Journals / Ankara Üniversitesi Eczacılık Fakültesi Dergisi / 2023 / Cilt: 47 - Sayı: 1

INTEGRATED BIOINFORMATIC ANALYSIS TO EVALUATE TARGET GENES AND PATHWAYS IN CHRONIC LYMPHOCYTIC LEUKEMIA

ENTEGRE BİYOİNFORMATİK ANALİZ İLE KRONİK LENFOSİTİK LÖSEMİDE HEDEF GENLERİN VE YOLAKLARIN BELİRLENMESİ

Pages
239–249
Publication Date
2023-01-20
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The goal of this research was to use bioinformatic analysis to comprehend the molecular mechanisms causing CLL and to investigate potential targets for the diagnosis and therapy of CLL.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Objective: The most common type of leukemia, chronic lymphocytic leukemia (CLL), is characterized by progressive accumulation of monoclonal B cells with a specific immunophenotype in the blood, bone marrow, and lymphoid organ.
📊
Key Findings & Data:Objective: The most common type of leukemia, chronic lymphocytic leukemia (CLL), is characterized by progressive accumulation of monoclonal B cells with a specific immunophenotype in the blood, bone marrow, and lymphoid organ.
💡
Conclusion & Impact:The goal of this research was to use bioinformatic analysis to comprehend the molecular mechanisms causing CLL and to investigate potential targets for the diagnosis and therapy of CLL.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Bioinformatic analysis · CLL · GEO

Abstract

Objective: The most common type of leukemia, chronic lymphocytic leukemia (CLL), is characterized by progressive accumulation of monoclonal B cells with a specific immunophenotype in the blood, bone marrow, and lymphoid organ. The goal of this research was to use bioinformatic analysis to comprehend the molecular mechanisms causing CLL and to investigate potential targets for the diagnosis and therapy of CLL.

Özet

Amaç: En yaygın lösemi türü olan kronik lenfositik lösemi (KLL), kanda, kemik iliğinde ve lenfoid organda spesifik bir immünofenotipe sahip monoklonal B hücrelerinin ilerleyici birikimi ile karakterize edilir. Bu çalışmanın amacı, biyoinformatik analiz yaparak KLL’nin altında yatan moleküler mekanizmaları araştırmak, KLL tanı ve tedavisi için potansiyel hedefleri belirlemektir.

🔖

Cite This Article

GÜNEŞ, B. A. (2023). INTEGRATED BIOINFORMATIC ANALYSIS TO EVALUATE TARGET GENES AND PATHWAYS IN CHRONIC LYMPHOCYTIC LEUKEMIA. Ankara Üniversitesi Eczacılık Fakültesi Dergisi, Cilt: 47 - Sayı: 1, 239–249. https://ajindex.com/articles/878130

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (1)

  1. Yan, H., Tian, S., Kleinstern, G., Wang, Z., Lee, J.H., Boddicker, N.J., Cerhan, J.R., Kay, N.E., Braggio, E., Slager, S.L. (2020). Chronic lymphocytic leukemia (CLL) risk is mediated by multiple enhancer variants within CLL risk loci. Human Molecular Genetics, 29(16), 2761-2774. [CrossRef] 2. Hallek, M. (2019). Chronic lymphocytic leukemia: 2020 update on diagnosis, risk stratification and treatment. Am J Hematology, 94(11), 1266-1287. [CrossRef] 3. Gao, C., Zhou, C., Zhuang, J., Liu, L., Wei, J., Liu, C., Huayao, Li, H., Sun, C. (2019). Identification of key candidate genes and miRNA‑mRNA target pairs in chronic lymphocytic leukemia by integrated bioinformatics analysis. Molecular Medicine Reports, 19, 362-374. [CrossRef] 4. Luo, P., Yang, Q., Cong, L.L., Wang, X.F., Li, Y.S., Zhong, X.M., Xie, R.T., Jia, C.Y., Yang, H.Q., Li, W.P., Cong, X.L., Xia, Q., Fu, D., Zeng, Q.H., Ma, Y.S. (2017). Identification of miR124a as a novel diagnostic and prognostic biomarker in nonsmall cell lung cancer for chemotherapy. Molecular Medicine Reports, 16, 238‑246. [CrossRef] 5. Kamaraj, B., Gopalakrishnan, C., Purohit, R. (2014). In silico analysis of miRNA‑mediated gene regulation in OCA and OA genes. Cell Biochemistry and Biophysics, 70,1923-1932. [CrossRef] 6. Zhang, Y., Han, X., Wu, H., Zhou, Y. (2017). Bioinformatics analysis of transcription profiling of solid pseudopapillary neoplasm of the pancreas. Molecular Medicine Reports, 16, 1635-1642. [CrossRef] 7. Yan, H., Zheng, G., Qu, J., Liu, Y., Huang, X., Zhang, E., Cai, Z. (2019). Identification of key candidate genes and pathways in multiple myeloma by integrated bioinformatics analysis. Journal of Cellular Physiology, 234(12), 23785-23797. [CrossRef] 8. Bustin, S.A., Dorudi, S. (2004). Gene expression profiling for molecular staging and prognosis prediction in colorectal cancer. Expert Review of Molecular Diagnostics, 4, 599–607. [CrossRef] 9. Nannini, M., Pantaleo, M.A., Maleddu, A., Astolfi, A., Formica, S., Biasco, G. (2009). Gene expression profiling in colorectal cancer using microarray technologies: results and perspectives. Cancer Treatment Reviews, 35, 201-209. [CrossRef] 10. Kulasingam, V., Diamandis, E.P. (2008). Strategies for discovering novel cancer biomarkers through utilization of emerging technologies. Nature Clinical Practice Oncology, 5, 588–599. [CrossRef] 11. Gao, C., Zhou, C., Zhuang, J., Liu, L., Wei, J., Liu, C., Li, H., Sun, C. (2019). Identification of key candidate genes and miRNA‑mRN