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Chuang Zhang,1,2,* Ruoxi Yu,1,2,* Zhi Li,1,2 Huicong Song,1,2 Dan Zang,1,2 Mingming Deng,1 Yibo Fan,1,2 Yunpeng Liu,1,2 Ye Zhang,1,2 Xiujuan Qu1,21Key Laboratory of Anticancer Drugs and Biotherapy, the First Hospital of China Medical University, Shenyang, Liaoning, 110001, People’s Republic of China; 2Department of Medical Oncology, The First Hospital of China Medical University, Shenyang 110001, Liaoning, People’s Republic of China *These authors contributed equally to this work Purpose: The chr1p/19q co-deletion is a favorable prognostic factor in patients with lower grade glioma. The aim of this study was to reveal key genes for prognosis and establish prognostic gene signatures based on genes encoded by chr1p/19q.Materials and methods: The data was downloaded from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA) and Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) between lower grade glioma tissue and normal brain were identified. The univariate COX regression, robust likelihood-base survival analysis (rbsurv) and multivariate COX regression analysis were used to establish the 4-gene-signature based on the DEGs. The receiver operating characteristic (ROC) curve and the Kaplan-Mere curve were used to verify the prediction accuracy of the signature. Gene Set Enrichment Analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were also performed to explore the reasons for good prognosis in patients with chr1p/19q deletion.Results: A total of 1346 DEGs were identified between lower grade glioma samples and normal brain samples in GSE16011, including 56 up-regulated mRNAs located on chr1p and 20 up-regulated mRNAs located on chr19q. We established a 4-gene-signature that was significantly associated with survival based on the 76 gene. The AUC of the 4-gene-signature for 5-year OS in TCGA and CGGA was 0.837 and 0.876, respectively, which was superior compared to other parameters such as chr1p/19q co-deletion, IDH mutant, WHO grade and histology type, especially in chr1p/19q non-co-deletion patients. GSEA and KEGG analysis suggested that the prolongation of chr1p/19q in patients could be associated with cell cycle and DNA mismatch repairing.Conclusions: We established a robust 4-gene-signature based on the chr1p/19q and we explored the potential function of these newly identified survival-associated genes by bioinformatics analysis. The 4-gene from the signature are promising molecular targets to be used in the future.Keywords: lower grade glioma, prognostic signature, 1p/19q co-deletion, rbsurv

Abstract Uterine corpus endometrial carcinoma (UCEC) is frequently diagnosed among women worldwide. However, there are different prognostic outcomes because of heterogeneity. Thus, the aim of the current study was to identify a gene signature that can predict the prognosis of patients with UCEC. UCEC gene expression profiles were first downloaded from the The Cancer Genome Atlas (TCGA) database. After data processing and forward screening, 11 390 key genes were selected. The UCEC samples were randomly divided into training and testing sets. In total, 996 genes with prognostic value were then examined by univariate Cox survival analysis with a P‐value

Uterine corpus endometrial carcinoma (UCEC) is frequently diagnosed among women worldwide. However, there are different prognostic outcomes because of heterogeneity. Thus, the aim of the current study was to identify a gene signature that can predict the prognosis of patients with UCEC. UCEC gene expression profiles were first downloaded from the The Cancer Genome Atlas (TCGA) database. After data processing and forward screening, 11 390 key genes were selected. The UCEC samples were randomly divided into training and testing sets. In total, 996 genes with prognostic value were then examined by univariate Cox survival analysis with a P‐value <0.01 in the training set. Next, using robust likelihood‐based survival modeling, we developed a six‐gene signature (CTSW, PCSK4, LRRC8D, TNFRSF18, IHH, and CDKN2A) with a prognostic function in UCEC. A prognostic risk score system was developed by multivariate Cox proportional hazard regression based on this six‐gene signature. According to the Kaplan‐Meier curve, patients in the high‐risk group had significantly poorer overall survival (OS) outcomes than those in the low‐risk group (log‐rank test P‐value <0.0001). This signature was further validated in the testing dataset and the entire TCGA dataset. In conclusion, we conducted an integrated study to develop a six‐gene signature for the prognostic prediction of patients with UCEC. Our findings may provide novel biomarkers for prognosis and have significant implications in the understanding of therapeutic targets for UCEC. Identification of a six‐gene signature with prognostic value for patients with endometrial carcinoma. [ABSTRACT FROM AUTHOR]

The symptoms of ovarian cancer at early stages are usually absent which makes the diagnosis in its early stages exceedingly difficult. Previous research has proven that ovarian cancer is a genetic disease, which depends on the alteration of multi-cancer related genes and anti-cancer genes, multi-stages and multi-pathways, involving a variety of oncogene activation and anti-oncogene inactivation. For a better understanding of the prognostic classification of ovarian cancer, gene expression profiles were used to analyze the prognostic factors of ovarian cancer, and the prognostic model was used to classify the ovarian cancer samples. The ovarian cancer samples data were downloaded from TCGA dataset. Rebust likelihood-based survival model was built to find the key genes that could function as prognostic markers. The samples were classified by unsupervised hierarchical clustering. Furthermore, Kaplan-Meier survival analysis was used to analyze the differences in the prognosis of the samples. The prognostic model was used to classify the samples, and then the best classification model was selected as the prognostic model of ovarian cancer. Finally, GEO datasets were used for external data validation. A total of 886 genes with influence on prognosis was obtained. Then genomic combinations of 11 genes were screened out by random sampling. Then the active number of influential factors was counted based on the expression level of featured genes. When the number of influencing factors is ≥7, the prognosis difference among these genes is the largest (P-value = 0.000775); and this was chosen as the final Classification model. To summary, a prognostic 11genes expression model was preliminarily built to classify the ovarian cancer samples., (© 2017 Wiley Periodicals, Inc.)