Multi-omics Approaches to Uncover Liquid-Based Cancer-Predicting Biomarkers in Lynch Syndrome

Multi-omics Approaches to Uncover Liquid-Based Cancer-Predicting Biomarkers in Lynch Syndrome | 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 Multi-omics Approaches to Uncover Liquid-Based Cancer-Predicting Biomarkers in Lynch Syndrome Minta Kärkkäinen, Tero Sievänen, Tia-Marje Korhonen, Joonas Tuomikoski, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5682364/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 Lynch syndrome is a genetic cancer-predisposing syndrome caused by pathogenic mutations in DNA mismatch repair (path_MMR) genes. Due to the elevated cancer risk, novel screening methods, alongside current surveillance techniques could enhance cancer risk stratification. Here we show how multi-omics integration could be utilized to pinpoint cancer-predicting biomarkers in Lynch Syndrome. We studied which blood-based circulating microRNAs and metabolites could predict Lynch Syndrome cancer occurrence within a 5.8-year prospective surveillance period. Methods The study cohort consisted of 116 Lynch Syndrome carriers who were healthy at the time of sampling, of whom 17 developed cancer during the surveillance. Principal Coordinate Analysis and Canonical Correlation Analysis were used to explore the relationships between single and multi-omics data, enabling the identification of patterns and correlations across different biological layers. Weighted Correlation Network Analysis was used to identify omics-level co-expression modules and to study how these modules are associated with future cancer incidence or path_MMR variant. Lasso Cox regression was used to identify cancer-predicting biomarkers. The initial model was internally validated by splitting the data randomly into 5 training and corresponding validation datasets. Biological functions of future cancer-associated circulating microRNAs were studied by conducting pathway analyses using miRWalk. Results Weighted Correlation Network Analysis revealed a circulating microRNA co-expression module significantly associated with future cancer incidence. The identified microRNAs regulate cancer-related pathways including PI3K/Akt signaling pathway. Also, the analysis detected a circulating metabolite module, consisting of ApoB containing lipoprotein classes, (low-, intermediate-, and very low-density lipoproteins), and included cholesterols, as well as phospholipids and sphingomyelins, that had distinct levels between the path_MMRvariants. Three biomarkers- hsa-miR-101-3p, hsa-miR-183-5p, and the among of triglycerides in high-density lipoprotein particles (HDL_TG)- significantly predicted cancer risk based on Lasso Cox regression, with a C-index of 0.76 (p-value = 0.0007), where elevated levels of these biomarkers were indicators of increased hazard ratio. In the internal validation, the model had an average C-index of 0.72. Conclusions The multi-omics approach and the identified biomarkers offer a promising tool for cancer risk identification in Lynch Syndrome while also uncovering underlying systemic molecular mechanisms. Lynch Syndrome multi-omics systemic biomarkers Lasso Cox regression cancer risk prediction circulating microRNAs circulating metabolites Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Lynch syndrome (LS) is estimated to affect approximately 1 in every 300 people worldwide( 1 ). This hereditary cancer risk syndrome significantly elevates the lifetime risk of cancer. LS is primarily caused by pathogenic mutations in the mismatch repair ( path_MMR ) genes, including MLH1, MSH2, MSH6 , and PMS2 . These mutations impair the DNA mismatch repair process, leading to increased risk in different types of cancers like endometrium, ovaries, stomach, small bowel, bile duct, pancreas, and upper urinary tract, with colorectal cancer (CRC) being the most common type in LS( 1 , 2 ). Due to high cancer risk, vigilant surveillance and innovative strategies for early detection and precise risk stratification are pivotal for effective risk mitigation in LS( 1 ). However, not all LS carriers develop cancer, indicating that other factors, such as lifestyle choices, can affect cancer risk( 3 ). Epigenetics and energy metabolism are key systemic molecular mechanisms through which lifestyle choices impact cancer risk( 4 , 5 ). One cancer hallmark is dysregulated epigenetics( 6 ). Epigenetic changes facilitate malignant transformation, for example, by affecting cell cycle regulation, hypoxia responses, and other processes through central signaling pathways like Tumor Necrosis Factor (TNF) alpha, Phosphoinositide 3-kinase (PI3K)/Akt and Hypoxia-Inducible Factor-1 (HIF-1)( 7 , 8 ). Circulating microRNAs (cmiR) are blood-based epigenetic modulators that regulate gene expression of multiple target tissues( 9 ). LS carriers have a distinct cmiR landscape compared to healthy non-carriers, potentially affecting LS cancer risk and carcinogenesis( 10 ). Cancer cells can also alter energy metabolism to generate more metabolic substrates, such as increasing cholesterol uptake to support membrane synthesis and rapid proliferation( 11 , 12 ). One hallmark of cancer is high glucose consumption, which promotes tumor growth and helps to adapt to new environments during metastasis( 13 ). Dysfunction in energy metabolism supports cancer development by fueling rapid proliferation, promoting genetic instability, evading apoptosis, and modifying the tumor microenvironment( 14 , 15 ). Systemic energy metabolism fluctuations can be examined by analyzing circulating metabolites (cMets). Our recent work demonstrated that LS carriers have a distinct cMet profile compared to the non-carrier control cohorts( 16 ). In summary, cancer cells manipulate both epigenetics and energy metabolism and fluctuations in cmiRs and cMets hold potential as biomarkers for assessing cancer susceptibility( 9 , 11 ). Multi-omics approaches provide a better biological assessment of cancer risk compared to relying solely on biomarkers from a single system or omics( 17 ). The LS cohort is ideal for biomarker research due to regular cancer screenings, such as colonoscopies( 18 ). Currently, cumulative cancer risk for distinct organs in the LS population is assessed primarily through the Prospective Lynch Syndrome database’s (PLSD) cumulative risk model. This model accounts for the mutation in MMR genes, age, sex, and previous cancer history( 2 , 18 ). Liquid biopsies offer a non-invasive and sensitive method for assessing cancer risk, enabling further stratification that incorporates lifestyle factors and personalized risk assessment for high-risk individuals. Previous studies have shown that cmiRs and cMets have potential as biomarkers for predicting cancer risk( 19 – 21 ). Our previous study demonstrated the potential of cmiR-biomarkers in predicting cancer incidence in the LS cohort( 19 ). Liquid-based biomarker identification offers a promising, non-invasive method for more precise cancer risk assessment and personalized stratification in LS. Here, we used a multi-omics analysis framework to study how individual and integrated cmiR and cMets data are associated with LS cancer risk (Fig. 1 ). Multi-omics integration was applied to the Lasso Cox survival model, which was used to identify the most significant potential biomarkers of cancer risk. This study presents the potential systemic biomarkers associated with cancer risk in LS. Materials and methods Clinical data The clinical data was obtained from the nationwide Finnish Lynch Syndrome Research Registry (LSRFi, www.lynchsyndrooma.fi). The data included age, sex, path_MMR variant, body-mass index (BMI), previous cancer diagnoses including specific diagnosis date and cancer type/organ, and whether the study subject had cancer during the surveillance (which is referred to as status). The status was categorized as follows: LS carriers who remained cancer-free throughout the follow-up period were classified as 'healthy,' while LS carriers diagnosed with cancer during follow-up were classified as 'future cancer’. LSRFi comprises ~1,800 LS carriers from ~380 families. In the current study, we used baseline medical records of Finnish cancer-free LS carriers whose cmiR expression profile and cMets levels were analyzed from serum samples (n = 116). The sample collection started in 2018 and lasted till 2020. The study subjects had been under surveillance for 5.8-years (until June 2024) for this specific study and continue to remain under surveillance. All data analyses for the study were conducted using the R programming language (v. 4.4.1). Sample collection Venous blood samples from LS carriers were collected in a fasted state during their surveillance colonoscopy visits, which confirmed a cancer-free status at the time of sampling. Samples were taken from the antecubital vein to standard serum tubes (455 092, Greiner). To separate serum, the whole blood samples were allowed to clot for 30 minutes at room temperature, centrifuged at 1800g for 10 min, and aliquoted. The aliquoted samples were stored at –80°C until analyzed. High-throughput microRNA sequencing CmiR isolations from blood serum were carried out using affinity column-based miRNeasy Serum/Plasma Advanced Kit (217204, Qiagen) according to the manufacturer's instructions. Small-RNA Library preparations were executed with QIAseq miRNA Library Preparation Kit (1103679, Qiagen) according to the manufacturer's instructions using multiplexing adapters. Sequencing of the small-RNA libraries was done with NextSeq 500 (Illumina) using NextSeq 500/550 High Output Kit v. 2.5 with 75 cycles (15057934, Illumina) to produce 75-base pair single-end reads with aimed mean sequencing depth of >5 M reads per sample as recommended by the manufacturer (Qiagen). More details are described here(10). CmiR data processing, alignment, and normalization Sequencing output data provided a FASTQ-format. These sequences were trimmed to 22 bp to enrich miR-sequences and then quality filtered with FastX-toolkit. Subsequently, the preprocessed reads were mapped to human mature miR-genome (miRbase v.22) with Bowtie alignment tool. Low expressed miRs were filtered out (miRs with count summary <1 in 50% of the samples), remaining miRs were normalized with the median of ratios method, and variance stabilized using DESeq2 package(23). The potential batch effect was removed using the sva package’s ComBat function in R(24). More details are described here(10,23). Metabolomics analysis Metabolites were analyzed with a targeted proton nuclear magnetic resonance ( 1 H-NMR) spectroscopy platform (Nightingale Health Ltd., Helsinki, Finland; biomarker quantification version 2020). The technical details of the method have been reported previously here(16). The platform quantifies 250 metabolite measures. Of these, 170 metabolites were selected for downstream analyses (STable 1). Eighty lipoprotein lipid ratios were excluded from the analyses due to their overlapping information compared to absolute lipid concentrations. The selected 170 metabolites were Box-Cox transformed(22) using the MASS package in R to ensure normally distributed data for downstream analyses. The Box-Cox transformation with lambda parameter was estimated from data for each variable separately. Principal coordinate analysis, Permanova and Anosim tests Principal Coordinate analysis (PCoA) of the Euclidean distances calculated from both omic datasets was performed using statistical procedures of ape package(25) in R. Permutational multivariate analysis of variance (PERMANOVA) was used to test whether cohorts' centroids and dispersion in the PCoA distance matrix significantly differ from each other. Analysis of Similarities (ANOSIM) was used to determine whether there is more similarity within the cohorts than between cohorts. PERMANOVA and ANOSIM statistical procedures were performed using the hagis package(26). Multi-omics correlations To detect correlations between cmiRs and cMets, omics integration and dimensionality reduction was done with R-package mixOmics(27). Canonical Correlation Analysis (CCA) with rCC function was used to create canonical variates, which are linear combinations of variables from the original datasets constructed so that the correlation between pairs of canonical variates is maximized. The correlation circle and correlation matrix were plotted, representing the strongest correlations. Weighted correlation network analysis The Weighted Correlation Network Analysis (WGCNA) was used to construct both the cmiR and cMets co-expression networks, using the R package WGCNA(28) (SMethods 1). First, the gene expression similarity matrix was created by calculating Spearman correlation coefficient between gene pairs. The matrix was converted into an adjacency matrix where soft-thresholding power ensured that the adjacency matrix met the scale-free topology criterion, with an R 2 value ideally approaching 0.90 (SFig. 1). However, for the cMets data, this criterion could not be fully achieved due to the lower connectivity and inherent variability within the cMets data. Therefore, a lower R 2 value (9) was accepted, a power at which the mean connectivity approached a low plateau (SFig. 1B). The adjacency matrix was transformed into a topological overlap matrix (TOM) and hierarchical clustering was applied to the TOM to group highly interconnected entities into modules. Modules were identified using a dynamic tree-cutting algorithm. For each module, an eigengene (ME) was calculated, representing the first principal component of the module's expression profile. The eigengene summarized the overall expression pattern of the module and was used as a representative measure of module activity. Module-phenotype association analyses Once modules were identified, their differences between phenotypic traits were assessed. A T-test was used to compare whether the mean of the module differed between future cancer and healthy groups. Tukey's Honest Significant Difference (HSD) was used to make multiple comparisons between path_MMR variant group means. A Linear Mixed-Effects Model (LMM) approach using lmer(29) package in R was used to analyze module- path_MMR variant correlations by involving both fixed ( path_MMR variant) and random effects (age, BMI). The fixed effect, path_MMR variant, represents the average effect of predictor variables on the response variable ME across all levels of the random effects (age and BMI). Predictive cmiR target gene and pathway analyses Target gene predictions were executed using the mirWalk(30) tool to investigate potential biological roles of the module cmiRs associated with future cancer status. The selected set of predicted miR-target genes exclusively included those targeting the 3′ untranslated region. To enhance the reliability of predictions, only those miR-target genes that were both included and verified in Targetscan, mirDB and miRTarBase databases were retained for subsequent gene set enrichment analysis (GSEA). The GSEA encompassed the evaluation of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Benjamini-Hochberg-adjusted p-values of <0.05 were deemed enriched. Identification of cancer predictive biomarkers and evaluation of their predictive capacity using Lasso Cox regression Least absolute shrinkage and selection method (Lasso)(31) regularized Cox regression was used to identify the most promising cancer-predictive biomarkers, using the glmnet and survival packages in R (SMethods1). Given the large set of predictive variables, and to avoid classification algorithm problems, like high dimensionality or multicollinearity, the number of both cMets and cmiRs were thresholded for biomarker identification. The number of cmiRs was thresholded, leaving only cmiRs of the modules from WGCNA (STables 2&3) that showed the strongest association with future cancer status. Overlapping cMets were excluded, leaving 52 metabolites for the analysis (STable 4). This reduction was done to eliminate redundancy, as the original list (STable 1) contained metabolites that overlapped extensively, either as subsets or specific components of broader categories. The response variable in the Cox regression model was the time to cancer diagnosis after serum sampling, measured in years, or for the healthy, the time until the final update from LSRFi in June 2024. The optimal value for the regularization parameter (lambda) was selected to shrink all predictors except the 5 most significant biomarkers from both the cmiR and cMets datasets (SFig. 2). The resulting 10 biomarkers (5 cmiRs and 5 cMets) were used to fit the initial Cox regression model on the entire study sample size. The ANOVA test was used to study whether each biomarker significantly contributed to the model fit. Biomarkers with the highest Chisq values were considered promising and retained in the final model. The final Cox regression model included three biomarkers to predict whether LS carriers would remain healthy over a 5.8-year surveillance period. Model performance was evaluated using Harrel’s Concordance Index (C-index), hazard ratios (HRs) and 95% confidence intervals (CIs). Proportional hazards assumptions were tested using Schoenfeld residuals (SFig. 3). Model performance was validated using internal validation, by splitting the data randomly five times into training (50 %) and validation (50 %) sets, stratified by cancer status. The performance was evaluated using the C-index, Brier Score (BS), Integrated Brier Score (IBS), Integrated Absolute Error (IAE), and Integrated Squared Error (ISE) on validation data that was not part of the model training, utilizing the SurvMetrics R package(32). Additionally, the model’s performance was tested for CRC risk prediction by excluding other cancer types from the dataset. Results The cohort characteristics The descriptive characteristics of the study subjects are presented in Table1. All study subjects were healthy at the time of serum sampling, which took place at the start of the surveillance period. Of the study subjects, 82 (71%) carried MLH1, 16 (14%) MSH2 , 17 (15%) MSH6 and 1 (<1%) PMS2 path_MMR variants. Due to there being only one PMS2 carrier, this study participant was excluded from path_MMR -omics association analyses. During 5.8-year surveillance 17 developed cancer (6 women, 11 men) and 99 remained healthy. No loss to follow-up occurred. The mean time for cancer diagnosis after sampling was 1.84 years. Of the study cohort, 45 (39%) had had cancer before surveillance started and 71 (61%) had no previous cancer. The mean age of the study participants was 55.6 years and BMI 27.1. The most prevalent cancer type was CRC (47%). Table 1. Descriptive characteristics of the study subjects. Variable Total cohort Cancer during surveillance Cancer-free after surveillance N (total=116) 116 17 99 Sex (N(%)) Female 59 (51 %) 6 (35 %) 53 (54 %) Male 57 (49 %) 11 (65 %) 46 (46 %) Age, years mean±(SD) at the time of sampling 55.6±(13.4) 56.8 ± (14.9) 55.3 ± (13.2) Body mass index, kg/m2 mean±(SD) 27.1 ± (5.6) 27.8 ± 4.6 26.9 ± 5.8 Path_MMR (N(%)) MLH1 82 (71 %) 14 (82 %) 68 (69 %) MSH2 16 (14 %) 2 (12 %) 14 (14 %) MSH6 17 (15 %) 1 (6 %) 16 (16 %) PMS2 1 (<1 %) 0 (0 %) 1 (1 %) Cancer history, N (%) Yes No 45 (39 %) 71 (61 %) 7 (41 %) 10 (59 %) 38 (38 %) 61 (62 %) Cancer-free time after sampling, mean± (SD) 4.55±(1.38) 1.84±(1.42) 5.01± (0.65) Cancer type during surveillance, N Bladder Breast Colorectal Esophageal Glioma Gastric Prostate Sebaceous gland Spinocellular 1 1 8 1 1 1 2 1 1 Path_MMR variant carriers exhibit distinct metabolomic landscapes The PCoA dimension reduction, PERMANOVA and ANOSIM analyses were used to identify overall omics-level differences between path_MMR variants, and future cancer and healthy groups. We did not observe a significant difference in cmiR landscape between different path_MMR variants (Fig. 2A). However, ANOSIM test revealed that there exists more similarity in cMets landscape within each path_MMR group than between the groups (Fig. 2B). Specifically, groups with path_MLH1 and path_MSH2 had distinct cMet profiles indicating that these variants differ more than others in the metabolomic landscape (Fig. 2B). Also, there was no significant difference in cmiR nor cMets landscapes between healthy and future cancer groups (Fig. 2C&D). In conclusion, path_MMR variants showed distinct whole cMets level landscapes, but we did not observe these differences in cmiR landscapes. Cholesterol-related cMets associate with groups of cmiRs We also studied how these omics datasets correlated with one another using CCA. CmiRs formed two major clusters with positive within-cluster correlation but no or negative correlation with cMets (Fig. 2E circle plot). In addition, distinct clusters were identified with cmiRs correlating with ApoA1 (HDL) and ApoB (LDL and VLDL) containing lipoprotein characteristics. Five cmiRs correlated positively with the amount of free cholesterol-, phospholipids-, and total lipids within S-sized HDL particles (Fig. 2E heatmap). They also correlated positively with a variety of cMet-variables of the ApoB-containing lipoprotein particles, such as triglycerides of the S-sized LDL particles and concentration, total lipids-, phospholipids-, and free cholesterol of the L- or XL-sized VLDL particles as well as the total concentration of triglycerides (Fig. 2E). Another set of five cmiRs correlated negatively with these same ApoB containing lipoprotein characteristics but did not correlate with HDL characteristics. Results indicate that some cmiRs are associated with lipid metabolism. CmiR and cMet co-expression networks associate with future cancer and path_MMR variant The WGCNA was used to detected cmiRs whose expression levels correlate with each other, suggesting shared regulatory mechanisms. After identifying co-expressed cmiRs modules we studied the associations between these modules and phenotypes of interest. WGCNA detected 11 cmiR co-expression modules (Fig. 3A, STable 2). The cmiRs in the grey module did not belong to any co-expression group. The pink module’s eigengene (MEpink), calculated from all cmiR expression levels within the module ( hsa-miR-101-3p, hsa-miR-182-5p, hsa-miR-183-5p, hsa-miR-25-3p, hsa-miR-4732-3p, hsa-miR-532-5p, hsa-miR-93-5p, hsa-miR-7-5p, and hsa-miR-660-5p ), had a significantly higher expression level in future cancer than the healthy group (Fig. 3B&C). We used miRWalk to study the future cancer-associated cmiR module’s target genes and the pathways they associate with. KEGG pathway analysis revealed that the module’s cmiRs are connected to biological pathways associated with various cancer mechanisms, including pathways in gastric cancer, prostate cancer, miRs in cancer, HIF-1 signaling, TNF signaling, and PI3K-Akt signaling (Fig. 3D). Results indicate that future cancer associated cmiRs potentially regulate cancer-related pathways. We also studied whether cmiR co-expression modules differ between path_MMR variants ( MLH1 , MSH2 , and MSH6 ) by utilizing Tukey's multiple comparison test and found no significant differences (STable 5). The results showed that path_MMR status is not a strong factor in regulating cmiR co-expression modules. The WGCNA was also conducted on cMets data to study whether the modules exhibited different levels between cancer and healthy groups or between path_MMR variants. The pipeline detected 6 modules (SFig. 4A, STable 6) but none of these differed significantly between future cancer and healthy groups (SFig. 4B). However, the MEturquoise had significantly different levels between MLH1 and MSH6 variant carriers (Fig. 4A). The MEturquoise mainly consisted of ApoB-containing lipoproteins (LDL, VLDL, IDL) with variable particle sizes and the cholesterol and triglycerides they carry and sphingomyelins (Fig. 4B). The Linear mixed model revealed that the MEturquoise was significantly associated with MSH2 (t-value = −2.07) and MSH6 (t-value = -2.07) path_MMR variants (SFig. 5A). The boxplot of the MEturquoise showed that the MLH1 variant carriers have higher module metabolite levels compared to MSH2 and MSH6 (SFig. 5B). The analysis highlights distinct profiles of LDL, VLDL, and IDL particles across these path_MMR variants where the particles have higher levels with MLH1 carriers. Hsa-miR-101-3p, hsa-miR-183-5p and triglycerides in HDL particles are potential cancer risk biomarkers in LS We applied Lasso Cox regression to determine significant cmiR and cMets predictors of cancer occurrence. The 10 top predictive features were: hsa-miR-101-3p , hsa-miR-183-5p , hsa-miR-182-5p , hsa-miR-4732-3p , hsa-miR-148b-3p , HDL_TG, Tyrosine, Glucose, Acetate, and GlycA (SFig. 6). The model was simplified by concluding only the strongest predictive biomarkers. Among these, hsa-miR-101-3p , hsa-miR-183-5p , and HDL_TG significantly predicted future cancer occurrence (Fig. 5A). Due to the dataset included distinct cancer types within the future cancer group (Table 1), we first evaluated the predictive capacity of the identified biomarkers for all LS-related cancers. The model incorporating all the 10 biomarkers had a C-index of 0.82 (p=0.0028) (SFig. 6). A reduced model using only the biomarkers— hsa-miR-101-3p , hsa-miR-183-5p , and HDL_TG— achieved a C-index of 0.76 (p=0.0007) (Fig. 5A). Additionally, we tested the model's performance specifically on CRC, as it is the predominant cancer type in LS. When incorporating all the 10 biomarkers, the model's C-index was 0.9 (p=0.021) (SFig. 7A). Interestingly, from this model for CRC, besides hsa-miR-101-3p , also glucose was a significant predictor (SFig. 7A), thus indicating that it could potentially also work as CRC-predicting biomarker in LS. The reduced CRC prediction model had a C-index of 0.8 (p=0.04) (SFig. 7B). However, in the model only hsa-miR-101-3p was a significant predictor (SFig. 7B). In summary, even the full 10-biomarker model for both, all LS-related cancers and the model for CRC, had higher overall accuracy, most biomarkers were not individually significant. The reduced model, focusing on hsa-miR-101-3p , hsa-miR-183-5p , and HDL_TG, highlighted their potential to predict cancer risk across all LS-related cancers. The Cox regression HR forest plot shows that elevated levels of these three biomarkers: hsa-miR-101-3p , hsa-miR-183-5p , and HDL_TG indicated an increased cancer risk (Fig. 5A). Additionally, we compared biomarker distributions between the groups, finding that hsa-miR-101-3p , hsa-miR-183-5p , and HDL_TG levels were significantly higher in the future cancer group compared to the healthy group (Fig. 5B). To validate the accuracy of these biomarkers as cancer predicting biomarkers, we conducted internal validation by randomly splitting the data 5 times to train and validation datasets where the model was trained with the train data and tested its performance using a validation set. In the validation, the model had an average C-index of 0.72, with low BS (0.102), IBS (0.087), IAE (0.10), and ISE (0.003) (Fig. 5C, SFig. 8). All iterations had prediction accuracy (C-index) ranging from moderate to good (SFig. 8A-F). The ROC curves consistently demonstrated predictions well above 0.5, supporting the potential of these biomarkers to predict overall cancer risk in LS (Fig. 5D). High levels of hsa-miR-101-3p , hsa-miR-183-5p , and HDL_TG indicate an association with increased cancer risk, positioning them as potential systemic biomarkers for predicting future cancer occurrence in LS. Discussion This study aimed to identify and select relevant biomarkers that significantly predict cancer risk in LS carriers using a liquid-based multi-omics data integration approach. We investigated both cmiRs and cMets at the single omics and multi-omics correlation levels. Notably, we found that MLH1 and MSH2 exhibited greater similarity in cMets landscape within their respective groups than between them. We also observed correlations between cmiR clusters and lipoprotein variables. The WGCNA revealed omics-level co-expression modules. The cmiR co-expression module (MEpink) was upregulated in the future cancer group compared to the healthy group, suggesting its potential as a biomarker for predicting future cancer occurrence. Additionally, a cMets cluster (MEturquoise) had significantly distinct levels between MLH1 and MSH6 variants. It consisted of lipid-related metabolites, primarily focused on cholesterol and ApoB containing lipoprotein particles, such as LDL, IDL, and VLDL. We identified three significant multi-omics biomarkers— hsa-miR-101-3p , hsa-miR-183-5p , and HDL_TG, that reflected LS cancer risk. These biomarkers were derived from two distinct system biology omics layers, cmiR and cMets, emphasizing the power of a multi-omics approach in uncovering key indicators for cancer susceptibility in LS carriers. Cancer-associated cmiRs regulate common cancer-related pathway s The WGCNA detected a cmiR module (MEpink) significantly associated with future cancer status. Interestingly, the target gene pathway analysis revealed some of the module cmiRs regulate PI3K/Akt signaling pathway. PI3K/Akt regulates cell growth, division, metabolism, protein synthesis, and survival(33). MEpink’s cmiRs also regulate for instance HIF-1 signaling pathway that controls proliferation, apoptosis, glucose metabolism, and promotes angiogenesis in addition to anaerobic metabolism(34,35). Elevated levels of HIF-1 are linked to tumor metastasis, poor patient prognosis as well as tumor resistance therapy(34). Tumor cells use the HIF-1 pathway to overcome hypoxic stress, where they activate survival pathways to secure essential biological processes to maintain for instance cell proliferation(34). In our results, the cmiRs within this module were upregulated in future cancer group. Additionally, our results revealed that high glucose levels were associated with elevated CRC cancer risk. This finding indicates a possible link between cmiRs associated with future cancer risk and elevated glucose levels. Path_MMR variants and their potential association with lipid metabolism The WGCNA also revealed that the cMets module, MEturquoise, showed higher levels in MLH1 carriers compared to MSH2 and MSH6 variant carriers, suggesting that mutations in MLH1 may influence lipid metabolism. Path_MMR variant significantly affects the cancer risk of LS carriers, where the highest risk is associated with MLH1 (36). MEturquoise mainly consisted of cholesterol within LDL-, IDL-, and VLDL particles in addition to high concentrations of these particles. The disorders in lipid metabolism are associated with a higher risk of tumor development by promoting cancer cell growth and metastatic lesion development(37,38). Cancer cells alter metabolic normalities to gain energy they need for cell proliferation and growth, for instance, using LDL as a cholesterol carrier(39,40). Stimulation of PI3K/Akt/mTOR signaling pathway causes transcription of the sterol regulatory element-binding proteins that contribute to cholesterol uptake and promote cancer cell growth(38). Our previous study showed significant similarities between the cMets profiles of healthy LS carriers and CRC patients, suggesting shared metabolic patterns that could contribute to LS cancer susceptibility(16). In support, the current study showed that the MLH1 variant might be associated with dysregulated lipid metabolism, potentially impacting LS carcinogenesis. Identified biomarkers’ role in cancer The three identified cancer-predicting biomarkers, hsa-miR-101-3p , hsa-miR-183-5p, and HDL_TG, have all been studied in the context of cancer risk and occurrence. In our models, specially hsa-miR-101-3p was a significant cancer risk predictor in the model for all LS-related cancers in addition to the model specifically for CRC. Hsa-miR-101-3p has been reported to inhibit the proliferation, migration, and invasion of cancer cells(41). However, studies are controversial on whether hsa-miR-101-3p’ srole is to work as a tumor suppressor or as an oncogene(41,42). In vivo studies have indicated it to work as an oncogene(41). For instance, hsa-miR-101 indicates poor prognosis in ovarian cancer patients and its overexpression promotes ovarian cancer sphere formation(43). We also found that hsa-miR-183-5p was an indicator of increased cancer risk. Previous research has reported that hsa-miR-183-5p is a potential biomarker for CRC, breast cancer, lung cancer, and hepatocellular carcinoma(44–47). Sanjabi et. al demonstrated that it was overexpressed in CRC samples when compared to healthy controls(44). The functionality of hsa-miR-183 has been studied by Macedo et al. in breast cancer cell lines(46) and showed that overexpressing hsa-miR-183 altered the proliferation and migration of MDA-MB-231 and MDA-MB-468 breast cancer cell lines. In silico analysis further identified retinoblastoma 1 (RB1), a well-known tumor suppressor protein, as a downstream target of hsa-miR-183 . In summary, both identified cmiRs indicated elevated cancer risk, and therefore were in line with previous findings. Functional assays using for instance LS cancer organoid cultures could be used