Integrated analysis of differentially expressed genes in esophageal squamous cell carcinoma using bioinformatics

Integrating network analysis with differential expression to uncover therapeutic and prognostic biomarkers in esophageal squamous cell carcinoma

Introduction: Esophageal squamous cell carcinoma (ESCC) accounts for over 90% of all esophageal tumors. However, the molecular mechanism underlying ESCC development and prognosis remains unclear, and there are still no effective molecular biomarkers for diagnosing or predicting the clinical outcome of patients with ESCC. Here, we used bioinformatics analysis to identify potential biomarkers and therapeutic targets for ESCC. Methodology: Differentially expressed genes (DEGs) between ESCC and normal esophageal tissue samples were obtained by comprehensively analyzing publicly available RNA-seq datasets from the TCGA and GTEX. Gene Ontology (GO) annotation and Reactome pathway analysis identified the biological roles of the DEGs. Moreover, the Cytoscape 3.10.1 platform and subsidiary tools such as CytoHubba were used to visualize the DEGs' protein-protein interaction (PPI) network and identify hub genes, Furthermore our results are validated by using Single-cell RNA analysis. Results: Identification of 2524 genes exhibiting altered expression enriched in pathways including keratinization, epidermal cell differentiation, G alpha(s) signaling events, and biological process of cell proliferation and division, extracellular matrix (ECM) disassembly, and muscle function. Moreover, upregulation of hallmarks E2F targets, G2M checkpoints, and TNF signaling. CytoHubba revealed 20 hub genes that had a valuable influence on the progression of ESCC in these patients. Among these, the high expression levels of four genes, CDK1 MAD2L1, PLK1, and TOP2A, were associated with critical dependence for cell survival in ESCC cell lines, as indicated by CRISPR dependency scores, gene expression data, and cell line metadata. We also identify the molecules targeting these essential hub genes, among which GSK461364 is a promising inhibitor of PLK1, BMS265246, and Valrubicin inhibitors of CDK1 and TOP2A, respectively. Moreover, we identified that elevated expression of MMP9 is associated with worse overall survival in ESCC patients, which may serve as potential prognostic biomarker or therapeutic target for ESCC. The single-cell RNA analysis showed MMP9 is highly expressed in myeloid, fibroblast, and epithelial cells, but low in T cells, endothelial cells, and B cells. This suggests MMP9's role in tumor progression and matrix remodeling, highlighting its potential as a prognostic marker and therapeutic target. Discussion: Our study identified key hub genes in ESCC, assessing their potential as therapeutic targets and biomarkers through detailed expression and dependency analyses. Notably, MMP9 emerged as a significant prognostic marker with high expression correlating with poor survival, underscoring its potential for targeted therapy. These findings enhance our understanding of ESCC pathogenesis and highlight promising avenues for treatment.

MAIP1-Related Tumor Immune Infiltration: As a Potential Prognostic Biomarker for Esophageal Cancer

Background

Esophageal cancer (EC), a common malignant tumor of digestive tract, is also one of the most deadly cancers. Accumulating studies have shown that the initiating and progressing multiple human diseases were closely related to the expression of MAIP. However, the specific roles and mechanisms of MAIP1 in EC remain incompletely defined.

Purpose

This study aims to determine the clinical significance of MAIP1 in EC and explores its potential molecular mechanisms regulating tumor immune infiltration.

Methods

We obtained RNA-seq datasets and corresponding clinical data for EC patients from the Cancer Genome Atlas (TCGA) database via the UCSC Xena browser to extract MAIP1 expression and plot survival curves to determine their prognosis. Based on the differential expression of MAIP1, EC patients were divided into high and low group to investigate the mechanism of MAIP1 in EC. In addition, the single sample gene set enrichment analysis (ssGSEA) quantified the expression of various immune cell signature marker genes and assessed the degree of immune infiltration in EC.

Results

In the TCGA-EC cohort, the overexpression of MAIP1 was observed in tumor tissues compared to normal tissues (p = 0.0038). Overall survival analysis showed that EC patients with the overexpression of MAIP1 presented a lower overall survival and worse prognosis (p = 0.004). Enrichment analysis revealed that the differential genes (DEGs) between high and low group are involved in biological functions such as extracellular matrix and organization extracellular structure. The results of ssGSEA showed that DCs, iDCs, macrophages, mast cells, and NK cells were significantly different in MAIP1^high and MAIP1^low groups, and all showed high expression in the MAIP1^low group.

Conclusion

We proposed that MAIP1 overexpression was associated with poor prognosis and tumor immune infiltration in EC. At present, there are few MAIP1-related tumor immune infiltration studies in EC, and further investigation is needed.

