scholarly journals Evolution of dominance in gene expression pattern associated with phenotypic robustness

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Kenji Okubo ◽  
Kunihiko Kaneko
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Regulatory Networks ◽  
Target Genes ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Diploid Cell ◽  
Mendelian Inheritance ◽  
Significant Difference ◽  
Phenotypic Robustness

Abstract Background Mendelian inheritance is a fundamental law of genetics. When we consider two genomes in a diploid cell, a heterozygote’s phenotype is dominated by a particular homozygote according to the law of dominance. Classical Mendelian dominance is concerned with which proteins are dominant, and is usually based on simple genotype–phenotype relationship in which one gene regulates one phenotype. However, in reality, some interactions between genes can exist, resulting in deviations from Mendelian dominance. Whether and how Mendelian dominance is generalized to the phenotypes of gene expression determined by gene regulatory networks (GRNs) remains elusive. Results Here, by using the numerical evolution of diploid GRNs, we discuss whether the dominance of phenotype evolves beyond the classical Mendelian case of one-to-one genotype–phenotype relationship. We examine whether complex genotype–phenotype relationship can achieve Mendelian dominance at the expression level by a pair of haplotypes through the evolution of the GRN with interacting genes. This dominance is defined via a pair of haplotypes that differ from each other but have a common phenotype given by the expression of target genes. We numerically evolve the GRN model for a diploid case, in which two GRN matrices are added to give gene expression dynamics and simulate evolution with meiosis and recombination. Our results reveal that group Mendelian dominance evolves even under complex genotype–phenotype relationship. Calculating the degree of dominance shows that it increases through the evolution, correlating closely with the decrease in phenotypic fluctuations and the increase in robustness to initial noise. We also demonstrate that the dominance of gene expression patterns evolves concurrently. This evolution of group Mendelian dominance and pattern dominance is associated with phenotypic robustness against meiosis-induced genome mixing, whereas sexual recombination arising from the mixing of genomes from the parents further enhances dominance and robustness. Due to this dominance, the robustness to genetic differences increases, while optimal fitness is sustained to a significant difference between the two genomes. Conclusion Group Mendelian dominance and gene-expression pattern dominance are achieved associated with the increase in phenotypic robustness to noise.

scholarly journals Evolution of Mendelian dominance in gene regulatory networks associated with phenotypic robustness

2021 ◽  
Author(s):  
Kenji Okubo ◽  
Kunihiko Kaneko
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Target Genes ◽  
Gene Expression Pattern ◽  
Mendelian Inheritance ◽  
Gene Expression Dynamics ◽  
Phenotypic Robustness ◽  
Robustness To Noise ◽  
Gene Regulatory

AbstractMendelian inheritance is a fundamental law of genetics. Considering two alleles in a diploid, a phenotype of a heterotype is dominated by a particular homotype according to the law of dominance. This picture is usually based on simple genotype-phenotype mapping in which one gene regulates one phenotype. However, in reality, some interactions between genes can result in deviation from Mendelian dominance.Here, by using the numerical evolution of diploid gene regulatory networks (GRNs), we discuss whether Mendelian dominance evolves beyond the classical case of one-to-one genotype-phenotype mapping. We examine whether complex genotype-phenotype mapping can achieve Mendelian dominance through the evolution of the GRN with interacting genes. Specifically, we extend the GRN model to a diploid case, in which two GRN matrices are added to give gene expression dynamics, and simulate evolution with meiosis and recombination. Our results reveal that Mendelian dominance evolves even under complex genotype-phenotype mapping. This dominance is achieved via a group of genotypes that differ from each other but have a common phenotype given by the expression of target genes. Calculating the degree of dominance shows that it increases through the evolution, correlating closely with the decrease in phenotypic fluctuations and the increase in robustness to initial noise. This evolution of Mendelian dominance is associated with phenotypic robustness against meiosis-induced genome mixing, whereas sexual recombination arising from the mixing of chromosomes from the parents further enhances dominance and robustness. Owing to this dominance, the robustness to genetic differences increases, while the optimal fitness is sustained up to a large difference between the two genomes. In summary, Mendelian dominance is achieved by groups of genotypes that are associated with the increase in phenotypic robustness to noise.Author summaryMendelian dominance is one of the most fundamental laws in genetics. When two conflicting characters occur in a single diploid, the dominant character is always chosen. Assuming that one gene makes one character, this law is simple to grasp. However, in reality, phenotypes are generated via interactions between several genes, which may alter Mendel’s dominance law. The evolution of robustness to noise and mutations has been investigated extensively using complex expression dynamics with gene regulatory networks. Here, we applied gene-expression dynamics with complex interactions to the case of a diploid and simulated the evolution of the gene regulatory network to generate the optimal phenotype given by a certain gene expression pattern. Interestingly, after evolution, Mendelian dominance is achieved via a group of genes. This group-based Mendelian dominance is shaped by phenotype insensitivity to genome mixing by meiosis and evolves concurrently with the robustness to noise. By focusing on the influence of phenotypic robustness, which has received considerable attention recently, our result provides a novel perspective as to why Mendel’s law of dominance is commonly observed.

