Detailed Molecular Mechanism and Potential Drugs for COL1A1 in Carboplatin-Resistant Ovarian Cancer

Abstract

Carboplatin resistance in ovarian cancer (OV) is a major medical problem. Thus, there is an urgent need to find novel therapeutic targets to improve the prognosis of patients with carboplatin-resistant OV. Accumulating evidence indicates that the gene COL1A1 (collagen type I alpha 1 chain) has an important role in chemoresistance and could be a therapeutic target. However, there have been no reports about the role of COL1A1 in carboplatin-resistant OV. This study aimed to establish the detailed molecular mechanism of COL1A1 and predict potential drugs for its treatment. We found that COL1A1 had a pivotal role in carboplatin resistance in OV by weighted gene correlation network analysis and survival analysis. Moreover, we constructed a competing endogenous RNA network (LINC00052/SMCR5-miR-98-COL1A1) based on multi-omics data and experiments to explore the upstream regulatory mechanisms of COL1A1. Two key pathways involving COL1A1 in carboplatin resistance were identified by co-expression analysis and pathway enrichment: the "ECM-receptor interaction" and "focal adhesion" Kyoto Encyclopedia of Genes and Genomes pathways. Furthermore, combining these results with those of cell viability assays, we proposed that ZINC000085537017 and quercetin were potential drugs for COL1A1 based on virtual screening and the TCMSP database, respectively. These results might help to improve the outcome of OV in the future.

Keywords: KEGG; carboplatin; ceRNA; drugs; ovarian cancer; virtual screening.

Figure 1

Flow diagram of the analysis procedure: data collection, preprocessing, analysis, and validation.

Figure 2

Identification of modules and genes associated with carboplatin-resistant OV. (A) Dendrogram of 8,791 genes in the top 70% median absolute deviations clustered based on a dissimilarity measure (1-TOM). (B) Heatmap of the correlation between module eigengenes and clinical traits. Each cell contains the correlation coefficient and p-value. (C) mRNA expression of COL1A1 in carboplatin-nonresistant OV (n = 84) and carboplatin-resistant OV (n = 14). *p < 0.05. (D) The overall survival of patients with high expression of COL1A1 was lower than that of patients with low expression (p < 0.05).

Figure 3

Upstream regulation mechanism of COL1A1 in carboplatin-resistant OV. (A) Bioinformatics methods were used to construct the ceRNA network of COL1A1. (B) ceRNA network of COL1A1. Red represents COL1A1, green represents miRNAs, blue represents lncRNAs. (C) Real-time PCR validation of candidates in ceRNA network. (mean ± SD, n =3). Asterisks indicate significant differences compared with the control group (*p < 0.05). Kaplan–Meier overall survival analyses for miR-98 (D), LINC00052 (E), and SMCR5 (F) in OV.

Figure 4

Downstream regulation mechanism of COL1A1 in carboplatin-resistant OV. (A) KEGG over-representation test pathway analysis of genes co-expressed with COL1A1 by clusterProfiler (p < 0.01). (B) Results of GSEA between carboplatin-resistant and nonresistant groups (p < 0.05 and FDR < 25%).

Figure 5

Potential drugs based on the structure of COL1A1. (A) Structure of ZINC000085537017. (B) 2D structure of ZINC000085537017’s binding mode in COL1A1. (C) 3D structure of ZINC000085537017’s binding mode in COL1A1. Yellow represents hydrogen bonds, green represents pi–pi interactions, and purple represents salt bridges. ZINC000085537017 (D) and quercetin (E) enhanced the cytotoxicity of carboplatin in resistant cells. Cells were pretreated with ZINC000085537017 or quercetin for 24 h, followed by incubation with carboplatin for 48 h, and were then subjected to cell viability assays. The results are presented as mean ± SD (n = 6) and were normalized to the control (*p < 0.05).

Figure 6

Possible mechanism of COL1A1 in carboplatin-resistant OV.