OncoScape: Exploring the cancer aberration landscape by genomic data fusion

Schlicker, Andreas; Michaut, Magali; Rahman, Rubayte; Wessels, Lodewyk

doi:10.1038/srep28103

Cited by 10 publications

(8 citation statements)

References 49 publications

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“…As a general role, this ceRNA interaction network suggests that downregulation of FER1L4 should result in the downregulation of RB1 mRNA, implying a positive correlation between their expression levels. Despite downregulation of FER1L4 and upregulation of RB1 that we and some other researchers [20][21][22][23][24][25][26][27]36] observed in colorectal tumors compared with their corresponding non-tumor tissues, a positive correlation between their expressions levels was identi ed when their expressions were analyzed just in tumor or polyp tissues in our study. This observation was consistent with their molecular relationship in the ceRNA network.…”

Section: Discussioncontrasting

confidence: 68%

“…RB1 is one of the rst described tumor suppressor genes in a wide variety of human cancers, but its involvement and function in colorectal cancer is largely controversial. Some researchers reported its downregulation [36,37], but, others observed its upregulation in CRC [20][21][22][23][25][26][27]. Here we also observed signi cant upregulation of RB1 in the colorectal tumor compared with their matched marginal non-tumor tissues.…”

Section: Discussionsupporting

confidence: 65%

“…However, there is little published data about involvement of FER1L4 in colorectal cancer [16]. On the other hand, the retinoblastoma gene, RB1, acts as a tumor suppressor gene in different tumor types, but its expression has been reported to increase in colorectal cancer [20][21][22][23][24][25][26][27]. The lncRNA FER1L4 and RB1 cooperate in a competing endogenous RNAs (ceRNAs) network involving in cancer biology, where, the expression of FER1L4 positively correlates with the RB1 expression.…”

Section: Introductionmentioning

confidence: 99%

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Correlation between expression levels of lncRNA FER1L4 and RB1 in patients with colorectal cancer

2021

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Colorectal cancer is one of the most common cancers in the world. Studies demonstrated that lncRNA FER1L4 was downregulated in different cancers and its expression was positively correlated with RB1 in a competing endogenous RNAs network. We investigated expression levels of FER1L4 lncRNA and RB1 in patients with colorectal cancer. 50 paired colorectal tumor and non-tumor marginal tissues, as well as 30 paired adenomatous colorectal polyps and matched adjacent normal tissues were obtained from the patients. Total RNA was extracted from the samples and cDNAs were synthesized. Their expression were quanti ed by qRT-PCR. Correlation between FER1L4 and RB1 expression levels was analyzed by Pearson correlation test. Finally, ROC curve analysis was used to evaluate their biomarker potency. We observed signi cant downregulation of FER1L4, but upregulation of RB1 in the colorectal tumors compared with non-tumor as well as with the adenomatous colorectal polyp tissues. However, RB1 expression was positively correlated with the FER1L4 expression both in the tumor and polyp samples. ROC curve analysis showed that both FER1L4 and RB1 expression levels could discriminate tumor from non-tumor and tumor from polyp samples. None of clinicopathological characteristics of patients were associated with FER1L4 or RB1 expression levels. Despite downregulation of FER1L4 and upregulation of RB1 in tumors compared with non-tumor tissues, the expression of RB1 was positively correlated with the expression of FER1L4 in the colorectal tumor as well as in the adenomatous colorectal polyp tissues.FER1L4 expression level might be considered as a potential biomarker for colorectal cancer development.

