SexAnnoDB

SexAnnoDB provides curated data on sex-specific regulations derived from multi-omics analyses of human cancers.

Key Features:

• Sex-biased competitive endogenous RNA networks: Analysis of regulatory interactions involving sex-biased non-coding RNAs across 27 cancer types.
• Sex-biased RNA binding protein-exon skipping networks: Identification of regulatory networks involving sex-biased RNA binding proteins and alternative splicing events.
• Sex-biased transcription factor-gene regulatory networks: Mapping of transcription factor-gene regulatory relationships with sex-specific patterns.
• Sex-biased expression quantitative trait loci: Identification of genetic variants associated with sex-biased gene expression.
• Sex-biased expression quantitative trait methylation: Analysis of methylation patterns associated with sex-biased gene expression.
• Sex-biased splicing quantitative trait loci: Identification of genetic variants associated with sex-biased splicing.
• Sex-biased cancer therapeutic drug target genes: Identification of 126 cancer therapeutic target sex-associated genes, including 9 genes showing sex-bias at both mRNA and protein levels.

Scientific Applications:

• Cancer sex disparity research: Investigation of molecular mechanisms underlying sex differences in cancer biology and outcomes.
• Therapeutic target identification: Discovery of sex-biased therapeutic targets such as S100A9, which is targeted by five drugs including FDA-approved calcium for colon and rectal cancers.
• Personalized treatment development: Support for basic experimental researchers and physicians in developing sex-informed personalized treatment plans.
• Epigenetic regulation analysis: Study of sex-biased epigenetic mechanisms, such as promoter hypermethylation effects on gene expression in sex-specific patient groups.

Methodology:

SexAnnoDB was constructed through comprehensive multi-omics analyses across 27 cancer types, including analysis of sex-biased competitive endogenous RNA networks, RNA binding protein-exon skipping events, transcription factor-gene regulatory networks, expression quantitative trait loci, expression quantitative trait methylation, splicing quantitative trait loci, and identification of sex-biased cancer therapeutic drug target genes.