High-risk non-muscle-invasive bladder cancer (HR-NMIBC) remains prone to recurrence and progression after transurethral resection of bladder tumor. Intravesical bacillus Calmette-Guérin (BCG) has long served as the backbone of treatment for HR-NMIBC. However, no uniform bladder-preserving pathway has been established for patients with BCG-unresponsive disease. Although radical cystectomy (RC) offers the most dependable oncological control, perioperative morbidity, the impact on quality of life, and patient reluctance restrict its routine use in real-world practice. Previous studies revealed that an initial bladder-preserving approach may not compromise survival while avoiding the morbidity of upfront RC. Accordingly, there remains a need for intravesical approaches that provide durable disease control with an acceptable safety profile while preserving the bladder. This review summarizes current advances in intravesical therapy for HR-NMIBC, covering treatment strategies, emerging therapeutics, intravesical delivery technologies, and representative preclinical platforms. We also examine the major barriers that blunt the activity of emerging intravesical treatments, including short intravesical drug dwell time, urinary dilution, the urothelial permeability barrier, tumor heterogeneity, field cancerization, and an immunosuppressive tumor microenvironment. Collectively, these strategies aim to refine treatment for HR-NMIBC and may provide a framework for improving the durability of bladder-preserving therapy.
Objective:To achieve accurate prediction of early recurrence (ER) in locally advanced gastric cancer (LAGC) patients after neoadjuvant therapy (NAT) and surgery, we constructed a deep learning fusion model integrating preoperative computed tomography (CT) imaging and perioperative clinicopathological features. Methods:We retrospectively enrolled 611 LAGC patients who received NAT from four tertiary teaching hospitals, including a training cohort (TC), an internal validation cohort (IVC), and an external validation cohort (EVC). ER was defined as recurrence occurring within 2 years post-surgery. Based on preoperative 2.5D CT images, we constructed a deep learning signature (DLS) using a ResNet50 architecture. In parallel, a clinical signature (CLIS) was developed through logistic regression analyses. To further improve predictive performance, a deep learning fusion signature (DLFS) was constructed by integrating the DLS and CLIS. Model performance was evaluated by discrimination, calibration, and clinical utility. Kaplan-Meier analysis was used to evaluate prognostic differences across risk groups. Bulk and single-cell transcriptomic analyses explored biological features. Results:Compared with the DLS and CLIS, the DLFS demonstrated superior performance in predicting ER, with area under the curve (AUC) values of 0.884 in the TC, 0.828 in the IVC, and 0.748 in the EVC. Calibration curves exhibited good agreement, and decision curve analysis indicated a higher net benefit. Risk stratification based on DLFS showed worse overall survival (OS) in the high-risk group (3-year OS: TC, 35.36% vs. 77.99%; IVC, 22.90% vs. 73.88%; EVC, 36.01% vs. 63.35%; all P<0.001). Moreover, proliferation-related pathways were enriched in the high-DLFS subgroup, accompanied by an increased proportion of malignant epithelial cells in single-cell analysis. Conclusions:The DLFS model, integrating preoperative CT imaging and perioperative clinicopathological variables, effectively predicts ER and survival in LAGC patients after NAT. Thus, it can serve as a useful tool to optimize prognostic monitoring.
