Composite indolent B-cell lymphomas may remain unrecognized when one component dominates the clinical presentation. Apparent relapse after therapy may, therefore, represent the emergence of a biologically distinct lymphoma rather than the recurrence of the original disease.

An 82-year-old woman presented with systemic low-grade B-cell lymphoma that was most consistent with marginal zone lymphoma (MZL). Staging demonstrated peripheral blood involvement and multifocal FDG-avid disease. Treatment with the CD20×CD3-bispecific antibody mosunetuzumab resulted in complete metabolic remission. During surveillance, a new, isolated cervical lymph node developed despite sustained systemic response. Biopsy revealed follicular lymphoma (Grade 1-2) with a germinal center phenotype and a BCL2 rearrangement, findings discordant with recurrence of MZL. Retrospective review of the original specimen identified in situ follicular neoplasia within the initial biopsy. The disease course was, therefore, reinterpreted as a synchronous composite lymphoma in which treatment of the dominant MZL unmasked a previously clinically silent B-cell neoplasm with follicular lineage. Local radiotherapy achieved remission of the follicular component.

New disease following treatment of an indolent lymphoma should not automatically be interpreted as relapse. Discordant clinical behavior, particularly focal progression after systemic response, warrants repeat biopsy and integrated pathologic reassessment. This case illustrates how therapy may reveal preexisting clonal heterogeneity and emphasizes the importance of temporality in distinguishing relapse from composite lymphoma.

The authors have confirmed clinical trial registration is not needed for this submission.

Systemic pro-inflammatory cytokine therapies (e.g., IL-2) represented early milestones in immunotherapy, but their use is hampered by low response rates and severe off-target toxicity. In contrast, immunocytokines deliver cytokines directly to tumors, reducing systemic toxicity and enhancing efficacy. Spatial-omics provides deep insights into the tumor microenvironment (TME), enabling identification of druggable targets and accelerating development of novel antibody platforms and cytokine payloads. However, variability in patient sample quality affects data integrity, and platform differences require distinct preprocessing workflows. Spatio-temporal data demand spatial clustering to define disease-relevant niches, yet a lack of consensus about what constitutes a niche complicates interpretation and reproducibility. To overcome these challenges, effort needs to be made to improve sample collection and processing, and to reconcile the diversity of platforms with their technical limitations in niche identification. By combining knowledge of key TME cell types and marker expression with cytokines identified from autoimmune datasets, innovative immunocytokines can be designed to improve targeting, effectiveness, and patient outcomes.

Clear cell renal cell carcinoma (ccRCC) is a kidney cancer in which immune activity is closely intertwined with von Hippel-Lindau (VHL) loss, hypoxia-inducible factor (HIF) signaling, angiogenesis, hypoxia, and metabolic adaptation. Although immune checkpoint inhibitor (ICI)-based regimens have changed the treatment landscape of advanced ccRCC, only a subset of patients achieve durable benefit. Commonly used biomarkers, such as programmed death-ligand 1 (PD-L1) expression, tumor mutation burden (TMB), and broad inflammatory gene signatures, have not been sufficient to explain this variation or to guide routine treatment selection. One reason is that immune infiltration in ccRCC is not synonymous with effective antitumor immunity. A tumor rich in CD8+ T cells may still be resistant if these cells are exhausted, metabolically restricted, spatially separated from tumor nests, or surrounded by suppressive myeloid, stromal, and vascular programs. Therefore, the key issue is not simply whether a tumor is immunologically "hot" or "cold," but which part of the antitumor response has failed. In this Mini Review, we discuss ccRCC immunotherapy response from an immune-ecological perspective. We focus on several treatment-relevant immune states, including T-cell-inflamed but dysfunctional tumors, myeloid-dominant suppressive tumors, angiogenesis- and hypoxia-skewed tumors, and immune-excluded tumors. We also consider how bulk transcriptomics, single-cell and spatial profiling, T-cell receptor sequencing, proteomics, metabolomics, and longitudinal liquid biopsy may help define these ecotypes and capture treatment-induced remodeling. This perspective may support more refined patient stratification and more mechanism-matched immunotherapy strategies in ccRCC.

To investigate the mechanisms underlying benzo[a]pyrene-induced esophageal cancer (EC), and to screen and identify the key targets and biomarkers associated with benzo[a]pyrene-related EC.

