Prognosis-related microRNAs in esophageal cancer

Construction of long non-coding RNA- and microRNA-mediated competing endogenous RNA networks in alcohol-related esophageal cancer

The current study aimed to explore the lncRNA–miRNA–mRNA networks associated with alcohol-related esophageal cancer (EC). RNA-sequencing and clinical data were downloaded from The Cancer Genome Atlas and the differentially expressed genes (DEGs), long non-coding RNAs (lncRNAs, DELs), and miRNAs (DEMs) in patients with alcohol-related and non-alcohol-related EC were identified. Prognostic RNAs were identified by performing Kaplan–Meier survival analyses. Weighted gene co-expression network analysis was employed to build the gene modules. The lncRNA–miRNA–mRNA competing endogenous RNA (ceRNA) networks were constructed based on our in silico analyses using data from miRcode, starBase, and miRTarBase databases. Functional enrichment analysis was performed for the genes in the identified ceRNA networks. A total of 906 DEGs, 40 DELs, and 52 DEMs were identified. There were eight lncRNAs and miRNAs each, including ST7-AS2 and miR-1269, which were significantly associated with the survival rate of patients with EC. Of the seven gene modules, the blue and turquoise modules were closely related to disease progression; the genes in this module were selected to construct the ceRNA networks. SNHG12–miR-1–ST6GAL1, SNHG3–miR-1–ST6GAL1, SPAG5-AS1–miR-133a–ST6GAL1, and SNHG12–hsa-miR-33a–ST6GA interactions, associated with the N-glycan biosynthesis pathway, may have key roles in alcohol-related EC. Thus, the identified biomarkers provide a novel insight into the molecular mechanism of alcohol-related EC.

Identification of prognostic miRNA biomarkers for esophageal cancer based on The Cancer Genome Atlas and Gene Expression Omnibus

MicroRNAs (miRNAs) in tumor and tumor-adjacent tissues can be effective diagnostic and prognostic markers to monitor tumor occurrence and progression. Despite improvements in the diagnosis and treatment of esophageal cancer (EC), the survival rate is <25%; consequently, more effective EC-specific prognostic biomarkers are urgently needed to design effective treatment regimens. In this study, we focused on identifying independent prognostic miRNA signatures in tumor and tumor-adjacent tissues in EC.We screened candidate miRNAs using a genome-wide miRNA transcriptome dataset from The Cancer Genome Atlas (TCGA) database that included 82 patients with esophageal adenocarcinoma (EADC) and 83 patients with esophageal squamous cell carcinoma (ESCC). We validated potential prognostic miRNA markers using a microarray profiling dataset that included information of 32 patients with EADC and 44 patients with ESCC from the Gene Expression Omnibus database. TCGA dataset was additionally used to identify differentially expressed mRNAs (DEMs) between the tumor and tumor-adjacent tissues. Univariate and multivariate Cox analyses were performed to detect the relationship between miRNAs and the overall survival of patients with EC. Kaplan-Meier method was applied to assess the survival differences between groups with differential miRNA expression. Lastly, functional enrichment analysis was conducted using miRWalk 2.0 online database for annotation.Although there was a considerable difference between the DEMs of EADC and ESCC, 73 DEMs were differentially expressed in both EADC and ESCC samples in TCGA dataset. Cox regression and Kaplan-Meier survival analyses showed that a higher expression of hsa-miR-186-5p and hsa-let-7d-5p was independently associated with a poor prognosis of EADC and ESCC, respectively. Furthermore, gene functional enrichment analysis revealed that the target genes of hsa-miR-186-5p and hsa-let-7d-5p participated in various cancer-related pathways, including the MAPK signaling pathway, proteoglycans in cancer, and AGE-RAGE signaling pathway.Our results revealed that hsa-miR-186-5p and hsa-let-7d-5p could be used as independent prognostic biomarkers for EADC and ESCC, respectively.

