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. 2022 Sep 27:13:990153.
doi: 10.3389/fgene.2022.990153. eCollection 2022.

Characterization of fatty acid metabolism-related lncRNAs in lung adenocarcinoma identifying potential novel prognostic targets

Yang Liu  1 , Xingshu Zhang  1 , Xuechao Cheng  1 , Qian Luo  1 , Mingyang Yu  1 , Kaijun Long  1 , Wendong Qu  1 , Yang Tang  1 , Ming Gong  1 , Lubiao Liang  1 , Xixian Ke  1 , Yongxiang Song  1
Affiliations

Characterization of fatty acid metabolism-related lncRNAs in lung adenocarcinoma identifying potential novel prognostic targets

Yang Liu et al. Front Genet. .

Abstract

Lung adenocarcinoma (LUAD), a malignant respiratory tumor with an extremely poor prognosis, has troubled the medical community all over the world. According to recent studies, fatty acid metabolism (FAM) and long non-coding RNAs (lncRNAs) regulation have shown exciting results in tumor therapy. In this study, the original LUAD patient data was obtained from the TCGA database, and 12 FAM-related lncRNAs (AL390755.1, AC105020.6, TMPO-AS1, AC016737.2, AC127070.2, LINC01281, AL589986.2, GAS6-DT, AC078993.1, LINC02198, AC007032.1, and AL021026.1) that were highly related to the progression of LUAD were finally identified through bioinformatics analysis, and a risk score model for clinical reference was constructed. The window explores the immunology and molecular mechanism of LUAD, aiming to shed the hoping light on LUAD treatment.

Keywords: biomarker; fatty acid metabolism; immune cells; long non-coding RNA; lung adenocarcinoma.

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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
The entire analytical process of the study.
FIGURE 2
FIGURE 2
Selection of FAM-related lncRNAs in LUAD patients. (A) Sankey relation diagram for target lncRNAs. (B) Heatmap of the correlation between FAM-related genes and the 12 prognostic FAM-related lncRNAs in TCGA entire set.
FIGURE 3
FIGURE 3
Prognostic model in training set validation. (A) Univariate Cox regression analysis. (B) The LASSO coefficient profile. (C) The 10-fold cross-validation for variable selection in the LASSO model. (D) Multivariate Cox regression analysis and 12 lncRNAs were finally selected. (E) Patient risk score distribution for the training set. (F) Survival status time between two risk groups in the training set. (G) 12 FAM-related lncRNAs distributed for each patient in the training set. (H) OS curve of the training set.
FIGURE 4
FIGURE 4
Prognostic value of risk score model in testing and entire sets. (A–D) Distribution of risk score, survival status, 12 hub lncRNA expression levels, and K-M survival curve (OS) in the testing set. (E–H) Distribution of risk score, survival status, 12 hub lncRNA expression levels, and K-M survival curve (OS) in the entire set.
FIGURE 5
FIGURE 5
Principal component analysis. (A,B) 2D PCA in training and the entire set. (C–E) PCA between two risk groups for entire gene expression profiles, 92 FAM related-genes, and profiles of the 12 FAM-related lncRNAs as an entire set.
FIGURE 6
FIGURE 6
Construction and validation of the nomogram. (A) Univariate Cox regression analysis indicated that disease stage, T stage, M stage, and risk score, were related to prognosis (p < 0.001) (B) Multivariate Cox regression analysis presented that the risk score was an independent factor affecting prognosis (p < 0.001). (C) The nomogram predicts the probability of the 1-, 3-, and 5-year OS. (D) The calibration plot.
FIGURE 7
FIGURE 7
Assessment of the prognostic risk model. (A–C) The 1-, 3-, and 5-year ROC curves of the training, testing set, and entire set. (D) ROC curves of all included features. (E) CI of the risk score and clinical characteristics. (F–I) OS curve of difference clustered by LUAD clinical features between two risk groups in the entire set.
FIGURE 8
FIGURE 8
Stratification Analysis of the FAM-related lncRNA prognostic risk score in immune features. (A–C) Heatmap, bar chart, and relative infiltrating proportion of 22 tumor-infiltrating immune cell types in two risk groups. (D,E) The score of immune functions comparing two risk groups by ssGSEA or ssGSEA score. (F–H) The comparison of immune-related scores between high- and low-risk groups.
FIGURE 9
FIGURE 9
Exploration of TMB and lncRNAs networks visualization. (A,B) 20 genes with high mutation frequencies in different risk subgroups. (C) TMB difference in two risk groups. (D) The correlation between risk score and TMB. (E) K-M curves of the patient OS in the high-TMB and low-TMB groups in the entire set. (F) The survival outcome predictive validity of TMB. (G) The correlation between risk score and immune subtype. (H) Sankey diagram: the connection degree between the FAM-related genes, FAM-related lncRNAs, and risk types.
FIGURE 10
FIGURE 10
The investigation of tumor immune factors and immunotherapy. (A) The immunotherapy prediction of high-risk and low-risk groups. (B) The correlation between 12 FAM-related lncRNAs and drugs. (C) TIDE prediction difference in the high-risk and low-risk patients.
FIGURE 11
FIGURE 11
Functional analysis. (A) Result of GO functional enrichment (top 10). (B) KEGG enrichment terms (top 30). (C) Circle diagram in KEGG analysis. (D) GSEA of the top 10 pathways significantly enriched in the high-risk group. (E) GSEA of the top 10 pathways in the low-risk group. (F) 12 FAM-related lncRNAs and differential FAM genes networks.
FIGURE 12
FIGURE 12
Expression of nine lncRNAs from the risk model in LUAD cell lines and bronchial epithelial cells.
See this image and copyright information in PMC

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