{"paper_id":"3d3802a4-1a44-438e-8a3d-ba3cfb192b77","body_text":"Enhanced Diagnostic Efficiency of a Novel Fecal Methylated Gene Model for Early Colorectal Cancer Detection | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Enhanced Diagnostic Efficiency of a Novel Fecal Methylated Gene Model for Early Colorectal Cancer Detection Peng Yun, Kamila Kulaixijiang, Jiang Pan, Luping Yang, Nengzhuang Wang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4180792/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background and Aims Methylation of stool DNA (sDNA) is a reliable noninvasive early diagnostic marker for colorectal cancer (CRC). Our study aimed to identify a new gene panel for the early diagnosis of CRC. Methods We conducted methyl-CpG binding domain isolated genome sequencing (MiGS) on 3 CpG island methylation phenotype (CIMP)-positive and 3 CIMP-negative CRC tissues and their corresponding normal adjacent tissues. Subsequently, by utilizing both the aforementioned data and public datasets, we identified a set of promising methylated sDNA markers for CRC. Finally, we developed a combined diagnostic model (CDM) for CRC based on the methylation status of PRDM12 , FOXE1 , and SDC2 and evaluated its performance in an independent multicenter validation cohort. Results A total of 1,062 participants were included in this study. The area under the curve (AUC) of the CDM was 0.979 (95% CI: 0.960–0.997), and the optimal sensitivity and specificity were 97.35% and 99.05%, respectively, in the training cohort (n = 231). In the independent validation cohort (n = 800), the AUC was 0.950 (95% CI: 0.927–0.973), along with the optimal sensitivity of 92.75% and specificity of 97.21%. When CRC and advanced adenoma (AAD) were used as diagnostic targets, the model AUC was 0.945 (95% CI: 0.922–0.969), with an optimal sensitivity of 91.89% and a specificity of 95.21%. The model sensitivity for nonadvanced adenoma patients was 68.66%. Conclusion The sDNA diagnostic model CDM, developed from both CIMP-P and CIMP-N, exhibited exceptional performance in CRC and could serve as a potential alternative strategy for CRC screening. Stool DNA Colorectal Cancer Early Diagnosis PRDM12 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Colorectal cancer (CRC) ranked second and third in tumor related occurrence and death worldwide, respectively; over 1.8 million new cases and 0.9 million deaths related to CRC occurred in 2020 [ 1 ] . Extensive evidence suggests that CRC patients can benefit from early detection and treatment, as the 5-year survival rate (81.7%-89.5%) of patients with early CRC (eCRC) is significantly greater than that of patients with advanced CRC (aCRC, 14.9%-30.4%) [ 2 – 3 ] . The main methods currently used for early screening of CRC include fecal occult blood tests (FOBTs), serum tumor marker (CEA, CA-199) analysis, and colonoscopy. Due to its invasive nature, lengthy preparation time, and high cost, colonoscopy is not applicable for large-scale screening of gastrointestinal diseases [ 4 ] . The sensitivities of FOBTs, CEA, and CA-199 for CRC screening are only 42%-73.8%, 32.0%-57.5%, and 16%-21%, respectively, which are far below the requirements for eCRC screening [ 5 – 9 ] . In the last 15 years, more than 30 fecal methylated genes for CRC diagnosis have been reported in more than 40 articles [ 4 , 10 ] . However, reviewing these studies, we found that the positive rate of stool DNA (sDNA) was generally 4.76%~52.4% lower than that in primary tumors (PTs) for the same locus or gene [ 5 , 10 – 13 ] . There are very few diagnostic panels with sensitivity and specificity greater than 90% [ 14 ] , which limits the clinical application of this technology. In addition to the inadequate polymerase chain reaction (PCR) system, this may also be related to the epigenetic heterogeneity of colorectal cancer [ 15 – 16 ] . The concept of the CpG island methylation phenotype (CIMP) in CRC was first proposed in 1999 by Minoru Toyota [ 17 ] . Although the concept was gradually cooling due to the lack of correlation between CIMP and treatment and prognosis, the presence of hypermethylated (or CIMP-positive, CIMP-P) and hypomethylated (or CIMP-negative, CIMP-N) subgroups in CRC has been firmly established [ 16 ] . In this study, we performed methyl CpG binding domain (MBD)-isolated genome sequencing (MiGS) [ 18 ] on 3 paired PTs and normal adjacent tissues (NATs) from patients with CIMP-N CRC and 3 PTs from patients with CIMP-P CRC to discover the most easily detectable methylated segments in CRC at the genome-wide level. By integrating methylation profiles from public datasets, we eliminated gene segments related to sex, age, and tumor stage. Finally, we optimized the fecal sample collection and PCR system and developed a CRC diagnosis model based on sDNA. The model exhibited exceptional performance in our independent multicenter validation cohort. Methods Sample Collection In this study, we collected 3 CIMP-P CRC PTs, 3 CIMP-N CRC PTs and paired NATs for MiGS and 31 PTs and 31 NATs from 31 individuals with CRC for pyrosequencing. In total, 1062 fecal samples consisting of the cohort Ⅲ (n = 31), cohort Ⅳ (n = 321) and cohort Ⅴ (n = 800) were collected from 282 individuals with CRC, 23 individuals with advanced adenoma (AAD), 67 individuals with non-AAD, 642 healthy controls (HCs), and 48 individuals with interfering disease (IFD). All specimens were collected from Changhai Hospital, 7th People's Hospital in Shanghai and Karamay Central Hospital in Karamay, and the distribution of sample sizes is shown in Additional file 1: Supplementary Table S1 . The demographic characteristics of all individuals are shown in Additional file 1: Supplementary Table S2 . The reference sites for CIMP-P CRC were 5 classical CIMP sites, namely, CACNA1G , IGF2 , NEUROG1 , RUNX3 , and SOCS1 [ 19 ] , as well as 11 genes reported in the literature, namely, CDKN21 , CRABP1 , MLH1 , CHFR , HIC1 , IGFBP3 , MGMT , MINT1 , MINT21 , CDKN2A/ARF , and WRN [ 16 ] . If the number of methylated genes was ≥ 11/16, the sample was classified as CIMP-P; otherwise, it was classified as CIMP-N. AAD was defined as villous adenoma or any polyp with high-grade dysplasia or adenoma larger than 1 cm in diameter. Non-AAD was defined as the collection of benign polyps other than AAD. HCs are defined as individuals who are endoscopically diagnosed without gastrointestinal tumors or polyps. This study was reviewed and approved by the Hospital Medical Ethics Committee of Changhai Hospital (2018-0016). All individuals in this study provided informed consent. DNA extraction All tissue DNA was obtained from postoperative paraffin-embedded pathological tissues. Experienced pathologists first cut enough tissue sections based on the pathological results, and then laboratory personnel followed the instructions of a tissue genomic DNA extraction kit (Qiagen Co., Germany) to extract DNA from the tissues. After receiving training from the study personnel and receiving proper instructions on the collection method, participants utilized a specialized specimen collector (Additional file 2: Supplementary Fig. S1 a, Sciendox Co., Xiamen, China) to gather approximately 10 g of fecal sample. The specimens were immediately sent to the laboratory for liquefaction by fecal liquefaction treatment equipment (Additional file 2: Supplementary Fig. S1 b), a filtrate was collected, and the specimens were stored at -80°C for less than 1 week or immediately processed for DNA extraction using the Magnetic Soil and Stool DNA Kit DP712 (Tiangen Co., Beijing, China) according to the manufacturer’s instructions. MiGS MiGS analysis was performed as previously described [ 18 ] . In brief, genomic DNA was fragmented to 150–600 bp by sonication in BW buffer, segments smaller than 100 bp were removed by QIAquick PCR Cleanup columns (Qiagen), and 5 µg of purified DNA fragments was used for immunoprecipitation. Methylated DNA was isolated by recombinant MBD-magnetic beads. The eluted DNA was extracted with phenol: chloroform, precipitated with isopropanol, and resuspended in 45 µL of dH 2 O. A ChIP-Seq Sample Prep Kit (Illumina) was used to construct the sequencing libraries on 10 ng of MBD-isolated DNA per sample. All sequencing results were aligned to NCBI Build 36.1/UCSC Hg18 using the University of California, Santa Cruz (UCSC) [ 20 ] server. Bisulfite treatment The purified genomic DNA was subjected to bisulfite treatment using an EZ DNA Methylation-Gold™ Kit (Zymo Research Co., Ltd.) following the manufacturer’s instructions. The eluted DNA was transferred to tubes for transformation, followed by washing with 30 µL of elution buffer. The eluted BisDNA was used entirely as the template for subsequent experiments. Pyrosequencing PCR products labeled with biotin were mixed with microbeads containing streptavidin. After denaturation, the sequencing template was single-stranded, and the methylation level of the CpG sites in the promoters of each gene was detected using a pyrophosphate sequencer. The primers used for pyrosequencing (Additional file 1: Supplementary Table S3) were designed with PyroMark Assay Design 2.0 software and synthesized by Sangon Biotech Co., Shanghai, China. The PCR system was 50 µL. The reaction program is shown in Additional file 1: Supplementary Table S4. The methylation index, which is equal to the peak height of methylated sites/(peak height of methylated sites + peak height of nonmethylated sites), was used to measure the degree of methylation. Fecal mt-msqPCR Fecal multiple-target methylation-specific quantitative polymerase chain reaction (mt-msqPCR) was established and optimized on 31 stool samples from cohort Ⅲ. The primers (Additional file 1: Supplementary Table S5) used were designed and synthesized by Sangon Bioengineering (Shanghai) Co., Ltd. The reference gene used was β-Actin, and the primers used for SDC2 were obtained from previous reports [ 21 ] . Our optimized reaction system is shown in Additional file 1: Supplementary Table S6. Each batch of samples was set up with a blank control (Tris-HCl buffer), a negative control (nonmethylated gene plasmid) and a positive control (methylated gene plasmid). Specifically, cohort V was designed as a multicenter, double-blind study to avoid the subjective influence of testers. Different people were responsible for specimen collection, detection and summarization of the data. Statistical analysis All the statistical analyses and figures were made using R software (version 4.3.1, https://cran.rstudio.com/ ). MiGS data was analyzed by the Mann‒Whitney U test. Paired Student’s t test was used to compare paired measurement data. The corrected P was calculated by the Benjamini‒Hochberg (BH) method. The intersection of the differentially methylated probes (DMPs) between the MiGS data and The Cancer Genome Atlas (TCGA) data was calculated using the “GenomicRanges” package in version 1.52.1. A circle plot was generated with 2 R packages, “shiny” version 1.8.0 and “circlize” version 0.4.15. The receiver operating characteristic (ROC) curve was plotted by “pROC” version 1.18.5, and the cutoff value, area under the curve (AUC) and 95% confidence interval (CI) were calculated, simultaneously. Pancancer and PRDM12 functions were analyzed by the R package TCGAplot [ 22 ] . Other plots were plotted using the “ggplot2” package in version 3.4.4. A logistic regression model of the target genes, LPFSM, was established based on the binary logistic regression method. If any target genes were positive, the combined diagnosis model (CDM) was used to determine that the gene was positive. The exact cutoff Ct values for each gene and CDM and LPFSM are presented in Additional file 1: Supplementary Table S7. Results Flowchart of the study The flowchart of our study is shown in Fig. 1 . First, we performed MiGS for 3 CIMP-P CRC PTs, 3 CIMP-N CRC PTs and paired NATs to identify DMPs across the whole genome. Then, we performed DMP analysis on the