scholarly journals TNMplot.com: A Web Tool for the Comparison of Gene Expression in Normal, Tumor and Metastatic Tissues

2021 ◽  
Vol 22 (5) ◽  
pp. 2622
Author(s):  
Áron Bartha ◽  
Balázs Győrffy
Keyword(s):  
Gene Expression ◽  
Statistical Significance ◽  
Tissue Expression ◽  
Gene Expression Omnibus ◽  
Tumor Formation ◽  
The Cancer Genome Atlas ◽  
Altered Expression ◽  
Multiple Tumor ◽  
Therapy Targets ◽  
Analysis Platform

Genes showing higher expression in either tumor or metastatic tissues can help in better understanding tumor formation and can serve as biomarkers of progression or as potential therapy targets. Our goal was to establish an integrated database using available transcriptome-level datasets and to create a web platform which enables the mining of this database by comparing normal, tumor and metastatic data across all genes in real time. We utilized data generated by either gene arrays from the Gene Expression Omnibus of the National Center for Biotechnology Information (NCBI-GEO) or RNA-seq from The Cancer Genome Atlas (TCGA), Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and The Genotype-Tissue Expression (GTEx) repositories. The altered expression within different platforms was analyzed separately. Statistical significance was computed using Mann–Whitney or Kruskal–Wallis tests. False Discovery Rate (FDR) was computed using the Benjamini–Hochberg method. The entire database contains 56,938 samples, including 33,520 samples from 3180 gene chip-based studies (453 metastatic, 29,376 tumorous and 3691 normal samples), 11,010 samples from TCGA (394 metastatic, 9886 tumorous and 730 normal), 1193 samples from TARGET (1 metastatic, 1180 tumorous and 12 normal) and 11,215 normal samples from GTEx. The most consistently upregulated genes across multiple tumor types were TOP2A (FC = 7.8), SPP1 (FC = 7.0) and CENPA (FC = 6.03), and the most consistently downregulated gene was ADH1B (FC = 0.15). Validation of differential expression using equally sized training and test sets confirmed the reliability of the database in breast, colon, and lung cancer at an FDR below 10%. The online analysis platform enables unrestricted mining of the database and is accessible at TNMplot.com.

scholarly journals TNMplot.com: a web tool for the comparison of gene expression in normal, tumor and metastatic tissues

2020 ◽  
Author(s):  
Áron Bartha ◽  
Balázs Győrffy
Keyword(s):  
Statistical Significance ◽  
Gene Array ◽  
Tumor Formation ◽  
Rna Seq ◽  
Sequencing Data ◽  
Multiple Tumor ◽  
Therapy Targets ◽  
Tumor Types ◽  
Analysis Platform ◽  
Target Effects

ABSTRACTGenes showing higher expression in either tumor or metastatic tissues can help in better understanding tumor formation, and can serve as biomarkers of progression or as therapy targets with minimal off-target effects. Our goal was to establish an integrated database using available transcriptome-level datasets and to create a web-platform enabling mining of this database by comparing normal, tumor and metastatic data across all genes in real time.We utilized data generated by either gene arrays or RNA-seq. Gene array data were manually selected from NCBI-GEO. RNA sequencing data was downloaded from the TCGA, TARGET, and GTEx repositories. TCGA and TARGET contain predominantly tumor and metastatic samples from adult and pediatric patients, while GTEx samples are from healthy tissues. Statistical significance was computed using Mann-Whitney or Kruskall-Wallis tests.The entire database contains 56,938 samples including 33,520 samples from 3,180 gene chip-based studies (453 metastatic, 29,376 tumorous and 3,691 normal samples), 11,010 samples from TCGA (394 metastatic, 9,886 tumorous and 730 normal), 1,193 samples from TARGET (1 metastatic, 1,180 tumor, 12 normal) and 11,215 normal samples from GTEx. The most consistently up-regulated genes across multiple tumor types were TOP2A (mean FC=7.8), SPP1 (FC=7.0) and CENPA (FC=6.03) and the most consistently down-regulated gene was ADH1B (mean FC=0.15). Validation of differential expression using equally sized training and test sets confirmed reliability of the database in breast, colon, and lung cancer (p<0.0001). The online analysis platform enables unrestricted mining of the database and is accessible at www.tnmplot.com.

