scholarly journals Detection of cancer-related gene expression profiles in severe cervical neoplasia

2004 ◽  
Vol 112 (1) ◽  
pp. 33-43 ◽  
Author(s):  
Igor Sopov ◽  
Till Sörensen ◽  
Mandy Magbagbeolu ◽  
Lars Jansen ◽  
Katrin Beer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cervical Neoplasia ◽  
Related Gene ◽  
Cancer Related Gene

Cancer-related gene expression profiles in NF1-associated pilocytic astrocytomas

Neurology ◽  
2001 ◽  
Vol 56 (7) ◽  
pp. 885-890 ◽  
Author(s):  
J. Li ◽  
A. Perry ◽  
C. D. James ◽  
D. H. Gutmann
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Related Gene ◽  
Pilocytic Astrocytomas ◽  
Cancer Related Gene

scholarly journals A Nomogram Based on Glycolysis-related Gene Expression Profilings Serve as a Novel Prognosis Risk Classifiers for Human Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Lingyu Zhang ◽  
Yu Li ◽  
Yibei Dai ◽  
Danhua Wang ◽  
Xuchu Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Expression Profiles ◽  
Area Under The Curve ◽  
Prognostic Index ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Related Gene ◽  
Metabolic Pattern

Abstract Metabolic pattern reconstruction is an important element in tumor progression. The metabolism of tumor cells is characterized by the abnormal increase of anaerobic glycolysis, regardless of the higher oxygen concentration, resulting in a large accumulation of energy from glucose sources, and contributes to rapid cell proliferation and tumor growth which is further referenced as the Warburg effect. We tried to reconstruct the metabolic pattern in the progression of cancer to screen which genetic changes are specific in cancer cells. A total of 12 common types of solid tumors were enrolled in the prospective study. Gene set enrichment analysis (GSEA) was implemented to analyze 9 glycolysis-related gene sets, which are closely related to the glycolysis process. Univariate and multivariate analyses were used to identify independent prognostic variables for the construction of a nomogram based on clinicopathological characteristics and a glycolysis-related gene prognostic index (GRGPI). The prognostic model based on glycolysis genes has the highest area under the curve (AUC) in LIHC (Liver hepatocellular carcinoma). 8-gene signatures (AURKA, CDK1, CENPA, DEPDC1, HMMR, KIF20A, PFKFB4, STMN1) were related to overall survival (OS) and recurrence-free survival (RFS). Further analysis demonstrates that the prediction model can accurately distinguish between high- and low-risk cancer patients among patients in different clusters in LIHC. A nomogram with a well-fitted calibration curve based on gene expression profiles and clinical characteristics improves discrimination in internal and external cohorts. Furthermore, the altering expression of metabolic genes related to glycolysis may contribute to the reconstruction of the tumor-related microenvironment.

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