to further study whether they work as oncogenes or tumor suppressors. Especially hsa-miR-101-3p was a significant indicator of increased cancer risk in both our models—one for all LS-related cancers and one specifically for CRC—and is widely reported in cancer research and could serve as a universal cancer risk estimator in LS. The third cancer-predicting biomarker we identified was HDL_TG, also associated with increased cancer risk. HDLs are dense, small (5–17 nm) ApoA1-containing lipoprotein particles composed of phospholipids, cholesteryl esters, triglycerides, free cholesterol, and sphingolipids. The best-characterized function of HDL is to carry cholesterol to the liver from the peripheral tissues(48). It navigates in circulation and takes free cholesterol and phospholipids from peripheral tissues and ApoB-containing lipoproteins by exchanging its triglycerides for the cholesterol from these particles(48). As mentioned before, cancer cells require a lot of energy and use for instance cholesterol to maintain their energy needs for proliferation, migration, and metastatic activities(49). The cancer cells impair reverse cholesterol transport(50). It has been reported that HDL-related components, like lipid transfer proteins (for instance cholesteryl ester transfer protein), correlate with distinct cancers(49). HDL triglyceride is demonstrated to correlate positively and significantly with triglyceride in VLDL, and the concentrations of triglyceride and cholesterol in HDL have a negative correlation(51). In summary, our results could indicate that increased HDL triglycerides imply a more favorable tumor microenvironment due to impaired reverse cholesterol transport, where the cholesterol esters are carried by LDL and VLDL that provide cholesterol to cancer cells. The prediction model accuracy The Cox proportional hazards model built in this study was used to test how well these identified biomarkers could predict LS carriers’ cancer risk. The Cox proportional model has widely been used in the biomarker identification and prediction of clinical outcomes of distinct cancers, like survival, reoccurrence, or risk prediction(52–55). Previous studies have reported predictive performance metrics for Lasso Cox regression models, such as a C-index of ~0.7 in pancreatic ductal adenocarcinoma and an AUC of 0.756 for predicting gastric cancer recurrence , (54,56). However, these models did not incorporate a multi-omics approach. Tyagi et al. used a multi-omics strategy to identify prognostic molecular features in prostate cancer, achieving an AUC of 0.67(57). In comparison, our model demonstrated a higher predictive accuracy, with a C-index of 0.76 for all cancer types and 0.8 for CRC, aligning with the performance range of other Lasso Cox regression models. In the internal validation, the model accuracy on validation data was, on average, 0.72. This suggests that the predictive capacity of these biomarkers, using the selected Lasso Cox regression method, is moderate. Thus, it is important to note that our model cannot serve as a reliable predictive tool in clinical practice without further validation to confirm its performance. However, the prediction model for all LS-related cancers had significant 95% CIs for each biomarker, indicating that all of the three biomarkers significantly predicted future cancer occurrence and, therefore, have the potential to work as LS cancer risk-reflecting biomarkers. Strengths of the study The strength of this study is its unique integration of multi-omics data, leveraging two systemic biomolecular levels to improve cancer prediction in LS. The dataset is remarkable, as there exist only a few prospective follow-up surveillances where participants almost certainly develop cancer. Due to high cancer risk, LS provides a good platform for identifying potential biomarkers. To date, distinct biomarkers including proteins, cytokines, metabolites, hormones, miRs, and circulating DNA have been explored, and many of them have been successfully verified as early cancer screening biomarkers(58). Biomarkers detected in this study can provide insights into the biological mechanisms underlying LS-associated cancers. Understanding how specific cmiRs or cMets affect cancer risk can lead to new therapeutic targets. Ultimately, identifying significant cancer predictors can lead to better clinical decision-making and improved patient outcomes. Particularly for LS carriers with high cancer incidence rates. Limitations Our study contained high-dimensional variables, leading to challenges such as handling highly correlated biomarkers which cause multicollinearity. To address these issues, we used the Lasso regularization method. This penalized regression approach identifies predictive features from high-dimensional data that handles collinearity by retaining only one of the correlated predictors(31). The small sample size and class imbalance could explain the moderate prediction accuracy of the model validation (C-index 0.72). The model could be further validated through external validation by collecting more data, but time and budget constraints limited this effort, and the use of synthetic data proved insufficient for this purpose. Also, we estimated HRs at the end of the surveillance period and assumed the constant effect of the biomarkers on the HR over time. In addition, the methodology approach also has a significant impact on rising results. In our previous pilot study, we identified LS-specific differentially expressed cmiRs as potential cancer-predicting biomarkers(19). Here, we used different approaches to identify key LS cancer-predicting biomarkers. Integrating two omics datasets offered a more comprehensive view of system-level responses to cancer development compared to single-omics approaches. In summary, the good model accuracy supports the suitability of the Lasso Cox regression for our data, and the identification of biomarkers across multiple omics layers highlights their potential to inform cancer risk and etiology. Conclusions In this study, we found promising biomarkers that could predict up-coming cancer in LS. Our findings operate as a proof of concept, providing a framework for future investigations aimed at discovering predictive circulating biomarkers specific to LS-related cancers by using a multi-omics approach. By assessing the impact of various biomarkers on cancer risk, a more detailed cancer risk prediction can be achieved for LS carriers. This approach can support the personalization of cancer screening plans based on individual risk profiles to complement the PLSD model as an additional risk assessment tool. By conducting further research and external validation on the identified biomarkers— hsa-miR-101-3p , hsa-miR-183-5p , and triglycerides in HDL—they could potentially be used as liquid-based LS cancer risk assessment tools that complement current screening methods. Abbreviations BS = Brier Score CCA = canonical correlation analysis C-index = concordance index cMets = circulating metabolites cmiR = circulating microRNA CRC = colorectal cancer HDL = high-density lipoprotein HIF-1 = hypoxia-inducible factor-1 HR = hazard ratio IAE = integrated absolute error IBS = integrated brier score IDL = intermediate-density lipoprotein ISE = integrated squared error Lasso = least absolute shrinkage and selection LDL = low-density lipoprotein LSRFi = Finnish Lynch Syndrome Research Registry LS = Lynch Syndrome ME = module eigengene MMR = mismatch repair gene PI3K = phosphoinositide 3-kinase PLSD = prospective Lynch Syndromes database path_MMR = pathogenic mutation in the mismatch repair gene TOM = topological overlap matrix TNF =tumor necrosis factor HSD = Tukey's honest significant difference VLDL = very low-density lipoprotein WGCNA = weighted correlation network analysis Declarations Ethics approval and consent to participate The acquired informed consent was collected from each participant. Helsinki and Uusimaa Health Care District (HUS/155/2021) and Central Finland Health Care District Ethics Committee (KSSHP D# 1U/2018 and 1/2019 and KSSHP 3/2016) had approved the data collection and data usage of LSRFi. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request. Competing interests TTS reports consultation fees from Mehiläinen, Nouscom, Tillots Pharma and Amgen, being a co-owner and CEO of Healthfund Finland Ltd., and a position in the Clinical Advisory Board and as a minor shareholder of Lynsight Ltd. Funding TJ reports grants from European Comission Union Marie Skłodowska-Curie Individual Fellowships, Mary and George C Ehrnrooth Foundation and K Albin Johanssons Foundations during the conduct of the study. TTS reports grants from Finnish Medical Foundation, Emil Aaltonen Foundation, Jane and Aatos Erkko Foundation, Relander Foundation, and Cancer Foundation Finland during the conduct of the study; personal fees from Amgen Finland, personal fees from Mehiläinen, personal fees from Nouscom, personal fees from Tillots Pharma, personal fees and other support from Lynsight, and other support from Healthfund Finland outside the submitted work. EKL reports grants from Päivikki and Sakari Sohlberg Foundation during the conduct of the study. JT reports personal fees from Seppo Säynäjäkankaa’s Foundation. No disclosures were reported by the other authors. Authors' contributions Conception and design: MK, EKL, and TJ; Laboratory analyses: MK, TS, and TJ; Bioinformatics analyses: MK, TS, and TMK; Data analyses: MK, TS, JT, SÄ, and TJ; Drafting the manuscript: MK, TS, TMK, EKL, and TJ. Patient sample collection and maintenance: TTS. Data management: TTS, JPM, and KP. Supervision: TMK, EKL, and TJ. All authors participated in revising the manuscript and approved the final version. Corresponding authors Correspondence to Tiina Jokela. Acknowledgments We would like to acknowledge all study participants, the LSRFi, Central Finland Hospital Nova’s pathology laboratory staff, and staff of the Sports and Health Science faculty laboratory members who participated in data collection. We thank you Nightingale Health Ltd. for analyzing metabolomics data. We would also like to acknowledge CSC for providing the computational resources that facilitated the analyses performed in this study. In addition, we thank BioRender for providing the tools to create scientific illustrations. References Dominguez-Valentin M, Sampson JR, Seppälä TT, ten Broeke SW, Plazzer JP, Nakken S, et al. Cancer risks by gene, age, and gender in 6350 carriers ofpathogenic mismatch repair variants: findings from the Prospective Lynch SyndromeDatabase. Genetics in Medicine. 2020 Jan 1;22(1):15–25. Bhattacharya P, Leslie SW, McHugh TW. Lynch Syndrome (Hereditary Nonpolyposis Colorectal Cancer). In: StatPearls. Treasure Island (FL): StatPearls Publishing; 2024. Power RF, Doherty DE, Horgan R, Fahey P, Gallagher DJ, Lowery MA, et al. Modifiable risk factors for cancer among people with lynch syndrome: an international, cross-sectional survey. Hereditary Cancer in Clinical Practice. 2024 Jun 14;22(1):10. Shankar E, Gupta K, Gupta S. Chapter 17 - Dietary and Lifestyle Factors in Epigenetic Regulation of Cancer. In: Bishayee A, Bhatia D, editors. Epigenetics of Cancer Prevention [Internet]. Academic Press; 2019. p. 361–94. Available from: https://www.sciencedirect.com/science/article/pii/B9780128124949000172 Locasale JW. Diet and Exercise in Cancer Metabolism. Cancer Discov. 2022 Oct 5;12(10):2249–57. Yu X, Zhao H, Wang R, Chen Y, Ouyang X, Li W, et al. Cancer epigenetics: from laboratory studies and clinical trials to precision medicine. Cell Death Discovery. 2024 Jan 15;10(1):28. Kanwal R, Gupta S. Epigenetic modifications in cancer. Clin Genet. 2012 Apr;81(4):303–11. Li T, Mao C, Wang X, Shi Y, Tao Y. Epigenetic crosstalk between hypoxia and tumor driven by HIF regulation. Journal of Experimental & Clinical Cancer Research. 2020 Oct 27;39(1):224. Peng Y, Croce CM. The role of MicroRNAs in human cancer. Signal Transduction and Targeted Therapy. 2016 Jan 28;1(1):15004. Sievänen T, Korhonen TM, Jokela T, Ahtiainen M, Lahtinen L, Kuopio T, et al. Systemic circulating microRNA landscape in Lynch syndrome. Int J Cancer. 2023 Mar 1;152(5):932–44. Martínez-Reyes I, Chandel NS. Cancer metabolism: looking forward. Nature Reviews Cancer. 2021 Oct 1;21(10):669–80. Nazih H, Bard JM. Cholesterol, Oxysterols and LXRs in Breast Cancer Pathophysiology. International Journal of Molecular Sciences [Internet]. 2020;21(4). Available from: https://www.mdpi.com/1422-0067/21/4/1356 Subramaniam S, Jeet V, Clements JA, Gunter JH, Batra J. Emergence of MicroRNAs as Key Players in Cancer Cell Metabolism. Clinical Chemistry. 2019 Sep 1;65(9):1090–101. Agbu P, Carthew RW. MicroRNA-mediated regulation of glucose and lipid metabolism. Nature Reviews Molecular Cell Biology. 2021 Jun 1;22(6):425–38. Suriya Muthukumaran N, Velusamy P, Akino Mercy CS, Langford D, Natarajaseenivasan K, Shanmughapriya S. MicroRNAs as Regulators of Cancer Cell Energy Metabolism. J Pers Med. 2022 Aug 18;12(8). Jokela T, Karppinen J, Kärkkäinen M, Mecklin JP, Walker S, Seppälä T, et al. Circulating metabolome landscape in Lynch Syndrome. 2023. Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biology. 2017 May 5;18(1):83. Dominguez-Valentin M, Haupt S, Seppälä TT, Sampson JR, Sunde L, Bernstein I, et al. Mortality by age, gene and gender in carriers of pathogenic mismatch repair gene variants receiving surveillance for early cancer diagnosis and treatment: a report from the prospective Lynch syndrome database. EClinicalMedicine. 2023 Apr;58:101909. Sievänen T, Jokela T, Hyvärinen M, Korhonen TM, Pylvänäinen K, Mecklin JP, et al. Circulating miRNA Signature Predicts Cancer Incidence in Lynch Syndrome—A Pilot Study. Cancer Prevention Research. 2024 Jun 4;17(6):243–54. Wang W, Rong Z, Wang G, Hou Y, Yang F, Qiu M. Cancer metabolites: promising biomarkers for cancer liquid biopsy. Biomarker Research. 2023 Jun 30;11(1):66. Hayes J, Peruzzi PP, Lawler S. MicroRNAs in cancer: biomarkers, functions and therapy. Trends in Molecular Medicine. 2014 Aug 1;20(8):460–9. Daimon T. Box–Cox Transformation. In: Lovric M, editor. International Encyclopedia of Statistical Science [Internet]. Berlin, Heidelberg: Springer Berlin Heidelberg; 2011. p. 176–8. Available from: https://doi.org/10.1007/978-3-642-04898-2_152 Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012 Mar 15;28(6):882–3. Paradis E, Schliep K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics. 2019 Feb 1;35(3):526–8. McCoy AG, Noel Z, Sparks AH, Chilvers M. hagis, an R Package Resource for Pathotype Analysis of Phytophthora sojae Populations Causing Stem and Root Rot of Soybean. Mol Plant Microbe Interact. 2019 Dec;32(12):1574–6. Rohart F, Gautier B, Singh A, Lê Cao KA. mixOmics: An R package for ’omics feature selection and multiple data integration. PLoS Comput Biol. 2017 Nov;13(11):e1005752. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008 Dec 29;9(1):559. Vestal BE, Wynn E, Moore CM. lmerSeq: an R package for analyzing transformed RNA-Seq data with linear mixed effects models. BMC Bioinformatics. 2022 Nov 16;23(1):489. Sticht C, De La Torre C, Parveen A, Gretz N. miRWalk: An online resource for prediction of microRNA binding sites. PLoS One. 2018;13(10):e0206239. Tibshirani R. The lasso method for variable selection in the Cox model. Stat Med. 1997 Feb 28;16(4):385–95. Zhou H, Wang H, Wang S, Zou Y. SurvMetrics: An R package for Predictive Evaluation Metrics in Survival Analysis. The R Journal. 2023 Feb 10;14:252–63. Hemmings BA, Restuccia DF. PI3K-PKB/Akt pathway. Cold Spring Harb Perspect Biol. 2012 Sep 1;4(9):a011189. Masoud GN, Li W. HIF-1α pathway: role, regulation and intervention for cancer therapy. Acta Pharm Sin B. 2015 Sep;5(5):378–89. Semenza GL. Targeting HIF-1 for cancer therapy. Nature Reviews Cancer. 2003 Dec 1;3(10):721–32. Holter S, Hall MJ, Hampel H, Jasperson K, Kupfer SS, Larsen Haidle J, et al. Risk assessment and genetic counseling for Lynch syndrome – Practice resource of the National Society of Genetic Counselors and the Collaborative Group of the Americas on Inherited Gastrointestinal Cancer. Journal of Genetic Counseling. 2022 Jun 1;31(3):568–83. Mehta N, Hordines J, Volpe C, Doerr R, Cohen SA. Cellular effects of hypercholesterolemia in modulation of cancer growth and metastasis: a review of the evidence. Surg Oncol. 1997 Nov;6(3):179–85. Deng CF, Zhu N, Zhao TJ, Li HF, Gu J, Liao DF, et al. Involvement of LDL and ox-LDL in Cancer Development and Its Therapeutical Potential. Front Oncol. 