Value of long non-coding RNA HAS2-AS1 as a diagnostic and prognostic marker of glioma / El valor de ARNncl HAS2-AS1 como marcador diagnóstico y pronóstico del glioma

Background: Glioma presents high incidence and poor prognosis, and therefore more effective treatments are needed. Studies have confirmed that long non-coding RNAs (lncRNAs) basically regulate various human diseases including glioma. It has been theorized that HAS2-AS1 serves as an lncRNA to exert an oncogenic role in varying cancers. This study aimed to assess the value of lncRNA HAS2-AS1 as a diagnostic and prognostic marker for glioma. Methods: The miRNA expression data and clinical data of glioma were downloaded from the TCGA database for differential analysis and survival analysis. In addition, pathological specimens and specimens of adjacent normal tissue from 80 patients with glioma were used to observe the expression of HAS2-AS1. The receiver operating characteristic (ROC) curve was used to analyze the diagnostic ability and prognostic value of HAS2-AS1 in glioma. Meanwhile, a Kaplan–Meier survival curve was plotted to evaluate the survival of glioma patients with different HAS2-AS1 expression levels. Results: HAS2-AS1 was significantly upregulated in glioma tissues compared with normal tissue. The survival curves showed that overexpression of HAS2-AS1 was associated with poor overall survival (OS) and progression-free survival (PFS). Several clinicopathological factors of glioma patients, including tumor size and WHO grade, were significantly correlated with HAS2-AS1 expression in tissues. The ROC curve showed an area under the curve (AUC) value of 0.863, indicating that HAS2-AS1 had good diagnostic value. The ROC curve for the predicted OS showed an AUC of 0.906, while the ROC curve for predicted PFS showed an AUC of 0.88. Both suggested that overexpression of HAS2-AS1 was associated with poor prognosis.Conclusions: Normal tissues could be clearly distinguished from glioma tissues based on HAS2-AS1 expression. Moreover, overexpression of HAS2-AS1 indicated poor prognosis in glioma patients.(AU)

Introducción: Los gliomas presentan una alta incidencia y un mal pronóstico, por lo que es necesario un tratamiento más efectivo. Algunos estudios han confirmado que los ARN no codificantes de cadena larga (ARNncl) regulan diferentes enfermedades, entre las que se incluyen los gliomas. Se ha postulado que HAS2-AS1 actúa como un ARNncl, con un efecto oncogénico en diferentes tipos de cáncer. Este estudio tiene como objetivo analizar el valor del ARNncl HAS2-AS1 como marcador diagnóstico y pronóstico de glioma. Métodos: Descargamos los datos clínicos y de expresión de micro-ARN de la base de datos del Atlas del Genoma del Cáncer (TCGA) para realizar el análisis diferencial y de supervivencia. También analizamos la expresión de HAS2-AS1 en muestras patológicas y muestras de tejido adyacente normal de 80 pacientes con glioma. Para analizar la capacidad diagnóstica y el valor pronóstico de HAS2-AS1 en el glioma, recurrimos a la curva ROC. También utilizamos curvas de Kaplan-Meier para evaluar la supervivencia de los pacientes con glioma con diferentes niveles de expresión de HAS2-AS1. Resultados: La expresión de HAS2-AS1 era significativamente mayor en las muestras patológicas que en el tejido normal. Las curvas de supervivencia demostraron que la sobreexpresión de HAS2-AS1 estaba relacionada con una menor supervivencia general y supervivencia libre de progresión. Algunos factores clínico-patológicos de los pacientes con glioma, como el tamaño del tumor y su grado, según la clasificación de la OMS, mostraron una correlación significativa con la expresión de HAS2-AS1 en los tejidos afectados. La curva ROC mostró un área bajo la curva de 0,863, lo que indica que la expresión de HAS2-AS1 posee un importante valor diagnóstico. El área bajo la curva de la supervivencia general estimada fue de 0,906; para la supervivencia libre de progresión estimada, de 0,88. Ambos valores muestran que la sobreexpresión de HAS2-AS1 se asocia con un mal pronóstico...(AU)

Current usage of pembrolizumab in triple negative breast cancer (TNBC).

