The pan-cancer analysis of gain-of-functional mutations to identify the common oncogenic signatures in multiple cancers

AbstractRNA stability is a crucial and often overlooked determinant of gene expression. Some of the regulators of mRNA stability are long known as key oncogenic or tumour suppressor factors. Nonetheless, the extent to which mRNA stability contributes to transcriptome remodeling in cancer is unknown, and the factors that modulate mRNA stability during cancer development and progression are largely uncharacterized. Here, by decoupling transcriptional and post-transcriptional effects in RNA-seq data of 7760 samples from 18 cancer types, we present a pan-cancer view of the mRNA stability changes that accompany tumour development and progression. We show that thousands of genes are dysregulated at the mRNA stability level, and identify the potential factors that drive these changes, including >80 RNA-binding proteins (RBPs) and microRNAs (miRNAs). Most RBPs and miRNAs have cancer type-specific activities, but a few show recurrent inactivation across multiple cancers, including the RBFOX family of RBPs and miR-29. Analysis of cell lines with phenotypic activation or inhibition of RBFOX1 and miR-29 confirms their role in modulation of genes that are dysregulated across multiple cancers, with functions in calcium signaling, extracellular matrix organization, and stemness. Overall, our study highlights the critical role of mRNA stability in shaping the tumour transcriptome, with recurrent post-transcriptional changes that are ~30% as frequent as transcriptional events. These results provide a resource for systematic interrogation of cancer-associated stability drivers and pathways.

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Molecular Mechanisms of PD-1 and PD-L1 Activity on a Pan-Cancer Basis: A Bioinformatic Exploratory Study

International Journal of Molecular Sciences ◽

10.3390/ijms22115478 ◽

2021 ◽

Vol 22 (11) ◽

pp. 5478

Author(s):

Siddarth Kannan ◽

Geraldine Martina O’Connor ◽

Emyr Yosef Bakker

Keyword(s):

Clinical Relevance ◽

Molecular Mechanisms ◽

Patient Survival ◽

Drug Repurposing ◽

Immune Checkpoint Blockade ◽

Multiple Cancers ◽

Significant Survival ◽

Cancer Types ◽

Repurposed Drugs ◽

Pan Cancer

Immune checkpoint blockade targeting PD-1 (PDCD1)/PD-L1 (CD274) is increasingly used for multiple cancers. However, efficacy and adverse-related events vary significantly. This bioinformatic study interrogated molecular differences pertaining to PDCD1/CD274 and their correlated genes on a pan-cancer basis to identify differences between cancer types. Patient RNA-seq data from fifteen cancer types were accessed on cBioPortal to determine the role of PDCD1/CD274 in patient survival and to identify positively and negatively correlated genes, which were also assessed for clinical relevance. Genes correlating with PDCD1/CD274 across multiple cancers were taken forward for drug repurposing via DRUGSURV and microRNA analysis using miRDB and miRabel. MicroRNAs were also screened for clinical relevance using OncomiR. Forty genes were consistently correlated with PDCD1/CD274 across multiple cancers, with the cancers themselves exhibiting a differential role for the correlated genes in terms of patient survival. Esophageal and renal cancers in particular stood out in this regard as having a unique survival profile. Forty-nine putative microRNAs were identified as being linked to the PDCD1/CD274 network, which were taken forward and further assessed for clinical relevance using OncomiR and previously published literature. One hundred and thirty significant survival associations for 46 microRNAs across fourteen groups of cancers were identified. Finally, a total of 23 putative repurposed drugs targeting multiple components of the PDCD1/CD274 network were identified, which may represent immunotherapeutic adjuvants. Taken together, these results shed light on the varying PDCD1/CD274 networks between individual cancers and signpost a need for more cancer-specific investigations and treatments.

