Melatonin, an endogenous indoleamine primarily synthesized in the pineal gland, has emerged as a promising adjunctive agent within integrative oncology due to its pleiotropic biological actions. Beyond its well-known chronobiological functions, melatonin exerts potent redox-regulatory, anti-inflammatory, oncostatic, and immune-modulating effects that are relevant across multiple stages of carcinogenesis and cancer therapy. Oxidative stress (OS), defined as an imbalance between reactive oxygen and nitrogen species (ROS/RNS) generation and antioxidant defenses, plays a central role in DNA damage, protein adduct formation, and lipid peroxidation, ultimately contributing to mutation accumulation, treatment resistance, and tumor progression. Melatonin modulates these OS-related processes through both receptor-dependent and receptor-independent mechanisms, including mitochondrial stabilization, enhancement of antioxidant enzyme activity, inhibition of pro-oxidant pathways, regulation of cell-cycle checkpoints, and promotion of apoptosis in malignant cells while protecting healthy tissues. Preclinical studies demonstrate synergistic interactions between melatonin and chemotherapy, radiotherapy, targeted agents, and immunotherapies, with consistent reductions in treatment toxicity and improvements in tumor control. Emerging clinical evidence supports its potential benefits in quality of life, sleep regulation, fatigue, and selected oncologic outcomes, although heterogeneity in dosing, formulations, and study design remains a key limitation. At the organizational and system levels, successful integration of melatonin into oncology practice requires interdisciplinary collaboration, standardized protocols, clinician awareness, regulatory clarity, and evidence-based implementation strategies. The aim of this narrative review is to synthesize current molecular, experimental, and clinical evidence on melatonin in integrative oncology, with particular emphasis on redox-related mechanisms, therapeutic interactions, and implementation challenges.

In vivo Chimeric Antigen Receptor (CAR)-T cell therapy reprograms a patient's own T cells directly inside the body, bypassing the complex and costly traditional manufacturing process. This is achieved by systemically delivering viral or non-viral vectors that genetically modify endogenous T lymphocytes to produce functional CAR-T cells de novo. By eliminating ex vivo cell processing, this strategy can simplify workflows, reduce costs, improve accessibility, and allow faster treatment. Key delivery platforms include engineered lentiviral and adeno-associated viral (AAV) vectors for lasting CAR expression and targeted lipid nanoparticles (LNPs) for transient mRNA delivery. Emerging technologies like biomaterial scaffolds and ultrasound stimulation further enable localized and spatiotemporally controlled T cell engineering. Clinically, early trials in relapsed/refractory multiple myeloma and B-cell malignancies have shown strong antitumor responses, even without preconditioning chemotherapy. Remaining challenges comprise achieving precise T cell targeting, overcoming the immunosuppressive tumor microenvironment, preventing antigen escape, and managing safety risks such as vector genotoxicity or LNP reactogenicity. Future translation will depend on combining synergistic regimens, refining vector design, and implementing tunable safety controls. The aim of the study is to highlight how in vivo CAR-T therapy is evolving from concept to clinical reality, poised to redefine adoptive cell therapy as a scalable and widely applicable pharmacologic intervention.

Hepatocellular carcinoma (HCC) remains a significant global health challenge, with therapeutic efficacy in advanced stages often limited by underlying liver dysfunction and adaptive resistance. In this review, the evolving landscape of molecular targets and combinatorial strategies is critically examined, with a particular focus on the transition from preclinical discovery to clinical application. While traditional molecular heterogeneity is acknowledged, the aim is to elucidate how emerging computational paradigms are redefining target discovery and therapeutic stratification in HCC. The primary purpose is to evaluate the role of Artificial Intelligence (AI) and Machine Learning (ML) as integrative tools for translating high-dimensional multi-omics data into clinically actionable insights for HCC management. Special attention is given to the capacity of AI-driven frameworks to analyze complex datasets derived from genomics, transcriptomics, proteomics, metabolomics, and epigenomics, thereby enabling the identification of novel predictive biomarkers, patient subgroups, and rational drug combinations. By synthesizing recent preclinical and clinical evidence, this review highlights how AI-guided approaches can accelerate biomarker validation and optimize therapeutic decision-making. Furthermore, the convergence of AI with spatial transcriptomics, digital pathology, and single-cell technologies is discussed as a transformative infrastructure for decoding tumor-microenvironment interactions and spatial heterogeneity. These integrative strategies provide unprecedented resolution into tumor evolution, immune landscapes, and resistance mechanisms. Collectively, the evidence reviewed supports the conclusion that AI-enabled, multi-omics-driven approaches are instrumental in advancing HCC treatment toward a new era of adaptive, spatially informed, and precision-based personalized medicine.

