The effects of PIKfyve inhibitor YM201636 on claudins and malignancy potential of nonsmall cell cancer cells

CLDN5: From structure and regulation to roles in tumors and other diseases beyond CNS disorders

Claudin-5 (CLDN5) is an essential component of tight junctions (TJs) and is critical for the integrity of the blood-brain barrier (BBB), ensuring homeostasis and protection from damage to the central nervous system (CNS). Currently, many researchers have summarized the role and mechanisms of CLDN5 in CNS diseases. However, it is noteworthy that CLDN5 also plays a significant role in tumor growth and metastasis. In addition, abnormal CLDN5 expression is involved in the development of respiratory diseases, intestinal diseases, cardiac diseases, and diabetic ocular complications. This paper aims to review the structure, expression, and regulation of CLDN5, focusing on its role in tumors, including its expression and regulation, effects on malignant phenotypes, and clinical significance. Furthermore, this paper will provide an overview of the role and mechanisms of CLDN5 in respiratory diseases, intestinal diseases, cardiac diseases, and diabetic ocular complications.

Targeting phosphoinositide signaling in cancer: relevant techniques to study lipids and novel avenues for therapeutic intervention

Phosphoinositides serve as essential players in numerous biological activities and are critical for overall cellular function. Due to their complex chemical structures, localization, and low abundance, current challenges in the phosphoinositide field include the accurate measurement and identification of specific variants, particularly those with acyl chains. Researchers are intensively developing innovative techniques and approaches to address these challenges and advance our understanding of the impact of phosphoinositide signaling on cellular biology. This article provides an overview of recent advances in the study of phosphoinositides, including mass spectrometry, lipid biosensors, and real-time activity assays using fluorometric sensors. These methodologies have proven instrumental for a comprehensive exploration of the cellular distribution and dynamics of phosphoinositides and have shed light on the growing significance of these lipids in human health and various pathological processes, including cancer. To illustrate the importance of phosphoinositide signaling in disease, this perspective also highlights the role of a family of lipid kinases named phosphatidylinositol 5-phosphate 4-kinases (PI5P4Ks), which have recently emerged as exciting therapeutic targets for cancer treatment. The ongoing exploration of phosphoinositide signaling not only deepens our understanding of cellular biology but also holds promise for novel interventions in cancer therapy.

Integrated bioinformatics and statistical approaches to explore molecular biomarkers for breast cancer diagnosis, prognosis and therapies

Integrated bioinformatics and statistical approaches are now playing the vital role in identifying potential molecular biomarkers more accurately in presence of huge number of alternatives for disease diagnosis, prognosis and therapies by reducing time and cost compared to the wet-lab based experimental procedures. Breast cancer (BC) is one of the leading causes of cancer related deaths for women worldwide. Several dry-lab and wet-lab based studies have identified different sets of molecular biomarkers for BC. But they did not compare their results to each other so much either computationally or experimentally. In this study, an attempt was made to propose a set of molecular biomarkers that might be more effective for BC diagnosis, prognosis and therapies, by using the integrated bioinformatics and statistical approaches. At first, we identified 190 differentially expressed genes (DEGs) between BC and control samples by using the statistical LIMMA approach. Then we identified 13 DEGs (AKR1C1, IRF9, OAS1, OAS3, SLCO2A1, NT5E, NQO1, ANGPT1, FN1, ATF6B, HPGD, BCL11A, and TP53INP1) as the key genes (KGs) by protein-protein interaction (PPI) network analysis. Then we investigated the pathogenetic processes of DEGs highlighting KGs by GO terms and KEGG pathway enrichment analysis. Moreover, we disclosed the transcriptional and post-transcriptional regulatory factors of KGs by their interaction network analysis with the transcription factors (TFs) and micro-RNAs. Both supervised and unsupervised learning's including multivariate survival analysis results confirmed the strong prognostic power of the proposed KGs. Finally, we suggested KGs-guided computationally more effective seven candidate drugs (NVP-BHG712, Nilotinib, GSK2126458, YM201636, TG-02, CX-5461, AP-24534) compared to other published drugs by cross-validation with the state-of-the-art alternatives top-ranked independent receptor proteins. Thus, our findings might be played a vital role in breast cancer diagnosis, prognosis and therapies.