Screening of Immune-related lncRNAs in Lung Adenocarcinoma and Establishing a Survival Prognostic Risk Prediction Model


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Abstract

Objective:This study aimed to improve lung adenocarcinoma (LUAD) prognosis prediction based on a signature of immune-related long non-coding RNAs (lncRNAs).

Methods:LUAD samples from the TCGA database were divided into the immunity_H group and the immunity_L group. Differentially expressed RNAs (DERs) between the two groups were identified. Optimized immune-related lncRNAs combination was obtained using LASSO Cox regression. A prognostic risk prediction (RS) model was built and further validated in the training and validation datasets. A network among lncRNAs in the RS model, their co-expressed DERs, and the related KEGG pathways were established. Critical lncRNAs were validated in LUAD tissue samples.

Results:In total, 255 DERs were obtained, and 11 immune-related lncRNAs were significantly related to prognosis. Six lncRNAs were demonstrated as an optimal combination for building the RS model, including LINC00944, LINC00930, LINC00607, LINC00582, LINC00543, and LINC00319. The KM curve and ROC curve revealed the RS model to be a reliable indicator for LUAD prognosis. LINC00944 and LINC00582 showed a co-expression relationship with the MS4A1. LINC00944, LINC00582, and MS4A1 were successfully validated in LUAD samples.

Conclusion:We have established a promising LUAD patient survival prediction model based on six immune-related lncRNAs. For LUAD patients, this prognostic model could guide personalized treatment.

About the authors

Wenxia Jiang

School of Clinical Medicine, Shanghai University of Medicine and Health Sciences

Author for correspondence.
Email: info@benthamscience.net

Xuyou Zhu

Department of Pathology, Tongji Hospital of Tongji University

Email: info@benthamscience.net

Jiaqi Bo

Department of Pathology, Tongji Hospital of Tongji University

Email: info@benthamscience.net

Jun Ma

Department of Nephrology, ing’an District Center Hospital of Shanghai, Fudan University

Author for correspondence.
Email: info@benthamscience.net

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