Screening and Comprehensive Analysis of Cancer-Associated tRNA-Derived Fragments

tRNA-derived fragments (tRFs) constitute a novel class of small non-coding RNA cleaved from tRNAs. In recent years, researches have shown the regulatory roles of a few tRFs in cancers, illuminating a new direction for tRF-centric cancer researches. Nonetheless, more specific screening of tRFs related to oncogenesis pathways, cancer progression stages and cancer prognosis is continuously demanded to reveal the landscape of the cancer-associated tRFs. In this work, by combining the clinical information recorded in The Cancer Genome Atlas (TCGA) and the tRF expression profiles curated by MINTbase v2.0, we systematically screened 1,516 cancer-associated tRFs (ca-tRFs) across seven cancer types. The ca-tRF set collectively combined the differentially expressed tRFs between cancer samples and control samples, the tRFs significantly correlated with tumor stage and the tRFs significantly correlated with patient survival. By incorporating our previous tRF-target dataset, we found the ca-tRFs tend to target cancer-associated genes and onco-pathways like ATF6-mediated unfolded protein response, angiogenesis, cell cycle process regulation, focal adhesion, PI3K-Akt signaling pathway, cellular senescence and FoxO signaling pathway across multiple cancer types. And cell composition analysis implies that the expressions of ca-tRFs are more likely to be correlated with T-cell infiltration. We also found the ca-tRF expression pattern is informative to prognosis, suggesting plausible tRF-based cancer subtypes. Together, our systematic analysis demonstrates the potentially extensive involvements of tRFs in cancers, and provides a reasonable list of cancer-associated tRFs for further investigations.

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An R Implementation of Tumor-Stroma-Immune Transcriptome Deconvolution Pipeline using DeMixT

10.1101/566075 ◽

2019 ◽

Author(s):

Shaolong Cao ◽

Zeya Wang ◽

Fan Gao ◽

Jingxiao Chen ◽

Feng Zhang ◽

...

Keyword(s):

Cancer Progression ◽

Expression Profiles ◽

Progression Free Survival ◽

Tumor Stroma ◽

R Package ◽

The Cancer Genome Atlas ◽

Biological Information ◽

Computationally Efficient ◽

Multiple Cancer ◽

Cancer Genome Atlas

AbstractThe deconvolution of transcriptomic data from heterogeneous tissues in cancer studies remains challenging. Available software faces difficulties for accurately estimating both component-specific proportions and expression profiles for individual samples. To address these challenges, we present a new R-implementation pipeline for the more accurate and efficient transcriptome deconvolution of high dimensional data from mixtures of more than two components. The pipeline utilizes the computationally efficient DeMixT R-package with OpenMP and additional cancer-specific biological information to perform three-component deconvolution without requiring data from the immune profiles. It enables a wide application of DeMixT to gene expression datasets available from cancer consortium such as the Cancer Genome Atlas (TCGA) projects, where, other than the mixed tumor samples, a handful of normal samples are profiled in multiple cancer types. We have applied this pipeline to two TCGA datasets in colorectal adenocarcinoma (COAD) and prostate adenocarcinoma (PRAD). In COAD, we found varying distributions of immune proportions across the Consensus Molecular Subtypes, from the highest to the lowest being CMS1, CMS3, CMS4 and CMS2. In PRAD, we found the immune proportions are associated with progression-free survival (p<0.01) and negatively correlated with Gleason scores (p<0.001). Our DeMixT-centered analysis protocol opens up new opportunities to investigate the tumor-stroma-immune microenvironment, by providing both proportions and component-specific expressions, and thus better define the underlying biology of cancer progression.Availability and implementation: An R package, scripts and data are available: https://github.com/wwylab/DeMixTallmaterials.

