Glioblastoma is an aggressive, malignant primary brain tumour and the most prevalent histological type of glioma. Our study attempted to investigate the independent predictors of overall survival (OS) and cancer-specific survival (CSS) in Asian patients with glioblastoma and establish predictive models for the OS and CSS of Asian patients with glioblastoma based on the machine learning algorithms. Data from Asian patients with glioblastoma in the SEER database were retrieved and stochastically grouped into a training set (n = 845) and a validation set (n = 362), and patients in our centre were assigned to the test set (n = 172). Univariate and multivariate Cox regression analyses were performed to evaluate the prognostic factors. Predictive models for OS and CSS were established based on eight machine learning algorithms, including Lasso Cox, random survival forest, CoxBoost, generalized boosted regression modelling (GBM), stepwise Cox and survival support vector machine, eXtreme Gradient Boosting, supervised principal component and partial least squares regression for Cox, and the selected predictive models were evaluated by the area under the ROC curves (AUC) and 95% confidence interval (CI), calibration curves and decision curve analyses in the training set, validation set and test set. In our retrospective study, age, tumour history, histologic type, surgery and chemotherapy were confirmed to be predictors of OS (p < 0.05); age, tumour history, histologic type, surgery and chemotherapy were identified as independent factors for CSS (p < 0.05). The predictive model for OS based on the GBM algorithm exhibited excellent predictive performance at 6 months (AUC = 0.837, 95% CI: 0.803-0.870), 12 months (AUC = 0.809, 95% CI: 0.780-0.839) and 24 months (AUC = 0.750, 95% CI: 0.717-0.783) in the training set, and the powerful predictive performance of the GBM model was confirmed in the validation and test sets, with good concordance between the predicted and observed OS rates demonstrated by calibration curves and clinical decision making performance suggested by the decision curve analyses curves. The predictive model based on the GBM algorithm for CSS also performed best = in the training set at 6 months (AUC = 0.808, 95% CI: 0.770-0.847), 12 months (AUC = 0.755, 95% CI: 0.721-0.789) and 24 months (AUC = 0.692, 95% CI: 0.657-0.728) in the training set, and convincing predictive effectiveness was also confirmed in the validation and test sets with good calibration and clinical utility. Age, tumour history, histologic type, surgery and chemotherapy were confirmed to be independent factors for OS; and age, tumour history, histologic type, surgery and chemotherapy were identified as prognostic factors for CSS in our retrospective study. The predictive model constructed for OS and CSS based on the GBM algorithm in Asian patients with glioblastoma can be used to accurately predict OS and CSS in clinical practice, which may help tailor personalized treatment regimens and provide significant benefits for these patients.

Tumor-associated macrophages (TAMs), derived from circulating monocytes recruited to tumor sites via chemotactic signals such as C-C motif ligand 2 (CCL2) and colony-stimulating factor-1 (CSF-1), are pivotal components of the tumor microenvironment (TME). Functionally polarized into distinct subtypes, TAMs play dual roles: proinflammatory M1-type TAMs enhance antitumor immunity through the secretion of cytokines such as interleukin-12 (IL-12) and tumor necrosis factor alpha (TNF-α) and direct tumor cell cytotoxicity, whereas M2-type TAMs promote tumor progression by facilitating angiogenesis, metastasis, and immunosuppression. This polarization is dynamically regulated by different cytokines, various signaling pathways, and metabolic cues within the TME. Spatial distribution analyses revealed that M2-like TAMs predominantly infiltrate hypoxic and stromal regions, where they secrete factors such as vascular endothelial growth factor (VEGF), transforming growth factor beta (TGF-β), and matrix metalloproteinases (MMPs) to remodel the extracellular matrix and suppress immune responses via programmed death-ligand 1 (PD-L1) and arginase-1 upregulation. Crucially, TAMs interact extensively with immune cells; M2-TAMs secrete interleukin-10 (IL-10) and TGF-β to inhibit cytotoxic T lymphocytes while expanding regulatory T (Treg) cells and impairing natural killer (NK) cell function via altered antigen presentation. Conversely, M1-TAMs synergize with dendritic cells to enhance T-cell priming. Therapeutically, targeting TAMs offers promising strategies, including colony-stimulating factor-1 receptor (CSF-1R) inhibitors, CCL2 antagonists, and nanoparticle-mediated repolarization of M2-TAMs toward the M1 phenotype. Emerging genetic approaches, such as clustered regularly interspaced short palindromic repeat-CRISPR-associated protein 9 (CRISPR-Cas9) editing, aim to disrupt protumorigenic pathways in TAMs. Additionally, TAM-related biomarkers (e.g., CD206 and CD163) are being evaluated for their prognostic and predictive utility in immunotherapies. Despite progress, challenges persist owing to TAM plasticity and TME heterogeneity across cancers. This review synthesizes TAM biology, immune crosstalk, and therapeutic advancements, providing a foundation for novel oncology strategies aimed at reprogramming TAMs to overcome treatment resistance and improve clinical outcomes.

