SexAnnoDB, a knowledgebase of sex-specific regulations from multi-omics data of human cancers

Abstract

Background: Sexual differences across molecular levels profoundly impact cancer biology and outcomes. Patient gender significantly influences drug responses, with divergent reactions between men and women to the same drugs. Despite databases on sex differences in human tissues, understanding regulations of sex disparities in cancer is limited. These resources lack detailed mechanistic studies on sex-biased molecules.

Methods: In this study, we conducted a comprehensive examination of molecular distinctions and regulatory networks across 27 cancer types, delving into sex-biased effects. Our analyses encompassed sex-biased competitive endogenous RNA networks, regulatory networks involving sex-biased RNA binding protein-exon skipping events, sex-biased transcription factor-gene regulatory networks, as well as sex-biased expression quantitative trait loci, sex-biased expression quantitative trait methylation, sex-biased splicing quantitative trait loci, and the identification of sex-biased cancer therapeutic drug target genes. All findings from these analyses are accessible on SexAnnoDB ( https://ccsm.uth.edu/SexAnnoDB/ ).

Results: From these analyses, we defined 126 cancer therapeutic target sex-associated genes. Among them, 9 genes showed sex-biased at both the mRNA and protein levels. Specifically, S100A9 was the target of five drugs, of which calcium has been approved by the FDA for the treatment of colon and rectal cancers. Transcription factor (TF)-gene regulatory network analysis suggested that four TFs in the SARC male group targeted S100A9 and upregulated the expression of S100A9 in these patients. Promoter region methylation status was only associated with S100A9 expression in KIRP female patients. Hypermethylation inhibited S100A9 expression and was responsible for the downregulation of S100A9 in these female patients.

Conclusions: Comprehensive network and association analyses indicated that the sex differences at the transcriptome level were partially the result of corresponding sex-biased epigenetic and genetic molecules. Overall, SexAnnoDB offers a discipline-specific search platform that could potentially assist basic experimental researchers or physicians in developing personalized treatment plans.

Keywords: Cancer; Multi-omics; Sex difference; Sex-biased regulatory network.

Fig. 1

Overview of SexAnnoDB. The molecules used to explore the sex difference in cancer include the genome level (single nucleotide variant, single-nucleotide polymorphisms), epigenome level (DNA methylation), transcriptome level (mRNA, lncRNA, miRNA, exon skipping events, and RNA editing events), and proteome level (protein). The analyses performed to explore the sex-biased regulations in cancer include differential analysis for each condition group, sex-biased network analysis, sex-biased quantitative trait loci studies, functional enrichment analysis, and drug-disease information. For detailed information on sex-related regulations, SexAnnoDB offers accessible and downloadable results. Visit https://ccsm.uth.edu/SexAnnoDB/ for more details

Fig. 2

Sex-biased molecular signatures in human cancers. (A)The number of sex-biased signatures and number of sex-biased genes which are drug target. (B)Top 20 enriched KEGG pathways of genes which have at least one sex-biased signature. (C)From left to right are the expression of S100A9 in four cancers, and pink represents the female tumor patients, blue represents the male tumor patients; the expression of S100A9 five cancers, and pink represents the female tumor patients, black represents the female normal tissues. the drug and disease information of S100A9. Calcium has been approved by the FDA for the treatment of colon and rectal cancers(*p.adjusted < 0.05, **p.adjusted < 0.01, ***p.adjusted < 0.001, ****p.adjusted < 0.001)

Fig. 3

Sex-biased mutations(A) The number of gene with sex-biased mutation.(B) The functional enrichmentment result of gene with sex-biased mutation for each cancer type.(C) The sex-biased cis-eQTL of rs12256605 and HTR7 in BLCA. (D) The sex-biased cis-sQTL of rs12256605 and exon_skip_499249 in THCA.

Fig. 4

DNA methylation-related sex-biased regulations and Sex-biased RNA editing. (A)The number of sex-biased related DNA methylation mediated genes. (B) Promoter methylation affected SRPX expression in SARC. Left is the heatmap of 10 CpG sites in SRPX promoter regions. (C) CpG sites associated with S100A9 in KIRP. (D) The PSI values of exon_skip_499249 in low methylation (0 ~ 0.2), middle methylation (0.2 ~ 0.6), and high methylation (0.6 ~ 1) groups. The patients were categorized into three groups based on the beta value of cg24870846. (E) RNA editing frequence of chr11:61007595 in CD6. (F) chr12:68851361 A-to-I editing in CPM 3’UTR. From left to right are the RNA editing frequency of chr12:68851361; the expression of CPM; and the mechanism of miRNA binding increase in CPM 3’UTR region through RNA editing variant(*FDR < 0.05, ** FDR < 0.01, ***FDR < 0.001, ****FDR < 0.0001).

Fig. 5

Sex-biased RBP-ES regulatory network analysis. (A) The pipeline to identify the functional ES events in the sex-biased RBP-ES network. (B) Number of RBP targeting ES events in sex-biased RBP-ES network of individual cancer type. (C) The sex-biased MSI1-exon_skip_470684 edges include two male-biased edges (LIHC, UVM) and four female-biased edges (ESCA, KIRC, PAAD, THCA). (D) Left: The expression of RBM24 in LIHC. Right: RBM24-ES regulatory network in LIHC. (*p. adjusted < 0.05, **p.adjusted < 0.01, ***p.adjusted < 0.001, ****p.adjusted < 0.001)

Fig. 6

Sex-biased TF-Gene regulatory network analysis. (A) The pipeline to identify the cancer therapeutic target genes in the sex-biased TF-gene network. (B) The number of sex-biased edges for each cancer type. (C) The number of targeting genes (white bar) and drug-targeted genes (red bar) in the sex-biased TF-gene network for each cancer type. (D) Left: the volcano plot of sex-biased genes in SARC. Pink marked as female-biased genes (log2FC< − 1 and p.adjusted < 0.05), blue marked as male-biased genes (log2FC > 1 and p.adjusted < 0.05). Right: sex-biased TF-Gene regulatory network of 7 sex-biased drug targeted genes in SARC

Fig. 7

Sex-biased ceRNAs in human cancers. (A) The pipeline to identify the cancer therapeutic targets in sex-biased ceRNA network. (B)Related drug information of ESR1. (C) The expression of HAND2-AS1(lncRNA), hsa-mir-206(miRNA), and ESR1(mRNA) in SARC (*p. adjusted < 0.05, **p.adjusted < 0.01, ***p.adjusted < 0.001, ****p.adjusted < 0.001)

Fig. 8

The sex-biased effect on cancer therapeutic targets. (A) Sex-biased cancer therapeutic drug target genes. The cancer-drug pairs were collected from the National Cancer Institute (https://www.cancer.gov/about-cancer/treatment/drugs/cancer-type) and the drug-gene pairs were collected from DrugBank. (B)The volcano plot of sex-biased genes in BRCA. Pink marked as female-biased genes (log2FC<–1 and p.adjusted < 0.05), blue marked as male-biased genes (log2FC > 1 and p.adjusted < 0.05). (C) The gene expression of EGFR in BRCA. (D) The protein expression of EGFR in BRCA. (E) Cancer-drug-sex-biased gene information in BRCA (*p. adjusted < 0.05, **p.adjusted < 0.01, ***p.adjusted < 0.001, ****p.adjusted < 0.001)