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Integrative Analysis of Cancer Omics Data for Prognosis Modeling

Genes ◽

10.3390/genes10080604 ◽

2019 ◽

Vol 10 (8) ◽

pp. 604 ◽

Cited By ~ 2

Author(s):

Wang ◽

Wu ◽

Keyword(s):

Integrative Analysis ◽

The Cancer Genome Atlas ◽

Cancer Prognosis ◽

Sufficient Information ◽

Cancer Type ◽

Multiple Cancer ◽

Cancer Genome Atlas ◽

Cancer Types ◽

Information Borrowing ◽

Penalization Technique

Prognosis modeling plays an important role in cancer studies. With the development of omics profiling, extensive research has been conducted to search for prognostic markers for various cancer types. However, many of the existing studies share a common limitation by only focusing on a single cancer type and suffering from a lack of sufficient information. With potential molecular similarity across cancer types, one cancer type may contain information useful for the analysis of other types. The integration of multiple cancer types may facilitate information borrowing so as to more comprehensively and more accurately describe prognosis. In this study, we conduct marginal and joint integrative analysis of multiple cancer types, effectively introducing integration in the discovery process. For accommodating high dimensionality and identifying relevant markers, we adopt the advanced penalization technique which has a solid statistical ground. Gene expression data on nine cancer types from The Cancer Genome Atlas (TCGA) are analyzed, leading to biologically sensible findings that are different from the alternatives. Overall, this study provides a novel venue for cancer prognosis modeling by integrating multiple cancer types.

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Identifying cancer type specific oncogenes and tumor suppressors using limited size data

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720016500311 ◽

2016 ◽

Vol 14 (06) ◽

pp. 1650031 ◽

Cited By ~ 4

Author(s):

Ana B. Pavel ◽

Cristian I. Vasile

Keyword(s):

Tumor Suppressors ◽

Molecular Mechanisms ◽

Lung Squamous Cell Carcinoma ◽

The Cancer Genome Atlas ◽

Driver Mutations ◽

Cancer Type ◽

Multiple Cancer ◽

Driver Genes ◽

Cancer Subtypes ◽

Cancer Types

Cancer is a complex and heterogeneous genetic disease. Different mutations and dysregulated molecular mechanisms alter the pathways that lead to cell proliferation. In this paper, we explore a method which classifies genes into oncogenes (ONGs) and tumor suppressors. We optimize this method to identify specific (ONGs) and tumor suppressors for breast cancer, lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) and colon adenocarcinoma (COAD), using data from the cancer genome atlas (TCGA). A set of genes were previously classified as ONGs and tumor suppressors across multiple cancer types (Science 2013). Each gene was assigned an ONG score and a tumor suppressor score based on the frequency of its driver mutations across all variants from the catalogue of somatic mutations in cancer (COSMIC). We evaluate and optimize this approach within different cancer types from TCGA. We are able to determine known driver genes for each of the four cancer types. After establishing the baseline parameters for each cancer type, we identify new driver genes for each cancer type, and the molecular pathways that are highly affected by them. Our methodology is general and can be applied to different cancer subtypes to identify specific driver genes and improve personalized therapy.

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ncRNA-eQTL: a database to systematically evaluate the effects of SNPs on non-coding RNA expression across cancer types

Nucleic Acids Research ◽

10.1093/nar/gkz711 ◽

2019 ◽

Vol 48 (D1) ◽

pp. D956-D963 ◽

Cited By ~ 11

Author(s):

Jiang Li ◽

Yawen Xue ◽

Muhammad Talal Amin ◽

Yanbo Yang ◽

Jiajun Yang ◽

...

Keyword(s):

Genome Wide Association Study ◽

Expression Profiles ◽

The Cancer Genome Atlas ◽

Eqtl Analysis ◽

Nucleotide Polymorphisms ◽

Survival Times ◽

Multiple Cancer ◽

Risk Alleles ◽

Genome Wide ◽

Cancer Types

Abstract Numerous studies indicate that non-coding RNAs (ncRNAs) have critical functions across biological processes, and single-nucleotide polymorphisms (SNPs) could contribute to diseases or traits through influencing ncRNA expression. However, the associations between SNPs and ncRNA expression are largely unknown. Therefore, genome-wide expression quantitative trait loci (eQTL) analysis to assess the effects of SNPs on ncRNA expression, especially in multiple cancer types, will help to understand how risk alleles contribute toward tumorigenesis and cancer development. Using genotype data and expression profiles of ncRNAs of >8700 samples from The Cancer Genome Atlas (TCGA), we developed a computational pipeline to systematically identify ncRNA-related eQTLs (ncRNA-eQTLs) across 33 cancer types. We identified a total of 6 133 278 and 721 122 eQTL-ncRNA pairs in cis-eQTL and trans-eQTL analyses, respectively. Further survival analyses identified 8312 eQTLs associated with patient survival times. Furthermore, we linked ncRNA-eQTLs to genome-wide association study (GWAS) data and found 262 332 ncRNA-eQTLs overlapping with known disease- and trait-associated loci. Finally, a user-friendly database, ncRNA-eQTL (http://ibi.hzau.edu.cn/ncRNA-eQTL), was developed for free searching, browsing and downloading of all ncRNA-eQTLs. We anticipate that such an integrative and comprehensive resource will improve our understanding of the mechanistic basis of human complex phenotypic variation, especially for ncRNA- and cancer-related studies.

