Bioinformatics analysis and identification of potential genes related to pathogenesis

Bioinformatics analysis and identification of potential genes related to pathogenesis An Academic presentation by Dr. Nancy Agnes, Head, Technical Operations, Statswork Group www.statswork.com Email: info@statswork.com

TODAY'S DISCUSSION Outline INTRODUCTION DATA PROCESSION GENE FUNCTION AND PATHWAYS GENE SET ENRICHMENT ANALYSIS CONCLUSION

INTRODUCTION Cervical cancer develops over a 10 to 25 year period, starting with inflammation and progressing via cervical intraepithelial neoplasia (CIN) to invasive malignancy.

CIN is a possibly premalignant alteration of the cervix's squamous cells.

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.

DATA PROCESSION The Gene Expression Omnibus (GEO) database was used to get GSE63514 gene expression 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 bioinformatics investigation, the array probes were mapped to their respective Gene IDs using the array annotations.

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.

Figure 1. The combined analysis and functional validation flowchart

GENE FUNCTION AND PATHWAYS The Cluster Profiler is an ontology-based R tool that compares gene clusters using biological word categorization and enrichment analysis better to understand the higherorder functions of biological systems.

DAVID -To identify the biological characteristics such as biological process (BP), cellular component (CC), and molecular function (MF) of significant DEGs, a standard functional annotation tool of bioinformatics resources were used.

Pathway enrichment analysis from the Kyoto Encyclopedia of Genes and Genomes was utilized to identify the critical pathways.

The cut-off threshold for substantial enrichment was established at AdjP-value 0.05.

GENE SET ENRICHMENT ANALYSIS The enrichment studies were carried out to see if a set of biological processes established in advance were enriched.

The enriched pathways were sorted by their normalized enrichment scores (NESs), using a cut-off of FDR 0.05 as the criterion.

CONCLUSION DEGs in CIN samples can be utilized to detect the disease's progression before it progresses to malignancy.

Researchers used a combinatorial method that included gene expression profiles, PPI networks, hubs, modules, and motifs to find possible prognostic indicators capable of differentiating progressive cervical illness.

Gene expression profiling found 537 DEGs in CIN samples (331 up-regulated genes and 206 down-regulated genes).

These genes influenced the cell cycle, DNA replication, Fanconi anemia route, p53 signaling pathway, homologous recombination, Oocyte meiosis, Mismatch repair, Pyrimidine metabolism, Progesterone-mediated oocyte maturation, and Drug metabolism, among other processes.

The four functional gene sets that were enriched were E2F-Targets, G2M-Checkpoint, Mitotic-Spindle, and Spermatogenesis.

A total of 31 DEGs were identified as potential hub genes for CIN high-grade risk. Out of 537. 13 genes may interact more closely in CIN categorization and have a strong diagnostic value.

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