Genome-scale analysis identifies GJB2 and ERO1LB as prognosis markers in patients with pancreatic cancer

Impairment of α-tubulin and F-actin interactions of GJB3 induces aneuploidy in urothelial cells and promotes bladder cancer cell invasion

BACKGROUND

We have previously identified an unsuspected role for GJB3 showing that the deficiency of this connexin protein induces aneuploidy in human and murine cells and accelerates cell transformation as well as tumor formation in xenograft models. The molecular mechanisms by which loss of GJB3 leads to aneuploidy and cancer initiation and progression remain unsolved.

METHODS

GJB3 expression levels were determined by RT-qPCR and Western blot. The consequences of GJB3 knockdown on genome instability were assessed by metaphase chromosome counting, multinucleation of cells, by micronuclei formation and by the determination of spindle orientation. Interactions of GJB3 with α-tubulin and F-actin was analyzed by immunoprecipitation and immunocytochemistry. Consequences of GJB3 deficiency on microtubule and actin dynamics were measured by live cell imaging and fluorescence recovery after photobleaching experiments, respectively. Immunohistochemistry was used to determine GJB3 levels on human and murine bladder cancer tissue sections. Bladder cancer in mice was chemically induced by BBN-treatment.

RESULTS

We find that GJB3 is highly expressed in the ureter and bladder epithelium, but it is downregulated in invasive bladder cancer cell lines and during tumor progression in both human and mouse bladder cancer. Downregulation of GJB3 expression leads to aneuploidy and genomic instability in karyotypically stable urothelial cells and experimental modulation of GJB3 levels alters the migration and invasive capacity of bladder cancer cell lines. Importantly, GJB3 interacts both with α-tubulin and F-actin. The impairment of these interactions alters the dynamics of these cytoskeletal components and leads to defective spindle orientation.

CONCLUSION

We conclude that deregulated microtubule and actin dynamics have an impact on proper chromosome separation and tumor cell invasion and migration. Consequently, these observations indicate a possible role for GJB3 in the onset and spreading of bladder cancer and demonstrate a molecular link between enhanced aneuploidy and invasive capacity cancer cells during tumor cell dissemination.

Topographic analysis of pancreatic cancer by TMA and digital spatial profiling reveals biological complexity with potential therapeutic implications

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal human malignancies. Tissue microarrays (TMA) are an established method of high throughput biomarker interrogation in tissues but may not capture histological features of cancer with potential biological relevance. Topographic TMAs (T-TMAs) representing pathophysiological hallmarks of cancer were constructed from representative, retrospective PDAC diagnostic material, including 72 individual core tissue samples. The T-TMA was interrogated with tissue hybridization-based experiments to confirm the accuracy of the topographic sampling, expression of pro-tumourigenic and immune mediators of cancer, totalling more than 750 individual biomarker analyses. A custom designed Next Generation Sequencing (NGS) panel and a spatial distribution-specific transcriptomic evaluation were also employed. The morphological choice of the pathophysiological hallmarks of cancer was confirmed by protein-specific expression. Quantitative analysis identified topography-specific patterns of expression in the IDO/TGF-β axis; with a heterogeneous relationship of inflammation and desmoplasia across hallmark areas and a general but variable protein and gene expression of c-MET. NGS results highlighted underlying genetic heterogeneity within samples, which may have a confounding influence on the expression of a particular biomarker. T-TMAs, integrated with quantitative biomarker digital scoring, are useful tools to identify hallmark specific expression of biomarkers in pancreatic cancer.

Plasma cell signatures predict prognosis and treatment efficacy for lung adenocarcinoma

PURPOSE

This study aims to identify key genes regulating tumor infiltrating plasma cells (PC) and provide new insights for innovative immunotherapy.

METHODS

Key genes related to PC were identified using machine learning in lung adenocarcinoma (LUAD) patients. A prognostic model called PC scores was developed using TCGA data and validated with GEO cohorts. We assessed the molecular background, immune features, and drug sensitivity of the high PC scores group. Real-time PCR was utilized to assess the expression of hub genes in both localized LUAD patients and LUAD cell lines.

RESULTS

We constructed PC scores based on seventeen PC-related hub genes (ELOVL6, MFI2, FURIN, DOK1, ERO1LB, CLEC7A, ZNF431, KIAA1324, NUCB2, TXNDC11, ICAM3, CR2, CLIC6, CARNS1, P2RY13, KLF15, and SLC24A4). Higher age, TNM stage, and PC scores independently predicted shorter overall survival. The AUC value of PC scores for one year, three years, and five years of overall survival were 0.713, 0.716, and 0.690, separately. The nomogram model that integrated age, stage, and PC scores showed significantly higher predictive value than stage alone (P < 0.01). High PC scores group exhibited an immune suppressing microenvironment with lower B, CD8 + T, CD4 + T, and dendritic cell infiltration. Docetaxel, gefitinib, and erlotinib had lower IC50 in high PC groups (P < 0.001). After validation through the local cohort and in vitro experiments, we ultimately confirmed three key potential targets: MFI2, KLF15, and CLEC7A.

CONCLUSION

We proposed a prediction mode which can effectively identify high-risk LUAD patients and found three novel genes closely correlated with PC tumor infiltration.

Proteomic associations with forced expiratory volume: a Mendelian randomisation study

BACKGROUND

A decline in forced expiratory volume (FEV1) is a hallmark of respiratory diseases that are an important cause of morbidity among the elderly. While some data exist on biomarkers that are related to FEV1, we sought to do a systematic analysis of causal relations of biomarkers with FEV1.

METHODS

Data from the population-based AGES-Reykjavik study were used. Serum proteomic measurements were done using 4782 DNA aptamers (SOMAmers). Data from 1479 participants with spirometric data were used to assess the association of SOMAmer measurements with FEV1 using linear regression. Bi-directional two-sample Mendelian randomisation (MR) analyses were done to assess causal relations of observationally associated SOMAmers with FEV1, using genotype and SOMAmer data from 5368 AGES-Reykjavik participants and genetic associations with FEV1 from a publicly available GWAS (n = 400,102).

RESULTS

In observational analyses, 530 SOMAmers were associated with FEV1 after multiple testing adjustment (FDR < 0.05). The most significant were Retinoic Acid Receptor Responder 2 (RARRES2), R-Spondin 4 (RSPO4) and Alkaline Phosphatase, Placental Like 2 (ALPPL2). Of the 257 SOMAmers with genetic instruments available, eight were associated with FEV1 in MR analyses. Three were directionally consistent with the observational estimate, Thrombospondin 2 (THBS2), Endoplasmic Reticulum Oxidoreductase 1 Beta (ERO1B) and Apolipoprotein M (APOM). THBS2 was further supported by a colocalization analysis. Analyses in the reverse direction, testing whether changes in SOMAmer levels were caused by changes in FEV1, were performed but no significant associations were found after multiple testing adjustments.

CONCLUSIONS

In summary, this large scale proteogenomic analyses of FEV1 reveals circulating protein markers of FEV1, as well as several proteins with potential causality to lung function.

Pan-cancer ion transport signature reveals functional regulators of glioblastoma aggression

Ion channels, transporters, and other ion-flux controlling proteins, collectively comprising the "ion permeome", are common drug targets, however, their roles in cancer remain understudied. Our integrative pan-cancer transcriptome analysis shows that genes encoding the ion permeome are significantly more often highly expressed in specific subsets of cancer samples, compared to pan-transcriptome expectations. To enable target selection, we identified 410 survival-associated IP genes in 33 cancer types using a machine-learning approach. Notably, GJB2 and SCN9A show prominent expression in neoplastic cells and are associated with poor prognosis in glioblastoma, the most common and aggressive brain cancer. GJB2 or SCN9A knockdown in patient-derived glioblastoma cells induces transcriptome-wide changes involving neuron projection and proliferation pathways, impairs cell viability and tumor sphere formation in vitro, perturbs tunneling nanotube dynamics, and extends the survival of glioblastoma-bearing mice. Thus, aberrant activation of genes encoding ion transport proteins appears as a pan-cancer feature defining tumor heterogeneity, which can be exploited for mechanistic insights and therapy development.

