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. 2022 Sep 21:13:1016449.
doi: 10.3389/fgene.2022.1016449. eCollection 2022.

A novel LUAD prognosis prediction model based on immune checkpoint-related lncRNAs

Yang Liu  1 , Mingyang Yu  1 , Xuechao Cheng  1 , Xingshu Zhang  1 , Qian Luo  1 , Sijin Liao  1 , Zhongzheng Chen  1 , Jianhao Zheng  1 , Kaijun Long  1 , Xingwei Wu  1 , Wendong Qu  1 , Ming Gong  1 , Yongxiang Song  1
Affiliations

A novel LUAD prognosis prediction model based on immune checkpoint-related lncRNAs

Yang Liu et al. Front Genet. .

Abstract

Lung adenocarcinoma (LUAD) is a malignant disease with an extremely poor prognosis, and there is currently a lack of clinical methods for early diagnosis and precise treatment and management. With the deepening of tumor research, more and more attention has been paid to the role of immune checkpoints (ICP) and long non-coding RNAs (lncRNAs) regulation in tumor development. Therefore, this study downloaded LUAD patient data from the TCGA database, and finally screened 14 key ICP-related lncRNAs based on ICP-related genes using univariate/multivariate COX regression analysis and LASSO regression analysis to construct a risk prediction model and corresponding nomogram. After multi-dimensional testing of the model, the model showed good prognostic prediction ability. In addition, to further elucidate how ICP plays a role in LUAD, we jointly analyzed the immune microenvironmental changes in LAUD patients and performed a functional enrichment analysis. Furthermore, to enhance the clinical significance of this study, we performed a sensitivity analysis of common antitumor drugs. All the above works aim to point to new directions for the treatment of LUAD.

Keywords: bioinformatic analyse; immune check point; lncRNA; lung adenocarcinoma; tumor microenvironment.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Workflow chart.
FIGURE 2
FIGURE 2
Genetic and expression variation of the MRDEGs in LUAD patients. (A) ICP-related gene expression profile. (B) Sankey relation diagram for ICP-related genes and lncRNAs. (C) The CNV frequency of 47 ICP genes in the LUAD cohort. (D) Heatmap for the correlations between randomly ICP-related genes and lncRNAs.
FIGURE 3
FIGURE 3
Risk model construction and validation. (A,B) Result of LASSO regression analysis. (C) Heatmap to show the expression of 14 lncRNAs between high- and low-risk groups in the training set. (D) Expression differences of 14 ICP-related lncRNAs in different risk groups in the training set. (E) Distribution of sample risk score and different patterns of survival status/time between the high-risk and low-risk groups in the training set. (F) Kaplan-Meier curve of high-risk and low-risk patients in the training set.
FIGURE 4
FIGURE 4
Risk model construction and validation in testing and entire sets. (A–D) The expression of 14 key prognostic lncRNAs in the testing set, the survival status of LUAD patients, the risk score, and the results of K-M analysis of survival analysis. (E–H) The expression of 14 key prognostic lncRNAs in the entire set, the survival status of LUAD patients, the risk score, and the results of K-M analysis of survival analysis.
FIGURE 5
FIGURE 5
Nomogram and independent prognostic factor analysis. (A,B) Result of univariate/multivariate Cox regression analyses. (C) Nomogram predicts the probability of the 1-, 3-, and 5-years OS. (D) Result of DCA. (E–G) 1-, 3-, and 5-years predicted prognosis.
FIGURE 6
FIGURE 6
Assessment of the predictive risk model and Principal component analysis. (A) The entire set's 1-, 3-, and 5-years ROC curves. (B) ROC curves of the clinical characteristics and risk score. (C,D) PCA results of testing and training sets. (E–H) The PCA result of entire gene expression profiles, ICPDEGs, ICP-related lncRNAs, and risk model according to the 14 hub lncRNAs, respectively.
FIGURE 7
FIGURE 7
TMB analysis. (A,B) The waterfall plot of somatic mutation features established with high- and low-risk groups. (C) Tumor mutation burden in the high-risk and low-risk groups. (D,E) Kaplan-Meier curve of the OS among the high- and low- TMB groups.
FIGURE 8
FIGURE 8
Immune infiltration discrepancy in different risk groups. (A) Heatmap of 22 tumor-infiltrating immune cell types in low-risk and high-risk groups. (B) Bar chart of the proportions for 22 immune cell types. (C) The ssGSEA scores of immune functions in low-risk and high-risk groups. (D) Immune cells in low-risk and high-risk groups. (E–G) The TME scores between high-risk and low-risk groups. (H,I) Survival analysis of combined immune cells. (J) Immune subtype.
FIGURE 9
FIGURE 9
Clinical immunotherapy analysis. (A) Results of drug sensitivity analysis. (B) Result of TIDE.
FIGURE 10
FIGURE 10
Functional enrichment analysis. (A) Result of GO enrichment. (B) Result of KEGG enrichment. (C,D) Result of GSEA.
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