Background Exploring the correlation between ovarian cancer and aging has great significance for understanding the pathogenesis of ovarian cancer and formulating targeted therapeutic regimens.Objective This computational study aims to identify and validate key genes in monocyte subtypes related to ovarian cancer and aging, exploring potential causal relationships.Methods We collected single-cell RNA sequencing data (GSE157007, GSE184880), GWAS data (14,049 samples and 40,941 controls from a European population), and eQTL data of ovarian cancer and aging. Using R software packages like Seurat and singleR, we conducted data integration, quality control, cell classification, and differential gene expression analysis to identify intersecting monocyte subtype genes in ovarian cancer and aging. We employed summary data-based Mendelian randomization (SMR) analysis and Heterogeneity in Dependent Instruments (HEIDI) tests to pinpoint causal genes. Further single-cell functional analyses (gene switching, cell communication, metabolic pathway analysis), Bulk RNA sequencing validation, functional enrichment, and protein-protein interaction (PPI) analyses elucidated these genes' biological roles.Results The dataset included 123,280 cells, revealing differential gene expression in classical monocytes (104 genes), intermediate monocytes (43 genes), and myeloid dendritic cells (39 genes). SMR and HEIDI identified causal relationships for 7 genes in classical monocytes, 3 in intermediate monocytes, and 3 in myeloid dendritic cells with ovarian cancer. Bulk RNA seq validation confirmed six monocyte genes as causal in ovarian cancer and aging. TREM1, SERPINB2, and CD44 were upregulated, while DST was downregulated; SLC11A1 and PNRC1 showed contradictory patterns. Interactions with NK and T cells involved LGALS9 - CD44/45 receptors. Riboflavin metabolism was a common enriched pathway.Conclusion This study identified six specific monocyte genes as potential therapeutic targets for ovarian cancer and aging.

RATIONALE AND OBJECTIVES:This retrospective study compared thermal ablation (TA) and surgical resection (SR) in treating hepatocellular carcinoma (HCC) based on multiparametric gadoxetic acid-enhanced MRI features, with an emphasis on the impact of tumor location, size, and liver function reserve. MATERIALS AND METHODS:Patients with early-stage HCC treated with either TA or SR at our hospital from January 2016 to August 2021 were included. Propensity score matching (PSM) with a 1:1 nearest-neighbor algorithm was employed to balance nineteen predefined covariates across treatment groups. Standardized imaging protocols and blinded consensus review were implemented. Survival outcomes, including overall survival (OS) and disease-free survival (DFS), were analyzed using Kaplan-Meier curves and Cox proportional hazards regression. Subgroup analyses stratified by tumor topography, size, and albumin-bilirubin grade were performed. RESULTS:Initially, 92 patients in the TA group and 181 patients in the SR group were included. After PSM, a balanced cohort of 50 patients in each group was achieved. No significant differences in OS (log-rank p = 0.822) or DFS (log-rank p = 0.268) were found between the TA and SR groups. The subgroup analyses after matching demonstrated no statistically significant differences in OS or DFS across the stratified groups (all p > 0.05). Further Cox analysis revealed that elevated alpha-fetoprotein (hazard ratio = 1.88, 95% CI: 1.04-3.39, p = 0.037) was an independent risk factor affecting DFS in the ablation group. CONCLUSION:This study indicates that TA and SR offer similar therapeutic outcomes in early-stage HCC, with comparable survival outcomes for patients.

