A novel knowledge-derived data potentizing method revealed unique liver cancer-associated genetic variants

Area of Science:

• Genomics
• Oncology
• Bioinformatics

Background:

• Next-generation sequencing (NGS) is crucial for identifying disease-associated genetic variants and advancing personalized medicine.
• NGS is effective for multifactorial diseases like cancer, but predicting markers for all cancer types and global populations remains challenging due to genetic diversity.

Purpose of the Study:

• To identify critical genetic factors associated with liver cancer using exome sequencing.
• To discover novel genetic variants and potential biomarkers for liver cancer.

Main Methods:

• Performed exome sequencing on four liver cancer samples.
• Applied knowledge-based filter chains and quality filters for data acquisition and curation.
• Analyzed liver-specific expression profiles to identify candidate genes.

Main Results:

• Identified 20 MNV-induced, 5 INDEL-induced, and 31 SNV-induced neoplasm-exclusive genes.
• Selected 17 genes as potential causative genetic factors for liver cancer based on expression profiles.
• Confirmed four novel variants (c.416T>C in SORD, c.1048_1049delGCinsCG in KRT6A, c.1159G>T in SVEP1, and c.430G>C in MRPL38) as critical genetic factors for liver cancer.

Conclusions:

• A novel data prioritizing rationale identified previously unaddressed liver cancer-associated variants.
• These findings offer potential for early liver cancer prediction and personalized therapeutic development.
• The data curation method can enhance screening of global genetic variants for disease management.