Genome-wide screening and co-expression network analysis identify recurrence-specific biomarkers of esophageal squamous cell carcinoma

SNMRS: An advanced measure for Co-expression network analysis

The challenge of identifying modules in a gene interaction network is important for a better understanding of the overall network architecture. In this work, we develop a novel similarity measure called Scaling-and-Shifting Normalized Mean Residue Similarity (SNMRS), based on the existing NMRS technique [1]. SNMRS yields correlation values in the range of 0 to +1 corresponding to negative and positive dependency. To study the performance of our measure, internal validation of extracted clusters resulting from different methods is carried out. Based on the performance, we choose hierarchical clustering and apply the same using the corresponding dissimilarity (distance) values of SNMRS scores, and utilize a dynamic tree cut method for extracting dense modules. The modules are validated using a literature search, KEGG pathway analysis, and gene-ontology analyses on the genes that make up the modules. Moreover, our measure can handle absolute, shifting, scaling, and shifting-and-scaling correlations and provides better performance than several other measures in terms of cluster-validity indices. Also, SNMRS based module detection method results in interesting biologically relevant patterns from gene microarray and RNA-seq dataset. A set of crucial genes having high relevance with the ESCC are also identified.

Cross-talk between next generation sequencing methodologies to identify genomic signatures of esophageal cancer

The asymptomatic behaviour of esophageal cancerous cells at early stages develops advanced clinical presentation of the disease, resulting in poor prognosis and curbed intervention of therapeutic modalities. The endeavours to detect diagnostic and prognostic markers have been proven futile at the clinical platform. While several biomarkers have been investigated, including CYFRA 21-1, carcinoembryonic antigen and squamous cell carcinoma antigen, their sensitivity has not proved consistently satisfactory across the various stages of esophageal cancer. Hence, there is an impending requirement of biomarkers for early diagnosis and better prognosis. In the recent past, next generation sequencing (NGS) tool has emerged as an important tool to highlight the hallmarks of esophageal cancer (EC). This review summarizes the changes/mutations occurred in tumor cells during carcinogenesis and addresses the contribution of NGS techniques, viz. whole genome sequencing (WGS), RNA-Sequencing and Exome sequencing (ES), in EC. Additionally, this review highlights the connection between the findings from these techniques. An effort has been made to emphasize the genes affected and involved signaling pathway in EC. Further, investigations of these mutated genes would not only shed light on the relevant genes to be studied but also help in the better management and cure through personalized therapy.

A five-gene signature to predict the overall survival time of patients with esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the six most commonly diagnosed tumor types in the Chinese population. Gene expression profiles help to predict the prognosis of patients with ESCC. Disease recurrence as the survival endpoint has been analyzed in the majority of previous studies; therefore, the aim of the present study was to construct a robust gene signature in order to determine the overall survival (OS) of patients with ESCC. The gene expression and clinical data of patients with ESCC were downloaded from The Cancer Genome Atlas (TCGA) database. Of the selected data (172 samples from surviving patients), 72 samples were randomly selected as modeling data, and verification was conducted using the entire dataset. Data from the Gene Expression Omnibus was analyzed simultaneously, and a venn diagram was constructed to determine the intersection between these two sets of results; a total of 97 genes were found to be associated with OS. Kyoto Encyclopedia of Genes and Genomes analysis demonstrated that these genes were primarily associated with specific pathways (Homo sapiens), including DNA replication, protein processing in endoplasmic reticulum and influenza A. A five-gene signature was identified with a robust likelihood-based survival modeling approach. Using regression coefficient modeling, a prognostic model consisting of the C-X-C motif chemokine ligand 8, DNA damage inducible transcript 3, RAB27A, member RAS oncogene family, replication factor C subunit 2 and elongation factor for RNA polymerase II 2 genes was constructed and validated. Based on these results, patients were subdivided into high and low-risk groups. Compared with the high-risk group, the OS time of patients in the low-risk group was significantly increased. Furthermore, it was determined that the five genes were all differentially expressed in ESCC tissues compared with normal tissues, indicating the potential role of these genes in ESCC initiation and progression. In another independent cohort, this five-gene signature was further confirmed and was considered as an independent prognostic biomarker for OS prediction in patients with ESCC. In conclusion, the OS of patients with ESCC may be predicted using this five-gene signature, which may be useful in identifying patients with high-risk ESCC.

RNA‑sequencing analysis of aberrantly expressed long non‑coding RNAs and mRNAs in a mouse model of ventilator‑induced lung injury

Long non-coding RNAs (lncRNAs) are closely associated with the regulation of various biological processes and are involved in the pathogenesis of numerous diseases. However, to the best of our knowledge, the role of lncRNAs in ventilator‑induced lung injury (VILI) has yet to be evaluated. In the present study, high‑throughput sequencing was applied to investigate differentially expressed lncRNAs and mRNAs (fold change >2; false discovery rate <0.05). Bioinformatics analysis was employed to predict the functions of differentially expressed lncRNAs. A total of 104 lncRNAs (74 upregulated and 30 downregulated) and 809 mRNAs (521 upregulated and 288 downregulated) were differentially expressed in lung tissues from the VILI group. Gene ontology analysis demonstrated that the differentially expressed lncRNAs and mRNAs were mainly associated with biological functions, including apoptosis, angiogenesis, neutrophil chemotaxis and skeletal muscle cell differentiation. The top four enriched pathways were the tumor necrosis factor (TNF) signaling pathway, P53 signaling pathway, neuroactive ligand‑receptor interaction and the forkhead box O signaling pathway. Several lncRNAs were predicted to serve a vital role in VILI. Subsequently, three lncRNAs [mitogen‑activated protein kinase kinase 3, opposite strand (Map2k3os), dynamin 3, opposite strand and abhydrolase domain containing 11, opposite strand] and three mRNAs (growth arrest and DNA damage‑inducible α, claudin 4 and thromboxane A2 receptor) were measured by reverse transcription‑quantitative polymerase chain reaction, in order to confirm the veracity of RNA‑sequencing analysis. In addition, Map2k3os small interfering RNA transfection inhibited the expression of stretch‑induced cytokines [TNF‑α, interleukin (IL)‑1β and IL‑6] in MLE12 cells. In conclusion, the results of the present study provided a profile of differentially expressed lncRNAs in VILI. Several important lncRNAs may be involved in the pathological process of VILI, which may be useful to guide further investigation into the pathogenesis for this disease.

