Immune-Related Long Non-coding RNA Signature and Clinical Nomogram to Evaluate Survival of Patients Suffering Esophageal Squamous Cell Carcinoma.

Ting Zhu,Zhupeng Li,Bin Wang,Zhifeng Ma,Guangmao Yu,Linhai Fu,Haiyong Wang,Desheng Wei,Chu Zhang

doi:10.3389/fcell.2021.641960

Abstract

Esophageal squamous cell carcinoma (ESCC) turns out to be one of the most prevalent cancer types, leading to a relatively high mortality among worldwide sufferers. In this study, gene microarray data of ESCC patients were obtained from the GEO database, with the samples involved divided into a training set and a validation set. Based on the immune-related differential long non-coding RNAs (lncRNAs) we identified, a prognostic eight-lncRNA-based risk signature was constructed following regression analyses. Then, the predictive capacity of the model was evaluated in the training set and validation set using survival curves and receiver operation characteristic curves. In addition, univariate and multivariate regression analyses based on clinical information and the model-based risk score also demonstrated the ability of the risk score in independently determining the prognosis of patients. Besides, based on the CIBERSORT tool, the abundance of immune infiltrates in tumor samples was scored, and a significant difference was presented between the high- and low- risk groups. Correlation analysis with immune checkpoints (PD1, PDL1, and CTLA4) indicated that the eight-lncRNA signature–based risk score was negatively correlated with PD1 expression, suggesting that the eight-lncRNA signature may have an effect in immunotherapy for ESCC. Finally, GO annotation was performed for the differential mRNAs that were co-expressed with the eight lncRNAs, and it was uncovered that they were remarkably enriched in immune-related biological functions. These results suggested that the eight-lncRNA signature–based risk model could be employed as an independent biomarker for ESCC prognosis and might play a part in evaluating the response of ESCC to immunotherapy with immune checkpoint blockade.

Highlights

Esophageal cancer is the eighth most prevalent cancer worldwide and the sixth in cancer mortality (Siegel et al, 2017)
In order to help clinicians better determine the prognosis of Esophageal squamous cell carcinoma (ESCC) patients, a nomogram was drawn using the eightlncRNA signature–based risk score plus clinical information in the training set
The area under the curve (AUC) values corresponding to 1, 2, and 3 years in the training set were 0.79, 0.83, and 0.8, respectively (Figure 4H), while those in the validation set were 0.8, 0.78, and 0.73, respectively (Figure 4I). These results demonstrated that the nomogram had a good ability to predict the prognosis of patients

Summary

Introduction

Esophageal cancer is the eighth most prevalent cancer worldwide and the sixth in cancer mortality (Siegel et al, 2017). There are two main histological types: esophageal adenocarcinoma (EAC) and esophageal squamous cell carcinoma (ESCC), which differ in etiology, pathogenesis, and biological characteristics. Despite the rapid increase in the incidence of EAC in western countries, lncRNA Signature Predicts the Prognosis of ESCC Patients. A comprehensive study of key molecular mechanisms related to the prognosis of ESCC is urgently needed. There have been few studies regarding the function of lncRNAs on ESCC prognosis, mainly attributed to the scarcity of relevant comprehensive and systematic analysis (Sun et al, 2014). ESCC gene expression data and related prognostic information are available in public databases, including the GEO database. LncRNA and mRNA data of ESCC were downloaded from GEO for analysis here

Methods

Results

Conclusion