Influence of migrasome-associated long noncoding RNAs on the immune microenvironment and prognosis in lung adenocarcinoma

Abstract

This study investigated the prognostic impact of migrasome-related long noncoding RNAs (lncRNAs) in lung adenocarcinoma (LUAD). We analyzed transcriptomic data from The Cancer Genome Atlas (TCGA) database, comprising 541 tumor samples and 59 normal tissue samples, to pinpoint key migrasome genes and related lncRNAs, using correlation analysis to detect those pertinent to patient outcomes. A risk score model based on 17 migrasome-related lncRNAs, constructed via univariate, LASSO, and multivariate Cox regression, was then validated in an independent dataset to ensure reliability. Our findings revealed that high-risk patients exhibited worse overall and progression-free survival, alongside altered immune features, such as potential immune evasion and an increased propensity for immunotherapy responsiveness. Moreover, Tumor Immune Dysfunction and Exclusion (TIDE) analyses suggested that individuals with higher scores could experience greater benefit from immune checkpoint inhibitors. Functional enrichment analysis supported the engagement of migrasome-related pathways and immune-regulatory processes that may drive disease progression. Additionally, principal component analysis (PCA) confirmed the robustness of our lncRNA-driven classifier, enabling accurate differentiation of risk cohorts. Overall, our study underscores the contribution of migrasome-related lncRNAs in predicting LUAD prognosis and informing clinical choices, shedding light on tumor biology and immunotherapy response. These results emphasize the clinical importance of migrasome-related lncRNAs as promising therapeutic targets and prognostic biomarkers in LUAD management.

Keywords: Immunotherapy; LUAD; LncRNAs; Migrasomes; Prognosis.

Fig. 1

Flowchart of the present study

Fig. 2

Identification of metastasis-related lncRNAs and development of prognostic models in LUAD. A Sankey diagram illustrating associations between metastasis genes and metastasis-related lncRNAs. B Ten-fold cross-validation for selecting the tuning parameter lambda in the LASSO Cox regression model. C LASSO coefficient profiles of 241 metastasis-related lncRNAs. D Heatmap showing correlations between metastasis genes and 9 metastasis-related lncRNAs

Fig. 3

Prognostic evaluation of the risk model in the entire, training, and test sets. A–C Risk score distributions for patients in low-risk and high-risk groups. D–F Survival status and time in low-risk versus high-risk groups. G–I Hierarchical clustering analysis of 9 Metastasis-related lncRNAs between low-risk and high-risk groups. J–L Kaplan-Meier overall survival curves comparing low-risk and high-risk groups. M Kaplan-Meier progression-free survival curves comparing low-risk and high-risk groups in the entire set

Fig. 4

Kaplan–Meier survival curves comparing low-risk and high-risk populations stratified by clinical variables. A, B Survival curves for patients aged ≤ 65 years (A) and > 65 years (B). C, D Survival curves for male (C) and female (D) patients. E, F Survival curves for clinical stages I-II (E) and III-IV (F). G, H Survival curves for T stages T1-2 (G) and T3-4 (H). I, J Survival curves for N stages N0 (I) and N1 (J). K, L Survival curves for M stages M0 (K) and M1 (L)

Fig. 5

Independent prognostic analysis and further validation of the risk model. Forest plots of A univariate and B multivariate Cox regression analyses showing the effects of clinical characteristics (including the risk signature) on OS. C Time-dependent ROC curves for OS at 1, 3, and 5 years. D Predictive accuracy of the risk model compared to clinicopathological characteristics. E Concordance index of the risk model and other clinical information. F Nomogram combining the risk signature and clinical factors. G Calibration curves for the nomogram-predicted OS at 1, 3, and 5 years

Fig. 6

Principal Component Analysis(PCA) distinguishing low-risk and high-risk groups. (A) PCA based on all genes. (B) PCA based on metastasis genes. (C) PCA based on metastasis-related lncRNAs. (D) PCA based on the 9 risk-associated lncRNAs

Fig. 7

Functional analysis of the risk model. (A, B) GO enrichment analysis showing significant biological processes (BP), cellular components (CC), and molecular functions (MF). (C, D) KEGG pathway analysis highlighting significantly enriched pathways. (E, F) GSEA comparing high-risk and low-risk groups based on the KEGG pathway database

Fig. 8

Differences in the tumor immune microenvironment between low-risk and high-risk groups. A Abundance ratios of immune cells in LUAD samples. B Differentially expressed immune cells between low-risk and high-risk score groups. C Immune function analysis in low-risk and high-risk score groups

Fig. 9

Relationship of model scores to TMB and TIDE. A, B Waterfall plots showing somatic mutation characteristics in the two groups. C Comparison of TMB between low-risk and high-risk groups. D Kaplan-Meier survival analysis based on TMB status. E Kaplan-Meier survival analysis for patients categorized by combined TMB status and risk score. F Comparison of TIDE scores between the two groups

Fig. 10

Drug sensitivity analysis. A BMS-754,807 exhibits higher sensitivity in the low-risk group. B SCH772984 is more effective in the high-risk group

Fig. 11

External validation of metastasis-related lncRNAs. A Expression of PAN3-AS1 in tumor tissues and paired normal tissues from the TCGA database. B Overall survival (OS) analysis of PAN3-AS1 in the Kaplan-Meier Plotter dataset. C PAN3-AS1 expression levels detected by qPCR in normal lung cells and LUAD cell lines. Significant differences compared to normal lung cells are indicated by asterisks (*p < 0.05; * *p < 0.01)