Survey of miRNA-miRNA cooperative regulation principles across cancer types

2018 ◽  
Vol 20 (5) ◽  
pp. 1621-1638 ◽  
Author(s):  
Tingting Shao ◽  
Guangjuan Wang ◽  
Hong Chen ◽  
Yunjin Xie ◽  
Xiyun Jin ◽  
...  

AbstractCooperative regulation among multiple microRNAs (miRNAs) is a complex type of posttranscriptional regulation in human; however, the global view of the system-level regulatory principles across cancers is still unclear. Here, we investigated miRNA-miRNA cooperative regulatory landscape across 18 cancer types and summarized the regulatory principles of miRNAs. The miRNA-miRNA cooperative pan-cancer network exhibited a scale-free and modular architecture. Cancer types with similar tissue origins had high similarity in cooperative network structure and expression of cooperative miRNA pairs. In addition, cooperative miRNAs showed divergent properties, including higher expression, greater expression variation and a stronger regulatory strength towards targets and were likely to regulate cancer hallmark-related functions. We found a marked rewiring of miRNA-miRNA cooperation between various cancers and revealed conserved and rewired network miRNA hubs. We further identified the common hubs, cancer-specific hubs and other hubs, which tend to target known anticancer drug targets. Finally, miRNA cooperative modules were found to be associated with patient survival in several cancer types. Our study highlights the potential of pan-cancer miRNA-miRNA cooperative regulation as a novel paradigm that may aid in the discovery of tumorigenesis mechanisms and development of anticancer drugs.

2018 ◽  
Author(s):  
Matthew H. Ung ◽  
Evelien Schaafsma ◽  
Daniel E. Mattox ◽  
George L. Wang ◽  
Chao Cheng

AbstractThe “dark matter” of the genome harbors several non-coding RNA species including IncRNAs, which have been implicated in neoplasias but remain understudied. RNA-seq has provided deep insights into the nature of lncRNAs in cancer but current RNA-seq data are rarely accompanied by longitudinal patient survival information. In contrast, a plethora of microarray studies have collected these clinical metadata that can be leveraged to identify novel associations between gene expression and clinical phenotypes. In this study, we developed an analysis framework that computationally integrates RNA-seq and microarray data to systematically screen 9,463 lncRNAs for association with mortality risk across 20 cancer types. In total, we identified a comprehensive list of associations between lncRNAs and patient survival and demonstrate that these prognostic lncRNAs are under selective pressure and may be functional. Our results provide valuable insights that facilitate further exploration of lncRNAs and their potential as cancer biomarkers and drug targets.


PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8797 ◽  
Author(s):  
Matthew Ung ◽  
Evelien Schaafsma ◽  
Daniel Mattox ◽  
George L. Wang ◽  
Chao Cheng

Background The “dark matter” of the genome harbors several non-coding RNA species including Long non-coding RNAs (lncRNAs), which have been implicated in neoplasia but remain understudied. RNA-seq has provided deep insights into the nature of lncRNAs in cancer but current RNA-seq data are rarely accompanied by longitudinal patient survival information. In contrast, a plethora of microarray studies have collected these clinical metadata that can be leveraged to identify novel associations between gene expression and clinical phenotypes. Methods In this study, we developed an analysis framework that computationally integrates RNA-seq and microarray data to systematically screen 9,463 lncRNAs for association with mortality risk across 20 cancer types. Results In total, we identified a comprehensive list of associations between lncRNAs and patient survival and demonstrate that these prognostic lncRNAs are under selective pressure and may be functional. Our results provide valuable insights that facilitate further exploration of lncRNAs and their potential as cancer biomarkers and drug targets.


Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 714-714
Author(s):  
Sumito Shingaki ◽  
Junji Koya ◽  
Mitsuhiro Yuasa ◽  
Yuki Saito ◽  
Marni B McClure ◽  
...  

