scholarly journals From Cellular Infiltration Assessment to a Functional Gene Set-Based Prognostic Model for Breast Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Huamei Li ◽  
Yiting Huang ◽  
Amit Sharma ◽  
Wenglong Ming ◽  
Kun Luo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Prognostic Model ◽  
Therapeutic Response ◽  
Cell Types ◽  
Functional Gene ◽  
Cellular Infiltration ◽  
Gene Set ◽  
Cancer Heterogeneity ◽  
Different Cell Types

BackgroundCancer heterogeneity is a major challenge in clinical practice, and to some extent, the varying combinations of different cell types and their cross-talk with tumor cells that modulate the tumor microenvironment (TME) are thought to be responsible. Despite recent methodological advances in cancer, a reliable and robust model that could effectively investigate heterogeneity with direct prognostic/diagnostic clinical application remained elusive.ResultsTo investigate cancer heterogeneity, we took advantage of single-cell transcriptome data and constructed the first indication- and cell type-specific reference gene expression profile (RGEP) for breast cancer (BC) that can accurately predict the cellular infiltration. By utilizing the BC-specific RGEP combined with a proven deconvolution model (LinDeconSeq), we were able to determine the intrinsic gene expression of 15 cell types in BC tissues. Besides identifying significant differences in cellular proportions between molecular subtypes, we also evaluated the varying degree of immune cell infiltration (basal-like subtype: highest; Her2 subtype: lowest) across all available TCGA-BRCA cohorts. By converting the cellular proportions into functional gene sets, we further developed a 24 functional gene set-based prognostic model that can effectively discriminate the overall survival (P = 5.9 × 10−33, n = 1091, TCGA-BRCA cohort) and therapeutic response (chemotherapy and immunotherapy) (P = 6.5 × 10−3, n = 348, IMvigor210 cohort) in the tumor patients.ConclusionsHerein, we have developed a highly reliable BC-RGEP that adequately annotates different cell types and estimates the cellular infiltration. Of importance, the functional gene set-based prognostic model that we have introduced here showed a great ability to screen patients based on their therapeutic response. On a broader perspective, we provide a perspective to generate similar models in other cancer types to identify shared factors that drives cancer heterogeneity.

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.

Dpp signaling thresholds in the dorsal ectoderm of the Drosophila embryo

Development ◽  
2000 ◽  
Vol 127 (15) ◽  
pp. 3305-3312 ◽  
Author(s):  
H.L. Ashe ◽  
M. Mannervik ◽  
M. Levine
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Histone Acetyltransferase ◽  
Cell Types ◽  
Drosophila Embryo ◽  
Present Evidence ◽  
Drosophila Homolog ◽  
Dorsal Ectoderm ◽  
Transgenic Embryos ◽  
Different Cell Types

The dorsal ectoderm of the Drosophila embryo is subdivided into different cell types by an activity gradient of two TGF(β) signaling molecules, Decapentaplegic (Dpp) and Screw (Scw). Patterning responses to this gradient depend on a secreted inhibitor, Short gastrulation (Sog) and a newly identified transcriptional repressor, Brinker (Brk), which are expressed in neurogenic regions that abut the dorsal ectoderm. Here we examine the expression of a number of Dpp target genes in transgenic embryos that contain ectopic stripes of Dpp, Sog and Brk expression. These studies suggest that the Dpp/Scw activity gradient directly specifies at least three distinct thresholds of gene expression in the dorsal ectoderm of gastrulating embryos. Brk was found to repress two target genes, tailup and pannier, that exhibit different limits of expression within the dorsal ectoderm. These results suggest that the Sog inhibitor and Brk repressor work in concert to establish sharp dorsolateral limits of gene expression. We also present evidence that the activation of Dpp/Scw target genes depends on the Drosophila homolog of the CBP histone acetyltransferase.

scholarly journals Hypercapnia Regulates Gene Expression and Tissue Function

2020 ◽  
Vol 11 ◽  
Author(s):  
Masahiko Shigemura ◽  
Lynn C. Welch ◽  
Jacob I. Sznajder
Keyword(s):  
Gene Expression ◽  
Lung Diseases ◽  
Transcriptional Response ◽  
Cell Types ◽  
Chronic Lung Diseases ◽  
Tissue Responses ◽  
Mammalian Tissues ◽  
Specific Tissue ◽  
Different Cell Types ◽  
Alveolar Gas Exchange

Carbon dioxide (CO2) is produced in eukaryotic cells primarily during aerobic respiration, resulting in higher CO2 levels in mammalian tissues than those in the atmosphere. CO2 like other gaseous molecules such as oxygen and nitric oxide, is sensed by cells and contributes to cellular and organismal physiology. In humans, elevation of CO2 levels in tissues and the bloodstream (hypercapnia) occurs during impaired alveolar gas exchange in patients with severe acute and chronic lung diseases. Advances in understanding of the biology of high CO2 effects reveal that the changes in CO2 levels are sensed in cells resulting in specific tissue responses. There is accumulating evidence on the transcriptional response to elevated CO2 levels that alters gene expression and activates signaling pathways with consequences for cellular and tissue functions. The nature of hypercapnia-responsive transcriptional regulation is an emerging area of research, as the responses to hypercapnia in different cell types, tissues, and species are not fully understood. Here, we review the current understanding of hypercapnia effects on gene transcription and consequent cellular and tissue functions.