Development of a prognostic nomogram based on an eight-gene signature for esophageal squamous cell carcinoma by weighted gene co-expression network analysis (WGCNA)

Background

Esophageal squamous cell carcinoma (ESCC) is a highly aggressive malignant tumor. This study aims to develop a robust prognostic model for ESCC.

Methods

Expression profiles of ESCC were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Co-expressed modules were constructed by weighted gene co-expression network analysis (WGCNA). Differentially expressed genes (DEGs) between ESCC and normal samples were identified with the screening criteria of adjusted P value <0.05 and log |fold change (FC)| >1. After univariate and multivariate Cox regression analysis, an 8-gene module was constructed. A receiver operating characteristic (ROC) curve for overall survival (OS) was used to assess the prediction efficacy of the risk score. A nomogram was developed based on the risk score, age, gender, and stage for 1-, 2- and 3-year survival. The potential biological functions and pathways of the 8 genes were predicted using the Metascape database.

Results

The 2 ESCC-related co-expression modules were built via WGCNA. Among all DEGs, 55 survival-related genes were identified for ESCC. Based on these genes, an 8-gene module was constructed, composed of CFAP53, FCGR2A, FCGR3A, GNGT1, IGF2, LINC01524, MAGEA3, and MAGEA6. The area under the curve (AUC) was 0.961, suggesting that the risk score could effectively predict the OS of patients with ESCC. Furthermore, the nomogram exhibited high accuracy in predicting the survival rate of ESCC patients at 1, 2, and 3 years. These genes were mainly involved in ESCC-related pathways such as extracellular matrix organization, collagen formation, and blood vessel development.

Conclusions

Our nomogram based on the 8-gene risk score could be a reliable prognostic tool for ESCC.

MicroRNA-148a-3p suppresses cell proliferation and migration of esophageal carcinoma by targeting CEP55

This study evaluated microRNA-148a-3p in esophageal carcinoma cells. The prediction of bioinformatics analysis revealed that microRNA-148a-3p may target CEP55. qRT-PCR and western blot showed that CEP55 level in esophageal carcinoma cells and tissue was dramatically higher than that of normal cells and tissue, while microRNA-148a-3p was the opposite. Forced expression of microRNA-148a-3p restrained cell malignant behaviors of esophageal carcinoma, and repression of microRNA-148a-3p resulted in the converse results in terms of cell function. Dual-luciferase assay confirmed that microRNA-148a-3p targeted CEP55. CEP55 attenuated the suppressive effect of microRNA-148a-3p on proliferation and migration of esophageal carcinoma cells, demonstrating that microRNA-148a-3p regulated function of esophageal carcinoma cells via decreasing CEP55 level. Microscopy observation indicated that cell morphology was also affected by the microRNA-148a-3p/CEP55 axis. Furthermore, western blot analysis revealed that the PI3K/AKT signaling pathway could be suppressed by activating the microRNA-148a-3p/CEP55 axis. Finally, in vivo experiments confirmed the effects of microRNA-148a-3p on tumorigenesis. Thus, microRNA-148a-3p could act as a repressor in esophageal carcinoma via binding to CEP55.

COL11A1 promotes esophageal squamous cell carcinoma proliferation and metastasis and is inversely regulated by miR-335-5p

Background

Esophagus squamous cell carcinoma (ESCC) is a sort of cancer that occurs in the esophageal epithelial tissue. This study performed integrated bioinformatics analysis of Gene Expression Omnibus (GEO) datasets GSE32424, GSE29968, and GSE130078. Collagen type XI alpha 1 (COL11A1) was identified as the hub gene in ESCC progression. The involvement of COL11A1 in ESCC development was next determined using in vitro functional tests.

Methods

Hub genes were identified through integrated bioinformatics analysis. The real-time reverse transcription-polymerase chain reaction was implemented for detecting the expression of COL11A1 mRNA in esophageal cancer cells. KYSE-30 cells were transfected using a vector encoding COL11A1. The proliferation of cells was determined using the Cell Counting Kit-8 (CCK-8) assay. Detection of the cell migration and invasion was made through making use of the transwell test. The development of ESCC cells in vivo was evaluated in naked mice. The interplay among COL11A1 and microRNA-335-5p (miR-335-5p) was discovered using a luciferase reporter experiment.

