Deciphering the role of complement system genes in pancreatic cancer susceptibility and prognosis

Abstract

Pancreatic ductal adenocarcinoma (PDAC) genetic susceptibility is partially identified. The complement system (CS) influences carcinogenesis and participates in immunological defense and homeostasis; however, its role in PDAC genetic susceptibility and prognosis is underexplored. The association of SNPs within 111 CS-related genes with PDAC risk is assessed in the PanGenEU study and validated in the UKBiobank. We investigate the association between the CS-related gene variation and PDAC risk, followed by an in-depth functional in silico study using TCGA and ICGC data. We assess whether CS-related genes are associated with prognosis at the germline and somatic levels. We investigate the immune infiltration of PDAC tumors according to their transcriptomic profile. Genetic variation in FCN1 and PLAT is significantly associated with PDAC risk. PDAC patients with elevated expression of IGHG3, IGKC, IGHM, F2R, F2RL2, CFI, A2M, or C4A display improved survival and higher infiltration of CD8⁺, B cells, and Th1 cells. Individuals with high expression levels of either FGA, SERPINE1, FGG, or F3 exhibit poorer survival, higher infiltration of Tregs, and lower infiltration of CD8⁺ cells. Results from this study suggest that CS-related genes play a role in PDAC genetic susceptibility and survival through specific immune cell infiltration.

Fig. 1. Forest plots from the meta-analysis of complement system-related genes associated with improved overall survival when considering PanGenEU, TCGA, ICGC-CA, and ICGC-AU (Fig. 1A-1G) and when including data from TCGA, ICGC-CA, and ICGC-AU (Fig. 1H).

The Hazard ratios (HR) and 95% confidence intervals (CI) were estimated using two-sided Cox proportional-hazards models under a dominance mode of inheritance. Meta-analysis p-values were adjusted for multiple comparisons using the Benjamini–Hochberg false discovery rate method.

Fig. 2. Forest plots from the meta-analysis of the complement system-related genes indicate an association with poorer overall survival when considering PanGenEU, TCGA, ICGC-CA, and ICGC-AU (Fig. 2A-2D).

Hazard ratios (HR) and 95% confidence intervals (CI) were estimated using two-sided Cox proportional-hazards regression models under a dominance mode of inheritance. Meta-analysis p-values were adjusted for multiple comparisons using the Benjamini–Hochberg false discovery rate method. Source data are provided as a Source Data file.

Fig. 3. Forest plots from the meta-analysis of complement system-related genes illustrate the protective signature that includes genes linked to improved overall survival, as analyzed in the PanGenEU, TCGA, ICGC-CA, and ICGC-AU databases (Fig. 3A).

Additionally, the risk signature considers CS-related genes tied to an increased risk of death in the TCGA, ICGC-CA, and ICGC-AU cohorts (Fig. 3 B). Hazard ratios (HR) and 95% confidence intervals (CI) are estimated using two-sided Cox proportional-hazards regression models under a dominance mode of inheritance. Meta-analysis p-values were adjusted for multiple comparisons using the Benjamini-Hochberg false discovery rate method. Source data are provided as a Source Data file.

Fig. 4. Complement-related gene expression is linked to distinct immune-cell infiltration patterns in PDAC.

A heatmap displays the log₂ fold-change (log₂FC) in the relative abundance of immune-cell populations when comparing samples with high expression of each complement system–related gene to those with low expression. The left block (PanGenEU, n = 142) corresponds to 21 immune cells derived from CIBERSORTx. The right block (TCGA-PAAD, n = 124) contains 12 immune cells inferred by Thorsson. Columns are ordered based on hierarchical clustering of the cell types shared between the two cohorts; the brace below indicates the two data sources. Rows represent the 12 complement system-related genes that were included in both expression matrices (IGHG3, IGKC, F2R, F2RL2, IGHM, CFI, A2M, signature 1, FGA, SERPINE1, FGG, F3). Color intensity reflects log₂FC (red = higher, blue = lower abundance in the high-expression group). Gray squares represent cell types for which all values were missing in one comparison group (e.g., Th1 cells in TCGA). Asterisks indicate significant differences that remain after the Benjamini–Hochberg correction (FDR < 0.05, two-sided Wilcoxon rank-sum test). One extreme outlier for T cells CD4 Naïve in TCGA was set to NA for that specific comparison only; the sample was retained for all other cell-type tests. Source data are provided as a Source Data file.

Fig. 5. The heatmap illustrates the relationships between gene-cell state types, highlighting significant differences in the abundance of 12 immune cell types in PanGenEU.

This is based on a comparison of high expression levels of complement system-related genes versus low expression levels. Color intensity reflects log₂FC (red = higher, blue = lower abundance in the high-expression group). Asterisks indicate significant differences that remain after the Benjamini–Hochberg correction (FDR < 0.05, two-sided Wilcoxon rank-sum test). Source data are provided as a Source Data file.

Fig. 6. Heatmap illustrating gene-ecotype relationships that highlights the significant differences in the abundance of 10 immune ecotypes in PanGenEU by comparing high expression levels of the complement system-related genes to low expression levels.

T Color intensity reflects log₂FC (red = higher, blue = lower abundance in the high-expression group). Asterisks indicate significant differences that remain after the Benjamini–Hochberg correction (FDR < 0.05, two-sided Wilcoxon rank-sum test). Source data are provided as a Source Data file.