PhD Scientific Days 2023

Translational Medicine - Posters O

Proteome and transcriptome analysis of healthy kidney tissue and clear cell renal cell carcinoma

Előadó neve

Dr. Bartha, Aron

Neptun code

RZ3SSP

Előadó munkahelye

Department of Bioinformatics, Semmelweis University

Előadó telefonszáma

+36709461583

Előadó e-mail címe

barthaaron92@gmail.com

Az előadás címe

Proteome and transcriptome analysis of healthy kidney tissue and clear cell renal cell carcinoma

Szerző(k) neve és munkahelye

Áron Bartha1,2, Zsuzsanna Darula3, Gyöngyi Munkácsy1,2, Éva Klement3, Péter Nyirády4, Balázs Győrffyb1,2

1. Dept. of Bioinformatics, Semmelweis University, Budapest, Hungary
2. Dept. of Pediatrics, Semmelweis University, Budapest, Hungary
3. Laboratory of Proteomics Research, BRC, Szeged Hungary
4. Dept. of Urology, Semmelweis University, Budapest, Hungary

Bemutatás módja

Poszter

Szekció

Translational Medicine - Posters O

Language of the presentation

English

Preferred session

Theoretical and Translational Medicine

Összefoglaló szövege

Clear cell renal carcinoma is the most frequent type of kidney cancer, with an increasing incidence rate worldwide. In this research, we used a proteotranscriptomic approach to differentiate normal and tumor tissues in clear cell renal cell carcinoma (ccRCC). Using transcriptomic data of patients with malignant and paired normal tissue samples from gene array cohorts, we identified the top genes over-expressed in ccRCC. We collected surgically resected ccRCC specimens to further investigate the transcriptomic results on the proteome level. The differential protein abundance was evaluated using targeted mass spectrometry (MS). We assembled a database of 558 renal tissue samples from NCBI GEO and used these to uncover the top genes with higher expression in ccRCC. For protein level analysis 162 malignant and normal kidney tissue samples were acquired. The most consistently upregulated genes were IGFBP3, PLIN2, PLOD2, PFKP, VEGFA, and CCND1 (p < 10−5 for each gene). Mass spectrometry further validated the differential protein abundance of these genes (IGFBP3, p = 7.53 × 10−18; PLIN2, p = 3.9 × 10−39; PLOD2, p = 6.51 × 10−36; PFKP, p = 1.01 × 10−47; VEGFA, p = 1.40 × 10−22; CCND1, p = 1.04 × 10−24). We also identified those proteins which correlate with overall survival. Finally, a support vector machine-based classification algorithm using the protein-level data was set up. We used transcriptomic and proteomic data to identify a minimal panel of proteins highly specific for clear cell renal carcinoma tissues. The introduced gene panel could be used as a promising tool in the clinical setting.

Funding: Supported by the ÚNKP-22-4-I-SE-18 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund.

University and Doctoral School

Semmelweis University, Doctoral School of Pathological Sciences

Supervisor

Prof. Dr. Balazs Gyorffy

Publication of my abstract

I do not give consent to the publication of my abstract on the website of the congress.

Kind

Szabad

Status

elfogadva

Accepted presentation method

poszter

Előadás fájl jóváhagyás

nem rendelkezett róla

Előadó

4084

Start

10:36

End

10:41

Authors (legacy)

Áron Bartha1,2, Zsuzsanna Darula3, Gyöngyi Munkácsy1,2, Éva Klement3, Péter Nyirády4, Balázs Győrffyb1,2

1. Dept. of Bioinformatics, Semmelweis University, Budapest, Hungary
2. Dept. of Pediatrics, Semmelweis University, Budapest, Hungary
3. Laboratory of Proteomics Research, BRC, Szeged Hungary
4. Dept. of Urology, Semmelweis University, Budapest, Hungary