A framework for Alternative Splicing Isoforms Detection in Lung Cancer/ Nourelislam Mohamed Awad Ahmed

Supervisor:
Walid Ibrahim Ali Al-Atabany
Mohamed El Hadidi

Publication:
1-Immunoinformatics approach of epitope prediction for SARS-CoV-2
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9019534/

2-A feature selection-based framework to identify biomarkers for cancer diagnosis: A focus on lung adenocarcinoma
https://pubmed.ncbi.nlm.nih.gov/36067196/

Thesis (M.A.)—Nile University, Egypt, 2022 .

"Includes bibliographical references"

Contents:
Certificate of approval…………………………………………………………………………………...II
Abstract …………………………………………………………………………………………………III
List of figures ………………………………………………………………………………………...…VI
List of tables ………………………………………………………………………………………….... VII
List of abbreviations …………………………………………………………………………………...VIII
Chapter 1: Introduction …………………………………………………………………………….….… 1
1.1 Lung cancer…………………………………………………………………………….….……....1
1.2 Alternative splicing…………………………………………………………………….….……... 1
1.3 RNA-seq technology in defining alternative splicing ……………………………….….…….....2
1.4 Detection of alternative splicing ………………………………………………………..……….. 2
1.5 Alternative splicing and cancer ………………………………………………………..………....2
1.6 Thesis objective ………………………………………………………………………….….…….. 3
Chapter 2: Literature review ………………………………………………………………………..……..4
2.1 RNA complexity ……………………………………………………………………………..…… 4
2.2 Alternative splicing and cancer …………………………………………………………….…… 5
2.3 Cancer biomarker and alternative splicing ……………………………………………….……. 7
2.4 Alternative splicing and cancer immunotherapy ………………………………………….…… 8
Chapter 3: Material and methods ……………………………………………………………………….. 10
3.1 Data collection and cleaning ……………………………………………………………………... 10
3.2 Mapping approach ……………………………………………………………...…………………10
3.3 Ttranscriptome assembly ……………………………………………………………………….....12
3.4 Ttranscriptome assembly assessment …………………………………………………………… 13
VIII
3.5 Differential expression analysis …………………………………………………………………...14
3.6 Statistical enrichment analysis …………………………………………………………………... 15
Chapter 4: Results ……………………………………………………………………………………….. 16
4.1 Quality control assessment ………………………………………………………………………. 16
4.2 Sequence mapping ………………………………………………………………………………....17
4.3 Transcriptome assembly ………………………………………………………………………….. 17
4.4 Transcriptome assembly assessment ……………………………………………………………... 18
4.5 Differential expression analysis …………………………………………………………...……… 18
4.6 Visualization of significant features of expression profile ……………………………………….20
4.7 Visualization of splicing events …………………………………………………………………….21
4.8 Enrichment analysis ……………………………………………………………………………… 27
Chapter 5: Discussion ……………………………………………………………………………….……. 39
5.1 Lung cancer and alternative splicing detection …………………………………………………. 39
5.2 Importance of alternative spliced isoform ………………………………………………….…..... 39
5.3 Correlation of alternative spliced isoforms with lung cancer …………………………….……...40
Chapter 6: Conclusion …………………………………………………………………………….………41
6.1 Conclusion ………………………………………………………………………………………….41
6.2 future directions …………………………………………………………………………………....41
Appendix ………………………………………………………………………………………………… 42
Bibliography …………………………………………………………………………………………….. 64

Abstract:
Among all malignancies, Lung cancer (LC) is the leading cause of death which occupying the most common tumor worldwide. Accumulated mutations in individual genes leading to the initiation and propagation of lung cancer. One of these mutations results from post-transcription modification of messenger RNA (mRNA) which is played a pivotal role in mRNA maturity. Alternative splicing (AS) is one of the main approaches in detecting this post-processing of mRNA, where is proposed to be the main determinant for the generation of various transcriptional variants within a single gene. Typically, there are different methods for the detection and quantifications of AS from RNA-seq data; isoform-based (IS), exon-based (EX), and event-based (EV), each of these methods relies on a different strategy for AS detection. Here, IS
IV
approach has been used for our analysis in AS detection, for the fact that it provides more biological interpretation, as it reflects the natural process of transcripts synthesis inside the cells, it relies on reconstructing the whole transcript structure within a single gene, instead of defining certain splicing events or junctions. Interestingly, our analysis is not only detecting the genes correlated with lung cancer but also their associated transcripts. Moreover, many potential LC biomarkers have been identified by our analysis, were strongly correlated with LC initiation, propagation, and metastasis.

Text in English, abstracts in English.