Microarray-based gene expression profiling in pancreatic ductal carcinoma: status quo and perspectives

Overexpression of a Long Non-Coding RNA BC037916 is Associated with Pancreatic Tumorigenesis and Poor Prognosis

Background

Pancreatic cancer is one of the most lethal malignancies. Accumulating evidence supports for the critical contribution of long noncoding RNAs (lncRNAs) to the cancer development and progression. We tried to identify novel lncRNAs involved in the pancreatic carcinogenesis.

Materials and Methods

Two independent datasets (Gene Expression Omnibus datasets: GSE16515 and GSE32688) were obtained from the Gene Expression Omnibus (GEO). The level of BC037916 was detected in pancreatic cancer tissues and adjacent no-tumorous tissues (n=86) by qRT-PCR. Effects of BC037916 on proliferation, apoptosis, and invasion of pancreatic cancer cells were examined.

Results

We identified a novel lncRNA BC037916 involved in the pancreatic carcinogenesis by analyzing GEO datasets. Quantitative RT-PCR analysis showed that 86.0% (74/86) pancreatic cancer tissues had increased BC037916 expression as compared with normal counterparts. Further, positive correlation was observed between BC037916 expression and clinical stage, primary tumor, and regional lymph node invasion. Importantly, BC037916 was an independent prognostic factor of pancreatic cancer. Functionally, knockdown of BC037916 repressed cell proliferation, inhibited cell invasion, halted cell cycle progression, and promoted apoptosis in both PANC-1 and SW1990 cells. In contrast, overexpression of BC037916 in CAPAN-1 had opposite effects. Moreover, silencing of BC037916 significantly inhibited the tumor growth of xenografted SW1990 cells in vivo. Results of Western blot assays suggested that BC037916 knockdown also suppressed the activation of JAK2/STAT3 and TGF-β signaling. Further experiments suggested that BC037916 positively regulated the expression of Twist through miR-3145-3p.

Conclusion

BC037916 exhibited oncogenic potential in pancreatic cancer development.

Revisiting the Concept of Stress in the Prognosis of Solid Tumors: A Role for Stress Granules Proteins?

Simple Summary

Stress Granules (SGs) were discovered in 1999 and while the first decade of research has focused on some fundamental questions, the field is now investigating their role in human pathogenesis. Since then, evidences of a link between SGs and cancerology are accumulating in vitro and in vivo. In this work we summarized the role of SGs proteins in cancer development and their prognostic values. We find that level of expression of protein involved in SGs formation (and not mRNA level) could serve a prognostic marker in cancer. With this review we strongly suggest that SGs (proteins) could be targets of choice to block cancer development and counteract resistance to improve patients care.

Abstract

Cancer treatments are constantly evolving with new approaches to improve patient outcomes. Despite progresses, too many patients remain refractory to treatment due to either the development of resistance to therapeutic drugs and/or metastasis occurrence. Growing evidence suggests that these two barriers are due to transient survival mechanisms that are similar to those observed during stress response. We review the literature and current available open databases to study the potential role of stress response and, most particularly, the involvement of Stress Granules (proteins) in cancer. We propose that Stress Granule proteins may have prognostic value for patients.

Bioorthogonal labeling cell-surface proteins expressed in pancreatic cancer cells to identify potential diagnostic/therapeutic biomarkers

To develop new diagnostic and therapeutic tools to specifically target pancreatic tumors, it is necessary to identify cell-surface proteins that may serve as potential tumor-specific targets. In this study we used an azido-labeled bioorthogonal chemical reporter to metabolically label N-linked glycoproteins on the surface of pancreatic cancer cell lines to identify potential targets that may be exploited for detection and/or treatment of pancreatic cancer. Labeled glycoproteins were tagged with biotin using click chemistry, purified by streptavidin-coupled magnetic beads, separated by gel electrophoresis, and identified by liquid chromatography-tandem mass spectrometry (MS). MS/MS analysis of peptides from 3 cell lines revealed 954 unique proteins enriched in the azido sugar samples relative to control sugar samples. A comparison of the proteins identified in each sample indicated 20% of these proteins were present in 2 cell lines (193 of 954) and 17 of the proteins were found in all 3 cell lines. Five of the 17 proteins identified in all 3 cell lines have not been previously reported to be expressed in pancreatic cancer; thus indicating that novel cell-surface proteins can be revealed through glycoprotein profiling. Western analysis of one of these glycoproteins, ecto-5'-nucleotidase (NT5E), revealed it is expressed in 8 out of 8 pancreatic cancer cell lines examined. Further, immunohistochemical analysis of human pancreatic tissues indicates NT5E is significantly overexpressed in pancreatic tumors compared to normal pancreas. Thus, we have demonstrated that metabolic labeling with bioorthogonal chemical reporters can be used to selectively enrich and identify novel cell-surface glycoproteins expressed in pancreatic ductal adenocarcinomas.

