scholarly journals Gene expression profiles of primary HPV16- and HPV18-infected early stage cervical cancers and normal cervical epithelium: identification of novel candidate molecular markers for cervical cancer diagnosis and therapy

Virology ◽  
2005 ◽  
Vol 331 (2) ◽  
pp. 269-291 ◽  
Author(s):  
Alessandro D. Santin ◽  
Fenghuang Zhan ◽  
Eliana Bignotti ◽  
Eric R. Siegel ◽  
Stefania Cané ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cervical Cancer ◽  
Cancer Diagnosis ◽  
Early Stage ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cervical Epithelium ◽  
Cervical Cancers ◽  
Normal Cervical Epithelium ◽  
Cancer Diagnosis And Therapy

scholarly journals Integrative machine learning analysis of multiple gene expression profiles in cervical cancer

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5285 ◽  
Author(s):  
Mei Sze Tan ◽  
Siow-Wee Chang ◽  
Phaik Leng Cheah ◽  
Hwa Jen Yap
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Cervical Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Hpv Infection ◽  
Gene Set Enrichment Analysis ◽  
Multiple Gene ◽  
Cervical Cancers ◽  
Learning Analysis

Although most of the cervical cancer cases are reported to be closely related to the Human Papillomavirus (HPV) infection, there is a need to study genes that stand up differentially in the final actualization of cervical cancers following HPV infection. In this study, we proposed an integrative machine learning approach to analyse multiple gene expression profiles in cervical cancer in order to identify a set of genetic markers that are associated with and may eventually aid in the diagnosis or prognosis of cervical cancers. The proposed integrative analysis is composed of three steps: namely, (i) gene expression analysis of individual dataset; (ii) meta-analysis of multiple datasets; and (iii) feature selection and machine learning analysis. As a result, 21 gene expressions were identified through the integrative machine learning analysis which including seven supervised and one unsupervised methods. A functional analysis with GSEA (Gene Set Enrichment Analysis) was performed on the selected 21-gene expression set and showed significant enrichment in a nine-potential gene expression signature, namely PEG3, SPON1, BTD and RPLP2 (upregulated genes) and PRDX3, COPB2, LSM3, SLC5A3 and AS1B (downregulated genes).

scholarly journals Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Baojie Wu ◽  
Shuyi Xi
Keyword(s):  
Gene Expression ◽  
Cervical Cancer ◽  
Differentially Expressed Genes ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Molecular Targets ◽  
Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Kegg Pathways

Abstract Background This study aimed to explore and identify key genes and signaling pathways that contribute to the progression of cervical cancer to improve prognosis. Methods Three gene expression profiles (GSE63514, GSE64217 and GSE138080) were screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were screened using the GEO2R and Venn diagram tools. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Gene set enrichment analysis (GSEA) was performed to analyze the three gene expression profiles. Moreover, a protein–protein interaction (PPI) network of the DEGs was constructed, and functional enrichment analysis was performed. On this basis, hub genes from critical PPI subnetworks were explored with Cytoscape software. The expression of these genes in tumors was verified, and survival analysis of potential prognostic genes from critical subnetworks was conducted. Functional annotation, multiple gene comparison and dimensionality reduction in candidate genes indicated the clinical significance of potential targets. Results A total of 476 DEGs were screened: 253 upregulated genes and 223 downregulated genes. DEGs were enriched in 22 biological processes, 16 cellular components and 9 molecular functions in precancerous lesions and cervical cancer. DEGs were mainly enriched in 10 KEGG pathways. Through intersection analysis and data mining, 3 key KEGG pathways and related core genes were revealed by GSEA. Moreover, a PPI network of 476 DEGs was constructed, hub genes from 12 critical subnetworks were explored, and a total of 14 potential molecular targets were obtained. Conclusions These findings promote the understanding of the molecular mechanism of and clinically related molecular targets for cervical cancer.

scholarly journals Impact of 70-Gene Signature Use on Adjuvant Chemotherapy Decisions in Patients With Estrogen Receptor–Positive Early Breast Cancer: Results of a Prospective Cohort Study

2017 ◽  
Vol 35 (24) ◽  
pp. 2814-2819 ◽  
Author(s):  
Anne Kuijer ◽  
Marieke Straver ◽  
Bianca den Dekker ◽  
Annelotte C.M. van Bommel ◽  
Sjoerd G. Elias ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Estrogen Receptor ◽  
Early Stage ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Signature ◽  
Early Stage Breast Cancer ◽  
Er Positive ◽  
Test Result

Purpose Gene-expression profiles increasingly are used in addition to conventional prognostic factors to guide adjuvant chemotherapy (CT) decisions. The Dutch guideline suggests use of validated gene-expression profiles in patients with estrogen receptor (ER) –positive, early-stage breast cancer without overt lymph node metastases. We aimed to assess the impact of a 70-gene signature (70-GS) test on CT decisions in patients with ER-positive, early-stage breast cancer. Patients and Methods In a prospective, observational, multicenter study in patients younger than 70 years old who had undergone surgery for ER-positive, early-stage breast cancer, physicians were asked whether they intended to administer adjuvant CT before deployment of the 70-GS test and after the test result was available. Results Between October 1, 2013, and December 31, 2015, 660 patients, treated in 33 hospitals, were enrolled. Fifty-one percent of patients had pT1cN0, BRII, HER2-Neu-negative breast cancer. On the basis of conventional clinicopathological characteristics, physicians recommended CT in 270 (41%) of the 660 patients and recommended withholding CT in 107 (16%) of the 660 patients. For the remaining 43% of patients, the physicians were unsure and unable to give advice before 70-GS testing. In patients for whom CT was initially recommended or not recommended, 56% and 59%, respectively, were assigned to a low-risk profile by the 70-GS (κ, 0.02; 95% CI, -0.08 to 0.11). After disclosure of the 70-GS test result, the preliminary advice was changed in 51% of patients who received a recommendation before testing; the definitive CT recommendation of the physician was in line with the 70-GS result in 96% of patients. Conclusion In this prospective, multicenter study in a selection of patients with ER-positive, early-stage breast cancer, 70-GS use changed the physician-intended recommendation to administer CT in half of the patients.

Gene Expression Profiles of Early Implant Adherent Cells in Smokers and Nonsmokers

2015 ◽  
Vol 41 (6) ◽  
pp. 640-645 ◽  
Author(s):  
Ghadeer Thalji ◽  
Lyndon F. Cooper ◽  
Salvador Nares
Keyword(s):  
Gene Expression ◽  
Early Stage ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Whole Genome ◽  
Adherent Cells ◽  
Mini Implants ◽  
Molecular Events ◽  
The Impact

The objective of this study was to evaluate the impact of smoking on the early molecular events involved in peri-implant healing at either a micro-roughened or a micro-roughened with superimposed nanofeatures surface implant in humans. Twenty-one subjects, 10 smokers and 11 nonsmokers received 4 mini-implants (2.2 × 5.0 mm; 2 of each surface). After 3 and 7 days, paired mini-implants were retrieved by reverse threading and RNA isolated from implant adherent cells. Whole genome microarrays were used interrogate the gene expression profiles. The study failed to identify differences in the gene expression profiles of implant adherent cells at this early stage of osseointegration (up to day 7) comparing smoker and nonsmoker individuals.

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