scholarly journals Tissue, age, sex, and disease patterns of matrisome expression in GTEx transcriptome data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tim O. Nieuwenhuis ◽  
Avi Z. Rosenberg ◽  
Matthew N. McCall ◽  
Marc K. Halushka
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Tissue Expression ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Normal Tissues ◽  
Disease Patterns ◽  
Generalized Matrix ◽  
Gene Transcripts ◽  
Matrix Control

AbstractThe extracellular matrix (ECM) has historically been explored through proteomic methods. Whether or not global transcriptomics can yield meaningful information on the human matrisome is unknown. Gene expression data from 17,382 samples across 52 tissues, were obtained from the Genotype-Tissue Expression (GTEx) project. Additional datasets were obtained from The Cancer Genome Atlas (TCGA) program and the Gene Expression Omnibus for comparisons. Gene expression levels generally matched proteome-derived matrisome expression patterns. Further, matrisome gene expression properly clustered tissue types, with some matrisome genes including SERPIN family members having tissue-restricted expression patterns. Deeper analyses revealed 382 gene transcripts varied by age and 315 varied by sex in at least one tissue, with expression correlating with digitally imaged histologic tissue features. A comparison of TCGA tumor, TCGA adjacent normal and GTEx normal tissues demonstrated robustness of the GTEx samples as a generalized matrix control, while also determining a common primary tumor matrisome. Additionally, GTEx tissues served as a useful non-diseased control in a separate study of idiopathic pulmonary fibrosis (IPF) matrix changes, while identifying 22 matrix genes upregulated in IPF. Altogether, these findings indicate that the transcriptome, in general, and GTEx in particular, has value in understanding the state of organ ECM.

scholarly journals Alternative polyadenylation drives oncogenic gene expression in pancreatic ductal adenocarcinoma

2019 ◽  
Author(s):  
Swati Venkat ◽  
Arwen A. Tisdale ◽  
Johann R. Schwarz ◽  
Abdulrahman A. Alahmari ◽  
H. Carlo Maurer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alternative Polyadenylation ◽  
Tissue Expression ◽  
The Cancer Genome Atlas ◽  
Ductal Adenocarcinoma ◽  
Tumor Type ◽  
Mirna Regulation ◽  
Sequencing Data ◽  
Altered Expression ◽  
Cancer Genome Atlas

ABSTRACTAlternative polyadenylation (APA) is a gene regulatory process that dictates mRNA 3’-UTR length, resulting in changes in mRNA stability and localization. APA is frequently disrupted in cancer and promotes tumorigenesis through altered expression of oncogenes and tumor suppressors. Pan-cancer analyses have revealed common APA events across the tumor landscape; however, little is known about tumor type-specific alterations that may uncover novel events and vulnerabilities. Here we integrate RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project and The Cancer Genome Atlas (TCGA) to comprehensively analyze APA events in 148 pancreatic ductal adenocarcinomas (PDAs). We report widespread, recurrent and functionally relevant 3’-UTR alterations associated with gene expression changes of known and newly identified PDA growth-promoting genes and experimentally validate the effects of these APA events on expression. We find enrichment for APA events in genes associated with known PDA pathways, loss of tumor-suppressive miRNA binding sites, and increased heterogeneity in 3’-UTR forms of metabolic genes. Survival analyses reveal a subset of 3’-UTR alterations that independently characterize a poor prognostic cohort among PDA patients. Finally, we identify and validate the casein kinase CK1α as an APA-regulated therapeutic target in PDA. Knockdown or pharmacological inhibition of CK1α attenuates PDA cell proliferation and clonogenic growth. Our single-cancer analysis reveals APA as an underappreciated driver of pro-tumorigenic gene expression in PDA via the loss of miRNA regulation.

scholarly journals A Human Pan-Cancer System Analysis of Procollagen-Lysine, 2-Oxoglutarate 5-Dioxygenase 3 (PLOD3)

2021 ◽  
Vol 22 (18) ◽  
pp. 9903
Author(s):  
Siming Gong ◽  
Yingjuan Duan ◽  
Changwu Wu ◽  
Georg Osterhoff ◽  
Nikolas Schopow ◽  
...  
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
System Analysis ◽  
Human Cancer ◽  
Soft Tissue Sarcomas ◽  
Tissue Expression ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Cancer Tissue ◽  
Potential Biomarker

