scholarly journals Comprehensive Analysis of Key Genes Associated with ceRNA Networks in Nasopharyngeal Carcinoma Based on Bioinformatics Analysis

2020 ◽  
Vol 108 (3) ◽  
pp. e535
Author(s):  
H. Xinyi ◽  
C. Chen ◽  
Y. Xu ◽  
Y. Zhang ◽  
C. Li ◽  
...  
Keyword(s):  
Nasopharyngeal Carcinoma ◽  
Bioinformatics Analysis ◽  
Comprehensive Analysis ◽  
Key Genes

scholarly journals Comprehensive analysis of key genes associated with ceRNA networks in nasopharyngeal carcinoma based on bioinformatics analysis

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Yuanji Xu ◽  
Xinyi Huang ◽  
Wangzhong Ye ◽  
Yangfan Zhang ◽  
Changkun Li ◽  
...  
Keyword(s):  
Nasopharyngeal Carcinoma ◽  
Bioinformatics Analysis ◽  
Comprehensive Analysis ◽  
Key Genes

scholarly journals Comprehensive analysis of key genes and microRNAs in radioresistant nasopharyngeal carcinoma

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Ya Guo ◽  
Yang Zhang ◽  
Shu Juan Zhang ◽  
Yi Nan Ma ◽  
Yun He
Keyword(s):  
Nasopharyngeal Carcinoma ◽  
Comprehensive Analysis ◽  
Key Genes

The identification of key genes in nasopharyngeal carcinoma by bioinformatics analysis of high-throughput data

2019 ◽  
Vol 46 (3) ◽  
pp. 2829-2840 ◽  
Author(s):  
Yanshan Ge ◽  
Zhengxi He ◽  
Yanqi Xiang ◽  
Dawei Wang ◽  
Yuping Yang ◽  
...  
Keyword(s):  
Nasopharyngeal Carcinoma ◽  
High Throughput ◽  
Bioinformatics Analysis ◽  
High Throughput Data ◽  
Key Genes

Identification of key genes in benign prostatic hyperplasia using bioinformatics analysis

2021 ◽  
Author(s):  
Peng Xiang ◽  
Dan Liu ◽  
Di Guan ◽  
Zhen Du ◽  
Yongxiu Hao ◽  
...  
Keyword(s):  
Benign Prostatic Hyperplasia ◽  
Bioinformatics Analysis ◽  
Prostatic Hyperplasia ◽  
Key Genes

Bioinformatics Analysis of the Signaling Pathways and Genes of Gossypol Induce Death of Nasopharyngeal Carcinoma Cells

2021 ◽  
Author(s):  
Kunjie Zhu ◽  
Junshang Ge ◽  
Yi He ◽  
Panchun Li ◽  
Xianjie Jiang ◽  
...  
Keyword(s):  
Nasopharyngeal Carcinoma ◽  
Signaling Pathways ◽  
Bioinformatics Analysis ◽  
Carcinoma Cells

scholarly journals Prediction and Analysis of Key Genes in Glioblastoma Based on Bioinformatics

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Hao Long ◽  
Chaofeng Liang ◽  
Xi’an Zhang ◽  
Luxiong Fang ◽  
Gang Wang ◽  
...  
Keyword(s):  
Glioblastoma Multiforme ◽  
Prediction Model ◽  
Basic Science ◽  
Prediction Accuracy ◽  
Genetic Basis ◽  
Bioinformatics Analysis ◽  
Clinical Treatment ◽  
Structural Level ◽  
Biotechnological Application ◽  
Key Genes

Understanding the mechanisms of glioblastoma at the molecular and structural level is not only interesting for basic science but also valuable for biotechnological application, such as the clinical treatment. In the present study, bioinformatics analysis was performed to reveal and identify the key genes of glioblastoma multiforme (GBM). The results obtained in the present study signified the importance of some genes, such as COL3A1, FN1, and MMP9, for glioblastoma. Based on the selected genes, a prediction model was built, which achieved 94.4% prediction accuracy. These findings might provide more insights into the genetic basis of glioblastoma.

Identification of potential key genes and key pathways related to clear cell renal cell carcinoma through bioinformatics analysis

2020 ◽  
Vol 52 (8) ◽  
pp. 853-863
Author(s):  
Wenxin Zhai ◽  
Haijiao Lu ◽  
Shenghua Dong ◽  
Jing Fang ◽  
Zhuang Yu
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Phosphoenolpyruvate Carboxykinase ◽  
Bioinformatics Analysis ◽  
Clear Cell ◽  
Interaction Network ◽  
Polymerase Chain Reaction Analysis ◽  
Cell Renal Cell Carcinoma ◽  
Key Genes

Abstract Clear cell renal cell carcinoma (ccRCC) is a common malignancy of the genitourinary system and is associated with high mortality rates. However, the molecular mechanism of ccRCC pathogenesis is still unclear, which translates to few effective diagnostic and prognostic biomarkers. In this study, we conducted a bioinformatics analysis on three Gene Expression Omnibus datasets and identified 437 differentially expressed genes (DEGs) related to ccRCC development and prognosis, of which 311 and 126 genes are respectively down-regulated and up-regulated. The protein–protein interaction network of these DEGs consists of 395 nodes and 1872 interactions and 2 prominent modules. The Staphylococcus aureus infection and complement and coagulation cascades are significantly enriched in module 1 and are likely involved in ccRCC progression. Forty-two hub genes were screened, of which von Willebrand factor, TIMP metallopeptidase inhibitor 1, plasminogen, formimidoyltransferase cyclodeaminase, solute carrier family 34 member 1, hydroxyacid oxidase 2, alanine-glyoxylate aminotransferase 2, phosphoenolpyruvate carboxykinase 1, and 3-hydroxy-3-methylglutaryl-CoA synthase 2 are possibly related to the prognosis of ccRCC. The differential expression of all nine genes was confirmed by quantitative real-time polymerase chain reaction analysis of the ccRCC and normal renal tissues. These key genes are potential biomarkers for the diagnosis and prognosis of ccRCC and warrant further investigation.

scholarly journals Screening and verifying key genes with poor prognosis in colon cancer through bioinformatics analysis

2020 ◽  
Vol 9 (11) ◽  
pp. 6720-6732
Author(s):  
Buyuan Dong ◽  
Mengyu Chai ◽  
Hao Chen ◽  
Qian Feng ◽  
Rong Jin ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Poor Prognosis ◽  
Bioinformatics Analysis ◽  
Key Genes
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