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

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.

FIGURE 1

Overall design of the present study. LUSC, lung squamous cell carcinoma; RFS, Recurrence-free survival.

FIGURE 2

Expression profiles of nine genes for prediction of RFS in stage I-IIIA LUSC by multivariate Cox regression.

FIGURE 3

Kaplan-Meier analysis of RFS with nine genes (including LINC02683, AC244517.5, LINC02418, LINC01322, AC011468.3, hsa-mir-6825, AC020637.1, AC027117.2, and SERPINB12) in stage I-IIIA LUSC.

FIGURE 4

(A) Scatter diagram of the risk score and survival status of patients with stage I-IIIA LUSC in the training cohort. (B) The heatmap of nine-gene expression profiles for predicting recurrence risk model. (C) Kaplan-Meier plot showed significance between high-risk and low-risk patients in RFS by the prognostic model (P < 0.05). (D) The ROC curve analysis for the recurrence risk model. ROC, receiver operating characteristic; AUC, area under the ROC curve.

FIGURE 5

The distribution of risk score and survival status of the nine-gene signature in the validation cohort (A) and entire cohort. (B) Kaplan-Meier curves of overall survival in the validation cohort (C) and entire cohort (D).

FIGURE 6

ROC analysis of the nine-gene risk model in the validation cohort (A) and the entire sample cohort (B).