Mitochondria are metabolic hubs that house their own genomes (mitochondrial DNA [mtDNA]), which encode components of the oxidative phosphorylation (OXPHOS) machinery. The mitochondrial central dogma not only governs compartmentalised metabolism but also intensively intertwines with multiple biological processes, and its dysregulation is a hallmark of cancer and metabolic diseases. In this review, we highlight recent advances in mitochondrial biogenesis from a metabolic perspective, with a particular emphasis on cancer. Metabolites act as donors for diverse chemical modifications, which have been systematically identified on mtDNA, rRNA, and tRNA. Besides, post-translational modifications of proteins involved in mtDNA replication, transcription, and translation has been revealed to connect metabolic signals with mitochondrial biogenesis. A comprehensive landscape of the mitochondrial central dogma has deepened our understanding of how mitochondria coordinate OXPHOS with other organelle-specific processes to obtain a flexible metabolic network, which potentiates tumor growth. Notably, non-canonical products and biological functions of the mitochondrial central dogma further reshape our concepts of cancer initiation and progression. Given that dysregulated mitochondrial biogenesis is found in multiple human disorders, including cancer, targeting this pathway offers new therapeutic opportunities. Genome-wide studies and drug screens have identified metabolic nodes and small molecules with potential to correct mitochondrial dysfunction in cancer, while emerging tools such as mtDNA editing enable precise intervention. Despite a maturing picture of mitochondrial biogenesis, many hidden players and functions remain to be uncovered to fully decipher mitochondrial biology in cancer.
The tumor microenvironment (TME) is a dynamic and immunosuppressive niche that critically governs cancer progression, immune evasion, and therapeutic resistance. Accurately modeling tumor–immune interactions in vitro is therefore essential for advancing our understanding of cancer biology and accelerating the development of effective immunotherapies. Co-culture systems have emerged as powerful platforms for this purpose, enabling the controlled study of cellular crosstalk between tumor cells and immune, stromal, or endothelial components under defined experimental conditions. Among available co-culture approaches, patient-derived tumor organoid–peripheral blood mononuclear cell (PDTO–PBMC) co-culture systems represent the most physiologically faithful platform currently available, preserving the histological, genetic, and phenotypic heterogeneity of the original tumor while enabling reconstitution of autologous immune responses. These systems allow the expansion of tumor-reactive T cells in an antigen–agnostic manner, providing a clinically relevant platform for evaluating immune checkpoint inhibitors (ICI), adoptive cell therapies (ACT), chimeric antigen receptor (CAR) T cell constructs, and combination immunotherapy regimens across multiple cancer types. This review comprehensively examines the spectrum of tumor–immune co-culture systems, from conventional 2D monolayer models to 3D spheroid and organoid-based platforms, critically comparing their biological fidelity, technical requirements, and translational utility. Particular emphasis is placed on the PDTO–PBMC co-culture model, its applications across diverse immune cell populations, including T cells, NK cells, macrophages, dendritic cells, and CAR T cells, and its potential as a functional companion diagnostic tool for precision immunotherapy. Current limitations, standardization challenges, and future directions including microfluidics, spatial transcriptomics, and artificial intelligence integration are also discussed.
Abstract Radiotherapy (RT) can initiate antitumor immunity through immunogenic cell death, tumor-associated antigen release, and innate immune engagement, but these responses are often too weak and transient to achieve durable systemic tumor control. This perspective identifies insufficient immune amplification as a central bottleneck in radioimmunotherapy and positions nano-immunoadjuvant (NIA) as programmable systems that enhance RT-induced immune priming, remodel the suppressive tumor microenvironment, and support adaptive antitumor immunity. Unlike conventional approaches focused mainly on radiosensitization, drug delivery, or immune initiation, we propose a “trigger–amplify–sustain” framework in which RT initiates immunity, NIA amplifies it, and immune checkpoint blockade maintains it. We also discuss key translational challenges, including radiation fractionation, scalable manufacturing, biomarker-guided patient selection, and regulatory evaluation, supporting a shift toward programmable radioimmunotherapy.
