A novel framework for identifying cellular signature LncRNAs and their functional coregulatory networks with prognostic implications at single-cell resolution.

Abstract

Introduction: Long non-coding RNAs have been implicated in crucial roles in developing malignant tumors. However, comprehensive analyses of lncRNA within the tumor microenvironment, particularly at the single-cell level across various tumor types, remain limited.

Objectives: This study aimed to establish a comprehensive methodological framework for prioritizing and identifying novel cancer-associated lncRNA biomarkers and potential therapeutic targets at the single-cell level, and to integrate these findings into a unified platform.

Methods: We developed SIGLNCOS, a computational framework for analyzing single-cell lncRNA regulation via: i) Constructing the lncRNA visualization landscape. ii) Identifying cellular signature lncRNA. iii) Functional enrichment of clncRNAs. iv) Identifying co-lncRNA pairs based on shared functional annotations and weighted functional association scores. Cell-composition-adjusted prognostic analysis evaluated the prognostic relevance of individual lncRNAs and co-lncRNA pairs.

Results: Applying SIGLNCOS, we identified 667 cellular-signature lncRNAs (clncRNAs) from over half a million single cells across 31 cancers, revealing their roles in immune modulation, tumor progression, and differentiation. NEAT1 was associated with distinct macrophage states and functional changes along pseudotime. SIGLNCOS identified 3,381 co-lncRNA pairs involved in key cancer-related pathways, revealing a complex landscape of lncRNA co-regulation. Notably, we uncovered distinct co-lncRNA networks associated with different thyroid cancer states, particularly between papillary thyroid carcinoma and anaplastic thyroid carcinoma. These networks exhibit functionally concordant regulatory effects that individual lncRNAs cannot, underscoring the importance of co-regulation in tumor evolution. The prognostic analysis demonstrated that co-lncRNA networks outperform individual lncRNAs in predicting patient outcomes, emphasizing their potential as biomarkers and therapeutic targets.

Conclusion: This study constructs pan-cancer lncRNA landscapes and develops SIGLNCOS, a comprehensive framework for uncovering lncRNA at single-cell resolution, offering new insights into cancer biology and potential avenues for targeted therapies and novel biomarker discovery. We further developed sCo-Lnc (https://bio-bigdata.hrbmu.edu.cn/scolnc), a web-based platform for visualizing novel lncRNA biomarkers across cancers.