TCGA-CRC datasets ( https://portal.gdc.cancer.gov/ ) and obtained the intersection of the DMPs of the two cohorts. In the second step, we validated and narrowed down the candidate genes. Pyrosequencing was performed for 31 CRC PTs and their NATs to verify the validity of the genome-wide methylation screening results. Fecal mt-msqPCR was established to examine the diagnostic efficiency of the candidate genes in stool. CDM and LPFSM were established to improve the diagnostic efficiency. In the third step, a multicenter cohort was recruited to validate the diagnostic efficiency of CDM and LPFSM by performing fecal mt-msqPCR. Finally, we investigated the function of PRDM12 using public datasets. Genome-wide candidate gene screening MiGS for cohort I revealed 596,168 to 791,389 methylated fragments in 9 specimens, for a total of 1,226,751 nonrepetitive methylated fragments (Additional file 1: Supplementary Table S8). Methylation fragments with a methylation difference greater than 20 between the CIMP-P and CIMP-N PTs were removed. Differential methylation analysis yielded 19,530 DMPs (Additional file 1: Supplementary Table S9), of which 12,558 (64.30%) were hypermethylated and 6,974 (35.70%) were hypomethylated (Fig. 2 b). Based on the filtering criteria, 25,751 DMPs were obtained by differential methylation analysis in cohort-Ⅱ, of which 21,527 (83.60%) DMPs were hypermethylated and 4,224 (16.40%) DMPs were hypomethylated (Fig. 2 c). By using the GenomicRanges package to take the gene coordinate intersection of two DMP datasets, a total of 1,662 DMPs were obtained, corresponding to 727 differentially methylated genes (DMGs) (Additional file 1: Supplementary Table S10). The majority of DMPs were located in the gene body (26.73%), while the fewest were found in the 3’UTR (1.02%) (Fig. 2 d). Based on the DMG profiles, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The top 10 terms for each of the 3 ontologies are shown in Fig. 2 e. The detailed results can be found in Additional file 1: Supplementary Table S11. KEGG pathway enrichment analysis revealed that the DMGs were mainly enriched in pathways such as neuroactive ligand‒receptor interaction, the PI3K-AKT signaling pathway, and the cAMP signaling pathway (Fig. 2 f, Additional file 1: Supplementary Table S12). To eliminate DNA methylation changes caused by cancer progression, we screened for DMPs between 177 early CRC (eCRC, stage Ⅰ-Ⅱ) and 147 advanced CRC (aCRC, stage Ⅲ-Ⅳ) samples in cohort II, resulting in a total of 15,359 DMPs (Additional file 1: Supplementary Table S13, Figure S2 ). The 53 overlapping portions of the candidate DMPs were removed. Analogously, 4,000 age-related DMPs were identified (Additional file 1: Supplementary Table S14), 50 overlapping DMPs were removed, and 50 gender-related DMPs were removed. Next, among the remaining DMPs, we identified 920 pairs of probes showing collinearity (Additional file 1: Supplementary Table S15, Figure S3). The 487 overlapping portions of candidate DMPs were removed. Finally, the number of candidate DMPs was reduced to 903, corresponding to 645 DMGs (Additional file 1: Supplementary Table S16). According to our previous study [ 23 ] , we selected 5 DMGs ( PRDM12 |cg09191327, FOXE1 |cg02157015, B3GAT2 |cg10766373, VIM |cg01003015, and SFRP2 |cg25645268) for further validation (Additional file 2: Supplementary Fig. S4). Pyrosequencing validation and establishment of fecal mt-msqPCR We performed pyrosequencing on the 5 candidate DMGs to verify their methylation status in PTs and NATs from 31 individuals with CRC. The average methylation index of all the DMGs in PTs was significantly greater than that in NATs (Fig. 3 a, P < 0.001). However, some patients showed hypomethylation of PRDM12 , FOXE1 , B3GAT2 , VIM , and SPRF2 in PTs compared to NATs. The hypermethylation ratio of SFRP2 was significantly lower than that of PRDM12 and FOXE1 (Additional file 2: Supplementary Fig. S5, P < 0.05). During the same period, preoperative stools were collected to establish fecal mt-msqPCR. After condition optimization, the methylation status of 5 candidate genes in PTs and Stool was detected. We found that only the percentage of patients with positive results for PRDM12 and FOXE1 in PTs reached 100%, while the percentage of patients with positive results in stools exceeded 80% (Fig. 3 b). The percentage of positive FOXE1 , B3GAT2 , VIM and SFRP2 in feces was significantly lower than that in cancer tissue (Fig. 3 b, P < 0.05), similar to previous reports [ 12 ] . SDC2 has previously been demonstrated to be an effective sDNA marker for diagnosing eCRC [ 11 ] . To enhance diagnostic efficiency, SDC2 was selected as a target gene alongside PRDM12 and FOXE1 in the training set. Performance of target genes for detecting CRC in the training set The training set (cohort Ⅳ) of this study included stool samples from 105 HCs, 13 patients with AAD and 113 patients with CRC. The ROC curves suggested that PRDM12 had the highest AUC [0.905 (95% CI: 0.868–0.942)] than those of FOXE1 [0.835 (95% CI: 0.791–0.879)] and SDC2 [0.900 (95% CI: 0.863–0.938)] (Fig. 3 c-e). The optimal sensitivities of PRDM12 , FOXE1 , and SDC2 for CRC were 80.53%, 67.26%, and 80.53%, respectively. The optimal specificities of PRDM12 (100%) and FOXE1 (100%) were slightly greater than that of SDC2 (99.05%). When AAD and CRC were utilized as diagnostic criteria, PRDM12 , FOXE1 , and SDC2 exhibited AUCs of 0.899, 0.840, and 0.895, respectively (Fig. 3 c-e). Although they all showed high specificity, none of the genes were more than 90% sensitive to CRC. Therefore, we developed two diagnostic models, the CDM and the LPFSM. The ROC curve demonstrated that CDM (Fig. 3 f) and LPFSM (Additional file 2: Supplementary Fig. S6) improved the AUC for CRC diagnosis, achieving scores of 0.979 (95% CI: 0.960–0.997) and 0.986 (95% CI: 0.970–1.000), respectively. The optimal sensitivity and specificity of the CDM were 97.35% and 99.05%, respectively, for CRC diagnosis and 96.83% and 99.05%, respectively, for CRC&AAD diagnosis. Performance of the CDM and LPFSM in the validation set The validation set (cohort Ⅴ) included 800 fecal samples from 537 HCs, 67 non-AAD patients, 10 AAD patients, 138 CRC patients and 47 IFD patients, with detailed clinical data presented in Additional file 1: Supplementary Table S2 . When HCs were used as controls, the AUCs of the CDM for CRC, eCRC, non-AAD and CRC&AAD were 0.950 (95% CI: 0.927–0.973), 0.959 (95% CI: 0.931–0.986), 0.829 (95% CI: 0.773–0.886) and 0.945 (95% CI: 0.922–0.969), respectively (Fig. 4 a-b). The optimal sensitivities of CDM for CRC, eCRC, non-AAD and CRC&AAD were 92.75%, 94.52%, 68.66% and 91.89%, respectively. When IFD and HC were utilized as controls, the AUC for CRC&AAD decreased slightly to 0.936 (95% CI: 0.912–0.959) (Fig. 4 c). The optimal specificity of CDM was 95.21%. Interestingly, 63.64% (7/11) of stomach adenocarcinomas (STADs) in IFD patients were CDM positive, suggesting that our target genes may also have certain diagnostic value for STAD. When STADs among IFD patients were removed, the AUC for CRC&AAD diagnosis increased to 0.941 (95% CI: 0.918–0.965) (Fig. 4 d). The diagnostic efficiency of LPFSM was slightly worse than that of CDM (Additional file 2: Supplementary Fig. S7 and Additional file 1: Supplementary Table S17). Functional exploration of PRDM12 We used various omics databases, including TCGA, to investigate the functional role of the PRDM12 gene. Our analysis revealed that PRDM12 has a highly conserved expression pattern in CRC in situ. Moreover, in the PanCancer Atlas dataset ( https://www.cbioportal.org/ ) comprising 526 CRC patients, the mutation rate of PRDM12 was remarkably low at only 0.38% (2/526), which is significantly lower than that of SDC2 [3.42% (18/526), P < 0.001] (Additional file 2: Supplementary Fig. S8). Pancancer analysis revealed that PRDM12 had almost no expression in 23 types of NATs (9 with no data and high expression in GBM) but was fully expressed in PTs. Furthermore, PRDM12 expression was significantly increased in 16 different types of PTs (Additional file 2: Supplementary Fig. S9a). Additionally, among the 33 major cancers, PRDM12 exhibited weak correlations with tumor mutation burden (TMB), microsatellite instability (MSI), and immune scores (Additional file 2: Supplementary Fig. S9b-d). Intriguingly, PRDM12 exhibited hypomethylation and restricted expression in normal adult human tissues (excluding those of the nervous system) (Additional file 2: Supplementary Fig. S10a; https://www.proteinatlas.org/ ). However, CRC cells are highly methylated, and their expression is increased [ 24 ] (Additional file 2: Supplementary Fig. S10b; https://mexpress.ugent.be/ ). According to the median expression of PRDM12 in CRC, the patients (n = 640) were divided into \"low-expression\" and \"high-expression\" groups (Fig. 5 a). The top 20 DEGs are shown in Additional file 2: Supplementary Fig. S11a and Additional File 1: Supplementary Table S18. Then, we performed GO gene set enrichment analysis (GO-GSEA) and KEGG-GSEA for the DEGs. The results showed that DEGs were mainly enriched in mitochondrial gene expression and ribosome assembly (Fig. 5 b, Additional file 1: Supplementary Table S19) and pathways enriched in cell cycle regulation (Fig. 5 c, Additional File 1: Supplementary Table S20). The STRING interaction network [ 25 ] suggested that EHMT2 (G9a) is the protein most closely related to PRDM12 (Fig. 5 d). DMFold2 [ 26 ] , the latest model for protein structure and function prediction, suggested that PRDM12 may interact with G9a and localize to the nucleus to regulate a wide range of biological processes (Additional file 2: Supplementary Fig. S11b-d). Discussion At present, colonoscopy and pathological biopsy are still the gold standards for the diagnosis of colorectal cancer. However, the disadvantages of colonoscopy are also obvious. First, colonoscopy is highly dependent on the experience of the operator. Second, complex preoperative preparation is not suitable for mass screening. Third, patients have poor experience with this invasive test. Methylated sDNA analysis is considered a promising method for large-scale CRC screening. To date, more than 30 methylated sDNA biomarkers have been developed, but only a few biomarkers have met the clinical need for diagnostic efficiency to some extent [ 27 ] . For example, methylated BMP3 combined with NDRG4 mutant KRAS was approved by the US. The FDA has a sensitivity of 92.3% and a specificity of 86.6% for CRC diagnosis [ 5 ] , and the sensitivity and specificity of SDC2 , which are approved by the China FDA, are 83.8%-89.1% and 98.0%, respectively [ 11 , 28 ] . Due to its suboptimal sensitivity and specificity and prolonged detection time, this method has yet to replace conventional screening methods. As a result, further development of a new panel/model with improved sensitivity and specificity is essential [ 29 ] . CRC methylation patterns exhibit heterogeneity, and a common classification approach in many studies involves categorizing CRC into hypermethylated and hypomethylated subtypes based on the CIMP [ 15 , 30 ] . In our study, we screened a specific gene set for CRC diagnosis using the CIMP concept. Due to the limited size of our in-house dataset, we implemented more rigorous screening criteria, resulting in all 6 PTs exhibiting hypermethylation irrespective of their CIMP phenotype. To obtain stage-insensitive CpGs, we retained only those DMCs that did not differ significantly between eCRC and aCRC patients. Similarly, age-, sex-related and collinear CpG sites were excluded. As a result, we identified a DMG set that included many previously unreported CRC methylation markers. We aimed to enhance the sensitivity of sDNA for diagnosing CRC, with the goal of identifying the most minimal marker for CRC detection. However, our study highlighted the challenges in achieving a significant breakthrough with single sDNA. Similar to