Accurate breast cancer prognosis remains a major challenge in precision oncology due to tumor heterogeneity and the complexity of integrating high-dimensional multi-omics data. Although multimodal learning approaches have improved predictive performance by combining clinical and molecular information, many existing methods rely on a single ensemble strategy that remains susceptible to prediction variance and limited robustness in high-dimensional, low-sample-size biomedical datasets. This study investigated whether integrating complementary ensemble strategies within a unified multimodal framework could improve the robustness and predictive performance of breast cancer prognosis. A heterogeneous multimodal ensemble framework was developed in which stacking was used to integrate complementary information from clinical, gene expression, and copy number variation (CNV) data through meta-learning, while bagging was incorporated to stabilize the meta-learning process via bootstrap aggregation. The outputs of the stacking and bagging branches were combined using weighted probability fusion. The framework was evaluated on the METABRIC breast cancer cohort and compared with unimodal models and a conventional stacking ensemble using an independent test set and stratified tenfold cross-validation. The proposed hybrid framework achieved a ROC-AUC of 0.936, outperforming unimodal clinical and molecular models (ROC-AUC = 0.8140.885) and the conventional stacking ensemble (ROC-AUC = 0.898). Stratified tenfold cross-validation further demonstrated consistent improvements in mean ROC-AUC, recall, F1-score, balanced accuracy, and Matthews correlation coefficient, indicating improved robustness and stable performance across the internal validation folds. On the independent test set, the hybrid framework reduced false-negative predictions and increased sensitivity relative to the stacking ensemble, demonstrating a more favorable balance between identifying high-risk patients and maintaining overall predictive performance. Rather than introducing a new ensemble algorithm, this study demonstrates that assigning complementary roles to stacking multimodal information integration and bagging for prediction stabilization provides an effective and robust framework for multi-omics breast cancer prognosis. The proposed hybrid strategy consistently improved predictive performance and robustness compared with conventional stacking while demonstrating stable performance across internal validation, supporting the use of complementary ensemble paradigms for multimodal prediction in precision oncology.
Radiotherapy (RT) is a cornerstone of solid tumor treatment, but its capacity to reshape the tumor microenvironment (TME) also has a less favorable consequences: acquired radioresistance, which can limit durable systemic antitumor immunity. Within this remodeling process, the CCL2/CCR2 axis has emerged as a key organizer of the immunosuppressive network. This review summarizes the transcriptional regulation and microenvironmental effects of the CCL2/CCR2 axis in response to radiation-induced stress. Evidence indicates that RT triggers DNA damage and a burst of reactive oxygen species (ROS), which in turn activate the ATM/NF-κB and STAT3 pathways. These signals induce surviving tumor cells and stromal components, such as cancer-associated fibroblasts (CAFs), to secrete CCL2 persistently and robustly. Elevated CCL2 then recruits peripheral monocytes and immature myeloid cells into the TME, where they differentiate into tumor-associated macrophages (TAMs) and myeloid-derived suppressor cells (MDSCs). Through the release of pro-tumorigenic factors, Arg-1, and nitric oxide, these myeloid populations not only drive effector T cell exhaustion but also promote abnormal angiogenesis and tissue fibrosis. These structural changes create physical barriers that hinder T cell infiltration, together shaping an immunosuppressive microenvironment. Importantly, the immunological effects of RT are schedule-dependent: conventional fractionated RT may sustain chronic CCL2 production and myeloid recruitment, whereas hypofractionated RT or stereotactic body radiation therapy may differentially regulate cGAS/STING/type Ⅰ interferon signaling, Trex1 induction, and CCL2/CCR2-mediated myeloid suppression. The review also discusses current clinical combination strategies, with attention to triple therapy that integrates CCL2/CCR2 blockade, RT, and immune checkpoint inhibitors (ICIs). In this approach, RT promotes antigen release, CCR2 inhibition limits myeloid and stromal barriers, and ICIs restore exhausted T cell function. Overall, targeting the CCL2/CCR2 axis offers a way to address bottlenecks in local control and may help RT function as an in situ vaccine that supports lasting, systemic antitumor immunity.