Abstract PD-L2 is a ligand for PD-1 immune checkpoint. In contrast to another ligand PD-L1, little is known about the biological relevance and regulatory mechanism of PD-L2 in cancer. Here we found by pan-cancer transcriptome analysis that PD-L2 was highly expressed in limited cancer types, especially in diffuse large B-cell lymphoma (DLBCL). In particular, PD-L2 expression was elevated in patients with PD-L2 genetic alterations, such as copy number amplifications and rearrangements causing promoter replacement or 3′-untranslated region (UTR) disruption. To evaluate the effect of PD-L1 and PD-L2 on the tumor microenvironment and clarify their similarities and differences at a similar expression level, we generated a A20-ovalbumin (OVA) murine B-cell lymphoma cell line lacking Pd-l1 and introduced exogenous Pd-l1 or Pd-l2 expression. Analysis of A20-OVA model showed that Pd-l2 expression accelerated tumor growth and attenuated CD8 + T cell infiltration in vivo, similar to Pd-l1 expression. Then, we performed multi-omics single-cell analysis in this model, constructing transcriptomic, surface phenotypic, and immune repertoire maps of > 20,000 cells from mock-, Pd-l1-, and Pd-l2-expressing A20-OVA tumors. Importantly, Pd-l1- and Pd-l2-expressing tumors exhibited similar cellular dynamics as well as transcriptomic and surface phenotypic changes in the tumor microenvironment. Specifically, a significant decrease of CD8 + T cells, particularly effector/memory cells showing high clonality, and regulatory T cells as well as a significant increase of myeloid-derived cells, including monocytes/macrophages and plasmacytoid dendritic cells (DCs), were observed in Pd-l1- and Pd-l2-expressing tumors. Differentially expressed gene analysis demonstrated the downregulation of response to bacterial molecules, including lipopolysaccharide, and antigen processing and presentation pathways in monocytes/macrophages and conventional and plasmacytoid DCs, respectively, in Pd-l1- and Pd-l2-expressing tumors. In line with this, pro-inflammatory cytokine‒inducible markers, such as Ly6A/E and I-A/I-E, were down-regulated in various cell types in Pd-l1- and Pd-l2-expressing tumors. These results suggest that delineates pleiotropic effects shared by PD-L1 and PD-L2, mainly enhancing anti-inflammatory, pro-tumorigenic responses in the tumor microenvironment. Given similar functions of PD-L1 and PD-L2, we hypothesized that the expression level of PD-1 ligands determines their biological relevance. Therefore, we aimed to dissect PD-L2 regulatory landscape by performing CRISPR tiling screening targeting 51 candidate regulatory elements predicted from Hi-C and DNase-seq data of a human transformed B cell line (GM12878). In addition to known cis-regulatory elements including the canonical transcription start site (TSS) and 3′-UTR, we identified a novel TSS, which was validated by cap analysis of gene expression (CAGE) with sequencing (CAGE-seq). Pan-cancer and -tissue expression analyses revealed that this novel element was expressed in 13% of DLBCL, but not in normal tissues nor other cancer types, suggestive of a unique PD-L2 regulatory mechanism in DLBCL. In addition, we identified an element located in the PD-L1 promoter which function as a distal silencer, suggesting functional complexity of this regulatory element. CRISPR-mediated knockout of other PD-L1 exons did not affect PD-L2 expression, suggesting that a silencer function in the PD-L1 promoter is independent of PD-L1 expression. The identified PD-L2 regulatory elements can be occupied by an array of trans-regulatory factors. Indeed, ENCODE ChIP-seq of GM12878 revealed that many chromatin-associated proteins (CAPs) were bound within the PD-L1/PD-L2 topology associating domain. Therefore, to determine key regulators, we performed loss-of-function CRISPR screening for 103 CAPs. This CRISPR screening identified seven negative (such as IRF4 and BATF) and two positive regulators of PD-L2 expression. CRISPR/Cas9-based inhibition exhibited differential usage of canonical and novel TSSs among these factors. Taken together, our findings reveal lineage-specific complex network of cis-regulatory elements and CAPs in regulating PD-L2 expression. These data provide insights into the molecular mechanisms underlying immune evasion and help refining immune-based therapeutic strategy in DLBCL. Disclosures Koya: 10x Genomics: Honoraria. Kogure: Takeda Pharmaceutical: Honoraria. Kataoka: Bristol-Myers Squibb: Research Funding; Japan Blood Products Organization: Research Funding; Teijin Pharma: Research Funding; Shionogi: Research Funding; Asahi Genomics: Current holder of individual stocks in a privately-held company; Otsuka Pharmaceutical: Honoraria, Research Funding; Takeda Pharmaceutical: Honoraria, Research Funding; Janssen Pharmaceutical: Honoraria; Kyowa Kirin: Honoraria, Research Funding; Sumitomo Dainippon Pharma: Honoraria, Research Funding; AstraZeneca: Honoraria; Chugai Pharmaceutical: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Astellas Pharma: Honoraria, Research Funding; Eisai: Honoraria, Research Funding; Celgene: Honoraria; Ono Pharmaceutical: Honoraria, Research Funding; Mochida Pharmaceutical: Research Funding; JCR Pharmaceuticals: Research Funding; MSD: Research Funding.


