Silencing PCCA Suppresses CRC Growth and Spread by Modulating EMT and M1 Macrophage Polarization

Abstract

Background: The progression and metastasis of colorectal cancer (CRC) remain major clinical challenges due to a lack of effective therapeutic targets. Our preliminary study identified the upregulation of the propionyl-CoA carboxylase alpha chain (PCCA) gene in CRC, prompting further investigation into its functional roles. Methods: Bioinformatics analysis, colorectal tumor tissues, and CRC cell lines were used to determine PCCA expression. Wound healing, Transwell, and cell counting kit-8 (CCK-8) assays were conducted to evaluate the impacts of PCCA expression on CRC cell migration, invasion, and proliferation. Western blotting was used to assess epithelial-mesenchymal transition (EMT) markers and associated signaling pathways. Mouse models, flow cytometry, and quantitative polymerase chain reaction (PCR) were performed to investigate the influences of PCCA on CRC tumor growth, lung metastasis, and macrophage polarization. Results: PCCA is highly expressed in CRC tumor tissues compared to normal tissues and is associated with a poor prognosis. Knocking down PCCA reduced CRC cell migration, invasion, and proliferation, which were associated with the upregulation of E-cadherin, the downregulation of N-cadherin, Vimentin, and Fibronectin, as well as the inactivation of the extracellular signal-regulated kinase (ERK)/glycogen synthase kinase 3 beta (GSK3β) signaling pathway. Moreover, PCCA knockdown suppressed CRC tumor growth and lung metastasis, accompanied by an increase in M1-macrophage polarization. Conclusion: Knockdown PCCA inhibits the progression and metastasis of CRC, which is associated with EMT reversion, ERK/GSK3β signaling inactivation, and M1-macrophage polarization. These findings suggest that PCCA is a potential target for controlling CRC.

Keywords: Colorectal cancer; Epithelial-mesenchymal transition; Macrophage polarisation; Propionyl-CoA carboxylase; Signal transduction.

Figure 1

PCCA was up-regulated in CRC and associated with poor prognosis. (A) Volcano plots illustrating differentially expressed genes in the GSE117606 dataset. (B) Expression levels of PCCA gene in the GEO dataset GSE117606. (C-D) OS and DFS of the subgroups of patients with PCCA high-expression (n=70) and low-expression (n=84) in the TCGA COAD-READ database. (E-F) The representative WB images and the quantification of PCCA expression in CRC cell lines (SW480, RKO, HT29, HCT116, DLD1) and normal colorectal epithelial cells (NCM460). (G-H) The representative WB images and the quantification of PCCA expression in 6 pairs of CRC tumor tissues (T) and adjacent normal tissues (N) from patients. (I) The heatmap displays the top 50 positively and negatively correlated genes with PCCA in CRC, and “TFF3” is highlighted in red boxes. (J) Gene Ontology (GO) enrichment analysis of PCCA-related genes, with “Positive regulation of epithelial to mesenchymal transition” prominently featured. (K) The correlation between PCCA levels and ESTIMATE scores, including Immune, Stromal, and overall Estimate Scores. The data are presented as means ± SEM (*P < 0.05; ** P < 0.01; ***P < 0.001). OS: overall survival, DFS: disease-free survival.

Figure 2

Knockdown PCCA inhibited the migration, invasion, and proliferation of CRC cells. (A-B) The representative WB images and the quantification of PCCA expression in DLD1 and HCT116 cells transfected with PCCA siRNA and NC RNA. (C-D) The representative images at indicated time points (0 hr, 24 hrs, 48 hrs) and the analysis of wound healing assays conducted on DLD1 and HCT116 cells transfected with PCCA siRNA and NC RNA. (E-G) The representative images and the analysis of transwell migration and invasion assays performed on DLD1 and HCT116 cells transfected with PCCA siRNA and NC RNA. (H) The proliferation of DLD1 and HCT116 cells transfected with PCCA siRNA and NC RNA was measured by CCK8 assays. Data are from one experiment representative of two (E-G) or three (A-D, H) independent experiments with similar results. PCCA si1, CRC cells transfected with PCCA siRNA-1; PCCA si2, CRC cells transfected with PCCA siRNA-2; NC, CRC cells transfected with NC RNA. The data are presented as Means ± SEM (*P < 0.05; ** P < 0.01; ***P < 0.001).

