Spinal ependymoma prognosis is closely correlated with tumor malignancy and biomarker levels, such as Ki-67 and p53, which reflect cellular proliferation and genetic instability. Despite their clinical significance, current methods to assess these biomarkers rely on invasive postoperative immunohistochemistry (IHC), delaying critical treatment decisions and limiting preoperative planning. While deep learning have revolutionized biomarker prediction in brain tumors, their application to spinal ependymomas remains underexplored due to the rarity of these tumors, insufficient datasets, and the technical challenges of analyzing spinal cord MRI. We used a deep learning model to predict molecular markers for spinal ependymomas using preoperative magnetic resonance imaging (MRI) scans and clinical information to predict biomarkers for spinal ependymoma.

Alloreactive T cells mediate graft-versus-leukemia (GvL) reactions and acute graft-versus-host disease (aGvHD) in AML patients following allogeneic hematopoietic stem cell transplantation.

Obesity is a major health challenge and fat accumulation in visceral depots is more strongly associated with metabolic comorbidities than deposition in subcutaneous depots. Epitranscriptomic regulation of gene expression by N-methyladenosine (mA) influences various aspects of RNA metabolism, however the mA methylome in human adipose tissue and its relationship with fat distribution has not yet been investigated in detail.

Premature ovarian insufficiency (POI) is associated with an increased risk of neurodegenerative diseases, but the underlying mechanisms remain unclear. Here, we integrated DNA methylome profiling of peripheral blood leukocytes and circulating steroid hormone analysis to identify potential mechanism linking POI to neurogenerative risk. Methylome analysis revealed distinct epigenetic signatures in POI patients, including hypomethylation at the SOAT1 promoter, a gene critical for cholesterol homeostasis. Gene set enrichment analysis (GSEA) implicated suppressed steroid biosynthesis, supported by significantly reduced circulating levels of steroids, including androstenedione, dehydroepiandrosterone (DHEA), aldosterone, cortisol, and cortisone in POI patients. Notably, neuroprotective steroids DHEA and pregnenolone exhibited an age-dependent decline exclusively in the POI group. Our findings suggest that SOAT1-mediated cholesterol dysmetabolism leads to steroidogenesis suppression and depletion of neuroprotective steroids. Epigenetic dysregulation of SOAT1 and steroidogenic genes, coupled with depletion of DHEA and pregnenolone might contribute to the elevated neurodegenerative risk in POI.

The blood biomarker hPG is linked to multiple solid tumors, including lung cancer. This study examined blood hPG levels of asymptomatic individuals and patients with non-small cell lung cancer (NSCLC), categorized by their smoking and chronic obstructive pulmonary disease (COPD) status. Plasma hPG levels were measured across five cohorts of patients, including 396 NSCLC patients, 200 NSCLC cancer-free COPD patients, 369 asymptomatic never smokers, 278 asymptomatic current smokers, and 235 asymptomatic former smokers. Receiver operating characteristic (ROC) curves assessed diagnostic accuracy. In asymptomatic current smokers, hPG levels were significantly higher (6.70 pM (IQR: 5.13-11.29)) than those in gender- and age-matched never smokers (2.50 pM (IQR: 1.70-3.70; p < 0.0001). In contrast, gender- and age-matched former smokers showed a return to normal hPG levels (2.29 pM (IQR: 1.61-2.97)). In multivariate analysis, age and smoking status were significantly associated with elevated levels of hPG (p-values of 0.0319 and < 0.0001, respectively). Levels of hPG in current smokers were not different from levels found in age-matched patients with NSCLC or COPD (6.60 pM (IQR: 4.36-11.22) and 6.07 pM (IQR: 3.99-11.69), respectively). In NSCLC and COPD patients, hPG levels were independent of the smoking status. When comparing asymptomatic and NSCLC-diagnosed former smokers, the AUC was 0.85 (95% CI:0.80-0.90, p < 0.0001). The AUC was equal to 0.53 (95% CI: 0.45-0.60, p = 0.4436) for current smokers. Our findings identify hPG as both a reversible marker of active smoking and a diagnostic biomarker of NSCLC. This dual role supports its potential use in risk stratification and early detection, particularly among non-COPD former smokers.