2022;12:803473. Xu H, Zhou S, Tang Q, Xia H, Bi F. Cholesterol metabolism: New functions and therapeutic approaches in cancer. Biochim Biophys Acta Rev Cancer. 2020 Aug;1874(1):188394. Inoue M, Niki M, Ozeki Y, Nagi S, Chadeka EA, Yamaguchi T, et al. High-density lipoprotein suppresses tumor necrosis factor alpha production by mycobacteria-infected human macrophages. Sci Rep. 2018 Apr 30;8(1):6736. Liu N, Yang C, Gao A, Sun M, Lv D. MiR-101: An Important Regulator of Gene Expression and Tumor Ecosystem. Cancers (Basel). 2022 Nov 28;14(23). Varambally S, Cao Q, Mani RS, Shankar S, Wang X, Ateeq B, et al. Genomic loss of microRNA-101 leads to overexpression of histone methyltransferase EZH2 in cancer. Science. 2008 Dec 12;322(5908):1695–9. Cui TX, Kryczek I, Zhao L, Zhao E, Kuick R, Roh MH, et al. Myeloid-Derived Suppressor Cells Enhance Stemness of Cancer Cells by Inducing MicroRNA101 and Suppressing the Corepressor CtBP2. Immunity. 2013 Sep 19;39(3):611–21. Sanjabi F, Nekouian R, Akbari A, Mirzaei R, Fattahi A. Plasma miR-183-5p in colorectal cancer patients as potential predictive lymph node metastasis marker. Journal of Cancer Research and Therapeutics [Internet]. 2022;18(4). Available from: https://journals.lww.com/cancerjournal/fulltext/2022/18040/plasma_mir_183_5p_in_colorectal_cancer_patients_as.8.aspx Zaporozhchenko IA, Morozkin ES, Skvortsova TE, Ponomaryova AA, Rykova EY, Cherdyntseva NV, et al. Plasma miR-19b and miR-183 as Potential Biomarkers of Lung Cancer. PLoS One. 2016;11(10):e0165261. Macedo T, Silva-Oliveira RJ, Silva VAO, Vidal DO, Evangelista AF, Marques MMC. Overexpression of mir-183 and mir-494 promotes proliferation and migration in human breast cancer cell lines. Oncol Lett. 2017 Jul;14(1):1054–60. Liang Z, Gao Y, Shi W, Zhai D, Li S, Jing L, et al. Expression and significance of microRNA-183 in hepatocellular carcinoma. ScientificWorldJournal. 2013;2013:381874. Bailey A, Mohiuddin SS. Biochemistry, High Density Lipoprotein. In: StatPearls. Treasure Island (FL): StatPearls Publishing; 2024. Pirro M, Ricciuti B, Rader DJ, Catapano AL, Sahebkar A, Banach M. High density lipoprotein cholesterol and cancer: Marker or causative? Progress in Lipid Research. 2018 Jul 1;71:54–69. Cruz PM, Mo H, McConathy W, Sabnis NA, Lacko AG. The role of cholesterol metabolism and cholesterol transport in carcinogenesis: a review of scientific findings, relevant to future cancer therapeutics. Frontiers in Pharmacology [Internet]. 2013;4. Available from: https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2013.00119 Barter PJ, Connor WE. The transport of triglyceride in the high-density lipoproteins of human plasma. J Lab Clin Med. 1975 Feb;85(2):260–72. Jardillier R, Koca D, Chatelain F, Guyon L. Prognosis of lasso-like penalized Cox models with tumor profiling improves prediction over clinical data alone and benefits from bi-dimensional pre-screening. BMC Cancer. 2022 Oct 5;22(1):1045. Calabrese F, Lunardi F, Pezzuto F, Fortarezza F, Vuljan SE, Marquette C, et al. Are There New Biomarkers in Tissue and Liquid Biopsies for the Early Detection of Non-Small Cell Lung Cancer? Journal of Clinical Medicine. 2019;8(3). Feng Y, Yang J, Duan W, Cai Y, Liu X, Peng Y. LASSO-derived prognostic model predicts cancer-specific survival in advanced pancreatic ductal adenocarcinoma over 50 years of age: a retrospective study of SEER database research. Front Oncol. 2023;13:1336251. Du X jie, Yang X rong, Wang Q cai, Lin G liang, Li P fei, Zhang W feng. Identification and validation of a five-gene prognostic signature based on bioinformatics analyses in breast cancer. Heliyon. 2023 Feb 1;9(2):e13185. Huang B, Ding F, Li Y. A practical recurrence risk model based on Lasso-Cox regression for gastric cancer. Journal of Cancer Research and Clinical Oncology. 2023 Nov 1;149(17):15845–54. Tyagi N, Roy S, Vengadesan K, Gupta D. Multi-omics approach for identifying CNV-associated lncRNA signatures with prognostic value in prostate cancer. Non-coding RNA Research. 2024 Mar 1;9(1):66–75. Zhou Y, Tao L, Qiu J, Xu J, Yang X, Zhang Y, et al. Tumor biomarkers for diagnosis, prognosis and targeted therapy. Signal Transduction and Targeted Therapy. 2024 May 20;9(1):132. Additional Declarations Competing interest reported. TTS reports consultation fees from Mehiläinen, Nouscom, Tillots Pharma and Amgen, being a co-owner and CEO of Healthfund Finland Ltd., and a position in the Clinical Advisory Board and as a minor shareholder of Lynsight Ltd. Supplementary Files Additionalfile1Supplementaryfigures.pdf Additional file 1: Supplementary Figures (.pdf). Additional figures 1-8 to support the presented results. Additionalfile2Supplementarytables.pdf Additional file 2: Supplementary Tables (.pdf). Additional tables 1-6 to support the presented results. Additionalfile3SupplementaryMethods.pdf Additional file 3: Supplementary Methods (.pdf). The R-scripts of the main analyses: WGCNA and Lasso Cox regression. Additionalfile4miRWalkGSEAresults.xlsx Additional file 4: MiRWalk_GSEA_results (.xlsx). The table presents the microRNAs included in the analysis, a list of target genes, a list of associated pathways, and the significance of the results. 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5682364","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":393796481,"identity":"524c229f-8926-4b4d-ab20-ca0f4a3489f0","order_by":0,"name":"Minta Kärkkäinen","email":"","orcid":"","institution":"University of Jyväskylä","correspondingAuthor":false,"prefix":"","firstName":"Minta","middleName":"","lastName":"Kärkkäinen","suffix":""},{"id":393796482,"identity":"3418373d-ab78-4183-9cf4-d377f6a16e8b","order_by":1,"name":"Tero Sievänen","email":"","orcid":"","institution":"University of Eastern Finland","correspondingAuthor":false,"prefix":"","firstName":"Tero","middleName":"","lastName":"Sievänen","suffix":""},{"id":393796483,"identity":"06bcc17e-5961-441c-a207-d9630fb3404f","order_by":2,"name":"Tia-Marje Korhonen","email":"","orcid":"","institution":"University of Jyväskylä","correspondingAuthor":false,"prefix":"","firstName":"Tia-Marje","middleName":"","lastName":"Korhonen","suffix":""},{"id":393796484,"identity":"7e86ebce-a70f-46ea-8616-25f34b5e27ab","order_by":3,"name":"Joonas Tuomikoski","email":"","orcid":"","institution":"University of Jyväskylä","correspondingAuthor":false,"prefix":"","firstName":"Joonas","middleName":"","lastName":"Tuomikoski","suffix":""},{"id":393796485,"identity":"d39a97ba-69d8-47d4-a47b-083116f5ce8a","order_by":4,"name":"Kirsi Pylvänäinen","email":"","orcid":"","institution":"The Wellbeing Services County of Central Finland","correspondingAuthor":false,"prefix":"","firstName":"Kirsi","middleName":"","lastName":"Pylvänäinen","suffix":""},{"id":393796486,"identity":"d79b8472-df3b-4d0b-bc73-5a9ef5f672e9","order_by":5,"name":"Sami Äyrämö","email":"","orcid":"","institution":"University of Jyväskylä","correspondingAuthor":false,"prefix":"","firstName":"Sami","middleName":"","lastName":"Äyrämö","suffix":""},{"id":393796487,"identity":"91d18a87-7ab6-4af4-b54d-21a4162deb18","order_by":6,"name":"Toni T. 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A) Serum samples were collected from 116 at the time healthy LS carriers, from which circulating microRNAs (cmiRs) and metabolites (cMets) were analyzed. The study subjects had a 5.8-year follow-up surveillance on whether they developed cancer. B) The surveillance information was combined with multi-omics data to study cancer-predicting systemic biomarkers.\u003c/p\u003e","description":"","filename":"Figure1Workflow.png","url":"https://assets-eu.researchsquare.com/files/rs-5682364/v1/bfdd5036f865fe25f03f84ae.png"},{"id":72879961,"identity":"b542de24-becf-495e-a3e6-1ba9db4c03c1","added_by":"auto","created_at":"2025-01-03 08:54:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1735786,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the two omics data used in the study. The single-omics landscape (principal coordinates V1\u0026amp;V2) of the traits of interest is presented, with panels A and B highlighting omics landscape differences between \u003cem\u003epath_MMR\u003c/em\u003e variant carriers, and panels C and D comparing future cancer vs. healthy groups during surveillance. Permanova and Anosim tests show whether there are significant differences in the composition of the omics landscapes between the groups. \u0026nbsp;Panel E shows the correlation matrix between the omics datasets, providing an integrated view of the relationships across different omics layers. The heatmap presents the strongest positive correlations as red and negative correlations as blue. In the correlation circle plot, cmiRs are depicted in blue and cMets in orange. Species that are close to each other in the circles have positive correlations. Species that are on the opposite sides of the circle have negative correlations.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5682364/v1/b2348e6faa374943c15a7581.png"},{"id":72879962,"identity":"f6d488da-c9bc-43f5-82d8-ef29f32d05b9","added_by":"auto","created_at":"2025-01-03 08:54:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1167824,"visible":true,"origin":"","legend":"\u003cp\u003eWGCNA on cmiR expression levels. A) The cluster dendrogram shows cmiR co-expression modules. B) The T-test reveals significant module eigengene (ME) differences between the future cancer group and the healthy group where MEs summarize the expression profiles of all genes within a given module, providing a single representative value for each sample. A positive t-value suggests that the healthy group has a higher mean ME value compared to the future cancer group, while a negative t-value indicates the opposite. The magnitude of the t-value reflects the size of the difference, with larger values indicating a stronger difference. C) A boxplot of the distribution of the MEpink among the groups, including the specific cmiRs within the module. The y-axis represents the ME values for the pink module (MEpink), derived from WGCNA. D) Pathway analysis of the pink module's target genes' biological roles. The y-axis indicates significantly enriched biological roles (BH \u0026lt; 0.05) and the x-axis the number of cmiR target genes related to the pathway. Stars highlight the central signaling pathways associated with malignant transformation.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5682364/v1/52d4d91cef8ac18ab02440b3.png"},{"id":72879964,"identity":"1eb1791a-1e80-4f91-983b-8db04d80b22f","added_by":"auto","created_at":"2025-01-03 08:54:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":351985,"visible":true,"origin":"","legend":"\u003cp\u003eWGCNA cMet module eigengene associations with \u003cem\u003epath_MMR\u003c/em\u003e gene variant where A) Tukey's multiple comparison test reveals module level differences and (adjusted p-values) between \u003cem\u003epath_MMR\u003c/em\u003e variants. The test compares the difference between the means of the two groups where positive values suggest the first group has a larger mean, while negative values indicate the second group has a larger mean. B) The table lists metabolites included in Module Turquoise, which showed significant difference between \u003cem\u003eMLH1\u003c/em\u003e and \u003cem\u003eMSH6\u003c/em\u003e variants. C) Presents a schematic figure of lipoproteins' role in cancer progression.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5682364/v1/1465620e6ae61bf4a59721bd.png"},{"id":72880195,"identity":"610538a6-6768-482c-b9b1-3211678a77bb","added_by":"auto","created_at":"2025-01-03 09:02:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":580609,"visible":true,"origin":"","legend":"\u003cp\u003eCox Proportional Hazards Model results and predictive accuracy on validation data. A) Forest plot displays the hazard ratios (HR), confidence intervals (CI), model C-index, and significance levels for each predictor variable, based on the model trained using the entire dataset. HRs for each predictor were above 1, indicating an increased cancer risk, with significant 95% CIs. B) Boxplot illustrates the distribution of key covariates between the healthy and future cancer groups. The significance of mean differences between the groups was assessed using a t-test. C) Internal validation results of the Cox regression model show average performance metrics across 5 iterations: Concordance Index (C-index), Brier Score (BS), Integrated Brier Score (IBS), Integrated Absolute Error (IAE), and Integrated Squared Error (ISE). A high C-index and low values for the other metrics indicate good prediction accuracy. D) Presents Receiver-Operating Characteristic (ROC) curves of the internal validation results.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5682364/v1/74c7ad8ef253d811bfc4a1a3.png"},{"id":72881637,"identity":"9c536b15-ba89-49db-ae07-411c7f351733","added_by":"auto","created_at":"2025-01-03 09:18:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4231891,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5682364/v1/18e22acc-bdde-45bd-a664-81d8b3fe43b3.pdf"},{"id":72880192,"identity":"b5b55737-8137-48c9-8d07-4d0357a40c6a","added_by":"auto","created_at":"2025-01-03 09:02:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":489333,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 1: Supplementary Figures (.pdf). Additional figures 1-8 to support the presented results.\u003c/p\u003e","description":"","filename":"Additionalfile1Supplementaryfigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5682364/v1/f0e14f67a35535c7586e2349.pdf"},{"id":72879957,"identity":"cd5182e4-043b-4c3f-a27a-fbe3ee2707c1","added_by":"auto","created_at":"2025-01-03 08:54:51","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":350719,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 2: Supplementary Tables (.pdf). Additional tables \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;1-6 to support the presented results.\u003c/p\u003e","description":"","filename":"Additionalfile2Supplementarytables.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5682364/v1/e41f008fb915b9aa5ac21d17.pdf"},{"id":72879966,"identity":"32a33ed7-7509-4158-94a4-346ed0bd8fd0","added_by":"auto","created_at":"2025-01-03 08:54:51","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":466268,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 3: Supplementary Methods (.pdf). The R-scripts of \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;the main analyses: WGCNA and Lasso Cox regression.\u003c/p\u003e","description":"","filename":"Additionalfile3SupplementaryMethods.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5682364/v1/6371f6be7c3e691e26ce563c.pdf"},{"id":72881511,"identity":"d5f657db-5c6e-4a55-b101-41a193edbeb4","added_by":"auto","created_at":"2025-01-03 09:10:51","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":20566,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 4: MiRWalk_GSEA_results (.xlsx). The table presents \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;the microRNAs included in the analysis, a list of target genes, a list of associated \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;pathways, and the significance of the results.\u003c/p\u003e","description":"","filename":"Additionalfile4miRWalkGSEAresults.