scholarly journals Integrative Analysis of Transcriptome Variation in Uterine Carcinosarcoma and Comparison to Sarcoma and Endometrial Carcinoma

2014 ◽  
Author(s):  
Natalie Davidson ◽  
Kjong-Van Lehmann ◽  
André Kahles ◽  
Alexendar Perez ◽  
Gunnar Rätsch
Keyword(s):  
Endometrial Carcinoma ◽  
Cancer Genomics ◽  
Expression Patterns ◽  
The Cancer Genome Atlas ◽  
Joint Analysis ◽  
Uterine Carcinosarcoma ◽  
Multiple Tumor ◽  
Depth Analysis ◽  
Similarities And Differences ◽  
Tumor Types

Large-scale cancer genomics has made a huge impact onto cancer research. It has allowed the characterization of tumor types in an unprecedented depth. More recent studies target the joint analysis of multiple tumor types to gain insight into similarities and differences on a molecular level. Here we present an analysis of Uterine Carcinosarcoma. The histological similarities to sarcomas and carcinomas warrants an in-depth analysis to Uterine Endometrial Carcinoma as well as Sarcomas and we have used data from The Cancer Genome Atlas to understand transcriptome similarities and diffrences between these tumor types. We have performed a differential transcriptome analysis of Uterine Carinosarcoma to Uterine samples from GTEx to find genes with tumor specifc splicing or expression patterns, which may not only be of interest for a deeper mechanistic understanding of the development and progression of Uterine Carcinosarcoma, but may also be potential tumor markers. Similarities and differences to Sarcomas and Endometrial Carcinomas present new opportunities for the development of new and targeted drug therapies. Finally we have also studied genetic determinants of gene expression and splicing changes and identifed germline variants that explain expression and splicing differences between individuals. This analysis demonstrates the opportunities of integrative comparative analysis between multiple tumor types.

scholarly journals Experimental and Meta-Analytic Validation of RNA Sequencing Signatures for Predicting Status of Microsatellite Instability

2021 ◽  
Vol 8 ◽  
Author(s):  
Maksim Sorokin ◽  
Elizaveta Rabushko ◽  
Victor Efimov ◽  
Elena Poddubskaya ◽  
Marina Sekacheva ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Esophageal Cancer ◽  
Microsatellite Instability ◽  
Endometrial Carcinoma ◽  
Rna Sequencing ◽  
Wide Spectrum ◽  
The Cancer Genome Atlas ◽  
Laboratory Diagnostics ◽  
Reaction Products ◽  
Uterine Carcinosarcoma

Microsatellite instability (MSI) is an important diagnostic and prognostic cancer biomarker. In colorectal, cervical, ovarian, and gastric cancers, it can guide the prescription of chemotherapy and immunotherapy. In laboratory diagnostics of susceptible tumors, MSI is routinely detected by the size of marker polymerase chain reaction products encompassing frequent microsatellite expansion regions. Alternatively, MSI status is screened indirectly by immunohistochemical interrogation of microsatellite binding proteins. RNA sequencing (RNAseq) profiling is an emerging source of data for a wide spectrum of cancer biomarkers. Recently, three RNAseq-based gene signatures were deduced for establishing MSI status in tumor samples. They had 25, 15, and 14 gene products with only one common gene. However, they were developed and tested on the incomplete literature of The Cancer Genome Atlas (TCGA) sampling and never validated experimentally on independent RNAseq samples. In this study, we, for the first time, systematically validated these three RNAseq MSI signatures on the literature colorectal cancer (CRC) (n = 619), endometrial carcinoma (n = 533), gastric cancer (n = 380), uterine carcinosarcoma (n = 55), and esophageal cancer (n = 83) samples and on the set of experimental CRC RNAseq samples (n = 23) for tumors with known MSI status. We found that all three signatures performed well with area under the curve (AUC) ranges of 0.94–1 for the experimental CRCs and 0.94–1 for the TCGA CRC, esophageal cancer, and uterine carcinosarcoma samples. However, for the TCGA endometrial carcinoma and gastric cancer samples, only two signatures were effective with AUC 0.91–0.97, whereas the third signature showed a significantly lower AUC of 0.69–0.88. Software for calculating these MSI signatures using RNAseq data is included.

