scholarly journals Tumors Widely Express Hundreds of Embryonic Germline Genes

Cancers ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3812
Author(s):  
Jan Willem Bruggeman ◽  
Naoko Irie ◽  
Paul Lodder ◽  
Ans M. M. van Pelt ◽  
Jan Koster ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cancer Treatment ◽  
Germ Cells ◽  
Expression Profiles ◽  
Cell Cancer ◽  
Cancer Genes ◽  
Germline Development ◽  
Multiple Tumor ◽  
Tumor Types ◽  
Gc Gene

We have recently described a class of 756 genes that are widely expressed in cancers, but are normally restricted to adult germ cells, referred to as germ cell cancer genes (GC genes). We hypothesized that carcinogenesis involves the reactivation of biomolecular processes and regulatory mechanisms that, under normal circumstances, are restricted to germline development. This would imply that cancer cells share gene expression profiles with primordial germ cells (PGCs). We therefore compared the transcriptomes of human PGCs (hPGCs) and PGC-like cells (PGCLCs) with 17,382 samples from 54 healthy somatic tissues (GTEx) and 11,003 samples from 33 tumor types (TCGA), and identified 672 GC genes, expanding the known GC gene pool by 387 genes (51%). We found that GC genes are expressed in clusters that are often expressed in multiple tumor types. Moreover, the amount of GC gene expression correlates with poor survival in patients with lung adenocarcinoma. As GC genes specific to the embryonic germline are not expressed in any adult tissue, targeting these in cancer treatment may result in fewer side effects than targeting conventional cancer/testis (CT) or GC genes and may preserve fertility. We anticipate that our extended GC dataset enables improved understanding of tumor development and may provide multiple novel targets for cancer treatment development.

scholarly journals Tumors widely express hundreds of embryonic germline genes

2020 ◽  
Author(s):  
Jan Willem Bruggeman ◽  
Naoko Irie ◽  
Paul Lodder ◽  
Ans M.M. van Pelt ◽  
Jan Koster ◽  
...  
Keyword(s):  
Cancer Treatment ◽  
Germ Cells ◽  
Expression Profiles ◽  
Primordial Germ Cells ◽  
Cell Cancer ◽  
Tumor Development ◽  
Gene Expression Profiles ◽  
Cancer Genes ◽  
Germline Development ◽  
Tumor Types

AbstractWe have recently described a class of 756 genes that are widely expressed in cancer, while normally restricted to adult germ cells, referred to as germ cell cancer genes (GC-genes). We hypothesized that carcinogenesis involves reactivation of biomolecular processes and regulatory mechanisms that, under normal circumstances, are restricted to germline development. This would imply that cancer cells share gene expression profiles with primordial germ cells (PGCs). We therefore compared the transcriptomes of human PGCs (hPGCs) and PGC-like cells (PGCLCs) with 17 382 samples from 54 healthy somatic tissues (GTEx) and 11 003 samples from 33 tumor types (TCGA), and identified 672 GC-genes, expanding the known GC-gene pool by 387 genes (51%). Because GC-genes specific to the embryonic germline are not expressed in any adult tissue, targeting these in cancer treatment may result in fewer side effects than targeting conventional cancer/testis (CT) or GC-genes and may preserve fertility. We anticipate that our extended GC-dataset enables improved understanding of tumor development and may provide multiple novel targets for cancer treatment development.

Utilizing Cancer - Functional Gene Set - Compound Networks to Identify Putative Drugs for Breast Cancer

2018 ◽  
Vol 21 (2) ◽  
pp. 74-83
Author(s):  
Tzu-Hung Hsiao ◽  
Yu-Chiao Chiu ◽  
Yu-Heng Chen ◽  
Yu-Ching Hsu ◽  
Hung-I Harry Chen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Therapy ◽  
Cancer Treatment ◽  
Cancer Survival ◽  
Expression Profiles ◽  
Functional Gene ◽  
Gene Set Enrichment Analysis ◽  
Gene Set ◽  
Gene Sets

