Objective: This study aimed to determine human papillomavirus (HPV)-related awareness and willingness to receive HPV vaccination among college students, in Wenzhou, and its associated factors. Methods: A cross-sectional epidemiological study was conducted among college students in Wenzhou to investigate their knowledge, attitude, and factors affecting their willingness to receive HPV vaccination. Results: A total of 1035 questionnaires were collected, of which 1002 were valid (males: 374, females: 628). In total, 904 (90.2%) college students had heard of HPV, with a lower rate among males than females (85.3% vs. 93.2%, p < 0.05) and 693 (69.2%) had heard of the HPV vaccine, with a significantly lower rate among males than females (53.7% vs. 78.3%, p < 0.05). Overall awareness of HPV and HPV vaccine among males and females was moderate, with lower awareness among males. A total of 55.9% of males and 80.4% of females indicated that they would be willing to receive the HPV vaccine, a significant difference (p < 0.001). The price, safety of HPV vaccine, and lack of knowledge about HPV and HPV vaccine were the major barriers to HPV vaccination for college students. Compared to females, inadequate knowledge of HPV was the main barrier factor for HPV vaccination among male college students. Conclusions: The overall knowledge level of males is lower than that of females. For male college students, providing more knowledge about HPV infection is helpful to promote their willingness to vaccinate. It is necessary to promote HPV-related knowledge for male and female college students, respectively.

Background: Most prostate cancers(PCa) rely on serum prostate-specific antigen (PSA) testing for biopsy confirmation, but the accuracy needs to be further improved. We need to continue to develop PCa prediction model with high clinical application value. Methods: Benign prostatic hyperplasia (BPH) and prostate cancer data were obtained from the Chinese National Clinical Medical Science Data Center for retrospective analysis. The model was constructed using the XGBoost algorithm, and patients’ age, body mass index (BMI), PSA-related parameters and serum biochemical parameters were used as model variables. Using decision analysis curve (DCA) to evaluate the clinical utility of the models. The shapley additive explanation (SHAP) framework was used to analyze the importance ranking and risk threshold of the variables. Results: A total of 1915 patients were included in this study, including 823 (43.0%) were BPH patients and 1092 (57.0%) were PCa patients. The XGBoost model provided better performance (AUC 0.82) compared with f/tPSA (AUC 0.75),tPSA (AUC 0.68) and fPSA (AUC 0.61), respectively. Based on SHAP values, f/tPSA was the most important variable, and the top five most important biochemical parameter variables were inorganic phosphorus (P), potassium (K), creatine kinase MB isoenzyme (CKMB), low-density lipoprotein cholesterol (LDL-C), and creatinine (Cre). PCa risk thresholds for these risk markers were f/tPSA (0.13), P (1.29 mmol/L), K (4.29 mmol/L), CKMB ( 11.6U/L), LDL-C (3.05mmol/L) and Cre (74.5-99.1umol/L). Conclusion: The present model has advantages of wide-spread availability and high net benefit, especially for underdeveloped countries and regions. Furthermore, these risk thresholds can assist in the diagnosis and screening of prostate cancer in clinical practice.

Background: Basement membranes (BMs) are associated with cell polarity, differentiation, migration, and survival. Previous studies have shown that BMs play a key role in the progression of cancer, and thus could serve as potential targets for inhibiting the development of cancer. However, the association between basement membrane-related genes (BMRGs) and clear cell renal cell carcinoma (ccRCC) remains unclear. To address that gap, we constructed a novel risk signature utilizing BMRGs to explore the relationship between ccRCC and BMs.Methods: We gathered transcriptome and clinical data from The Cancer Genome Atlas (TCGA) and randomly separated the data into training and test sets to look for new potential biomarkers and create a predictive signature of BMRGs for ccRCC. We applied univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses to establish the model. The risk signature was further verified and evaluated through principal component analysis (PCA), the Kaplan-Meier technique, and time-dependent receiver operating characteristics (ROC). A nomogram was constructed to predict the overall survival (OS). The possible biological pathways were investigated through functional enrichment analysis. In this study, we also determined tumor mutation burden (TMB) and performed immunological analysis and immunotherapeutic drug analysis between the high- and low-risk groups.Results: We identified 33 differentially expressed genes and constructed a risk model of eight BMRGs, including COL4A4, FREM1, CSPG4, COL4A5, ITGB6, ADAMTS14, MMP17, and THBS4. The PCA analysis showed that the signature could distinguish the high- and low-risk groups well. The K-M and ROC analysis demonstrated that the model could predict the prognosis well from the areas under the curves (AUCs), which was 0.731. Moreover, the nomogram showed good predictability. Univariate and multivariate Cox regression analysis validated that the model results supported the hypothesis that BMRGs were independent risk factors for ccRCC. Furthermore, immune cell infiltration, immunological checkpoints, TMB, and the half-inhibitory concentration varied considerably between high- and low-risk groups.Conclusion: Employing eight BMRGs to construct a risk model as a prognostic indicator of ccRCC could provide us with a potential progression trajectory as well as predictions of therapeutic response.

Background: Bladder cancer (BC), as the most common urinary system tumor type and the main cause of tumor-related death, has an unsatisfactory prognosis. In recent years, related literature has proposed that cell pyroptosis is an inflammatory form of programmed cell death. However, in BC, the relationship between the expression of pyroptosis-related genes and the prognosis has not been elucidated. Methods: We got the RNA sequencing data from TCGA and GEO datasets. Fifty-two pyroptosis-related genes were extracted for further explore. Then, we compared the gene expression levels between the normal bladder and BC tissues. After that, we develop and validate a pyroptosis-related gene prognostic model and made following functional enrichment analysis and single-sample gene set enrichment analysis of the differentially expressed genes between the high- and low-risk groups. Results: Twenty-nine differentially expressed genes (DEGs) were found between normal and tumor tissues. Based on the median score calculated by the risk score formula from 8 pyroptosis-related genes, 414 patients were equally divided into low- and high-risk subgroups. The survival probability of BC patients in the high-risk group was significantly lower than that in the low-risk group (P < 0.001). Through multivariate analysis, our risk score is an independent factor predicting OS in BC patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis show that high-risk populations are rich in immune-related genes and have a decreased immune status. All the above results have been externally verified from GEO cohort. Conclusion: Pyroptosis-related genes are closely related to tumor immunity and are a potential prognostic tool for predicting BCs.