The human microbiome has garnered significant interest in recent years as an important driver of human health and disease. Likewise, it has been suggested that the intra-tumoral microbiome may be associated with specific features of cancer such as tumour progression and metastasis. However, additional research is needed to validate these findings in diverse populations. In this study, we characterized the intra-tumoral microbiota of 883 Malaysian breast cancer patients using transcriptomic data from bulk tumours and investigated their association with clinical variables and immune scores. We found that the tumour microbiome was not associated with breast cancer molecular subtype, cancer stage, tumour grade, or patient age, but was weakly associated with immune scores. We also found that the tumour microbiome was associated with immune scores in our cohort using random forest models, suggesting the possibility of an interaction between the tumour microbiome and the tumour immune microenvironment in Asian breast cancer.

Intercellular communication signals in the tumor microenvironment are closely related to behaviors such as cancer cell proliferation and immune evasion. However, the specific roles of intercellular signaling pathways in intrahepatic cholangiocarcinoma (ICC) have not yet been fully characterized. In this study, we analyzed publicly available single-cell RNA sequencing (scRNA-seq) data derived from paired samples of two intrahepatic cholangiocarcinoma (ICC) tissues and two adjacent normal tissues, thoroughly examining their cellular composition. InferCNV analysis was employed to compare tumor cells and normal cells, and pseudotime analysis was used to identify the growth and differentiation trajectories of the cells. Additionally, intercellular communication analysis was conducted to elucidate the communication networks between cells. Our analysis delineated the cellular ecosystem of ICC, identifying cell subclusters with shared characteristics between ICC and normal tissues. Notably, we characterized a distinct C7-E-T subcluster that exhibited high expression of CXCR4 and BPTF, markers associated with cancer stem cells (CSCs). Further investigation revealed that the MIF intercellular signaling pathway promotes the progression of ICC by activating intracellular signals in the MYC pathway. This study highlights the dysregulation of intercellular signaling pathways within tumor clusters, which influences the onset and progression of ICC. The cancer stem cell subpopulation (CXCR4hiBPTFhiE-T) exerts a significant influence on ICC progression by secreting relevant signaling molecules via the MIF signaling pathway.

To explore the efficacy of a deep learning (DL) model in predicting perineural invasion (PNI) in prostate cancer (PCa) by conducting multiparametric MRI (mpMRI)-based tumor heterogeneity analysis.

Liquid biopsy assays using cerebrospinal fluid (CSF) can revolutionize care for children with central nervous system (CNS) tumors by enabling precise monitoring of therapeutic responses and detecting recurrence or measurable residual disease (MRD). These assays can detect cell-free, circulating tumor DNA (ctDNA) via somatic alterations, though accurately measuring low-abundance ctDNA in CSF is challenging.

This review provides a comprehensive synthesis of current knowledge on immunotherapy resistance in non-small cell lung cancer (NSCLC), a disease that accounts for approximately 85% of all lung cancer cases and remains the leading cause of cancer-related death worldwide. Although immune checkpoint inhibitors (ICIs) have significantly improved survival for a subset of patients with advanced NSCLC, over 70% of cases ultimately exhibit primary or acquired resistance, underscoring the urgent need to understand the underlying mechanisms. The review categorizes resistance into tumor-intrinsic and tumor-extrinsic processes and provides an in-depth mechanistic analysis of how factors such as tumor antigen loss, impaired antigen presentation, cGAS-STING pathway dysregulation, metabolic reprogramming in tumor microenvironment (TME), immune cell exhaustion, and microbiomes collectively contribute to immune escape. In parallel, the influence of the lung and gut microbiome on shaping immunotherapy responses is discussed, with emphasis on microbial dysbiosis, immunosuppressive metabolite production, and TME remodeling. Therapeutic strategies to overcome resistance are also discussed, including combination approaches involving chemotherapy, radiotherapy, and antiangiogenic agents, as well as epigenetic modulators (HDAC and BET inhibitors). Moreover, the review explores bispecific antibodies, antibody-drug conjugates, and small-molecule agents that enhance T cell function or disrupt immunosuppressive signaling networks. By integrating insights from preclinical models and clinical trials, the review underscores the necessity of biomarker-guided patient stratification, combination immunotherapy approaches, and interventions that restore tumor immunogenicity. It concludes that a multipronged therapeutic strategy, one that addresses both immune evasion and TME-induced suppression, holds the greatest promise for improving response durability and advancing personalized immunotherapy for NSCLC.