Abstract Purpose Hepatocellular carcinoma (HCC) has a high risk of recurrence even after curative-intent hepatic resection. Although several postoperative adjuvant therapies have been investigated, the optimal strategy remains uncertain because direct head-to-head comparisons are limited. This study aimed to compare and rank the efficacy and safety of adjuvant therapies for HCC using a network meta-analysis (NMA). Methods A systematic search of MEDLINE via PubMed, Embase, CENTRAL, Web of Science, Scopus, regional databases, clinical trial registries, and conference proceedings was conducted to identify randomized controlled trials evaluating postoperative adjuvant therapies after curative-intent hepatic resection for HCC. The primary outcome was recurrence-free survival (RFS); secondary outcomes included overall survival (OS) and safety. A frequentist NMA was performed to estimate pooled hazard ratios (HRs) with 95% confidence intervals (CIs). Treatments were ranked using P -scores. Results Twenty-eight randomized controlled trials involving 4,830 participants were included. Most patients had hepatitis B virus-related HCC and preserved liver function (Child–Pugh A). For OS, adjuvant 125 I-brachytherapy (HR = 0.36, 95%CI 0.17–0.78), IMRT (HR = 0.44, 95%CI 0.23–0.86), and 131 I-metuximab (HR = 0.46, 95%CI 0.28–0.77) were associated with the greatest reductions in mortality compared with observation. For RFS, autologous formalin-fixed tumor vaccine (AFTV) ranked highest ( P -score = 0.92), followed by adjuvant Cidan capsule plus TACE (0.90), 125 I-brachytherapy (0.84), and IMRT (0.80). Most therapies showed manageable toxicity. Immune checkpoint inhibitors demonstrated a favorable benefit-risk profile, while locoregional therapies were associated with expected procedure-related adverse events. Conclusion Internal radiation and brachytherapy approaches ( 125 I-brachytherapy, IMRT, and 131 I-metuximab) were associated with the greatest OS benefit, whereas AFTV and adjuvant Cidan capsule plus TACE showed the strongest RFS in the network. However, this finding is based on limited evidence (single small trial) and should be considered hypothesis-generating rather than definitive. These findings support risk-adapted integration of radiotherapy and immunotherapy after resection, although further large-scale head-to-head trials are needed.
Abstract Human epidermal growth factor receptor 2 (HER2) is overexpressed in 25%–30% of breast cancer cases and was previously correlated with inferior outcomes. HER2 amplification in breast cancer was discovered in the 1980’s and the monoclonal antibody has largely been available for clinical use since the 2000’s. Introduction of HER2-targeted therapies like trastuzumab into treatment paradigm has markedly improved survival outcomes but cardiac dysfunction is a well-known adverse effect. Despite more than two decades of targeting HER2 pathway, the appropriate use of anti-HER2 agents in a patient with pre-existing cardiac dysfunction has not been conclusively established. This report describes management of early-stage HER2-positive breast cancer in a 65-year-old woman with pre-existing cardiomyopathy. Contraindications to use of HER2-targeted monoclonal antibodies prompted the use of lapatinib, an oral tyrosine kinase inhibitor (TKI) with relatively shorter half-life, absence of antibody dependent cellular cytotoxicity and lower incidence of cardiotoxicity. Because it is taken orally, lapatinib allows for dose adjustments and even interruption in case of adverse event. This allows for rapid withdrawal of its effect on the heart making it favorable compared to use of trastuzumab in this context. Our patient, receiving neoadjuvant chemotherapy with lapatinib, demonstrated a complete metabolic response on imaging without worsening cardiac dysfunction. This case report highlights a gap in literature regarding the management of HER2-positive breast cancer in patients with pre-existing cardiac dysfunction, while also offering guidance for potential future use of TKIs as HER2-directed therapy in this population.
