Transcriptional profiling of peripheral blood in pancreatic adenocarcinoma patients identifies diagnostic biomarkers

CLEC4D as a Novel Prognostic Marker Boosts the Proliferation and Migration of Gastric Cancer via the NF-κB/AKT Signaling Pathway

Purpose

The functions of C-type lectin domain family 4 member D (CLEC4D), one member of the C-type lectin/C-type lectin-like domain superfamily, in immunity have been well described, but its roles in cancer biology remain largely unknown.

Patients and Methods

This study aims to explore the role of CLEC4D in gastric cancer (GC). Bioinformatics preliminarily analyzed the expression of CLEC4D in gastric cancer. Immunohistochemical staining was used to detect the expression level and clinical pathological characteristics of CLEC4D in gastric cancer. The biological function of CLEC4D in gastric cancer cell lines was verified through in vitro and in vivo experiments.

Results

In this study, CLEC4D expression was found to be markedly increased in gastric cancer (GC) tissues compared with matched normal gastric tissues, and high CLEC4D expression independently predicted unfavorable overall survival in patients with GC. Knockdown of CLEC4D markedly inhibited GC cell proliferation and migration. Mechanistically, CLEC4D knockdown deactivated the Akt and NF-κB signaling pathways in GC cells.

Conclusion

Together, these results demonstrate that aberrantly increased CLEC4D expression promotes cancer phenotypes via the Akt and NF-κB signaling pathways in GC cells.

Identification and Validation of the Diagnostic Markers for Inflammatory Bowel Disease by Bioinformatics Analysis and Machine Learning

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract which is mediated by the inappropriate immune responses. This study was aimed to identify novel diagnostic biomarkers for diagnosis of IBD and explore the relationship between the diagnostic biomarkers and infiltrated immune cells. GSE38713, GSE53306, and GSE75214 downloaded from the Gene Expression Omnibus (GEO) database were split into training and testing sets. Differentially expressed genes (DEGs) were screened using the "limma" package. Gene Ontology (GO) and KEGG pathway enrichment analysis of DEGs were performed by clusterProfiler package. The LASSO regression and support vector machine recursive feature elimination (SVM-RFE) algorithms were conducted to identify novel diagnostic biomarkers. The receiver operating characteristic (ROC) curve was applied to evaluate the diagnostic value of the candidate biomarkers. The relationship of the candidate biomarkers and infiltrating immune cells in IBD were evaluated by CIBERSOTR. Quantitative Real-Time PCR (qRT-PCR) was applied to measure the expression level of the biomarkers in IBD. A total of 289 dysregulated genes were identified as DEGs in IBD. These DEGs were significantly enriched in chemokine signaling pathway and cytokine-cytokine receptor interaction. RHOU was identified as a critical diagnostic gene in IBD, which was confirmed using ROC curve and qRT-PCR assays. Immune cell infiltration analysis showed that RHOU was correlated with macrophages M2, dendritic cells resting, mast cells resting, T cells CD4 memory resting, macrophages M0, and mast cells activated. Our results imply that RHOU may be a potential diagnostic biomarker for IBD.

Prognostic assessment capability of a five-gene signature in pancreatic cancer: a machine learning based-study

BACKGROUND

A prognostic assessment method with good sensitivity and specificity plays an important role in the treatment of pancreatic cancer patients. Finding a way to evaluate the prognosis of pancreatic cancer is of great significance for the treatment of pancreatic cancer.

METHODS

In this study, GTEx dataset and TCGA dataset were merged together for differential gene expression analysis. Univariate Cox regression and Lasso regression were used to screen variables in the TCGA dataset. Screening the optimal prognostic assessment model is then performed by gaussian finite mixture model. Receiver operating characteristic (ROC) curves were used as an indicator to assess the predictive ability of the prognostic model, the validation process was performed on the GEO datasets.

RESULTS

Gaussian finite mixture model was then used to build 5-gene signature (ANKRD22, ARNTL2, DSG3, KRT7, PRSS3). Receiver operating characteristic (ROC) curves suggested the 5-gene signature performed well on both the training and validation datasets.