Proteomic associations with forced expiratory volume - a Mendelian randomisation study

A decline in forced expiratory volume (FEV1) is a hallmark of obstructive respiratory diseases, an important cause of morbidity among the elderly. While some data exist on biomarkers that are related to FEV1, we sought to do a systematic analysis of causal relations of biomarkers with FEV1. Data from the general population-based AGES-Reykjavik study were used. Proteomic measurements were done using 4,782 DNA aptamers (SOMAmers). Data from 1,648 participants with spirometric data were used to assess the association of SOMAmer measurements with FEV1 using linear regression. Bi-directional Mendelian randomisation (MR) analyses were done to assess causal relations of observationally associated SOMAmers with FEV1, using genotype and SOMAmer data from 5,368 AGES-Reykjavik participants and genetic associations with FEV1 from a publicly available GWAS (n = 400,102). In observational analyses, 473 SOMAmers were associated with FEV1 after multiple testing adjustment. The most significant were R-Spondin 4, Alkaline Phosphatase, Placental Like 2 and Retinoic Acid Receptor Responder 2. Of the 235 SOMAmers with genetic data, eight were associated with FEV1 in MR analyses. Three were directionally consistent with the observational estimate, Thrombospondin 2 (THBS2), Endoplasmic Reticulum Oxidoreductase 1 Beta and Apolipoprotein M. THBS2 was further supported by a colocalization analysis. Analyses in the reverse direction, testing whether changes in SOMAmer levels were caused by changes in FEV1, were performed but no significant associations were found after multiple testing adjustments. In summary, this large scale proteogenomic analyses of FEV1 reveals protein markers of FEV1, as well as several proteins with potential causality to lung function.

Pan-cancer analysis of the prognostic and immunological role of GJB2: a potential target for survival and immunotherapy

Background

GJB2 plays an essential role in the growth and progression of several cancers. However, asystematic pan-cancer analysis of GJB2 is lacking. Therefore, in this study, we performed a comprehensive pan-cancer analysis to determine the potential role of GJB2 in prognostic prediction and cancer immunotherapy response.

Methods

The differential expression of GJB2 in the tumor and adjacent normal tissues of various cancer types was analyzed using the TIMER, GEPIA, and Sangerbox databases. GEPIA and Kaplan-Meier plotter databases were used to analyze the survival outcomes based on GJB2 expression levels in pan-cancer. Furthermore, the association of GJB2 expression with the immune checkpoint (ICP) genes, tumor mutational load (TMB), microsatellite instability (MSI), neoantigens, and tumor infiltration of immune cells was analyzed using via the Sangerbox database. The cBioPortal database was used to determine the characteristics of GJB2 gene alterations in the cancer tissues. The STRING database was used to identify the GJB2-binding proteins. GEPIA database was used to identify the GJB2 co-expressed genes. DAVID was used to perform the functional enrichment analysis of gene ontology (GO) terms and KEGG pathways associated with GJB2. Finally, the mechanistic role of GJB2 in pancreatic adenocarcinoma (PAAD) was analyzed using the LinkedOmics database.

Results

The GJB2 gene was highly expressed in a variety of tumors. Furthermore, GJB2 expression levels showed significant positive or negative association with the survival outcomes in various cancers. GJB2 expression levels cor related with tumor mutational burden, microsatellite instability, neoantigens, and tumor infiltration of immune cells in multiple cancers. This suggested that GJB2 played a critical role in the tumor microenvironment. Functional enrichment analysis showed that the biological role of GJB2 in tumors included modulation of gap junction-mediated intercellular transport, regulation of cell communication by electrical coupling, ion transmembrane transport, autocrine signaling, apoptotic signaling pathway, NOD-like receptor signaling pathway, p53 signaling pathway, and PI3K-Akt signaling pathway.

Conclusions

Our study demonstrated that GJB2 played a significant role in tumorigenesis and tumor immunity in multiple cancers. Furthermore, GJB2 is a potential prognostic biomarker and a promising therapeutic target in multiple types of cancers.

The roles of connexins and gap junctions in the progression of cancer

Gap junctions (GJs), which are composed of connexins (Cxs), provide channels for direct information exchange between cells. Cx expression has a strong spatial specificity; however, its influence on cell behavior and information exchange between cells cannot be ignored. A variety of factors in organisms can modulate Cxs and subsequently trigger a series of responses that have important effects on cellular behavior. The expression and function of Cxs and the number and function of GJs are in dynamic change. Cxs have been characterized as tumor suppressors in the past, but recent studies have highlighted the critical roles of Cxs and GJs in cancer pathogenesis. The complex mechanism underlying Cx and GJ involvement in cancer development is a major obstacle to the evolution of therapy targeting Cxs. In this paper, we review the post-translational modifications of Cxs, the interactions of Cxs with several chaperone proteins, and the effects of Cxs and GJs on cancer. Video Abstract.

Robust identification of common genomic biomarkers from multiple gene expression profiles for the prognosis, diagnosis, and therapies of pancreatic cancer

Pancreatic cancer (PC) is one of the leading causes of cancer-related death globally. So, identification of potential molecular signatures is required for diagnosis, prognosis, and therapies of PC. In this study, we detected 71 common differentially expressed genes (cDEGs) between PC and control samples from four microarray gene-expression datasets (GSE15471, GSE16515, GSE71989, and GSE22780) by using robust statistical and machine learning approaches, since microarray gene-expression datasets are often contaminated by outliers due to several steps involved in the data generating processes. Then we detected 8 cDEGs (ADAM10, COL1A2, FN1, P4HB, ITGB1, ITGB5, ANXA2, and MYOF) as the PC-causing key genes (KGs) by the protein-protein interaction (PPI) network analysis. We validated the expression patterns of KGs between case and control samples by box plot analysis with the TCGA and GTEx databases. The proposed KGs showed high prognostic power with the random forest (RF) based prediction model and Kaplan-Meier-based survival probability curve. The KGs regulatory network analysis detected few transcriptional and post-transcriptional regulators for KGs. The cDEGs-set enrichment analysis revealed some crucial PC-causing molecular functions, biological processes, cellular components, and pathways that are associated with KGs. Finally, we suggested KGs-guided five repurposable drug molecules (Linsitinib, CX5461, Irinotecan, Timosaponin AIII, and Olaparib) and a new molecule (NVP-BHG712) against PC by molecular docking. The stability of the top three protein-ligand complexes was confirmed by molecular dynamic (MD) simulation studies. The cross-validation and some literature reviews also supported our findings. Therefore, the finding of this study might be useful resources to the researchers and medical doctors for diagnosis, prognosis and therapies of PC by the wet-lab validation.

The global change of gene expression pattern caused by PTEN mutation affects the prognosis of glioblastoma

Glioblastoma (GBM), an aggressive primary tumor, is common in humans, accounting for 12–15% of all intracranial tumors, and has median survival of fewer than 15 months. Since a growing body of evidence suggests that conventional drugs are ineffective against GBM, our goal is to find emerging therapies that play a role in its treatment. This research constructs a risk model to predict the prognosis of GBM patients. A set of genes associated with GBM was taken from a GBM gene data bank, and clinical information on patients with GBM was retrieved from the Cancer Genome Atlas (TCGA) data bank. One-way Cox and Kaplan–Meier analyses were performed to identify genes in relation to prognosis. Groups were classified into high and low expression level of PTEN expression. Prognosis-related genes were further identified, and multi-factor Cox regression analysis was used to build risk score equations for the prognostic model to construct a survival prognostic model. The area under the ROC curve suggested that the pattern had high accuracy. When combined with nomogram analysis, GJB2 was considered an independent predictor of GBM prognosis. This study provides a potential prognostic predictive biological marker for GBM patients and confirms that GJB2 is a key gene for GBM progression.

