Unveiling the therapeutic potential of senescence-related IQGAP2 in pancreatic Cancer through post-GWAS genomic and scRNA-seq analyses

Abstract

Background: Pancreatic cancer (PC) is a highly aggressive tumor with a poor prognosis and few treatment options available. While cellular senescence has been linked to the advancement of various cancers, its specific role in PC is not well understood.

Method: We employed Mendelian randomization (MR) alongside single-cell RNA sequencing (scRNA-seq) to explore the involvement of senescence-associated genes (SAGs) in PC. A summary-data-based MR (SMR) analysis was performed to evaluate the connection between SAG expression and the risk of developing PC, using HEIDI test and colocalization analysis to reduce confounding variables. Additionally, scRNA-seq data were used to further examine SAG expression within pancreatic cancer cells and assess their potential as therapeutic targets.

Results: The SMR analysis revealed a significant correlation between IQGAP2 expression levels and the risk of PC (P__FDR < 0.05), which was corroborated by HEIDI test (P__HEIDI > 0.01) and colocalization analysis (PPH4 = 0.79). Further MR analyses that IQGAP2 plays a causal role in PC at both genetic and protein levels. Functional enrichment analyses indicated that IQGAP2 participates in cytoskeletal organization, cell migration, signal transduction, along with pathways pertinent to PC development. The scRNA-seq findings demonstrated heightened IQGAP2 expression specifically in epithelial cells; drug prediction analyses identified it as a promising target for therapy.

Conclusion: There is a strong association between IQGAP2 and the risk factors for the progression of PC, positioning it as an attractive candidate for targeted therapies. This investigation sheds new light on mechanisms underlying PC while paving the way for precision-targeted treatment strategies.

Keywords: Cellular senescence; Drug target; IQGAP2; Pancreatic cancer; Summary-data-based MR; scRNA-seq.

Fig. 1

Research Design Diagram

Fig. 2

SMR analysis results. A Forest plot of positive results for SMR analysis in three queues. B Venn diagram of three datasets

Fig. 3

SMR locus plot and effects plot of the correlation between PC and IQGAP2 gene expression

Fig. 4

IQGAP2 and PC colocalization area map

Fig. 5

PPI network of IQGAP2

Fig. 6

Functional enrichment analysis results. A GO functional enrichment analysis. B KEGG pathway enrichment analysis

Fig. 7

Tissue expression profile of IQGAP2. A Differential mRNA expression of IQGAP2. B Differential protein expression of IQGAP2

Fig. 8

Single-cell type expression in PC tissue for IQGAP2. A and B represent a total of 23 clusters of cells that are ultimately divided into 6 types of cells. C and D indicates significant enrichment of IQGAP2 in epithelial cells

Fig. 9

Molecular docking of IQGAP2 protein with ellagic acid drug molecule