A meta analysis of pancreatic microarray datasets yields new targets as cancer genes and biomarkers

A comprehensive meta-analysis of transcriptome data to identify signature genes associated with pancreatic ductal adenocarcinoma

PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) has a five-year survival rate of less than 5%. Absence of symptoms at primary tumor stages, as well as high aggressiveness of the tumor can lead to high mortality in cancer patients. Most patients are recognized at the advanced or metastatic stage without surgical symptom, because of the lack of reliable early diagnostic biomarkers. The objective of this work was to identify potential cancer biomarkers by integrating transcriptome data.

METHODS

Several transcriptomic datasets comprising of 11 microarrays were retrieved from the GEO database. After pre-processing, a meta-analysis was applied to identify differentially expressed genes (DEGs) between tumor and nontumor samples for datasets. Next, co-expression analysis, functional enrichment and survival analyses were used to determine the functional properties of DEGs and identify potential prognostic biomarkers. In addition, some regulatory factors involved in PDAC including transcription factors (TFs), protein kinases (PKs), and miRNAs were identified.

RESULTS

After applying meta-analysis, 1074 DEGs including 539 down- and 535 up-regulated genes were identified. Pathway enrichment analyzes using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed that DEGs were significantly enriched in the HIF-1 signaling pathway and focal adhesion. The results also showed that some of the DEGs were assigned to TFs that belonged to 23 conserved families. Sixty-four PKs were identified among the DEGs that showed the CAMK family was the most abundant group. Moreover, investigation of corresponding upstream regions of DEGs identified 11 conserved sequence motifs. Furthermore, weighted gene co-expression network analysis (WGCNA) identified 8 modules, more of them were significantly enriched in Ras signaling, p53 signaling, MAPK signaling pathways. In addition, several hubs in modules were identified, including EMP1, EVL, ELP5, DEF8, MTERF4, GLUP1, CAPN1, IGF1R, HSD17B14, TOM1L2 and RAB11FIP3. According to survival analysis, it was identified that the expression levels of two genes, EMP1 and RAB11FIP3 are related to prognosis.

CONCLUSION

We identified several genes critical for PDAC based on meta-analysis and system biology approach. These genes may serve as potential targets for the treatment and prognosis of PDAC.

Using meta-analysis and machine learning to investigate the transcriptional response of immune cells to Leishmania infection

BACKGROUND

Leishmaniasis is a parasitic disease caused by the Leishmania protozoan affecting millions of people worldwide, especially in tropical and subtropical regions. The immune response involves the activation of various cells to eliminate the infection. Understanding the complex interplay between Leishmania and the host immune system is crucial for developing effective treatments against this disease.

METHODS

This study collected extensive transcriptomic data from macrophages, dendritic, and NK cells exposed to Leishmania spp. Our objective was to determine the Leishmania-responsive genes in immune system cells by applying meta-analysis and feature selection algorithms, followed by co-expression analysis.

RESULTS

As a result of meta-analysis, we discovered 703 differentially expressed genes (DEGs), primarily associated with the immune system and cellular metabolic processes. In addition, we have substantiated the significance of transcription factor families, such as bZIP and C2H2 ZF, in response to Leishmania infection. Furthermore, the feature selection techniques revealed the potential of two genes, namely G0S2 and CXCL8, as biomarkers and therapeutic targets for Leishmania infection. Lastly, our co-expression analysis has unveiled seven hub genes, including PFKFB3, DIAPH1, BSG, BIRC3, GOT2, EIF3H, and ATF3, chiefly related to signaling pathways.

CONCLUSIONS

These findings provide valuable insights into the molecular mechanisms underlying the response of immune system cells to Leishmania infection and offer novel potential targets for the therapeutic goals.

A Comprehensive Transcriptional Signature in Pancreatic Ductal Adenocarcinoma Reveals New Insights into the Immune and Desmoplastic Microenvironments

Pancreatic ductal adenocarcinoma (PDAC) prognoses and treatment responses remain devastatingly poor due partly to the highly heterogeneous, aggressive, and immunosuppressive nature of this tumor type. The intricate relationship between the stroma, inflammation, and immunity remains vaguely understood in the PDAC microenvironment. Here, we performed a meta-analysis of stroma-, and immune-related gene expression in the PDAC microenvironment to improve disease prognosis and therapeutic development. We selected 21 PDAC studies from the Gene Expression Omnibus and ArrayExpress databases, including 922 samples (320 controls and 602 cases). Differential gene enrichment analysis identified 1153 significant dysregulated genes in PDAC patients that contribute to a desmoplastic stroma and an immunosuppressive environment (the hallmarks of PDAC tumors). The results highlighted two gene signatures related to the immune and stromal environments that cluster PDAC patients into high- and low-risk groups, impacting patients' stratification and therapeutic decision making. Moreover, HCP5, SLFN13, IRF9, IFIT2, and IFI35 immune genes are related to the prognosis of PDAC patients for the first time.

Efficient Selection of Enhancers and Promoters from MIA PaCa-2 Pancreatic Cancer Cells by ChIP-lentiMPRA

A library of active genome regulatory elements (putative promoters and enhancers) from MIA PaCa-2 pancreatic adenocarcinoma cells was constructed using a specially designed lentiviral vector and a massive parallel reporter assay (ChIP-lentiMPRA). Chromatin immunoprecipitation of the cell genomic DNA by H3K27ac antibodies was used for primary enrichment of the library for regulatory elements. Totally, 11,264 unique genome regions, many of which are capable of enhancing the expression of the CopGFP reporter gene from the minimal CMV promoter, were identified. The regions tend to be located near promoters. Based on the proximity assay, we found an enrichment of highly expressed genes among those associated with three or more mapped distal regions (2 kb distant from the 5'-ends of genes). It was shown significant enrichment of genes related to carcinogenesis or Mia PaCa-2 cell identity genes in this group. In contrast, genes associated with 1-2 distal regions or only with proximal regions (within 2 kbp of the 5'-ends of genes) are more often related to housekeeping functions. Thus, ChIP-lentiMPRA is a useful strategy for creating libraries of regulatory elements for the study of tumor-specific gene transcription.

In vitro model of predicting metastatic ability using tumor derived extracellular vesicles; beyond seed soil hypothesis

Lung cancer progression is often driven by metastasis, which has resulted in a considerable increase in lung cancer-related deaths. Cell-derived extracellular vesicles (EVs), particularly exosomes, serve key roles in cellular signal transmission via microenvironment, however, their biological relevance in cancer development and metastasis still needs to be clear. Here, we demonstrate that extracellular vesicles (EVs) derived from lung cancer bone metastatic patients exhibited a great capacity to promote the progression of lung cancer cells. We carried out a comprehensive meta-analysis to identify the gene expression profile of bone metastases using publicly available microarray datasets. Furthermore, mRNA expression of six identified genes was quantified by real time PCR in lung cancer with and without bone metastasis and healthy individual derived EVs. In addition, we utilized a very novel approach by to study how lung cancer cells uptake EVs by co-culturing EVs with lung cells. We observed that EVs obtained from bone metastases patients were efficiently ingested by lung cancer cells. Morevore, integration and uptake of these EVs lead to increased lung cancer cell proliferation, migration, invasion, and sphere formation. We discovered that EV uptake increase the expression of SPP1, CD44, and POSTN genes in lung cancer cells. The data obtained from this study, support to the possibility that circulating EVs play a significant role in the formation of the pre-metastatic niche, eventually leading to metastasis.