Aim and Objective: The number of anticancer drugs available currently is limited, and some of them have low treatment response rates. Moreover, developing a new drug for cancer therapy is labor intensive and sometimes cost prohibitive. Therefore, “repositioning” of known cancer treatment compounds can speed up the development time and potentially increase the response rate of cancer therapy. This study proposes a systems biology method for identifying new compound candidates for cancer treatment in two separate procedures. Materials and Methods: First, a “gene set–compound” network was constructed by conducting gene set enrichment analysis on the expression profile of responses to a compound. Second, survival analyses were applied to gene expression profiles derived from four breast cancer patient cohorts to identify gene sets that are associated with cancer survival. A “cancer–functional gene set– compound” network was constructed, and candidate anticancer compounds were identified. Through the use of breast cancer as an example, 162 breast cancer survival-associated gene sets and 172 putative compounds were obtained. Results: We demonstrated how to utilize the clinical relevance of previous studies through gene sets and then connect it to candidate compounds by using gene expression data from the Connectivity Map. Specifically, we chose a gene set derived from a stem cell study to demonstrate its association with breast cancer prognosis and discussed six new compounds that can increase the expression of the gene set after the treatment. Conclusion: Our method can effectively identify compounds with a potential to be “repositioned” for cancer treatment according to their active mechanisms and their association with patients’ survival time.

scholarly journals Graphene oxide selectively targets cancer stem cells, across multiple tumor types: Implications for non-toxic cancer treatment, via “differentiation-based nano-therapy”

Oncotarget ◽  
2015 ◽  
Vol 6 (6) ◽  
pp. 3553-3562 ◽  
Author(s):  
Marco Fiorillo ◽  
Andrea F. Verre ◽  
Maria Iliut ◽  
Maria Peiris-Pagés ◽  
Bela Ozsvari ◽  
...  
Keyword(s):  
Stem Cells ◽  
Graphene Oxide ◽  
Cancer Stem Cells ◽  
Cancer Treatment ◽  
Multiple Tumor ◽  
Tumor Types

scholarly journals Identification of Lung-Cancer-Related Genes with the Shortest Path Approach in a Protein-Protein Interaction Network

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Bi-Qing Li ◽  
Jin You ◽  
Lei Chen ◽  
Jian Zhang ◽  
Ning Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Shortest Path ◽  
Protein Interaction ◽  
Expression Profiles ◽  
Shortest Paths ◽  
Gene Expression Profiles ◽  
Cancer Genes ◽  
Ppi Network ◽  
Protein Protein Interaction

Lung cancer is one of the leading causes of cancer mortality worldwide. The main types of lung cancer are small cell lung cancer (SCLC) and nonsmall cell lung cancer (NSCLC). In this work, a computational method was proposed for identifying lung-cancer-related genes with a shortest path approach in a protein-protein interaction (PPI) network. Based on the PPI data from STRING, a weighted PPI network was constructed. 54 NSCLC- and 84 SCLC-related genes were retrieved from associated KEGG pathways. Then the shortest paths between each pair of these 54 NSCLC genes and 84 SCLC genes were obtained with Dijkstra’s algorithm. Finally, all the genes on the shortest paths were extracted, and 25 and 38 shortest genes with a permutationPvalue less than 0.05 for NSCLC and SCLC were selected for further analysis. Some of the shortest path genes have been reported to be related to lung cancer. Intriguingly, the candidate genes we identified from the PPI network contained more cancer genes than those identified from the gene expression profiles. Furthermore, these genes possessed more functional similarity with the known cancer genes than those identified from the gene expression profiles. This study proved the efficiency of the proposed method and showed promising results.

PADGE: analysis of heterogeneous patterns of differential gene expression

2007 ◽  
Vol 32 (1) ◽  
pp. 154-159 ◽  
Author(s):  
Li Li ◽  
Amitabha Chaudhuri ◽  
John Chant ◽  
Zhijun Tang
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Web Application ◽  
Statistical Power ◽  
Profile Analysis ◽  
Expression Profiles ◽  
Differentially Expressed ◽  
Cancer Genes ◽  
Data Set ◽  
Differential Gene