CONCLUSIONS

This 5-gene signature performed well on both our chosen training dataset and validation dataset and provided a new way to predict the prognosis of pancreatic cancer patients.

RNA-sequencing for transcriptional profiling of whole blood in early stage and metastatic pancreatic cancer patients

We investigated the transcriptional profile of whole blood in early and metastatic stages of pancreatic cancer (PaC) patients to identify potential diagnostic factors for early diagnosis. Blood samples from 18 participants (6 healthy individuals, 6 patients in early stage (I/II) PaC, and 6 patients in metastatic PaC) were analyzed by RNA-sequencing. The expression levels of identified genes were subsequently compared with their expression in pancreatic tumor tissues based on TCGA data reported in UALCAN and GEPIA2 databases. Overall, 331 and 724 genes were identified as differentially expressed genes in early and metastatic stages, respectively. Of these, 146 genes were shared by early and metastatic stages. Upregulation of PTCD3 and UBA52 genes and downregulation of A2M and ARID1B genes in PaC patients were observed from early stage to metastasis. TCGA database showed increasing trend in expression levels of these genes from stage I to IV in pancreatic tumor tissue. Finally, we found that low expression of PTCD3, A2M, and ARID1B genes and high expression of UBA52 gene were positively correlated with PaC patients survival. We identified a four-gene set (PTCD3, UBA52, A2M, and ARID1B) expressed in peripheral blood of early stage and metastatic PaC patients that may be useful for PaC early diagnosis.

Crucial Genes in Aortic Dissection Identified by Weighted Gene Coexpression Network Analysis

Background

Aortic dissection (AD) is a lethal vascular disease with high mortality and morbidity. Though AD clinical pathology is well understood, its molecular mechanisms remain unclear. Specifically, gene expression profiling helps illustrate the potential mechanism of aortic dissection in terms of gene regulation and its modification by risk factors. This study was aimed at identifying the genes and molecular mechanisms in aortic dissection through bioinformatics analysis.

Method

Nine patients with AD and 10 healthy controls were enrolled. The gene expression in peripheral mononuclear cells was profiled through next-generation RNA sequencing. Analyses including differential expressed gene (DEG) via DEGseq, weighted gene coexpression network (WGCNA), and VisANT were performed to identify crucial genes associated with AD. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was also utilized to analyze Gene Ontology (GO).

Results

DEG analysis revealed that 1,113 genes were associated with AD. Of these, 812 genes were markedly reduced, whereas 301 genes were highly expressed, in AD patients. DEGs were rich in certain categories such as MHC class II receptor activity, MHC class II protein complex, and immune response genes. Gene coexpression networks via WGCNA identified 3 gene hub modules, with one positively and 2 negatively correlated with AD, respectively. Specifically, module 37 was the most strongly positively correlated with AD with a correlation coefficient of 0.72. Within module 37, five hub genes (AGFG1, MCEMP1, IRAK3, KCNE1, and CLEC4D) displayed high connectivity and may have clinical significance in the pathogenesis of AD.

Conclusion

Our analysis provides the possible association of specific genes and gene modules for the involvement of the immune system in aortic dissection. AGFG1, MCEMP1, IRAK3, KCNE1, and CLEC4D in module M37 were highly connected and strongly linked with AD, suggesting that these genes may help understand the pathogenesis of aortic dissection.

