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. 2024 Aug 8;14(8):969.
doi: 10.3390/biom14080969.

Proteomics and Bioinformatics Identify Drug-Resistant-Related Genes with Prognostic Potential in Cholangiocarcinoma

Kankamol Kerdkumthong  1 , Sittiruk Roytrakul  2 , Kawinnath Songsurin  1 , Kandawasri Pratummanee  1 , Phanthipha Runsaeng  1   3 , Sumalee Obchoei  1   3
Affiliations

Proteomics and Bioinformatics Identify Drug-Resistant-Related Genes with Prognostic Potential in Cholangiocarcinoma

Kankamol Kerdkumthong et al. Biomolecules. .

Abstract

Drug resistance is a major challenge in the treatment of advanced cholangiocarcinoma (CCA). Understanding the mechanisms of drug resistance can aid in identifying novel prognostic biomarkers and therapeutic targets to improve treatment efficacy. This study established 5-fluorouracil- (5-FU) and gemcitabine-resistant CCA cell lines, KKU-213FR and KKU-213GR, and utilized comparative proteomics to identify differentially expressed proteins in drug-resistant cells compared to parental cells. Additionally, bioinformatics analyses were conducted to explore the biological and clinical significance of key proteins. The drug-resistant phenotypes of KKU-213FR and KKU-213GR cell lines were confirmed. In addition, these cells demonstrated increased migration and invasion abilities. Proteomics analysis identified 81 differentially expressed proteins in drug-resistant cells, primarily related to binding functions, biological regulation, and metabolic processes. Protein-protein interaction analysis revealed a highly interconnected network involving MET, LAMB1, ITGA3, NOTCH2, CDH2, and NDRG1. siRNA-mediated knockdown of these genes in drug-resistant cell lines attenuated cell migration and cell invasion abilities and increased sensitivity to 5-FU and gemcitabine. The mRNA expression of these genes is upregulated in CCA patient samples and is associated with poor prognosis in gastrointestinal cancers. Furthermore, the functions of these proteins are closely related to the epithelial-mesenchymal transition (EMT) pathway. These findings elucidate the potential molecular mechanisms underlying drug resistance and tumor progression in CCA, providing insights into potential therapeutic targets.

Keywords: 5-fluorouracil; EMT; cholangiocarcinoma; drug resistance; gemcitabine; quantitative proteomics.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Phenotypic study of drug-resistant CCA cell lines. Drug sensitivity assay of KKU-213FR (a) and KKU-213GR (b). (c) Cell migration assay. (d) Cell invasion assay: magnification 100×; scale bar, 100 μm. The data are presented as mean ± SD from three replicates. Significant differences between treated versus untreated controls or parental versus drug-resistant cells are indicated by * p < 0.05, ** p < 0.01, and *** p < 0.001.
Figure 2
Figure 2
Proteomics analysis of KKU-213A, KKU-213FR, and KKU-213GR cell lines. (a) Proteomics analysis workflow. (b) Partial least squares-discriminant analysis (PLS-DA) of all identified proteins. Venn diagram of upregulated proteins (c) and down-regulated proteins (d) in KKU-213FR and KKU-213GR compared to KKU-213A. (e) Heatmap with group averages of upregulated (red frame) and down-regulated (green frame) proteins shared among KKU-213A, KKU-213FR, and KKU-213GR.
Figure 3
Figure 3
Gene Ontology analysis of upregulated proteins shared in KKU-213FR and KKU-213GR. The analysis represents the proteins hits and their respective percentages. Analysis based on molecular functions (a), biological processes (b), and protein classes (c).
Figure 4
Figure 4
Identification of significant genes in CCA. (a) The Venn diagram represents 80 upregulated proteins from the proteomic analysis and 25 upregulated genes in CCA tissue compared to normal tissue. (b) Heatmap analysis displaying the expression of 25 overlapping genes that were significantly upregulated in CCA patients’ tissues based on TCGA data (left) and proteomics data (right). (c) PPI analysis of the 25 upregulated genes via STRING. (d) Box plot showing the mRNA expression of six selected genes (a focused network) in CCA patients’ tissues (red box) vs. normal tissues (grey box) based on TCGA data. Significant differences between groups are indicated by * p < 0.05. (e) The mRNA expression of six selected genes verified by RT-qPCR in KKU-213A, KKU-213FR, and KKU-213GR. Significant differences between the KKU-213A and KKU-213FR groups, as well as between the KKU-213A and KKU-213GR groups, are indicated by *** p < 0.001.
Figure 5
Figure 5
Survival analyses of six selected genes in GI tract cancer patients. The overall survival map (a) and disease-free survival map (b) are displayed. Red shading indicates poor prognosis, and blue shading indicates good prognostic potential for each gene. The box with a prominent border represents statistical significance. Kaplan–Meier plots are used to represent overall survival (c) and disease-free survival (d).
Figure 6
Figure 6
The correlation analysis of six focused genes in GI tract cancers includes. (a) Scatter plots depicting the expression correlation between gene pairs in GI tract cancers. (b) Summary of the significant correlations among gene pairs in GI tract cancers. Yellow color indicates a positive correlation, blue color indicates a negative correlation, and grey color indicates no significant correlation between genes. Significant correlations between gene pairs are indicated by *** p < 0.001, while NS denotes non-significance.
Figure 7
Figure 7
Phenotypic study after knockdown of six selected genes in KKU-213FR and KKU-213GR cells. (a) mRNA expression after siRNA transfection. (b) Cell migration assay. (c) Cell invasion assay. (d) Drug sensitivity assay. Data are presented as mean ± SD from three replicates. Significant differences between siRNA-transfected cells and siNC-transfected controls are indicated by * p < 0.05, ** p < 0.01, and *** p < 0.001, magnification 100×; scale bar, 100 μm.
Figure 8
Figure 8
Hypothetical role of six selected genes in CCA progression. A schematic illustration delineating the putative role of the selected genes, elucidating their hypothesized involvement in driving drug resistance and promoting the progression of CCA cells. This figure was constructed using BioRender (2023).
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