A novel nanobody as therapeutics target for EGFR-positive colorectal cancer therapy: exploring the effects of the nanobody on SW480 cells using proteomics approach

Background

The epidermal growth factor receptor (EGFR) overexpression is found in metastatic colorectal cancer (mCRC). Targeted molecular therapies such as monoclonal antibodies (mAbs) and tyrosine kinase inhibitors (TKI) are becoming more precise, targeting specifically for cancer therapeutics. However, there are adverse effects of currently available anti-EGFR drugs, including drug-resistant and side effects. Nanobodies can overcome these limitations. Our previous study has found that cell-penetrable nanobodies targeted at EGFR-tyrosine kinase were significantly reduced EGFR-positive lung cancer cells viability and proliferation. The aim of the present study was to determine the effect of cell-penetrable nanobody (R9VH36) on cell viability and proteomic profile in EGFR-positive human colorectal cancer cell lines.

Methods

The human colorectal carcinoma cell line (SW480) was treated with R9VH36, compared with gefitinib. Cell viability was monitored using the MTT cell viability assay. The proteomic profiling was analyzed by LC–MS/MS .

Results

The half-maximal inhibitory concentration (IC₅₀) values determined for R9VH36 and gefitinib against SW480 were 527 ± 0.03 nM and 13.31 ± 0.02 μM, respectively. Moreover, both the gefitinib-treated group and nanobody-treated group had completely different proteome profiles. A total 6626 differentially expressed proteins were identified. PCA analysis revealed different proteome profiling in R9VH36 experiment. There were 8 proteins in R9VH36 that significantly exhibited opposite expression directions when compared to gefitinib. These proteins are involved in DNA-damage checkpoint processes.

Conclusion

The proteomics explored those 6,626 proteins had different expressions between R9VH36 and gefitinib. There were 8 proteins in R9VH36 exhibited opposite expression direction when comparing to gefitinib. Our findings suggest that R9VH36 has the potential to be an alternative remedy for treating EGFR-positive colon cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12953-022-00190-6.

A predictive biomarker panel for bone metastases: Liquid biopsy approach

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Data mining of published microarray datasets directed us to the identification of a multi gene panel involving of 15 genes that are particular to bone metastases.

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Serum exosomal markers HSP90AA1, SPP1, IL3, and PTK2 found in the present study might be useful in detecting the early spread of bone metastases leading to better clinical outcomes.

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This multi-gene panel and their related pathways may assist as promising conclusion predictors using novel approaches of exosome as liquid biopsy and their application in therapeutic targets in breast and lung cancer patients with bone metastases.

Bone metastases is one of the common metastatic site and leading cause of cancer-related mortality in progressive cancer patients. The purpose of the present study is to establish a liquid biopsy based multi-gene classifier and associated signalling pathways for early diagnosis of bone metastases. We used publically available microarray datasets and analysed them in a platform/chip-specific manner using GeneSpring software. Analyses of gene expression datasets identified 15 consistently over-expressed genes with statistical significance. Further, expression profile of same set of 15 genes were compared in breast and lung cancer exosome derived mRNA with (n = 10) and without (n = 10) bone metastases against healthy controls. ROC curve analysis performed individually for all the 15 genes shortlisted the 5 most relevant genes with significant sensitivity and specificity in both cancers. This liquid biopsy-based bone metastases predictor using multi-gene panel is a unique approach with potential clinical applications for effective management of aggressive cancers.

The Case for GNMT as a Biomarker and a Therapeutic Target in Pancreatic Cancer

The high mortality rate for pancreatic cancer (PC) is due to the lack of specific symptoms at early tumor stages and a high biological aggressiveness. Reliable biomarkers and new therapeutic targets would help to improve outlook in PC. In this study, we analyzed the expression of GNMT in a panel of pancreatic cancer cell lines and in early-stage paired patient tissue samples (normal and diseased) by quantitative reverse transcription-PCR (qRT-PCR). We also investigated the effect of 1,2,3,4,6-penta-O-galloyl-β-d-glucopyranoside (PGG) as a therapeutic agent for PC. We find that GNMT is markedly downregulated (p < 0.05), in a majority of PC cell lines. Similar results are observed in early-stage patient tissue samples, where GNMT expression can be reduced by a 100-fold or more. We also show that PGG is a strong inhibitor of PC cell proliferation, with an IC₅₀ value of 12 ng/mL, and PGG upregulates GNMT expression in a dose-dependent manner. In conclusion, our data show that GNMT has promise as a biomarker and as a therapeutic target for PC.

Unraveling the Molecular Tumor-Promoting Regulation of Cofilin-1 in Pancreatic Cancer

Simple Summary

Unraveling the mechanistic regulations that influence tumor behavior is an important step towards treatment. However, in vitro studies capture only small parts of the complex signaling cascades leading to tumor development. Mechanistic modeling, instead, allows a more holistic view of complex signaling pathways and their crosstalk. These models are able to suggest mechanistic regulations that can be validated by targeted and thus more cost-effective experiments. This article presents a logical model of pancreatic cancer cells with high cofilin-1 expression. The model includes migratory, proliferative, and apoptotic pathways as well as their crosstalk. Based on this model, mechanistic regulations affecting tumor promotion could be unraveled. Moreover, it was applied to screen for new therapeutic targets. The development of resistance mechanisms is a common limitation of cancer therapies. Therefore, new approaches are needed to identify optimal treatments. One is suggested in this article, indicating the surface protein CD44 as a promising target.

Abstract

Cofilin-1 (CFL1) overexpression in pancreatic cancer correlates with high invasiveness and shorter survival. Besides a well-documented role in actin remodeling, additional cellular functions of CFL1 remain poorly understood. Here, we unraveled molecular tumor-promoting functions of CFL1 in pancreatic cancer. For this purpose, we first show that a knockdown of CFL1 results in reduced growth and proliferation rates in vitro and in vivo, while apoptosis is not induced. By mechanistic modeling we were able to predict the underlying regulation. Model simulations indicate that an imbalance in actin remodeling induces overexpression and activation of CFL1 by acting on transcription factor 7-like 2 (TCF7L2) and aurora kinase A (AURKA). Moreover, we could predict that CFL1 impacts proliferation and apoptosis via the signal transducer and activator of transcription 3 (STAT3). These initial model-based regulations could be substantiated by studying protein levels in pancreatic cancer cell lines and human datasets. Finally, we identified the surface protein CD44 as a promising therapeutic target for pancreatic cancer patients with high CFL1 expression.

GVES: machine learning model for identification of prognostic genes with a small dataset

Machine learning may be a powerful approach to more accurate identification of genes that may serve as prognosticators of cancer outcomes using various types of omics data. However, to date, machine learning approaches have shown limited prediction accuracy for cancer outcomes, primarily owing to small sample numbers and relatively large number of features. In this paper, we provide a description of GVES (Gene Vector for Each Sample), a proposed machine learning model that can be efficiently leveraged even with a small sample size, to increase the accuracy of identification of genes with prognostic value. GVES, an adaptation of the continuous bag of words (CBOW) model, generates vector representations of all genes for all samples by leveraging gene expression and biological network data. GVES clusters samples using their gene vectors, and identifies genes that divide samples into good and poor outcome groups for the prediction of cancer outcomes. Because GVES generates gene vectors for each sample, the sample size effect is reduced. We applied GVES to six cancer types and demonstrated that GVES outperformed existing machine learning methods, particularly for cancer datasets with a small number of samples. Moreover, the genes identified as prognosticators were shown to reside within a number of significant prognostic genetic pathways associated with pancreatic cancer.