A Novel Three-miRNA Signature Identified Using Bioinformatics Predicts Survival in Esophageal Carcinoma

Objective

We identified differentially expressed microRNAs (DEMs) between esophageal carcinoma (ESCA) tissues and normal esophageal tissues. We then constructed a novel three-miRNA signature to predict the prognosis of ESCA patients using bioinformatics analysis. Materials and Methods. We combined two microarray profiling datasets from the Gene Expression Omnibus (GEO) database and RNA-seq datasets from the Cancer Genome Atlas (TCGA) database to analyze DEMs in ESCA. The clinical data from 168 ESCA patients were selected from the TCGA database to assess the prognostic role of the DEMs. The TargetScan, miRDB, miRWalk, and DIANA websites were used to predict the miRNA target genes. Functional enrichment analysis was conducted using the Database for Annotation, Visualization, and Integrated Discovery (David), and protein-protein interaction (PPI) networks were obtained using the Search Tool for the Retrieval of Interacting Genes database (STRING).

Results

With cut-off criteria of P < 0.05 and |log2FC| > 1.0, 33 overlapping DEMs, including 27 upregulated and 6 downregulated miRNAs, were identified from GEO microarray datasets and TCGA RNA-seq count datasets. The Kaplan–Meier survival analysis indicated that a three-miRNA signature (miR-1301-3p, miR-431-5p, and miR-769-5p) was significantly associated with the overall survival of ESCA patients. The results of univariate and multivariate Cox regression analysis showed that the three-miRNA signature was a potential prognostic factor in ESCA. Furthermore, the gene functional enrichment analysis revealed that the target genes of the three miRNAs participate in various cancer-related pathways, including viral carcinogenesis, forkhead box O (FoxO), vascular endothelial growth factor (VEGF), human epidermal growth factor receptor 2 (ErbB2), and mammalian target of rapamycin (mTOR) signaling pathways. In the PPI network, three target genes (MAPK1, RB1, and CLTC) with a high degree of connectivity were selected as hub genes.

Conclusions

Our results revealed that a three-miRNA signature (miR-1301-3p, miR-431-5p, and miR-769-5p) is a potential novel prognostic biomarker for ESCA.

Upregulation of miR-17-92 cluster is associated with progression and lymph node metastasis in oesophageal adenocarcinoma

The occurrence of lymph node metastasis (LNM) and depth of tumour infiltration are significant prognostic factors in oesophageal adenocarcinoma (OAC), however no reliable prognostic biomarkers have been established so far. Aim of this study was to characterize microRNAs (miRs) of OAC patients, who primarily underwent oesophagectomy, in order to identify specific alterations during tumour progression and LNM. MicroRNA array-based quantification analysis of 754 miRs, including tumour specimens of 12 patients with pT2 OAC from three different centres (detection group), was performed. We identified miR-17, miR-19a/b, miR-20a, and miR-106a, showing the best predictive power for LNM. These miRs were validated by quantitative real time-PCR (qRT-PCR) in 43 patients with different tumour stages (pT1: n = 21; pT2: n = 12 and pT3: n = 10) (training group) (p < 0.05), demonstrating that increasing levels of identified miRs were associated with advanced depth of tumour infiltration. These findings were verified in another independent group of 46 pT2 OAC patients (validation group). Quantitative RT-PCR analysis of the miR-panel confirmed these results except for miR-19a (p < 0.05 each). Logistic regression analysis identified miR-17 and miR-20a (p = 0.025 and p = 0.022, respectively) to be independent variables for prediction of LNM. The mathematical prediction model was used in the validation group, and the estimated prognosis was compared to the actual postsurgical follow-up. This comprehensive data demonstrated the importance of miR-17-92 cluster and miR-106a for progression as well as LNM in OAC indicating that those might be feasible prognostic biomarkers.

New Strategies in Esophageal Carcinoma: Translational Insights from Signaling Pathways and Immune Checkpoints

Esophageal cancer remains a highly lethal malignancy in which relatively modest therapeutic advances have been made over the past several decades. Cytotoxic therapy remains the mainstay of treatment for both advanced esophageal adenocarcinoma and squamous cell carcinoma (SCC), with incremental benefit conferred by antibodies targeting HER2 and VEGFR in selected patients. However, intrinsic or acquired resistance in this disease almost invariably occurs and remains a major challenge. Moreover, although large-scale exome and whole-genome sequencing efforts have identified a variety of somatic mutations and copy number variations, particularly amplifications, in esophageal cancer, the ability to translate these findings successfully into actionable therapeutic approaches has been elusive. More recently, immunotherapeutic strategies, most notably immune checkpoint inhibitors, have demonstrated benefit to a subset of patients with both esophageal adenocarcinoma and SCC and represent an area of active clinical investigation. In this article, we discuss some of the insights derived from past trials of esophageal cancer, highlight ongoing research efforts in this arena, and emphasize the need to refine our approach to treating patients based on distinct anatomic, histologic, and molecular features. Clin Cancer Res; 22(17); 4283-90. ©2016 AACR.

