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. 2024 Nov 22;15(1):696.
doi: 10.1007/s12672-024-01566-0.

Single-cell analysis uncovers liver susceptibility to pancreatic cancer metastasis via myeloid cell characterization

Aizier Ainiwaer #  1 , Zhenwei Qian #  2 , Jianxun Wang  3 , Qi Zhao  4 , Yinying Lu  5   6
Affiliations

Single-cell analysis uncovers liver susceptibility to pancreatic cancer metastasis via myeloid cell characterization

Aizier Ainiwaer et al. Discov Oncol. .

Abstract

The liver is the predominant metastatic site for diverse cancers, including pancreatic and colorectal cancers (CRC), etc. The high incidence of hepatic metastasis of pancreatic cancer is an important reason for its refractory and high mortality. Therefore, it is important to understand how metastatic pancreatic cancer affects the hepatic tumor immune microenvironment (TME) in patients. Here, we characterized the TME of liver metastases unique to pancreatic cancer by comparing them with CRC liver metastases. We integrated two single-cell RNA-seq (scRNA-seq) datasets including tumor samples of pancreatic cancer liver metastasis (P-LM), colorectal cancer liver metastasis (C-LM), primary pancreatic cancer (PP), primary colorectal cancer (PC), as well as samples of peripheral blood mono-nuclear cells (PBMC), adjacent normal pancreatic tissues (NPT), to better characterize the heterogeneities of the microenvironment of two kinds of liver metastases. We next performed comparative analysis on cellular compositions between P-LM and C-LM, found that Mph_SPP1, a subset of macrophages associated with angiogenesis and tumor invasion, was more enriched in the P-LM group, indicating this kind of macrophages provide a TME niche more vulnerable for pancreatic cancers. Analysis of the developmental trajectory implied that Mph_SPP1 may progressively be furnished with increased expression of genes regulating endothelium. Cell-cell communications analysis revealed that Mph_SPP1 potentially interacts with endothelial cells in P-LM via FN1/SPP1-ITGAV/ITGB1, implying this macrophage subset may construct an immunosuppressive TME for pancreatic cancer by regulating endothelial cells. We also found that Mph_SPP1 has a prognostic value in pancreatic adenocarcinoma that is not present in colon adenocarcinoma or rectum adenocarcinoma. This study provides a new perspective for understanding the characteristics of the hepatic TME in patients with liver metastatic cancer. And it provides a subset of macrophages specifically associated with the liver metastasis of pancreatic cancer, and its detection and intervention have potential value for preventing the metastasis of pancreatic cancer to the liver.

Keywords: Colorectal cancer; Liver metastasis; Pancreatic cancer; SPP1; scRNA-seq.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Data integration and analysis of cellular compositions and functions of each site. A The schematics of our study. Transcriptome data of different tissue types were collected including 8 PDAC samples and 13 CRC samples. The tissue types include P-LM, C-LM, PP, PC, NPT and PBMC. B UMAP plot of the combined datasets before batch correction, colored by sample source. C UMAP plot of integrated dataset after batch correction with CCA, colored by sample source. D UMAP plot of cells from the 23 samples profiled in this study, with each cell color coded to indicate the associated cell types. E Dot plot of the normalized average expression of the top 3 enriched genes for each cluster. F Heatmap showing the functional enrichment analysis of all cell types. G Dot plot depicting the 9 cell subtypes enrichment in 9 groups of samples as calculated using RO/E. One subset was assumed as an enriched population in a specific tissue if the corresponding RO/E value is greater than 1
Fig. 2
Fig. 2
The subclusters of myeloid cells have different physiological functions and are specifically enriched in different sites. A UMAP plot of subclusters of myeloid cells, with each cell color coded to indicate the associated cell types. B Dot plot of the normalized average expression of the top 3–5 enriched genes for each cluster. C Heatmap showing the functional enrichment analysis of subclusters. D Dot plot showing the functional scores (M1 macrophage, M2 macrophage, phagocytosis; proinflammatory; Kupffer, and MoMF) of myeloid subclusters. E Analysis of pathway enrichment and clustering of P-LM and C-LM based on distinct gene expressions. Each plot represents a known pathway, and the size of plot indicated the number of enriched genes. The pathways in both P-LM and C-LM were divided into two clusters (C1 and C2). F Dot plot depicting the enrichment of myeloid subclusters in 9 groups of samples as calculated using RO/E. One subset was assumed as an enriched population in a specific tissue if the corresponding RO/E value is greater than 1
Fig. 3
Fig. 3
Analysis of developmental course of myeloid subclusters and characteristic of Mph_SPP1 development. A The development trajectories of 15 myeloid subclusters were depicted by 5 lineages. B Developmental trends in lineage 4. C Visualization of the expression of individual genes in lineage 4. Genes in the lineage 4 were partitioned into 5 clusters. D Pseudo-temporal change curve of genes with a high level of expression matching to Mph_SPP1 cluster, including CHI3L1, CCL18, FABP4, and PTX3
Fig. 4
Fig. 4
Differences in intercellular interactions between the P-LM and C-LM groups. A Comparison of the number and strength of interactions between P-LM and C-LM. B Cell interactions network diagram for differential interaction strength (P-LM/C-LM). The thickness of the line represented the strength of interactions. The red lines represent relatively stronger myeloid cell interactions in P-LM, and the blue lines represent relatively stronger myeloid cell interactions in C-LM. C Heatmap showing differential number of cell interactions and differential interaction strength(P-LM/C-LM). D Bar plots of the ranking of signaling axes by overall information flow differences in the interaction networks between P-LM and C-LM. The top signaling pathways with red-colored labels are more enriched in C-LM, the middle ones with black-colored labels are equally enriched in P-LM and C-LM, and the bottom ones with green-colored labels are more enriched in the P-LM. E Chord diagrams showing VEGF signaling pathway network in P-LM and C-LM. F Dot plot comparing the significant ligand-receptor pairs in C-LM and P-LM, which contribute to the signaling from Mph_SPP1 to Endothelial cells. Dot color reflects communication probabilities and dot size represents computed p-values. Empty space means the communication probability is zero. p-values are computed from a one-sided permutation test
Fig. 5
Fig. 5
The potential significance of Mph_SPP1 and Mph_SPP1-specific markers for PAAD, COAD, and READ prognosis. Survival analysis and prognostic factor analysis were performed. For survival analysis, the Kaplan–Meier method and the log-rank test were employed. For prognostic factors, COX proportional hazard regression model was used. A Kaplan–Meier curves for the high- and low- Mph_SPP1 signature score groups in PAAD (left) and COAD (right). B Univariate Cox regression analysis of PAAD identified top genes of Mph_SPP1 as independent clinical characteristic for overall survival prediction. C Kruskal–Wallis test showed that Mph_SPP1 was correlated with the progressing stages of PAAD but not with COAD and READ
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