Yan Shi

We performed an integrated genomic, transcriptomic and proteomic characterization of 373 endometrial carcinomas using array-and sequencing-based technologies. Uterine serous tumours and ∼25% of high-grade endometrioid tumours had extensive copy number alterations, few DNA methylation changes, low oestrogen receptor/progesterone receptor levels, and frequent TP53 mutations. Most endometrioid tumours had few copy number alterations or TP53 mutations, but frequent mutations in PTEN, CTNNB1, PIK3CA, ARID1A and KRAS and novel mutations in the SWI/SNF chromatin remodelling complex gene ARID5B. A subset of endometrioid tumours that we identified had a markedly increased transversion mutation frequency and newly identified hotspot mutations in POLE. Our results classified endometrial cancers into four categories: POLE ultramutated, microsatellite instability hypermutated, copy-number low, and copy-number high. Uterine serous carcinomas share genomic features with ovarian serous and basal-like breast carcinomas. We demonstrated that the genomic features of endometrial carcinomas permit a reclassification that may affect post-surgical adjuvant treatment for women with aggressive tumours. © 2013 Macmillan Publishers Limited. All rights reserved.

H19 RNA has been characterized as an oncogenic long non-coding RNA (lncRNA) in breast and colon cancer. However, the role and function of lncRNA H19 in glioma development remain unclear. In this study, we identified that H19/miR-675 signaling was critical for glioma progression. By analyzing glioma gene expression data sets, we found increased H19 in high grade gliomas. H19 depletion via siRNA inhibited invasion in glioma cells. Further, we found H19 positively correlated with its derivate miR-675 expression and reduction of H19 inhibited miR-675 expression. Bioinformatics and luciferase reporter assays showed that miR-675 modulated Cadherin 13 expression by directly targeting the binding site within the 3' UTR. Finally, introduction of miR-675 abrogated H19 knockdown-induced cell invasion inhibition in glioma cells. To our knowledge, it is first time to demonstrate that H19 regulates glioma development by deriving miR-675 and provide important clues for understanding the key roles of lncRNA-miRNA functional network in glioma.

// Wenkang Luan 1,* , Yingyi Wang 1,* , Xincheng Chen 1,* , Yan Shi 1,* , Jiajia Wang 1 , Junxia Zhang 1 , Jin Qian 2 , Ri Li 1 , Tao Tao 1 , Wenjin Wei 1 , Qi Hu 1 , Ning Liu 1 and Yongping You 1 1 Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China 2 Department of Neurosurgery, People’s Hospital of Xuancheng City, Anhui, China * These authors contributed equally to this work Correspondence to: Yongping You, email: // Keywords : let-7a microRNA, c-Myc, hnRNPA1, PKM2, glucose metabolism, aerobic glycolysis, glioma Received : October 08, 2014 Accepted : February 04, 2015 Published : February 10, 2015 Abstract Tumor cells metabolize more glucose to lactate in aerobic or hypoxic conditions than non-tumor cells. Pyruvate kinase isoenzyme type M2 (PKM2) is crucial for tumor cell aerobic glycolysis. We established a role for let-7a/c-Myc/hnRNPA1/PKM2 signaling in glioma cell glucose metabolism. PKM2 depletion via siRNA inhibits cell proliferation and aerobic glycolysis in glioma cells. C-Myc promotes up-regulation of hnRNPA1 expression, hnRNPA1 binding to PKM pre-mRNA, and the subsequent formation of PKM2. This pathway is downregulated by the microRNA let-7a, which functionally targets c-Myc, whereas hnRNPA1   blocks the biogenesis of let-7a to counteract its ability to downregulate the c-Myc/hnRNPA1/PKM2 signaling pathway. The down-regulation of c-Myc/ hnRNPA1/PKM2 by let-7a is verified using a glioma xenograft model. These results suggest that let-7a, c-Myc and hnRNPA1 from a feedback loop, thereby regulating PKM2 expression to modulate glucose metabolism of   glioma cells. These findings elucidate a new pathway mediating aerobic glycolysis in gliomas and provide an attractive potential target for therapeutic intervention.

Background: Cancer-associated fibroblasts (CAFs) are involved in tumor growth, angiogenesis, metastasis, and resistance to therapy. We sought to explore the CAFs characteristics in hepatocellular carcinoma (HCC) and establish a CAF-based risk signature for predicting the prognosis of HCC patients. Methods: The signal-cell RNA sequencing (scRNA-seq) data was obtained from the GEO database. Bulk RNA-seq data and microarray data of HCC were obtained from the TCGA and GEO databases respectively. Seurat R package was applied to process scRNA-seq data and identify CAF clusters according to the CAF markers. Differential expression analysis was performed to screen differentially expressed genes (DEGs) between normal and tumor samples in TCGA dataset. Then Pearson correlation analysis was used to determine the DEGs associated with CAF clusters, followed by the univariate Cox regression analysis to identify CAF-related prognostic genes. Lasso regression was implemented to construct a risk signature based on CAF-related prognostic genes. Finally, a nomogram model based on the risk signature and clinicopathological characteristics was developed. Results: Based on scRNA-seq data, we identified 4 CAF clusters in HCC, 3 of which were associated with prognosis in HCC. A total of 423 genes were identified from 2811 DEGs to be significantly correlated with CAF clusters, and were narrowed down to generate a risk signature with 6 genes. These six genes were primarily connected with 39 pathways, such as angiogenesis, apoptosis, and hypoxia. Meanwhile, the risk signature was significantly associated with stromal and immune scores, as well as some immune cells. Multivariate analysis revealed that risk signature was an independent prognostic factor for HCC, and its value in predicting immunotherapeutic outcomes was confirmed. A novel nomogram integrating the stage and CAF-based risk signature was constructed, which exhibited favorable predictability and reliability in the prognosis prediction of HCC. Conclusion: CAF-based risk signatures can effectively predict the prognosis of HCC, and comprehensive characterization of the CAF signature of HCC may help to interpret the response of HCC to immunotherapy and provide new strategies for cancer treatment.