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SurvSig: Harnessing gene expression signatures to uncover heterogeneity in lung neuroendocrine neoplasms

  • 0HCEMM Cancer Genomics and Epigenetics Core Group, Szeged, Hungary.
Computational and Structural Biotechnology Journal +

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June 30, 2025

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June 30, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Lung neuroendocrine neoplasms (NENs) exhibit diverse phenotypes due to distinct molecular profiles. A new website, SurvSig, aids researchers in analyzing gene signatures for improved lung NEN classification and survival prediction.

Area Of Science

  • Oncology
  • Genomics
  • Bioinformatics

Background

  • Lung neuroendocrine neoplasms (NENs) display significant clinical heterogeneity.
  • This heterogeneity stems from underlying biological differences, including genetic mutations, epigenetic alterations, and immune microenvironment variations.
  • Current classification often relies on a limited number of genes, potentially missing finer distinctions.

Purpose Of The Study

  • To address the underrepresentation of lung NENs in pan-cancer studies.
  • To provide a tool for exploring gene signatures and their correlation with patient survival.
  • To enhance the accuracy of prognostic classifiers for lung NENs.

Main Methods

  • Development of a freely available website, SurvSig (https://survsig.hcemm.eu/).
  • The website allows users to upload gene sets for analysis.
  • Functionalities include patient clustering, survival comparison, and gene expression signature exploration.

Main Results

  • Lung NEN subtypes show differential mutation patterns, epigenetic changes, and immune microenvironment activities.
  • Broader gene signatures can lead to finer patient separation and identification of differential survival.
  • The SurvSig tool facilitates the exploration of these biological differences.

Conclusions

  • Leveraging distinct biological differences in lung NENs improves prognostic classifier accuracy.
  • The SurvSig website serves as a valuable resource for researchers studying lung NENs.
  • Enhanced gene expression-based prognostic models can aid in personalized treatment strategies.

Keywords: