lncRNAs in Different Cancer Types at Spatial and Single-Cell Resolution
Long non-coding RNAs (lncRNAs) play pivotal roles in gene regulation and disease, including cancer. Their less abundance and reliance on bulk data lacking specificity has limited the understanding about their functions. Our study focuses on unearthing new lncRNAs and investigating their functions across various cancers using single-cell and spatial transcriptomics data. We adapted a computational pipeline to identify previously unknown lncRNAs across 11 cancer types. LncRNA functions were assessed by identifying their cell-type specificity and distinct spatial distributions across the tissue.
First, lncRNAs are computationally validated, comparing to existing databases, and experimentally validated using spatial long read sequencing methods. Further, genome-wide computation of spatial autocorrelation identified coexpression with cancer-associated coding-genes across the tissue and co-localization with regulatory features and disease associated genetic variants suggest possible functional association. The identified lncRNAs were analyzed for responses to immunotherapy and prognostic value.
Through the comprehensive mapping and functional annotation, we have identified potential cancer-associated lncRNAs. Annotations are available as a website to serve as a pan-cancer atlas of single-cell and spatially resolved lnRNAs. These cell-type specific lncRNAs could complement established biomarkers because they reflect the unique characteristics of specific cell populations within tumors, offering more precise insights into disease progression and treatment response.
The lncRNA expression and annotations can be accessed and visualized in the interactive website SPA-Lnc: https://spanclnc.click/pages/about
The codes that were used to build and host the website can be found at https://github.com/GenomicsMachineLearning/SPanC-Lnc_Backend and https://github.com/GenomicsMachineLearning/SPanC-Lnc_Website