scholarly journals Hard wiring of normal tissue-specific chromosome-wide gene expression levels is an additional factor driving cancer type-specific aneuploidies

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Sushant Patkar ◽  
Kerstin Heselmeyer-Haddad ◽  
Noam Auslander ◽  
Daniela Hirsch ◽  
Jordi Camps ◽  
...  
Keyword(s):  
Gene Expression ◽  
Normal Tissue ◽  
Cancer Type ◽  
Tissue Specific ◽  
Expression Levels ◽  
Specific Distribution ◽  
Tumor Origin ◽  
Chromosome Arm ◽  
Gains And Losses ◽  
Gene Expression Levels

Abstract Background Many carcinomas have recurrent chromosomal aneuploidies specific to the tissue of tumor origin. The reason for this specificity is not completely understood. Methods In this study, we looked at the frequency of chromosomal arm gains and losses in different cancer types from the The Cancer Genome Atlas (TCGA) and compared them to the mean gene expression of each chromosome arm in corresponding normal tissues of origin from the Genotype-Tissue Expression (GTEx) database, in addition to the distribution of tissue-specific oncogenes and tumor suppressors on different chromosome arms. Results This analysis revealed a complex picture of factors driving tumor karyotype evolution in which some recurrent chromosomal copy number reflect the chromosome arm-wide gene expression levels of the their normal tissue of tumor origin. Conclusions We conclude that the cancer type-specific distribution of chromosomal arm gains and losses is potentially “hardwiring” gene expression levels characteristic of the normal tissue of tumor origin, in addition to broadly modulating the expression of tissue-specific tumor driver genes.

scholarly journals Cancer-type specific aneuploidies hard-wire chromosome-wide gene expression patterns of their tissue of origin

2019 ◽  
Author(s):  
Noam Auslander ◽  
Kerstin Heselmeyer-Haddad ◽  
Sushant Patkar ◽  
Daniela Hirsch ◽  
Jordi Camps ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Tumor Evolution ◽  
Cancer Type ◽  
Expression Levels ◽  
Normal Tissues ◽  
Chromosome Arm ◽  
Tissue Of Origin ◽  
Chromosomal Aneuploidies ◽  
Gene Expression Levels

SUMMARYMost carcinomas have characteristic chromosomal aneuploidies specific to the tissue of tumor origin. The reason for this specificity is unknown. As aneuploidies directly affect gene expression, we hypothesized that cancer-type specific aneuploidies, which emerge at early stages of tumor evolution, confer adaptive advantages to the physiological requirements of the tissue of origin. To test this hypothesis, we compared chromosomal aneuploidies reported in the TCGA database to chromosome arm-wide gene expression levels of normal tissues from the GTEx database. We find that cancer-type specific chromosomal aneuploidies mirror differential gene expression levels specific to the respective normal tissues which cannot be explained by copy number alterations of resident cancer driver genes. We propose that cancer-type specific aneuploidies “hard-wire” chromosome arm-wide gene expression levels present in normal tissues, favoring clonal expansion and tumorigenesis.One sentence summaryThe clonal evolution of cancer is initiated by tissue-specific transcriptional requirements

scholarly journals High heterogeneity undermines generalization of differential expression results in RNA-Seq analysis

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Weitong Cui ◽  
Huaru Xue ◽  
Lei Wei ◽  
Jinghua Jin ◽  
Xuewen Tian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Small Sample ◽  
Differentially Expressed ◽  
Cancer Type ◽  
Rna Seq ◽  
Sample Sizes ◽  
Large Sample ◽  
Expression Levels ◽  
Gene Expression Levels

