UNASSIGNED: A considerable proportion of hypertensive patients may experience lacunar infarction. Therefore, early identification of the risk for lacunar infarction in hypertensive patients is particularly important. This study aimed to develop and validate a concise nomogram for predicting lacunar infarction in hypertensive patients.
UNASSIGNED: Retrospectively analyzed the clinical data of 314 patients with accurate history of hypertension in the Second Affiliated Hospital of Wannan Medical College from January 2021 to December 2022. All the patients were randomly assigned to the training set (n=220) and the validation set (n=94) with 7:3. The diagnosis of lacunar infarction in patients was confirmed using cranial CT or MRI. The independent risk factors of lacunar infarction were determined by Least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression analysis. The nomogram was built based on the independent risk factors. The nomogram\'s discrimination, calibration, and clinical usefulness were evaluated by receiver operating characteristics (ROC) curve, calibration curve, and decision curve analysis (DCA) analysis, respectively.
UNASSIGNED: The incidence of lacunar infarction was 34.50% and 33.00% in the training and validation sets, respectively. Five independent predictors were made up of the nomogram, including age (OR=1.142, 95% CI: 1.089-1.198, P<0.001), diabetes mellitus (OR=3.058, 95% CI: 1.396-6.697, P=0.005), atrial fibrillation (OR=3.103, 95% CI: 1.328-7.250, P=0.009), duration of hypertension (OR=1.130, 95% CI: 1.045-1.222, P=0.002), and low-density lipoprotein (OR=2.147, 95% CI: 1.250-3.688, P=0.006). The discrimination with area under the curve (AUC) was 0.847 (95% CI: 0.789-0.905) in the training set and was a slight increase to 0.907 (95% CI: 0.838-0.976) in the validation set. The calibration curve showed high coherence between the predicted and actual probability of lacunar infarction. Moreover, the DCA analysis indicated that the nomogram had a higher overall net benefit of the threshold probability range in both two sets.
UNASSIGNED: Age, diabetes mellitus, atrial fibrillation, duration of hypertension, and low-density lipoprotein were significant predictors of lacunar infarction in hypertensive patients. The nomogram based on the clinical data was constructed, which was a useful visualized tool for clinicians to assess the risk of the lacunar infarction in hypertensive patients.

This study aimed to characterize PANoptosis-related genes with immunoregulatory features in osteoarthritis (OA) and investigate their potential diagnostic and therapeutic implications. Gene expression data from OA patients and healthy controls were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis and functional enrichment analysis were conducted to identify PANoptosis-related genes (PRGs) associated with OA pathogenesis. A diagnostic model was developed using LASSO regression, and the diagnostic value of key PRGs was evaluated using Receiver Operating Characteristic Curve (ROC) analysis. The infiltration of immune cells and potential small molecule agents were also examined. A total of 39 differentially expressed PANoptosis-related genes (DE-PRGs) were identified, with functional enrichment analysis revealing their involvement in inflammatory response regulation and immune modulation pathways. Seven key PRGs, including CDKN1A, EZH2, MEG3, NR4A1, PIK3R2, S100A8, and SYVN1, were selected for diagnostic model construction, demonstrating high predictive performance in both training and validation datasets. The correlation between key PRGs and immune cell infiltration was explored. Additionally, molecular docking analysis identified APHA-compound-8 as a potential therapeutic agent targeting key PRGs. This study identified and analyzed PRGs in OA, uncovering their roles in immune regulation. Seven key PRGs were used to construct a diagnostic model with high predictive performance. The identified PRGs\' correlation with immune cell infiltration was elucidated, and APHA-compound-8 was highlighted as a potential therapeutic agent. These findings offer novel diagnostic markers and therapeutic targets for OA, warranting further in vivo validation and exploration of clinical applications.

