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.

Senescent immune cells secrete varied inflammatory factors that weaken the systemic anti-tumor ability and promote the proliferation and metastasis of tumor cells. Tumor cells could also accelerate the immune cellular senescence through diverse mechanisms. However, there has been a lack of indicators to quantify the senescence levels of different immune cell types. A model for Identifying Senescent Immune Cells and Samples was developed to explore the role of senescent immune cells in the tumor immune microenvironment (TIME). By integrating bulk and single-cell RNA-seq data, we constructed immune cell gene expression profiles for 23 cancer types using a deconvolution algorithm. By calculating the cellular senescence scores, we found that tumor samples exhibited higher senescence levels than normal samples. Monocytes/macrophages were prone to co-senescence with other cell subtypes. Differentially expressed genes in the high- and low-immune cellular senescence scores groups were enriched in the senescence pathway. Patients with higher levels of immunosenescence were associated with better prognosis. At the single-cell level, the number and strength of cell-to-cell interactions increased following immune cellular senescence in most cancers. Samples with senescent immune cells exhibited poorer immunotherapy response. Our study advances our understanding of senescent immune cells in the TIME, provides insights into cancer-specific relationships between immune cellular senescence and immune characteristics, and offers a model for identifying these senescent immune cells.

Abnormal cellular respiration plays a critical role in carcinogenesis. However, the molecular mechanisms underlying dysregulation of respiratory gene expression across different cancer types remain unclear. Here, we developed a computational framework that provides an analytical approach for exploring the molecular alterations and clinical relevance of respiratory genes in pan-cancer. We identified a total of 53 gene signatures in the three stages of respiration (including glycolysis, tricarboxylic acid cycle, and oxidative phosphorylation) through this framework and found that they were broadly differentially expressed and genetically altered across 33 cancer types. Pathway analysis manifested that the expression levels of almost all respiratory gene signatures were remarkably associated with the activation or inhibition of numerous oncogenic pathways, such as metabolism, angiogenesis, cell proliferation, and apoptosis. Survival analysis highlighted the oncogenic or tumor suppressor potential of the respiratory gene signatures. In particular, VCAN has shown significant oncogenic features in multiple cancer types. Finally, we identified a number of respiratory gene signatures that could be potential therapeutic targets, including VCAN. We also predicted small-molecule compounds targeting respiratory gene signatures or components of pathways regulated by them. Overall, our comprehensive analysis has greatly enhanced the understanding of molecular alterations of respiratory genes in tumorigenesis and progression, and provided insights into developing new therapeutic strategies.

Discovering the biological basis of aging is one of the greatest remaining challenges for biomedical field. Work on the biology of aging has discovered a range of interventions and pathways that control aging rate. Thus, we developed AgingBank (http://bio-bigdata.hrbmu.edu.cn/AgingBank) which was a manually curated comprehensive database and high-throughput analysis platform that provided experimentally supported multi-omics data relevant to aging in multiple species. AgingBank contained 3771 experimentally verified aging-related multi-omics entries from studies across more than 50 model organisms, including human, mice, worms, flies and yeast. The records included genome (single nucleotide polymorphism, copy number variation and somatic mutation), transcriptome [mRNA, long non-coding RNA (lncRNA), microRNA (miRNA) and circular RNA (circRNA)], epigenome (DNA methylation and histone modification), other modification and regulation elements (transcription factor, enhancer, promoter, gene silence, alternative splicing and RNA editing). In addition, AgingBank was also an online computational analysis platform containing five useful tools (Aging Landscape, Differential Expression Analyzer, Data Heat Mapper, Co-Expression Network and Functional Annotation Analyzer), nearly 112 high-throughput experiments of genes, miRNAs, lncRNAs, circRNAs and methylation sites related with aging. Cancer & Aging module was developed to explore the relationships between aging and cancer. Submit & Analysis module allows users upload and analyze their experiments data. AginBank is a valuable resource for elucidating aging-related biomarkers and relationships with other diseases.

Long non-coding RNAs (lncRNAs) that emanate from enhancer regions (defined as enhancer-associated lncRNAs, or elncRNAs) are emerging as critical regulators in disease progression. However, their biological characteristics and clinical relevance have not been fully portrayed. Here, based on the traditional expression quantitative loci (eQTL) and our optimized residual eQTL method, we comprehensively described the genetic effect on elncRNA expression in more than 300 lymphoblastoid cell lines. Meanwhile, a chromatin atlas of elncRNAs relative to the genetic regulation state was depicted. By applying the maximum likelihood estimate method, we successfully identified causal elncRNAs for protein-coding gene expression reprogramming and showed their associated single nucleotide polymorphisms (SNPs) favor binding of transcription factors. Further epigenome analysis revealed two immune-associated elncRNAs AL662844.4 and LINC01215 possess high levels of H3K27ac and H3K4me1 in human cancer. Besides, pan-cancer analysis of 3D genome, transcriptome, and regulatome data showed they potentially regulate tumor-immune cell interaction through affecting MHC class I genes and CD47, respectively. Moreover, our study showed there exist associations between elncRNA and patient survival. Finally, we made a user-friendly web interface available for exploring the regulatory relationship of SNP-elncRNA-protein-coding gene triplets (http://bio-bigdata.hrbmu.edu.cn/elncVarReg). Our study provides critical mechanistic insights for elncRNA function and illustrates their implications in human cancer.

