scholarly journals Comprehensive assessment of TP53 loss of function using multiple combinatorial mutagenesis libraries

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Vincent Carbonnier ◽  
Bernard Leroy ◽  
Shai Rosenberg ◽  
Thierry Soussi
Keyword(s):  
Functional Data ◽  
Large Scale ◽  
The Cancer Genome Atlas ◽  
Saturation Mutagenesis ◽  
Missense Mutations ◽  
Expression Data ◽  
Loss Of Function ◽  
Proteomic Data ◽  
Cancer Genome Atlas ◽  
Combinatorial Mutagenesis

AbstractThe diagnosis of somatic and germline TP53 mutations in human tumors or in individuals prone to various types of cancer has now reached the clinic. To increase the accuracy of the prediction of TP53 variant pathogenicity, we gathered functional data from three independent large-scale saturation mutagenesis screening studies with experimental data for more than 10,000 TP53 variants performed in different settings (yeast or mammalian) and with different readouts (transcription, growth arrest or apoptosis). Correlation analysis and multidimensional scaling showed excellent agreement between all these variables. Furthermore, we found that some missense mutations localized in TP53 exons led to impaired TP53 splicing as shown by an analysis of the TP53 expression data from the cancer genome atlas. With the increasing availability of genomic, transcriptomic and proteomic data, it is essential to employ both protein and RNA prediction to accurately define variant pathogenicity.

scholarly journals LOXL1 confers antiapoptosis and promotes gliomagenesis through stabilizing BAG2

2020 ◽  
Vol 27 (11) ◽  
pp. 3021-3036 ◽  
Author(s):  
Hua Yu ◽  
Jun Ding ◽  
Hongwen Zhu ◽  
Yao Jing ◽  
Hu Zhou ◽  
...  
Keyword(s):  
Molecular Chaperone ◽  
Glioma Cell ◽  
Lysyl Oxidase ◽  
The Cancer Genome Atlas ◽  
Loss Of Function ◽  
Protein Levels ◽  
Important Mediator ◽  
Cancer Genome Atlas ◽  
Potential Strategy ◽  
Glioma Progression

Abstract The lysyl oxidase (LOX) family is closely related to the progression of glioma. To ensure the clinical significance of LOX family in glioma, The Cancer Genome Atlas (TCGA) database was mined and the analysis indicated that higher LOXL1 expression was correlated with more malignant glioma progression. The functions of LOXL1 in promoting glioma cell survival and inhibiting apoptosis were studied by gain- and loss-of-function experiments in cells and animals. LOXL1 was found to exhibit antiapoptotic activity by interacting with multiple antiapoptosis modulators, especially BAG family molecular chaperone regulator 2 (BAG2). LOXL1-D515 interacted with BAG2-K186 through a hydrogen bond, and its lysyl oxidase activity prevented BAG2 degradation by competing with K186 ubiquitylation. Then, we discovered that LOXL1 expression was specifically upregulated through the VEGFR-Src-CEBPA axis. Clinically, the patients with higher LOXL1 levels in their blood had much more abundant BAG2 protein levels in glioma tissues. Conclusively, LOXL1 functions as an important mediator that increases the antiapoptotic capacity of tumor cells, and approaches targeting LOXL1 represent a potential strategy for treating glioma. In addition, blood LOXL1 levels can be used as a biomarker to monitor glioma progression.

scholarly journals TRIM27 interacts with Iκbα to promote the growth of human renal cancer cells through regulating the NF-κB pathway

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chengwu Xiao ◽  
Wei Zhang ◽  
Meimian Hua ◽  
Huan Chen ◽  
Bin Yang ◽  
...  
Keyword(s):  
Cell Lines ◽  
Prognostic Indicator ◽  
The Cancer Genome Atlas ◽  
Mrna Levels ◽  
Loss Of Function ◽  
Protein Levels ◽  
Kaplan Meier ◽  
Cancer Genome Atlas

Abstract Background The tripartite motif (TRIM) family proteins exhibit oncogenic roles in various cancers. The roles of TRIM27, a member of the TRIM super family, in renal cell carcinoma (RCC) remained unexplored. In the current study, we aimed to investigate the clinical impact and roles of TRIM27 in the development of RCC. Methods The mRNA levels of TRIM27 and Kaplan–Meier survival of RCC were analyzed from The Cancer Genome Atlas database. Real-time PCR and Western blotting were used to measure the mRNA and protein levels of TRIM27 both in vivo and in vitro. siRNA and TRIM27 were exogenously overexpressed in RCC cell lines to manipulate TRIM27 expression. Results We discovered that TRIM27 was elevated in RCC patients, and the expression of TRIM27 was closely correlated with poor prognosis. The loss of function and gain of function results illustrated that TRIM27 promotes cell proliferation and inhibits apoptosis in RCC cell lines. Furthermore, TRIM27 expression was positively associated with NF-κB expression in patients with RCC. Blocking the activity of NF-κB attenuated the TRIM27-mediated enhancement of proliferation and inhibition of apoptosis. TRIM27 directly interacted with Iκbα, an inhibitor of NF-κB, to promote its ubiquitination, and the inhibitory effects of TRIM27 on Iκbα led to NF-κB activation. Conclusions Our results suggest that TRIM27 exhibits an oncogenic role in RCC by regulating NF-κB signaling. TRIM27 serves as a specific prognostic indicator for RCC, and strategies targeting the suppression of TRIM27 function may shed light on future therapeutic approaches.