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A Pan-Cancer Study: The Immunological and Prognostic Significance of Aberrant SPP1 Expression on Tumors

10.21203/rs.3.rs-855946/v1 ◽

2021 ◽

Author(s):

Tian peng Huang ◽

Wei Ye ◽

Xue jiao Lin

Keyword(s):

Tumor Immunity ◽

Cancer Progression ◽

Immune Cell ◽

Prognostic Significance ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Multiple Cancer ◽

Multiple Tumor ◽

Cancer Types

Abstract Background Secretory phosphoprotein 1 (SPP1) is a glyco-phosphoprotein that is widely expressed in a variety of cancer cells. Current studies have identified that SPP1 is differentially expressed in a variety of cancer cell species. However, there are few studies on the level of SPP1 expression in different types of cancer and its clinical significance. Methods In this study, we analyzed SPP1 levels and its significance in 33 different cancer types by using The Cancer Genome Atlas (TCGA) database. The study analyzed the correlation between SPP1 expression and tumor immunity. Results The results showed that SPP1 transcript levels were aberrantly expressed in most tumors. Univariate Cox analysis showed that SPP1 was strongly associated with Overall survival in multiple tumor types. We also found that SPP1 was significantly correlated with tumor immune microenvironment, tumor immune cells, and tumor infiltrating lymphocyte markers. The correlation of SPP1 with Tumor mutational load (TMB) and Microsatellite instability (MSI) also predicts its role in assessing the efficacy of immunotherapy. Gene set enrichment analysis of 33 cancer types provided further evidence for the relationship between SPP1 levels and cancer progression and immune cell infiltration. Conclusion our study concludes that SPP1 plays an important role in tumorigenesis and tumor immunity and can be used as a marker for the assessment of clinical indicators in multiple cancer types.

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Identification of Key Differentially Expressed MicroRNAs in Cancer Patients Through Pan-cancer Analysis

10.1101/388892 ◽

2018 ◽

Author(s):

Yu Hu ◽

Hayley Dingerdissen ◽

Samir Gupta ◽

Robel Kahsay ◽

Vijay Shanker ◽

...

Keyword(s):

Differential Expression ◽

Cancer Progression ◽

Expression Profiles ◽

Expression Patterns ◽

Differential Expression Analysis ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Literature Mining ◽

Wide Range ◽

Cancer Types

AbstractA number of microRNAs (miRNAs) functioning in gene silencing have been associated with cancer progression. However, common expression patterns of abnormally expressed miRNAs and their potential roles in multiple cancer types have not yet been evaluated. To minimize the difference of patients, we collected miRNA sequencing data of 575 patients with tumor and adjacent non-tumorous tissues from 14 cancer types from The Cancer Genome Atlas (TCGA), and performed differential expression analysis using DESeq2 and edgeR. The results showed that cancer types can be grouped based on the distribution of miRNAs with different expression patterns. We found 81 significantly differentially expressed miRNAs (SDEmiRNAs) unique to one of the 14 cancers may affect patient survival rate, and 21 key SDEmiRNAs (nine overexpressed and 12 under-expressed) associated with at least eight cancers and enriched in more than 60% of patients per cancer, including four newly identified SDEmiRNAs (hsa-mir-4746, hsa-mir-3648, hsa-mir-3687, and hsa-mir-1269a). The downstream effect of these 21 SDEmiRNAs on cellular functions was evaluated through enrichment and pathway analysis of 7,186 protein-coding gene targets from literature mining with known differential expression profiles in cancers. It enables identification of their functional similarity in cell proliferation control across a wide range of cancers and to build common regulatory networks over cancer-related pathways. This is validated by construction of a regulatory network in PI3K pathway. This study provides evidence of the value of further analysis on SDEmiRNAs as potential biomarkers and therapeutic targets for cancer diagnosis and treatment.

