Authors :
Yasotha S.; Manivannan R.; Kamalakannan Dhanabalan; Mathiyazhagan M.; Santhosh M.; Selva Suriya S.; Thenmozhi C.
Volume/Issue :
Volume 11 - 2026, Issue 7 - July
Google Scholar :
https://tinyurl.com/2bpktc2a
Scribd :
https://tinyurl.com/3r9bzkpn
DOI :
https://doi.org/10.38124/ijisrt/26jul1437
Note : A published paper may take 4-5
working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and
ResearchGate.
Abstract :
One of the leading causes of cancer-related mortality is now breast cancer, which is worldwide nowadays an
unavoidable challenge of novel therapeutic strategies. This study aims to investigate the mechanism of molecular which
approaches the anticancer potential of Wrightia tinctoria bark extract. Based on pharmacokinetic properties and
physicochemical guidelines of the drug, active phytoconstituents were identified & screened. On identifying common targets,
potential protein targets were predicted and linked up with breast cancer-associated genes. A protein-protein interaction
(PPI) network & pathway analysis were determined using topological analysis. Functional enrichment & pathway analysis
were performed to clarify biological relevance, and survival analysis was performed for clinical validation. A total of 239
phytochemical targets and 1595 disease-associated genes were identified by submitting 25 common targets. The key hub
genes which are included are CCND1, CDK4, ESR1, MDM2, HIF1A & others. Enrichment analysis includes PI3K-Akt &
JAK-STAT signalling. Survival analysis confirmed the predictive significance of CCND1 & CDK4. This information
suggests that ‘Wrightia tinctoria’ has multi-target therapeutic potential & serves as the best source for breast cancer
development.
Keywords :
Breast Cancer; Wrightia tincoria bark; In-silico; Hub Genes.
References :
- National Cancer Institute. Breast Cancer Treatment (PDQ®).
- World Health Organization. Breast cancer.
- Bray F, Laversanne M, Weiderpass E, Soerjomataram I. Global cancer statistics 2022. CA Cancer J Clin. 2024.
- Harbeck N, Penault-Llorca F, Cortes J, et al. Breast cancer. Nat Rev Dis Primers. 2019; 5:66.
- Waks AG, Winer EP. Breast cancer treatment: A review. JAMA. 2019;321(3):288-300.
- Gradishar WJ, Anderson BO, Abraham J, et al. NCCN guidelines insights: Breast cancer. J Natl Compr Canc Netw. 2024;22(1):1-15.
- Modi S, Jacot W, Yamashita T, et al. Trastuzumab deruxtecan in previously treated HER2-positive breast cancer. N Engl J Med. 2022;386(7):610-21.
- Cortes J, Cescon DW, Rugo HS, et al. Pembrolizumab plus chemotherapy in advanced TNBC. N Engl J Med. 2020; 382:810-21.
- Elbashir MK, et al. Identification of hub genes associated with breast cancer using different network scoring methods. Applied Sciences. 2023. https://www.mdpi.com/2076-3417/13/4/2403
- Niraj Kale, Sanket Rathod, Snehal More, Namdeo Shinde. Phyto-Pharmacological Profile of Wrightia tinctoria. Asian Journal of Research in Pharmaceutical Sciences. 2021; 11(4):301-8. doi: 10.52711/2231-5659.2021.00047
- Kim TH, et al. Network Pharmacological Analysis of Herbal Folk Medicines and Their Therapeutic Mechanisms. Molecules. 2023;28(21):6678. https://pmc.ncbi.nlm.nih.gov/articles/PMC9955970/
- Mohanraj, K.; Karthikeyan, B.S.; Vivek-Ananth, R.P.; Chand, R.P.; Aparna, S.R.; Mangalapandi, P.; Samal, A. IMPPAT: A Curated Database of Indian Medicinal Plants, Phytochemistry and Therapeutics. Sci. Rep. 2018, 8, 4329. https://doi.org/10.1038/s41598-018-22631-z.
- Daina, A.; Michielin, O.; Zoete, V. SwissADME: A Free Web Tool to Evaluate Pharmacokinetics, Drug-Likeness and Medicinal Chemistry Friendliness of Small Molecules. Sci. Rep. 2017, 7, 42717. https://doi.org/10.1038/srep42717.