scholarly journals Mining The Cancer Genome Atlas gene expression data for lineage markers in distinguishing bladder urothelial carcinoma and prostate adenocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ewe Seng Ch’ng
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
The Cancer Genome Atlas ◽  
Relative Importance ◽  
Expression Data ◽  
Gene Expressions ◽  
Urothelial Carcinomas ◽  
Cancer Genome Atlas ◽  
Lineage Markers ◽  
Genome Atlas

AbstractDistinguishing bladder urothelial carcinomas from prostate adenocarcinomas for poorly differentiated carcinomas derived from the bladder neck entails the use of a panel of lineage markers to help make this distinction. Publicly available The Cancer Genome Atlas (TCGA) gene expression data provides an avenue to examine utilities of these markers. This study aimed to verify expressions of urothelial and prostate lineage markers in the respective carcinomas and to seek the relative importance of these markers in making this distinction. Gene expressions of these markers were downloaded from TCGA Pan-Cancer database for bladder and prostate carcinomas. Differential gene expressions of these markers were analyzed. Standard linear discriminant analyses were applied to establish the relative importance of these markers in lineage determination and to construct the model best in making the distinction. This study shows that all urothelial lineage genes except for the gene for uroplakin III were significantly expressed in bladder urothelial carcinomas (p < 0.001). In descending order of importance to distinguish from prostate adenocarcinomas, genes for uroplakin II, S100P, GATA3 and thrombomodulin had high discriminant loadings (> 0.3). All prostate lineage genes were significantly expressed in prostate adenocarcinomas(p < 0.001). In descending order of importance to distinguish from bladder urothelial carcinomas, genes for NKX3.1, prostate specific antigen (PSA), prostate-specific acid phosphatase, prostein, and prostate-specific membrane antigen had high discriminant loadings (> 0.3). Combination of gene expressions for uroplakin II, S100P, NKX3.1 and PSA approached 100% accuracy in tumor classification both in the training and validation sets. Mining gene expression data, a combination of four lineage markers helps distinguish between bladder urothelial carcinomas and prostate adenocarcinomas.

scholarly journals Large-scale profiling of microRNAs for The Cancer Genome Atlas

2015 ◽  
Vol 44 (1) ◽  
pp. e3-e3 ◽  
Author(s):  
Andy Chu ◽  
Gordon Robertson ◽  
Denise Brooks ◽  
Andrew J. Mungall ◽  
Inanc Birol ◽  
...  
Keyword(s):  
Large Scale ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Upregulation of SGOL2 Predicts Poor Prognosis and Facilitates Hepatocellular Carcinoma Progression via Dysregulating the Cell Cycle by Directly Activating MAD2

2021 ◽  
Author(s):  
Qingqing Hu ◽  
Xiaochu Hu ◽  
Yalei Zhao ◽  
Lingjian Zhang ◽  
Ya Yang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
The Cancer Genome Atlas ◽  
Loss Of Function ◽  
Protein Levels ◽  
Cancer Genome Atlas ◽  
Centromeric Protein ◽  
Hcc Cells

Abstract Background: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Shugoshin-like protein 2 (SGOL2) is a centromeric protein that ensures the correct and orderly process of mitosis by protecting and maintaining centripetal adhesions during meiosis and mitosis. However, the role of SGOL2 in cancer is not well understood. Methods: The mRNA and protein levels of SGOL2 and survival analysis were conducted in The Cancer Genome Atlas (TCGA) and further validated in 2 independent cohorts. Differential genes correlated with SGOL2 and mitotic arrest deficient 2 like 1 (MAD2) were obtained using LinkedOmics. Subsequently, loss-of-function and rescue assays were carried out in vitro and in vivo to assess the functions of SGOL2 in hepatic tumorigenisis. Findings: We found that SGOL2 was significantly overexpressed in HCC and predicted unfavorable overall survival in HCC patients. Next, we identified 47 differentially expressed genes positively correlated with both SGOL2 and MAD2 to be mainly involved in the cell cycle. In addition, SGOL2 downregulation suppressed the migration, invasion, proliferation, stemness and EMT of HCC cells and inhibited tumorigenesis in vivo. Furthermore, SGOL2 promoted tumor proliferation by activating MAD2-induced cell cycle dysregulation, which could be reversed by the MAD2 inhibitor M2I-1. We also proved that SGOL2 activated MAD2 by directly binding with MAD2. Conclusions: The results of this study showed that SGOL2 acts as an oncogene in HCC cells by directly activating MAD2 and then dysregulating the cell cycle, thereby providing a potential target for HCC patients in the future.

