High-confidence Mapping and Clustered Patterns of LncRNA-associated SNP Reveal Recurrent Non-coding Variants in Colorectal Cancer Transcriptomes

Long non-coding RNAs (lncRNAs) are important regulators of gene expression and cellular homeostasis, yet sequence-level variation within these transcripts remains poorly characterized in colorectal cancer (CRC). Although evidence suggests that lncRNA-embedded variants influence transcriptional regulation and disease progression, the exact mechanistic function of these variants is not well understood. Publicly available CRC transcriptome sequencing libraries were systematically obtained and subjected to rigorous quality control to ensure high analytical reliability. Filtered libraries were aligned to a curated reference set of lncRNA transcripts derived from the RefSeq human genome assembly. Variant detection was carried out using RNA-seq-based variant-calling approaches, followed by stringent filtering based on alternate read depth and genotype confidence to minimize false-positive calls. Recurrence-based filtering, combined with hierarchical clustering of transcriptomes using shared variant profiles, was applied to identify reproducible patterns of sequence variation across independent datasets. Initial variant detection identified more than one hundred thousand candidate single nucleotide variants within lncRNA transcripts. Application of depth-, confidence-, and recurrence-based filtering substantially reduced this set to a focused catalogue of highly recurrent variants shared across multiple transcriptome clusters. In total, 549 high-confidence single nucleotide variants distributed across 99 lncRNAs were identified. Several of the transcript-embedded variants have been previously implicated in CRC biology, supporting the biological relevance of the detected variants. These findings demonstrated that expressed lncRNA-associated SNPs are non-randomly distributed in CRC transcriptomes and potentially   accumulate in non-coding variant hotspots. This study provides a scalable framework for prioritizing recurrent lncRNA-associated sequence variants and offers a resource for future functional validation, biomarker-oriented analyses, and precision oncology studies in CRC.