Clinical significance of upregulated Rho GTPase activating protein 12 causing resistance to tyrosine kinase inhibitors in hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is a major health challenge with high incidence and poor survival rates in China. Systemic therapies, particularly tyrosine kinase inhibitors (TKIs), are the first-line treatment for advanced HCC, but resistance is common. The Rho GTPase family member Rho GTPase activating protein 12 (ARHGAP12), which regulates cell adhesion and invasion, is a potential therapeutic target for overcoming TKI resistance in HCC.

Overexpressed KCNK1 regulates potassium channels affecting molecular mechanisms and biological pathways in bladder cancer.

Background: This study aimed to explore the expression, molecular mechanism and its biological function of potassium two pore domain channel subfamily K member 1 (KCNK1) in bladder cancer (BC).

Methods: We integrated large numbers of external samples (n = 1486) to assess KCNK1 mRNA expression levels and collected in-house samples (n = 245) for immunohistochemistry (IHC) experiments to validate at the KCNK1 protein level. Single-cell RNA sequencing (scRNA-seq) analysis was performed to further assess KCNK1 expression and cellular communication.

Cross-Domain Pattern Classification With Distribution Adaptation Based on Evidence Theory.

In pattern classification, there may not exist labeled patterns in the target domain to train a classifier. Domain adaptation (DA) techniques can transfer the knowledge from the source domain with massive labeled patterns to the target domain for learning a classification model. In practice, some objects in the target domain are easily classified by this classification model, and these objects usually can provide more or less useful information for classifying the other objects in the target domain.

Combination of Transferable Classification With Multisource Domain Adaptation Based on Evidential Reasoning.

In applications of domain adaptation, there may exist multiple source domains, which can provide more or less complementary knowledge for pattern classification in the target domain. In order to improve the classification accuracy, a decision-level combination method is proposed for the multisource domain adaptation based on evidential reasoning. The classification results obtained from different source domains usually have different reliabilities/weights, which are calculated according to domain consistency.