LINC01234 Accelerates the Progression of Breast Cancer via the miR-525-5p/Cold Shock Domain-Containing E1 Axis

Backgrounds. Long noncoding RNAs (lncRNAs) are strongly associated with the development of breast cancer (BC). As yet, the function of LINC01234 in BC remains unknown. Methods. Using biological information, the potential lncRNA, miRNA, and target gene were predicted. LINC01234 and miR-525-5p expression in BC tissues was detected using quantitative real-time reverse transcription polymerase chain reaction. Fluorescence in situ hybridization was used to determine the distribution of LINC01234. Cell proliferation was analyzed using CCK-8 assay, colony formation, terminal deoxynucleotidyl transferase dUTP nick end labeling staining, and apoptosis evaluated using flow cytometry. Western blotting was used to evaluate protein expression. Dual-luciferase® reporter, RNA pull-down, and RNA immunoprecipitation assays were performed to analyze the binding relationships among LINC01234, miR-525-5p, and cold shock domain-containing E1 (CSDE1). Results. We screened out LINC01234, found to be significantly increased in BC tissues, associated with a poor prognosis, and positively correlated with tumor size of BC. Knockdown of LINC01234 suppressed BC cell growth and facilitated apoptosis. Dual-luciferase reporter®, RNA pull-down, and RNA immunoprecipitation assays confirmed that LINC01234 and CSDE1 directly interacted with miR-525-5p. Upregulation of miR-525-5p and suppression of CSDE1 inhibited BC cell growth and induced cell apoptosis. Conclusion. Upregulation of LINC01234 contributes to the development of BC through the miR-525-5p/CSDE1 axis. LINC01234 may be one of the potential diagnostic and treatment targets for BC.

The prognostic value and biological significance of gap junction beta protein 2 (GJB2 or Cx26) in cervical cancer

Objective

To evaluate the prognostic value and explore the biological significance of gap junction protein beta 2 (GJB2 or Cx26) in cervical cancer (CC).

Methods

We first compared GJB2 expression between CC and normal tissues using public databases and immunohistochemistry (IHC). Based on The Cancer Genome Atlas data (TCGA cohort, n = 304) and tissue microarray samples (OBC cohort, n = 111), we explored the prognostic value of GJB2 for CC patients using bioinformatics analysis and IHC scoring. To explore the biological significance of GJB2, Gene set enrichment analysis (GSEA) and Gene Ontology (GO) were performed. The impact of GJB2 on the immune microenvironment was analyzed by CIBERSORTx and ESTIMATE algorithms. We finally investigated the relationship between GJB2 and drug sensitivity based on the Genomics of Drug Sensitivity in Cancer (GDSC).

Results

The expression of GJB2 was significantly increased in CC over normal tissues. Both the TCGA and OBC cohort found that patients with high GJB2 expression had shorter overall survival (OS) time, and high GJB2 expression was the independent risk factor for prognosis (TCGA: HR, 2.566; 95% CI, 1.066–6.180; p = 0.036; OBC: HR, 2.198; 95% CI, 1.019–4.741; p = 0.045). GJB2 was correlated with patient clinical factors such as tumor size and differentiation grade. The p53 signaling pathway and toll-like receptor pathway may be regulated by GJB2. The abundance of various immune cells was significantly different between the low and high GJB2 expression groups. The ImmuneScore was significantly increased in the high GJB2 expression group. In addition, the expression level of GJB2 was positively correlated with the natural log of the half-maximal inhibitory concentration (LN_IC50) value of cisplatin/paclitaxel (Spearman r = 0.238/0.153, p &lt; 0.001).

Conclusion

GJB2 can serve as a potential prognostic marker of poor survival and a therapeutic target in CC. Moreover, GJB2 may affect the immune microenvironment and is correlated with chemoresistance.

Deeper insights into long-term survival heterogeneity of pancreatic ductal adenocarcinoma (PDAC) patients using integrative individual- and group-level transcriptome network analyses

Pancreatic ductal adenocarcinoma (PDAC) is categorized as the leading cause of cancer mortality worldwide. However, its predictive markers for long-term survival are not well known. It is interesting to delineate individual-specific perturbed genes when comparing long-term (LT) and short-term (ST) PDAC survivors and integrate individual- and group-based transcriptome profiling. Using a discovery cohort of 19 PDAC patients from CHU-Liège (Belgium), we first performed differential gene expression analysis comparing LT to ST survivor. Second, we adopted systems biology approaches to obtain clinically relevant gene modules. Third, we created individual-specific perturbation profiles. Furthermore, we used Degree-Aware disease gene prioritizing (DADA) method to develop PDAC disease modules; Network-based Integration of Multi-omics Data (NetICS) to integrate group-based and individual-specific perturbed genes in relation to PDAC LT survival. We identified 173 differentially expressed genes (DEGs) in ST and LT survivors and five modules (including 38 DEGs) showing associations to clinical traits. Validation of DEGs in the molecular lab suggested a role of REG4 and TSPAN8 in PDAC survival. Via NetICS and DADA, we identified various known oncogenes such as CUL1 and TGFB1. Our proposed analytic workflow shows the advantages of combining clinical and omics data as well as individual- and group-level transcriptome profiling.

Quercitrin restrains the growth and invasion of lung adenocarcinoma cells by regulating gap junction protein beta 2

Lung adenocarcinoma (LUAD) is the most prevalent subtype of non-small cell lung cancer (NSCLC) with high lethality, and quercitrin exhibits anticancer characteristics. Here, we attempted to uncover the anticancer activity of quercitrin in LUAD. In this work, quercitrin prohibited the cell viability and clone-formation of LUAD cells in vitro. Meanwhile, quercitrin treatment reduced the aggressive phenotypes in LUAD cells. Further, Gap Junction Protein Beta 2 (GJB2) expression was aberrantly higher in LUAD when compared within control tissue. The higher expression of GJB2 is associated with an inferior overall survival for patients with LUAD. Finally, the reintroduction of GJB2 offset the inhibiting influence of quercitrin in LUAD cells. Altogether, these findings disclosed that quercitrin suppressed the growth and metastatic-related traits of LUAD cells partly via regulating GJB2 expression.

A deep convolutional neural network for segmentation of whole-slide pathology images identifies novel tumour cell-perivascular niche interactions that are associated with poor survival in glioblastoma

BACKGROUND

Glioblastoma is the most aggressive type of brain cancer with high-levels of intra- and inter-tumour heterogeneity that contribute to its rapid growth and invasion within the brain. However, a spatial characterisation of gene signatures and the cell types expressing these in different tumour locations is still lacking.

METHODS

We have used a deep convolutional neural network (DCNN) as a semantic segmentation model to segment seven different tumour regions including leading edge (LE), infiltrating tumour (IT), cellular tumour (CT), cellular tumour microvascular proliferation (CTmvp), cellular tumour pseudopalisading region around necrosis (CTpan), cellular tumour perinecrotic zones (CTpnz) and cellular tumour necrosis (CTne) in digitised glioblastoma histopathological slides from The Cancer Genome Atlas (TCGA). Correlation analysis between segmentation results from tumour images together with matched RNA expression data was performed to identify genetic signatures that are specific to different tumour regions.

RESULTS

We found that spatially resolved gene signatures were strongly correlated with survival in patients with defined genetic mutations. Further in silico cell ontology analysis along with single-cell RNA sequencing data from resected glioblastoma tissue samples showed that these tumour regions had different gene signatures, whose expression was driven by different cell types in the regional tumour microenvironment. Our results further pointed to a key role for interactions between microglia/pericytes/monocytes and tumour cells that occur in the IT and CTmvp regions, which may contribute to poor patient survival.

CONCLUSIONS

This work identified key histopathological features that correlate with patient survival and detected spatially associated genetic signatures that contribute to tumour-stroma interactions and which should be investigated as new targets in glioblastoma. The source codes and datasets used are available in GitHub: https://github.com/amin20/GBM_WSSM .