Abstract Background RNA sequencing (RNA-Seq) has been widely applied in oncology for monitoring transcriptome changes. However, the emerging problem that high variation of gene expression levels caused by tumor heterogeneity may affect the reproducibility of differential expression (DE) results has rarely been studied. Here, we investigated the reproducibility of DE results for any given number of biological replicates between 3 and 24 and explored why a great many differentially expressed genes (DEGs) were not reproducible. Results Our findings demonstrate that poor reproducibility of DE results exists not only for small sample sizes, but also for relatively large sample sizes. Quite a few of the DEGs detected are specific to the samples in use, rather than genuinely differentially expressed under different conditions. Poor reproducibility of DE results is mainly caused by high variation of gene expression levels for the same gene in different samples. Even though biological variation may account for much of the high variation of gene expression levels, the effect of outlier count data also needs to be treated seriously, as outlier data severely interfere with DE analysis. Conclusions High heterogeneity exists not only in tumor tissue samples of each cancer type studied, but also in normal samples. High heterogeneity leads to poor reproducibility of DEGs, undermining generalization of differential expression results. Therefore, it is necessary to use large sample sizes (at least 10 if possible) in RNA-Seq experimental designs to reduce the impact of biological variability and DE results should be interpreted cautiously unless soundly validated.

scholarly journals Analyzing cancer gene expression data through the lens of normal tissue-specificity

2021 ◽  
Author(s):  
H. Robert Frost
Keyword(s):  
Gene Expression ◽  
Normal Tissue ◽  
Tissue Specificity ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Genetic Alterations ◽  
Cancer Type ◽  
Tissue Specific ◽  
Normal Tissues ◽  
Cancer Types

AbstractThe genetic alterations that underlie cancer development are highly tissue-specific with the majority of driving alterations occurring in only a few cancer types and with alterations common to multiple cancer types often showing a tissue-specific functional impact. This tissue-specificity means that the biology of normal tissues carries important information regarding the pathophysiology of the associated cancers, information that can be leveraged to improve the power and accuracy of cancer genomic analyses. Research exploring the use of normal tissue data for the analysis of cancer genomics has primarily focused on the paired analysis of tumor and adjacent normal samples. Efforts to leverage the general characteristics of normal tissue for cancer analysis has received less attention with most investigations focusing on understanding the tissue-specific factors that lead to individual genomic alterations or dysregulated pathways within a single cancer type. To address this gap and support scenarios where adjacent normal tissue samples are not available, we explored the genome-wide association between the transcriptomes of 21 solid human cancers and their associated normal tissues as profiled in healthy individuals. While the average gene expression profiles of normal and cancerous tissue may appear distinct, with normal tissues more similar to other normal tissues than to the associated cancer types, when transformed into relative expression values, i.e., the ratio of expression in one tissue or cancer relative to the mean in other tissues or cancers, the close association between gene activity in normal tissues and related cancers is revealed. As we demonstrate through an analysis of tumor data from The Cancer Genome Atlas and normal tissue data from the Human Protein Atlas, this association between tissue-specific and cancer-specific expression values can be leveraged to improve the prognostic modeling of cancer, the comparative analysis of different cancer types, and the analysis of cancer and normal tissue pairs.

Somatostatin receptor subtypes 1-5 gene expression in multiple sites in the same individual.

2012 ◽  
Vol 30 (4_suppl) ◽  
pp. 182-182
Author(s):  
Michael Anthony Hall ◽  
Tanja Milosavljevic ◽  
Peter Casey ◽  
Catherine T. Anthony ◽  
Eugene Woltering
Keyword(s):  
Gene Expression ◽  
Liver Metastasis ◽  
Primary Tumor ◽  
Normal Tissue ◽  
Somatostatin Receptor ◽  
Receptor Expression ◽  
Receptor Subtypes ◽  
Expression Levels ◽  
Somatostatin Receptor Subtypes ◽  
Gene Expression Levels

