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Many research has focused on the variety or heterogeneity of distinct solid tumour types in recent years. Cancer cells and tumours have more complicated gene expression network patterns than normal cells and organs. The enrichment studies were carried out to see if a set of biological processes established in advance were enriched. Read More with Us: https://bit.ly/3mwNjib Why Statswork? Plagiarism Free | Unlimited Support | Prompt Turnaround Times | Subject Matter Expertise | Experienced Bio-statisticians & Statisticians | Statistics across Methodologies | Wide Range of Tools & Technologies Supports | Tutoring Services | 24/7 Email Support | Recommended by Universities Contact Us: Website: www.statswork.com Email: [email protected] #UnitedKingdom: +44 1618184707 #India: +91 4446313550 WhatsApp: +91 8754467066
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Bioinformatics analysisand identification ofpotential genes related topathogenesis
An Academic presentation by
Dr. Nancy Agnes, Head, Technical Operations, Statswork Group www.statswork.comEmail: [email protected]
Outline
TODAY'S DISCUSSION
INTRODUCTION
DATA PROCESSION
GENE FUNCTION AND PATHWAYS
GENE SET ENRICHMENT ANALYSIS
CONCLUSION
Cervical cancer develops over a 10 to 25 year period, starting with inflammationand progressing via cervical intraepithelial neoplasia (CIN) to invasivemalignancy.
CIN is a possibly premalignant alteration of the cervix's squamous cells.
Many research has focused on the variety or heterogeneity of distinct solidtumour types in recent years.
Cancer cells and tumours have more complicated gene expression networkpatterns than normal cells and organs.
INTRODUCTION
The Gene Expression Omnibus (GEO) database was used to get GSE63514 geneexpression profiles.
The GSE63514 was a 128-sample expression profiling based on the GPL570 platform(24 normal samples, 76 CIN samples and 28 cervical cancer samples).
From a flash-frozen biopsy, all samples were cryosectioned. Prior to bioinformaticsinvestigation, the array probes were mapped to their respective Gene IDs using the arrayannotations.
If a probe matches several genes, it will be deleted. If a gene matches several probes,the average value will be calculated.
Based on the number of genes filtered out, an appropriate threshold was determined.The study's workflow is depicted in Figure 1.
DATA PROCESSION
Figure 1. The combined analysis and functional validation flowchart
The Cluster Profiler is an ontology-based R tool that compares gene clusters usingbiological word categorization and enrichment analysis better to understand the higher-order functions of biological systems.
DAVID -To identify the biological characteristics such as biological process (BP), cellularcomponent (CC), and molecular function (MF) of significant DEGs, a standard functionalannotation tool of bioinformatics resources were used.
Pathway enrichment analysis from the Kyoto Encyclopedia of Genes and Genomes wasutilized to identify the critical pathways.
The cut-off threshold for substantial enrichment was established at AdjP-value 0.05.
GENE FUNCTION AND PATHWAYS
The enrichment studies were carried out to see if a set of biologicalprocesses established in advance were enriched.
The enriched pathways were sorted by their normalized enrichmentscores (NESs), using a cut-off of FDR 0.05 as the criterion.
GENE SET ENRICHMENT ANALYSIS
DEGs in CIN samples can be utilized to detect the disease's progression before itprogresses to malignancy.
Researchers used a combinatorial method that included gene expression profiles,PPI networks, hubs, modules, and motifs to find possible prognostic indicatorscapable of differentiating progressive cervical illness.
Gene expression profiling found 537 DEGs in CIN samples (331 up-regulatedgenes and 206 down-regulated genes).
These genes influenced the cell cycle, DNA replication, Fanconi anemia route, p53signaling pathway, homologous recombination, Oocyte meiosis, Mismatch repair,Pyrimidine metabolism, Progesterone-mediated oocyte maturation, and Drugmetabolism, among other processes.
CONCLUSION
The four functional gene sets that were enriched were E2F-Targets, G2M-Checkpoint, Mitotic-Spindle, andSpermatogenesis.
A total of 31 DEGs were identified as potential hub genes for CIN high-grade risk. Out of 537. 13 genes mayinteract more closely in CIN categorization and have a strong diagnostic value.