INTRODUCTION: The use of immune checkpoint inhibitors (ICI) targeting the PD-1/PD-L1 pathway has changed the landscape in the treatment of triple negative breast cancer (TNBC). The ICI pembrolizumab in combination with chemotherapy now forms a standard of care for the treatment of advanced PD-L1 positive TNBC and as part of neoadjuvant therapy for high-risk early-stage disease. Evidence in this space is rapidly advancing. AREAS COVERED: This review aims to highlight the evolving role of immunotherapy in TNBC management and to discuss current challenges. The studies in this review were searched from PubMed and ClinicalTrials.gov. EXPERT OPINION: The KEYNOTE-522 trial demonstrated that the addition of peri-operative pembrolizumab to neoadjuvant chemotherapy improves patient outcomes in early-stage TNBC. However, critical questions remain including how to select which patients truly gain benefit from the addition of pembrolizumab; the optimal duration of therapy, and the optimal adjuvant therapy depending on pathologic response.

Trajectory of cardiac troponin T following moderate-to-severe COVID-19 and the association with cardiac abnormalities.

BACKGROUND: COVID-19 has been associated with cardiac troponin T (cTnT) elevations and changes in cardiac structure and function, but the link between cardiac dysfunction and high-sensitive cardiac troponin T (hs-cTnT) in the acute and convalescent phase is unclear. OBJECTIVE: To assess whether hs-cTnT concentrations are associated with cardiac dysfunction and structural abnormalities after hospitalization for COVID-19, and to evaluate the performance of hs-cTnT to rule out cardiac pathology. METHODS: Patients hospitalized with COVID-19 had hs-cTnT measured during the index hospitalization and after 3-and 12 months, when they also underwent an echocardiographic study. A subset also underwent cardiovascular magnetic resonance imaging (CMR) after 6 months. Cardiac abnormalities were defined as left ventricular hypertrophy or dysfunction, right ventricular dysfunction, or CMR late gadolinium. RESULTS: We included 189 patients with hs-cTnT concentrations measured during hospitalization for COVID-19, and after 3-and 12 months: Geometric mean (95%CI) 13 (11-15) ng/L, 7 (6-8) ng/L and 7 (6-8) ng/L, respectively. Cardiac abnormalities after 3 months were present in 45 (30%) and 3 (8%) of patients with hs-cTnT ≥ and < 5 ng/L at 3 months, respectively (negative predictive value 92.3% [95%CI 88.5-96.1%]). The performance was similar in patients with and without dyspnea. Hs-cTnT decreased from hospitalization to 3 months (more pronounced in intensive care unit-treated patients) and remained unchanged from 3 to 12 months, regardless of the presence of cardiac abnormalities. CONCLUSION: Higher hs-cTnT concentrations in the convalescent phase of COVID-19 are associated with the presence of cardiac pathology and low concentrations (< 5 ng/L) may support in ruling out cardiac pathology following the infection.

The identification of novel schizophrenia-related metabolites using untargeted lipidomics.

Human lipidome still remains largely unexplored among Chinese schizophrenia patients. We aimed to identify novel lipid molecules associated with schizophrenia and cognition among schizophrenia patients. The current study included 96 male schizophrenia patients and 96 gender-matched healthy controls. Untargeted lipidomics profiling was conducted among all participants. Logistic regression models were used to assess metabolite associations with schizophrenia. We further assessed the incremental predictive value of identified metabolites beyond conventional risk factors on schizophrenia status. In addition, identified metabolites were tested for association with cognitive function among schizophrenia patients using linear regression models. A total of 34 metabolites were associated with schizophrenia. Addition of these identified metabolites to age, body mass index, smoking, and education significantly increased the risk reclassification of schizophrenia. Among the schizophrenia-related metabolites, 10 were further associated with cognition in schizophrenia patients, including four metabolites associated with immediate memory, two metabolites associated with delayed memory, three metabolites associated with visuospatial, four metabolites associated with language, one metabolite associated with attention, and two metabolites associated with the total score. Our findings provide novel insights into the biological mechanisms of schizophrenia, suggesting that lipid metabolites may serve as potential diagnostic or therapeutic targets of schizophrenia.

