Background/Objectives: Hepatocellular carcinoma (HCC) is a primary malignancy often driven by metabolic syndrome, fatty liver disease, and chronic hepatitis. These conditions foster a pro-inflammatory microenvironment that promotes tumor progression. Viniferin, a natural oligostilbene, has gained attention for its potential bioactivity. This study utilized an in silico network pharmacology approach to elucidate the pharmacokinetic properties and molecular mechanisms of ε- and δ-viniferin against HCC within the context of metabolic and inflammatory liver pathologies. Methods: ADMET profiles were characterized using SwissADME and pkCSM. Therapeutic targets were identified by intersecting viniferin-associated molecules with disease genes from GeneCards. A protein-protein interaction (PPI) network was constructed, supplemented by GO and KEGG enrichment analyses. Molecular docking and 200 ns of molecular dynamics (MD) simulations evaluated the binding affinity and structural stability between viniferin isomers and identified hub proteins. Results: Both ε- and δ-viniferin showed favorable drug-like properties, including high gastrointestinal absorption and low hepatotoxicity. We identified 247 overlapping targets, with network analysis highlighting ten essential hub genes, including AKT1, HSP90AA1, ESR1, HIF1A, NFKB1, GSK3B, PTGS2, APP, MTOR, and PIK3CA. Enrichment analysis confirmed their involvement in critical oncogenic pathways. Molecular docking showed strong interactions with APP, HSP90AA1, and AKT1, while MD simulations validated the long-term stability of ε-viniferin within the APP binding pocket. Conclusions: These findings provide mechanistic insights into viniferin as a multi-target agent for HCC, justifying further experimental validation in pre-clinical models.

Background/Objectives: P-glycoprotein (P-gp, ABCB1/MDR1) is a key ATP-binding cassette transporter involved in multidrug resistance in cancer, limiting intracellular accumulation of various chemotherapeutic (CT) agents. Several antidepressants (ADs) have been shown to modulate P-gp function. This dual pharmacological profile raises the possibility of repurposing ADs as chemosensitizers to enhance anticancer drug efficacy. The objective of this review was to summarize the available evidence on the combined use of ADs and chemotherapeutics to overcome P-gp-mediated resistance. Methods: A systematic search was performed in PubMed, Scopus, and PsycInfo/PsycArticles databases using a comprehensive search string combining terms for P-gp, ADs, chemotherapy, and drug resistance. Inclusion criteria were preclinical or clinical studies investigating the effect of ADs in combination with chemotherapeutics on P-gp-mediated resistance in cancer models. Eleven relevant studies were identified and qualitatively analyzed. Results: Across diverse cancer models, including colon, breast, and multidrug-resistant cell lines, several ADs significantly enhanced the cytotoxicity of many chemotherapeutic agents. The proposed mechanisms involved downregulation of P-gp expression, inhibition of efflux activity, and increased intracellular drug accumulation. Conclusions: The combination of ADs with CT agents shows promising potential in overcoming P-gp-mediated multidrug resistance, enhancing antitumor efficacy in preclinical models. Further translational and clinical research is needed to validate these findings, optimize dosing strategies, and assess the risk-benefit profile in cancer patients, particularly those with comorbid depressive disorders.

Measurable residual disease (MRD) has become a central determinant of prognosis and treatment planning in acute myeloid leukemia (AML). MRD assessment is now aided by a wide range of technologies, including next-generation sequencing, PCR-based assays, multiparameter flow cytometry, and emerging approaches such as liquid biopsy platforms and imaging-based detection. These modalities differ in sensitivity, applicability, and interpretive framework, yet each offers distinct advantages in specific disease contexts. Beyond technical issues, MRD is becoming increasingly integrated into clinical practice. In non-intensive treatment settings, where targeted and low-intensity regimens rely on dynamic disease monitoring to guide ongoing management, MRD is increasingly being used to inform therapeutic decisions. In the peri-transplant setting, MRD status influences conditioning strategies, donor selection, and the use of post-transplant interventions. Despite the growing evidence supporting the clinical relevance of MRD across these scenarios, challenges remain regarding standardization, optimal timing of assessment, and the interpretation of discordant results. This review summarizes the full landscape of MRD detection methods and examines the evolving role of MRD in contemporary AML management, emphasizing current applications and areas requiring further refinement.

Cholangiocarcinoma (CCA) is a highly heterogeneous malignancy in which numerous biomarker candidates have been reported, yet few progress to clinical use. Beyond biological complexity, this low translational yield reflects the lack of systematic criteria for prioritizing biomarkers during the discovery stage. In particular, tumor-derived signals identified in tissue often fail to persist in clinically accessible biofluids, as cross-compartment signal behavior is rarely evaluated explicitly.