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Iron-Bound Lipocalin-2 from Tumor-Associated Macrophages Drives Breast Cancer Progression Independent of Ferroportin

Metabolites ◽

10.3390/metabo11030180 ◽

2021 ◽

Vol 11 (3) ◽

pp. 180

Author(s):

Christina Mertens ◽

Matthias Schnetz ◽

Claudia Rehwald ◽

Stephan Grein ◽

Eiman Elwakeel ◽

...

Keyword(s):

Breast Cancer ◽

Tumor Progression ◽

Tumor Cells ◽

Cancer Progression ◽

Lung Metastases ◽

Expression Profiles ◽

The Cancer Genome Atlas ◽

Lipocalin 2 ◽

Tumor Associated Macrophages

Macrophages supply iron to the breast tumor microenvironment by enforced secretion of lipocalin-2 (Lcn-2)-bound iron as well as the increased expression of the iron exporter ferroportin (FPN). We aimed at identifying the contribution of each pathway in supplying iron for the growing tumor, thereby fostering tumor progression. Analyzing the expression profiles of Lcn-2 and FPN using the spontaneous polyoma-middle-T oncogene (PyMT) breast cancer model as well as mining publicly available TCGA (The Cancer Genome Atlas) and GEO Series(GSE) datasets from the Gene Expression Omnibus database (GEO), we found no association between tumor parameters and Lcn-2 or FPN. However, stromal/macrophage-expression of Lcn-2 correlated with tumor onset, lung metastases, and recurrence, whereas FPN did not. While the total iron amount in wildtype and Lcn-2−/− PyMT tumors showed no difference, we observed that tumor-associated macrophages from Lcn-2−/− compared to wildtype tumors stored more iron. In contrast, Lcn-2−/− tumor cells accumulated less iron than their wildtype counterparts, translating into a low migratory and proliferative capacity of Lcn-2−/− tumor cells in a 3D tumor spheroid model in vitro. Our data suggest a pivotal role of Lcn-2 in tumor iron-management, affecting tumor growth. This study underscores the role of iron for tumor progression and the need for a better understanding of iron-targeted therapy approaches.

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Pan-Cancer Analysis of PIMREG as a Biomarker for the Prognostic and Immunological Role

Frontiers in Genetics ◽

10.3389/fgene.2021.687778 ◽

2021 ◽

Vol 12 ◽

Author(s):

Hua Zhu ◽

Xinyao Hu ◽

Yingze Ye ◽

Zhihong Jian ◽

Yi Zhong ◽

...

Keyword(s):

Signaling Pathway ◽

Chemokine Receptors ◽

Antigen Processing ◽

Immune Cell ◽

Nk Cell ◽

Cell Lineage ◽

Cell Receptor ◽

The Cancer Genome Atlas ◽

Cancer Types ◽

Pan Cancer

Phosphatidylinositol binding clathrin assembly protein interacting mitotic regulator (PIMREG) localizes to the nucleus and can significantly elevate the nuclear localization of clathrin assembly lymphomedullary leukocythemia gene. Although there is some evidence to support an important action for PIMREG in the occurrence and development of certain cancers, currently no pan-cancer analysis of PIMREG is available. Therefore, we intended to estimate the prognostic predictive value of PIMREG and to explore its potential immune function in 33 cancer types. By using a series of bioinformatics approaches, we extracted and analyzed datasets from Oncomine, The Cancer Genome Atlas, Cancer Cell Lineage Encyclopedia (CCLE) and the Human Protein Atlas (HPA), to explore the underlying carcinogenesis of PIMREG, including relevance of PIMREG to prognosis, microsatellite instability (MSI), tumor mutation burden (TMB), tumor microenvironment (TME) and infiltration of immune cells in various types of cancer. Our findings indicate that PIMREG is highly expressed in at least 24 types of cancer, and is negatively correlated with prognosis in major cancer types. In addition, PIMREG expression was correlated with TMB in 24 cancers and with MSI in 10 cancers. We revealed that PIMREG is co-expressed with genes encoding major histocompatibility complex, immune activation, immune suppression, chemokine and chemokine receptors. We also found that the different roles of PIMREG in the infiltration of different immune cell types in different tumors. PIMREG can potentially influence the etiology or pathogenesis of cancer by acting on immune-related pathways, chemokine signaling pathway, regulation of autophagy, RIG-I like receptor signaling pathway, antigen processing and presentation, FC epsilon RI pathway, complement and coagulation cascades, T cell receptor pathway, NK cell mediated cytotoxicity and other immune-related pathways. Our study suggests that PIMREG can be applied as a prognostic marker in a variety of malignancies because of its role in tumorigenesis and immune infiltration.