We have devised a novel analysis approach, percentile analysis for differential gene expression (PADGE), for identifying genes differentially expressed between two groups of heterogeneous samples. PADGE was designed to compare expression profiles of sample subgroups at a series of percentile cutoffs and to examine the trend of relative expression between sample groups as expression level increases. Simulation studies showed that PADGE has more statistical power than t-statistics, cancer outlier profile analysis (COPA) (Tomlins SA, Rhodes DR, Perner S, Dhanasekaran SM, Mehra R, Sun XW, Varambally S, Cao X, Tchinda J, Kuefer R, Lee C, Montie JE, Shah RB, Pienta KJ, Rubin MA, Chinnaiyan AM. Science 310: 644–648, 2005), and kurtosis (Teschendorff AE, Naderi A, Barbosa-Morais NL, Caldas C. Bioinformatics 22: 2269–2275, 2006). Application of PADGE to microarray data sets in tumor tissues demonstrated its utility in prioritizing cancer genes encoding potential therapeutic targets or diagnostic markers. A web application was developed for researchers to analyze a large gene expression data set from heterogeneous biological samples and identify differentially expressed genes between subsets of sample classes using PADGE and other available approaches. Availability: http://www.cgl.ucsf.edu/Research/genentech/padge/ .

scholarly journals Targeting Signaling Pathways in Cancer Stem Cells for Cancer Treatment

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Jeffrey Koury ◽  
Li Zhong ◽  
Jijun Hao
Keyword(s):  
Stem Cells ◽  
Cancer Stem Cells ◽  
Cancer Treatment ◽  
Signaling Pathways ◽  
Small Subset ◽  
Self Renewal ◽  
Multiple Tumor ◽  
Notch Pathways ◽  
Tumor Types ◽  
Heterogeneous Tumor

The Wnt, Hedgehog, and Notch pathways are inherent signaling pathways in normal embryogenesis, development, and hemostasis. However, dysfunctions of these pathways are evident in multiple tumor types and malignancies. Specifically, aberrant activation of these pathways is implicated in modulation of cancer stem cells (CSCs), a small subset of cancer cells capable of self-renewal and differentiation into heterogeneous tumor cells. The CSCs are accountable for tumor initiation, growth, and recurrence. In this review, we focus on roles of Wnt, Hedgehog, and Notch pathways in CSCs’ stemness and functions and summarize therapeutic studies targeting these pathways to eliminate CSCs and improve overall cancer treatment outcomes.

scholarly journals Discovering novel driver mutations from pan-cancer analysis of mutational and gene expression profiles

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242780
Author(s):  
Houriiyah Tegally ◽  
Kevin H. Kensler ◽  
Zahra Mungloo-Dilmohamud ◽  
Anisah W. Ghoorah ◽  
Timothy R. Rebbeck ◽  
...  
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Driver Mutations ◽  
Literature Mining ◽  
Cancer Genes ◽  
Multiple Cancer ◽  
Driver Genes

As the genomic profile across cancers varies from person to person, patient prognosis and treatment may differ based on the mutational signature of each tumour. Thus, it is critical to understand genomic drivers of cancer and identify potential mutational commonalities across tumors originating at diverse anatomical sites. Large-scale cancer genomics initiatives, such as TCGA, ICGC and GENIE have enabled the analysis of thousands of tumour genomes. Our goal was to identify new cancer-causing mutations that may be common across tumour sites using mutational and gene expression profiles. Genomic and transcriptomic data from breast, ovarian, and prostate cancers were aggregated and analysed using differential gene expression methods to identify the effect of specific mutations on the expression of multiple genes. Mutated genes associated with the most differentially expressed genes were considered to be novel candidates for driver mutations, and were validated through literature mining, pathway analysis and clinical data investigation. Our driver selection method successfully identified 116 probable novel cancer-causing genes, with 4 discovered in patients having no alterations in any known driver genes: MXRA5, OBSCN, RYR1, and TG. The candidate genes previously not officially classified as cancer-causing showed enrichment in cancer pathways and in cancer diseases. They also matched expectations pertaining to properties of cancer genes, for instance, showing larger gene and protein lengths, and having mutation patterns suggesting oncogenic or tumor suppressor properties. Our approach allows for the identification of novel putative driver genes that are common across cancer sites using an unbiased approach without any a priori knowledge on pathways or gene interactions and is therefore an agnostic approach to the identification of putative common driver genes acting at multiple cancer sites.