RUNX2 and LAMC2: promising pancreatic cancer biomarkers identified by an integrative data mining of pancreatic adenocarcinoma tissues

Pancreatic carcinoma (PC) is a severe disease associated with high mortality. Although strategies for cancer therapy have made great progress, outcomes of pancreatic carcinoma patients remain extremely poor. Therefore, it is urgent to find novel biomarkers and therapeutic targets. To identify biomarkers for early diagnosis and therapy, three mRNA microarray datasets and two miRNA datasets were selected, and combinative analysis was performed by GEO2R. Functional and pathway enrichment analysis were performed using DAVID database. MiRTarBase, miRWalk and Diana Tools were used to get key genes. TCGA, HPA and western blotting were used to verify diagnostic and prognostic value of key genes. By integrating mRNA and miRNA expression profiles, we identified 114 differentially expressed genes and 114 differentially expressed miRNAs, respectively. Then, three overlapping key genes, RUNX2, LAMC2 and FBXO32, were found. Their protein levels in pancreatic tissue from PC patients and normal people were analyzed by immunohistochemical staining and western blotting. RUNX2 showed a potential property to identify PC. Aberrant over-expression of LAMC2 was associated with poor prognosis of PC patients, tumor status and subtypes. In summary, our current study identified that RUNX2 and LAMC2 may be promising targets for early diagnosis and therapy of PC patients.

Evaluation of the diagnostic ability of laminin gene family for pancreatic ductal adenocarcinoma

A poor outcome for pancreatic ductal adenocarcinoma (PDAC) patients is still a challenge worldwide. The aim of our study is to investigate the potential of key laminin subunits for being used both as a diagnostic and prognostic biomarker for PDAC patients. We evaluated the mRNA expression and prognostic value of laminin gene family in PDAC tissues using online public databases. Moreover, the relationship between key laminin subunits in PDAC blood cells and circulating tumor cells (CTCs) and the distinguishing ability of joint serum levels with carbohydrate antigen 19-9 (CA19-9) was analyzed. Two key differentially expressed subunits (LAMA3 and LAMC2) that are associated with prognosis of PDAC patients were found to show a potential for distinguishing between PDAC and non-tumor tissues. LAMA3 and LAMC2 expression were found to be positively related with CTC quantity in PDAC blood (R=0.628, p=0.029; R=0.776, p=0.003, respectively) using IgG chips. Furthermore, serum LAMC2 levels offered significant improvement over using CA19-9 alone for the discrimination of PDAC. Joint serum LAMC2 and CA19-9 levels increased the net benefit proportion in early stage/operational PDAC patients. Using integrated profiling, we identified LAMA3 and LAMC2 as potential therapeutic targets and prognostic markers for PDAC, for which further validation is warranted.

ANKRD22, a novel tumor microenvironment-induced mitochondrial protein promotes metabolic reprogramming of colorectal cancer cells

Background: The leading cause of poor prognosis in colorectal cancer (CRC) is the presence of colorectal cancer-initiating cells (CCICs). The interplay between the tumor microenvironment (TME) and CRC cells induces reacquisition of initiating cell characteristics, but the underlying mechanisms remain elusive.

Methods: Candidate molecules were screened by global differential cDNA expression profiles of CCICs, which were enriched from patient-derived tumor xenograft models. Luciferase reporters and chromatin immunoprecipitation assays were used to explore the mechanism of TME factors regulating the transcription of ANKRD22. The effects of Ankyrin repeat domain-containing protein 22 (ANKRD22) on energy metabolism were monitored by extracellular flux and ¹³C-based metabolic flux analysis. Mass spectrometry was used to identify the interacting partners of ANKRD22. Morphological changes of CCICs overexpressing ANKRD22 were observed by electron microscopy. The effects of ANKRD22 on mitochondrial lipid metabolism were analyzed by lipidomics.

Results: We identified a novel nucleus-encoded mitochondrial membrane protein, ANKRD22, which was upregulated in CCICs. We found that ANKRD22 was induced by the p38/MAX pathway activated by different TME stimuli. As a key transcription factor, MAX promoted the transcription of ANKRD22. Expression of ANKRD22 promoted glycolysis associated with a decrease in ATP/ADP and an increase in AMP/ATP levels, which were related to its interaction with pyruvate dehydrogenase kinase isoform 1 (PDK1) and multiple subunits of ATP synthase. Further, in CCICs, ANKRD22 cooperated with the lipid transport protein, Extended Synaptotagmin-1 (E-Syt1), to transport excess lipids into mitochondria and reduced the number of mitochondria in an autophagy-independent manner, thus meeting the metabolic requirements of CCICs.