Neural regulation of gastric cancer (GC) has conventionally been parsed into isolated axes-vagal, sympathetic, or sensory. This compartmentalized view is increasingly untenable. The stomach is embedded in a multilayered neural architecture in which tumor-infiltrating nerves, the enteric nervous system (ENS), and long-range brain-gut pathways operate at distinct yet interconnected spatial scales. These layers do not simply deliver unidirectional "nerve-to-tumor" commands; they dynamically shape epithelial behavior, stromal and immune states, organ physiology, and ultimately treatment responses. Here we propose a three-level framework for neural regulation in GC: local neural remodeling within tumors, the ENS as an intrinsic organ-level network, and central control through brain-gut axes. Within this framework, we emphasize that the same neural pathway can exert opposing effects depending on disease stage, anatomical compartment, neuronal subtype, and the prevailing microenvironmental and systemic context. Critically, neural regulation must be understood as a bidirectional, multi-modal dialogue: nerves modulate every cellular compartment of the tumor microenvironment (TME), while TME components (e.g. cancer cells, immune infiltrates, vascular endothelium, and fibroblasts) reciprocally remodel neural architecture and function. This dialogue is further conditioned by macroenvironmental factors (psychosocial stress, dietary metabolism, and gut microbiota) and local physicochemical cues (mechanical forces, epigenetic states, metabolites, secreted protein signals, and even cell-surface RNAs). Unraveling this complexity will require not only classical neural tracing but also a new generation of technologies-tissue-specific secretomes, in vivo cell-cell interaction labeling, and cell-surface nucleic-acid sequencing at single-cell resolution.
Objective:Breast cancer remains a leading cause of global cancer mortality, characterized by profound heterogeneity. While immune checkpoint blockade (ICB) has transformed oncology, its efficacy in breast cancer is often hindered by "immune-cold" microenvironments and immune exclusion. Programmed cell death (PCD) is a critical regulator of tumor immune microenvironment (TIME). However, its role in the breast cancer immune microenvironment remains poorly understood. Methods:We integrated multi-omics data from six breast cancer cohorts (N=3,764) to develop a programmed cell death learning signature (PCDsig) using over 100 machine learning combinations. The model was benchmarked against 29 published signatures. Single-cell transcriptomic analysis decoded the immune landscape and cellular crosstalk. The role of adaptor-related protein complex 1 subunit sigma 1 (AP1S1) was validated through a clinical cohort, in vitro functional assays, and in vivo syngeneic mouse models. Results:PCDsig significantly stratified patient prognosis across all cohorts, consistently outperforming 29 existing models. High PCDsig scores correlated with immune-excluded phenotypes, reduced CD8+ T cell infiltration, and lower immunophenoscores. Single-cell analysis revealed that high-PCDsig tumors utilize vascular endothelial growth factor A (VEGFA) signaling to foster an immunosuppressive microenvironment. AP1S1 was identified as the core driver of immune exclusion. And our clinical cohort supported the immune exclusion effect of AP1S1. AP1S1 knockdown impaired tumor progression in vitro and fundamentally remodeled the tumor immune ecosystem in vivo. Combining AP1S1 inhibition with anti-programmed cell death ligand 1 (anti-PD-L1) therapy exerted profound synergistic effects, driven by massive infiltration and functional activation of cytotoxic Granzyme B (GZMB)+CD8+ T cells. Conclusions:Our study establishes the PCDsig we developed is a potential prognostic and predictive biomarker for breast cancer. We provide the first evidence of AP1S1 as a core immunomodulatory oncogene that mediates immune exclusion. Targeting AP1S1 represents a highly promising strategy to sensitize cold breast tumors to ICB, offering a new perspective for precision immunotherapy.
