Development of an alkaliptosis-related lncRNA risk model and immunotherapy target analysis in lung adenocarcinoma

Abstract

Background: Lung cancer has the highest mortality rate among all cancers worldwide. Alkaliptosis is characterized by a pH-dependent form of regulated cell death. In this study, we constructed a model related to alkaliptosis-associated long non-coding RNAs (lncRNAs) and developed a prognosis-related framework, followed by the identification of potential therapeutic drugs.

Methods: The TCGA database was utilized to obtain RNA-seq-based transcriptome profiling data, clinical information, and mutation data. We conducted multivariate Cox regression analysis to identify alkaliptosis-related lncRNAs. Subsequently, we employed the training group to construct the prognostic model and utilized the testing group to validate the model's accuracy. Calibration curves were generated to illustrate the discrepancies between predicted and observed outcomes. Principal Component Analysis (PCA) was performed to investigate the distribution of LUAD patients across high- and low-risk groups. Additionally, Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) were conducted. Immune cell infiltration and Tumor Mutational Burden (TMB) analyses were carried out using the CIBERSORT and maftools algorithms. Finally, the "oncoPredict" package was employed to predict immunotherapy sensitivity and to further forecast potential anti-tumor immune drugs. qPCR was used for experimental verification.

Results: We identified 155 alkaliptosis-related lncRNAs and determined that 5 of these lncRNAs serve as independent prognostic factors. The progression-free survival (PFS) and overall survival (OS) rates of the low-risk group were significantly higher than those of the high-risk group. The risk signature functions as a prognostic factor that is independent of other variables. Different stages (I-II and III-IV) effectively predict the survival rates of lung adenocarcinoma (LUAD) patients, and these lncRNAs can reliably forecast these signatures. GSEA revealed that processes related to chromosome segregation and immune response activation were significantly enriched in both the high- and low-risk groups. The high-risk group exhibited a lower fraction of plasma cells and a higher proportion of activated CD4 memory T cells. Additionally, the OS of the low TMB group was significantly lower compared to the high TMB group. Furthermore, drug sensitivity was significantly greater in the high-risk group than in the low-risk group. These lncRNAs may serve as biomarkers for treating LUAD patients.

Conclusion: In summary, the construction of an alkaliptosis-related lncRNA prognostic model and drug sensitivity analysis in LUAD patients provides new insights into the clinical diagnosis and treatment of advanced LUAD patients.

Keywords: alkaliptosis; immunotherapy sensitivity; lncRNAs; lung adenocarcinoma; prognosis.

FIGURE 1

Identification of alkaliptosis-related lncRNAs. (A) The Sankey diagram showed the co-expression relationship of alkaliptosis-related genes and alkaliptosis-related lncRNAs. (B) Heatmap showed the relationship between alkaliptosis-related genes and alkaliptosis-related lncRNAs. (C) Cross-validation of LASSO regression. (D) Trajectory of each independent variable. *, p < 0.05; **, p < 0.01; ***, p < 0.001.

FIGURE 2

Kaplan–Meier survival analyses of LUAD patients. We divided patients into high-risk and low-risk groups based on median risk and predicted overall survival (OS) and progression-free survival (PFS) for each group. (A) OS in all groups. (B) OS in the training groups. (C) OS in the testing groups. (D) PFS in all groups.

FIGURE 3

Assessment of the prognostic value of alkaliptosis-related lncRNA models. Survival curves of high-risk and low-risk LUAD patients; Heatmap showed high-and low-risk levels of 5 lncRNAs in the (A) all group, (B) training group, and (C) testing groups.

FIGURE 4

Analysis of the independent prognostic value of clinical features. (A) Univariate and (B) multivariate independent prognostic analysis to determine whether clinical characteristics are independently associated with OS. (C) 1-, 3-, and 5-year area under the ROC curve (AUC) of clinical indicators in all groups. (D) AUC values of clinical indicators.

FIGURE 5

Nomograms and survival curves were used to predict OS and survival in LUAD patients. (A) Calibration curves for 1-year, 3-year, and 5-year. (B) Prognostic nomogram for OS of LUAD patients. (C, D) Survival analyses of LUAD Patients at stages I–II and stages III–IV.

FIGURE 6

Principal component analysis. PCA observed the distribution of (A) All genes, (B) Risk lncRNAs, (C) Alkaliptosis-related genes, and (D) Alkaliptosis-related lncRNAs.

FIGURE 7

Functional enrichment analysis. GO enrichment analyses of the alkaliptosis-related lncRNAs were shown by (A) circle diagram and (B) bubble chart.

FIGURE 8

GSEA enrichment analyses. GSEA enrichment analyses of the alkaliptosis-related lncRNAs in (A) high-risk group and (B) low-risk group.

FIGURE 9

The proportions and differences of 22 types of immune cells in high-risk and low-risk groups were analyzed. (A) The percentage histogram illustrates the relative percentages of immune cells. (B) The violin plot displays the differences in immune cell infiltration fractions between high-risk and low-risk LUAD patients, with low-risk samples represented in blue and high-risk samples in red. *, p < 0.05; **, p < 0.01; ***, p < 0.001.

FIGURE 10

The tumor mutational burden (TMB) and somatic mutation frequencies were analyzed in high- and low-risk lung adenocarcinoma (LUAD) patients. The waterfall plot illustrates the top fifteen mutated genes in LUAD patients for the (A) high-risk group (250 samples) and (B) low-risk group (246 samples). (C) The violin plot presents the TMB results. (D) Kaplan-Meier curves demonstrate the differences in overall survival (OS) between LUAD patients with high and low TMB. (E) Survival curves for LUAD patients are shown based on varying TMB and risk scores.

FIGURE 11

Immunotherapy and drug sensitivity analysis. Different drug sensitivity of (A) 5-fluorouracil, (B) AZD6738, (C) ERK_6604, (D) Savolitinib, and (E) SCH772984. ***, p < 0.001.

FIGURE 12

lncRNA expression and drug verification. (A, B) qPCR analysis of 5 lncRNAs in LUAD vs normal cells. And the outcomes of anti-PD-1 antibody and 5-fluorouracil on lncRNA expression. ***, p < 0.001.