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Poster Display session

303P - Identifying novel prognostic markers in small cell lung cancer through comprehensive in-silico analysis

Date

28 Mar 2025

Session

Poster Display session

Presenters

Vivien Teglas

Citation

Journal of Thoracic Oncology (2025) 20 (3): S181-S207. 10.1016/S1556-0864(25)00632-X

Authors

V.B. Teglas1, B. Megyesfalvi2, B. Ferencz1, B. Szigeti2, K.S. Senuma Pang1, M.D. Pozonec1, B. Szeitz2, B. Dome3, Z. Megyesfalvi1

Author affiliations

  • 1 Semmelweis University, Budapest/HU
  • 2 National Koranyi Institute of Pulmonology, Budapest/HU
  • 3 Comprehensive Cancer Center, Medical University of Vienna, Vienna/AT

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Abstract 303P

Background

Small cell lung cancer (SCLC) is among the most aggressive malignancies, characterized by rapid disease progression. The early onset of distant organ metastases underlines the urgent need for reliable prognostic markers to guide the development of personalized treatment and follow-up strategies. Here, we aimed to identify and validate robust prognostic markers in SCLC by analyzing publicly available transcriptomic datasets and conducting subsequent immunohistochemistry (IHC) analyses.

Methods

Three previously published SCLC tissue transcriptomic datasets were accessed, quantifying over 50,000 transcripts accompanied by overall survival data. Cox regression analysis was conducted, using the clinical stage as a stratification variable to identify genes with potential prognostic significance among the 16,594 overlapping genes. 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, we will perform IHC analyses on a cohort of 50 surgically resected SCLC specimens using specific antibodies.

Results

Our comprehensive bioinformatic analysis revealed 25 genes with potential prognostic significance in SCLC, exhibiting (marginally) significant associations (p < 0.15) across all three cohorts. Altogether, we identified three unfavorable (HR>1) and 22 favorable (HR

Conclusions

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.

Legal entity responsible for the study

National Koranyi Institute of Pulmonology, Budapest, Hungary.

Funding

B.D. was supported by the Austrian Science Fund (FWF I3522, FWF I3977, and I4677) and the ‘BIOSMALL’ EU HORIZON-MSCA-2022-SE-01 project. B.D. and Z.M. were supported by funding from the Hungarian National Research, Development, and Innovation Office (2020-1.1.6-JO?VŐ, TKP2021-EGA-33, FK-143751 and FK-147045). Z. M. was supported by the New National Excellence Program of the Ministry for Innovation and Technology of Hungary (UNKP-20-3, UNKP-21-3 and UNKP-23-5), and by the Bolyai Research Scholarship of the Hungarian Academy of Sciences. Z.M. is also the recipient of the International Association for the Study of Lung Cancer/International Lung Cancer Foundation Young Investigator Grant (2022).

Disclosure

All authors have declared no conflicts of interest.

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