Follistatin alters myostatin gene expression in C2C12 muscle cells

2004 ◽  
Vol 52 (2) ◽  
pp. 135-141 ◽  
Author(s):  
H. Kocams¸ ◽  
N. Gulmez ◽  
S. Aslan ◽  
M. Nazlı
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Time Course ◽  
Expression Patterns ◽  
Mrna Level ◽  
Gene Expression Pattern ◽  
Muscle Cells ◽  
Treated Group ◽  
Myostatin Gene ◽  
Significant Difference

The objective of the present study was to determine the effects of follistatin addition on myostatin and follistatin gene expression patterns in C2C12 muscle cells. C2C12 cells were administered with 100 ng/ml recombinant human (rh) follistatin in Dulbecco's modified Eagle medium (DMEM) containing 10% fetal bovine serum (FBS), 4 mM glutamine and antibiotics daily for three days. Rh follistatin was not added in the control wells. Follistatin and myostatin gene cDNAs were synthesised by reverse transcriptase polymerase chain reaction (RT-PCR).The time course of follistatin gene expression pattern was similar in both the control and the follistatin-treated group. Myostatin mRNA level significantly increased in the follistatin-treated group after 24 h of culture (Fig. 3, P < 0.01). Amounts then sharply decreased (Fig. 3, P < 0.01) at 48 h of culture, whereas there was no significant difference between the control and the follistatin-treated group at 72 h of culture. Our results demonstrated that myostatin and follistatin mRNA were expressed in C2C12 cells and rh follistatin changed the myostatin expression pattern.

Third coronary artery in monkey heart

2004 ◽  
Vol 52 (3) ◽  
pp. 253-257 ◽  
Author(s):  
Valentina Nikolić ◽  
Gordana Teofilovski-Parapid ◽  
Gordana Stanković ◽  
Biljana Parapid ◽  
S. Malobabić ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Time Course ◽  
Expression Patterns ◽  
Mrna Level ◽  
Gene Expression Pattern ◽  
Treated Group ◽  
Myostatin Gene ◽  
Significant Difference ◽  
Fetal Bovine

The objective of the present study was to determine the effects of follistatin addition on myostatin and follistatin gene expression patterns in C2C12 muscle cells. C2C12 cells were administered with 100 ng/ml recombinant human (rh) follistatin in Dulbecco's modified Eagle medium (DMEM) containing 10% fetal bovine serum (FBS), 4 mM glutamine and antibiotics daily for three days. Rh follistatin was not added in the control wells. Follistatin and myostatin gene cDNAs were synthesised by reverse transcriptase polymerase chain reaction (RT-PCR).The time course of follistatin gene expression pattern was similar in both the control and the follistatin-treated group. Myostatin mRNA level significantly increased in the follistatin-treated group after 24 h of culture (Fig. 3, P < 0.01). Amounts then sharply decreased (Fig. 3, P < 0.01) at 48 h of culture, whereas there was no significant difference between the control and the follistatin-treated group at 72 h of culture. Our results demonstrated that myostatin and follistatin mRNA were expressed in C2C12 cells and rh follistatin changed the myostatin expression pattern.

scholarly journals Verticillium wilt resistant and susceptible olive cultivars express a very different basal set of genes in roots

