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

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.