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5682364/v1/32473030f0c5473d7b8ce066.xlsx"}],"financialInterests":"Competing interest reported. TTS reports consultation fees from Mehiläinen, Nouscom, Tillots Pharma and Amgen, being a co-owner and CEO of Healthfund Finland Ltd., and a position in the Clinical Advisory Board and as a minor shareholder of Lynsight Ltd.","formattedTitle":"Multi-omics Approaches to Uncover Liquid-Based Cancer-Predicting Biomarkers in Lynch Syndrome","fulltext":[{"header":"Background","content":"\u003cp\u003eLynch syndrome (LS) is estimated to affect approximately 1 in every 300 people worldwide(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). This hereditary cancer risk syndrome significantly elevates the lifetime risk of cancer. LS is primarily caused by pathogenic mutations in the mismatch repair (\u003cem\u003epath_MMR\u003c/em\u003e) genes, including \u003cem\u003eMLH1, MSH2, MSH6\u003c/em\u003e, and \u003cem\u003ePMS2\u003c/em\u003e. These mutations impair the DNA mismatch repair process, leading to increased risk in different types of cancers like endometrium, ovaries, stomach, small bowel, bile duct, pancreas, and upper urinary tract, with colorectal cancer (CRC) being the most common type in LS(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Due to high cancer risk, vigilant surveillance and innovative strategies for early detection and precise risk stratification are pivotal for effective risk mitigation in LS(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). However, not all LS carriers develop cancer, indicating that other factors, such as lifestyle choices, can affect cancer risk(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEpigenetics and energy metabolism are key systemic molecular mechanisms through which lifestyle choices impact cancer risk(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). One cancer hallmark is dysregulated epigenetics(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Epigenetic changes facilitate malignant transformation, for example, by affecting cell cycle regulation, hypoxia responses, and other processes through central signaling pathways like Tumor Necrosis Factor (TNF) alpha, Phosphoinositide 3-kinase (PI3K)/Akt and Hypoxia-Inducible Factor-1 (HIF-1)(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Circulating microRNAs (cmiR) are blood-based epigenetic modulators that regulate gene expression of multiple target tissues(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). LS carriers have a distinct cmiR landscape compared to healthy non-carriers, potentially affecting LS cancer risk and carcinogenesis(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Cancer cells can also alter energy metabolism to generate more metabolic substrates, such as increasing cholesterol uptake to support membrane synthesis and rapid proliferation(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). One hallmark of cancer is high glucose consumption, which promotes tumor growth and helps to adapt to new environments during metastasis(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Dysfunction in energy metabolism supports cancer development by fueling rapid proliferation, promoting genetic instability, evading apoptosis, and modifying the tumor microenvironment(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Systemic energy metabolism fluctuations can be examined by analyzing circulating metabolites (cMets). Our recent work demonstrated that LS carriers have a distinct cMet profile compared to the non-carrier control cohorts(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In summary, cancer cells manipulate both epigenetics and energy metabolism and fluctuations in cmiRs and cMets hold potential as biomarkers for assessing cancer susceptibility(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMulti-omics approaches provide a better biological assessment of cancer risk compared to relying solely on biomarkers from a single system or omics(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The LS cohort is ideal for biomarker research due to regular cancer screenings, such as colonoscopies(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Currently, cumulative cancer risk for distinct organs in the LS population is assessed primarily through the Prospective Lynch Syndrome database\u0026rsquo;s (PLSD) cumulative risk model. This model accounts for the mutation in \u003cem\u003eMMR\u003c/em\u003e genes, age, sex, and previous cancer history(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Liquid biopsies offer a non-invasive and sensitive method for assessing cancer risk, enabling further stratification that incorporates lifestyle factors and personalized risk assessment for high-risk individuals. Previous studies have shown that cmiRs and cMets have potential as biomarkers for predicting cancer risk(\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Our previous study demonstrated the potential of cmiR-biomarkers in predicting cancer incidence in the LS cohort(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Liquid-based biomarker identification offers a promising, non-invasive method for more precise cancer risk assessment and personalized stratification in LS.\u003c/p\u003e \u003cp\u003eHere, we used a multi-omics analysis framework to study how individual and integrated cmiR and cMets data are associated with LS cancer risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Multi-omics integration was applied to the Lasso Cox survival model, which was used to identify the most significant potential biomarkers of cancer risk. This study presents the potential systemic biomarkers associated with cancer risk in LS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cem\u003eClinical data\u0026nbsp;\u003cbr\u003e\u003c/em\u003eThe clinical data was obtained from the nationwide Finnish Lynch Syndrome Research Registry (LSRFi, www.lynchsyndrooma.fi). The data included age, sex,\u0026nbsp;\u003cem\u003epath_MMR\u003c/em\u003e variant, body-mass index (BMI), previous cancer diagnoses including specific diagnosis date and cancer type/organ, and whether the study subject had cancer during the surveillance (which is referred to as status). The status was categorized as follows: LS carriers who remained cancer-free throughout the follow-up period were classified as 'healthy,' while LS carriers diagnosed with cancer during follow-up were classified as 'future cancer’.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLSRFi comprises ~1,800 LS carriers from ~380 families. In the current study, we used baseline medical records of Finnish cancer-free LS carriers whose cmiR expression profile and cMets levels were analyzed from serum samples (n = 116). The sample collection started in 2018 and lasted till 2020. The study subjects had been under surveillance for 5.8-years (until June 2024) for this specific study and continue to remain under surveillance. All data analyses for the study were conducted using the R programming language (v. 4.4.1). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSample collection\u003cbr\u003e\u003c/em\u003eVenous blood samples from LS carriers were collected in a fasted state during their surveillance colonoscopy visits, which confirmed a cancer-free status at the time of sampling. Samples were taken from the antecubital vein to standard serum tubes (455 092, Greiner). To separate serum, the whole blood samples were allowed to clot for 30 minutes at room temperature, centrifuged at 1800g for 10 min, and aliquoted. The aliquoted samples were stored at –80°C until analyzed.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHigh-throughput microRNA sequencing\u003cbr\u003e\u003c/em\u003eCmiR isolations from blood serum were carried out using affinity column-based miRNeasy Serum/Plasma Advanced Kit (217204, Qiagen) according to the manufacturer's instructions. Small-RNA Library preparations were executed with QIAseq miRNA Library Preparation Kit (1103679, Qiagen) according to the manufacturer's instructions using multiplexing adapters. Sequencing of the small-RNA libraries was done with NextSeq 500 (Illumina) using NextSeq 500/550 High Output Kit v. 2.5 with 75 cycles (15057934, Illumina) to produce 75-base pair single-end reads with aimed mean sequencing depth of \u0026gt;5 M reads per sample as recommended by the manufacturer (Qiagen). More details are described here(10).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCmiR data processing, alignment, and normalization\u003cbr\u003e\u003c/em\u003eSequencing output data provided a FASTQ-format. These sequences were trimmed to 22 bp to enrich miR-sequences and then quality filtered with FastX-toolkit. Subsequently, the preprocessed reads were mapped to human mature miR-genome (miRbase v.22) with Bowtie alignment tool. Low expressed miRs were filtered out (miRs with count summary \u0026lt;1 in 50% of the samples), remaining miRs were normalized with the median of ratios method, and variance stabilized using DESeq2 package(23). The potential batch effect was removed using the sva package’s ComBat function in R(24). More details are described here(10,23).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMetabolomics analysis\u003cbr\u003e\u003c/em\u003eMetabolites were analyzed with a targeted proton nuclear magnetic resonance (\u003csup\u003e1\u003c/sup\u003eH-NMR) spectroscopy platform (Nightingale Health Ltd., Helsinki, Finland; biomarker quantification version 2020). The technical details of the method have been reported previously here(16). The platform quantifies 250 metabolite measures. Of these, 170 metabolites were selected for downstream analyses (STable 1). Eighty lipoprotein lipid ratios were excluded from the analyses due to their overlapping information compared to absolute lipid concentrations. The selected 170 metabolites were Box-Cox transformed(22) using the MASS package in R to ensure normally distributed data for downstream analyses. The Box-Cox transformation with lambda parameter was estimated from data for each variable separately.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePrincipal coordinate analysis, Permanova and Anosim tests\u003cbr\u003e\u003c/em\u003ePrincipal Coordinate analysis (PCoA) of the Euclidean distances calculated from both omic datasets was performed using statistical procedures of ape package(25) \u0026nbsp;in R. Permutational multivariate analysis of variance (PERMANOVA) was used to test whether cohorts' centroids and dispersion in the PCoA distance matrix significantly differ from each other. Analysis of Similarities (ANOSIM) was used to determine whether there is more similarity within the cohorts than between cohorts. PERMANOVA and ANOSIM statistical procedures were performed using the hagis package(26).\u003cem\u003e\u003cbr\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMulti-omics correlations\u003cbr\u003e\u003c/em\u003eTo detect correlations between cmiRs and cMets, omics integration and dimensionality reduction was done with R-package mixOmics(27). Canonical Correlation Analysis (CCA) with rCC function was used to create canonical variates, which are linear combinations of variables from the original datasets constructed so that the correlation between pairs of canonical variates is maximized. The correlation circle and correlation matrix were plotted, representing the strongest correlations.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWeighted correlation network analysis\u003cbr\u003e\u003c/em\u003eThe Weighted Correlation Network Analysis (WGCNA) was used to construct both the cmiR and cMets co-expression networks, using the R package WGCNA(28) (SMethods 1). First, the gene expression similarity matrix was created by calculating Spearman correlation coefficient between gene pairs. The matrix was converted into an adjacency matrix where soft-thresholding power ensured that the adjacency matrix met the scale-free topology criterion, with an R\u003csup\u003e2\u003c/sup\u003e value ideally approaching 0.90 (SFig. 1). However, for the cMets data, this criterion could not be fully achieved due to the lower connectivity and inherent variability within the cMets data. Therefore, a lower R\u003csup\u003e2\u0026nbsp;\u003c/sup\u003evalue (9) was accepted, a power at which the mean connectivity approached a low plateau (SFig. 1B). The adjacency matrix was transformed into a topological overlap matrix (TOM) and hierarchical clustering was applied to the TOM to group highly interconnected entities into modules. Modules were identified using a dynamic tree-cutting algorithm. For each module, an eigengene (ME) was calculated, representing the first principal component of the module's expression profile. The eigengene summarized the overall expression pattern of the module and was used as a representative measure of module activity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eModule-phenotype association analyses\u003cbr\u003e\u003c/em\u003eOnce modules were identified, their differences between phenotypic traits were assessed. \u0026nbsp;A T-test was used to compare whether the mean of the module differed between future cancer and healthy groups. Tukey's Honest Significant Difference (HSD) was used to make multiple comparisons between\u0026nbsp;\u003cem\u003epath_MMR\u003c/em\u003e variant group means. A Linear Mixed-Effects Model (LMM) approach using lmer(29) package in R was used to analyze module-\u003cem\u003epath_MMR\u003c/em\u003e variant correlations by involving both fixed (\u003cem\u003epath_MMR\u003c/em\u003e variant) and random effects (age, BMI). The fixed effect,\u0026nbsp;\u003cem\u003epath_MMR\u003c/em\u003e variant, represents the average effect of predictor variables on the response variable ME across all levels of the random effects (age and BMI).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePredictive cmiR target gene and pathway analyses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTarget gene predictions were executed using the mirWalk(30) tool to investigate potential biological roles of the module cmiRs associated with future cancer status. The selected set of predicted miR-target genes exclusively included those targeting the 3′ untranslated region. To enhance the reliability of predictions, only those miR-target genes that were both included and verified in Targetscan, mirDB and miRTarBase databases were retained for subsequent gene set enrichment analysis (GSEA). The GSEA encompassed the evaluation of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Benjamini-Hochberg-adjusted p-values of \u0026lt;0.05 were deemed enriched.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIdentification of cancer predictive biomarkers and evaluation of their predictive capacity using Lasso Cox regression\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLeast absolute shrinkage and selection method (Lasso)(31) regularized Cox regression was used to identify the most promising cancer-predictive biomarkers, using the glmnet and survival packages in R (SMethods1). Given the large set of predictive variables, and to avoid classification algorithm problems, like high dimensionality or multicollinearity, the number of both cMets and cmiRs were thresholded for biomarker identification. The number of cmiRs was thresholded, leaving only cmiRs of the modules from WGCNA (STables 2\u0026amp;3) that showed the strongest association with future cancer status. Overlapping cMets were excluded, leaving 52 metabolites for the analysis (STable 4). This reduction was done to eliminate redundancy, as the original list (STable 1) contained metabolites that overlapped extensively, either as subsets or specific components of broader categories.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe response variable in the Cox regression model was the time to cancer diagnosis after serum sampling, measured in years, or for the healthy, the time until the final update from LSRFi in June 2024. The optimal value for the regularization parameter (lambda) was selected to shrink all predictors except the 5 most significant biomarkers from both the cmiR and cMets datasets (SFig. 2). The resulting 10 biomarkers (5 cmiRs and 5 cMets) were used to fit the initial Cox regression model on the entire study sample size. The ANOVA test was used to study whether each biomarker significantly contributed to the model fit. Biomarkers with the highest Chisq values were considered promising and retained in the final model. The final Cox regression model included three biomarkers to predict whether LS carriers would remain healthy over a 5.8-year surveillance period. Model performance was evaluated using Harrel’s Concordance Index (C-index), hazard ratios (HRs) and 95% confidence intervals (CIs).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eProportional hazards assumptions were tested using Schoenfeld residuals (SFig. 3). Model performance was validated using internal validation, by splitting the data randomly five times into training (50 %) and validation (50 %) sets, stratified by cancer status. The performance was evaluated using the C-index, Brier Score (BS), Integrated Brier Score (IBS), Integrated Absolute Error (IAE), and Integrated Squared Error (ISE) on validation data that was not part of the model training, utilizing the SurvMetrics R package(32). Additionally, the model’s performance was tested for CRC risk prediction by excluding other cancer types from the dataset.