Integrated transcriptome meta-analysis of pancreatic ductal adenocarcinoma and matched adjacent pancreatic tissues

A comprehensive meta-analysis of publicly available gene expression microarray data obtained from human-derived pancreatic ductal adenocarcinoma (PDAC) tissues and their histologically matched adjacent tissue samples was performed to provide diagnostic and prognostic biomarkers, and molecular targets for PDAC. An integrative meta-analysis of four submissions (GSE62452, GSE15471, GSE62165, and GSE56560) containing 105 eligible tumor-adjacent tissue pairs revealed 344 differentially over-expressed and 168 repressed genes in PDAC compared to the adjacent-to-tumor samples. The validation analysis using TCGA combined GTEx data confirmed 98.24% of the identified up-regulated and 73.88% of the down-regulated protein-coding genes in PDAC. Pathway enrichment analysis showed that “ECM-receptor interaction”, “PI3K-Akt signaling pathway”, and “focal adhesion” are the most enriched KEGG pathways in PDAC. Protein-protein interaction analysis identified FN1, TIMP1, and MSLN as the most highly ranked hub genes among the DEGs. Transcription factor enrichment analysis revealed that TCF7, CTNNB1, SMAD3, and JUN are significantly activated in PDAC, while SMAD7 is inhibited. The prognostic significance of the identified and validated differentially expressed genes in PDAC was evaluated via survival analysis of TCGA Pan-Cancer pancreatic ductal adenocarcinoma data. The identified candidate prognostic biomarkers were then validated in four external validation datasets (GSE21501, GSE50827, GSE57495, and GSE71729) to further improve reliability. A total of 28 up-regulated genes were found to be significantly correlated with worse overall survival in patients with PDAC. Twenty-one of the identified prognostic genes (ITGB6, LAMC2, KRT7, SERPINB5, IGF2BP3, IL1RN, MPZL2, SFTA2, MET, LAMA3, ARNTL2, SLC2A1, LAMB3, COL17A1, EPSTI1, IL1RAP, AK4, ANXA2, S100A16, KRT19, and GPRC5A) were also found to be significantly correlated with the pathological stages of the disease. The results of this study provided promising prognostic biomarkers that have the potential to differentiate PDAC from both healthy and adjacent-to-tumor pancreatic tissues. Several novel dysregulated genes merit further study as potentially promising candidates for the development of more effective treatment strategies for PDAC.

Genetic predisposition and prediction protocol for epithelial neoplasms in disease-free individuals: A systematic review

Background

Epithelial neoplasm is an important global health-care problem, with high morbidity and mortality rates. Early diagnosis and appropriate treatment are essential for increased life survival. Prediction of occurrence of malignancy in a disease-free individual by any means will be a great breakthrough for healthy living.

Aims and Objectives

The aims and objectives were to predict the genetic predisposition and propose a prediction protocol for epithelial malignancy of various systems in our body, in a disease-free individual.

Methods

We have searched databases both manually and electronically, published in English language in Cochrane group, Google search, MEDLINE and PubMed from 2000 to 2019. We have included all the published, peer-reviewed, narrative reviews; randomized controlled trials; case-control studies; and cohort studies and excluded the abstract-only articles and duplicates. Specific words such as "etiological factors," "pathology and mutations," "signs and symptoms," "genetics and IHC marker," and "treatment outcome" were used for the search. A total of 1032 citations were taken, and only 141 citations met the inclusion criteria and were analyzed.

Results

After analyzing various articles, the etiological factors, clinical signs and symptoms, genes and the pathology involved and the commonly used blood and tissue markers were analyzed. A basic investigation strategy using immunohistochemistry markers was established.

Conclusion

The set of proposed biomarkers should be studied in future to predict genetic predisposition in disease-free individuals.

Controlling technical variation amongst 6693 patient microarrays of the randomized MINDACT trial

Gene expression data obtained in large studies hold great promises for discovering disease signatures or subtypes through data analysis. It is also prone to technical variation, whose removal is essential to avoid spurious discoveries. Because this variation is not always known and can be confounded with biological signals, its removal is a challenging task. Here we provide a step-wise procedure and comprehensive analysis of the MINDACT microarray dataset. The MINDACT trial enrolled 6693 breast cancer patients and prospectively validated the gene expression signature MammaPrint for outcome prediction. The study also yielded a full-transcriptome microarray for each tumor. We show for the first time in such a large dataset how technical variation can be removed while retaining expected biological signals. Because of its unprecedented size, we hope the resulting adjusted dataset will be an invaluable tool to discover or test gene expression signatures and to advance our understanding of breast cancer.

Carcinoembryonic antigen cell adhesion molecule 6 (CEACAM6) in Pancreatic Ductal Adenocarcinoma (PDA): An integrative analysis of a novel therapeutic target

We investigated biomarker CEACAM6, a highly abundant cell surface adhesion receptor that modulates the extracellular matrix (ECM) in pancreatic ductal adenocarcinoma (PDA). The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) RNA-Seq data from PDA patients were analyzed for CEACAM6 expression and evaluated for overall survival, association, enrichment and correlations. A CRISPR/Cas9 Knockout (KO) of CEACAM6 in PDA cell line for quantitative proteomics, mitochondrial bioenergetics and tumor growth in mice were conducted. We found CEACAM6 is over-expressed in primary and metastatic basal and classical PDA subtypes. Highest levels are in classical activated stroma subtype. CEACAM6 over-expression is universally a poor prognostic marker in KRAS mutant and wild type PDA. High CEACAM6 expression is associated with low cytolytic T-cell activity in both basal and classical PDA subtypes and correlates with low levels of T-_REG markers. In HPAF-II cells knockout of CEACAM6 alters ECM-cell adhesion, catabolism, immune environment, transmembrane transport and autophagy. CEACAM6 loss increases mitochondrial basal and maximal respiratory capacity. HPAF-II CEACAM6−/− cells are growth suppressed by >65% vs. wild type in mice bearing tumors. CEACAM6, a key regulator affects several hallmarks of PDA including the fibrotic reaction, immune regulation, energy metabolism and is a novel therapeutic target in PDA.

Mining Public Toxicogenomic Data Reveals Insights and Challenges in Delineating Liver Steatosis Adverse Outcome Pathways

Exposure to chemicals contributes to the development and progression of fatty liver, or steatosis, a process characterized by abnormal accumulation of lipids within liver cells. However, lack of knowledge on how chemicals cause steatosis has prevented any large-scale assessment of the 80,000+ chemicals in current use. To address this gap, we mined a large, publicly available toxicogenomic dataset associated with 18 known steatogenic chemicals to assess responses across assays (in vitro and in vivo) and species (i.e., rats and humans). We identified genes that were differentially expressed (DEGs) in rat in vivo, rat in vitro, and human in vitro studies in which rats or in vitro primary cell lines were exposed to the chemicals at different doses and durations. Using these DEGs, we performed pathway enrichment analysis, analyzed the molecular initiating events (MIEs) of the steatosis adverse outcome pathway (AOP), and predicted metabolite changes using metabolic network analysis. Genes indicative of oxidative stress were among the DEGs most frequently observed in the rat in vivo studies. Nox4, a pro-fibrotic gene, was down-regulated across these chemical exposure conditions. We identified eight genes (Cyp1a1, Egr1, Ccnb1, Gdf15, Cdk1, Pdk4, Ccna2, and Ns5atp9) and one pathway (retinol metabolism), associated with steatogenic chemicals and whose response was conserved across the three in vitro and in vivo systems. Similarly, we found the predicted metabolite changes, such as increases of saturated and unsaturated fatty acids, conserved across the three systems. Analysis of the target genes associated with the MIEs of the current steatosis AOP did not provide a clear association between these 18 chemicals and the MIEs, underlining the multi-factorial nature of this disease. Notably, our overall analysis implicated mitochondrial toxicity as an important and overlooked MIE for chemical-induced steatosis. The integrated toxicogenomics approach to identify genes, pathways, and metabolites based on known steatogenic chemicals, provide an important mean to assess development of AOPs and gauging the relevance of new testing strategies.

