Systematic pan-cancer analysis identified NCOA4 as an immunological and prognostic biomarker and validated in lung adenocarcinoma

Abstract

Background: Cancer immunotherapy has revolutionized treatment, yet predicting patient responses remains challenging. The potential of nuclear receptor coactivator 4 (NCOA4) as a biomarker has been identified in different types of cancer. However, its role as an immunological and prognostic factor across cancers, particularly lung adenocarcinoma (LUAD), still needs to be fully understood. This study aims to address this gap by systematically analyzing NCOA4's expression and its correlation with clinical outcomes and immune responses in pan-cancer.

Objective: To evaluate NCOA4 as a prognostic biomarker and explore its association with immune microenvironments across different cancer types, specifically focusing on LUAD.

Methods: We comprehensively analyzed data from the CCLE, GTEx, and TCGA databases. NCOA4 expression was analyzed in both normal and tumor tissues, and its correlation with overall survival was assessed using univariate Cox regression and Kaplan-Meier analysis. Immune infiltration was evaluated using the ESTIMATE, TIMER, and xCELL algorithms. We comprehensively analyzed its correlation with six genomic instability markers and the DNA methylation- and mRNA-based stemness index in certain tumors to investigate the potential role of NCOA4 in mediating cancer genomic heterogeneity and stemness. PPI network analysis and KEGG/GO enrichment analysis were conducted to identify associated proteins and pathways. LUAD samples were stained using immunohistochemistry (IHC). TIDE algorithm was used to predict immune checkpoint blockade (ICB) response within the TCGA-LUAD cohort.

Results: NCOA4 expression was significantly higher in tumor tissues than normal tissues in 18 cancer types, including LUAD, while it was lower in 5. Survival outcomes in specific cancers were found to be inversely associated with NCOA4 expression, as indicated by the results of univariate Cox regression and Kaplan-Meier analysis. Analysis of immune infiltration demonstrated a significant association between NCOA4 and the presence of immune cells, including CD8 + T cells, neutrophils, and dendritic cells. Genomic instability markers, including TMB and MSI, showed significant correlations with NCOA4 expression, indicating a potential role in immunotherapy response. Stemness index analyses suggested NCOA4's involvement in regulating tumor stemness. PPI network and KEGG/GO enrichment analyses implicated NCOA4 in immune-related biological processes and pathways. IHC staining of LUAD tissues confirmed higher NCOA4 expression in tumors versus non-cancerous tissues. The TIDE algorithm predicted that higher NCOA4 expression levels, as indicated by elevated TIDE scores, were associated with poorer ICB response rates in the TCGA-LUAD cohort.

Conclusion: NCOA4 exhibits context-dependent associations with prognosis, immune infiltration, and genomic instability across cancers. While experimental validation in LUAD supports its candidacy as a biomarker, mechanistic studies are required to establish causal relationships. These findings highlight NCOA4's potential utility in stratifying patients for immunotherapy and ferroptosis-targeted therapies.

Keywords: Immunological biomarker; Immunotherapy; Lung adenocarcinoma; NCOA4; Prognostic biomarker; Tumor microenvironment.

Fig. 1

NCOA4 expression analysis across normal and tumor tissues. A NCOA4 expression levels in 31 normal tissue types from the GTEx database. B Relative NCOA4 expression in various tumor cell lines from the CCLE database. C Comparison of NCOA4 expression between matched tumor and normal tissues for multiple cancer types from the TCGA and GTEx databases. D NCOA4 expression patterns across different tumor stages for various cancer types. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. GTEx, Genotype-Tissue Expression; CCLE, Cancer Cell Line Encyclopaedia; TCGA, The Cancer Genome Atlas

Fig. 2

Prognostic value of NCOA4 expression in pan-cancer. Univariate Cox regression analyses for overall survival (OS, A), progression-free interval (PFI, B), disease-free interval (DFI, C), and disease-specific survival (DSS, D) in various cancer types

Fig. 3

Kaplan-Meier survival curves for high and low NCOA4 expression groups in different cancer types

Fig. 4

Correlation of NCOA4 expression with immune cell infiltration and tumor microenvironment (TME). Correlation analysis between NCOA4 and immune cell infiltration levels in different cancer types using the TIMER (A) and Xcell (B) algorithms. C Association between NCOA4 expression and stromal, immune, and ESTIMATE scores in various tumors. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

Fig. 5

Correlation of NCOA4 with immune-related genes and genomic instability markers in pan-cancer. A Analysis of the correlation between NCOA4 expression and immune checkpoint molecules. B The correlation between NCOA4 expression and panels of chemokines, chemokine receptors, and MHC molecules is analyzed. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

Fig. 6

Correlation between NCOA4 expression and tumor stemness indices in various tumor types. A Correlation analysis between NCOA4 expression and TMB. B Correlation analysis between NCOA4 expression and MSI. C Correlation analysis between NCOA4 expression and NEO. D Correlation analysis between NCOA4 expression and purity. E Correlation analysis between NCOA4 expression and MATH. TMB, tumor mutation burden; MSI, microsatellite instability; NEO, tumor neoantigens; MATH, mutant-allele tumor heterogeneity

Fig. 7

Correlation between NCOA4 expression and tumor stemness. A Correlation analysis between NCOA4 expression and mRNAsi. B Correlation analysis between NCOA4 expression and DNAsi. mRNAsi, mRNA expression-based stemness index; DNAsi, DNA methylation-based stemness index

Fig. 8

NCOA4 is involved in the regulation of multiple immune-related signaling pathways. A PPI network analysis of NCOA4. B GO enrichment analysis of NCOA4. C KEGG enrichment analysis of NCOA4. PPI, Protein-Protein Interaction; GO, Gene Ontology; KEGG, Kyoto Encyclopaedia of Genes and Genomes

Fig. 9

NCOA4 Expression in LUAD and Its Association with Clinicopathological Features and ICB Response. A IHC staining of NCOA4 in a LUAD tissue microarray. B Violin plots showing NCOA4 expression levels in LUAD tissue. C Kaplan-Meier survival analyses of high or low NCOA4 expression based on tissue microarray IHC results for 65 patients. D Computationally predicted TIDE scores in TCGA-LUAD patients stratified by high and low NCOA4 expression levels. Scale bars, 200 and 50 μm (inset)

Fig. 10

Schematic diagram hypothesizing the relationship between NCOA4 and ferroptosis, and proposed strategies for combining ferroptosis modulation with immune checkpoint blockade. A Fe2 + produces reactive oxygen species (ROS) via the Fenton reaction. In exosomes, ferritin degradation is mediated by NCOA4. The NCOA4 binds ferritin, mediating its autophagic degradation in ferritinophagy. Ferroptosis has a dual role in anti-tumor immunity, dependent on the nature of the immune cell. B Inflamed phenotype: Inflamed tumors are associated with high immunogenicity, intact antigen presentation, and abundant CD8 + T cell infiltration. Ferroptosis inducers can hinder immune checkpoint inhibitor immunotherapy by killing infiltrated T cells and impairing dendritic cell function, whereas ferroptosis inhibitors do not exhibit such effects. Immune-excluded phenotypes are characterized by high levels of infiltrated MDSCs, TAMs, or Tregs. Ferroptosis inducers might be applied to eliminate MDSCs, TAMs, and Tregs. Immune-desert type: Tumors have low immunogenicity and limited immune cell infiltration, leading to poor response to Immunotherapy. However, some targeted drugs or chemoradiotherapy can induce ferroptosis, enhancing tumor cell immunogenicity and improving the effectiveness of combined Immunotherapy. (Created with bioRender.com)