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eThe cohort characteristics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe descriptive characteristics of the study subjects are presented in Table1. All study subjects were healthy at the time of serum sampling, which took place at the start of the surveillance period. Of the study subjects, 82 (71%) carried \u003cem\u003eMLH1,\u0026nbsp;\u003c/em\u003e16 (14%) \u003cem\u003eMSH2\u003c/em\u003e, 17 (15%) \u003cem\u003eMSH6\u003c/em\u003e and 1 (\u0026lt;1%) \u003cem\u003ePMS2\u003c/em\u003e \u003cem\u003epath_MMR\u003c/em\u003e variants. Due to there being only one \u003cem\u003ePMS2\u003c/em\u003e carrier, this study participant was excluded from\u003cem\u003e\u0026nbsp;path_MMR\u003c/em\u003e-omics association analyses. During 5.8-year surveillance 17 developed cancer (6 women, 11 men) and 99 remained healthy. No loss to follow-up occurred. The mean time for cancer diagnosis after sampling was 1.84 years. Of the study cohort, 45 (39%) had had cancer before surveillance started and 71 (61%) had no previous cancer. The mean age of the study participants was 55.6 years and BMI 27.1. The most prevalent cancer type was CRC (47%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Descriptive characteristics of the study subjects.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCancer during surveillance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCancer-free after surveillance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN (total=116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSex (N(%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59 (51 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (35 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53 (54 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57 (49 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (65 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46 (46 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge, years mean\u0026plusmn;(SD) at the time of sampling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55.6\u0026plusmn;(13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56.8 \u0026plusmn; (14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55.3 \u0026plusmn; (13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBody mass index, kg/m2 mean\u0026plusmn;(SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.1 \u0026plusmn; (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.8 \u0026plusmn; 4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.9 \u0026plusmn; 5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePath_MMR\u003c/em\u003e (N(%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMLH1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e82 (71 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (82 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e68 (69 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMSH2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (14 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (12 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (14 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMSH6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17 (15 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (6 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (16 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePMS2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (\u0026lt;1 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (1 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCancer history, N (%)\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e45 (39 %)\u003c/p\u003e\n \u003cp\u003e71 (61 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7 (41 %)\u003c/p\u003e\n \u003cp\u003e10 (59 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e38 (38 %)\u003c/p\u003e\n \u003cp\u003e61 (62 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCancer-free time after sampling,\u003cbr\u003e\u0026nbsp;mean\u0026plusmn; (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.55\u0026plusmn;(1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.84\u0026plusmn;(1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.01\u0026plusmn;\u0026nbsp;(0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCancer type during surveillance, N\u003c/p\u003e\n \u003cp\u003eBladder\u003c/p\u003e\n \u003cp\u003eBreast\u003c/p\u003e\n \u003cp\u003eColorectal\u003c/p\u003e\n \u003cp\u003eEsophageal\u003c/p\u003e\n \u003cp\u003eGlioma\u003c/p\u003e\n \u003cp\u003eGastric\u003c/p\u003e\n \u003cp\u003eProstate\u003c/p\u003e\n \u003cp\u003eSebaceous gland\u003c/p\u003e\n \u003cp\u003eSpinocellular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003ePath_MMR variant carriers exhibit distinct metabolomic landscapes\u003cbr\u003e\u003c/em\u003eThe PCoA dimension reduction, PERMANOVA and ANOSIM analyses were used to identify overall omics-level differences between\u003cem\u003e\u0026nbsp;path_MMR\u003c/em\u003e variants, and future cancer and healthy groups. We did not observe a significant difference in cmiR landscape between different \u003cem\u003epath_MMR\u003c/em\u003e variants (Fig. 2A). However, ANOSIM test revealed that there exists more similarity in cMets landscape within each \u003cem\u003epath_MMR\u003c/em\u003e group than between the groups (Fig. 2B). Specifically, groups with \u003cem\u003epath_MLH1\u003c/em\u003e and \u003cem\u003epath_MSH2\u003c/em\u003e had distinct cMet profiles indicating that these variants differ more than others in the metabolomic landscape (Fig. 2B). Also, there was no significant difference in cmiR nor cMets landscapes between healthy and future cancer groups (Fig. 2C\u0026amp;D). In conclusion, \u003cem\u003epath_MMR\u003c/em\u003e variants showed distinct whole cMets level landscapes, but we did not observe these differences in cmiR landscapes.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCholesterol-related cMets associate with groups of cmiRs\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe also studied how these omics datasets correlated with one another using CCA. CmiRs formed two major clusters with positive within-cluster correlation but no or negative correlation with cMets (Fig. 2E circle plot). In addition, distinct clusters were identified with cmiRs correlating with ApoA1 (HDL) and ApoB (LDL and VLDL) containing lipoprotein characteristics. Five cmiRs correlated positively with the amount of free cholesterol-, phospholipids-, and total lipids within S-sized HDL particles (Fig. 2E heatmap). They also correlated positively with a variety of cMet-variables of the ApoB-containing lipoprotein particles, such as triglycerides of the S-sized LDL particles and concentration, total lipids-, phospholipids-, and free cholesterol of the L- or XL-sized VLDL particles as well as the total concentration of triglycerides (Fig. 2E). Another set of five cmiRs correlated negatively with these same ApoB containing lipoprotein characteristics but did not correlate with HDL characteristics. Results indicate that some cmiRs are associated with lipid metabolism.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCmiR and cMet co-expression networks associate with future cancer and path_MMR variant\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe WGCNA was used to detected cmiRs whose expression levels correlate with each other, suggesting shared regulatory mechanisms. After identifying co-expressed cmiRs modules we studied the associations between these modules and phenotypes of interest. WGCNA detected 11 cmiR co-expression modules (Fig. 3A, STable 2). The cmiRs in the grey module did not belong to any co-expression group. The pink module\u0026rsquo;s eigengene (MEpink), calculated from all cmiR expression levels within the module (\u003cem\u003ehsa-miR-101-3p, hsa-miR-182-5p, hsa-miR-183-5p, hsa-miR-25-3p, hsa-miR-4732-3p, hsa-miR-532-5p, hsa-miR-93-5p, hsa-miR-7-5p, and hsa-miR-660-5p\u003c/em\u003e), had a significantly higher expression level in future cancer than the healthy group (Fig. 3B\u0026amp;C). We used miRWalk to study the future cancer-associated cmiR module\u0026rsquo;s target genes and the pathways they associate with. KEGG pathway analysis revealed that the module\u0026rsquo;s cmiRs are connected to biological pathways associated with various cancer mechanisms, including pathways in gastric cancer, prostate cancer, miRs in cancer, HIF-1 signaling, TNF signaling, and PI3K-Akt signaling (Fig. 3D). Results indicate that future cancer associated cmiRs potentially regulate cancer-related pathways.\u003c/p\u003e\n\u003cp\u003eWe also studied whether cmiR co-expression modules differ between \u003cem\u003epath_MMR\u003c/em\u003e variants (\u003cem\u003eMLH1\u003c/em\u003e,\u003cem\u003e\u0026nbsp;MSH2\u003c/em\u003e, and \u003cem\u003eMSH6\u003c/em\u003e) by utilizing Tukey\u0026apos;s multiple comparison test and found no significant differences (STable 5). The results showed that \u003cem\u003epath_MMR\u003c/em\u003e status is not a strong factor in regulating cmiR co-expression modules.\u003c/p\u003e\n\u003cp\u003eThe WGCNA was also conducted on cMets data to study whether the modules exhibited different levels between cancer and healthy groups or between \u003cem\u003epath_MMR\u003c/em\u003e variants. The pipeline detected 6 modules (SFig. 4A, STable 6) but none of these differed significantly between future cancer and healthy groups (SFig. 4B). However, the MEturquoise had significantly different levels between \u003cem\u003eMLH1\u003c/em\u003e and \u003cem\u003eMSH6\u003c/em\u003e variant carriers (Fig. 4A). The MEturquoise mainly consisted of ApoB-containing lipoproteins (LDL, VLDL, IDL) with variable particle sizes and the cholesterol and triglycerides they carry and sphingomyelins (Fig. 4B). The Linear mixed model revealed that the MEturquoise was significantly associated with \u003cem\u003eMSH2\u003c/em\u003e (t-value\u0026thinsp;=\u0026thinsp;\u0026minus;2.07) and \u003cem\u003eMSH6\u003c/em\u003e (t-value\u0026thinsp;=\u0026thinsp;-2.07) \u003cem\u003epath_MMR\u003c/em\u003e variants (SFig. 5A). The boxplot of the MEturquoise showed that the \u003cem\u003eMLH1\u003c/em\u003e variant carriers have higher module metabolite levels compared to \u003cem\u003eMSH2\u003c/em\u003e and \u003cem\u003eMSH6\u003c/em\u003e (SFig. 5B). The analysis highlights distinct profiles of LDL, VLDL, and IDL particles across these \u003cem\u003epath_MMR\u003c/em\u003e variants where the particles have higher levels with \u003cem\u003eMLH1\u003c/em\u003e carriers.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHsa-miR-101-3p, hsa-miR-183-5p and triglycerides in HDL particles are potential cancer risk biomarkers in LS\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe applied Lasso Cox regression to determine significant cmiR and cMets predictors of cancer occurrence. The 10 top predictive features were: \u003cem\u003ehsa-miR-101-3p\u003c/em\u003e, \u003cem\u003ehsa-miR-183-5p\u003c/em\u003e,\u003cem\u003e\u0026nbsp;hsa-miR-182-5p\u003c/em\u003e, \u003cem\u003ehsa-miR-4732-3p\u003c/em\u003e, \u003cem\u003ehsa-miR-148b-3p\u003c/em\u003e, HDL_TG, Tyrosine, Glucose, Acetate, and GlycA (SFig. 6). The model was simplified by concluding only the strongest predictive biomarkers. Among these, \u003cem\u003ehsa-miR-101-3p\u003c/em\u003e, \u003cem\u003ehsa-miR-183-5p\u003c/em\u003e, and HDL_TG significantly predicted future cancer occurrence (Fig. 5A).\u003c/p\u003e\n\u003cp\u003eDue to the dataset included distinct cancer types within the future cancer group (Table 1), we first evaluated the predictive capacity of the identified biomarkers for all LS-related cancers. The model incorporating all the 10 biomarkers had a C-index of 0.82 (p=0.0028) (SFig. 6). A reduced model using only the biomarkers\u0026mdash;\u003cem\u003ehsa-miR-101-3p\u003c/em\u003e, \u003cem\u003ehsa-miR-183-5p\u003c/em\u003e, and HDL_TG\u0026mdash; achieved a C-index of 0.76 (p=0.0007) (Fig. 5A). Additionally, we tested the model\u0026apos;s performance specifically on CRC, as it is the predominant cancer type in LS. When incorporating all the 10 biomarkers, the model\u0026apos;s C-index was 0.9 (p=0.021) (SFig. 7A). Interestingly, from this model for CRC, besides \u003cem\u003ehsa-miR-101-3p\u003c/em\u003e, also glucose was a significant predictor (SFig. 7A), thus indicating that it could potentially also work as CRC-predicting biomarker in LS. The reduced CRC prediction model had a C-index of 0.8 (p=0.04) (SFig. 7B). However, in the model only \u003cem\u003ehsa-miR-101-3p\u0026nbsp;\u003c/em\u003ewas a significant predictor\u003cem\u003e\u0026nbsp;\u003c/em\u003e(SFig. 7B). In summary, even the full 10-biomarker model for both, all LS-related cancers and the model for CRC, had higher overall accuracy, most biomarkers were not individually significant. The reduced model, focusing on \u003cem\u003ehsa-miR-101-3p\u003c/em\u003e,\u003cem\u003e\u0026nbsp;hsa-miR-183-5p\u003c/em\u003e, and HDL_TG, highlighted their potential to predict cancer risk across all LS-related cancers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Cox regression HR forest plot shows that elevated levels of these three biomarkers: \u003cem\u003ehsa-miR-101-3p\u003c/em\u003e, \u003cem\u003ehsa-miR-183-5p\u003c/em\u003e, and HDL_TG indicated an increased cancer risk (Fig. 5A). Additionally, we compared biomarker distributions between the groups, finding that \u003cem\u003ehsa-miR-101-3p\u003c/em\u003e, \u003cem\u003ehsa-miR-183-5p\u003c/em\u003e, and HDL_TG levels were significantly higher in the future cancer group compared to the healthy group (Fig. 5B). To validate the accuracy of these biomarkers as cancer predicting biomarkers, we conducted internal validation by randomly splitting the data 5 times to train and validation datasets where the model was trained with the train data and tested its performance using a validation set. In the validation, the model had an average C-index of 0.72, with low BS (0.102), IBS (0.087), IAE (0.10), and ISE (0.003) (Fig. 5C, SFig. 8). All iterations had prediction accuracy (C-index) ranging from moderate to good (SFig. 8A-F). The ROC curves consistently demonstrated predictions well above 0.5, supporting the potential of these biomarkers to predict overall cancer risk in LS (Fig. 5D). High levels of \u003cem\u003ehsa-miR-101-3p\u003c/em\u003e, \u003cem\u003ehsa-miR-183-5p\u003c/em\u003e, and HDL_TG indicate an association with increased cancer risk, positioning them as potential systemic biomarkers for predicting future cancer occurrence in LS.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to identify and select relevant biomarkers that significantly predict cancer risk in LS carriers using a liquid-based multi-omics data integration approach. We investigated both cmiRs and cMets at the single omics and multi-omics correlation levels. Notably, we found that \u003cem\u003eMLH1\u003c/em\u003e and \u003cem\u003eMSH2\u003c/em\u003e exhibited greater similarity in cMets landscape within their respective groups than between them. We also observed correlations between cmiR clusters and lipoprotein variables. The WGCNA revealed omics-level co-expression modules. The cmiR co-expression module (MEpink) was upregulated in the future cancer group compared to the healthy group, suggesting its potential as a biomarker for predicting future cancer occurrence. Additionally, a cMets cluster (MEturquoise) had significantly distinct levels between \u003cem\u003eMLH1\u003c/em\u003e and \u003cem\u003eMSH6\u003c/em\u003e variants. It consisted of lipid-related metabolites, primarily focused on cholesterol and ApoB containing lipoprotein particles, such as \u0026nbsp;LDL, IDL, and VLDL. We identified three significant multi-omics biomarkers—\u003cem\u003ehsa-miR-101-3p\u003c/em\u003e, \u003cem\u003ehsa-miR-183-5p\u003c/em\u003e, and HDL_TG, that reflected LS cancer risk. These biomarkers were derived from two distinct system biology omics layers, cmiR and cMets, emphasizing the power of a multi-omics approach in uncovering key indicators for cancer susceptibility in LS carriers.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCancer-associated cmiRs regulate common cancer-related pathway\u003c/em\u003es\u003c/p\u003e\n\u003cp\u003eThe WGCNA detected a cmiR module (MEpink) significantly associated with future cancer status. Interestingly, the target gene pathway analysis revealed some of the module cmiRs regulate PI3K/Akt signaling pathway. PI3K/Akt regulates cell growth, division, metabolism, protein synthesis, and survival(33). MEpink’s cmiRs also regulate for instance HIF-1 signaling pathway that controls proliferation, apoptosis, glucose metabolism, and promotes angiogenesis in addition to anaerobic metabolism(34,35). Elevated levels of HIF-1 are linked to tumor metastasis, poor patient prognosis as well as tumor resistance therapy(34). Tumor cells use the HIF-1 pathway to overcome hypoxic stress, where they activate survival pathways to secure essential biological processes to maintain for instance cell proliferation(34). In our results, the cmiRs within this module were upregulated in future cancer group. Additionally, our results revealed that high glucose levels were associated with elevated CRC cancer risk. This finding indicates a possible link between cmiRs associated with future cancer risk and elevated glucose levels.