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jorge A. Ramírez-Tejero ◽  
Jaime Jiménez-Ruiz ◽  
Alicia Serrano ◽  
Angjelina Belaj ◽  
Lorenzo León ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Growth And Development ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Expression Level ◽  
Resistant Cultivars ◽  
Pathogen Invasion ◽  
Wide Range ◽  
Olive Cultivars

Abstract Background Olive orchards are threatened by a wide range of pathogens. Of these, Verticillium dahliae has been in the spotlight for its high incidence, the difficulty to control it and the few cultivars that has increased tolerance to the pathogen. Disease resistance not only depends on detection of pathogen invasion and induction of responses by the plant, but also on barriers to avoid the invasion and active resistance mechanisms constitutively expressed in the absence of the pathogen. In a previous work we found that two healthy non-infected plants from cultivars that differ in V. dahliae resistance such as ‘Frantoio’ (resistant) and ‘Picual’ (susceptible) had a different root morphology and gene expression pattern. In this work, we have addressed the issue of basal differences in the roots between Resistant and Susceptible cultivars. Results The gene expression pattern of roots from 29 olive cultivars with different degree of resistance/susceptibility to V. dahliae was analyzed by RNA-Seq. However, only the Highly Resistant and Extremely Susceptible cultivars showed significant differences in gene expression among various groups of cultivars. A set of 421 genes showing an inverse differential expression level between the Highly Resistant to Extremely Susceptible cultivars was found and analyzed. The main differences involved higher expression of a series of transcription factors and genes involved in processes of molecules importation to nucleus, plant defense genes and lower expression of root growth and development genes in Highly Resistant cultivars, while a reverse pattern in Moderately Susceptible and more pronounced in Extremely Susceptible cultivars were observed. Conclusion According to the different gene expression patterns, it seems that the roots of the Extremely Susceptible cultivars focus more on growth and development, while some other functions, such as defense against pathogens, have a higher expression level in roots of Highly Resistant cultivars. Therefore, it seems that there are constitutive differences in the roots between Resistant and Susceptible cultivars, and that susceptible roots seem to provide a more suitable environment for the pathogen than the resistant ones.

Biological Image Analysis via Matrix Approximation

2011 ◽  
pp. 166-170
Author(s):  
Jieping Ye ◽  
Ravi Janardan ◽  
Sudhir Kumar
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Controlled Vocabulary ◽  
Gene Expression Patterns ◽  
Image Content ◽  
Gene Expressions ◽  
Microarray Gene Expression ◽  
Individual Gene

Understanding the roles of genes and their interactions is one of the central challenges in genome research. One popular approach is based on the analysis of microarray gene expression data (Golub et al., 1999; White, et al., 1999; Oshlack et al., 2007). By their very nature, these data often do not capture spatial patterns of individual gene expressions, which is accomplished by direct visualization of the presence or absence of gene products (mRNA or protein) (e.g., Tomancak et al., 2002; Christiansen et al., 2006). For instance, the gene expression pattern images of a Drosophila melanogaster embryo capture the spatial and temporal distribution of gene expression patterns at a given developmental stage (Bownes, 1975; Tsai et al., 1998; Myasnikova et al., 2002; Harmon et al., 2007). The identification of genes showing spatial overlaps in their expression patterns is fundamentally important to formulating and testing gene interaction hypotheses (Kumar et al., 2002; Tomancak et al., 2002; Gurunathan et al., 2004; Peng & Myers, 2004; Pan et al., 2006). Recent high-throughput experiments of Drosophila have produced over fifty thousand images (http://www. fruitfly.org/cgi-bin/ex/insitu.pl). It is thus desirable to design efficient computational approaches that can automatically retrieve images with overlapping expression patterns. There are two primary ways of accomplishing this task. In one approach, gene expression patterns are described using a controlled vocabulary, and images containing overlapping patterns are found based on the similarity of textual annotations. In the second approach, the most similar expression patterns are identified by a direct comparison of image content, emulating the visual inspection carried out by biologists [(Kumar et al., 2002); see also www.flyexpress.net]. The direct comparison of image content is expected to be complementary to, and more powerful than, the controlled vocabulary approach, because it is unlikely that all attributes of an expression pattern can be completely captured via textual descriptions. Hence, to facilitate the efficient and widespread use of such datasets, there is a significant need for sophisticated, high-performance, informatics-based solutions for the analysis of large collections of biological images.

