NOX4 as an emerging prognostic and immunological biomarker across pan-cancer analyses

Abstract

Despite extensive research highlighting the pivotal role of NOX4 in the development of various malignancies, no systematic pan-cancer analysis has been conducted to evaluate its comprehensive prognostic value and potential immunological functions. In this study, we explored the prognostic significance of NOX4 and its role in immune modulation across different cancer types. We performed a pan-cancer analysis of NOX4 expression and its prognostic implications using multiple databases, including TCGA, GEPIA, GTEx, UALCAN, TISCH, GDSC, PDB, TIMER, and cBioPortal. This analysis provided a detailed assessment of NOX4 expression profiles, clinical correlations, genetic variants, tumor microenvironment interactions, immune relevance, therapeutic potential, and related gene functions through various multi-omics approaches. To further investigate these findings, we collected clinical samples from selected cancer types and validated NOX4 expression through immunohistochemistry, H&E staining, and RT-PCR. Our results revealed that NOX4 was overexpressed in most tumors, although its expression was significantly reduced in certain renal cancers. Moreover, we observed distinct correlations between NOX4 expression and patient prognosis. Notably, NOX4 expression was significantly associated with tumor infiltration, indicating its potential as a target for immunotherapy. Additionally, NOX4 expression showed significant correlations with immune checkpoint proteins (ICP), tumor mutation burden (TMB), microsatellite instability (MSI), RNA stemness scores (RNAss), DNA stemness scores (DNAss), RNA methylation, and DNA methylation.

Keywords: Immune microenvironment; NOX4; Pan-cancer; Prognosis.

Fig. 1

Overview of the study design.

Fig. 2

Expression levels of NOX4 in pan-cancer. (A) TIMER2.0 displays the expression of NOX4 across TCGA tumors and corresponding normal tissues. (B) The expression of NOX4 in tumor and normal tissues, as shown on the UALCAN website, which provides protein level data for NOX4 across various tumor types. (C) RT-PCR analysis of NOX4 expression in different tumor types, indicating potential variations in NOX4 regulation across cancers. (D) Expression of NOX4 protein in KIRC provided by the CPTAC website. (E) NOX4 expression through unsupervised cluster analysis for pan-cancer subtypes (S1 to S11) of different tumor types in CPTAC. (F) Single-cell map of KIRC from the GSE159115 dataset. (G) NOX4 expression in KIRC’s single-cell map. (H) Differential expression analysis of NOX4 in KIRC’s single-cell map, revealing significant upregulation in specific cell populations. (I) Hematoxylin and eosin staining of adjacent normal tissue and KIRC. (J) Immunohistochemical staining of NOX4 in KIRC and adjacent normal tissue. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Fig. 3

Prognostic value of NOX4 across tumors. (A) Overall survival (OS), (B) Disease-specific survival (DSS), (C, D) GEPIA2.0 analysis of the impact of NOX4 expression on overall survival (C) and disease-free survival (D) in TCGA pan-cancer.

Fig. 4

Expression and clinical staging analysis of NOX4. (A) T staging, (B) N staging, (C) Pathological stage.

Fig. 5

Mutation status of NOX4 in TCGA carcinoma. The cBioPortal provides data on mutation type, mutation site, and a 3D structure diagram of the NOX4 sequence.

Fig. 6

(A) Correlation between NOX4 expression and tumor mutation burden (TMB). (B) Correlation between NOX4 expression and microsatellite instability (MSI).

Fig. 7

(A) Pan-cancer correlation analysis of NOX4 expression with 44 RNA methylation genes. The correlation matrix illustrates the relationships between NOX4 expression and RNA methylation genes across tumors. Each cell in the matrix represents the correlation between NOX4 expression and a specific RNA methylation molecule (listed on the right) in a particular tumor type (listed at the bottom). The correlation coefficient and p-value are indicated by the color of each cell. (B) DNA methylation levels of NOX4 across tumors. *P < 0.05, **P < 0.01, ***P < 0.001.

Fig. 8

(A) Stemness scores based on DNA methylation derived from the Stemness group.(B) Stemness scores based on RNA expression derived from the Stemness group. *P < 0.05.

Fig. 9

Nine tumors showing the highest correlation coefficients between NOX4 expression and the tumor microenvironment.

Fig. 10

Correlation between NOX4 expression and immune cell infiltration levels. *P < 0.05, **P < 0.01, ***P < 0.001.

Fig. 11

(A) Pan-cancer correlation analysis of NOX4 expression with immune checkpoint genes. (B) Pan-cancer correlation analysis of NOX4 expression with immune pathway marker genes reveals significant associations (*P < 0.05).

Fig. 12

(A) Correlation between NOX4 expression and drug sensitivity (top 25 drugs) in pan-cancer, based on GDSC data. (B) NOX4 docking profile with a drug molecule, shown in the 3D structure of the ligand-receptor interaction in the left panel. The right panel displays a 2D representation of the interaction between the ligand and receptor within the binding pocket.

Fig. 13

NOX4-related gene enrichment analysis. (A) Using the STRING tool, we identified experimentally validated NOX4-binding proteins. (B) Heatmap of NOX4-binding proteins across different tumor types, including NOX1, NOX3, TLR4, CYBB, DUOX1, and DUOX2. (C) KEGG pathway analysis based on NOX4-binding and interacting genes. (D) Bubble plot of GO analysis (biological processes, BP). (E) Bubble plot of GO analysis (cellular components, CC). (F) Bubble plot of GO analysis (molecular functions, MF).