scholarly journals Pan-cancer analysis of m5C regulator genes reveals consistent epigenetic landscape changes in multiple cancers

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Yuting He ◽  
Xiao Yu ◽  
Menggang Zhang ◽  
Wenzhi Guo
Keyword(s):  
Copy Number Variations ◽  
Regulatory Genes ◽  
The Cancer Genome Atlas ◽  
Cancer Pathogenesis ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Reversible Modification ◽  
Regulator Genes ◽  
Modified Nucleobases ◽  
Pan Cancer

Abstract Background 5-Methylcytosine (m5C) is a reversible modification to both DNA and various cellular RNAs. However, its roles in developing human cancers are poorly understood, including the effects of mutant m5C regulators and the outcomes of modified nucleobases in RNAs. Methods Based on The Cancer Genome Atlas (TCGA) database, we uncovered that mutations and copy number variations (CNVs) of m5C regulatory genes were significantly correlated across many cancer types. We then assessed the correlation between the expression of individual m5C regulators and the activity of related hallmark pathways of cancers. Results After validating m5C regulators’ expression based on their contributions to cancer development and progression, we observed their upregulation within tumor-specific processes. Notably, our research connected aberrant alterations to m5C regulatory genes with poor clinical outcomes among various tumors that may drive cancer pathogenesis and/or survival. Conclusion Our results offered strong evidence and clinical implications for the involvement of m5C regulators.

scholarly journals Pan-cancer analysis of m5C regulator genes reveals consistent epigenetic landscape changes in multiple cancers

2021 ◽  
Author(s):  
Yuting He ◽  
Xiao Yu ◽  
Menggang Zhang ◽  
Wenzhi Guo
Keyword(s):  
Copy Number Variations ◽  
Regulatory Genes ◽  
The Cancer Genome Atlas ◽  
Cancer Pathogenesis ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Reversible Modification ◽  
Regulator Genes ◽  
Modified Nucleobases ◽  
Pan Cancer

Abstract Backgroud5-methylcytosine (m5C) is a reversible modification to both DNA and various cellular RNAs. However, its roles in developing human cancers are poorly understood, including the effects of mutant m5C regulators and the outcomes of modified nucleobases in RNAs. Methods Based on The Cancer Genome Atlas (TCGA) database, we uncovered that mutations and copy number variations (CNVs) of m5C regulatory genes were significantly correlated across 33 cancer types. We then assessed the correlation between the expression of individual m5C regulators and the activity of related hallmark pathways of cancers. After validating m5C regulators' expression based on their contributions to cancer development and progression, we observed their up-regulation within tumor-specific processes. Results Notably, our research connected aberrant alterations to m5C regulatory genes with poor clinical outcomes among various tumors that may drive cancer pathogenesis and/or survival. Conclusions Our results offered strong evidence and clinical implications for the involvement of m5C regulators.

scholarly journals A pan-cancer analysis of prognostic genes

PeerJ ◽  
2016 ◽  
Vol 3 ◽  
pp. e1499 ◽  
Author(s):  
Jordan Anaya ◽  
Brian Reon ◽  
Wei-Min Chen ◽  
Stefan Bekiranov ◽  
Anindya Dutta
Keyword(s):  
Microarray Data ◽  
The Cancer Genome Atlas ◽  
Rna Seq ◽  
Cancer Pathogenesis ◽  
Gene Sets ◽  
Protective Genes ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Measure Of Association ◽  
Pan Cancer

Numerous studies have identified prognostic genes in individual cancers, but a thorough pan-cancer analysis has not been performed. In addition, previous studies have mostly used microarray data instead of RNA-SEQ, and have not published comprehensive lists of associations with survival. Using recently available RNA-SEQ and clinical data from The Cancer Genome Atlas for 6,495 patients, we have investigated every annotated and expressed gene’s association with survival across 16 cancer types. The most statistically significant harmful and protective genes were not shared across cancers, but were enriched in distinct gene sets which were shared across certain groups of cancers. These groups of cancers were independently recapitulated by both unsupervised clustering of Cox coefficients (a measure of association with survival) for individual genes, and for gene programs. This analysis has revealed unappreciated commonalities among cancers which may provide insights into cancer pathogenesis and rationales for co-opting treatments between cancers.