High-dimensional covariance matrices tests for analyzing multi-tumor gene expression data

2021 ◽  
pp. 096228022110092
Author(s):  
Abdullah Qayed ◽  
Dong Han
Keyword(s):  
Gene Expression ◽  
Empirical Studies ◽  
The Cancer Genome Atlas ◽  
Covariance Structures ◽  
Gene Expressions ◽  
Multiple Tumor ◽  
Gene Sets ◽  
Cancer Genome Atlas ◽  
Tumor Types ◽  
Tumor Gene Expression

By collecting multiple sets per subject in microarray data, gene sets analysis requires characterize intra-subject variation using gene expression profiling. For each subject, the data can be written as a matrix with the different subsets of gene expressions (e.g. multiple tumor types) indexing the rows and the genes indexing the columns. To test the assumption of intra-subject (tumor) variation, we present and perform tests of multi-set sphericity and multi-set identity of covariance structures across subjects (tumor types). We demonstrate by both theoretical and empirical studies that the tests have good properties. We applied the proposed tests on The Cancer Genome Atlas (TCGA) and tested covariance structures for the gene expressions across several tumor types.

scholarly journals Identification of Hub Genes Correlated With Poor Prognosis for Patients With Uterine Corpus Endometrial Carcinoma by Integrated Bioinformatics Analysis and Experimental Validation

2021 ◽  
Vol 11 ◽  
Author(s):  
Yi Yuan ◽  
Zhengzheng Chen ◽  
Xushan Cai ◽  
Shengxiang He ◽  
Dong Li ◽  
...  
Keyword(s):  
Endometrial Carcinoma ◽  
Molecular Mechanisms ◽  
Expression Patterns ◽  
Female Genital Tract ◽  
Therapeutic Targets ◽  
Public Health Problem ◽  
The Cancer Genome Atlas ◽  
Major Public Health Problem ◽  
Hub Genes ◽  
Uterine Corpus

Uterine Corpus Endometrial Carcinoma (UCEC) is one of the most common malignancies of the female genital tract and there remains a major public health problem. Although significant progress has been made in explaining the progression of UCEC, it is still warranted that molecular mechanisms underlying the tumorigenesis of UCEC are to be elucidated. The aim of the current study was to investigate key modules and hub genes related to UCEC pathogenesis, and to explore potential biomarkers and therapeutic targets for UCEC. The RNA-seq dataset and corresponding clinical information for UCEC patients were obtained from the Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were screened between 23 paired UCEC tissues and adjacent non-cancerous tissues. Subsequently, the co-expression network of DEGs was determined via weighted gene co-expression network analysis (WGCNA). The Blue and Brown modules were identified to be significantly positively associated with neoplasm histologic grade. The highly connected genes of the two modules were then investigated as potential key factors related to tumor differentiation. Additionally, a protein-protein interaction (PPI) network for all genes in the two modules was constructed to obtain key modules and nodes. 10 genes were identified by both WGCNA and PPI analyses, and it was shown by Kaplan-Meier curve analysis that 6 out of the 10 genes were significantly negatively related to the 5-year overall survival (OS) in patients (AURKA, BUB1, CDCA8, DLGAP5, KIF2C, TPX2). Besides, according to the DEGs from the two modules, lncRNA-miRNA-mRNA and lncRNA-TF-mRNA networks were constructed to explore the molecular mechanism of UCEC-related lncRNAs. 3 lncRNAs were identified as being significantly negatively related to the 5-year OS (AC015849.16, DUXAP8 and DGCR5), with higher expression in UCEC tissues compared to non-tumor tissues. Finally, quantitative Real-time PCR was applied to validate the expression patterns of hub genes. Cell proliferation and colony formation assays, as well as cell cycle distribution and apoptosis analysis, were performed to test the effects of representative hub genes. Altogether, this study not only promotes our understanding of the molecular mechanisms for the pathogenesis of UCEC but also identifies several promising biomarkers in UCEC development, providing potential therapeutic targets for UCEC.

scholarly journals A Comprehensive Survey of Immune Cytolytic Activity-Associated Gene Co-Expression Networks across 17 Tumor and Normal Tissue Types

