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.

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 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 Atlas of tissue-specific and tissue-preferential gene expression in ecologically and economically significant conifer Pinus sylvestris

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11781
Author(s):  
Sandra Cervantes ◽  
Jaana Vuosku ◽  
Tanja Pyhäjärvi
Keyword(s):  
Gene Expression ◽  
Pinus Sylvestris ◽  
Tissue Specificity ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Biological Databases ◽  
Ploidy Levels ◽  
Tissue Specific ◽  
Genomic Resources ◽  
Wide Range

Despite their ecological and economical importance, conifers genomic resources are limited, mainly due to the large size and complexity of their genomes. Additionally, the available genomic resources lack complete structural and functional annotation. Transcriptomic resources have been commonly used to compensate for these deficiencies, though for most conifer species they are limited to a small number of tissues, or capture only a fraction of the genes present in the genome. Here we provide an atlas of gene expression patterns for conifer Pinus sylvestris across five tissues: embryo, megagametophyte, needle, phloem and vegetative bud. We used a wide range of tissues and focused our analyses on the expression profiles of genes at tissue level. We provide comprehensive information of the per-tissue normalized expression level, indication of tissue preferential upregulation and tissue-specificity of expression. We identified a total of 48,001 tissue preferentially upregulated and tissue specifically expressed genes, of which 28% have annotation in the Swiss-Prot database. Even though most of the putative genes identified do not have functional information in current biological databases, the tissue-specific patterns discovered provide valuable information about their potential functions for further studies, as for example in the areas of plant physiology, population genetics and genomics in general. As we provide information on tissue specificity at both diploid and haploid life stages, our data will also contribute to the understanding of evolutionary rates of different tissue types and ploidy levels.

scholarly journals Identification of the innate normal tissue specific genes and acquired tumor specific genes in determining the tumor transcriptional profiles

2019 ◽  
Author(s):  
Haiwei Wang ◽  
Xinrui Wang ◽  
Liangpu Xu ◽  
Ji Zhang ◽  
Hua Cao
Keyword(s):  
Dna Methylation ◽  
Cell Carcinoma ◽  
Normal Tissue ◽  
Expression Profiles ◽  
Lung Squamous Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Cancer Type ◽  
Tumor Type ◽  
Tissue Specific ◽  
Tumor Tissues

Abstract Background: For a specific cancer type, the transcriptional profile is determined by the combination of innate transcriptional features of the original normal tissue and the acquired transcriptional characteristics mediated by genomic and epigenetic aberrations in the tumor development. However, the classification of innate normal tissue specific genes and acquired tumor specific genes is not studied in a pan-cancer manner. Methods: The innate and acquired gene expression profiles in each tumor type were studied using The Cancer Genome Atlas (TCGA) RNA-seq dataset. The prognostic effects of the tumor acquired genes were determined by “survival” package in R software. The methylation of the tumor acquired genes was delineated using TCGA HumanMethylation450 microarray data. Results: 90% liver hepatocellular carcinoma (LIHC) specific genes are derived from innate normal liver specific genes. On the contrary, 90.3% kidney clear cell carcinoma (KIRC) specific genes and 90.9 % lung squamous cell carcinoma (LUSC) specific genes are acquired in the tumor developmental progress. The innate normal tissue specific genes are down regulated in tumor tissues, while, the tumor acquired specific genes are up regulated in the tumor tissues. The innate normal tissue specific genes and the tumors acquired specific genes are both associated with the tumor overall survival in some tumor types. The hyper-DNA methylation of normal tissue specific genes is contributing to the inhibition of normal tissue specific genes expression in cancer cells. And the tumor acquired specific genes are activated by hypo-DNA methylation and genomic aberrations. Conclusions: Our results provide descriptions of the specific transcriptional features across cancer types and suggest that the tumor acquired specific genes are potential targets for anti-cancer therapy.

scholarly journals Atlas of tissue-specific and tissue-preferential gene expression in ecologically and economically significant conifer Pinus sylvestris

2020 ◽  
Author(s):  
Sandra Cervantes ◽  
Jaana Vuosku ◽  
Dorota Paczesniak ◽  
Tanja Pyhäjärvi
Keyword(s):  
Gene Expression ◽  
Pinus Sylvestris ◽  
Tissue Specificity ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Biological Databases ◽  
Ploidy Levels ◽  
Tissue Specific ◽  
Genomic Resources ◽  
Wide Range