several previously reported methylated sDNA markers [ 10 , 31 ] , the rates of positivity for B3GAT2 , VIM , and SFRP2 in feces were notably lower than those observed in PTs. There may be several underlying factors that can account for our analysis. First, fecal samples contain fewer tumor cells [ 32 ] , necessitating an experimental system with high sensitivity. Second, the composition of feces, including DNA from bacteria, along with residues from various animal and plant foods, is complex. Third, dietary structure, digestive function, and intestinal condition can also impact the outcomes of the test [ 33 ] . Therefore, by combining PRDM12 with previously reported genes in our DMG set, namely, FOXE1 and SDC2 , we established a 3-target model, CDM, that exhibited exceptional performance in detecting eCRC, as validated in both the training and independent validation cohorts. The sensitivity and specificity of CDM for eCRC patients were 94.52% and 97.21%, respectively, and those for CRC patients and patients with AADs were 91.89% and 95.21%, respectively, both of which are better than those of the kits reported thus far. Furthermore, in the verification group, we added a variety of diseases, such as gastrointestinal malignancies, benign diseases, and extralimentary tumors, as interfering diseases, and the results showed that only STAD may interfere with the diagnosis of CRC by CDM. Therefore, in clinical diagnosis and treatment, attention should be given to the identification of upper and lower gastrointestinal symptoms. The progression from atypical hyperplastic colorectal polyps to malignant colorectal adenocarcinoma involves the accumulation of genetic and epigenetic alterations [ 34 – 35 ] , resulting in epigenetic reprogramming of human colonocytes. Different types of cancers display unique methylation patterns, and certain genes, including PRDM12 , exhibit similar patterns across multiple cancer types [ 36 – 37 ] . Considering the potential role of PRDM12 in the occurrence of CRC, we explored its function. PRDM12 belongs to the PRDI-BF1 (positive regulatory domain I-binding factor 1) homologous domain (PRDM)-containing protein family, which is a subfamily of Kruppel-like zinc finger proteins [ 38 ] . This family plays a crucial role in regulating cancer development [ 39 ] . PRDM12 , which is restrictively expressed in normal human tissues, shows elevated expression in eCRC and consistently maintains a lower expression level, suggesting its potential widespread involvement in cancer development. PRDM12 is known to play a role in the development of sensory neurons [ 40 ] ; however, the role of PRDM12 in cancer has been poorly studied. PRDM12 is an important transcriptional regulator capable of regulating neural differentiation and formation in combination with solid tumors and hematological malignancies [ 38 , 41 ] . AG Reid [ 42 ] et al. reported that 15% of patients with chronic myeloid leukemia have deletion of the PRDM12 gene, which is related to rapid progression and short-term survival. Pancancer analysis suggested that PRDM12 expression is poorly correlated with patient prognosis and immunotherapy outcomes. Subsequently, we stratified CRC patients into high-expression and low-expression groups, revealing that the DEGs were primarily enriched in the cell cycle pathway. Protein interaction analysis demonstrated a close association between PRDM12 and EHMT2 (G9a), while DMFold2 predicted its involvement in various cellular processes. The findings of Yang et al [ 41 ] . In P19 embryonic carcinoma cells, PRDM12 recruits G9a to methylate lysine 9 of histone H3 (H3K9me), resulting in a decrease in chromatin structure and an increase in the proportion of G1-phase cells. However, this alteration hinders cell cycle progression, suggesting that PRDM12 may function as a tumor suppressor gene. The present study has certain limitations. First, 25% of participants in the validation set were symptomatic patients with CRC, which could lead to an overestimation of the sensitivity of the PRDM12 test. Second, the sensitivity of PRDM12 in detecting STAD is nearly 65%, which may result in an increase in the number of endoscopies performed by individuals during physical examinations. However, from a different perspective, this could broaden the application range of PRDM12 . Third, controlling the quality of specimens proves to be challenging. Patients collect their own specimens, and the use of regular toilets instead of squatting toilets in most wards contributes to difficulties in sampling and increases the possibility of specimens being soaked in water. In summary, CIMP-based genome-wide methylation profiles provide valuable epigenetic information that can be used to develop novel methylation markers. The three-target diagnostic model constructed in this study further improved the diagnostic efficiency of methylated sDNA for eCRC. Abbreviations sDNA:stool DNA CRC colorectal cancer AAD advanced adenoma PTs primary tumors NATs normal adjacent tissues TCGA The Cancer Genome Atlas HC healthy control IFD interfering disease DMP differentially methylated probe DMG differentially methylated gene mt-msqPCR multiple-target methylation-specific quantitative polymerase chain reaction CDM combined diagnosis model LPFSM logistic regression model of PRDM12 FOXE1 and SDC2 MiGS methyl CpG binding domain isolated genome sequencing DMRs differentially methylated regions CIMP-P CpG island methylation phenotype positive CIMP-N CIMP negative MtI methylation index ROC, receiver operating characteristic AUC area under curve STAD Stomach adenocarcinoma GO-GSEA Gene Ontology–Gene Set Enrichment Analysis KEGG-GSEA Kyoto Encyclopedia of Genes and Genomes–GSEA. Declarations Acknowledgments We express our sincere gratitude to the medical staff of the Department of Anorectal Surgery at Changhai Hospital for their invaluable contribution in collecting specimens. We are also thankful to Sciendox Co. for their innovative fecal collection. Additionally, we extend our heartfelt to the researchers at Sciendox Co. for their diligent efforts in optimizing the msqMSP experimental conditions. Special thanks to Dr. Xiong Wang from the Department of Laboratory Medicine at Tongji Hospital, Huazhong University of Science and Technology, for his assistance with the bioinformatics analysis. Authors' contributions PY, KK, JP, ZZ, MZ, and HY contributed to conceptualization; PY, JP, and HY contributed to data curation; PY, JP, and HY were involved in clinical data collection and formal analysis; MZ and HY were involved in funding acquisition; PY, KK, JP, NW, LY, YZ, HC, and ZX contributed to the investigation; LY, NW, KK, YZ, PY, and ZX were involved in methodology; ZZ, MZ and HY contributed to project administration; ZZ, MZ and HY contributed to supervision; PY wrote the first version of the manuscript; YP, KK, JP, ZX, and HY contributed to writing, review and editing. All the authors have read and approved the final manuscript. Funding This work was supported by the National Natural Science Foundation of China (81872225, 82273465) and supported in part by grants from the National Natural Science Foundation of China (grant no. 82260322 to M. Z.) and from the Natural Science Foundation of Xinjiang Uygur Autonomous Region for Outstanding Young Scientists (grant no. 2021D01E34 to M. Z.). Ethics approval and consent to participate This study was reviewed and approved by the Hospital Medical Ethics Committee of the Officers Hospital of Naval Medical University (2018-0016). All the authors declare that this study was performed in accordance with the Declaration of Helsinki. Consent for publication The first author wrote the first version of the manuscript, and all the authors reviewed the subsequent drafts and agreed to submit articles for publication. Data availability The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request. Competing Interests The authors declare that they have no competing interests. References Hyuna Sung,Jacques Ferlay,Rebecca L. Siegel, et al. 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Advances in CpG Island Methylator Phenotype Colorectal Cancer Therapies.[J]. Front Oncol, 2021, 11: 629390. Karpiński Paweł,Sąsiadek Maria Małgorzata. CpG Island Methylator Phenotype—A Hope for the Future or a Road to Nowhere?[J]. International Journal of Molecular Sciences, 2022, 23(2): 830. Minoru Toyota,Nita Ahuja,Mutsumi Ohe-Toyota, et al. CpG island methylator phenotype in colorectal cancer[J]. Proceedings of the National Academy of Sciences, 1999, 96(15): 8681–8686. David Serre,Byron H Lee,Angela H Ting. MBD-isolated Genome Sequencing provides a high-throughput and comprehensive survey of DNA methylation in the human genome[J]. Nucleic Acids Res, 2010, 38(2): 391–399. Daniel J Weisenberger,Kimberly D Siegmund,Mihaela Campan, et al. CpG island methylator phenotype underlies sporadic microsatellite instability and is tightly associated with BRAF mutation in colorectal cancer.[J]. Nature Genetics, 2006, (7): 787–93. Ben Langmead,Cole Trapnell,Mihai Pop, et al. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome[J]. Genome Biology, 2009, 10(3): R25. Feng Niu,Jialing Wen,Xinhui Fu, et al. Stool DNA Test of Methylated Syndecan-2 for the Early Detection of Colorectal Neoplasia[J]. Cancer Epidemiology, Biomarkers & Prevention, 2017, 26(9): 1411–1419. Chenqi Liao,Xiong Wang. TCGAplot: an R package for integrative pan-cancer analysis and visualization of TCGA multi-omics data[J]. Bmc Bioinformatics, 2023, 24(1):483. Hongli Yan,Ae-jin Choi,Byron H. Lee, et al. Identification and Functional Analysis of Epigenetically Silenced MicroRNAs in Colorectal Cancer Cells[J]. Plos One, 2011, 6(6):e20628. Alexander Koch,Jana Jeschke,Wim Van Criekinge, et al. MEXPRESS update 2019[J]. Nucleic Acids Research, 2019, 47(W1):W561-W565. Damian Szklarczyk,Rebecca Kirsch,Mikaela Koutrouli, et al. The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest[J]. Nucleic Acids Research, 2022, 51(D1):D638-D646. Wei Zheng,Qiqige Wuyun,Yang Li, et al. Improving deep learning protein monomer and complex structure prediction using DeepMSA2 with huge metagenomics data[J]. Nature Methods, 2024, 21(2):279–289. Aasma Shaukat,Theodore R Levin. Current and future colorectal cancer screening strategies[J]. Nat Rev Gastroenterol Hepatol, 2022, 19(8): 521–531. Yoon Dae Han,Tae Jeong Oh,Tae-Ha Chung, et al. Early detection of colorectal cancer based on presence of methylated syndecan-2 (SDC2) in stool DNA[J]. Clinical Epigenetics, 2019, 11(1): 51. Sarah Cheuk Hei Chan,Jessie Qiaoyi Liang. Advances in tests for colorectal cancer screening and diagnosis[J]. Expert Rev Mol Diagn, 2022, 22(4): 449–460. Yuba R. Bhandari,Vinod Krishna,Rachael Powers, et al. Transcription factor expression repertoire basis for epigenetic and transcriptional subtypes of colorectal cancers[J]. Proceedings of the National Academy of Sciences, 2023, 120(31): e2301536120. N.R. Shruthi,M.K. Makalakshmi,Alakesh Das, et al. An Updated Review on Molecular Biomarkers in Diagnosis and Therapy of Colorectal Cancer[J]. Current Cancer Drug Targets, 2023, doi: 10.2174/01156800 96270555231113074003. Online ahead of print. Dalia Hamza,Rehab Elhelw,Mahmoud Elhariri, et al. Genotyping and antimicrobial resistance patterns of Helicobacter pylori in human and dogs associated with A2142G and A2143G point mutations in clarithromycin resistance.[J]. Microb Pathog, 2018, 123: 330–338. Chih-Chi Li,Wei-Fan Hsu,Po-Chieh Chiang, et al. Characterization of markers, functional properties, and microbiome composition in human gut-derived bacterial extracellular vesicles[J]. Gut Microbes, 2023, 15(2): 2288200. To Kenneth-KW,Tong Christy-WS,Wu Mingxia, et al. MicroRNAs in the prognosis and therapy of colorectal cancer: From bench to bedside[J]. World Journal of Gastroenterology, 2018, 24(27): 2949–2973. Gerhard Jung,Eva Hernández-Illán,Leticia Moreira, et al. Epigenetics of colorectal cancer: biomarker and therapeutic potential[J]. Nature Reviews Gastroenterology & Hepatology, 2020, 17(2): 111–130. Chagovets Vitaliy,Starodubtseva Natalia,Tokareva Alisa, et al. Specific changes in amino acid profiles in monocytes of patients with breast, lung, colorectal and ovarian cancers[J]. Frontiers in Immunology, 2024, 14: 1332043. Hidayati Husainy Hasbullah,Marahaini Musa. Gene Therapy Targeting p53 and KRAS for Colorectal Cancer Treatment: A Myth or the Way Forward?