Liver fibrosis represents the main risk factor not only for liver-related but also for overall mortality in metabolic dysfunction-associated steatotic liver disease (MASLD) patients, being metabolic dysfunction-associated steatohepatitis (MASH) its more severe clinical form. We recently developed a non-invasive algorithm termed BMP8A Fibrosis Score (BFS) which is able to identify MASH patients with advanced liver fibrosis. The aim of this study was to validate the BFS comparing its diagnostic accuracy with that of other scoring systems developed to assess liver fibrosis in MASH patients. Serum BMP8A was measured in 302 patients with biopsy-proven MASH: 171 with non- or mild fibrosis (F0-F2) and 131 with advanced fibrosis (F3-F4) recruited from seven university hospitals located in different cities in Spain. BFS, Fibrosis-4 (FIB-4) Index, NAFLD Fibrosis Score (NFS), Hepamet Fibrosis Score (HFS), and AST-to-Platelet Ratio Index (APRI) were calculated for each patient. The diagnostic accuracy of the scoring systems was determined according to the area under the receiver operating characteristic (AUROC) curve, sensitivity, specificity, positive (PPV) and negative (NPV) predictive values, and likelihood ratios (LR). BFS showed higher overall accuracy than the other liver fibrosis algorithms calculated in the study cohort, presenting an AUROC of 0.750 for predicting advanced liver fibrosis (F3-F4), and correctly classifying 70.9% of F3-F4 patients with a sensitivity of 58.0%, a specificity of 80.7%, a 71.5% NPV, a 69.7% PPV, a 3.0 LR+, and a 0.5 LR-; the other predictive scores correctly classified a lower percentage of these patients (63.6% for FIB-4 ≥ 2.67, 63.2% for HFS ≥ 0.47, 57.3% for APRI ≥ 1.5 and 56.9% for NFS ≥ 0.675). BFS eliminates the grey area as it uses a single cut-off value (0.46), which is its key advantage over the others, reducing the number of patients with undetermined results (43.4% for FIB-4, 39.1% APRI, 37.4% for HFS, and 24.1% NFS). In sum, BFS properly classified more patients with advanced liver fibrosis (F3-F4) than the other scoring systems, eliminating indeterminate results and improving risk stratification.

The methyltransferase-like (METTL) family members are the central ‘writers’ of epitranscriptome modifications, catalyzing N6-methyladenosine (mA), N7-methylguanosine (mG), 3-methylcytosine (mC) and other chemical markers that modify DNA, RNA, and proteins (both histones and non-histone proteins) to dynamically regulate gene expression. The METTL family is distinguished by structural diversity, substrate specificity and multifaceted roles in epigenetic regulation. Dysregulation of METTL proteins has been demonstrated to disrupt RNA stability, translational efficiency and signaling pathways, which has been associated with tumorigenesis, neurodegeneration and immune dysfunction. At present, there are still limitations in the knowledge of the cooperative networks among METTL members and with other major signaling pathways. The objective of the present study is to elucidate the regulatory mechanisms mediated by METTL across different levels, laying the groundwork for subsequent development of precision therapies targeting phenotypic enzyme modifications. This review comprehensively delineates the structural characteristics and molecular functions of METTLs, their cooperative interactions, and their pathophysiological regulatory networks organized by signaling pathways rather than disease categories. We evaluate their diagnostic potential as biomarkers and their therapeutic implications, with particular focus on emerging METTL inhibitors that have entered clinical trials. By systematically exploring the mechanisms behind their context-dependent functions and analyzing their potential for clinical translation, we provide a foundation for precision therapies targeting these core regulators of nucleic acid and protein methylation.