Conclusion: ANKRD22 induced by TME promotes the metabolic reprogramming of CRC cells. Our study has identified ANKRD22/E-Syt1 as a potential target for eradicating CCICs.

Identification of a Nine-Gene Signature and Establishment of a Prognostic Nomogram Predicting Overall Survival of Pancreatic Cancer

Background: Pancreatic cancer is highly lethal and aggressive with increasing trend of mortality in both genders. An effective prediction model is needed to assess prognosis of patients for optimization of treatment. Materials and Methods: Seven datasets of mRNA expression and clinical data were obtained from gene expression omnibus (GEO) database. Level 3 mRNA expression and clinicopathological data were obtained from The Cancer Genome Atlas pancreatic ductal adenocarcinoma (TCGA-PAAD) dataset. Differentially expressed genes (DEGs) between pancreatic tumor and normal tissue were identified by integrated analysis of multiple GEO datasets. Univariate and Lasso Cox regression analyses were applied to identify overall survival-related DEGs and establish a prognostic gene signature whose performance was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell's concordance index (C-index) and calibration curve. GSE62452 and GSE57495 were used for external validation. Gene set enrichment analysis (GSEA) and tumor immunity analysis were applied to elucidate the molecular mechanisms and immune relevance. Multivariate Cox regression analysis was used to identify independent prognostic factors in pancreatic cancer. Finally, a prognostic nomogram was established based on the TCGA PAAD dataset. Results: A nine-gene signature comprising MET, KLK10, COL17A1, CEP55, ANKRD22, ITGB6, ARNTL2, MCOLN3, and SLC25A45 was established to predict overall survival of pancreatic cancer. The ROC curve and C-index indicated good performance of the nine-gene signature at predicting overall survival in the TCGA dataset and external validation datasets relative to classic AJCC staging. The nine-gene signature could classify patients into high- and low-risk groups with distinct overall survival and differentiate tumor from normal tissue. Univariate Cox regression revealed that the nine-gene signature was an independent prognostic factor in pancreatic cancer. The nomogram incorporating the gene signature and clinical prognostic factors was superior to AJCC staging in predicting overall survival. The high-risk group was enriched with multiple oncological signatures and aggressiveness-related pathways and associated with significantly lower levels of CD4⁺ T cell infiltration. Conclusion: Our study identified a nine-gene signature and established a prognostic nomogram that reliably predict overall survival in pancreatic cancer. The findings may be beneficial to therapeutic customization and medical decision-making.

Network-based integration of mRNA and miRNA profiles reveals new target genes involved in pancreatic cancer

Pancreatic cancer is regarded as the most fatal and aggressive malignancy cancer due to its low 5-year survival rate and poor prognosis. The approaches of early diagnosis and treatment are limited, which makes it urgent to identify the complex mechanism of pancreatic oncogenesis. In this study, we used RNA-seq to investigate the transcriptomic (mRNA and miRNA) profiles of pancreatic cancer in paired tumor and normal pancreatic samples from ten patients. More than 1000 differentially expressed genes were identified, nearly half of which were also found to be differentially expressed in the majority of examined patients. Functional enrichment analysis revealed that these genes were significantly enriched in multicellular organismal and metabolic process, secretion, mineral transport, and intercellular communication. In addition, only 24 differentially expressed miRNAs were found, all of which have been reported to be associated with pancreatic cancer. Furthermore, an integrated miRNA-mRNA interaction network was generated using multiple resources. Based on the calculation of disease correlation scores developed here, several genes present in the largest connected subnetwork, such as albumin, ATPase H⁺ /K⁺ exchanging alpha polypeptide and carcinoembryonic antigen-related cell adhesion molecule 1, were considered as novel genes that play important roles in the development of pancreatic cancer. Overall, our data provide new insights into further understanding of key molecular mechanisms underlying pancreatic tumorigenesis.

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.

