# Dynamic changes of synergy relationship between lncRNA and immune checkpoint in cancer progression

## TL;DR

This study explores how lncRNAs and immune checkpoints work together during cancer progression and identifies key genes that affect patient outcomes and immunotherapy response.

## Contribution

A novel method to identify stage-specific lncRNA-ICP synergistic pairs and their associations with immune dynamics and patient survival.

## Key findings

- Key lncRNAs like MALAT1 and CRNDE show varied involvement in cancer progression across multiple cancer types.
- lncRNA-ICP synergy genes correlate with immune cell dynamics and are consistent across cancers and stages.
- Stage 4 gene expression significantly predicts survival and immunotherapy response in patients.

## Abstract

In the battle between tumors and the immune system, immune evasion based on immune checkpoints (ICPs) is a critical mechanism for tumor progression. Long noncoding RNAs (lncRNAs) are key players in tumorigenesis and immune responses; however, the mechanisms underlying the synergistic relationship between lncRNAs and ICPs in cancer progression remain poorly understood. Manually curated ICPs and high-confidence lncRNA-messenger RNA (mRNA) interactions were integrated via a protein-protein interaction (PPI) network to construct an initial set of lncRNA-ICP pairs. Stage-specific synergy scores were then performed and used to identify stage-specific synergistic pairs for each cancer type. Our findings indicate that several key genes, including MALAT1 and CRNDE, are widely involved in cancer progression and exhibit various patterns in multiple cancers. Genes within the lncRNA-ICP synergy network were associated with the dynamic changes of immune cells during cancer progression, and these relationships remain relatively stable across different cancers and stages. The relationships of the synergistic pairs we identified demonstrate consistency with spatial transcriptomics data in skin cutaneous melanoma. Notably, the overall expression of genes identified in Stage 4 could significantly differentiate patients’ survival outcomes. Moreover, the genes we identified could distinguish patients’ responses to immunotherapy.