The overexpression of the enzymes involved in the degradation of procollagen lysine is correlated with various tumor entities. Procollagen-lysine, 2-oxoglutarate 5-dioxygenase 3 (PLOD3) expression was found to be correlated to the progression and migration of cancer cells in gastric, lung and prostate cancer. Here, we analyzed the gene expression, protein expression, and the clinical parameters of survival across 33 cancers based on the Clinical Proteomic Tumor Analysis Consortium (CPTAC), function annotation of the mammalian genome 5 (FANTOM5), Gene Expression Omnibus (GEO), Genotype-Tissue Expression (GTEx), Human Protein Atlas (HPA) and The Cancer Genome Atlas (TCGA) databases. Genetic alteration, immune infiltration and relevant cellular pathways were analyzed in detail. PLOD3 expression negatively correlated with survival periods and the infiltration level of CD8+ T cells, but positively correlated to the infiltration of cancer associated fibroblasts in diverse cancers. Immunohistochemistry in colon carcinomas, glioblastomas, and soft tissue sarcomas further confirm PLOD 3 expression in human cancer tissue. Moreover, amplification and mutation accounted for the largest proportion in esophageal adenocarcinoma and uterine corpus endometrial carcinoma, respectively; the copy number alteration of PLOD3 appeared in all cancers from TCGA; and molecular mechanisms further proved the effect of PLOD3 on tumorigenesis. In particular, PLOD3 expression appears to have a tumor immunological effect, and is related to multiple immune cells. Furthermore, it is also associated with tumor mutation burden and microsatellite instability in various tumors. PLOD3 acts as an inducer of various cancers, and it could be a potential biomarker for prognosis and targeted treatment.

scholarly journals Sex, age, tissue, and disease patterns of matrisome expression in GTEx transcriptome data

2021 ◽  
Author(s):  
Tim O. Nieuwenhuis ◽  
Avi Z. Rosenberg ◽  
Matthew N. McCall ◽  
Marc K. Halushka
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Tissue Expression ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Normal Tissues ◽  
Disease Patterns ◽  
Cancer Genome Atlas ◽  
Unknown Gene ◽  
Gene Expression Levels

AbstractThe extracellular matrix (ECM) has historically been explored through proteomic methods. Whether or not global transcriptomics can yield meaningful information on the human matrisome is unknown. Gene expression data from 17,382 samples across 52 tissues, were obtained from the Genotype-Tissue Expression (GTEx) project. Additional datasets were obtained from The Cancer Genome Atlas (TCGA) program and the Gene Expression Omnibus for comparisons. Gene expression levels generally recapitulated proteome-derived matrisome expression patterns. Further, matrisome gene expression properly clustered tissue types, with some matrisome genes including SERPIN family members having tissue-restricted expression patterns. Deeper analyses revealed 388 genes varied by age and 222 varied by sex in at least one tissue, with expression correlating with digitally imaged histologic tissue features. A comparison of TCGA tumor, TCGA adjacent normal and GTEx normal tissues demonstrated robustness of the GTEx samples as a generalized control, while also determining a common primary tumor matrisome. Additionally, GTEx tissues served as a useful non-diseased control in a separate study of idiopathic pulmonary fibrosis matrix changes. Altogether, these findings indicate that the transcriptome, in general, and GTEx in particular, has value in understanding the state of organ ECM.

scholarly journals Novel Epigenetic Eight-Gene Signature Predictive of Poor Prognosis and MSI-Like Phenotype in Human Metastatic Colorectal Carcinomas

Cancers ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 158
Author(s):  
Valentina Condelli ◽  
Giovanni Calice ◽  
Alessandra Cassano ◽  
Michele Basso ◽  
Maria Grazia Rodriquenz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Poor Prognosis ◽  
Cpg Island ◽  
Colon Adenocarcinoma ◽  
Gene Signature ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Cpg Island Methylator Phenotype ◽  
Prognostic Information ◽  
Global Methylation

