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. 2022 Jul 29:13:951214.
doi: 10.3389/fimmu.2022.951214. eCollection 2022.

The cancer-associated fibroblast-related signature predicts prognosis and indicates immune microenvironment infiltration in gastric cancer

Tsz Kin Mak  1 , Xing Li  1 , Huaping Huang  1 , Kaiming Wu  1 , Zhijian Huang  1   2 , Yulong He  1   2 , Changhua Zhang  1   2
Affiliations

The cancer-associated fibroblast-related signature predicts prognosis and indicates immune microenvironment infiltration in gastric cancer

Tsz Kin Mak et al. Front Immunol. .

Abstract

Background: Gastric cancer (GC) is one of the most common cancers, with a wide range of symptoms and outcomes. Cancer-associated fibroblasts (CAFs) are newly identified in the tumor microenvironment (TME) and associated with GC progression, prognosis, and treatment response. A novel CAF-associated prognostic model is urgently needed to improve treatment strategies.

Methods: The detailed data of GC samples were downloaded from The Cancer Genome Atlas (TCGA), GSE62254, GSE26253, and GSE84437 datasets, then obtained 18 unique CAF-related genes from the research papers. Eight hundred eight individuals with GC were classified as TCGA or GSE84437 using consensus clustering by the selected CAF-related genes. The difference between the two subtypes revealed in this study was utilized to create the "CAF-related signature score" (CAFS-score) prognostic model and validated with the Gene Expression Omnibus (GEO) database.

Results: We identified two CAF subtypes characterized by high and low CAFS-score in this study. GC patients in the low CAFS-score group had a better OS than those in the high CAFS-score group, and the cancer-related malignant pathways were more active in the high CAFS-score group, compared to the low CAFS-score group. We found that there was more early TNM stage in the low CAFS-score subgroup, while there was more advanced TNM stage in the high CAFS-score subgroup. The expression of TMB was significantly higher in the low CAFS-score subgroup than in the high CAFS-score subgroup. A low CAFS-score was linked to increased microsatellite instability-high (MSI-H), mutation load, and immunological activation. Furthermore, the CAFS-score was linked to the cancer stem cell (CSC) index as well as chemotherapeutic treatment sensitivity. The patients in the high CAFS-score subgroup had significantly higher proportions of monocytes, M2 macrophages, and resting mast cells, while plasma cells and follicular helper T cells were more abundant in the low-risk subgroup. The CAFS-score was also highly correlated with the sensitivity of chemotherapeutic drugs. The low CAFS-score group was more likely to have an immune response and respond to immunotherapy. We developed a nomogram to improve the CAFS-clinical score's usefulness.

Conclusion: The CAFS-score may have a significant role in the TME, clinicopathological characteristics, prognosis, CSC, MSI, and drug sensitivity, according to our investigation of CAFs in GC. We also analyzed the value of the CAFS-score in immune response and immunotherapy. This work provides a foundation for improving prognosis and responding to immunotherapy in patients with GC.