Arginine methylation is a critical post-translational modification that modulates protein stability, enzymatic activity, and subcellular localization, thereby shaping cell fate decisions and maintaining cellular homeostasis. As the principal enzymes catalyzing this modification, protein arginine methyltransferases (PRMTs) participate in key biological processes, including transcriptional and post-transcriptional regulation as well as signal transduction. Dysregulated PRMT activity has been increasingly linked to tumor initiation, progression, and therapeutic resistance. This review summarizes PRMT classification, structural and functional characteristics, and upstream regulatory mechanisms, offering a framework for understanding their diverse roles in cancer biology and therapeutic relevance. We further discuss the mechanistic contributions of PRMTs to multiple cancer hallmarks and highlight recent advances in the development of PRMT inhibitors. Finally, we examine current strategies for clinical translation, with particular emphasis on combination approaches involving chemotherapy, targeted therapy, and immunotherapy, thereby offering a foundation for advancing PRMT-targeted precision oncology.

Neuroblastoma (NB) is a heterogeneous tumor, ranging from cases with spontaneous regression (MS stage) to high risk (HR) tumors. Resistance to therapy presents a major challenge in the treatment and management of HR-NB, contributing to a poor prognosis. In these patients, the resistance to conventional treatment is also related to non-genetic features, such as the cellular plasticity. Novel studies have demonstrated the presence of two distinct cell phenotypes: adrenergic (ADRN) and mesenchymal (MES), which reflect the heterogeneity of NB. MicroRNAs (miRNAs) are small, endogenous and non-coding RNAs with the ability to regulate gene expression and may have a crucial role in controlling cell plasticity. However, the role of miRNAs in NB plasticity has not been investigated yet. We investigated miRNA signature in NB cells subtypes (MES and ADRN) and in the extracellular vesicles (EVs) released by them, to identify potential MES related biomarkers. Differentially expressed miRNAs were identified by RT-qPCR and subjected to gene ontology, KEGG pathway, and protein-protein interaction network analyses. Candidate miRNAs were validated in plasma-derived EVs from NB patients. We identified miR-199a-3p as strongly upregulated in the MES cells subtype. Moreover, its expression levels were significantly higher in primary cell lines derived from HR patients compared to low-risk (LR) ones. This was confirmed by a bioinformatics analysis in patient tissue obtained from TARGET NB dataset. Protein-protein interaction analysis uncovered a complex network, with FN1, CD44, and YAP1, identified as key genes upregulated by miR-199a-3p, all of which are closely associated with the MES phenotype. Among the miRNAs significantly upregulated in EVs derived from MES cell lines, miR-584a-5p was significantly higher in EVs isolated from plasma of HR and L/Intermediate(I)R patients compared to MS. MiR-584-5p is typically considered a tumor suppressor; to support this role miR-584a-3p resulted significantly upregulated in L/IR tumor tissue, both in the INSS and COG classifications (TARGET NB database). Our findings identified specific miRNAs as MES phenotype related biomarkers. Further studies should investigate the potential impact of miRNAs on plasticity-related pathways in order to open new therapeutic strategies.

Solid pseudopapillary neoplasm (SPN) of the pancreas is a rare but distinct disease that remains poorly understood, especially at proteome level. We report comprehensive mass spectrometry-based proteomic analyses of SPN (n = 13) and characterize differences from other pancreatic neoplasms, pancreatic ductal adenocarcinoma (n = 11) and neuroendocrine tumor (n = 10). We discovered that the SPN proteome is uniquely distinct from that of other pancreatic neoplasms. Lysosome-related proteins are enriched and upstream lysosomal processes transcriptional regulators, MITF and TFE3, are overexpressed in SPN. MITF protein expression is more specific for SPN than TFE3, previously considered the most specific immunohistochemical marker. Since lysosomal-related processes are connected to biological energy generation processes, we profiled metabolic pathways and found that SPN is characterized by higher fatty acid oxidation and lower glycolysis than PDAC and high proteasome pathway activity with many proteasomal proteins upregulated, suggesting a possible link to metabolic adaptation mechanisms in low-nutrient environments. Proteomics characterizes SPN as an immune-cold tumor with low MHC class I expression. Proteome-based receptor tyrosine kinase (RTK) pathway profiling suggests PDGFRA and ERBB2 (HER2) as potential candidates for targeted therapy. Our results provide unique proteomic contribution to the understanding of SPN biology and highlight differences from other pancreatic tumors.

