Programmed cell death pathways in lung adenocarcinoma: illuminating tumor drug resistance and therapeutic opportunities through single-cell analysis

Abstract

Lung adenocarcinoma (LUAD) is a major contributor to cancer-related deaths, distinguished by its pronounced tumor heterogeneity and persistent challenges in overcoming drug resistance. In this study, we utilized single-cell RNA sequencing (scRNA-seq) to dissect the roles of programmed cell death (PCD) pathways, including apoptosis, necroptosis, pyroptosis, and ferroptosis, in shaping LUAD heterogeneity, immune infiltration, and prognosis. Among these, ferroptosis and pyroptosis were most significantly associated with favorable survival outcomes, highlighting their potential roles in enhancing anti-tumor immunity. Distinct PCD-related LUAD subtypes were identified, characterized by differential pathway activation and immune cell composition. Subtypes enriched with cytotoxic lymphocytes and dendritic cells demonstrated improved survival outcomes and increased potential responsiveness to immunotherapy. Drug sensitivity analysis revealed that these subtypes exhibited heightened sensitivity to targeted therapies and immune checkpoint inhibitors, suggesting opportunities for personalized treatment strategies. Our findings emphasize the interplay between PCD pathways and the tumor microenvironment, providing insights into the mechanisms underlying tumor drug resistance and immune evasion. By linking molecular and immune features to clinical outcomes, this study highlights the potential of targeting PCD pathways to enhance therapeutic efficacy and overcome resistance in LUAD. These results contribute to a growing framework for developing precise and adaptable cancer therapies tailored to specific tumor characteristics.

Keywords: Drug resistance; Immune infiltration; Lung adenocarcinoma; Programmed cell death; Single-cell RNA sequencing; Therapeutic targets; Tumor heterogeneity.

Fig. 1

Single-cell RNA sequencing (scRNA-seq) analysis of lung adenocarcinoma (LUAD) samples. A t-SNE plot showing sample type distribution across four datasets: GSM4506698, GSM4506699, GSM4506700, and GSM4506701. Each color represents a different dataset, highlighting distinct clustering. B t-SNE plot illustrating cell cycle phase distribution (G1, G2M, and S) within the samples. C t-SNE plot displaying cell ploidy status, categorized as aneuploid, diploid, or undefined. D Bar plot of cell type proportions in each dataset, revealing a predominance of diploid cells in GSM4506700 and GSM4506701 and a higher proportion of aneuploid cells in GSM4506698 and GSM4506699. E Bar plot depicting cell cycle phase distribution (G1, G2M, S) across datasets, showing balanced proportions

Fig. 2

Programmed cell death (PCD) pathway enrichment in LUAD cells. A Heatmap of PCD pathway activation in malignant versus non-malignant cells across datasets, highlighting distinct activation profiles in pathways like parthanatos, ferroptosis, and oxeiptosis. B Bar plot showing activation scores for key PCD pathways in aneuploid and diploid cells within each dataset. Aneuploid cells in GSM4506701 exhibit notable enrichment in netotic cell death pathways, while diploid cells show higher activation in cuproptosis and pyroptosis pathways. C Heatmap of PCD-related gene expression across aneuploid and diploid cells, revealing differential profiles by cell type. D Bar plots showing PCD-related gene expression, with genes such as NPC2, NAPSA, and SAT1 elevated in aneuploid cells and FTL, FTL1, and FTL2 elevated in diploid cells

Fig. 3

Identification of PCD-related clusters in LUAD patients. A Cumulative distribution function (CDF) plot from consensus clustering, identifying k = 2 as the optimal cluster number. B Consensus matrix for k = 2, illustrating clear separation of LUAD samples into Cluster 1 and Cluster 2. C Kaplan–Meier survival curve comparing overall survival between clusters, with Cluster 2 showing significantly improved survival (p < 0.0001). D Violin plot of PCD scores by cluster, indicating a significantly higher PCD score in Cluster 1. E Heatmap of PCD-related gene expression across clusters, with genes like PCBP2, HSBP1, BAG3, JUN, and DAP1 highly expressed in Cluster 2. F Heatmap of pathway enrichment scores for PCD pathways (alkaliptosis, cuproptosis, autophagy, oxeiptosis, pyroptosis), showing notable differences between clusters

Fig. 4

Drug sensitivity analysis in PCD-related LUAD clusters. Box plot of IC50 values for selected drugs between Cluster 1 and Cluster 2, highlighting significant drug sensitivity differences, with Cluster 2 showing higher sensitivity to certain drugs, suggesting cluster-specific therapeutic vulnerabilities

Fig. 5

Immune infiltration in PCD-related LUAD clusters. A Violin plots comparing immune score, stromal score, ESTIMATE score, and tumor purity between clusters (using ESTIMATE algorithm). Cluster 2 shows significantly higher immune and stromal scores and a higher ESTIMATE score, while Cluster 1 has higher tumor purity. B Heatmap of immune cell infiltration levels by cluster, with elevated infiltration of immune cell types (e.g., B cells, cytotoxic lymphocytes, monocytes, dendritic cells) in Cluster 2. C Box plot of immune checkpoint expression between clusters, showing significantly higher expression of checkpoints (e.g., YTHDF1, NRP1, B2M, TNFSF15) in Cluster 2, indicating potential responsiveness to immunotherapy

Fig. 6

Pathway enrichment analysis of differentially expressed genes (DEGs) between clusters. A Circular plot of Gene Ontology (GO) enrichment for DEGs, showing significant pathways, including small GTPase signaling, positive regulation of cell development, and transcription factor binding. B Circular plot of KEGG pathway enrichment for DEGs, revealing significant pathways such as PI3K-Akt signaling, human papillomavirus infection, and Kaposi sarcoma-associated herpesvirus infection

Fig. 7

Risk stratification based on PCD-related genes in TCGA dataset. A Risk score distribution plot with a cutoff separating high-risk and low-risk groups. B Scatter plot of survival status versus risk score, showing higher mortality among high-risk patients. C Heatmap of gene expression (BAMBI, TMCC2, NOX4, DKK1, CBS) in high-risk versus low-risk groups, illustrating distinct expression patterns associated with risk

Fig. 8

Prognostic analysis of PCD-related subtypes in TCGA and GSE31210 datasets. A Kaplan–Meier survival curves for high- and low-risk groups in TCGA dataset, with the low-risk group showing better prognosis (p < 0.0001). B ROC curve for TCGA dataset, with AUC values of 0.85, 0.88, and 0.84 at 1, 3, and 5 years, demonstrating strong predictive performance. C Kaplan–Meier survival curves for GSE31210 dataset, reaffirming significant survival differences between risk groups. D ROC curve for GSE31210, supporting the model’s prognostic accuracy with high AUC values