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Gene & Protein in Disease

EDITORIAL
Unveiling hidden genes: New drivers of cancer

Amancio Carnero *
1,2
2
1 IBIS, HUVR/CSIC/Univ Sevilla, Avda Manuel Siurot sn, Sevilla, Spain, CIBER DE CANCER, IS
Carlos III, Madrid, Spain

Identifying new genes involved in cancer is crucial for cancer research. This process
helps in discovering new oncogenes/tumor suppressors and establishing gene alterations
in tumors whose drivers have not yet been identified. It also helps identify genes that
1,2
modify tumor behavior and drive tumor evolution, leading to tumor resistance to
therapy, metastasis, or complications within the microenvironment. Some common
3,4
approaches used by researchers are as follows:
1. Genome-wide association studies: These studies analyze the genomes of individuals
with and without cancer to identify genetic variations associated with cancer risk. 5
2. Sequencing technologies: Next-generation sequencing allows researchers to
sequence entire genomes or specific parts of the genome to identify mutations,
alterations, or variations contributing to cancer development. 6-8
3. Methylome analysis: This analysis involves studying and mapping DNA methylation
patterns across the genome to identify regions of DNA methylation and understand
the relevant impact on gene activity. 9
4. Functional genomics: This involves studying gene functions and their interactions
within cells. Techniques such as short hairpin RNA or short interfering RNA
screening and clustered regularly interspaced short palindromic repeats/cas9
protein system (CRISPR/Cas9) gene editing can help researchers manipulate genes
to understand their roles in cancer. 10,11
5. Expression profiling: Comparing gene expression patterns between cancer cells and
healthy cells can help identify genes upregulated or downregulated in cancer. This is
*Corresponding author: achieved through techniques such as microarray analysis or RNA sequencing. 12-14
Amancio Carnero 6. Bioinformatics and data analysis: Computational approaches are crucial for
(acarnero-ibis@us.es)
analyzing large genomic datasets to identify potential candidate genes involved in
Citation: Carnero A. Unveiling cancer pathways or mechanisms. 15
hidden genes: New drivers 7. Animal models: Using genetically engineered animals that mimic human cancers
of cancer. Gene Protein Dis.
16
2025;4(1):2892. can help identify genes involved in cancer development and progression. This is
doi: 10.36922/gpd.2892 accomplished through whole genome CRISPR/Cas9 gene knockout and functional
Received: February 5, 2024 phenotype analysis. 17
Published online: October 7, 2024 8. Clinical studies and patient data analysis: Analyzing data from patients with cancer,
Copyright: © 2024 Author(s). including genetic profiles, treatment responses, and outcomes, provides insights
This is an Open-Access article into genetic factors influencing cancer susceptibility and progression. 18,19
distributed under the terms of the
Creative Commons Attribution The integration of clinical data with genomic information and patient outcomes
License, permitting distribution, offers a comprehensive understanding of the genetic landscape of cancer. This integrative
and reproduction in any medium,
provided the original work is approach facilitates the identification of novel genes, pathways, and molecular
properly cited. mechanisms involved in cancer development. Furthermore, it supports the advancement
of targeted therapies and personalized medicine for patients with cancer.
Publisher’s Note: AccScience
Publishing remains neutral with The use of artificial intelligence (AI) and complex algorithms to analyze and extract
regard to jurisdictional claims in
published maps and institutional conclusions from various databases has become a pivotal aspect of cancer research.
affiliations. AI facilitates the analysis of vast datasets, accelerating the identification of new genes,

Volume 4 Issue 1 (2025) 1 doi: 10.36922/gpd.2892