Potential targets of benzo[a]pyrene (BaP) were predicted using PharmMapper, SwissTargetPrediction, and ChEMBL databases, and were intersected with differentially expressed genes (DEGs) from the GEO database to screen candidate key genes. Subsequently, diagnostic models were constructed using 14 machine learning algorithms based on the identified key genes. Meanwhile, a prognostic model of key genes was constructed based on the TCGA esophageal cancer cohort, and the correlation between these key genes and tumor immune infiltration was further explored. Additional explainability was provided via SHAP analysis by determining the contributions of key features. Molecular docking was performed to verify the binding between BaP and core targets.

A total of 82 genes were identified as potential targets of EC induced by BaP. These key genes were found to be mainly involved in core tumor-related pathways, cell cycle regulation, MAPK signaling, and immune-inflammatory pathways, covering the crucial biological processes underlying malignant transformation of EC. Subsequently, 12 core genes (ACOT9、ACOX3、AURKA、HMGCR、INHBA、MMP3、MSR1、SHC1、SORT1、MAOB、MMP13、CDK4) were identified as key regulators by machine learning analysis. Among them, SORT1 and ACOX3 were significantly down-regulated, while AURKA and MMP13 were markedly up-regulated (P < 0.05). The 12-gene prognostic model enables efficient prognostic stratification of esophageal cancer patients, and core genes are implicated in the remodeling of the esophageal cancer immunosuppressive microenvironment through the regulation of immune cell infiltration. Molecular docking revealed strong binding ability between BaP and target proteins.

Bioinformatics analysis and molecular docking results revealed significant associations between BaP and 12 core esophageal cancer-related genes. BaP could stably bind to core proteins including AURKA, CDK4, MMP13 and INHBA, which is potentially correlated with altered cell cycle, metabolic disorders and dysregulated tumor immune microenvironment in esophageal cancer. 12 core genes were identified via machine learning, which offers new perspectives for the interdisciplinary field of environmental toxicology and precision oncology and provides a foundation for the development of individualized therapeutic strategies.

Multiple primary lung cancer (MPLC) is an increasingly recognized subtype characterized by distinct lesions with independent origins. While recent studies have profiled the immune landscape of MPLC, its tumor-intrinsic metabolic features and immunoregulatory interactions remain largely unexplored.

Single-cell RNA sequencing data from 11 single primary lung cancer (SPLC) tumors and 8 samples from 4 MPLC patients were analyzed using dimensionality reduction, clustering, and cell type annotation. Subtype-specific metabolic features and intercellular communication patterns were investigated through pathway enrichment and cell-cell interaction analyses. A prognostic model was constructed using Lasso-Cox regression. Immune microenvironment characteristics were assessed using deconvolution algorithms and immune-related signatures. Drug sensitivity prediction and functional assays were performed to explore potential therapeutic implications.

This study identified a metabolically distinct malignant epithelial subpopulation enriched in MPLC tumors, characterized by upregulation of amino acid metabolism pathways and active MHC-II-mediated interactions with immunosuppressive CD4⁺ Treg cells and mast cells. A metabolism-based eight-gene prognostic model was developed and validated in independent lung adenocarcinoma cohorts, effectively stratifying patient survival outcomes. High-risk patients exhibited immunosuppressive tumor microenvironment features, reduced immunotherapy response potential, and distinct drug sensitivity profiles. Functional assays confirmed that key metabolic genes, spermine oxidase (SMOX) and spermine synthase (SMS), promoted tumor proliferation and invasion, accompanied by transcriptional changes in PI3K/mTOR pathway components, highlighting their potential roles in poor prognosis and therapeutic vulnerability.

This study provides a systematic characterization of malignant subpopulations in MPLC, highlighting metabolic reprogramming and immunoregulatory features that contribute to poor prognosis. These findings provide a rationale for metabolism-based prognostic stratification and highlight potential therapeutic strategies to improve clinical outcomes.

Prediction models for postoperative recurrence in papillary thyroid carcinoma (PTC) have increased substantially in recent years. However, recurrence outcomes are inconsistently defined across studies, particularly with respect to structural and biochemical recurrence, and the quality and clinical applicability of existing models remain uncertain.

To systematically review and critically appraise multivariable prediction models for structural postoperative recurrence in pathologically confirmed PTC and to evaluate their predictive performance, methodological quality, and risk of bias.

PubMed, Embase, and the Cochrane Library were searched from inception to February 2026. Studies developing or validating multivariable prediction models for structural recurrence in adult patients with PTC were included. Data extraction was guided by the CHARMS checklist, and risk of bias was assessed using PROBAST. Findings were synthesized narratively, and an exploratory meta-analysis of discrimination performance from validation studies was conducted where appropriate.