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePath_MMR variants and their potential association with lipid metabolism\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe WGCNA also revealed that the cMets module, MEturquoise, showed higher levels in \u003cem\u003eMLH1\u003c/em\u003e carriers compared to \u003cem\u003eMSH2\u003c/em\u003e and \u003cem\u003eMSH6\u003c/em\u003e variant carriers, suggesting that mutations in \u003cem\u003eMLH1\u003c/em\u003e may influence lipid metabolism. \u003cem\u003ePath_MMR\u003c/em\u003e variant significantly affects the cancer risk of LS carriers, where the highest risk is associated with \u003cem\u003eMLH1\u003c/em\u003e(36). MEturquoise mainly consisted of cholesterol within LDL-, IDL-, and VLDL particles in addition to high concentrations of these particles. The disorders in lipid metabolism are associated with a higher risk of tumor development by promoting cancer cell growth and metastatic lesion development(37,38). Cancer cells alter metabolic normalities to gain energy they need for cell proliferation and growth, for instance, using LDL as a cholesterol carrier(39,40). Stimulation of PI3K/Akt/mTOR signaling pathway causes transcription of the sterol regulatory element-binding proteins that contribute to cholesterol uptake and promote cancer cell growth(38). Our previous study showed significant similarities between the cMets profiles of healthy LS carriers and CRC patients, suggesting shared metabolic patterns that could contribute to LS cancer susceptibility(16). In support, the current study showed that the \u003cem\u003eMLH1\u003c/em\u003e variant might be associated with dysregulated lipid metabolism, potentially impacting LS carcinogenesis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIdentified biomarkers’ role in cancer\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe three identified cancer-predicting biomarkers, \u003cem\u003ehsa-miR-101-3p\u003c/em\u003e, \u003cem\u003ehsa-miR-183-5p,\u003c/em\u003e and HDL_TG, have all been studied in the context of cancer risk and occurrence. In our models, specially \u003cem\u003ehsa-miR-101-3p\u0026nbsp;\u003c/em\u003ewas a significant cancer risk predictor in the model for all LS-related cancers in addition to the model specifically for CRC. \u003cem\u003eHsa-miR-101-3p\u0026nbsp;\u003c/em\u003ehas been reported to inhibit the proliferation, migration, and invasion of cancer cells(41). However, studies are controversial on whether \u003cem\u003ehsa-miR-101-3p’\u003c/em\u003esrole is to work as a tumor suppressor or as an oncogene(41,42). In vivo studies have indicated it to work as an oncogene(41). For instance, \u003cem\u003ehsa-miR-101\u003c/em\u003e indicates poor prognosis in ovarian cancer patients and its overexpression promotes ovarian cancer sphere formation(43). We also found that \u003cem\u003ehsa-miR-183-5p\u003c/em\u003e was an indicator of increased cancer risk. Previous research has reported that \u003cem\u003ehsa-miR-183-5p\u003c/em\u003e is a potential biomarker for CRC, breast cancer, lung cancer, and hepatocellular carcinoma(44–47). Sanjabi et. al demonstrated that it was overexpressed in CRC samples when compared to healthy controls(44). The functionality of \u003cem\u003ehsa-miR-183\u003c/em\u003e has been studied by Macedo et al. in breast cancer cell lines(46) and showed that overexpressing \u003cem\u003ehsa-miR-183\u003c/em\u003e altered the proliferation and migration of MDA-MB-231 and MDA-MB-468 breast cancer cell lines. In silico analysis further identified retinoblastoma 1 (RB1), a well-known tumor suppressor protein, as a downstream target of \u003cem\u003ehsa-miR-183\u003c/em\u003e. In summary, both identified cmiRs indicated elevated cancer risk, and therefore were in line with previous findings. Functional assays using for instance LS cancer organoid cultures could be used to further study whether they work as oncogenes or tumor suppressors. Especially \u003cem\u003ehsa-miR-101-3p\u003c/em\u003e was a significant indicator of increased cancer risk in both our models—one for all LS-related cancers and one specifically for CRC—and is widely reported in cancer research and could serve as a universal cancer risk estimator in LS.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe third cancer-predicting biomarker we identified was HDL_TG, also associated with increased cancer risk. HDLs are dense, small (5–17 nm) ApoA1-containing lipoprotein particles composed of phospholipids, cholesteryl esters, triglycerides, free cholesterol, and sphingolipids. The best-characterized function of HDL is to carry cholesterol to the liver from the peripheral tissues(48). It navigates in circulation and takes free cholesterol and phospholipids from peripheral tissues and ApoB-containing lipoproteins by exchanging its triglycerides for the cholesterol from \u0026nbsp; these particles(48). As mentioned before, cancer cells require a lot of energy and use for instance cholesterol to maintain their energy needs for proliferation, migration, and metastatic activities(49). The cancer cells impair reverse cholesterol transport(50). It has been reported that HDL-related components, like lipid transfer proteins (for instance cholesteryl ester transfer protein), correlate with distinct cancers(49). \u0026nbsp;HDL triglyceride is demonstrated to correlate positively and significantly with triglyceride in VLDL, and the concentrations of triglyceride and cholesterol in HDL have a negative correlation(51). In summary, our results could indicate that increased HDL triglycerides imply a more favorable tumor microenvironment due to impaired reverse cholesterol transport, where the cholesterol esters are carried by LDL and VLDL that provide cholesterol to cancer cells.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe prediction model accuracy\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe Cox proportional hazards model built in this study was used to test how well these identified biomarkers could predict LS carriers’ cancer risk. The Cox proportional model has widely been used in the biomarker identification and prediction of clinical outcomes of distinct cancers, like survival, reoccurrence, or risk prediction(52–55). Previous studies have reported predictive performance metrics for Lasso Cox regression models, such as a C-index of ~0.7 in pancreatic ductal adenocarcinoma and an AUC of 0.756 for predicting gastric cancer recurrence\u003csup\u003e,\u003c/sup\u003e(54,56). However, these models did not incorporate a multi-omics approach. Tyagi et al. used a multi-omics strategy to identify prognostic molecular features in prostate cancer, achieving an AUC of 0.67(57). In comparison, our model demonstrated a higher predictive accuracy, with a C-index of 0.76 for all cancer types and 0.8 for CRC, aligning with the performance range of other Lasso Cox regression models. In the internal validation, the model accuracy on validation data was, on average, 0.72. This suggests that the predictive capacity of these biomarkers, using the selected Lasso Cox regression method, is moderate. Thus, it is important to note that our model cannot serve as a reliable predictive tool in clinical practice without further validation to confirm its performance. However, the prediction model for all LS-related cancers had significant 95% CIs for each biomarker, indicating that all of the three biomarkers significantly predicted future cancer occurrence and, therefore, have the potential to work as LS cancer risk-reflecting biomarkers.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStrengths of the study\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe strength of this study is its unique integration of multi-omics data, leveraging two systemic biomolecular levels to improve cancer prediction in LS. The dataset is remarkable, as there exist only a few prospective follow-up surveillances where participants almost certainly develop cancer. Due to high cancer risk, LS provides a good platform for identifying potential biomarkers. To date, distinct \u0026nbsp;biomarkers including proteins, cytokines, metabolites, hormones, miRs, and circulating DNA have been explored, and many of them have been successfully verified as early cancer screening biomarkers(58). Biomarkers detected in this study can provide insights into the biological mechanisms underlying LS-associated cancers. Understanding how specific cmiRs or cMets affect cancer risk can lead to new therapeutic targets. Ultimately, identifying significant cancer predictors can lead to better clinical decision-making and improved patient outcomes. Particularly for LS carriers with high cancer incidence rates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLimitations\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOur study contained high-dimensional variables, leading to challenges such as handling highly correlated biomarkers which cause multicollinearity. To address these issues, we used the Lasso regularization method. This penalized regression approach identifies predictive features from high-dimensional data that handles collinearity by retaining only one of the correlated predictors(31). The small sample size and class imbalance could explain the moderate prediction accuracy of the model validation (C-index 0.72). The model could be further validated through external validation by collecting more data, but time and budget constraints limited this effort, and the use of synthetic data proved insufficient for this purpose. Also, we estimated HRs at the end of the surveillance period and assumed the constant effect of the biomarkers on the HR over time. \u0026nbsp;In addition, the methodology approach also has a significant impact on rising results. In our previous pilot study, we identified LS-specific differentially expressed cmiRs as potential cancer-predicting biomarkers(19). Here, we used different approaches to identify key LS cancer-predicting biomarkers. Integrating two omics datasets offered a more comprehensive view of system-level responses to cancer development compared to single-omics approaches. In summary, the good model accuracy supports the suitability of the Lasso Cox regression for our data, and the identification of biomarkers across multiple omics layers highlights their potential to inform cancer risk and etiology.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, we found promising biomarkers that could predict up-coming cancer in LS. Our findings operate as a proof of concept, providing a framework for future investigations aimed at discovering predictive circulating biomarkers specific to LS-related cancers by using a multi-omics approach. By assessing the impact of various biomarkers on cancer risk, a more detailed cancer risk prediction can be achieved for LS carriers. This approach can support the personalization of cancer screening plans based on individual risk profiles to complement the PLSD model as an additional risk assessment tool. By conducting further research and external validation on the identified biomarkers\u0026mdash;\u003cem\u003ehsa-miR-101-3p\u003c/em\u003e, \u003cem\u003ehsa-miR-183-5p\u003c/em\u003e, and triglycerides in HDL\u0026mdash;they could potentially be used as liquid-based LS cancer risk assessment tools that complement current screening methods.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBS = Brier Score\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCCA = canonical correlation analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eC-index = concordance index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ecMets = circulating metabolites\u003c/p\u003e\n\u003cp\u003ecmiR = circulating microRNA\u003c/p\u003e\n\u003cp\u003eCRC = colorectal cancer\u003c/p\u003e\n\u003cp\u003eHDL = high-density lipoprotein\u003c/p\u003e\n\u003cp\u003eHIF-1 = hypoxia-inducible factor-1\u003c/p\u003e\n\u003cp\u003eHR = hazard ratio\u003c/p\u003e\n\u003cp\u003eIAE = integrated absolute error\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIBS = integrated brier score\u003c/p\u003e\n\u003cp\u003eIDL = intermediate-density lipoprotein\u003c/p\u003e\n\u003cp\u003eISE = integrated squared error\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLasso = least absolute shrinkage and selection\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLDL = low-density lipoprotein\u003c/p\u003e\n\u003cp\u003eLSRFi = Finnish Lynch Syndrome Research Registry\u003c/p\u003e\n\u003cp\u003eLS = Lynch Syndrome\u003c/p\u003e\n\u003cp\u003eME = module eigengene\u003c/p\u003e\n\u003cp\u003eMMR = mismatch repair gene\u003c/p\u003e\n\u003cp\u003ePI3K = phosphoinositide 3-kinase\u003c/p\u003e\n\u003cp\u003ePLSD = prospective Lynch Syndromes database\u003c/p\u003e\n\u003cp\u003epath_MMR = pathogenic mutation in the mismatch repair gene\u003c/p\u003e\n\u003cp\u003eTOM = topological overlap matrix\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTNF =tumor necrosis factor\u003c/p\u003e\n\u003cp\u003eHSD = Tukey's honest significant difference\u003c/p\u003e\n\u003cp\u003eVLDL = very low-density lipoprotein\u003c/p\u003e\n\u003cp\u003eWGCNA = weighted correlation network analysis\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe acquired informed consent was collected from each participant. Helsinki and Uusimaa Health Care District (HUS/155/2021) and Central Finland Health Care District Ethics Committee (KSSHP D# 1U/2018 and 1/2019 and KSSHP 3/2016) had approved the data collection and data usage of LSRFi.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003cbr\u003e\u003c/em\u003eThe datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTTS reports consultation fees from Mehiläinen, Nouscom, Tillots Pharma and Amgen, being a co-owner and CEO of Healthfund Finland Ltd., and a position in the Clinical Advisory Board and as a minor shareholder of Lynsight Ltd.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTJ reports grants from European Comission Union Marie Skłodowska-Curie Individual Fellowships, Mary and George C Ehrnrooth Foundation and K Albin Johanssons Foundations during the conduct of the study. TTS reports grants from Finnish Medical Foundation, Emil Aaltonen Foundation, Jane and Aatos Erkko Foundation, Relander Foundation, and Cancer Foundation Finland during the conduct of the study; personal fees from Amgen Finland, personal fees from Mehiläinen, personal fees from Nouscom, personal fees from Tillots Pharma, personal fees and other support from Lynsight, and other support from Healthfund Finland outside the submitted work. EKL reports grants from Päivikki and Sakari Sohlberg Foundation during the conduct of the study. JT reports personal fees from Seppo Säynäjäkankaa’s Foundation. No disclosures were reported by the other authors.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors' contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eConception and design: MK, EKL, and TJ; Laboratory analyses: MK, TS, and TJ; Bioinformatics analyses: MK, TS, and TMK; Data analyses: MK, TS, JT, SÄ, and TJ; Drafting the manuscript: MK, TS, TMK, EKL, and TJ. Patient sample collection and maintenance: TTS. Data management: TTS, JPM, and KP. Supervision: TMK, EKL, and TJ. All authors participated in revising the manuscript and approved the final version.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCorresponding authors\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Tiina Jokela.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgments\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge all study participants, the LSRFi, Central Finland Hospital Nova’s pathology laboratory staff, and staff of the Sports and Health Science faculty laboratory members who participated in data collection. We thank you Nightingale Health Ltd. for analyzing metabolomics data. We would also like to acknowledge CSC for providing the computational resources that facilitated the analyses performed in this study. In addition, we thank BioRender for providing the tools to create scientific illustrations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDominguez-Valentin M, Sampson JR, Sepp\u0026auml;l\u0026auml; TT, ten Broeke SW, Plazzer JP, Nakken S, et al. Cancer risks by gene, age, and gender in 6350 carriers ofpathogenic mismatch repair variants: findings from the Prospective Lynch SyndromeDatabase. Genetics in Medicine. 