Long noncoding RNAs (lncRNAs) are emerging as critical regulators of gene expression and they play fundamental roles in immune regulation. Here we introduce an integrated algorithm, ImmLnc, for identifying lncRNA regulators of immune-related pathways. We comprehensively chart the landscape of lncRNA regulation in the immunome across 33 cancer types and show that cancers with similar tissue origin are likely to share lncRNA immune regulators. Moreover, the immune-related lncRNAs are likely to show expression perturbation in cancer and are significantly correlated with immune cell infiltration. ImmLnc can help prioritize cancer-related lncRNAs and further identify three molecular subtypes (proliferative, intermediate, and immunological) of non-small cell lung cancer. These subtypes are characterized by differences in mutation burden, immune cell infiltration, expression of immunomodulatory genes, response to chemotherapy, and prognosis. In summary, the ImmLnc pipeline and the resulting data serve as a valuable resource for understanding lncRNA function and to advance identification of immunotherapy targets.

Long noncoding RNAs (lncRNAs) have been implicated in cancer biogenesis and prognosis. However, we still lack knowledge on their function during glioma progression. In this study, we analyzed the lncRNA expression profile across 907 glioma patients in grades II, III, and IV. Widespread dynamic expression of lncRNAs during glioma progression was revealed, and we identified 33 onco-lncRNAs and 61 tumor suppressor lncRNAs. We found that the expression of these oncogenic lncRNAs is regulated by grade-specific expressed transcription factors. Based on the “guilt by association” rule, we predicted the potential functions of oncogenic lncRNAs, and the majority of these lncRNAs are involved in cancer hallmarks. Especially we found that CARD8-AS1 regulates the metastatic potential of glioma cell lines in vitro. Integrating clinical information, we identified the 12 protective and 8 risk lncRNAs (such as PWAR6 and CARD8-AS1) in glioma. Finally, an lncRNA-gene functional module was identified to be associated with the survival of patients. The predictive ability of this module signature was further validated in an independent dataset. Our results revealed the dynamic transcriptome transition during glioma progression, indicating that the lncRNA signature could be a useful biomarker that may improve upon our understanding of the molecular mechanisms underlying glioma progression.

Immune system gene regulation perturbation has been found to be a major cause of the development of various types of cancer. Numbers of mechanisms contribute to gene expression regulation, thus, systematically identification of potential regulons of immune-related pathways is critical to cancer immunotherapy. Here, we comprehensively chart the landscape of transcription factors, microRNAs, RNA binding proteins and long noncoding RNAs regulation in 17 immune-related pathways across 33 cancers. The potential immunology regulons are likely to exhibit higher expressions in immune cells, show expression perturbations in cancer, and are significantly correlated with immune cell infiltrations. We also identify a panel of clinically relevant immunology regulons across cancers. Moreover, the regulon atlas of immune-related pathways helps prioritizing cancer-related genes (i.e. ETV7, miR-146a-5p, ZFP36 and HCP5). We further identified two molecular subtypes of glioma (cold and hot tumour phenotypes), which were characterized by differences in immune cell infiltrations, expression of checkpoints, and prognosis. Finally, we developed a user-friendly resource, ImmReg (http://bio-bigdata.hrbmu.edu.cn/ImmReg/), with multiple modules to visualize, browse, and download immunology regulation. Our study provides a comprehensive landscape of immunology regulons, which will shed light on future development of RNA-based cancer immunotherapies.

Single-cell transcriptome has enabled the transcriptional profiling of thousands of immune cells in complex tissues and cancers. However, subtle transcriptomic differences in immune cell subpopulations and the high dimensionality of transcriptomic data make the clustering and annotation of immune cells challenging. Herein, we introduce ImmCluster (http://bio-bigdata.hrbmu.edu.cn/ImmCluster) for immunology cell type clustering and annotation. We manually curated 346 well-known marker genes from 1163 studies. ImmCluster integrates over 420 000 immune cells from nine healthy tissues and over 648 000 cells from different tumour samples of 17 cancer types to generate stable marker-gene sets and develop context-specific immunology references. In addition, ImmCluster provides cell clustering using seven reference-based and four marker gene-based computational methods, and the ensemble method was developed to provide consistent cell clustering than individual methods. Five major analytic modules were provided for interactively exploring the annotations of immune cells, including clustering and annotating immune cell clusters, gene expression of markers, functional assignment in cancer hallmarks, cell states and immune pathways, cell–cell communications and the corresponding ligand–receptor interactions, as well as online tools. ImmCluster generates diverse plots and tables, enabling users to identify significant associations in immune cell clusters simultaneously. ImmCluster is a valuable resource for analysing cellular heterogeneity in cancer microenvironments.

