M2‐phenotype tumour‐associated macrophages upregulate the expression of prognostic predictors MMP14 and INHBA in pancreatic cancer

Pancreatic cancer is one of the most lethal gastrointestinal tumours, the most common pathological type is pancreatic adenocarcinoma (PAAD). In recent year, immune imbalanced in tumour microenvironment has been shown to play an important role in the evolution of tumours progression, and the efficacy of immunotherapy has been gradually demonstrated in clinical practice. In this study, we propose to construct an immune‐related prognostic risk model based on immune‐related genes MMP14 and INHBA expression that can assess the prognosis of pancreatic cancer patients and identify potential therapeutic targets for pancreatic cancer, to provide new ideas for the treatment of pancreatic cancer. We also investigate the correlation between macrophage infiltration and MMP14 and INHBA expression. First, the gene expression data of pancreatic cancer and normal pancreatic tissue were obtained from The Cancer Genome Atlas Program (TCGA) and The Genotype‐Tissue Expression public database (GTEx). The differentially expressed immune‐related genes between pancreatic cancer samples and normal sample were screened by R software. Secondly, univariate Cox regression analysis were used to evaluate the relationship between immune‐related genes and the prognosis of pancreatic cancer patients. A polygenic risk score model was constructed by Cox regression analysis. The prognostic nomogram was constructed, and its performance was evaluated comprehensively by internal calibration curve and C‐index. Using the risk model, each patient gets a risk score, and was divided into high‐ or low‐ risk groups. The proportion of 22 types of immune cells infiltration in pancreatic cancer samples was inferred by CIBERSOFT algorithm, correlation analysis (Pearson method) was used to analyse the correlation between the immune‐related genes and immunes cells. Then, we applied macrophage conditioned medium to culture pancreatic cancer cell line PANC1, detected the expression of MMP14 and INHBA by qRT‐PCR and Western blot methods. Knock‐down MMP14 and INHBA in PANC1 cells by transfected with shRNA lentiviruses. Detection of migration ability of pancreatic cells was done by trans‐well cell migration assay. A subcutaneous xenograft tumour model of human pancreatic cancer in nude mice was constructed. In conclusion, an immune‐related gene prognostic model was constructed, patients with high‐risk scores have poorer survival status, M2‐phenotype tumour‐associated macrophages (TAMs) up‐regulate two immune‐related genes, MMP14 and INHBA, which were used to establish the prognostic model. Knock‐down of MMP14 and INHBA inhibited invasion of pancreatic cancer.     

Combining bioinformatics techniques to explore the molecular mechanisms involved in pancreatic cancer metastasis and prognosis

This article aims to explore the underlying molecular mechanisms and prognosis‐related genes in pancreatic cancer metastasis. Pancreatic cancer metastasis‐related gene chip data were downloaded from GENE EXPRESSION OMNIBUS(GEO)database. Differentially expressed genes were screened after R‐package pre‐treatment. Functional annotations and related signalling pathways were analysed using DAVID software. GEPIA (Gene Expression Profiling Interactive Analysis) was used to perform prognostic analysis, and differential genes associated with prognosis were screened and validated using data from GEO. We screened 40 healthy patients, 40 primary pancreatic cancer and 40 metastatic pancreatic cancer patients, collected serum, designed primers and used qPCR to test the expression of prognosis‐related genes in each group. 109 differentially expressed genes related with pancreatic cancer metastasis were screened, of which 49 were up‐regulated and 60 were down‐regulated. Functional annotation and pathway analysis revealed differentially expressed genes were mainly concentrated in protein activation cascade, extracellular matrix construction, decomposition, etc In the biological process, it is mainly involved in signalling pathways such as PPAR, PI3K‐Akt and ECM receptor interaction. Prognostic analysis showed the expression levels of four genes were significantly correlated with the overall survival time of patients with pancreatic cancer, namely SCG5, CRYBA2, CPE and CHGB. qPCR experiments showed the expression of these four genes was decreased in both the primary pancreatic cancer group and the metastatic pancreatic cancer group, and the latter was more significantly reduced. Pancreatic cancer metastasis is closely related to the activation of PPAR pathway, PI3K‐Akt pathway and ECM receptor interaction. SCG5, CRYBA2, CPE and CHGB genes are associated with the prognosis of pancreatic cancer, and their low expression suggests a poor prognosis.     

