scholarly journals Identification of tRNA-derived ncRNAs in TCGA and NCI-60 panel cell lines and development of the public database tRFexplorer

Database ◽  
2019 ◽  
Vol 2019 ◽  
Author(s):  
Alessandro La Ferlita ◽  
Salvatore Alaimo ◽  
Dario Veneziano ◽  
Giovanni Nigita ◽  
Veronica Balatti ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Expression Profile ◽  
Molecular Mechanisms ◽  
Gene Expression Regulation ◽  
The Cancer Genome Atlas ◽  
Tumor Type ◽  
Public Database ◽  
The Public ◽  
Cancer Genome Atlas ◽  
First Time

Abstract Next-generation sequencing is increasing our understanding and knowledge of non-coding RNAs (ncRNAs), elucidating their roles in molecular mechanisms and processes such as cell growth and development. Within such a class, tRNA-derived ncRNAs have been recently associated with gene expression regulation in cancer progression. In this paper, we characterize, for the first time, tRNA-derived ncRNAs in NCI-60. Furthermore, we assess their expression profile in The Cancer Genome Atlas (TCGA). Our comprehensive analysis allowed us to report 322 distinct tRNA-derived ncRNAs in NCI-60, categorized in tRNA-derived fragments (11 tRF-5s, 55 tRF-3s), tRNA-derived small RNAs (107 tsRNAs) and tRNA 5′ leader RNAs (149 sequences identified). In TCGA, we were able to identify 232 distinct tRNA-derived ncRNAs categorized in 53 tRF-5s, 58 tRF-3s, 63 tsRNAs and 58 5′ leader RNAs. This latter group represents an additional evidence of tRNA-derived ncRNAs originating from the 5′ leader region of precursor tRNA. We developed a public database, tRFexplorer, which provides users with the expression profile of each tRNA-derived ncRNAs in every cell line in NCI-60 as well as for each TCGA tumor type. Moreover, the system allows us to perform differential expression analyses of such fragments in TCGA, as well as correlation analyses of tRNA-derived ncRNAs expression in TCGA and NCI-60 with gene and miRNA expression in TCGA samples, in association with all omics and compound activities data available on CellMiner. Hence, the tool provides an important opportunity to investigate their potential biological roles in absence of any direct experimental evidence. Database URL: https://trfexplorer.cloud/

scholarly journals HNRNPH1-stabilized LINC00662 promotes ovarian cancer progression by activating the GRP78/p38 pathway

Oncogene ◽  
2021 ◽  
Author(s):  
Yong Wu ◽  
Qinhao Guo ◽  
Xingzhu Ju ◽  
Zhixiang Hu ◽  
Lingfang Xia ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Progression ◽  
Prognostic Indicator ◽  
Mapk Signaling ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Proliferation And Metastasis ◽  
Nuclear Ribonucleoprotein

AbstractNumerous studies suggest an important role for copy number alterations (CNAs) in cancer progression. However, CNAs of long intergenic noncoding RNAs (lincRNAs) in ovarian cancer (OC) and their potential functions have not been fully investigated. Here, based on analysis of The Cancer Genome Atlas (TCGA) database, we identified in this study an oncogenic lincRNA termed LINC00662 that exhibited a significant correlation between its CNA and its increased expression. LINC00662 overexpression is highly associated with malignant features in OC patients and is a prognostic indicator. LINC00662 significantly promotes OC cell proliferation and metastasis in vitro and in vivo. Mechanistically, LINC00662 is stabilized by heterogeneous nuclear ribonucleoprotein H1 (HNRNPH1). Moreover, LINC00662 exerts oncogenic effects by interacting with glucose-regulated protein 78 (GRP78) and preventing its ubiquitination in OC cells, leading to activation of the oncogenic p38 MAPK signaling pathway. Taken together, our results define an oncogenic role for LINC00662 in OC progression mediated via GRP78/p38 signaling, with potential implications regarding therapeutic targets for OC.

scholarly journals SEMA6D Expression and Patient Survival in Breast Invasive Carcinoma

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Dongquan Chen ◽  
Yufeng Li ◽  
Lizhong Wang ◽  
Kai Jiao
Keyword(s):  
Breast Cancer ◽  
Expression Profile ◽  
Heart Development ◽  
Invasive Carcinoma ◽  
Patient Survival ◽  
The Cancer Genome Atlas ◽  
Breast Cancer Patients ◽  
Genomic Expression ◽  
Cancer Genome Atlas ◽  
Public Datasets

Breast cancer (BC) is the second most common cancer diagnosed in American women and is also the second leading cause of cancer death in women. Research has focused heavily on BC metastasis. Multiple signaling pathways have been implicated in regulating BC metastasis. Our knowledge of regulation of BC metastasis is, however, far from complete. Identification of new factors during metastasis is an essential step towards future therapy. Our labs have focused on Semaphorin 6D (SEMA6D), which was implicated in immune responses, heart development, and neurogenesis. It will be interesting to know SEMA6D-related genomic expression profile and its implications in clinical outcome. In this study, we examined the public datasets of breast invasive carcinoma from The Cancer Genome Atlas (TCGA). We analyzed the expression of SEMA6D along with its related genes, their functions, pathways, and potential as copredictors for BC patients’ survival. We found 6-gene expression profile that can be used as such predictors. Our study provides evidences for the first time that breast invasive carcinoma may contain a subtype based on SEMA6D expression. The expression of SEMA6D gene may play an important role in promoting patient survival, especially among triple negative breast cancer patients.

