the protocol by the manufacturer. Extracted gDNA was then fragmented into segments of about 400 bp with a Covaris S220 ultrasonicator. The extracted and shredded gDNA was bisulfite-converted with the EZ DNA Methylation-Lightning kit (Zymo Research, Inc., Irvine, CA, USA). Sequencing libraries were prepared from the bisulfite-converted DNA by using the Accel-NGS Methyl-seq DNA library kit (Swift Biosciences (Ann Arbor, MI, USA) and consequently sequenced to an average depth of 22.5x with NovaSeq6000 (Illumina, San Diego, CA, USA) equipment, using paired-end sequencing (2 × 150 bp). On average, samples had about 325 million reads each. 2.3. Genomic Data and Methylation Processing Quality checks and trimming were performed by using FastQC [ 27 ]TrimGalore v0.4.5 (a wrapper tool around Cutadapt [ 28 ], which removed adapter sequences and poor-quality bases and reads). The remaining high-quality reads (average Phred score > 35) were aligned to a bisulfiteconverted human genome (Ensembl 91 assembly, hg38) by using the Bismark Bisulfite Read Mapper (v0.20.0) [ 29 ], which makes use of Bowtie 1.2.1.1 alignment software [ 30 ]. On average, 89% of the sequences were aligned. Information regarding sequencing and alignment data quality at the sample level is reported in Supplementary Table S2. Methylation calls for every single C analyzed were performed by the Bismark bismark_methylation_extractor script. For each CpG, the beta values (β) were calculated as β= CGmethylated/(CGmethylated + CGunmethylated), where CGmethylated is the number of methylated cytosine and (CGmethylated + CGunmethylated) is the sum of methylated and unmethylated cytosine (total number of reads) at that position. The bisulfite conversion rate was estimated from cytosine (C) in non-CpG context and in average was above 99% (98.8–99.7%). CpGs were first selected based on their coverage: CpGs not covered by at least 5 uniquely mapped reads in all samples were discarded for subsequent analyses, such as those CpG with high beta difference variance, ending with 14,848,915 CpG sites. 2.4. Analyses of Differentially Methylated Position/Region To identify differentially methylated regions (DMRs), we conducted a pairwise analysis using custom R scripts, with beta values as the input for all analyses. Shortly, for each filtered CpG site from the previous step, a paired Wilcoxon test was applied to compare methylation levels. p-Values were adjusted for multiple testing by using the Benjamini–Hochberg (BH) algorithm, and CpGs with a corrected p-value less than 0.05 and a methylation difference of at least 0.2 between the two groups were retained. CpGs located at SNP positions with a population global minor allele frequency (MAF) greater than 1% (dbSNP Build 155) were excluded to minimize the impact of genetic variation on methylation analysis. DMRs were defined by aggregating CpGs located within a maximum distance of 50 base pairs. Only regions containing three or more CpGs, with at least 60% of the CpGs in the region meeting the significance criteria, were selected for further analysis. This approach ensured the identification of robust regions with biologically meaningful methylation differences. Cancers 2025,17, 654 5 of 19 2.5. Pathway Enrichment Analysis To analyze the biological meaning of our DMRs and add an extra layer of validation, we performed pathway enrichment with KEGG [ 31 ] pathways. As a first step, an extensive mapping of the DMRs was performed to find putative cis-regulatory regions related to specific genes by using the rGREAT function [ 32 ]. Afterwards, with the associated gene information, we proceeded to perform pathway enrichment by using ClusterProfiler [ 33 ], with a p-value of 0.05 and limiting the search to Homo sapiens. The resulting pathways were visualized by using the R package enrichplot [34]. 