PhD Scientific Days 2020

Pathology and Oncology III. Lectures

TNMplot: Transcriptome Based Database for the Comparison of Gene Expression Profiles of Normal, Malignant and Metastatic Tissues

Előadó neve

Dr. Bartha, Aron

Előadó munkahelye

Department of Bioinformatics and 2nd Department of Pediatrics, Semmelweis University, H-1094 Budapest, Hungary

Előadó telefonszáma

+36709461583

Előadó e-mail címe

bartha.aron@med.semmelweis-univ.hu

Az előadás címe

TNMplot: Transcriptome Based Database for the Comparison of Gene Expression Profiles of Normal, Malignant and Metastatic Tissues

Szerző(k) neve és munkahelye

Áron Bartha, Balázs Győrffy

Department of Bioinformatics and 2nd Department of Pediatrics, Semmelweis University, H-1094 Budapest, Hungary

Szekció

Pathology and Oncology III. Lectures

Language of the presentation

Hungarian

Section, first choice

Pathology and Oncology

Section, second choice

Theoretical and Translational Medicine

Összefoglaló szövege

Introduction: Over the past two decades robust amount of cancer related data have became available online, including both RNA sequencing (GDC) and gene-chip based data (NCBI-GEO). Currently, there is a lack of user-friendly web-tool, that allows the comparison of normal, tumor and metastatic data within and across these databases.
Objectives: To create a database which enables the comparison of normal, tumor and metastatic data across all genes and multiple databases.
Methods: Our database is built on two platforms, gene chip and RNA-seq. The gene chip data were processed from the NCBI-GEO database using a total of 3180 assays, from which we manually selected the appropriate samples, followed by the normalization process using the MAS5 algorithm. RNA sequencing data was downloaded from TCGA, TARGET and GTEx databases. TCGA and TARGET contain mainly tumor and metastatic data from adult and pediatric patients, while data found in GTEx are from healthy tissues. A total of 23.418 sample sequences from the three databases were used, which were normalized using the DESeq2 algorithm.
Results: A total of 33.520 samples were included from 3180 gene chip-based assays, comprising of 453 metastatic, 29.376 tumorous and 3691 normal samples, across 38 tissue types. From the TCGA database, we used 11010 samples (394 metastatic, 9886 tumorous and 730 normal) representing 33 tissue types, from TARGET we used 1193 samples (1 metastatic, 1180 tumor, 12 normal) in 7 tissue types, based on GTEx data we withdrew 11.215 normal samples over 53 tissue types.
Conclusion: In this study, we created a transcriptome-based database containing 56.938 samples. This database serves as a base for the establishment of an online application to track the development and progression of tumors.

Additional Information

Supervisor: Balázs Győrffy
E-mail address: gyorffy.balazs@med.semmelweis-univ.hu

Bemutatás módja

Szóbeli

Kind

Szabad

Status

elfogadva

Accepted presentation method

szóbeli

Előadás fájl jóváhagyás

nem rendelkezett róla

Előadó

4084

Start

18:15

End

18:30

Authors (legacy)

Áron Bartha, Balázs Győrffy

Department of Bioinformatics and 2nd Department of Pediatrics, Semmelweis University, H-1094 Budapest, Hungary