Endothelial-cell (EC) states may influence the hepatocellular carcinoma (HCC) microenvironment, but EC-derived bulk-tissue scores can also capture tumour purity and cellular admixture. We re-derived and stress-tested a previously proposed vascular immune accessibility score. GSE149614 was analysed with sample-aware doublet detection, ambient-RNA correction, pan-cell-type specificity testing, and patient-level pseudobulk contrasts. A deterministic algorithm selected two 15-gene modules. Independent single-cell validation used GSE151530, with all score genes excluded from unsupervised tumour endothelial-cell (TEC) clustering. Bulk sensitivity analyses used TCGA-LIHC, GSE14520, and ICGC LIRI-JP; TCGA models incorporated ABSOLUTE purity. Spatial analyses used two unique GSE245908 sections. Only 9/25 original immune-adhesive genes and 6/25 barrier genes passed EC-specificity thresholds. GSE151530 included 50,023 HCC cells from 25 patients and 32 specimens, including 2,299 quality-controlled TECs. At the patient level, both modules were EC enriched (median TEC-minus-non-TEC differences 1.438 and 3.222; paired Wilcoxon P = 2.21 × 10^-5 and 1.94 × 10^-5). The modules mapped onto 12 independently derived TEC clusters. The revised score correlated modestly with TCGA ABSOLUTE purity (rho=-0.137; P = 0.010). Clinical Cox models were non-significant in TCGA-LIHC, GSE14520, and ICGC LIRI-JP, and cohort directions were inconsistent. Spatial results replicated in only one of two sections. The revised score was not evaluable in the 24-sample NanoString immunotherapy cohort because only 3/30 genes were present. The reproducible result is an LSEC-like-to-angiogenic EC transcriptomic contrast. The data do not support direct immune-accessibility, directionally consistent prognostic, or immunotherapy-selection claims.

Background Hypertrophic cardiomyopathy (HCM) exhibits complex phenotypic heterogeneity. Left ventricular myocardial strain and atrioventricular coupling hold significant clinical value in cardiovascular diseases; however, the impact of different left ventricular hypertrophy (LVH) distribution patterns on myocardial strain and atrioventricular coupling remains unclear in HCM. Objective To investigate left ventricular strain and left atrioventricular coupling in HCM patients with different LVH distribution patterns and obstruction using cardiac magnetic resonance feature tracking. Methods A total of 199 HCM patients were divided into four LVH patterns (P1–P4): P1 (isolated septal hypertrophy), P2 (septal+other segments hypertrophy, non-apical), P3 (apical+other segments hypertrophy) and P4 (isolated apical hypertrophy). We measured left ventricular global longitudinal strain (LVGLS), global radial strain (LVGRS), global circumferential strain (LVGCS) and left atrioventricular coupling index (LACI). Parameters were compared across groups and between obstructive HCM (HOCM) and non-obstructive HCM (HNCM). Results Strain differed significantly among the four groups (all p<0.05). LVGLS impairment was most severe in P2. P4 showed better LVGRS and LVGCS than the other groups. Absolute LVGRS and LVGCS were higher in HOCM than HNCM (both p<0.05), but LVGLS did not differ. However, after indexing LVGRS to left ventricular maximal wall thickness (LVMWT) (LVGRS/LVMWT), the difference between HOCM and HNCM was no longer significant (p=0.999). HOCM also had higher LACI, left atrial volumes (all p<0.05). On multivariable analysis, P2 was independently associated with LVGLS impairment (β=2.14, adjusted p=0.007). P4 was independently associated with better LVGRS and LVGCS (adjusted p<0.05). Obstruction was independently associated with LVGCS (adjusted p<0.05). LACI showed no independent association with hypertrophy pattern or obstruction (all adjusted p>0.05). Conclusion LVH patterns on cardiac MR effectively differentiate myocardial strain in HCM. P2 identifies a subtype with severe LVGLS impairment, whereas P4 shows preserved LVGRS and LVGCS. Obstruction independently correlates with compensatory enhancement of LVGCS with larger left atrial volumes, whereas the augmentation of LVGRS may be attributable to wall thickness differences. LACI is not independently associated with LVH pattern or obstruction.