Genome-wide bioinformatics analysis reveals CTCFL is upregulated in high-grade epithelial ovarian cancer

Epithelial ovarian cancer (EOC) is the most lethal gynecological malignancy that threatens the health of females. Previous studies have demonstrated that the survival outcomes of patients with different EOC grades varied. Therefore, the EOC grade is considered to serve as a distinctive prognostic factor. To date, the evaluation of ovarian cancer grade relies on pathological examination and a quantitative index for diagnosis is lacking. Furthermore, the dysregulation of genes has been demonstrated to exert pivotal functions in the carcinogenesis of EOCs. Therefore, the identification of effective biomarkers associated with EOC grade is of importance for the development of therapeutic regimens, and also contributes to the prediction of EOC prognosis. Microarrays have been increasingly applied for the identification of potential molecular biomarkers for numerous diseases including EOC. In the present study, four public microarray datasets (GSE26193, GSE63885, GSE30161 and GSE9891) were analyzed. A total of 6,103 upregulated probes corresponding to 5,766 genes, and 4,004 downregulated probes corresponding to 3,707 genes were identified in the GSE26193, GSE63885 and GSE30161 datasets. ALK and LTK ligand 2 was the most downregulated gene associated with the tumor grade, while CCCTC-binding factor like (CTCFL), EGF like domain multiple 6, radical S-adenosyl methionine domain containing 2 and SAM and HD domain containing deoxynucleoside triphosphate triphosphohydrolase 1 were the most upregulated genes associated with EOC grade. The GSE9891 dataset was added for further analysis. Only one probe (1552368_at) encoding for CTCFL was identified to be consistently upregulated in the four examined datasets. Immunohistochemical analysis was used to detect the expression of CTCFL between low- and high-grade EOC tissues and revealed that the EOC grade was closely associated with CTCFL level. This was corroborated via the reverse transcription-quantitative polymerase chain reaction. Taken together, the results of the present study suggested that CTCFL is upregulated in high-grade epithelial ovarian cancer.

Molecular prognosticators in clinically and pathologically distinct cohorts of head and neck squamous cell carcinoma-A meta-analysis approach

Head and neck squamous cell carcinomas (HNSCC) includes multiple subsites that exhibit differential treatment outcome, which is in turn reflective of tumor stage/histopathology and molecular profile. This study hypothesized that the molecular profile is an accurate prognostic adjunct in patients triaged based on clinico-pathological characteristics. Towards this effect, publically available micro-array datasets (n = 8), were downloaded, classified based on HPV association (n = 83) and site (tongue n = 88; laryngopharynx n = 53; oropharynx n = 51) and re-analyzed (Genespring; v13.1). The significant genes were validated in respective cohorts in The Cancer Genome Atlas (TCGA) for correlation with clinico-pathological parameters/survival. The gene entities (n = 3258) identified from HPV based analysis, when validated in TCGA identified the subset specifically altered in HPV+ HNSCC (n = 63), with three genes showing survival impact (RPP25, NUDCD2, NOVA1). Site-specific meta-analysis identified respective differentials (tongue: 3508, laryngopharynx: 4893, oropharynx: 2386); validation in TCGA revealed markers with high incidence (altered in >10% of patients) in tongue (n = 331), laryngopharynx (n = 701) and oropharynx (n = 404). Assessment of these genes in clinical sub-cohorts of TCGA indicated that early stage tongue (MTFR1, C8ORF33, OTUD6B) and laryngeal cancers (TWISTNB, KLHL13 and UBE2Q1) were defined by distinct prognosticators. Similarly, correlation with perineural/angiolymophatic invasion, identified discrete marker panels with survival impact (tongue: NUDCD1, PRKC1; laryngopharynx: SLC4A1AP, PIK3CA, AP2M1). Alterations in ANO1, NUDCD1, PIK3CA defined survival in tongue cancer patients with nodal metastasis (node+ECS-), while EPS8 is a significant differential in node+ECS- laryngopharyngeal cancers. In oropharynx, wherein HPV is a major etiological factor, distinct prognosticators were identified in HPV+ (ECHDC2, HERC5, GGT6) and HPV- (GRB10, EMILIN1, FNDC1). Meta-analysis in combination with TCGA validation carried out in this study emphasized on the molecular heterogeneity inherent within HNSCC; the feasibility of leveraging this information for improving prognostic efficacy is also established. Subject to large scale clinical validation, the marker panel identified in this study can prove to be valuable prognostic adjuncts.

Upregulation of nucleoprotein AHNAK is associated with poor outcome of pancreatic ductal adenocarcinoma prognosis via mediating epithelial-mesenchymal transition

The nucleoprotein AHNAK (AHNAK) is a large scaffold protein that is involved in several biological processes. Previous studies have suggested a possible relation between AHNAK and the epithelial-mesenchymal transition (EMT). However, the role of AHNAK in pancreatic ductal adenocarcinoma (PDAC) has not been unveiled. The present study focuses on identifying the potential value of the biological effects of AHNAK in PDAC, which is one of the most lethal malignancies. Bioinformatic analysis was carried for driver gene prediction, and we proved that AHNAK was a driver gene of pancreatic adenocarcinoma and a predictor of poor outcomes of PDAC by clinical characteristics analysis and in vitro experiments. High AHNAK expression was associated with short disease-free survival and poor overall survival. In vitro assays showed that AHNAK was associated with cell proliferation and migration, and a positive relation was observed between AHNAK and the EMT. In conclusion, AHNAK is a crucial biomarker that may promote cellular proliferation and migration and thus impact PDAC outcomes via the EMT, which suggests that AHANK might be a potential target for PDAC.

Assessing The Predictive Value Of Regulatory Molecules For Patient Outcome In Pancreatic Cancer: A Computational Approach

Pancreatic Cancer (PC) can be characterized as one of the most lethal cancers considering its poor diagnosis and symptoms in early stages. To assess the predictive value of regulatory molecules in terms of differentially expressed genes, we first performed a thorough search of gene expression profiling studies in pancreatic cohorts. We obtained the genes that have been identified and validated experimentally to be associated with patient outcome and also differentially expressed in tumors compared with adjacent non-tumor tissues. A two-step upstream analysis on the derived set of the genes under study was performed. The subsequent promoter and pathway analysis unveiled candidate transcription factors and regulatory molecules that potentially have regulated the detected differentially expressed genes. Predictive analysis was applied in the identified regulators and classification algorithms were implemented to model accurately patient outcome. In view of our findings, Gaussian Naïve Bayes model exhibited the highest classification accuracy and f-score concerning the predictive value of regulatory molecules in PC (accuracy =0.85, f-score =0.84).

Identification of prognostic genes in colorectal cancer through transcription profiling of multi-stage carcinogenesis

Colorectal cancer is a complex multistage process following the adenoma-carcinoma sequence. Additional research on the basis of molecular dysregulations, particularly in the precancerous stage, may provide insight into the realization of potential biomarkers and therapeutic targets for the disease. In the present study, the expression profile of human multistage colorectal mucosa tissues, including healthy, adenoma and adenocarcinoma samples, was downloaded. Genes that were consistently differentially expressed in precancerous tissues and cancer samples were collected. Based on a merged biological network, the biggest connected component composed of these identified genes and their one-step neighbors were retrieved to conduct random walk with restart algorithm, in order to identify genes significantly affected during carcinogenesis. Therefore, 35 genes significantly affected by carcinogenic dysregulation were successfully identified. Survival and Cox analysis indicated that the expression of these genes was an independent prognostic factor confirmed by six cohorts. In summary, based on the transcription profile of multi-stage carcinogenesis and bioinformatics analysis, 35 genes significantly associated with patient survival were successfully identified, which may serve as promising therapeutic targets for the disease.

