Integrated analysis of multiple microarray studies to identify potential pathogenic gene modules in preeclampsia

2021 ◽  
Vol 120 ◽  
pp. 104631
Author(s):  
Heze Xu ◽  
Yin Xie ◽  
Yanan Sun ◽  
Rong Guo ◽  
Dan Lv ◽  
...  
2007 ◽  
Vol 28 (2) ◽  
pp. 85
Author(s):  
Traude H Beilharz ◽  
Thomas Preiss

Microarray studies in Saccharomyces cerevisiae have set the benchmark for genome-wide analyses, available data-sets covering practically every stage of gene expression from DNA-binding by transcription factors to mRNA export, sub-cellular localisation, translation and decay. A theme to emerge from such data has been the prevalence of coordinate gene regulation. Thus, gene modules or ?regulons? are well recognised at the level of gene transcription and the activity of transcription factors provides an obvious molecular explanation for such coordination. More surprising was the organisation of mRNAs into co-regulated ?post-transcriptional operons?. RNA-binding proteins (RBPs), but also ribonucleoprotein (RNP) complexes involving noncoding RNA, have been proposed as the conceptual equivalent of transcription factors at this level.


2020 ◽  
Author(s):  
Yuxiang Ge ◽  
Wang Ding ◽  
Chong Bian ◽  
Huijie Gu ◽  
Jun Xu ◽  
...  

Abstract Background: Osteosarcoma (OS), one of the utmost common and malignant cancer, accounts for over 30% among skeletal sarcomas. Although great efforts have been made, the mechanism of OS still remains largely unknown. Here, we intend to identify gene modules and candidate biomarkers for clinical diagnosis of patients with OS, and reveal the mechanisms of OS progression.Methods: Weighted gene co-expression network analysis (WGCNA) was conducted to build a co-expression network and investigate the relationship between modules and clinical traits. Functional enrichment analysis was performed on module genes. Protein-protein interaction (PPI) network was constructed to identify the hub gene and the expression level of hub genes was validated based on another dataset.Results: A total of 9854 genes were included in WGCNA, and 17 gene modules were constructed. Gene module related with OS in sacrum was mainly enriched in skeletal system development, bone development and extracellular structure organization. Furthermore, we screened the top 10 hub genes and further validated 5 of the 10 (MMP13, DCN, GNG2, PCOLCE and RUNX2), the expression of which were upregulated as compared with normal tissues.Conclusion: The hub gene we identified show great promise as prognostic markers for the management of OS and our findings also provide new insight for molecular mechanism of OS.


Placenta ◽  
2021 ◽  
Vol 105 ◽  
pp. 104-118
Author(s):  
Qingling Kang ◽  
Wei Li ◽  
Juan Xiao ◽  
Nan Yu ◽  
Lei Fan ◽  
...  

Aging ◽  
2019 ◽  
Vol 11 (16) ◽  
pp. 6109-6119 ◽  
Author(s):  
Cuihua Zou ◽  
Jie Wang ◽  
Xiaohua Huang ◽  
Chongdong Jian ◽  
Donghua Zou ◽  
...  

2020 ◽  
Vol 3 (10) ◽  
pp. e202000654
Author(s):  
Mario Cocco ◽  
Matthew A Care ◽  
Amel Saadi ◽  
Muna Al-Maskari ◽  
Gina Doody ◽  
...  

The activated B-cell (ABC) to plasmablast transition encompasses the cusp of antibody-secreting cell (ASC) differentiation. We explore this transition with integrated analysis in human cells, focusing on changes that follow removal from CD40-mediated signals. Within hours of input signal loss, cell growth programs shift toward enhanced proliferation, accompanied by ER-stress response, and up-regulation of ASC features. Clustering of genomic occupancy for IRF4, BLIMP1, XBP1, and CTCF with histone marks identifies a dichotomy: XBP1 and IRF4 link to induced but not repressed gene modules in plasmablasts, whereas BLIMP1 links to modules of ABC genes that are repressed, but not to activated genes. Between ABC and plasmablast states, IRF4 shifts away from AP1/IRF composite elements while maintaining occupancy at IRF and ETS/IRF elements. This parallels the loss of BATF expression, which is identified as a potential BLIMP1 target. In plasmablasts, IRF4 acquires an association with CTCF, a feature maintained in plasma cell myeloma lines. Thus, shifting occupancy links IRF4 to both ABC and ASC gene expression, whereas BLIMP1 occupancy links to repression of the activation state.


Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 2487-2487
Author(s):  
Ondrej Havranek ◽  
Jason R. Westin ◽  
Min Zhang ◽  
Seema Rawal ◽  
Larry W. Kwak ◽  
...  