Angiogenic gene expression in primary neuroendocrine tumors and their metastases.

2016 ◽  
Vol 34 (4_suppl) ◽  
pp. 200-200
Author(s):  
Tanja Milosavljevic ◽  
Michael Anthony Hall ◽  
Luis Del Valle ◽  
Elise Juge ◽  
Jovanny Zabaleta ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Expression ◽  
Neuroendocrine Tumors ◽  
Primary Tumor ◽  
Expression Profiles ◽  
Current Treatment ◽  
Tumor Site ◽  
Tissue Samples ◽  
Multiple Tumor ◽  
Gene And Protein Expression

200 Background: Neuroendocrine tumors (NETs) are neoplasms arising from the cells of the nervous, endocrine and hormonal systems. They most commonly originate in the small bowel (SB) and frequently metastasize to the lymph nodes (LNs) and/or liver (LV). Current treatment of metastatic NETs involves a variety of approaches including antiangiogenic therapies. Our group demonstrated that there are significant histologic and functional differences between the primary NETs and their nodal or organ metastases. We hypothesized that sampling of multiple tumor sites within the same individual will reveal differential expression profiles of angiogenesis-related genes. Methods: Tissue-matched normal and tumor tissue samples were obtained from patients with well differentiated NETs who underwent simultaneous removal of their primary tumor, nodal, and organ metastasis. High quality RNA was extracted from each tumor site using TRIzol and RNeasy Mini kit. Gene expression of 28 well-documented angiogenesis-related genes was assessed using Custom Quantitative RT-PCR array. These gene expression trends were validated by Illumina microarray and TaqMan analysis. Immunohistochemistry (IHC) staining was performed using Avidin-Biotin-Peroxidase complex, with the markers: SSTR2 and FGFR3. Results: Normal SB, LN, and LV gene expression of 28 genes was compared to that of the tumor sample at each tumor site [4-fold change, p ≤ 0.01]. A consistent up-regulation of SSTR2 and SSTR1 was seen in 18/24 (75%) samples. Up-regulation of FGFR3 and SSTR5 was observed in 13/24 (50%) of tumors. TGFA and IGF were consistently down-regulated in 12/24 (50%) and 10/24 (42%) of tumor samples, respectively. Six genes expression trends were validated by TaqMan analysis and Illumina microarray analysis. IHC staining revealed higher SSTR2 and FGFR3 protein expression in all three tumor sites compared to the control. Conclusions: Expression of angiogenesis-related genes varies between the primary tumor (SB) and metastatic sites (LV, LN) within the same individual. We found a positive correlation between SSTR2 and FGFR3 gene and protein expression levels in all three tumor sites.

scholarly journals Identification of a Gene Expression Signature Common to Distinct Cancer Pathways

2012 ◽  
Vol 11 ◽  
pp. CIN.S9542 ◽  
Author(s):  
Niklaus Fankhauser ◽  
Igor Cima ◽  
Peter Wild ◽  
Wilhelm Krek
Keyword(s):  
Gene Expression ◽  
Web Application ◽  
Cell Transformation ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Expression Signature ◽  
Malignant Cell ◽  
Cancer Genes ◽  
Data Set ◽  
Cancer Pathways

Mutations in cancer-causing genes induce changes in gene expression programs critical for malignant cell transformation. Publicly available gene expression profiles produced by modulating the expression of distinct cancer genes may therefore represent a rich resource for the identification of gene signatures common to seemingly unrelated cancer genes. We combined automatic retrieval with manual validation to obtain a data set of high-quality gene microarray profiles. This data set was used to create logical models of the signaling events underlying the observed expression changes produced by various cancer genes and allowed to uncover unknown and verifiable interactions. Data clustering revealed novel sets of gene expression profiles commonly regulated by distinct cancer genes. Our method allows retrieval of significant new information and testable hypotheses from a pool of deposited cancer gene expression experiments that are otherwise not apparent or appear insignificant from single measurements. The complete results are available through a web-application at http://biodata.ethz.ch/cgi-bin/geologic .

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.

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