Objective:This study aimed to evaluate the long-term effectiveness of a colorectal cancer (CRC) screening strategy combining fecal immunochemical testing (FIT) and questionnaire-based risk assessment (QRA), and to characterize post-polypectomy CRC risk within this screening framework. Methods:A total of 295,706 participants who underwent FIT or QRA were enrolled in a CRC screening program in China. Those with positive results were classified as high risk and referred for colonoscopy. Absolute risk reductions (ARRs) and numbers needed to screen (NNS) were estimated for CRC incidence and mortality. Cox proportional hazards models estimated hazard ratios (HRs) and 95% confidence intervals (95% CIs), compared to low-risk non-attenders. Among participants with polyps, post-polypectomy CRC risk was evaluated by comparing index colonoscopy findings with negative findings. Results:During a median follow-up of 11.41 years, 1,267 CRC cases and 387 CRC-specific deaths were identified. Among high-risk individuals, colonoscopy attendance was associated with 10-year ARRs of 0.83% for CRC incidence and 0.31% for CRC mortality, corresponding to NNS values of 121 and 323, respectively. Compared with low-risk colonoscopy non-attenders, high-risk colonoscopy attenders showed no significantly increased CRC incidence over 15 years and had lower CRC-specific mortality at 10 and 15 years, with HRs of 0.59 (95% CI: 0.36-0.97) and 0.57 (95% CI: 0.37-0.88), respectively. In contrast, high-risk colonoscopy non-attenders had markedly increased risks of both CRC incidence (HR=2.79, 95% CI: 2.39-3.25) and mortality (HR=2.82, 95% CI: 2.14-3.71). Individuals with high-risk polyps had a markedly elevated CRC risk as early as 3 years after index colonoscopy (HR=5.75, 95% CI: 1.90-17.36). Conclusions:Within a two-step FIT-QRA screening strategy, colonoscopy attendance among high-risk individuals was associated with long-term benefit and a risk profile closer to that of the low-risk population, whereas individuals with high-risk polyps showed markedly elevated CRC risk from 3 years after polypectomy.
The postoperative management of resectable solid tumors depends on clinicopathological risk stratification. However, these static criteria often fail to identify occult minimal residual disease (MRD), resulting in suboptimal adjuvant treatment characterized by either overtreatment or undertreatment. Circulating tumor DNA (ctDNA)-based MRD detection has emerged as a transformative molecular strategy to bridge this gap, allowing for the identification of residual disease at extremely low molecular abundance. Recent technological advances in ultra-sensitive assays have enabled the clinical translation of ctDNA assessment across diverse solid malignancies. Increasing evidence indicates that postoperative MRD status offers superior prognostic stratification and identifies molecular recurrence significantly earlier than conventional imaging. Furthermore, MRD is increasingly being evaluated as a dynamic tool to tailor adjuvant therapy, facilitating treatment intensification for MRD-positive cohorts and potential de-escalation for MRD-negative populations. This review summarizes the evolution of ctDNA-based MRD detection technologies and the clinical evidence supporting MRD-guided adjuvant strategies. We critically examine the nuances between tumor-informed and tumor-agnostic approaches. Additionally, cancer-specific variations in ctDNA shedding and clinical evidence maturity are highlighted, along with the integration of MRD into prospective interventional trial designs. We argue that MRD is currently best utilized as a biological stratification and trial-enabling tool, rather than a definitive standalone criterion for routine practice. Furthermore, technical, biological, and clinical hurdles hindering widespread implementation, including sensitivity thresholds, background noise, and standardization gaps, are addressed. Results from ongoing interventional trials will be pivotal in defining optimal thresholds and monitoring frameworks, ultimately determining whether MRD-guided precision management improves survival outcomes.