Thirteen retrospective studies met the inclusion criteria, all of which were conducted in East Asian populations. Reported discrimination was generally acceptable, with most AUC or C-index values exceeding 0.70. However, all studies were judged to have a high overall risk of bias, primarily due to limitations in the analysis domain, including inadequate handling of overfitting, insufficient sample size justification, limited reporting of missing data, and reliance on internal validation. Only two studies performed external validation. Exploratory pooling of validation AUCs suggested moderate predictive performance but substantial heterogeneity across studies.

Current prediction models for structural recurrence in PTC show promise for individualized risk estimation but remain limited by methodological weaknesses, heterogeneous modelling approaches, inadequate assessment of calibration, and scarce external validation. Future studies should adopt standardized recurrence definitions, improve reporting transparency, and prioritize robust external validation before routine clinical implementation can be recommended.

To investigate the association between gut-airway microbiota dysbiosis, serum queuine levels, and early malignant transformation in patients with chronic obstructive pulmonary disease (COPD). We further explored whether the potential mechanistic role of queuine in enhancing lung epithelial cell viability under cigarette smoke exposure.

Stable COPD patients were stratified into a high relative abundance of Proteobacteria group (CH) and a low relative abundance of Proteobacteria group (CL) using 16S rRNA gene sequencing of fecal samples. Airway microbiota profiles were analyzed in parallel to assess gut-lung axis coupling. Serum queuine concentrations were quantified using LC-MS/MS in healthy controls, COPD subgroups (CL and CH), and COPD patients complicated by lung cancer. Clinical symptoms (CAT, mMRC, SCSS) and spirometry (FEV₁/FVC, FEV₁, FEV₁% predicted, FVC, FEF_25-75%) were assessed. In vitro experiments were performed using cigarette smoke extract (CSE)-stimulated lung cancer epithelial A549 cells and bronchial epithelial BEAS-2B cells to determine the effects of queuine on cell viability. Chest CT imaging was analyzed to quantify pulmonary nodules as an indicator of in vivo epithelial proliferative activity.

The α-diversity of gut microbiota did not differ between CH and CL. In contrast, β-diversity showed separation (PERMANOVA P = 0.062), with CH characterized by Proteobacteria enrichment and relative depletion of Firmicutes, Bacteroidota, and Actinobacteriota. Airway communities showed concordant remodeling with shifts in taxa consistent with dysbiosis. Serum queuine concentrations increased stepwise from healthy controls to COPD, were higher in CH than CL, and were highest in COPD complicated by lung cancer. Despite comparable pulmonary function and symptom scores between CH and CL groups, the CH group exhibited a significantly higher number of pulmonary nodules on CT imaging, particularly ground-glass nodules. In vitro, queuine significantly enhanced the viability of CSE-stimulated A549 lung cancer cells but failed to rescue CSE-induced growth inhibition in BEAS-2B cells.

COPD-associated gut microbiota dysbiosis, particularly enrichment of Proteobacteria, is closely associated with elevated systemic queuine levels. Excess queuine enhances cell viability of smoke-exposed lung cancer epithelial cells and is associated with increased pulmonary nodules in vivo. These findings identify queuine as a microbiota-derived metabolic mediator that may connect COPD-related dysbiosis to abnormal proliferation of lung epithelial cells.

Solid pseudopapillary neoplasm is a rare epithelial tumor of the pancreas with low malignant potential and a marked predilection for adolescent girls and young women. Although the overall prognosis is favorable after complete resection, preoperative characterization and surgical planning become more challenging in giant tumors with intratumoral degeneration, hemorrhage, and adjacent vascular compression. We report a 15-year-old girl who presented with acute abdominal pain persisting for 24 hours after strenuous physical activity. Imaging revealed a giant mixed solid-cystic mass in the pancreatic body and tail, and computed tomography angiography and venography demonstrated narrowing of the splenic vein with collateral venous circulation. After multidisciplinary assessment of oncologic safety and perioperative bleeding risk, the patient underwent laparoscopic distal pancreatectomy with splenectomy. Histopathology confirmed solid pseudopapillary neoplasm with negative margins and no nodal metastasis; ectopic splenic tissue was identified in the peripancreatic fat. Immunohistochemistry showed nuclear/cytoplasmic positivity for beta-catenin, loss of E-cadherin, positivity for CD10 and CD56, and partial positivity for lymphoid enhancer-binding factor 1, supporting the diagnosis. The postoperative course was uneventful, and no evidence of recurrence was detected on short-term follow-up. This case suggests that giant solid pseudopapillary neoplasm may have been related to acute abdominal pain in the setting of intratumoral degeneration, hemorrhagic change, and local tension effect. Surgical strategy should balance oncologic safety, bleeding risk, and organ preservation. In patients requiring splenectomy, long-term infection prevention should include immunization planning, fever emergency counseling, and individualized antibiotic prophylaxis according to local guidance and risk stratification.

