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. 2023 Apr 25:2023:3781091.
doi: 10.1155/2023/3781091. eCollection 2023.

Cancer-Associated Fibroblast Risk Model for Prediction of Colorectal Carcinoma Prognosis and Therapeutic Responses

Yan Wang  1 , Zhengbo Chen  2 , Gang Zhao  2 , Qiang Li  1
Affiliations

Cancer-Associated Fibroblast Risk Model for Prediction of Colorectal Carcinoma Prognosis and Therapeutic Responses

Yan Wang et al. Mediators Inflamm. .

Abstract

Colorectal carcinoma (CRC) is a malignant tumor of the digestive system. Cancer-associated fibroblasts (CAFs) are important cellular elements in the tumor microenvironment of CRC, which contribute to CRC progression and immune escape. To predict the survival outcome and therapeutic responses of CRC patients, we identified genes connected with stromal CAF and generated a risk model. In this study, we used multiple algorithms to reveal CAF-related genes in the Gene Expression Omnibus and The Cancer Genome Atlas datasets and construct a risk model composed by prognostic CAF-associated genes. Then, we evaluated whether the risk score could predict CAF infiltrations and immunotherapy in CRC and confirmed the expression of the risk model in CAFs. Our results showed that CRC patients with high CAF infiltrations and stromal score had worse prognosis than those with low-CAF infiltrations and stromal score. We obtained 88 stromal CAF-associated hub-genes and generated a CAF risk model consisting of ZNF532 and COLEC12. Compared with low-risk group, the overall survival in high-risk group was shorter. The relationship between risk score, ZNF532 and COLEC12, and stromal CAF infiltrations and CAF markers was positive. In addition, the effect of immunotherapy in the high-risk group was not as good as that in the low-risk group. Patients with the high-risk group were enriched in chemokine signaling pathway, cytokine-cytokine receptor interaction, and focal adhesion. Finally, we confirmed that the expressions of ZNF532 and COLEC12 in risk model were widely distributed in fibroblasts of CRC, and the expression levels were higher in fibroblasts than CRC cells. In conclusion, the prognostic CAF signature of ZNF532 and COLEC12 can be applied not only to predict the prognosis of CRC patients but also to evaluate the immunotherapy response in CRC patients, and these findings provide the possibility for further development of individualized treatment for CRC.

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Conflict of interest statement

The authors report that there are no competing interests to declare.

Figures

Figure 1
Figure 1
The schematic diagram of the workflow.
Figure 2
Figure 2
High CAF and stromal scores in CRC had a bad prognosis. High CAF immune infiltration level was associated with poor prognosis in GSE39582 (a) and TCGA-COAD (b) cohorts. High stromal score was associated with poor prognosis in GSE39582 (c) and TCGA-COAD (d) cohorts.
Figure 3
Figure 3
WGCNA was used to explore stromal CAF-related hub-genes and perform functional enrichment analysis. The soft-thresholding power in GSE39582 (a) and TCGA-COAD (b) cohorts. Clustering dendrograms exhibiting hub-genes with alike expression profiles were converged into coexpression modules in GSE39582 (c) and TCGA-COAD (d) cohorts. MEturquoise module was most closely connected with the CAF proportion and stromal score in GSE39582 (e) and TCGA-COAD (f) cohorts. (g) Venn diagram showed the shared hub-genes in GSE39582 and TCGA-COAD cohorts. (h) GO enrichment analysis of 88 shared hub-genes. (i) KEGG enrichment analysis of 88 shared hub-genes.
Figure 4
Figure 4
Construction of the prognostic model. (a) Univariate Cox analysis. (b) LASSO Cox regression analysis. (c) Survival analysis in GSE39582 cohort. (d) Survival analysis in TCGA-COAD cohort.
Figure 5
Figure 5
Risk score was positively connected with CAF infiltrations and CAF markers. Risk score was positively associated with CAF abundances in GSE39582 (a) and TCGA-COAD (b) cohorts. CAF markers, ZNF532 and COLEC12, were highly expressed in high-risk group, both in GSE39582 (c) and TCGA-COAD (d) cohorts. CAF markers were positively connected with risk score, ZNF532, and COLEC12 in GSE39582 (e) and TCGA-COAD (f) cohorts.
Figure 6
Figure 6
Multidimensional validation for risk score. Comparison of the effect of immunotherapy between the high- and low-risk groups in GSE39582 (a) and TCGA-COAD (d) cohorts. Comparison of the TIDE level between the high-and low-risk groups in GSE39582 (b) and TCGA-COAD (e) cohorts. Receiver-operating characteristic curves of the risk score in forecasting treatment effects in GSE39582 (c) and TCGA-COAD (f) cohorts.
Figure 7
Figure 7
GSEA showing possible associations between high- (a) and low-risk (b) groups and disease phenotypes.
Figure 8
Figure 8
SNP analysis. The top 20 mutational genes in high- (a) and low-risk (b) groups. (c) Analysis of correlation between TMB and the risk groups. (d) Comparison of TMB value between the high- and low-risk groups.
Figure 9
Figure 9
Single-cell RNA sequencing analysis of CRC. UMAP map of cell clusters (a) and types (b). (c) Distribution of ZNF532 and COLEC12 in each cell type. (d) Fibroblasts were divided into 8 subpopulations. (e) The expression of top 5 genes in each fibroblast subpopulation. (f) GSVA analysis of fibroblast subpopulations. (g) Distribution of ZNF532 and COLEC12 in each fibroblast subpopulation.
Figure 10
Figure 10
Multidimensional expression validation. The levels of ZNF532 and COLEC12 in the fibroblasts and large intestine were compared by Wilcoxon analysis (a) and exhibited in the heat map (b). (c) q-PCR was applied to verify the expression of ZNF532 and COLEC12 in fibroblasts and SW480. ∗∗p < 0.01.
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