Identification of Significant Modules and Targets of Xian-Lian-Jie-Du Decoction Based on the Analysis of Transcriptomics, Proteomics and Single-Cell Transcriptomics in Colorectal Tumor

Abstract

Purpose: Colorectal cancer (CRC) remains the third most common tumor worldwide. Ulcerative colitis (UC) could cause chronic inflammation and ulcers in the colon and rectum. UC is a risk factor for a high incidence of CRC, and the incidence of UC-associated CRC (UC-CRC) is still increasing. Chinese medicine prescription, Xian-Lian-Jie-Du decoction (XLJDD), has been proven its efficacy in some UC-CRC patients. However, the mechanism of XLJDD in treating UC-CRC remains unknown. This study aimed to investigate the mechanism of XLJDD in treating UC-CRC.

Methods: We constructed an AOM/DSS mouse model that could simulate the various stages of UC-CRC in humans. XLJDD and its 5 main components are used to treat the AOM/DSS model, respectively. With the power of high-throughput sequencing technology, we described the mechanism of XLJDD from transcriptomics, proteomics, and single-cell transcriptomics.

Results: Our results showed that XLJDD could effectively suppress the occurrence and development of colorectal tumors. Using the weighted correlation network analysis (WGCNA), several mRNA and protein modules that respond to XLJDD have been identified. Moreover, two essential genes, Mfsd2a and Ccdc85c, were caught our attention. They were prognostic markers in CRC patients, and their expression could be significantly modulated by XLJDD, showing their potential as effective targets of XLJDD. In addition, we also discovered that XLJDD could affect the cell composition of the colorectal tumor environment, especially in the infiltration of B cells.

Conclusion: We demonstrated that XLJDD could prevent the initiation and development of colorectal tumors by modulating the expression of Mfsd2a and Ccdc85c and reducing the infiltration of B cells in the tumor microenvironment of colorectal tumor.

Keywords: B cell infiltration; Ccdc85c; Mfsd2a; colorectal tumor; inflammation pathway.

Figure 1

Alleviating effects of XLJDD on AOM/DSS induced tumor progression. (A) Total chromatograms of typical extracts from Xian-Lian-Jie-Du decoction using UPLC-LTQ-Orbitrap-MS. (B) A schematic overview of the AOM/DSS mice model construction. (C) The morphological display of the colorectal tissues in each group. (D) H&E staining of colorectal paraffin sections (longitudinal and horizontal slice of paraffin sections). (E) Immunohistochemical staining of Ki-67 expression in colorectal tissues. (F and G) Body weight of mice was monitored weekly. (H and I) The number and volume of nodules were measured. Results are represented as mean ± SD, n=7; *P<0.05, **P<0.01.

Figure 2

Weighted gene co-expression network analysis of transcriptome profiles. (A) Topological overlap matrix (TOM) plot showing the correlations of each module. (B) Module preservations median rank and Zsummary score of all modules were presented. (C) Functional enrichment analyses for different modules corresponding to the colors. (D) The correlations between the modules and the NF-XLJDD group. (E) Heatmap of correlations between modules and each group. Colors correspond to correlations, while red means positively correlated and blue means negatively correlated. Correlations values and P values are also labeled. (F) Venn diagrams of comparing purple module and differentially expressed genes (DE genes) between NF-XLJDD and NF-MOD groups. (G) The enrichment analysis of 14 differential genes. (H) Gene set enrichment analysis showed enrichment pathways between the NF-XLJDD and the NF-MOD groups.

Figure 3

Mfsd2a may be a tumor suppressor activated by XLJDD. (A) Candidate genes expression in cancer tissues (Red) is compared with normal tissues (Black) in the TCGA-COAD dataset. (B) Kaplan-Meier survival analyses of Mfsd2a expression (low vs high). (C)The expression of Mfsd2a in our results of transcriptomic sequencing. (D) The expression results of Mfsd2a measured by qPCR. (E) Representative images for IHC staining of Mfsd2a. *P<0.05, **P<0.01.

Figure 4

Weighted gene co-expression network analysis of proteomics profiles. (A) Topological overlap matrix (TOM) plot showing the correlations of each module. (B) Module preservations median rank and Zsummary score of all modules were presented. (C) The Eigengenes expression pattern in each group. (D) The correlations between various modules and their corresponding pathways. (E) Heatmap of correlations between modules and each group. Colors correspond to correlations, while red means positively correlated and blue means negatively correlated. Correlations values and P values are also labeled. (F) The correlations between the functional modules and NF-XLJDD, HDF-XLJDD, P2, and P4 groups, respectively. (G) The maps of module membership with gene significance were displayed. (H) The Venn diagram of differential genes in NF-XLJDD, HDF-XLJDD, P2, and P4 and their corresponding modules.

Figure 5

Ccdc85c may be an effective target for XLJDD. (A) Gene set enrichment analysis showed enrichment pathways between the two groups. (B) The expression levels of Ccdc85c and Kaplan-Meier survival analysis from TCGA data. (C) The expression of Ccdc85c in our results of proteomics sequencing. (D) The expression results of Ccdc85c measured by qPCR. *P<0.05, ns, not significant.

Figure 6

The alteration of tumor microenvironment caused by XLJDD. (A) The PCA result of single-cell sequencing profiles. (B) t-distributed stochastic neighbor embedding (t-SNE) plot of all cells from control, HDF-MOD, and HDF-XLJDD group. (C) Uniform manifold approximation and projection (UMAP) plot of all cells from control, HDF-MOD, and HDF-XLJDD group. (D) The t-SNE plot of control, HDF-MOD, and HDF-XLJDD group. (E) The correlations between the various clusters. (F) Histogram of the ratio of clusters in control, HDF-MOD and HDF-XLJDD group.

Figure 7

Identification of B cells clusters by Single-cell Atlas. (A) The t-SNE plot and UMAP plot showed the predicted type of cells. (B) The t-SNE plot of cluster with marker genes expression, the red dots represent the cells with high expression of the marker gene in the total detected cells. (C–F) The enrichment analysis of differential genes in B cells (cluster 2, cluster 17, cluster 23, cluster 25) between control and HDF-MOD groups. (G–J) The enrichment analysis of differential genes in B cells (cluster 2, cluster 17, cluster 23, cluster 25) between HDF-MOD and HDF-XLJDD groups.