Radiomics can quantify intratumoral heterogeneity on MRI, providing complementary information beyond clinical predictors. This study aimed to evaluate whether integrating radiomic features from pre-operative biparametric MRI (bpMRI) with standard clinical variables improves pre-treatment prediction of biochemical recurrence after radical prostatectomy. We further assessed whether radiomic features add prognostic value to established predictors and compared model performance against the D'Amico classification.

In this retrospective single-center study (2015-2023), 395 men who underwent pre-operative bpMRI (3T Siemens Magnetom Skyra) before radical prostatectomy were included. Lesions were automatically detected using the in-house developed PROVIZ framework, and radiomic features were extracted from index lesions using PyRadiomics v3.1.0. A total of 153 features, including first-order, textural, shape, and anatomical descriptors, were derived from T2-weighted (T2W), ADC, and high b-value diffusion-weighted (DWI, b=1500 s/mm²) images. Clinical variables included prostate specific antigen (PSA), Gleason Grade Group (GGG), PI-RADS v2.1, clinical T stage, and age. A stacked ensemble model (Random Forest and regularized Logistic Regression as base model; Logistic Regression as meta-model) was developed using five-fold stratified cross-validation, SMOTE balancing, Optuna hyperparameter tuning, and isotonic regression-based probability calibration. Performance was evaluated using AUC, calibration, and decision-curve analysis (DCA). Prognostic value was assessed with Kaplan-Meier and Cox regression analyses.

The combined model achieved an AUC of 0.85 (95% CI 0.83-0.87), outperforming radiomics-only (0.78) and clinical-only (0.72) models. Calibration was strong (slope = 1.01; Brier = 0.13). DCA showed higher net benefit than D'Amico classification. High-risk patients (probability ≥ 0.26) had significantly shorter recurrence-free survival (log-rank p<0.001; HR = 5.03, 95% CI 2.7-9.9). The most influential predictors were Gleason Grade Group and PSA, together with radiomic first-order/texture features.

Integrating bpMRI-derived radiomic features with standard clinical variables improved pre-treatment prediction of biochemical recurrence after radical prostatectomy. The combined model provided better discrimination between high-risk and low-risk recurrence groups and showed higher clinical net benefit than D'Amico. The results support the potential role of radiomics for refining individualized recurrence risk assessment in prostate cancer. External validation in independent cohorts is required before clinical implementation.

Friend and/or family caregivers (FCGs) of those with terminal cancer are heavily relied upon to address the emotional and physical needs of the patient facing death. Although increasing research has focused on testing supportive interventions for FCGs who deliver home-based care at end of life, limited attention has targeted strategies that support grief processing for bereaved FCGs during the months following the patients' death. Both natural environments and meditation practices are shown to support directed attention, reflection, and the capacity to remain present with painful thoughts and emotions that are essential for grief processing. This mixed-method study assesses the potential of a new tailored online audio nature-meditative intervention to support grief recovery and healing among 70 bereaved cancer caregivers who are in the first year of bereavement. A primary goal is to assess the acceptability and feasibility of both content and delivery methods. Measures for the 6-week intervention include numbers eligible vs. consented; numbers consented vs. completed; number of weeks using the intervention, and self-report feasibility/acceptability. Potential effect size estimate data for future larger scale work will be collected at three time points: baseline (Time 1; study week 0), at the intervention end (Time 2; week 6), and at follow-up (Time 3; study week 12) on quality of life, bereavement (grief), directed attention, anxiety and depressive symptoms. Additionally, semi-structured interviews with a diverse representative subsample of 15 caregivers will be conducted at the end of the 12-week study to further assess acceptability and feasibility in more depth. Data analyses will include descriptive statistics, linear mixed models, and content analysis. Findings will be used to assess and enhance the intervention prior to wider scale testing with the long-term goal of establishing an evidence-based intervention for supporting bereaved cancer caregivers as they process grief and adapt to their loss.