A novel platelet risk score for stratifing the tumor immunophenotypes, treatment responses and prognosis in bladder carcinoma: results from real-world cohorts

PDE3B regulates KRT6B and increases the sensitivity of bladder cancer cells to copper ionophores

Cuproptosis is a new Cu-dependent programmed cell death manner that has shown regulatory functions in many tumor types, however, its mechanism in bladder cancer remains unclear. Here, we reveal that Phosphodiesterase 3B (PDE3B), a cuproptosis-associated gene, could reduce the invasion and migration of bladder cancer. PDE3B is downregulated in bladder cancer tissues, which is correlated with better prognosis. Conversely, overexpression of PDE3B in bladder cancer cell could significantly resist invasion and migration, which is consistent with the TCGA database results. Future study demonstrate the anti-cancer effect of PDE3B is mediated by Keratin 6B (KRT6B) which leads to the keratinization. Therefore, PDE3B can reduce KRT6B expression and inhibit the invasion and migration of bladder cancer. Meanwhile, increased expression of PDE3B was able to enhance the sensitivity of Cuproptosis drug thiram. This study show that PDE3B/KRT6B is a potential cancer therapeutic target and PDE3B activation is able to increase the sensitivity of bladder cancer cells to copper ionophores.

Improving platelet-RNA-based diagnostics: a comparative analysis of machine learning models for cancer detection and multiclass classification

Liquid biopsy demonstrates excellent potential in patient management by providing a minimally invasive and cost-effective approach to detecting and monitoring cancer, even at its early stages. Due to the complexity of liquid biopsy data, machine-learning techniques are increasingly gaining attention in sample analysis, especially for multidimensional data such as RNA expression profiles. Yet, there is no agreement in the community on which methods are the most effective or how to process the data. To circumvent this, we performed a large-scale study using various machine-learning techniques. First, we took a closer look at existing datasets and filtered out some patients to assert data collection quality. The final data collection included platelet RNA samples acquired from 1397 cancer patients (17 types of cancer) and 354 asymptomatic, presumed healthy, donors. Then, we assessed an array of different machine-learning models and techniques (e.g., feature selection of RNA transcripts) in pan-cancer detection and multiclass classification. Our results show that simple logistic regression performs the best, reaching a 68% cancer detection rate at a 99% specificity level, and multiclass classification accuracy of 79.38% when distinguishing between five cancer types. In summary, by revisiting classical machine-learning models, we have exceeded the previously used method by 5% and 9.65% in cancer detection and multiclass classification, respectively. To ease further research, we open-source our code and data processing pipelines (https://gitlab.com/jopekmaksym/improving-platelet-rna-based-diagnostics), which we hope will serve the community as a strong baseline.