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Section: Discussioncontrasting

confidence: 68%

Section: Discussionsupporting

confidence: 65%

Section: Introductionmentioning

confidence: 99%

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Correlation between expression levels of lncRNA FER1L4 and RB1 in patients with colorectal cancer

2021

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“…GScore has been implemented to consider: (1) genomic feature evidence by mutation consequence, functional impact, and population allele frequency; (2) relevance in cancer estimated by Cancer Gene Census (CGC) of COSMIC v84 [ 30 ], TumorPortal resource [ 31 ], Tamborero et al [ 32 ], and OncoScape [ 33 ]; (3) essentiality from RNA interference (RNAi) experiments in cancer cell lines from Achilles project [ 34 , 35 ] and; (4) clinical implications based on its pathogenicity supporting evidence (taken from COSMIC and ClinVar). GScore weight assignation for non-ranked gene lists and for VCF files are described in Additional file 1 : Tables S2 and S3, respectively.…”

Section: Methodsmentioning

confidence: 99%

PanDrugs: a novel method to prioritize anticancer drug treatments according to individual genomic data

et al. 2018

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BackgroundLarge-sequencing cancer genome projects have shown that tumors have thousands of molecular alterations and their frequency is highly heterogeneous. In such scenarios, physicians and oncologists routinely face lists of cancer genomic alterations where only a minority of them are relevant biomarkers to drive clinical decision-making. For this reason, the medical community agrees on the urgent need of methodologies to establish the relevance of tumor alterations, assisting in genomic profile interpretation, and, more importantly, to prioritize those that could be clinically actionable for cancer therapy.ResultsWe present PanDrugs, a new computational methodology to guide the selection of personalized treatments in cancer patients using the variant lists provided by genome-wide sequencing analyses. PanDrugs offers the largest database of drug-target associations available from well-known targeted therapies to preclinical drugs. Scoring data-driven gene cancer relevance and drug feasibility PanDrugs interprets genomic alterations and provides a prioritized evidence-based list of anticancer therapies. Our tool represents the first drug prescription strategy applying a rational based on pathway context, multi-gene markers impact and information provided by functional experiments. Our approach has been systematically applied to TCGA patients and successfully validated in a cancer case study with a xenograft mouse model demonstrating its utility.ConclusionsPanDrugs is a feasible method to identify potentially druggable molecular alterations and prioritize drugs to facilitate the interpretation of genomic landscape and clinical decision-making in cancer patients. Our approach expands the search of druggable genomic alterations from the concept of cancer driver genes to the druggable pathway context extending anticancer therapeutic options beyond already known cancer genes. The methodology is public and easily integratable with custom pipelines through its programmatic API or its docker image. The PanDrugs webtool is freely accessible at http://www.pandrugs.org.Electronic supplementary materialThe online version of this article (10.1186/s13073-018-0546-1) contains supplementary material, which is available to authorized users.

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“…OncoScape [23] is an R package software for cancer gene prioritization that has a Web portal for interactive analyses. OncoScape can access five complementary data types across 11 different cancers to identify new candidate cancer genes and explore cancer aberrations by using a fusion of genomic data.…”

Section: Overview and Categories Of Public Web-based Tools For Analyzmentioning

confidence: 99%

A survey and evaluation of Web-based tools/databases for variant analysis of TCGA data

Zhang

Jiang

et al. 2018

Briefings in Bioinformatics

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The Cancer Genome Atlas (TCGA) is a publicly funded project that aims to catalog and discover major cancer-causing genomic alterations with the goal of creating a comprehensive 'atlas' of cancer genomic profiles. The availability of this genome-wide information provides an unprecedented opportunity to expand our knowledge of tumourigenesis. Computational analytics and mining are frequently used as effective tools for exploring this byzantine series of biological and biomedical data. However, some of the more advanced computational tools are often difficult to understand or use, thereby limiting their application by scientists who do not have a strong computational background. Hence, it is of great importance to build user-friendly interfaces that allow both computational scientists and life scientists without a computational background to gain greater biological and medical insights. To that end, this survey was designed to systematically present available Web-based tools and facilitate the use TCGA data for cancer research.

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OncoScape: Exploring the cancer aberration landscape by genomic data fusion

Cited by 10 publications

References 49 publications

Correlation between expression levels of lncRNA FER1L4 and RB1 in patients with colorectal cancer

Correlation between expression levels of lncRNA FER1L4 and RB1 in patients with colorectal cancer

PanDrugs: a novel method to prioritize anticancer drug treatments according to individual genomic data

A survey and evaluation of Web-based tools/databases for variant analysis of TCGA data

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