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BayCount: A Bayesian Decomposition Method for Inferring Tumor Heterogeneity using RNA-Seq Counts

10.1101/218511 ◽

2017 ◽

Cited By ~ 1

Author(s):

Fangzheng Xie ◽

Mingyuan Zhou ◽

Yanxun Xu

Keyword(s):

Rna Sequencing ◽

Count Data ◽

Decomposition Method ◽

Tumor Heterogeneity ◽

Negative Binomial ◽

Expression Profiles ◽

The Cancer Genome Atlas ◽

Cancer Prognosis ◽

Real World Data ◽

Tumor Sample

AbstractTumors are heterogeneous - a tumor sample usually consists of a set of subclones with distinct transcriptional profiles and potentially different degrees of aggressiveness and responses to drugs. Understanding tumor heterogeneity is therefore critical for precise cancer prognosis and treatment. In this paper, we introduce BayCount, a Bayesian decomposition method to infer tumor heterogeneity with highly over-dispersed RNA sequencing count data. Using negative binomial factor analysis, BayCount takes into account both the between-sample and gene-specific random effects on raw counts of sequencing reads mapped to each gene. For the posterior inference, we develop an efficient compound Poisson based blocked Gibbs sampler. Simulation studies show that BayCount is able to accurately estimate the subclonal inference, including number of subclones, the proportions of these subclones in each tumor sample, and the gene expression profiles in each subclone. For real-world data examples, we apply BayCount to The Cancer Genome Atlas lung cancer and kidney cancer RNA sequencing count data and obtain biologically interpretable results. Our method represents the first effort in characterizing tumor heterogeneity using RNA sequencing count data that simultaneously removes the need of normalizing the counts, achieves statistical robustness, and obtains biologically/clinically meaningful insights. The R package BayCount implementing our model and algorithm is available for download.

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Enolase 1 Correlated With Cancer Progression and Immune-Infiltrating in Multiple Cancer Types: A Pan-Cancer Analysis

Frontiers in Oncology ◽

10.3389/fonc.2020.593706 ◽

2021 ◽

Vol 10 ◽

Author(s):

Wenhua Xu ◽

Wenna Yang ◽

Chunfeng Wu ◽

Xiaocong Ma ◽

Haoyu Li ◽

...

Keyword(s):

Cancer Progression ◽

Stress Protein ◽

Enrichment Analysis ◽

Tumor Stage ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Multiple Cancer ◽

Clinical Prognosis ◽

Multiple Tumor ◽

Normal Tissues

Enolase 1 (ENO1) is an oxidative stress protein expressed in endothelial cells. This study aimed to investigate the correlation of ENO1 with prognosis, tumor stage, and levels of tumor-infiltrating immune cells in multiple cancers. ENO1 expression and its influence on tumor stage and clinical prognosis were analyzed by UCSC Xena browser, Gene Expression Profiling Interactive Analysis (GEPIA), The Cancer Genome Atlas (TCGA), and GTEx Portal. The ENO1 mutation analysis was performed by cBio Portal, and demonstrated ENO1 mutation (1.8%) did not impact on tumor prognosis. The relationship between ENO1 expression and tumor immunity was analyzed by Tumor Immune Estimation Resource (TIMER) and GEPIA. The potential functions of ENO1 in pathways were investigated by Gene Set Enrichment Analysis. ENO1 expression was significantly different in tumor and corresponding normal tissues. ENO1 expression in multiple tumor tissues correlated with prognosis and stage. ENO1 showed correlation with immune infiltrates including B cells, CD8+ and CD4+ T cells, macrophages, neutrophils, and dendritic cells, and tumor purity. ENO1 was proved to be involved in DNA replication, cell cycle, apoptosis, glycolysis process, and other processes. These findings indicate that ENO1 is a potential prognostic biomarker that correlates with cancer progression immune infiltration.

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