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Survey of miRNA-miRNA cooperative regulation principles across cancer types

Briefings in Bioinformatics ◽

10.1093/bib/bby038 ◽

2018 ◽

Vol 20 (5) ◽

pp. 1621-1638 ◽

Cited By ~ 5

Author(s):

Tingting Shao ◽

Guangjuan Wang ◽

Hong Chen ◽

Yunjin Xie ◽

Xiyun Jin ◽

...

Keyword(s):

Posttranscriptional Regulation ◽

Drug Targets ◽

System Level ◽

Modular Architecture ◽

Scale Free ◽

Cancer Types ◽

The Common ◽

Cancer Network ◽

Pan Cancer ◽

Regulatory Landscape

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.

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Comprehensive Pan-Cancer Analysis and the Regulatory Mechanism of ASF1B, a Gene Associated With Thyroid Cancer Prognosis in the Tumor Micro-Environment

Frontiers in Oncology ◽

10.3389/fonc.2021.711756 ◽

2021 ◽

Vol 11 ◽

Author(s):

Jing Ma ◽

Wei Han ◽

Kai Lu

Keyword(s):

Gene Expression ◽

Survival Analysis ◽

Thyroid Cancer ◽

Prognostic Marker ◽

Enrichment Analysis ◽

Differential Analysis ◽

Expression Levels ◽

Multiple Cancers ◽

Mtorc1 Signaling ◽

Pan Cancer

BackgroundThe incidence of thyroid cancer, whose local recurrence and metastasis lead to death, has always been high and the pathogenesis of papillary thyroid carcinoma (PTC) has not been clearly elucidated. Therefore, the research for more accurate prognosis-related predictive biomarkers is imminent, and a key gene can often be a prognostic marker for multiple tumors.MethodsGene expression profiles of various cancers in the TCGA and GTEx databases were downloaded, and genes significantly associated with the prognosis of THCA were identified by combining differential analysis with survival analysis. Then, a series of bioinformatics tools and methods were used to analyze the expression of the gene in each cancer and the correlation of each expression with prognosis, tumor immune microenvironment, immune neoantigens, immune checkpoints, DNA repair genes, and methyltransferases respectively. The possible biological mechanisms were also investigated by GSEA enrichment analysis.Results656 differentially expressed genes were identified from two datasets and 960 DEGs that were associated with disease-free survival in THCA patients were screened via survival analysis. The former and the latter were crossed to obtain 7 key genes, and the gene with the highest risk factor, ASF1B, was selected for this study. Differential analysis of multiple databases showed that ASF1B was commonly and highly expressed in pan-cancer. Survival analysis showed that high ASF1B expression was significantly associated with poor patient prognosis in multiple cancers. In addition, ASF1B expression levels were found to be associated with tumor immune infiltration in THCA, KIRC, LGG, and LIHC, and with tumor microenvironment in BRCA, LUSC, STAD, UCEC, and KIRC. Further analysis of the relationship between ASF1B expression and immune checker gene expression suggested that ASF1B may regulate tumor immune patterns in most tumors by regulating the expression levels of specific immune checker genes. Finally, GSEA enrichment analysis showed that ASF1B high expression was mainly enriched in cell cycle, MTORC1 signaling system, E2F targets, and G2M checkpoints pathways.ConclusionsASF1B may be an independent prognostic marker for predicting the prognosis of THCA patients. The pan-cancer analysis suggested that ASF1B may play an important role in the tumor micro-environment and tumor immunity and it has the potential of serving as a predictive biomarker for multiple cancers.

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Pan-cancer analysis of differential DNA methylation patterns

BMC Medical Genomics ◽

10.1186/s12920-020-00780-3 ◽

2020 ◽

Vol 13 (S10) ◽

Author(s):

Mai Shi ◽

Stephen Kwok-Wing Tsui ◽

Hao Wu ◽

Yingying Wei

Keyword(s):

Dna Methylation ◽

Statistical Power ◽

Differential Methylation ◽

Cancer Type ◽

Summary Statistics ◽

Multiple Cancer ◽

Cancer Types ◽

The Common ◽

Methylation Patterns ◽

Pan Cancer

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.

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