scholarly journals The Functional Characterization of Epigenetically Related lncRNAs Involved in Dysregulated CeRNA–CeRNA Networks Across Eight Cancer Types

2021 ◽  
Vol 9 ◽  
Author(s):  
Dahua Xu ◽  
Liqiang Wang ◽  
Sainan Pang ◽  
Meng Cao ◽  
Wenxiang Wang ◽  
...  
Keyword(s):  
Functional Characterization ◽  
Cancer Hallmarks ◽  
Diagnosis And Prognosis ◽  
Competing Endogenous Rnas ◽  
Cancer Types ◽  
Cancer Outcome ◽  
The Individual ◽  
Molecular Therapeutic ◽  
Molecular Therapeutic Targets ◽  
Pan Cancer

Numerous studies have demonstrated that lncRNAs could compete with other RNAs to bind miRNAs, as competing endogenous RNAs (ceRNAs), to regulate each other. On the other hand, ceRNAs were found to be recurrently dysregulated in cancer status. However, limited studies considered the upstream epigenetic regulatory factors that disrupted the normal competing mechanism. In the present study, we constructed the lncRNA-associated dysregulated ceRNA networks across eight cancer types. lncRNAs in the individual dysregulated network and pan-cancer core dysregulated ceRNA subnetwork were found to play more important roles than mRNAs. Integrating lncRNA methylation profiles, we identified 49 epigenetically related (ER) lncRNAs involved in the dysregulated ceRNA networks, including 18 epigenetically activated (EA) lncRNAs, 18 epigenetically silenced (ES) lncRNAs, and 13 rewired ER lncRNAs across eight cancer types. Furthermore, we evaluated the epigenetic regulating patterns of these lncRNAs and screened nine pan-cancer ER lncRNAs (six EA and three ES lncRNAs). The nine lncRNAs were found to regulate the cancer hallmarks by competing with mRNAs. Moreover, we found that integrating the expression and methylation profiles of the nine lncRNAs could predict cancer incidence in eight cancer types robustly and the cancer outcome of several cancer types. These results provide an improved understanding of methylation regulation to ceRNA and offer novel potential molecular therapeutic targets for the diagnosis and prognosis across different cancer types.

scholarly journals Complex impact of DNA methylation on transcriptional dysregulation across 22 human cancer types

2020 ◽  
Vol 48 (5) ◽  
pp. 2287-2302 ◽  
Author(s):  
Zishan Wang ◽  
Jiaqi Yin ◽  
Weiwei Zhou ◽  
Jing Bai ◽  
Yunjin Xie ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Transcriptional Regulation ◽  
Human Cancer ◽  
Network Module ◽  
Regulatory Activity ◽  
Valuable Resource ◽  
Cancer Hallmarks ◽  
Transcriptional Dysregulation ◽  
Cancer Types ◽  
Pan Cancer

Abstract Accumulating evidence has demonstrated that transcriptional regulation is affected by DNA methylation. Understanding the perturbation of DNA methylation-mediated regulation between transcriptional factors (TFs) and targets is crucial for human diseases. However, the global landscape of DNA methylation-mediated transcriptional dysregulation (DMTD) across cancers has not been portrayed. Here, we systematically identified DMTD by integrative analysis of transcriptome, methylome and regulatome across 22 human cancer types. Our results revealed that transcriptional regulation was affected by DNA methylation, involving hundreds of methylation-sensitive TFs (MethTFs). In addition, pan-cancer MethTFs, the regulatory activity of which is generally affected by DNA methylation across cancers, exhibit dominant functional characteristics and regulate several cancer hallmarks. Moreover, pan-cancer MethTFs were found to be affected by DNA methylation in a complex pattern. Finally, we investigated the cooperation among MethTFs and identified a network module that consisted of 43 MethTFs with prognostic potential. In summary, we systematically dissected the transcriptional dysregulation mediated by DNA methylation across cancer types, and our results provide a valuable resource for both epigenetic and transcriptional regulation communities.

scholarly journals Immunomediated Pan-cancer Regulation Networks are Dominant Fingerprints after Treatment of Cell Lines with Demethylation

2016 ◽  
Vol 15 ◽  
pp. CIN.S31809
Author(s):  
Manama El Baroudi ◽  
Caterina Cinti ◽  
Enrico Capobianco
Keyword(s):  
Immune System ◽  
Cell Lines ◽  
Network Inference ◽  
Cancer Hallmarks ◽  
Regulation Mechanisms ◽  
Cancer Types ◽  
Synergistic Modulation ◽  
Cancer Immunotherapies ◽  
Post Transcriptional Regulation ◽  
Pan Cancer