Association between preoperative peripheral blood mononuclear cell gene expression profiles, early postoperative organ function recovery potential and long-term survival in advanced heart failure patients undergoing mechanical circulatory support

BACKGROUND

Multiorgan dysfunction syndrome contributes to adverse outcomes in advanced heart failure (AdHF) patients after mechanical circulatory support (MCS) implantation and is associated with aberrant leukocyte activity. We tested the hypothesis that preoperative peripheral blood mononuclear cell (PBMC) gene expression profiles (GEP) can predict early postoperative improvement or non-improvement in patients undergoing MCS implantation. We believe this information may be useful in developing prognostic biomarkers.

METHODS & DESIGN

We conducted a study with 29 patients undergoing MCS-surgery in a tertiary academic medical center from 2012 to 2014. PBMC samples were collected one day before surgery (day -1). Clinical data was collected on day -1 and day 8 postoperatively. Patients were classified by Sequential Organ Failure Assessment score and Model of End-stage Liver Disease Except INR score (measured eight days after surgery): Group I = improving (both scores improved from day -1 to day 8, n = 17) and Group II = not improving (either one or both scores did not improve from day -1 to day 8, n = 12). RNA-sequencing was performed on purified mRNA and analyzed using Next Generation Sequencing Strand. Differentially expressed genes (DEGs) were identified by Mann-Whitney test with Benjamini-Hochberg correction. Preoperative DEGs were used to construct a support vector machine algorithm to predict Group I vs. Group II membership.

RESULTS

Out of 28 MCS-surgery patients alive 8 days postoperatively, one-year survival was 88% in Group I and 27% in Group II. We identified 28 preoperative DEGs between Group I and II, with an average 93% prediction accuracy. Out of 105 DEGs identified preoperatively between year 1 survivors and non-survivors, 12 genes overlapped with the 28 predictive genes.

CONCLUSIONS

In AdHF patients following MCS implantation, preoperative PBMC-GEP predicts early changes in organ function scores and correlates with long-term outcomes. Therefore, gene expression lends itself to outcome prediction and warrants further studies in larger longitudinal cohorts.

ANKRD22 promotes progression of non-small cell lung cancer through transcriptional up-regulation of E2F1

Lung cancer is the leading cause of death among all malignancies due to rapid tumor progression and relapse; however, the underlying molecular mechanisms of tumor progression are unclear. In the present study, we identified ANKRD22 as a novel tumor-associated gene in non-small cell lung cancer (NSCLC). According to the clinical correlation analysis, ANKRD22 was highly expressed in primary cancerous tissue compared with adjacent cancerous tissue, and high expression levels of ANKRD22 were significantly correlated with relapse and short overall survival time. Knockdown and overexpression analysis revealed that ANKRD22 promoted tumor progression by increasing cell proliferation. In xenograft assays, knockdown of ANKRD22 or in vivo treatment with ANKRD22 siRNA inhibited tumor growth. Furthermore, ANKRD22 was shown to participate in the transcriptional regulation of E2F1, and ANKRD22 promoted cell proliferation by up-regulating the expression of E2F1 which enhanced cell cycle progression. Therefore, our studies indicated that ANKRD22 up-regulated the transcription of E2F1 and promoted the progression of NSCLC by enhancing cell proliferation. These findings suggest that ANKRD22 could potentially act as a novel therapeutic target for NSCLC.

Basal-like breast cancer: molecular profiles, clinical features and survival outcomes

BACKGROUND

Basal-like constitutes an important molecular subtype of breast cancer characterised by an aggressive behaviour and a limited therapy response. The outcome of patients within this subtype is, however, divergent. Some individuals show an increased risk of dying in the first five years, and others a long-term survival of over ten years after the diagnosis. In this study, we aim at identifying markers associated with basal-like patients' survival and characterising subgroups with distinct disease outcome.