scholarly journals Evolution of Mendelian Dominance in Gene Regulatory Networks Associated With Phenotypic Robustness

2021 ◽  
Author(s):  
Kenji Okubo ◽  
Kunihiko Kaneko
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Target Genes ◽  
Classical Case ◽  
Mendelian Inheritance ◽  
Degree Of Dominance ◽  
Gene Expression Dynamics ◽  
Phenotypic Robustness ◽  
Robustness To Noise ◽  
Gene Regulatory

Abstract Background: Mendelian inheritance is a fundamental law of genetics. Considering two alleles in a diploid, a phenotype of a heterotype is dominated by a particular homotype according to the law of dominance. This picture is usually based on simple genotype-phenotype mapping in which one gene regulates one phenotype. However, in reality, some interactions between genes can result in deviation from Mendelian dominance. Result: Here, by using the numerical evolution of diploid gene regulatory networks (GRNs), we discuss whether Mendelian dominance evolves beyond the classical case of one-to-one genotype-phenotype mapping. We examine whether complex genotype-phenotype mapping can achieve Mendelian dominance through the evolution of the GRN with interacting genes. Specifically, we extend the GRN model to a diploid case, in which two GRN matrices are added to give gene expression dynamics, and simulate evolution with meiosis and recombination. Our results reveal that Mendelian dominance evolves even under complex genotype-phenotype mapping. This dominance is achieved via a group of genotypes that differ from each other but have a common phenotype given by the expression of target genes. Calculating the degree of dominance shows that it increases through the evolution, correlating closely with the decrease in phenotypic fluctuations and the increase in robustness to initial noise. This evolution of Mendelian dominance is associated with phenotypic robustness against meiosis-induced genome mixing, whereas sexual recombination arising from the mixing of chromosomes from the parents further enhances dominance and robustness. Owing to this dominance, the robustness to genetic differences increases, while the optimal fitness is sustained up to a large difference between the two genomes. Conclusion: Mendelian dominance is achieved by groups of genotypes that are associated with the increase in phenotypic robustness to noise.

scholarly journals Selection and Validation of Reference Genes for RT-qPCR Analysis in Spinacia oleracea under Abiotic Stress

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Hao Xie ◽  
Bo Li ◽  
Yu Chang ◽  
Xiaoyan Hou ◽  
Yue Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Reference Genes ◽  
18S Rrna ◽  
Spinacia Oleracea ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Housekeeping Genes ◽  
Qpcr Analysis ◽  
The Stability

Reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) is an accurate and convenient method for mRNA quantification. Selection of optimal reference gene(s) is an important step in RT-qPCR experiments. However, the stability of housekeeping genes in spinach (Spinacia oleracea) under various abiotic stresses is unclear. Evaluating the stability of candidate genes and determining the optimal gene(s) for normalization of gene expression in spinach are necessary to investigate the gene expression patterns during development and stress response. In this study, ten housekeeping genes, 18S ribosomal RNA (18S rRNA), actin, ADP ribosylation factor (ARF), cytochrome c oxidase subunit 5C (COX), cyclophilin (CYP), elongation factor 1-alpha (EF1α), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), histone H3 (H3), 50S ribosomal protein L2 (RPL2), and tubulin alpha chain (TUBα) from spinach, were selected as candidates in roots, stems, leaves, flowers, and seedlings in response to high temperature, CdCl2, NaCl, NaHCO3, and Na2CO3 stresses. The expression of these genes was quantified by RT-qPCR and evaluated by NormFinder, BestKeeper, and geNorm. 18S rRNA, actin, ARF, COX, CYP, EF1α, GAPDH, H3, and RPL2 were detected as optimal reference genes for gene expression analysis of different organs and stress responses. The results were further confirmed by the expression pattern normalized with different reference genes of two heat-responsive genes. Here, we optimized the detection method of the gene expression pattern in spinach. Our results provide the optimal candidate reference genes which were crucial for RT-qPCR analysis.

