Gene expression correlation analysis predicts involvement of high- and low-confidence risk genes in different stages of prostate carcinogenesis

The Prostate ◽  
2010 ◽  
Vol 70 (16) ◽  
pp. 1746-1759
Author(s):  
Kojiro Yano
Keyword(s):  
Gene Expression ◽  
Correlation Analysis ◽  
Risk Genes ◽  
Expression Correlation ◽  
Prostate Carcinogenesis ◽  
Gene Expression Correlation

scholarly journals bc-GenExMiner 3.0: new mining module computes breast cancer gene expression correlation analyses

Database ◽  
2013 ◽  
Vol 2013 (0) ◽  
pp. bas060-bas060 ◽  
Author(s):  
P. Jezequel ◽  
J.-S. Frenel ◽  
L. Campion ◽  
C. Guerin-Charbonnel ◽  
W. Gouraud ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Gene ◽  
Expression Correlation ◽  
Correlation Analyses ◽  
Gene Expression Correlation ◽  
Breast Cancer Gene

UPEFinder: A Bioinformatic Tool for the Study of Uncharacterized Proteins Based on Gene Expression Correlation and the PageRank Algorithm

2020 ◽  
Vol 19 (12) ◽  
pp. 4795-4807
Author(s):  
José González-Gomariz ◽  
Guillermo Serrano ◽  
Carlos M. Tilve-Álvarez ◽  
Fernando J. Corrales ◽  
Elizabeth Guruceaga ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pagerank Algorithm ◽  
Expression Correlation ◽  
Bioinformatic Tool ◽  
Gene Expression Correlation

scholarly journals FlowerNet: A Gene Expression Correlation Network for Anther and Pollen Development

2015 ◽  
Vol 167 (4) ◽  
pp. 1717-1730 ◽  
Author(s):  
Simon Pearce ◽  
Alison Ferguson ◽  
John King ◽  
Zoe A. Wilson
Keyword(s):  
Gene Expression ◽  
Pollen Development ◽  
Correlation Network ◽  
Expression Correlation ◽  
Gene Expression Correlation

scholarly journals Gene expression atlas of a developing tissue by single cell expression correlation analysis

2019 ◽  
Vol 16 (8) ◽  
pp. 750-756 ◽  
Author(s):  
Josephine Bageritz ◽  
Philipp Willnow ◽  
Erica Valentini ◽  
Svenja Leible ◽  
Michael Boutros ◽  
...  
Keyword(s):  
Gene Expression ◽  
Correlation Analysis ◽  
Single Cell ◽  
Expression Correlation ◽  
Gene Expression Atlas ◽  
Expression Atlas ◽  
Cell Expression

scholarly journals Gene Expression Correlation Analysis Reveals MYC-NAC Regulatory Network in Cotton Pigment Gland Development

2021 ◽  
Vol 22 (9) ◽  
pp. 5007
Author(s):  
Hailiang Cheng ◽  
Xiaoxu Feng ◽  
Dongyun Zuo ◽  
Youping Zhang ◽  
Qiaolian Wang ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Correlation Analysis ◽  
Regulatory Network ◽  
Pigment Gland ◽  
Interaction Network ◽  
Nac Transcription Factors ◽  
Expression Correlation ◽  
Dna Binding Sites ◽  
Gene Expression Correlation ◽  
Gland Development

