scholarly journals Cervical Cancer Development: Implications of HPV16 E6E7-NFX1-123 Regulated Genes

Cancers ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 6182
Author(s):  
Kevin M. Quist ◽  
Isaiah Solorzano ◽  
Sebastian O. Wendel ◽  
Sreenivasulu Chintala ◽  
Cen Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cervical Cancer ◽  
Disease Progression ◽  
Patient Survival ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Models ◽  
Cervical Precancer ◽  
Regulatory Effects ◽  
Cervical Cancer Development

High-risk human papillomavirus (HR HPV) causes nearly all cervical cancers, half of which are due to HPV type 16 (HPV16). HPV16 oncoprotein E6 (16E6) binds to NFX1-123, and dysregulates gene expression, but their clinical implications are unknown. Additionally, HPV16 E7’s role has not been studied in concert with NFX1-123 and 16E6. HR HPVs express both oncogenes, and transformation requires their expression, so we sought to investigate the effect of E7 on gene expression. This study’s goal was to define gene expression profiles across cervical precancer and cancer stages, identify genes correlating with disease progression, assess patient survival, and validate findings in cell models. We analyzed NCBI GEO datasets containing transcriptomic data linked with cervical cancer stage and utilized LASSO analysis to identify cancer-driving genes. Keratinocytes expressing 16E6 and 16E7 (16E6E7) and exogenous NFX1-123 were tested for LASSO-identified gene expression. Ten out of nineteen genes correlated with disease progression, including CEBPD, NOTCH1, and KRT16, and affected survival. 16E6E7 in keratinocytes increased CEBPD, KRT16, and SLPI, and decreased NOTCH1. Exogenous NFX1-123 in 16E6E7 keratinocytes resulted in significantly increased CEBPD and NOTCH1, and reduced SLPI. This work demonstrates the clinical relevance of CEBPD, NOTCH1, KRT16, and SLPI, and shows the regulatory effects of 16E6E7 and NFX1-123.

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 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 A Study of Gene Expression Changes in Human Spinal and Oculomotor Neurons; Identifying Potential Links to Sporadic ALS

Genes ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 448
Author(s):  
Aayan N. Patel ◽  
Dennis Mathew
Keyword(s):  
Gene Expression ◽  
Disease Progression ◽  
Motor Neurons ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Tissue Samples ◽  
Disease Symptoms ◽  
Sporadic Als ◽  
Oculomotor Neurons ◽  
Als Patients

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that causes compromised function of motor neurons and neuronal death. However, oculomotor neurons are largely spared from disease symptoms. The underlying causes for sporadic ALS as well as for the resistance of oculomotor neurons to disease symptoms remain poorly understood. In this bioinformatic-analysis, we compared the gene expression profiles of spinal and oculomotor tissue samples from control individuals and sporadic ALS patients. We show that the genes GAD2 and GABRE (involved in GABA signaling), and CALB1 (involved in intracellular Ca2+ ion buffering) are downregulated in the spinal tissues of ALS patients, but their endogenous levels are higher in oculomotor tissues relative to the spinal tissues. Our results suggest that the downregulation of these genes and processes in spinal tissues are related to sporadic ALS disease progression and their upregulation in oculomotor neurons confer upon them resistance to ALS symptoms. These results build upon prevailing models of excitotoxicity that are relevant to sporadic ALS disease progression and point out unique opportunities for better understanding the progression of neurodegenerative properties associated with sporadic ALS.

scholarly journals Potential Biomarkers of Colorectal Adenoma–Dysplasia–Carcinoma Progression: mRNA Expression Profiling and In Situ Protein Detection on TMAs Reveal 15 Sequentially Upregulated and 2 Downregulated Genes

2009 ◽  
Vol 31 (1) ◽  
pp. 19-29
Author(s):  
Orsolya Galamb ◽  
Ferenc Sipos ◽  
Sándor Spisák ◽  
Barnabás Galamb ◽  
Tibor Krenács ◽  
...  
Keyword(s):  
Gene Expression ◽  
Disease Progression ◽  
Colorectal Adenoma ◽  
Protein Level ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Protein Detection ◽  
Yield Potential ◽  
Prostaglandin D2 ◽  
Potential Biomarkers

Background: As most colorectal cancers (CRC) develop from villous adenomas, studying alterations in gene expression profiles across the colorectal adenoma–dysplasia–carcinoma sequence may yield potential biomarkers of disease progression.Methods: Total RNA was extracted, amplified, and biotinylated from colonic biopsies of 15 patients with CRC, 15 with villous adenoma and 8 normal controls. Gene expression profiles were evaluated using HGU133Plus2.0 microarrays and disease progression associated data were validated with RT-PCR. The potential biomarkers were also tested at the protein level using tissue microarray samples of 103 independent and 16 overlapping patients.Results: 17 genes were validated to show sequentially altered expression at mRNA level through the normal–adenoma–dysplasia–carcinoma progression. Prostaglandin-D2 receptor (PTGDR) and amnionless homolog (AMN) genes revealed gradually decreasing expression while the rest of 15 genes including osteonectin, osteopontin, collagen IV–alpha 1, biglycan, matrix GLAprotein, and von Willebrand factor demonstrated progressively increasing expression. Similar trends of expression were confirmed at protein level for PTGDR, AMN, osteopontin and osteonectin.Conclusion: Downregulated AMN and PTGDR and upregulated osteopontin and osteonectin were found as potential biomarkers of colorectal carcinogenesis and disease progression to be utilized for prospective biopsy screening both at mRNA and protein levels. Gene alterations identified here may also add to our understanding of CRC progression.

Ependymoma gene expression profiles associated with histological subtype, proliferation, and patient survival

2007 ◽  
Vol 113 (3) ◽  
pp. 325-337 ◽  
Author(s):  
Inna Lukashova-v.Zangen ◽  
Susanne Kneitz ◽  
Camelia-Maria Monoranu ◽  
Stefan Rutkowski ◽  
Bernward Hinkes ◽  
...  
Keyword(s):  
Gene Expression ◽  
Patient Survival ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Histological Subtype
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