Integrative Functional Genomic Analysis of CRISPR Screening Data to Prioritize Host Dependency Factors in Coronavirus Infection
Keywords:
SARS-CoV-2, CRISPR-Cas9 screening, Host dependency factors, Functional genomics, Gene prioritizationAbstract
Electrophoretic Coronavirus infection depends on multiple host cellular factors that regulate viral entry, replication, and intracellular processes. Identifying reliable host dependency factors is essential for understanding coronavirus–host interactions and supporting future antiviral research. The present study investigated potential host dependency factors associated with SARS-CoV-2 infection through comparative analysis of publicly available genome-wide CRISPR-Cas9 screening data obtained from the NCBI Gene Expression Omnibus (GSE178699). The analysis included 18,053 non-control gene labels across five cellular models: Vero, Huh7, Calu-3, Caco-2, and HEK293A_T. Gene-level log2 screening differences, cross-model consistency, guide coverage, and exploratory prioritization scores were evaluated using descriptive statistical approaches and Pearson correlation analysis. ACE2 demonstrated the highest exploratory priority score (3.661), with positive screening differences exceeding the selected threshold in three cellular models. Additional candidates, including GYPB, KRTAP1-1, HNF1B, and PCBD1, exhibited variable screening responses. The strongest correlation was observed between Vero and Calu-3 cells (r = 0.247), indicating limited similarity across cellular models. These findings highlight substantial heterogeneity in coronavirus-associated screening profiles and emphasize the importance of cross-model comparisons for candidate prioritization. Provisional experimental contrasts, the absence of formal statistical significance testing, and limited functional validation restrict definitive biological interpretation. Overall, the study provides a preliminary framework for prioritizing potential coronavirus host dependency factors and supports further functional genomic investigations and experimental validation.
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