This study aims to construct a robust artificial intelligence (AI) model to predict early recurrence of hepatocellular carcinoma (HCC) following surgical resection, leveraging clinical blood biomarkers, pathological parameters, and MRI-derived features. We included 240 hepatectomy patients from two medical centers, collecting clinical blood biomarkers, MRI features, and postoperative pathological data. Feature reduction was conducted using Spearman correlation and the least absolute shrinkage and selection operator (LASSO) regression. Predictive models were constructed using five machine learning algorithms and validated on an external dataset. The models were subsequently compared. The ExtraTrees, XGBoost, and LightGBM models exhibited high predictive performance in the training set, with AUCs of 0.816 (95% CI 0.748-0.884), 0.978 (95% CI 0.958-0.998), and 0.898 (95% CI 0.846-0.950), respectively. In the validation set, their AUC values were 0.759 (95% CI 0.641-0.876), 0.789 (95% CI 0.684-0.894), and 0.760 (95% CI 0.650-0.869). Decision curve analysis indicated favorable net benefits for predicting early recurrence across all three models. Tumor margin and age were identified as significant factors, showing strong associations with early recurrence. This study developed AI model utilizing clinical blood biomarkers, MRI features, and pathological information to predict early recurrence of HCC after surgery. The models demonstrated good predictive performance and showed clinical applicability in predicting early recurrence, potentially assisting clinicians in identifying high-risk patients, guiding individualized surveillance, and optimizing postoperative management. However, inherent biases in this retrospective study necessitate further research for validation and refinement.

This study developed and validated a mitochondrial apoptosis-related pathology transfer learning model (MAR-PTL) for ovarian cancer prognosis by integrating digital pathology features with mitochondrial apoptosis gene expression. We constructed a transfer learning framework combining deep learning features extracted from H&E slides using ResNet50 architecture with transcriptomic data. Patients were categorized into high- and low-risk groups based on model-generated risk scores, and functional enrichment analysis along with single-cell RNA sequencing were performed to elucidate underlying mechanisms. The MAR-PTL model demonstrated superior prognostic performance (C-index = 0.78) compared to conventional methods. Notably, BCL2L2 emerged as the core prognostic gene, showing significant correlations with specific ResNet features, including a negative correlation with ResNet592 and positive correlations with ResNet373, 737, and 938. Mechanistically, high-risk groups exhibited downregulated ribosomal pathways and upregulated immune-inflammatory pathways. Furthermore, single-cell analysis revealed that BCL2L2 + tumor cells displayed distinct metabolic profiles enriched in respirasome assembly pathways and preferentially interacted with fibroblasts and endothelial cells via MDK-NCL and PPIA-BSG ligand-receptor pairs. Collectively, the MAR-PTL model provides a novel approach for prognostication by capturing the interplay between mitochondrial apoptosis and pathological features, identifying BCL2L2 as a key regulator of progression through metabolic reprogramming and tumor-stromal interactions, thereby offering potential therapeutic targets for high-risk patients.

INTRODUCTION:To investigate the causal relationship between 1-palmitoyl-GPG (16:0) and serous ovarian cancer (SOC), and explore the underlying mechanisms. METHOD:Two-sample Mendelian randomization (MR) and mediation effect analyses were employed to determine the causal effects of 1-palmitoyl-GPG (16:0) on serous ovarian cancer (SOC), focusing particularly on naive CD4+ T cell proportions as potential mediators. Single-cell RNA sequencing, immune infiltration analysis, and bulk machine learning algorithms were also integrated to examine the expression and impact of palmitoyl-CoA synthesis genes in CD4+ T cells. Lasso regression was utilized to refine the set of marker genes, and CatBoost machine learning algorithm was applied for predictive modeling. SHAP analysis was performed to interpret the model results. RESULTS:MR and mediation analyses indicated that 1-palmitoyl-GPG (16:0) has a causal effect on SOC, partly mediated by the proportion of naive CD4+ T cells, and partly through direct effects potentially involving metabolic gene expression (e.g., PIGB) in CD4+ T cells. Single-cell and immune infiltration analyses confirmed that key palmitoyl-CoA synthesis genes, including PIGB, were highly expressed in CD4+ T cells and may contribute to SOC both indirectly, by influencing naive CD4+ T cell proportions, and directly through metabolic modulation within CD4+ subsets. The bulk RNA-seq machine learning model showed good predictive performance on an independent validation dataset. SHAP analysis was used to interpret feature contributions, with PIGB having the greatest impact on model predictions. The immune-related genes, including upregulated PIGB, GZMA, PRF1, S100A4, and CCL5, while downregulated AHNAK and LGALS1 (except in fibroblasts). Furthermore, different patterns of gene expression were observed in different CD4+ T cell clusters, which corresponded to various developmental statuses and functional roles. We identified a causal relationship between 1-palmitoyl-GPG (16:0) and SOC, which is mediated by naive CD4+ T cells and key synthesis genes. CONCLUSION AND DISCUSSION:Our findings provide new insights into the metabolic and immunological mechanisms underlying SOC, and highlight potential targets for therapeutic interventions.