Robust Selection Algorithm (RSA) for Multi-Omic Biomarker Discovery; Integration with Functional Network Analysis to Identify miRNA Regulated Pathways in Multiple Cancers

MicroRNAs (miRNAs) play a crucial role in the maintenance of cellular homeostasis by regulating the expression of their target genes. As such, the dysregulation of miRNA expression has been frequently linked to cancer. With rapidly accumulating molecular data linked to patient outcome, the need for identification of robust multi-omic molecular markers is critical in order to provide clinical impact. While previous bioinformatic tools have been developed to identify potential biomarkers in cancer, these methods do not allow for rapid classification of oncogenes versus tumor suppressors taking into account robust differential expression, cutoffs, p-values and non-normality of the data. Here, we propose a methodology, Robust Selection Algorithm (RSA) that addresses these important problems in big data omics analysis. The robustness of the survival analysis is ensured by identification of optimal cutoff values of omics expression, strengthened by p-value computed through intensive random resampling taking into account any non-normality in the data and integration into multi-omic functional networks. Here we have analyzed pan-cancer miRNA patient data to identify functional pathways involved in cancer progression that are associated with selected miRNA identified by RSA. Our approach demonstrates the way in which existing survival analysis techniques can be integrated with a functional network analysis framework to efficiently identify promising biomarkers and novel therapeutic candidates across diseases.

Prognostic markers in esophageal cancer: from basic research to clinical use

Esophageal cancer (EC) remains a leading cause of cancer-related death in Asian countries. Due to the biology of EC, including aggressive local invasion, early metastasis and drug resistance, EC has a low survival rate. Therefore, molecular markers for prognosis judgment are urgently required so as to identify subgroups of patients that will benefit from more aggressive therapeutic interventions. So far, many genes and miRNAs, such as VEGF, cyclin D1, and miR-21, have been shown to be valuable when predicting the prognosis of EC. Some circulating molecules, including miR-200c, miR-1246, miR-31, have been identified as the independent risk factors for poor survival. However, the function and mechanism of these molecules in EC remains unclear. More clinical studies should be performed to promote the clinical use of prognosis-related markers in the management of EC.

MicroRNA signature characterizes primary tumors that metastasize in an esophageal adenocarcinoma rat model

Objective

To establish a miRNA signature for metastasis in an animal model of esophageal adenocarcinoma (EAC).

Background

The incidence of esophageal adenocarcinoma (EAC) has dramatically increased and esophageal cancer is now the sixth leading cause of cancer deaths worldwide. Mortality rates remain high among patients with advanced stage disease and esophagectomy is associated with high complication rates. Hence, early identification of potentially metastatic disease would better guide treatment strategies.

Methods

The modified Levrat’s surgery was performed to induce EAC in Sprague-Dawley rats. Primary EAC and distant metastatic sites were confirmed via histology and immunofluorescence. miRNA profiling was performed on primary tumors with or without metastasis. A unique subset of miRNAs expressed in primary tumors and metastases was identified with Ingenuity Pathway Analysis (IPA) along with upstream and downstream targets. miRNA-linked gene expression analysis was performed on a secondary cohort of metastasis positive (n=5) and metastasis negative (n=28) primary tumors.

Results

The epithelial origin of distant metastasis was established by IF using villin (VIL1) and mucin 5AC (MUC5AC) antibodies. miRNome analysis identified four down-regulated miRNAs in metastasis positive primary tumors compared to metastasis negative tumors: miR-92a-3p (p=0.0001), miR-141-3p (p=0.0022), miR-451-1a (p=0.0181) and miR133a-3p (p=0.0304). Six target genes identified in the top scoring networks by IPA were validated as significantly, differentially expressed in metastasis positive primary tumors: Ago2, Akt1, Kras, Bcl2L11, CDKN1B and Zeb2.

Conclusion

In vivo metastasis was confirmed in the modified Levrat’s model. Analysis of the primary tumor identified a distinctive miRNA signature for primary tumors that metastasized.