[J]. International Journal of Molecular Sciences, 2021, 22(21): 11941. Anna Sorrentino,Antonio Federico,Monica Rienzo, et al. PR/SET Domain Family and Cancer: Novel Insights from the Cancer Genome Atlas[J]. International Journal of Molecular Sciences, 2018, 19(10): 3250. Monica Rienzo,Erika Di Zazzo,Amelia Casamassimi, et al. PRDM12 in Health and Diseases[J]. International Journal of Molecular Sciences, 2021, 22(21): 12030. Ya-Chun Chen,Michaela Auer-Grumbach,Shinya Matsukawa, et al. Transcriptional regulator PRDM12 is essential for human pain perception[J]. Nature Genetics, 2015, 47(7): 803–808. Chia-Ming Yang,Yoichi Shinkai. PRDM12 is induced by retinoic acid and exhibits anti-proliferative properties through the cell cycle modulation of P19 embryonic carcinoma cells[J]. Cell Struct Funct, 2013,38(2): 197–206. A G Reid,E P Nacheva. A potential role for PRDM12 in the pathogenesis of chronic myeloid leukaemia with derivative chromosome 9 deletion[J]. Leukemia, 2003, 18: 178–180. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1SupplementarayTables.xlsx Additionalfile2Supplementaryfigures.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-4180792\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":285160974,\"identity\":\"a53e60f6-409d-47ba-9ebc-2166f3b81fe0\",\"order_by\":0,\"name\":\"Peng Yun\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The First Affiliated Hospital of Naval Military Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Peng\",\"middleName\":\"\",\"lastName\":\"Yun\",\"suffix\":\"\"},{\"id\":285160975,\"identity\":\"6176d409-ad5f-4eaf-87be-6de907d6fb1b\",\"order_by\":1,\"name\":\"Kamila 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Yan\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYBACAxDxAESyN0CFDhCjJQFE8sCUEqcFREgkEKnFnL338IuEgsPy5pLPn2662cYgx3cjgfFzAR4tlj3n0iwSDA4b7pydY3Y7t43BWPJGArP0DHwOu5FjZgDUwrjhdg4bSEvihhsJbMw8RGix33Dz+DOQlnpitBg/AGoBGs4AdliCAUEtZ86YAZWlJ284A/RLzjkJw5lnHjZL49VyvMf4w4c/1rYbjgMdllNmI893PPngZ3xagIBNgoGhGcYBshkYG/BrYGBg/sDAUEdI0SgYBaNgFIxkAAA/IlQA6ig/QwAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"The First Affiliated Hospital of Naval Military Medical University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Hongli\",\"middleName\":\"\",\"lastName\":\"Yan\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-03-28 08:42:13\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4180792/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4180792/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":53967808,\"identity\":\"5ebae93d-53ff-4152-aecb-6ac3732f7657\",\"added_by\":\"auto\",\"created_at\":\"2024-04-02 20:01:57\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1857385,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eThe flowchart of this study.\\u003c/strong\\u003e CIMP-P: CpG island methylation phenotype positive; CIMP-N: CIMP-negative; PTs: Primary tumors; NATs: Normal adjacent tissues; TCGA: The Cancer Genome Atlas; CRC: Colorectal Cancer; AAD: Advanced adenoma; HC: Healthy control; IFD: Interfering disease; DMP: Differentially methylated probe; DMG: Differentially methylated genes; mt-msqPCR: Multiple-target methylation-specific quantitative polymerase chain reaction.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4180792/v1/6779b676bb9aacf7a79a253b.png\"},{\"id\":53967810,\"identity\":\"dfcb4bc8-bcf5-402d-89e4-b3103ffef69a\",\"added_by\":\"auto\",\"created_at\":\"2024-04-02 20:01:57\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1124186,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eGenome-wide candidate gene screening.\\u003c/strong\\u003e (a) A Circos plot showing CIMP-P, CIMP-N and NATs on different chromosomes. The circles from outer to inner indicate CIMP-P, CIMP-N and NATs. The lines in the center indicate collinear methyl probe pairs. The 5 target genes and their chromosomal positions are externally labeled. (b) The methylation levels of candidate probes in the in-house dataset. (c) The methylation levels of candidate probes in the TCGA dataset. (d) Classification of DMPs according to their location relative to their genomic distribution. (e) The top 10 significantly enriched GO terms of the candidate DMGs in the BP, MF and CC categories. (f) The top 10 significantly enriched KEGG terms of the candidate DMGs. GO: Gene Ontology; BP: Biological process; MF: Molecular function; CC: Cellular component. DMGs: Differentially methylated genes; KEGG: Kyoto Encyclopedia of Genes and Genomes.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4180792/v1/3d935d0b5f936036cec0aa3a.png\"},{\"id\":53968054,\"identity\":\"bb4035ed-8287-4a62-82a9-3c6e55a1a996\",\"added_by\":\"auto\",\"created_at\":\"2024-04-02 20:09:57\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":644638,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003ePerformance of candidate target genes in pyrosequencing and training sets.\\u003c/strong\\u003e (a) Pyrosequencing was performed to validate the methylation indexes of the five target genes on 31 PTs and paired NATs. (b) Fecal mt-msqPCR was established to validate the positive rates of the five target genes in PTs and stools. (C-F) ROC curves and associated AUCs showing the efficiency of \\u003cem\\u003ePRDM12\\u003c/em\\u003e(c), \\u003cem\\u003eFOXE1\\u003c/em\\u003e (d), \\u003cem\\u003eSDC2\\u003c/em\\u003e (e), and CDM (f) individually in diagnosing CRC (or CRC and AAD) using fecal mt-msqPCR. (g) Polar bar chart depicting the positive rates of various diagnostic objects. CDM: Combined diagnostic model. LPFSM: logistic regression model of \\u003cem\\u003ePRDM12\\u003c/em\\u003e, \\u003cem\\u003eFOXE1\\u003c/em\\u003e and \\u003cem\\u003eSDC2\\u003c/em\\u003e; Tris: tumor in situ; eCRC: early colorectal cancer; aCRC: advanced CRC.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4180792/v1/cd410cd2004b31df43a38d8e.png\"},{\"id\":53967811,\"identity\":\"4738c195-e281-487e-b4f2-05ed191d58ca\",\"added_by\":\"auto\",\"created_at\":\"2024-04-02 20:01:57\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":661773,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003ePerformance of the CDM in the validation set.\\u003c/strong\\u003e The ROC curve and associated AUC were used to assess the diagnostic efficiency of the CDM. (a-b) When HCs served as the control group, the CDM was used to construct diagnostic ROC curves for different patient groups. (c) When HCs and IFD patients served as the control group, the CDM yielded diagnostic ROC curves for CRC and AAD. (d) Using HCs and patients with IFD without STAD as the control group, the CDM was used to construct diagnostic ROC curves for CRC and AAD. CDM: Combined diagnostic model. AAD: advanced adenoma; IFD: interfering disease; STAD: stomach adenocarcinoma.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4180792/v1/2414064b5599bcbcf8db9d6e.png\"},{\"id\":53967812,\"identity\":\"04040da1-a344-4bd2-b9ed-f8891cd371bb\",\"added_by\":\"auto\",\"created_at\":\"2024-04-02 20:01:57\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1582818,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFunctional exploration of PRDM12.\\u003c/strong\\u003e (a) According to the RNA expression level of PRDM12 in PTs, CRC patients in the TCGA public dataset were divided into a low-expression group (n=321) and a high-expression group (n=319). Each point represents a case. GO-GSEA (b) and KEGG-GSEA (c) of the DEGs between the low-expression group and high-expression group. (d) The STRING interaction network suggested that EHMT2 (G9a) is the protein most closely related to PRDM12. Different colors represent different functional clusters. TCGA: The Cancer Genome Atlas; GO-GSEA: Gene Ontology–Gene Set Enrichment Analysis; KEGG-GSEA: Kyoto Encyclopedia of Genes and Genomes–GSEA.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4180792/v1/59d36ade8003b9209fe93a6f.png\"},{\"id\":54606639,\"identity\":\"44665d97-e2fd-45a8-ae42-04ec896b6294\",\"added_by\":\"auto\",\"created_at\":\"2024-04-13 03:09:09\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2958082,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4180792/v1/c4138070-ff36-4953-afea-3ae2f95204b5.pdf\"},{\"id\":53967814,\"identity\":\"37c7e470-751c-41bd-85a8-b3fc3cfd6983\",\"added_by\":\"auto\",\"created_at\":\"2024-04-02 20:01:57\",\"extension\":\"xlsx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":5919451,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Additionalfile1SupplementarayTables.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4180792/v1/0707f06f41200596bba1f036.xlsx\"},{\"id\":53967815,\"identity\":\"a669269e-ca5b-4a94-ace4-9c1c2ccf891c\",\"added_by\":\"auto\",\"created_at\":\"2024-04-02 20:01:57\",\"extension\":\"docx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":5030953,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Additionalfile2Supplementaryfigures.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4180792/v1/2a6950b1068ea03dc4c90ee3.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Enhanced Diagnostic Efficiency of a Novel Fecal Methylated Gene Model for Early Colorectal Cancer Detection\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eColorectal cancer (CRC) ranked second and third in tumor related occurrence and death worldwide, respectively; over 1.8\\u0026nbsp;million new cases and 0.9\\u0026nbsp;million deaths related to CRC occurred in 2020\\u003csup\\u003e[\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]\\u003c/sup\\u003e. Extensive evidence suggests that CRC patients can benefit from early detection and treatment, as the 5-year survival rate (81.7%-89.5%) of patients with early CRC (eCRC) is significantly greater than that of patients with advanced CRC (aCRC, 14.9%-30.4%)\\u003csup\\u003e[\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]\\u003c/sup\\u003e. The main methods currently used for early screening of CRC include fecal occult blood tests (FOBTs), serum tumor marker (CEA, CA-199) analysis, and colonoscopy. Due to its invasive nature, lengthy preparation time, and high cost, colonoscopy is not applicable for large-scale screening of gastrointestinal diseases\\u003csup\\u003e[\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]\\u003c/sup\\u003e. The sensitivities of FOBTs, CEA, and CA-199 for CRC screening are only 42%-73.8%, 32.0%-57.5%, and 16%-21%, respectively, which are far below the requirements for eCRC screening\\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR6 CR7 CR8\\\" citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eIn the last 15 years, more than 30 fecal methylated genes for CRC diagnosis have been reported in more than 40 articles\\u003csup\\u003e[\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]\\u003c/sup\\u003e. However, reviewing these studies, we found that the positive rate of stool DNA (sDNA) was generally 4.76%~52.4% lower than that in primary tumors (PTs) for the same locus or gene\\u003csup\\u003e[\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e, \\u003cspan additionalcitationids=\\\"CR11 CR12\\\" citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]\\u003c/sup\\u003e. There are very few diagnostic panels with sensitivity and specificity greater than 90% \\u003csup\\u003e[\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]\\u003c/sup\\u003e, which limits the clinical application of this technology. In addition to the inadequate polymerase chain reaction (PCR) system, this may also be related to the epigenetic heterogeneity of colorectal cancer\\u003csup\\u003e[\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]\\u003c/sup\\u003e. The concept of the CpG island methylation phenotype (CIMP) in CRC was first proposed in 1999 by Minoru Toyota \\u003csup\\u003e[\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]\\u003c/sup\\u003e. Although the concept was gradually cooling due to the lack of correlation between CIMP and treatment and prognosis, the presence of hypermethylated (or CIMP-positive, CIMP-P) and hypomethylated (or CIMP-negative, CIMP-N) subgroups in CRC has been firmly established\\u003csup\\u003e[\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eIn this study, we performed methyl CpG binding domain (MBD)-isolated genome sequencing (MiGS)\\u003csup\\u003e[\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]\\u003c/sup\\u003e on 3 paired PTs and normal adjacent tissues (NATs) from patients with CIMP-N CRC and 3 PTs from patients with CIMP-P CRC to discover the most easily detectable methylated segments in CRC at the genome-wide level. By integrating methylation profiles from public datasets, we eliminated gene segments related to sex, age, and tumor stage. Finally, we optimized the fecal sample collection and PCR system and developed a CRC diagnosis model based on sDNA. The model exhibited exceptional performance in our independent multicenter validation cohort.