2.6. Copy-Number Alteration Detection To evaluate large-scale copy-number alterations (CNAs), the tool ichorCNA [ 35 ] was used. Read counts for each APL, CRC, and respective normal samples were calculated with the HMMcopy Suite [ 36 ]: each genome was divided into 10 kb non-overlapping bins, and aligned reads were counted based on overlap within each bin; then, read counts were normalized to correct for GC content and mappability biases. Centromeres were filtered based on chromosome gap coordinates obtained from UCSC (University of California at Santa Cruz) for hg38. Normal samples were used to create a reference dataset. These data were further used to correct systematic biases arising from library construction, sequencing platform, and DNA-specific artifacts. Copy-number analysis was performed on autosomal chromosomes only, using ichorCNA default parameters. 2.7. Somatic Mutation Detection To overcome the bisulfite conversion problem, which can cause cytosine to appear as thymine during sequencing and complicate the detection of true mutations, a pipeline based on EpiDiverse/snp (https://github.com/EpiDiverse/snp (accessed on 28 January 2022)) [ 37 ] was created. In summary, bam files were first preprocessed by using samtools (sort, calmd, and index); then, a double-masking procedure, which manipulates specific nucleotides and base quality (BQ) scores on alignments, was applied. For each sample pair, pileup files were created with samtools. The somatic function of VarScan2 [ 38 , 39 ] was used to call somatic single-nucleotide variants (SNVs) and small insertions/deletions (INDELs) from the comparison between APLs or CRCs and NATs. Only positions that were present in both files and met the minimum coverage in both files were compared. Only mutations identified by the tool as somatic were annotated by SnpEff [ 40 ]. To summarize, analyze, and visualize the mutations, the VCF output from VarScan2 and SnpEff was converted into Mutation Annotation Format (MAF) files and analyzed by using the R Bioconductor package Maftools [41]. 2.8. CpG Island Methylator Phenotype (CIMP) To predict the CIMP status of each sample, we utilized CpGs derived from promoter regions that exhibited a high standard deviation (SD > 0.2) of methylation levels in tumor tissues and a low methylation level (mean β < 0.05) in normal tissues. Consensus clustering analysis based on the K-means algorithm was performed by using the R package ConsensusClusterPlus [ 42 ]. Given the limited number of available samples for DNA methylation profiling in this study, we classified samples with hypermethylation patterns as CIMP-H, while all other samples were categorized as CIMP-L/N. Cancers 2025,17, 654 6 of 19 2.9. Microsatellite Instability (MSI) Status Prediction Standard short tandem repeat DNA sequence search methods are not applicable to bisulfite-converted data, as the conversion alters the DNA sequence and prevents the direct detection of microsatellite instability (MSI). To address this, we determined MSI status by examining potential frameshift mutations in genes associated with the mismatch repair (MMR) pathway, like RNF43, PMS2, MSH6, MSH2, and MLH1. Additionally, we applied the CpG biomarker panel used in the MSIMEP method [ 43 ], originally developed for microarray DNA methylation profiling, to our whole-genome bisulfite sequencing (WGBS) data. By leveraging these established biomarkers, we adapted the approach to WGBS, enabling us to estimate MSI status in our dataset effectively. 2.10. Principal Component Analysis (PCA) Principal component analysis (PCA) reduces the dimensionality of multivariate data to two or three dimensions, which can be visualized graphically with minimal loss of information. PCA was performed on epigenomics data to illustrate the differences between APL histologies by using the prcomp function in R package stats. The R package factoextra was used to visualize the PCA output. 3. Results We obtained 168 fresh frozen samples, fully characterized clinically and histologically by using standardized criteria (described in Section 2and Supplementary Table S1). To ensure robustness and generalizability, samples were divided into a discovery dataset of 48 APL samples paired with normal adjacent tissue (NAT), a validation dataset of 26 non-paired APL samples, and a third dataset comprising 23 paired CRC samples (Table 1). The discovery dataset was used to explore and identify the APL signature, performing differential methylation analysis and CNV/mutation analysis, while the validation dataset was used exclusively for assessing our signature