Value of long non-coding RNA HAS2-AS1 as a diagnostic and prognostic marker of glioma.

BACKGROUND: Glioma presents high incidence and poor prognosis, and therefore more effective treatments are needed. Studies have confirmed that long non-coding RNAs (lncRNAs) basically regulate various human diseases including glioma. It has been theorized that HAS2-AS1 serves as an lncRNA to exert an oncogenic role in varying cancers. This study aimed to assess the value of lncRNA HAS2-AS1 as a diagnostic and prognostic marker for glioma. METHODS: The miRNA expression data and clinical data of glioma were downloaded from the TCGA database for differential analysis and survival analysis. In addition, pathological specimens and specimens of adjacent normal tissue from 80 patients with glioma were used to observe the expression of HAS2-AS1. The receiver operating characteristic (ROC) curve was used to analyze the diagnostic ability and prognostic value of HAS2-AS1 in glioma. Meanwhile, a Kaplan-Meier survival curve was plotted to evaluate the survival of glioma patients with different HAS2-AS1 expression levels. RESULTS: HAS2-AS1 was significantly upregulated in glioma tissues compared with normal tissue. The survival curves showed that overexpression of HAS2-AS1 was associated with poor overall survival (OS) and progression-free survival (PFS). Several clinicopathological factors of glioma patients, including tumor size and WHO grade, were significantly correlated with HAS2-AS1 expression in tissues. The ROC curve showed an area under the curve (AUC) value of 0.863, indicating that HAS2-AS1 had good diagnostic value. The ROC curve for the predicted OS showed an AUC of 0.906, while the ROC curve for predicted PFS showed an AUC of 0.88. Both suggested that overexpression of HAS2-AS1 was associated with poor prognosis. CONCLUSIONS: Normal tissues could be clearly distinguished from glioma tissues based on HAS2-AS1 expression. Moreover, overexpression of HAS2-AS1 indicated poor prognosis in glioma patients. Therefore, HAS2-AS1 could be used as a diagnostic and prognostic marker for glioma.

The Combination of circEPSTI1 and MIF Offers Diagnostic Value for Endometrial Cancer.

Background: Circular RNAs (circRNAs) exhibit unique patterns of expression and high levels of stability in patient plasma samples such that they represent ideal non-invasive biomarkers that can be leveraged to detect a wide array of diseases including endometrial cancer (EC). This study was designed to identify circRNAs with potential diagnostic utility in serum samples from EC patients while also evaluating the utility of macrophage migration inhibitory factor (MIF) as a biomarker when screening for this form of cancer in the clinic. Methods: Levels of circEPSTI1 and MIF were assessed in the plasma of EC patients and healthy subjects (n=186 each) through qPCR and ELISAs. The diagnostic utility of these biomarkers was assessed with receiver operating characteristic curve (ROC) analyses. Results: Relative to healthy subjects, EC patient serum contained significantly elevated circEPSTI1 and MIF. An association was noted between circEPSTI1 expression in stages, histologic grade, and residual tumor. ROC curves confirmed that serum circEPSTI1 levels distinguished between controls and patients with EC with an Area of 0.835 and serum MIF levels distinguished between controls and patients with EC with an Area of 0.6646. When instead diagnosing patients based on the combination of MIF and circEPSTI1, the Area further rose to 0.8604. Conclusion: Assessing the combination of circEPSTI1 and MIF may be a viable approach to reliably diagnosing EC.

White matter biomarker for predicting de novo Parkinson's disease using tract-based spatial statistics: a machine learning-based model.