AbstractDespite their ecological and economical importance, conifers genomic resources are limited, mainly due to the large size and complexity of their genomes. Additionally, the available genomic resources lack complete structural and functional annotation. Transcriptomic resources have been commonly used to compensate for these deficiencies, though for most conifer species they are limited to a small number of tissues, or capture only a fraction of the genes present in the genome.Here we provide an atlas of gene expression patterns for conifer Pinus sylvestris across five tissues: embryo, megagametophyte, needle, phloem, and vegetative bud. We used a wide range of tissues and focused our analyses on the expression profiles of genes at tissue level. We provide comprehensive information of the per-tissue normalized expression level, indication of tissue preferential upregulation and tissue-specificity of expression. We identified a total of 48,001 tissue preferentially upregulated and tissue specifically expressed genes, of which 28% have annotation in the Swiss-Prot database. Even though most of the putative genes identified do not have functional information in current biological databases, the tissue-specific patterns discovered provide valuable information about their potential functions for further studies, as for example in the areas of plant physiology, population genetics, and genomics in general. As we provide information on tissue specificity at both diploid and haploid life stages, our data will also contribute to the understanding of evolutionary rates of different tissue types and ploidy levels.

scholarly journals Gene Expression Signatures Based on Variability can Robustly Predict Tumor Progression and Prognosis

2015 ◽  
Vol 14 ◽  
pp. CIN.S23862 ◽  
Author(s):  
Wikum Dinalankara ◽  
Héctor Corrada Bravo
Keyword(s):  
Gene Expression ◽  
Normal Tissue ◽  
Tissue Expression ◽  
Cancer Prognosis ◽  
Clinical Settings ◽  
Cancer Type ◽  
Genomic Signatures ◽  
Normal Expression ◽  
Gene Expression Signatures ◽  
Cancer Types

Gene expression signatures are commonly used to create cancer prognosis and diagnosis methods, yet only a small number of them are successfully deployed in the clinic since many fail to replicate performance on subsequent validation. A primary reason for this lack of reproducibility is the fact that these signatures attempt to model the highly variable and unstable genomic behavior of cancer. Our group recently introduced gene expression anti-profiles as a robust methodology to derive gene expression signatures based on the observation that while gene expression measurements are highly heterogeneous across tumors of a specific cancer type relative to the normal tissue, their degree of deviation from normal tissue expression in specific genes involved in tissue differentiation is a stable tumor mark that is reproducible across experiments and cancer types. Here we show that constructing gene expression signatures based on variability and the anti-profile approach yields classifiers capable of successfully distinguishing benign growths from cancerous growths based on deviation from normal expression. We then show that this same approach generates stable and reproducible signatures that predict probability of relapse and survival based on tumor gene expression. These results suggest that using the anti-profile framework for the discovery of genomic signatures is an avenue leading to the development of reproducible signatures suitable for adoption in clinical settings.

scholarly journals ExSurv: A Web Resource for Prognostic Analyses of Exons across Human Cancers Using Clinical Transcriptomes

2016 ◽  
Vol 15s2 ◽  
pp. CIN.S39367 ◽  
Author(s):  
Seyedsasan Hashemikhabir ◽  
Gungor Budak ◽  
Sarath Chandra Janga
Keyword(s):  
Biomarker Discovery ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Treatment Options ◽  
The Cancer Genome Atlas ◽  
Cancer Type ◽  
Biomedical Sciences ◽  
Regulatory Pathways ◽  
Web Resource ◽  
Cancer Types

Survival analysis in biomedical sciences is generally performed by correlating the levels of cellular components with patients’ clinical features as a common practice in prognostic biomarker discovery. While the common and primary focus of such analysis in cancer genomics so far has been to identify the potential prognostic genes, alternative splicing – a posttranscriptional regulatory mechanism that affects the functional form of a protein due to inclusion or exclusion of individual exons giving rise to alternative protein products, has increasingly gained attention due to the prevalence of splicing aberrations in cancer transcriptomes. Hence, uncovering the potential prognostic exons can not only help in rationally designing exon-specific therapeutics but also increase specificity toward more personalized treatment options. To address this gap and to provide a platform for rational identification of prognostic exons from cancer transcriptomes, we developed ExSurv ( https://exsurv.soic.iupui.edu ), a web-based platform for predicting the survival contribution of all annotated exons in the human genome using RNA sequencing-based expression profiles for cancer samples from four cancer types available from The Cancer Genome Atlas. ExSurv enables users to search for a gene of interest and shows survival probabilities for all the exons associated with a gene and found to be significant at the chosen threshold. ExSurv also includes raw expression values across the cancer cohort as well as the survival plots for prognostic exons. Our analysis of the resulting prognostic exons across four cancer types revealed that most of the survival-associated exons are unique to a cancer type with few processes such as cell adhesion, carboxylic, fatty acid metabolism, and regulation of T-cell signaling common across cancer types, possibly suggesting significant differences in the posttranscriptional regulatory pathways contributing to prognosis.

scholarly journals CancerSiamese: one-shot learning for primary and metastatic tumor classification

