Results Three distinct m6A modification clusters were identified. The m6A clusters were significantly associated with clinical features, prognosis, and immune cell infiltration. The three clusters were highly consistent with the three tumor immune phenotypes, i.e., immune-excluded, immune-inflamed, and immune-desert. Comprehensive bioinformatics analysis revealed that high m6Ascore was closely associated with tumor progression, poor prognosis, and immunotherapy non-response. m6A regulators were dysregulated in HCC tissues. Hence, they play a role as predictors of poor prognosis. Tissue microarray demonstrated that overexpressed YTHDF1 was associated with low CD3+ and CD8+ T cell infiltration in HCC. Conclusion Our findings demonstrate that m6A modification patterns play a crucial role in the tumor immune microenvironment and the prognosis of HCC. High YTHDF1 expression is closely associated with low CD3+ and CD8+ T cell infiltration in HCC.Tendons connect the muscle abdomen of skeletal muscles to the bone, which transmits the force generated by the muscle abdomen contraction and pulls the bone into motion. Tendon injury is a common clinical condition occurring in certain populations, such as repeated tendon strains in athletes. And it can lead to substantial pain and loss of motor function, in severe cases, significant disability. Tendon healing and regeneration have attracted growing interests. Some treatments including growth factors, stem cell therapies and rehabilitation programs have been tried to improve tendon healing. However, the basic cellular biology and pathology of tendons are still not fully understood, and the management of tendon injury remains a considerable challenge. Regulating gene expression at post-transcriptional level, microRNA (miRNA) has been increasingly recognized as essential regulators in the biological processes of tendon healing and regeneration. A wide range of miRNAs in tendon injury have been shown to play vital roles in maintaining and regulating its physiological function, as well as regulating the tenogenic differentiation potential of stem cells. In this review, we show the summary of the latest information on the role of miRNAs in tendon healing and regeneration, and also discuss potentials for miRNA-directed diagnosis and therapy in tendon injuries and tendinopathy, which may provide new theoretical foundation for tenogenesis and tendon healing.Proper differentiation of odontoblasts is crucial for the development of tooth roots. Previous studies have reported the osteogenic/odontogenic potential of pre-odontoblasts during root odontoblast differentiation. However, the underlying molecular pathway that orchestrates these processes remains largely unclear. In this study, ablation of transforming growth factor-β receptor type 2 (Tgfbr2) in root pre-odontoblasts resulted in abnormal formation of root osteodentin, which was associated with ectopic osteogenic differentiation of root odontoblasts. Disrupting TGF-β signaling caused upregulation of Wnt signaling characterized by increased Wnt6, Wnt10a, Tcf-1, and Axin2 expression. Interestingly, inhibiting Wnt signaling by deleting Wntless (wls) in Osteocalcin (Ocn)-Cre; Tgfbr2 fl/fl ; Wls fl/fl mice or overexpressing the Wnt antagonist Dkk1 in Ocn-Cre; Tgfbr2 fl/fl ; ROSA26 Dkk1 mice decreased ectopic osteogenic differentiation and arrested odontoblast differentiation. Our results suggest that TGF-β signaling acts with Wnt signaling to regulate root odontogenic differentiation.Breast cancer (BRCA) has become the highest incidence of cancer due to its heterogeneity. To predict the prognosis of BRCA patients, sensitive biomarkers deserve intensive investigation. Herein, we explored the role of N 6-methyladenosine-related long non-coding RNAs (m6A-related lncRNAs) as prognostic biomarkers in BRCA patients acquired from The Cancer Genome Atlas (TCGA; n = 1,089) dataset and RNA sequencing (RNA-seq) data (n = 196). Pearson's correlation analysis, and univariate and multivariate Cox regression were performed to select m6A-related lncRNAs associated with prognosis. Twelve lncRNAs were identified to construct an m6A-related lncRNA prognostic signature (m6A-LPS) in TCGA training (n = 545) and validation (n = 544) cohorts. Based on the 12 lncRNAs, risk scores were calculated. Then, patients were classified into low- and high-risk groups according to the median value of risk scores. Distinct immune cell infiltration was observed between the two groups. Patients with low-risk score had higher immune score and upregulated expressions of four immune-oncology targets (CTLA4, PDCD1, CD274, and CD19) than patients with high-risk score. On the contrary, the high-risk group was more correlated with overall gene mutations, Wnt/β-catenin signaling, and JAK-STAT signaling pathways. In addition, the stratification analysis verified the ability of m6A-LPS to predict prognosis. Moreover, a nomogram (based on risk score, age, gender, stage, PAM50, T, M, and N stage) was established to evaluate the overall survival (OS) of BRCA patients. Thus, m6A-LPS could serve as a sensitive biomarker in predicting the prognosis of BRCA patients and could exert positive influence in personalized immunotherapy.Aging is an inevitable time-dependent process associated with a gradual decline in many physiological functions. Importantly, some studies have supported that aging may be involved in the development of lung adenocarcinoma (LUAD). However, no studies have described an aging-related gene (ARG)-based prognosis signature for LUAD. Accordingly, in this study, we analyzed ARG expression data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). After LASSO and Cox regression analyses, a six ARG-based signature (APOC3, EPOR, H2AFX, MXD1, PLCG2, and YWHAZ) was constructed using TCGA dataset that significantly stratified cases into high- and low-risk groups in terms of overall survival (OS). Cox regression analysis indicated that the ARG signature was an independent prognostic factor in LUAD.  https://www.selleckchem.com/products/ku-0060648.html  A nomogram based on the ARG signature and clinicopathological factors was developed in TCGA cohort and validated in the GEO dataset. Moreover, to visualize the prediction results, we established a web-based calculator yurong.