Identification of Cancer Associated Fibroblasts Related Genes Signature to Facilitate Improved Prediction of Prognosis and Responses to Therapy in Patients with Pancreatic Cancer

Abstract

Pancreatic cancer (PC) is highly aggressive, with a 5-year survival rate of 12.8%, making early detection vital. However, non-specific symptoms and precursor lesions complicate diagnosis. Existing tools for the early detection of PC are limited. CAFs are crucial in cancer progression, invasion, and metastasis, yet their role in PC is poorly understood. This study analyzes mRNA data from PC samples to identify CAF-related genes and drugs for PC treatment using algorithms like EPIC, xCell, MCP-counter, and TIDE to quantify CAF infiltration. Weighted gene co-expression network analysis (WGCNA) identified 26 hub genes. Our analyses revealed eight prognostic genes, leading to establishing a six-gene model for assessing prognosis. Correlation analysis showed that the CAF risk score correlates with CAF infiltration and related markers. We also identified six potential drugs, observing significant differences between high-CAF and low-CAF risk groups. High CAF risk scores were associated with lower responses to immunotherapy and higher tumor mutation burdens. GSEA indicated that these scores are enriched in tumor microenvironment pathways. In summary, these six model genes can predict overall survival and responses to chemotherapy and immunotherapy for pancreatic cancer, offering valuable insights for future clinical strategies.

Keywords: WGCNA; cancer associated fibroblast; chemotherapy; immunotherapy; molecular docking; pancreatic cancer; prognosis.

Figure 1

Workflow of the research on TCGA-PAAD and GSE183795 and GSE78229 (A). Overall survival analysis by log-rank. K-M curves show the pancreatic cancer patients with higher stromal-score related to bad overall survival in GSE183795 and GSE78229 (B) and TCGA-PAAD (C).

Figure 2

Co-express network of the WGCNA. Based on the scale topology tree in GSE183795 and GSE78229 (A) and TCGA-PAAD (B), the soft threshold power was decided. The cluster gene tree shows the depth of cut needed <0.2 displayed in GSE183795 and GSE78229 (C) and TCGA-PAAD (D). Module-trait relationships show different module colors’ relation values in GSE183795 and GSE78229 (E) and TCGA-PAAD (F). Scatter graphs of the module membership and gene significance of each gene in the magenta module in GSE183795 and GSE78229 for CAF (G) and for StromalScore (H); the brown module in brown TCGA-PAAD for CAF (I) and for StromalScore (J) (MM: module membership, GS: gene significance). The horizontal axis is the correlation between gene and co-expression modules, and the vertical axis is about the gene and phenotype.

Figure 3

(A) The Venn diagram presented the intersection of TCGA-PAAD brown and GSE183795 and GSE78229 magenta module genes. (B) Univariate Cox analysis for the screening of overall survival-associated genes in TCGA-PAAD.

Figure 4

Spearman’s correlation analyses revealed the CAF risk score was strongly and positively correlated with stromal scores and multi-estimated CAF infiltrations in GSE183795 and GSE78229 (A) and TCGA-PAAD (B) cohorts. The heatmap revealing the expression patterns of CAF markers identified two CAF genes with the CAF risk score in GSE183795 and GSE78229 (C) and TCGA-PAAD (D) cohorts. The CAF risk score and two signature genes were positively correlated with the literature that reported CAF markers in GSE183795 and GSE78229 (E) and TCGA-PAAD (F) cohorts. (* means p < 0.05, ** means p < 0.01, *** means p < 0.001).

Figure 5

Sensitivity drugs: about the GSE183795 and GSE78229 (A) and TCGA-PAAD (B) including Staurosporine, Dasatinib, OTX015, BMS-536924, Luminespib, IGF1R. Immunotherapy responses of the high-/low-risk groups based on TIDE scores. The box plots (C) in GSE183795 and GSE78229 and TCGA-PAAD, violin plots (D) in GSE183795 and GSE78229 and TCGA-PAAD, and the curve showing the sensitivity of different methods in GSE183795 and GSE78229 (E) and TCGA-PAAD (F).

Figure 6

Molecular docking graphs MMP2 binding with OTX015 (A), FSTL1 with OTX015 (B), GFPT2 with Luminespib (C), and CTSK with Staurosporine (D).

Figure 7

GSEA and ssGSEA enriched GO biological function in GEO datasets. Gene set enrichment analysis (GSEA) sets between high-CAF risk groups (A) and low risk group (B) in GSE183795 and GSE78229. ssGSEA results showed that the CAF risk score was positively correlated with bone morphogenesis (C), cell fate specification involved in pattern (D), embryonic neurocranium morphogenesis (E), fibroblast growth factor receptor signaling pathway (F), and mesenchyme development (G) in GSE183795 and GSE78229.

Figure 8

The mRNA expression levels of the two CAF genes in the fibroblasts and pancreatic cancer cell lines were illustrated in the heat map (A) and compared by Wilcoxon analysis (B). Protein expression in pancreatic cancer tissues (C).