Dhania Novitasari1,2, Riris Istighfari Jenie2,3, Jun-Ya Kato4, Edy Meiyanto5,6. 1. Doctoral Student in the Faculty of Pharmacy, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia. 2. Cancer Chemoprevention Research Center, Faculty of Pharmacy, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia. 3. Macromolecular Engineering Laboratory, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Gadjah Mada, Sekip Utara, Yogyakarta, 55281, Indonesia. 4. Laboratory of Tumor Cell Biology, Nara Institute of Science and Technology, Ikoma, Nara, 630-0192, Japan. 5. Cancer Chemoprevention Research Center, Faculty of Pharmacy, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia. edy_meiyanto@ugm.ac.id. 6. Macromolecular Engineering Laboratory, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Gadjah Mada, Sekip Utara, Yogyakarta, 55281, Indonesia. edy_meiyanto@ugm.ac.id.
Abstract
BACKGROUND: The poor outcomes from triple-negative breast cancer (TNBC) therapy are mainly because of TNBC cells' heterogeneity, and chemotherapy is the current approach in TNBC treatment. A previous study reported that CCA-1.1, the alcohol-derivative from monocarbonyl PGV-1, exhibits anticancer activities against several cancer cells, as well as in TNBC. This time, we utilized an integrative bioinformatics approach to identify potential biomarkers and molecular mechanisms of CCA-1.1 in inhibiting proliferation in TNBC cells. METHODS: Genomics data expression were collected through UALCAN, derived initially from TCGA-BRCA data, and selected for TNBC-only cases. We predict CCA-1.1 potential targets using SMILES-based similarity functions across six public web tools (BindingDB, DINIES, Swiss Target Prediction, Polypharmacology browser/PPB, Similarity Ensemble Approach/SEA, and TargetNet). The overlapping genes between the CCA-1.1 target and TNBC (CPTGs) were selected and used in further assessment. Gene ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) network analysis were generated in WebGestalt. The protein-protein interaction (PPI) network was established in STRING-DB, and then the hub-genes were defined through Cytoscape. The hub-gene's survival analysis was processed via CTGS web tools using TCGA database. RESULTS: KEGG pathway analysis pointed to cell cycle process which enriched in CCA-1.1 potential targets. We also identified nine CPTGs that are responsible in mitosis, including AURKB, PLK1, CDK1, TPX2, AURKA, KIF11, CDC7, CHEK1, and CDC25B. CONCLUSION: We suggested CCA-1.1 possibly regulated cell cycle process during mitosis, which led to cell death. These findings needed to be investigated through experimental studies to reinforce scientific data of CCA-1.1 therapy against TNBC.
BACKGROUND: The poor outcomes from triple-negative breast cancer (TNBC) therapy are mainly because of TNBC cells' heterogeneity, and chemotherapy is the current approach in TNBC treatment. A previous study reported that CCA-1.1, the alcohol-derivative from monocarbonyl PGV-1, exhibits anticancer activities against several cancer cells, as well as in TNBC. This time, we utilized an integrative bioinformatics approach to identify potential biomarkers and molecular mechanisms of CCA-1.1 in inhibiting proliferation in TNBC cells. METHODS: Genomics data expression were collected through UALCAN, derived initially from TCGA-BRCA data, and selected for TNBC-only cases. We predict CCA-1.1 potential targets using SMILES-based similarity functions across six public web tools (BindingDB, DINIES, Swiss Target Prediction, Polypharmacology browser/PPB, Similarity Ensemble Approach/SEA, and TargetNet). The overlapping genes between the CCA-1.1 target and TNBC (CPTGs) were selected and used in further assessment. Gene ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) network analysis were generated in WebGestalt. The protein-protein interaction (PPI) network was established in STRING-DB, and then the hub-genes were defined through Cytoscape. The hub-gene's survival analysis was processed via CTGS web tools using TCGA database. RESULTS: KEGG pathway analysis pointed to cell cycle process which enriched in CCA-1.1 potential targets. We also identified nine CPTGs that are responsible in mitosis, including AURKB, PLK1, CDK1, TPX2, AURKA, KIF11, CDC7, CHEK1, and CDC25B. CONCLUSION: We suggested CCA-1.1 possibly regulated cell cycle process during mitosis, which led to cell death. These findings needed to be investigated through experimental studies to reinforce scientific data of CCA-1.1 therapy against TNBC.