Background: Parkinson's disease (PD) is an irreversible, chronic degenerative disease of the central nervous system, potentially associated with cerebral white matter (WM) lesions. Investigating the microstructural alterations within the WM in the early stages of PD can help to identify the disease early and enable intervention to reduce the associated serious threats to health. Methods: This study selected 227 cases from the Parkinson's Progression Markers Initiative (PPMI) database, including 152 de novo PD patients and 75 normal controls (NC). Whole-brain voxel analysis of the WM was performed using the tract-based spatial statistics (TBSS) method. The WM regions with statistically significant differences (P<0.05) between the PD and NC groups were identified and used as masks. The mask was applied to each case's fractional anisotropy (FA) image to extract voxel values as feature vectors. Geometric dimensionality reduction was then applied to eliminate redundant values in the feature vectors. Subsequently, the cases were randomly divided into a training group (158 cases, including 103 PD patients and 55 NC) and a test group (69 cases, including 49 PD patients and 20 NC). The least absolute shrinkage and selection operator (LASSO) regression algorithm was employed to extract the minimal set of relevant features, then the random forest (RF) algorithm was utilized for classification using 5-fold cross validation. The resulting model was further integrated with clinical factors to create a comprehensive prediction model. Results: In comparison to the NC group, the FA values in PD patients exhibited a statistically significant decrease (P<0.05), indicating the presence of widespread WM lesions across multiple brain regions. Moreover, the PD prediction model, constructed based on these WM lesion regions, yielded prediction accuracy (ACC) and area under the receiver operating characteristic (ROC) curve (AUC) values of 0.778 and 0.865 in the validation set, and 0.783 and 0.831 in the test set, respectively. Furthermore, the performance of the integrated model showed some improvement, with ACC and AUC values in the test set reaching 0.804 and 0.844, respectively. Conclusions: The quantitative calculation of WM lesion area on FA images using the TBSS method can serve as a neuroimaging biomarker for diagnosing and predicting early PD at the individual level. When integrated with clinical variables, the predictive performance improves.

The prognostic biomarker TPGS2 is correlated with immune infiltrates in pan-cancer: a bioinformatics analysis.

Background: Tubulin polyglutamylase complex subunit 2 (TPGS2) is an element of the neuronal polyglutamylase complex that plays a role in the post-translational addition of glutamate residues to C-terminal tubulin tails. Recent research has shown that TPGS2 is associated with some tumors, but the roles of TPGS2 in tumor immunity remain unclear. Methods: The research data were mainly sourced from The Cancer Genome Atlas. The data were analyzed to identify potential correlations between TPGS2 expression and survival, gene alterations, the tumor mutational burden (TMB), microsatellite instability (MSI), immune infiltration, and various immune-related genes across various cancers. The Wilcoxon rank-sum test was used to identify the significance. A log-rank test and univariate Cox regression analysis were performed to assess the survival state of the patients. Spearman's correlation coefficients were used to show the correlations. Results: TPGS2 exhibited abnormal expression patterns in most types of cancers, and has promising prognostic potential in adrenocortical carcinoma and liver hepatocellular carcinoma. Further, TPGS2 expression was significantly correlated with molecular and immune subtypes. Moreover, the single-cell analyses showed that the expression of TPGS2 was associated with the cell cycle, metastasis, invasion, inflammation, and DNA damage. In addition, the immune cell infiltration analysis and gene-set enrichment analysis demonstrated that a variety of immune cells and immune processes were associated with TPGS2 expression in various cancers. Further, immune regulators, including immunoinhibitors, immunostimulators, the major histocompatibility complex, chemokines, and chemokine receptors, were correlated with TPGS2 expression in different cancer types. Finally, the TMB and MSI, which have been identified as powerful predictors of immunotherapy, were shown to be correlated with the expression of TPGS2 across human cancers. Conclusions: TPGS2 is aberrantly expressed in most cancer tissues and might be associated with immune cell infiltration in the tumor microenvironment. TPGS2 could serve not only as a biomarker for predicting clinical outcomes, but also as a promising biomarker for evaluating and developing new approaches to immunotherapy in many types of cancers, especially colon adenocarcinoma and stomach adenocarcinoma.

Monocytes predict prognosis and successful treatment in older patients with miliary tuberculosis.