Affymetrix基因表达谱芯片在新疆维吾尔族与汉族胰腺癌组织样本间差异表达基因筛选中的运用

目的:运用基因表达谱芯片筛选并分析新疆维吾尔族与汉族胰腺癌组织样本间的差异表达基因。方法:收集我院2014年1月至2016年6月间行手术切除的维吾尔族与汉族胰腺导管细胞癌组织并提取总RNA,选取经Nanodrop 2000与Agilent 2100仪器质检合格的样本总RNA采用Affymetrix基因表达谱芯片筛选出差异表达基因并绘制统计图,运用基因本体(GO)分析及信号通路(Pathway)分析对这些差异表达基因的生物信息进行汇总分析。结果:通过基因表达谱芯片分析,新疆维吾尔族与汉族胰腺癌组织样本间共检测到1063个基因存在差异表达,在维吾尔族胰腺癌标本中显著上调表达的基因共281个,差异表达倍数最高的为IGLV1-44基因(差异倍数:9.99)下调表达的基因共782个,差异表达倍数最高的为CPB1基因(差异倍数:33.76);在Gene Ontology数据库中共检索到815个上述差异表达基因具有明确的GO分类,差异表达倍数最高的为CPB1基因(差异倍数:33.76);Pathway分析中共检测到30条信号通路包含有上述差异表达基因,共涉及196个基因,其中以FAK信号通路差异表达基因富集程度最高,差异表达倍数最高的基因为COL11A1基因(差异倍数:5.02)。结论:基因表达谱芯片分析结果显示,在新疆维吾尔族与汉族胰腺癌组织样本间存在大量的差异表达基因,这些基因与胰腺癌的增殖分化、侵袭转移及多药耐药等特性密切相关,且参与了多条生物体内重要信号转导通路的调控。     

基于生物信息学分析寻找胰腺癌新的诊断和治疗靶点

胰腺癌作为一种消化系统高度恶性的肿瘤性疾病,其发生和进展的分子机制仍不确定。为寻找与胰腺癌发生和进展有关的新的有效治疗靶点和潜在的生物标志物。利用GEO数据库中的GEO2R在线工具对胰腺癌组织和正常对照组织的基因表达进行差异分析并对差异表达基因(DEGs)进行GO功能和KEGG通路富集分析。然后通过GEPIA数据库中胰腺癌的转录数据对候选基因的表达进行验证。Kaplan-Meier法分析各候选基因的预后价值。利用starBase数据库中的7个预测程序对候选基因上游潜在的miRNAs进行预测。此外,还使用miRNet和starBase预测了hsa-miR-20b-5p的上游lncRNAs并利用lncATLAS数据库对潜在的lncRNAs进行亚细胞定位。在本研究中,我们发现胰腺癌组织中LAMA3基因的转录水平明显高于健康对照组织。同时,LAMA3的过表达与胰腺癌患者较差的临床预后相关。随后,预测了21个可能靶向LAMA3的潜在上游miRNAs。在预测到的miRNA-mRNA调控轴中,has-miR-20b-5p-LAMA3轴在胰腺癌的发生和进展中具有较高的潜力。进一步研究发现,FGD5-AS1潜在的抑制has-miR-20b-5p-LAMA3调控轴的作用可能能够在胰腺癌中作为诊断和治疗的有效靶点。FGD5-AS1-has-miR-20b-5p-LAMA3调控网络在胰腺癌发生和发展中的具有关键作用,可作为胰腺癌临床诊断和治疗的潜在靶点和生物标志物。     

Concurrent Gene Signatures for Han Chinese Breast Cancers

The interplay between copy number variation (CNV) and differential gene expression may be able to shed light on molecular process underlying breast cancer and lead to the discovery of cancer-related genes. In the current study, genes concurrently identified in array comparative genomic hybridization (CGH) and gene expression microarrays were used to derive gene signatures for Han Chinese breast cancers.We performed 23 array CGHs and 81 gene expression microarrays in breast cancer samples from Taiwanese women. Genes with coherent patterns of both CNV and differential gene expression were identified from the 21 samples assayed using both platforms. We used these genes to derive signatures associated with clinical ER and HER2 status and disease-free survival.Distributions of signature genes were strongly associated with chromosomal location: chromosome 16 for ER and 17 for HER2. A breast cancer risk predictive model was built based on the first supervised principal component from 16 genes (RCAN3, MCOLN2, DENND2D, RWDD3, ZMYM6, CAPZA1, GPR18, WARS2, TRIM45, SCRN1, CSNK1E, HBXIP, CSDE1, MRPL20, IKZF1, and COL20A1), and distinct survival patterns were observed between the high- and low-risk groups from the combined dataset of 408 microarrays. The risk score was significantly higher in breast cancer patients with recurrence, metastasis, or mortality than in relapse-free individuals (0.241 versus 0, P<0.001). The concurrent gene risk predictive model remained discriminative across distinct clinical ER and HER2 statuses in subgroup analysis. Prognostic comparisons with published gene expression signatures showed a better discerning ability of concurrent genes, many of which were rarely identifiable if expression data were pre-selected by phenotype correlations or variability of individual genes.We conclude that parallel analysis of CGH and microarray data, in conjunction with known gene expression patterns, can be used to identify biomarkers with prognostic values in breast cancer.     