METHODS

We explored the genomic and transcriptomic profiles of 351 basal-like samples from the METABRIC and ROCK data sets. Two selection methods, labelled Differential and Survival filters, were employed to determine genes/probes that are differentially expressed in tumour and control samples, and are associated with overall survival. These probes were further used to define molecular subgroups, which vary at the microRNA level and in DNA copy number.

RESULTS

We identified the expression signature of 80 probes that distinguishes between two basal-like subgroups with distinct clinical features and survival outcomes. Genes included in this list have been mainly linked to cancer immune response, epithelial-mesenchymal transition and cell cycle. In particular, high levels of CXCR6, HCST, C3AR1 and FPR3 were found in Basal I; whereas HJURP, RRP12 and DNMT3B appeared over-expressed in Basal II. These genes exhibited the highest betweenness centrality and node degree values and play a key role in the basal-like breast cancer differentiation. Further molecular analysis revealed 17 miRNAs correlated to the subgroups, including hsa-miR-342-5p, -150, -155, -200c and -17. Additionally, increased percentages of gains/amplifications were detected on chromosomes 1q, 3q, 8q, 10p and 17q, and losses/deletions on 4q, 5q, 8p and X, associated with reduced survival.

CONCLUSIONS

The proposed signature supports the existence of at least two subgroups of basal-like breast cancers with distinct disease outcome. The identification of patients at a low risk may impact the clinical decisions-making by reducing the prescription of high-dose chemotherapy and, consequently, avoiding adverse effects. The recognition of other aggressive features within this subtype may be also critical for improving individual care and for delineating more effective therapies for patients at high risk.

Untargeted LC-HRMS-Based Metabolomics for Searching New Biomarkers of Pancreatic Ductal Adenocarcinoma: A Pilot Study

Pancreatic ductal adenocarcinoma is one of the most lethal tumors since it is usually detected at an advanced stage in which surgery and/or current chemotherapy have limited efficacy. The lack of sensitive and specific markers for diagnosis leads to a dismal prognosis. The purpose of this study is to identify metabolites in serum of pancreatic ductal adenocarcinoma patients that could be used as diagnostic biomarkers of this pathology. We used liquid chromatography-high-resolution mass spectrometry for a nontargeted metabolomics approach with serum samples from 28 individuals, including 16 patients with pancreatic ductal adenocarcinoma and 12 healthy controls. Multivariate statistical analysis, which included principal component analysis and partial least squares, revealed clear separation between the patient and control groups analyzed by liquid chromatography-high-resolution mass spectrometry using a nontargeted metabolomics approach. The metabolic analysis showed significantly lower levels of phospholipids in the serum from patients with pancreatic ductal adenocarcinoma compared with serum from controls. Our results suggest that the liquid chromatography-high-resolution mass spectrometry-based metabolomics approach provides a potent and promising tool for the diagnosis of pancreatic ductal adenocarcinoma patients using the specific metabolites identified as novel biomarkers that could be used for an earlier detection and treatment of these patients.

VNN1, a potential biomarker for pancreatic cancer-associated new-onset diabetes, aggravates paraneoplastic islet dysfunction by increasing oxidative stress

In our previous clinical microarray analysis, we were the first to report on Vanin-1 (VNN1) as a novel clinically derived biomarker of pancreatic cancer-associated new-onset diabetes (PCAND). The functional mechanisms of VNN1 in the pathogenesis of PCAND, however, are not completely understood. In the present study, we further extend our previous clinical study to include laboratory research. The functions and mechanisms of neoplastic overexpressed VNN1 in PCAND have been explored using a co-culture model. Furthermore, the serum concentrations and discrimination power of downstream molecules of VNN1 were tested in a PCAND cohort. Pancreatic ductal adenocarcinoma (PDA) overexpressed VNN1 further aggravates paraneoplastic islet dysfunction; decreases in GSH/PPAR-γ concentrations and increases in ROS/cysteamine might be primary cause of this effect. Clinical serum analyses revealed that the expression profiles of these molecules were aberrant in the PCAND group. Our results further demonstrated that PCAND is a type of paraneoplastic diabetes. As the only clinically derived biomarker for PCAND screening available today, the biological role of VNN1 in triggering oxidative stress within the pancreatic microenvironment is important. The molecules downstream of VNN1 are also potential biomarkers for PCAND screening.

