Wilms tumor is the most common kidney tumor in children aged 0-14 years. MicroRNAs are small, noncoding RNAs linked to the development of malignant tumors. Several studies have shown the association between single nucleotide polymorphism in miR-27a and cancer risk. This study aimed to explore the potential impact of the miR-27a rs895819 T>C polymorphism on Wilms tumor susceptibility.

The rs895819 T>C polymorphism was genotyped using the TaqMan method in 145 patients with Wilms tumors and 531 controls. Logistic regression models were used to assess the association between this polymorphism and Wilms tumor risk. A stratified analysis was also performed based on age, sex, and clinical stage.

The rs895819 T>C polymorphism showed genotypic distribution consistent with Hardy-Weinberg equilibrium (P = 0.749). The differences were not statistically significant. The miR-27a rs895819 T>C polymorphism was not significantly associated with Wilms tumor susceptibility, and the stratified analysis did not yield any significant differences.

Our study provides evidence of a lack of association between the miR-27a rs895819 T>C polymorphism and Wilms tumor susceptibility. Further validation through larger sample sizes and additional genetic polymorphisms is warranted.

Prostate cancer is the second most diagnosed cancer in men worldwide and a significant cause of cancer-related death. Proteogenomic analysis offers insights into how genomic mutations influence protein expression and can identify novel biomarkers. This study aimed to investigate the impact of missense mutations on protein abundance in prostate cancer versus healthy tissues using SILAC-based quantitative proteomics.

Mass spectrometry data from prostate tumors and adjacent healthy tissues were analyzed using stable isotope labeling. Peptides were classified based on their abundance into RefSeq and Variant Abundant groups. Missense mutations were mapped via RefSeq and dbPepVar databases. Protein intensity metrics were compared, and Spearman’s correlation was used to evaluate the relationship between mutation presence and protein abundance.

Functional enrichment revealed that RefSeq Abundant proteins are involved in normal metabolic and structural functions, while Variant Abundant proteins are enriched in tumor-related pathways such as immune evasion and apoptosis suppression. A significant negative correlation was found between protein intensity difference and ratio (p < 0.05), indicating that missense mutations contribute to altered protein expression. Mutation hotspot analysis identified recurrent alterations in genes such as PPIF and ACTB. PROVEAN was used to evaluate the functional impact of variants, identifying several as deleterious to protein stability and function.

Missense mutations are associated with altered protein abundance and may promote oncogenic processes in prostate cancer. These findings enhance the understanding of genome-proteome interactions and could support the development of targeted biomarkers and therapies.

Given the high burden of hepatocellular carcinoma (HCC), risk stratification in patients with cirrhosis is critical but remains inadequate. In this study, we aimed to develop and validate an HCC prediction model by integrating radiomics and deep learning features from liver and spleen computed tomography (CT) images into the established age-male-ALBI-platelet (aMAP) clinical model.

Patients were enrolled between 2018 and 2023 from a Chinese multicenter, prospective, observational cirrhosis cohort, all of whom underwent 3-phase contrast-enhanced abdominal CT scans at enrollment. The aMAP clinical score was calculated, and radiomic (PyRadiomics) and deep learning (ResNet-18) features were extracted from liver and spleen regions of interest. Feature selection was performed using the least absolute shrinkage and selection operator.

Among 2,411 patients (median follow-up: 42.7 months [IQR: 32.9–54.1]), 118 developed HCC (three-year cumulative incidence: 3.59%). Chronic hepatitis B virus infection was the main etiology, accounting for 91.5% of cases. The aMAP-CT model, which incorporates CT signatures, significantly outperformed existing models (area under the receiver-operating characteristic curve: 0.809–0.869 in three cohorts). It stratified patients into high-risk (three-year HCC incidence: 26.3%) and low-risk (1.7%) groups. Stepwise application (aMAP → aMAP-CT) further refined stratification (three-year incidences: 1.8% [93.0% of the cohort] vs. 27.2% [7.0%]).

The aMAP-CT model improves HCC risk prediction by integrating CT-based liver and spleen signatures, enabling precise identification of high-risk cirrhosis patients. This approach personalizes surveillance strategies, potentially facilitating earlier detection and improved outcomes.

Actively identifying the risk factors and predictive indicators associated with portal vein thrombosis (PVT) in liver cirrhosis (LC) can enable early diagnosis and treatment, which is of great significance for prolonging the survival of patients with LC. Hemodynamic disturbances, advanced LC, vascular endothelial injury, and mutations in thrombophilic genetic factors are established risk factors for PVT-LC. Venous dilatation and decreased blood flow velocity contribute to hemodynamic disturbances. The severity of LC can be assessed by the degree of portal hypertension, liver metabolic function biomarkers, and validated liver scoring systems. Iatrogenic interventions, endotoxemia, and metabolic syndrome may induce vascular endothelial injury and hypercoagulability, the latter of which can be quantified via coagulation-anticoagulation-fibrinolysis biomarkers. Mutations in thrombophilic genetic factors, such as Factor V Leiden, MTHFR C667T, and JAK2 V617F, disrupt coagulation-anticoagulation homeostasis and predispose patients to PVT-LC. This review specifically focuses on comprehensively delineating established risk factors and predictive indicators for PVT-LC, thereby providing a theoretical foundation for the construction of clinically applicable PVT predictive models to guide early interventions and improve the prognosis. Future research should further validate the associations between recently proposed risk factors and PVT-LC, while simultaneously establishing cutoff values for indicators with robust predictive value to construct a clinically applicable PVT prediction framework.

Human papillomavirus (HPV) infection is the primary cause of cervical, anogenital, and oropharyngeal cancers in the United States. These cancers are preventable through HPV vaccination. Research is critically needed to identify effective strategies for promoting HPV vaccination among high-risk groups. This study develops a risk prediction model to identify patients who are unlikely to complete HPV vaccination, with the goal of using the model to direct resources and increase vaccination rates.

We assessed vaccination status along with patient, provider, and clinic characteristics that predict vaccination completion. We then developed a predictive model to assess the likelihood of completing HPV vaccination, which can be used to target interventions based on patient needs. We used a retrospective cohort from a large integrated delivery system in Oregon. Using logistic regression with data available in the electronic health record, we created a risk model to determine the likelihood of vaccination completion among patients aged 11–17 years.

In a cohort of 61,788 patients, 40,570 (65.7%) had received at least one dose of the HPV vaccine. The full model included 17 demographic, clinical, provider, and community characteristics, achieving a bootstrap-corrected C-statistic of 0.67 with adequate calibration. The reduced model, which retained five demographic and clinical characteristics (age, language, race, ethnicity, and prior vaccinations), had a bootstrap-corrected C-statistic of 0.65 and adequate calibration.

Our findings suggest that a risk prediction model can guide the implementation of targeted interventions and the intensity of those interventions based on the likelihood of vaccination completion.