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.
Bacterial outer membrane vesicles (OMVs) are spherical nanostructures that originate from Gram-negative bacteria. They are gaining attention as powerful tools in cancer diagnostics and therapy due to their unique biological properties. These vesicles, which range from 50 to 250 nm in size, carry molecular components from their parent bacteria, allowing them to play important roles in bacterial defense and microbial ecosystems. Their lipid bilayer structure facilitates targeted drug delivery, while their natural immunogenic properties hold promise for cancer immunotherapy by helping overcome immune evasion in the tumor microenvironment. Moreover, OMVs have potential as biomarkers in liquid biopsies, particularly for cancers associated with bacteria, such as gastric and colorectal cancers. Their ability to interact with the intratumoral microbiota further indicates their relevance in tumor pathogenesis. This review aims to provide a comprehensive overview of the fundamental biology of OMVs and their emerging applications in cancer therapy.
Objective: Non-diagnostic thyroid nodules (Bethesda I) account for 5%-20% of all thyroid nodules. Accurate differentiation of benign and malignant nodules can reduce unnecessary surgeries and repeat biopsies. Herein we evaluated the diagnostic efficacy of multigene testing in non-diagnostic thyroid nodules and developed a predictive model integrating molecular and clinical data. Methods: In this prospective cohort study, 1,175 patients with thyroid nodules were evaluated for inclusion, of which 218 patients with Bethesda I nodules met our inclusion criteria. The primary outcome was diagnostic accuracy of molecular testing, and the secondary outcome was the performance of a predictive model integrating molecular and clinical data. Results: Final histopathology identified 165 benign and 53 malignant nodules. Molecular testing detected 10 distinct point mutations and seven gene fusions. Among benign nodules, 147 tested negative and 18 tested positive, whereas 44 malignant nodules tested positive and nine tested negative. In nodules with ultrasound grades 4-5 and fine-needle aspiration cytology (FNAC) results categorized as non-diagnostic, molecular testing achieved sensitivity of 83.00%, specificity of 89.00%, positive predictive value (PPV) of 71.00%, negative predictive value (NPV) of 94.20%, and overall accuracy of 87.60%. The predictive model incorporated 18 clinical and 19 molecular features. Eleven non-zero predictors were selected via least absolute shrinkage and selection operator (LASSO), and the model achieved area under curve (AUC) of 0.95 in the training set and 0.96 in the testing set. Decision curve analysis indicated greater net benefit compared with conventional diagnostic approaches. Conclusions: Molecular testing significantly improved diagnostic accuracy for Bethesda I thyroid nodules. Integrating molecular and clinical data enabled the development of a robust predictive model, facilitating precise, individualized patient management and reducing the need for repeat FNAC and unnecessary surgeries.
With the advancement of surgical techniques and enhanced management of early gastric cancer (EGC), minimally invasive function-preserving surgical approaches have emerged as a common goal for patients and clinicians. Laparoscopic-endoscopic cooperative surgery combined with sentinel lymph node navigation surgery (LECS-SNNS) has drawn increasing interest because of its dual benefits of minimal invasiveness and organ function preservation. However, robust evidence-based support for guiding clinical implementation remains limited. To address this gap, we systematically evaluated available studies on the clinical application of LECS-SNNS in EGC and integrated expert insights to formulate 20 recommendations. These included preoperative assessment, surgical techniques, intraoperative endoscopic procedures, pathological evaluation, postoperative care, and follow-up. This consensus aimed to provide comprehensive guidance for the standardized application of LECS-SNNS, thereby advancing precise, minimally invasive, and function-preserving treatment for EGC.
