Cap-dependent mRNA translation and the ubiquitin-proteasome system cooperate to promote ERBB2-dependent esophageal cancer phenotype

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.

Patient-specific cancer genes contribute to recurrently perturbed pathways and establish therapeutic vulnerabilities in esophageal adenocarcinoma

The identification of cancer-promoting genetic alterations is challenging particularly in highly unstable and heterogeneous cancers, such as esophageal adenocarcinoma (EAC). Here we describe a machine learning algorithm to identify cancer genes in individual patients considering all types of damaging alterations simultaneously. Analysing 261 EACs from the OCCAMS Consortium, we discover helper genes that, alongside well-known drivers, promote cancer. We confirm the robustness of our approach in 107 additional EACs. Unlike recurrent alterations of known drivers, these cancer helper genes are rare or patient-specific. However, they converge towards perturbations of well-known cancer processes. Recurrence of the same process perturbations, rather than individual genes, divides EACs into six clusters differing in their molecular and clinical features. Experimentally mimicking the alterations of predicted helper genes in cancer and pre-cancer cells validates their contribution to disease progression, while reverting their alterations reveals EAC acquired dependencies that can be exploited in therapy.

Single-cell RNA sequencing reveals diverse intratumoral heterogeneities and gene signatures of two types of esophageal cancers

Single-cell RNA sequencing and transcriptome analysis enable novel discovery and precise characterization of new cell types and states, which improves the understanding of the cellular context of tumorigenesis. Herein, we applied this powerful approach to analyze 368 single cells from three esophageal squamous cell carcinoma (ESCC) and two esophageal adenocarcinoma (EAC) tumors. Using inferred copy number variation analysis, we successfully distinguished carcinoma cells from heterogeneous cellular populations, identifying gene signatures and crucial cancer-related signaling pathways related to ESCC and EAC. In particular, we found that NOTCH signaling was exclusively activated in ESCC, but not in EAC. ESCC tumors with higher NOTCH activity were associated with significantly worse survival rates than those with lower NOTCH activity. Collectively, this study revealed that ESCC and EAC are distinct in terms of cellular transcriptome profiles, which leads to a wide range of intratumoral cellular heterogeneity. The findings suggest that different therapeutic strategies that target the differences between two types of esophageal cancers are required, guiding cancer-specific future drug development.

Toxicoproteomic analysis of human lung epithelial cells exposed to steel industry ambient particulate matter (PM) reveals possible mechanism of PM related carcinogenesis

Toxicoproteomic analysis of steel industry ambient particulate matter (PM) that contain high concentrations of PAHs and metals was done by treating human lung cancer cell-line, A549 and the cell lysates were analysed using quantitative label-free nano LC-MS/MS. A total of 18,562 peptides representing 1576 proteins were identified and quantified, with 196 proteins had significantly altered expression in the treated cells. Enrichment analyses revealed that proteins associated to redox homeostsis, metabolism, and cellular energy generation were inhibited while, proteins related to DNA damage and repair and other stresses were over expressed. Altered activities of several tumor associated proteins were observed. Protein-protein interaction network and biological pathway analysis of these differentially expressed proteins were carried out to obtain a systems level view of proteome changes. Together it could be inferred that PM exposure induced oxidative stress which could have lead into DNA damage and tumor related changes. However, lowering of cellular metabolism, and energy production could reduce its ability to overcome these stress. This kind of disequilibrium between the DNA damage and ability of the cells to repair the DNA damage may lead into genomic instability that is capable of acting as the driving force during PM induced carcinogenesis.

Metabolism-related enzyme alterations identified by proteomic analysis in human renal cell carcinoma

The renal cell carcinoma (RCC) is one of the most common types of kidney neoplasia in Western countries; it is relatively resistant to conventional chemotherapy and radiotherapy. Metabolic disorders have a profound effect on the degree of malignancy and treatment resistance of the tumor. However, the molecular characteristics related to impaired metabolism leading to the initiation of RCC are still not very clear. In this study, two-dimensional electrophoresis (2-DE) and mass spectra (MS) technologies were utilized to identify the proteins involved in energy metabolism of RCC. A total of 73 proteins that were differentially expressed in conventional RCC, in comparison with the corresponding normal kidney tissues, were identified. Bioinformatics analysis has shown that these proteins are involved in glycolysis, urea cycle, and the metabolic pathways of pyruvate, propanoate, and arginine/proline. In addition, some were also involved in the signaling network of p53 and FAS. These results provide some clues for new therapeutic targets and treatment strategies of RCC.