Background: The increasing number of patients with miliary tuberculosis (MTB) is a concern in an aging society because of its high mortality rate. Several prognostic biomarkers for MTB have been identified; however, the predictive ability of monocytes as biomarkers remains unknown. This study demonstrates the usefulness of monocytes as prognostic biomarkers for MTB. Materials and methods: We retrospectively compared the clinical findings of 52 patients with MTB hospitalized between April 2013 and October 2021. The predictive ability of biomarkers for 3-month prognosis and their cutoff values were calculated. Survival times and longitudinal changes in monocytes after initiating treatment were compared. Results: A smaller number of monocytes (#M), higher lymphocyte-monocyte ratio (LMR), higher neutrophil-monocyte ratio, and poorer performance status were associated with death within 3 months. #M was an independent prognostic factor. #M and LMR exhibited the highest predictive performance compared to others using receiver operating characteristic curve analysis (area under the curve = 0.86 and 0.85, respectively). Survival time was shorter in patients with #M ≤ 200 cells/µL and LMR > 2.5. Rapidly increasing #M after treatment was related to better prognosis in patients with #M ≤ 200 cells/µL at diagnosis. Conclusions: #M at diagnosis and longitudinal changes in monocytes are related to MTB prognosis.

Comprehensive Analysis Reveals Deoxyribonuclease 1 as a Potential Prognostic and Diagnostic Biomarker in Human Cancers.

BACKGROUND: Deoxyribonuclease 1 (DNASE1) is an important gene associated with several cancers, including liver, bladder, and gastric cancer. It has been linked to autoimmune illnesses, including systemic lupus erythematosus, which may lead to cancer formation. However, the role of DNASE1 in cancer has not been studied. MATERIALS AND METHODS: We performed a pan-cancer analysis using bioinformatics tools, including Tumor Immune Estimation Resource (TIMER), Gene Expression Profiling Interactive Analysis (GEPIA), and University of Alabama at Birmingham Cancer Data Analysis Portal (UALCAN) databases, Kaplan-Meier plotter, and cBioPortal, to investigate the expression of DNASE1 across various cancers as well as its association with immune infiltration and genetic alterations. Public datasets were used to validate DNASE1 expression in kidney renal clear cell carcinoma (KIRC) and kidney papillary renal cell carcinoma (KIRP) samples. RESULTS: DNASE1 was found to be highly expressed in many cancers, such as bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), head and neck squamous cell carcinoma (HNSC), and was lowly expressed in other cancers, including KIRC, KIRP, and thyroid carcinoma (THCA). Additionally, TIMER results showed an association of DNASE1 with immune cell infiltration in KIRC and KIRP. Survival analysis indicated that high DNASE1 expression was associated with poor prognosis in KIRC. We also discovered that altered DNASE1 expression was related to poor prognosis in The Cancer Genome Atlas (TCGA) tumors. CONCLUSION: DNASE1 could potentially be used as a prognostic and diagnostic biomarker for KIRC and as a diagnostic biomarker for KIRP.

Identification of a circulating three-miRNA panel for the diagnosis of primary open angle glaucoma.

PURPOSE: Conventional diagnosis of primary open angle glaucoma (POAG) needs a combination of ophthalmic examinations. An efficient assay is urgently needed for a timely POAG diagnosis. We aim to explore differential expressions of circulating microRNAs (miRNA) and provide novel miRNA biomarkers for POAG diagnosis. METHODS: A total of 180 POAG patients and 210 age-related cataract (ARC) patients were enrolled. We collected aqueous humor (AH) and plasma samples from the recruited patients. The expressions of candidate miRNAs were measured using quantitative real time polymerase chain reaction. The diagnostic ability of candidate miRNAs was analyzed by receiver operating characteristic curve. RESULTS: The expressions of miR-21-5p and miR-29b-3p were downregulated significantly in AH and plasma of POAG and miR-24-3p expression was significantly increased in AH and plasma of POAG, comparing with those of ARC. A three-miRNA panel was constructed by a binary logistic regression. And the panel could differentiate between POAG and ARC with an area under the curve of 0.8867 (sensitivity = 78.0%, specificity = 83.3%) in aqueous humor and 0.7547 (sensitivity = 73.8%, specificity = 81.2%) in plasma. Next, we verified the three-miRNA panel working as a potential diagnostic biomarker stable and reliable. At last, we identified related function and regulation pathways in vitro. CONCLUSIONS: In conclusion, we built and identified a circulating three-miRNA panel as a potential diagnostic biomarker for POAG. It may be developed into an efficient assay and help improve the POAG diagnosis in the future.