2020 Jan 1;22(1):15\u0026ndash;25. \u003c/li\u003e\n\u003cli\u003eBhattacharya P, Leslie SW, McHugh TW. Lynch Syndrome (Hereditary Nonpolyposis Colorectal Cancer). In: StatPearls. Treasure Island (FL): StatPearls Publishing; 2024. \u003c/li\u003e\n\u003cli\u003ePower RF, Doherty DE, Horgan R, Fahey P, Gallagher DJ, Lowery MA, et al. Modifiable risk factors for cancer among people with lynch syndrome: an international, cross-sectional survey. Hereditary Cancer in Clinical Practice. 2024 Jun 14;22(1):10. \u003c/li\u003e\n\u003cli\u003eShankar E, Gupta K, Gupta S. Chapter 17 - Dietary and Lifestyle Factors in Epigenetic Regulation of Cancer. In: Bishayee A, Bhatia D, editors. Epigenetics of Cancer Prevention [Internet]. Academic Press; 2019. p. 361\u0026ndash;94. Available from: https://www.sciencedirect.com/science/article/pii/B9780128124949000172\u003c/li\u003e\n\u003cli\u003eLocasale JW. Diet and Exercise in Cancer Metabolism. Cancer Discov. 2022 Oct 5;12(10):2249\u0026ndash;57. \u003c/li\u003e\n\u003cli\u003eYu X, Zhao H, Wang R, Chen Y, Ouyang X, Li W, et al. Cancer epigenetics: from laboratory studies and clinical trials to precision medicine. Cell Death Discovery. 2024 Jan 15;10(1):28. \u003c/li\u003e\n\u003cli\u003eKanwal R, Gupta S. Epigenetic modifications in cancer. Clin Genet. 2012 Apr;81(4):303\u0026ndash;11. \u003c/li\u003e\n\u003cli\u003eLi T, Mao C, Wang X, Shi Y, Tao Y. Epigenetic crosstalk between hypoxia and tumor driven by HIF regulation. Journal of Experimental \u0026amp; Clinical Cancer Research. 2020 Oct 27;39(1):224. \u003c/li\u003e\n\u003cli\u003ePeng Y, Croce CM. The role of MicroRNAs in human cancer. Signal Transduction and Targeted Therapy. 2016 Jan 28;1(1):15004. \u003c/li\u003e\n\u003cli\u003eSiev\u0026auml;nen T, Korhonen TM, Jokela T, Ahtiainen M, Lahtinen L, Kuopio T, et al. Systemic circulating microRNA landscape in Lynch syndrome. Int J Cancer. 2023 Mar 1;152(5):932\u0026ndash;44. \u003c/li\u003e\n\u003cli\u003eMart\u0026iacute;nez-Reyes I, Chandel NS. Cancer metabolism: looking forward. Nature Reviews Cancer. 2021 Oct 1;21(10):669\u0026ndash;80. \u003c/li\u003e\n\u003cli\u003eNazih H, Bard JM. Cholesterol, Oxysterols and LXRs in Breast Cancer Pathophysiology. International Journal of Molecular Sciences [Internet]. 2020;21(4). Available from: https://www.mdpi.com/1422-0067/21/4/1356\u003c/li\u003e\n\u003cli\u003eSubramaniam S, Jeet V, Clements JA, Gunter JH, Batra J. Emergence of MicroRNAs as Key Players in Cancer Cell Metabolism. Clinical Chemistry. 2019 Sep 1;65(9):1090\u0026ndash;101. \u003c/li\u003e\n\u003cli\u003eAgbu P, Carthew RW. MicroRNA-mediated regulation of glucose and lipid metabolism. Nature Reviews Molecular Cell Biology. 2021 Jun 1;22(6):425\u0026ndash;38. \u003c/li\u003e\n\u003cli\u003eSuriya Muthukumaran N, Velusamy P, Akino Mercy CS, Langford D, Natarajaseenivasan K, Shanmughapriya S. MicroRNAs as Regulators of Cancer Cell Energy Metabolism. J Pers Med. 2022 Aug 18;12(8). \u003c/li\u003e\n\u003cli\u003eJokela T, Karppinen J, K\u0026auml;rkk\u0026auml;inen M, Mecklin JP, Walker S, Sepp\u0026auml;l\u0026auml; T, et al. Circulating metabolome landscape in Lynch Syndrome. 2023. \u003c/li\u003e\n\u003cli\u003eHasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biology. 2017 May 5;18(1):83. \u003c/li\u003e\n\u003cli\u003eDominguez-Valentin M, Haupt S, Sepp\u0026auml;l\u0026auml; TT, Sampson JR, Sunde L, Bernstein I, et al. Mortality by age, gene and gender in carriers of pathogenic mismatch repair gene variants receiving surveillance for early cancer diagnosis and treatment: a report from the prospective Lynch syndrome database. EClinicalMedicine. 2023 Apr;58:101909. \u003c/li\u003e\n\u003cli\u003eSiev\u0026auml;nen T, Jokela T, Hyv\u0026auml;rinen M, Korhonen TM, Pylv\u0026auml;n\u0026auml;inen K, Mecklin JP, et al. Circulating miRNA Signature Predicts Cancer Incidence in Lynch Syndrome\u0026mdash;A Pilot Study. Cancer Prevention Research. 2024 Jun 4;17(6):243\u0026ndash;54. \u003c/li\u003e\n\u003cli\u003eWang W, Rong Z, Wang G, Hou Y, Yang F, Qiu M. Cancer metabolites: promising biomarkers for cancer liquid biopsy. Biomarker Research. 2023 Jun 30;11(1):66. \u003c/li\u003e\n\u003cli\u003eHayes J, Peruzzi PP, Lawler S. MicroRNAs in cancer: biomarkers, functions and therapy. Trends in Molecular Medicine. 2014 Aug 1;20(8):460\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eDaimon T. Box\u0026ndash;Cox Transformation. In: Lovric M, editor. International Encyclopedia of Statistical Science [Internet]. Berlin, Heidelberg: Springer Berlin Heidelberg; 2011. p. 176\u0026ndash;8. Available from: https://doi.org/10.1007/978-3-642-04898-2_152\u003c/li\u003e\n\u003cli\u003eLove MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. \u003c/li\u003e\n\u003cli\u003eLeek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012 Mar 15;28(6):882\u0026ndash;3. \u003c/li\u003e\n\u003cli\u003eParadis E, Schliep K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics. 2019 Feb 1;35(3):526\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eMcCoy AG, Noel Z, Sparks AH, Chilvers M. hagis, an R Package Resource for Pathotype Analysis of Phytophthora sojae Populations Causing Stem and Root Rot of Soybean. Mol Plant Microbe Interact. 2019 Dec;32(12):1574\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eRohart F, Gautier B, Singh A, L\u0026ecirc; Cao KA. mixOmics: An R package for \u0026rsquo;omics feature selection and multiple data integration. PLoS Comput Biol. 2017 Nov;13(11):e1005752. \u003c/li\u003e\n\u003cli\u003eLangfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008 Dec 29;9(1):559. \u003c/li\u003e\n\u003cli\u003eVestal BE, Wynn E, Moore CM. lmerSeq: an R package for analyzing transformed RNA-Seq data with linear mixed effects models. BMC Bioinformatics. 2022 Nov 16;23(1):489. \u003c/li\u003e\n\u003cli\u003eSticht C, De La Torre C, Parveen A, Gretz N. miRWalk: An online resource for prediction of microRNA binding sites. PLoS One. 2018;13(10):e0206239. \u003c/li\u003e\n\u003cli\u003eTibshirani R. The lasso method for variable selection in the Cox model. Stat Med. 1997 Feb 28;16(4):385\u0026ndash;95. \u003c/li\u003e\n\u003cli\u003eZhou H, Wang H, Wang S, Zou Y. SurvMetrics: An R package for Predictive Evaluation Metrics in Survival Analysis. The R Journal. 2023 Feb 10;14:252\u0026ndash;63. \u003c/li\u003e\n\u003cli\u003eHemmings BA, Restuccia DF. PI3K-PKB/Akt pathway. Cold Spring Harb Perspect Biol. 2012 Sep 1;4(9):a011189. \u003c/li\u003e\n\u003cli\u003eMasoud GN, Li W. HIF-1\u0026alpha; pathway: role, regulation and intervention for cancer therapy. Acta Pharm Sin B. 2015 Sep;5(5):378\u0026ndash;89. \u003c/li\u003e\n\u003cli\u003eSemenza GL. Targeting HIF-1 for cancer therapy. Nature Reviews Cancer. 2003 Dec 1;3(10):721\u0026ndash;32. \u003c/li\u003e\n\u003cli\u003eHolter S, Hall MJ, Hampel H, Jasperson K, Kupfer SS, Larsen Haidle J, et al. Risk assessment and genetic counseling for Lynch syndrome \u0026ndash; Practice resource of the National Society of Genetic Counselors and the Collaborative Group of the Americas on Inherited Gastrointestinal Cancer. Journal of Genetic Counseling. 2022 Jun 1;31(3):568\u0026ndash;83. \u003c/li\u003e\n\u003cli\u003eMehta N, Hordines J, Volpe C, Doerr R, Cohen SA. Cellular effects of hypercholesterolemia in modulation of cancer growth and metastasis: a review of the evidence. Surg Oncol. 1997 Nov;6(3):179\u0026ndash;85. \u003c/li\u003e\n\u003cli\u003eDeng CF, Zhu N, Zhao TJ, Li HF, Gu J, Liao DF, et al. Involvement of LDL and ox-LDL in Cancer Development and Its Therapeutical Potential. Front Oncol. 2022;12:803473. \u003c/li\u003e\n\u003cli\u003eXu H, Zhou S, Tang Q, Xia H, Bi F. Cholesterol metabolism: New functions and therapeutic approaches in cancer. Biochim Biophys Acta Rev Cancer. 2020 Aug;1874(1):188394. \u003c/li\u003e\n\u003cli\u003eInoue M, Niki M, Ozeki Y, Nagi S, Chadeka EA, Yamaguchi T, et al. High-density lipoprotein suppresses tumor necrosis factor alpha production by mycobacteria-infected human macrophages. Sci Rep. 2018 Apr 30;8(1):6736. \u003c/li\u003e\n\u003cli\u003eLiu N, Yang C, Gao A, Sun M, Lv D. MiR-101: An Important Regulator of Gene Expression and Tumor Ecosystem. Cancers (Basel). 2022 Nov 28;14(23). \u003c/li\u003e\n\u003cli\u003eVarambally S, Cao Q, Mani RS, Shankar S, Wang X, Ateeq B, et al. Genomic loss of microRNA-101 leads to overexpression of histone methyltransferase EZH2 in cancer. Science. 2008 Dec 12;322(5908):1695\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eCui TX, Kryczek I, Zhao L, Zhao E, Kuick R, Roh MH, et al. Myeloid-Derived Suppressor Cells Enhance Stemness of Cancer Cells by Inducing MicroRNA101 and Suppressing the Corepressor CtBP2. Immunity. 2013 Sep 19;39(3):611\u0026ndash;21. \u003c/li\u003e\n\u003cli\u003eSanjabi F, Nekouian R, Akbari A, Mirzaei R, Fattahi A. Plasma miR-183-5p in colorectal cancer patients as potential predictive lymph node metastasis marker. Journal of Cancer Research and Therapeutics [Internet]. 2022;18(4). Available from: https://journals.lww.com/cancerjournal/fulltext/2022/18040/plasma_mir_183_5p_in_colorectal_cancer_patients_as.8.aspx\u003c/li\u003e\n\u003cli\u003eZaporozhchenko IA, Morozkin ES, Skvortsova TE, Ponomaryova AA, Rykova EY, Cherdyntseva NV, et al. Plasma miR-19b and miR-183 as Potential Biomarkers of Lung Cancer. PLoS One. 2016;11(10):e0165261. \u003c/li\u003e\n\u003cli\u003eMacedo T, Silva-Oliveira RJ, Silva VAO, Vidal DO, Evangelista AF, Marques MMC. Overexpression of mir-183 and mir-494 promotes proliferation and migration in human breast cancer cell lines. Oncol Lett. 2017 Jul;14(1):1054\u0026ndash;60. \u003c/li\u003e\n\u003cli\u003eLiang Z, Gao Y, Shi W, Zhai D, Li S, Jing L, et al. Expression and significance of microRNA-183 in hepatocellular carcinoma. ScientificWorldJournal. 2013;2013:381874. \u003c/li\u003e\n\u003cli\u003eBailey A, Mohiuddin SS. Biochemistry, High Density Lipoprotein. In: StatPearls. Treasure Island (FL): StatPearls Publishing; 2024. \u003c/li\u003e\n\u003cli\u003ePirro M, Ricciuti B, Rader DJ, Catapano AL, Sahebkar A, Banach M. High density lipoprotein cholesterol and cancer: Marker or causative? Progress in Lipid Research. 2018 Jul 1;71:54\u0026ndash;69. \u003c/li\u003e\n\u003cli\u003eCruz PM, Mo H, McConathy W, Sabnis NA, Lacko AG. The role of cholesterol metabolism and cholesterol transport in carcinogenesis: a review of scientific findings, relevant to future cancer therapeutics. Frontiers in Pharmacology [Internet]. 2013;4. Available from: https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2013.00119\u003c/li\u003e\n\u003cli\u003eBarter PJ, Connor WE. The transport of triglyceride in the high-density lipoproteins of human plasma. J Lab Clin Med. 1975 Feb;85(2):260\u0026ndash;72. \u003c/li\u003e\n\u003cli\u003eJardillier R, Koca D, Chatelain F, Guyon L. Prognosis of lasso-like penalized Cox models with tumor profiling improves prediction over clinical data alone and benefits from bi-dimensional pre-screening. BMC Cancer. 2022 Oct 5;22(1):1045. \u003c/li\u003e\n\u003cli\u003eCalabrese F, Lunardi F, Pezzuto F, Fortarezza F, Vuljan SE, Marquette C, et al. Are There New Biomarkers in Tissue and Liquid Biopsies for the Early Detection of Non-Small Cell Lung Cancer? Journal of Clinical Medicine. 2019;8(3). \u003c/li\u003e\n\u003cli\u003eFeng Y, Yang J, Duan W, Cai Y, Liu X, Peng Y. LASSO-derived prognostic model predicts cancer-specific survival in advanced pancreatic ductal adenocarcinoma over 50 years of age: a retrospective study of SEER database research. Front Oncol. 2023;13:1336251. \u003c/li\u003e\n\u003cli\u003eDu X jie, Yang X rong, Wang Q cai, Lin G liang, Li P fei, Zhang W feng. Identification and validation of a five-gene prognostic signature based on bioinformatics analyses in breast cancer. Heliyon. 2023 Feb 1;9(2):e13185. \u003c/li\u003e\n\u003cli\u003eHuang B, Ding F, Li Y. A practical recurrence risk model based on Lasso-Cox regression for gastric cancer. Journal of Cancer Research and Clinical Oncology. 2023 Nov 1;149(17):15845\u0026ndash;54. \u003c/li\u003e\n\u003cli\u003eTyagi N, Roy S, Vengadesan K, Gupta D. Multi-omics approach for identifying CNV-associated lncRNA signatures with prognostic value in prostate cancer. Non-coding RNA Research. 2024 Mar 1;9(1):66\u0026ndash;75. \u003c/li\u003e\n\u003cli\u003eZhou Y, Tao L, Qiu J, Xu J, Yang X, Zhang Y, et al. Tumor biomarkers for diagnosis, prognosis and targeted therapy. Signal Transduction and Targeted Therapy. 2024 May 20;9(1):132. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"[email protected]","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":"Lynch Syndrome, multi-omics, systemic biomarkers, Lasso Cox regression, cancer risk prediction, circulating microRNAs, circulating metabolites","lastPublishedDoi":"10.21203/rs.3.rs-5682364/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5682364/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLynch syndrome is a genetic cancer-predisposing syndrome caused by pathogenic mutations in DNA mismatch repair (path_MMR) genes. Due to the elevated cancer risk, novel screening methods, alongside current surveillance techniques could enhance cancer risk stratification. Here we show how multi-omics integration could be utilized to pinpoint cancer-predicting biomarkers in Lynch Syndrome. We studied which blood-based circulating microRNAs and metabolites could predict Lynch Syndrome cancer occurrence within a 5.8-year prospective surveillance period.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study cohort consisted of 116 Lynch Syndrome carriers who were healthy at the time of sampling, of whom 17 developed cancer during the surveillance. Principal Coordinate Analysis and Canonical Correlation Analysis were used to explore the relationships between single and multi-omics data, enabling the identification of patterns and correlations across different biological layers. Weighted Correlation Network Analysis was used to identify omics-level co-expression modules and to study how these modules are associated with future cancer incidence or path_MMR variant. Lasso Cox regression was used to identify cancer-predicting biomarkers. The initial model was internally validated by splitting the data randomly into 5 training and corresponding validation datasets. Biological functions of future cancer-associated circulating microRNAs were studied by conducting pathway analyses using miRWalk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWeighted Correlation Network Analysis revealed a circulating microRNA co-expression module significantly associated with future cancer incidence. The identified microRNAs regulate cancer-related pathways including PI3K/Akt signaling pathway. Also, the analysis detected a circulating metabolite module, consisting of ApoB containing lipoprotein classes, (low-, intermediate-, and very low-density lipoproteins), and included cholesterols, as well as phospholipids and sphingomyelins, that had distinct levels between the path_MMRvariants. Three biomarkers- hsa-miR-101-3p, hsa-miR-183-5p, and the among of triglycerides in high-density lipoprotein particles (HDL_TG)- significantly predicted cancer risk based on Lasso Cox regression, with a C-index of 0.76 (p-value = 0.0007), where elevated levels of these biomarkers were indicators of increased hazard ratio. In the internal validation, the model had an average C-index of 0.72.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe multi-omics approach and the identified biomarkers offer a promising tool for cancer risk identification in Lynch Syndrome while also uncovering underlying systemic molecular mechanisms.\u003c/p\u003e","manuscriptTitle":"Multi-omics Approaches to Uncover Liquid-Based Cancer-Predicting Biomarkers in Lynch Syndrome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-03 08:54:46","doi":"10.21203/rs.3.rs-5682364/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"4649f8b0-bfb1-48d2-ae7f-a20196aa7ec0","owner":[],"postedDate":"January 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-03T08:54:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-03 08:54:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5682364","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5682364","identity":"rs-5682364","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}