Pre-neoplastic pancreas cells enter a partially mesenchymal state following transient TGF-β exposure

Pancreatic ductal adenocarcinoma (PDAC) is a deadly disease and a major health problem in the United States. While the cytokine TGF-β has been implicated in PDAC development, it can exert both pro-tumorigenic and anti-tumorigenic effects that are highly context dependent and incompletely understood. Using three-dimensional (3D) cultures of KrasG12D-expressing mouse pancreatic epithelial cells we demonstrated that while exposure to exogenous TGF-β induced growth arrest of the KrasG12D cells, its subsequent removal allowed the cells to enter a hyper-proliferative, partially mesenchymal (PM), and progenitor-like state. This state was highly stable and was maintained by autocrine TGF-β signaling. While untreated KrasG12D cells formed cystic lesions in vivo, PM cells formed ductal structures resembling human PanINs, suggesting that they had attained increased oncogenic potential. Supporting this hypothesis, we determined that the PM cells share salient molecular and phenotypic features with the quasi-mesenchymal/squamous subtype of human PDAC, which has the worst prognosis of any of the recently identified subtypes. Transient pulses of TGF-β have been observed during pancreatitis, a major risk factor for PDAC. Our data suggest that transient TGF-β exposure is sufficient to induce the acquisition of stable PDAC-associated phenotypes in pre-neoplastic KrasG12D cells, providing novel molecular insight into the complex role of TGF-β in tumorigenesis.

Exact association test for small size sequencing data

Background

Recent statistical methods for next generation sequencing (NGS) data have been successfully applied to identifying rare genetic variants associated with certain diseases. However, most commonly used methods (e.g., burden tests and variance-component tests) rely on large sample sizes. Notwithstanding, due to its-still high cost, NGS data is generally restricted to small sample sizes, that cannot be analyzed by most existing methods.

Methods

In this work, we propose a new exact association test for sequencing data that does not require a large sample approximation, which is applicable to both common and rare variants. Our method, based on the Generalized Cochran-Mantel-Haenszel (GCMH) statistic, was applied to NGS datasets from intraductal papillary mucinous neoplasm (IPMN) patients. IPMN is a unique pancreatic cancer subtype that can turn into an invasive and hard-to-treat metastatic disease.

Results

Application of our method to IPMN data successfully identified susceptible genes associated with progression of IPMN to pancreatic cancer.

Conclusions

Our method is expected to identify disease-associated genetic variants more successfully, and corresponding signal pathways, improving our understanding of specific disease’s etiology and prognosis.

Analysis of K-Ras Interactions by Biotin Ligase Tagging

BACKGROUND

Mutations of the human K-Ras 4B (K-Ras) G protein are associated with a significant proportion of all human cancers. Despite this fact, a comprehensive analysis of K-Ras interactions is lacking. Our investigations focus on characterization of the K-Ras interaction network.

MATERIALS AND METHODS

We employed a biotin ligase-tagging approach, in which tagged K-Ras proteins biotinylate neighbor proteins in a proximity-dependent fashion, and proteins are identified via mass spectrometry (MS) sequencing.

RESULTS

In transfected cells, a total of 748 biotinylated proteins were identified from cells expressing biotin ligase-tagged K-Ras variants. Significant differences were observed between membrane-associated variants and a farnesylation-defective mutant. In pancreatic cancer cells, 56 K-Ras interaction partners were identified. Most of these were cytoskeletal or plasma membrane proteins, and many have been identified previously as potential cancer biomarkers.

CONCLUSION

Biotin ligase tagging offers a rapid and convenient approach to the characterization of K-Ras interaction networks.

Identification and Validation of a Diagnostic and Prognostic Multi-Gene Biomarker Panel for Pancreatic Ductal Adenocarcinoma

Late diagnosis and systemic dissemination essentially contribute to the invariably poor prognosis of pancreatic ductal adenocarcinoma (PDAC). Therefore, the development of diagnostic biomarkers for PDAC are urgently needed to improve patient stratification and outcome in the clinic. By studying the transcriptomes of independent PDAC patient cohorts of tumor and non-tumor tissues, we identified 81 robustly regulated genes, through a novel, generally applicable meta-analysis. Using consensus clustering on co-expression values revealed four distinct clusters with genes originating from exocrine/endocrine pancreas, stromal and tumor cells. Three clusters were strongly associated with survival of PDAC patients based on TCGA database underlining the prognostic potential of the identified genes. With the added information of impact of survival and the robustness within the meta-analysis, we extracted a 17-gene subset for further validation. We show that it did not only discriminate PDAC from non-tumor tissue and stroma in fresh-frozen as well as formalin-fixed paraffin embedded samples, but also detected pancreatic precursor lesions and singled out pancreatitis samples. Moreover, the classifier discriminated PDAC from other cancers in the TCGA database. In addition, we experimentally validated the classifier in PDAC patients on transcript level using qPCR and exemplify the usage on protein level for three proteins (AHNAK2, LAMC2, TFF1) using immunohistochemistry and for two secreted proteins (TFF1, SERPINB5) using ELISA-based protein detection in blood-plasma. In conclusion, we present a novel robust diagnostic and prognostic gene signature for PDAC with future potential applicability in the clinic.

Integrative multi-platform meta-analysis of gene expression profiles in pancreatic ductal adenocarcinoma patients for identifying novel diagnostic biomarkers

Applying differentially expressed genes (DEGs) to identify feasible biomarkers in diseases can be a hard task when working with heterogeneous datasets. Expression data are strongly influenced by technology, sample preparation processes, and/or labeling methods. The proliferation of different microarray platforms for measuring gene expression increases the need to develop models able to compare their results, especially when different technologies can lead to signal values that vary greatly. Integrative meta-analysis can significantly improve the reliability and robustness of DEG detection. The objective of this work was to develop an integrative approach for identifying potential cancer biomarkers by integrating gene expression data from two different platforms. Pancreatic ductal adenocarcinoma (PDAC), where there is an urgent need to find new biomarkers due its late diagnosis, is an ideal candidate for testing this technology. Expression data from two different datasets, namely Affymetrix and Illumina (18 and 36 PDAC patients, respectively), as well as from 18 healthy controls, was used for this study. A meta-analysis based on an empirical Bayesian methodology (ComBat) was then proposed to integrate these datasets. DEGs were finally identified from the integrated data by using the statistical programming language R. After our integrative meta-analysis, 5 genes were commonly identified within the individual analyses of the independent datasets. Also, 28 novel genes that were not reported by the individual analyses (‘gained’ genes) were also discovered. Several of these gained genes have been already related to other gastroenterological tumors. The proposed integrative meta-analysis has revealed novel DEGs that may play an important role in PDAC and could be potential biomarkers for diagnosing the disease.

Identification of genes highly downregulated in pancreatic cancer through a meta-analysis of microarray datasets: implications for discovery of novel tumor-suppressor genes and therapeutic targets

PURPOSE

The lack of specific symptoms at early tumor stages, together with a high biological aggressiveness of the tumor contribute to the high mortality rate for pancreatic cancer (PC), which has a 5-year survival rate of about 7%. Recent failures of targeted therapies inhibiting kinase activity in clinical trials have highlighted the need for new approaches towards combating this deadly disease.

METHODS

In this study, we have identified genes that are significantly downregulated in PC, through a meta-analysis of large number of microarray datasets. We have used qRT-PCR to confirm the downregulation of selected genes in a panel of PC cell lines.

RESULTS

This study has yielded several novel candidate tumor-suppressor genes (TSGs) including GNMT, CEL, PLA2G1B and SERPINI2. We highlight the role of GNMT, a methyl transferase associated with the methylation potential of the cell, and CEL, a lipase, as potential therapeutic targets. We have uncovered genetic links to risk factors associated with PC such as smoking and obesity. Genes important for patient survival and prognosis are also discussed, and we confirm the dysregulation of metabolic pathways previously observed in PC.

