PhD Scientific Days 2025

Pathological and Oncological Sciences III.

Identifying Novel Prognostic Markers in Small Cell Lung Cancer Through Comprehensive In-silico Analysis

Előadó neve

Dr. Téglás Vivien Beatrix

Neptun code

E73O2O

Előadó munkahelye

Department of Thoracic Surgery, Semmelweis University and National Institute of Oncology, Budapest, Hungary; National Koranyi Institute of Pulmonology, Budapest, Hungary

Előadó telefonszáma

209535885

Előadó e-mail címe

teglas.vivien@stud.semmelweis.hu

Az előadás címe

Identifying Novel Prognostic Markers in Small Cell Lung Cancer Through Comprehensive In-silico Analysis

Szerző(k) neve és munkahelye

Vivien Beatrix Téglás¹, Botond Megyesfalvi², Bence Ferencz¹, Balázs Szigeti¹, Senuma Pang Kaito Skyler³, Maria Dorothea Pozonec¹, Milica Kontic⁴, Filip Markovic⁴, Luka Brcic⁵, Balázs Döme⁶, Zsolt Megyesfalvi⁷, Beáta Szeitz²

1: Department of Thoracic Surgery, Semmelweis University and National Institute of Oncology, Budapest, Hungary; National Koranyi Institute of Pulmonology, Budapest, Hungary
2: National Koranyi Institute of Pulmonology, Budapest, Hungary
3: Department of Thoracic Surgery, Semmelweis University and National Institute of Oncology, Budapest, Hungary
4: University of Belgrade Faculty of Medicine, Belgrade, Serbia
5: Medical University of Graz, Graz, Austria
6: National Koranyi Institute of Pulmonology, Budapest, Hungary; Department of Thoracic Surgery, Semmelweis University and National Institute of Oncology, Budapest, Hungary; Department of Thoracic Surgery, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria; Department of Translational Medicine, Lund University, Lund, Sweden
7: National Koranyi Institute of Pulmonology, Budapest, Hungary; Department of Thoracic Surgery, Semmelweis University and National Institute of Oncology, Budapest, Hungary; Department of Thoracic Surgery, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria

Bemutatás módja

Szóbeli

Szekció

Pathological and Oncological Sciences III.

Language of the presentation

English

Preferred session

Pathological and Oncological Sciences

Összefoglaló szövege

Introduction: Small cell lung cancer (SCLC) is among the most aggressive malignancies, characterized by rapid disease progression.

Aims: We aimed to identify and validate robust prognostic markers in SCLC by analyzing publicly available transcriptomic datasets and conducting subsequent immunohistochemistry (IHC) analyses.

Method: Three previously published SCLC tissue transcriptomic datasets, accompanied by overall survival (OS) data, were accessed. Cox regression analysis was conducted to identify genes with potential prognostic significance. The most promising markers across the three cohorts were identified based on their alignment with corresponding protein expression data and insights from our comprehensive literature search. To validate our in-silico findings, IHC analyses were performed on a cohort of 50 surgically resected SCLC specimens.

Results: Our comprehensive bioinformatic analysis revealed 25 genes with potential prognostic significance in SCLC. Protein expression data supported the survival association for 1 unfavorable and 11 favorable markers (p<0.15). Following our in-depth selection process, PFN2, CTSB, PTPN6, and SLC35C1 emerged as the most promising markers for further validation. The IHC analysis revealed that high PFN2 expression in tumor cells was associated with worse OS (p=0.14), supporting its role as an unfavorable prognostic marker. In contrast, higher expression of CTSB (p=0.3) and PTPN6 (p=0.17) in immune cells was correlated with improved OS, suggesting these proteins may reflect immune competence within the tumor microenvironment. No significant association was found for SLC35C1.

Conclusion: Analyzing publicly available gene and protein expression datasets facilitates the identification of novel prognostic markers applicable to the diverse SCLC population, paving the way toward more personalized management of SCLC patients.

Funding:
This research was funded by the TKP2021‐EGA‐33 and by the PC2022-II-19/1/2022. BD and ZM were supported by additional funding from the KH130356 to BD, 2020‐1.1.6‐JÖVŐ to BD, ZM, and FB, and FK‐143751 to BD and ZM. BD was also supported by the FWF I3522, FWF I3977, and I4677. ZM was supported by the UNKP‐20‐3, UNKP‐21‐3, and UNKP-23-5, and by the Bolyai Research Scholarship of the Hungarian Academy of Sciences.

University

Semmelweis University

Supervisor

Dr. Zsolt Megyesfalvi PhD

Publication of my abstract

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

phd.section.field

in doctoral studies after complex exam (PhD)

Kind

Szabad

Status

elfogadva

Accepted presentation method

szóbeli

Előadás fájl jóváhagyás

jóváhagyta

Előadó

7986

Start

16:00

End

16:15