Identification of Prognostic Genes in Lung Adenocarcinoma Immune Microenvironment
Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer and a leading cause of cancer-related deaths worldwide. The tumor immune microenvironment, composed of infiltrating immune and stromal cells, plays a critical role in the prognosis of LUAD patients. Recent advancements in high-throughput sequencing technology have revolutionized tumor research, enabling the analysis of large-scale clinical data to improve cancer diagnosis, treatment, and prevention. This study aimed to explore the correlation between immune and stromal scores in LUAD patients and identify differentially expressed genes (DEGs) with prognostic value. Using the ESTIMATE algorithm, the immune and stromal scores of LUAD patients from The Cancer Genome Atlas (TCGA) database were calculated, and DEGs were identified based on these scores. The prognostic significance of these genes was further validated using the Gene Expression Omnibus (GEO) database, Kaplan-Meier (K-M) plotter, and bioinformatics tools such as STRING and gene set enrichment analysis (GSEA).
Background and Significance
The tumor microenvironment is a complex network of malignant tumor cells, mesenchymal cells, inflammatory mediators, endothelial cells, stromal cells, immune cells, and normal epithelial cells. This microenvironment significantly influences cancer progression, including LUAD. The ESTIMATE algorithm, developed by Yoshihara et al., predicts the infiltration of stromal and immune cells in tumor samples based on transcriptional profiles. This algorithm has been successfully applied to various cancers, including prostate cancer, glioblastoma, colon cancer, thyroid cancer, breast cancer, and acute myeloid leukemia, to identify genes with prognostic value.
Study Design and Methodology
The study analyzed 522 LUAD patients from the TCGA database with complete clinical information. The clinicopathological factors included age, gender, T stage, N stage, M stage, and tumor-node-metastasis (TNM) stage. Among the patients, 53.6% were female, and 46.4% were male, with an average age of 66 years. The T stage distribution was T1 (33.0%), T2 (53.8%), T3 (9.0%), and T4 (3.6%). The N stage distribution was N0 (64.2%), N1 (18.8%), N2 (14.4%), and N3 (0.4%). The M stage distribution was M0 (67.6%) and M1 (4.8%). Over half of the patients were in stages I and II with no metastasis.
The ESTIMATE algorithm was used to calculate immune and stromal scores for all patients. The scores were divided into high and low groups based on their median values. The stromal scores ranged from -372.83 to 351.82, and the immune scores ranged from -172.02 to 656.87. Kaplan-Meier survival analysis revealed that patients with low immune scores had significantly poorer overall survival (P = 0.013), while the stromal score did not show a clear distinction in survival (P = 0.038).
Identification of Differentially Expressed Genes
Differential expression analysis was conducted on LUAD expression profiles from the TCGA database to identify genes associated with immune and stromal scores. A total of 72 DEGs were identified, including 64 up-regulated and 8 down-regulated genes. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed that these DEGs were primarily related to immune processes.
A protein-protein interaction (PPI) network was constructed using the STRING database, containing 49 nodes and 172 edges. The MCODE algorithm was used to cluster the PPI network, identifying two significant modules. The top ten genes with the highest interaction scores were identified using Cytoscape. Survival analysis of the 72 DEGs revealed that 22 genes were significantly associated with LUAD prognosis (P < 0.05). Among these, six genes were validated in the GEO database, and five of these genes (ABI3BP, CSF2RB, KBTBD8, PKHD1L1, and SCML4) were further confirmed in the K-M plotter.
Functional and Pathway Analysis
Functional enrichment analysis of the 22 prognostic genes showed their involvement in immune response. GSEA using the Biocarta gene sets revealed that the five key genes (CSF2RB, PKHD1L1, ABI3BP, SCML4, and KBTBD8) were positively enriched in several signaling pathways. The most significant pathways included TCR, IL2RB, FCER1, FMLP, CTCF, TOB1, CTLA4, GH, IL7, NO2IL12, IL12, and TH1TH2 signaling pathways.
Discussion
The study identified five key genes (CSF2RB, PKHD1L1, ABI3BP, SCML4, and KBTBD8) with significant prognostic value in LUAD. ABI3BP, a potential biomarker of lung cancer, has been shown to be suppressed in lung cancer cell lines. Up-regulation of ABI3BP in gallbladder cancer inhibits H3K27 methylation induced by EZH2, suggesting its tumor-suppressive role. CSF2RB acts as an apoptosis inducer in cancer cells, while PKHD1L1 inhibits cell growth and invasion in thyroid cancer cells, making them potential therapeutic targets for LUAD. The roles of KBTBD8 and SCML4 in cancer remain unexplored and warrant further investigation.
The signaling pathways associated with these genes, such as TCR, IL2RB, and CTLA4, are critical in immune regulation and cancer progression. However, the specific mechanisms of these pathways in LUAD need further validation through in vivo and in vitro experiments.
Limitations and Future Directions
While this study successfully identified prognostic genes using the ESTIMATE algorithm and bioinformatics tools, the findings require experimental validation using human samples. Future research should focus on validating the prognostic and therapeutic potential of the identified genes through clinical studies and functional experiments.
Conclusion
This study utilized the ESTIMATE algorithm and integrated bioinformatics approaches to identify key genes in the LUAD immune microenvironment. The five key genes (CSF2RB, PKHD1L1, ABI3BP, SCML4, and KBTBD8) show promise as prognostic markers and therapeutic targets. Further research is essential to validate their roles and explore their potential in improving LUAD prognosis and treatment.
doi.org/10.1097/CM9.0000000000001367
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