Author

Sumeyye Su

ORCID Identifier(s)

0000-0001-5221-413X

Graduation Semester and Year

2020

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Mathematics

Department

Mathematics

First Advisor

Leili Shahriyari

Second Advisor

Tuncay Aktosun

Abstract

Recent advances in biotechnology led to generation of large complex biological and clinical data sets that can be used to infer the underlying mechanism of many diseases and arrive at personalized treatments. One of these data sets are the whole genome profiles, including a good collection of publicly available human gene expression data sets. In the first part of this study, we analyzed gene expression profiles of patients with renal cell carcinoma (RCC). We found that the regulator of G-protein signaling 5 (RGS5) might play a crucial role in initiation and progression of RCC, and it might be prognostic. We observed that a high expression level of RGS5 is associated with better survival months. Importantly, when the grade of tumor increases, the RGS5 expression level significantly decreases. Although there is no difference between expression level of RGS5 in male and female patients with primary tumors in the right kidney, among patients with primary tumors in the left kidney, females have a significantly higher RGS5 expression than male patients. Interestingly, we also observed a significant association between the high expression level of RGS5 and low serum calcium level and elevated white blood cells level. v Moreover, the outcome of cancer treatments especially immunotherapeutic interventions depends on tumor immune environments. In recent years, the devel- opment of various immunotherapies has improved overall survival months of some cancer patients, including renal cancer patients. However, for all immunotherapeutic interventions, only a small groups of patients respond to the treatments. Therefore, it is crucial to investigate and classify immune variations of tumors to identify the groups of patients who might benefit from each treatment option. In the second part of this study, we estimate the percentage of each immune cell type in 526 TCGA renal tumors using “digital mass cytometry”. K-mean clustering of tumors based on their immune variations indicates the existence of four distinct classes of renal cell carcinoma: Cluster 1 (CD4 < CD8 ≈ MΦ), in which the numbers of macrophages and CD8+ T-cells are approximately the same, and the number of CD4+ T-cells is slightly less than the number of CD8+ T-cells; Cluster 2 (CD8 < CD4 < MΦ), in which the number of macrophages is significantly higher than the number of CD4+ and CD8+ T-cells; Cluster 3 (CD4 < MΦ < CD8), in which the number of CD8+ T-cells is significantly higher than the number of macrophages and CD4+ T-cells; and Cluster 4 (CD8 < CD4 ≈ MΦ) in which the numbers of macrophages and CD4+ T-cells are approximately the same, and the number of CD8+ T-cells is significantly less than CD4+ T-cells. Moreover, we observe a high positive correlation between the number of CD8+ T-cells and the expression levels of IFNG and PDCD1. Importantly, higher stage and grade of tumors have a significantly higher percentage of CD8+ T-cells in tumors. In addition, the primary tumors of patients, who were tumor free at the last time of follow up, have a higher percentage of NK cells and mast cells compared to the patients with tumors at the last time of follow up.

Keywords

Renal cell carcinoma, RGS5, Data analysis, RNA-seq data, Immune classification, Digital mass cytometry, T-cells, PD-1

Disciplines

Mathematics | Physical Sciences and Mathematics

Comments

Degree granted by The University of Texas at Arlington

Included in

Mathematics Commons

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