In various tumor types, cell division cycle-associated 7 (CDCA7) is involved in chromatin remodeling and DNA methylation. However, its biological functions and regulatory mechanisms in gastric cancer (GC) remain unknown. This investigation intended to identify the function of CDCA7 in GC progression and elucidate its epigenetic regulatory mechanisms.

Differentially expressed genes (DEGs) were detected from the GSE19826, TCGA-GC, and GSE56807 datasets. Networks of protein-protein interactions (PPI) and hub genes were discovered by the DMNC and Clustering Coefficient algorithms. Receiver operating characteristic (ROC) analysis and expression profiling were undertaken to determine diagnostic performance. In vitro assays, including CCK-8 assays, clonogenic assays, flow cytometry, dot blots, co-immunoprecipitation (Co-IP), chromatin immunoprecipitation (ChIP), and Western blots, were applied to evaluate the role of CDCA7 and its interaction with helicase, lymphoid-specific (HELLS).

169 overlapping genes were discovered, enriched in Cell adhesion molecules and ECM-receptor interaction. CDCA7 is highly expressed in GC and has high clinical diagnostic value. Knockdown of CDCA7 causes apoptosis and suppresses GC cell invasion, migration, and proliferation. Mechanistically, CDCA7 physically interacts with HELLS and promotes HELLS recruitment to chromatin. Knockdown of CDCA7 reduces global 5 hmC/5 mC levels and histone methylation (H3K9me3 and H4K20me3), while HELLS overexpression partially reverses these effects. Functionally, HELLS overexpression also partially reverses the antiproliferative and proapoptotic effects of CDCA7 knockdown.

CDCA7 promotes GC progression by interacting with HELLS to regulate DNA methylation and chromatin stability, suggesting that the CDCA7-HELLS axis may serve as a potential diagnostic biomarker and therapeutic target for GC.

Esophageal cancer (EC) ranks among the most lethal gastrointestinal malignancies. Due to challenges in early diagnosis, molecular heterogeneity, and therapeutic resistance, patient prognosis remains extremely poor, necessitating the development of novel biomarkers and therapeutic targets. As a core effector of the Hippo signaling pathway, the potential significance of Yes-associated protein 1 (YAP1) has garnered increasing attention. This paper aims to systematically summarize the multi-omics research, molecular mechanisms, and preclinical/translational evidence for YAP1, covering its activation pathways, biological functions, clinical significance, and therapeutic strategies. We elucidated YAP1's multidimensional regulatory network in EC, including Hippo-dependent and -independent mechanisms, cross-regulation with environmental risk factors, and its role in malignant phenotypes such as cell proliferation, apoptosis, epithelial-mesenchymal transition (EMT), and metastasis. The potential of YAP1 as a diagnostic, prognostic, and predictive biomarker is evaluated, alongside summarizing its role in mediating chemotherapy, radiotherapy, and immune tolerance mechanisms, along with recent advances in targeted therapies. This provides a theoretical foundation for subsequent basic research and precision medicine translation. As a potential hub in the EC signaling network, it is considered to play a key role in driving tumor progression and treatment resistance through multiple pathways. Targeting YAP1 holds broad clinical promise but faces challenges including functional duality, subtype heterogeneity, and complex resistance mechanisms. Future efforts should focus on developing highly selective inhibitors, integrating multi-omics technologies and innovative models to advance clinical translation and provide new strategies for precision treatment of EC patients.