Genome-Scale Analysis Identified NID2, SPARC, and MFAP2 as Prognosis Markers of Overall Survival in Gastric Cancer

BACKGROUND Gastric cancer is the most common gastrointestinal tumor, and the rates of recurrence and metastasis are high. Research results on molecular biomarkers used for prognosis of gastric cancer remain inconclusive. This study aimed to explore the gene expression module of gastric cancer and to determine potential prognostic biomarkers. MATERIAL AND METHODS Three microarray datasets (GSE13911, GSE79973, and GSE29272) from Gene Expression Omnibus (GEO), including 206 pairs of gastric tumors and adjacent normal samples, were used for analysis of differentially expressed genes (DEGs). The 3 microarray datasets yielded 144 genes associated with the progression and prognosis of gastric cancer. After this, a risk score model was developed for result validation using an independent dataset from The Cancer Genome Atlas. RESULTS The validation of the independent dataset showed significantly increased NID2, SPARC, and MFAP2 expression in gastric tumor tissues, which were associated with poor outcomes in gastric cancer patients. Moreover, the high risk score obtained was associated with poor overall survival (HR: 1.787; 1.069-2.986; P=0.027). Subgroup analyses revealed that these significant prognostic values were detected in patients aged <65.0 years, tumors in the antrum/distal colon, grade 3 tumors, or TNM-M0 stages of cancer. CONCLUSIONS The findings of this study show that NID2, SPARC, and MFAP2 are upregulated in gastric tumor tissues and are significantly associated with poor overall survival. Therefore, the predictive values of the risk score model employed for the prognosis of gastric cancer could be improved by using these 3 upregulated DEGs.

miR-193a-3p Promotes the Invasion, Migration, and Mesenchymal Transition in Glioma through Regulating BTRC

BACKGROUND

The present study is aimed at exploring the specific expression of miR-193a-3p and the mechanism underlying miR-193a-3p-mediated mesenchymal transition (MT), invasion, and migration in glioma.

METHODS

The gene expression profile datasets of GSE39486 and GSE25676 were downloaded from the National Center for Biotechnology (NCBI). Data regarding the expression of miR-193a-3p and survival curves were derived from Chinese Glioma Genome Atlas (CGGA). Online websites including miRWalk, DIANA, and starbase were employed to predict the target genes for miR-193a-3p. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by the Omicsbean online software. Module analysis of the protein-protein interaction (PPI) networks was performed by the plug-in Molecular Complex Detection (MCODE), and the degrees of genes were calculated by CytoHubba plug-in of Cytoscape. Survival curves were based on the Gene Expression Profile Interaction Analysis (GEPIA). Transwell, wound healing, and Western blot experiments were performed to investigate the effects of miR-193a-3p and beta-transducin repeat containing E3 ubiquitin protein ligase (BTRC) on the invasion, migration, and MT of glioma.

RESULTS

miR-193a-3p was highly expressed in glioma tissues and significantly correlated with poor survival in patients with glioma. The target genes for miR-193a-3p were involved in many cancer-related signaling pathways. The PPI showed 11 genes with both high degrees and MCODE scores in the network. Survival analysis demonstrated that the expression of BTRC was significantly correlated with the prognosis of patients with glioma. The results from the transwell, wound healing, and Western blot analyses suggested that miR-193a-3p promoted the invasion, migration, and MT of glioma cells, which could be reversed by BTRC.

CONCLUSIONS

miR-193a-3p was upregulated in patients with glioma and could affect the invasion, migration, and MT of glioma by regulating BTRC.

Construction and validation of an immunity-related prognostic signature for breast cancer

Breast cancer is one of the most lethal malignancies among women, and understanding the effects of host immunity on disease progression offers the potential to improve immunotherapies against it. Here, we constructed an immunity-related gene (IRG)-based prognostic signature to stratify breast cancer patients and predict their survival. We identified differentially-expressed genes by analyzing the breast cancer transcriptome data from The Cancer Genome Atlas. Univariate Cox regression revealed 179 survival-correlated IRGs, 12 of which we used to construct an immunity-based prognostic signature that stratified breast cancer patients into high- and low-risk groups. The signature was an independent predictor for survival and was validated in an independent dataset. We also investigated the correlations between our prognostic signature and immune infiltrates and found that signature-derived risk scores correlated negatively with infiltration of B cells, CD4+ T cells, CD8+ T cells, neutrophils and dendritic cells. Our results show that the proposed prognostic signature reflects the tumor immune microenvironment, which makes it a potential indicator for survival that warrants further research to assess its clinical utility.

Identification of prognostic risk factors for pancreatic cancer using bioinformatics analysis

Background

Pancreatic cancer is one of the most common malignant cancers worldwide. Currently, the pathogenesis of pancreatic cancer remains unclear; thus, it is necessary to explore its precise molecular mechanisms.

Methods

To identify candidate genes involved in the tumorigenesis and proliferation of pancreatic cancer, the microarray datasets GSE32676, GSE15471 and GSE71989 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between Pancreatic ductal adenocarcinoma (PDAC) and nonmalignant samples were screened by GEO2R. The Database for Annotation Visualization and Integrated Discovery (DAVID) online tool was used to obtain a synthetic set of functional annotation information for the DEGs. A PPI network of the DEGs was established using the Search Tool for the Retrieval of Interacting Genes (STRING) database, and a combination of more than 0.4 was considered statistically significant for the PPI. Subsequently, we visualized the PPI network using Cytoscape. Functional module analysis was then performed using Molecular Complex Detection (MCODE). Genes with a degree ≥10 were chosen as hub genes, and pathways of the hub genes were visualized using ClueGO and CluePedia. Additionally, GenCLiP 2.0 was used to explore interactions of hub genes. The Literature Mining Gene Networks module was applied to explore the cocitation of hub genes. The Cytoscape plugin iRegulon was employed to analyze transcription factors regulating the hub genes. Furthermore, the expression levels of the 13 hub genes in pancreatic cancer tissues and normal samples were validated using the Gene Expression Profiling Interactive Analysis (GEPIA) platform. Moreover, overall survival and disease-free survival analyses according to the expression of hub genes were performed using Kaplan-Meier curve analysis in the cBioPortal online platform. The relationship between expression level and tumor grade was analyzed using the online database Oncomine. Lastly, the eight snap-frozen tumorous and adjacent noncancerous adjacent tissues of pancreatic cancer patients used to detect the CDK1 and CEP55 protein levels by western blot.

Conclusions

Altogether, the DEGs and hub genes identified in this work can help uncover the molecular mechanisms underlying the tumorigenesis of pancreatic cancer and provide potential targets for the diagnosis and treatment of this disease.

Identification of Candidate Genes Associated with Breast Cancer Prognosis

Breast cancer (BC) is the most malignant tumor in women. The molecular mechanisms underlying tumorigenesis still need to be further elucidated. It is necessary to investigate novel candidate genes involved in breast cancer progression and prognosis. In this study, we commit to explore candidate genes that associate with prognosis and therapy in BC by a comprehensive bioinformatic analysis. Four GEO datasets (GSE5764, GSE7904, GSE20711, and GSE29431) and the BC-related transcriptome data in TCGA database were downloaded and used to identify the differently expressed genes (DEGs). The function of DEGs was analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis. The protein-protein interaction (PPI) network of DEGs was constructed to identify hub genes. Prognostic candidate genes were identified through survival analysis. In addition, potential therapeutic targets were identified by constructed gene-drug interaction network through Comparative Toxicogenomics Database. A total of 547 DEGs (302 up and 245 down) were identified. Three core-subnetwork and 25 hub genes were identified in PPI network. Seven genes (namely COL12A1, QPRT, MRPL13, KRT14, KRT15, LAMB3, and MYBPC1) were identified as crucial prognostic candidate genes, which significantly associated with breast cancer overall survival. Furthermore, two representative candidate genes (COL12A1 and LAMB3) were optionally chosen for verification by reverse transcription and quantitative real-time polymerase chain reaction (RT-PCR). What's more, the gene-drugs interaction analysis indicates several antitumor drugs that could affect the expression of these prognostic markers, such as doxorubicin, cisplatin, and tamoxifen. These results identified seven crucial candidate genes that may serve as prognosis biomarkers and novel therapeutic targets of breast cancer, which may facilitate further understanding the molecular pathogenesis and providing potential therapeutic strategies for BC.

