Unveiling the Role of Sialylation-Related LncRNAs in Lung Adenocarcinoma: A New Prognostic Model.

Background: Lung adenocarcinoma (LUAD) patients have a poor overall survival rate. Sialylation is closely related to lung cancer progression, and long non-coding RNAs (lncRNAs) are crucial in tumorigenesis. However, how sialylation and lncRNAs affect LUAD is unclear. Methods: This study utilized the TCGA-LUAD dataset, GSE31210, GSE131907, and a set of 109 sialylation-related genes (SRGs). Differential expression analysis was performed on TCGA-LUAD tumor and control samples to identify differentially expressed lncRNAs (DE-lncRNAs). Spearman correlation analysis with SRGs was then conducted to screen for sialylation-related lncRNAs (SRDLs). Risk-signature models were constructed using univariate and multivariate Cox regression analyses, followed by a proportional hazards (PH) test. Independent prognostic factors were identified, and a nomogram was developed to predict 1-, 3-, and 5-year survival. Gene expression in key cell populations was examined using GSE131907, and GSEA, tumor immune microenvironment assessment, and drug sensitivity analyses were performed. Finally, prognostic genes were validated using RT-qPCR. Results: A total of 139 SRDLs were identified. A four-gene risk model (LINC00115, LINC00173, LINC00968, LINC01352) was constructed. Both the risk score and T stage were independent prognostic factors. Single-cell analysis of GSE131907 identified eight cell types, with myeloid cells emerging as key contributors. The high-risk group was associated with cell-cycle-related pathways, whereas the low-risk group was enriched in neuroactive ligand-receptor interaction pathways. Immune infiltration analysis revealed multiple significant correlations, and tumor mutation burden scores differed between the two groups. RT-qPCR validated the observed gene expression patterns. Discussion: The four-lncRNA sialylation signature predicted LUAD outcomes and pointed to myeloid cells as therapeutic targets. Prospective validation and mechanistic studies are, however, needed. Conclusion: Four sialylation-associated lncRNAs were identified in LUAD, enabling the construction of an effective risk model. Myeloid cells were highlighted as key contributors, offering valuable insights for personalized treatment and prognostic intervention.