Background:Hypertension (HTN) and type 2 diabetes mellitus (T2DM) frequently coexist, synergistically increasing heart failure risk. The specific incremental impairment of T2DM on left atrioventricular mechanics in hypertensive patients remains poorly characterised. This study aimed to assess whether the presence of T2DM is associated with further alterations in cardiac deformation and atrioventricular coupling beyond hypertension alone. Methods:We performed a retrospective analysis including 130 hypertensive patients (74 HTN-only, 56 HTN-T2DM) and 42 age- and sex-matched controls, all undergoing 3.0 T cardiac magnetic resonance. Intergroup comparisons of atrioventricular function and deformation were adjusted for age, sex, BMI, heart rate and SBP using ANCOVA. Multivariable regression was applied to identify independent determinants of left atrioventricular deformation and coupling, and the independent effect of T2DM. Results:Key cardiac parameters demonstrated graded impairment from controls to HTN-only and HTN-T2DM groups (all P < 0.05). This progressive decline was evident in LV systolic function [peak global longitudinal strain: -19.99% (-21.16, -19.13) vs. -17.15% (-19.18, -15.58) vs. -16.03% (-17.86, -13.94)] and LA phasic function [reservoir strain/εs: 48 ± 10% vs. 40 ± 14% vs. 33 ± 15%; conduit strain/εe: 33% (27, 36) vs. 21% (16, 31) vs. 16% (10, 24)]. Consequently, the left atrioventricular coupling index (LACI) was significantly elevated in the HTN-T2DM group [24% (23, 30)] compared to both the HTN-only [22% (18, 28)] and control groups [17% (16, 20)]. Multivariable linear regression analysis indicated that in the overall population, hypertensive patients with and without T2DM independently reduced left atrial εs, εe and left ventricular GLS, and significantly increased LACI; the detrimental effects were more marked in the HTN-T2DM group (all P < 0.05). In the hypertensive subgroup, after adjusting for confounding factors, comorbid T2DM remained an independent risk factor for reduced LA reservoir function (εs: β = -6.09, P = 0.018), impaired LA conduit function (εe: β = -5.58, P = 0.002), and worsened LV systolic function (GLS: β = -1.37, P = 0.010). Conclusion:Hypertensive patients with T2DM demonstrate more significant impairment of myocardial deformation, and worse left atrioventricular uncoupling compared with those with HTN alone, underscoring the need for integrated cardiometabolic management in this high-risk population.

The impact of cryptococcosis on cerebral structure and MRI characteristics in people living with HIV (PWH) who have cryptococcal meningoencephalitis remains underexplored. Existing evidence mainly comes from small-scale studies.This study aimed to clarify the effects of cryptococcal meningoencephalitis on brain structural changes and MRI features in PWH. A total of 190 patients with intracranial cryptococcosis were enrolled and categorized, based on HIV serostatus, into the PWH group (n = 127) and the people linving without HIV(PWOH) group (n = 63). Laboratory parameters, brain volumes, and MRI findings were compared between the two groups. In the two groups, continuous variables were compared using the independent samples t-test, while categorical data were analyzed using the χ² test. The PWH with cryptococcosis group was significantly younger than the PWOH group (51 ± 13 years vs. 60 ± 14 years, P < 0.05). Additionally, this group showed lower peripheral blood leukocyte, platelet, hemoglobin, and lymphocyte counts (all P < 0.05).Cerebrospinal fluid (CSF) analysis in PWH showed lower protein levels (0.97 ± 0.9 vs. 1.31 ± 1.11), decreased cell counts—predominantly polymorphonuclear cells—and increased glucose levels (2.18 ± 1.03 vs. 1.64 ± 1.29) compared with the PWOH. Brain volumetric analysis revealed that the PWH with cryptococcosis group had a reduction in total brain tissue volume (70.1 ± 3.9 vs. 72.0 ± 4.3), a decreased proportion of white matter (33.3 ± 2.9 vs. 35.3 ± 3.3), and an increased proportion of CSF space (29.9 ± 3.9 vs. 28.0 ± 4.3) (all P < 0.05). Additionally, the relative volume proportions of the hippocampus (0.68 ± 0.27 vs. 0.52 ± 0.25) and basal ganglia (1.71 ± 0.41 vs. 1.48 ± 0.34) were significantly larger in the PWH (P < 0.05). On MRI, leptomeningeal enhancement was more frequently observed in the PWOH group, whereas the PWH with cryptococcosis group demonstrated a higher incidence of lacunar infarcts. PWH with intracranial cryptococcosis show cerebral white matter atrophy, accompanied by compensatory expansion of cerebrospinal fluid (CSF) volume and a relative enlargement of deep gray matter nuclei, particularly affecting the hippocampus and basal ganglia. These structural changes are associated with inflammatory alterations in CSF and an elevated risk of vascular complications, such as lacunar infarction.