We developed an extracellular vesicle (EV)-guided, multi-compartment proof-of-concept framework to assess biomarker robustness and translatability early in discovery. EV proteomes from three biologically distinct CCA cell lines and a normal cholangiocyte were analyzed using multivariate and machine-learning-assisted approaches to identify conserved EV-associated features. These were integrated with public transcriptomic, epigenetic, copy-number, promoter usage, and miRNA regulatory data. Tissue relevance was assessed using TCGA/GTEx RNA-seq datasets, and exploratory signal behavior was examined in pooled serum- and urine-derived EVs from CCA patients and controls.

EV proteomics revealed marked molecular heterogeneity across CCA models but identified a small subset of conserved EV-associated proteins. SERPINF2 was used as a representative example, showing consistently reduced EV-associated abundance across all CCA models with coordinated regulation across multiple molecular layers. SERPINF2 expression was independent of patient sex and tumor stage and clearly distinguished tumor from normal bile duct tissue. Exploratory biofluid analyses demonstrated compartment-dependent signal behavior, with SERPINF2 depletion detectable in urine-derived EVs but not in serum-derived EVs.

Rather than validating a single biomarker, this study presents an EV-guided, multi-compartment framework for prioritizing biomarker candidates at the discovery stage. By explicitly accounting for tumor heterogeneity and compartment-specific signal preservation, this proof-of-concept approach provides a practical decision-support strategy for identifying biomarkers with greater translational potential in heterogeneous cancers such as CCA.

The Neuroblastoma Breakpoint Family (NBPF) consists of 23 genes, 9 of which are pseudogenes, and is characterized by extensive duplication events and species-specific diversification in Homo sapiens, as well as by the presence of a unique protein domain known as Olduvai (also referred to as DUF1220 or the NBPF domain). Previous studies have attempted to define subfamilies based on the presence of HLS triplet domains; however, this classification has become increasingly unclear with the identification of additional NBPF members. The family remains poorly understood, and the functions of many genes are still unknown, although several have been hypothesized to play key roles in cell proliferation and developmental processes, particularly in neural and skeletal tissues. In this study, we systematically analyzed all available data on the NBPF gene family using the PRISMA-S methodology to infer the biological functions in which these genes may be involved. We also generated multiple phylogenetic trees to support the creation of coherent subfamilies and to correlate the origin of each subfamily with homologous genes in our last common ancestor with the Pan genus, providing what we believe to be one of the most comprehensive phylogenetic reconstructions including all currently annotated NBPF members. Through comparative genomic and phylogenetic analyses, we propose that the NBPF may have originated from a duplication of the PDE4DIP gene, with NBPF26 representing the ancestral member from which the remaining NBPF genes diverged via lineage-specific segmental duplications. In this systematic review and comparative genomic study, we present the first integrative synthesis of our knowledge of the NBPF, encompassing its evolutionary origins, structural dynamics, expression across tissues, and clinical associations.

Osteosarcoma (OS), the most common primary malignant bone tumor in children and young adults, is characterized by aggressive behavior, frequent metastasis, and resistance to chemotherapy, resulting in poor clinical outcomes. Increasing evidence indicates that OS progression is not solely driven by tumor-intrinsic factors but is strongly influenced by dynamic interactions within the tumor microenvironment (TME). This literature review synthesizes current research on the roles of endothelial cells, fibroblasts, mesenchymal stromal cells, immune populations, and osteoclasts in OS pathogenesis, with emphasis on cell-cell interactions mediated by direct contact, soluble factors, and extracellular vesicles. The studies demonstrate that these interactions promote tumor proliferation, immune evasion, extracellular matrix remodeling, metastatic dissemination, and therapeutic resistance. Adaptive responses of both tumor and stromal cells to environmental stressors contribute to chemoresistance and disease progression. Collectively, our findings highlight the multifactorial nature of OS driven by complex cellular crosstalk within the TME. Understanding these mechanisms highlights the limitations of conventional chemotherapy and encourages the development of combined therapeutic approaches, including targeted therapies, immunomodulation, and microenvironmental interventions. Continued investigation into tumor-microenvironment interactions may facilitate the identification of actionable targets and improve personalized treatment approaches for OS.