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSample Collection\\u003c/h2\\u003e \\u003cp\\u003eIn this study, we collected 3 CIMP-P CRC PTs, 3 CIMP-N CRC PTs and paired NATs for MiGS and 31 PTs and 31 NATs from 31 individuals with CRC for pyrosequencing. In total, 1062 fecal samples consisting of the cohort Ⅲ (n\\u0026thinsp;=\\u0026thinsp;31), cohort Ⅳ (n\\u0026thinsp;=\\u0026thinsp;321) and cohort Ⅴ (n\\u0026thinsp;=\\u0026thinsp;800) were collected from 282 individuals with CRC, 23 individuals with advanced adenoma (AAD), 67 individuals with non-AAD, 642 healthy controls (HCs), and 48 individuals with interfering disease (IFD). All specimens were collected from Changhai Hospital, 7th People's Hospital in Shanghai and Karamay Central Hospital in Karamay, and the distribution of sample sizes is shown in Additional file 1: Supplementary Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e. The demographic characteristics of all individuals are shown in Additional file 1: Supplementary Table \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003eThe reference sites for CIMP-P CRC were 5 classical CIMP sites, namely, \\u003cem\\u003eCACNA1G\\u003c/em\\u003e, \\u003cem\\u003eIGF2\\u003c/em\\u003e, \\u003cem\\u003eNEUROG1\\u003c/em\\u003e, \\u003cem\\u003eRUNX3\\u003c/em\\u003e, and \\u003cem\\u003eSOCS1\\u003c/em\\u003e \\u003csup\\u003e[\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]\\u003c/sup\\u003e, as well as 11 genes reported in the literature, namely, \\u003cem\\u003eCDKN21\\u003c/em\\u003e, \\u003cem\\u003eCRABP1\\u003c/em\\u003e, \\u003cem\\u003eMLH1\\u003c/em\\u003e, \\u003cem\\u003eCHFR\\u003c/em\\u003e, \\u003cem\\u003eHIC1\\u003c/em\\u003e, \\u003cem\\u003eIGFBP3\\u003c/em\\u003e, \\u003cem\\u003eMGMT\\u003c/em\\u003e, \\u003cem\\u003eMINT1\\u003c/em\\u003e, \\u003cem\\u003eMINT21\\u003c/em\\u003e, \\u003cem\\u003eCDKN2A/ARF\\u003c/em\\u003e, and \\u003cem\\u003eWRN\\u003c/em\\u003e\\u003csup\\u003e[\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]\\u003c/sup\\u003e. If the number of methylated genes was \\u0026ge;\\u0026thinsp;11/16, the sample was classified as CIMP-P; otherwise, it was classified as CIMP-N. AAD was defined as villous adenoma or any polyp with high-grade dysplasia or adenoma larger than 1 cm in diameter. Non-AAD was defined as the collection of benign polyps other than AAD. HCs are defined as individuals who are endoscopically diagnosed without gastrointestinal tumors or polyps.\\u003c/p\\u003e \\u003cp\\u003e This study was reviewed and approved by the Hospital Medical Ethics Committee of Changhai Hospital (2018-0016). All individuals in this study provided informed consent.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eDNA extraction\\u003c/h2\\u003e \\u003cp\\u003eAll tissue DNA was obtained from postoperative paraffin-embedded pathological tissues. Experienced pathologists first cut enough tissue sections based on the pathological results, and then laboratory personnel followed the instructions of a tissue genomic DNA extraction kit (Qiagen Co., Germany) to extract DNA from the tissues.\\u003c/p\\u003e \\u003cp\\u003e After receiving training from the study personnel and receiving proper instructions on the collection method, participants utilized a specialized specimen collector (Additional file 2: Supplementary Fig. \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003ea, Sciendox Co., Xiamen, China) to gather approximately 10 g of fecal sample. The specimens were immediately sent to the laboratory for liquefaction by fecal liquefaction treatment equipment (Additional file 2: Supplementary Fig. \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003eb), a filtrate was collected, and the specimens were stored at -80\\u0026deg;C for less than 1 week or immediately processed for DNA extraction using the Magnetic Soil and Stool DNA Kit DP712 (Tiangen Co., Beijing, China) according to the manufacturer\\u0026rsquo;s instructions.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMiGS\\u003c/h2\\u003e \\u003cp\\u003eMiGS analysis was performed as previously described\\u003csup\\u003e[\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]\\u003c/sup\\u003e. In brief, genomic DNA was fragmented to 150\\u0026ndash;600 bp by sonication in BW buffer, segments smaller than 100 bp were removed by QIAquick PCR Cleanup columns (Qiagen), and 5 \\u0026micro;g of purified DNA fragments was used for immunoprecipitation. Methylated DNA was isolated by recombinant MBD-magnetic beads. The eluted DNA was extracted with phenol: chloroform, precipitated with isopropanol, and resuspended in 45 \\u0026micro;L of dH\\u003csub\\u003e2\\u003c/sub\\u003eO. A ChIP-Seq Sample Prep Kit (Illumina) was used to construct the sequencing libraries on 10 ng of MBD-isolated DNA per sample. All sequencing results were aligned to NCBI Build 36.1/UCSC Hg18 using the University of California, Santa Cruz (UCSC) \\u003csup\\u003e[\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]\\u003c/sup\\u003e server.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eBisulfite treatment\\u003c/h2\\u003e \\u003cp\\u003eThe purified genomic DNA was subjected to bisulfite treatment using an EZ DNA Methylation-Gold\\u0026trade; Kit (Zymo Research Co., Ltd.) following the manufacturer\\u0026rsquo;s instructions. The eluted DNA was transferred to tubes for transformation, followed by washing with 30 \\u0026micro;L of elution buffer. The eluted BisDNA was used entirely as the template for subsequent experiments.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePyrosequencing\\u003c/h2\\u003e \\u003cp\\u003ePCR products labeled with biotin were mixed with microbeads containing streptavidin. After denaturation, the sequencing template was single-stranded, and the methylation level of the CpG sites in the promoters of each gene was detected using a pyrophosphate sequencer. The primers used for pyrosequencing (Additional file 1: Supplementary Table S3) were designed with PyroMark Assay Design 2.0 software and synthesized by Sangon Biotech Co., Shanghai, China. The PCR system was 50 \\u0026micro;L. The reaction program is shown in Additional file 1: Supplementary Table S4. The methylation index, which is equal to the peak height of methylated sites/(peak height of methylated sites\\u0026thinsp;+\\u0026thinsp;peak height of nonmethylated sites), was used to measure the degree of methylation.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eFecal mt-msqPCR\\u003c/h2\\u003e \\u003cp\\u003eFecal multiple-target methylation-specific quantitative polymerase chain reaction (mt-msqPCR) was established and optimized on 31 stool samples from cohort Ⅲ. The primers (Additional file 1: Supplementary Table S5) used were designed and synthesized by Sangon Bioengineering (Shanghai) Co., Ltd. The reference gene used was β-Actin, and the primers used for \\u003cem\\u003eSDC2\\u003c/em\\u003e were obtained from previous reports\\u003csup\\u003e[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]\\u003c/sup\\u003e. Our optimized reaction system is shown in Additional file 1: Supplementary Table S6. Each batch of samples was set up with a blank control (Tris-HCl buffer), a negative control (nonmethylated gene plasmid) and a positive control (methylated gene plasmid). Specifically, cohort V was designed as a multicenter, double-blind study to avoid the subjective influence of testers. Different people were responsible for specimen collection, detection and summarization of the data.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical analysis\\u003c/h2\\u003e \\u003cp\\u003eAll the statistical analyses and figures were made using R software (version 4.3.1, \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://cran.rstudio.com/\\u003c/span\\u003e\\u003cspan address=\\\"https://cran.rstudio.com/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). MiGS data was analyzed by the Mann‒Whitney U test. Paired Student\\u0026rsquo;s t test was used to compare paired measurement data. The corrected \\u003cem\\u003eP\\u003c/em\\u003e was calculated by the Benjamini‒Hochberg (BH) method. The intersection of the differentially methylated probes (DMPs) between the MiGS data and The Cancer Genome Atlas (TCGA) data was calculated using the \\u0026ldquo;GenomicRanges\\u0026rdquo; package in version 1.52.1. A circle plot was generated with 2 R packages, \\u0026ldquo;shiny\\u0026rdquo; version 1.8.0 and \\u0026ldquo;circlize\\u0026rdquo; version 0.4.15. The receiver operating characteristic (ROC) curve was plotted by \\u0026ldquo;pROC\\u0026rdquo; version 1.18.5, and the cutoff value, area under the curve (AUC) and 95% confidence interval (CI) were calculated, simultaneously. Pancancer and \\u003cem\\u003ePRDM12\\u003c/em\\u003e functions were analyzed by the R package TCGAplot\\u003csup\\u003e[\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]\\u003c/sup\\u003e. Other plots were plotted using the \\u0026ldquo;ggplot2\\u0026rdquo; package in version 3.4.4. A logistic regression model of the target genes, LPFSM, was established based on the binary logistic regression method. If any target genes were positive, the combined diagnosis model (CDM) was used to determine that the gene was positive. The exact cutoff Ct values for each gene and CDM and LPFSM are presented in Additional file 1: Supplementary Table S7.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eFlowchart of the study\\u003c/h2\\u003e \\u003cp\\u003eThe flowchart of our study is shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. First, we performed MiGS for 3 CIMP-P CRC PTs, 3 CIMP-N CRC PTs and paired NATs to identify DMPs across the whole genome. Then, we performed DMP analysis on the TCGA-CRC datasets (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://portal.gdc.cancer.gov/\\u003c/span\\u003e\\u003cspan address=\\\"https://portal.gdc.cancer.gov/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) and obtained the intersection of the DMPs of the two cohorts. In the second step, we validated and narrowed down the candidate genes. Pyrosequencing was performed for 31 CRC PTs and their NATs to verify the validity of the genome-wide methylation screening results. Fecal mt-msqPCR was established to examine the diagnostic efficiency of the candidate genes in stool. CDM and LPFSM were established to improve the diagnostic efficiency. In the third step, a multicenter cohort was recruited to validate the diagnostic efficiency of CDM and LPFSM by performing fecal mt-msqPCR. Finally, we investigated the function of \\u003cem\\u003ePRDM12\\u003c/em\\u003e using public datasets.