Human epididymal protein 4 and its combined detection show good diagnostic value in lung cancer: A retrospective study.

OBJECTIVES: This study aimed to assess the diagnostic value of human epididymal protein 4 (HE4), a potential novel biomarker for lung cancer, and its combined detection with five other conventional biomarkers in lung cancer diagnosis and subtyping. METHODS: In this retrospective study, 115 lung cancer patients, 50 patients with benign pulmonary disease, and 50 healthy controls were included. Serum HE4, progastrin-releasing peptide (ProGRP), squamous cell carcinoma (SCC) antigen, cytokeratin-19 fragment (CYFRA21-1), neuron-specific enolase (NSE), and carcinoembryonic antigen (CEA) were analyzed using the electrochemiluminescence immunoassay and chemiluminescence immunoassay. The receiver operating characteristic curve was performed to analyze the diagnostic efficacy of individual biomarkers in identifying both lung cancer and its histologic subtypes. RESULTS: All six biomarkers showed significantly elevated levels in the lung cancer group compared to both benign pulmonary disease and control groups (P < 0.05). Among the biomarkers evaluated, HE4 exhibited the highest diagnostic performance for lung cancer, lung adenocarcinoma, and lung squamous cell carcinoma with area under the curve (AUC) values of 0.921, 0.891, and 0.937, respectively. ProGRP was the optimal biomarker for small cell lung cancer with an AUC of 0.973. The combination of all six biomarkers yielded the largest AUCs in the diagnosis of lung cancer subtypes (0.937 for lung adenocarcinoma, 0.998 for lung squamous cell carcinoma, and 0.985 for small cell lung cancer). Furthermore, specific combinations, such as HE4 + CEA, HE4 + SCC, and ProGRP + HE4 + NSE, showed strong diagnostic performance in lung cancer. CONCLUSIONS: HE4 and its combined detection held substantial clinical significance in the diagnosis of lung cancer and its histologic subtyping, especially for lung adenocarcinoma and lung squamous cell carcinoma.

Proteomic profiling of tumor microenvironment and prognosis risk prediction in stage I lung adenocarcinoma.

OBJECTIVES: With the increasing popularity of CT screening, more cases of early-stage lung cancer are being diagnosed. However, 24.5% of stage I non-small-cell lung cancer (NSCLC) patients still experience treatment failure post-surgery. Biomarkers to predict lung cancer patients at high risk of recurrence are needed. MATERIALS AND METHODS: We collected protein mass spectrometry data from the Taiwan Lung Cancer Moonshot Project and performed bioinformatics analysis on proteins with differential expressions between tumor and adjacent normal tissues in 74 stage I lung adenocarcinoma (LUAD) cases, aiming to explore the tumor microenvironment related prognostic biomarkers. Findings were further validated in 6 external cohorts. RESULTS: The analysis of differentially expressed proteins revealed that the most enriched categories of diseases and biological functions were cellular movement, immune cell trafficking, and cancer. Utilizing proteomic profiling of the tumor microenvironment, we identified five prognostic biomarkers (ADAM10, MIF, TEK, THBS2, MAOA). We then developed a risk score model, which independently predicted recurrence-free survival and overall survival in stage I LUAD. Patients with high risk scores experienced worse recurrence-free survival (adjusted hazard ratio = 8.28, p < 0.001) and overall survival (adjusted hazard ratio = 6.88, p = 0.013). Findings had been also validated in the external cohorts. CONCLUSION: The risk score model derived from proteomic profiling of tumor microenvironment can be used to predict recurrence risk and prognosis of stage I LUAD.

Extracellular mitochondrial components as new biomarkers for lupus nephritis.