Objective: This study aimed to evaluate the associations of baseline income, cumulative income exposure, and income volatility with the incidence of pancreatic and biliary tract cancers in a nationwide Korean cohort. Methods: We analyzed 3,361,091 adults aged 30-65 years who underwent the 2012 National Health Insurance Service (NHIS) health screening. Income level was derived from insurance premium data assessed over the five years preceding baseline (2008-2012) and categorized into baseline income quartiles, cumulative exposure to low or high income, and income volatility based on annual percentage changes. Incident pancreatic and biliary tract cancers were identified using diagnostic codes and the copayment reduction registry. Associations were evaluated using Cox proportional hazards models with adjustment for demographic, lifestyle, and clinical covariates, and cumulative incidence was compared using Kaplan-Meier curves. Results: During a median follow-up of 9.6 years, 14,469 pancreatic cancers and 6,647 biliary tract cancers were newly diagnosed. Lower baseline income was associated with a higher risk of pancreatic and biliary tract cancers, whereas sustained high-income exposure was associated with reduced risk. Cumulative low-income exposure showed a positive linear trend with pancreatic cancer incidence. Income volatility was modestly associated with pancreatic cancer and was positively associated with biliary tract cancer in the fully adjusted model. These associations were generally consistent across subgroups, with a stronger inverse association between prolonged high-income exposure and pancreatic cancer among individuals without diabetes. Conclusions: Income level and income stability were significantly associated with the incidence of pancreatic and biliary tract cancers. Lower baseline income was associated with higher risk, whereas sustained high-income exposure was protective. Income volatility was associated with increased cancer risk, particularly for biliary tract cancer. These findings highlight the importance of incorporating income dynamics into cancer prevention strategies and addressing socioeconomic instability among vulnerable populations.
The National Health Commission of the People's Republic of China Guidelines for Diagnosis and Treatment of Colorectal Cancer (2025 edition), based on evidence-based medicine, integrates cutting-edge international advances with Chinese clinical practice, and supplements and completes the previous versions. This version of the guidelines, retains the core diagnostic and treatment framework, highlights new contents such as "Surgical treatment of anal canal cancer" and "New technologies and advances in diagnosis and treatment", and systematically summarizes the core points in the surgical treatment, medical oncology treatment, radiation oncology treatment, imaging, and pathology treatment. It is designed to help clinicians quickly grasp the key points of the guidelines and promote the standardization, precision, and consistence of colorectal cancer diagnosis and treatment.
Objective:Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer. Despite significant advances in immunotherapy, treatment responses vary substantially among individuals. Metabolic reprogramming, as a hallmark of cancer, plays a crucial role in tumor progression and immune evasion. However, the interplay between metabolic features and tumor immune microenvironment in LUAD remains to be systematically elucidated. Methods:We analyzed data from 1,231 LUAD patients across seven global cohorts and developed an integrated Metabolism-Related Signature (iMRS) using machine learning approaches based on 114 metabolic features. The signature's ability to predict immunotherapy response was validated using 9 immunotherapy cohorts (n=712, including LUAD, melanoma, and glioma). An in-house LUAD tissue cohort (n=146) confirmed the prognostic significance of SLC25A1, a key gene within the signature, and its spatial relationship with immune cells. In vivo and in vitro experiments investigated SLC25A1's role in cancer promotion, immune exclusion, and its impact on programmed cell death protein 1 (PD-1) therapy efficacy. Results:iMRS demonstrated superior prognostic performance in LUAD patients, outperforming 129 published LUAD signatures. In immunotherapy cohorts, responders showed significantly lower iMRS scores. High iMRS was associated with reduced immune activity and "cold" tumor characteristics. SLC25A1 (correlation coefficient=0.54, P<0.05), a key gene in the signature, showed the highest expression in CD8 desert phenotype and correlated with poor prognosis. Multiplexed immunofluorescence revealed exclusion patterns between SLC25A1 and immune cells (CD4+ T cells and CD20+ B cells). SLC25A1 knockdown reduced lung metastasis and enhanced anti-PD-1 efficacy by increasing CD8+ T cell abundance and cytotoxicity [increased interferon-γ (IFN-γ)+/GZMB+ CD8+ T cells]. Conclusions:iMRS provides personalized immunotherapy prediction for LUAD patients. SLC25A1, identified as a novel immune-exclusion related oncogene, represents a promising therapeutic target for LUAD treatment.
