Downregulation of dermatopontin in cholangiocarcinoma cells suppresses CCL19 secretion of macrophages and immune infiltration

Abstract

Objective: The tumor microenvironment (TME) in cholangiocarcinoma (CHOL) is typically characterized by a low level of immune infiltration, which accounts for the dismal prognosis of this patient population. This study sought to investigate the mechanisms underlying the reduced infiltration of immune cells into the CHOL TME.

Methods: We constructed a Least Absolute Shrinkage and Selection Operator (LASSO) regression model to identify prognosis-related differentially expressed genes (DEGs). The 'Corrplot' package was employed to analyze the correlation between dermatopontin (DPT) and immune infiltration in CHOL. The Tumor and Immune System Interaction Database (TISIDB) was used to evaluate the association between DPT and immunology. Single-cell analysis was conducted to localize CCL19 secretions. Western blot and qPCR were utilized to detect DPT expression, while immunofluorescence was performed to investigate the cellular localization of DPT. Additionally, ELISA analysis was employed to assess the alteration in CCL19 secretion in cancer-associated fibroblasts (CAFs) and macrophages.

Results: Our findings revealed that CHOL patients with low DPT expression had a poorer prognosis. Enrichment analysis demonstrated a positive correlation between DPT levels and the infiltration of immunomodulators and immune cells. Moreover, high DPT levels were associated with enhanced anti-PD-1/PD-L1 immunotherapeutic responses. Furthermore, DPT expression impacted the landscape of gene mutations, showing a negative association with tumor grade, stage, and lymph node metastasis. Based on the results of protein peptides analysis and cell experiments, it was inferred that the downregulation of DPT in CHOL cells effectively suppressed the secretion of CCL19 in macrophages.

Conclusions: DPT is a novel prognosis-related biomarker for CHOL patients, and this study provides preliminary insights into the mechanism by which DPT promotes the infiltration of immune cells into the CHOL TME.

Keywords: CCL19; Cholangiocarcinoma; DPT; Immune infiltration; Macrophages; Tumor microenvironment.

Fig. 1

Screening of the intersected DEGs of CHOL. A–B Volcano plot (|log (FC)|> 1, p < 0.05) and heatmap plot of DEGs in GSE26566 and GSE45001 datasets. C Using DESeq2, edgeR, and limma algorithms, DEGs in CHOL from TCGA were visualized by volcano plot and heatmap plot. D Intersection of DEGs from GEO and TCGA database. E Overlapping DEGs and log-rank p genes

Fig. 2

DPT is a favorable biomarker for the prognostic prediction of CHOL. A Cross-validation for tuning parameter screening in the LASSO regression model. B Coefficient profiles in the LASSO regression model. C ROC curve verified the validity of the model. D Kaplan–Meier analysis verified the clinical prognostic effect of the target genes. E Constructing a cox-forest model to predict overall survival risk and protective factors in patients with CHOL. F The prognosis values of the risk model in TCGA-CHOL database. G Distribution of risk score, survival time, and candidate genes in high- and low-risk groups. H ROC curve was used to demonstrate the survival rate at 1, 3, and 4 years, respectively. I–K The expression of these nine genes was displayed with GEO (GSE26566 and GSE45001) and TCGA datasets (*, p < 0.05; **, p < 0.01; ***, p < 0.001; ****, p < 0.0001)

Fig. 3

DPT can enhance the prognosis of CHOL patients by increasing immune infiltration. A Heatmap shows the DEGs of DPT low- and High-expression groups using TCGA-CHOL Dataset. B–C GO and KEGG analysis of DPT-related DEGs. D Analysis of the correlation between DPT and the level of immune cell infiltration. E Assessment of the associations between DPT and scores (stromal score, immune score, and estimate score) with the ESTIMATE algorithm. F Investigation of the relationship between scores (stromal score, immune score, and estimate score) and CHOL patients’ prognosis

Fig. 4

Relationship between DPT and immunoinhibitors, immunostimulators, and MHC molecules. A–B TISIDB was used to investigate the correlation between DPT expression and immunoinhibitors in CHOL. C–D TISIDB was used to investigate the correlation between DPT expression and immunostimulators in CHOL. E–F TISIDB was used to investigate the correlation between DPT expression and MHC molecules in CHOL

Fig. 5

DPT may significantly improve patient response and survival to anti-PD-1/PD-L1 Immunotherapy. A The response effect in different therapeutic target study groups varied with the expression level of DPT. B The diagnostic specificity and sensitivity of different therapeutic targets for CHOL. C–D Impact of different immunotherapies on survival

Fig. 6

The landscape of DPT mutations and its links to clinical subgroups in CHOL. A camoip database showed the gene mutation frequency in the high- and low-DPT group. B Exhibition of mutation rate and major mutation types of DPT in CHOL using cBioPortal database. D–F Using UALAN website to show the relationship between DPT and tumor stage, grade, and lymph node grade of CHOL. G–I Using UALCAN website to show the relationship between DPT and tumor stage, grade, and lymph node grade of LIHC

Fig. 7

DPT may target CCL19 to promote immune cell infiltration in CHOL. A–B The intersection of DPT-related DEGs and chemokine genes in GSE26566 and TCGA datasets. C Exhibition of the top 10 genes most associated with DPT in CHOL using cBioPortal database. D–F Analysis of the correlation between DPT and chemokine CCL19, adhesion molecule SELP (Green represents chemokines). G Exploration of the relationship between DPT and macrophages, CAFs using Timer2.0 database. H Exploration of the relationship between CCL19 and macrophages, CAFs using Timer2.0 database

Fig. 8

Single-cell analysis of CCL19 expression in different cells of CHOL TME. A–C The cells of CHOL were divided into 8 cell groups via principal component analysis and t-distributed stochastic neighbor embedding (tSNE). D The markers of these 8 cell groups and the clusters in which they are expressed. E Heatmap was used to mark the top 5 DEGs of each cell group. F The synthetic position of DPT in the CHOL TME was demonstrated using a bubble diagram

Fig. 9

DPT and CCL19 is lowly expressed in CHOL cells and clinical tissues. A Detection of the DPT expression in normal bile duct epithelial and CHOL via immunohistochemistry. B–C Through qPCR and WB analysis, the expression of DPT mRNA and protein in normal bile duct epithelial and CHOL specimens was detected. D qPCR was performed to assess the expression of CCL19 mRNA in CHOL tissues. E–F mRNA and protein level of DPT in normal biliary cells (HIBEC) and various CHOL cell lines (QBC939, MZ, SK, RBE, HUCCT1, and HUH28). G Construction of CHOL cell lines overexpressing DPT by lentivirus transfection

Fig. 10

DPT overexpression in CHOL cells stimulates macrophages to produce CCL19. A The signal peptide structure in front of the DPT protein sequence was found using the SignalP-5.0 website. B The sequence near the cleavage site of DPT signal peptide was consistent in various species using MegAlign analysis (human, mouse, rat, dog, chicken, monkey, and chimpanzee). C Confocal microscopy exhibited the localization of DPT in various CHOL cell lines. D–E Through ELISA assay, the effect of exocrine DPT on the secretion of CCL19 by macrophages but CAFs was detected