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

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01_title.pdfAttached File115.99 kBAdobe PDFView/Open
02_prelimpages.pdf500.68 kBAdobe PDFView/Open
03_contents.pdf134.89 kBAdobe PDFView/Open
04_abstract.pdf6.28 kBAdobe PDFView/Open
05_chapter 1.pdf215.78 kBAdobe PDFView/Open
06_chapter 2.pdf1.7 MBAdobe PDFView/Open
07_chapter 3.pdf435.6 kBAdobe PDFView/Open
08_chapter 4.pdf2.82 MBAdobe PDFView/Open
09_chapter 5.pdf231.25 kBAdobe PDFView/Open
10_chapter 6.pdf554.22 kBAdobe PDFView/Open
11_annexures.pdf3.68 MBAdobe PDFView/Open
80_recommendation.pdf138.7 kBAdobe PDFView/Open
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