Cancer remains a leading cause of mortality worldwide, with early detection being critical for improving survival rates. Traditional diagnostic methods, such as tissue biopsies and imaging, face limitations in invasiveness, cost, and accessibility, making liquid biopsy a compelling non-invasive alternative. Among liquid biopsy approaches, circulating cell-free DNA (cfDNA) analysis has gained prominence for its ability to capture tumor-derived genetic and epigenetic alterations. This review summarizes key cfDNA biomarkers, including gene mutations, copy number variations (CNVs), DNA methylation, fragmentation patterns, and end motifs (EMs), and highlights their utility in cancer detection and monitoring. By integrating these multi-modal cfDNA biomarkers, feature fusion approaches have not only enhanced the performance of cancer classification models but also stabilized low-abundance signals, thus ensuring more reliable cancer detection and monitoring. Furthermore, the diagnostic power of cfDNA analysis has been further amplified by machine learning (ML), with both traditional ML and deep learning (DL) methods demonstrating strong predictive performance in routine clinical liquid biopsy applications. However, challenges remain, including tumor heterogeneity, standardization of data processing, model explainability, and cost constraints. Future advancements should focus on refining multi-modal feature integration, developing explainable AI (XAI) models, and optimizing cost-effective strategies to enhance clinical applicability. As computational methodologies advance, the integration of cfDNA biomarkers with ML frameworks holds great promise to reshape non-invasive cancer detection by enabling earlier diagnostics, more accurate prognostic evaluation and personalized treatment strategies.

Measurable residual disease (MRD) assessment has become a cornerstone in the management of acute myeloid leukemia (AML), offering critical prognostic information and guiding post-remission therapy. Conventional MRD detection methods, including multiparameter flow cytometry (MFC), quantitative PCR (qPCR), and next-generation sequencing (NGS), have demonstrated strong predictive value but are limited by technical complexity, marker specificity, and accessibility. This review explores the current landscape of MRD monitoring in AML, covering cytogenetic, immunophenotypic, and molecular approaches, with particular emphasis on the strengths and limitations of each. We further examine promising emerging technologies—namely DNA methylation profiling and surface-enhanced Raman scattering (SERS)—as non-invasive alternatives. DNA methylation-based assays capitalize on the epigenetic dysregulation characteristic of AML, while proof-of-concept studies indicate SERS as a promising alternative for cancer subtypes, stages or specific mutation detection by analyzing biofluids or extracted DNA from blood. Together, these developments hold the potential to overcome current diagnostic limitations, enabling more universal and precise MRD assessment. Ongoing research and validation will determine their future integration into standard clinical practice.

Alternative splicing (AS) is a crucial post-transcriptional regulatory mechanism that is frequently disrupted in cancer, leading to the generation of tumor-specific splice variants. These aberrant splicing events, often driven by mutations in splice sites or splicing factors (SFs), produce abnormal mRNA transcripts and protein isoforms that contribute to tumor initiation, progression, and immune evasion. Recent advancements in cancer immunotherapy have positioned AS-derived neoantigens as a novel and promising class of tumor-specific targets. These neoepitopes significantly expand the pool of immunogenic antigens for mRNA vaccines and adoptive cell transfer therapies, triggering robust and targeted anti-tumor immune responses. This review offers a comprehensive overview of the molecular mechanisms driving the generation of AS-derived neoantigens, their tumorigenic and immunological properties, and the antigen processing and presentation pathways involved. Additionally, we discuss emerging therapeutic strategies that exploit these neoantigens, such as splicing modulation and personalized immunotherapies, while also addressing current challenges and future prospects for translating AS-derived neoantigens into precision cancer immunotherapy.

