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

IF 4.1 2区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Computational and structural biotechnology journal Pub Date : 2025-06-06 eCollection Date: 2025-01-01 DOI:10.1016/j.csbj.2025.06.010
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
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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.

利用基因表达特征揭示肺神经内分泌肿瘤的异质性。
癌症基因组学领域的进步使研究人员和临床医生能够识别出肺神经内分泌肿瘤(NENs)亚型分化的通路和调控网络。肺NEN亚型的临床异质性反映了潜在的生物学差异,包括不同的突变模式、表观遗传变化和免疫微环境活动。虽然在许多情况下,只有少数潜在基因被用来区分患者,但更广泛的基因特征可能会导致更精细的分离,并有助于识别有差异生存的患者。肺NENs在泛癌症研究中的代表性大大不足,导致缺乏探索数据集的选择。为此,我们开发了一个免费的网站(https://survsig.hcemm.eu/),允许用户上传感兴趣的潜在基因,进行患者聚类,比较生存率并探索肺NENs的基因表达特征。利用这些生物学差异可以提高基于基因表达的预后分类器(如SurvSig)的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computational and structural biotechnology journal
Computational and structural biotechnology journal Biochemistry, Genetics and Molecular Biology-Biophysics
CiteScore
9.30
自引率
3.30%
发文量
540
审稿时长
6 weeks
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
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