Abstract Purpose Triple-negative breast cancer (TNBC) is a high-risk molecular subtype defined by absence of estrogen receptors, progesterone receptors, and human epidermal growth factor receptor 2 (HER2) overexpression. Immune checkpoint blockade (ICB) benefits only 20% to 40% of patients, with T cell exhaustion driven by chronic programmed death receptor 1 (PD-1, PDCD1) inhibitory signalling being the dominant barrier. This work presents a computational pipeline that converts single-cell immune phenotypes into per-patient synthetic immune-cell engineering recommendations, filling the gap for quantitative patient-level ligand design parameters. Methods The pipeline was applied to the GSE176078 single-cell RNA sequencing (scRNA-seq) atlas (100,064 cells, 26 treatment-naive TNBC patients). After quality control, normalisation, and Leiden clustering, T cells were extracted and scored for exhaustion and cytotoxicity using defined gene-set modules. The PDCD1/CD2 ratio was computed per cell and aggregated per patient. Cross-modal validation used TCGA-BRCA bulk RNA-seq through univariate Cox regression and Kaplan–Meier analysis. A targeted ligand-receptor proxy screen was performed across five receptor-ligand pairs and a rule-based DesignPriorityScore was assessed by bootstrap resampling (n = 200). Results Leiden clustering was validated at adjusted rand index (ARI) 0.288 to 0.311 and normalised mutual information (NMI) 0.616 to 0.671. Three patient phenotype groups were identified based on exhaustion burden and PDCD1/CD2 imbalance. The bulk PDCD1/CD2 ratio showed an exploratory association with overall survival in TCGA-BRCA (HR=0.47, 95%CI 0.28–0.79; P < 0.005), reflecting immune infiltration rather than per-cell exhaustion state. Patient rankings were stable across bootstrap resamples (mean Spearman ρ > 0.85; top-quartile retention > 90%). Conclusion This pipeline shows that per-patient PDCD1/CD2 ratios derived from scRNA-seq can be translated into ranked synthetic ligand engineering priorities, offering a prototype framework for single-cell-informed synthetic immunology design in TNBC immunotherapy.
Abstract The liver is the primary target organ for hematogenous metastasis of colorectal cancer, and colorectal cancer liver metastasis is one of the key and challenging aspects in its treatment. In order to improve the diagnosis and comprehensive treatment of colorectal cancer liver metastasis, the guideline development group has summarized advanced experiences and the latest achievements from both domestic and international sources, and has once again revised and updated the Guideline for the diagnosis and comprehensive treatment of colorectal cancer liver metastases (2025 edition) to continuously provide guidance and reference for clinical practice in this field.
Abstract Small-cell lung cancer (SCLC) has long been characterized by its aggressive clinical course and a deceptive initial sensitivity to chemotherapy that almost invariably yields to recalcitrant disease. Recent genomic and transcriptomic profiling has dismantled the historical view of SCLC as a monolithic entity, revealing a complex landscape of inter- and intra-tumoral heterogeneity defined by the differential expression of key transcription factors—ASCL1, NEUROD1, POU2F3, and YAP1. Central to this heterogeneity is the phenomenon of lineage infidelity, a form of cellular plasticity where SCLC cells transit between molecularly distinct states to evade physiological and therapeutic pressures. In this review, we decode the epigenetic and transcriptional networks that govern these subtype transitions, highlighting how the loss of canonical lineage markers drives the emergence of variant phenotypes with altered metabolic and tumor profiles. We argue that this plasticity is not merely a byproduct of tumor evolution but a fundamental engine of chemoresistance. Crucially, we identify the acquired therapeutic vulnerabilities inherent to each state, proposing a transition from broad-spectrum cytotoxic regimens to a trajectory-based precision medicine framework. By targeting the gatekeepers of lineage identity and exploiting the transient weakness of emerging subtypes, we outline a roadmap for overcoming the historical stalemate in SCLC treatment and achieving sustained clinical responses.