CD109 Overexpression in Pancreatic Cancer Identified by Cell-Surface Glycoprotein Capture

BACKGROUND

The development of novel targeted cancer therapies and/or diagnostic tools is dependent upon an understanding of the differential expression of molecular targets between normal tissues and tumors. Many of these potential targets are cell-surface receptors; however, our knowledge of the cell-surface proteins upregulated in pancreatic tumors is limited, thus impeding the development of targeted therapies for pancreatic cancer. To develop new diagnostic and therapeutic tools to specifically target pancreatic tumors, we sought to identify cell-surface proteins that may serve as potential tumor-specfic targets.

METHODS

Membrane glycoproteins on the pancreatic cancer cell lines BxPC-3 were labeled with the bifunctional linker biocytin hydrazide. Following proteolytic digestion, biotinylated glycopeptides were captured with streptavidin-coupled beads then released by PNGaseF-mediated endoglycosidase cleavage and identified by liquid chromatography-tandem mass spectrometry (MS). A protein identified by the cell-surface glycoprotein capture procedure, CD109, was evaluated by western analysis of lysates of pancreatic cancer cell lines and by immunohistochemistry in sections of pancreatic ductal adenocarcinoma and non- neoplastic pancreatic tissues.

RESULTS

MS/MS analysis of glycopeptides captured from BxPC-3 cells revealed 18 proteins predicted or known to be associated with the plasma membrane, including CD109, which has not been reported in pancreatic cancer. Western analysis of CD109 in lysates prepared from pancreatic cancer cell lines revealed it was expressed in 6 of 8 cell lines, with a high level of expression in BxPC-3, MIAPaCa-2, and Panc-1 cells. Immunohistochemical analyses of human pancreatic tissues indicate CD109 is significantly overexpressed in pancreatic tumors compared to normal pancreas.

CONCLUSIONS

The selective capture of glycopeptides from the surface of pancreatic cancer cell lines can reveal novel cell-surface glycoproteins expressed in pancreatic ductal adenocarcinomas.

Examination of apoptosis signaling in pancreatic cancer by computational signal transduction analysis

Background

Pancreatic ductal adenocarcinoma (PDAC) remains an important cause of cancer death. Changes in apoptosis signaling in pancreatic cancer result in chemotherapy resistance and aggressive growth and metastasizing. The aim of this study was to characterize the apoptosis pathway in pancreatic cancer computationally by evaluation of experimental data from high-throughput technologies and public data bases. Therefore, gene expression analysis of microdissected pancreatic tumor tissue was implemented in a model of the apoptosis pathway obtained by computational protein interaction prediction.

Methodology/Principal Findings

Apoptosis pathway related genes were assembled from electronic databases. To assess expression of these genes we constructed a virtual subarray from a whole genome analysis from microdissected native tumor tissue. To obtain a model of the apoptosis pathway, interactions of members of the apoptosis pathway were analysed using public databases and computational prediction of protein interactions. Gene expression data were implemented in the apoptosis pathway model. 19 genes were found differentially expressed and 12 genes had an already known pathophysiological role in PDAC, such as Survivin/BIRC5, BNIP3 and TNF-R1. Furthermore we validated differential expression of IL1R2 and Livin/BIRC7 by RT-PCR and immunohistochemistry. Implementation of the gene expression data in the apoptosis pathway map suggested two higher level defects of the pathway at the level of cell death receptors and within the intrinsic signaling cascade consistent with references on apoptosis in PDAC. Protein interaction prediction further showed possible new interactions between the single pathway members, which demonstrate the complexity of the apoptosis pathway.

Conclusions/Significance

Our data shows that by computational evaluation of public accessible data an acceptable virtual image of the apoptosis pathway might be given. By this approach we could identify two higher level defects of the apoptosis pathway in PDAC. We could further for the first time identify IL1R2 as possible candidate gene in PDAC.

A six-gene signature predicts survival of patients with localized pancreatic ductal adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) remains a lethal disease. For patients with localized PDAC, surgery is the best option, but with a median survival of less than 2 years and a difficult and prolonged postoperative course for most, there is an urgent need to better identify patients who have the most aggressive disease.