Keywords: CAFS-score; CAFs gene; Gastric cancer; immune microenvironment infiltration; immune therapy.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Genetic and transcriptional alterations of CAFs-related genes in GC. (A) Mutation frequencies of 18 CAFs-related genes in 433 patients with STAD from the TCGA cohort. (B) Frequencies of CNV gain, loss, and non-CNV among CAFs-related genes. (C) Locations of CNV alterations in CAFs-related genes on 23 chromosomes. (D) Expression distributions of 1 8 CAFs-related genes in normal and GC tissues. (E) Interactions among CAFs-related genes in GC. The connecting line among CAFs-related genes indicates their interaction, and the thickness of lines represents the strength of the association between CAFs-related genes. Blue and pink represent negative and positive correlations, respectively. *P<0.05, **P<0.01, ***P<0.001.
Figure 2
Figure 2
Identification of CAFs subtypes in GC. (A) Consensus matrix heatmap defining two clusters (k = 2) and their correlation area. (B) Univariate analysis indicating 18 CAFs-related genes corelated with the OS time. (C) GSVA of biological pathways between two distinct subtypes. (Red and blue represent activated and inhibited pathways, respectively). (D) Differences in clinicopathologic features and expression levels of CAFs-related genes between the two distinct subtypes. (E) The 22 infiltrating immune cell types in the two GC subtypes. **P<0.01, ***P<0.001.
Figure 3
Figure 3
Identification of gene subtypes based on DEGs. (A) Kaplan-Meier curves for RFS of the three gene subtypes (log-rank tests, p < 0.001). (B) Relationships between clinicopathologic features and the three gene subtypes. (C) Differences in the expression of 18 CAFs-related genes among the three gene subtypes. *P<0.05, **P<0.01, ***P<0.001.
Figure 4
Figure 4
Establishment risk assessment model and survival outcomes in GC. (A) Alluvial diagram of the subtype distributions in groups with different CAFS-score and survival outcomes. (B) Differences in CAFS-score between two CAF subtypes. (C) Differences in CAFS-score between three gene subtypes. (D, E) Ranked dot and scatter plots representing the CAFS-score distribution and patient survival status. (F-H) Kaplan-Meier analysis of the RFS between the two risk groups in the TCGA, GSE62254, and GSE26253 cohort.
Figure 5
Figure 5
Clinicopathologic characteristics of TCGA in CAFS-score. (A) The Univariate Cox regression analysis in CAFS-score subgroups. (B) The multiple Cox regression analysis in CAFS-score subgroups. (C) The clinicopathologic characteristics of GC patients in the TCGA cohort. (D, E) The staging and the immune subtypes was significantly related to the risk between the two CAFS-score subgroups, respectively. *P<0.05, ***P<0.001.
Figure 6
Figure 6
Characteristic in gene mutation and relationship of CAFS-score with MSI and CSC index. (A, B) Significantly mutated genes in the mutated GC samples of the high and the low risk groups, respectively. Mutated genes (rows, top 20) are ordered by mutation rate; samples (columns) are arranged to emphasize mutual exclusivity among mutations. The right shows mutation percentage, and the top shows the overall number of mutations. The color-coding indicates the mutation type. (C) The TMB of two differen risk subgroups. (D) Relationships between CAFS-score and TMR in three gene subtypes. (E) Relationships between CAFS-score and CSC index. (F, G) Relationships between CAFS-score and MSI.
Figure 7
Figure 7
Immune Infiltration in two CAFS-score subgroup (TCGA). (A) Composition of immune cells in two CAFS-score subgroup. (B) The Relative immune infiltration score of 22 immune cells between low- and high-risk groups. (C) Relationships between CAFS-score and different immune cells. (D) Correlations between the abundance of immune cells and four genes in the proposed model. *P<0.05, **P<0.01, ***P<0.001
Figure 8
Figure 8
The prognostic value of CAFS-score in immunotherapy from TCGA cohort. (A–D) TIDE, MSI, T cell exclusion, and T cell dysfunction score in two CAFS-score subgroup, respectively. (E) ROC analysis of CAFS-score, TIDE, and TIS on OS in GC cohort. ***P<0.001, ns, P>0.05.
Figure 9
Figure 9
The prognostic value of CAFS-score in immunotherapy from TCGA cohort. (A–D) The vioplot of the difference expression of CTLA4 and PD-1 between high- and low-risk groups.
Figure 10
Figure 10
Relationships between CAFS-score and medicine sensitivity. Lower IC50 of indicated chemo-therapeutics drugs in low (A–C) and high (D–F) CAFS-score group, respectively.
Figure 11
Figure 11
Construction and validation of a nomogram. (A) Nomogram for predicting the 1-, 3-, and 5-year OS of GC patients in TCGA cohort. (B-D) ROC curves for predicting the 1-, 3-, and 5-year ROC curves in TCGA, GSE62254, and GSE26253 cohorts. (E-G) Calibration curves of the nomogram for predicting of 1-, 3-, and 5-year OS in the TCGA, GSE62254, and GSE26253 cohorts. ***P<0.001.
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