A bioinformatics analysis for diagnostic roles of the family in esophageal cancer.

BACKGROUND

Esophageal cancer (EC) is the eighth most commonly occurring cancer worldwide and the sixth leading cause of cancer-related deaths. The therapeutic effect of EC patients is not ideal, and new biomarkers are needed to guide diagnosis and prognosis of EC patients. family transcription factors are among the most important links in the cell cycle regulatory network. dysregulation not only promotes the early stages of tumor development but also the progression of benign tumors to malignant tumors. is expected to be a new biomarker. The prognostic significance of the family in EC requires further research.

METHODS

We analyzed The Cancer Genome Atlas (TCGA), Gene Expression Profiling Interactive Analysis (GEPIA), and GeneMANIA databases to obtain RNA-sequencing data and clinical data. The clinical data included age, gender, race, stage, type, status, etc. The prognosis outcome included overall survival (OS) and progression-free interval (PFI). Subsequently, we conducted further research on gene expressions, enrichment analysis, interaction network, and prognostic values by R software, containing ggplot2, ComplexHeatmap, DESeq2, pROC R package, based on -test, Wilcoxon rank sum test, Spearman rank correlation analysis, log-rank test and COX model.

RESULTS

We found that mRNA transcription levels of , were more highly expressed in esophageal carcinoma (ESCA) tissues than in normal tissues. expression was correlated with tumor stage [Pr(>F)=0.00856]. -related genes played a role in development and differentiation, and were prevalent in the endoplasmic reticulum lumen, Golgi lumen, and lipoprotein particle, catalyzing translation activities and lipid metabolism. Each gene was found to be related to each other to some degree. The GeneMANIA network analysis revealed links between E2Fs and other genes. We compared the correlations between 24 kinds of tumor-infiltrating immune cells and . (AUC =0.945, CI: 0.890-1.000) and (AUC =0.958, CI: 0.920-0.996) exhibited higher predictive power accuracy. However, only was closely related to OS [HR =1.91 (1.16-3.16), P=0.011].

CONCLUSIONS

We discover that is a prognostic biomarker. family may take part in the development of EC through lipid metabolism pathways, which is helpful to predict the prognosis of EC patients and guide accurate diagnosis and treatment.