The A818-6 system as an in-vitro model for studying the role of the transportome in pancreatic cancer

Background

The human pancreatic cancer cell line A818–6 can be grown in vitro either as a highly malignant, undifferentiated monolayer (ML) or as three-dimensional (3D) single layer hollow spheres (HS) simulating a benign, highly differentiated, duct-like pancreatic epithelial structure. This characteristic allowing A818–6 cells to switch from one phenotype to another makes these cells a unique system to characterize the cellular and molecular modifications during differentiation on one hand and malignant transformation on the other hand. Ion channels and transport proteins (transportome) have been implicated in malignant transformation. Therefore, the current study aimed to analyse the transportome gene expression profile in the A818–6 cells growing as a monolayer or as hollow spheres.

Methods & Results

The study identified the differentially expressed transportome genes in both cellular states of A818–6 using Agilent and Nanostring arrays and some targets were validated via immunoblotting. Additionally, these results were compared to a tissue Affymetrix microarray analysis of pancreatic adenocarcinoma patients’ tissues. The overall transcriptional profile of the ML and HS cells confirmed the formerly described mesenchymal features of ML and epithelial nature of HS which was further verified via high expression of E-cadherin and low expression of vimentin found in HS in comparison to ML. Among the predicted features between HS and ML was the involvement of miRNA-9 in this switch. Importantly, the bioinformatics analysis also revealed substantial number (n = 126) of altered transportome genes. Interestingly, three genes upregulated in PDAC tissue samples (GJB2, GJB5 and SLC38A6) were found to be also upregulated in ML and 3 down-regulated transportome genes (KCNQ1, TRPV6 and SLC4A) were also reduced in ML.

Conclusion

This reversible HS/ML in vitro system might help in understanding the pathophysiological impact of the transportome in the dedifferentiation process in pancreatic carcinogenesis. Furthermore, the HS/ML model represents a novel system for studying the role of the transportome during the switch from a more benign, differentiated (HS) to a highly malignant, undifferentiated (ML) phenotype.

Uncovering Prognosis-Related Genes and Pathways by Multi-Omics Analysis in Lung Cancer

Lung cancer is one of the leading causes of death worldwide. Therefore, understanding the factors linked to patient survival is essential. Recently, multi-omics analysis has emerged, allowing for patient groups to be classified according to prognosis and at a more individual level, to support the use of precision medicine. Here, we combined RNA expression and miRNA expression with clinical information, to conduct a multi-omics analysis, using publicly available datasets (the cancer genome atlas (TCGA) focusing on lung adenocarcinoma (LUAD)). We were able to successfully subclass patients according to survival. The classifiers we developed, using inferred labels obtained from patient subtypes showed that a support vector machine (SVM), gave the best classification results, with an accuracy of 0.82 with the test dataset. Using these subtypes, we ranked genes based on RNA expression levels. The top 25 genes were investigated, to elucidate the mechanisms that underlie patient prognosis. Bioinformatics analyses showed that the expression levels of six out of 25 genes (ERO1B, DPY19L1, NCAM1, RET, MARCH1, and SLC7A8) were associated with LUAD patient survival (p < 0.05), and pathway analyses indicated that major cancer signaling was altered in the subtypes.

Dysregulation of pseudogene/lncRNA-hsa-miR-363-3p-SPOCK2 pathway fuels stage progression of ovarian cancer

Objective: Ovarian cancer is one of the most common and lethal cancer types in women. The molecular mechanism of ovarian cancer progression is still unclear.

Results: Here, we first reported that expression levels of three genes, GJB2, S100A2 and SPOCK2, were significantly higher in advanced stage than that in early stage of ovarian cancer, and upregulation of them indicated poor prognosis of patients with ovarian cancer. Subsequently, 8, 6 and 20 miRNAs were predicted to target GJB2, S100A2 and SPOCK2, respectively. Among these miRNA-mRNA pairs, hsa-miR-363-3p-SPOCK2 axis was the most potential in suppressing progression of ovarian cancer. Mechanistically, we found that hsa-miR-363-3p-SPOCK2 axis was involved in regulation of actin cytoskeleton. Moreover, 6 pseudogenes and 8 lncRNAs were identified to potentially inhibit hsa-miR-363-3p-SPOCK2 axis in ovarian cancer.

Conclusions: Collectively, we elucidate a regulatory role of pseudogene/lncRNA-hsa-miR-363-3p-SPOCK2 pathway in progression of ovarian cancer, which may provide effective therapeutic approaches and promising prognostic biomarkers for ovarian cancer.

Materials and methods: Differentially expressed genes (DEGs) in ovarian cancer were first screened using GSE12470, after which DEGs expression were validated using GEPIA. Kaplan-Meier analysis was employed to assess the prognostic values. Potential miRNAs were predicted by seven target prediction databases, and upstream lncRNAs and pseudogenes of hsa-miR-363-3p were forecasted through miRNet or starBase. UALCAN and starBase were used to obtain the co-expressed genes of SPOCK. Enrichment analysis for these co-expressed genes was performed by Enrichr.

Integrated transcriptomic analysis reveals hub genes involved in diagnosis and prognosis of pancreatic cancer

BACKGROUND

The hunt for the molecular markers with specificity and sensitivity has been a hot area for the tumor treatment. Due to the poor diagnosis and prognosis of pancreatic cancer (PC), the excision rate is often low, which makes it more urgent to find the ideal tumor markers.

METHODS

Robust Rank Aggreg (RRA) methods was firstly applied to identify the differentially expressed genes (DEGs) between PC tissues and normal tissues from GSE28735, GSE15471, GSE16515, and GSE101448. Among these DEGs, the highly correlated genes were clustered using WGCNA analysis. The co-expression networks and molecular complex detection (MCODE) Cytoscape app were then performed to find the sub-clusters and confirm 35 candidate genes. For these genes, least absolute shrinkage and selection operator (lasso) regression model was applied and validated to build a diagnostic risk score model. Cox proportional hazard regression analysis was used and validated to build a prognostic model.

RESULTS

Based on integrated transcriptomic analysis, we identified a 19 gene module (SYCN, PNLIPRP1, CAP2, GNMT, MAT1A, ABAT, GPT2, ADHFE1, PHGDH, PSAT1, ERP27, PDIA2, MT1H, COMP, COL5A2, FN1, COL1A2, FAP and POSTN) as a specific predictive signature for the diagnosis of PC. Based on the two consideration, accuracy and feasibility, we simplified the diagnostic risk model as a four-gene model: 0.3034*log2(MAT1A)-0.1526*log2(MT1H) + 0.4645*log2(FN1) -0.2244*log2(FAP), log2(gene count). Besides, a four-hub gene module was also identified as prognostic model = - 1.400*log2(CEL) + 1.321*log2(CPA1) + 0.454*log2(POSTN) + 1.011*log2(PM20D1), log2(gene count).

CONCLUSION

Integrated transcriptomic analysis identifies two four-hub gene modules as specific predictive signatures for the diagnosis and prognosis of PC, which may bring new sight for the clinical practice of PC.

