Repositioning of Hypoglycemic Drug Linagliptin for Cancer Treatment

Abstract

Background: Drug repositioning, development of new uses for marketed drugs, is an effective way to discover new antitumor compounds. In this study, we used a new method, filtering compounds via molecular docking to find key targets combination.

Methods: The data of gene expression in cancer and normal tissues of colorectal, breast, and liver cancer were obtained from The Cancer Genome Atlas Project (TCGA). The key targets combination was obtained from the protein-protein interaction network (PPI network) and the correlation analysis of the targets. Molecular docking was used to reposition the drugs which were obtained from DrugBank. MTT proliferation assay and animal experiments were used to verify the activity of candidate compounds. Flow cytometric analysis of proliferation, cell cycle and apoptosis, slice analysis, gene regulatory network, and Western blot were performed to elucidate the mechanism of drug action.

Results: CDK1 and AURKB were identified as a pair of key targets by the analysis of different expression gene from TCGA. Three compounds, linagliptin, mupirocin, and tobramycin, from 12 computationally predicted compounds, were verified to inhibit cell viability in HCT116 (colorectal), MCF7 (breast), and HepG2 (liver) cancer cells. Linagliptin, a hypoglycemic drug, was proved to inhibit cell proliferation by cell cycle arrest and induce apoptosis in HCT116 cells, and suppress tumor growth in nude mice bearing HCT116 cells. Linagliptin reduced the tumor size and decreased the expression of Ki67, a nuclear protein expressed in all proliferative cells. Gene regulatory network and Western blot analysis suggested that linagliptin inhibited tumor cell proliferation and promoted cell apoptosis through suppressing the expression and phosphorylation of Rb, plus down-regulating the expression of Pro-caspase3 and Bcl-2, respectively.

Conclusion: The combination of key targets based on the protein-protein interaction network that were built by the different gene expression of TCGA data to reposition the marketed drugs turned out to be a new approach to discover new antitumor drugs. Hypoglycemic drug linagliptin could potentially lead to novel therapeutics for the treatment of tumors, especially for colorectal cancer. Gene regulatory network is a valuable method for predicting and explaining the mechanism of drugs action.

Keywords: cell proliferation and apoptosis; drug repositioning; gene regulatory network; molecular docking; multi-target anti-tumor drug screening; the oncology genome atlas project; xenograft tumor mice.

Figure 1

The results of screening of novel drug compounds for CDK1 and AURKB (A): up: The comparison of conformations of the initial ligand before and after docking with CDK1; below: The interactions between initial ligand and CDK1; (B): up: The comparison of conformations of the initial ligand before and after docking with AURKB; below: the interactions between initial ligand and AURKB; (C) The structures of candidate compounds.

Figure 2

Candidate compounds inhibit cell viability in HCT116 cells, MCF7 cells, and HepG2 cells (A): Linagliptin inhibits viability of HCT116 cells, MCF7 cells, and HepG2 cells, a showed linagliptin inhibits cell viability in HCT 116 cell line, b showed linagliptin inhibits cell viability in MCF7 cell line, c showed linagliptin inhibits cell viability in HepG2 cell line; (B): Mupirocin inhibits cell viability in HCT116 cells, MCF7 cells, and HepG2 cells, a showed mupirocin inhibits cell viability in HCT 116 cell line, b showed mupirocin inhibits cell viability in MCF7 cell line, c showed mupirocin inhibits cell viability in HepG2 cell line; (C): Tobramycin inhibits viability of HCT116 cells, MCF7 cells and HepG2 cells, a showed tobramycin inhibits cell viability in HCT 116 cell line, b showed tobramycin inhibits cell viability in MCF7, c showed tobramycin inhibits cell viability in HepG2 cell line.

Figure 3

Linagliptin induces HCT 116 cell cycle arrest and apoptosis (A): Control fluorescence intensity of CFSE; (B): Fluorescence intensity of CFSE in linagliptin treated group (HCT 116 cells were treated with 100 μM linagliptin for 24 h); (C): Statistical difference between control group and linagliptin treated group in A; D–F: Cell cycle analysis by flow cytometry. (D): Cell cycle of control group; (E): 50 μM linagliptin induced cell cycle arrest at 48 h; (F): 100 μM linagliptin induced cell cycle arrest at 48 h; (G, H): Apoptosis analysis by flow cytometry. (G): Control group; (H): 100 μM linagliptin induced HCT 116 cell apoptosis at 48 h.

Figure 4

Linagliptin inhibits tumor growth in HCT116 xenograft nude mice (A): Growth curve of tumor volume in nude mice; (B): Effect of linagliptin and Cyclophosphamide treatment on body weight of nude mice; (C): Change of tumor volume from control, cyclophosphamide, 50 mg/kg and 500 mg/kg linagliptin groups; (D): Linagliptin reduced cell proliferation markers, Ki67, as determined by immunohistochemical staining (40×) and the graphs of H&E staining; (E): The statistical analysis of Ki67 in control and linagliptin treated group showed in (D).

Figure 5

The mechanism of linagliptin inhibits tumor growth in HCT116 (A): GO analysis of down-regulated genes from linagliptin effect gene regulatory network; (B): KEGG analysis of down-regulated genes from linagliptin effect gene regulatory network; (C): GO analysis of up-regulated genes from linagliptin effect gene regulatory network; (D): KEGG analysis of up-regulated genes from linagliptin effect gene regulatory network; (E): the expression levels of p53, Rb, pRb^s780, pRb^s807/811, Pro-caspase3 and Bcl-2.