Objective:Age-associated molecular heterogeneity is well described in female breast cancer but remains insufficiently characterized in male breast cancer (MBC). We profiled age-stratified clinical and molecular differences between younger (≤55 years) male breast cancer (YMBC) and older (>55 years) male breast cancer (OMBC). Methods:We retrospectively analyzed 347 patients with MBC diagnosed at Fudan University Shanghai Cancer Center by integrating clinicopathological data, RNA sequencing, and whole-exome sequencing (WES). Survival, differential expression, and mutational signature analyses were performed. Tumor microenvironment features were inferred using xCell and ESTIMATE, and weighted gene co-expression network analysis (WGCNA) was conducted to identify age-associated co-expression modules. Candidate therapeutics were prioritized using the Genomics of Drug Sensitivity in Cancer (GDSC) resource and evaluated using patient-derived organoids (PDOs). Results:Compared with OMBC, YMBC more frequently had human epidermal growth factor receptor 2 (HER2)-positive status (14.91% vs. 4.02%) and triple-negative tumors (4.92% vs. 1.78%), and had worse 5-year recurrence-free survival (hazard ratio=2.19, P=0.018). Transcriptomic analyses indicated enrichment of neural-related programs and reduced immune-related signaling in YMBC, and xCell/ESTIMATE supported lower immune infiltration. Consistently, WGCNA identified age-associated modules linking neural-related programs with reduced immune infiltration. Immunohistochemistry supported increased perineural invasion and lower CD8+ T cell infiltration in YMBC. GDSC-guided prioritization with PDO testing nominated sepantronium bromide (YM155) as a candidate vulnerability in YMBC. WES showed a higher NBPF10 mutation frequency in YMBC (54.5% vs. 14.3%, P<0.05). Conclusions:Integrated multi-omics profiling revealed age-stratified clinical and molecular heterogeneity in MBC. YMBC patients demonstrated inferior recurrence-free survival, neural signaling enrichment, an immune-cold microenvironment, and enriched NBPF10 mutations. These findings support age as a meaningful stratification variable in MBC risk assessment and treatment planning, and highlight the need for caution when considering treatment de-escalation in younger patients, while nominating YM155 as a candidate agent for prospective evaluation.
Medical imaging serves as a critical source of evidence for cancer diagnosis, treatment, and follow-up. However, most existing medical imaging artificial intelligence (AI) systems still operate on predefined tasks and inputs, limiting their ability to address the evolving needs of clinical practice and research. AI agents use foundation models as central engines for reasoning and orchestration. They organize analytical processes according to task objectives, invoke external tools and data resources, and adapt subsequent actions based on intermediate results. They may therefore facilitate a shift in cancer imaging AI from isolated model applications toward continuous workflow support. This review outlines the fundamental principles, system components, and evaluation approaches for AI agents from a cancer imaging perspective and summarizes representative advances in image interpretation, clinical decision support, and research workflows. Current evidence suggests that agents can connect previously fragmented imaging, clinical, and knowledge resources, thereby potentially improving the continuity and traceability of complex tasks. Nevertheless, clinical evidence for AI agents remains limited, as most studies have been conducted in controlled settings and have yet to demonstrate consistent value in real-world clinical workflows. Clinical translation is further constrained by the computational demands and potential error propagation inherent in multistep execution, as well as by the dependence of agent performance on specific tools, data environments, and workflow configurations. Future research should move beyond task-level performance toward system-level validation, with particular emphasis on whether agents can reliably organize information, interpret findings, and collaborate effectively with clinicians. Such evidence will be essential to clarify their practical role in cancer imaging care and research.
Objective:Esophageal cancer has a poor prognosis and limited treatment options. Ferroptosis, an iron-dependent cell death pathway, is a promising therapeutic target; however, its significance in esophageal cancer remains largely unexplored. Here, we investigated the prognostic significance of ferroptosis-related genes in esophageal cancer and identified a key functional regulator that may serve as a therapeutic target. Methods:We analyzed ferroptosis-related gene expression profiles with The Cancer Genome Atlas-Esophageal Carcinoma (TCGA-ESCA) cohort and constructed a prognostic risk model using LASSO Cox regression analysis. Among the genes in this model, GTP cyclohydrolase 1 (GCH1) was selected for functional investigation, based on its established role in antioxidant defense. Subsequently, in vitro experiments were performed to assess the effects of GCH1 knockdown on cell proliferation, migration, clonogenicity, and ferroptosis-related biochemical indicators. The role of GCH1 in antitumor immunity was evaluated through co-culture of esophageal cancer cells with activated T cells, and drug sensitivity was assessed using cytotoxicity assays. Results:A prognostic model consisting of nine ferroptosis-related genes (STC2, TRIB3, HMGB3, CXCL8, GCH1, PARP10, APOE, MTIM, and GPER1) with reliable risk stratification was constructed. The prognostic model could reflect the differences in drug responses and immune cell infiltration. GCH1 knockdown suppressed esophageal cancer cell proliferation, migration, and clonogenicity. Furthermore, GCH1 knockdown increased the intracellular levels of reactive oxygen species, lipid peroxidation, and ferrous iron (Fe2+). Co-culture assays demonstrated that GCH1 knockdown in tumor cells increased the production of granzyme B and interferon-γ by CD8+ T cells. Moreover, GCH1 silencing sensitized esophageal cancer cells to both sorafenib and cisplatin. Conclusions:This study established a ferroptosis-related prognostic model for esophageal cancer and identified GCH1 as a critical regulator that contributes to esophageal cancer progression and drug resistance. These findings suggest that targeting GCH1 may be a promising strategy to improve drug sensitivity and clinical outcomes in esophageal cancer.