H3K27-altered diffuse midline glioma (DMG) is a fatal disease, including four subtypes H3.3-mutant, H3.1/H3.2-mutant, H3-wildtype with EZHIP overexpression, and EGFR-mutant. H3F3B, another gene encoding histone H3.3 in addition to H3F3A, was ever reported to be mutated in DMGs. However, the clinical and molecular characteristics of H3F3B-mutant DMGs is yet understood. The clinical and radiological information of 9 patients with H3F3B-mutant DMG were retrospectively collected. Tumor specimens underwent DNA methylation profiling and next-generation sequencing. All tumors harbored somatic H3F3B p.K27I mutation. Average patient age was 46 ± 6.86 years, 6 tumors located in spinal cord, 5 tumors involved brainstem and 2 arose in the thalamus. Immunohistochemistry showed these tumors exhibited completely or mosaic-like loss of H3K27me3 expression. Unsupervised t-distributed stochastic neighbor embedding (t-SNE) analysis of DNA methylation profiles showed that H3F3B-mutant DMGs formed a unique methylation cluster separate from other gliomas with H3K27me3 loss and DMGs with canonical histone H3 mutation. PPM1D and NF1 were frequently mutated in H3F3B-mutant DMGs. Survival analysis showed that H3F3B-mutant DMGs had poor prognosis comparable to H3K27M-mutant DMGs. Taken together, H3F3B mutation also cause a loss of H3K27 trimethylation in DMGs and result in poor prognosis. The distinct characteristics of DNA methylation and mutational spectrum between H3F3B-mutant DMGs and canonical H3K27M-mutant DMGs might suggest divergent underlying mechanism of gliomagenesis.

Biomolecular phase separation has emerged as a fundamental mechanism governing intracellular spatial organization and functional compartmentalization, and is increasingly recognized as a critical factor in tumor initiation and progression. Through multivalent molecular interactions, biomolecular phase separation contributes to the formation of condensates that mediate the assembly of membraneless organelles, coordination of signaling pathways, and transcriptional programs. Under physiological conditions, condensation contributes to the maintenance of gene expression homeostasis, stress adaptation, and metabolic balance. In cancer cells, however, biomolecular condensates (BMCs) often exhibit aberrant behavior, accompanied by alterations in their structure, components, and regulatory mechanisms. Such perturbations may disrupt cellular homeostasis and influence key biological processes including gene regulation, signal transduction, metabolic reprogramming, and immune responses, thereby modulating various cancer hallmarks. Although the mechanistic understanding of BMCs remains incomplete, their intrinsic plasticity and environmental sensitivity make them attractive therapeutic targets for cancer treatment. This review provides a comprehensive overview of the regulatory factors and functional mechanisms of BMCs in cancer biology, with a particular focus on their involvement in diverse cancer hallmarks. This review further summarizes emerging therapeutic strategies targeting condensation, aiming to inspire novel treatment opportunities.

The factors associated with extrahepatic recurrence (EHR) after curative resection for hepatocellular carcinoma (HCC) have rarely been investigated. This study examined the pre- and postoperative predictors of EHR after curative resection in HCC patients over a ten-year follow-up period.

The exploration of how dysbiosis relates to lung masses is still nascent, with few studies focusing on the microbial characteristics across various sites. Therefore, we categorized the microbiota into feces and bronchoalveolar fluid (BALF) groups to compare microbial characteristics between benign and malignant masses, analyze their clinical correlations, and develop predictive models for lung cancer.

