Transcriptomic and protein-protein interaction analyses applying various methods including super-resolution and time-lapse microscopy showed that TGM2 directly binds to the tumor suppressor p53, leading to its inactivation and escape of apoptosis induction.We demonstrate here that TGM2 is an essential survival factor in CRC, highlighting the therapeutic potential of TGM2 inhibitors in CRC patients with high TGM2 expression. The inactivation of p53 by TGM2 binding indicates a general anti-apoptotic function, which may be relevant in cancers beyond CRC.Artificial intelligence (AI) is about to make itself indispensable in the health care sector. Examples of successful applications or promising approaches range from the application of pattern recognition software to pre-process and analyze digital medical images, to deep learning algorithms for subtype or disease classification, and digital twin technology and in silico clinical trials. Moreover, machine-learning techniques are used to identify patterns and anomalies in electronic health records and to perform ad-hoc evaluations of gathered data from wearable health tracking devices for deep longitudinal phenotyping. In the last years, substantial progress has been made in automated image classification, reaching even superhuman level in some instances. Despite the increasing awareness of the importance of the genetic context, the diagnosis in hematology is still mainly based on the evaluation of the phenotype. Either by the analysis of microscopic images of cells in cytomorphology or by the analysis of cell populations in bidimensional plots obtained by flow cytometry. Here, AI algorithms not only spot details that might escape the human eye, but might also identify entirely new ways of interpreting these images. With the introduction of high-throughput next-generation sequencing in molecular genetics, the amount of available information is increasing exponentially, priming the field for the application of machine learning approaches. The goal of all the approaches is to allow personalized and informed interventions, to enhance treatment success, to improve the timeliness and accuracy of diagnoses, and to minimize technically induced misclassifications. The potential of AI-based applications is virtually endless but where do we stand in hematology and how far can we go?The TP53 gene continues to hold distinction as the most frequently mutated gene in cancer. Since its discovery in 1979, hundreds of research groups have devoted their efforts toward understanding why this gene is so frequently selected against by tumors, with the hopes of harnessing this information toward the improved therapy of cancer. The result is that this protein has been meticulously analyzed in tumor and normal cells, resulting in over 100,000 publications, with an average of 5000 papers published on p53 every year for the past decade. The journey toward understanding p53 function has been anything but straightforward; in fact, the field is notable for the numerous times that established paradigms not only have been shifted, but in fact have been shattered or reversed. In this review, we will discuss the manuscripts, or series of manuscripts, that have most radically changed our thinking about how this tumor suppressor functions, and we will delve into the emerging challenges for the future in this important area of research. It is hoped that this review will serve as a useful historical reference for those interested in p53, and a useful lesson on the need to be flexible in the face of established paradigms.CircRNAs play essential roles in various physiological processes and involves in many diseases, in particular cancer. Global downregulation of circRNA expression has been observed in hepatocellular carcinoma (HCC) in many studies. Previous studies revealed that the pre-mRNA 3' end processing complex participates in circRNA cyclization and plays an important role in HCC tumorigenesis. Therefore, we explored the role of CPSF4, for 3' end formation and cleavage, in circRNA formation. Clinical research has shown that CPSF4 expression is upregulated in HCC and that high expression of CPSF4 is associated with poor prognosis in HCC patients. Mechanistic studies have demonstrated that CPSF4 reduces the levels of circRNAs, which possess a polyadenylation signal sequence and this decrease in circRNAs reduces the accumulation of miRNA and disrupts the miRNA-mediated gene silencing in HCC. Experiments in cell culture and xenograft mouse models showed that CPSF4 promotes the proliferation of HCC cells and enhances tumorigenicity. Moreover, CPSF4 antagonizes the tumor suppressor effect of its downstream circRNA in HCC. In summary, CPSF4 acts as an oncogene in HCC through circRNA inhibition and disruption of miRNA-mediated gene silencing.Recurrence of metastatic breast cancer stemming from acquired endocrine and chemotherapy resistance remains a health burden for women with luminal (ER+) breast cancer. Disseminated ER+ tumor cells can remain viable but quiescent for years to decades. Contributing factors to metastatic spread include the maintenance and expansion of breast cancer stem cells (CSCs). Breast CSCs frequently exist as a minority population in therapy resistant tumors. https://www.selleckchem.com/products/escin.html In this study, we show that cytoplasmic complexes composed of steroid receptor (SR) co-activators, PELP1 and SRC-3, modulate breast CSC expansion through upregulation of the HIF-activated metabolic target genes PFKFB3 and PFKFB4. Seahorse metabolic assays demonstrated that cytoplasmic PELP1 influences cellular metabolism by increasing both glycolysis and mitochondrial respiration. PELP1 interacts with PFKFB3 and PFKFB4 proteins, and inhibition of PFKFB3 and PFKFB4 kinase activity blocks PELP1-induced tumorspheres and protein-protein interactions with SRC-3. PFKFB4 knockdown inhibited in vivo emergence of circulating tumor cell (CTC) populations in mammary intraductal (MIND) models. Application of PFKFB inhibitors in combination with ER targeted therapies blocked tumorsphere formation in multiple models of advanced breast cancer including tamoxifen (TamR) and paclitaxel (TaxR) resistant models, murine tumor cells, and ER+ patient-derived organoids (PDxO). Together, our data suggest that PELP1, SRC-3, and PFKFBs cooperate to drive ER+ tumor cell populations that include CSCs and CTCs. Identifying non-ER pharmacological targets offers a useful approach to blocking metastatic escape from standard of care ER/estrogen (E2)-targeted strategies to overcome endocrine and chemotherapy resistance.