The co-occurrence of epidermal growth-factor receptor mutation and or acquired exon 14 skipping (ex14) mutation in non-small-cell lung cancer (NSCLC) is extraordinarily rare. No more than five cases across multiple studies have been well documented. Here, we report the largest –ex14 co-mutation series to date-seven tumours diagnosed between 2019 and 2024 at three Chinese tertiary hospitals—and compare them with 12 ex14-only and 709 -only cohorts. Co-mutated tumours were dominated by -L858R (71%) and frequently harboured high-level amplification (57%). Four co-mutations emerged as resistance after first- or third-generation tyrosine-kinase inhibitors (TKIs), with a median post-acquisition progression-free survival (PFS) of 1.5 months and an overall survival (OS) of less than 6 months. Three tumours exhibited heterogeneous but occasionally durable control. Building on prior evidence that the cytoplasmic kinase domains of EGFR and MET physically interact, we computationally modelled their domain interactions. Compared to EGFR-19del, EGFR-L858R was predicted to have stronger binding affinity to MET, which may contribute to its clinical enrichment. The poor outcomes associated with concurrent EGFR–MET activation likely reflect the enhanced potential of EGFR-L858R to engage MET signaling and may lead to resistance to EGFR-TKIs even without overt MET amplification. In contrast, EGFR-19del showed weaker MET interaction and may retain EGFR-TKI sensitivity unless MET was secondarily upregulated or amplified. These findings highlight the need for routine molecular re-profiling at progression and support prospective trials of dual EGFR/MET blockade administered upfront in co-alteration or at resistance with acquired MET activation.

The development of brain metastasis is a major cause of significantly reduced survival in breast cancer patients. The initiation and progression of breast cancer brain metastasis (BCBM) involve multiple distinct molecular pathways and reprogramming of the tumor microenvironment (TME). This review systematically summarizes key mechanisms underlying BCBM, including epithelial-mesenchymal transition (EMT), extracellular matrix (ECM) remodeling, and the spatiotemporal dynamics of metabolic reprogramming regulated by critical signaling pathways during brain colonization. In particular, we highlight emerging mechanisms of breaching the specialized brain multifunctional barriers. Furthermore, this review provides an in-depth analysis of the cooperative immune-suppressive network within the BCBM TME, emphasizing the crosstalk among various immune cell components (such as T cells, B cells, macrophages, neutrophils, NK cells, MDSCs) and intracranial-specific cellular elements (including astrocytes, microglia, brain metastasis-associated fibroblasts). Through the complex interplay, these cells collectively facilitate immune evasion and metastatic outgrowth. Accordingly, we discuss the current clinical management of BCBM and potential future directions. Deeper mechanistic insights are expected to offer novel biomarkers and reveal new targets for developing precision therapeutic strategies against BCBM.

Metabolic rewiring is a defining feature of malignant cells, enabling them to dynamically exploit nutrient resources to meet bioenergetic problems at different growth stages. Beyond the classical Warburg effect, recent studies have shown that neoplasms demonstrate a marked dependency on lipid metabolism, using free fatty acids to support cellular proliferation and regeneration via fatty acid oxidation (FAO). As a central component of lipid metabolism, FAO exerts dual immunomodulatory functions within tumors. Although numerous studies have described the enzymatic reactions of the FAO pathway in different malignancies, relatively few have investigated the pharmacological disruption of these enzymatic checkpoints and the resulting immunological consequences. Moreover, existing therapeutic strategies have failed to achieve a risk-benefit balance, limiting the clinical translation of FAO-directed approaches. To better understand the therapeutic implications of FAO, we investigated the mechanistic pathways mediated by mitochondrial rate-limiting enzymes, with a particular focus on the carnitine palmitoyltransferase 1 enzyme family-the critical gatekeeper controlling the entry of fatty acids into mitochondrial oxidation instead of CPT2. We comprehensively evaluated its role in tumor biology and also highlight future research directions to inform rational intervention strategies.

Current methods for post-treatment cancer surveillance and recurrence monitoring rely mainly on biophysical imaging methods like CT and MRI. Limitations associated with these approaches include risk of radiation, high cost and sophistication in operation. Minimally invasive blood test is a very attractive alternative but there is no biomarker that is of sufficient sensitivity for this purpose. In this study, we attempted to discover novel breast cancer-associated blood plasma proteins that can fill this gap. We tested the hypothesis that genes that are co-amplified HER2 can be used as a surrogate biomarker for detection of HER2+ breast cancer. Following identification of HER2-coamplified genes via copy number variation analysis, a series of bioinformatic tools were used that eventually led to the identification of WFIKKN2 as a novel cancer-associated blood plasma protein. ELISA analysis of more than 120 plasma samples from non-cancer and cancer patients with HER2+ breast cancer revealed WFIKKN2 to have sensitivity and specificity of up to 89% and 60%, respectively. While not ideal as a diagnostic biomarker due to its moderate specificity, the high sensitivity of WFIKKN2 is suitable for the purpose of post-treatment surveillance and recurrence monitoring. The data warrants WFIKKN2 to be further evaluated through clinical studies to validate its clinical utility.