Abstract Purpose To provide a comprehensive overview of research in AI-driven anticancer drug design and to recommend future research directions for both researchers and clinicians. Methods A thorough literature search was conducted across the Web of Science Core Collection databases. Analysis was conducted using Excel 2025, CiteSpace, VOSviewer, and R. Results This bibliometric analysis encompassed a total of 15,554 publications. China emerged as the leading contributor in terms of total publication output, followed by the United States, India, and South Korea. Harvard University produced the highest volume of publications. Professor Alex Zhavoronkov was identified as the most prolific author in this domain, while Professor Michael Patrick Menden received the highest number of citations. The terms “artificial intelligence,” “immunotherapy,” and “breast cancer” were the most frequently mentioned, with significant attention given to breast, prostate, lung, and liver cancers. Conclusion AI-driven anticancer drug design emphasizes both established and novel targets, particularly for high-prevalence cancers. To enhance translational impact, future initiatives should focus on clinically oriented innovations. Key priorities must include the integration of multi-omics data, development of specialized cohorts, improved accessibility to data, and the combination of wet and dry lab tests. It will also be crucial to address data compliance and regulatory challenges to ensure sustained progress in this field.
Abstract Tumor-infiltrating lymphocyte (TIL) therapy has become a promising immunotherapy for the treatment of solid tumors and has shown substantial therapeutic potential in recent years, offering new options for patients with cancer. Despite its encouraging clinical outcomes, TIL therapy continues to face several research challenges and unresolved issues. Interleukin-2 (IL-2) is a key cytokine in TIL therapy and plays a critical role by promoting TIL proliferation and enhancing their anti-tumor activity. However, the administration of IL-2 may also lead to a range of adverse effects. This study reviews recent clinical research progress in TIL therapy, with particular emphasis on the dual role of IL-2 in this treatment approach. By examining current clinical trial data and recent research findings, this study evaluates both the beneficial effects and potential risks of IL-2 in TIL therapy and aims to provide guidance for future research and clinical practice.
Abstract Purpose Little is known about how the brain regulates extracerebral tumors. There is a significant difference in the incidence of hematologic maligancies between Han Chinese and European/American populations, with the most pronounced difference being more than 8–10 fold for multiple myeloma (MM) and chronic lymphocytic leukemia (CLL). Here, we aimed to investigate the genetic predisposition specific to each hematologic malignancy and to determine whether variations in cortical architecture contribute to population-level differences in their incidence. Methods In this study, we investigated causal relationships between cortical structural characteristics and eight hematologic malignancies using Mendelian randomization analyses based on data from the ENIGMA3, CHIMGEN, and FinnGen cohorts. Results MM showed a positive association with the surface area of the pars triangularis, whereas CLL was positively associated with the cortical thickness of the rostral anterior cingulate and rostral middle frontal regions. In addition, European/American populations exhibited a larger pars triangularis surface area and greater thickness of the rostral anterior cingulate and rostral middle frontal compared with Han Chinese populations. Findings from pathway analysis and transcriptome-wide association study provided additional evidence supporting these causal associations. Conclusions This study provides the first evidence examining the impact of cortical structural features on specific extracerebral cancer types. The findings further suggest that variations in cortical architecture may contribute to ancestry-related differences in the incidence of certain hematologic malignancies.
Eyelid tumors, particularly sebaceous carcinoma and meibomian gland carcinoma (MGC), are rare but highly aggressive malignancies characterized by diagnostic difficulty, early dissemination, and limited therapeutic options. Progress in understanding their biology and developing effective treatments has been hampered by the scarcity of robust, patient-derived preclinical models. Conditional reprogramming (CR) technology, which combines Rho-associated kinase (ROCK) inhibition with feeder fibroblast co-culture, enables rapid and reversible expansion of primary epithelial cells while preserving their genetic stability and lineage-specific phenotypic. Over the past decade, CR has emerged as a powerful platform for cancer modeling, drug sensitivity testing, and mechanistic studies across multiple epithelial malignancies. However, its application in ocular oncology remains underexplored. This review summarizes the biological principles and technical features of CR technology, highlights its established applications in epithelial cancer research, and focuses on its emerging relevance to eyelid tumors, particularly MGC. We further address current challenges, including tissue acquisition, standardization and reconstruction of the tumor microenvironment, and the future perspectives integrating CR with organoid culture, multi-omics profiling, and artificial intelligence-based analyses to advance functional precision oncology in eyelid malignancies.
