Study of DEGs
Initiative #2
Compare DEGs in Primary and Metastatic Melanoma To Comprehend Genetic Changes Associated to Melanoma Progression And Metastatis
Background
Melanoma is a complex and aggressive skin cancer that can be challenging to diagnose and treat. Understanding the differences in gene expression between primary and metastatic melanoma samples can provide valuable insights into the molecular mechanisms driving tumor progression and metastasis.
Research study objective
This study aims to identify and compare differentially expressed genes (DEGs) in primary and metastatic melanoma samples to better comprehend the genetic changes associated with melanoma progression, which potentially leads to finding novel therapies.
Research questions
What are the key DEGs that distinguish primary and metastatic melanoma samples?
How do the DEGs in primary and metastatic melanoma samples relate to known melanoma-related pathways and biological processes?
Can the identified DEGs be validated in wet lab tests like qPCR?
Study Design
Data Collection
⦿ Retrieve high-throughput gene expression data from publicly available databases such as The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO).
- GSE8401: 83 samples with primary and metastatic
- GSE46517:121 samples ( primary, metastatic nevus , and normal)
⦿ Select datasets that include both primary and metastatic melanoma samples.
⦿ Ensure the datasets have sufficient sample sizes and are well-characterized for clinical and pathological features.
Data Preprocessing
⦿ Nextflow (differential abundance.nf)
⦿ Normalize and filter the data to remove any technical artifacts and ensure consistency across datasets.
⦿ Perform quality control checks to ensure that the data are reliable and accurate.
Identification of DEGs
⦿ Nextflow (differential abundance.nf) for DEGs
⦿ Use statistical methods such as DESeq2 or limma to identify DEGs between primary and metastatic melanoma samples.
⦿ Apply a threshold for statistical significance (e.g., p < 0.05) and fold change (e.g., |log2(fold\_change)| > 1) to filter the results.
Pathway Enrichment Analysis
⦿ Use bioinformatics tools such as DAVID or GSEA to identify enriched biological pathways and gene ontology (GO) terms associated with the DEGs.
⦿ Analyze the pathways and GO terms to identify key biological processes and signaling pathways involved in melanoma progression.
Hub Gene Identification
⦿ Use network analysis tools such as Cytoscape to identify hub genes that are central to the DEGs and their associated pathways.
⦿ Analyze the hub genes to identify potential key regulators of melanoma progression.
Wet lab validation by qPCR
Deliverables (expected outcomes):
1. DEGs Identification
A list of DEGs that distinguish primary and metastatic melanoma samples.
2. Pathway Enrichment
Identification of enriched biological pathways and GO terms associated with the DEGs.
3. Hub Gene Identification
A set of hub genes that are central to the DEGs and their associated pathways.
Citations:
[1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9303152/
[2] https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2020.581985/full
[3] https://www.degruyter.com/document/doi/10.1515/med-2020-0190/html
[4] https://www.nature.com/articles/s41598-022-17468-6
[5] https://bmccancer.biomedcentral.com/articles/10.1186/s12885-020-07372-5