Artificial intelligence (AI) excels at efficiently processing large volumes of data and extracting valuable insights. Deep Learning (DL), a subfield of AI, utilizes multi-layer neural network algorithms to analyze various types of data, mimicking the neural network architecture of the human brain. One of the most prominent features of DL is its end-to-end learning mechanism, which excels at automatic feature extraction and pattern recognition in data. As multi-omics technologies rapidly evolve, the volume of omics data from cancer samples has surged, presenting a significant challenge in managing this vast amount of information. Due to its strong data processing capabilities, DL is increasingly applied across a range of cancer research areas, such as early detection and screening, diagnosis, molecular subtype classification, discovery of biomarkers, and predicting patient prognosis and treatment responses. DL integrates high-dimensional data from fields such as genomics, epigenomics, transcriptomics, proteomics, radiomics, and single-cell omics, enhancing our understanding of cancer development and advancing personalized treatment approaches. This paper reviews various DL models and their roles in analyzing complex data patterns, providing a review of DL applications in cancer multi-omics analysis research and emphasizing its potential in early detection, diagnosis, classification, and prognosis prediction. As DL models are introduced continuously, we expect their application in cancer research to become more extensive, thus propelling the advancement of cancer medicine.

The mortality rate of liver cancer is gradually increasing worldwide due to the increasing risk factors such as fatty liver, diabetes, and alcoholic cirrhosis. The diagnostic methods of liver cancer include ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI), among others. The treatment of liver cancer includes surgical resection, transplantation, ablation, and chemoembolization; however, treatment still faces multiple challenges due to its insidious development, high rate of recurrence after surgical resection, and high failure rate of transplantation. The emergence of liposomes has provided new insights into the treatment of liver cancer. Due to their excellent carrier properties and maneuverability, liposomes can be used to perform a variety of functions such as aiding in imaging diagnoses, combinatorial therapies, and integrating disease diagnosis and treatment. In this paper, we further discuss such advantages. Graphical Abstract:

During radiotherapy, reactive oxygen species- (ROS-) induced apoptosis is one of the main mechanism of radiation. Based on KillerRed which can induce ROS burst in different cell substructures, here we hypothesized that KillerRed targeting mitochondria (mtKR) could induce ROS to enhance apoptosis by radiation. In this study, empty vector, mtKR, and mtmCherry plasmids were successfully constructed, and mitochondrial localization were detected in COS-7 and HeLa cells. After HeLa cells were transfected and irradiated by visible light and X-rays, ROS levels, mitochondrial membrane potential (Δψ_m), ATPase activities, adenosine triphosphate (ATP) content, apoptosis, and the expressions of mRNA and protein were measured, respectively. Data demonstrated that the ROS levels significantly increased after light exposure, and adding extra radiation, voltage-dependent anion channel 1 (VDAC1) protein increased in the mitochondria, while Na⁺-K⁺ and Ca²⁺-Mg²⁺ ATPase activities, ATP content, and Δψ_m significantly reduced. Additionally, the cell apoptotic rates dramatically increased, which referred to the increase of cytochrome c (Cyt c), caspase-9, and caspase-3 mRNA expressions, and Cyt c protein was released from the mitochondria into the cytoplasm; caspase-9 and -3 were activated. These results indicated that mtKR can increase the production of ROS, enhance mitochondrial dysfunction, and strengthen apoptosis by radiation via Cyt c/caspase-3 pathway.

Molecular imaging is an advanced technology that utilizes specific probes or markers in conjunction with cutting-edge imaging techniques to observe and analyze the localization, distribution, activity, and interactions of biomolecules within living organisms. Tumor molecular imaging, by enabling the visualization and quantification of molecular characteristics of tumor cells, facilitates a deeper and more comprehensive understanding of tumors, providing valuable insights for early diagnosis, treatment monitoring, and cancer biology research. However, the image quality of molecular imaging still requires improvement, and nanotechnology has significantly propelled the advancement of molecular imaging. Currently, nanoparticle-based tumor molecular imaging technologies encompass radionuclide imaging, fluorescence imaging, magnetic resonance imaging, ultrasound imaging, photoacoustic imaging, and multimodal imaging, among others. As our understanding of the tumor microenvironment deepens, the design of nanoparticle probes for tumor molecular imaging has also evolved, offering new perspectives and expanding the applications of tumor molecular imaging. Beyond diagnostics, there is a marked trend towards integrated diagnosis and therapy, with image-guided treatment playing a pivotal role. This includes image-guided surgery, photodynamic therapy, and chemodynamic therapy. Despite continuous advancements and innovative developments in molecular imaging, many of these remain in the experimental stage and require breakthroughs before they can be fully integrated into clinical practice.

Cancer is the second most common cause of death among humans in the world, and the threat that it presents to human health is becoming more and more serious. The mechanisms of cancer development have not yet been fully elucidated, and new therapies are changing with each passing day. Evidence from the literature has validated the finding that the composition and modification of gut microbiota play an important role in the development of many different types of cancer. The results also demonstrate that there is a bidirectional interaction between the gut microbiota and radiotherapy treatments for cancer. In a nutshell, the modifications of the gut microbiota caused by radiotherapy have an effect on tumor radiosensitivity and, as a result, affect the efficacy of radiotherapy and show a certain radiation toxicity, which leads to numerous side effects. What is of new research significance is that the “gut-organ axis” formed by the gut microbiota may be one of the most interesting potential mechanisms, although the relevant research is still very limited. In this review, we combine new insights into the relationship between the gut microbiota, cancer, and radiotherapy. Based on our current comprehensive understanding of this relationship, we give an overview of the new cancer treatments based on the gut microbiota.