BACKGROUND: Long noncoding RNAs (lncRNAs) and N6-methyladenosine (m6A) modification of RNA play pivotal roles in tumorigenesis and cancer progression. However, knowledge regarding the expression patterns of m6A-related lncRNAs and their corresponding m6A regulators in prostate cancer (PCa) is limited. This study aimed to delineate the landscape of m6A-related lncRNAs, develop a predictive model, and identify the critical m6A regulators of prognostic lncRNAs in PCa.
METHODS: Clinical and transcriptome data of PCa patients were downloaded from The Cancer Genome Atlas (TCGA) database. Prognostic m6A-related lncRNAs were subsequently identified through Pearson correlation and univariate Cox regression analyses. The prognostic lncRNAs were clustered into two groups by consensus clustering analysis, and a risk signature model was constructed using least absolute shrinkage and selection operator (LASSO) regression analysis of the lncRNAs. This model was evaluated using survival, clinicopathological, and immunological analyses. Furthermore, based on the constructed lncRNA-m6A regulatory network and RT-qPCR results, RBM15 was identified as a critical regulator of m6A-related lncRNAs. The biological roles of RBM15 in PCa were explored through bioinformatics analysis and biological experiments.
RESULTS: Thirty-four prognostic m6A-related lncRNAs were identified and categorized into two clusters with different expression patterns and survival outcomes in PCa patients. Seven m6A lncRNAs (AC105345.1, AL354989.1, AC138028.4, AC022211.1, AC020558.2, AC004076.2, and LINC02666) were selected to construct a risk signature with robust predictive ability for overall survival and were correlated with clinicopathological characteristics and the immune microenvironment of PCa patients. Among them, LINC02666 and AC022211.1 were regulated by RBM15. In addition, RBM15 expression correlated with PCa progression, survival, and the immune response. Patients with elevated RBM15 expression were more susceptible to the drug AMG-232. Moreover, silencing RBM15 decreased the viability of PCa cells and promoted apoptosis.
CONCLUSIONS: RBM15 is involved in the regulation of prognostic lncRNAs in the risk signature and has a robust predictive ability for PCa, making it a promising biomarker in PCa.

BACKGROUND: PDAC, also known as pancreatic ductal adenocarcinoma, is often diagnosed at a late stage due to nonspecific symptoms and a distinct lack of reliable biomarkers for timely diagnosis. Ferroptosis, a novel non-apoptotic cell death mode discovered in recent years, is strongly linked to the progression of PDAC and the evasion of the immune system. The objective of this study is to discover a novel ceRNA biomarker associated with ferroptosis and investigate its possible molecular mechanisms and therapeutic potential in PDAC.
METHODS: Based on the FerrDb and TCGA databases, the R survival package was used to screen for ferroptosis-related mRNAs associated with PDAC prognosis. The ferroptosis-related ceRNA network was identified by miRTarBase, miRNet, and starBase and visualized using Cytoscape. The LASSO regression analysis was used to build a risk model associated with ceRNA. Additionally, we investigated the correlation between the ceRNA axis and the infiltration of immune cells in PDAC by employing the ssGSEA algorithm. Spearman correlation analysis was used to investigate the association between the ceRNA network and the expression levels of immune checkpoint genes in PDAC. The prediction of potential medications for PAAD patients with high risk scores was conducted using the R package oncoPredict and the Genomics of Drug Sensitivity in Cancer (GDSC) repository. Expression levels of LINC02535 in clinical specimens and PDAC cell lines were determined using qRT-PCR. CCK-8, colony formation, EdU, wound healing, and transwell assays were performed to assess the impact of reducing LINC02535 on the growth, migration, and invasion of PDAC cell lines BxPC3 and PANC1.
RESULTS: We first discovered a new LINC02535/miR-30c-5p/EIF2S1 axis associated with ferroptosis and created a prognostic nomogram for predicting overall survival. Meanwhile, the risk scores of the LINC02535/miR-30c-5p/EIF2S1 axis associated with ferroptosis were linked to immune subtypes in PDAC. The high immune infiltration subtype exhibited elevated ceRNA risk scores and EIF2S1 expression. The correlation analysis revealed a positive correlation between ceRNA risk scores and four immune cells, namely Activated CD4 T cell, Memory B cell, Neutrophil, and Type 2 T helper cell, as well as four immune checkpoint genes, namely CD274, HAVCR2, PDCD1LG2, and TIGIT. The analysis of drug sensitivity indicated that individuals with a high-risk score may exhibit greater sensitivity to inhibitors targeting MEK1/2 compared to those with a low-risk score. In our validation experiments, it was observed that the expression of LINC02535 was increased in both PDAC tissues and cell lines. Additionally, the inhibition of LINC02535 resulted in decreased proliferation, migration, and invasion of PDAC cells. Rescue experiments demonstrated that LINC02535 promoted PDAC cell growth and metastasis by upregulating EIF2S1 expression.
CONCLUSIONS: To summarize, a novel ferroptosis-associated LINC02535/miR-30c-5p/EIF2S1 ceRNA network for PDAC patients was established. The analysis of this network\'s functionality offers potential insights for clinical decision-making and the advancement of precision medicine.