Prognostic gene expression profiling in esophageal cancer: a systematic review

Background

Individual variability in prognosis of esophageal cancer highlights the need for advances in personalized therapy. This systematic review aimed at elucidating the prognostic role of gene expression profiles and at identifying gene signatures to predict clinical outcome.

Methods

A systematic search of the Medline, Embase and the Cochrane library databases (2000-2015) was performed. Articles associating gene expression profiles in patients with esophageal adenocarcinoma or squamous cell carcinoma to survival, response to chemo(radio)therapy and/or lymph node metastasis were identified. Differentially expressed genes and gene signatures were extracted from each study and combined to construct a list of prognostic genes per outcome and histological tumor type.

Results

This review includes a total of 22 studies. Gene expression profiles were related to survival in 9 studies, to response to chemo(radio)therapy in 7 studies, and to lymph node metastasis in 9 studies. The studies proposed many differentially expressed genes. However, the findings were heterogeneous and only 12 (ALDH1A3, ATR, BIN1, CSPG2, DOK1, IFIT1, IFIT3, MAL, PCP4, PHB, SPP1) of the 1.112 reported genes were identified in more than 1 study. Overall, 16 studies reported a prognostic gene signature, which was externally validated in 10 studies.

Conclusion

This systematic review shows heterogeneous findings in associating gene expression with clinical outcome in esophageal cancer. Larger validated studies employing RNA next-generation sequencing are required to establish gene expression profiles to predict clinical outcome and to select optimal personalized therapy.

Landscape of expression profiles in esophageal carcinoma by The Cancer Genome Atlas data

In this study, we explored the gene and microRNA (miRNA) expressions profile of esophageal carcinoma. The expression data for messenger RNAs and miRNAs in normal and cancerous esophageal tissues were obtained from the Cancer Genome Atlas database and then the differentially expressed genes and miRNAs were identified. As a result, we identified 2962 genes and 45 miRNAs differentially expressed in esophageal carcinoma compared with normal esophageal tissues. Subsequently, the altered gene functions and signaling pathways were investigated using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, and these differentially expressed genes were significantly enriched in the cell cycle, cell migration, mitogen-activated protein kinase (MAPK) and toll-like receptor signaling pathway, and so on. Then the regulatory relationships between the differentially expressed miRNAs and genes were examined with Targetscan and Miranda, and the potential target sites of transcription factors (TFs) in the promoter regions of these miRNAs and genes were identified using the TRANSFAC database. Finally the TF-miRNA-gene network in esophageal cancer was established, summarizing the regulatory links among the TFs, differentially expressed miRNAs and differentially expressed genes. Factors such as core promoter-binding protein (CPBP), nuclear factor of activated T-cells 1 (NFAT-1), miR-30c-5p, were located in the central hub of this network, highlighting their vital roles in esophageal tumorigenesis. These findings may extend our understanding of the molecular mechanisms underlying esophageal carcinoma and promote new perspectives for prevention, diagnosis and treatment.

Ruan X, Chen Y. Genome-Wide Transcriptome Analysis of CD36 Overexpression in HepG2.2.15 Cells to Explore Its Regulatory Role in Metabolism and the Hepatitis B Virus Life Cycle

Hepatitis B virus (HBV) is a hepatocyte-specific DNA virus whose gene expression and replication are closely associated with hepatic metabolic processes. Thus, a potential anti-viral strategy is to target the host metabolic factors necessary for HBV gene expression and replication. Recent studies revealed that fatty acid translocase CD36 is involved in the replication, assembly, storage, and secretion of certain viruses, such as hepatitis C virus (HCV) and human immunodeficiency virus (HIV). However, the relationship between CD36 and the HBV life cycle remains unclear. Here, we showed, for the first time, that increased CD36 expression enhances HBV replication in HepG2.2.15 cells. To understand the underlying molecular basis, we performed genome-wide sequencing of the mRNA from HepG2.2.15-CD36 overexpression (CD36OE) cells and HepG2.2.15-vector cells using RNA Sequencing (RNA-seq) technology to analyze the differential transcriptomic profile. Our results identified 141 differentially expressed genes (DEGs) related to CD36 overexpression, including 79 upregulated genes and 62 downregulated genes. Gene ontology and KEGG pathway analysis revealed that some of the DEGs were involved in various metabolic processes and the HBV life cycle. The reliability of the RNA-Seq data was confirmed by qPCR analysis. Our findings provide clues to build a link between CD36, host metabolism and the HBV life cycle and identified areas that require further investigation.