2020 ◽  
Vol 13 (S10) ◽  
Author(s):  
Mai Shi ◽  
Stephen Kwok-Wing Tsui ◽  
Hao Wu ◽  
Yingying Wei

Abstract Background DNA methylation is a key epigenetic regulator contributing to cancer development. To understand the role of DNA methylation in tumorigenesis, it is important to investigate and compare differential methylation (DM) patterns between normal and case samples across different cancer types. However, current pan-cancer analyses call DM separately for each cancer, which suffers from lower statistical power and fails to provide a comprehensive view for patterns across cancers. Methods In this work, we propose a rigorous statistical model, PanDM, to jointly characterize DM patterns across diverse cancer types. PanDM uses the hidden correlations in the combined dataset to improve statistical power through joint modeling. PanDM takes summary statistics from separate analyses as input and performs methylation site clustering, differential methylation detection, and pan-cancer pattern discovery. We demonstrate the favorable performance of PanDM using simulation data. We apply our model to 12 cancer methylome data collected from The Cancer Genome Atlas (TCGA) project. We further conduct ontology- and pathway-enrichment analyses to gain new biological insights into the pan-cancer DM patterns learned by PanDM. Results PanDM outperforms two types of separate analyses in the power of DM calling in the simulation study. Application of PanDM to TCGA data reveals 37 pan-cancer DM patterns in the 12 cancer methylomes, including both common and cancer-type-specific patterns. These 37 patterns are in turn used to group cancer types. Functional ontology and biological pathways enriched in the non-common patterns not only underpin the cancer-type-specific etiology and pathogenesis but also unveil the common environmental risk factors shared by multiple cancer types. Moreover, we also identify PanDM-specific DM CpG sites that the common strategy fails to detect. Conclusions PanDM is a powerful tool that provides a systematic way to investigate aberrant methylation patterns across multiple cancer types. Results from real data analyses suggest a novel angle for us to understand the common and specific DM patterns in different cancers. Moreover, as PanDM works on the summary statistics for each cancer type, the same framework can in principle be applied to pan-cancer analyses of other functional genomic profiles. We implement PanDM as an R package, which is freely available at http://www.sta.cuhk.edu.hk/YWei/PanDM.html.


2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Bai-xia Zhang ◽  
Jian Li ◽  
Hao Gu ◽  
Qiang Li ◽  
Qi Zhang ◽  
...  

Due to the proved clinical efficacy, Shuang-Huang-Lian (SHL) has developed a variety of dosage forms. However, the in-depth research on targets and pharmacological mechanisms of SHL preparations was scarce. In the presented study, the bioinformatics approaches were adopted to integrate relevant data and biological information. As a result, a PPI network was built and the common topological parameters were characterized. The results suggested that the PPI network of SHL exhibited a scale-free property and modular architecture. The drug target network of SHL was structured with 21 functional modules. According to certain modules and pharmacological effects distribution, an antitumor effect and potential drug targets were predicted. A biological network which contained 26 subnetworks was constructed to elucidate the antipneumonia mechanism of SHL. We also extracted the subnetwork to explicitly display the pathway where one effective component acts on the pneumonia related targets. In conclusions, a bioinformatics approach was established for exploring the drug targets, pharmacological activity distribution, effective components of SHL, and its mechanism of antipneumonia. Above all, we identified the effective components and disclosed the mechanism of SHL from the view of system.


Genes ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 466
Author(s):  
Chen Chen ◽  
Samuel Haddox ◽  
Yue Tang ◽  
Fujun Qin ◽  
Hui Li

Gene fusions and their products (RNA and protein) have been traditionally recognized as unique features of cancer cells and are used as ideal biomarkers and drug targets for multiple cancer types. However, recent studies have demonstrated that chimeric RNAs generated by intergenic alternative splicing can also be found in normal cells and tissues. In this study, we aim to identify chimeric RNAs in different non-neoplastic cell lines and investigate the landscape and expression of these novel candidate chimeric RNAs. To do so, we used HEK-293T, HUVEC, and LO2 cell lines as models, performed paired-end RNA sequencing, and conducted analyses for chimeric RNA profiles. Several filtering criteria were applied, and the landscape of chimeric RNAs was characterized at multiple levels and from various angles. Further, we experimentally validated 17 chimeric RNAs from different classifications. Finally, we examined a number of validated chimeric RNAs in different cancer and non-cancer cells, including blood from healthy donors, and demonstrated their ubiquitous expression pattern.


2021 ◽  
Vol 22 (9) ◽  
pp. 4384
Author(s):  
Divya Sahu ◽  
Yu-Lin Chang ◽  
Yin-Chen Lin ◽  
Chen-Ching Lin

The genes influencing cancer patient mortality have been studied by survival analysis for many years. However, most studies utilized them only to support their findings associated with patient prognosis: their roles in carcinogenesis have not yet been revealed. Herein, we applied an in silico approach, integrating the Cox regression model with effect size estimated by the Monte Carlo algorithm, to screen survival-influential genes in more than 6000 tumor samples across 16 cancer types. We observed that the survival-influential genes had cancer-dependent properties. Moreover, the functional modules formed by the harmful genes were consistently associated with cell cycle in 12 out of the 16 cancer types and pan-cancer, showing that dysregulation of the cell cycle could harm patient prognosis in cancer. The functional modules formed by the protective genes are more diverse in cancers; the most prevalent functions are relevant for immune response, implying that patients with different cancer types might develop different mechanisms against carcinogenesis. We also identified a harmful set of 10 genes, with potential as prognostic biomarkers in pan-cancer. Briefly, our results demonstrated that the survival-influential genes could reveal underlying mechanisms in carcinogenesis and might provide clues for developing therapeutic targets for cancers.