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eGenome-wide candidate gene screening\\u003c/h2\\u003e \\u003cp\\u003eMiGS for cohort I revealed 596,168 to 791,389 methylated fragments in 9 specimens, for a total of 1,226,751 nonrepetitive methylated fragments (Additional file 1: Supplementary Table S8). Methylation fragments with a methylation difference greater than 20 between the CIMP-P and CIMP-N PTs were removed. Differential methylation analysis yielded 19,530 DMPs (Additional file 1: Supplementary Table S9), of which 12,558 (64.30%) were hypermethylated and 6,974 (35.70%) were hypomethylated (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eb). Based on the filtering criteria, 25,751 DMPs were obtained by differential methylation analysis in cohort-Ⅱ, of which 21,527 (83.60%) DMPs were hypermethylated and 4,224 (16.40%) DMPs were hypomethylated (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ec). By using the GenomicRanges package to take the gene coordinate intersection of two DMP datasets, a total of 1,662 DMPs were obtained, corresponding to 727 differentially methylated genes (DMGs) (Additional file 1: Supplementary Table S10). The majority of DMPs were located in the gene body (26.73%), while the fewest were found in the 3\\u0026rsquo;UTR (1.02%) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ed).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eBased on the DMG profiles, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The top 10 terms for each of the 3 ontologies are shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ee. The detailed results can be found in Additional file 1: Supplementary Table S11. KEGG pathway enrichment analysis revealed that the DMGs were mainly enriched in pathways such as neuroactive ligand‒receptor interaction, the PI3K-AKT signaling pathway, and the cAMP signaling pathway (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ef, Additional file 1: Supplementary Table S12).\\u003c/p\\u003e \\u003cp\\u003eTo eliminate DNA methylation changes caused by cancer progression, we screened for DMPs between 177 early CRC (eCRC, stage Ⅰ-Ⅱ) and 147 advanced CRC (aCRC, stage Ⅲ-Ⅳ) samples in cohort II, resulting in a total of 15,359 DMPs (Additional file 1: Supplementary Table S13, Figure \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e). The 53 overlapping portions of the candidate DMPs were removed. Analogously, 4,000 age-related DMPs were identified (Additional file 1: Supplementary Table S14), 50 overlapping DMPs were removed, and 50 gender-related DMPs were removed. Next, among the remaining DMPs, we identified 920 pairs of probes showing collinearity (Additional file 1: Supplementary Table S15, Figure S3). The 487 overlapping portions of candidate DMPs were removed. Finally, the number of candidate DMPs was reduced to 903, corresponding to 645 DMGs (Additional file 1: Supplementary Table S16). According to our previous study\\u003csup\\u003e[\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]\\u003c/sup\\u003e, we selected 5 DMGs (\\u003cem\\u003ePRDM12\\u003c/em\\u003e|cg09191327, \\u003cem\\u003eFOXE1\\u003c/em\\u003e|cg02157015, \\u003cem\\u003eB3GAT2\\u003c/em\\u003e|cg10766373, \\u003cem\\u003eVIM\\u003c/em\\u003e|cg01003015, and \\u003cem\\u003eSFRP2\\u003c/em\\u003e|cg25645268) for further validation (Additional file 2: Supplementary Fig. S4).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePyrosequencing validation and establishment of fecal mt-msqPCR\\u003c/h2\\u003e \\u003cp\\u003eWe performed pyrosequencing on the 5 candidate DMGs to verify their methylation status in PTs and NATs from 31 individuals with CRC. The average methylation index of all the DMGs in PTs was significantly greater than that in NATs (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ea, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). However, some patients showed hypomethylation of \\u003cem\\u003ePRDM12\\u003c/em\\u003e, \\u003cem\\u003eFOXE1\\u003c/em\\u003e, \\u003cem\\u003eB3GAT2\\u003c/em\\u003e, \\u003cem\\u003eVIM\\u003c/em\\u003e, and SPRF2 in PTs compared to NATs. The hypermethylation ratio of \\u003cem\\u003eSFRP2\\u003c/em\\u003e was significantly lower than that of \\u003cem\\u003ePRDM12\\u003c/em\\u003e and \\u003cem\\u003eFOXE1\\u003c/em\\u003e (Additional file 2: Supplementary Fig. S5, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). During the same period, preoperative stools were collected to establish fecal mt-msqPCR. After condition optimization, the methylation status of 5 candidate genes in PTs and Stool was detected. We found that only the percentage of patients with positive results for \\u003cem\\u003ePRDM12\\u003c/em\\u003e and \\u003cem\\u003eFOXE1\\u003c/em\\u003e in PTs reached 100%, while the percentage of patients with positive results in stools exceeded 80% (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eb). The percentage of positive \\u003cem\\u003eFOXE1\\u003c/em\\u003e, \\u003cem\\u003eB3GAT2\\u003c/em\\u003e, \\u003cem\\u003eVIM\\u003c/em\\u003e and \\u003cem\\u003eSFRP2\\u003c/em\\u003e in feces was significantly lower than that in cancer tissue (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eb, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), similar to previous reports\\u003csup\\u003e[\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]\\u003c/sup\\u003e. \\u003cem\\u003eSDC2\\u003c/em\\u003e has previously been demonstrated to be an effective sDNA marker for diagnosing eCRC\\u003csup\\u003e[\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]\\u003c/sup\\u003e. To enhance diagnostic efficiency, \\u003cem\\u003eSDC2\\u003c/em\\u003e was selected as a target gene alongside \\u003cem\\u003ePRDM12\\u003c/em\\u003e and \\u003cem\\u003eFOXE1\\u003c/em\\u003e in the training set.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePerformance of target genes for detecting CRC in the training set\\u003c/h2\\u003e \\u003cp\\u003eThe training set (cohort Ⅳ) of this study included stool samples from 105 HCs, 13 patients with AAD and 113 patients with CRC. The ROC curves suggested that \\u003cem\\u003ePRDM12\\u003c/em\\u003e had the highest AUC [0.905 (95% CI: 0.868\\u0026ndash;0.942)] than those of \\u003cem\\u003eFOXE1\\u003c/em\\u003e [0.835 (95% CI: 0.791\\u0026ndash;0.879)] and \\u003cem\\u003eSDC2\\u003c/em\\u003e [0.900 (95% CI: 0.863\\u0026ndash;0.938)] (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ec-e). The optimal sensitivities of \\u003cem\\u003ePRDM12\\u003c/em\\u003e, \\u003cem\\u003eFOXE1\\u003c/em\\u003e, and \\u003cem\\u003eSDC2\\u003c/em\\u003e for CRC were 80.53%, 67.26%, and 80.53%, respectively. The optimal specificities of \\u003cem\\u003ePRDM12\\u003c/em\\u003e (100%) and \\u003cem\\u003eFOXE1\\u003c/em\\u003e (100%) were slightly greater than that of \\u003cem\\u003eSDC2\\u003c/em\\u003e (99.05%). When AAD and CRC were utilized as diagnostic criteria, \\u003cem\\u003ePRDM12\\u003c/em\\u003e, \\u003cem\\u003eFOXE1\\u003c/em\\u003e, and \\u003cem\\u003eSDC2\\u003c/em\\u003e exhibited AUCs of 0.899, 0.840, and 0.895, respectively (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ec-e).\\u003c/p\\u003e \\u003cp\\u003eAlthough they all showed high specificity, none of the genes were more than 90% sensitive to CRC. Therefore, we developed two diagnostic models, the CDM and the LPFSM. The ROC curve demonstrated that CDM (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ef) and LPFSM (Additional file 2: Supplementary Fig. S6) improved the AUC for CRC diagnosis, achieving scores of 0.979 (95% CI: 0.960\\u0026ndash;0.997) and 0.986 (95% CI: 0.970\\u0026ndash;1.000), respectively. The optimal sensitivity and specificity of the CDM were 97.35% and 99.05%, respectively, for CRC diagnosis and 96.83% and 99.05%, respectively, for CRC\\u0026amp;AAD diagnosis.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePerformance of the CDM and LPFSM in the validation set\\u003c/h2\\u003e \\u003cp\\u003eThe validation set (cohort Ⅴ) included 800 fecal samples from 537 HCs, 67 non-AAD patients, 10 AAD patients, 138 CRC patients and 47 IFD patients, with detailed clinical data presented in Additional file 1: Supplementary Table \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e. When HCs were used as controls, the AUCs of the CDM for CRC, eCRC, non-AAD and CRC\\u0026amp;AAD were 0.950 (95% CI: 0.927\\u0026ndash;0.973), 0.959 (95% CI: 0.931\\u0026ndash;0.986), 0.829 (95% CI: 0.773\\u0026ndash;0.886) and 0.945 (95% CI: 0.922\\u0026ndash;0.969), respectively (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ea-b). The optimal sensitivities of CDM for CRC, eCRC, non-AAD and CRC\\u0026amp;AAD were 92.75%, 94.52%, 68.66% and 91.89%, respectively. When IFD and HC were utilized as controls, the AUC for CRC\\u0026amp;AAD decreased slightly to 0.936 (95% CI: 0.912\\u0026ndash;0.959) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ec). The optimal specificity of CDM was 95.21%. Interestingly, 63.64% (7/11) of stomach adenocarcinomas (STADs) in IFD patients were CDM positive, suggesting that our target genes may also have certain diagnostic value for STAD. When STADs among IFD patients were removed, the AUC for CRC\\u0026amp;AAD diagnosis increased to 0.941 (95% CI: 0.918\\u0026ndash;0.965) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ed). The diagnostic efficiency of LPFSM was slightly worse than that of CDM (Additional file 2: Supplementary Fig. S7 and Additional file 1: Supplementary Table S17).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eFunctional exploration of\\u003c/b\\u003e \\u003cb\\u003ePRDM12\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003eWe used various omics databases, including TCGA, to investigate the functional role of the \\u003cem\\u003ePRDM12\\u003c/em\\u003e gene. Our analysis revealed that \\u003cem\\u003ePRDM12\\u003c/em\\u003e has a highly conserved expression pattern in CRC in situ. Moreover, in the PanCancer Atlas dataset (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.cbioportal.org/\\u003c/span\\u003e\\u003cspan address=\\\"https://www.cbioportal.org/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) comprising 526 CRC patients, the mutation rate of \\u003cem\\u003ePRDM12\\u003c/em\\u003e was remarkably low at only 0.38% (2/526), which is significantly lower than that of \\u003cem\\u003eSDC2\\u003c/em\\u003e [3.42% (18/526), \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001] (Additional file 2: Supplementary Fig. S8). Pancancer analysis revealed that \\u003cem\\u003ePRDM12\\u003c/em\\u003e had almost no expression in 23 types of NATs (9 with no data and high expression in GBM) but was fully expressed in PTs. Furthermore, \\u003cem\\u003ePRDM12\\u003c/em\\u003e expression was significantly increased in 16 different types of PTs (Additional file 2: Supplementary Fig. S9a). Additionally, among the 33 major cancers, \\u003cem\\u003ePRDM12\\u003c/em\\u003e exhibited weak correlations with tumor mutation burden (TMB), microsatellite instability (MSI), and immune scores (Additional file 2: Supplementary Fig. S9b-d).\\u003c/p\\u003e \\u003cp\\u003eIntriguingly, \\u003cem\\u003ePRDM12\\u003c/em\\u003e exhibited hypomethylation and restricted expression in normal adult human tissues (excluding those of the nervous system) (Additional file 2: Supplementary Fig. S10a; \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.proteinatlas.org/\\u003c/span\\u003e\\u003cspan address=\\\"https://www.proteinatlas.org/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). However, CRC cells are highly methylated, and their expression is increased \\u003csup\\u003e[\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]\\u003c/sup\\u003e (Additional file 2: Supplementary Fig. S10b; \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://mexpress.ugent.be/\\u003c/span\\u003e\\u003cspan address=\\\"https://mexpress.ugent.be/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). According to the median expression of \\u003cem\\u003ePRDM12\\u003c/em\\u003e in CRC, the patients (n\\u0026thinsp;=\\u0026thinsp;640) were divided into \\\"low-expression\\\" and \\\"high-expression\\\" groups (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003ea). The top 20 DEGs are shown in Additional file 2: Supplementary Fig. S11a and Additional File 1: Supplementary Table S18. Then, we performed GO gene set enrichment analysis (GO-GSEA) and KEGG-GSEA for the DEGs. The results showed that DEGs were mainly enriched in mitochondrial gene expression and ribosome assembly (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eb, Additional file 1: Supplementary Table S19) and pathways enriched in cell cycle regulation (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003ec, Additional File 1: Supplementary Table S20).