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

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Янв. 3, 2025

Background Lynch syndrome is a genetic cancer-predisposing 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 risk stratification. Here we show how multi-omics integration be utilized pinpoint cancer-predicting biomarkers Syndrome. We studied which blood-based circulating microRNAs and metabolites predict Syndrome occurrence within 5.8-year prospective period. Methods The study cohort consisted of 116 carriers who were healthy at time sampling, whom 17 developed during surveillance. Principal Coordinate Analysis Canonical Correlation used explore relationships between single data, enabling identification patterns correlations across different biological layers. Weighted Network was identify omics-level co-expression modules these are associated with future incidence or path_MMR variant. Lasso Cox regression biomarkers. initial model internally validated splitting data randomly into 5 training corresponding validation datasets. Biological functions cancer-associated conducting pathway analyses using miRWalk. Results revealed microRNA module significantly incidence. identified regulate cancer-related pathways including PI3K/Akt signaling pathway. Also, analysis detected metabolite module, consisting ApoB containing lipoprotein classes, (low-, intermediate-, very low-density lipoproteins), included cholesterols, as well phospholipids sphingomyelins, that had distinct levels path_MMRvariants. Three biomarkers- hsa-miR-101-3p, hsa-miR-183-5p, among triglycerides high-density particles (HDL_TG)- predicted based on regression, C-index 0.76 (p-value = 0.0007), where indicators increased hazard ratio. In internal validation, an average 0.72. Conclusions approach offer promising tool for while also uncovering underlying systemic molecular mechanisms.

Язык: Английский

Establishing a clinical prediction model for diabetic foot ulcers in type 2 diabetic patients with lower extremity arteriosclerotic occlusion using machine learning

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 5, 2025

The burden of diabetic foot ulcers (DFU) is exacerbated in patients with concomitant arteriosclerotic occlusion disease (ASO) the lower extremities, who experience more severe symptoms and poorer prognoses. study aims to develop a predictive model grounded machine learning (ML) algorithms, specifically tailored forecast occurrence DFU extremity ASO. involves data from diagnosed ASO January 1, 2011 August 31, 2023. We conducted quality control on data. Subsequently, dataset was divided into training set comprising before 2020 validation onwards. Patients were stratified group or non-DFU based DFU. Intergroup comparisons analyze differences between these two groups. Logistic regression analyses, 3 kinds nomogram formulated estimate risk among Internal undertaken using bootstrap method, combing external temporal validation, results visually presented through Receiver Operating Characteristic (ROC) curve Calibration curve. To evaluate clinical practicality model, Decision Curve Analysis (DCA) Clinical Impact (CIC) employed. Body Mass Index (BMI), hypertension, coronary heart disease, nephropathy, number leg artery occlusions, controlling glucose by insulin injection, age, cigarettes smoked per day, diastolic blood pressure, C-reactive protein (CRP) utilized construct prediction model. This exhibited high performance (AUC = 0.962), both internal further confirmed its accuracy reproducibility 0.968 AUC 0.977, respectively). Additionally, DCA CIC demonstrated this excellent reproducibility, along broad practicality. It provides good reference for diagnosis treatment

Язык: Английский

Variable screening and model construction for prognosis of elderly patients with lower-grade gliomas based on LASSO-Cox regression: a population-based cohort study

Frontiers in Immunology, Год журнала: 2024, Номер 15

Опубликована: Сен. 11, 2024

This study aimed to identify prognostic factors for survival and develop a nomogram predict the probability of elderly patients with lower-grade gliomas (LGGs).

Язык: Английский