Parallel Gene Expression Changes in Sarcoidosis Involving the Lacrimal Gland, Orbital Tissue, or Blood

IMPORTANCE

Sarcoidosis is a major cause of ocular or periocular inflammation. The pathogenesis of sarcoidosis is incompletely understood and diagnosis often requires a biopsy.

OBJECTIVE

To determine how gene expression in either orbital adipose tissue or the lacrimal gland affected by sarcoidosis compares with gene expression in other causes of orbital disease and how gene expression in tissue affected by sarcoidosis compares with gene expression in peripheral blood samples obtained from patients with sarcoidosis.

DESIGN, SETTING, AND PARTICIPANTS

In a multicenter, international, observational study, gene expression profiling of formalin-fixed biopsy specimens, using GeneChipp U133 Plus 2 microarrays (Affymetrix), was conducted between October 2012 and January 2014 on tissues biopsied from January 2000 through June 2013. Participants included 12 patients with orbital sarcoidosis (7 in adipose tissue; 5 affecting the lacrimal gland) as well as comparable tissue from 6 healthy individuals serving as controls or patients with thyroid eye disease, nonspecific orbital inflammation, or granulomatosis with polyangiitis. In addition, results were compared with gene expression in peripheral blood samples obtained from 12 historical individuals with sarcoidosis.

MAIN OUTCOMES AND MEASURES

Significantly differentially expressed transcripts defined as a minimum of a 1.5-fold increase or a comparable decrease and a false discovery rate of P < .05.

RESULTS

Signals from 2449 probe sets (transcripts from approximately 1522 genes) were significantly increased in the orbital adipose tissue from patients with sarcoidosis. Signals from 4050 probe sets (approximately 2619 genes) were significantly decreased. Signals from 3069 probe sets (approximately 2001 genes) were significantly higher and 3320 (approximately 2283 genes) were significantly lower in the lacrimal gland for patients with sarcoidosis. Ninety-two probe sets (approximately 69 genes) had significantly elevated signals and 67 probe sets (approximately 56 genes) had significantly lower signals in both orbital tissues and in peripheral blood from patients with sarcoidosis. The transcription factors, interferon-response factor 1, interferon-response factor 2, and nuclear factor κB, were strongly implicated in the expression of messenger RNA upregulated in common in the 3 tissues.

CONCLUSIONS AND RELEVANCE

Gene expression in sarcoidosis involving the orbit or lacrimal gland can be distinguished from gene expression patterns in control tissue and overlaps with many transcripts upregulated or downregulated in the peripheral blood of patients with sarcoidosis. These observations suggest that common pathogenic mechanisms contribute to sarcoidosis in different sites. The observations support the hypothesis that a pattern of gene expression profiles could provide diagnostic information in patients with sarcoidosis.

Pancreatic cancer stromal biology and therapy

Pancreatic cancer is one of the most lethal malignancies. Significant progresses have been made in understanding of pancreatic cancer pathogenesis, including appreciation of precursor lesions or premalignant pancreatic intraepithelial neoplasia (PanINs), description of sequential transformation from normal pancreatic tissue to invasive pancreatic cancer and identification of major genetic and epigenetic events and the biological impact of those events on malignant behavior. However, the currently used therapeutic strategies targeting tumor epithelial cells, which are potent in cell culture and animal models, have not been successful in the clinic. Presumably, therapeutic resistance of pancreatic cancer is at least in part due to its drastic desmoplasis, which is a defining hallmark for and circumstantially contributes to pancreatic cancer development and progression. Improved understanding of the dynamic interaction between cancer cells and the stroma is important to better understanding pancreatic cancer biology and to designing effective intervention strategies. This review focuses on the origination, evolution and disruption of stromal molecular and cellular components in pancreatic cancer, and their biological effects on pancreatic cancer pathogenesis.