performance in the agnostic identification of APLs by using the signature method. Finally, the CRC dataset was used to compare and understand the differences between the precancerous state and the invasive cancer state. The research pipeline is surmised in Figure 1. Cancers2025,17,xFORPEERREVIEW6of20   2.9.MicrosatelliteInstability(MSI)StatusPrediction StandardshorttandemrepeatDNAsequencesearchmethodsarenotapplicableto bisulfite-converteddata,astheconversionalterstheDNAsequenceandpreventsthedirectdetectionofmicrosatelliteinstability(MSI).Toaddressthis,wedeterminedMSIstatusbyexaminingpotentialframeshiftmutationsingenesassociatedwiththemismatch repair(MMR)pathway,likeRNF43,PMS2,MSH6,MSH2,andMLH1.Additionally,we appliedtheCpGbiomarkerpanelusedintheMSIMEPmethod[43],originallydeveloped formicroarrayDNAmethylationprofiling,toourwhole-genomebisulfitesequencing (WGBS)data.Byleveragingtheseestablishedbiomarkers,weadaptedtheapproachto WGBS,enablingustoestimateMSIstatusinourdataseteffectively. 2.10.PrincipalComponentAnalysis(PCA) Principalcomponentanalysis(PCA)reducesthedimensionalityofmultivariatedata totwoorthreedimensions,whichcanbevisualizedgraphicallywithminimallossofinformation.PCAwasperformedonepigenomicsdatatoillustratethedifferencesbetween APLhistologiesbyusingtheprcompfunctioninRpackagestats.TheRpackagefactoextra wasusedtovisualizethePCAoutput. 3.Results Weobtained168freshfrozensamples,fullycharacterizedclinicallyandhistologicallybyusingstandardizedcriteria(describedinSection2andSupplementaryTableS1). Toensurerobustnessandgeneralizability,samplesweredividedintoadiscoverydataset of48APLsamplespairedwithnormaladjacenttissue(NAT),avalidationdatasetof26 non-pairedAPLsamples,andathirddatasetcomprising23pairedCRCsamples(Table 1).ThediscoverydatasetwasusedtoexploreandidentifytheAPLsignature,performing differentialmethylationanalysisandCNV/mutationanalysis,whilethevalidationdataset wasusedexclusivelyforassessingoursignatureperformanceintheagnosticidentificationofAPLsbyusingthesignaturemethod.Finally,theCRCdatasetwasusedtocompare andunderstandthedifferencesbetweentheprecancerousstateandtheinvasivecancer state.TheresearchpipelineissurmisedinFigure1.  Figure1.Researchpipeline.Graphicalrepresentationofpipelinefollowedinthepresentwork. Figure 1. Research pipeline. Graphical representation of pipeline followed in the present work. Cancers 2025,17, 654 7 of 19 3.1. DMR Analysis Shows Unique Methylation Patterns Between APL Subtypes and CRC We conducted a comprehensive analysis to identify differentially methylated regions (DMRs) among APL subtypes. Our analysis employed two key approaches: first, pairwise comparisons of each APL subtype—SSL, TA, and VA/TVA—against their corresponding non-adjacent normal tissue (NAT) samples, allowing us to identify subtype-specific methylation changes relative to their normal tissue baseline. Second, direct comparisons among the three APL subtypes were performed to uncover DMRs that highlight methylation differences unique to each histological group. The results of these analyses are summarized in Table 2, which presents the total number of DMRs identified in each comparison. These findings quantify the significant methylation differences observed both within individual subtypes (relative to NAT) and among the subtypes themselves. In total, 122,348 DMRs were identified, including 6263 hypermethylated and 116,050 hypomethylated regions. To prioritize the most biologically relevant signals, we focused on hypermethylated DMRs unique to each APL subtype. These regions were selected based on their absence in NAT samples and their distinct presence in a single subtype, ensuring the identification of robust, subtype-specific signals. Figure 2a illustrates the different analytical comparisons performed among the three APL subtypes, with red circles highlighting the unique hypermethylated regions that represent key methylation patterns enriched in each histological subtype. Overall, our findings reveal extensive DMRs distinguishing APL subtypes from one another, as well as from normal mucosa and CRC. We found 2003 hypermethylated regions across the APL subtypes which were absent in the normal adjacent tissues. It should be noted that the DMRs were