Cancers ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 307
Author(s):  
Tian Tian ◽  
Ji Wan ◽  
Yan Han ◽  
Haoran Liu ◽  
Feng Gao ◽  
...  
Keyword(s):  
Cytolytic Activity ◽  
The Cancer Genome Atlas ◽  
Immune Activity ◽  
Multiple Tumor ◽  
Normal Tissues ◽  
Tumor Tissues ◽  
Comprehensive Survey ◽  
Cancer Genome Atlas ◽  
Normal Networks ◽  
Tumor Types

Cytolytic immune activity in solid tissue can be quantified by transcript levels of two genes, GZMA and PRF1, which is named the CYT score. A previous study has investigated the molecular and genetic properties of tumors associated CYT, but a systematic exploration of how co-expression networks across different tumors are shaped by anti-tumor immunity is lacking. Here, we examined the connectivity and biological themes of CYT-associated modules in gene co-expression networks of 14 tumor and 3 matched normal tissues constructed from the RNA-Seq data of the “The Cancer Genome Atlas” project. We first found that tumors networks have more diverse CYT-correlated modules than normal networks. We next identified and investigated tissue-specific CYT-associated modules across 14 tumor types. Finally, a common CYT-associated network across 14 tumor types was constructed. Two common modules have mixed signs of correlation with CYT in different tumors. Given the tumors and normal tissues surveyed, our study presents a systematic view of the regulation of cytolytic immune activity across multiple tumor tissues.

Association with immune checkpoint inhibitor efficacy of a 27-gene classifier in renal cell cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 4575-4575
Author(s):  
Robert Seitz ◽  
Tyler J Nielsen ◽  
Brock Lloyd Schweitzer ◽  
David R. Gandara ◽  
Mamta Parikh ◽  
...  
Keyword(s):  
Immune Checkpoint ◽  
Renal Cell ◽  
Checkpoint Inhibitors ◽  
Expression Patterns ◽  
Cell Cancer ◽  
The Cancer Genome Atlas ◽  
Tumor Type ◽  
Multiple Tumor ◽  
Diagnostic Threshold ◽  
One Year

4575 Background: The 27-gene immuno-oncology (IO) signature that incorporates expression from activated inflammatory cells, cancer associated fibroblasts, and tumor cells to produce a binary classifier has been shown to be associated with efficacy to immune checkpoint inhibitors (ICIs) in breast, lung, and bladder cancers. Here we created clustered heat maps using data from The Cancer Genome Atlas (TCGA) to confirm the classifier function and diagnostic threshold in renal cell carcinoma (RCC), then applied the predefined algorithm to RNAseq data from a community RCC cohort treated with ICI therapy. Methods: Previously, we described the selection of 939 genes from the TCGA breast and lung datasets that comprise mesenchymal (M), mesenchymal stem-like (MSL), and immunodulatory (IM) gene expression patterns centered upon the twenty-seven genes selected for the IO score (AACR, 2021). We created an expression dataset using these genes in clear cell (n = 403) and papillary (n = 203) RCC and used k-means clustering to organize the genes and cases (k=3). We assessed the 27-gene classification of cases by utilizing area under the curve for phenotypic classification and determining the sensitivity and specificity of the previously established threshold compared to optimal accuracy for quantitating the fraction of cases enriched into the IM+ cluster (likely sensitive to ICIs) as opposed to the M or MSL clusters (likely insensitive). Finally, the IO score was evaluated in a small multi-institutional RNAseq dataset of forty-three RCC patients treated with an ICI for which there was definitive one-year progression free survival (PFS) data. Results: The 27-gene IO signature applied to the TCGA sample data had an AUC of 90.3 for stratification of cases into IM+ as opposed to M and MSL clusters while the established threshold for likely sensitive enriched 90% of cases into the appropriate IM cluster as opposed 28% into the M and MSL. Efficacy was defined by PFS. Given this result, the 27-gene IO signature was applied with the predefined threshold to the forty-three ICI treated patients. Patients who had a IO+ score by the 27-gene signature had significantly better one-year PFS compared to patients with a negative IO score (hazard ratio = 0.235, 95% CI = 0.069 - 0.803, p < 0.01). Median PFS was 5.2 months for patients classified as IO score negative versus 8.6 months for those classified as IO score+. Conclusions: The 27-gene IO signature has been validated across multiple tumor types and here in RCC to classify the tumor immune microenvironment without changing the algorithm or threshold. Results demonstrate that the 27-gene classifier has a strong correlation with efficacy of ICI therapy in RCC. This is the fourth tumor type in which the same algorithm has been validated as a predictor of ICI efficacy. These data support this assay as a strong pan-cancer immune system classifier worthy of further prospective study for ICI therapy.