Despite guideline recommendations for stringent lipid-lowering targets, achievement rates in high cardiovascular risk patients with type 2 diabetes mellitus (T2DM) remain suboptimal, contributing to residual cardiovascular risk. This study aimed to investigate the relationship between achieving low-density lipoprotein cholesterol (LDL-C) target and subclinical cardiac function in patients with T2DM, utilising cardiac magnetic resonance feature tracking (CMR-FT). This study recruited 118 patients with T2DM from January 2019 to December 2024. Differences in left ventricular function characteristics were compared between patients who achieved the LDL-C target and those who did not. Multivariable linear regression and logistic regression analyses were employed to investigate the association between lipid management and subclinical left ventricular dysfunction. The robustness of the analysis was verified through sensitivity analyses, including treating LDL-C as a continuous variable and excluding specific subpopulations. Of the 118 patients included in the study, 46 (38.9

Objective To develop and externally validate a pretreatment multiparametric MRI-based combined imaging model integrating conventional radiomics, habitat radiomics, and 2.5D deep learning features for predicting early complete response (CR) after induction chemoimmunotherapy in locally advanced nasopharyngeal carcinoma (LANPC) Methods This retrospective study included 500 patients with LANPC who received induction chemoimmunotherapy between January 2021 and June 2025. Patients from one center were randomly divided into training and internal validation cohorts, and patients from another center formed an external test cohort. Pretreatment T1-weighted, T2-weighted fat-suppressed, and contrast-enhanced T1-weighted MRI were used to extract conventional radiomics, habitat radiomics, and 2.5D deep learning features. Clinical, conventional radiomics, deep learning, habitat radiomics, and combined imaging models were developed in the training cohort and evaluated using discrimination, calibration, decision curve analysis, and DeLong tests. Results Early CR was achieved in 51 of 195 patients in the training cohort, 25 of 84 in the internal validation cohort, and 59 of 221 in the external test cohort. The combined imaging model achieved the highest AUCs of 0.922, 0.847, and 0.821, respectively. In the external test cohort, the combined model achieved a sensitivity of 0.859 and a negative predictive value of 0.932. SHAP analysis indicated that both 2.5D deep learning and habitat radiomics features contributed prominently to model predictions. Conclusion A pretreatment multiparametric MRI-based combined imaging model showed favorable performance for predicting early CR after induction chemoimmunotherapy in LANPC. This model may provide a noninvasive tool for pretreatment response stratification, although prospective multicenter validation is warranted.

Objective: To investigate the specific alterations in intrinsic functional activity of local brain regions in patients with Long-Standing Chronic Insomnia (LCI), elucidate its neuropathological mechanisms, and provide imaging evidence for precision diagnosis and treatment. Methods: A total of 66 LCI patients and 46 healthy controls were enrolled. Resting-state functional magnetic resonance imaging (rs-fMRI) was employed, combined with multidimensional metrics including amplitude of low-frequency fluctuation (ALFF), percent amplitude of fluctuation (PerAF), regional homogeneity (ReHo), and degree centrality (DC) to systematically assess brain functional differences between groups. Demographic data and clinical scale data including the Pittsburgh Sleep Quality Index (PSQI) were collected. Group comparisons were performed using Mann-Whitney U test and chi-square test, and Spearman rank correlation analysis was conducted for correlation analysis. Multiple comparison correction was performed using Gaussian random field (GRF) theory. Results: Significant differences were observed in age, education, and clinical scale scores including PSQI scores ( p < 0.05), but not in SDS scores ( p > 0.05). Compared to controls, LCI patients showed decreased ALFF and KCC-ReHo in the left inferior parietal lobule, reduced PerAF in the right dorsolateral prefrontal cortex, and increased weighted-DC in the left insula. Conclusion: LCI may involve a core imbalance: hypofunction of the parietal-frontal cognitive control network and enhanced hub property of the insular salience network. This interaction between local and whole-brain dysfunction may represent a core neuropathological feature at a specific stage of LCI, providing a theoretical basis for the development of future therapeutic targets.