Oral squamous cell carcinoma (OSCC) is a prevalent form of head and neck cancer that typically develops on the lip or within the oral cavity. Although there have been advances in early detection and treatment, the prognosis for patients, particularly those with advanced-stage disease, remains poor. Liquid biopsy, particularly through the analysis of cell-free DNA (cfDNA) in plasma and urine, has emerged as a promising tool for non-invasive cancer detection and monitoring. This study assessed cfDNA concentration dynamics in plasma and urine samples from 32 OSCC patients, with 5 undergoing genomic characterization by targeted next-generation sequencing (NGS). CfDNA levels were higher in patients compared to healthy controls and showed transient increases following treatment initiation, likely reflecting tumor cell death, followed by a gradual return to baseline. However, cfDNA concentrations were not significantly associated with tumor stage, recurrence, or progression-free survival. Targeted NGS analysis revealed a heterogeneous mutational landscape, identifying 76 variants across tumor tissue and initial cfDNA, with 30.3% shared between both sources. Recurrent hotspot mutations were detected in several important genes, including TP53, PIK3CA, KRAS, APC, and FBXW7. Urine cfDNA also captured several mutations absent from plasma or tissue, supporting its complementary value. These findings demonstrate that cfDNA analysis can dynamically reflect treatment response and capture tumor heterogeneity in OSCC. While informative, cfDNA quantification alone offers limited prognostic reliability, reinforcing the need for a multidimensional approach that includes genomic and clinical evaluation. Overall, this study supports the potential of liquid biopsy as a real-time, non-invasive tool for molecular monitoring and personalized management of OSCC patients.

Glioblastoma (GBM) persists as one of the greatest challenges in the treatment of human cancer, despite extensive efforts to leverage the therapeutic potential of immunotherapy. While checkpoint blockade and other forms of immunotherapy have revolutionized the treatment of various cancers, their therapeutic efficacy in GBM has been hindered by the profound immunosuppressive environment, spatial heterogeneity, and dynamic immune metabolic challenges associated with the tumor microenvironment. In this review, we will synthesize recent advances and insights to develop a next-generation framework for GBM immunotherapy based on systems biology approaches to understanding the complex interplay between GBM and the immune system, as opposed to single-axis approaches to immune activation and modulation. We will discuss how the functional competence of the interferon system, myeloid antigen presentation status, T-cell clone status, spatial organization of the immune microenvironment, and resource competition between GBM and the immune system dictate therapeutic responsiveness. Furthermore, the current paper elucidates how recent advances in spatial transcriptomics, single-cell analysis, and high-parameter imaging enable us to understand how immune phenotype status varies across GBM regions and treatment status, and how this information can be used to develop predictive and pharmacodynamic biomarkers of therapeutic efficacy and failure. We will then discuss how these advances form the basis for rational combination approaches to GBM immunotherapy, which involve the integration of checkpoint blockade with metabolic reprogramming, myeloid modulation, and interferon system reactivation, and how artificial intelligence-based analytics and adaptive clinical trial design can guide the development of biomarker-based therapeutic selection approaches.

Blood platelets are derived from megakaryocytes with functions extending beyond hemostasis to inflammation, immunity, and cancer. Myeloproliferative neoplasms (MPNs) are clonal stem cell disorders driven by somatic mutations affecting JAK-STAT signaling, leading to excessive myeloid proliferation. Thrombosis affects approximately one-fifth of patients at diagnosis and remains elevated throughout the disease course, while the paradoxical coexistence of bleeding further complicates clinical management. In addition, MPNs may progress to advanced disease stages, including bone marrow fibrosis and transformation to acute myeloid leukemia, leading to ineffective hematopoiesis, worsening symptom burden, and poor clinical outcomes. This review outlines how peripherally circulating platelets provide a unique window into MPN pathophysiology, with emphasis on their functional and molecular abnormalities. We summarize current understanding of platelet-mediated hemostatic imbalance across MPN subtypes. We discuss the potential of platelet transcriptomics and proteomics to reveal disease-specific signatures. We further highlight emerging platelet-associated candidates with potential utility as dynamic biomarkers for both the pathological marrow niche and thrombotic and bleeding risk. Together, these insights underscore the potential of platelet-based approaches to complement existing diagnostic and prognostic strategies in MPNs.

Gastric cancer (GC) remains a major global health burden, with its unfavorable prognosis primarily driven by extensive tumor heterogeneity. Traditional bulk omics, while informative, are inherently limited by the averaging effect of diverse cell populations and fail to capture the critical spatial molecular disparities within the tumor and its microenvironment (TME). Single-cell omics can capture cellular heterogeneity but lack spatial context. Therefore, there is an urgent clinical need for spatial multi-omics to provide a high-definition dissection of GC heterogeneity and to optimize therapeutic efficacy. This review first outlines briefly the evolution of spatial technologies, including transcriptomics, proteomics, metabolomics, genomics and epigenomics, and their transformative applications in GC research. We further explore how these platforms refine molecular classification beyond traditional models, identify next-generation biomarkers, and decode the intricate cellular interactions governing immune evasion and metastasis. Next, we highlight the pivotal role of spatial profiling in unravelling the multidimensional mechanisms of resistance to chemotherapy, targeted therapy and immunotherapy. Finally, we address current technical bottlenecks and discuss prospects for clinical translation.