CONCLUSIONS

While many of the genes downregulated in our dataset are associated with protein products normally produced by the pancreas for excretion, we have uncovered some genes whose downregulation appear to play a more causal role in PC. These genes will assist in providing a better understanding of the disease etiology of PC, and in the search for new therapeutic targets and biomarkers.

AHNAK2 is a potential prognostic biomarker in patients with PDAC

BACKGROUND

AHNAK nucleoprotein 2 (AHNAK2) belongs to the AHNAK protein family. The studies of AHNAK2 are limited. A recent study reported that AHNAK2 might be a biomarker for pancreatic ductal adenocarcinoma (PDAC); however, tissue-based experiments have not been conducted. The aim of this study was to determine the tissue expression of AHNAK2 and to find the correlation between AHNAK2 and overall survival rate in PDAC.

RESULTS

AHNAK2 is highly expressed in PDAC (n=79) compared with adjacent normal tissues (n=64, P<0.001). Overexpression of AHNAK2 showed a significant relationship with a lower overall survival rate (P=0.033) in PDAC patients. The predictive value of increased expression of AHNAK2 remains relevant in patients with AJCC grade above II (n=43, P=0.006) or lymph node metastasis (n=32, P=0.004). Cox regression analysis showed that AHNAK2 expression (P=0.003) and pathology grade (P<0.001) are independent prognostic factors for PDAC. The nomogram model was performed to predict the 1- and 3-year survival rates based on Cox regression. The C-index was 0.61. The calibration curves were also made to show the association between the observed and predicted probability of the overall survival rates.

MATERIALS AND METHODS

AHNAK2 expression was performed in tissue microarrays by immunohistochemistry. The overall survival rate analysis was performed using the Kaplan-Meier method, Cox proportional hazards regression, and a nomogram model.

CONCLUSIONS

AHNAK2 is overexpressed in PDAC tissues and is an independent prognostic factor in patients with PDAC.

Early detection of pancreatic cancer: Where are we now and where are we going?

Pancreatic cancer (PC) is one of the most lethal malignancies. Recent studies indicate that patients with incidentally diagnosed PC have better prognosis than those with symptoms and that there is a sufficient window for early detection. However, effective early diagnosis remains difficult and depends mainly on imaging modalities and the development of screening methodologies with highly sensitive and specific biomarkers. This review summarizes recent advances in effective screening for early diagnosis of PC using imaging modalities and novel molecular biomarkers discovered from various "omics" studies including genomics, epigenomics, non-coding RNA, metabonomics, liquid biopsy (CTC, ctDNA and exosomes) and microbiomes, and their use in body fluids (feces, urine and saliva). Although many biomarkers for early detection of PC have been discovered through various methods, larger scale and rigorous validation is required before their application in the clinic. In addition, more effective and specific biomarkers of PC are urgently needed.

Meta-analysis of gene expression in relapsed childhood B-acute lymphoblastic leukemia

Background

Relapsed pediatric B-acute lymphoblastic leukemia (B-ALL) remains as the leading cause of cancer death among children. Other than stem cell transplantation and intensified chemotherapy, no other improved treatment strategies have been approved clinically. Gene expression profiling represents a powerful approach to identify potential biomarkers and new therapeutic targets for various diseases including leukemias. However, inadequate sample size in many individual experiments has failed to provide adequate study power to yield translatable findings. With the hope of getting new insights into the biological mechanisms underpinning relapsed ALL and identifying more promising biomarkers or therapeutic targets, we conducted a meta-analysis of gene expression studies involving ALL from 3 separate studies.

Method

By using the keywords “acute lymphoblastic leukemia”, and “microarray”, a total of 280 and 275 microarray datasets were found listed in Gene Expression Omnibus database GEO and ArrayExpress database respectively. Further manual inspection found that only three studies (GSE18497, GSE28460, GSE3910) were focused on gene expression profiling of paired diagnosis-relapsed pediatric B-ALL. These three datasets which comprised of a total of 108 matched diagnosis-relapsed pediatric B-ALL samples were then included for this meta-analysis using RankProd approach.

Results

Our analysis identified a total of 1795 upregulated probes which corresponded to 1527 genes (pfp < 0.01; FC > 1), and 1493 downregulated probes which corresponded to 1214 genes (pfp < 0.01; FC < 1) respectively. S100A8 appeared as the top most overexpressed gene (pfp < 0.01, FC = 1.8) and is a potential target for further validation. Based on gene ontology biological process annotation, the upregulated genes were most enriched in cell cycle processes (enrichment score = 15.3), whilst the downregulated genes were clustered in transcription regulation (enrichment score = 12.6). Elevated expression of cell cycle regulators (e.g kinesins, AURKA, CDKs) was the key genetic defect implicated in relapsed ALL, and serve as attractive targets for therapeutic intervention.

Conclusion

We identified S100A8 as the most overexpressed gene, and the cell cycle pathway as the most promising biomarker and therapeutic target for relapsed childhood B-ALL. The validity of the results warrants further investigation.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-017-3103-1) contains supplementary material, which is available to authorized users.

New developments in the early diagnosis of pancreatic cancer

Pancreatic cancer is an aggressive carcinoma of the digestive system and radical resection, which is available to very few patients, is the only possibility for cure. Since therapeutic choices are limited at the advanced stage, screening and early diagnostic tools are indispensable for a better prognosis. Areas covered: This review illustrates serologic and imaging examinations, and carbohydrate antigens, microRNAs, methylation biomarkers, molecules in exosomes, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography and endoscopic retrograde cholangiopancreatography, among other topics. No matter which approach is used, the accuracy of early diagnosis is extremely low. Combining different methods greatly improves the accuracy of early diagnosis. This review was conducted utilizing PubMed with key search words pancreatic cancer, early diagnosis, biomarkers and imaging. Expert commentary: Appropriate combination of biomarkers and imaging technologies will become standard practice in the future. Because the incidence of and mortality from pancreatic cancer is rising, further study of new approaches for the early detection of pancreatic tumors is essential.

Cell cycle related genes up-regulated in human colorectal development predict the overall survival of late-stage colorectal cancer patients

A tumor can be perceived as a special "organ" that undergoes aberrant and poorly regulated organogenesis. Embryonic development and carcinogenesis share striking similarities in their cellular behavior and underlying molecular mechanisms. This intimate association makes embryonic development a viable reference model for studying cancer thereby circumventing the potentially misleading complexity of tumor heterogeneity. Therefore, on the basis of global expression profile, the genes simultaneously activated (up-regulated in terms of expression profile) or suppressed (down-regulated) in both the embryonic development and cancer stage, probably contain profound information on the molecular mechanism of cancer. In this study, the Affymetrix expression profile of 1593 colorectal cancer samples was downloaded from Gene Expression Omnibus. The 1396 differentially expressed probes were robustly obtained using 660 colorectal normal and cancer samples, the expression pattern of which was analyzed using our human colorectal developmental data. All of these 1396 probes were classified into 27 distinct patterns based on their expression patterns during the developmental process. By means of gene set enrichment analysis, we collected 393 V probes simultaneously up-regulated in both development and carcinogenesis and 207 A probes down-regulated in both. Functional enrichment analysis indicated that the V probes were significantly related to cell cycle regulation. Notably, 28 cell-cycle related probes within the V probe group were found to be significantly associated with an overall survival of Stage III/IV patients (GSE17536 cross validation, n = 96, p = 5.70 × 10(-3); GSE29621, n = 36, p = 1.70 × 10(-3); GSE39084, n = 38, p = 0.05; GSE39582, n = 264, p = 0.047; GSE17537, n = 36, p = 5.90 × 10(-3)).