LincRNAs enriched with high H3K4me1 and low H3K4me3 signals often have the enhancer-like features which are named as enhancer-associated lincRNAs (elincRNAs). ElincRNAs are considered to be indispensable for target gene transcription, which play important roles in development, signaling events, and even diseases. In this study, we developed a regularized regression model to identify elincRNAs by integrating the genomic, epigenomic, and regulatory data. Application of the proposed method to mouse ESCs reveals that besides the basic well-known epigenetic features H3K4me1 and H3K4me3, more specific epigenetic features, such as high DNA methylation, high H3K122ac, and H3K36me3 were contributed to mark elincRNAs with the best accuracy and precision. Finally, 3729 elincRNAs were identified in mouse ESCs. Furthermore, the elincRNAs and canonical lincRNAs exhibit distinct genomic features, and elincRNAs have the higher CGI enrichment and lower sequence conservation. Through the analysis of transcription regulation, we found that elincRNAs were significantly regulated by NANOG, POU5F1, SOX2 and ESRRB, and were involved in the core transcriptional regulatory circuitry controlling ES cell state Function enrichment analysis further discovered that elincRNAs tended to regulate specific embryonic development biological processes. These results indicated that these two types of lincRNAs had both specific epigenetic and transcriptional regulation mechanism and display distinct functional characters. In conclusion, we presented a credible computational model to prioritize novel elincRNAs, and depicted the atlas of elincRNAs in mouse ESCs, which would help dissect the function roles of lncRNAs during the mammalian development and diseases.

RNA-binding proteins (RBPs) are key players of gene expression and perturbations of RBP-RNA regulatory network have been observed in various cancer types. Here, we propose a computational method, RBPreg, to identify the RBP regulators by integration of single cell RNA-Seq (N = 233,591) and RBP binding data. Pan-cancer analyses suggest that RBP regulators exhibit cancer and cell specificity and perturbations of RBP regulatory network are involved in cancer hallmark-related functions. We prioritize an oncogenic RBP-HNRNPK, which is highly expressed in tumors and associated with poor prognosis of patients. Functional assays performed in cancer cells reveal that HNRNPK promotes cancer cell proliferation, migration, and invasion in vitro and in vivo. Mechanistic investigations further demonstrate that HNRNPK promotes tumorigenesis and progression by directly binding to MYC and perturbed the MYC targets pathway in lung cancer. Our results provide a valuable resource for characterizing RBP regulatory networks in cancer, yielding potential biomarkers for precision medicine.

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RNA-binding proteins (RBPs) are key players of gene expression and perturbation of RBP-RNA regulatory network has been observed in various cancer types. Here, we propose a computational method, RBPreg, to identify the RBP regulators by integration of single cell RNA-Seq (N = 233,591) and RBP binding data. Pan-cancer analyses suggest that RBP regulators exhibit cancer and cell specificity and perturbation of RBP regulatory network is involved in cancer hallmark-related functions. We prioritize an oncogenic RBP-HNRNPK, which is highly expressed in tumors and associated with poor prognosis of patients. Functional assays performed in cancer cells reveal that HNRNPK promotes cancer cell proliferation, migration, and invasion in vitro and in vivo. Mechanistic investigations further demonstrate that HNRNPK promotes tumorigenesis and progression by directly binding to MYC and perturbed the MYC targets pathway in lung cancer. Our results provide a valuable resource for characterizing RBP regulatory networks in cancer, yielding potential biomarkers for precision medicine.