Translation initiation complex eIF4F is a therapeutic target for dual mTOR kinase inhibitors in non-Hodgkin lymphoma

Deregulated mRNA translation has been implicated in disease development and in part is controlled by a eukaryotic initiation complex eIF4F (composed of eIF4E, eIF4G and eIF4A). We demonstrate here that the cap bound fraction from lymphoma cells was enriched with eIF4G and eIF4E indicating that lymphoma cells exist in an activated translational state. Moreover, 77% (110/142) of diffuse large B cell lymphoma tumors expressed eIF4E and this was associated with an inferior event free survival. Over-expression of wild-type eIF4E (eIF4E(WT)) but not cap-mutant eIF4E (eIF4E(cap mutant)) increased the activation of the eIF4F complex. Treatment with the active-site dual mTOR inhibitor CC214-1 reduced the level of the eIF4F complex by decreasing the cap bound fraction of eIF4G and increasing the levels of 4E-BP1. CC214-1 inhibited both the cap dependent and global protein translation. CC214-1 inhibited c-Myc, and cyclin D3 translation by decreasing polysomal fractions from lymphoma cells. Inhibition of eIF4E with shRNA further decreased the CC214-1 induced inhibition of the eIF4F complex, c-Myc, cyclin D3 translation, and colony formation. These studies demonstrate that the eIF4F complex is deregulated in aggressive lymphoma and that dual mTOR therapy has therapeutic potential in these patients.

Gene expression profile of human esophageal squamous carcinoma cell line TE-1

Esophageal squamous cell carcinoma (ESCC) is one of the most common and deadly causes of cancer worldwide. However, to date, the mechanisms underlying its pathogenesis remain unclear. The present study investigated the gene expression profile of human esophageal cancer cell line TE-1, a cell model for ESCC, to gain insight to the genetic regulation of this disease. Human esophageal cancer TE-1 cells and normal esophageal HET-1A cells were cultured for isolation of total RNA. Differential expression of RNA transcripts was assessed using the Agilent 4×44 K microarray, combined with real-time PCR (qRT-PCR) for validation. Classification and function of the differential genes were illustrated by bioinformatics processing including hierarchical clustering and gene ontology (GO) analysis. We identified 4,986 transcripts with differential expression (fold-change ≥1.5, P<0.05), including 2,368 up-regulated and 2,618 down-regulated transcripts. GO analysis showed that the dysregulated transcripts were associated with biological process, cellular component, and molecular function. After bioinformatic analysis of significantly regulated signaling pathways, we found these transcripts may target 35 gene pathways, including p53 signaling, glioma, ubiquitin-mediated proteolysis, insulin signaling, cell cycle, inositol phosphate metabolism, mTOR signaling, and MAPK signaling. The differentially expressed transcripts were screened between the esophageal cancer cell line TE-1 and normal esophageal cell line HET-1A, as well as their target gene pathways. Further data mining is related to prevention and treatment of esophageal cancer.

Synergistic effects of TOR and proteasome pathways on the yeast transcriptome and cell growth

The proteasome has been implicated in gene transcription through a variety of mechanisms. How the proteasome regulates genome-wide transcription in relation to nutrient signalling pathways is largely unknown. Using chemical inhibitors to compromise the functions of the proteasome and/or TORC1, we reveal that the proteasome and TORC1 synergistically promote the expression of de novo purine and amino acid biosynthetic genes, and restrict the transcription of those associated with proteolysis, starvation and stress responses. Genetic analysis demonstrates that TORC1 negatively regulates both the Yak1 and Rim15 kinases to modulate starvation-specific gene expression mediated by the Msn2/4 and Gis1 transcription factors. Compromising proteasome function induces starvation-specific gene transcription in exponential-phase cells and abrogates the strict control of such expression by Yak1 and Rim15 in rapamycin-treated cells, confirming that the proteasome functions to ensure stringent control of the starvation response by the TOR pathway. Synergy between the two pathways is also exhibited on cell growth control. Rpn4-dependent upregulation of proteasomal genes and a catalytically competent 20S proteasome are essential for yeast cells to respond to reduced TORC1 activity. These data suggest that the proteasome and the TOR signalling pathway synergistically regulate a significant portion of the genome to coordinate cell growth and starvation response.