Search

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.

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.

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