Results

In vitro studies showed the upregulation of COL11A1 in ESCC cell lines obtained from ESCC patients and upregulation of COL11A1 was correlated with poor disease-free survival of ESCC patients, thereby implying an oncogenic involvement of COL11A1 in ESCC. Overexpression of COL11A1 enhanced the proliferation of ESCC cells, invasion, and migration; whereas COL11A1 knockdown impeded the proliferation of ESCC cells, invasion, and migration. Additionally, miRNA pathway analysis in combination with TargetScan’s online prediction and the luciferase reporter assay suggested miR-335-5p targeting and negatively regulating the COL11A1 3' untranslated region (3'UTR) within ESCC cells. MiR-335-5p overexpression diminished the development of ESCC cells. Additionally, co-expression of COL11A1 ameliorated the repressive influence of miR-335-5p overexpression on the growth and metastasis of ESCC cells.

Conclusions

Using comprehensive bioinformatics analysis, the current study identified COL11A1 as an oncogene in ESCC. The mechanistic studies indicated that COL11A1 promoted ESCC cell progression and that miR-335-5p negatively regulated the expression of COL11A1 in ESCC.

Overexpression of cyclin-dependent kinase 1 in esophageal squamous cell carcinoma and its clinical significance

Cyclin‐dependent kinase 1 (CDK1) plays a significant role in certain malignancies. However, it remains unclear whether CDK1 plays a role in esophageal squamous cell carcinoma (ESCC). The aim of this study was to analyze the expression and clinical value of CDK1 in ESCC. CDK1 protein in 151 ESCC tissues and 138 normal esophageal tissues was detected by immunohistochemistry. RNA‐seq of eight pairs of ESCC and adjacent esophageal specimens was performed to evaluate the levels of CDK1 mRNA. Microarray and external RNA‐seq data from 664 cases of ESCC and 1733 cases of control tissues were used to verify the difference in CDK1 expression between the two groups. A comprehensive analysis of all data was performed to evaluate the difference in CDK1 between ESCC tissues and control tissues. Further, functional enrichment analyses were performed based on differentially expressed genes (DEGs) of ESCC and co‐expressed genes (CEGs) of CDK1. In addition, a lncRNA‐miRNA‐CDK1 network was constructed. The expression of CDK1 protein was obviously increased in ESCC tissues (3.540 ± 2.923 vs. 1.040 ± 1.632, P < 0.001). RNA‐seq indicated that the mRNA level of CDK1 was also highly expressed in ESCC tissues (5.261 ± 0.703 vs. 2.229 ± 1.161, P < 0.0001). Comprehensive analysis revealed consistent up‐regulation of CDK1 (SMD = 1.41; 95% CI 1.00–1.83). Further, functional enrichment analyses revealed that the functions of these genes were mainly concentrated in the cell cycle. A triple regulatory network of PVT1‐hsa‐miR‐145‐5p/hsa‐miR‐30c‐5p‐CDK1 was constructed using in silico analysis. In summary, overexpression of CDK1 is closely related to ESCC tumorigenesis.

Integrated analysis of ceRNA network and tumor-infiltrating immune cells in esophageal cancer

Background: Esophageal cancer (ESCA) is one of the most commonly diagnosed cancers in the world. Tumor immune microenvironment is closely related to tumor prognosis. The present study aimed at analyzing the competing endogenous RNA (ceRNA) network and tumor-infiltrating immune cells in ESCA.

Methods: The expression profiles of mRNAs, lncRNAs, and miRNAs were downloaded from the Cancer Genome Atlas database. A ceRNA network was established based on the differentially expressed RNAs by Cytoscape. CIBERSORT was applied to estimate the proportion of immune cells in ESCA. Prognosis-associated genes and immune cells were applied to establish prognostic models basing on Lasso and multivariate Cox analyses. The survival curves were constructed with Kaplan–Meier method. The predictive efficacy of the prognostic models was evaluated by the receiver operating characteristic (ROC) curves.

Results: The differentially expressed mRNAs, lncRNAs, and miRNAs were identified. We constructed the ceRNA network including 23 lncRNAs, 19 miRNAs, and 147 mRNAs. Five key molecules (HMGB3, HOXC8, HSPA1B, KLHL15, and RUNX3) were identified from the ceRNA network and five significant immune cells (plasma cells, T cells follicular helper, monocytes, dendritic cells activated, and neutrophils) were selected via CIBERSORT. The ROC curves based on key genes and significant immune cells all showed good sensitivity (AUC of 3-year survival: 0.739, AUC of 5-year survival: 0.899, AUC of 3-year survival: 0.824, AUC of 5-year survival: 0.876). There was certain correlation between five immune cells and five key molecules.

Conclusion: The present study provides an effective bioinformatics basis for exploring the potential biomarkers of ESCA and predicting its prognosis.