Plant NAC (NAM, ATAF1/2, and CUC2) family is involved in various development processes including Programmed Cell Death (PCD) associated development. However, the relationship between NAC family and PCD-associated cotton pigment gland development is largely unknown. In this study, we identified 150, 153 and 299 NAC genes in newly updated genome sequences of G. arboreum, G. raimondii and G. hirsutum, respectively. All NAC genes were divided into 8 groups by the phylogenetic analysis and most of them were conserved during cotton evolution. Using the vital regulator of gland formation GhMYC2-like as bait, expression correlation analysis screened out 6 NAC genes which were low-expressed in glandless cotton and high-expressed in glanded cotton. These 6 NAC genes acted downstream of GhMYC2-like and were induced by MeJA. Silencing CGF1(Cotton Gland Formation1), another MYC-coding gene, caused almost glandless phenotype and down-regulated expression of GhMYC2-like and the 6 NAC genes, indicating a MYC-NAC regulatory network in gland development. In addition, predicted regulatory mechanism showed that the 6 NAC genes were possibly regulated by light, various phytohormones and transcription factors as well as miRNAs. The interaction network and DNA binding sites of the 6 NAC transcription factors were also predicted. These results laid the foundation for further study of gland-related genes and gland development regulatory network.

WWOX—the FRA16D cancer gene: Expression correlation with breast cancer progression and prognosis

2006 ◽  
Vol 32 (2) ◽  
pp. 153-157 ◽  
Author(s):  
E. Płuciennik ◽  
R. Kusińska ◽  
P. Potemski ◽  
R. Kubiak ◽  
R. Kordek ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Progression ◽  
Breast Cancer Progression ◽  
Cancer Gene ◽  
Expression Correlation ◽  
Gene Expression Correlation

scholarly journals Low GNG12 Expression Predicts Adverse Outcomes: A Potential Therapeutic Target for Osteosarcoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Jinghong Yuan ◽  
Zhao Yuan ◽  
Aifang Ye ◽  
Tianlong Wu ◽  
Jingyu Jia ◽  
...  
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Correlation Analysis ◽  
Therapeutic Target ◽  
Competing Endogenous Rna ◽  
Network Construction ◽  
Expression Correlation ◽  
Kaplan Meier ◽  
Metastatic Osteosarcoma ◽  
Potential Biomarker

BackgroundG protein subunit gamma 12 (GNG12) is observed in some types of cancer, but its role in osteosarcoma is unknown. This study hypothesized that GNG12 may be a potential biomarker and therapeutic target. We aimed to identify an association between GNG12 and osteosarcoma based on the Gene Expression Omnibus and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) databases.MethodsOsteosarcoma samples in GSE42352 and TARGET database were selected as the test cohorts. As the external validation cohort, 78 osteosarcoma specimens from The Second Affiliated Hospital of Nanchang University were collected. Patients with osteosarcoma were divided into high and low GNG12 mRNA-expression groups; differentially expressed genes were identified as GNG12-related genes. The biological function of GNG12 was annotated using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, gene set enrichment analysis, and immune infiltration analysis. Gene expression correlation analysis and competing endogenous RNA regulatory network construction were used to determine potential biological regulatory relationships of GNG12. Overall survival, Kaplan–Meier analysis, and log-rank tests were calculated to determine GNG12 reliability in predicting survival prognosis.ResultsGNG12 expression decreased in osteosarcoma samples. GNG12 was a highly effective biomarker for osteosarcoma [area under the receiver operating characteristic (ROC) curve (AUC) = 0.920], and the results of our Kaplan–Meier analysis indicated that overall survival and progression-free survival differed significantly between low and high GNG-expression group (p < 0.05). Functional analyses indicated that GNG12 may promote osteosarcoma through regulating the endoplasmic reticulum. Expression correlation analysis and competing endogenous RNA network construction showed that HOTTIP/miR-27a-3p may regulate GNG12 expression. Furthermore, the subunit suppresses adaptive immunity via inhibiting M1 and M2 macrophage infiltration. GNG12 was inhibited in metastatic osteosarcoma compared with non-metastatic osteosarcoma, and its expression predicted survival of patients (1, 3, and 5-year AUCs were 0.961, 0.826, and 0.808, respectively).ConclusionThis study identified GNG12 as a potential biomarker for osteosarcoma prognosis, highlighting its potential as an immunotherapy target.

Sign in / Sign up

Export Citation Format

Share Document