Abstract
Background and objective: Esophageal cancer(ESCA) ranks eleventh in incidence and eighth in mortality among malignant tumors in the world. Due to the paucity of effective early diagnostic approach, a lot of patients have missed the first-rank treatment time frame and were already in the advanced phase at their first diagnosis. The continuous reforming of high-throughput sequencing technologies and analytical techniques has provided novel concepts and approaches for the study of cancer biomarkers in esophageal cancer. The development of cancer is a complex biological process with multi-gene concernment, multi-factor mutual effect and multi-phase development. This process includes the mutations in proto-oncogenes, changes in transcript expression profiles, and abnormalities of protein structure, function, or expression levels. The study of the molecular mechanism of ESCA using high-throughput sequencing technology will lay theoretic foundation for the early diagnosis and targeted therapy of ESCA.Materials and methods: In this study, a search was conducted in tow commonly used public databases, UCSC XENA and GEO, one UCSC XENA RNA-seq data and tow GEO datasets were included in this study. Differential expression analysis was implemented by using limma in R software.Weighted gene co-expression network analysis (WGCNA) was used to analyze the gene transcriptome expression profile consisting of 181 ESCA tissues and 181 normal tissues as controls to construct topology network. We constructed gene modules and searched for gene modules that were closely participant to ESCA, and gene ontology (GO) and KEGG pathway enrichment analysis were implemented to probe into the functions of the DEGs and differentially expressed hub genes in key modules. By combining the consequences of differential gene expression analysis with WGCNA consequences(hub genes), we procured a 30 of differentially expressed genes in module that were closely participant to ESCA. Next, we procured the expression data of these genes from normalized transcriptome expression data to construct ESCA predictive model. Then, ten-fold cross validation combining with machine learning algorithms were used to construct prediction models for ESCA. Finally, we also verified the four screened biomarkers which used to build the predictive model with the GEO data sets.Results: Analysis of differentially expressed genes were conducted by using the limma packages and differentially expressed genes were defined as |log2FC|>1 and adj.P.Val < 0.01. After comparison the results from limma, a total of 15814 genes were up-regulated in ESCA, a total of 6176 gene were down-regulated in ESCA.A total of 7 gene modules were identified from WGCNA, 2 modules of them are strongly corelative with ESCA (Brown module: R2=0.87, Lightcyan module: R2=-0.75, both P <0.001). Brown module is closely related to ESCA.The consequences of WGCNA analysis combined with differentially expressed genes revealed that there were 4419 differentially expressed genes in the brown module which were closely related to ESCA. 30 hub gene were screened by kWithin top 30 from brown module, and all of them are differentially expressed.GO analysis of differetially expressed genes from brown module revealed that these genes are from immunoglobulin complex, “chromosome, centromeric region”, condensed chromosome, “immunoglobulin complex, circulating”, condensed chromosome, centromeric region, and other components, and they participated in biological function such as antigen binding, immunoglobulin receptor binding, ATPase activity, cadherin binding, DNA helicase activity, etc., involved in biological processes such as adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains, mitotic nuclear division, lymphocyte mediated immunity, nuclear division, and DNA replication; KEGG pathway analysis shows the brown module differentially expressed genes are mainly enriched in signal pathways such as cell cycle, pathogenic escherichia coli infection, DNA replication, IL-17 signaling pathway and human T-cell leukemia virus 1 infection. This shed new light on molecular mechanisms of the development of ESCA.Twelve ESCA prediction models constructed from 30 gene expression matrices from 362 subjects by using 10-fold cross-validation combined with machine learning algorithms revealed good prediction performance in validation dataset, among which models from gbm, BoostGLM, C5.0 algorithms revealed higher accuracy than from other algorithms. Although the transparent or semi-transparent models constructed by JRip, PART, and Rpart algorithms have acceptable accuracy in validation dataset, their sensitivity are lower. From a comprehensive perspective, two black box algorithm models including gbm and BoostGLM models are selected as the final model. This study has successfully constructed ESCA prediction models with accuracies higher than 0.97.Finally, three of the four screened biomarkers were validated.Conclusions: In current study, differential expression analysis and WGCNA of ESCA participant RNA-seq data available in public database were used to screen DEGs and genes that were closely participant with ESCA. Consequences from GO and KEGG analysis further revealed the underlying mechanisms of ESCA. Normalized gene expression data was feed to several different machine learning techniques and 10-fold cross validation was used to construct high accuracy ESCA predictive models. Eventually, several ESCA predictive models with accuracy higher than 0.96 in validation group were constructed. At the meantime, three biomarkers(G3BP1, CHEK1 and MOB1A) were screened and validated, in particular, G3BP1 may be a potential therapeutic target, as overall survival analysis have shown it to be an adverse prognostic factor. Current study has lay the basis of applying RNA-seq data in the early genetic diagnosis of ESCA, and a prognostic marker that might contribute to treatment of ESCA.
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