Identification of an extracellular vesicle-related gene signature in the prediction of pancreatic cancer clinical prognosis

Although extracellular vesicles (EVs) in body fluid have been considered to be ideal biomarkers for cancer diagnosis and prognosis, it is still difficult to distinguish EVs derived from tumor tissue and normal tissue. Therefore, the prognostic value of tumor-specific EVs was evaluated through related molecules in pancreatic tumor tissue. NA sequencing data of pancreatic adenocarcinoma (PAAD) were acquired from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). EV-related genes in pancreatic cancer were obtained from exoRBase. Protein–protein interaction (PPI) network analysis was used to identify modules related to clinical stage. CIBERSORT was used to assess the abundance of immune and non-immune cells in the tumor microenvironment. A total of 12 PPI modules were identified, and the 3-PPI-MOD was identified based on the randomForest package. The genes of this model are involved in DNA damage and repair and cell membrane-related pathways. The independent external verification cohorts showed that the 3-PPI-MOD can significantly classify patient prognosis. Moreover, compared with the model constructed by pure gene expression, the 3-PPI-MOD showed better prognostic value. The expression of genes in the 3-PPI-MOD had a significant positive correlation with immune cells. Genes related to the hypoxia pathway were significantly enriched in the high-risk tumors predicted by the 3-PPI-MOD. External databases were used to verify the gene expression in the 3-PPI-MOD. The 3-PPI-MOD had satisfactory predictive performance and could be used as a prognostic predictive biomarker for pancreatic cancer.     

Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice.     

Expression of Tumor Necrosis Factor-Alpha-Mediated Genes Predicts Recurrence-Free Survival in Lung Cancer

In this study, we conducted a meta-analysis on high-throughput gene expression data to identify TNF-α-mediated genes implicated in lung cancer. We first investigated the gene expression profiles of two independent TNF-α/TNFR KO murine models. The EGF receptor signaling pathway was the top pathway associated with genes mediated by TNF-α. After matching the TNF-α-mediated mouse genes to their human orthologs, we compared the expression patterns of the TNF-α-mediated genes in normal and tumor lung tissues obtained from humans. Based on the TNF-α-mediated genes that were dysregulated in lung tumors, we developed a prognostic gene signature that effectively predicted recurrence-free survival in lung cancer in two validation cohorts. Resampling tests suggested that the prognostic power of the gene signature was not by chance, and multivariate analysis suggested that this gene signature was independent of the traditional clinical factors and enhanced the identification of lung cancer patients at greater risk for recurrence.     

Identification of germline genomic copy number variation in familial pancreatic cancer

Adenocarcinoma of the pancreas is a significant cause of cancer mortality, and up to 10?% of cases appear to be familial. Heritable genomic copy number variants (CNVs) can modulate gene expression and predispose to disease. Here, we identify candidate predisposition genes for familial pancreatic cancer (FPC) by analyzing germline losses or gains present in one or more high-risk patients and absent in a large control group. A total of 120 FPC cases and 1,194 controls were genotyped on the Affymetrix 500K array, and 36 cases and 2,357 controls were genotyped on the Affymetrix 6.0 array. Detection of CNVs was performed by multiple computational algorithms and partially validated by quantitative PCR. We found no significant difference in the germline CNV profiles of cases and controls. A total of 93 non-redundant FPC-specific CNVs (53 losses and 40 gains) were identified in 50 cases, each CNV present in a single individual. FPC-specific CNVs overlapped the coding region of 88 RefSeq genes. Several of these genes have been reported to be differentially expressed and/or affected by copy number alterations in pancreatic adenocarcinoma. Further investigation in high-risk subjects may elucidate the role of one or more of these genes in genetic predisposition to pancreatic cancer.     

