Mohaned Adil

Colorectal cancer (CRC) remains a major global health burden, with rising incidence and mortality, particularly in developing countries. The tumor microenvironment (TME) plays a critical role in CRC progression by facilitating angiogenesis, immune evasion, and metastasis through complex intercellular communication. Among the key mediators of this communication are exosomes—nano-sized extracellular vesicles—that transport a variety of bioactive molecules, including competing endogenous RNAs (ceRNAs). These ceRNAs, such as long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), and mRNAs, act as molecular sponges for microRNAs (miRNAs), thereby regulating gene expression and influencing cancer-related pathways like Wnt/β-catenin and PI3K/Akt. This review explores the emerging role of exosomal ceRNAs derived from the TME in CRC progression, emphasizing their involvement in promoting tumor cell proliferation, invasion, metastasis, and resistance to therapy. By elucidating the intricate crosstalk between exosomal ceRNAs and the TME, we highlight their potential as novel biomarkers and therapeutic targets, offering new avenues for personalized treatment strategies in colorectal malignancies. Despite growing interest in exosomes and non-coding RNAs in colorectal cancer (CRC), limited attention has been given to the specific role of exosomal ceRNAs derived from the tumor microenvironment (TME). Existing reviews have predominantly addressed broader aspects of exosomal non-coding RNAs (e.g., miRNAs, lncRNAs, and circRNAs in general CRC progression, metastasis, or as biomarkers) or focused on exosomal communication in other malignancies. However, the intricate mechanisms by which TME-derived exosomal ceRNAs establish regulatory networks to drive CRC pathology—particularly through miRNA sponging in stromal-tumor crosstalk—remain underexplored and insufficiently synthesized. This review addresses these critical gaps by uniquely emphasizing exosomal ceRNAs sourced from the TME (including CAFs, macrophages, and other stromal components), elucidating their convergent roles in orchestrating proliferation, invasion, immune evasion, and chemoresistance. By integrating recent evidence into a TME-centric framework, we provide novel insights into potential diagnostic and therapeutic applications, advancing beyond general exosomal RNA overviews toward targeted strategies for CRC.

Neoadjuvant therapy is a cornerstone of modern oncology, yet its efficacy is traditionally assessed only after treatment completion, creating a “black box” period where patients may receive ineffective, toxic therapies. This review explores the transformative potential of circulating tumor DNA (ctDNA) as a dynamic biomarker to overcome this limitation. The short half-life of ctDNA allows for real-time, non-invasive monitoring of tumor burden through serial liquid biopsies. A substantial body of evidence across breast, lung, colorectal, and other solid tumors demonstrates that on-treatment ctDNA dynamics are a powerful predictor of pathological response and long-term survival. Rapid ctDNA clearance is strongly associated with favorable outcomes, providing a rationale for treatment de-escalation, while persistent ctDNA identifies non-responders who may benefit from therapy intensification. Despite its promise, the clinical adoption of ctDNA is hindered by challenges including a lack of assay standardization, the existence of low-shedding tumors, and biological confounders like clonal hematopoiesis. The future of precision neoadjuvant care lies in integrating ctDNA with imaging and other biological data within a multimodal framework, leveraging artificial intelligence to guide personalized, response-adapted treatment strategies. This paradigm shift from static to dynamic assessment promises to unlock the full potential of neoadjuvant therapy, maximizing efficacy while minimizing toxicity for individual patients. Graphical Abstract

Cancer stem cells (CSCs) represent a critical subpopulation within tumors, driving progression, metastasis, and, notably, resistance to conventional therapies. This inherent recalcitrance underscores an urgent need for targeted eradication strategies. Chimeric Antigen Receptor (CAR)-based cellular immunotherapies, including CAR-T, CAR-NK, and CAR-NKT cells, have revolutionized the treatment of hematological malignancies and are now emerging as potent tools against CSCs in solid tumors. This review provides a comprehensive overview of CSC biology, the foundational principles of CAR engineering, and the current clinical landscape of these cellular immunotherapies. It delves into the specific challenges encountered when targeting CSCs, such as tumor heterogeneity, the pervasive immunosuppressive tumor microenvironment, and the risk of on-target/off-tumor toxicities. Furthermore, the report explores advanced engineering strategies, including dual-targeting and logic-gated CARs, the strategic modulation of CSC metabolism, and the transformative potential of multi-omics integration and artificial intelligence. By synthesizing current knowledge and highlighting innovative approaches, this review aims to illuminate the path toward overcoming stem-like resistance and achieving durable cancer remission through engineered cellular immunotherapies.