Identification of potential hub genes associated with the pathogenesis and prognosis of pancreatic duct adenocarcinoma using bioinformatics meta-analysis of multi-platform datasets

Pancreatic duct adenocarcinoma (PDAC) is a highly malignant type of cancer with a low five-year survival rate. Gene alterations are crucial to the molecular pathogenesis of PDAC. Therefore, the present study analyzed gene expression profiles to reveal genes involved in the tumorigenesis of PDAC. A total of eight gene expression profiles (GSE15471, GSE16515, GSE41368, GSE62165, GSE62452, GSE71729, GSE71989 and GSE91035) and a PDAC dataset were acquired from the Gene Expression Omnibus and The Cancer Genome Atlas (TCGA) database, respectively. Differentially expressed genes (DEGs) were screened using functional annotation, Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and protein-protein interaction (PPI) network construction. A Cox proportional hazards model was then constructed and used to analyze the data. A total of 136 DEGs (67 up- and 69 downregulated genes) were identified between PDAC tissues and normal tissues. The ‘extracellular matrix-related’ genes were the most enriched in the GO term analysis. ‘Pancreatic secretion’, ‘phosphoinositide-3-kinase–protein kinase B/Akt (PI3K-Akt) signaling pathway’, ‘protein digestion and absorption’ and ‘ECM-receptor interaction’ were the most enriched categories in KEGG pathway analysis. Following PPI network construction, the 10 most significant genes [albumin, epidermal growth factor, matrix metalloproteinase (MMP) 9, epidermal growth factor receptor, fibronectin 1, MMP1, plasminogen activator inhibitor-1, tissue inhibitor of metalloproteinase 1, plasminogen activator urokinase (PLAU) and PLAU receptor) exhibiting a high degree of connectivity, were identified as the hub genes likely to be associated with the pathogenesis of PDAC. In addition, a prognostic predictive system for PDAC, composed of five genes (laminin subunit γ 2, laminin subunit β 3, serpin family B member 5, amphiregulin and secreted frizzled related protein 4), was constructed. This was validated in the GSE62452 dataset (using 66 PDAC samples with outcome data) and TCGA PDAC dataset (using 146 PDAC samples with outcome data). In conclusion, the present study revealed potential hub genes involved in PDAC progression, providing directive significance for individualized clinical decision-making and molecular-targeting therapy in patients with PDAC.

Identification of Key Genes and Pathways in Pancreatic Cancer Gene Expression Profile by Integrative Analysis

Background: Pancreatic cancer is one of the malignant tumors that threaten human health. Methods: The gene expression profiles of GSE15471, GSE19650, GSE32676 and GSE71989 were downloaded from the gene expression omnibus database including pancreatic cancer and normal samples. The differentially expressed genes between the two types of samples were identified with the Limma package using R language. The gene ontology functional and pathway enrichment analyses of differentially-expressed genes were performed by the DAVID software followed by the construction of a protein–protein interaction network. Hub gene identification was performed by the plug-in cytoHubba in cytoscape software, and the reliability and survival analysis of hub genes was carried out in The Cancer Genome Atlas gene expression data. Results: The 138 differentially expressed genes were significantly enriched in biological processes including cell migration, cell adhesion and several pathways, mainly associated with extracellular matrix-receptor interaction and focal adhesion pathway in pancreatic cancer. The top hub genes, namely thrombospondin 1, DNA topoisomerase II alpha, syndecan 1, maternal embryonic leucine zipper kinase and proto-oncogene receptor tyrosine kinase Met were identified from the protein–protein interaction network. The expression levels of hub genes were consistent with data obtained in The Cancer Genome Atlas. DNA topoisomerase II alpha, syndecan 1, maternal embryonic leucine zipper kinase and proto-oncogene receptor tyrosine kinase Met were significantly linked with poor survival in pancreatic adenocarcinoma. Conclusions: These hub genes may be used as potential targets for pancreatic cancer diagnosis and treatment.

Identification of Potential Crucial Genes and Key Pathways in Breast Cancer Using Bioinformatic Analysis

Background: The molecular mechanism of tumorigenesis remains to be fully understood in breast cancer. It is urgently required to identify genes that are associated with breast cancer development and prognosis and to elucidate the underlying molecular mechanisms. In the present study, we aimed to identify potential pathogenic and prognostic differentially expressed genes (DEGs) in breast adenocarcinoma through bioinformatic analysis of public datasets.

Methods: Four datasets (GSE21422, GSE29431, GSE42568, and GSE61304) from Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) dataset were used for the bioinformatic analysis. DEGs were identified using LIMMA Package of R. The GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses were conducted through FunRich. The protein-protein interaction (PPI) network of the DEGs was established through STRING (Search Tool for the Retrieval of Interacting Genes database) website, visualized by Cytoscape and further analyzed by Molecular Complex Detection (MCODE). UALCAN and Kaplan–Meier (KM) plotter were employed to analyze the expression levels and prognostic values of hub genes. The expression levels of the hub genes were also validated in clinical samples from breast cancer patients. In addition, the gene-drug interaction network was constructed using Comparative Toxicogenomics Database (CTD).

Results: In total, 203 up-regulated and 118 down-regulated DEGs were identified. Mitotic cell cycle and epithelial-to-mesenchymal transition pathway were the major enriched pathways for the up-regulated and down-regulated genes, respectively. The PPI network was constructed with 314 nodes and 1,810 interactions, and two significant modules are selected. The most significant enriched pathway in module 1 was the mitotic cell cycle. Moreover, six hub genes were selected and validated in clinical sample for further analysis owing to the high degree of connectivity, including CDK1, CCNA2, TOP2A, CCNB1, KIF11, and MELK, and they were all correlated to worse overall survival (OS) in breast cancer.

Conclusion: These results revealed that mitotic cell cycle and epithelial-to-mesenchymal transition pathway could be potential pathways accounting for the progression in breast cancer, and CDK1, CCNA2, TOP2A, CCNB1, KIF11, and MELK may be potential crucial genes. Further, it could be utilized as new biomarkers for prognosis and potential new targets for drug synthesis of breast cancer.

Transcriptome profiling revealed multiple genes and ECM-receptor interaction pathways that may be associated with breast cancer

Background

Exploration of the genes with abnormal expression during the development of breast cancer is essential to provide a deeper understanding of the mechanisms involved. Transcriptome sequencing and bioinformatics analysis of invasive ductal carcinoma and paracancerous tissues from the same patient were performed to identify the key genes and signaling pathways related to breast cancer development.

Methods

Samples of breast tumor tissue and paracancerous breast tissue were obtained from 6 patients. Sequencing used the Illumina HiSeq platform. All. Only perfectly matched clean reads were mapped to the reference genome database, further analyzed and annotated based on the reference genome information. Differentially expressed genes (DEGs) were identified using the DESeq R package (1.10.1) and DEGSeq R package (1.12.0). Using KOBAS software to execute the KEGG bioinformatics analyses, enriched signaling pathways of DEGs involved in the occurrence of breast cancer were determined. Subsequently, quantitative real time PCR was used to verify the accuracy of the expression profile of key DEGs from the RNA-seq result and to explore the expression patterns of novel cancer-related genes on 8 different clinical individuals.

Results

The transcriptomic sequencing results showed 937 DEGs, including 487 upregulated and 450 downregulated genes in the breast cancer specimens. Further quantitative gene expression analysis was performed and captured 252 DEGs (201 downregulated and 51 upregulated) that showed the same differential expression pattern in all libraries. Finally, 6 upregulated DEGs (CST2, DRP2, CLEC5A, SCD, KIAA1211, DTL) and 6 downregulated DEGs (STAC2, BTNL9, CA4, CD300LG, GPIHBP1 and PIGR), were confirmed in a quantitative real time PCR comparison of breast cancer and paracancerous breast tissues from 8 clinical specimens. KEGG analysis revealed various pathway changes, including 20 upregulated and 21 downregulated gene enrichment pathways. The extracellular matrix–receptor (ECM-receptor) interaction pathway was the most enriched pathway: all genes in this pathway were DEGs, including the THBS family, collagen and fibronectin. These DEGs and the ECM-receptor interaction pathway may perform important roles in breast cancer.

Conclusion

Several potential breast cancer-related genes and pathways were captured, including 7 novel upregulated genes and 76 novel downregulated genes that were not found in other studies. These genes are related to cell proliferation, movement and adhesion. They may be important for research into breast cancer mechanisms, particularly CST2 and CA4. A key signaling pathway, the ECM-receptor interaction signal pathway, was also identified as possibly involved in the development of breast cancer.

Electronic supplementary material

The online version of this article (10.1186/s11658-019-0162-0) contains supplementary material, which is available to authorized users.