Major reason for mortality among systemic lupus erythematosus patients is renal failure due to the deposition of immune complexes in the glomeruli. Being a chronic disease with multiple relapses and remissions across the lifespan, it's important to know the degree of nephritis for diagnosis as well as the long-term clinical management of the patients. Currently, renal biopsy is being used as the gold standard to diagnose and define the stages of the disease. However, renal biopsy being invasive only provides a localized picture of nephritis, and has the risk of bleeding. Additionally, it is also cost-intensive. Hence, a reliable, non-invasive biomarker is required for lupus nephritis. This study has evaluated extracellular mitochondrial components, including cell-free mitochondria, and cell-free mitochondrial DNA as probable biomarkers of the degree of nephritis. Both showed a significant correlation with proteinuria and protein-creatinine ratio. Our study substantiates their usage as clinical biomarkers of nephritis upon their validation in a larger cohort of lupus nephritis patients and other forms of nephritis. Although the current data suggest using cell-free mitochondria as a biomarker of lupus nephritis is better than the cell-free mitochondrial DNA.

Finding the best subgroup with differential treatment effect with multiple outcomes.

Precision medicine aims to identify specific patient subgroups that may benefit the most from a particular treatment than the whole population. Existing definitions for the best subgroup in subgroup analysis are based on a single outcome and do not consider multiple outcomes; specifically, outcomes of different types. In this article, we introduce a definition for the best subgroup under a multiple-outcome setting with continuous, binary, and censored time-to-event outcomes. Our definition provides a trade-off between the subgroup size and the conditional average treatment effects (CATE) in the subgroup with respect to each of the outcomes while taking the relative contribution of the outcomes into account. We conduct simulations to illustrate the proposed definition. By examining the outcomes of urinary tract infection and renal scarring in the RIVUR clinical trial, we identify a subgroup of children that would benefit the most from long-term antimicrobial prophylaxis.

PIVKA-II combined with alpha-fetoprotein for the diagnostic value of hepatic tumors in children: a multicenter, prospective observational study.

BACKGROUND: To investigate whether protein induced by vitamin K antagonist-II (PIVKA-II) combined with alpha-fetoprotein (AFP) can improve the diagnostic and differential diagnostic accuracy of childhood hepatic tumors. METHODS: A multi-center prospective observational study was performed at nine regional institutions around China. Children with hepatic mass (Group T) were divided into hepatoblastoma group (Group THB) and hemangioendothelioma group (Group THE), children with extrahepatic abdominal mass (Group C). Peripheral blood was collected from each patient prior to surgery or chemotherapy. The area under the curve (AUROC) was used to evaluate the diagnostic efficiency of PIVKA-II and the combined tumor markers with AFP. RESULTS: The mean levels of PIVKA-II and AFP were both significantly higher in Group T than Group C (p = 0.001, p < 0.001), in Group THB than Group THE (p = 0.018, p = 0.013) and in advanced HB than non-advanced HB (p = 0.001, p = 0.021). For the diagnosis of childhood hepatic tumors, AUROC of PIVKA-II (cut-off value 32.6 mAU/mL) and AFP (cut-off value 120 ng/mL) was 0.867 and 0.857. The differential diagnostic value of PIVKA-II and AFP in hepatoblastoma from hemangioendothelioma was further assessed, AUROC of PIVKA-II (cut-off value 47.1mAU/mL) and AFP (cut-off value 560 ng/mL) was 0.876 and 0.743. The combined markers showed higher AUROC (0.891, 0.895 respectively) than PIVKA-II or AFP alone. CONCLUSIONS: The serum level of PIVKA-II was significantly higher in children with hepatic tumors, especially those with malignant tumors. The combination of PIVKA-II with AFP further increased the diagnostic performance. TRIAL REGISTRATION: Clinical Trials, NCT03645655. Registered 20 August 2018, https://www. CLINICALTRIALS: gov/ct2/show/NCT03645655 .

Clinical characteristics and salivary biomarkers of burning mouth syndrome.

OBJECTIVES: To investigate the clinical characteristics and salivary biomarkers in each type of burning mouth syndrome (BMS) patients. MATERIALS AND METHODS: Ninety-eight postmenopausal female patients with BMS were included. Fifty and 21 patients were assigned to the primary and secondary groups, respectively. Twenty-seven patients with both primary and secondary characteristics were assigned to the intermediate group. Comprehensive clinical characteristics and salivary biomarkers were analyzed. RESULTS: Significant differences in age, proportion of hyposalivator patients based on unstimulated whole saliva (UWS), symptom distribution, severties of burning sensation and effect of oral complaints in daily life (Eff-life), and positive symptom distress index (PSDI) were observed among the three groups. The primary group had significant higher UWS flow rate, fewer UWS hyposalivator proportions, and lesser severity of Eff-life than the secondary group. The intermediate group had significantly greater intensities of burning sensation and Eff-life and higher PSDI score than did the primary group. The primary group had significantly higher cortisol and dehydroepiandrosterone (DHEA) levels in stimulated whole saliva than did the secondary group. CONCLUSIONS: This study's findings show that clinical characteristics differentiate each BMS type. Cortisol and DHEA levels are potential salivary biomarkers for discriminating between the primary and secondary types of BMS.

