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Osteoarthritis (OA) is a chronic degenerative disease of the bone and joints. Immune-related genes and immune cell infiltration are important in OA development. We analyzed immune-related genes and immune infiltrates to identify OA diagnostic markers. The datasets GSE51588, GSE55235, GSE55457, GSE82107, and GSE114007 were downloaded from the Gene Expression Omnibus database. First, R software was used to identify differentially expressed genes (DEGs) and differentially expressed immune-related genes (DEIRGs), and functional correlation analysis was conducted. Second, CIBERSORT was used to evaluate infiltration of immune cells in OA tissue. Finally, the least absolute shrinkage and selection operator logistic regression algorithm and support vector machine-recurrent feature elimination algorithm were used to screen and verify diagnostic markers of OA. A total of 711 DEGs and 270 DEIRGs were identified in this study. Functional enrichment analysis showed that the DEGs and DEIRGs are closely related to cellular calcium ion homeostasis, ion channel complexes, chemokine signaling pathways, and JAK-STAT signaling pathways. Differential analysis of immune cell infiltration showed that M1 macrophage infiltration was increased but that mast cell and neutrophil infiltration were decreased in OA samples. The machine learning algorithm cross-identified 15 biomarkers (BTC, PSMD8, TLR3, IL7, APOD, CIITA, IFIH1, CDC42, FGF9, TNFAIP3, CX3CR1, ERAP2, SEMA3D, MPO, and plasma cells). According to pass validation, all 15 biomarkers had high diagnostic efficacy (AUC > 0.7), and the diagnostic efficiency was higher when the 15 biomarkers were fitted into one variable (AUC = 0.758). We developed 15 biomarkers for OA diagnosis. The findings provide a new understanding of the molecular mechanism of OA from the perspective of immunology. [ABSTRACT FROM AUTHOR]

Objectives: Since no report on the genetic characteristics of RET fusions in female patients with lung cancer is available, this study revealed the genetic and prognostic characteristics of female patients with lung cancer harboring RET fusion gene for the first time. Materials and Methods: The molecular portfolios of 1,652 patients with lung cancer who underwent targeted next-generation sequencing for screening candidate oncogenic drivers in their histological specimens from January 2016 to December 2018 were investigated in this study. Results: RET fusions were identified in 23 cases, 15 females [2.2% (15/685)] and eight males [0.9% (8/902)]. The most common fusions were KIF5B–RET in females [80% (12/15)] and CCDC6–RET in males [50% (4/8)], along with some rare RET fusions, including SLC39A8–RET, ITIH2–RET, FYCO1–RET and SLC25A36–RET in females, and MIR3924–RET, ZBTB41–RET and ITGA8–RET in males. Interestingly, the highly positive, moderate positive, and negative rates of PD–L1 staining in females were 33.3%, 8.3% and 58.3%, respectively; whereas those in males were 0%, 57.1% and 42.9%. Additionally, the progression-free survival (PFS) of stage IV patients was comparatively shorter in females, shown by the medians of 4.0 months in females and 6.0 months in males (P = 0.029). A 43-year-old female patient with metastatic lung adenocarcinoma, who harbored KIF5B–RET fusion and had highly positive PD–L1 staining, received nivolumab as second-line treatment. A partial response was achieved and remained for more than five months. Conclusion: Unique genetic characteristics and poor prognosis are found in female patients with lung cancer harboring RET fusion gene. Immune checkpoint inhibitors are a potential option for patients with high expression of PD–L1. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is the most common and fatal tumor in the gastrointestinal system. Its incidence and mortality rate have increased in recent years. Hypoxia, a persistent physiological tumor feature, plays a vital role in CRC tumorigenesis, metastasis, and tumor microenvironment (TME). Therefore, we constructed a hypoxia-related gene (HRG) nomogram to predict overall survival (OS) and explored the role of HRGs in the CRC TME. The Cancer Genome Atlas (TCGA) dataset was used as the training set, and two Gene Expression Omnibus datasets (GSE39582 and GSE103479) were used as the testing sets. HRGs were identified using the Gene Set Enrichment Analysis (GSEA) database. An HRG prognostic model was constructed in the training set using the least absolute shrinkage and selection operator regression algorithm and validated in the testing sets. Then, we analyzed tumor-infiltrating cells (TICs) using the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm. Furthermore, single-cell next-generation RNA sequencing (RNA-seq) was used to investigate HRG expression in different TICs in the GSE139555 dataset. Finally, reverse transcription polymerase chain reactions (RT-PCR) were used to validate HRG mRNA expression in ten pairs of CRC normal and cancer tissue samples. A six HRG prognostic signature was constructed, with a superior OS prediction ability in CRC patients (area under the receiver operating characteristic curve (AUC) at one year: 0.693, AUC at three years: 0.712, and AUC at five years: 0.780). GSEA enrichment analysis identified six pathways enriched in the high-risk group. The TIC analysis indicated that the high-risk group had lower T-cell expression and higher neutrophil expression than the low-risk group. Furthermore, immune-related genes had an inseparable relationship with the HRG prognostic signature. Based on single-cell RNA-seq data, we found elevated hexokinase 1 (HK1) and glucose-6-phosphate isomerase (GPI) gene expression in natural killer (NK) and CD8+ T cells. RT-PCR in ten CRC normal-tumor tissue pairs showed that expression of the signature's six HRGs varied differently in cancerous and paracancerous tissues. The constructed HRG signature successfully predicted the OS of whole-stage CRC patients. In addition, we showed that the signature's six HRGs were closely associated with the TME in CRC, where hypoxia inhibits the antitumor function of T cells. [ABSTRACT FROM AUTHOR]