Objective:Lymphovascular space invasion(LVSI)is a high-risk factor for lymph node metastasis,relapse,and poor prognosis in patients with endometrioid endometrial carcinoma(EEC).However,the diagnosis of LVSI still relies on traditional pathological methods.Moreover,the high-risk factors and mechanism for LVSI remain unclear.Thus,this study developed an interpretable machine learning(ML)model to accurately predict LVSI status in patients with EEC. Methods:The study collected data from 832 patients with EEC at Peking University People's Hospital.Patients were randomly divided into training(n=582)and internal validation(n=250)cohorts.A prospective external validation cohort included 129 patients with EEC from Nanjing Drum Tower Hospital.Using 21 parameters,6 ML strategies were used to build prediction models.The global and local interpretation of feature significance was performed using the SHapley Additive exPlanations(SHAP)approach.Data from NanoString nCounter evaluation was subjected to pathway enrichment and Spearman correlation analysis to investigate the mechanistic basis of LVSI. Results:Among the six ML models,the XGBoost model had the best performance.The XGBoost model correctly predicted the risk of LVSI in the training set[area under the curve(AUC):0.982,95%confidence interval(95%CI):0.972-0.991],the internal validation set(AUC:0.818,95%CI:0.776-0.860),and the external test set(AUC:0.748,95%CI:0.618-0.879).The calibration curve indicated that the XGBoost model exhibited favorable consistency between the predicted and actual risks.SHAP analysis identified age,carbohydrate antigen 125(CA125),low-density lipoprotein(LDL),and neutrophil as the top four variables contributing to XGBoost model predictions.Analysis of NanoString data indicated that LVSI may be closely associated with the PI3K-Akt signaling pathway. Conclusions:We developed an interpretable ML model for preoperative LVSI risk prediction in patients with EEC.This model may aid clinicians by informing individualized clinical decision-making.
Objective:The heterogeneity of epithelial cells and their interaction with the immune microenvironment play crucial roles in tumor progression,but the underlying mechanisms remain unclear. Methods:We analyzed single-cell transcriptomic data from normal and tumor tissues to characterize epithelial cells and their microenvironment.Key genes were identified and used,via survival analysis and multiple machine learning methods,to construct a prognostic model termed the Epithelial Signature(EpiSig).We further validated,through a series of experiments,the critical immunological roles of the key genes incorporated into the EpiSig model. Results:Tumor tissues showed a marked increase in epithelial cells,a reduction in natural killer(NK)/T cells,and cell co-occurrence patterns distinct from normal tissues.We identified differentially expressed genes in tumor epithelial cells and integrated multiple machine-learning algorithms to construct the EpiSig model.This model effectively stratified patient prognosis,with the high-EpiSig group exhibiting significantly worse survival;receiver operator characteristic curve(ROC)and principal component analysis(PCA)analyses further supported its accuracy and robustness.Immune analyses indicated lower immune cell infiltration,decreased human leukocyte antigen(HLA)expression,and elevated programmed cell death ligand 1/programmed cell death protein 1(PD-L1/PD-1)in the high-EpiSig group,reflecting a more pronounced immunosuppressive microenvironment.The core gene KRT18 correlated strongly with EpiSig scores(R=0.65)and promoted lung adenocarcinoma(LUAD)cell proliferation in functional assays.Further,low KRT18 tumors showed higher CD4+/CD8+T cell and CD20+B cell infiltration;in a mouse LLC subcutaneous tumor model,both Krt18 knockdown and PD-1 blockade suppressed tumor growth,with greater efficacy in combination.Mechanistically,KRT18 activated NF-kB/p65 to upregulate PD-L1,promoting immune exclusion and impairing T-cell effector function. Conclusions:This study highlights the close relationship between epithelial cell heterogeneity and immune microenvironment alterations in tumors,and presents the EpiSig as a robust tool for prognostic prediction.KRT18 may serve as a promising therapeutic target in LUAD.
