Machine learning and bioinformatics models to identify gene expression patterns of Glioblastoma associated with disease progression and mortality

Zakia Zinat Choudhury, Utpala Nanda Chowdhury, Shamim Ahmad, M. Babul Islam, Julian M.W. Quinn, Mohammad Ali Moni

Research output: Book chapter/Published conference paperConference paperpeer-review

1 Citation (Scopus)

Abstract

Glioblastoma (GB), the most frequent and aggressive form of malignant brain tumor, causes rapid death. The genetic mutation is the main reason for this fatal disease. Henceforth, it is essential to identify the causal genetic targets associated with GB survival. Plenty of publicly accessible gene expression and clinical data for GB patients from the Gene expression omnibus and the Broad Institute Cancer Genome Atlas (TCGA) dataset can allow us to study patient fatality prediction and thus to identify new GB biomarkers. In this study we applied bioinformatics and network-based approach to identify the altered genes associated with the GB comparing with normal mRNA expression data from the brain tissues. Total of 325 genes were found as differentially expressed in GB. Gene set enrichment analysis through protein-protein interaction (PPI), gene ontology (GO) and KEGG pathways also revealed their significance. We then applied machine-learning approach incorporating Cox Proportional Hazard models to the both clinical and RNA-Seq datasets to determine target biomarkers that affect the survival of the GB patients. Three genes (PHLDA1, IQGAP and, SPARC) were identified by using univariate approach that have a significant effect on the GB progression. Thus, our combined Machine Learning and Bioinformatics approach revealed the target signature genes for GB progression that could be useful to develop potential drug targets for the GB.

Original languageEnglish
Title of host publicationThe 6th International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering
Subtitle of host publicationIC4ME2 2021
EditorsAbu Bakar Md. Ismail, Md. Ekramul Hamid, Md. Hasnat Kabir
Place of PublicationBangladesh
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781665406376
ISBN (Print)9781665406383 (Print on demand)
DOIs
Publication statusPublished - 2021
Event6th International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering, IC4ME2 2021 - University of Rajshahi, Rajshahi, Bangladesh
Duration: 26 Dec 202127 Dec 2021
https://ieeexplore.ieee.org/xpl/conhome/9768399/proceeding (Proceedings)
https://web.archive.org/web/20220808043553/http://dept.ru.ac.bd/ic4me2/2021/ (Archived conference website)

Conference

Conference6th International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering, IC4ME2 2021
Country/TerritoryBangladesh
CityRajshahi
Period26/12/2127/12/21
OtherThe International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2-2021) will be held from December 26~27, 2021 at University of Rajshahi in Bangladesh. This conference is a sequel of our first conference ICMEIE-2015. This is the 6th episode of IC4ME2 conference which is going to take place. This year, this conference will be hosted in collaboration with Rajshahi University of Engineering and Technology (RUET) and Kyushu Institute of Technology (Kyutech), Japan. The conference will gather world-class researchers, engineers and educators engaged in the fields of Materials, Electronics, Chemical and Information Engineering to meet and present their latest activities. The main theme of this conference is Networking and Collaboration. Accepted papers will be submitted for inclusion into IEEE Xplore; subject to meeting IEEE Xplore’s scope and quality requirements. Papers outside the scope of IEEE will be published in the conference proceedings only. You are cordially invited to attend this interesting event.
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