Identification of DNA-Repair-Related Five-Gene Signature to Predict Prognosis in Patients with Esophageal Cancer

Esophageal cancer (ESCA) is a leading cause of cancer-related mortality, with poor prognosis worldwide. DNA damage repair is one of the hallmarks of cancer. Loss of genomic integrity owing to inactivation of DNA repair genes can increase the risk of cancer progression and lead to poor prognosis. We aimed to identify a novel gene signature related to DNA repair to predict the prognosis of ESCA patients. Based on gene expression profiles of ESCA patients from The Cancer Genome Atlas and gene set enrichment analysis, 102 genes related to DNA repair were identified as candidates. After stepwise Cox regression analysis, we established a five-gene prognostic model comprising DGCR8, POM121, TAF9, UPF3B, and BCAP31. Kaplan-Meier survival analysis confirmed a strong correlation between the prognostic model and survival. Moreover, we verified the clinical value of the prognostic signature under the influence of different clinical parameters. We found that small-molecule drugs (trametinib, selumetinib, and refametinib) could help to improve patient survival. In summary, our study provides a novel and promising prognostic signature based on DNA-repair-related genes to predict survival of patients with ESCA. Systematic data mining provides a theoretical basis for further exploring the molecular pathogenesis of ESCA and identifying therapeutic targets.

Time series expression pattern of key genes reveals the molecular process of esophageal cancer

Background: Esophageal cancer is one of the most poorly diagnosed and fatal cancers in the world. Although a series of studies on esophageal cancer have been reported, the molecular pathogenesis of the disease is still elusive. Aim: To investigate the molecular process of esophageal cancer comprehensively and deeply. Methods: Differential expression analysis was performed to identify differentially expressed genes (DEGs) in different stages of esophageal cancer. Then exacting gene interaction modules and hub genes were identified in module interaction network. Further, though survival analysis, methylation analysis, pivot analysis, and enrichment analysis, some important molecules and related function or pathway were identified to elucidate potential mechanism in esophageal cancer. Results: A total of 7457 DEGs and 14 gene interaction modules were identified. These module genes were significantly involved in the positive regulation of protein transport, gastric acid secretion, insulin-like growth factor receptor binding and other biological processes (BPs), as well as p53 signaling pathway, ERBB signaling pathway and epidermal growth factor receptor (EGFR) signaling pathway. Then, transcription factors (TFs) (including HIF1A) and ncRNAs (including CRNDE and hsa-mir-330-3p) significantly regulate dysfunction modules were identified. Further, survival analysis showed that GNGT2 was closely related to survival of esophageal cancer. And DEGs with strong methylation regulation ability were identified, including SST and SH3GL2. Conclusion: These works not only help us to reveal the potential regulatory factors in the development of disease, but also deepen our understanding of its deterioration mechanism.

Identification of candidate aberrantly methylated and differentially expressed genes in Esophageal squamous cell carcinoma

Aberrant methylated genes (DMGs) play an important role in the etiology and pathogenesis of esophageal squamous cell carcinoma (ESCC). In this study, we aimed to integrate three cohorts profile datasets to ascertain aberrant methylated-differentially expressed genes and pathways associated with ESCC by comprehensive bioinformatics analysis. We downloaded data of gene expression microarrays (GSE20347, GSE38129) and gene methylation microarrays (GSE52826) from the Gene Expression Omnibus (GEO) database. Aberrantly differentially expressed genes (DEGs) were obtained by GEO2R tool. The David database was then used to perform Gene ontology (GO) analysis and Kyoto Encyclopedia of Gene and Genome pathway enrichment analyses on selected genes. STRING and Cytoscape software were used to construct a protein-protein interaction (PPI) network, then the modules in the PPI networks were analyzed with MCODE and the hub genes chose from the PPI networks were verified by Oncomine and TCGA database. In total, 291 hypomethylation-high expression genes and 168 hypermethylation-low expression genes were identified at the screening step, and finally found six mostly changed hub genes including KIF14, CDK1, AURKA, LCN2, TGM1, and DSG1. Pathway analysis indicated that aberrantly methylated DEGs mainly associated with the P13K-AKT signaling, cAMP signaling and cell cycle process. After validation in multiple databases, most hub genes remained significant. Patients with high expression of AURKA were associated with shorter overall survival. To summarize, we have identified six feasible aberrant methylated-differentially expressed genes and pathways in ESCC by bioinformatics analysis, potentially providing valuable information for the molecular mechanisms of ESCC. Our data combined the analysis of gene expression profiling microarrays and gene methylation profiling microarrays, simultaneously, and in this way, it can shed a light for screening and diagnosis of ESCC in future.