BACKGROUND: Frailty is a multifactorial syndrome; through this study, we aimed to investigate the physiological, psychological, and social factors associated with frailty and frailty worsening in community-dwelling older adults.
METHODS: We conducted a cross-sectional and longitudinal study using data from the \"Community Empowerment and Well-Being and Healthy Long-term Care: Evidence from a Cohort Study (CEC),\" which focuses on community dwellers aged 65 and above in Japan. The sample of the cross-sectional study was drawn from a CEC study conducted in 2014 with a total of 673 participants. After excluding those who were frail during the baseline assessment (2014) and at the 3-year follow-up (2017), the study included 373 participants. Frailty assessment was extracted from the Kihon Checklist, while social relationships were assessed using the Social Interaction Index (ISI). Variable selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression and their predictive abilities were tested. Factors associated with frailty status and worsening were identified through the Maximum-min Hillclimb algorithm applied to Bayesian networks (BNs).
RESULTS: At baseline, 14.1% (95 out of 673) participants were frail, and 24.1% (90 out of 373) participants experienced frailty worsening at the 3-years follow up. LASSO regression identified key variables for frailty. For frailty identification (cross-sectional), the LASSO model\'s AUC was 0.943 (95%CI 0.913-0.974), indicating good discrimination, with Hosmer-Lemeshow (H-L) test p = 0.395. For frailty worsening (longitudinal), the LASSO model\'s AUC was 0.722 (95%CI 0.656-0.788), indicating moderate discrimination, with H-L test p = 0.26. The BNs found that age, multimorbidity, function status, and social relationships were parent nodes directly related to frailty. It revealed an 85% probability of frailty in individuals aged 75 or older with physical dysfunction, polypharmacy, and low ISI scores; however, if their social relationships and polypharmacy status improve, the probability reduces to 50.0%. In the longitudinal-level frailty worsening model, a 75% probability of frailty worsening in individuals aged 75 or older with declined physical function and ISI scores was noted; however, if physical function and ISI improve, the probability decreases to 25.0%.
CONCLUSIONS: Frailty and its progression are prevalent among community-dwelling older adults and are influenced by various factors, including age, physical function, and social relationships. BNs facilitate the identification of interrelationships among these variables, quantify the influence of key factors. However, further research is required to validate the proposed model.

BACKGROUND: The presence of frailty decreases the overall survival of cancer patients. An accurate and operational diagnostic method is needed to help clinicians choose the most appropriate treatment to improve patient outcomes.
METHODS: Data were collected from 10 649 cancer patients who were prospectively enrolled in the Investigation on Nutritional Status and its Clinical Outcomes of Common Cancers (INSCOC) project in China from July 2013 to August 2022. The training cohort and validation cohort were randomly divided at a ratio of 7:3. The multivariable logistic regression analysis, multivariate Cox regression analyses, and the least absolute shrinkage and selection operator (LASSO) method were used to develop the nomogram. The concordance index and calibration curve were used to assess the diagnostic utility of the nomogram model.
RESULTS: The 10 risk factors associated with frailty in cancer patients were age, AJCC stage, liver cancer, hemoglobin, radiotherapy, surgery, hand grip strength (HGS), calf circumference (CC), PG-SGA score and QOL from the QLQ-C30. The diagnostic nomogram model achieved a good C index of 0.847 (95% CI, 0.832-0.862, P < 0.001) in the training cohort and 0.853 (95% CI, 0.83-0.876, P < 0.001) in the validation cohort. The prediction nomogram showed 1-, 3-, and 5-year mortality C indices in the training cohort of 0.708 (95% CI, 0.686-0.731), 0.655 (95% CI, 0.627-0.683), and 0.623 (95% CI, 0.568-0.678). The 1-, 3-, and 5-year C indices in the validation cohort were similarly 0.743 (95% CI, 0.711-0.777), 0.680 (95% CI, 0.639-0.722), and 0.629 (95% CI, 0.558-0.700). In addition, the calibration curves and decision curve analysis (DCA) were well-fitted for both the diagnostic model and prediction model.
CONCLUSIONS: The nomogram model provides an accurate method to diagnose frailty in cancer patients. Using this model could lead to the selection of more appropriate therapy and a better prognosis for cancer patients.