A Thirteen-Gene Expression Signature Predicts Survival of Patients with Pancreatic Cancer and Identifies New Genes of Interest

Background

Currently, prognostication for pancreatic ductal adenocarcinoma (PDAC) is based upon a coarse clinical staging system. Thus, more accurate prognostic tests are needed for PDAC patients to aid treatment decisions.

Methods and Findings

Affymetrix gene expression profiling was carried out on 15 human PDAC tumors and from the data we identified a 13-gene expression signature (risk score) that correlated with patient survival. The gene expression risk score was then independently validated using published gene expression data and survival data for an additional 101 patients with pancreatic cancer. Patients with high-risk scores had significantly higher risk of death compared to patients with low-risk scores (HR 2.27, p = 0.002). When the 13-gene score was combined with lymph node status the risk-score further discriminated the length of patient survival time (p<0.001). Patients with a high-risk score had poor survival independent of nodal status; however, nodal status increased predictability for survival in patients with a low-risk gene signature score (low-risk N1 vs. low-risk N0: HR = 2.0, p = 0.002). While AJCC stage correlated with patient survival (p = 0.03), the 13-gene score was superior at predicting survival. Of the 13 genes comprising the predictive model, four have been shown to be important in PDAC, six are unreported in PDAC but important in other cancers, and three are unreported in any cancer.

Conclusions

We identified a 13-gene expression signature that predicts survival of PDAC patients and could prove useful for making treatment decisions. This risk score should be evaluated prospectively in clinical trials for prognostication and for predicting response to chemotherapy. Investigation of new genes identified in our model may lead to novel therapeutic targets.     

A real-time PCR-based quantitative assay for 3-methylcytosine demethylase activity of ALKBH3

Human AlkB homolog 3 (ALKBH3), a homolog of the Escherichia coli protein AlkB, demethylates 1-methyladenine and 3-methylcytosine (3-meC) in single-stranded DNA and RNA by oxidative demethylation. Immunohistochemical analyses on clinical cancer specimens and knockdown experiments using RNA interference in vitro and in vivo indicate that ALKBH3 is a promising molecular target for the treatment of prostate, pancreatic, and non-small cell lung cancer. Therefore, an inhibitor for ALKBH3 demethylase is expected to be a first-in-class molecular-targeted drug for cancer treatment. Here, we report the development of a novel, quantitative real-time PCR-based assay for ALKBH3 demethylase activity against 3-meC by highly active recombinant ALKBH3 protein using a silkworm expression system. This assay enables us to screen for inhibitors of ALKBH3 demethylase, which may result in the development of a novel molecular-targeted drug for cancer therapy.     

Identification of TWIST-interacting genes in prostate cancer

Prostate cancer is one of the most common cancers in men worldwide, and the number of diagnosed patients has dramatically increased in recent years. Currently, the clinical parameters used to diagnose prostate cancer, such as Gleason score, pathological tumor staging, and prostate-specific antigen(PSA) expression level, are considered insufficient to inform recommendation to guide clinical practice. Thus, identification of a novel biomarker is necessary. TWIST is one of the well-studied targets and is correlated with cancer invasion and metastasis in several human cancers. We have investigated two largest prostate cancer patient cohorts available in GEO database and found that TWIST expression is positive correlated with Gleason score and associated with poorer survival. By using a prostate cancer cohort and a prostate cancer cell line dataset, we have identified three potential downstream targets of TWIST, PPM1 A, SRP72 and TBCB. TWIST's prognostic capacity is lost when the gene is mutated. Further investigation in the prostate cancer cohort revealed that gene expression of SERPINA, STX7, PDIA2, FMP5, GP1 BB, VGLL4,KCNMA1, SHMT2, SAA4 and DIDO1 influence the prognostic significance of TWIST and vice versa. Importantly, eight out of these ten genes are prognostic indicator by itself. In conclusion, our study has further confirmed that TWIST is a prognostic marker in prostate cancer, identified its potential downstream targets and genes that could possibly give additional prognostic value to predict TWIST-mediated prostate cancer progression.     

