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LncRNAs are not only well-known as non-coding elements, but also serve as templates for peptide translation, playing important roles in fundamental cellular processes and diseases. Here, we describe a database, TransLnc (http://bio-bigdata.hrbmu.edu.cn/TransLnc/), which aims to provide comprehensive experimentally supported and predicted lncRNA peptides in multiple species. TransLnc currently documents approximate 583 840 peptides encoded by 33 094 lncRNAs. Six types of direct and indirect evidences supporting the coding potential of lncRNAs were integrated, and 65.28% peptides entries were with at least one type of evidence. Considering the strong tissue-specific expression of lncRNAs, TransLnc allows users to access lncRNA peptides in any of the 34 tissues involved in. In addition, both the unique characteristic and homology relationship were also predicted and provided. Importantly, TransLnc provides computationally predicted tumour neoantigens from peptides encoded by lncRNAs, which would provide novel insights into cancer immunotherapy. There were 220 791 and 237 915 candidate neoantigens binding by major histocompatibility complex (MHC) class I or II molecules, respectively. Several flexible tools were developed to aid retrieve and analyse, particularly lncRNAs tissue expression patterns, clinical relevance across cancer types. TransLnc will serve as a valuable resource for investigating the translation capacity of lncRNAs and greatly extends the cancer immunopeptidome.

Background: Enhancers are emerging regulatory regions controlling gene expression in diverse cancer types. However, the functions of enhancer regulatory circuit perturbations driven by copy number variations (CNVs) in malignant glioma are unclear. Therefore, we aimed to investigate the comprehensive enhancer regulatory perturbation and identify potential biomarkers in glioma. Results: We performed a meta-analysis of the enhancer centered regulatory circuit perturbations in 683 gliomas by integrating CNVs, gene expression, and transcription factors (TFs) binding. We found widespread CNVs of enhancers during glioma progression, and CNVs were associated with the perturbations of enhancer activities. In particular, the degree of perturbations for amplified enhancers was much greater accompanied by the glioma malignant progression. In addition, CNVs and enhancers cooperatively regulated the expressions of cancer-related genes. Genome-wide TF binding profiles revealed that enhancers were pervasively regulated by TFs. A network-based analysis of TF-enhancer-gene regulatory circuits revealed a core TF-gene module (58 interactions including seven genes and 14 TFs) that was associated survival of patients with glioma (p < 0.001). Finally, we validated this prognosis-associated TF-gene regulatory module in an independent cohort. In summary, our analyses provided new molecular insights for enhancer-centered transcriptional perturbation in glioma therapy. Conclusion: Integrative analysis revealed enhancer regulatory perturbations in glioma and also identified a network module that was associated with patient survival, thereby providing novel insights into enhancer-centered cancer therapy.

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.

Additional file 2: Supplemental materials and methods, and supplemental figure S1-S9.Figure S1. Numbers of m6A-regulated lncRNAs across tissues. Figure S2. Numbers of m6A-regulated TE lncRNAs across tissues in four data resources. Figure S3. Distribution of odds ratios for comparison between TE lncRNAs and non-TE lncRNAs across tissues in four data resources. Figure S4. Number of m6A peaks correlated with TE lncRNAs in four resources. Figure S5.Co-expression between m6A regulators and m6A-modified TE lncRNAs. A, River plot showing the expression correlation between m6A modified TE lncRNAs and m6A regulators. B, Bar plots showing the number of m6A regulators correlated with each m6A modified TE lncRNA. Color indicated the different data resources. C-E, Scatter plots showing the correlation between the expression of lncRNAs and m6A regulators. C for PVT1 and YTHDF2; D for SOX2-OT and HNRNPA2B1; E for KCNK15-AS1 and ALKBH5. Figure S6. Numbers of TE lncRNAs correlated with m6A regulators. Figure S7. Number of m6A modified TE lncRNAs across cancer types. Figure S8. GSEA for m6A modified lncRNAs in HCC, LGG and GBM. A, F11-AS1 in HCC; B, LINC01018 in HCC; C, MIR325HG in LGG, D, MIR325HG in GBM. Figure S9. GSEA figures for m6A-modified lncRNAs in HCC, LGG and GBM. A, F11-AS1 enriched in DNA repair pathway in HCC; B, LINC01018 enriched in DNA repair pathway in HCC; C, MIR325HG enriched in EMT pathway in LGG, D, MIR325HG enriched in EMT pathway in GBM.

