The knotty problem of RNA structure prediction - Science | AAAS

ORCID: orcid.org/0000-0002-9388-94211, Zamira Guerra Soares1, Krista Pipho2, Fabio Hecht1, Sara Blick-Nitko1, Isaac S. Harris1,3 nAff6, Stephano S. Mello1,3 nAff7 & … Paul L. Boutz ORCID: orcid.org/0000-0003-0788-31363,4,5

We’re sharing this article early to provide faster access to peer-reviewed, accepted research. It is citable and carries a permanent DOI. This version is subject to further edits and will be replaced automatically by the final Version of Record. All legal disclaimers apply. AbstractOvarian cancer, with high mortality and often late diagnosis, shows high recurrence despite treatment. The variable effectiveness of immunotherapy highlights the urgent need for personalized, advanced therapeutic strategies. To investigate T-cell marker genes, single-cell RNA-sequencing (scRNA-seq) data were sourced from the Gene Expression Omnibus (GEO) database. Additionally, bulk RNA-sequencing data along with clinical information from ovarian cancer patients were retrieved from the Cancer Genome Atlas (TCGA) database to establish a prognostic signature. This study involved survival analysis to evaluate associations between different risk groups, and explored cellular communication and relevant pathway analyses, including metabolic pathways.