Figure 3

Knockdown PCCA suppressed the EMT and inactivated the ERK1/2 and GSK3β signaling in CRC cells. (A-B) The representative WB images and the quantification of the expression of epithelioid markers (E-cadherin) and mesenchymal markers (N-cadherin, Vimentin, and Fibronectin) in DLD1 and HCT116 cells transfected with PCCA siRNA and NC RNA. (C-D) The representative WB images and the quantification of the phosphorylation of ERK1/2 and GSK3β signaling in DLD1 and HCT116 cells transfected with PCCA siRNA and NC RNA. Data are from one experiment representative of three independent experiments with similar results. PCCA si1, CRC cells transfected with PCCA siRNA-1; PCCA si2, CRC cells transfected with PCCA siRNA-2; NC, CRC cells transfected with NC RNA. The data are presented as Means ± SEM (*P < 0.05; ** P < 0.01; ***P < 0.001).

Figure 4

Knockdown PCCA suppressed CRC tumor growth and lung metastasis. (A-B) The representative WB images and the quantification of PCCA expression in HCT116-PCCA sh2 or NC. (C-D) The representative images and the quantification of the colony formation assay were performed in HCT116-PCCA sh2 or NC. (E) The diagram of the subcutaneous tumor and lung metastasis models. (F) The tumor growth curves of HCT116-PCCA sh2 or NC in nude mice (n=7). (G-H) The images and results of lung metastasis after 8 weeks of tumor cell tail vein injection (n=10). Data are from one experiment representative of two (E-H) or three (A-D) independent experiments with similar results. PCCA sh2, stable transformation strain of HCT116 with PCCA sh2RNA; NC, stable transformation strain of HCT116 with NC RNA. The data are presented as Means ± SEM (*P < 0.05; ** P < 0.01; ***P < 0.001).

Figure 5

Knockdown PCCA increased the polarization of TAMs to an M1-like phenotype. (A) CIBERSORT was utilized to predict the percentages of M1- and M2-like TAMs in the subgroups of metastatic CRC patients with high (29 cases) or low (29 cases) levels of PCCA in the TCGA database. (B) The representative flow cytometry dot plots depicting the polarization of TAMs in the tumor tissues of HCT116-PCCA sh2 or NC (n=7). (C) The statistical analysis of the proportions of myeloid cells (CD11b⁺) and TAMs in leukocytes (CD45⁺), as well as the fold changes of M1-like (CD11c⁺CD206^-) and M2-like (CD11c^-CD206⁺) TAMs in the tumor tissues of HCT116-PCCA sh2 or NC (n=7). (D) Heatmap showing the transcription levels of M1 and M2 marker genes in the tumor tissue of HCT116-PCCA sh2 or NC. (E) The transcription levels of Nos2 (from mice) and NOS2 (from humans) in the tumor tissues of HCT116-PCCA sh2 or NC (n=7). (F) The transcription levels of M1 and M2 TAM marker genes in bone marrow-derived macrophages co-cultured with the condition media from HCT116-PCCA sh2 or NC tumor cell cultures. The experiment was conducted in triplicates. (G) The levels of the PCCA gene in the GSE11237 dataset. (H) The violin plot illustrated the estimated IC50 for cis-platinum in the subgroups of CRC patients with high (190 cases) or low (191 cases) levels of PCCA in the TCGA COAD-READ dataset. PCCA sh2, stable transformation strain of HCT116 with PCCA sh2RNA; NC, stable transformation strain of HCT116 with NC RNA. The data are presented as Means ± SEM (*P < 0.05; ** P < 0.01; ***P < 0.001).