HOX Genes Expression Pattern Is Related to Phenotypical and Genotypical Subgroups but Not to Treatment Response in Pediatric ALL.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 1288-1288
Author(s):  
Julia Starkova ◽  
Blanka Vicenova ◽  
Roman Krejci ◽  
Harry A. Drabkin ◽  
Jan Trka
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Hox Genes ◽  
Conflicts Of Interest ◽  
Fusion Gene ◽  
Expression Patterns ◽  
Hox Gene ◽  
Age Related ◽  
Genes Expression ◽  
Significant Difference

Abstract Abstract 1288 Poster Board I-310 Homeodomain (HOX) genes encode transcription factors important for embryonic development. They are involved in normal hemopoiesis regulation and likely also in leukemogenesis as a result of translocations and other aberrations present in leukemias. In previous work Drabkin et al. demonstrated that HOX gene expression patterns differentiate major cytogenetic groups in acute myeloid leukemias. In this study we focused on HOX gene expression in pediatric acute lymphoblastic leukemias (ALL). We were interested if certain HOX genes or expression pattern could distinguish subpopulations of ALL. We analyzed the expression pattern of 21 HOX genes from HOXA and HOXB clusters and non-cluster HOX genes, CDX1 and CDX2 using qRT-PCR approach. We looked at 54 patients chosen according to phenotypic (T-ALL, BCP-ALL), prognostic (PGR – prednisone good responders, PPR – prednisone poor responders) and genotypic (BCR/ABL, MLL/AF4, TEL/AML1, hyperdiploid) characteristics. Overall analysis comparing all studied groups showed that HOXA7 (Kruskal-Wallis test p=0.000045), HOXA3 (p=0.000098), HOXB3 (p=0.00015), HOXA4 (p=0.000619) and HOXB4 (p=0.001925) genes were differently expressed among groups. Wilcoxon signed-rank test, a non-parametric statistical analysis comparing two groups against each other, showed that HOXA3, A4 and B3 distinguish BCP-ALL (w/o fusion gene) and T-ALL. Interestingly, particular HOX genes expression showed significant difference among the groups: HOXA7 gene is significantly downregulated in hyperdiploid ALL (p=0.03) compared to all other subgroups. Furthermore, HOXB7 gene is specifically upregulated in TEL/AML-positive patients (p=0.0048 vs BCP-ALL w/o fusion gene) and CDX2 is downregulated in BCR/ABL-positive patients (p=0.001 vs hyperdiploid; p=0.006 vs TEL/AML1; p=0.03 vs MLL/AF4). Suprisingly, TEL/AML1-positive patients have similar expression of HOXA1-A4 as T-ALL patients. HOX genes expression pattern seemed to differ in MLL/AF4-positive patients according to the age at diagnosis. Three patients younger than 2 months at presentation clustered together in clear contrast to the MLL/AF4-positive patient diagnosed at the age of 13 years with secALL who presented with very low overall expression of all HOX genes. Next, we looked for diversity and similarity between groups. We determined how many HOX genes were expressed differently (p<0.05) and similarly (p=1.0) between particular ALL subtypes. The most outlying couples were T-ALL vs PPR (11 genes differently expressed), T-ALL vs PGR (9 genes) and T-ALL vs TEL/AML1 (6 genes). In contrast, the closest groups were BCR/ABL vs PPR, MLL/AF4 vs T-ALL and MLL/AF4 vs PPR. Our data demonstrate that BCP-ALL (w/o known fusion gene) can be distinguished from T-ALL by the HOX gene expression (in particular HOXA3, HOXB3, HOXA4). Like in AML, expression pattern differs also among the major cytogenetical subgroups of ALL. On the other hand, within the BCP-ALL subgroup, no expression difference was found between patients with good (PGR) and poor (PPR) response to the initial steroid therapy which is known to be an excellent predictor of outcome. HOX genes of interest emerged from our analysis: low expression of HOXA7 in hyperdiploid ALL, highly expressed HOXB7 in TEL/AML1-positive ALL and specifically downregulated CDX2 in BCR/ABL-positive ALL. Age-related differences in expression in MLL/AF4-positive ALL seem to link the expression pattern rather with the relative maturity of the cell undergoing (pre)malignant transformation than with the specific changes caused by the leukemogenesis itself. This hypothesis must be tested in comparison to the HOX genes expression in sorted subtypes of normal T and B precursors. This work was supported by MSM0021620813, IGA NR/9526 and GACR 301/08/P532. Disclosures No relevant conflicts of interest to declare.

