Search

Malignant glioma with a mesenchymal (MES) signature is characterized by shorter survival time due to aggressive dissemination and resistance to chemoradiotherapy. Here, this study used the TCGA database as the training set and the CGGA database as the testing set. Consensus clustering was performed on the two data sets, and it was found that two groups had distinguished prognostic and molecular features. Cox analysis and Lasso regression analysis were used to construct MES signature-based risk score model of glioma. Our results show that MES signature-based risk score model can be used to assess the prognosis of glioma. Three methods (ROC curve analyses, univariate Cox regression analysis, multivariate Cox regression analysis) were used to investigate the prognostic role of texture parameters. The result showed that the MES-related gene signature was proved to be an independent prognostic factor for glioma. Furthermore, functional analysis of the gene related to the risk signature showed that the genes sets were closely related to the malignant process of tumors. Finally, FCGR2A and EHD2 were selected for functional verification. Silencing these two genes inhibited the proliferation, migration and invasion of gliomas and reduced the expression of mesenchymal marker genes. Collectively, MES-related risk signature seems to provide a novel target for predicting the prognosis and treatment of glioma.

Wheat stripe rust is one of the most destructive diseases to affect wheat. Although the major resistant wheat varieties have made a great contribution to global food security, yield losses from stripe rust still occur in large wheat growing areas when climatic conditions are unstable. Despite this threat, resistance levels and yield losses of these elite wheat cultivars under wheat stripe rust infection have not been well studied. Based on this investigation of natural infection conditions over 2 years, analysis of the area-under-the-disease-progress-curves differentiated the susceptible cultivars Mianmai 367 (MM367; 788.59), Jinmai 47 (JM47; 1,087.71), and Avocet Susceptible (AvS; 1,314.59) from resistant cultivars Xikemai 18 (XKM18; 177.50) and Xiaoyan 6 (XY6; 545.67). Stripe rust resulted in a 2-year mean yield loss of 32% for all tested varieties. The susceptible varieties JM47, AvS, and MM367 lost 64, 55, and 21% of grain yield, respectively. On the contrary, rust-resistant cultivars XKM18 and XY6 lost only 11 and 28%, respectively. In addition, stripe rust resulted in reduced kernel hardness, flour yield, and flour whiteness. Dough and gluten properties were also affected. Overall, results revealed that the grain yield and quality loss values of the resistant wheat cultivars were less than in the susceptible cultivars. Disease-resistant cultivars such as XKM18 should be promoted and recommended for application. It may also be suggested that growing a susceptible variety such as MM367 could be feasible in combination with fungicide application under high disease pressure.

Background Long non-coding RNAs (lncRNAs) have been considered as one type of gene expression regulator for cancer development, but it is not clear how these are regulated. This study aimed to identify a specific lncRNA that promotes glioma progression. Methods RNA sequencing (RNA-seq) and quantitative real-time PCR were performed to screen differentially expressed genes. CCK-8, transwell migration, invasion assays, and a mouse xenograft model were performed to determine the functions of TMEM44-AS1. Co-IP, ChIP, Dual-luciferase reporter assays, RNA pulldown, and RNA immunoprecipitation assays were performed to study the molecular mechanism of TMEM44-AS1 and the downstream target. Results We identified a novel lncRNA TMEM44-AS1, which was aberrantly expressed in glioma tissues, and that increased TMEM44-AS1 expression was correlated with malignant progression and poor survival for patients with glioma. Expression of TMEM44-AS1 increased the proliferation, colony formation, migration, and invasion of glioma cells. Knockdown of TMEM44-AS1 in glioma cells reduced cell proliferation, colony formation, migration and invasion, and tumor growth in a nude mouse xenograft model. Mechanistically, TMEM44-AS1 is directly bound to the SerpinB3, and sequentially activated Myc and EGR1/IL-6 signaling; Myc transcriptionally induced TMEM44-AS1 and directly bound to the promoter and super-enhancer of TMEM44-AS1, thus forming a positive feedback loop with TMEM44-AS. Further studies demonstrated that Myc interacts with MED1 regulates the super-enhancer of TMEM44-AS1. More importantly, a novel small-molecule Myc inhibitor, Myci975, alleviated TMEM44-AS1-promoted the growth of glioma cells. Conclusions Our study implicates a crucial role of the TMEM44-AS1-Myc axis in glioma progression and provides a possible anti-glioma therapeutic agent.

Two adult plant stripe rust resistance QTL, QYrto.swust-3AS and QYrto.swust-3BS, were identified and mapped in common wheat cultivar Toni. The two QTL were located to corresponding positions in the wheat physical map position based on flanking SNP markers. Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is one of the most important foliar diseases of wheat. Characterization and utilization of resistance genes are the most effective, economic and environmental-friendly way to control the disease. The wheat cultivar Toni resistant at the adult plant stage to predominant Chinese Pst races was crossed with the susceptible genotype Mingxian 169. A recombinant inbred line population comprising 171 lines was tested in the field at three locations in the 2016 and 2017 crop seasons. The Affymetrix Axiom® 35 K single-nucleotide polymorphism (SNP) Wheat Breeder’s Genotyping Array was used to map quantitative trait loci (QTL) for adult plant resistance to stripe rust. Inclusive composite interval mapping identified stable QTL QYrto.swust-3AS and QYrto.swust-3BS that explained 31.6–48.2% and 21.9–56.3% of the variation in stripe rust severity and infection type, respectively. The two QTL regions were anchored to the wheat IWGSC Ref Seq v1.0 sequence. QYrto.swust-3AS was localized to a 2.22-Mb interval flanked by SNP markers AX-95240191 and AX-94828890. Among 65 HC (high confidence) annotated genes in this region, 11 (16.9%) contained NB-ARC domains and 9 (13.8%) contained protein kinase domains and thus could contribute to disease resistance. QYrto.swust-3BS was localized to a 4.77-Mb interval flanked by SNP markers AX-94509749 and AX-94998050. One hundred and thirty three HC genes are annotated in this region. Among them, 14 (10.5%) protein kinase domain genes may contribute to disease resistance. The linked markers should be useful for marker-assisted selection in breeding for resistance.

Glioma is one of the most common malignant tumors of the central nervous system, and its prognosis is extremely poor. Aberrant methylation of lncRNA promoter region is significantly associated with the prognosis of glioma patients. In this study, we investigated the potential impact of methylation of lncRNA promoter region in glioma patients to establish a signature of nine lncRNA methylated genes for determining glioma patient prognosis. Methylation data and clinical follow-up data were obtained from The Cancer Genome Atlas (TCGA). The multistep screening strategy identified nine lncRNA methylated genes that were significantly associated with the overall survival (OS) of glioma patients. Subsequently, we constructed a risk signature that containing nine lncRNA methylated genes. The risk signature successfully divided the glioma patients into high-risk and low-risk groups. Compared with the low-risk group, the high-risk group had a worse prognosis, higher glioma grade, and older age. Furthermore, we identified two lncRNAs termed PCBP1-AS1 and LINC02875 that may be involved in the malignant progression of glioma cells by using the TCGA database. Loss-of-function assays confirmed that knockdown of PCBP1-AS1 and LINC02875 inhibited the proliferation, migration, and invasion of glioma cells. Therefore, the nine lncRNA methylated genes signature may provide a novel predictor and therapeutic target for glioma patients.

Unfortunately, Fig. 2 and Fig. 3 were interchanged in the results section. The figures should swap positions, whereas the legends should stay in the given order.