scholarly journals A pan-cancer analysis of prognostic genes

2015 ◽  
Author(s):  
Jordan Anaya ◽  
Brian J. Reon ◽  
Wei-Min Chen ◽  
Stefan Bekiranov ◽  
Anindya Dutta
Keyword(s):  
Microarray Data ◽  
The Cancer Genome Atlas ◽  
Rna Seq ◽  
Cancer Pathogenesis ◽  
Gene Sets ◽  
Protective Genes ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Measure Of Association ◽  
Pan Cancer

AbstractNumerous studies have identified prognostic genes in individual cancers, but a thorough pan-cancer analysis has not been performed. In addition, previous studies have mostly used microarray data instead of RNA-SEQ, and have not published comprehensive lists of associations with survival. Using recently available RNA-SEQ and clinical data from the The Cancer Genome Atlas for 6,495 patients, we have investigated every annotated and expressed gene’s association with survival across 16 cancer types. The most statistically significant harmful and protective genes were not shared across cancers, but were enriched in distinct gene sets which were shared across certain groups of cancers. These groups of cancers were independently reconstructed by unsupervised clustering of Cox coefficients (a measure of association with survival) for individual genes or for gene programs. This analysis has revealed unappreciated commonalities among cancers which may provide insights into cancer pathogenesis and rationales for co-opting treatments between cancers.

scholarly journals Integrated Dissection of lncRNA-Perturbated Triplets Reveals Novel Prognostic Signatures Across Cancer Types

2020 ◽  
Vol 21 (17) ◽  
pp. 6087
Author(s):  
Yunzhen Wei ◽  
Limeng Zhou ◽  
Yingzhang Huang ◽  
Dianjing Guo
Keyword(s):  
Cancer Biology ◽  
Noncoding Rna ◽  
The Cancer Genome Atlas ◽  
Dynamic Changes ◽  
Computational Framework ◽  
Potential Biomarker ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Tcga Dataset ◽  
Pan Cancer

Long noncoding RNA (lncRNA)/microRNA(miRNA)/mRNA triplets contribute to cancer biology. However, identifying significative triplets remains a major challenge for cancer research. The dynamic changes among factors of the triplets have been less understood. Here, by integrating target information and expression datasets, we proposed a novel computational framework to identify the triplets termed as “lncRNA-perturbated triplets”. We applied the framework to five cancer datasets in The Cancer Genome Atlas (TCGA) project and identified 109 triplets. We showed that the paired miRNAs and mRNAs were widely perturbated by lncRNAs in different cancer types. LncRNA perturbators and lncRNA-perturbated mRNAs showed significantly higher evolutionary conservation than other lncRNAs and mRNAs. Importantly, the lncRNA-perturbated triplets exhibited high cancer specificity. The pan-cancer perturbator OIP5-AS1 had higher expression level than that of the cancer-specific perturbators. These lncRNA perturbators were significantly enriched in known cancer-related pathways. Furthermore, among the 25 lncRNA in the 109 triplets, lncRNA SNHG7 was identified as a stable potential biomarker in lung adenocarcinoma (LUAD) by combining the TCGA dataset and two independent GEO datasets. Results from cell transfection also indicated that overexpression of lncRNA SNHG7 and TUG1 enhanced the expression of the corresponding mRNA PNMA2 and CDC7 in LUAD. Our study provides a systematic dissection of lncRNA-perturbated triplets and facilitates our understanding of the molecular roles of lncRNAs in cancers.

scholarly journals Pan-cancer driver copy number alterations identified by joint expression/CNA data analysis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Gaojianyong Wang ◽  
Dimitris Anastassiou
Keyword(s):  
Gene Expression ◽  
Copy Number ◽  
The Cancer Genome Atlas ◽  
Copy Number Alterations ◽  
Multiple Cancer ◽  
Cancer Driver ◽  
Large Gene ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Pan Cancer

Abstract Analysis of large gene expression datasets from biopsies of cancer patients can identify co-expression signatures representing particular biomolecular events in cancer. Some of these signatures involve genomically co-localized genes resulting from the presence of copy number alterations (CNAs), for which analysis of the expression of the underlying genes provides valuable information about their combined role as oncogenes or tumor suppressor genes. Here we focus on the discovery and interpretation of such signatures that are present in multiple cancer types due to driver amplifications and deletions in particular regions of the genome after doing a comprehensive analysis combining both gene expression and CNA data from The Cancer Genome Atlas.