Connexins in cancer: bridging the gap to the clinic

Gap junctions comprise arrays of intercellular channels formed by connexin proteins and provide for the direct communication between adjacent cells. This type of intercellular communication permits the coordination of cellular activities and plays key roles in the control of cell growth and differentiation and in the maintenance of tissue homoeostasis. After more than 50 years, deciphering the links among connexins, gap junctions and cancer, researchers are now beginning to translate this knowledge to the clinic. The emergence of new strategies for connexin targeting, combined with an improved understanding of the molecular bases underlying the dysregulation of connexins during cancer development, offers novel opportunities for clinical applications. However, different connexin isoforms have diverse channel-dependent and -independent functions that are tissue and stage specific. This can elicit both pro- and anti-tumorigenic effects that engender significant challenges in the path towards personalised medicine. Here, we review the current understanding of the role of connexins and gap junctions in cancer, with particular focus on the recent progress made in determining their prognostic and therapeutic potential.

Modulation of YrdC promotes hepatocellular carcinoma progression via MEK/ERK signaling pathway

Accumulating evidence suggested that YrdC involved in growth, telomere homeostasis, translation and the N6-threonylcarbamoylation (t6A) of tRNA was abnormally expressed in the progression of tumor. However, the role of YrdC in hepatocellular carcinoma remained elusive. Our study aimed to investigate the clinical significance and oncogenic phenotypes of YrdC in hepatocellular carcinoma, and to determine its related mechanism of this disease. With the usage of GEO datasets, we analyzed the expression of YrdC in hepatocellular carcinoma (HCC). Kaplan-Meier survival analysis was used to evaluate the prognostic significance of hepatocellular carcinoma patients in TCGA. Gain- and loss-of-function analyses in vitro of YrdC were also performed to evaluate its effects on oncogenic phenotypes and relevant signaling pathways. YrdC expression was not only dysregulated in hepatocellular carcinoma tissue but also related to the prognosis of patients with hepatocellular carcinoma. In addition, YrdC depletion suppressed the capability of proliferation, migration and invasion of huh7 cells, while there was opposite result for YrdC overexpression. Our data also unraveled that YrdC promoted the progression of HCC by activating MEK/ERK signaling pathways. Together, our findings indicated that YrdC was a potential prognosis marker for hepatocellular carcinoma, and therapeutic strategies targeting YrdC might hold promise in improving the treatment of hepatocellular carcinoma.

ID2 and GJB2 promote early-stage breast cancer progression by regulating cancer stemness

PURPOSE

Ductal carcinoma in situ (DCIS) is a non-invasive form of breast cancer which could progress to or recur as invasive breast cancer. The underlying molecular mechanism of DCIS progression is yet poorly understood, and appropriate biomarkers to distinguish benign form of DCIS from potentially invasive tumor are urgently needed.

METHODS

To identify the key regulators of DCIS progression, we performed gene-expression analysis of syngeneic breast cancer cell lines MCF10A, DCIS.com, and MCF10CA and cross-referenced the targets with patient cohort data.

RESULTS

We identified ID2 as a critical gene for DCIS initiation and found that ID2 promoted DCIS formation by enhancing cancer stemness of pre-malignant cells. ID2 also plays a pivotal role in survival of the aggressive cancer cells. In addition, we identified INHBA and GJB2 as key regulators for the transition of benign DCIS to aggressive phenotype. These two genes regulate migration, colonization, and stemness of invasive cancer cells. Upregulation of ID2 and GJB2 predicts poor prognosis after breast-conserving surgery. Finally, we found a natural compound Helichrysetin as ID2 inhibitor which suppresses DCIS formation in vitro and in vivo.

CONCLUSION

Our results indicate that ID2 is a key driver of DCIS formation and therefore is considered to be a potential target for prevention of DCIS, while INHBA and GJB2 play vital roles in progression of DCIS to IDC and they may serve as potential prognosis markers.

Epigenomics of Pancreatic Cancer: A Critical Role for Epigenome-Wide Studies

Several challenges present themselves when discussing current approaches to the prevention or treatment of pancreatic cancer. Up to 45% of the risk of pancreatic cancer is attributed to unknown causes, making effective prevention programs difficult to design. The most common type of pancreatic cancer, pancreatic ductal adenocarcinoma (PDAC), is generally diagnosed at a late stage, leading to a poor prognosis and 5-year survival estimate. PDAC tumors are heterogeneous, leading to many identified cell subtypes within one patient’s primary tumor. This explains why there is a high frequency of tumors that are resistant to standard treatments, leading to high relapse rates. This review will discuss how epigenetic technologies and epigenome-wide association studies have been used to address some of these challenges and the future promises these approaches hold.

Genome-scale analysis identifies SERPINE1 and SPARC as diagnostic and prognostic biomarkers in gastric cancer

Background

Gastric cancer (GC) is one of the most common types of malignancy and is associated with high morbidity and mortality rates around the world. With poor clinical outcomes, potential biomarkers for diagnosis and prognosis are important to investigate.

Objective

The aim of this study is to investigate the gene expression module of GC and to identify potential diagnostic and prognostic biomarkers.

Method

Microarray data (GSE13911, GSE29272, GSE54129, and GSE79973), including 293 stomach tumor tissues and 196 normal tissues, were analyzed to identify differentially expressed genes (DEGs). DEGs were identified in four profiles by intersecting four overlapping subsets, including 90 downregulated and 45 upregulated DEGs in common. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway analyses have been showed that extracellular matrix was the most enriched signal pathway. Furthermore, hub genes were analyzed by protein-protein interaction network and clinical outcomes were assessed by Kaplan-Meier survival analysis. Two independent datasets were used to validate the differential expression of two hub genes: Serpin Family E Member 1 (SERPINE1) and Secreted Protein Acidic and Cysteine Rich (SPARC).

Results

Validation of independent datasets indicated that SERPINE1 and SPARC expression were drastically increased in gastric tumor tissues and associated with poor outcomes in GC patients. The expression of SERPINE1 was related to race (Asian and White) (P< 0.05).

Conclusion

SERPINE1 and SPARC were significantly upregulated in gastric tissues and associated with poor outcomes. The investigations of SERPINE1 and SPARC may promote their predictive and prognostic value in GC.

Integrative analysis of gene expression profiles reveals specific signaling pathways associated with pancreatic duct adenocarcinoma

Background

Pancreatic duct adenocarcinoma (PDAC) remains a major health problem because conventional cancer treatments are relatively ineffective against it. Microarray studies have linked many genes to pancreatic cancer, but the available data have not been extensively mined for potential insights into PDAC. This study attempted to identify PDAC-associated genes and signaling pathways based on six microarray-based profiles of gene expression in pancreatic cancer deposited in the gene expression omnibus database.

Methods

Pathway network methods were used to analyze core pathways in six publicly available pancreatic cancer gene (GSE71989, GSE15471, GSE16515, GSE32676, GSE41368 and GSE28735) expression profiles. Genes potentially linked to PDAC were assessed for potential impact on survival time based on data in The Cancer Genome Atlas and International Cancer Genome Consortium databases, and the expression of one candidate gene (CKS2) and its association with survival was examined in 102 patients with PDAC from our hospital. Effects of CKS2 knockdown were explored in the PDAC cell lines BxPC-3 and CFPAC-1.

Results

The KEGG signaling pathway called “pathway in cancer” may play an important role in pancreatic cancer development and progression. Five genes (BIRC5, CKS2, ITGA3, ITGA6 and RALA) in this pathway were significantly associated with survival time in patients with PDAC. CKS2 was overexpressed in PDAC samples from our hospital, and higher CKS2 expression in these patients was associated with shorter survival time. CKS2 knockdown substantially inhibited PDAC cell proliferation in vitro.

Conclusions

Analysis integrating existing microarray datasets allowed identification of the “pathway in cancer” as an important signaling pathway in PDAC. This integrative approach may be powerful for identifying genes and pathways involved in cancer.