Increased B Cell-Activating Factor Promotes Tumor Invasion and Metastasis in Human Pancreatic Cancer

B cell-activating factor (BAFF) is a cytokine belonging to the tumor necrosis factor (TNF) superfamily. It has been reported that BAFF is elevated in patients with autoimmune pancreatitis and contributes to the malignant potential of blood cancers and solid tumors. In this study, clinical evidence of increased BAFF levels in patients with pancreatic ductal adenocarcinoma (PDAC) was obtained, and the roles and mechanisms of BAFF in PDAC were clarified in human tissues of PDAC and from in vitro data of PDAC cell lines. Serum levels of BAFF in patients with PDAC were significantly higher than in healthy subjects (p = 0.0121). Patients with UICC stage IV PDAC (T1-4, N0-1, M1) had significantly higher levels of serum BAFF compared to patients with PDAC (p = 0.0182). BAFF was remarkably expressed in infiltrating B lymphocytes surrounding pancreatic cancer in human pancreatic tissues, suggesting that BAFF may play a role in progression of pancreatic cancer. PDAC cell lines were cultured with human recombinant BAFF, and morphology and gene expression were analyzed; pancreatic cancer cells changed to a fibroblast-like morphology, and showed altered gene expression of E-cadherin, vimentin and Snail. These BAFF-induced changes reflect enhanced cell motility and invasion. BAFF-R-overexpressing cell clones confirmed the association between these BAFF-induced changes and epithelial-mesenchymal transition (EMT)-related genes. BAFF was elevated in patients with metastatic advanced PDAC and induced alterations in PDAC cells via regulation of EMT-related genes. Elucidation of the precise role and mechanism of control of BAFF may lead to new therapeutic approaches with the aim of improving pancreatic cancer survival.     

A Multistep High-Content Screening Approach to Identify Novel Functionally Relevant Target Genes in Pancreatic Cancer

In order to foster the systematic identification of novel genes with important functional roles in pancreatic cancer, we have devised a multi-stage screening strategy to provide a rational basis for the selection of highly relevant novel candidate genes based on the results of functional high-content analyses. The workflow comprised three consecutive stages: 1) serial gene expression profiling analyses of primary human pancreatic tissues as well as a number of in vivo and in vitro models of tumor-relevant characteristics in order to identify genes with conspicuous expression patterns; 2) use of ‘reverse transfection array’ technology for large-scale parallelized functional analyses of potential candidate genes in cell-based assays; and 3) selection of individual candidate genes for further in-depth examination of their cellular roles. A total of 14 genes, among them 8 from “druggable” gene families, were classified as high priority candidates for individual functional characterization. As an example to demonstrate the validity of the approach, comprehensive functional data on candidate gene ADRBK1/GRK2, which has previously not been implicated in pancreatic cancer, is presented.     

Circ_0008542 in osteoblast exosomes promotes osteoclast-induced bone resorption through m6A methylation

With an increasing aging society, China is the world’s fastest growing markets for oral implants. Compared with traditional oral implants, immediate implants cause marginal bone resorption and increase the failure rate of osseointegration, but the mechanism is still unknown. Therefore, it is important to further study mechanisms of tension stimulus on osteoblasts and osteoclasts at the early stage of osseointegration to promote rapid osseointegration around oral implants. The results showed that exosomes containing circ_0008542 from MC3T3-E1 cells with prolonged tensile stimulation promoted osteoclast differentiation and bone resorption. Circ_0008542 upregulated Tnfrsf11a (RANK) gene expression by acting as a miR-185-5p sponge. Meanwhile, the circ_0008542 1916-1992 bp segment exhibited increased m6A methylation levels. Inhibiting the RNA methyltransferase METTL3 or overexpressing the RNA demethylase ALKBH5 reversed osteoclast differentiation and bone resorption induced by circ_0008542. Injection of circ_0008542 + ALKBH5 into the tail vein of mice reversed the same effects in vivo. Site-directed mutagenesis study demonstrated that 1956 bp on circ_0008542 is the m6A functional site with the abovementioned biological functions. In conclusion, the RNA methylase METTL3 acts on the m6A functional site of 1956 bp in circ_0008542, promoting competitive binding of miRNA-185-5p by circ_0008542, and leading to an increase in the target gene RANK and the initiation of osteoclast bone absorption. In contrast, the RNA demethylase ALKBH5 inhibits the binding of circ_0008542 with miRNA-185-5p to correct the bone resorption process. The potential value of this study provides methods to enhance the resistance of immediate implants through use of exosomes releasing ALKBH5.Subject terms: Epigenetics, Predictive markers