Porphyrinic MOF-based nanocomposites provide an applied attraction that overcomes the harmful effects of existing anticancer drugs. In this work, a nanorod porphyrinic metal-organic framework, PCN-222, was synthesized to serve as a high-surface-area drug carrier with photochemical properties. This material was then used for the production of Au nanoparticles. The PCN-222/Au/DOX NCs were provided with a low dosage of doxorubicin (DOX) as a novel nanocomposite with multifunctional anti-cancer properties on gastric cancer cells. The cytotoxicity effect was surveyed with an MTT assay on the MKN-45 cell line. The Annexin-V/propidium iodide technique and the colorimetric methods were utilized for apoptosis detection and caspase 8 and 9 pathway determination, respectively. The produced intracellular reactive oxygen species (ROS) were assigned using flow cytometry. Besides, the antibacterial activity of the nanocomposite was examined by conducting minimum inhibitory concentration (MIC) and Minimum Bactericidal Concentration (MBC) tests on Helicobacter pylori under LED light and dark conditions. Achieving a 91% ratio of the apoptotic cells versus the total dead cells rate, the high ROS production with the main Florence intensity (MIF) value of about 247.5, and an approximately two-fold increase in caspase 9 compared to caspase 8, with a MIC value of 375 µg/mL result under LED light for antibacterial activity, make the PCN-222/Au/DOX NCs an applicable promising agent with anticancer properties for gastric cancer cure.

Early and accurate brain tumor classification is vital for clinical diagnosis and treatment. Although Convolutional Neural Networks (CNNs) are widely used in medical image analysis, they often struggle to focus on critical information adequately and have limited feature extraction capabilities. To address these challenges, this study proposes a novel Residual Network based on Multi-dimensional Attention and Pinwheel Convolution (Res-MAPNet) for Magnetic Resonance Imaging (MRI) based brain tumor classification. Res-MAPNet is developed on two key modules: the Coordinated Local Importance Enhancement Attention (CLIA) module and the Pinwheel-Shaped Attention Convolution (PSAConv) module. CLIA combines channel attention, spatial attention, and direction-aware positional encoding to focus on lesion areas. PSAConv enhances spatial feature perception through asymmetric padding and grouped convolution, expanding the receptive field for better feature extraction. The proposed model classifies two publicly brain tumor datasets into glioma, meningioma, pituitary tumor, and no tumor. The experimental results show that the proposed model achieves 99.51% accuracy in the three-classification task and 98.01% accuracy in the four-classification task, better than the existing mainstream models. Ablation studies validate the effectiveness of CLIA and PSAConv, which are 4.41% and 4.45% higher than the ConvNeXt baseline, respectively. This study provides an efficient and robust solution for brain tumor computer-aided diagnosis systems with potential for clinical applications.

Natural killer (NK) cells are a crucial part of the innate immune system and serve as an important effector for killing tumor cells through direct cytolytic activity or immunomodulatory signaling to T cells and antigen presenting cells. NK cells are correlated with increased tumor control and better overall patient survival across various types of cancers including non-small cell lung cancer (NSCLC). Despite their promising potential for anti-tumor killing, NK cell function is often diminished within the tumor microenvironment. There are many factors that lead to decreased tumor-infiltrating NK cell killing, including immunoinhibitory factors from tumor cells and resident tissues, acquired immune tolerance, NK cell exhaustion, and the hypoxic state of the tumor microenvironment. Unleashing NK cell activity therefore has high potential to create a new class of immunotherapy that could combat both primary and acquired resistance to current checkpoint inhibitors. In this review we discuss mechanistic details of NK cell tumor killing, NK cell immunosuppression, and gaps in knowledge regarding highly complex microenvironment-specific effects on NK cell function. We also discuss the promise and limitations of emerging NK-cell based therapeutic strategies.

As a central regulator of the JAK/STAT signaling pathway, STAT1 generates functionally distinct α and β protein isoforms through alternative splicing. We systematically investigated the expression patterns and prognostic relevance of STAT1 isoform-specific transcripts across pan-cancer tissues, with a particular focus on ovarian cancer (OV), and elucidated their potential mechanisms in modulating the tumor immune microenvironment (TIME) to influence patient prognosis.