Objectives To verify the feasibility and clinical value of synthetic magnetic resonance imaging (SyMRI) for evaluating total maturation score (TMS) and to explore its correlation with gestational age (GA) in newborns. Methods A total of 106 neonates within 28 days of birth were recruited for this study. Each subject underwent MRI examinations, including axial T1- and T2-weighted imaging and SyMRI sequences. TMS was calculated using SyMRI (SyTMS) and conventional T1- and T2-weighted images (cTMS), respectively. The consistency between cTMS and SyTMS was evaluated, along with the parameters of glial cell migration band involution (B score), germinal matrix presence (G score), myelination (M score), and cortical infolding (C score). Analysis of variance was used to compare the mean values of SyTMS in the preterm groups (including preterm cerebral changes group, preterm with normal routine MRI findings group, and preterm cerebral hemorrhage group) and the full-term control group. Subsequently, Pearson correlation analysis was conducted to examine the relationship between SyTMS and GA. Results The correlation coefficients of TMS, M, G, B, and C scores between SyTMS and cTMS were 0.96, 0.87, 0.85, 0.91, and 1.00, respectively ( P < 0.01). Additionally, GA and SyTMS were strongly and positively correlated [r = 0.74; linear regression equation: SyTMS = -5.63 + 0.50 × GA (weeks)]. Conclusions The SyTMS score in the neonatal period is equivalent to the TMS score obtained using conventional MRI. SyTMS scores of premature infants with normal MRI structures and signals remained lower than those of full-term infants during the neonatal period.

Preserved Ratio Impaired Spirometry (PRISm) is considered an early stage of chronic obstructive pulmonary disease (COPD), which may either revert to normal or progress to COPD. Therefore, early identification is crucial for improving patient prognosis. In this study, we developed multiple machine learning (ML) models based on inspiratory and/or expiratory breath-hold chest computed tomography (CT) images to identify PRISm. A total of 270 subjects were prospectively enrolled, and clinical models, radiomics models, and combined clinical-radiomics models were constructed using inspiratory, expiratory, and dual-phase CT images, respectively. The results demonstrated that combined models outperformed clinical models alone across all three phases. Among them, the logistic regression (LR)-based combined models using expiratory or dual-phase CT achieved the best performance, with comparable area under the receiver operating characteristic curve (AUC) values and superior performance to the inspiratory-phase models. Specifically, the AUCs (95% confidence intervals [CI]) of the clinical model in the training, internal, and external validation sets were 0.825 (0.750-0.900), 0.771 (0.639-0.903), and 0.778 (0.653-0.904), respectively. For the expiratory-phase combined model, the AUCs were 0.901 (0.845-0.956), 0.819 (0.680-0.957), and 0.817 (0.695-0.940), while for the dual-phase combined model, they were 0.901 (0.846-0.955), 0.821 (0.684-0.957), and 0.813 (0.694-0.932), indicating that adding inspiratory data did not significantly improve model performance. Based on these findings, we recommend that single-phase expiratory CT scans, combined with clinical features and analyzed using LR models, be prioritized in clinical practice for efficient PRISm identification, providing support for early diagnosis and timely intervention.

