Contribution of different types of -omic data on personalized diagnosis of cancer

Download

HIBIT22_paper_107.pdf

Date

2022-10

Author

Tanıl, Ezgi
Kızılilsoley, Nehir
Nikerel, Emrah

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

199
views

66
downloads

Cancer is a complex disease which is one of the main causes of death globally. Survival rate of cancer increases if early diagnosis and application of appropriate treatment are available [1,2]. Understanding differential response from one patient to another can aid in drug targeting, understanding disease progression and finding efficient treatment strategies. Tumour cells shown to have altered metabolism and the most commonly defined example is the Warburg effect in which tumour cells shown to use lactic acid fermentation instead of oxidative phosphorylation due to the increased energy demand caused by fast proliferation rate [3]. These alterations are controlled at genomic, transcriptomic, fluxomic and metabolomic levels causes the necessity of using systems biology approaches for investigations of cancer. GSMMs became an important tool for simulation and analysis of such metabolic alterations. By integration of omic data to genome scale metabolic models, tissue/disease/patient specific flux distributions can be obtained and analyzed for personalized metabolic responses to diseases and treatments. By linking genotype to phenotype, they can allow identification of personalized treatment strategies for complex metabolic diseases. Additionally, providing cancer-specific and easily measurable markers makes risk group or population wide regular screening with less invasive methods possible. In this study, contribution of genomic, transcriptomic and fluxomic data on classification of lung and pancreatic cancer, which is hard to diagnose at early stages, was performed by integrating genomic and transcriptomic data obtained from TCGA and NCBI-GEO databases and calculated personalized flux distributions from integration of transcriptome data to GSMMs. Using random forest classification of multi-omic data, individual effects of each omic level information as well as genes/reactions/metabolites that have potential to be used as biomarkers are demonstrated.

URI

https://hibit2022.ims.metu.edu.tr
https://hdl.handle.net/11511/101355

Conference Name

The International Symposium on Health Informatics and Bioinformatics

Collections

Graduate School of Informatics, Conference / Seminar

Suggestions

OpenMETU
Core

Structural mapping and network analysis of patient-specific mutations in glioblastoma Kaya, Tuğba; Tunçbağ, Nurcan; Doğan, Tunca; Department of Bioinformatics (2018) Cancer is one of the most common cause of death worldwide. It occurs as a result of a collection of somatic deviations from normal state. Therefore, many efforts have been invested to profile mutations in different types of tumors; such as, the Cancer Genome Atlas (TCGA) which deposits multiple omic data for more than 11,000 tumor samples. In this thesis, we present a pipeline which retrieves patient-specific mutation data in Glioblastoma from TCGA, maps these mutations on the protein structures in Protein ...
Characterization of liposomal celecoxib formulation as a drug delivery system in colorectal cancer cell lines Erdoğ, Aslı; Banerjee, Sreeparna; Department of Biotechnology (2012) Colorectal carcinoma (CRC) is one of the most common cancers and is the leading cause of cancer deaths in much of the developed world. Owing to the high incidence of drug resistance and potential toxic effects of chemotherapy drugs, much research is currently underway to design better strategies for smart drug delivery systems. Cyclooxygenase-2 (COX-2) pathway is associated with poor prognosis in colon carcinomas. The selective COX-2 inhibitor drug Celecoxib (CLX) has been shown to posses COX-2 independent ...
Development and Characterization of PEG-B-PCL Micelles Carrying Anticancer Agents Işık, Gülhan; Tezcaner, Ayşen; Hasırcı, Nesrin; Department of Biotechnology (2022-2-9) Cancer is a disease that decreases the quality of life. Many cancer drugs are either toxic or not effective due to their fast removal by reticuloendothelial system. Therefore, nano-sized drug delivery systems, especially the ones carrying the drugs directly to tumor, gained attention in the last decades. The aim of the study was to prepare nano-sized drug carrying micelles (drug conjugated and drug loaded) from methoxy polyethylene glycol-block-polycaprolactone (mPEG-b-PCL). In order to conjugate drugs, mPE...
Investigation of Effects of Microenvironment and Immune Cells onTumor Growth by Agent Based Modeling Orta Doğu Teknik Üniversitesi, Deniz Bilimleri Enstitüsü (Orta Doğu Teknik Üniversitesi Enformatik Enstitüsü; 2022-10) Cancer is one of the diseases that humanity suffered for a long time. We understand many aspects of this disease, but there is still much to learn. Because it is a complex disease that affects many processes, organs, cell types, and so on. We need to investigate cellular components, interactions and processes that are related to cancer as a whole to understand its structure. In this scope, investigating the microenvironment of tumors and interactions in it can reveal fundamental features of cancer. One of t...
Cost-effectiveness analysis of a prospective breast cancer screening program in Turkey Astım, Engin; Voyvoda, Ebru; Yılmaz, H. Hakan; Department of Economics (2011) Cancer is the second leading cause of death among the world and it has an increasing share among all causes of death. Economical burden of cancer is increasing especially in high and middle-income countries. Leaving cancer in competitive markets would lead to inefficiencies; hence governments should intervene in the market and make public decisions in struggling cancer. Among all cancer types breast cancer has the highest incidence and mortality rates in females. Causes of breast cancer still remains indete...

Citation Formats

E. Tanıl, N. Kızılilsoley, and E. Nikerel, “Contribution of different types of -omic data on personalized diagnosis of cancer,” Erdemli, Mersin, TÜRKİYE, 2022, p. 3107, Accessed: 00, 2023. [Online]. Available: https://hibit2022.ims.metu.edu.tr.