A systems biology approach to study the biology characteristics of esophageal squamous cell carcinoma by integrating microRNA and messenger RNA expression profiling

Prognostic signature analysis and survival prediction of esophageal cancer based on N6-methyladenosine associated lncRNAs

Esophageal cancer (ESCA) has a bad prognosis. Long non-coding RNA (lncRNA) impacts on cell proliferation. However, the prognosis function of N6-methyladenosine (m6A)-associated lncRNAs (m6A-lncRNAs) in ESCA remains unknown. Univariate Cox analysis was applied to investigate prognosis related m6A-lncRNAs, based on which the samples were clustered. Wilcoxon rank and Chi-square tests were adopted to compare the clinical traits, survival, pathway activity and immune infiltration in different clusters where overall survival, clinical traits (N stage), tumor-invasive immune cells and pathway activity were found significantly different. Through least absolute shrinkage and selection operator and proportional hazard (Lasso-Cox) model, five m6A-lncRNAs were selected to construct the prognostic signature (m6A-lncSig) and risk score. To investigate the link between risk score and clinical traits or immunological microenvironments, Chi-square test and Spearman correlation analysis were utilized. Risk score was found connected with N stage, tumor stage, different clusters, macrophages M2, B cells naive and T cells CD4 memory resting. Risk score and tumor stage were found as independent prognostic variables. And the constructed nomogram model had high accuracy in predicting prognosis. The obtained m6A-lncSig could be taken as potential prognostic biomarker for ESCA patients. This study offers a theoretical foundation for clinical diagnosis and prognosis of ESCA.

Heterogeneity Analysis of Esophageal Squamous Cell Carcinoma in Cell Lines, Tumor Tissues and Patient-Derived Xenografts

Esophageal Squamous Cell Carcinoma (ESCC) is the predominant type of Esophageal Cancer (EC), accounting for nearly 88% of EC incidents worldwide. Importantly, it is also a life-threatening cancer for patients diagnosed in advanced stages, with only a 20% 5-year survival rate due to a limited number of actionable targets and therapeutic options. Increasing evidence has shown that inter-tumor and intra-tumor heterogeneity are widely distributed across ESCC tumor tissues. In our work, multi-omics data from ESCC cell lines, tumor tissue, normal tissue and Patient-Derived Xenograft (PDX) tissues were analyzed to investigate the heterogeneity among ESCC samples at the DNA, RNA, and protein level. We identified enrichment of ECM-receptor interaction and Focal adhesion pathways from the subset of protein-coding genes with non-silent mutations in ESCC patients. We also found that TP53, TTN, KMT2D, CSMD3, DNAH5, MUC16 and DST are the most frequently mutated genes in ESCC patient samples. Out of the identified genes, TP53 is the most frequently mutated, with 84 distinct non-silent mutation variants. We observed that p.R248Q, p.R175G/H, and p.R273C/H are the most common TP53 mutation variants. The diversity of TP53 mutations reveal its importance in ESCC progression and may also provide promising targets for precision therapeutics. Additionally, we identified the Olfactory transduction as the top signaling pathway, enriched from genes uniquely expressed in The Cancer Genome Atlas (TCGA)-ESCC patient tumor tissues, which may provide implications for the exact roles of the corresponding genes in ESCC. Cyclic nucleotide-gated channel subunit beta 1(CNGB1), a gene belonging to the Olfactory transduction pathway, was found exclusively overexpressed in ESCC. Expression of CNGB1 could serve as a marker, indicating potential diagnostic or therapeutic value. Finally, we investigated heterogeneity in the context of the ESCC PDX model, which is an emerging tool used to predict drug response and recapitulate tumor behavior in vivo. We observed trans-species heterogeneity in as high as 75% of the identified proteins, indicating that the ambiguity of proteins should be addressed by specific strategies to avoid drawing false conclusions. The identification and characterization of gene mutation and expression heterogeneity across different ESCC datasets, including various novel TP53 mutations, ECM-receptor interaction, Focal adhesion, and Olfactory transduction pathways (CNGB1), provide researchers with evidence and implications for accurate research and precision therapeutic development.

Integrative analysis of differential genes and identification of a "2-gene score" associated with survival in esophageal squamous cell carcinoma

Background

Developments in high‐throughput genomic technologies have led to improved understanding of the molecular underpinnings of esophageal squamous cell carcinoma (ESCC). However, there is currently no model that combines the clinical features and gene expression signatures to predict outcomes.