Identification of Aneuploid Circulating Tumor Cells in Soft-Tissue Sarcoma Patients: A Pilot Study

Oncology ◽  
2020 ◽  
Vol 98 (12) ◽  
pp. 893-896
Author(s):  
Andrea Napolitano ◽  
Alessandro Minelli ◽  
Daniele Santini ◽  
Giuseppe Tonini ◽  
Bruno Vincenzi
Keyword(s):  
Overall Survival ◽  
Pilot Study ◽  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Tumor Cells ◽  
In Silico ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
First Time ◽  
Genome Atlas

<b><i>Background:</i></b> Circulating tumor cells (CTCs) have been identified and shown to have prognostic and predictive roles in several types of carcinoma. More recently, aneuploid CTCs have become subject of a growing interest, as aneuploidy is considered a hallmark of cancer often associated with poor prognosis. Here, we aimed to identify for the first time aneuploid CTCs in soft-tissue sarcoma (STS) patients and show supportive in silico evidence on the prognostic role of aneuploidy in mesenchymal cancers. <b><i>Methods:</i></b> In our pilot study, we collected blood from 4 metastatic STS patients and 4 age- and sex-matched healthy controls. After sample processing, cells were cyto-centrifuged onto glass slides and FISH was performed using 5 probes. The in silico analysis was performed using data from The Cancer Genome Atlas cohort of STS patients, using the validated Aneuploidy Score. We divided the patients in two populations (aneuploidy-high, Ane-Hi, and aneuploidy-low, Ane-Lo) using the median value of the Aneuploidy Score as a cutoff. Kaplan-Meier curves associated with log-rank test were used to compare progression-free and overall survival between groups. GraphPad Prism 8.0 (La Jolla, CA, USA) was used for statistical analyses. <b><i>Results:</i></b> Aneuploid CTCs were identified in all 4 STS patients and in none of the controls, with a median value of 4 (range 3–6) per 7 mL of blood. Ane-Hi patients showed a significantly worse progression-free and overall survival compared to Ane-Lo patients. The same trend was maintained when analyzing the data based on the different histologies. <b><i>Conclusions:</i></b> We identified for the first time aneuploid CTCs in STS patients using fluorescence in situ hybridization in a surface marker-independent way. We also showed that the Aneuploidy Score has a prognostic value both in terms of progression-free survival and overall survival in STS patients using The Cancer Genome Atlas data, regardless of the histology.

scholarly journals Pan-Cancer Analysis of the Genomic Alterations and Mutations of the Matrisome

Cancers ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2046 ◽  
Author(s):  
Valerio Izzi ◽  
Martin N. Davis ◽  
Alexandra Naba
Keyword(s):  
Cancer Progression ◽  
Protein Function ◽  
The Cancer Genome Atlas ◽  
Copy Number Alterations ◽  
Cellular Functions ◽  
Genomic Alterations ◽  
Genes Encoding ◽  
Cancer Genome Atlas ◽  
Biomechanical Changes ◽  
Pan Cancer

The extracellular matrix (ECM) is a master regulator of all cellular functions and a major component of the tumor microenvironment. We previously defined the “matrisome” as the ensemble of genes encoding ECM proteins and proteins modulating ECM structure or function. While compositional and biomechanical changes in the ECM regulate cancer progression, no study has investigated the genomic alterations of matrisome genes in cancers and their consequences. Here, mining The Cancer Genome Atlas (TCGA) data, we found that copy number alterations and mutations are frequent in matrisome genes, even more so than in the rest of the genome. We also found that these alterations are predicted to significantly impact gene expression and protein function. Moreover, we identified matrisome genes whose mutational burden is an independent predictor of survival. We propose that studying genomic alterations of matrisome genes will further our understanding of the roles of this compartment in cancer progression and will lead to the development of innovative therapeutic strategies targeting the ECM.

scholarly journals The Gasdermin E gene Potential as a Pan-Cancer Biomarker, While Discriminating between Different Tumor Types

Cancers ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1810 ◽  
Author(s):  
Joe Ibrahim ◽  
Ken Op de Beeck ◽  
Erik Fransen ◽  
Marc Peeters ◽  
Guy Van Camp
Keyword(s):  
The Cancer Genome Atlas ◽  
Tumor Type ◽  
E Gene ◽  
Normal Tissues ◽  
Incidence And Mortality ◽  
Cancer Genome Atlas ◽  
Cancer Setting ◽  
Tumor Types ◽  
Methylation Patterns ◽  
Pan Cancer

