Ferroptosis-related long noncoding RNA signature predicts the prognosis of clear cell renal cell carcinoma / Liu Jiawen

Clear Cell Renal Cell Carcinoma (ccRCC) is very common and accounts for most kidney cancer deaths. While many studies are being conducted in finding the prognosis signatures of ccRCC, we believe that ferroptosis, that involves programmed cell death dependent on iron accumulation has therapeutic p...

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Main Author: Liu , Jiawen
Format: Thesis
Published: 2022
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Online Access:http://studentsrepo.um.edu.my/14729/1/Liu_Jiawen.pdf
http://studentsrepo.um.edu.my/14729/2/Liu_Jiawen.pdf
http://studentsrepo.um.edu.my/14729/
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spelling my.um.stud.147292024-01-21T23:56:56Z Ferroptosis-related long noncoding RNA signature predicts the prognosis of clear cell renal cell carcinoma / Liu Jiawen Liu , Jiawen Q Science (General) QH301 Biology Clear Cell Renal Cell Carcinoma (ccRCC) is very common and accounts for most kidney cancer deaths. While many studies are being conducted in finding the prognosis signatures of ccRCC, we believe that ferroptosis, that involves programmed cell death dependent on iron accumulation has therapeutic potential in ccRCC. Recent research showed that long noncoding RNAs (lncRNAs) have been shown to be involved in ferroptosis-related tumor processes and are closely related to survival in patients with ccRCC. Hence in this study we aim to further explore the role of ferroptosis-related lncRNAs (FRLs) in ccRCC, hoping to establish a signature to predict the survival outcome of ccRCC. Here we analyzed transcriptome data from The Cancer Genome Atlas database (TCGA) and ferroptosis-related genes (FRGs) from FerrDb to identify FRLs using Pearson’s correlation. Lasso Cox regression analysis and multivariate Cox proportional hazards models screened seventeen optimal FRLs for developing prognostic signatures. Kaplan–Meier survival curves and receiver operating characteristic (ROC) curves were then plotted for validating the sensitivity, specificity, and accuracy of the identified signatures. CIBERSORT algorithm were deployed to explore the role of these FRLs in tumor microenvironment (TME). It was concluded that these models demonstrate excellent performance in predicting prognosis among patients with ccRCC and which also indicated association with the clinicopathologic parameters such as tumor grade, tumor stage and tumor immune infiltration. In conclusion, our findings provide novel insights into ferroptosis-related lncRNAs in ccRCC which are important targets for investigating the tumorigenesis of ccRCC. 2022-05 Thesis NonPeerReviewed application/pdf http://studentsrepo.um.edu.my/14729/1/Liu_Jiawen.pdf application/pdf http://studentsrepo.um.edu.my/14729/2/Liu_Jiawen.pdf Liu , Jiawen (2022) Ferroptosis-related long noncoding RNA signature predicts the prognosis of clear cell renal cell carcinoma / Liu Jiawen. Masters thesis, Universiti Malaya. http://studentsrepo.um.edu.my/14729/
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Student Repository
url_provider http://studentsrepo.um.edu.my/
topic Q Science (General)
QH301 Biology
spellingShingle Q Science (General)
QH301 Biology
Liu , Jiawen
Ferroptosis-related long noncoding RNA signature predicts the prognosis of clear cell renal cell carcinoma / Liu Jiawen
description Clear Cell Renal Cell Carcinoma (ccRCC) is very common and accounts for most kidney cancer deaths. While many studies are being conducted in finding the prognosis signatures of ccRCC, we believe that ferroptosis, that involves programmed cell death dependent on iron accumulation has therapeutic potential in ccRCC. Recent research showed that long noncoding RNAs (lncRNAs) have been shown to be involved in ferroptosis-related tumor processes and are closely related to survival in patients with ccRCC. Hence in this study we aim to further explore the role of ferroptosis-related lncRNAs (FRLs) in ccRCC, hoping to establish a signature to predict the survival outcome of ccRCC. Here we analyzed transcriptome data from The Cancer Genome Atlas database (TCGA) and ferroptosis-related genes (FRGs) from FerrDb to identify FRLs using Pearson’s correlation. Lasso Cox regression analysis and multivariate Cox proportional hazards models screened seventeen optimal FRLs for developing prognostic signatures. Kaplan–Meier survival curves and receiver operating characteristic (ROC) curves were then plotted for validating the sensitivity, specificity, and accuracy of the identified signatures. CIBERSORT algorithm were deployed to explore the role of these FRLs in tumor microenvironment (TME). It was concluded that these models demonstrate excellent performance in predicting prognosis among patients with ccRCC and which also indicated association with the clinicopathologic parameters such as tumor grade, tumor stage and tumor immune infiltration. In conclusion, our findings provide novel insights into ferroptosis-related lncRNAs in ccRCC which are important targets for investigating the tumorigenesis of ccRCC.
format Thesis
author Liu , Jiawen
author_facet Liu , Jiawen
author_sort Liu , Jiawen
title Ferroptosis-related long noncoding RNA signature predicts the prognosis of clear cell renal cell carcinoma / Liu Jiawen
title_short Ferroptosis-related long noncoding RNA signature predicts the prognosis of clear cell renal cell carcinoma / Liu Jiawen
title_full Ferroptosis-related long noncoding RNA signature predicts the prognosis of clear cell renal cell carcinoma / Liu Jiawen
title_fullStr Ferroptosis-related long noncoding RNA signature predicts the prognosis of clear cell renal cell carcinoma / Liu Jiawen
title_full_unstemmed Ferroptosis-related long noncoding RNA signature predicts the prognosis of clear cell renal cell carcinoma / Liu Jiawen
title_sort ferroptosis-related long noncoding rna signature predicts the prognosis of clear cell renal cell carcinoma / liu jiawen
publishDate 2022
url http://studentsrepo.um.edu.my/14729/1/Liu_Jiawen.pdf
http://studentsrepo.um.edu.my/14729/2/Liu_Jiawen.pdf
http://studentsrepo.um.edu.my/14729/
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