A workflow summarizing the strategy to identify accessible common cancer biomarkers.

<p>See text for details. Reg: regulation; Exp: expression; CNV: copy number variation; MCN: minimal connected network; PPI: protein-protein interactions; TF: Transcription factor.</p

The minimal connected network of TFs regulated in cancer cell lines.

<p>The Snow web tool identified a significant human curated protein-protein interaction subnetwork involving 70 out of the 88 TFs correlatively regulated in cancer cell lines. The first connected component as shown here is considered as the minimal connected network (MCN) connecting these TFs. Each node represent a protein. Edges are the protein-protein interactions validated by at least two experimental evidences. Nodes shaded in violet represent the top ten most central TFs in the MCN. Node-ranking was based on the betweenness centrality scores.</p

A Minimal Connected Network of Transcription Factors Regulated in Human Tumors and Its Application to the Quest for Universal Cancer Biomarkers

<div><p>A universal cancer biomarker candidate for diagnosis is supposed to distinguish, within a broad range of tumors, between healthy and diseased patients. Recently published studies have explored the universal usefulness of some biomarkers in human tumors. In this study, we present an integrative approach to search for potential common cancer biomarkers. Using the TFactS web-tool with a catalogue of experimentally established gene regulations, we could predict transcription factors (TFs) regulated in 305 different human cancer cell lines covering a large panel of tumor types. We also identified chromosomal regions having significant copy number variation (CNV) in these cell lines. Within the scope of TFactS catalogue, 88 TFs whose activity status were explained by their gene expressions and CNVs were identified. Their minimal connected network (MCN) of protein-protein interactions forms a significant module within the human curated TF proteome. Functional analysis of the proteins included in this MCN revealed enrichment in cancer pathways as well as inflammation. The ten most central proteins in MCN are TFs that trans-regulate 157 known genes encoding secreted and transmembrane proteins. In publicly available collections of gene expression data from 8,525 patient tissues, 86 genes were differentially regulated in cancer compared to inflammatory diseases and controls. From TCGA cancer gene expression data sets, 50 genes were significantly associated to patient survival in at least one tumor type. Enrichment analysis shows that these genes mechanistically interact in common cancer pathways. Among these cancer biomarker candidates, TFRC, MET and VEGFA are commonly amplified genes in tumors and their encoded proteins stained positive in more than 80% of malignancies from public databases. They are linked to angiogenesis and hypoxia, which are common in cancer. They could be interesting for further investigations in cancer diagnostic strategies.</p> </div

Patient sample repartition and cancer-specific gene expression analysis.

<p>Microarray gene expression data representing 8,525 patients samples were downloaded from GEO. A- 78% of patients had different cancer types; 14% are healthy individual and were sampled from different tissues; 8% of patients had inflammation/sepsis and were investigated from the whole blood and other tissues. B- differential expression of the MCN top ten central TFs target gene list coding for secreted and transmembrane proteins were analyzed. Among these genes, as shown in the Venn-diagram, 140 probe sets (86 unique genes) were found to be cancer-specific. GI: Gastro-intestinal.</p

Signaling pathway enrichment in the MCN proteins.

<p>All proteins (nodes) in the MCN were submitted to DAVID web tool for KEGG pathway enrichment analysis. Significant pathways are shown by categories according to the −log10(p-value) and the percentage of intersection between the submitted list and queried annotations.</p

<i>SSTR1, SSTR2, SSTR3, SSTR4 and SSTR5</i> gene expression analysis.

<p><i>SSTR1-5</i> gene expression analysis was performed by QRT-PCR on total RNA from five human NET cell lines: CNDT2.5, KRJ-1, QGP-1, NCI-H720 and NCI-H727. The absolute mRNA copy numbers are adjusted by <i>β-actin</i> mRNA copy number. Results were plotted using the 2^−ΔΔCt method with <i>β-actin</i> expression (set to 1) from each individual sample as endogenous reference.</p

Gene expression of CNDT2.5 cells and 1 µM octreotide treated cells.

<p>Microarray analysis detected 25 differentially expressed genes in octreotide treated CNDT2.5 cells compared to CNDT2.5 untreated cells (control). Upregulated genes are in yellow and downregulated genes are in blue. Genes were clustered according to Euclidian distance, as indicated in the figure. Of the 25 genes, 6 were selected for further analysis and they are indicated by a red asterisk.</p

ANXA1, ARHGAP18, EMP1, GDF15, TGFBR2 and TNFSF15 Western blot analysis.

<p>CNDT2.5 cells cultured in the absence or presence of 1 µM octreotide were collected at 1 week (wk), 4 months, 10 months and 16 months (mo) to prepare total lysates. Octreotide induces protein expression level of octreotide treated CNDT2.5 cells for 10 and 16 months compared to untreated cells (5A). β-actin was used as endogenous control. Fold changes are illustrated in 5B.</p

Result of Immunohistochemistry on paraffin embedded SI-NET specimens.

<p>Primary tumour (P); Liver metastases (L); Mesentery metastases (M); Untreated (UT); Treated (T).</p><p>Intensity in >50% of tumour cells: +++ strong, ++ moderate, + weak, – negative.</p

CNDT2.5 cells growth in the presence of 1 µM octreotide was kinetically evaluated.

<p>Cells were cultured in the absence or presence of 1 µM octreotide (oct). WST-1 assay was used to evaluate cell growth. Cell proliferation ratio for each time point was converted to a percentage of the mean value relative to CNDT2.5 cells growth, set to 100%. Plotted results are means ± SD from triplicate wells. Significance was calculated by using Two-Way ANOVA followed by Bonferroni test; comparing with untreated CNDT2.5 cells. *** = <i>p</i><0.001.</p