Epithelial–mesenchymal transition (EMT) is a process by which tumour cells lose epithelial characteristics, become mesenchymal and highly motile. EMT pathways also induce stem cell features and resistance to apoptosis. Identifying and targeting this pool of tumour cells is a major challenge. Protein kinase C (PKC) inhibition has been shown to eliminate breast cancer stem cells but has never been assessed in hepatocellular cancer (HCC). We investigated ZEB family of EMT inducer expression as a biomarker for metastatic HCC and evaluated the efficacy of PKC inhibitors for HCC treatment. We showed that ZEB1 positivity predicted patient survival in multiple cohorts and also validated as an independent biomarker of HCC metastasis. ZEB1-expressing HCC cell lines became resistant to conventional chemotherapeutic agents and were enriched in CD44high/CD24low cell population. ZEB1- or TGFβ-induced EMT increased PKCα abundance. Probing public databases ascertained a positive association of ZEB1 and PKCα expression in human HCC tumours. Inhibition of PKCα activity by small molecule inhibitors or by PKCA knockdown reduced viability of mesenchymal HCC cells in vitro and in vivo. Our results suggest that ZEB1 expression predicts survival and metastatic potential of HCC. Chemoresistant/mesenchymal HCC cells become addicted to PKC pathway and display sensitivity to PKC inhibitors such as UCN-01. Stratifying patients according to ZEB1 and combining UCN-01 with conventional chemotherapy may be an advantageous chemotherapeutic strategy.
Resistance to adjuvant chemotherapy is a major clinical problem in the treatment of colorectal cancer (CRC). The aim of this study was to elucidate the role of an epithelial to mesenchymal transition (EMT)‐inducing protein, ZEB2, in chemoresistance of CRC, and to uncover the underlying mechanism. We performed IHC for ZEB2 and association analyses with clinical outcomes on primary CRC and matched CRC liver metastases in compliance with observational biomarker study guidelines. ZEB2 expression in primary tumours was an independent prognostic marker of reduced overall survival and disease‐free survival in patients who received adjuvant FOLFOX chemotherapy. ZEB2 expression was retained in 96% of liver metastases. The ZEB2‐dependent EMT transcriptional programme activated nucleotide excision repair (NER) pathway largely via upregulation of the ERCC1 gene and other components in NER pathway, leading to enhanced viability of CRC cells upon oxaliplatin treatment. ERCC1‐overexpressing CRC cells did not respond to oxaliplatin in vivo, as assessed using a murine orthotopic model in a randomised and blinded preclinical study. Our findings show that ZEB2 is a biomarker of tumour response to chemotherapy and risk of recurrence in CRC patients. We propose that the ZEB2‐ERCC1 axis is a key determinant of chemoresistance in CRC.
As medical science and technology progress towards the era of “big data”, a multi-dimensional dataset pertaining to medical diagnosis and treatment is becoming accessible for mathematical modelling. However, these datasets are frequently inconsistent, noisy, and often characterized by a significant degree of redundancy. Thus, extensive data processing is widely advised to clean the dataset before feeding it into the mathematical model. In this context, Artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) algorithms based on artificial neural networks (ANNs) and their types, are being used to produce a precise and cross-sectional illustration of clinical data. For prostate cancer patients, datasets derived from the prostate-specific antigen (PSA), MRI-guided biopsies, genetic biomarkers, and the Gleason grading are primarily used for diagnosis, risk stratification, and patient monitoring. However, recording diagnoses and further stratifying risks based on such diagnostic data frequently involves much subjectivity. Thus, implementing an AI algorithm on a PC’s diagnostic data can reduce the subjectivity of the process and assist in decision making. In addition, AI is used to cut down the processing time and help with early detection, which provides a superior outcome in critical cases of prostate cancer. Furthermore, this also facilitates offering the service at a lower cost by reducing the amount of human labor. Herein, the prime objective of this review is to provide a deep analysis encompassing the existing AI algorithms that are being deployed in the field of prostate cancer (PC) for diagnosis and treatment. Based on the available literature, AI-powered technology has the potential for extensive growth and penetration in PC diagnosis and treatment to ease and expedite the existing medical process.
Conventional anticancer treatments, such as radiotherapy and chemotherapy, have significantly improved cancer therapy. Nevertheless, the existing traditional anticancer treatments have been reported to cause serious side effects and resistance to cancer and even to severely affect the quality of life of cancer survivors, which indicates the utmost urgency to develop effective and safe anticancer treatments. As the primary focus of cancer nanotheranostics, nanomaterials with unique surface chemistry and shape have been investigated for integrating cancer diagnostics with treatment techniques, including guiding a prompt diagnosis, precise imaging, treatment with an effective dose, and real-time supervision of therapeutic efficacy. Several theranostic nanosystems have been explored for cancer diagnosis and treatment in the past decade. However, metal-based nanotheranostics continue to be the most common types of nonentities. Consequently, the present review covers the physical characteristics of effective metallic, functionalized, and hybrid nanotheranostic systems. The scope of coverage also includes the clinical advantages and limitations of cancer nanotheranostics. In light of these viewpoints, future research directions exploring the robustness and clinical viability of cancer nanotheranostics through various strategies to enhance the biocompatibility of theranostic nanoparticles are summarised.
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