2020 ◽  
Author(s):  
Milad Mostav ◽  
Yu-Chiao Chiu ◽  
Yidong Chen ◽  
Yufei Huang
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Metastatic Cancer ◽  
Single Gene ◽  
Marker Gene ◽  
Metastatic Tumor ◽  
Saliency Map ◽  
Similarity Function ◽  
Cancer Type ◽  
Cancer Types

AbstractWe consider cancer classification based on one single gene expression profile. We proposed CancerSiamese, a new one-shot learning model, to predict the cancer type of a query primary or metastatic tumor sample based on a support set that contains only one known sample for each cancer type. CancerSiamese receives pairs of gene expression profiles and learns a representation of similar or dissimilar cancer types through two parallel Convolutional Neural Networks joined by a similarity function. We trained CancerSiamese for both primary and metastatic cancer type predictions using samples from TCGA and MET500. Test results for different N-way predictions yielded an average accuracy improvement of 8% and 4% over the benchmark 1-Nearest Neighbor (1-NN) classifier for primary and metastatic tumors, respectively. Moreover, we applied the guided gradient saliency map and feature selection to CancerSiamese to identify and analyze the marker-gene candidates for primary and metastatic cancers. Our work demonstrated, for the first time, the feasibility of applying one-shot learning for expression-based cancer type prediction when gene expression data of cancer types are limited and could inspire new and ingenious applications of one-shot and few-shot learning solutions for improving cancer diagnosis, treatment planning, and our understanding of cancer.

Gene expression data analysis for characterizing shared and type specific mechanisms of HCC and B-CLL

2018 ◽  
Vol 44 (1) ◽  
pp. 86-97
Author(s):  
Ceren Sucularli ◽  
Ugur Toprak ◽  
Melda Arslantas
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
B Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
B Cell Lymphoma ◽  
Specific Gene ◽  
Cancer Type ◽  
Expression Data ◽  
Cancer Types

Abstract Background Comparing gene expression profiles using gene expression datasets of different types of tumors is frequently used to identify molecular mechanisms of cancer. This study aimed to find shared and type specific gene expression profiles of hepatocellular carcinoma (HCC) and B-cell chronic lymphocytic leukemia (B-CLL). Material and methods Gene expression microarrays for HCC and B-CLL and RNA-sequencing expression data for liver HCC and lymphoid neoplasm diffuse large B-cell lymphoma (DLBC) were analyzed and differentially expressed probe sets or genes for each cancer type were detected. Probe sets and genes that were shared or specifically expressed in both cancer types were identified. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) terms for Biological Process (BP) annotations were performed. Results According to our analysis shared upregulated genes were mainly annotated in cell cycle processes. Some of the genes that changed only in HCC were annotated in cell cycle and metabolic processes, and some of the genes, altered only in B-CLL, were annotated in immune response and hemopoiesis. Conclusion These results contribute to cancer research that aim to find the conserved gene expression profiles in different cancer types and widen the knowledge of HCC and B-CLL specific mechanisms.

scholarly journals Pan-Cancer Transcriptional Models Predicting Chemosensitivity in Human Tumors

2021 ◽  
Vol 20 ◽  
pp. 117693512110024
Author(s):  
Jason D Wells ◽  
Jacqueline R Griffin ◽  
Todd W Miller
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Human Tumors ◽  
Molecular Characteristics ◽  
Survival Times ◽  
Chemotherapy Drugs ◽  
Testing Dataset ◽  
Cancer Models ◽  
Cancer Types ◽  
Pan Cancer

Motivation: Despite increasing understanding of the molecular characteristics of cancer, chemotherapy success rates remain low for many cancer types. Studies have attempted to identify patient and tumor characteristics that predict sensitivity or resistance to different types of conventional chemotherapies, yet a concise model that predicts chemosensitivity based on gene expression profiles across cancer types remains to be formulated. We attempted to generate pan-cancer models predictive of chemosensitivity and chemoresistance. Such models may increase the likelihood of identifying the type of chemotherapy most likely to be effective for a given patient based on the overall gene expression of their tumor. Results: Gene expression and drug sensitivity data from solid tumor cell lines were used to build predictive models for 11 individual chemotherapy drugs. Models were validated using datasets from solid tumors from patients. For all drug models, accuracy ranged from 0.81 to 0.93 when applied to all relevant cancer types in the testing dataset. When considering how well the models predicted chemosensitivity or chemoresistance within individual cancer types in the testing dataset, accuracy was as high as 0.98. Cell line–derived pan-cancer models were able to statistically significantly predict sensitivity in human tumors in some instances; for example, a pan-cancer model predicting sensitivity in patients with bladder cancer treated with cisplatin was able to significantly segregate sensitive and resistant patients based on recurrence-free survival times ( P = .048) and in patients with pancreatic cancer treated with gemcitabine ( P = .038). These models can predict chemosensitivity and chemoresistance across cancer types with clinically useful levels of accuracy.

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