Myelofibrosis is characterized by aberrant JAK/STAT signaling, with approved therapy including the JAK inhibitor ruxolitinib. Preclinical evidence implicates BCL-2 family proteins in MF pathogenesis and therapeutic response. Here, we evaluated baseline and on-treatment expression of , (encoding BCL-xL), and in 19 myelofibrosis patients receiving ruxolitinib. Quantitative PCR fold-change (FC) values, relative to healthy donors, revealed reduced baseline (mean FC 0.15) and (0.32) expression, with showing a non-significant trend toward upregulation. Baseline and expression was significantly higher in patients achieving spleen response (responders;  = 7) compared to non-responders (: 0.30 vs 0.07,  = 0.0130; : 2.73 vs 0.52,  = 0.0096). Logistic regression confirmed both as independent predictors of response. We derived a combinatorial score (CS = FC * FC), which outperformed individual genes in response prediction, as confirmed in logistic regression analysis (OR, 7.5;  = 0.0028). ROC-defined cutoff (0.06) stratified patients by likelihood of response (OR 3.3,  = 0.0037). Longitudinal analysis showed no significant overall change in gene expression on ruxolitinib, but responders exhibited BCL2 down-regulation at loss of response ( = 0.0419). Overall, these preliminary findings suggest that and expression, individually and via a simple CS, predict response to ruxolitinib in myelofibrosis. While limited by small sample size and retrospective design, our data support prospective validation and exploration of BCL-2 pathway modulation as a therapeutic strategy.

With the advancement of novel technologies such as whole-genome sequencing, single-cell sequencing, and spatial transcriptomics, single-omics analyses have already promoted the research of tumorigenesis as well as development and have partly elucidated the evolutionary processes of lung cancer. However, it is still difficult to distinguish these confounding features via single dimensional approaches due to the complexity, heterogeneity and cell-cell interactions with the immune microenvironment in lung cancer. Multi-omics approaches provide a holistic framework for constructing detailed tumor ecosystem landscapes, thereby facilitating the development of a more robust classification system for precision diagnosis and treatment, and aiding in the discovery of novel cancer biomarkers. In this review, we summarize the potential and applications of multi-omics approaches in characterizing intratumor heterogeneity and the tumor microenvironment throughout the course of lung cancer development. By further discussing the discovery and application of diagnostic and therapeutic biomarkers across precancerous lesions, early-stage lung cancer, tumor progression, metastasis, and therapy resistance, we outline the current challenges and future prospects of using multi-omics to identify reliable biomarkers. Moreover, we emphasize that integrative multi-omics models hold great promise for elucidating the complex interactions within the lung cancer ecosystem, thereby contributing to improved diagnostic accuracy, optimized therapeutic strategies, and better patient outcomes.

Gliomas constitute a major category of primary brain malignancies, characterized by limited therapeutic options and generally poor prognoses. Despite the promising outcomes of immunotherapies, particularly immune checkpoint inhibitors (ICIs), in various cancers, their clinical efficacy in gliomas has remained modest. This limited efficacy is largely attributed to the brain's immune-privileged status and the profoundly immunosuppressive nature of the glioma tumor microenvironment (TME). These challenges underscore the urgent need to improve understanding of the glioma TME and to develop innovative strategies that enhance the effectiveness of immunotherapies. This review provides a comprehensive overview of recent advances in glioma immunobiology and immunotherapy, with emphasis on ongoing clinical trials and emerging combinatorial strategies. Current efforts to combine ICIs with modalities such as radiotherapy and chemotherapy are highlighted, aiming to remodel the TME, improve antigen presentation, and stimulate more robust antitumor immune responses. The evolving landscape of glioma immunotherapy offers renewed hope for enhanced patient outcomes.Clinical trial registration Not applicable.