Epigenetics is involved in tumor progression and drug resistance in human colorectal carcinoma (CRC). This study addressed the hypothesis that the DNA methylation profiling may predict the clinical behavior of metastatic CRCs (mCRCs). The global methylation profile of two human mCRC subgroups with significantly different outcome was analyzed and compared with gene expression and methylation data from The Cancer Genome Atlas COlon ADenocarcinoma (TCGA COAD) and the NCBI GENE expression Omnibus repository (GEO) GSE48684 mCRCs datasets to identify a prognostic signature of functionally methylated genes. A novel epigenetic signature of eight hypermethylated genes was characterized that was able to identify mCRCs with poor prognosis, which had a CpG-island methylator phenotype (CIMP)-high and microsatellite instability (MSI)-like phenotype. Interestingly, methylation events were enriched in genes located on the q-arm of chromosomes 13 and 20, two chromosomal regions with gain/loss alterations associated with adenoma-to-carcinoma progression. Finally, the expression of the eight-genes signature and MSI-enriching genes was confirmed in oxaliplatin- and irinotecan-resistant CRC cell lines. These data reveal that the hypermethylation of specific genes may provide prognostic information that is able to identify a subgroup of mCRCs with poor prognosis.

scholarly journals Functions and clinical significance of KLRG1 in the development of lung adenocarcinoma and immunotherapy

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiaodong Yang ◽  
Yuexin Zheng ◽  
Zhihai Han ◽  
Xiliang Zhang
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Lung Adenocarcinoma ◽  
Molecular Mechanisms ◽  
Killer Cell ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Normal Lung ◽  
Using Data ◽  
Low Expression

Abstract Background As a marker of differentiation, Killer cell lectin like receptor G1 (KLRG1) plays an inhibitory role in human NK cells and T cells. However, its clinical role remains inexplicit. This work intended to investigate the predictive ability of KLRG1 on the efficacy of immune-checkpoint inhibitor in the treatment of lung adenocarcinoma (LUAD), as well as contribute to the possible molecular mechanisms of KLRG1 on LUAD development. Methods Using data from the Gene Expression Omnibus, the Cancer Genome Atlas and the Genotype-Tissue Expression, we compared the expression of KLRG1 and its related genes Bruton tyrosine kinase (BTK), C-C motif chemokine receptor 2 (CCR2), Scm polycomb group protein like 4 (SCML4) in LUAD and normal lung tissues. We also established stable LUAD cell lines with KLRG1 gene knockdown and investigated the effect of KLRG1 knockdown on tumor cell proliferation. We further studied the prognostic value of the four factors in terms of overall survival (OS) in LUAD. Using data from the Gene Expression Omnibus, we further investigated the expression of KLRG1 in the patients with different responses after immunotherapy. Results The expression of KLRG1, BTK, CCR2 and SCML4 was significantly downregulated in LUAD tissues compared to normal controls. Knockdown of KLRG1 promoted the proliferation of A549 and H1299 tumor cells. And low expression of these four factors was associated with unfavorable overall survival in patients with LUAD. Furthermore, low expression of KLRG1 also correlated with poor responses to immunotherapy in LUAD patients. Conclusion Based on these findings, we inferred that KLRG1 had significant correlation with immunotherapy response. Meanwhile, KLRG1, BTK, CCR2 and SCML4 might serve as valuable prognostic biomarkers in LUAD.

scholarly journals BART Cancer: a web resource for transcriptional regulators in cancer genomes

NAR Cancer ◽  
2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Zachary V Thomas ◽  
Zhenjia Wang ◽  
Chongzhi Zang
Keyword(s):  
Gene Expression ◽  
Cancer Research ◽  
Computational Prediction ◽  
Transcriptional Regulators ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Regulation Of Transcription ◽  
Data Resource ◽  
Web Resource ◽  
Cancer Types

