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. 2024 Dec 30;14(1):31749.
doi: 10.1038/s41598-024-82563-9.

Weighted gene co-expression network analysis reveals key stromal prognostic markers in pancreatic cancer

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Weighted gene co-expression network analysis reveals key stromal prognostic markers in pancreatic cancer

G Mantini et al. Sci Rep. .

Abstract

In recent years, it has been shown that stroma compartment can favor tumor proliferation and aggressiveness. Although extensive research with network analyses such as Weighted Gene Co-expression Network Analysis (WGCNA) has been conducted on pancreatic cancer and its stromal components, WGCNA has not previously been applied to isolate and identify genes associated with the abundance of stroma and survival outcome from bulk RNA data. We investigated the gene expression profile and clinical information of 140 pancreatic ductal adenocarcinoma patients from TCGA. Network analysis was performed using WGCNA and four modules were found to be associated to patients' clinical traits. Specifically, one module of 2459 genes, was associated to stromal sample content. Subsequently, those genes were further analyzed for survival association through log-rank test and Cox regression. HPGDS and ITGA9-AS1 emerged as significant indicators of favorable prognosis while KCMF1 and YARS1 were implicated in poorer prognostic outcomes. Importantly, HPGDS was found to be stromal-specific in the TMA cohort of Human Protein Atlas. Single sample GSEA showed that the stromal module is enriched for stromal signature of Moffitt and Puleo. These findings suggest that we uncovered a stromal specific signature through WGCNA and found putative prognostic markers.

Keywords: Biomarkers; PDAC; Stroma; Survival; WGCNA.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Choice of soft thresholds parameter for network construction. a) Correlation of resulting network with scale free topology network for each selected threshold (on the left); mean connectivity of resulting network for each selected threshold (on the right). b) Association between modules and clinical traits was assessed by correlating each module with clinical traits (TMB, Acinar mean, Islet mean, and Stromal mean), providing both correlation coefficients and p-values. Positive correlations are indicated in red, while negative correlations are shown in blue.
Fig. 2
Fig. 2
Survival analyses on module associated to stromal content. a) KM curves of significant stromal prognostic markers. b) Univariate cox-regression test on significant prognostic markers.
Fig. 3
Fig. 3
Cox regression adjusted for stage. Multivariate cox-regression of four putative prognostic markers (YARS1, KCMF1, ITGA9-AS1 and HPGDS) adjusted for stage.
Fig. 4
Fig. 4
Validation of stromal expression of HPGDS. (a-b) HPGDS expression in stromal compartment of PDAC tissue. (c-d) Low expression of HPGDS in stromal compartment of PDAC tissue. Images are taken by Human Protein Atlas.
Fig. 5
Fig. 5
Validation of stromal signatures in module associated to stromal content. ssGSEA of stromal genes identified by WGCNA and enriched for known stromal signatures of Moffitt and Puleo.

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