scholarly journals A new genome-wide method to identify genes with bimodal gene expression

2020 ◽  
Author(s):  
Josivan Ribeiro Justino ◽  
Clovis F. Reis ◽  
Andre Faustino Fonseca ◽  
Sandro Jose de Souza ◽  
Beatriz Stransky
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
The Cancer Genome Atlas ◽  
Cancer Type ◽  
Bimodal Gene ◽  
Cancer Genome Atlas ◽  
Tumor Types ◽  
The Impact

AbstractA new method is presented to detect bimodality in gene expression data using the Gaussian Mixture Models to cluster samples in each mode. We have used the method to search for bimodal genes in data from 25 tumor types available from The Cancer Genome Atlas. The method identified 554 genes with bimodal gene expression, of which 46 were identified in more than one cancer type. To further illustrate the impact of the method, we show that 96 out of the 554 genes with bimodal expression patterns presented different prognosis when patients belonging to the two expression peaks are compared. The software to execute the method and the corresponding documentation are available at https://github.com/LabBiosystemUFRN/Bimodality_Genes.

scholarly journals Targeting AURKA in Cancer: molecular mechanisms and opportunities for Cancer therapy

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Ruijuan Du ◽  
Chuntian Huang ◽  
Kangdong Liu ◽  
Xiang Li ◽  
Zigang Dong
Keyword(s):  
Cancer Therapy ◽  
Molecular Mechanisms ◽  
Aurora Kinase ◽  
Interacting Proteins ◽  
Multiple Tumor ◽  
Oncogenic Pathways ◽  
Wide Range ◽  
The Family ◽  
Tumor Types ◽  
Cancer Tissues

AbstractAurora kinase A (AURKA) belongs to the family of serine/threonine kinases, whose activation is necessary for cell division processes via regulation of mitosis. AURKA shows significantly higher expression in cancer tissues than in normal control tissues for multiple tumor types according to the TCGA database. Activation of AURKA has been demonstrated to play an important role in a wide range of cancers, and numerous AURKA substrates have been identified. AURKA-mediated phosphorylation can regulate the functions of AURKA substrates, some of which are mitosis regulators, tumor suppressors or oncogenes. In addition, enrichment of AURKA-interacting proteins with KEGG pathway and GO analysis have demonstrated that these proteins are involved in classic oncogenic pathways. All of this evidence favors the idea of AURKA as a target for cancer therapy, and some small molecules targeting AURKA have been discovered. These AURKA inhibitors (AKIs) have been tested in preclinical studies, and some of them have been subjected to clinical trials as monotherapies or in combination with classic chemotherapy or other targeted therapies.

scholarly journals Deep Learning for Automatic Subclassification of Gastric Carcinoma Using Whole-Slide Histopathology Images

Cancers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 3811
Author(s):  
Hyun-Jong Jang ◽  
In-Hye Song ◽  
Sung-Hak Lee
Keyword(s):  
Deep Learning ◽  
Relative Proportion ◽  
The Cancer Genome Atlas ◽  
Cancer Tissue ◽  
Mucinous Tumor ◽  
Receiver Operating Characteristic Curves ◽  
Tumor Tissues ◽  
Cancer Genome Atlas ◽  
Stomach Adenocarcinoma ◽  
Tumor Types

