Interactions between NAD+ metabolism and immune cell infiltration in ulcerative colitis: subtype identification and development of novel diagnostic models

Abstract

Background: Ulcerative colitis (UC) is a chronic inflammatory disease of the colonic mucosa with increasing incidence worldwide. Growing evidence highlights the pivotal role of nicotinamide adenine dinucleotide (NAD+) metabolism in UC pathogenesis, prompting our investigation into the subtype-specific molecular underpinnings and diagnostic potential of NAD+ metabolism-related genes (NMRGs).

Methods: Transcriptome data from UC patients and healthy controls were downloaded from the GEO database, specifically GSE75214 and GSE87466. We performed unsupervised clustering based on differentially expressed NAD+ metabolism-related genes (DE-NMRGs) to classify UC cases into distinct subtypes. GSEA and GSVA identified potential biological pathways active within these subtypes, while the CIBERSORT algorithm assessed differential immune cell infiltration. Weighted gene co-expression network analysis (WGCNA) combined with differential gene expression analysis was used to pinpoint specific NMRGs in UC. Robust gene features for subtyping and diagnosis were selected using two machine learning algorithms. Nomograms were constructed and their effectiveness was evaluated using receiver operating characteristic (ROC) curves. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) was conducted to verify gene expression in cell lines.

Results: In our study, UC patients were classified into two subtypes based on DE-NMRGs expression levels, with Cluster A exhibiting enhanced self-repair capabilities during inflammatory responses and Cluster B showing greater inflammation and tissue damage. Through comprehensive bioinformatics analyses, we identified four key biomarkers (AOX1, NAMPT, NNMT, PTGS2) for UC subtyping, and two (NNMT, PARP9) for its diagnosis. These biomarkers are closely linked to various immune cells within the UC microenvironment, particularly NAMPT and PTGS2, which were strongly associated with neutrophil infiltration. Nomograms developed for subtyping and diagnosis demonstrated high predictive accuracy, achieving area under curve (AUC) values up to 0.989 and 0.997 in the training set and up to 0.998 and 0.988 in validation sets. RT-qPCR validation showed a significant upregulation of NNMT and PARP9 in inflamed versus normal colonic epithelia, underscoring their diagnostic relevance.

Conclusion: Our study reveals two NAD+ subtypes in UC, identifying four biomarkers for subtyping and two for diagnosis. These findings could suggest potential therapeutic targets and contribute to advancing personalized treatment strategies for UC, potentially improving patient outcomes.

Keywords: NAD+ metabolism; bioinformatics; diagnosis; immune cell infiltration; machine learning; subtype; ulcerative colitis.

Figure 1

Flowchart of the research.

Figure 2

Identification and enrichment analysis of DEGs. (A, B) Two datasets (GSE75214, GSE87466) were combined into one dataset after removing batch effects. Sample relationships before and after batch effect removal. (C, D) Volcano plot and heatmap showing DEGs between UC samples and normal samples. (E) GO enrichment analysis results of DEGs. (F) KEGG enrichment analysis results of DEGs.

Figure 3

Identification of two NAD+ subtypes in UC. (A) Venn diagram showing the overlapping genes between DEGs and NMRGs. (B) Consensus cumulative distribution function (CDF) plot showing the area under the curve for k = 2-9. (C) Relative change in the area under the CDF curve. (D) Tracking plot showing the sample subtypes for different values of (k). (E) Consensus matrix heatmap for k = 2. (F) PCA plot showing the distribution of the two subtypes. (G, H) Boxplot (G) and heatmap (H) displaying the differential expression of DE-NMRGs between the two NAD+ subtypes. * p < 0.05; *** p < 0.001.

Figure 4

Pathway enrichment analysis reveals distinct biological behaviors of NAD+ subtypes in UC. (A, B) GSEA highlights pathways significantly enriched in subtype A and B. (C, D) GSVA result, (C) Enriched pathways based on KEGG pathways. (D) Enriched pathways based on Reactome pathways.

Figure 5

Immune cell infiltration profiles related to NAD+ subtypes in UC. (A) Heatmap showing the relative abundance of 22 immune cell types in different NAD+ subtype samples. (B) Boxplot visualizing the distribution and variability of immune cell relative abundance in NAD+ subtypes. (C) Correlation matrix describing the interactions between different immune cells. ns, not significant; * p < 0.05; ** p < 0.01; *** p < 0.001.

Figure 6

WGCNA between NAD+ subtypes. (A) Sample dendrogram generated after clustering using Pearson correlation coefficients and removal of outliers. (B) Determination of the soft-thresholding power in WGCNA. (C) Dendrogram of all DEGs between subtypes, clustered based on differential measurements, dividing genes into six different modules, each representing a co-expressed gene cluster. (D) Bar graph illustrating the significance measurements of the identified gene modules. (E) Heatmap of the UC module feature genes and their correlations with different NAD+ subtypes. (F, G) Scatter plots demonstrating the relationship between module membership and gene significance within the black and blue modules.

Figure 7

Construction of an NAD+ related typing model. (A) Venn diagram showing the intersection of DEGs between subtypes, key module genes identified by the WGCNA algorithm, and NMRGs, resulting in eight intersecting genes. (B, C) Feature gene selection using LASSO regression. (D, E) Feature gene selection using RF algorithms. (F) Venn diagram displaying four candidate hub genes identified by the aforementioned machine learning algorithms as the core of the predictive model. (G) Nomogram of the NAD+ related typing model in the training set. (H, I) The ROC curves of the four hub genes (AOX1, NAMPT, NNMT, and PTGS2) and the nomogram in the training set.

Figure 8

Validation of the NAD+ related typing model. (A) Nomogram of NAD+ related typing model in the validation set. (B, C) ROC curves for the four hub genes (AOX1, NAMPT, NNMT, and PTGS2) and the nomogram in the validation set. (D) Heatmap of the Spearman correlation coefficients between the expression of the four hub genes and the content of various immune cells. *p < 0.05; **p < 0.01; ***p < 0.001. (E) Network diagram illustrating the interrelationships among the four hub genes.

Figure 9

Construction of an NAD+ related diagnostic model in UC. (A) Heatmap of the UC module feature genes and their correlations with UC and normal in WGCNA. (B) Venn diagram showing the intersection of DEGs between UC and normal, key module genes identified by the WGCNA algorithm, and NMRGs, resulting in seven intersecting genes. (C, D) Feature gene selection using LASSO regression. (E, F) Feature gene selection using RF algorithms. (G) Venn diagram displaying two candidate hub genes identified by the aforementioned machine learning algorithms as the core of the predictive model. (H) Nomogram of NAD+ related diagnostic model in the training set.

Figure 10

Validation of the NAD+ related diagnostic model in UC. (A, B) ROC curves for the two hub genes (NNMT and PARP9) and the nomogram in the training set. (C–H) ROC curves for the two hub genes (NNMT and PARP9) and the nomograms in the validation sets. (I, J) RT-qPCR experiment results of two hub genes (NNMT and PARP9) in NAD+ related diagnostic model. **p < 0.01; ***p< 0.001.