found not to be correlated with age (average Spearman correlation of 0.06) or sample origin (average Spearman correlation of 0.09). After analyzing the genomic distribution of DMRs, we observed their widespread presence across all chromosomes (Figure 2b). The Circos plot provides a visual representation of the human genome, with the outermost ring corresponding to chromosomes 1 to 22. The next two rings depict the density of DMRs along the chromosome sequences, shown as a dot plot and a density plot. The innermost layer highlights the 30 most significant DMRs identified for subsequent PCA analysis, with each line representing a specific region. Gene names are included when a DMR overlaps with a known gene. Table 2. Summary of DMRs in all analyses. Comparison DMRs Hyper DMRs Hypo DMRs SSL vs. SSL NAT 6317 1271 5046 TA vs. TA NAT 36,297 861 35,436 VA/TVA vs. VA/TVA NAT 65,794 1807 63,987 SSL vs. TA 1299 137 1162 SSL vs. VA/TVA 11,955 2096 9859 TA vs. VA/TVA 651 91 560 SSL NAT vs. TA NAT 4 SSL NAT vs. VA/TVA NAT 30 TA NAT vs. VA/TVA NAT 1 Cancers 2025,17, 654 8 of 19 Cancers2025,17,xFORPEERREVIEW8of20    Figure2.(a)DMRselection.TheUpSetplotusedtovisualizetheanalyticalcomparisonsamongthe APLs.Toenrichthehistologicalsubtypesignals,onlythoseregionsthatdonotoverlapwithAPL vs.NATresultsareconsidered(redcircles).Allidentifiedregionsandtheirpossibleintersections arerepresentedbyablackdot.(b)Circosplotofgenomicregions,displayingthedistributionof DMRsacrossallchromosomes.Theoutermostlayerrepresentsthechromosomes,followedbyadot plotindicatingthegenomicpositionofeachindividualDMR.ThenextlayerisadensityplotsummarizingthequantityofDMRsalongeachchromosome.Theinnermostlayerhighlightsthetop30 DMRs(annotatedwithgenenameswhenapplicable)thatsignificantlycontributetothePCAanalysisshowninFigure3a. Figure 2. (a) DMR selection. The UpSet plot used to visualize the analytical comparisons among the APLs. To enrich the histological subtype signals, only those regions that do not overlap with APL vs. NAT results are considered (red circles). All identified regions and their possible intersections are represented by a black dot. (b) Circos plot of genomic regions, displaying the distribution of DMRs across all chromosomes. The outermost layer represents the chromosomes, followed by a dot plot indicating the genomic position of each individual DMR. The next layer is a density plot summarizing the quantity of DMRs along each chromosome. The innermost layer highlights the top 30 DMRs (annotated with gene names when applicable) that significantly contribute to the PCA analysis shown in Figure 3a. Cancers 2025,17, 654 9 of 19 Cancers2025,17,xFORPEERREVIEW9of20    Figure3.(a)Principalcomponentanalysis(PCA)ofdifferentiallymethylatedregions,showingthe firsttwocomponentsofvarianceinbetavaluesforSSL(red),TA(orange),andVA/TVA(lightblue). Smallsymbolsrepresentindividualsamplevariability,whilelargersymbolsindicatethecentroid (mean)ofeachgroupinPCAspace.(b)Boxplotillustratingthestatisticaldifferencesinthefirst principalcomponent’scontributionsamongtheAPLsubtypes,withsignificancedeterminedbythe Kruskal–Wallistest. AfteranalyzingthegenomicdistributionofDMRs,weobservedtheirwidespread presenceacrossallchromosomes(Figure2b).TheCircosplotprovidesavisualrepresentationofthehumangenome,withtheoutermostringcorrespondingtochromosomes1to 22.ThenexttworingsdepictthedensityofDMRsalongthechromosomesequences, shownasadotplotandadensityplot.Theinnermostlayerhighlightsthe30most Figure 3. (a) Principal component analysis (PCA) of differentially methylated regions, showing the first two components of variance in beta values for SSL (red), TA (orange), and VA/TVA (light blue). Small symbols represent individual sample variability, while larger symbols indicate the centroid (mean) of each group in PCA space. (b) Boxplot illustrating the statistical differences in the first principal component’s contributions among the APL subtypes, with significance determined by the Kruskal–Wallis test. In total, 803 genes associated with DMRs were identified (Supplementary Table S3). Among these, some genes, such as CDKN2A and AMER2, are known to be epigenetically silenced in CRC. However, further transcriptomic analysis is needed to