Establishment and validation of serum lipid-based nomogram for predicting the risk of prostate cancer.

BACKGROUND: This study aimed to explore the value of combined serum lipids with clinical symptoms to diagnose prostate cancer (PCa), and to develop and validate a Nomogram and prediction model to better select patients at risk of PCa for prostate biopsy. METHODS: Retrospective analysis of 548 patients who underwent prostate biopsies as a result of high serum prostate-specific antigen (PSA) levels or irregular digital rectal examinations (DRE) was conducted. The enrolled patients were randomly assigned to the training groups (n = 384, 70%) and validation groups (n = 164, 30%). To identify independent variables for PCa, serum lipids (TC, TG, HDL, LDL, apoA-1, and apoB) were taken into account in the multivariable logistic regression analyses of the training group, and established predictive models. After that, we evaluated prediction models with clinical markers using decision curves and the area under the curve (AUC). Based on training group data, a Nomogram was developed to predict PCa. RESULTS: 210 (54.70%) of the patients in the training group were diagnosed with PCa. Multivariate regression analysis showed that total PSA, f/tPSA, PSA density (PSAD), TG, LDL, DRE, and TRUS were independent risk predictors of PCa. A prediction model utilizing a Nomogram was constructed with a cut-off value of 0.502. The training and validation groups achieved area under the curve (AUC) values of 0.846 and 0.814 respectively. According to the decision curve analysis (DCA), the prediction model yielded optimal overall net benefits in both the training and validation groups, which is better than the optimal net benefit of PSA alone. After comparing our developed prediction model with two domestic models and PCPT-RC, we found that our prediction model exhibited significantly superior predictive performance. Furthermore, in comparison with clinical indicators, our Nomogram's ability to predict prostate cancer showed good estimation, suggesting its potential as a reliable tool for prognostication. CONCLUSIONS: The prediction model and Nomogram, which utilize both blood lipid levels and clinical signs, demonstrated improved accuracy in predicting the risk of prostate cancer, and consequently can guide the selection of appropriate diagnostic strategies for each patient in a more personalized manner.

Identifying stage-associated hub genes in bladder cancer via weighted gene co-expression network and robust rank aggregation analyses.

BACKGROUND: Bladder cancer (BC) is among the most frequent cancers globally. Although substantial efforts have been put to understand its pathogenesis, its underlying molecular mechanisms have not been fully elucidated. METHODS: The robust rank aggregation approach was adopted to integrate 4 eligible bladder urothelial carcinoma microarray datasets from the Gene Expression Omnibus. Differentially expressed gene sets were identified between tumor samples and equivalent healthy samples. We constructed gene co-expression networks using weighted gene co-expression network to explore the alleged relationship between BC clinical characteristics and gene sets, as well as to identify hub genes. We also incorporated the weighted gene co-expression network and robust rank aggregation to screen differentially expressed genes. RESULTS: CDH11, COL6A3, EDNRA, and SERPINF1 were selected from the key module and validated. Based on the results, significant downregulation of the hub genes occurred during the early stages of BC. Moreover, receiver operating characteristics curves and Kaplan-Meier plots showed that the genes exhibited favorable diagnostic and prognostic value for BC. Based on gene set enrichment analysis for single hub gene, all the genes were closely linked to BC cell proliferation. CONCLUSIONS: These results offer unique insight into the pathogenesis of BC and recognize CDH11, COL6A3, EDNRA, and SERPINF1 as potential biomarkers with diagnostic and prognostic roles in BC.

Binary-blend fibber-based capture assay of circulating tumor cells for clinical diagnosis of colorectal cancer.

BACKGROUND: In addition to conventional approaches, detecting and characterizing CTCs in patient blood allows for early diagnosis of cancer metastasis. METHODS: We blended poly(ethylene oxide) (PEO) into nylon-6 through electrospinning to generate a fibrous matbased circulating tumour cells (CTCs) assay. The contents of nylon-6 and PEO in the electrospun blend fibrous mats (EBFMs) were optimized to facilitate high cell-substrate affinity and low leukocyte adsorption. RESULTS: Compared with the IsoFlux System, a commercial instrument for CTC detection, the CTC assay of EBFMs exhibited lower false positive readings and high sensitivity and selectivity with preclinical specimens. Furthermore, we examined the clinical diagnosis accuracy of colorectal cancer, using the CTC assay and compared the results with those identified through pathological analyses of biopsies from colonoscopies. Our positive expressions of colorectal cancer through CTC detection completely matched those recognized through the pathological analyses for the individuals having stage II, III, and IV colorectal cancer. Nevertheless, two in four individuals having stage I colorectal cancer, recognized through pathological analysis of biopsies from colonoscopies, exhibited positive expression of CTCs. Ten individuals were identified through pathological analysis as having no colorectal tumours. Nevertheless, two of these ten individuals exhibited positive expression of CTCs. CONCLUSIONS: Thus, in this population, the low cost EBFMs exhibited considerable capture efficiency for the non-invasive diagnosis of colorectal cancer.

MT1-MMP is not a good prognosticator of cancer survival: evidence from 11 studies.

MT1-MMP exhibits diverse expressions in patients with cancer and could be considered as potential prognostic biomarker of cancer. We performed a meta-analysis aiming to provide more sufficient evidence that MT1-MMP expression is associated with poor overall survival in several types of cancers. We systematically searched the studies from databases and carefully identified based on eligibility criteria. The association between MT1-MMP expression and overall survival in cancers was estimated using Review Manager. A total of 11 literatures which included 1,918 cancer patients were combined in the final analysis. Meta-analysis revealed that MT1-MMP overexpression was associated with an unfavorable overall survival and the pooled hazard ratio (HR) and corresponding 95 % confidence interval (CI) was 2.46 (95 % CI 1.75-3.47). From subgroup analyses, we identified that MT1-MMP was an independent prognostic factor for lung cancer and gastric cancer, and HRs (95 % CI) were 3.73 (95 % CI 2.67-5.21) and 2.46 (95 % CI 1.69-3.59), respectively. In conclusion, MT1-MMP is a potential prognostic factor in human cancers.