Pan-cancer studies are particularly relevant not only for addressing the complexity of the inherently observed heterogeneity but also for identifying clinically relevant features that may be common to the cancer types. Immune system regulations usually reveal synergistic modulation with other cancer mechanisms and in combination provide insights on possible advances in cancer immunotherapies. Network inference is a powerful approach to decipher pan-cancer systems dynamics. The methodology proposed in this study elucidates the impacts of epigenetic treatment on the drivers of complex pan-cancer regulation circuits involving cell lines of five cancer types. These patterns were observed from differential gene expression measurements following demethylation with 5-azacytidine. Networks were built to establish associations of phenotypes at molecular level with cancer hallmarks through both transcriptional and post-transcriptional regulation mechanisms. The most prominent feature that emerges from our integrative network maps, linking pathway landscapes to disease and drug-target associations, refers primarily to a mosaic of immune-system crosslinked influences. Therefore, characteristics initially evidenced in single cancer maps become motifs well summarized by network cores and fingerprints.

scholarly journals Characterization of the Survival Influential Genes in Carcinogenesis

2021 ◽  
Vol 22 (9) ◽  
pp. 4384
Author(s):  
Divya Sahu ◽  
Yu-Lin Chang ◽  
Yin-Chen Lin ◽  
Chen-Ching Lin
Keyword(s):  
Cell Cycle ◽  
Cox Regression ◽  
Monte Carlo Algorithm ◽  
Functional Modules ◽  
Protective Genes ◽  
Cancer Types ◽  
Underlying Mechanisms ◽  
Patient Prognosis ◽  
Pan Cancer

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.

scholarly journals Oncology Clinicians’ Challenges to Providing Palliative Cancer Care—A Theoretical Domains Framework, Pan-Cancer System Survey

2021 ◽  
Vol 28 (2) ◽  
pp. 1483-1494
Author(s):  
Sharlette Dunn ◽  
Madelene A. Earp ◽  
Patricia Biondo ◽  
Winson Y. Cheung ◽  
Marc Kerba ◽  
...  
Keyword(s):  
Online Survey ◽  
Social Issues ◽  
Theoretical Domains Framework ◽  
Advanced Cancer Patients ◽  
Behaviour Change Wheel ◽  
Negative Perceptions ◽  
Cancer Types ◽  
Multifaceted Interventions ◽  
Competing Priorities ◽  
Pan Cancer

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.

scholarly journals Chromosomal copy number heterogeneity predicts survival rates across cancers

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Erik van Dijk ◽  
Tom van den Bosch ◽  
Kristiaan J. Lenos ◽  
Khalid El Makrini ◽  
Lisanne E. Nijman ◽  
...  
Keyword(s):  
Copy Number ◽  
Cancer Survival ◽  
Survival Rates ◽  
Cancer Prognosis ◽  
Disease Outcomes ◽  
Distinct Disease ◽  
Cancer Types ◽  
Chromosomal Copy Number ◽  
Chromosomal Copy ◽  
Pan Cancer

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.

scholarly journals The biogenesis and biological function of PIWI-interacting RNA in cancer

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Silu Chen ◽  
Shuai Ben ◽  
Junyi Xin ◽  
Shuwei Li ◽  
Rui Zheng ◽  
...  
Keyword(s):  
Cancer Diagnosis ◽  
Precancerous Lesions ◽  
Biological Activities ◽  
Cancer Hallmarks ◽  
Diagnosis And Prognosis ◽  
Small Ncrnas ◽  
Bioinformatics Databases ◽  
Non Coding Rnas ◽  
Underlying Mechanisms ◽  
Proliferative Signaling

AbstractSmall non-coding RNAs (ncRNAs) are vital regulators of biological activities, and aberrant levels of small ncRNAs are commonly found in precancerous lesions and cancer. PIWI-interacting RNAs (piRNAs) are a novel type of small ncRNA initially discovered in germ cells that have a specific length (24–31 nucleotides), bind to PIWI proteins, and show 2′-O-methyl modification at the 3′-end. Numerous studies have revealed that piRNAs can play important roles in tumorigenesis via multiple biological regulatory mechanisms, including silencing transcriptional and posttranscriptional gene processes and accelerating multiprotein interactions. piRNAs are emerging players in the malignant transformation of normal cells and participate in the regulation of cancer hallmarks. Most of the specific cancer hallmarks regulated by piRNAs are involved in sustaining proliferative signaling, resistance to cell death or apoptosis, and activation of invasion and metastasis. Additionally, piRNAs have been used as biomarkers for cancer diagnosis and prognosis and have great potential for clinical utility. However, research on the underlying mechanisms of piRNAs in cancer is limited. Here, we systematically reviewed recent advances in the biogenesis and biological functions of piRNAs and relevant bioinformatics databases with the aim of providing insights into cancer diagnosis and clinical applications. We also focused on some cancer hallmarks rarely reported to be related to piRNAs, which can promote in-depth research of piRNAs in molecular biology and facilitate their clinical translation into cancer treatment.

scholarly journals Pan-Cancer Transcriptional Models Predicting Chemosensitivity in Human Tumors

