Cell subpopulations in the blood and joint fluid of patients with gout are poorly understood. Single-cell RNA sequencing and bioinformatic tools were used to identify cell subsets and their gene signatures in blood and synovial fluid (SF) cells, determine their relationships, characterize the diversity, and evaluate interactions among specific cell types. We identified 34 subpopulations (5 types of B cells, 16 types of T and natural killer cells, 9 types of monocytes, and 4 other cell types) in the blood of five healthy subjects and seven patients with acute gouty, and the SF of three patients with acute gout. We found that naïve CD4 T cells and classical monocytes cell populations were enriched in patients with gout, whereas plasmacytoid dendritic cells and intermediate monocytes were more abundant in healthy subjects. SF was enriched in Th1/Th17 cells, effector memory CD8 T cells, mucosal-associated invariant T cells, and macrophages. Subclusters of these cell subpopulations showed different compositions between healthy subjects and those with acute gout, according to blood and SF samples. At the cellular level, the inflammation score of a subpopulation or subcluster was highest in SF, following by the blood of acute gout patients and healthy person, whereas energy score showed the opposite trend. We also detected specific cell–cell interactions for interleukin-1, tumor necrosis factor-α, and transforming growth factor-β1 expression in the cells of patients with acute gout. Our study reveals cellular and molecular insights on inflammatory responses to hyperuricemia or uric crystal and may provide therapeutic guidance to improve treatments for gout.

Long noncoding RNA MIAT promotes non-small cell lung cancer proliferation and metastasis through MMP9 activation Long noncoding RNA MIAT promotes non-small cell lung cancer proliferation and metastasis through MMP9 activation

This study was designed to characterize the microbiomes of the lung tissues of lung cancer patients. RNA-sequencing was performed on lung tumor samples from 49 patients with lung cancer. Metatranscriptomics data were analyzed using SAMSA2 and Kraken2 software. 16S rRNA sequencing was also performed. The heterogeneous cellular landscape and immune repertoires of the lung samples were examined using xCell and TRUST4, respectively. We found that nine bacteria were significantly enriched in the lung tissues of cancer patients, and associated with reduced overall survival (OS). We also found that subjects with mutations in the epidermal growth factor receptor gene were less likely to experience the presence of Pseudomonas. aeruginosa. We found that the presence of CD8+ T-cells, CD4+ naive T-cells, dendritic cells, and CD4+ central memory T cells were associated with a good prognosis, while the presence of pro B-cells was associated with a poor prognosis. Furthermore, high clone numbers were associated with a high ImmuneScore for all immune receptor repertoires. Clone numbers and diversity were significantly higher in unpresented subjects compared to presented subjects. Our results provide insight into the microbiota of human lung cancer, and how its composition is linked to the tumor immune microenvironment, immune receptor repertoires, and OS.

Gender affects cancer susceptibility. Currently, there are only a few studies on Y chromosome-linked long noncoding RNAs (lncRNAs), and the potential association between lncRNAs and cancers in males has not been fully elucidated. Here, we examined the expression of testis-specific transcript Y-linked 15 (TTTY15) in 37 males with non-small cell lung cancer (NSCLC), and performed circular chromosome conformation capture with next-generation sequencing to determine the genomic interaction regions of the TTTY15 gene. Our results showed that the expression levels of TTTY15 were lower in NSCLC tissues. Lower TTTY15 expression levels were associated with Tumor-Node-Metastasis (TNM) stage. A TTTY15 knockdown promoted malignant transformation of NSCLC cells. Based on the bioinformatics analysis of circular chromosome conformation capture data, we found that T-box transcription factor 4 (TBX4) may be a potential target gene of TTTY15. The RNA immunoprecipitation and chromatin immunoprecipitation results showed that TTTY15 may interact with DNA (cytosine-5)-methyltransferase 3A (DNMT3A), and the TTTY15 knockdown increased the binding of DNMT3A to the TBX4 promoter. We concluded that low TTTY15 expression correlates with worse prognosis among patients with NSCLC. TTTY15 promotes TBX4 expression via DNMT3A-mediated regulation. The identification of lncRNAs encoded by male-specific genes may help to identify potential targets for NSCLC therapy.

To identify genetic variants and polygenic risk score (PRS) relating to female gout and asymptomatic hyperuricaemia (AH) in a genome-wide association study (GWAS). Gout, AH and normouricemia controls were included from Taiwan biobank and China Medical University Hospital. All participants were divided into discovery and replication cohorts for GWAS. PRS was estimated according to whether the variant exhibited a protective effect on the phenotypes or not. Each cohort was separated into two groups by the age of 50 years old. A total of 59 472 females were enrolled, and gout and AH occupied 1.60% and 19.59%, respectively. Six variants located in genes SLC2A9, C5orf22, CNTNAP2 and GLRX5 were significantly predictors of female gout in those aged ≥50. For those aged <50 years old, only the variant rs147750368 (SPANXN1) on chromosome X was found. Most variants located in genes SLC2A9, ZNF518B, PKD2 and ABCG2 were found to be significantly related to AH in both age groups. The PRS could explain ∼0.59% to 0.89% of variance of gout in variants with protective effects, which showed 6.2 times of mean PRS in the risk variants, but only 1.2 times in the AH phenotype. Moreover, the PRS also revealed a dose-response trend between AH rates and quartile scores. The variants in gene SLC2A9 are the major genetic factors for females associated with gout in those aged ≥50. PRS can provide a more robust prediction of the gout/AH under a homogeneous selection of variants that show effects on the traits.

Hepatocellular carcinoma (HCC) is one of the most common lethal cancers worldwide and is often related to late diagnosis and poor survival outcome. More evidence is demonstrating that gene-based prognostic models can be used to predict high-risk HCC patients. Therefore, our study aimed to construct a novel prognostic model for predicting the prognosis of HCC patients. We used multivariate Cox regression model with three hybrid penalties approach including least absolute shrinkage and selection operator (Lasso), adaptive lasso and elastic net algorithms for informative prognostic-related genes selection. Then, the best subset regression was used to identify the best prognostic gene signature. The prognostic gene-based risk score was constructed using the Cox coefficient of the prognostic gene signature. The model was evaluated by Kaplan–Meier (KM) and receiver operating characteristic curve (ROC) analyses. A novel four-gene signature associated with prognosis was identified and the risk score was constructed based on the four-gene signature. The risk score efficiently distinguished the patients into a high-risk group with poor prognosis. The time-dependent ROC analysis revealed that the risk model had a good performance with an area under the curve (AUC) of 0.780, 0.732, 0.733 in 1-, 2- and 3-year prognosis prediction in The Cancer Genome Atlas (TCGA) dataset. Moreover, the risk score revealed a high diagnostic performance to classify HCC from normal samples. The prognosis and diagnosis prediction performances of risk scores were verified in external validation datasets. Functional enrichment analysis of the four-gene signature and its co-expressed genes involved in the metabolic and cell cycle pathways was constructed. Overall, we developed a novel-gene-based prognostic model to predict high-risk HCC patients and we hope that our findings can provide promising insight to explore the role of the four-gene signature in HCC patients and aid risk classification.