scholarly journals PABPC1 relevant bioinformatic profiling and prognostic value in gliomas

2020 ◽  
Vol 16 (1) ◽  
pp. 4279-4288 ◽  
Author(s):  
Qiangwei Wang ◽  
Zhiliang Wang ◽  
Zhaoshi Bao ◽  
Chuanbao Zhang ◽  
Zheng Wang ◽  
...  
Keyword(s):  
Biological Process ◽  
The Cancer Genome Atlas ◽  
Analysis Tool ◽  
Expression Data ◽  
Clinical Value ◽  
Mrna Expression Data ◽  
R Language ◽  
Molecular Features ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Aim: We aimed at investigating molecular features and potential clinical value of PABPC1 in gliomas. Materials & methods: We assembled totally 1000 glioma samples with mRNA expression data from Chinese Glioma Genome Atlas and The Cancer Genome Atlas. We utilized R language as the main analysis tool. Gene Ontology was performed for functional analysis. Results: PABPC1 was downregulated in gliomas with higher malignance and PABPC1 may contribute as potential predictor of proneural subtype in gliomas. Higher expression of PABPC1 was significantly related to better prognosis and related to biological process of translation. Conclusion: Our finding improves the understanding of PABPC1 as a novel biomarker with potential therapeutic connotations.

scholarly journals Overexpression of CLEC18B Associates With the Proliferation, Migration, and Prognosis of Glioblastoma

ASN NEURO ◽  
2018 ◽  
Vol 10 ◽  
pp. 175909141878194 ◽  
Author(s):  
Rui-Ming Guo ◽  
Cheng-Bin Zhao ◽  
Peng Li ◽  
Liang Zhang ◽  
Su-Hua Zang ◽  
...  
Keyword(s):  
Inverse Correlation ◽  
The Cancer Genome Atlas ◽  
Cell Counting ◽  
Migration And Invasion ◽  
Loss Of Function ◽  
Lectin Domain ◽  
Kaplan Meier ◽  
Cancer Genome Atlas ◽  
Interfering Rna

C-type lectin domain family 18 member B (CLEC18B), encoding a superfamily of CLEC, has been found to be expressed in some of cancer cells, which possibly indicates it associated with cancer. However, the defined functional characterizations of CLEC18B in glioblastoma multiforme (GBM) progression still remain unclear. To this end, clinical relevance of CLEC18B expression with GBM patients’ prognosis was analyzed both in The Cancer Genome Atlas dataset of 174 tissues and 40 GBM tumor tissues collected from our hospital by using the Kaplan–Meier survival and the Cox proportional hazard model. The role of CLEC18B in GBM was determined by loss-of-function assay using small interfering RNA approach in vitro. Functional and signaling analyses were also performed to understand how CLEC18B facilitated the aggressiveness of GBM at molecular and cellular levels using Cell Counting Kit-8 assay, wound-healing, transwell, and Western blot analyses. Results from our analyses showed that CLEC18B was markedly elevated in both GBM tissues and cells, and exhibited strong inverse correlation with overall survival in GBM patients. Moreover, CLEC18B was identified as an independent predictor of patient survival. Functionally, knockdown of CLEC18B inhibited the growth, migration, and invasion of GBM cells. Mechanistic studies revealed that silencing of CLEC18B resulted in downregulation of Wnt/β-catenin signaling activity. Collectively, our findings provide clinical, molecular, and cellular evidence of CLEC18B as a promising prognostic biomarker and therapeutic target for GBM.

scholarly journals Downregulated SPINK4 is associated with poor survival in colorectal cancer

BMC Cancer ◽  
2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Xiaojie Wang ◽  
Qian Yu ◽  
Waleed M. Ghareeb ◽  
Yiyi Zhang ◽  
Xingrong Lu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Expression Data ◽  
Poor Survival ◽  
Protein Levels ◽  
Normal Tissues ◽  
Human Protein Atlas ◽  
Cancer Genome Atlas ◽  
Independent Indicator