2021 ◽  
Vol 28 (2) ◽  
pp. 1483-1494
Author(s):  
Sharlette Dunn ◽  
Madelene A. Earp ◽  
Patricia Biondo ◽  
Winson Y. Cheung ◽  
Marc Kerba ◽  
...  

Despite the known benefits, healthcare systems struggle to provide early, integrated palliative care (PC) for advanced cancer patients. Understanding the barriers to providing PC from the perspective of oncology clinicians is an important first step in improving care. A 33-item online survey was emailed to all oncology clinicians working with all cancer types in Alberta, Canada, from November 2017 to January 2018. Questions were informed by Michie’s Theoretical Domains Framework and Behaviour Change Wheel (BCW) and queried (a) PC provision in oncology clinics, (b) specialist PC consultation referrals, and (c) working with PC consultants and home care. Respondents (n = 263) were nurses (41%), physicians (25%), and allied healthcare professionals (18%). Barriers most frequently identified were “clinicians’ limited time/competing priorities” (64%), “patients’ negative perceptions of PC” (63%), and clinicians’ capability to manage patients’ social issues (63%). These factors mapped to all three BCW domains: motivation, opportunity, and capability. In contrast, the least frequently identified barriers were clinician motivation and perceived PC benefits. Oncology clinicians’ perceptions of barriers to early PC were comparable across tumour types and specialties but varied by professional role. The main challenges to early integrated PC include all three BCW domains. Notably, motivation is not a barrier for oncology clinicians; however, opportunity and capability barriers were identified. Multifaceted interventions using these findings have been developed, such as tip sheets to enhance capability, reframing PC with patients, and earlier specialist PC nursing access, to enhance clinicians’ use of and patients’ benefits from an early PC approach.


Cells ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 433
Author(s):  
Bijesh George ◽  
P. Mukundan Pillai ◽  
Aswathy Mary Paul ◽  
Revikumar Amjesh ◽  
Kim Leitzel ◽  
...  

To define the growing significance of cellular targets and/or effectors of cancer drugs, we examined the fitness dependency of cellular targets and effectors of cancer drug targets across human cancer cells from 19 cancer types. We observed that the deletion of 35 out of 47 cellular effectors and/or targets of oncology drugs did not result in the expected loss of cell fitness in appropriate cancer types for which drugs targeting or utilizing these molecules for their actions were approved. Additionally, our analysis recognized 43 cellular molecules as fitness genes in several cancer types in which these drugs were not approved, and thus, providing clues for repurposing certain approved oncology drugs in such cancer types. For example, we found a widespread upregulation and fitness dependency of several components of the mevalonate and purine biosynthesis pathways (currently targeted by bisphosphonates, statins, and pemetrexed in certain cancers) and an association between the overexpression of these molecules and reduction in the overall survival duration of patients with breast and other hard-to-treat cancers, for which such drugs are not approved. In brief, the present analysis raised cautions about off-target and undesirable effects of certain oncology drugs in a subset of cancers where the intended cellular effectors of drug might not be good fitness genes and that this study offers a potential rationale for repurposing certain approved oncology drugs for targeted therapeutics in additional cancer types.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Erik van Dijk ◽  
Tom van den Bosch ◽  
Kristiaan J. Lenos ◽  
Khalid El Makrini ◽  
Lisanne E. Nijman ◽  
...  

AbstractSurvival rates of cancer patients vary widely within and between malignancies. While genetic aberrations are at the root of all cancers, individual genomic features cannot explain these distinct disease outcomes. In contrast, intra-tumour heterogeneity (ITH) has the potential to elucidate pan-cancer survival rates and the biology that drives cancer prognosis. Unfortunately, a comprehensive and effective framework to measure ITH across cancers is missing. Here, we introduce a scalable measure of chromosomal copy number heterogeneity (CNH) that predicts patient survival across cancers. We show that the level of ITH can be derived from a single-sample copy number profile. Using gene-expression data and live cell imaging we demonstrate that ongoing chromosomal instability underlies the observed heterogeneity. Analysing 11,534 primary cancer samples from 37 different malignancies, we find that copy number heterogeneity can be accurately deduced and predicts cancer survival across tissues of origin and stages of disease. Our results provide a unifying molecular explanation for the different survival rates observed between cancer types.


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