Colorectal liver metastases (CRLM) represent a significant clinical challenge, as they are a leading cause of morbidity and mortality in patients with colorectal cancer (CRC). Early detection, accurate diagnosis, and precise treatment planning are crucial for improving patient outcomes. Magnetic resonance imaging (MRI) has emerged as a cornerstone in evaluating CRLM. This article provides a comprehensive review of recent innovations in MRI for CRLM diagnosis and treatment, with a particular focus on precision surgical models. Additionally, the application of artificial intelligence (AI) and radiomics is explored, highlighting their potential in automating lesion detection, evaluating treatment response, and predicting patient survival. The integration of these advanced imaging techniques and AI-based models holds promise for enhancing clinical decision-making, enabling personalized treatment strategies, and improving patient outcomes in CRLM. As these technologies continue to evolve, they could revolutionize the management of CRLM, offering non-invasive, accurate, and cost-effective solutions for early detection, monitoring, and prognosis prediction in CRC patients.

In this study, spatial and single-cell transcriptome techniques were used to investigate the role of beta-galactoside alpha-2,6-sialyltransferase 1 (ST6GAL1) in promoting peritoneal metastasis in ovarian cancer epithelial cells. We collected single-cell transcriptomic (GSE130000) and spatial transcriptomic datasets (GSE211956) from the Gene Expression Omnibus and RNA-sequencing data from The Cancer Genome Atlas. The Robust Cell Type Decomposition (RCTD) approach was implemented to integrate spatial and single-cell transcriptomic data. In addition, pseudo-time trajectory analysis, cell-cell communication networks, transcription factor activity profiling, spatial interaction mapping, and prognostic significance of gene expression were assessed. A significant enrichment of ST6GAL1 was observed in the epithelial cells of ovarian cancer, particularly in peritoneal metastases, which exhibited elevated metabolic activity compared to primary tumors. The levels of ST6GAL1 were significantly high in peritumoral and adjacent non-tumorous tissues, with increased metabolic activity, while the tumor core demonstrated ST6GAL1-negative epithelial cells. Extensive cell-cell communication and transcription factor networks were unraveled, potentially influencing vascular permeability and intracellular signaling. Clinically, high expression of ST6GAL1 in epithelial cells is associated with diminished progression-free survival, indicating its prognostic potential. In conclusion, ST6GAL1 is likely to significantly impact the progression and metastasis of ovarian cancer.

BACKGROUND AND PURPOSE:This study aimed to evaluate the value of intravoxel incoherent motion (IVIM) magnetic resonance (MR) in monitoring radiation-induced thyroid injury and predicting the risk of hypothyroidism (HT) for patients with nasopharyngeal carcinoma (NPC) undergoing intensity-modulated radiotherapy (IMRT). MATERIALS AND METHODS:Eligible patients were enrolled and underwent IVIM MR before, halfway through, and upon completion of IMRT. Thyroid function was assessed periodically before and after treatment. The changes in IVIM parameters (D, D*, and f) throughout IMRT and their association with HT were analyzed. RESULTS:A total of 48 patients were recruited, and 14 (14/48, 29.2 %) developed HT, with a median onset time of 7 months following treatment. 12 of the 45 surviving patients (12/45, 26.7 %) developed HT 1 year after IMRT. The D gradually increased during IMRT (p < 0.001), with the rate of increase in the first half of IMRT significantly greater than that in the second half (9.50 % vs. 0.94 %, p = 0.003). The D* initially increased and subsequently decreased during IMRT (p = 0.036). Female, N3 stage, and ΔDhalf(%) were identified as independent risk factors for HT 1 year after IMRT. A nomogram was developed with favorable performance and accuracy. CONCLUSIONS:A high incidence of HT with a short latency period is still observed in NPC. When formulating IMRT plan for female or N3 stage NPC, efforts should be made to minimize the thyroid exposure. IVIM MR shows promise as a tool for dynamically monitoring radiation-related thyroid injury, with ΔDhalf(%) demonstrating potential for early prediction of HT 1 year after IMRT.