Abstract Background SPINK4 is known as a gastrointestinal peptide in the gastrointestinal tract and is abundantly expressed in human goblet cells. The clinical significance of SPINK4 in colorectal cancer (CRC) is largely unknown. Methods We retrieved the expression data of 1168 CRC patients from 3 Gene Expression Omnibus (GEO) datasets (GSE24551, GSE39582, GSE32323) and The Cancer Genome Atlas (TCGA) to compare the expression level of SPINK4 between CRC tissues and normal colorectal tissues and to evaluate its value in predicting the survival of CRC patients. At the protein level, these results were further confirmed by data mining in the Human Protein Atlas and by immunohistochemical staining of samples from 81 CRC cases in our own center. Results SPINK4 expression was downregulated in CRC compared with that in normal tissues, and decreased SPINK4 expression at both the mRNA and protein levels was associated with poor prognosis in CRC patients from all 3 GEO datasets, the TCGA database and our cohort. Additionally, lower SPINK4 expression was significantly related to higher TNM stage. Moreover, in multivariate regression, SPINK4 was confirmed as an independent indicator of poor survival in CRC patients in all databases and in our own cohort. Conclusions We concluded that reduced expression of SPINK4 relates to poor survival in CRC, functioning as a novel indicator.

scholarly journals Comprehensive characterization of protein-protein interaction network perturbations by human disease mutations

2020 ◽  
Author(s):  
Feixiong Cheng ◽  
Junfei Zhao ◽  
Yang Wang ◽  
Weiqiang Lu ◽  
Zehui Liu ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Large Scale ◽  
Human Cancer ◽  
The Cancer Genome Atlas ◽  
Binary Interaction ◽  
Missense Mutations ◽  
Cancer Subtypes ◽  
Protein Protein Interaction ◽  
Protein Interfaces ◽  
Disease Mutations

AbstractTechnological and computational advances in genomics and interactomics have made it possible to identify rapidly how disease mutations perturb interaction networks within human cells. In this study, we investigate at large-scale the effects of network perturbations caused by disease mutations within the human three-dimensional (3D), structurally-resolved macromolecular interactome. We show that disease-associated germline mutations are significantly enriched in sequences encoding protein-protein interfaces compared to mutations identified in healthy subjects from the 1000 Genomes and ExAC projects; these interface mutations correspond to protein-protein interaction (PPI)-perturbing alleles including p.Ser127Arg in PCSK9 at the PCSK9-LDLR interface. In addition, somatic missense mutations are significantly enriched in PPI interfaces compared to non-interfaces in 10,861 human exomes across 33 cancer subtypes/types from The Cancer Genome Atlas. Using a binomial statistical model, we computationally identified 470 PPIs harboring a statistically significant excess number of missense mutations at protein-protein interfaces (termed putative oncoPPIs) in pan-cancer analysis. We demonstrate that the oncoPPIs, including histone H4 complex in individual cancer types, are highly correlated with patient survival and drug resistance/sensitivity in human cancer cell lines and patient-derived xenografts. We experimentally validate the network effects of 13 oncoPPIs using a systematic binary interaction assay. We further showed that ALOX5 p.Met146Lys at the ALOX5-MAD1L1 interface and RXRA p.Ser427Phe at the RXRA-PPARG interface promote significant tumor cell growth using cell line-based functional assays, providing a functional proof-of-concept. In summary, if broadly applied, this human 3D interactome network analysis offers a powerful tool for prioritizing alleles with mutations altering PPIs that may contribute to the pathobiology of human diseases, and may offer disease-specific targets for genotype-informed therapeutic discovery.

scholarly journals Explainable autoencoder-based representation learning for gene expression data

2021 ◽  
Author(s):  
Yang Yu ◽  
Pathum Kossinna ◽  
Wenyuan Liao ◽  
Qingrun Zhang
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Hidden Variables ◽  
Representation Learning ◽  
The Cancer Genome Atlas ◽  
Expression Data ◽  
Rna Seq ◽  
Gene Expression Data Analysis ◽  
Cancer Genome Atlas ◽  
Modern Machine

Modern machine learning methods have been extensively utilized in gene expression data analysis. In particular, autoencoders (AE) have been employed in processing noisy and heterogenous RNA-Seq data. However, AEs usually lead to "black-box" hidden variables difficult to interpret, hindering downstream experimental validation and clinical translation. To bridge the gap between complicated models and biological interpretations, we developed a tool, XAE4Exp (eXplainable AutoEncoder for Expression data), which integrates AE and SHapley Additive exPlanations (SHAP), a flagship technique in the field of eXplainable AI (XAI). It quantitatively evaluates the contributions of each gene to the hidden structure learned by an AE, substantially improving the expandability of AE outcomes. By applying XAE4Exp to The Cancer Genome Atlas (TCGA) breast cancer gene expression data, we identified genes that are not differentially expressed, and pathways in various cancer-related classes. This tool will enable researchers and practitioners to analyze high-dimensional expression data intuitively, paving the way towards broader uses of deep learning.

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