Ultrasound-guided percutaneous lung needle biopsy(US-PLNB)is a pivotal diagnostic technique for subpleural pulmonary lesions,offering significant advantages such as real-time imaging,absence of ionizing radiation,and high operational flexibility.To standardize its clinical application and enhance both procedural safety and diagnostic accuracy,a multidisciplinary panel of experts was convened by eleven authoritative Chinese academic societies,including Chinese Society of Ultrasound in Medicine.Based on the latest domestic and international evidence-based medicine,this panel developed Chinese expert consensus on ultrasound-guided percutaneous lung needle biopsy(2025 edition).This consensus systematically elaborates on the indications and contraindications,preprocedural assessment and preparation,intraoperative techniques,complication prevention and management,and post-procedural care for US-PLNB.Furthermore,it puts forward 18 specific evidence-based recommendations.The primary aim of this consensus is to provide radiologists and clinicians with standardized operational guidance,promote the standardized application of US-PLNB,and ultimately improve patient care for individuals with pulmonary diseases.
Digestive system cancers represent a group of malignancies with high global incidence and a particularly heavy disease burden in China. Among these, gastric cancer, colorectal cancer, and biliary tract cancer represent critical human epidermal growth factor receptor 2 (HER2)-positive subtypes that require focused clinical attention. Antibody–drug conjugates (ADCs), which utilize HER2-targeted monoclonal antibodies to deliver cytotoxic agents directly to tumor cells, have revolutionized precision therapy in HER2-positive cancers, marking a significant milestone in oncologic treatment. Currently, trastuzumab deruxtecan and disitamab vedotin have been approved for the treatment of advanced gastric cancer. This expert consensus emphasizes the safety management of HER2 ADCs in digestive system cancers, considering the unique anatomical complexity and the vulnerability of the mucosal barrier in this system. It underscores the essential role of multidisciplinary team collaboration in standardizing the management of adverse events and offers evidence-based recommendations to guide clinical practice across primary and secondary care settings. Furthermore, it underscores the value of a “physician-led, patient-engaged” model in patient education. The safety management experience accumulated in this context also offers important insights for the clinical application of ADCs targeting other oncogenic pathways.
Robot surgery is an important trend in contemporary colorectal cancer surgical treatment. The Robotic Surgery Group, Colorectal Cancer Committee of Chinese Medical Doctor Association organized experts in relevant fields across the country to update and revise the application standards of robotic colorectal cancer surgery based on the Expert consensus on robotic surgery for colorectal cancer (2015 edition) and the revised version in 2020, in accordance with the development of robotic surgery concepts, technologies, and equipment in recent years, in order to promote the application and promotion of robotic surgery.
As the third leading cause of cancer-related mortality worldwide, hepatocellular carcinoma (HCC) constitutes a substantial global health burden. Immunotherapy has transformed HCC management, encompassing immune checkpoint inhibitors, cytokine-based therapies, adoptive cell therapies, and oncolytic viruses. Clinical research is increasingly focused on combination immunotherapy, which offers improved treatment prospects, particularly for patients with unresectable HCC. This review provides a concise overview of the HCC immune microenvironment, emphasizing the role of immune cells in HCC pathogenesis and progression. Additionally, it examines the molecular mechanisms underlying HCC immunotherapy, summarizes relevant clinical trials and their outcomes, discusses key therapeutic agents and combination strategies, and addresses current challenges and future directions in the field.
Lung cancer remains the leading cause of cancer-related mortality worldwide, largely due to its asymptomatic nature in early stages and consequent late diagnosis. The development of reliable, non-invasive biomarkers for early detection is critical to improving prognosis and survival rates. Recent advances in omics technologies and liquid biopsy have led to the identification of novel biomarkers, including circulating tumor DNA (ctDNA), microRNAs (miRNAs), exosomes, and protein signatures. These biomarkers show promise in enhancing diagnostic accuracy, risk stratification, and monitoring of disease progression. This review highlights the current landscape of emerging biomarkers for early lung cancer detection, evaluates their clinical utility, and discusses the challenges in their translation to routine clinical practice. Integration of these biomarkers with imaging and artificial intelligence-based diagnostic tools may offer a transformative approach for early lung cancer diagnosis.