182 Background: Metastatic tumors may be fundamentally different than the primary tumor. This phenomenon may partially explain resistance of metastatic disease to therapy. We evaluated the gene expression levels of somatostatin receptor subtypes 1-5 (SSTR 1-5) in patients with disseminated neuroendocrine tumors (NETS) undergoing cytoreduction of their primary tumor and its nodal and liver metastasis. Methods: We compared the gene expression levels for SSTR 1-5 in primary tumor and their nodal and liver metastasis. The small bowel primary (SB), a mesenteric lymph node (LN) and a liver metastasis and their normal tissue counterparts were evaluated in four patients. RNA samples from each tissue underwent gene expression analysis using a customized Real Time Quantitative PCR (RT-qPCR) gene array. Normal tissue gene expression was compared to that obtained from the tumor sample at each site. Results: SSTR 2 was overexpressed (four-fold or greater, p≤ 0.01) compared to control levels in 8/12 (67%) specimens; 4/4 (100%) of the liver specimens, 3/4 (75%) of the SB specimens, and 1/4 (25%) of the LN specimens. SSTR 2 gene overexpression was not observed in all three tumor sites in any patient. No tumor had SSTR 2 downregulation. SSTR 5 was overexpressed (four-fold or greater, p≤ 0.01) compared to control levels in 6/12 (50%) specimens; 3/4 (75%) of the liver specimens, 2/4 (50%) of the SB specimens, and 1/4 (25%) of the LN specimens. SSTR 5 gene expression was up-regulated in all three tumor sites in one individual. SSTR 5 was down-regulated (7-fold, p<0.01) in one LN specimen. Changes in gene expression levels of SSTR 3 and SSTR 4 showed inconsistency between tumor sites, whereas that of SSTR1 was observed only at the metastatic sites. Conclusions: These results explain the observed variability in somatostatin receptor expression seen in 111In pentetreotide scans in multiple tumor sites from the same individual. The observation that gene expression varies from metastasis to metastasis may also help explain the difficulty in designing therapies that cure patients rather than inducing partial remissions.

scholarly journals Analyzing cancer gene expression data through the lens of normal tissue-specificity

2021 ◽  
Vol 17 (6) ◽  
pp. e1009085
Author(s):  
H. Robert Frost
Keyword(s):  
Gene Expression ◽  
Normal Tissue ◽  
Tissue Specificity ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Genetic Alterations ◽  
Cancer Type ◽  
Tissue Specific ◽  
Normal Tissues ◽  
Cancer Types

The genetic alterations that underlie cancer development are highly tissue-specific with the majority of driving alterations occurring in only a few cancer types and with alterations common to multiple cancer types often showing a tissue-specific functional impact. This tissue-specificity means that the biology of normal tissues carries important information regarding the pathophysiology of the associated cancers, information that can be leveraged to improve the power and accuracy of cancer genomic analyses. Research exploring the use of normal tissue data for the analysis of cancer genomics has primarily focused on the paired analysis of tumor and adjacent normal samples. Efforts to leverage the general characteristics of normal tissue for cancer analysis has received less attention with most investigations focusing on understanding the tissue-specific factors that lead to individual genomic alterations or dysregulated pathways within a single cancer type. To address this gap and support scenarios where adjacent normal tissue samples are not available, we explored the genome-wide association between the transcriptomes of 21 solid human cancers and their associated normal tissues as profiled in healthy individuals. While the average gene expression profiles of normal and cancerous tissue may appear distinct, with normal tissues more similar to other normal tissues than to the associated cancer types, when transformed into relative expression values, i.e., the ratio of expression in one tissue or cancer relative to the mean in other tissues or cancers, the close association between gene activity in normal tissues and related cancers is revealed. As we demonstrate through an analysis of tumor data from The Cancer Genome Atlas and normal tissue data from the Human Protein Atlas, this association between tissue-specific and cancer-specific expression values can be leveraged to improve the prognostic modeling of cancer, the comparative analysis of different cancer types, and the analysis of cancer and normal tissue pairs.

scholarly journals Integrated Analysis to Study the Relationship between Tumor-Associated Selenoproteins: Focus on Prostate Cancer