- Lipinski, C.A. Lead- and Drug-Like Compounds: The Rule-of-Five Revolution. Drug Discov. Today Technol. 2004, 1, 337–341. https://doi.org/10.1016/j.ddtec.2004.11.007.
- Kim, S.; Chen, J.; Cheng, T.; Gindulyte, A.; He, J.; He, S.; Li, Q.; Shoemaker, B.A.; Thiessen, P.A.; Yu, B.; et al. PubChem in 2021: New Data Content and Improved Web Interfaces. Nucleic Acids Res. 2021, 49, D1388–D1395. https://doi.org/10.1093/nar/gkaa971.
- Gfeller, D.; Grosdidier, A.; Wirth, M.; Daina, A.; Michielin, O.; Zoete, V. SwissTargetPrediction: A Web Server for Target Prediction of Bioactive Small Molecules. Nucleic Acids Res. 2014, 42, W32–W38. https://doi.org/10.1093/nar/gku293.
- Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Res. 2003, 13, 2498–2504. https://doi.org/10.1101/gr.1239303.
- Stelzer, G.; Rosen, N.; Plaschkes, I.; Zimmerman, S.; Twik, M.; Fishilevich, S.; Stein, T.I.; Nudel, R.; Lieder, I.; Mazor, Y.; et al. The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses. Curr. Protoc. Bioinform. 2016, 54, 1.30.1–1.30.33. https://doi.org/10.1002/cpbi.5.
- Oliveros, J.C. Venny. An Interactive Tool for Comparing Lists with Venn’s Diagrams. 2007–2015. Available online: https://bioinfogp.cnb.csic.es/tools/venny/ (accessed on 22 January 2026).
- Szklarczyk, D.; Gable, A.L.; Nastou, K.C.; Lyon, D.; Kirsch, R.; Pyysalo, S.; Doncheva, N.T.; Legeay, M.; Fang, T.; Bork, P.; et al. The STRING Database in 2021: Customizable Protein–Protein Networks, and Functional Characterization of User-Uploaded Gene/Measurement Sets. Nucleic Acids Res. 2021, 49, D605–D612. https://doi.org/10.1093/nar/gkaa1074.
- Ge, S.X.; Jung, D.; Yao, R. ShinyGO: A Graphical Gene-Set Enrichment Tool for Animals and Plants. Bioinformatics 2020, 36, 2628–2629. https://doi.org/10.1093/bioinformatics/btz931.
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G. Gene ontology: a tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000 May;25(1):25-9. doi: 10.1038/75556
- Chandrashekar, D.S.; Bashel, B.; Balasubramanya, S.A.H.; Creighton, C.J.; Ponce-Rodriguez, I.; Chakravarthi, B.V.S.K.; Varambally, S. UALCAN: A Portal for Facilitating Tumour Subgroup Gene Expression and Survival Analyses. Neoplasia 2017, 19, 649–658. https://doi.org/10.1016/j.neo.2017.05.002.
One of the leading causes of cancer-related mortality is now breast cancer, which is worldwide nowadays an
unavoidable challenge of novel therapeutic strategies. This study aims to investigate the mechanism of molecular which
approaches the anticancer potential of Wrightia tinctoria bark extract. Based on pharmacokinetic properties and
physicochemical guidelines of the drug, active phytoconstituents were identified & screened. On identifying common targets,
potential protein targets were predicted and linked up with breast cancer-associated genes. A protein-protein interaction
(PPI) network & pathway analysis were determined using topological analysis. Functional enrichment & pathway analysis
were performed to clarify biological relevance, and survival analysis was performed for clinical validation. A total of 239
phytochemical targets and 1595 disease-associated genes were identified by submitting 25 common targets. The key hub
genes which are included are CCND1, CDK4, ESR1, MDM2, HIF1A & others. Enrichment analysis includes PI3K-Akt &
JAK-STAT signalling. Survival analysis confirmed the predictive significance of CCND1 & CDK4. This information
suggests that ‘Wrightia tinctoria’ has multi-target therapeutic potential & serves as the best source for breast cancer
development.
Keywords :
Breast Cancer; Wrightia tincoria bark; In-silico; Hub Genes.