The Identification and Interpretation of cis-Regulatory Noncoding Mutations in Cancer

Area of Science:

• Genomic Medicine
• Cancer Genomics

Background:

• Cancer research traditionally focuses on protein-coding mutations.
• The noncoding genome, larger than the coding regions, contains regulatory sequences with functional cancer-implicated mutations.
• Differentiating pathogenic noncoding mutations (NCMs) from background noise is computationally and technically difficult.

Purpose of the Study:

• To review current high-throughput sequencing data and in silico methods for interrogating the noncoding genome in cancer.
• To provide an overview of data resources, computational, and molecular techniques for identifying functional NCMs.

Main Methods:

• Review of up-to-date high-throughput sequencing studies.
• In silico computational methods for noncoding genome analysis.
• Overview of data resources and molecular techniques.

Main Results:

• The noncoding genome harbors functional mutations affecting cancer-related genes and pathways.
• Challenges exist in distinguishing pathogenic NCMs from passenger mutations.
• Various computational and sequencing approaches are available for NCM identification.

Conclusions:

• Understanding the noncoding genome is crucial for cancer characterization, biomarker discovery, and therapeutic target identification.
• Advanced computational and molecular tools are essential for navigating the complexities of noncoding mutations in cancer genomics.
• This review guides researchers in the search for functional NCMs.