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. 2024 May 16;10(10):e31403.
doi: 10.1016/j.heliyon.2024.e31403. eCollection 2024 May 30.

Prognostic role of MUCIN family and its relationship with immune characteristics and tumor biology in diffuse-type gastric cancer

Xiao-Xiao Luo  1 , Shi-Zhen Li  2 , Lu Wang  1 , Ai-Lin Luo  3 , Hong Qiu  1 , Xiang-Lin Yuan  1
Affiliations

Prognostic role of MUCIN family and its relationship with immune characteristics and tumor biology in diffuse-type gastric cancer

Xiao-Xiao Luo et al. Heliyon. .

Abstract

The main component of O-glycoproteins, mucin, is known to play important roles in physiological conditions and oncogenic processes, particularly correlated with poor prognosis in different carcinomas. Diffuse-type gastric cancer (DGC) has long been associated with genomic stability and unfavorable clinical outcomes. To investigate further, we obtained clinical information and the RNA-seq data of the TCGA-STAD cohort. Through the use of unsupervised clustering methods and GSEA, we identified two distinct clusters, characterized by higher and lower expression of MUC2 and MUC20, denoted as cluster 1 and cluster 2, respectively. Subsequently, employing CIBERSORT, it was determined that cluster 2 exhibited a higher tumor mutation burden (TMB) and a greater abundance of CD8+ T cells and activated CD4+ memory T cells, in addition to immune checkpoints (ICPs). On the other hand, cluster 1 showed a lower TIDE score estimation, indicating a higher probability of tumor immune escape. Furthermore, overexpression of MUC15 and MUC20 was confirmed through qPCR and Western blotting, and their specific roles in mediating the epithelial-mesenchymal transition (EMT) process of GC cells (SNU484 and Hs746t) were validated via CCK-8 assay and wound healing assay in vitro. These findings highlight the potential prognostic value of MUC20 and offer insights into the prospects of immunotherapy for DGC by targeting MUC20.

Keywords: Diffuse type gastric cancer; MUCINs; Prognostic factor; Tumor biology; Tumor immune microenvironment.

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Conflict of interest statement

