Related Experiment Video
Updated: Aug 4, 2026
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
The integrative bioinformatic analysis deciphers the predicted molecular target gene and pathway from curcumin
Dhania Novitasari1,2, Riris Istighfari Jenie2,3, Jun-Ya Kato4
1Doctoral Student in the Faculty of Pharmacy, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia.
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.
Insights
This study reveals that CCA-1.1 may inhibit triple-negative breast cancer (TNBC) by targeting the cell cycle during mitosis. Further research is needed to confirm CCA-1.1
Area of Science:
- Oncology
- Bioinformatics
- Molecular Biology
Background:
- Triple-negative breast cancer (TNBC) has poor therapeutic outcomes due to cellular heterogeneity.
- Chemotherapy remains the primary treatment for TNBC.
- CCA-1.1, a derivative of monocarbonyl PGV-1, shows anticancer potential, including against TNBC.
Purpose of the Study:
- To identify potential biomarkers for CCA-1.1's anticancer effects in TNBC.
- To elucidate the molecular mechanisms underlying CCA-1.1's inhibition of TNBC cell proliferation.
Main Methods:
- Integrative bioinformatics approach using TCGA-BRCA and UALCAN databases.
- Prediction of CCA-1.1 targets via multiple public web tools.
- Gene Ontology and KEGG pathway analysis, PPI network construction, and hub-gene identification.
Main Results:
- KEGG pathway analysis indicated cell cycle regulation as a key mechanism.
- Nine cancer-prostate-testis genes (CPTGs) involved in mitosis were identified as potential targets.
- Hub genes related to mitosis were identified using network analysis.
Conclusions:
- CCA-1.1 likely regulates the cell cycle during mitosis, leading to cancer cell death.
- Experimental validation is required to confirm CCA-1.1's therapeutic potential for TNBC.
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