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe STRING interaction network\\u003csup\\u003e[\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]\\u003c/sup\\u003e suggested that EHMT2 (G9a) is the protein most closely related to PRDM12 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003ed). DMFold2\\u003csup\\u003e[\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]\\u003c/sup\\u003e, the latest model for protein structure and function prediction, suggested that PRDM12 may interact with G9a and localize to the nucleus to regulate a wide range of biological processes (Additional file 2: Supplementary Fig. S11b-d).\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eAt present, colonoscopy and pathological biopsy are still the gold standards for the diagnosis of colorectal cancer. However, the disadvantages of colonoscopy are also obvious. First, colonoscopy is highly dependent on the experience of the operator. Second, complex preoperative preparation is not suitable for mass screening. Third, patients have poor experience with this invasive test. Methylated sDNA analysis is considered a promising method for large-scale CRC screening. To date, more than 30 methylated sDNA biomarkers have been developed, but only a few biomarkers have met the clinical need for diagnostic efficiency to some extent\\u003csup\\u003e[\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]\\u003c/sup\\u003e. For example, methylated \\u003cem\\u003eBMP3\\u003c/em\\u003e combined with \\u003cem\\u003eNDRG4\\u003c/em\\u003e mutant KRAS was approved by the US. The FDA has a sensitivity of 92.3% and a specificity of 86.6% for CRC diagnosis\\u003csup\\u003e[\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]\\u003c/sup\\u003e, and the sensitivity and specificity of \\u003cem\\u003eSDC2\\u003c/em\\u003e, which are approved by the China FDA, are 83.8%-89.1% and 98.0%, respectively\\u003csup\\u003e[\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]\\u003c/sup\\u003e. Due to its suboptimal sensitivity and specificity and prolonged detection time, this method has yet to replace conventional screening methods. As a result, further development of a new panel/model with improved sensitivity and specificity is essential\\u003csup\\u003e[\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eCRC methylation patterns exhibit heterogeneity, and a common classification approach in many studies involves categorizing CRC into hypermethylated and hypomethylated subtypes based on the CIMP\\u003csup\\u003e[\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]\\u003c/sup\\u003e. In our study, we screened a specific gene set for CRC diagnosis using the CIMP concept. Due to the limited size of our in-house dataset, we implemented more rigorous screening criteria, resulting in all 6 PTs exhibiting hypermethylation irrespective of their CIMP phenotype. To obtain stage-insensitive CpGs, we retained only those DMCs that did not differ significantly between eCRC and aCRC patients. Similarly, age-, sex-related and collinear CpG sites were excluded. As a result, we identified a DMG set that included many previously unreported CRC methylation markers.\\u003c/p\\u003e \\u003cp\\u003eWe aimed to enhance the sensitivity of sDNA for diagnosing CRC, with the goal of identifying the most minimal marker for CRC detection. However, our study highlighted the challenges in achieving a significant breakthrough with single sDNA. Similar to several previously reported methylated sDNA markers\\u003csup\\u003e[\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]\\u003c/sup\\u003e, the rates of positivity for \\u003cem\\u003eB3GAT2\\u003c/em\\u003e, \\u003cem\\u003eVIM\\u003c/em\\u003e, and \\u003cem\\u003eSFRP2\\u003c/em\\u003e in feces were notably lower than those observed in PTs. There may be several underlying factors that can account for our analysis. First, fecal samples contain fewer tumor cells\\u003csup\\u003e[\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]\\u003c/sup\\u003e, necessitating an experimental system with high sensitivity. Second, the composition of feces, including DNA from bacteria, along with residues from various animal and plant foods, is complex. Third, dietary structure, digestive function, and intestinal condition can also impact the outcomes of the test\\u003csup\\u003e[\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eTherefore, by combining \\u003cem\\u003ePRDM12\\u003c/em\\u003e with previously reported genes in our DMG set, namely, \\u003cem\\u003eFOXE1\\u003c/em\\u003e and \\u003cem\\u003eSDC2\\u003c/em\\u003e, we established a 3-target model, CDM, that exhibited exceptional performance in detecting eCRC, as validated in both the training and independent validation cohorts. The sensitivity and specificity of CDM for eCRC patients were 94.52% and 97.21%, respectively, and those for CRC patients and patients with AADs were 91.89% and 95.21%, respectively, both of which are better than those of the kits reported thus far. Furthermore, in the verification group, we added a variety of diseases, such as gastrointestinal malignancies, benign diseases, and extralimentary tumors, as interfering diseases, and the results showed that only STAD may interfere with the diagnosis of CRC by CDM. Therefore, in clinical diagnosis and treatment, attention should be given to the identification of upper and lower gastrointestinal symptoms.\\u003c/p\\u003e \\u003cp\\u003eThe progression from atypical hyperplastic colorectal polyps to malignant colorectal adenocarcinoma involves the accumulation of genetic and epigenetic alterations\\u003csup\\u003e[\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]\\u003c/sup\\u003e, resulting in epigenetic reprogramming of human colonocytes. Different types of cancers display unique methylation patterns, and certain genes, including \\u003cem\\u003ePRDM12\\u003c/em\\u003e, exhibit similar patterns across multiple cancer types\\u003csup\\u003e[\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e]\\u003c/sup\\u003e. Considering the potential role of \\u003cem\\u003ePRDM12\\u003c/em\\u003e in the occurrence of CRC, we explored its function. \\u003cem\\u003ePRDM12\\u003c/em\\u003e belongs to the PRDI-BF1 (positive regulatory domain I-binding factor 1) homologous domain (PRDM)-containing protein family, which is a subfamily of Kruppel-like zinc finger proteins\\u003csup\\u003e[\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]\\u003c/sup\\u003e. This family plays a crucial role in regulating cancer development\\u003csup\\u003e[\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e]\\u003c/sup\\u003e. \\u003cem\\u003ePRDM12\\u003c/em\\u003e, which is restrictively expressed in normal human tissues, shows elevated expression in eCRC and consistently maintains a lower expression level, suggesting its potential widespread involvement in cancer development. \\u003cem\\u003ePRDM12\\u003c/em\\u003e is known to play a role in the development of sensory neurons \\u003csup\\u003e[\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]\\u003c/sup\\u003e; however, the role of \\u003cem\\u003ePRDM12\\u003c/em\\u003e in cancer has been poorly studied.\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003ePRDM12\\u003c/em\\u003e is an important transcriptional regulator capable of regulating neural differentiation and formation in combination with solid tumors and hematological malignancies \\u003csup\\u003e[\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e]\\u003c/sup\\u003e. AG Reid\\u003csup\\u003e[\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e]\\u003c/sup\\u003e et al. reported that 15% of patients with chronic myeloid leukemia have deletion of the \\u003cem\\u003ePRDM12\\u003c/em\\u003e gene, which is related to rapid progression and short-term survival. Pancancer analysis suggested that \\u003cem\\u003ePRDM12\\u003c/em\\u003e expression is poorly correlated with patient prognosis and immunotherapy outcomes. Subsequently, we stratified CRC patients into high-expression and low-expression groups, revealing that the DEGs were primarily enriched in the cell cycle pathway. Protein interaction analysis demonstrated a close association between \\u003cem\\u003ePRDM12\\u003c/em\\u003e and EHMT2 (G9a), while DMFold2 predicted its involvement in various cellular processes. The findings of Yang et al\\u003csup\\u003e[\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e]\\u003c/sup\\u003e. In P19 embryonic carcinoma cells, \\u003cem\\u003ePRDM12\\u003c/em\\u003e recruits G9a to methylate lysine 9 of histone H3 (H3K9me), resulting in a decrease in chromatin structure and an increase in the proportion of G1-phase cells. However, this alteration hinders cell cycle progression, suggesting that \\u003cem\\u003ePRDM12\\u003c/em\\u003e may function as a tumor suppressor gene.\\u003c/p\\u003e \\u003cp\\u003eThe present study has certain limitations. First, 25% of participants in the validation set were symptomatic patients with CRC, which could lead to an overestimation of the sensitivity of the \\u003cem\\u003ePRDM12\\u003c/em\\u003e test. Second, the sensitivity of \\u003cem\\u003ePRDM12\\u003c/em\\u003e in detecting STAD is nearly 65%, which may result in an increase in the number of endoscopies performed by individuals during physical examinations. However, from a different perspective, this could broaden the application range of \\u003cem\\u003ePRDM12\\u003c/em\\u003e. Third, controlling the quality of specimens proves to be challenging. Patients collect their own specimens, and the use of regular toilets instead of squatting toilets in most wards contributes to difficulties in sampling and increases the possibility of specimens being soaked in water.\\u003c/p\\u003e \\u003cp\\u003eIn summary, CIMP-based genome-wide methylation profiles provide valuable epigenetic information that can be used to develop novel methylation markers. The three-target diagnostic model constructed in this study further improved the diagnostic efficiency of methylated sDNA for eCRC.