2020 ◽  
Vol 21 (18) ◽  
pp. 6694
Author(s):  
Francesca Capone ◽  
Andrea Polo ◽  
Angela Sorice ◽  
Alfredo Budillon ◽  
Susan Costantini
Keyword(s):  
Gene Expression ◽  
Patient Outcome ◽  
Androgen Receptor ◽  
Integrated Analysis ◽  
Cancer Type ◽  
Bioinformatics Analyses ◽  
Human Interactome ◽  
Expression Levels ◽  
Network Analyses ◽  
Gene Expression Levels

Selenoproteins are proteins that contain selenium within selenocysteine residues. To date, twenty-five mammalian selenoproteins have been identified; however, the functions of nearly half of these selenoproteins are unknown. Although alterations in selenoprotein expression and function have been suggested to play a role in cancer development and progression, few detailed studies have been carried out in this field. Network analyses and data mining of publicly available datasets on gene expression levels in different cancers, and the correlations with patient outcome, represent important tools to study the correlation between selenoproteins and other proteins present in the human interactome, and to determine whether altered selenoprotein expression is cancer type-specific, and/or correlated with cancer patient prognosis. Therefore, in the present study, we used bioinformatics approaches to (i) build up the network of interactions between twenty-five selenoproteins and identify the most inter-correlated proteins/genes, which are named HUB nodes; and (ii) analyze the correlation between selenoprotein gene expression and patient outcome in ten solid tumors. Then, considering the need to confirm by experimental approaches the correlations suggested by the bioinformatics analyses, we decided to evaluate the gene expression levels of the twenty-five selenoproteins and six HUB nodes in androgen receptor-positive (22RV1 and LNCaP) and androgen receptor–negative (DU145 and PC3) cell lines, compared to human nontransformed, and differentiated, prostate epithelial cells (EPN) by RT-qPCR analysis. This analysis confirmed that the combined evaluation of some selenoproteins and HUB nodes could have prognostic value and may improve patient outcome predictions.

scholarly journals A novel feature extraction method based on highly expressed SNPs for tissue-specific gene prediction

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Jasbir Dhaliwal ◽  
John Wagner
Keyword(s):  
Gene Expression ◽  
Feature Extraction ◽  
Extraction Method ◽  
Specific Gene ◽  
Gene Products ◽  
Tissue Specific ◽  
Expression Levels ◽  
Feature Extraction Method ◽  
Tissue Specific Gene ◽  
Gene Expression Levels

Abstract Background Gene expression provides a means for an organism to produce gene products necessary for the organism to live. Variation in the significant gene expression levels can distinguish the gene and the tissue in which the gene is expressed. Tissue-specific gene expression, often determined by single nucleotide polymorphisms (SNPs), provides potential molecular markers or therapeutic targets for disease progression. Therefore, SNPs are good candidates for identifying disease progression. The current bioinformatics literature uses gene network modeling to summarize complex interactions between transcription factors, genes, and gene products. Here, our focus is on the SNPs’ impact on tissue-specific gene expression levels. To the best of our knowledge, we are not aware of any studies that distinguish tissue-specific genes using SNP expression levels. Method We propose a novel feature extraction method based on highly expressed SNPs using k-mers as features. We also propose optimal k-mer and feature sizes used in our approach. Determining the optimal sizes is still an open research question as it depends on the dataset and purpose of the analysis. Therefore, we evaluate our algorithm’s performance on a range of k-mer and feature sizes using a multinomial naive Bayes (MNB) classifier on genes in the 49 human tissues from the Genotype-Tissue Expression (GTEx) portal. Conclusions Our approach achieves practical performance results with k-mers of size 3. Based on the purpose of the analysis and the number of tissue-specific genes under study, feature sizes [7, 8, 9] and [8, 9, 10] are typically optimal for the machine learning model.