Objective:Gastric cancer(GC)is heterogeneous,and current mismatch repair(MMR)-based classifications incompletely predict response to immune checkpoint inhibitors(ICIs). Methods:RNA sequencing(RNA-seq)and immune infiltration profiles from 189 resected GC were used to derive four refined immune-MMR subtypes(R1-R4)by integrating MMR status,survival,and tumor microenvironment(TME)features.Multi-omics profiling and pathway analysis defined subtype biology.External transcriptomic cohorts and an ICI-treated cohort were classified with Nearest Template Prediction(NTP).Immune response-associated genes were identified from responder vs.non-responder comparisons within the ICI-sensitive subtype and validated by multiplex immunohistochemistry(mIHC). Results:R1 showed the best prognosis and highest immunotherapy response with objective response rate(ORR)54.5%,while R4 had the worst prognosis.R2 represented an immune-unresponsive deficient mismatch repair(dMMR)subset,and R3 captured an immune-active proficient mismatch repair(pMMR)subgroup with moderate therapy sensitivity.Multi-omics integration revealed subtype-specific pathways(e.g.,ECM remodeling in R1,metabolic reprogramming in R2).Reclassification of pMMR tumors based on transcriptional similarity to R1 identified a New R3 subset with enhanced immune features and higher ICI response.Eight immune response-associated genes(e.g.,CXCL10,CXCL11,ELN,GAD1,IL32,MT1E,OR2I1P,SLC3A1)were identified and validated by mIHC for predictive relevance. Conclusions:This immune-based molecular framework refines risk stratification beyond conventional MMR categories,identifies ICI-sensitive subsets among both dMMR and pMMR tumors,and proposes candidate biomarkers for patient selection.
Objective:Although distance from the inferior tumor edge to the anal verge(DTAV)is a key predictor for sphincter-preserving surgery(SPS)in mid-low rectal cancer,its utility is limited in the"decision-gray zone"(DTAV,3-8 cm).Therefore,this study aimed to develop and validate a multiparametric magnetic resonance imaging-based nomogram for individualized preoperative prediction of SPS feasibility. Methods:This dual-center retrospective study included 335 patients with rectal adenocarcinoma(DTAV 3-8 cm).Patients were divided into training(n=263)and external validation(n=72)cohorts,and predictors were identified using multivariate logistic regression analysis.Model discrimination was assessed using area under the receiver operating curve(AUC)and calibration via the Hosmer-Lemeshow test.Subgroup analyses were performed across DTAV strata. Results:Four independent predictors were identified:larger DTAV[odds ratio(OR)=5.00,P<0.001)],larger pubococcygeal overlap distance(PCOD)(OR=1.08,P=0.001),transverse diameter of mesorectal fat(TMS)(OR=1.07,P=0.017),and subcutaneous adipose tissue thickness(SAT)(OR=0.94,P=0.016).The Sphincter Preservation Assessment in Rectal Cancer(SPARC)nomogram achieved an AUC of 0.928[95%confidence interval(95%CI):0.890-0.956]in the training cohort,outperforming DTAV alone(AUC=0.884,P=0.031)and maintaining an AUC of 0.916(95%CI:0.827-0.969)in external validation.Subgroup analysis showed notably improved predictions in the 5-8 cm DTAV subgroup.Decision curve analysis demonstrated a pronounced net clinical benefit across a wide range of threshold probabilities.Interobserver agreement was excellent(intraclass correlation coefficient,0.890-0.997). Conclusions:The SPARC nomogram reliably predicted SPS feasibility by integrating tumor location with pelvic anatomy and fat distribution.This provides valuable and evidence-based preoperative guidance,especially within the DTAV 3-8 cm gray zone.
