Open reading frames associated with cancer in the dark matter of the human genome

Area of Science:

• Genomics
• Proteomics
• Cancer Biology

Background:

• The human genome contains numerous uncharacterized proteins (open reading frames, ORFs) representing potential novel targets for cancer therapy.
• Systematic analysis of these ORFs, termed the 'dark matter' of the proteome, is crucial for identifying druggable targets and biomarkers.
• Existing data mining tools can be leveraged to build a knowledge base for target discovery and validation.

Purpose of the Study:

• To systematically screen the human proteome's dark matter for proteins associated with cancer.
• To identify and characterize novel cancer-associated open reading frames (OncoORFs) for potential therapeutic targeting.

Main Methods:

• Screening of human dark matter proteome ORFs using the Genetic Association Database for neoplasm association.
• Utilizing the Phenome-Genome Integrator tool to establish phenotypic associations with cancer.
• Employing batch analysis for protein expression, gene ontology, motifs, domains, and genome-wide association studies to characterize identified ORFs.

Main Results:

• Sixty-two ORFs were identified with associations to neoplasms.
• Expression Quantitative Trait Loci (eQTL) analysis pinpointed thirteen ORFs relevant to cancer traits.
• Further analysis confirmed the relevance of these OncoORFs in various tumors and their association with a broad spectrum of human diseases.

Conclusions:

• The identified OncoORFs are linked to diverse diseases and disorders, highlighting the complexity of the uncharacterized proteome in human pathology.
• This study successfully illuminates the 'dark matter' of the proteome, paving the way for novel cancer target discovery and research.