Methods

We obtained data from the GSE53625 database of Chinese ESCC patients who had undergone surgical treatment. The R packages, Limma and WGCNA, were used to identify and construct a co‐expression network of differentially expressed genes, respectively. The Cox regression model was used, and a nomogram prediction model was constructed.

Results

A total of 3654 differentially expressed genes were identified. Bioinformatics enrichment analysis was conducted. Multivariate analysis of the clinical cohort revealed that age and adjuvant therapy were independent factors for survival, and these were entered into the clinical nomogram. After integrating the gene expression profiles, we identified a “2‐gene score” associated with overall survival. The combinational model is composed of clinical data and gene expression profiles. The C‐index of the combined nomogram for predicting survival was statistically higher than the clinical nomogram. The calibration curve revealed that the combined nomogram and actual observation showed better prediction accuracy than the clinical nomogram alone.

Conclusions

The integration of gene expression signatures and clinical variables produced a predictive model for ESCC that performed better than those based exclusively on clinical variables. This approach may provide a novel prediction model for ESCC patients after surgery.

Exploration of gene functions for esophageal squamous cell carcinoma using network-based guilt by association principle

Gene networks have been broadly used to predict gene functions based on guilt by association (GBA) principle. Thus, in order to better understand the molecular mechanisms of esophageal squamous cell carcinoma (ESCC), our study was designed to use a network-based GBA method to identify the optimal gene functions for ESCC. To identify genomic bio-signatures for ESCC, microarray data of GSE20347 were first downloaded from a public functional genomics data repository of Gene Expression Omnibus database. Then, differentially expressed genes (DEGs) between ESCC patients and controls were identified using the LIMMA method. Afterwards, construction of differential co-expression network (DCN) was performed relying on DEGs, followed by gene ontology (GO) enrichment analysis based on a known confirmed database and DEGs. Eventually, the optimal gene functions were predicted using GBA algorithm based on the area under the curve (AUC) for each GO term. Overall, 43 DEGs and 67 GO terms were gained for subsequent analysis. GBA predictions demonstrated that 13 GO functions with AUC>0.7 had a good classification ability. Significantly, 6 out of 13 GO terms yielded AUC>0.8, which were determined as the optimal gene functions. Interestingly, there were two GO categories with AUC>0.9, which included cell cycle checkpoint (AUC=0.91648), and mitotic sister chromatid segregation (AUC=0.91597). Our findings highlight the clinical implications of cell cycle checkpoint and mitotic sister chromatid segregation in ESCC progression and provide the molecular foundation for developing therapeutic targets.

Comprehensive analysis of differential co-expression patterns reveal transcriptional dysregulation mechanism and identify novel prognostic lncRNAs in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most common malignancies worldwide and occurs at a relatively high frequency in People's Republic of China. However, the molecular mechanism underlying ESCC is still unclear. In this study, the mRNA and long non-coding RNA (lncRNA) expression profiles of ESCC were downloaded from the Gene Expression Omnibus database, and then differential co-expression analysis was used to reveal the altered co-expression relationship of gene pairs in ESCC tumors. A total of 3,709 mRNAs and 923 lncRNAs were differentially co-expressed between normal and tumor tissues, and we found that most of the gene pairs lost associations in the tumor tissues. The differential regulatory networking approach deciphered that transcriptional dysregulation was ubiquitous in ESCC, and most of the differentially regulated links were modulated by 37 TFs. Our study also found that two novel lncRNAs (ADAMTS9-AS1 and AP000696.2) might be essential in the development of ectoderm and epithelial cells, which could significantly stratify ESCC patients into high-risk and low-risk groups, and were much better than traditional clinical tumor markers. Further inspection of two risk groups showed that the changes in TF-target regulation in the high-risk patients were significantly higher than those in the low-risk patients. In addition, four signal transduction-related DCmRNAs (ERBB3, ENSA, KCNK7, MFSD5), which were differentially co-expressed with the two lncRNAs, might also have the predictive capacity. Our findings will enhance the understanding of ESCC transcriptional dysregulation from a view of cross-link of lncRNA and mRNA, and the two-lncRNA combination may serve as a novel prognostic biomarker for clinical applications of ESCC.