Lung cancer is the most lethal malignancy worldwide,largely due to its late detection after its progression to advanced stages.Over the last decade,artificial intelligence(AI)applications have shown significant potential in transforming lung cancer diagnostics by improving the speed,accuracy,and personalization of early detection strategies.This review provides a comprehensive overview of current AI application landscape in early lung cancer diagnosis,encompassing medical imaging,histopathology,liquid biopsy,natural language processing of electronic health records,and genomic profiling.We explain how machine learning,deep learning,and transformer-based models are employed in lung cancer diagnosis,and summarize recent cutting-edge advances,including multimodal AI platforms and Food and Drug Administration(FDA)-approved computer-aided diagnosis/detection(CAD)systems.Furthermore,we evaluate the challenges that impede clinical translation,including data heterogeneity,interpretability,and privacy,and present prospective directions such as federated learning and multi-omics integration.Through a comprehensive analysis of the dynamic evolution of AI applications in oncology,we aim to inform researchers,clinicians,and policymakers about its diagnostic potential and translational relevance in clinical practice.
Objective: To identify chromatin regulators (CRs)-based molecular subtypes and risk scores for accurately predicting biochemical recurrence (BCR) after radical prostatectomy (RAP) in prostate cancer (PCa) patients. Methods: Differentially expressed genes (DEGs) between tumor and normal samples from The Cancer Genome Atlas (TCGA) and gene expression omnibus (GEO) databases were intersected with CR-related and prognostic genes. Consensus clustering, risk score analysis, functional analysis, immune microenvironment, m6A, and heterogeneity assessments were performed using R software. In vitro validation used DU145 and C42B PCa cell lines. Topoisomerase II alpha (TOP2A) was knocked down via siRNA. Assays included CCK-8 proliferation, colony formation, transwell migration/invasion, wound healing, and western blotting (WB) for pathway validation. Results: TOP2A and peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PPARGC1A) defined molecular subtypes and a risk score in TCGA, validated in a GEO dataset. Cluster 2 exhibited significantly shorter BCR-free survival vs. cluster 1 in TCGA [hazard ratio (HR): 2.21; 95% confidence interval (95% CI): 1.32-3.73; P=0.003)], GEO (HR: 2.05; 95% CI: 1.05-4.02; P=0.010), and MSKCC2010 (HR: 5.93; 95% CI: 1.96-17.87; P<0.001). Similar survival differences were observed between high-and low-risk groups (defined by the median risk score). Cluster 2 showed greater tumor heterogeneity and higher m6A gene expression. Gene set variation analysis (GSVA) revealed downregulated cell-cycle pathways in cluster 2, alongside suppressed tumor-infiltrating immune cells. TOP2A knockdown significantly impaired PCa cell proliferation, colony formation, migration, and invasion. Mechanistically, it suppressed phosphoinositide 3-kinase (PI3K)/AKT serine/threonine kinase (AKT) pathway activation, reducing phosphorylated PI3K and AKT levels without altering total protein. Conclusions: TOP2A and PPARGC1A effectively stratify PCa subtypes for RAP patients. TOP2A drives malignant progression via the PI3K/AKT pathway.