scholarly journals Clinical and Prognostic Significance of TZAP Expression in Cervical Cancer

Medicina ◽  
2020 ◽  
Vol 56 (5) ◽  
pp. 207
Author(s):  
Won-Jin Park ◽  
Jae-Hee Park ◽  
Ho-Yong Shin ◽  
Jae-Ho Lee
Keyword(s):  
Cervical Cancer ◽  
Survival Analysis ◽  
Prognostic Value ◽  
Prognostic Significance ◽  
Telomeric Repeat ◽  
The Cancer Genome Atlas ◽  
Genetic Changes ◽  
Cancer Pathogenesis ◽  
Cancer Genome Atlas ◽  
Cancer Types

Background and Objectives: Telomeric zinc finger-associated protein (TZAP) is a telomere-associated factor that was previously called ZBTB48. This protein binds preferentially to long telomeres, competing with telomeric repeat factors 1 and 2. Genetic changes in TZAP may be associated with cancer pathogenesis; however, this relationship has not yet been elucidated for any type of cancer. In this study, we aimed to examine the clinicopathologic and prognostic value of TZAP expression in cervical cancer (CC). Materials and Methods: The data were extracted from The Cancer Genome Atlas cohorts by OncoLnc (21 cancer types, 7700 cancers). The prognostic value of TZAP for different stages of 264 CCs was examined using survival analysis. Results: The TZAP expression did not differ significantly between CC and normal matched tissues. Age, cancer stage, and viral infection were not associated with TZAP expression. Survival analysis revealed a shorter overall survival in CC patients with a lower TZAP expression (χ2 = 3.62, p = 0.057). The prognostic value of TZAP expression was greater in patients with N1 stage CC (χ2 = 5.64, p = 0.018). Conclusion: TZAP expression is a possible prognostic marker for CC, especially stage N1 CC.

scholarly journals Pan-cancer analysis of non-coding recurrent mutations and their possible involvement in cancer pathogenesis

NAR Cancer ◽  
2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Chie Kikutake ◽  
Minako Yoshihara ◽  
Mikita Suyama
Keyword(s):  
The Cancer Genome Atlas ◽  
Whole Genome Sequencing Data ◽  
Sequencing Data ◽  
Huge Number ◽  
Protein Coding ◽  
Coding Regions ◽  
Cancer Pathogenesis ◽  
Recurrent Mutations ◽  
Cancer Genome Atlas ◽  
Pan Cancer

Abstract Cancer-related mutations have been mainly identified in protein-coding regions. Recent studies have demonstrated that mutations in non-coding regions of the genome could also be a risk factor for cancer. However, the non-coding regions comprise 98% of the total length of the human genome and contain a huge number of mutations, making it difficult to interpret their impacts on pathogenesis of cancer. To comprehensively identify cancer-related non-coding mutations, we focused on recurrent mutations in non-coding regions using somatic mutation data from COSMIC and whole-genome sequencing data from The Cancer Genome Atlas (TCGA). We identified 21 574 recurrent mutations in non-coding regions that were shared by at least two different samples from both COSMIC and TCGA databases. Among them, 580 candidate cancer-related non-coding recurrent mutations were identified based on epigenomic and chromatin structure datasets. One of such mutation was located in RREB1 binding site that is thought to interact with TEAD1 promoter. Our results suggest that mutations may disrupt the binding of RREB1 to the candidate enhancer region and increase TEAD1 expression levels. Our findings demonstrate that non-coding recurrent mutations and coding mutations may contribute to the pathogenesis of cancer.

scholarly journals Global identification and characterization of tRNA-derived RNA fragment landscapes across human cancers

NAR Cancer ◽  
2020 ◽  
Vol 2 (4) ◽  
Author(s):  
Xiwei Sun ◽  
Juze Yang ◽  
Mengqian Yu ◽  
Dongxia Yao ◽  
Liyuan Zhou ◽  
...  
Keyword(s):  
The Cancer Genome Atlas ◽  
Cancer Driver ◽  
Cancer Pathogenesis ◽  
Genomic Landscape ◽  
High Sequence Conservation ◽  
Rna Fragments ◽  
Cancer Genome Atlas ◽  
Cancer Types