Alternative splicing (AS) plays an important role in gene regulation, and AS perturbations are frequently observed in cancer. RNA binding protein (RBP) is one of the molecular determinants of AS, and perturbations in RBP-gene network activity are causally associated with cancer development. Here, we performed a systematic analysis to characterize the perturbations in AS events across 18 cancer types. We showed that AS alterations were prevalent in cancer and involved in cancer-related pathways. Given that the extent of AS perturbation was associated with disease severity, we proposed a computational pipeline to identify RBP regulators. Pan-cancer analysis identified a number of conserved RBP regulators, which play important roles in regulating AS of genes involved in cancer hallmark pathways. Our application analysis revealed that the expression of 68 RBP regulators helped in cancer subtyping. Specifically, we identified four subtypes of kidney cancer with differences in cancer hallmark pathway activities and prognosis. Finally, we identified the small molecules that can potentially target the RBP genes and suggested potential candidates for cancer therapy. In summary, our comprehensive AS perturbation landscape analysis identified RBPs as potential therapeutic targets in cancer and provided novel insights into the regulatory functions of RBPs in cancer.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disease in early childhood, and growing up to be a major cause of disability in children. However, the underlying molecular mechanism of ASD remains elusive. Hence, we represented integrated multifactor analysis exploring dysfunctional modules based on RNA-Seq data from corpus callosum in 6 patients with ASD and 6 normal individuals. According to protein-protein interactions (PPIs) and WGCNA, we performed co-expression modules analysis for ASD-associated genes, and identified 25 modules with differentially expressed genes (DEGs), observing that genes in these modules were significantly involved in various biological processes in nervous system, sensory system, phylogenetic system and variety of signaling pathways. Then, based on transcriptional and post-transcriptional regulations, integrating transcription factor (TF)-target and RNA-associated interactions, significant regulators of co-expression modules were identified as pivot regulators, including 67 pivot TFs, 13 pivot miRNAs and 6 pivot lncRNAs. GO and KEGG pathway enrichment analysis demonstrated that the pivot miRNAs significantly enriched in neural or mental-associated biological progresses. The pivot TFs were mainly involved in various regulation of transcription, immune system and organs development. Finally, our work deciphered a multifactor dysfunctional co-expression subnetwork involved in ASD, helps uncover core dysfunctional modules for this disease and improves our understanding of its underlying molecular mechanism.

The placenta separating embryo and mother plays a major role in evading rejection by the maternal immune system. Tumor cells utilize similar strategies to coexist in an immunologically hostile environment. Exploration of placental development help us increase understanding of the immune aspects of pan-cancer. However, a system-wide placental development analysis of the underlying immune changes remains elusive. Here, we constructed the comprehensive time-resolved atlas of human placental tissues and presented an integrated proteotranscriptomic analysis. The liquid chromatography tandem mass spectrometry (LC-MS/MS) was used to quantify proteins extracted from 15 immature placenta and 6 mature placenta tissues. And the quantitative transcriptome atlas was depicted based on RNA-seq. We found that the amount and abundance of proteins detected in immature placenta were more than mature placenta. Moreover, protein abundance was incompletely predicted by transcriptome, presenting functional differences. In addition, dynamically expressed proteins ubiquitously involved in human placenta development and were significantly related to immune pathways. Based on the striking similarities between placental cells and cancer cells, co-differentially expressed genes (co-DEGs) from placenta time-resolved multi-omics data were identified to dissect cancer-related genes and cluster cancer types with similar tissue origins. As results, we identified key immunomodulators in the placenta, whose expression tend to dysregulated in pan-cancer and are strongly correlated with pan-cancer immune scores as well as clinical outcomes. Overall, our study provides a resource to view the altered immune regulation driven by the placenta and demonstrates the benefit of placental analysis in cancer tumorigenesis. Funding Statement: This work was supported by the CAMS Innovation Fund for Medical Sciences (CIFMS) (Grant no.2016-I2M-1-001), National Key R&D Program of China (2017YFC0906603), the Open Project Program of the State Key Laboratory of Proteomics (SKLP-O201705), the CAMS Innovation Fund for Medical Sciences (CIFMS) (Grant no.2016-I2M-3-005), the National Natural Science Foundation of China (31571331, 61873075, 31871338), Natural Science Foundation for Distinguished Young Scholars of Heilongjiang Province (JQ2019C004). Declaration of Interests: The authors declare no competing interests. Ethical Approval Statement: All donors signed informed consent forms. The use of human tissue samples and experimental procedures for this study were reviewed and approved by the Ethics Committee of the Cancer Institute and Hospital, Chinese Academy of Medical Sciences.

Summary Circular RNAs (circRNAs) have been identified as naturally occurring RNAs that are highly represented in the eukaryotic transcriptome. Although a large number of circRNAs have been reported, the underlying regulatory mechanism of circRNAs biogenesis remains largely unknown. Here, we integrated in-depth multi-omics data including epigenome, transcriptome, and non-coding RNA and identified candidate circRNAs in six cellular contexts. Next, circRNAs were divided into two classes (high versus low) with different expression levels. Machine learning models were constructed that predicted circRNA expression levels based on 11 different histone modifications and host gene expression. We found that the models achieve great accuracy in predicting high versus low expressed circRNAs. Furthermore, the expression levels of host genes of circRNAs, H3k36me3, H3k79me2, and H4k20me1 contributed greatly to the classification models in six cellular contexts. In summary, all these results suggest that epigenetic modifications, particularly histone modifications, can effectively predict expression levels of circRNAs., Graphical Abstract, Highlights • CircRNAs exhibit specific expression in various cellular contexts • High and low expressed circRNAs exhibit different biological functions • Histone modifications are significantly correlated with circRNAs expression • Machine learning models were constructed for predicting circRNAs expression, Bioinformatics; Omics; Transcriptomics