Vascular endothelial growth factor receptor 2 (VEGFR2) is a critical therapeutic target in hepatocellular carcinoma (HCC) due to its role in angiogenesis and tumor progression. While several inhibitors are currently used, clinical utility is often limited by resistance and adverse effects, necessitating the discovery of novel therapeutic agents. The aim of this study was to identify and characterize novel, highly effective VEGFR2 inhibitors using an integrated computational pipeline to advance the development of new HCC treatments.

A comprehensive dataset from the ChEMBL database was curated and standardized for Quantitative Structure-Activity Relationship (QSAR) modeling. A binary classification framework was employed, where a Light Gradient Boosting Machine (LGBM) model demonstrated superior predictive performance. Two lead compounds and a reference were selected for in-depth molecular modeling. Their binding poses were predicted via molecular docking and subsequently subjected to 200 ns Molecular Dynamics (MD) simulations to assess stability and conformational dynamics. Thermodynamic binding affinities were calculated using the Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) method.

The LGBM model achieved high accuracy and a robust Matthews Correlation Coefficient (MCC) on an independent test set. MD analysis, including Root Mean Square Deviation (RMSD) and Radius of Gyration (Rg), confirmed stable binding throughout the 200 ns trajectory. MMPBSA calculations validated the binding affinities, identifying van der Waals and electrostatic interactions as the primary driving forces for complex stability.

This study successfully bridges machine learning with advanced molecular simulations, offering a validated workflow for the rational design and optimization of novel small-molecule VEGFR2 inhibitors.

Chronic myeloid leukemia (CML) is a hematopoietic malignancy originating from hematopoietic stem cells. It is characterized by the Philadelphia chromosome, which arises from a reciprocal translocation between chromosomes 9 and 22. The breakpoint cluster region::Abelson murine leukemia 1 (BCR::ABL1) fusion protein produced from this chromosome is the main factor responsible for disease onset. Tyrosine kinase inhibitors (TKIs) have led to significant advances in CML treatment and contributed to improved patient survival rates. Nonetheless, a substantial number of patients develop resistance to TKIs, which remains a major challenge in CML therapy. Currently, two mechanisms are considered responsible for TKIs resistance in CML: BCR::ABL1-dependent resistance, involving mutations or overexpression of BCR::ABL1, and BCR::ABL1-independent resistance, which does not depend on BCR::ABL1. This review discusses the recent findings on the resistance mechanisms mediated by BCR::ABL1 mutations. It also focuses on bypass pathways, the B-cell/CLL lymphoma 2 family, tumor suppressor genes, microRNAs, and molecular chaperones as independent resistance mechanisms. Furthermore, the potential for combination therapies targeting these resistance mechanisms is discussed, anticipating further advances in research aimed at overcoming TKI resistance in CML.

Extrachromosomal DNA (ecDNA) constitutes a principal factor in the amplification of oncogenes and the progression of tumors in solid malignancies. This review synthesizes emerging mechanistic, genomic, and immunologic evidence across multiple tumor types, including glioblastoma, lung, breast, gastrointestinal, hepatobiliary, urothelial, prostate, gynecologic, pediatric, and head-and-neck cancers, with the goal of clarifying the role of ecDNA in immune escape and therapy resistance and outlining its translational implications for precision oncology. ecDNA comprises substantial acentromeric circular elements that serve as transcriptional hubs, modulate enhancer-promoter interactions, and undergo dynamic copy-number cycling, thereby fostering intratumoral heterogeneity and resistance to therapy. Recurrent oncogenic cargos, including epidermal growth factor receptor (EGFR), v-myc avian myelocytomatosis viral oncogene homolog (MYC), erb-b2 receptor tyrosine kinase 2, also known as human epidermal growth factor receptor 2 (ERBB2/HER2), and cyclin D1 (CCND1), are frequently located in ecDNA. They can interconvert with intrachromosomal homogeneously staining regions (HSRs) under treatment pressure. Emerging evidence links ecDNA to an immune-cold phenotype, characterized by downregulation of antigen presentation and decreased responsiveness to immune checkpoint inhibitors. We further emphasize diagnostic and translational methodologies that incorporate ecDNA detection through liquid biopsy and the spatial mapping of tumor topology. Finally, we propose a comprehensive clinical implementation framework that integrates ecDNA profiling, longitudinal monitoring, and immune microenvironment assessment to guide precision therapy. Gaining a deeper understanding of ecDNA biology has the potential to ultimately transform it from merely a prognostic biomarker into a targetable element within cancer therapy.