scholarly journals Integration of GWAS Summary Statistics and Gene Expression Reveals Target Cell Types Underlying Kidney Function Traits

2020 ◽  
Vol 31 (10) ◽  
pp. 2326-2340 ◽  
Author(s):  
Yong Li ◽  
Stefan Haug ◽  
Pascal Schlosser ◽  
Alexander Teumer ◽  
Adrienne Tin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Kidney Function ◽  
Cell Types ◽  
Gene Set Enrichment Analysis ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Rna Seq ◽  
Cell Type ◽  
Gene Set Enrichment ◽  
Gene Set

BackgroundGenetic variants identified in genome-wide association studies (GWAS) are often not specific enough to reveal complex underlying physiology. By integrating RNA-seq data and GWAS summary statistics, novel computational methods allow unbiased identification of trait-relevant tissues and cell types.MethodsThe CKDGen consortium provided GWAS summary data for eGFR, urinary albumin-creatinine ratio (UACR), BUN, and serum urate. Genotype-Tissue Expression Project (GTEx) RNA-seq data were used to construct the top 10% specifically expressed genes for each of 53 tissues followed by linkage disequilibrium (LD) score–based enrichment testing for each trait. Similar procedures were performed for five kidney single-cell RNA-seq datasets from humans and mice and for a microdissected tubule RNA-seq dataset from rat. Gene set enrichment analyses were also conducted for genes implicated in Mendelian kidney diseases.ResultsAcross 53 tissues, genes in kidney function–associated GWAS loci were enriched in kidney (P=9.1E-8 for eGFR; P=1.2E-5 for urate) and liver (P=6.8·10-5 for eGFR). In the kidney, proximal tubule was enriched in humans (P=8.5E-5 for eGFR; P=7.8E-6 for urate) and mice (P=0.0003 for eGFR; P=0.0002 for urate) and confirmed as the primary cell type in microdissected tubules and organoids. Gene set enrichment analysis supported this and showed enrichment of genes implicated in monogenic glomerular diseases in podocytes. A systematic approach generated a comprehensive list of GWAS genes prioritized by cell type–specific expression.ConclusionsIntegration of GWAS statistics of kidney function traits and gene expression data identified relevant tissues and cell types, as a basis for further mechanistic studies to understand GWAS loci.

scholarly journals Differential Regulation of Human Interferon A Gene Expression by Interferon Regulatory Factors 3 and 7

2009 ◽  
Vol 29 (12) ◽  
pp. 3435-3450 ◽  
Author(s):  
Pierre Génin ◽  
Rongtuan Lin ◽  
John Hiscott ◽  
Ahmet Civas
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Transcriptional Activation ◽  
Cell Types ◽  
Inhibitory Effect ◽  
Human Interferon ◽  
Gene Promoters ◽  
Nucleotide Substitutions ◽  
D Modules ◽  
Different Cell Types

ABSTRACT Differential expression of the human interferon A (IFN-A) gene cluster is modulated following paramyxovirus infection by the relative amounts of active interferon regulatory factor 3 (IRF-3) and IRF-7. IRF-3 expression activates predominantly IFN-A1 and IFN-B, while IRF-7 expression induces multiple IFN-A genes. IFN-A1 gene expression is dependent on three promoter proximal IRF elements (B, C, and D modules, located at positions −98 to −45 relative to the mRNA start site). IRF-3 binds the C module of IFN-A1, while other IFN-A gene promoters are responsive to the binding of IRF-7 to the B and D modules. Maximal expression of IFN-A1 is observed with complete occupancy of the three modules in the presence of IRF-7. Nucleotide substitutions in the C modules of other IFN-A genes disrupt IRF-3-mediated transcription, whereas a G/A substitution in the D modules enhances IRF7-mediated expression. IRF-3 exerts dual effects on IFN-A gene expression, as follows: a synergistic effect with IRF-7 on IFN-A1 expression and an inhibitory effect on other IFN-A gene promoters. Chromatin immunoprecipitation experiments reveal that transient binding of both IRF-3 and IRF-7, accompanied by CBP/p300 recruitment to the endogenous IFN-A gene promoters, is associated with transcriptional activation, whereas a biphasic recruitment of IRF-3 and CBP/p300 represses IFN-A gene expression. This regulatory mechanism contributes to differential expression of IFN-A genes and may be critical for alpha interferon production in different cell types by RIG-I-dependent signals, leading to innate antiviral immune responses.

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