Kolos Nemes, Gabriella Mihalekné Fűr, Alexandra Benő, Christopher W Schultz, Petronella Topolcsányi, Éva Magó, Parth Desai, Nobuyuki Takahashi, Mirit I Aladjem, William Reinhold, Yves Pommier, Anish Thomas, Lorinc S Pongor
{"title":"SurvSig: Harnessing gene expression signatures to uncover heterogeneity in lung neuroendocrine neoplasms.","authors":"Kolos Nemes, Gabriella Mihalekné Fűr, Alexandra Benő, Christopher W Schultz, Petronella Topolcsányi, Éva Magó, Parth Desai, Nobuyuki Takahashi, Mirit I Aladjem, William Reinhold, Yves Pommier, Anish Thomas, Lorinc S Pongor","doi":"10.1016/j.csbj.2025.06.010","DOIUrl":null,"url":null,"abstract":"<p><p>The advances in the field of cancer genomics have enabled researchers and clinicians to identify altered pathways and regulatory networks that differentiate subtypes manifesting as differential phenotypes of lung neuroendocrine neoplasms (NENs). The clinical heterogeneity observed among lung NEN subtypes reflects underlying biological distinctions, including differential mutation patterns, epigenetic changes and immune microenvironment activities. Although in many cases only a handful of underlying genes are used to differentiate patients, broader gene signatures might result in finer separation and help identify patients with differential survival. Lung NENs are vastly underrepresented in pan-cancer studies, resulting in lacking options to explore datasets. To this end, we developed a freely available website (https://survsig.hcemm.eu/) which allows users to upload potential genes of interest, perform patient clustering, compare survival and explore gene expression signature of lung NENs. Leveraging these biological differences enhances the accuracy of gene expression-based prognostic classifiers like SurvSig.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2574-2583"},"PeriodicalIF":4.1000,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12205313/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational and structural biotechnology journal","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1016/j.csbj.2025.06.010","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
The advances in the field of cancer genomics have enabled researchers and clinicians to identify altered pathways and regulatory networks that differentiate subtypes manifesting as differential phenotypes of lung neuroendocrine neoplasms (NENs). The clinical heterogeneity observed among lung NEN subtypes reflects underlying biological distinctions, including differential mutation patterns, epigenetic changes and immune microenvironment activities. Although in many cases only a handful of underlying genes are used to differentiate patients, broader gene signatures might result in finer separation and help identify patients with differential survival. Lung NENs are vastly underrepresented in pan-cancer studies, resulting in lacking options to explore datasets. To this end, we developed a freely available website (https://survsig.hcemm.eu/) which allows users to upload potential genes of interest, perform patient clustering, compare survival and explore gene expression signature of lung NENs. Leveraging these biological differences enhances the accuracy of gene expression-based prognostic classifiers like SurvSig.
期刊介绍:
Computational and Structural Biotechnology Journal (CSBJ) is an online gold open access journal publishing research articles and reviews after full peer review. All articles are published, without barriers to access, immediately upon acceptance. The journal places a strong emphasis on functional and mechanistic understanding of how molecular components in a biological process work together through the application of computational methods. Structural data may provide such insights, but they are not a pre-requisite for publication in the journal. Specific areas of interest include, but are not limited to:
Structure and function of proteins, nucleic acids and other macromolecules
Structure and function of multi-component complexes
Protein folding, processing and degradation
Enzymology
Computational and structural studies of plant systems
Microbial Informatics
Genomics
Proteomics
Metabolomics
Algorithms and Hypothesis in Bioinformatics
Mathematical and Theoretical Biology
Computational Chemistry and Drug Discovery
Microscopy and Molecular Imaging
Nanotechnology
Systems and Synthetic Biology