RT-PCR analysis of selected genes induced by sLF.

<p>Real-time RT-PCR analysis of ten selected genes displaying altered expression in response to sLF in HEK293 cells. Gray and black bars denote the expression of the indicated genes for non-treated and treated with tetracycline in the sLF cells, respectively. Each sample was analyzed in duplicate, and the average relative expression level of 15 clones is presented. *Significant (P<0.05; Student's <i>t</i>-test).</p

Genome-Wide Pathway Analysis Reveals Different Signaling Pathways between Secreted Lactoferrin and Intracellular Delta-Lactoferrin

<div><p>Human lactoferrin (LF) is a multifunctional protein involved in immunomodulation, cellular growth, and differentiation. In addition to its secreted form (sLF), an alternative form (ΔLF) lacking the signal sequence has been found to be downregulated in cancer. Although the signaling pathways mediated by LF have been studied in a few cell models, there have been no relevant systemic approaches. Therefore, this study was carried out to identify and compare signaling networks provoked by the two LF isoforms. For this, the two forms were overexpressed in HEK293 cells using the Flp-In T-Rex system, after which genome-wide expression analysis of 18,367 genes was conducted. Pathway analysis of the genes showing altered expression identified pathways which are responsible for cell survival and apoptosis. In addition, the pathways mediated by the two LF forms were within distantly related networks. GPCR, PI3K complex, and POU5F1, which are involved in receptor-mediated pathways, were centered in the sLF network, whereas RIF1, NOS3, and RNPS1, which are involved in intracellular signaling, were centered in the ΔLF network. These results suggest that structural differences between the LF isoforms, mainly glycosylation, determine the fate of LF signaling. Furthermore, these findings provide information relating to the role of ΔLF which is downregulated during carcinogenesis.</p> </div

RT-PCR analysis of selected genes induced by ΔLF.

<p>Gray and black bars denote the expression of the indicated genes for non-treated and treated with tetracycline in the ΔLF cells, respectively. Each sample was analyzed in duplicate, and the average relative expression level of 15 clones is presented. *Significant (P<0.05; Student's <i>t</i>-test).</p

Pre-mRNA levels in ΔLF-overexpressing cells.

<p>Real-time RT-PCR was carried out to measure the pre-mRNA (A) and mature-mRNA levels (B) of the three selected genes. Primers spanning an exon and its downstream intron were used for pre-mRNA and primers spanning two exons were used for mature-mRNA. Gray and black bars denote the expression of the indicated genes for non-treated and treated with tetracycline in the ΔLF cells, respectively. Each clone was analyzed in duplicate, and the average relative expression level of 15 clones is presented with the standard error. *Significant (P<0.05; Student's <i>t</i>-test).</p

Highest confidence network of genes regulated by sLF.

<p>Highest confidence network of genes displaying altered expression levels in response to secreted sLF in HEK293 cells. According to IPA, the network is relevant to ‘Genetic Disorder, Hematological Disease, Metabolic Disease’. Genes that were upregulated are presented in red, whereas those that were downregulated are presented in green, where the intensity of the color reflects the magnitude of the expression change, as indicated in the scale bar. Each interaction is supported by at least one literature reference, with solid lines representing direct interactions and dashed lines representing indirect interactions.</p

Genome-wide expression analysis in LF-overexpressing HEK293 cells.

<p>Expression histogram of control (X-axis) vs. sLF (Y-axis) (A) and control vs. ΔLF (B). Expression levels of 18,367 genes were measured by Phalanx Human 32K microarray and are presented on a log scale. Best fit and two-fold difference lines were added. Altered expression was observed more often in ΔLF cells than in sLF cells, indicating that ΔLF generally regulated more genes.</p

Overexpression of human LF in HEK293 cells using Flp-In T-Rex system.

<p>(A) End-point RT-PCR analysis of LF. RT-PCR was conducted for RNAs from HEK293 cells transfected with T-Rex LF expression vectors with signal sequence (sLF) or without signal sequence and 26 N-terminal amino acid residues (ΔLF), non-treated with (Tet(−), top panel) or treated with tetracycline (Tet(+), bottom panel) for 24 or 36 h. C, vector alone. M, size marker. (B) Real-time RT-PCR analysis of LF. Each reaction per clone was carried out in duplicate, and the average of 15 clones is presented along with the standard error. (C) Immunoblot analysis of LF. LF protein expressed from recombinant vector was immunobloted using anti-LF antibody from cell lysate and culture media. LF, commercially available sLF (80 kDa). C, vector alone. LFs expressed from vectors with and without the signal sequence and 26 N-terminal amino acid residues are marked by arrows. Signals below the LF bands appearing in all samples in the medium are non-specifically-reacted serum proteins. The result of ELISA for the LF in medium is shown at the bottom.</p

Cluster and heat map analysis with genes of altered methylation for five cancers.

<p>A. Cluster analysis was carried out using a methylation index (β-value) taken from Illumina Human Methylation 27 Bead Chip database registered at the GEO. Hierarchical clustering with the correlation distance was shown for 50 tumor tissues from the breast, colon, liver, lung, and stomach cancer. B. Hierarchical cluster analysis was conducted for 90 genes that were related with “cancer” from the five cancer types. The bottom panel is a heat map analysis wherein the rows and columns represent genes and cancer cell lines, respectively.</p

Identification and Comparison of Aberrant Key Regulatory Networks in Breast, Colon, Liver, Lung, and Stomach Cancers through Methylome Database Analysis

<div><p>Aberrant methylation of specific CpG sites at the promoter is widely responsible for genesis and development of various cancer types. Even though the microarray-based methylome analyzing techniques have contributed to the elucidation of the methylation change at the genome-wide level, the identification of key methylation markers or top regulatory networks appearing common in highly incident cancers through comparison analysis is still limited. In this study, we in silico performed the genome-wide methylation analysis on each 10 sets of normal and cancer pairs of five tissues: breast, colon, liver, lung, and stomach. The methylation array covers 27,578 CpG sites, corresponding to 14,495 genes, and significantly hypermethylated or hypomethylated genes in the cancer were collected (FDR adjusted p-value <0.05; methylation difference >0.3). Analysis of the dataset confirmed the methylation of previously known methylation markers and further identified novel methylation markers, such as GPX2, CLDN15, and KL. Cluster analysis using the methylome dataset resulted in a diagram with a bipartite mode distinguishing cancer cells from normal cells regardless of tissue types. The analysis further revealed that breast cancer was closest with lung cancer, whereas it was farthest from colon cancer. Pathway analysis identified that either the “cancer” related network or the “cancer” related bio-function appeared as the highest confidence in all the five cancers, whereas each cancer type represents its tissue-specific gene sets. Our results contribute toward understanding the essential abnormal epigenetic pathways involved in carcinogenesis. Further, the novel methylation markers could be applied to establish markers for cancer prognosis.</p></div

Highest confidence network of genes displaying altered methylation levels in breast cancer.

<p>In the network, hypermethylated genes in cancer are colored in red, whereas the hypomethylated genes are shown in green. The intensity of the color reflects the magnitude of methylation change. According to IPA, the network is relevant to “cellular movement, cellular development, and cancer.” The highest confidence network for the other four cancers is added in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097818#pone.0097818.s002" target="_blank">Fig. S2</a>. Each interaction is supported by at least one literature reference, with solid lines representing direct interactions, and dashed lines representing indirect interactions.</p