Objective: Chemotherapy-based regimens remain the standard first-and second-line treatment options for patients with driver gene-negative non-small cell lung cancer (NSCLC). However, in real-world settings, certain patients cannot tolerate chemotherapy or opt to decline it. Immune checkpoint inhibitors (ICIs) constitute the preferred chemotherapy-free alternative. To enhance patient prognosis, this study aimed to examine the efficacy of ICIs combined with anlotinib in real-world scenarios. Methods: This prospective, multicenter, real-world study evaluated the efficacy and safety of ICIs combined with anlotinib in patients with advanced NSCLC. Patients undergoing first-or second-line treatment were enrolled. The primary endpoint was progression-free survival (PFS), while the secondary endpoints included overall survival (OS), objective response rate (ORR), disease control rate (DCR), and safety. Results: In total, 242 patients were enrolled from 28 centers. The median PFS for the entire cohort was 7.8 [95% confidence interval (95% CI), 7.0-9.5] months, OS events occurred in 112 (46.3%) patients, with a current median OS of 17.0 (95% CI, 15.1-19.4) months. The ORR and DCR were 36.0% (95% CI, 30.2%-42.2%) and 97.9% (95% CI, 95.3%-99.1%), respectively. The median PFS was 9.8 (95% CI, 7.4-12.5) months for first-line therapy and 6.9 (95% CI, 6.0-8.3) months for second-line therapy. Treatment-related adverse events (AEs) occurred in 198 (81.8%) patients, with grade 3-4 AEs reported in 22 (9.1%) patients. Conclusions: This multicenter, real-world study demonstrates that the anlotinib-ICI combination regimen exhibits clinically meaningful efficacy and tolerability as a chemotherapy-free alternative for advanced NSCLC, offering viable evidence to guide treatment for patients who are unsuitable for conventional chemotherapy.
Objective: We investigated the clinical value of a novel circulating tumor cell (CTC) detection method-subtraction enrichment combined with immunostaining and fluorescence in situ hybridization (SEiFISH)-in ovarian cancer (OC). This study evaluated the diagnostic and prognostic significance of chromosome 8 aneuploidy in CTCs and circulating tumor endothelial cells (CTECs) for preoperative diagnosis, treatment efficacy assessment, and recurrence monitoring. Methods: A total of 331 patients were enrolled, including 56 with newly diagnosed primary OC, 265 with benign ovarian tumors, and 10 with borderline tumors. Peripheral blood CTCs and CTECs were detected using SEiFISH; their quantity and ploidy characteristics were analyzed in relation to clinical indicators. To assess dynamic CTC changes during disease progression and treatment response, 72 patients were followed longitudinally, of whom 19 experienced recurrence. Results: The CTC detection rate in OC patients was 92.9%, with significantly higher counts than that in the benign tumor group (median 5 vs. 2). Receiver operating characteristic analysis demonstrated good diagnostic performance for total CTCs [area under the curve (AUC)=0.699], with triploid CTCs achieving the highest efficacy (AUC=0.792), surpassing carbohydrate antigen 125 (CA125) (AUC=0.702). Postoperative follow-up showed that 70% of patients exhibited concurrent decreases in CTCs and CA125 levels, indicating disease improvement. In 30% of patients, CTC levels did not correlate with changes in CA125 levels. Individual case evidence suggests that CTC alterations may serve as an early indicator of recurrence or metastasis. Among the 19 recurrent cases, 73.7% showed elevated CTCs at recurrence that decreased following treatment. In four patients, CTCs reflected disease progression earlier than CA125, indicating higher sensitivity for recurrence monitoring. Conclusions: CTCs with chromosome 8 aneuploidy demonstrate significant clinical value in the preoperative diagnosis, treatment efficacy evaluation, and recurrence monitoring of OC. Dynamic CTC changes may serve as a more sensitive indicator than CA125 for disease surveillance, supporting the translational potential of CTC-based biomarkers in OC.
