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Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

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Identification of Potential Driver Genes Based on Multi-Genomic Data in Cervical Cancer.

Yuexun Xu¹, Hui Luo², Qunchao Hu²

¹Department of Gynecology and Obstetrics, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, China.

Frontiers in Genetics

|March 5, 2021

Summary

This study identifies key genetic mutations and molecular subtypes in cervical cancer, revealing potential new targets for precision treatments and improving our understanding of this common women's cancer.

Keywords:

TCGA cervical cancer driver mutation molecular classification multi-platform analysis

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Area of Science:

Oncology
Genomics
Molecular Biology

Background:

Cervical cancer is a leading cause of cancer death in women globally.
Complex molecular alterations in cervical cancer present challenges for classification and treatment.
Genome characterization is crucial for understanding cervical cancer's complexity.

Purpose of the Study:

To identify driver mutations in cervical cancer.
To depict molecular classification of cervical cancer.
To analyze genomic and molecular profiles for therapeutic insights.

Main Methods:

Integrative multi-platform analysis of The Cancer Genome Atlas (TCGA) cervical cancer cohort (284 cases).
Analysis of mutation data, copy number variation (CNV) using GISTIC, and methylation profiles.
Unsupervised consensus clustering to identify molecular subtypes.

Main Results:

Identified top 10 mutated genes (e.g., TTN, PIK3CA) and frequent chromosome arm-level CNVs (losses in 4p, 11p, 11q; gains in 20q, 3q, 1q).
Detected four distinct molecular subtypes based on CNV and methylation profiles.
Identified 10 potential novel driver genes (e.g., GPR107, Rb1, DDX3X).

Conclusions:

Comprehensive genetic characterization of cervical cancer.
Discovery of novel driver genes offers potential for targeted therapies.
Findings advance precision medicine approaches for cervical cancer treatment.