BackgroundOsteoarthritis (OA) is one of the most common age-related degenerative diseases. In recent years, some studies have shown that pathological changes in the synovial membrane occur earlier than those in the cartilage in OA. However, the molecular mechanism of synovitis in the pathological process of OA has not been elucidated. This study aimed to identify novel biomarkers associated with OA and to emphasize the role of immune cells in the pathogenesis of OA.MethodsMicroarray datasets were obtained from the Gene Expression Omnibus (GEO) and ArrayExpress databases and were then analyzed using R software. To determine differential immune cell subtype infiltration, the CIBERSORT deconvolution algorithm was used. Quantitative reverse transcription PCR (qRT-PCR) was used to determine the relative expressions of selected genes. Besides, Western blotting was used to assess the protein expression levels in osteoarthritic chondrocytes.ResultsAfter analyzing the database profiles, two potential biomarkers, collagen type 3 alpha 1 chain (COL3A1), and matrix metalloproteinase 9 (MMP9), associated with OA were discovered, which were confirmed by qRT-PCR and Western blotting. Specifically, the results revealed that, as the concentration of IL-1β increased, so did the gene and protein expression levels of COL3A1 and MMP9.ConclusionThe findings provide valuable information and direction for future research into novel targets for OA immunotherapy and diagnosis and aids in the discovery of the underlying biological mechanisms of OA pathogenesis.
Prostate cancer is still a significant global health burden in the coming decade. Novel biomarkers for detection and prognosis are needed to improve the survival of distant and advanced stage prostate cancer patients. The tumor microenvironment is an important driving factor for tumor biological functions. To investigate RNA prognostic biomarkers for prostate cancer in the tumor microenvironment, we obtained relevant data from The Cancer Genome Atlas (TCGA) database. We used the bioinformatics tools Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm and weighted coexpression network analysis (WGCNA) to construct tumor microenvironment stromal-immune score-based competitive endogenous RNA (ceRNA) networks. Then, the Cox regression model was performed to screen RNAs associated with prostate cancer survival. The differentially expressed gene profile in tumor stroma was significantly enriched in microenvironment functions, like immune response, cancer-related pathways, and cell adhesion-related pathways. Based on these differentially expressed genes, we constructed three ceRNA networks with 152 RNAs associated with the prostate cancer tumor microenvironment. Cox regression analysis screened 31 RNAs as the potential prognostic biomarkers for prostate cancer. The most interesting 8 prognostic biomarkers for prostate cancer included lncRNA LINC01082, miRNA hsa-miR-133a-3p, and genes TTLL12, PTGDS, GAS6, CYP27A1, PKP3, and ZG16B. In this systematic study for ceRNA networks in the tumor environment, we screened out potential biomarkers to predict prognosis for prostate cancer. Our findings might apply a valuable tool to improve prostate cancer clinical management and the new target for mechanism study and therapy.
Objectives: This study aimed to explore the clinical value of N-terminal pro-brain natriuretic peptide (NT-proBNP) in predicting moderate-to-severe bronchopulmonary dysplasia (BPD)/death, and to establish an effective clinical predictive nomogram.Methods: We retrospectively analyzed very low birth weight infants (VLBWs) with gestational age ≤ 32 weeks. The NT-proBNP values were determined on the 1st, 3rd, 7th, 14th, 21st, and 28th days after birth. The correlation between NT-proBNP level and moderate-to-severe BPD/death was evaluated. Receiver operating characteristic (ROC) curve analysis was used to evaluate the prediction ability. Then, we used multivariable logistic regression to build the prediction model and nomogram, and calibration of the model was assessed by calibration curve.Results: In total, 556 VLBWs were involved, among whom 229 developed BPD (mild: n = 109; moderate: n = 68; severe: n = 52) and 18 died. The NT-proBNP level in the moderate-to-severe BPD/death group was significantly higher than that in the no-to-mild BPD group from the 3rd to 28th day (P < 0.001). When the natural logarithm of the serum NT-ProBNP level increased by 1 unit at day 7 (±2 days) of life, the risk of moderate and severe BPD/death was the highest (OR = 3.753; 95% CI: 2.984~4.720), and ROC analysis identified an optimal cutoff point of 3360 ng/L (sensitivity: 80.0%; specificity: 86.2%; AUC: 0.861). After adjusting for confounding factors, the level of NT-proBNP at day 7 (±2 days) of life still had important predictive value for the development of moderate-to-severe BPD/death, significantly improving the predictive ability of the model.Conclusion: The level of NT-proBNP at day 7 (±2 days) of life can be used as an early promising biomarker for VLBWs to develop moderate-to-severe BPD/death. We constructed an early predictive nomogram to help clinicians identify high-risk populations.
Background: Purulent meningitis (PM) is an important cause of mortality and morbidity in the newborn population throughout the world. The subtle of specific clinical signs and low success rates of lumbar puncture make diagnosis of PM more difficult in preterm than in older children. The objective of this study was to establish a predict model for preterm PM in hopes of helping clinicians develop new diagnostic and treatment strategies.Methods: Premature infants who were admitted to The First Affiliated Hospital of Zhengzhou University from September 2017 to March 2020 were enrolled in this study. All the patients underwent lumbar puncture. We collected data encompassing maternal diseases and neonatal clinical features. Cerebrospinal fluid (CSF) culture is the gold standard for diagnosing meningitis. The PM was diagnosed according to the diagnostic criteria. All statistical analyses were performed using R 3.63 (https://www.r-project.org/). Logistic regression and least absolute shrinkage and selection operator (LASSO) regression analyses were used to establish a risk prediction model of PM. The Brier score, calibration slope, and concordance (C)-index were used to verify the accuracy of prediction model.Results: A total of 168 preterm infants were enrolled in this study, 80 boys and 88 girls, the gestational age (GA) was 26. 43-36.86 weeks (32.45±2.79 weeks), the birth weight (BW) was 700-3,400 g (1,814.05±568.84 g).There were 77 preterm infants with PM while 91 without. We identified seven variables as independent risk factors for PM in preterm infants by LASSO analysis [the optimal λ was 0.080960, and log(λ) = −2.5138],including procalcitonin (PCT) on the 1st day after birth, prenatal glucocorticoid use, albumin, the 1-minute Apgar score, the use of non-invasive biphasic positive airway pressure, hemoglobin, and sex. These were used to construct a risk prediction nomogram and verified its accuracy. The Brier score was 0.17, the calibration slope was 0.966, and the concordance index was 0.82018.Conclusions: Our prediction model could predict the risk of PM in preterm infants. Using this prediction model, it may be able to provide reference to determine whether lumbar puncture is performed and whether antibiotics are applied as soon as possible.
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