Surgical and molecular pathology of pancreatic neoplasms

BACKGROUND

Histologic characteristics have proven to be very useful for classifying different types of tumors of the pancreas. As a result, the major tumor types in the pancreas have long been classified based on their microscopic appearance.

MAIN BODY

Recent advances in whole exome sequencing, gene expression profiling, and knowledge of tumorigenic pathways have deepened our understanding of the underlying biology of pancreatic neoplasia. These advances have not only confirmed the traditional histologic classification system, but also opened new doors to early diagnosis and targeted treatment.

CONCLUSION

This review discusses the histopathology, genetic and epigenetic alterations and potential treatment targets of the five major malignant pancreatic tumors - pancreatic ductal adenocarcinoma, pancreatic neuroendocrine tumor, solid-pseudopapillary neoplasm, acinar cell carcinoma and pancreatoblastoma.

A comparative evaluation of data-merging and meta-analysis methods for reconstructing gene-gene interactions

BACKGROUND

We address the problem of integratively analyzing multiple gene expression, microarray datasets in order to reconstruct gene-gene interaction networks. Integrating multiple datasets is generally believed to provide increased statistical power and to lead to a better characterization of the system under study. However, the presence of systematic variation across different studies makes network reverse-engineering tasks particularly challenging. We contrast two approaches that have been frequently used in the literature for addressing systematic biases: meta-analysis methods, which first calculate opportune statistics on single datasets and successively summarize them, and data-merging methods, which directly analyze the pooled data after removing eventual biases. This comparative evaluation is performed on both synthetic and real data, the latter consisting of two manually curated microarray compendia comprising several E. coli and Yeast studies, respectively. Furthermore, the reconstruction of the regulatory network of the transcription factor Ikaros in human Peripheral Blood Mononuclear Cells (PBMCs) is presented as a case-study.

RESULTS

The meta-analysis and data-merging methods included in our experimentations provided comparable performances on both synthetic and real data. Furthermore, both approaches outperformed (a) the naïve solution of merging data together ignoring possible biases, and (b) the results that are expected when only one dataset out of the available ones is analyzed in isolation. Using correlation statistics proved to be more effective than using p-values for correctly ranking candidate interactions. The results from the PBMC case-study indicate that the findings of the present study generalize to different types of network reconstruction algorithms.

CONCLUSIONS

Ignoring the systematic variations that differentiate heterogeneous studies can produce results that are statistically indistinguishable from random guessing. Meta-analysis and data merging methods have proved equally effective in addressing this issue, and thus researchers may safely select the approach that best suit their specific application.

Identification of key regulators of pancreatic cancer progression through multidimensional systems-level analysis

BACKGROUND

Pancreatic cancer is an aggressive cancer with dismal prognosis, urgently necessitating better biomarkers to improve therapeutic options and early diagnosis. Traditional approaches of biomarker detection that consider only one aspect of the biological continuum like gene expression alone are limited in their scope and lack robustness in identifying the key regulators of the disease. We have adopted a multidimensional approach involving the cross-talk between the omics spaces to identify key regulators of disease progression.

METHODS

Multidimensional domain-specific disease signatures were obtained using rank-based meta-analysis of individual omics profiles (mRNA, miRNA, DNA methylation) related to pancreatic ductal adenocarcinoma (PDAC). These domain-specific PDAC signatures were integrated to identify genes that were affected across multiple dimensions of omics space in PDAC (genes under multiple regulatory controls, GMCs). To further pin down the regulators of PDAC pathophysiology, a systems-level network was generated from knowledge-based interaction information applied to the above identified GMCs. Key regulators were identified from the GMC network based on network statistics and their functional importance was validated using gene set enrichment analysis and survival analysis.

RESULTS

Rank-based meta-analysis identified 5391 genes, 109 miRNAs and 2081 methylation-sites significantly differentially expressed in PDAC (false discovery rate ≤ 0.05). Bimodal integration of meta-analysis signatures revealed 1150 and 715 genes regulated by miRNAs and methylation, respectively. Further analysis identified 189 altered genes that are commonly regulated by miRNA and methylation, hence considered GMCs. Systems-level analysis of the scale-free GMCs network identified eight potential key regulator hubs, namely E2F3, HMGA2, RASA1, IRS1, NUAK1, ACTN1, SKI and DLL1, associated with important pathways driving cancer progression. Survival analysis on individual key regulators revealed that higher expression of IRS1 and DLL1 and lower expression of HMGA2, ACTN1 and SKI were associated with better survival probabilities.

CONCLUSIONS

It is evident from the results that our hierarchical systems-level multidimensional analysis approach has been successful in isolating the converging regulatory modules and associated key regulatory molecules that are potential biomarkers for pancreatic cancer progression.

Meta-analysis of transcriptome data identifies a novel 5-gene pancreatic adenocarcinoma classifier

PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) is largely incurable due to late diagnosis. Superior early detection biomarkers are critical to improving PDAC survival and risk stratification.

EXPERIMENTAL DESIGN

Optimized meta-analysis of PDAC transcriptome datasets identified and validated key PDAC biomarkers. PDAC-specific expression of a 5-gene biomarker panel was measured by qRT-PCR in microdissected patient-derived FFPE tissues. Cell-based assays assessed impact of two of these biomarkers, TMPRSS4 and ECT2, on PDAC cells.

RESULTS

A 5-gene PDAC classifier (TMPRSS4, AHNAK2, POSTN, ECT2, SERPINB5) achieved on average 95% sensitivity and 89% specificity in discriminating PDAC from non-tumor samples in four training sets and similar performance (sensitivity = 94%, specificity = 89.6%) in five independent validation datasets. This classifier accurately discriminated PDAC from chronic pancreatitis (AUC = 0.83), other cancers (AUC = 0.89), and non-tumor from PDAC precursors (AUC = 0.92) in three independent datasets. Importantly, the classifier distinguished PanIN from healthy pancreas in the PDX1-Cre;LSL-KrasG12D PDAC mouse model. Discriminatory expression of the PDAC classifier genes was confirmed in microdissected FFPE samples of PDAC and matched surrounding non-tumor pancreas or pancreatitis. Notably, knock-down of TMPRSS4 and ECT2 reduced PDAC soft agar growth and cell viability and TMPRSS4 knockdown also blocked PDAC migration and invasion.

CONCLUSIONS

This study identified and validated a highly accurate 5-gene PDAC classifier for discriminating PDAC and early precursor lesions from non-malignant tissue that may facilitate early diagnosis and risk stratification upon validation in prospective clinical trials. Cell-based experiments of two overexpressed proteins encoded by the panel, TMPRSS4 and ECT2, suggest a causal link to PDAC development and progression, confirming them as potential therapeutic targets.

Meta-Analysis of Tumor Stem-Like Breast Cancer Cells Using Gene Set and Network Analysis

Generally, cancer stem cells have epithelial-to-mesenchymal-transition characteristics and other aggressive properties that cause metastasis. However, there have been no confident markers for the identification of cancer stem cells and comparative methods examining adherent and sphere cells are widely used to investigate mechanism underlying cancer stem cells, because sphere cells have been known to maintain cancer stem cell characteristics. In this study, we conducted a meta-analysis that combined gene expression profiles from several studies that utilized tumorsphere technology to investigate tumor stem-like breast cancer cells. We used our own gene expression profiles along with the three different gene expression profiles from the Gene Expression Omnibus, which we combined using the ComBat method, and obtained significant gene sets using the gene set analysis of our datasets and the combined dataset. This experiment focused on four gene sets such as cytokine-cytokine receptor interaction that demonstrated significance in both datasets. Our observations demonstrated that among the genes of four significant gene sets, six genes were consistently up-regulated and satisfied the p-value of < 0.05, and our network analysis showed high connectivity in five genes. From these results, we established CXCR4, CXCL1 and HMGCS1, the intersecting genes of the datasets with high connectivity and p-value of < 0.05, as significant genes in the identification of cancer stem cells. Additional experiment using quantitative reverse transcription-polymerase chain reaction showed significant up-regulation in MCF-7 derived sphere cells and confirmed the importance of these three genes. Taken together, using meta-analysis that combines gene set and network analysis, we suggested CXCR4, CXCL1 and HMGCS1 as candidates involved in tumor stem-like breast cancer cells. Distinct from other meta-analysis, by using gene set analysis, we selected possible markers which can explain the biological mechanisms and suggested network analysis as an additional criterion for selecting candidates.