IGSF3 tissue expression in squamous cell carcinoma of the oropharynx: a novel tool for prognosis assessment in HPV-related and HPV-unrelated disease.

Biomarkers are not broadly used in the management of head and neck cancers (HNCs). Biomarkers have been beneficial in the management of other cancers, however, not in HNCs. Therefore, we observed the immunopositivity of a novel biomarker called immunoglobulin superfamily member 3 (IGSF3) in tumor tissues in HPV-related and HPV-unrelated OPSCC. Two patient cohorts (C1 and C2) from separate time periods were available for this study (total N = 282). Both consisted of OPSCC patients treated at the Helsinki University Hospital (HUS, Helsinki, Finland) during 2000-2016. For HPV determination, HPV mRNA in situ hybridization was used. Immunohistochemistry was used to assess IGSF3 immunopositivity in cancer tissues. Overall survival (OS) was used as endpoint in the statistical analysis. In C1, stronger immunopositivity of IGSF3 in tumor-infiltrating lymphocytes (TILs) correlated with favorable OS (p = 0.005). Stronger IGSF3 immunopositivity in tumor cells (TCs) was associated with HPV negativity (p = 0.017). Stronger IGSF3 immunopositivity in TILs correlated with HPV positivity (p < 0.001). Elevated IGSF3 immunopositivity in TILs associates with HPV-related tumors and may signify favorable prognosis. The immunopositivity of IGSF3 differs between HPV-related and HPV-unrelated OPSCC.

Serum metabolomics improves risk stratification for incident heart failure.

AIMS: Prediction and early detection of heart failure (HF) is crucial to mitigate its impact on quality of life, survival, and healthcare expenditure. Here, we explored the predictive value of serum metabolomics (168 metabolites detected by proton nuclear magnetic resonance [1H-NMR] spectroscopy) for incident HF. METHODS AND RESULTS: Leveraging data of 68 311 individuals and >0.8 million person-years of follow-up from the UK Biobank cohort, we (i) fitted per-metabolite Cox proportional hazards models to assess individual metabolite associations, and (ii) trained and validated elastic net models to predict incident HF using the serum metabolome. We benchmarked discriminative performance against a comprehensive, well-validated clinical risk score (Pooled Cohort Equations to Prevent HF [PCP-HF]). During a median follow-up of ≈12.3 years, several metabolites showed independent association with incident HF (90/168 adjusting for age and sex, 48/168 adjusting for PCP-HF). Performance-optimized risk models effectively retained key predictors representing highly correlated clusters (≈80% feature reduction). Adding metabolomics to PCP-HF improved predictive performance (Harrel's C: 0.768 vs. 0.755, ΔC = 0.013, [95% confidence interval [CI] 0.004-0.022], continuous net reclassification improvement [NRI]: 0.287 [95% CI 0.200-0.367], relative integrated discrimination improvement [IDI]: 17.47% [95% CI 9.463-27.825]). Models including age, sex and metabolomics performed almost as well as PCP-HF (Harrel's C: 0.745 vs. 0.755, ΔC = 0.010 [95% CI -0.004 to 0.027], continuous NRI: 0.097 [95% CI -0.025 to 0.217], relative IDI: 13.445% [95% CI -10.608 to 41.454]). Risk and survival stratification was improved by integrating metabolomics. CONCLUSION: Serum metabolomics improves incident HF risk prediction over PCP-HF. Scores based on age, sex and metabolomics exhibit similar predictive power to clinically-based models, potentially offering a cost-effective, standardizable, and scalable single-domain alternative.