Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/523575
Title: | Identification of novel genes pathways and mutations associated with Acute Myeloid Leukemia |
Researcher: | Nithya R |
Guide(s): | Santhy K S |
Keywords: | Life Sciences Plant and Animal Science Zoology |
University: | Avinashilingam Institute for Home Science and Higher Education for Women |
Completed Date: | 2023 |
Abstract: | Cancer ranks as a leading cause of death and an important barrier to increasing life newlineexpectancy around the world. Acute myeloid leukemia (AML) is characterized by proliferative, poorly differentiated cells of the hematopoietic system. Indeed, molecular newlineanalysis of leukemic blasts from AML patients suggested that there was an obvious newlineheterogeneity in the gene expression and mutations. However, the detailed pathophysiology of AML is still unclear, especially the complicated molecular mechanisms. Therefore, the identification of effective therapeutic strategies and better understanding of the mechanism of AML is needed. This study focuses on the differential gene expression, prognostic newlinecharacteristic of the PTPN11 mutation, co-expressed genes associated with blast cell %, newlinepathways and position specific disease-prone and neutral mutations underlying in AML. The newlinestudy revealed that PTPN11 gene mutation is associated with differential expression and newlineshorter median survival time. Co-expression network analysis also revealed that six novel newlinegenes namely ITGB1, JUN, ATM, MYC, NOTCH1, and PTPN11 were related to the development of AML. This report emphasizes on the involvement of metabolic pathways, newlineMAPK signalling pathway and pathways in cancer. Further, a machine learning classification model developed with sensitivity, specificity, accuracy and AUC of 90.94%, 89.05%,90.31%and 0.93, respectively. Also, amino acid residues arginine (Arg), glycine (Gly) and cysteine (Cys) were found to be more prevalent at disease prone sites. Finally, a novel mutation site predictor namely MLCanPred (https://ml-canpred.web.app) was developed for predicting the disease prone sites in AML, by using XGB classifier, in collaboration with IIT Madras. This study will establish the framework for further research that can develop therapeutics for the treatment of AML and also to recommend the right therapy for this cancer. |
Pagination: | 184 p. |
URI: | http://hdl.handle.net/10603/523575 |
Appears in Departments: | Department of Zoology |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 115.99 kB | Adobe PDF | View/Open |
02_prelimpages.pdf | 500.68 kB | Adobe PDF | View/Open | |
03_contents.pdf | 134.89 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 6.28 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 215.78 kB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 1.7 MB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 435.6 kB | Adobe PDF | View/Open | |
08_chapter 4.pdf | 2.82 MB | Adobe PDF | View/Open | |
09_chapter 5.pdf | 231.25 kB | Adobe PDF | View/Open | |
10_chapter 6.pdf | 554.22 kB | Adobe PDF | View/Open | |
11_annexures.pdf | 3.68 MB | Adobe PDF | View/Open | |
80_recommendation.pdf | 138.7 kB | Adobe PDF | View/Open |
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