The prognostic value of sialylation-related long non-coding RNAs in lung adenocarcinoma.

There has been increasing interest in the role of epigenetic modification in cancers recently. Among the various modifications, sialylation has emerged as a dominant subtype implicated in tumor progression, metastasis, immune evasion, and chemoresistance. The prognostic significance of sialylation-related molecules has been demonstrated in colorectal cancer. However, the potential roles and regulatory mechanisms of sialylation in lung adenocarcinoma (LUAD) have not been thoroughly investigated. Through Pearson correlation, univariate Cox hazards proportional regression, and random survival forest model analyses, we identified several prognostic long non-coding RNAs (lncRNAs) associated with aberrant sialylation and tumor progression, including LINC00857, LINC00968, LINC00663, and ITGA9-AS1. Based on the signatures of four lncRNAs, we classified patients into two clusters with different landscapes using a non-negative matrix factorization approach. Collectively, patients in Cluster 1 (C1) exhibited worse prognoses than those in Cluster 2 (C2), as well as heavier tumor mutation burden. Functional enrichment analysis showed the enrichment of several pro-tumor pathways in C1, differing from the upregulated Longevity and programmed cell death pathways in C2. Moreover, we profiled immune infiltration levels of important immune cell lineages in two subgroups using MCPcounter scores and single sample gene set enrichment analysis scores, revealing a relatively immunosuppressive microenvironment in C1. Risk analysis indicated that LINC00857 may serve as a pro-tumor regulator, while the other three lncRNAs may be protective contributors. Consistently, we observed upregulated LINC00857 in C1, whereas increased expressive levels of LINC00968, LINC00663, and ITGA9-AS1 were observed in C2. Finally, drug sensitivity analysis suggested that patients in the two groups may benefit from different therapeutic strategies, contributing to precise treatment in LUAD. By integrating multi-omics data, we identified four core sialylation-related lncRNAs and successfully established a prognostic model to distinguish patients with different characterizations. These findings may provide some insights into the underlying mechanism of sialylation, and offer a new stratification way as well as clinical guidance in LUAD.