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cdiv class=\\\"DefinitionList\\\"\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003esDNA:stool DNA\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eCRC\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003ecolorectal cancer\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eAAD\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eadvanced adenoma\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003ePTs\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eprimary tumors\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eNATs\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003enormal adjacent tissues\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eTCGA\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eThe Cancer Genome Atlas\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eHC\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003ehealthy control\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eIFD\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003einterfering disease\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eDMP\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003edifferentially methylated probe\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eDMG\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003edifferentially methylated gene\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003emt-msqPCR\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003emultiple-target methylation-specific quantitative polymerase chain reaction\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eCDM\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003ecombined diagnosis model\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eLPFSM\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003elogistic regression model of \\u003cem\\u003ePRDM12 FOXE1\\u003c/em\\u003e and \\u003cem\\u003eSDC2\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eMiGS\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003emethyl CpG binding domain isolated genome sequencing\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eDMRs\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003edifferentially methylated regions\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eCIMP-P\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eCpG island methylation phenotype positive\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eCIMP-N\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eCIMP negative\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eMtI\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003emethylation index\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eROC, receiver operating characteristic\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e\\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eAUC\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003earea under curve\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eSTAD\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eStomach adenocarcinoma\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eGO-GSEA\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eGene Ontology\\u0026ndash;Gene Set Enrichment Analysis\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eKEGG-GSEA\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eKyoto Encyclopedia of Genes and Genomes\\u0026ndash;GSEA.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe express our sincere gratitude to the medical staff of the Department of Anorectal Surgery at Changhai Hospital for their invaluable contribution in collecting specimens. We are also thankful to Sciendox Co. for their innovative fecal collection. Additionally, we extend our heartfelt to the researchers at Sciendox Co. for their diligent efforts in optimizing the msqMSP experimental conditions. Special thanks to Dr. Xiong Wang from the Department of Laboratory Medicine at Tongji Hospital, Huazhong University of Science and Technology, for his assistance\\u0026nbsp;with the\\u0026nbsp;bioinformatics analysis.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026apos; contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003ePY, KK, JP, ZZ, MZ, and HY contributed to conceptualization; PY, JP, and HY contributed to data curation; PY, JP, and HY were involved in clinical data collection and formal analysis; MZ and HY\\u0026nbsp;were\\u0026nbsp;involved in funding acquisition; PY, KK, JP, NW, LY, YZ, HC, and ZX contributed to\\u0026nbsp;the\\u0026nbsp;investigation; LY, NW, KK, YZ, PY, and ZX were involved in methodology; ZZ, MZ and HY contributed to project administration; ZZ, MZ and HY contributed to supervision; PY wrote the first version of the manuscript; YP, KK, JP, ZX, and HY contributed to writing, review and editing. All the authors have read and approved the final manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was supported by\\u0026nbsp;the\\u0026nbsp;National Natural Science Foundation of China (81872225, 82273465) and supported in part by grants from the National Natural Science Foundation of China (grant no.\\u0026nbsp;82260322 to M. Z.) and from\\u0026nbsp;the\\u0026nbsp;Natural Science Foundation of Xinjiang Uygur Autonomous Region for Outstanding Young Scientists (grant no. 2021D01E34 to M. Z.).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was reviewed and approved by the Hospital Medical Ethics Committee of the Officers Hospital of Naval Medical University (2018-0016). All the authors declare that this study was performed in accordance with the Declaration of Helsinki.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe first author wrote the first version of the manuscript, and all the authors reviewed the subsequent drafts and agreed to submit articles for publication.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets used and\\u0026nbsp;analyzed\\u0026nbsp;during the current study are available from the corresponding author\\u0026nbsp;upon\\u0026nbsp;reasonable request.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting Interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no competing interests.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eHyuna Sung,Jacques Ferlay,Rebecca L. 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The STRING database in 2023: protein\\u0026ndash;protein association networks and functional enrichment analyses for any sequenced genome of interest[J]. Nucleic Acids Research, 2022, 51(D1):D638-D646.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWei Zheng,Qiqige Wuyun,Yang Li, et al. Improving deep learning protein monomer and complex structure prediction using DeepMSA2 with huge metagenomics data[J]. Nature Methods, 2024, 21(2):279\\u0026ndash;289.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAasma Shaukat,Theodore R Levin. Current and future colorectal cancer screening strategies[J]. Nat Rev Gastroenterol Hepatol, 2022, 19(8): 521\\u0026ndash;531.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYoon Dae Han,Tae Jeong Oh,Tae-Ha Chung, et al. Early detection of colorectal cancer based on presence of methylated syndecan-2 (SDC2) in stool DNA[J]. 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Online ahead of print.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDalia Hamza,Rehab Elhelw,Mahmoud Elhariri, et al. Genotyping and antimicrobial resistance patterns of Helicobacter pylori in human and dogs associated with A2142G and A2143G point mutations in clarithromycin resistance.[J]. Microb Pathog, 2018, 123: 330\\u0026ndash;338.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChih-Chi Li,Wei-Fan Hsu,Po-Chieh Chiang, et al. Characterization of markers, functional properties, and microbiome composition in human gut-derived bacterial extracellular vesicles[J]. Gut Microbes, 2023, 15(2): 2288200.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eTo Kenneth-KW,Tong Christy-WS,Wu Mingxia, et al. MicroRNAs in the prognosis and therapy of colorectal cancer: From bench to bedside[J]. World Journal of Gastroenterology, 2018, 24(27): 2949\\u0026ndash;2973.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGerhard Jung,Eva Hern\\u0026aacute;ndez-Ill\\u0026aacute;n,Leticia Moreira, et al. Epigenetics of colorectal cancer: biomarker and therapeutic potential[J]. Nature Reviews Gastroenterology \\u0026amp; Hepatology, 2020, 17(2): 111\\u0026ndash;130.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChagovets Vitaliy,Starodubtseva Natalia,Tokareva Alisa, et al. Specific changes in amino acid profiles in monocytes of patients with breast, lung, colorectal and ovarian cancers[J]. Frontiers in Immunology, 2024, 14: 1332043.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHidayati Husainy Hasbullah,Marahaini Musa. Gene Therapy Targeting p53 and KRAS for Colorectal Cancer Treatment: A Myth or the Way Forward?[J]. International Journal of Molecular Sciences, 2021, 22(21): 11941.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAnna Sorrentino,Antonio Federico,Monica Rienzo, et al. PR/SET Domain Family and Cancer: Novel Insights from the Cancer Genome Atlas[J]. International Journal of Molecular Sciences, 2018, 19(10): 3250.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMonica Rienzo,Erika Di Zazzo,Amelia Casamassimi, et al. PRDM12 in Health and Diseases[J]. International Journal of Molecular Sciences, 2021, 22(21): 12030.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYa-Chun Chen,Michaela Auer-Grumbach,Shinya Matsukawa, et al. Transcriptional regulator PRDM12 is essential for human pain perception[J]. Nature Genetics, 2015, 47(7): 803\\u0026ndash;808.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChia-Ming Yang,Yoichi Shinkai. PRDM12 is induced by retinoic acid and exhibits anti-proliferative properties through the cell cycle modulation of P19 embryonic carcinoma cells[J]. Cell Struct Funct, 2013,38(2): 197\\u0026ndash;206.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eA G Reid,E P Nacheva. A potential role for PRDM12 in the pathogenesis of chronic myeloid leukaemia with derivative chromosome 9 deletion[J]. Leukemia, 2003, 18: 178\\u0026ndash;180.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Stool DNA, Colorectal Cancer, Early Diagnosis, PRDM12\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4180792/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4180792/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackground and Aims\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMethylation of stool DNA (sDNA) is a reliable noninvasive early diagnostic marker for colorectal cancer (CRC). Our study aimed to identify a new gene panel for the early diagnosis of CRC.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe conducted methyl-CpG binding domain isolated genome sequencing (MiGS) on 3 CpG island methylation phenotype (CIMP)-positive and 3 CIMP-negative CRC tissues and their corresponding normal adjacent tissues. Subsequently, by utilizing both the aforementioned data and public datasets, we identified a set of promising methylated sDNA markers for CRC. Finally, we developed a combined diagnostic model (CDM) for CRC based on the methylation status of \\u003cem\\u003ePRDM12\\u003c/em\\u003e, \\u003cem\\u003eFOXE1\\u003c/em\\u003e, and \\u003cem\\u003eSDC2\\u003c/em\\u003e and evaluated its performance in an independent multicenter validation cohort.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA total of 1,062 participants were included in this study. The area under the curve (AUC) of the CDM was 0.979 (95% CI: 0.960–0.997), and the optimal sensitivity and specificity were 97.35% and 99.05%, respectively, in the training cohort (n = 231). In the independent validation cohort (n = 800), the AUC was 0.950 (95% CI: 0.927–0.973), along with the optimal sensitivity of 92.75% and specificity of 97.21%. When CRC and advanced adenoma (AAD) were used as diagnostic targets, the model AUC was 0.945 (95% CI: 0.922–0.969), with an optimal sensitivity of 91.89% and a specificity of 95.21%. The model sensitivity for nonadvanced adenoma patients was 68.66%.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusion\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe sDNA diagnostic model CDM, developed from both CIMP-P and CIMP-N, exhibited exceptional performance in CRC and could serve as a potential alternative strategy for CRC screening.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Enhanced Diagnostic Efficiency of a Novel Fecal Methylated Gene Model for Early Colorectal Cancer Detection\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-04-02 20:01:52\",\"doi\":\"10.21203/rs.3.rs-4180792/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"114af747-fc34-49c6-82b9-ddfde20488bc\",\"owner\":[],\"postedDate\":\"April 2nd, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-04-15T13:42:03+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-04-02 20:01:52\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4180792\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4180792\",\"identity\":\"rs-4180792\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}