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Xiao-Xiao Luo reports financial support was provided by Hubei Provincial Natural Science Foundation of China (grants 2023AFB208). If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
The DEGs and their distributions between normal and tumor tissue. A. Volcano plot showed upregulated and down regulated genes between normal and tumor tissues. B. Heatmap showed top 20 genes upregulated and downregulated between normal and tumor tissues.
Fig. 2
Fig. 2
Relative mRNA expression levels in DGC and normal tissues. A. Membrane-bound MUCINs (MUC1, MUC3A, MUC4, MUC12, MUC15, MUC16, MUC17, MUC20, MUC21, and MUC22) and secreted MUCINs (MUC2, MUC5AC, MUC5B, MUC6) were labeled with red and blue titles, respectively. Statistical analyses were performed using unpaired t-test. *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001.
Fig. 3
Fig. 3
Identification of prognostic-related genes. A. Univariate Cox analyses showed the hazard ratios (HRs) of selected CLDN and MUNCIN genes with forest plot. B. Multivariate Cox regression analyses showed four selected MUCIN genes and clinicopathological features in DGC patients.
Fig. 4
Fig. 4
Identification of clusters. A. Heat map of sample clustering. B. Cumulative distribution function curve. C. Delta area of related genes. D. Expression of targeted MUNCINs between clusters E. Survival curves of cluster1 and cluster2 in test cohort.
Fig. 5
Fig. 5
Construction and validation of predicting model for overall survival. A.-D. Receiver operating characteristic (ROC) curve of 1,3,5 and 7 years of selected MUCINs. E. Nomogram showed the potential prognostic role for four selected MUCINs. F. Receiver operating characteristic (ROC) curve of 1,3,5 and 7 years of nomogram model. G. Kaplan–Meier survival curve showed the overall survival in two clusters.
Fig. 6
Fig. 6
Comparison of various metabolic patterns of DGC. A. GSEA analysis of top 6 metabolic patterns between clusters. B. GSEA analysis of top 6 most immune-related biological pathways between clusters. The TGF-beta, WNT, and MAPK signaling pathways were up-regulated in high-MUC20-expression cluster.
Fig. 7
Fig. 7
Mutation burden and CNV analysis. A.-B. Barplot showed tumor mutation burden between clusters. C.-D. Numbers of mutation genes and CNV analysis between clusters.
Fig. 8
Fig. 8
Immune cell infiltration pattern in tumor and normal tissue. A. Barplot showed the distribution of 22 immune cells in tumor samples. B. Boxplot showed the distribution of 22 immune cells in tumor tissue between clusters. C. Heatmap showed correlation between four targeted MUNICNs and the 22 immune cells infiltration in tumor tissue. ***p < 0.001, **p < 0.01, *p < 0.05, ns p > 0.05.
Fig. 9
Fig. 9
Distributions of ICBs between clusters. A. Boxplot showed expression of ICBs between clusters. B. Heatmap showed correlation between four targeted MUNICNs and ICBs in tumor tissue. C. Boxplot showed T-cell inflammation score between clusters. D-E. Boxplot showed wilcox. test and chisq. test of TIDE score between clusters. ***p < 0.001, **p < 0.01, *p < 0.05, ns p > 0.05.
Fig. 10
Fig. 10
Experimental verification of dysregulated expression of MUCIN family members in GC cells. (A–D) Detection of mRNA levels of (A) MUC2, (B) MUC15, (C) MUC19 and (D) MUC20 in GES-1, SNU484 and Hs746t cells through qPCR. (E) Representative Western blot images of MUC15, MUC19 and MUC20 in GES-1, SNU484 and Hs746t cells, raw data in supplementary materials FigureS 5 A-D. GAPDH was served as an internal reference. (F–H) Quantification of protein levels of (F) MUC15, (G) MUC19 and (H) MUC20 in GES-1, SNU484 and Hs746t cells based upon protein bands. *P < 0.05; **P < 0.01; ns: no statistical significance.
Fig. 11
Fig. 11
Effects of MUC15 and MUC20 knockdown on cell viability and migratory abilities of GC cells. (A) Representative immunofluorescence photographs of MUC15 and MUC20 in SNU484 cells in the context of transfection of si-MUC15, si-MUC20 or controls. Scale bar, 20 μm. (B) Quantification of fluorescence intensity of MUC15 and MUC20 in the figure above. (C, D) Representative immunofluorescence photographs of MUC15 and MUC20 and quantification results in Hs746t cells transfected with si-MUC15, si-MUC20 or controls. (E, F) Detection of OD values at 450 nm in SNU484 and Hs746t cells in the context of si-MUC15, si-MUC20 or control transfection by CCK-8 assay. (G) Representative wound healing photographs of SNU484 cells under transfection of si-MUC15, si-MUC20 or controls at 0 h and 24 h. Scale bar, 50 μm. (H) Calculation of wound distance (%) in the figure above. (I, J) Representative 0- and 24-h wound healing images in Hs746t cells transfected with si-MUC15, si-MUC20 or controls as well as quantification of wound distance (%). **P < 0.01.
Fig. 12
Fig. 12
Effects of MUC15 and MUC20 knockdown on EMT process in GC cells. (A, B) Representative immunofluorescence photographs of E-cadherin in SNU484 and Hs746t cells in the context of si-MUC15, si-MUC20 or control transfection. Scale bar, 20 μm. (C, D) Calculation of fluorescence intensity of E-cadherin in the figures above. (E–H) Representative immunofluorescence photographs and fluorescence intensity of N-cadherin in SNU484 and Hs746t cells with si-MUC15, si-MUC20 or control transfection. **P < 0.01.
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