Chenyu Liu, Qianyi Lu, Jian Li, Di Wang, Zhuoru Wang, Wenli Chen, Yakun Zhang, Caiyu Zhang, Yue Gao, Shangwei Ning
{"title":"Dynamic changes of synergy relationship between lncRNA and immune checkpoint in cancer progression.","authors":"Chenyu Liu, Qianyi Lu, Jian Li, Di Wang, Zhuoru Wang, Wenli Chen, Yakun Zhang, Caiyu Zhang, Yue Gao, Shangwei Ning","doi":"10.1093/bib/bbaf370","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":7.7000,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12306437/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Briefings in bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/bib/bbaf370","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data.
The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.