confirm their functional silencing, which is beyond the scope of the current study. To further investigate whether specific chromosomes exhibit a higher density of DMRs, we performed an enrichment analysis of DMRs per chromosome, normalizing Cancers 2025,17, 654 16 of 19 The findings underscore the potential of DMRs as robust biomarkers for early detection, offering opportunities to improve diagnostic accuracy and stratify patients based on their risk of progression to colorectal cancer (CRC). Additionally, the observed differences in CNVs and somatic mutations among APL subtypes further reinforce the importance of a multi-omics approach to fully characterize the molecular landscape of these lesions. Beyond their diagnostic value, these molecular alterations present opportunities for the development of targeted preventive strategies. Epigenetic modifications offer an attractive avenue for therapeutic intervention due to their reversible nature. The identification of subtype-specific alterations could pave the way for personalized medicine approaches, enabling tailored surveillance and treatment strategies for individuals at higher risk of CRC progression. While our study provides a comprehensive framework for understanding the molecular biology of APLs, further research is required to validate these findings in larger cohorts and to explore their functional relevance. Future studies should also investigate the integration of these molecular signatures into existing clinical workflows to enhance early detection and improve patient outcomes. Finally, the incorporation of artificial intelligence and machine learning algorithms must be performed to improve feature selection and detection rates. In conclusion, this study contributes to the growing body of knowledge on early colorectal tumorigenesis and highlights the potential of multi-omics strategies to transform the diagnosis, prevention, and management of CRC. By advancing our understanding of APL subtypes, we hope to facilitate the development of novel tools and strategies aimed at reducing the global burden of colorectal cancer. Supplementary Materials: The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/cancers17040654/s1, Figure S1: Heatmap of methylation profiles in all the groups, Figure S2: (a) Heatmap of CIMP status. (b) Heatmap of MSI status, Table S1: Description of the samples included in the study, Table S2: Data quality metrics per sample, Table S3: List of genes with at least one associated DMR, Table S4: Comprehensive list of somatic mutations identified in the study. Author Contributions: Conceptualization, F.M.M. and A.B.; methodology, F.M.M.; software, F.M.M.; validation, F.M.M.; formal analysis F.M.M.; resources, P.C.-N. and K.K.; data curation, F.M.M.; writing—original draft preparation, J.C.H.-A.; writing—review and editing, J.C.H.-A., F.M.M., P.C.-N., A.P.-L., M.H.A.R., and K.K.; visualization, F.M.M.; supervision, P.C.-N., K.K., M.H.A.R., and A.P.-L.; project administration, P.C.-N. and K.K.; funding acquisition, P.C.-N. and K.K. All authors have read and agreed to the published version of the manuscript. Funding: ThisworkwassupportedbygrantPID2021-122952OB-I00fundedbyAEI 10.13039/501100011033 and by the ERDF—A way of making Europe; the Networking Biomedical Research Centre in the subject area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN); the CERCA Programme/Generalitat de Catalunya (B2SLab is certified as 2021 SGR 01052); and private funding by UniversalDx S.A. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are available on a reasonable request from the corresponding author. Acknowledgments: We would like to express our sincere gratitude to Kathryn Lang for her valuable assistance with reviewing and refining the manuscript and for providing insightful feedback that significantly enhanced the quality of this work. Cancers 2025,17, 654 17 of 19 Conflicts of Interest: F.M.M., J.C.H.-A., and P.C.-N. are employees of Universal Diagnostics S.A., K.K., and A.B. are employees of UniversalDx d.o.o. 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. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. References 1. 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