scholarly journals Transcriptomic Analysis Identifies Gene Networks Regulated by Estrogen Receptor α (ERα) and ERβ that Control Distinct Effects of Different Botanical Estrogens

2014 ◽  
Vol 12 (1) ◽  
pp. nrs.12001 ◽  
Author(s):  
Ping Gong ◽  
Zeynep Madak-Erdogan ◽  
Jilong Li ◽  
Jianlin Cheng ◽  
C Michael Greenlief ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Gene Networks ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Licorice Root ◽  
Target Cells ◽  
Human Breast ◽  
Mcf7 Cells ◽  
Gene Regulations

The estrogen receptors (ERs) ERα and ERβ mediate the actions of endogenous estrogens as well as those of botanical estrogens (BEs) present in plants. BEs are ingested in the diet and also widely consumed by postmenopausal women as dietary supplements, often as a substitute for the loss of endogenous estrogens at menopause. However, their activities and efficacies, and similarities and differences in gene expression programs with respect to endogenous estrogens such as estradiol (E2) are not fully understood. Because gene expression patterns underlie and control the broad physiological effects of estrogens, we have investigated and compared the gene networks that are regulated by different BEs and by E2. Our aim was to determine if the soy and licorice BEs control similar or different gene expression programs and to compare their gene regulations with that of E2. Gene expression was examined by RNA-Seq in human breast cancer (MCF7) cells treated with control vehicle, BE or E2. These cells contained three different complements of ERs, ERα only, ERα+ERβ, or ERβ only, reflecting the different ratios of these two receptors in different human breast cancers and in different estrogen target cells. Using principal component, hierarchical clustering, and gene ontology and interactome analyses, we found that BEs regulated many of the same genes as did E2. The genes regulated by each BE, however, were somewhat different from one another, with some genes being regulated uniquely by each compound. The overlap with E2 in regulated genes was greatest for the soy isoflavones genistein and S-equol, while the greatest difference from E2 in gene expression pattern was observed for the licorice root BE liquiritigenin. The gene expression pattern of each ligand depended greatly on the cell background of ERs present. Despite similarities in gene expression pattern with E2, the BEs were generally less stimulatory of genes promoting proliferation and were more pro-apoptotic in their gene regulations than E2. The distinctive patterns of gene regulation by the individual BEs and E2 may underlie differences in the activities of these soy and licorice-derived BEs in estrogen target cells containing different levels of the two ERs.

scholarly journals Identification of Gene Expression Pattern Related to Breast Cancer Survival Using Integrated TCGA Datasets and Genomic Tools

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Zhenzhen Huang ◽  
Huilong Duan ◽  
Haomin Li
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Patients ◽  
Expression Pattern ◽  
Clinical Data ◽  
Cancer Genomics ◽  
Expression Patterns ◽  
Gene List ◽  
Gene Expression Pattern ◽  
Cancer Development

Several large-scale human cancer genomics projects such as TCGA offered huge genomic and clinical data for researchers to obtain meaningful genomics alterations which intervene in the development and metastasis of the tumor. A web-based TCGA data analysis platform called TCGA4U was developed in this study. TCGA4U provides a visualization solution for this study to illustrate the relationship of these genomics alternations with clinical data. A whole genome screening of the survival related gene expression patterns in breast cancer was studied. The gene list that impacts the breast cancer patient survival was divided into two patterns. Gene list of each of these patterns was separately analyzed on DAVID. The result showed that mitochondrial ribosomes play a more crucial role in the cancer development. We also reported that breast cancer patients with low HSPA2 expression level had shorter overall survival time. This is widely different to findings of HSPA2 expression pattern in other cancer types. TCGA4U provided a new perspective for the TCGA datasets. We believe it can inspire more biomedical researchers to study and explain the genomic alterations in cancer development and discover more targeted therapies to help more cancer patients.

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