Intertumoral immune heterogeneity is a critical reason for distinct clinical benefits of immunotherapy in lung adenocarcinoma (LUAD). Tumor immunophenotype (immune 'Hot' or 'Cold') suggests immunological individual differences and potential clinical treatment guidelines. However, employing epigenome signatures to determine tumor immunophenotypes and responsive treatment is not well understood. To delineate the tumor immunophenotype and immune heterogeneity, we first distinguished the immune 'Hot' and 'Cold' tumors of LUAD based on five immune expression signatures. In terms of clinical presentation, the immune 'Hot' tumors usually had higher immunoactivity, lower disease stages and better survival outcomes than 'Cold' tumors. At the epigenome levels, we observed that distinct DNA methylation patterns between immunophenotypes were closely associated with LUAD development. Hence, we identified a set of five CpG sites as the immunophenotype-related methylation signature (iPMS) for tumor immunophenotyping and further confirmed its efficiency based on a machine learning framework. Furthermore, we found iPMS and immunophenotype-related immune checkpoints (IPCPs) could contribute to the risk of tumor progression, implying IPCP has the potential to be a novel immunotherapy blockade target. After further parsing of the role of iPMS-predicted immunophenotypes, we found immune 'Hot' was a protective factor leading to better survival outcomes when patients received the anti-PD-1/PD-L1 immunotherapy. And iPMS was also a well-performed signature (AUC = 0.752) for predicting the durable/nondurable clinical benefits. In summary, our study explored the role of epigenome signature in clinical tumor immunophenotyping. Utilizing iPMS to characterize tumor immunophenotypes will facilitate developing personalized epigenetic anticancer approaches.

The nervous system is one of the most complex biological systems, and nervous system disease (NSD) is a major cause of disability and mortality. Extensive evidence indicates that numerous dysregulated microRNAs (miRNAs) are involved in a broad spectrum of NSDs. A comprehensive review of miRNA-mediated regulatory will facilitate our understanding of miRNA dysregulation mechanisms in NSDs. In this work, we summarized currently available databases on miRNAs and NSDs, star NSD miRNAs, NSD spectrum width, miRNA spectrum width and the distribution of miRNAs in NSD sub-categories by reviewing approximately 1000 studies. In addition, we characterized miRNA-miRNA and NSD-NSD interactions from a network perspective based on miRNA-NSD benchmarking data sets. Furthermore, we summarized the regulatory principles of miRNAs in NSDs, including miRNA synergistic regulation in NSDs, miRNA modules and NSD modules. We also discussed computational challenges for identifying novel miRNAs in NSDs. Elucidating the roles of miRNAs in NSDs from a network perspective would not only improve our understanding of the precise mechanism underlying these complex diseases, but also provide novel insight into the development, diagnosis and treatment of NSDs.

BACKGROUND: Genome-wide association studies (GWAS) have successfully identified a large number of single nucleotide polymorphisms (SNPs) that are associated with a wide range of human diseases. However, many of these disease-associated SNPs are located in non-coding regions and have remained largely unexplained. Recent findings indicate that disease-associated SNPs in human large intergenic non-coding RNA (lincRNA) may lead to susceptibility to diseases through their effects on lincRNA expression. There is, therefore, a need to specifically record these SNPs and annotate them as potential candidates for disease. DESCRIPTION: We have built LincSNP, an integrated database, to identify and annotate disease-associated SNPs in human lincRNAs. The current release of LincSNP contains approximately 140,000 disease-associated SNPs (or linkage disequilibrium SNPs), which can be mapped to around 5,000 human lincRNAs, together with their comprehensive functional annotations. The database also contains annotated, experimentally supported SNP-lincRNA-disease associations and disease-associated lincRNAs. It provides flexible search options for data extraction and searches can be performed by disease/phenotype name, SNP ID, lincRNA name and chromosome region. In addition, we provide users with a link to download all the data from LincSNP and have developed a web interface for the submission of novel identified SNP-lincRNA-disease associations. CONCLUSIONS: The LincSNP database aims to integrate disease-associated SNPs and human lincRNAs, which will be an important resource for the investigation of the functions and mechanisms of lincRNAs in human disease. The database is available at http://bioinfo.hrbmu.edu.cn/LincSNP.