BACKGROUND: Breast cancer (BC) is a heterogeneous disease, with the ductal subtype exhibiting significant cellular diversity that influences prognosis and response to treatment. Single-cell RNA sequencing data from the GEO database were utilized in this study to investigate the underlying mechanisms of cellular heterogeneity and to identify potential prognostic markers and therapeutic targets.
METHODS: Bioinformatics analysis was conducted using R packages to analyze the single-cell sequencing data. The presence of highly variable genes and differences in malignant potency within the same BC samples were examined. Differential gene expression and biological function between Type 1 and Type 2 ductal epithelial cells were identified. Lasso regression and Cox proportional hazards regression analyses were employed to identify genes associated with patient prognosis. Experimental validation was performed in vitro and in vivo to confirm the functional relevance of the identified genes.
RESULTS: The analysis revealed notable heterogeneity among BC cells, with the presence of highly variable genes and differences in malignant behavior within the same samples. Significant disparities in gene expression and biological function were identified between Type 1 and Type 2 ductal epithelial cells. Through regression analyses, CYP24A1 and TFPI2 were identified as pivotal genes associated with patient prognosis. Kaplan-Meier curves demonstrated their prognostic significance, and experimental validation confirmed their inhibitory effects on malignant behaviors of ductal BC cells.
CONCLUSIONS: This study highlights the cellular heterogeneity in ductal subtype breast cancer and delineates the differential gene expressions and biological functions between Type 1 and Type 2 ductal epithelial cells. The genes CYP24A1 and TFPI2 emerged as promising prognostic markers and therapeutic targets, exhibiting inhibitory effects on BC cell malignancy in vitro and in vivo. These findings offer the potential for improved BC management and the development of targeted treatment strategies.

BACKGROUND: To explore the impact of ARGs on the prognosis of NSCLC, and its correlation with clinicopathological parameters and immune microenvironment. Preliminary research on the biological functions of CEBPA in NSCLC.
METHODS: Using consensus clustering analysis to identify molecular subtypes of ARGs in NSCLC patients; employing LASSO regression and multivariate Cox analysis to select 7 prognostic risk genes and construct a prognostic risk model; validating independent prognostic factors of NSCLC using forest plot analysis; analyzing immune microenvironment correlations using ESTIMATE and ssGSEA; assessing correlations between prognostic risk genes via qPCR and Western blot in NSCLC; measuring mRNA and protein expression levels of knocked down and overexpressed CEBPA in NSCLC using CCK-8 and EdU assays; evaluating the effects of knocked down and overexpressed CEBPA on cell proliferation using Transwell experiments; examining the correlation of CEBPA with T cells and B cells using mIHC analysis.
RESULTS: Consensus clustering analysis identified three molecular subtypes, suggesting significant differential expression of these ARGs in NSCLC prognosis and clinical pathological parameters. There was significant differential expression between the two risk groups in the prognostic risk model, with P < 0.001. The risk score of the prognostic risk model was also P < 0.001. CEBPA exhibited higher mRNA and protein expression levels in NSCLC cell lines. Knockdown of CEBPA significantly reduced mRNA and protein expression levels of CEBPB, YWHAZ, ABL1, and CDK1 in H1650 and A549 cells. siRNA-mediated knockdown of CEBPA markedly inhibited proliferation, migration, and invasion of NSCLC cells, whereas overexpression of CEBPA showed the opposite trend. mIHC results indicated a significant increase in CD3 + CD4+, CD3 + CD8+, and CD20 + cell counts in the high CEBPA expression group.
CONCLUSIONS: The risk score of the prognostic risk model can serve as an independent prognostic factor, guiding the diagnosis and treatment of NSCLC. CEBPA may serve as a potential tumor biomarker and immune target, facilitating further exploration of the biological functions and immunological relevance in NSCLC.