The roles of endogenous nitric oxide (NO) derived from the entire NO synthases (NOSs) system have yet to be fully elucidated. We addressed this issue in mice in which all three NOS isoforms were deleted. Under basal conditions, the triple n/i/eNOSs−/− mice displayed significantly longer mean alveolar linear intercept length, increased alveolar destructive index, reduced lung elastic fiber content, lower lung field computed tomographic value, and greater end-expiratory lung volume as compared with wild-type (WT) mice. None of single NOS−/− or double NOSs−/− genotypes showed such features. These findings were observed in the triple n/i/eNOSs−/− mice as early as 4 weeks after birth. Cyclopaedic and quantitative comparisons of mRNA expression levels between the lungs of WT and triple n/i/eNOSs−/− mice by cap analysis of gene expression (CAGE) revealed that mRNA expression levels of three Wnt ligands and ten Wnt/β-catenin signaling components were significantly reduced in the lungs of triple n/i/eNOSs−/− mice. These results provide the first direct evidence that complete disruption of all three NOS genes results in spontaneous pulmonary emphysema in juvenile mice in vivo possibly through down-regulation of the Wnt/β-catenin signaling pathway, demonstrating a novel preventive role of the endogenous NO/NOS system in the occurrence of pulmonary emphysema. [ABSTRACT FROM AUTHOR]

In order to explore the role of exosomal circRNAs in the occurrence and development of sepsis, we looked for potential diagnostic markers to accurately identify sepsis and to lay a molecular basis for precise treatment. Ultracentrifugation was used to extract exosomes from the serum of patients with sepsis and healthy individuals. Then, changes in circRNA expression in exosomes were studied by circRNA microarray analysis. Gene ontology (GO) analysis and Kyoto City Encyclopaedia of Genes and Genomes (KEGG) pathway analysis were used to annotate the biological functions and pathways of genes, and a circRNA-miRNA-mRNA regulatory network was constructed. In the microarray analysis, 132 circRNAs were significantly differentially expressed, including 80 and 52 that were upregulated and downregulated, respectively. RT-qPCR verified the results of microarray analysis: hsa_circRNA_104484 and hsa_circRNA_104670 were upregulated in sepsis serum exosomes. ROC analysis showed that hsa_circRNA_104484 and hsa_circRNA_104670 in serum exosomes have the potential to be used as diagnostic markers for sepsis. The circRNA-miRNA-mRNA network predicted the potential regulatory pathways of differentially expressed circRNAs. There are differences in the expression of circRNA in serum exosomes between patients with sepsis and healthy individuals, which may be involved in the occurrence and development of the disease. Among them, elevations in hsa_circRNA_104484 and hsa_circRNA_104670 could be used as novel diagnostic biomarkers and molecular therapeutic targets. [ABSTRACT FROM AUTHOR]

Nonlinear complexity is a characteristic of hydrologic processes. Using fewer model parameters is recommended to reduce error. This study investigates, and compares, the ability of gene expression programming (GEP) and artificial neural network (ANN) techniques in modeling ET by using fewer meteorological parameters in the karst area of northwest Guangxi province, China. Over a 5-year period (2008-2012), meteorological data consisting of maximum and minimum air temperature, relative humidity, wind speed, and sunshine duration were collected from four weather stations: BaiSe, DuAn, HeChi, and RongAn. The ET calculated by the FAO-56 PM equation was used as a reference to evaluate results for GEP, ANN, and Hargreaves models. The coefficient of determination ( R ) and the root mean square error (RMSE) were used as statistical indicators. Evaluations revealed that GEP, and ANN, can be used to successfully model ET. In most cases, when using the same input variables, ANN models were superior to GEP. We then established ET equations with fewer parameters under various conditions. GEP can produce simple explicit mathematical formulations which are easier to use than the ANN models. [ABSTRACT FROM AUTHOR]