METHODS AND FINDINGS

We analyzed the gene expression profiles of primary tumors from patients with localized compared to metastatic disease and identified a six-gene signature associated with metastatic disease. We evaluated the prognostic potential of this signature in a training set of 34 patients with localized and resected PDAC and selected a cut-point associated with outcome using X-tile. We then applied this cut-point to an independent test set of 67 patients with localized and resected PDAC and found that our signature was independently predictive of survival and superior to established clinical prognostic factors such as grade, tumor size, and nodal status, with a hazard ratio of 4.1 (95% confidence interval [CI] 1.7-10.0). Patients defined to be high-risk patients by the six-gene signature had a 1-year survival rate of 55% compared to 91% in the low-risk group.

CONCLUSIONS

Our six-gene signature may be used to better stage PDAC patients and assist in the difficult treatment decisions of surgery and to select patients whose tumor biology may benefit most from neoadjuvant therapy. The use of this six-gene signature should be investigated in prospective patient cohorts, and if confirmed, in future PDAC clinical trials, its potential as a biomarker should be investigated. Genes in this signature, or the pathways that they fall into, may represent new therapeutic targets. Please see later in the article for the Editors' Summary.

Prognostic signature for pancreatic cancer: are we close?

Pancreatic cancer is a deadly disease with an annual incidence rate nearly equal to its mortality rate. Incremental improvement in outcome has been seen in the last 25 years, illustrating the critical need for novel approaches and intensive research investment. Expression profiling of pancreatic cancers has led to an explosion of informative gene-expression changes and the identification of new diagnostic and prognostic markers. However, the search for genes that are of functional significance in these large datasets continues to be much more challenging. One approach to focusing on genes or pathways that are likely to be more biologically relevant is to study those that are of prognostic significance. This review will therefore focus on the advantages of a prognostic gene signature for pancreatic cancer, the advances that have been made thus far, the approaches used and the challenges that remain.

Gene expression profiling of liver metastases and tumour invasion in pancreatic cancer using an orthotopic SCID mouse model

The prognosis of pancreatic adenocarcinoma is affected by early metastases and local tumour invasion beyond surgical margins. Gene expression profiling in pancreatic cancer tissue is complicated due to the high amount of RNAses being present in human tissue and that of suitable models. In order to demonstrate early metastases, the models should take into account the anatomical environment of the tumour. Using the orthotopic transplantation of pancreatic tumour cells in SCID (severe combined immunodeficiency) mice, these interactions are taken into consideration. In order to identify genes associated with local tumour invasion and metastases in ductal pancreatic cancer, we investigated a human pancreatic tumour cell line derived from an orthopic pancreatic tumour model in SCID mice. Differential gene expression was performed on the basis of microarray technique. The human MiaPaca-2 cell line was implanted orthotopically in SCID mice. Transcriptional profiling was performed on fresh frozen tissue derived from the primary tumour, the tumour invasion front and the liver metastases. Differentially expressed genes were identified using statistical analyses, and were validated with external databases and with immunohistochemistry. A total of 1066 of 14 500 genes were significantly differentially expressed. Comparing the primary tumour with the tumour invasion front, there were 614 statistically significant up- and 348 downregulated genes. Twenty-five statistically significant up- and 181 downregulated genes were identified comparing the liver metastases with the primary tumour. Eight genes (PAI-1, BNIP3l, VEGF, NSE, RGS4, HSP27, GADD45A, PTPN14) were chosen and validated in a semi-quantitative immunohistochemical analysis, which revealed a positive correlation to the array data. Overrepresentation analyses revealed a total of 66 significantly regulated pathways associated with cell proliferation, cell stress, cell communication metabolic and cytokine function. In conclusion, model marker genes for local invasion and liver metastases can be identified using transcriptional profiling in the SCID mouse. Overrepresentation analysis secures a good and fast overview about the significantly regulated genes and can assign genes to certain pathways. These marker genes can be related to the apoptotic cascade, angiogenesis and cell interaction.

Applications of microarray technology to pancreatic cancer research and drug development

As one of the deadliest forms of malignancy in the US and worldwide, pancreatic cancer is becoming a top research priority as therapies at present have had markedly limited or no success in controlling this disease. The arrival and advancement of microarray technology in the last decade offers the hope of new diagnostic, prognostic and therapeutic applications. The purpose of this review is to describe the basic principles of microarray technology and techniques and its use in pancreatic cancer research for drug discovery and development, with a focus on the relevant published data obtained from the literature.