Immune checkpoint inhibitors (ICIs) are a cornerstone of systemic therapy for renal cell carcinoma (RCC), used both in the adjuvant and metastatic settings across various lines of treatment, often in combination with vascular endothelial growth factor receptor tyrosine kinase inhibitors (VEGFR-TKIs). These therapies are associated with endocrine immune-related adverse events (irAEs), which can be irreversible and life-threatening if not promptly managed. Using data from the Food and Drug Administration Adverse Reporting System (FAERS), this study aimed to evaluate the real-world occurrence of endocrine irAEs in all approved VEGFR-TKI + ICI combinations for RCC, and to compare these findings with the corresponding VEGFR-TKI or ICI monotherapies. The immune doublet ipilimumab + nivolumab was not considered in this analysis.

FAERS database from 2019 Q1 to 2024 Q2 was queried using OpenVigil 2.1-MedDRA-v24 and AERSMine to identify endocrine irAEs reports. Reports were filtered by age, gender, and report severity. The frequency of reported endocrine irAEs associated with VEGFR-TKI + ICI combination therapies was compared to that reported for VEGFR-TKI or ICI monotherapy.

Compared with VEGFR-TKI monotherapies, VEGFR-TKI + ICI combinations showed a significant disproportionate reporting of endocrine irAEs, mostly associated with the combination regimens. In contrast, when compared with ICI monotherapy, VEGFR-TKI + ICI showed more heterogeneous disproportionality signals, with generally lower reporting of hypothalamus, pituitary, and hyperglycemic disorders, whereas hypoglycemia and thyroid irAEs were more frequently reported, except for autoimmune thyroid diseases.

Combination therapy, compared with VEGFR-TKI monotherapy, was associated with a higher reporting frequency of specific endocrine irAEs, whereas comparisons with ICI monotherapy yielded mixed signals, highlighting regimen- and event-specific differences.

The rapid growth and accessibility of artificial intelligence (AI) and machine learning (ML) have opened many avenues to revolutionize biomedical research, particularly in oncogenesis. Oncogenesis is a hallmark process in the development of cancer, involving the amplification of proto-oncogenes and the subsequent dysregulation of molecular signaling networks. These pathways-including the RAS/RAF/MEK/ERK, PI3K-AKT, JAK-STAT, TGF-β/Smad, Wnt/β-Catenin, and Notch cascades-have been studied extensively in isolation, with major strides achieved in understanding how they drive cancer. However, there are still many considerations regarding how these networks interact. Ongoing studies show that crosstalk among these pathways occurs through feedback loops, shared intermediates, and compensatory activation, creating a complex network that enables tumor cells to adapt and metastasize. New developments in AI and ML have enabled modeling and prediction of these interactions for pathway discovery, mapping oncogenic crosstalk, predicting drug resistance and therapeutic responses, and complex data analysis. Novel technologies such as feature selection algorithms and convolutional neural networks have demonstrated immense translational potential to bridge computational predictions in cancer genomics with clinical applications. Similar models have also proven useful for learning from genomic datasets and reducing multidimensionality in heterogeneous multiomics data. As current AI/ML approaches continue to develop, it is also important to consider the limitations of batch effects, model generalizability, and potential bias in training datasets. This review aims to integrate the most recent AI and ML applications in uncovering the hidden interactions within oncogenic networks that drive tumorigenesis, heterogeneity, and resistance to therapies. Moreover, this review aims to synthesize the functionality of emerging computational methods that elucidate these insights, as well as the transformative implications of AI-guided systems biology on precision oncology and combinatorial therapies.