Due to the elevated rates of incidence and mortality of cancer, early and accurate detection is crucial for achieving optimal treatment. Molecular biomarkers remain important screening and detection tools, especially in light of novel blood-based assays. DNA methylation in cancer has been linked to tumorigenesis, but its value as a biomarker has not been fully explored. In this study, we have investigated the methylation patterns of the Gasdermin E gene across 14 different tumor types using The Cancer Genome Atlas (TCGA) methylation data (N = 6502). We were able to identify six CpG sites that could effectively distinguish tumors from normal samples in a pan-cancer setting (AUC = 0.86). This combination of pan-cancer biomarkers was validated in six independent datasets (AUC = 0.84–0.97). Moreover, we tested 74,613 different combinations of six CpG probes, where we identified tumor-specific signatures that could differentiate one tumor type versus all the others (AUC = 0.79–0.98). In all, methylation patterns exhibited great variation between cancer and normal tissues, but were also tumor specific. Our analyses highlight that a Gasdermin E methylation biomarker assay, not only has the potential for being a methylation-specific pan-cancer detection marker, but it also possesses the capacity to discriminate between different types of tumors.

scholarly journals Silencing of lncRNA MIR497HG via CRISPR/Cas13d Induces Bladder Cancer Progression Through Promoting the Crosstalk Between Hippo/Yap and TGF-β/Smad Signaling

2020 ◽  
Vol 7 ◽  
Author(s):  
Changshui Zhuang ◽  
Ying Liu ◽  
Shengqiang Fu ◽  
Chaobo Yuan ◽  
Jingwen Luo ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Cell Line ◽  
Cancer Progression ◽  
Molecular Mechanisms ◽  
Transforming Growth Factor ◽  
Transforming Growth Factor Β ◽  
Pathological Process ◽  
The Cancer Genome Atlas ◽  
Migration And Invasion

A subset of long non-coding RNAs (lncRNAs), categorized as miRNA-host gene lncRNAs (lnc-miRHGs), is processed to produce miRNAs and involved in cancer progression. This work aimed to investigate the influences and the molecular mechanisms of lnc-miRHGs MIR497HG in bladder cancer (BCa). The miR-497 and miR-195 were derived from MIR497HG. We identified that lnc-miRHG MIR497HG and two harbored miRNAs, miR-497 and miR-195, were downregulated in BCa by analyzing The Cancer Genome Atlas and our dataset. Silencing of MIR497HG by CRISPR/Cas13d in BCa cell line 5637 promoted cell growth, migration, and invasion in vitro. Conversely, overexpression of MIR497HG suppressed cell progression in BCa cell line T24. MiR-497/miR-195 mimics rescued significantly the oncogenic roles of knockdown of MIR497HG by CRISPR/Cas13d in BCa. Mechanistically, miR-497 and miR-195 co-ordinately suppressed multiple key components in Hippo/Yap and transforming growth factor β signaling and particularly attenuated the interaction between Yap and Smad3. In addition, E2F4 was proven to be critical for silencing MIR497HG transcription in BCa cells. In short, we propose for the first time to reveal the function and mechanisms of MIR497HG in BCa. Blocking the pathological process may be a potential strategy for the treatment of BCa.

scholarly journals CAPRI: Efficient Inference of Cancer Progression Models from Cross-sectional Data

2014 ◽  
Author(s):  
Daniele Ramazzotti ◽  
Giulio Caravagna ◽  
Loes Olde Loohuis ◽  
Alex Graudenzi ◽  
Ilya Korsunsky ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Directed Acyclic Graphs ◽  
The Cancer Genome Atlas ◽  
Scoring Method ◽  
Cross Sectional ◽  
Progression Model ◽  
Acyclic Graphs ◽  
Cancer Genome Atlas ◽  
Atypical Chronic Myeloid Leukemia ◽  
Insight Into

We devise a novel inference algorithm to effectively solve the cancer progression model reconstruction problem. Our empirical analysis of the accuracy and convergence rate of our algorithm, CAncer PRogression Inference (CAPRI), shows that it outperforms the state-of-the-art algorithms addressing similar problems. Motivation: Several cancer-related genomic data have become available (e.g., The Cancer Genome Atlas, TCGA) typically involving hundreds of patients. At present, most of these data are aggregated in a cross-sectional fashion providing all measurements at the time of diagnosis. Our goal is to infer cancer ?progression? models from such data. These models are represented as directed acyclic graphs (DAGs) of collections of ?selectivity? relations, where a mutation in a gene A ?selects? for a later mutation in a gene B. Gaining insight into the structure of such progressions has the potential to improve both the stratification of patients and personalized therapy choices. Results: The CAPRI algorithm relies on a scoring method based on a probabilistic theory developed by Suppes, coupled with bootstrap and maximum likelihood inference. The resulting algorithm is efficient, achieves high accuracy, and has good complexity, also, in terms of convergence properties. CAPRI performs especially well in the presence of noise in the data, and with limited sample sizes. Moreover CAPRI, in contrast to other approaches, robustly reconstructs different types of confluent trajectories despite irregularities in the data. We also report on an ongoing investigation using CAPRI to study atypical Chronic Myeloid Leukemia, in which we uncovered non trivial selectivity relations and exclusivity patterns among key genomic events.

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

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.

Sign in / Sign up

Export Citation Format

Share Document