Identification of biomarkers of human pancreatic adenocarcinomas by expression profiling and validation with gene expression analysis in endoscopic ultrasound-guided fine needle aspiration samples

AIM: To compare gene expression profiles of pancreatic adenocarcinoma tissue specimens, human pancreatic and colon adenocarcinoma and leukemia cell lines and normal pancreas samples in order to distinguish differentially expressed genes and to validate the differential expression of a subset of genes by quantitative real-time RT-PCR (RT-QPCR) in endoscopic ultrasound-guided fine needle aspiration (EUS-guided FNA) specimens.

METHODS: Commercially dedicated cancer cDNA macroarrays (Atlas Human Cancer 1.2) containing 1176 genes were used. Different statistical approaches (hierarchical clustering, principal component analysis (PCA) and SAM) were used to analyze the expression data. RT-QPCR and immunohistochemical studies were used for validation of results.

RESULTS: RT-QPCR validated the increased expression of LCN2 (lipocalin 2) and for the first time PLAT (tissue-type plasminogen activator or tPA) in malignant pancreas as compared with normal pancreas. Immunohistochemical analysis confirmed the increased expression of LCN2 protein localized in epithelial cells of ducts invaded by carcinoma. The analysis of PLAT and LCN2 transcripts in 12 samples obtained through EUS-guided FNA from patients with pancreatic adenocarcinoma showed significantly increased expression levels in comparison with those found in normal tissues, indicating that a sufficient amount of high quality RNA can be obtained with this technique.

CONCLUSION: Expression profiling is a useful method to identify biomarkers and potential target genes. Molecular analysis of EUS-guided FNA samples in pancreatic cancer appears as a valuable strategy for the diagnosis of pancreatic adenocarcinomas.

Meta-analysis of microarray data on pancreatic cancer defines a set of commonly dysregulated genes

Pancreatic ductal adenocarcinoma is the eighth most common cancer with the lowest overall 5-year relative survival rate of any tumor type today. Expression profiling using microarrays has been widely used to identify genes associated with pancreatic cancer development. To extract maximum value from the available gene expression data, we applied a meta-analysis to search for commonly differentially expressed genes in pancreatic ductal adenocarcinoma. We obtained data sets from four different gene expression studies on pancreatic cancer. We selected a consensus set of 2984 genes measured in all four studies and applied a meta-analysis approach to evaluate the combined data. Of the genes identified as differentially expressed, several were validated using RT-PCR and immunohistochemistry. Additionally, we used a class discovery algorithm to identify a gene expression signature. Our meta-analysis revealed that the pancreatic cancer gene expression data sets shared a significant number of up- and downregulated genes, independent of the technology used. This interstudy crossvalidation approach generated a set of 568 genes that were consistently and significantly dysregulated in pancreatic cancer. Of these, 364 (64.1%) were upregulated and 204 (35.9%) were downregulated in pancreatic cancer. Only 127 (22%) were described in the published individual analyses. Functional annotation of the genes revealed that genes presumably associated with the cell adhesion-mediated drug resistance pathway are frequently overexpressed in pancreatic cancer. Meta-analysis is an important tool for the identification and validation of differentially expressed genes. These could represent good candidates for novel diagnostic and therapeutic approaches to pancreatic cancer.

Identification and validation of commonly overexpressed genes in solid tumors by comparison of microarray data

Cancers originating from epithelial cells are the most common malignancies. No common expression profile of solid tumors compared to normal tissues has been described so far. Therefore we were interested if genes differentially expressed in the majority of carcinomas could be identified using bioinformatic methods. Complete data sets were downloaded for carcinomas of the prostate, breast, lung, ovary, colon, pancreas, stomach, bladder, liver, and kidney, and were subjected to an expression analysis using SAM. In each experiment, a gene was scored as differentially expressed if the q value was below 25%. Probe identifiers were unified by comparing the respective probe sequences to the Unigene build 155 using BlastN. To obtain differentially expressed genes within the set of analyzed carcinomas, the number of experiments in which differential expression was observed was counted. Differential expression was assigned to genes if they were differentially expressed in at least eight experiments of tumors from different origin. The identified candidate genes ADRM1, EBNA1BP2, FDPS, FOXM1, H2AFX, HDAC3, IRAK1, and YY1 were subjected to further validation. Using this comparative approach, 100 genes were identified as upregulated and 21 genes as downregulated in the carcinomas.