Electronic supplementary material

The online version of this article (10.1186/s40880-018-0289-9) contains supplementary material, which is available to authorized users.

Nucleolar and spindle-associated protein 1 is a tumor grade correlated prognosis marker for glioma patients

AIMS

Despite therapeutic advances in glioma management including surgery, radiation, and chemotherapy, the improvement of patient outcome is far from satisfactory. Nucleolar and spindle-associated protein 1 (NUSAP1) is an important functional protein during mitosis, and its abnormal expression is implicated in progression of different types of tumors. However, the role of NUSAP1 in gliomas remains unclear.

METHODS

NUSAP1 expression in gliomas with different grades was investigated based on GEO glioma datasets. Kaplan-Meier survival analysis was used to evaluate its prognostic significance. In vitro assays were also performed to evaluate effects of NUSAP1 on malignant phenotypes of glioma cells by silencing NUSAP1.

RESULTS

NUSAP1 expression was correlated not only with glioma grade but also with prognosis of glioma patients. NUSAP1 depletion suppressed proliferation of U251 cells by inducing cell cycle arrest at G2/M phase and apoptosis. NUSAP1 depletion rendered U251 cells impaired migratory ability as well.

CONCLUSION

NUSAP1 is a potential prognosis marker for glioma patients and therapeutic strategies targeting NUSAP1 might hold promise in improving glioma treatment.

Analysis of differential gene expression profile identifies novel biomarkers for breast cancer

Breast cancer is the most prevalent cancer diagnosis in women. We aimed to identify biomarkers for breast cancer prognosis. mRNA expression profiling was performed using Gene Chip Human Transcriptome Array 2.0. Microarray analysis and series test of cluster (STC) analysis were used to screen the differential expressed mRNAs and the expression trend of genes. Immumohistochemical staining with 100 clinical specimens was used to validate two differentially expressed genes, ITGA11 and Jab1. In the present study, significantly enriched Gene Ontology (GO) terms and pathways were identified. 26 model profiles were used to summarize the expression pattern of differentially expressed genes. Results of immunohistochemistry were consistent with those of the microarray, in that ITGA11 and Jab1 were differentially expressed with the same trend. Survival analyses using the Kaplan–Meier method demonstrated that breast cancer patients with high levels of either ITGA11 or Jab1 had a significant association with worse prognosis. Our study identified ITGA11 and Jab1 as novel biomarkers for breast cancer.

A five-gene based risk score with high prognostic value in colorectal cancer

Colorectal cancer (CRC) is one of the most frequently occurring malignancies worldwide. The outcomes of patients with similar clinical symptoms or at similar pathological stages remain unpredictable. This inherent clinical diversity is most likely due to the genetic heterogeneity. The present study aimed to create a predicting tool to evaluate patient survival based on genetic profile. Firstly, three Gene Expression Omnibus (GEO) datasets (GSE9348, GSE44076 and GSE44861) were utilized to identify and validate differentially expressed genes (DEGs) in CRC. The GSE14333 dataset containing survival information was then introduced in order to screen and verify prognosis-associated genes. Of the 66 DEGs, the present study screened out 46 biomarkers closely associated to patient overall survival. By Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis, it was demonstrated that these genes participated in multiple biological processes which were highly associated with cancer proliferation, drug-resistance and metastasis, thus further affecting patient survival. The five most important genes, MET proto-oncogene, receptor tyrosine kinase, carboxypeptidase M, serine hydroxymethyltransferase 2, guanylate cyclase activator 2B and sodium voltage-gated channel a subunit 9 were selected by a random survival forests algorithm, and were further made up to a linear risk score formula by multivariable cox regression. Finally, the present study tested and verified this risk score within three independent GEO datasets (GSE14333, GSE17536 and GSE29621), and observed that patients with a high risk score had a lower overall survival (P<0.05). Furthermore, this risk score was the most significant compared with other predicting factors including age and American Joint Committee on Cancer stage, in the model, and was able to predict patient survival independently and directly. The findings suggest that this survival associated DEGs-based risk score is a powerful and accurate prognostic tool and is promisingly implemented in a clinical setting.

Biomarkers identified for prostate cancer patients through genome-scale screening

Prostate cancer is a threat to men and usually occurs in aged males. Though prostate specific antigen level and Gleason score are utilized for evaluation of the prostate cancer in clinic, the biomarkers for this malignancy have not been widely recognized. Furthermore, the outcome varies across individuals receiving comparable treatment regimens and the underlying mechanism is still unclear. We supposed that genetic feature may be responsible for, at least in part, this process and conducted a two-cohort study to compare the genetic difference in tumorous and normal tissues of prostate cancer patients. The Gene Expression Omnibus dataset were used and a total of 41 genes were found significantly differently expressed in tumor tissues as compared with normal prostate tissues. Four genes (SPOCK3, SPON1, PTN and TGFB3) were selected for further evaluation after Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis and clinical association analysis. MIR1908 was also found decreased expression level in prostate cancer whose target genes were found expressing in both prostate tumor and normal tissues. These results indicated that these potential biomarkers deserve attention in prostate cancer patients and the underlying mechanism should be further investigated.

Mind the Gaps in Tumor Immunity: Impact of Connexin-Mediated Intercellular Connections

Gap junctions (GJs)-mediated intercellular communications (GJICs) are connexin (Cx)-formed plasma membrane channels that allow for the passage of small molecules between adjacent cells, and are involved in several physiopathological processes, including immune responses against cancer. In general, tumor cells are poorly coupled through GJs, mainly due to low Cx expression or reduced channel activity, suggesting that Cxs may have tumor suppressor roles. However, more recent data indicate that Cxs and/or GJICs may also in some cases promote tumor progression. This dual role of Cx channels in tumor outcome may be due, at least partially, to the fact that GJs not only interconnect cells from the same type, such as cancer cells, but also promote the intercellular communication of tumor cells with different types of cells from their microenvironment, and such diverse intercellular interactions have distinctive impact on tumor development. For example, whereas GJ-mediated interactions among tumor cells and microglia have been implicated in promotion of tumor growth, tumor cells delivery to dendritic cells of antigenic peptides through GJs have been associated with enhanced immune-mediated tumor elimination. In this review, we provide an updated overview on the role of GJICs in tumor immunity, focusing on the pro-tumor and antitumor effect of GJs occurring among tumor and immune cells. Accumulated data suggest that GJICs may act as tumor suppressors or enhancers depending on whether tumor cells interact predominantly with antitumor immune cells or with stromal cells. The complex modulation of immune-tumor cell GJICs should be taken into consideration in order to potentiate current cancer immunotherapies.

LncRNA plasmacytoma variant translocation 1 is an oncogene in bladder urothelial carcinoma

Bladder cancer (BC) is the most lethal malignant cancer of the genitourinary system, and bladder urothelial carcinoma (BUC) is the most common type of BC. The long non-coding RNA (lncRNA) plasmacytoma variant translocation 1 (PVT1) is overexpressed in several malignant tumors, including BC. Using a lncRNA array and quantitative real-time PCR, we detected greater expression of PVT1 in BUC tissues and cell lines resistant to doxorubicin (DOX) and cisplatin (DDP) than in DOX- and DDP-sensitive cells. PVT1 knockdown reduced proliferation and invasion by a DOX- and DDP-resistant T24/DR BUC cells, arrested cells in G1 phase, and increased apoptosis. PVT1 knockdown also sensitized T24/DR cells to DOX and DDP, and suppressed expression of multidrug resistance 1 (MDR1) and multidrug resistance associated protein 1 (MRP1). Wnt/β-catenin pathway activation in T24/DR cells reversed the effects of PVT1 knockdown on metastasis-associated behavior and chemoresistance. In sum, lncRNA PVT1 is overexpressed in multidrug resistant BUC tissues and cell lines, and PVT1 knockdown reduces BUC cell proliferation, invasiveness, and chemoresistance by modulating Wnt/β-catenin signaling. These results provide new insight into BUC chemoresistance mechanisms and suggest potential therapeutic targets for anti-BUC therapeutics.