Histomorphologic types of gastric cancer (GC) have significant prognostic values that should be considered during treatment planning. Because the thorough quantitative review of a tissue slide is a laborious task for pathologists, deep learning (DL) can be a useful tool to support pathologic workflow. In the present study, a fully automated approach was applied to distinguish differentiated/undifferentiated and non-mucinous/mucinous tumor types in GC tissue whole-slide images from The Cancer Genome Atlas (TCGA) stomach adenocarcinoma dataset (TCGA-STAD). By classifying small patches of tissue images into differentiated/undifferentiated and non-mucinous/mucinous tumor tissues, the relative proportion of GC tissue subtypes can be easily quantified. Furthermore, the distribution of different tissue subtypes can be clearly visualized. The patch-level areas under the curves for the receiver operating characteristic curves for the differentiated/undifferentiated and non-mucinous/mucinous classifiers were 0.932 and 0.979, respectively. We also validated the classifiers on our own GC datasets and confirmed that the generalizability of the classifiers is excellent. The results indicate that the DL-based tissue classifier could be a useful tool for the quantitative analysis of cancer tissue slides. By combining DL-based classifiers for various molecular and morphologic variations in tissue slides, the heterogeneity of tumor tissues can be unveiled more efficiently.

scholarly journals Enhance the Immune Checkpoint Inhibitors Efficacy with Radiotherapy Induced Immunogenic Cell Death: A Comprehensive Review and Latest Developments

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 678 ◽  
Author(s):  
Adrien Procureur ◽  
Audrey Simonaggio ◽  
Jean-Emmanuel Bibault ◽  
Stéphane Oudard ◽  
Yann-Alexandre Vano
Keyword(s):  
Cell Death ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Tumor Antigens ◽  
Checkpoint Inhibitors ◽  
Mitotic Cell ◽  
Immunogenic Cell Death ◽  
Dna Damages ◽  
Multiple Tumor ◽  
Tumor Types

The immunogenic cell death (ICD) is defined as a regulated cell death able to induce an adaptive immunity. It depends on different parameters including sufficient antigenicity, adjuvanticity and favorable microenvironment conditions. Radiation therapy (RT), a pillar of modern cancer treatment, is being used in many tumor types in curative, (neo) adjuvant, as well as metastatic settings. The anti-tumor effects of RT have been traditionally attributed to the mitotic cell death resulting from the DNA damages triggered by the release of reactive oxygen species. Recent evidence suggests that RT may also exert its anti-tumor effect by recruiting tumor-specific immunity. RT is able to induce the release of tumor antigens, to act as an immune adjuvant and thus to synergize with the anti-tumor immunity. The advent of new efficient immunotherapeutic agents, such as immune checkpoint inhibitors (ICI), in multiple tumor types sheds new light on the opportunity of combining RT and ICI. Here, we will describe the biological and radiobiological rationale of the RT-induced ICD. We will then focus on the interest to combine RT and ICI, from bench to bedside, and summarize the clinical data existing with this combination. Finally, RT technical adaptations to optimize the ICD induction will be discussed.

scholarly journals Hyaluronan-mediated motility receptor expression functions as a prognostic biomarker in uterine carcinosarcoma based on bioinformatics analysis

2021 ◽  
Vol 49 (6) ◽  
pp. 030006052110210
Author(s):  
Hui Sun ◽  
Li Ma ◽  
Jie Chen
Keyword(s):  
Interaction Network ◽  
Maximal Clique ◽  
Receptor Expression ◽  
The Cancer Genome Atlas ◽  
Uterine Carcinosarcoma ◽  
Sequencing Data ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Normal Tissues ◽  
Potential Biomarker

Objective Uterine carcinosarcoma (UCS) is a rare, aggressive tumour with a high metastasis rate and poor prognosis. This study aimed to explore potential key genes associated with the prognosis of UCS. Methods Transcriptional expression data were downloaded from the Gene Expression Profiling Interactive Analysis database and differentially expressed genes (DEGs) were subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses using Metascape. A protein–protein interaction network was constructed using the STRING website and Cytoscape software, and the top 30 genes obtained through the Maximal Clique Centrality algorithm were selected as hub genes. These hub genes were validated by clinicopathological and sequencing data for 56 patients with UCS from The Cancer Genome Atlas database. Results A total of 1894 DEGs were identified, and the top 30 genes were considered as hub genes. Hyaluronan-mediated motility receptor (HMMR) expression was significantly higher in UCS tissues compared with normal tissues, and elevated expression of HMMR was identified as an independent prognostic factor for shorter survival in patients with UCS. Conclusions These results suggest that HMMR may be a potential biomarker for predicting the prognosis of patients with UCS.

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