Objective To develop and evaluate a comprehensive AI-driven pipeline for automated segmentation and multi-class classification of ovarian tumors in ultrasound images using advanced transformer-based models and radiomic analysis. Materials and methods The study was a multi-center, retrospective study that included ultrasound data of 1,907 patients in 4 clinical centers, with 1,364 patients being used as a model training and validation and 543 as an external test. Five transformer-based segmentation models were used to outline tumour regions namely SegFormer, Swin-UNet, DPT, nnU-Net and HRFormer. Dice Similarity Coefficient (DSC), Hausdorff Distance (HD) and Relative Absolute Volume Difference (RAVD) were used to measure segmentation accuracy. Radiomic features (n = 215) were taken out through SERA platform, following the IBSI instructions. Mutual Information (MI), LASSO and Recursive Feature Elimination (RFE) were used as feature selection methods. Five models were considered to classify them: TabTransformer, TabNet, MLP, XGBoost, and KAN. Three clinically significant classification tasks, including tumour type, histological subtype, and FIGO staging, were covered. The models were checked by five-fold cross-validation and externally tested. SHAP analysis delivered interpretability. Results SegFormer was found to be the most accurate in terms of segmentation (mean DSC > 91%), which is higher than other U-Net-based models in earlier research. To classify, the TabTransformer selected by MI had the highest test accuracies in all tasks: tumour type (92.7 per cent.), histological subtype (92.1 per cent.), and FIGO stage (92.0 per cent). The accuracy of the external tests was over 90 per cent in all the tasks. SHAP analysis demonstrated the important radiomic features that make classification decisions, which is clinically transparent. There was no difference in performance between centers and imaging conditions. Conclusions The suggested pipeline proves to be very accurate, general and interpretable in automated ovarian cancer diagnostics based on ultrasound imaging. It provides a powerful framework of clinical implementation and future application with multimodal data to support decisions better.

Background Gastric cancer (GC) remains a major global health concern, ranking as the fifth most prevalent malignancy and the fourth leading cause of cancer-related mortality worldwide. Although early detection can increase the 5-year survival rate of early gastric cancer (EGC) to over 90%, more than 80% of cases are diagnosed at advanced stages due to subtle clinical symptoms and diagnostic challenges. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has shown great promise in addressing these limitations. Objectives This systematic review aims to evaluate the performance, applications, and limitations of ML and DL models in GC management, with a focus on their use in detection, diagnosis, treatment planning, and prognosis prediction across diverse clinical imaging and data modalities. Methods Following the PRISMA 2020 guidelines, a comprehensive literature search was conducted in MEDLINE, Web of Science, and Scopus for studies published between 2004 and May 2025. Eligible studies applied ML or DL algorithms for diagnostic or prognostic tasks in GC using data from endoscopy, computed tomography (CT), pathology, or multi-modal sources. Two reviewers independently performed study selection, data extraction, and risk of bias assessment. Results A total of 59 studies met the inclusion criteria. DL models, particularly convolutional neural networks (CNNs), demonstrated strong performance in EGC detection, with reported sensitivities up to 95.3% and Area Under the Curve (AUCs) as high as 0.981, often exceeding expert endoscopists. CT-based radiomics and DL models achieved AUCs ranging from 0.825 to 0.972 for tumor staging and metastasis prediction. Pathology-based models reported accuracies up to 100% for EGC detection and AUCs up to 0.92 for predicting treatment response. Cross-modality approaches combining radiomics and pathomics achieved AUCs up to 0.951. Key challenges included algorithmic bias, limited dataset diversity, interpretability issues, and barriers to clinical integration. Conclusion ML and DL models have demonstrated substantial potential to improve early detection, diagnostic accuracy, and individualized treatment in GC. To advance clinical adoption, future research should prioritize the development of large, diverse datasets, implement explainable AI frameworks, and conduct prospective clinical trials. These efforts will be essential for integrating AI into precision oncology and addressing the increasing global burden of gastric cancer.