Abstract Transfer RNA-derived RNA fragments (tRFs) are a class of small non-coding RNAs that are abundant in many organisms, but their role in cancer has not been fully explored. Here, we report a functional genomic landscape of tRFs in 8118 specimens across 15 cancer types from The Cancer Genome Atlas. These tRFs exhibited characteristics of widespread expression, high sequence conservation, cytoplasmic localization, specific patterns of tRNA cleavage and conserved cleavage in tissues. A cross-tumor analysis revealed significant commonality among tRF expression subtypes from distinct tissues of origins, characterized by upregulation of a group of tRFs with similar size and activation of cancer-associated signaling. One of the largest superclusters was composed of 22 nt 3′-tRFs upregulated in 13 cancer types, all of which share the activation of Ras/MAPK, RTK and TSC/mTOR signaling. tRF-based subgrouping provided clinically relevant stratifications and significantly improved outcome prediction by incorporating clinical variables. Additionally, we discovered 11 cancer driver tRFs using an effective approach for accurately exploring cross-tumor and platform trends. As a proof of concept, we performed comprehensive functional assays on a non-microRNA driver tRF, 5′-IleAAT-8-1-L20, and validated its oncogenic roles in lung cancer in vitro and in vivo. Our study also provides a valuable tRF resource for identifying diagnostic and prognostic biomarkers, developing cancer therapy and studying cancer pathogenesis.

scholarly journals A novel similarity score based on gene ranks to reveal genetic relationships among diseases

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10576
Author(s):  
Dongmei Luo ◽  
Chengdong Zhang ◽  
Liwan Fu ◽  
Yuening Zhang ◽  
Yue-Qing Hu
Keyword(s):  
Genetic Relationships ◽  
Similarity Measures ◽  
Similarity Score ◽  
The Cancer Genome Atlas ◽  
Genetic Mechanisms ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Pan Cancer ◽  
Cancer Studies

Knowledge of similarities among diseases can contribute to uncovering common genetic mechanisms. Based on ranked gene lists, a couple of similarity measures were proposed in the literature. Notice that they may suffer from the determination of cutoff or heavy computational load, we propose a novel similarity score SimSIP among diseases based on gene ranks. Simulation studies under various scenarios demonstrate that SimSIP has better performance than existing rank-based similarity measures. Application of SimSIP in gene expression data of 18 cancer types from The Cancer Genome Atlas shows that SimSIP is superior in clarifying the genetic relationships among diseases and demonstrates the tendency to cluster the histologically or anatomically related cancers together, which is analogous to the pan-cancer studies. Moreover, SimSIP with simpler form and faster computation is more robust for higher levels of noise than existing methods and provides a basis for future studies on genetic relationships among diseases. In addition, a measure MAG is developed to gauge the magnitude of association of anindividual gene with diseases. By using MAG the genes and biological processes significantly associated with colorectal cancer are detected.

scholarly journals Chromoanagenesis Landscape in 10,000 TCGA Patients

Cancers ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 4197
Author(s):  
Roni Rasnic ◽  
Michal Linial
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Learning Algorithm ◽  
The Cancer Genome Atlas ◽  
Cancer Type ◽  
Whole Genome ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Tumor Biopsies ◽  
Pan Cancer

During the past decade, whole-genome sequencing of tumor biopsies and individuals with congenital disorders highlighted the phenomenon of chromoanagenesis, a single chaotic event of chromosomal rearrangement. Chromoanagenesis was shown to be frequent in many types of cancers, to occur in early stages of cancer development, and significantly impact the tumor’s nature. However, an in-depth, cancer-type dependent analysis has been somewhat incomplete due to the shortage in whole genome sequencing of cancerous samples. In this study, we extracted data from The Pan-Cancer Analysis of Whole Genome (PCAWG) and The Cancer Genome Atlas (TCGA) to construct and test a machine learning algorithm that can detect chromoanagenesis with high accuracy (86%). The algorithm was applied to ~10,000 unlabeled TCGA cancer patients. We utilize the chromoanagenesis assignment results, to analyze cancer-type specific chromoanagenesis characteristics in 20 TCGA cancer types. Our results unveil prominent genes affected in either chromoanagenesis or non-chromoanagenesis tumorigenesis. The analysis reveals a mutual exclusivity relationship between the genes impaired in chromoanagenesis versus non-chromoanagenesis cases. We offer the discovered characteristics as possible targets for cancer diagnostic and therapeutic purposes.

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