Background: As a novel type of isothiocyanate derived from radish seeds from cruciferous vegetables, sulforaphene (SFE, 4-methylsufinyl-3-butenyl isothiocyanate) has various important biological effects, such as anti-oxidative and anti-bacterial effects. Recently, sulforaphene has attracted increasing attention for its anti-tumor effects and its ability to suppress the development of multiple tumors through different regulatory mechanisms. However, it has not yet been widely investigated for the treatment of esophageal cancer. Methods: We observed an increased apoptosis in esophageal cancer cells on sulforaphene treatment through flow cytometry (FCM) analysis and transmission electron microscopy (TEM). Through mass spectrometry (MS) analysis, we further detected global changes in the proteomes and phosphoproteomes of esophageal cancer cells on sulforaphene treatment. The molecular mechanism of sulforaphene was verified by western blot,the effect and mechanism of SFE on esophageal cancer was further verified by patient-derived xenograft mouse model. Results: We identified multiple cellular processes that were changed after sulforaphene treatment by proteomics. We found that sulforaphene could repress the phosphorylation of CREB through MSK2, leading to suppression of Bcl-2 and further promoted cell apoptosis. Additionally, we confirmed that sulforaphene induces tumor cell apoptosis in mice. Interestingly, we also observed the obvious inhibition of cell migration and invasion caused by sulforaphene treatment by inhibiting the expression of cadherin, indicating the complex effects of sulforaphene on the development of esophageal cancer. Conclusions: Our data demonstrated that sulforaphene induced cell apoptosis and inhibits the invasion of esophageal cancer through a mechanism involving the inhibition of the MSK2–CREB–Bcl2 and cadherin pathway. Sulforaphene could therefore serve as a promising anti-tumor drug for the treatment of esophageal cancer.

Artificial intelligence (AI) has potential to revolutionize the field of oncology by enhancing the precision of cancer diagnosis, optimizing treatment strategies, and personalizing therapies for a variety of cancers. This review examines the limitations of conventional diagnostic techniques and explores the transformative role of AI in diagnosing and treating cancers such as lung, breast, colorectal, liver, stomach, esophageal, cervical, thyroid, prostate, and skin cancers. The primary objective of this paper is to highlight the significant advancements that AI algorithms have brought to oncology within the medical industry. By enabling early cancer detection, improving diagnostic accuracy, and facilitating targeted treatment delivery, AI contributes to substantial improvements in patient outcomes. The integration of AI in medical imaging, genomic analysis, and pathology enhances diagnostic precision and introduces a novel, less invasive approach to cancer screening. This not only boosts the effectiveness of medical facilities but also reduces operational costs. The study delves into the application of AI in radiomics for detailed cancer characterization, predictive analytics for identifying associated risks, and the development of algorithm-driven robots for immediate diagnosis. Furthermore, it investigates the impact of AI on addressing healthcare challenges, particularly in underserved and remote regions. The overarching goal of this platform is to support the development of expert recommendations and to provide universal, efficient diagnostic procedures. By reviewing existing research and clinical studies, this paper underscores the pivotal role of AI in improving the overall cancer care system. It emphasizes how AI-enabled systems can enhance clinical decision-making and expand treatment options, thereby underscoring the importance of AI in advancing precision oncology

Objective: Tertiary Lymphoid Structures (TLSs) are ectopic lymphoid aggregates that form within the tumor microenvironment (TME) and are increasingly recognized as potential prognostic biomarkers in various cancers. However, the spatial heterogeneity and prognostic value of TLSs in esophageal squamous cell carcinoma (ESCC) remain poorly defined. This study aimed to characterize the spatial distribution patterns of TLSs and tumor-infiltrating lymphocytes (TILs), and to establish a refined prognostic model for ESCC patients in both surgery-only and neoadjuvant therapy cohorts.Methods: The TLSs were quantified through microscopic evaluation and digital slide analysis and correlated with prognosis by Cox regression and Kaplan-Meier analyses. The heterogeneity and clinical prognostic value of TLSs were explored by analyzing their distribution, density, and maximum diameter in different regions of ESCC patients.Results: TLSs showed spatial distribution heterogeneity in the tumor area, adjacent area, and marginal area, with consistent differences observed across different paraffin blocks. The distribution of iTIL and sTIL also exhibited certain spatial heterogeneity. In the surgical cohort (n = 117), the median Overall Survival (OS) and Disease-Free Survival (DFS) were 33 months and 15 months, respectively. Univariate analyses showed that TLS presence in tumor (TG), TLS-rich regions (TR), TLS ratio in normal regions (NR), tumor-stroma ratio (TSR), and both iTIL and sTIL levels were significantly associated with OS (p < 0.05). Multivariate analysis confirmed N stage, TG, TR, TLS abundance in adjacent regions (NA), and TLS density in tumor (NT), along with TSR, iTIL, and sTIL, as independent predictors of prognosis (p < 0.05). High TLS presence in tumor regions (TG-high) was associated with significantly improved OS (log-rank p = 0.026).Conclusion: This study demonstrates that TLSs and TILs in ESCC are not only prognostically relevant but also spatially heterogeneous. The refined spatial immune profiling across multiple tumor regions improves prognostic stratification and may inform personalized treatment planning in ESCC.

The integration of Artificial Intelligence (AI) in healthcare has transformed clinical practices by improving the accuracy of diagnosis, optimizing surgical procedures, improving the patient experience, and accelerating drug development. This article provides a comprehensive overview of the many applications of artificial intelligence (AI) across several industries, including fast diagnostics, where ML algorithms greatly enhance the accuracy and velocity of disease diagnosis. State-of-the-art robotic-assisted minimally invasive surgical procedures that shorten patients' hospitalization and improve their chances of recovery, cutting-edge AI applications in healthcare monitoring, and medication development. The article also looks at the primary challenges that AI in healthcare will inevitably encounter, such as differences in product quality, a shortage of skilled workers, privacy and ethical issues, and the need for improved regulatory frameworks. Despite the challenges, AI contributes significant advantages to the healthcare industry, providing novel and remarkable contributions to the efficiency of medical procedures and the progress of medical outcomes. The paper emphasizes the importance of cooperation in overcoming current challenges and enhancing the acceptability of AI technology in clinical settings. This will ensure that AI-driven innovations continue to enhance the standards of patient care.

BCR–ABL, the pioneering gene fusion resulting from chromosomal translocation, has marked a major milestone in understanding genetic alterations in cancer. Initially, gene fusions were linked solely to chromosomal rearrangements, serving as diagnostic markers and cancer drivers. However, advancements in high-throughput sequencing and bioinformatics have revealed additional mechanisms underlying gene fusion. The term “gene fusion” primarily refers to fusion events at the DNA level, whereas “chimeric RNA” encompasses a wide range of transcripts containing exons from different parental genes, including gene fusion transcripts. Recent developments have identified numerous chimeric RNAs in various cancer types, extending even to non-cancerous tissues. Chimeric RNAs, originating from events such as trans-splicing, read-through, and intergenic splicing, form a complex landscape with varied functions. While some chimeric RNAs have defined roles and therapeutic potential, a comprehensive understanding of their diverse functions remains a priority. Exploring the full spectrum of chimeric RNA activities is crucial for revealing their clinical and therapeutic implications. In addition, chimeric RNAs are key players in tumorigenesis, affecting cellular processes, and driving cancer progression. Understanding their intricate interactions with cellular pathways is essential for developing targeted therapies and precision medicine approaches. The dynamic nature of chimeric RNAs highlights the need for ongoing research to fully harness their diagnostic and therapeutic potential.