Tissue Transglutaminase (TG2) is a potential therapeutic target in the treatment of chemoresistant breast cancer

Tissue transglutaminase (TG2) has been suggested to be a key player in the progression and metastasis of chemoresistant breast cancer. One of the foremost survival signalling pathways implicated in causing drug resistance in breast cancer is the constitutive activation of NFκB (Nuclear Factor -kappa B) induced by TG2. This study provides a mechanism by which TG2 constitutively activates NFκB which in turn confers chemoresistance to breast cancer cells against doxorubicin. Breast cancer cell lines with varying expression levels of TG2 as well as TG2 null breast cancer cells transfected with TG2 were used as the major cell models for this study. This study made use of cell permeable and impermeable TG2 inhibitors, specific TG2 and Rel A/ p65 targeting siRNA, TG2 functional blocking antibodies, IKK inhibitors and a specific targeting peptide against Rel A/p65 to investigate the pathway of activation involved in the constitutive activation of NFκB by TG2 leading to drug resistance. Crucial to the activation of Rel A/p65 and drug resistance in the breast cancer cells is the interaction between the complex of IκBα and Rel A/p65 with TG2 which results in the dimerization of Rel A/p65 and polymerization of IκBα. The association of TG2 with the IκBα-NFκB complex was determined to be independent of IKKα/β function. The polymerized IκBα is degraded in the cytoplasm by the μ-calpain pathway which allows the cross linked Rel A/ p65 dimers to translocate into the nucleus. Using R283 and ZDON (cell permeable TG2 activity inhibitors) and specific TG2 targeting siRNA, the Rel A/ p65 dimer formation could be inhibited. Co-immunoprecipitation studies confirmed that the phosphorylation of the Rel A/p65 dimers at the Ser536 residue by IKKε took place in the cell nucleus. Importantly, this study also investigated the transcriptional regulation of the TGM2 gene by the pSer536 Rel A/ p65 dimer and the importance of this TG2-NFκB feedback loop in conferring drug resistance to breast cancer cells. This data provides evidence that TG2 could be a key therapeutic target in the treatment of chemoresistant breast cancer

Predicting Customer Churn in E-Commerce Using Statistical and Machine Learning Methods

This research work aims to develop prediction models and analytical insights to overcome customer churn issues through data-driven approaches. The attrition rate of consumers in e-commerce is a significant issue requiring effective retention strategies. A novel methodology is proposed comprising data preprocessing, using statistical analysis techniques developing the model and carrying out tailored retention strategies. The model is used to identify crucial churn influencers and propose practical recommendations for enhancing consumer retention. The significance of this work lies in its potential to allow e-commerce ventures with insights with the intention of price savings strategies, enhanced revenue measures, and better consumer fulfillment. This research will influence the e-commerce business by facilitating evidence-based methods for reducing customer turnover and increasing long-term customer value. The resulting accuracy of the proposed model using Logistic Regression results in  87 percentage of accuracy which is a good metric to assess overall model performance. The Kaplan-Meier curve is used to check the survival probability of consumers and identify consumers more likely to churn over time

Transcriptional dynamics of colorectal cancer risk associated variation at 11q23.1 correlate with tuft cell abundance and marker expression in silico

Colorectal cancer (CRC) is characterised by heritable risk that is not well understood. Heritable, genetic variation at 11q23.1 is associated with increased colorectal cancer (CRC) risk, demonstrating eQTL effects on 3 cis- and 23 trans-eQTL targets. We sought to determine the relationship between 11q23.1 cis- and trans-eQTL target expression and test for potential cell-specificity. scRNAseq from 32,361 healthy colonic epithelial cells was aggregated and subject to weighted gene co-expression network analysis (WGCNA). One module (blue) included 19 trans-eQTL targets and was correlated with POU2AF2 expression only. Following unsupervised clustering of single cells, the expression of 19 trans-eQTL targets was greatest and most variable in cluster number 11, which transcriptionally resembled tuft cells. 14 trans-eQTL targets were found to demarcate this cluster, 11 of which were corroborated in a second dataset. Intra-cluster WGCNA and module preservation analysis then identified twelve 11q23.1 trans-eQTL targets to comprise a network that was specific to cluster 11. Finally, linear modelling and differential abundance testing showed 11q23.1 trans-eQTL target expression was predictive of cluster 11 abundance. Our findings suggest 11q23.1 trans-eQTL targets comprise a POU2AF2-related network that is likely tuft cell-specific and reduced expression of these genes correlates with reduced tuft cell abundance in silico

Development of Preclinical Ultrasound Imaging Techniques to Identify and Image Sentinel Lymph Nodes in a Cancerous Animal Model

Lymph nodes (LNs) are believed to be the first organs targeted by colorectal cancer cells detached from a primary solid tumor because of their role in draining interstitial fluids. Better detection and assessment of these organs have the potential to help clinicians in stratification and designing optimal design of oncological treatments for each patient. Whilst highly valuable for the detection of primary tumors, CT and MRI remain limited for the characterization of LNs. B-mode ultrasound (US) and contrast-enhanced ultrasound (CEUS) can improve the detection of LNs and could provide critical complementary information to MRI and CT scans; however, the European Federation of Societies for Ultrasound in Medicine and Biology (EFSUMB) guidelines advise that further evidence is required before US or CEUS can be recommended for clinical use. Moreover, knowledge of the lymphatic system and LNs is relatively limited, especially in preclinical models. In this pilot study, we have created a mouse model of metastatic cancer and utilized 3D high-frequency ultrasound to assess the volume, shape, and absence of hilum, along with CEUS to assess the flow dynamics of tumor-free and tumor-bearing LNs in vivo. The aforementioned parameters were used to create a scoring system to predict the likelihood of a disease-involved LN before establishing post-mortem diagnosis with histopathology. Preliminary results suggest that a sum score of parameters may provide a more accurate diagnosis than the LN size, the single parameter currently used to predict the involvement of an LN in disease