2021 ◽  
Vol 20 ◽  
pp. 117693512110024
Author(s):  
Jason D Wells ◽  
Jacqueline R Griffin ◽  
Todd W Miller
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Human Tumors ◽  
Molecular Characteristics ◽  
Survival Times ◽  
Chemotherapy Drugs ◽  
Testing Dataset ◽  
Cancer Models ◽  
Cancer Types ◽  
Pan Cancer

Motivation: Despite increasing understanding of the molecular characteristics of cancer, chemotherapy success rates remain low for many cancer types. Studies have attempted to identify patient and tumor characteristics that predict sensitivity or resistance to different types of conventional chemotherapies, yet a concise model that predicts chemosensitivity based on gene expression profiles across cancer types remains to be formulated. We attempted to generate pan-cancer models predictive of chemosensitivity and chemoresistance. Such models may increase the likelihood of identifying the type of chemotherapy most likely to be effective for a given patient based on the overall gene expression of their tumor. Results: Gene expression and drug sensitivity data from solid tumor cell lines were used to build predictive models for 11 individual chemotherapy drugs. Models were validated using datasets from solid tumors from patients. For all drug models, accuracy ranged from 0.81 to 0.93 when applied to all relevant cancer types in the testing dataset. When considering how well the models predicted chemosensitivity or chemoresistance within individual cancer types in the testing dataset, accuracy was as high as 0.98. Cell line–derived pan-cancer models were able to statistically significantly predict sensitivity in human tumors in some instances; for example, a pan-cancer model predicting sensitivity in patients with bladder cancer treated with cisplatin was able to significantly segregate sensitive and resistant patients based on recurrence-free survival times ( P = .048) and in patients with pancreatic cancer treated with gemcitabine ( P = .038). These models can predict chemosensitivity and chemoresistance across cancer types with clinically useful levels of accuracy.

scholarly journals Estimating the predictive power of silent mutations on cancer classification and prognosis

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Tal Gutman ◽  
Guy Goren ◽  
Omri Efroni ◽  
Tamir Tuller
Keyword(s):  
Gene Expression ◽  
Predictive Power ◽  
Predictive Ability ◽  
Cancer Classification ◽  
Silent Mutation ◽  
Cancer Type ◽  
Coding Region ◽  
Probability Of Survival ◽  
Diagnosis And Prognosis ◽  
Cancer Types

AbstractIn recent years it has been shown that silent mutations, in and out of the coding region, can affect gene expression and may be related to tumorigenesis and cancer cell fitness. However, the predictive ability of these mutations for cancer type diagnosis and prognosis has not been evaluated yet. In the current study, based on the analysis of 9,915 cancer genomes and approximately three million mutations, we provide a comprehensive quantitative evaluation of the predictive power of various types of silent and non-silent mutations over cancer classification and prognosis. The results indicate that silent-mutation models outperform the equivalent null models in classifying all examined cancer types and in estimating the probability of survival 10 years after the initial diagnosis. Additionally, combining both non-silent and silent mutations achieved the best classification results for 68% of the cancer types and the best survival estimation results for up to nine years after the diagnosis. Thus, silent mutations hold considerable predictive power over both cancer classification and prognosis, most likely due to their effect on gene expression. It is highly advised that silent mutations are integrated in cancer research in order to unravel the full genomic landscape of cancer and its ramifications on cancer fitness.

scholarly journals Pan-Cancer Molecular Patterns and Biological Implications Associated with a Tumor-Specific Molecular Signature

Cells ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 45
Author(s):  
Darío Rocha ◽  
Iris A. García ◽  
Aldana González Montoro ◽  
Andrea Llera ◽  
Laura Prato ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Cell Cycle Progression ◽  
Uncertainty Assessment ◽  
Cell Cycle Checkpoints ◽  
Molecular Signature ◽  
Cancer Subtypes ◽  
United States Food ◽  
Cancer Types ◽  
Molecular Patterns ◽  
Pan Cancer

Studying tissue-independent components of cancer and defining pan-cancer subtypes could be addressed using tissue-specific molecular signatures if classification errors are controlled. Since PAM50 is a well-known, United States Food and Drug Administration (FDA)-approved and commercially available breast cancer signature, we applied it with uncertainty assessment to classify tumor samples from over 33 cancer types, discarded unassigned samples, and studied the emerging tumor-agnostic molecular patterns. The percentage of unassigned samples ranged between 55.5% and 86.9% in non-breast tissues, and gene set analysis suggested that the remaining samples could be grouped into two classes (named C1 and C2) regardless of the tissue. The C2 class was more dedifferentiated, more proliferative, with higher centrosome amplification, and potentially more TP53 and RB1 mutations. We identified 28 gene sets and 95 genes mainly associated with cell-cycle progression, cell-cycle checkpoints, and DNA damage that were consistently exacerbated in the C2 class. In some cancer types, the C1/C2 classification was associated with survival and drug sensitivity, and modulated the prognostic meaning of the immune infiltrate. Our results suggest that PAM50 could be repurposed for a pan-cancer context when paired with uncertainty assessment, resulting in two classes with molecular, biological, and clinical implications.

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