Meta-Analyses of Microarray Datasets Identifies ANO1 and FADD as Prognostic Markers of Head and Neck Cancer

The head and neck squamous cell carcinoma (HNSCC) transcriptome has been profiled extensively, nevertheless, identifying biomarkers that are clinically relevant and thereby with translational benefit, has been a major challenge. The objective of this study was to use a meta-analysis based approach to catalog candidate biomarkers with high potential for clinical application in HNSCC. Data from publically available microarray series (N = 20) profiled using Agilent (4X44K G4112F) and Affymetrix (HGU133A, U133A_2, U133Plus 2) platforms was downloaded and analyzed in a platform/chip-specific manner (GeneSpring software v12.5, Agilent, USA). Principal Component Analysis (PCA) and clustering analysis was carried out iteratively for segregating outliers; 140 normal and 277 tumor samples from 15 series were included in the final analysis. The analyses identified 181 differentially expressed, concordant and statistically significant genes; STRING analysis revealed interactions between 122 of them, with two major gene clusters connected by multiple nodes (MYC, FOS and HSPA4). Validation in the HNSCC-specific database (N = 528) in The Cancer Genome Atlas (TCGA) identified a panel (ECT2, ANO1, TP63, FADD, EXT1, NCBP2) that was altered in 30% of the samples. Validation in treatment naïve (Group I; N = 12) and post treatment (Group II; N = 12) patients identified 8 genes significantly associated with the disease (Area under curve>0.6). Correlation with recurrence/re-recurrence showed ANO1 had highest efficacy (sensitivity: 0.8, specificity: 0.6) to predict failure in Group I. UBE2V2, PLAC8, FADD and TTK showed high sensitivity (1.00) in Group I while UBE2V2 and CRYM were highly sensitive (>0.8) in predicting re-recurrence in Group II. Further, TCGA analysis showed that ANO1 and FADD, located at 11q13, were co-expressed at transcript level and significantly associated with overall and disease-free survival (p<0.05). The meta-analysis approach adopted in this study has identified candidate markers correlated with disease outcome in HNSCC; further validation in a larger cohort of patients will establish their clinical relevance.

Developmental genes significantly afflicted by aberrant promoter methylation and somatic mutation predict overall survival of late-stage colorectal cancer

Carcinogenesis is an exceedingly complicated process, which involves multi-level dysregulations, including genomics (majorly caused by somatic mutation and copy number variation), DNA methylomics, and transcriptomics. Therefore, only looking into one molecular level of cancer is not sufficient to uncover the intricate underlying mechanisms. With the abundant resources of public available data in the Cancer Genome Atlas (TCGA) database, an integrative strategy was conducted to systematically analyze the aberrant patterns of colorectal cancer on the basis of DNA copy number, promoter methylation, somatic mutation and gene expression. In this study, paired samples in each genomic level were retrieved to identify differentially expressed genes with corresponding genetic or epigenetic dysregulations. Notably, the result of gene ontology enrichment analysis indicated that the differentially expressed genes with corresponding aberrant promoter methylation or somatic mutation were both functionally concentrated upon developmental process, suggesting the intimate association between development and carcinogenesis. Thus, by means of random walk with restart, 37 significant development-related genes were retrieved from a priori-knowledge based biological network. In five independent microarray datasets, Kaplan-Meier survival and Cox regression analyses both confirmed that the expression of these genes was significantly associated with overall survival of Stage III/IV colorectal cancer patients.

Comparative study of joint analysis of microarray gene expression data in survival prediction and risk assessment of breast cancer patients

Microarray gene expression data sets are jointly analyzed to increase statistical power. They could either be merged together or analyzed by meta-analysis. For a given ensemble of data sets, it cannot be foreseen which of these paradigms, merging or meta-analysis, works better. In this article, three joint analysis methods, Z -score normalization, ComBat and the inverse normal method (meta-analysis) were selected for survival prognosis and risk assessment of breast cancer patients. The methods were applied to eight microarray gene expression data sets, totaling 1324 patients with two clinical endpoints, overall survival and relapse-free survival. The performance derived from the joint analysis methods was evaluated using Cox regression for survival analysis and independent validation used as bias estimation. Overall, Z -score normalization had a better performance than ComBat and meta-analysis. Higher Area Under the Receiver Operating Characteristic curve and hazard ratio were also obtained when independent validation was used as bias estimation. With a lower time and memory complexity, Z -score normalization is a simple method for joint analysis of microarray gene expression data sets. The derived findings suggest further assessment of this method in future survival prediction and cancer classification applications.

Integrated genomic approaches identify major pathways and upstream regulators in late onset Alzheimer's disease

Previous studies have evaluated gene expression in Alzheimer’s disease (AD) brains to identify mechanistic processes, but have been limited by the size of the datasets studied. Here we have implemented a novel meta-analysis approach to identify differentially expressed genes (DEGs) in published datasets comprising 450 late onset AD (LOAD) brains and 212 controls. We found 3124 DEGs, many of which were highly correlated with Braak stage and cerebral atrophy. Pathway Analysis revealed the most perturbed pathways to be (a) nitric oxide and reactive oxygen species in macrophages (NOROS), (b) NFkB and (c) mitochondrial dysfunction. NOROS was also up-regulated, and mitochondrial dysfunction down-regulated, in healthy ageing subjects. Upstream regulator analysis predicted the TLR4 ligands, STAT3 and NFKBIA, for activated pathways and RICTOR for mitochondrial genes. Protein-protein interaction network analysis emphasised the role of NFKB; identified a key interaction of CLU with complement; and linked TYROBP, TREM2 and DOK3 to modulation of LPS signalling through TLR4 and to phosphatidylinositol metabolism. We suggest that NEUROD6, ZCCHC17, PPEF1 and MANBAL are potentially implicated in LOAD, with predicted links to calcium signalling and protein mannosylation. Our study demonstrates a highly injurious combination of TLR4-mediated NFKB signalling, NOROS inflammatory pathway activation, and mitochondrial dysfunction in LOAD.

AHNAK: the giant jack of all trades

The nucleoprotein AHNAK is an unusual and somewhat mysterious scaffolding protein characterised by its large size of approximately 700 kDa. Several aspects of this protein remain uncertain, including its exact molecular function and regulation on both the gene and protein levels. Various studies have attempted to annotate AHNAK and, notably, protein interaction and expression analyses have contributed greatly to our current understanding of the protein. The implicated biological processes are, however, very diverse, ranging from a role in the formation of the blood-brain barrier, cell architecture and migration, to the regulation of cardiac calcium channels and muscle membrane repair. In addition, recent evidence suggests that AHNAK might be yet another accomplice in the development of tumour metastasis. This review will discuss the different functional roles of AHNAK, highlighting recent advancements that have added foundation to the proposed roles while identifying ties between them. Implications for related fields of research are noted and suggestions for future research that will assist in unravelling the function of AHNAK are offered.