This study aimed to explore the relationship between Ephrin-A3 expression and postoperative overall survival (OS) in hepatocellular carcinoma (HCC) patients and to develop a prognostic nomogram integrating clinical, pathological, MRI features, and Ephrin-A3 expression. We conducted a retrospective analysis of 111 HCC patients, from whom clinical, pathological, and MRI data were collected, and Ephrin-A3 immunohistochemistry was performed. Using an optimal cut-off value of averaged optical density (AOD) derived from Ephrin-A3 staining to predict OS, patients were categorized into high and low expression groups. The cohort was randomly split into training (70%) and validation (30%) sets. Univariate and multivariate Cox regression analyses identified Ephrin-A3 expression, tumor margin, nonrim arterial phase hyperenhancement, and extrahepatic metastasis as independent predictors of OS. A nomogram was subsequently developed to predict postoperative OS. The model demonstrated concordance indexes of 0.76 and 0.72 in the training and validation sets, respectively. The area under the curve (AUC) values for predicting 12-, 36-, and 60-month OS were 0.790, 0.790, and 0.776 in the training set, and 0.970, 0.737, and 0.770 in the validation set. Kaplan–Meier survival analysis confirmed significantly longer OS in the low-risk group in both datasets. Calibration curves indicated strong agreement between predicted and observed survival probabilities, and decision curve analysis (DCA) confirmed the clinical utility of the model. In conclusion, Ephrin-A3 serves as an independent prognostic factor for OS in HCC patients after surgery, and the constructed nomogram exhibits favorable predictive performance for postoperative survival.

PurposeTo explore whether magnetic resonance imaging (MRI) features, when employed with the MRI structural report "DISTANCE," allow the prediction of the overall survival (OS) and progression-free survival (PFS) of patients with T3-stage rectal cancer and to provide information for improved preoperative diagnosis and prognosis evaluation of T3-stage rectal cancer.MethodsThis is a retrospective analysis of 205 cases of T3-stage rectal cancer from January 2014 to January 2021. Univariate, multivariate, and LASSO Cox regression analyses were performed to identify prognostic factors, construct OS and PFS feature nomograms, evaluate the value of MRI features in predicting OS and PFS, and visualize their impact on OS and PFS using Kaplan-Meier survival curves.ResultsThe circumferential resection margin (CRM), obturator lymph node (Obturator N), extramural depth (EMD), maximum short axis of lymph node (maximum short axis of N), and mrEMVI were identified as independent predictors of OS and PFS. The nomogram model predicted the AUCs of OS at 2, 3, and 5 years as 0.806, 0.775, and 0.815, respectively, and those for PFS as 0.695, 0.729, and 0.726 at 2, 3, and 5 years, respectively.ConclusionThe CRM, EMD, Obturator N, maximum short axis of N, and mrEMVI from the MRI structural report "DISTANCE" should be employed for the prognosis prediction of T3-stage rectal cancer.

Background:Immunotherapy combined with targeted therapy is a key approach for patients with unresectable hepatocellular carcinoma (HCC). This study aimed to evaluate the prognostic value of clinical and CT features in Camrelizumab plus Apatinib treatment for these patients. Materials and Methods:A retrospective study was conducted on unresectable HCC patients who received camrelizumab plus Apatinib treatment from June 2019 to August 2021. Clinical and contrast-enhanced CT features were analyzed. Logistic regression identified predictors of objective response, and Cox regression assessed prognostic factors for progression-free survival (PFS) and overall survival (OS). Nomograms were constructed based on independent predictors. Results:Among 109 patients, the median OS was 20 months, median PFS was 9 months, and the ORR was 43.1%. Independent predictors of objective response included AFP ≥ 400 ng/mL (OR = 6.31), NLR ≥ 3.2 (OR = 3.72), tumor numbers ≥ 3 (OR = 3.93), and ΔAER < 15% (OR = 10.99), the AUC for objective response model was 0.874. Independent factors for PFS included AFP ≥ 400 ng/mL (HR = 2.04) and ΔAER < 15% (HR = 2.57), resulting in a model AUC of 0.859. Independent factors for OS were NLR ≥ 3.2 (HR = 2.07), Tumor numbers ≥ 3 (HR = 2.68), and extrahepatic metastasis (HR = 2.32), with an AUC of 0.848 and 0.866 for 1- and 2-year survival, respectively. Conclusion:Clinical and contrast-enhanced show significant prognostic value in patients receiving Camrelizumab plus Apatinib. The developed models may assist in identifying responsive patients and personalizing treatment strategies.