The mutation spectrum in CRC
After analyzing the data, we discover the missense mutations are the most common. The waterfall diagram revealed the 30 frequently mutated genes from the TCGA cohort (Figure 1A). At the same time, we explored 30 frequently mutated genes from the ICGC cohort contained Chinese CRC samples (Figure 1B). The results showed that some genes frequently mutated in both two cohorts. Thus, the Venn diagram was used to identify the intersecting part and visualizes the 17 common genes shared by these two cohorts, including APC, TP53, TTN, KRAS, MUC16, MUC4, SYNE1, FLG, FAT4, OBSCN, FAT3, RYR2, PIK3CA, FBXW7, DNAH11, MUC5B and ZFHX4 (Figure 1C).
MUC4 mutation is tightly linked with high TMB and inferior prognosis in CRC patients
The result revealed that mutation in TTN, MUC16, MUC4, SYNE1, FLG, FAT4, OBSCN, FAT3, RYR2, PIK3CA, FBXW7, DNAH11, MUC5B and ZFHX4 presented relatively higher TMB in the CRC samples (Figure 2A). Kaplan- Meier survival analysis was conducted to investigate the potential relationship between these gene mutations and the prognosis of CRC patients (Figure 2B). Among the mutated genes,only MUC4 mutation (P= 0.002) was related to an inferior prognosis. The univariate and multivariate Cox regression analyses were conformed to determine if MUC4 mutation was an independent prognostic parameter (Table 1). The results obtained from the univariate Cox model suggested that MUC4 mutation was correlated to a worsening of the outcome (HR=2.084 p=0.003, 95% CI [1.286-3.376]). Afterward, the entire variables were analyzed via multivariate Cox regression. Further statistical tests revealed that the MUC4 mutation leads to an inferior prognosis (HR=2.093, p =0.003 95% CI [1.290–3.396]), suggesting that MUC4 mutation could act as an independent prognostic parameter in CRC patients.
Table 1. Exploration of the independent prognostic parameters in colorectal cancer.
Factors
|
Univariate
|
Multivariate
|
HR(95%CI)
|
p-value
|
|
HR
|
p-value
|
age(year) (<65, ≥65)
|
1.57(0.99-2.50)
|
0.06
|
|
|
|
gender (male, female)
|
1.37(0.88-2.12)
|
0.16
|
|
|
|
stage (low, high)
|
2.82(1.79-4.43)
|
0.00
|
|
2.82(1.79-4.43)
|
<0.001
|
TMB (low, high)
|
1.00(0.99-1.01)
|
0.91
|
|
|
|
MUC4 (wide, mutant)
|
2.08(1.29-3.38)
|
<0.001
|
|
2.09(1.29-3.40)
|
<0.001
|
CI: confidence interval; HR: hazard ratio.
Enrichment pathway analysis of MUC4 mutation
We performed GSEA enrichment analysis to explore the underlying mechanism of MUC4 mutation,the result revealed that natural killer cell mediated cytotoxicity were mainly enriched in patients with MUC4 mutation. Thus, we concluded that MUC4 mutation regulated pathways referred to the immune system (Figure 3).
Correlation of MUC4 mutation with tumor-infiltrating immune cells in CRC
We further explored the connection between MUC4 mutation and tumor-infiltrating immune cells in CRC patients. we compared the landscape of the immune cell in the MUC4 mutation group and wild group by CIBERSORT algorithm (Figure 4A). The difference analytical results showed that CD8 T cells, activated NK cells and M1 macrophages were comparatively higher infiltrating in tumors with MUC4 mutation, while resting memory CD4 T cells were higher infiltrating in the wild group (Figure 4B). The result of Correlation analysis revealed that CD8+T cells had a negative association with resting memory CD4+T cells (Figure 4C).