Abstract:This study's goal is to determine similarities and differences in the molecular pathways or potential functions of the various targeted regions or genes of the Vegf family-VegfA, VegfB, VegfC, and Pgf-using the BXD genetic reference panel. Data from whole genome expression profiles of retinas from the well-characterized mouse recombinant inbred (RI) strain population derived from C57BL/6J X DBA/2J (BXD) were analyzed. Multiple analytical tools and statistical strategies were used to investigate the expression … Show more
“…From the GeneNetwork platform, we first obtained the expression levels of VC key genes ( Gulo , Rgn , Slc23a1 , and Slc23a2 ) and VD key genes ( Vdr , Gc , Cyp24a1 , Trpv6 , Cyp27b1 , Cyp2r1 , Dhcr7 , and Rxra ) from whole genome mRNA expression profiles in four different mouse and human organ tissues (spleen, liver, lung and kidney). Next, network graphs of these key genes were created using a threshold of absolute values greater than 0.35 as we previously reported 16 .…”
Vitamin C (VC) and vitamin D (VD) have been widely used as the dietary supplements and in treatment of diseases both independently and in combination. Whether there is a connection between their pathways is critical for their therapeutic applications. Using whole-genome expression profiles, we performed multiple measures of associations, networks, eQTL mappings and expressions of key genes of interest in VC and VD functions. Several key genes in their pathways were found to be associated. Gc and Rgn play important roles connecting VC and VD pathways in mice. The r values of expression levels between Gc and Rgn in mouse spleen, liver, lung, and kidney are 0.937, 0.558, 0.901, and 0.617, respectively. The expression QTLs of Gc and Rgn are mapped onto the same locations, i.e., 68–76 MB in chromosome 7 and 26–36 MB in chromosome 9. In humans, there are positive correlations between CYP27B1 and SLC23A1 expression levels in kidney (r = 0.733) and spleen (r = 0.424). SLC23A2 and RXRA are minimally associated in both mouse and human. These data indicate that pathways of VC and VD are not independent but affect each other, and this effect is different between mice and humans during VC and VD synthesis and transportation.
“…From the GeneNetwork platform, we first obtained the expression levels of VC key genes ( Gulo , Rgn , Slc23a1 , and Slc23a2 ) and VD key genes ( Vdr , Gc , Cyp24a1 , Trpv6 , Cyp27b1 , Cyp2r1 , Dhcr7 , and Rxra ) from whole genome mRNA expression profiles in four different mouse and human organ tissues (spleen, liver, lung and kidney). Next, network graphs of these key genes were created using a threshold of absolute values greater than 0.35 as we previously reported 16 .…”
Vitamin C (VC) and vitamin D (VD) have been widely used as the dietary supplements and in treatment of diseases both independently and in combination. Whether there is a connection between their pathways is critical for their therapeutic applications. Using whole-genome expression profiles, we performed multiple measures of associations, networks, eQTL mappings and expressions of key genes of interest in VC and VD functions. Several key genes in their pathways were found to be associated. Gc and Rgn play important roles connecting VC and VD pathways in mice. The r values of expression levels between Gc and Rgn in mouse spleen, liver, lung, and kidney are 0.937, 0.558, 0.901, and 0.617, respectively. The expression QTLs of Gc and Rgn are mapped onto the same locations, i.e., 68–76 MB in chromosome 7 and 26–36 MB in chromosome 9. In humans, there are positive correlations between CYP27B1 and SLC23A1 expression levels in kidney (r = 0.733) and spleen (r = 0.424). SLC23A2 and RXRA are minimally associated in both mouse and human. These data indicate that pathways of VC and VD are not independent but affect each other, and this effect is different between mice and humans during VC and VD synthesis and transportation.
“…This data is generated from the analysis of similarities and differences in regulation of gene expression levels, most correlated genes to their expression levels, and overlapped genes among the top 500 genes that are most correlated to each of genes (VegfA, VegfB, VegfC, and pgf) in Vegf family. Data is obtained from the whole genome expression data generated from normal retina of eighty strains of mice, including 75 BXD RI strains, the parental strains (C57BL/6J and DBA/2J), the reciprocal crosses, and the BALB/cByJ mice ( http://www.genenetwork.org/webqtl/main.py ) [1] , [2] , [3] . Overlapped genes from top 500 genes among VegfA, VegfB and VegfC are shown in Fig.…”
Section: Datamentioning
confidence: 99%
“…Rn values are the summary of absolute values for all R values of top correlated genes. Ra is the average of Rn [1] . Fig.…”
Section: Datamentioning
confidence: 99%
“… Data accessibility All the data are presented in this article. Additional information can be found from GeneNetwork ( http://genenetwork.org/webqtl/main.py ) and [1] . Related research article Jinglin Cui, Lidi Liu, Hang Lu, Dongmei Wei, Yan Jiao, Monica M. Jablonski, Robert W. Williams, Weikuan Gu, P Hong Chen.…”
This data is generated from the analysis of similarities and differences in gene expression levels and pathways among genes (VegfA, VegfB, VegfC, and Pgf) in Vegf family using whole genome expression data generated from normal retina of eighty strains of mice. The results have been published in doi:10.1016/j.exer.2018.06.0210.1016/j.exer.2018.06.024 (Cui et al., 2018) [1]. Fig. 1 shows the expression quantitative trait loci (eQTL) that regulate the expression level of each gene in the Vegf family. The other three figure show the overlapped genes among the top 500 genes that their expression levels are most closely correlated to each of the Vegf genes. The four tables contain the information of top 50 genes that their expression levels are most closely correlated to each of the Vegf genes, and the correlation of the top 50 genes from one gene to the other genes in the Vegf family.
“…There have been too many researches about long non-coding RNA and microRNA in the field of oncology. Also, some molecular has been recognized as novel biomarkers for prognosis, including XIST [2], PVT1 [3], and MALT1 [4]. Small nucleolar RNA host gene 4 (SNHG4), a novel non-coding RNA, has first reported in the research of directly irradiated and bystander cells and was found to be up-regulated in irradiated TK6 cells but were repressed in bystander cells [5].…”
Background and object: Emerging evidence shows that non-coding RNA functions as new gene regulators and prognostic markers in several cancers, including liver cancer. Here, we focused on the small nucleolar RNA host gene 4 (SNHG4) in liver cancer prognosis based on The Cancer Genome Atlas (TCGA) data.
Methods: The expression data and clinical information were downloaded from TCGA. Chi-square tests evaluated the correlation between SNHG4 expression and clinical parameters. Differences in survival between high and low expression groups (optic cutoff value determined by ROC) from Cox regression analysis were compared, and P-value was calculated by a log-rank test. Kaplan–Meier curves were compared with the log-rank test. GSEA and ceRNA network were conducted to explore the potential mechanism.
Results: Data mining of lncRNA expression data for 371 patients with primary tumor revealed overexpression of SNHG4 in liver cancer. High SNHG4 expression was correlated with histological type (P = 0.01), histologic grade (P = 0.001), stage (P = 0.01), T classification (P = 0.004) and survival status (P = 0.013). Patients with high SNHG4 expression had poor overall survival and relapse-free survival compared with those with low SNHG4 expression. Multivariate analysis identified SNHG4 as an independent prognostic factor of poor survival in liver cancer. GSEA revealed related signaling pathway and ceRNA network explored the further mechanism.
Conclusion: High SNHG4 expression is an independent predictor of poor prognosis in liver cancer.
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