Polycystic ovary syndrome (PCOS) is strongly associated with major depressive disorder (MDD), but the shared pathophysiological mechanisms between them are ambiguous, and the aim of this study was to explore the shared genetic features and associated pathways between these two disorders. MDD-related genes and mitochondrial function genes were downloaded from the GeneCards database. Weighted gene co-expression network analysis of Merge Cohort (GSE80432 and GSE34526) was performed to identify PCOS-related genes. Overlaps between PCOS-related genes, MDD-related genes, and mitochondrial function genes were defined as mitochondrial function-related shared genes. Functional enrichment analysis and protein-protein interaction (PPI) network analysis were performed on the shared genes. Functional genes were then identified using Last Absolute Shrinkage and Selection Operator Regression (LASSO), and a support vector machine (SVM-RFE) was constructed to measure the accuracy of the calculations. Finally, the results were tested using the whole blood datasets GSE54250 (for PCOS) and GSE98793 (for MDD) as external validation sets. A total of 498 PCOS-related genes, 5909 MDD-related genes, and 7232 mitochondrial function genes were acquired, and totally, 40 shared genes were obtained from the overlap of the above three. The shared mitochondrial function genes were enriched for biological processes mainly involving cholesterol biosynthetic process, lipid metabolic process, triglyceride biosynthetic process, response to drug phosphatidic acid biosynthetic process, and endoplasmic reticulum membrane. Based on LASSO regression and SVM-RFE model, NPAS2 and NTS were identified as characteristic genes shared by two disorders. According to two external validation sets for PCOS and MDD, NPAS2 was finally identified as a key shared gene. Our analysis identified a mitochondrial functional gene-NPAS2-as the most critical candidate for linking PCOS and MDD. The present findings may provide new insights into the diagnosis and treatment of PCOS and MDD comorbidities.

BACKGROUND: Despite evidence showing a connection between inflammation and endometrial cancer (EC) risk, the surveys on genetic correlation and cohort studies investigating the impact on long-term outcomes have yet to be refined. We aimed to address the impact of inflammation factors on the pathogenesis, progression and consequences of EC.
METHODS: For the genetic correlation analyses, a two-sample of Mendelian randomization (MR) study was applied to investigate inflammation-related single-nucleotide polymorphisms involved with endometrial cancer from GWAS databases. The observational retrospective study included consecutive patients diagnosed with EC (stage I to IV) with surgeries between January 2010 and October 2020 at the Cancer Hospital of Shantou University Medical College.
RESULTS: The 2-sample MR surveys indicated no causal relationship between inflammatory cytokines and endometrial cancer. 780 cases (median age, 55.0 years ) diagnosed with EC were included in the cohort and followed up for an average of 6.8 years. Increased inflammatory parameters at baseline were associated with a higher FIGO stage and invasive EC risk (odds ratios [OR] 1.01 to 4.20). Multivariate-cox regression suggested that multiple inflammatory indicators were significantly associated with overall survival (OS) and progression-free survival (PFS) (P < 0.05). Nomogram models based on inflammatory risk and clinical factors were developed for OS and PFS with C-index of 0.811 and 0.789, respectively. LASSO regression for the validation supported the predictive efficacy of inflammatory and clinical factors on the long-term outcomes of EC.
CONCLUSIONS: Despite the fact that the genetic surveys did not show a detrimental impact of inflammatory cytokines on the endometrial cancer risk, our cohort study suggested that inflammatory level was associated with the progression and long-term outcomes of EC. This evidence may contribute to new strategies targeted at decreasing inflammation levels during EC therapy.