Metabolic reprogramming and epigenetic modification are two hallmarks of cancer. Protein lysine lactylation (Kla) is a novel type of glycolysis lactate-triggered posttranslational modification. However, the role of Kla in breast cancer (BC) remains largely unknown. Here, western blot, and immunohistochemical (IHC) staining of BC tissues revealed that global Kla levels were upregulated in BC tissues, and high levels of Kla were correlated with poor prognosis of patients with BC. A series of in vitro and in vivo assays demonstrated that interruption of glycolysis by lactate dehydrogenase (LDH) inhibitor or silencing LDHA and LDHB repressed the malignant behaviors of BC cells. Moreover, 4D label-free quantitative lactylproteomics analysis of BC tissues and cells revealed that lactylated proteins widely existed in several subcellular compartments and were closely associated with various cancer-related biological processes. Notably, two previously unresearched sites of histone lactylation, H4K79 lactylation (H4K79la) and H4K91 lactylation (H4K91la), were identified to be hyperlactylated in cancer tissues and cells. Glycolytic genes, such as lactate dehydrogenase A (LDHA), phosphoglycerate kinase 1 (PGK1), and hexokinase 1 (HK1) were identified to be the potential candidate genes epigenetically regulated by H4K79la and H4K91la by intersecting through chromatin immunoprecipitation sequencing (ChIP-seq), RNA sequencing (RNA-seq), and TCGA-BRCA database. Pharmacological inhibition of glycolysis downregulated H4K79 and H4K91 lactylation and suppressed the expression of glycolytic genes, whereas treatment with sodium lactate exhibited the opposite effects. Additionally, E1A-binding protein p300 (P300) acted as lysine lactyltransferase to regulate H4K79la and H4K91la, and control the transcription and expression of downstream glycolytic genes in BC cells. The results revealed an intriguing positive feedback loop formed by glycolysis/H4K79la/H4K91la/glycolytic genes in BC, highlighting the relationship between metabolic reprogramming and epigenetic regulation. These findings provide new therapeutic targets for patients with BC.

The impact of diffusion-weighted magnetic resonance imaging guided dose-painting intensity-modulated radiation therapy on head and neck squamous cell carcinoma remains unclear. Our objective aimed at comparing the outcomes and toxicities of DWI-guided DP-IMRT for HNSCC.

Mesothelin (MSLN) is an attractive therapeutic target for precision cancer treatments. However, MSLN can be cleaved and shed from tumor cells, resulting in the presence of soluble MSLN (sMSLN), which significantly hinders the efficacy of MSLN-targeted therapies. Here, we identify a MSLN-targeting designed ankyrin repeat protein (DARPin) M7, which specifically binds to the protease-sensitive C-terminal region of MSLN. Furthermore, we develop two auristatin-based DARPin-drug conjugates (DARPin-DCs), M7A-DC and M7GA-DC. We show that M7A-DC and M7GA-DC are effectively internalized by MSLN-positive cells, leading to the release of MMAE that induces lethal effects as well as bystander killing against MSLN-negative cells. Compared to ADCs, M7A-DC and M7GA-DC exhibit improved tumor spheroid penetration and cytotoxic activity in 3D models. Notably, M7GA-DC demonstrates enhanced tumor control and improved survival benefits in pancreatic cancer models with limited MSLN expression. Combination therapy with PD-1 blockade further promotes long-term immunological memory formation by activation of dendritic cells and reprogramming of the tumor microenvironment. These findings highlight the translational potential of DARPin-DC and its promising prospects for clinical combination with immunotherapies in the treatment of solid tumors, including refractory pancreatic cancer.

Cancer-associated fibroblasts (CAFs) are key components of the tumor microenvironment (TME). CAF phenotypes are highly heterogeneous and exert anti- and protumorigenic effects. We present a mathematical model that describes cancer-immune-CAF interactions and exploits the heterogeneity of CAF phenotypes to predict cancer progression and treatment response. By simulating multiple treatment options, including targeted monotherapies alone, two different immunotherapies, and a combination of therapies, we have found that CAF composition can impact treatment outcomes, potentially resulting in comparable effectiveness of single-drug treatments and combinatorial approaches or even the ineffectiveness of multicombination therapies. These findings suggest that CAF composition can be a promising indicator, in some cases guiding the choice towards less invasive therapies without compromising effectiveness. Our model indicates that accounting for CAF characteristics might facilitate the matching of targeted treatments, supporting clinical decision-making.

Solitary fibrous tumors (SFTs) of the central nervous system (CNS) are rare, aggressive mesenchymal neoplasms with high recurrence and metastasis rates. Here, we perform a comprehensive multi-omics analysis of 189 cases of CNS SFTs integrating 94 whole genome sequencing, 88 transcriptomics, 7 single-nucleus RNA sequencing and 3 spatial transcriptome sequencing. We find that receptor tyrosine kinase mutations are significantly more prevalent besides the widespread NAB2-STAT6 fusion and correlate with tumor grade. Transcriptomic analysis reveals four molecular subtypes-classical, neural-like, inflamed and migratory-each associated with distinct clinical and biological characteristics. Single-nucleus RNA sequencing identifies five tumor cell states, with the SFT_classical state serving as a precursor to other states influenced by hypoxia and inflammation. Notably, we identify FER kinase as a key therapeutic target, with FER inhibition significantly reducing tumor cell proliferation, migration and invasion. These findings provide important insights into CNS SFT biology and suggest potential therapeutic strategies for this challenging tumor type.