A Combined RNA Signature Predicts Recurrence Risk of Stage I-IIIA Lung Squamous Cell Carcinoma

Li Sun; Juan Li; Xiaomeng Li; Xuemei Yang; Shujun Zhang; Xue Wang; Nan Wang; Kanghong Xu; Xinquan Jiang; Yi Zhang

doi:10.3389/fgene.2021.676464

A Combined RNA Signature Predicts Recurrence Risk of Stage I-IIIA Lung Squamous Cell Carcinoma

Front Genet. 2021 Jun 14:12:676464. doi: 10.3389/fgene.2021.676464. eCollection 2021.

Authors

Li Sun¹, Juan Li², Xiaomeng Li^{1

3}, Xuemei Yang², Shujun Zhang², Xue Wang¹, Nan Wang¹, Kanghong Xu¹, Xinquan Jiang¹, Yi Zhang⁴

Affiliations

¹ School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, China.
² Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
³ Department of Hematology, Jining First People's Hospital, Jining, China.
⁴ Respiratory and Critical Care Medicine Department, Qilu Hospital, Shandong University, Jinan, China.

Abstract

Objective: Recurrence remains the main cause of the poor prognosis in stage I-IIIA lung squamous cell carcinoma (LUSC) after surgical resection. In the present study, we aimed to identify the long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) related to the recurrence of stage I-IIIA LUSC. Moreover, we constructed a risk assessment model to predict the recurrence of LUSC patients.

Methods: RNA sequencing data (including miRNAs, lncRNAs, and mRNAs) and relevant clinical information were obtained from The Cancer Genome Atlas (TCGA) database. The differentially expressed lncRNAs, miRNAs, and mRNAs were identified using the "DESeq2" package of the R language. Univariate Cox proportional hazards regression analysis and Kaplan-Meier curve were used to identify recurrence-related genes. Stepwise multivariate Cox regression analysis was carried out to establish a risk model for predicting recurrence in the training cohort. Moreover, Kaplan-Meier curves and receiver operating characteristic (ROC) curves were adopted to examine the predictive performance of the signature in the training cohort, validation cohort, and entire cohort.

Results: Based on the TCGA database, we analyzed the differentially expressed genes (DEGs) among 27 patients with recurrent stage I-IIIA LUSC and 134 patients with non-recurrent stage I-IIIA LUSC, and identified 431 lncRNAs, 36 miRNAs, and 746 mRNAs with different expression levels. Out of these DEGs, the optimal combination of DEGs was finally determined, and a nine-joint RNA molecular signature was constructed for clinical prediction of recurrence, including LINC02683, AC244517.5, LINC02418, LINC01322, AC011468.3, hsa-mir-6825, AC020637.1, AC027117.2, and SERPINB12. The ROC curve proved that the model had good predictive performance in predicting recurrence. The area under the curve (AUC) of the prognostic model for recurrence-free survival (RFS) was 0.989 at 3 years and 0.958 at 5 years (in the training set). The combined RNA signature also revealed good predictive performance in predicting the recurrence in the validation cohort and entire cohort.

Conclusions: In the present study, we constructed a nine-joint RNA molecular signature for recurrence prediction of stage I-IIIA LUSC. Collectively, our findings provided new and valuable clinical evidence for predicting the recurrence and targeted treatment of stage I-IIIA LUSC.

Keywords: RNA signature; TCGA; biomarker; lung squamous cell carcinoma; recurrence.