Abstract Background The immune microenvironment in follicular lymphoma (FL) impacts its clinical course, but the interaction between FL cells and host immune cells is poorly understood, and may be influenced by large genetic abnormalities in FL cells. Regions of copy number variation (CNV) and copy-neutral loss of heterozygosity (cnLOH) are detectable by single nucleotide polymorphism (SNP) arrays and frequently found in FL, but the critical “driver genes” within them are largely unidentified. Methods Cell suspensions from tumor biopsies of 66 untreated FL patients were sorted into “B cell” and non-B fractions by immunomagnetic depletion targeting CD3 or CD19 and CD20 respectively. High-resolution Illumina Omni5 SNP arrays were used to profile genomic DNA from B cell fractions and germline DNA (from non-B fractions or peripheral blood cells). Nexus Copy Number software (BioDiscovery) compared paired profiles to determine tumor-specific CNV and cnLOH abnormalities of each patient. Genes within overlapping recurrently-altered regions were identified by the JISTIC algorithm (PMID: 20398270). For 43 of these patients, whole-genome gene expression profiling (GEP) of both fractions was done on Illumina HT12v4 arrays. CONEXIC module network analysis (PMID: 21129771) identified candidate driver genes, based on correlation of their expression in B-cell fractions with that of modules of genes in B-cell or non-B fractions. Results Comparing tumor vs. germline profiles in SNP array analysis clarified the detection of tumor-specific CNV, and enabled the detection of cnLOH. The aggregate genomic profile of regions affected by CNV in our 66 FL samples was highly similar to results of previous FL studies. Most frequent (each in 25-35% of samples) were deletions of 1p36 or a large part of 6q, amplifications of 1q, 7p/q, 12q, 17q, or 18p/q, and cnLOH at 16p. The distribution of these abnormalities suggested that FL can be divided into subgroups based on several large mutually-exclusive genomic aberrations: -10q, -16p, +12q, and, less clearly, -1p/1q+. Novel analysis combining copy number values with corresponding SNP frequencies also identified abnormalities of lower frequency within samples, suggestive of tumor subclones with potential growth advantages, notably including deletions at 13q14 and 19p12 and amplification of 16p13. JISTIC identified 715 expressed genes within amplified regions and 413 expressed genes within deleted regions (329 genes) or regions of cnLOH (84 genes). CONEXIC identified 62 and 68 of these genes as candidate drivers regulating expression of gene modules in tumor B cells and infiltrating immune cells, respectively. Several regulators of B-cell modules were already described in FL or other hematological malignancies: MDM2 (12q15, amplified in 26%), an E3 ubiquitin ligase whose targets include TP53; NME1 (17q21.33, amplified in 21%), part of the nucleoside diphosphate kinase complex, overexpressed and correlated with poor prognosis in AML; or B-cell receptor-associated CD79B (17q23.3, amplified in 21%), mutated and functionally significant in diffuse large B-cell lymphoma. Validating MDM2 as a driver gene, Gene Set Enrichment Analysis showed strong positive association between expression of MDM2 and that of proliferation signatures in B cells, including signatures of genes downregulated by TP53. Genes affecting the interaction between tumor B cells and the FL microenvironment plausibly regulate module expression in both B cells and non-B cells. Such dual candidate driver genes included PHIP (6q14.1, deleted in 27%), a binding partner of insulin receptor substrate-1, overexpressed in melanoma and linked to its metastasis and progression; SMARCC2 (12q13.2, amplified in 25%), part of the ATP-dependent chromatin remodeling complex SNF/SWI, mutated in some carcinomas; SFR1 (10q25.1, deleted in 18%), involved in DNA homologous recombination; and BUD31(7q22.1, amplified in 21%), a homolog of a yeast protein involved in pre-mRNA splicing. Conclusions CNV and cnLOH abnormalities are frequent in FL, and may identify subgroups within FL. Integrated analysis finds known candidate driver genes within recurrently-altered regions, appearing to regulate expression of gene modules in B cells. Novel candidate driver genes that appear to regulate modules in both B and non-B cells may shape the FL microenvironment in important ways, and are being investigated experimentally. Disclosures: No relevant conflicts of interest to declare.


2016 ◽  
Vol 9 (2) ◽  
pp. 149-157 ◽  
Author(s):  
Xiaowei Jia ◽  
Haotian Yu ◽  
Hui Zhang ◽  
Yanfang Si ◽  
Dengmei Tian ◽  
...  

2017 ◽  
Vol 12 (1) ◽  
Author(s):  
Feifei Wang ◽  
Ruliang Wang ◽  
Qiuwen Li ◽  
Xueling Qu ◽  
Yixin Hao ◽  
...  

2020 ◽  
Author(s):  
Yuxiang Ge ◽  
Wang Ding ◽  
Chong Bian ◽  
Huijie Gu ◽  
Jun Xu ◽  
...  

Abstract Background: Osteosarcoma (OS) is the most common type of musculoskeletal malignant tumor, accounting for over 30% of primary skeletal sarcomas. Although great efforts have been made, the mechanism of OS still remains largely unknown. In this study, we aim to identify gene modules and representative candidate biomarkers for clinical diagnosis of patients with OS, and reveal the mechanisms of OS progression.Methods: Weighted gene co-expression network analysis (WGCNA) was conducted to construct a co-expression network and investigate the relationship between modules and clinical traits. Functional enrichment analysis was performed on module genes. Protein-protein interaction (PPI) network was constructed to identify the hub gene and the expression level of hub genes was validated based on another dataset.Results: A total of 9854 genes were included in WGCNA, and 17 gene modules were constructed. Gene module related with OS in sacrum was mainly enriched in skeletal system development, bone development and extracellular structure organization. Furthermore, we screened the top 10 hub genes and further validated 5 of the 10 (MMP13, DCN, GNG2, PCOLCE and RUNX2), the expression of which were upregulated as compared with normal tissues.Conclusion: The hub gene we identified show great promise as prognostic markers for the management of OS and our findings also provide new insight for molecular mechanism of OS.


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