Abstract Dysregulation of gene expression plays an important role in cancer development. Identifying transcriptional regulators, including transcription factors and chromatin regulators, that drive the oncogenic gene expression program is a critical task in cancer research. Genomic profiles of active transcriptional regulators from primary cancer samples are limited in the public domain. Here we present BART Cancer (bartcancer.org), an interactive web resource database to display the putative transcriptional regulators that are responsible for differentially regulated genes in 15 different cancer types in The Cancer Genome Atlas (TCGA). BART Cancer integrates over 10000 gene expression profiling RNA-seq datasets from TCGA with over 7000 ChIP-seq datasets from the Cistrome Data Browser database and the Gene Expression Omnibus (GEO). BART Cancer uses Binding Analysis for Regulation of Transcription (BART) for predicting the transcriptional regulators from the differentially expressed genes in cancer samples compared to normal samples. BART Cancer also displays the activities of over 900 transcriptional regulators across cancer types, by integrating computational prediction results from BART and the Cistrome Cancer database. Focusing on transcriptional regulator activities in human cancers, BART Cancer can provide unique insights into epigenetics and transcriptional regulation in cancer, and is a useful data resource for genomics and cancer research communities.

scholarly journals Development and validation of a 13-m6a related lncRNA prognostic signature for lung adenocarcinoma

2021 ◽  
Author(s):  
Pingfan Wu ◽  
Xiaowen Zhao ◽  
Ling Xue ◽  
Xiaojing Yang ◽  
Yuxiang Shi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Adenocarcinoma ◽  
Treatment Strategies ◽  
Risk Groups ◽  
Gene Expression Omnibus ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
P53 Signaling ◽  
Selection Operator

Abstract Considerable evidence suggests that N6-methyladenosine (m6A) is involved in the regulation of long non-coding RNA (lncRNA), whichparticipates in the occurrence, development and prognosis of tumorscancerBut the relationship between m6A regulators-related lncRNA (mRlncRNA) and lung adenocarcinoma (LUAD) remains unclear. This study aims to determine a feature based on mRlncRNA for prognostic evaluation of LUAD patients. By integrating the gene expression data of LUAD and normal samples from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database, the m6A gene and mRlncRNA with imbalanced expression were screened out. Then we used the least absolute shrinkage and selection operator (LASSO) to obtain the 13-lncRNA prognostic signature in the TCGA training cohort. Patients were divided into two risk groups based on the risk score of lncRNAs characteristics, and their overall survival (OS) was significantly different. The predictive power of this signature was verified in TCGA testing cohort and entire TCGA cohort. These landmark lncRNAs were involved in several biologiocal processes and pathways related to cell cycle, DNA replication, P53 signaling pathway and mismatch repair. Besides, the high-risk group was low-response to cisplatin, while high-response to mitomycin, docetaxel and immunotherapy. In conclusion, we identified a 13-mRlncRNA model associated with prognosis and treatment sensitivity in LUAD, which may provide clues about the influence of m6A on lncRNA in LUAD and promote the further improvement of LUAD individualized treatment strategies.

scholarly journals Data mining of immune-related prognostic genes in metastatic melanoma microenvironment

2020 ◽  
Vol 40 (11) ◽  
Author(s):  
Wei Han ◽  
Biao Huang ◽  
Xiao-Yu Zhao ◽  
Guo-Liang Shen
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Metastatic Melanoma ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Protein Protein Interaction ◽  
Subtype Identification ◽  
Cancer Genome Atlas ◽  
Immune Related Genes

Abstract Skin cutaneous melanoma (SKCM) is one of the most deadly malignancies. Although immunotherapies showed the potential to improve the prognosis for metastatic melanoma patients, only a small group of patients can benefit from it. Therefore, it is urgent to investigate the tumor microenvironment in melanoma as well as to identify efficient biomarkers in the diagnosis and treatments of SKCM patients. A comprehensive analysis was performed based on metastatic melanoma samples from the Cancer Genome Atlas (TCGA) database and ESTIMATE algorithm, including gene expression, immune and stromal scores, prognostic immune-related genes, infiltrating immune cells analysis and immune subtype identification. Then, the differentially expressed genes (DEGs) were obtained based on the immune and stromal scores, and a list of prognostic immune-related genes was identified. Functional analysis and the protein–protein interaction network revealed that these genes enriched in multiple immune-related biological processes. Furthermore, prognostic genes were verified in the Gene Expression Omnibus (GEO) databases and used to predict immune infiltrating cells component. Our study revealed seven immune subtypes with different risk values and identified T cells as the most abundant cells in the immune microenvironment and closely associated with prognostic outcomes. In conclusion, the present study thoroughly analyzed the tumor microenvironment and identified prognostic immune-related biomarkers for metastatic melanoma.

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