Chromosomal Aberration Patterns of Aggressive B-Cell Lymphomas Closely Correlate with Molecularly Defined Disease Subtypes and Regional mRNA Expression. An Analysis of 213 Tumor Samples within the German MMML Network.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 2025-2025
Author(s):  
Swen Wessendorf
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Chromosomal Aberrations ◽  
Molecular Mechanisms ◽  
Prognostic Information ◽  
Expression Levels ◽  
Chromosome Arm ◽  
Mrna Gene ◽  
Mrna Gene Expression ◽  
Gene Expression Levels

Recently, it has been demonstrated that aggressive B-cell non-Hodgkin lymphomas (B-NHL) split up into distinct subgroups such as molecular Burkitt (mBL) and molecular non-Burkitt lymphomas (non-mBL). The non-mBL group can further be devided in activated B-cell (ABC) and germinal center B-cell (GCB) like lymphomas. In the present investigation we aimed at unravelling correlations of single or multiple chromosomal imbalances, their respective regional mRNA gene expression levels and the above mentioned molecular subgroups. Aggressive B-NHL samples (n=255) were investigated within the German network project Molecular Mechanisms in Malignant Lymphomas (MMML). Data from genomic arrayCGH analysis were evaluable for 213 lymphoma cases. Consensus review diagnoses according to WHO criteria were: DLBCL (n=154), atypical BL (n=20), typical BL (n=15) and other aggressive B-NHL (n=24). For all cases gene expression analysis was done using the Affymetrix U133A GeneChip. FISH data were available for translocations involving the MYC locus and for t(14;18). Using arrayCGH, chromosomal gains in more than 10% of all cases have been identified on 18q(24.4%), 3q(19.8%), 1q(19.1%), 12q(16.7%) 2p(16.3 %), 7p and 7q(16.3%), 6p(13.2%) and 11q(12.5%). Deletions occurred most frequently on chromosome arm 6q(20.3%), 17p(15.4%), 9p(14.8%) and 8p(10%). Molecular BLs had significantly less chromosomal aberrations than non-mBLs but frequently show gains of chromosome arm 1q. Non-mBLs show higher genomic complexity and strikingly differing aberration patterns for GCB (more than 20% gains on 1q, 2p, 7q, 11q, 12q) and ABC (more than 20% gains on 3q, 9p, 18q, loss on 6q) type lymphomas. These findings were also supported by results of genomic clustering algorithms such as non negative matrix factorization. For a large number of recurrent chromosomal aberrations we were able to delineate minimal aberrant chromosomal regions and to correlate respective regional mRNA gene expression levels. Strong proportional correlations were found for gains of 18q21.1-q21.33 and losses of 6q21-q24.3. In conclusion, genomic aberration patterns closely correlate with distinct molecular subtypes of aggressive B-NHL. These aberration patterns might serve as surrogate markers providing diagnostic and prognostic information as well as a basis for targeted gene investigations and risk adapted therapy studies.

scholarly journals A Sparse and Low-Rank Regression Model for Identifying the Relationships Between DNA Methylation and Gene Expression Levels in Gastric Cancer and the Prediction of Prognosis

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 854
Author(s):  
Yishu Wang ◽  
Lingyun Xu ◽  
Dongmei Ai
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Dna Methylation ◽  
Regression Model ◽  
The Cancer Genome Atlas ◽  
Low Rank ◽  
Expression Levels ◽  
Rank Regression ◽  
Prediction Of Prognosis ◽  
Gene Expression Levels

DNA methylation is an important regulator of gene expression that can influence tumor heterogeneity and shows weak and varying expression levels among different genes. Gastric cancer (GC) is a highly heterogeneous cancer of the digestive system with a high mortality rate worldwide. The heterogeneous subtypes of GC lead to different prognoses. In this study, we explored the relationships between DNA methylation and gene expression levels by introducing a sparse low-rank regression model based on a GC dataset with 375 tumor samples and 32 normal samples from The Cancer Genome Atlas database. Differences in the DNA methylation levels and sites were found to be associated with differences in the expressed genes related to GC development. Overall, 29 methylation-driven genes were found to be related to the GC subtypes, and in the prognostic model, we explored five prognoses related to the methylation sites. Finally, based on a low-rank matrix, seven subgroups were identified with different methylation statuses. These specific classifications based on DNA methylation levels may help to account for heterogeneity and aid in personalized treatments.

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