Comprehensive analysis of lactate-related gene profiles and immune characteristics in lupus nephritis

Abstract

Objectives: The most frequent cause of kidney damage in systemic lupus erythematosus (SLE) is lupus nephritis (LN), which is also a significant risk factor for morbidity and mortality. Lactate metabolism and protein lactylation might be related to the development of LN. However, there is still a lack of relative research to prove the hypothesis. Hence, this study was conducted to screen the lactate-related biomarkers for LN and analyze the underlying mechanism.

Methods: To identify differentially expressed genes (DEGs) in the training set (GSE32591, GSE127797), we conducted a differential expression analysis (LN samples versus normal samples). Then, module genes were mined using WGCNA concerning LN. The overlapping of DEGs, critical module genes, and lactate-related genes (LRGs) was used to create the lactate-related differentially expressed genes (LR-DEGs). By using a machine-learning algorithm, ROC, and expression levels, biomarkers were discovered. We also carried out an immune infiltration study based on biomarkers and GSEA.

Results: A sum of 1259 DEGs was obtained between LN and normal groups. Then, 3800 module genes in reference to LN were procured. 19 LR-DEGs were screened out by the intersection of DEGs, key module genes, and LRGs. Moreover, 8 pivotal genes were acquired via two machine-learning algorithms. Subsequently, 3 biomarkers related to lactate metabolism were obtained, including COQ2, COQ4, and NDUFV1. And these three biomarkers were enriched in pathways 'antigen processing and presentation' and 'NOD-like receptor signaling pathway'. We found that Macrophages M0 and T cells regulatory (Tregs) were associated with these three biomarkers as well.

Conclusion: Overall, the results indicated that lactate-related biomarkers COQ2, COQ4, and NDUFV1 were associated with LN, which laid a theoretical foundation for the diagnosis and treatment of LN.

Keywords: GEO; bioinformatics; infiltrating immunocytes; lactate; lupus nephritis.

Figure 1

The flow diagram of the research design.

Figure 2

Identification of Lactate-related DEGs in LN. (A) Volcano plot showing the DEGs between LN and normal groups, including 583 down-regulated and 676 up-regulated genes. (B) Heatmap showing the TOP50 DEGs. (C) Disease and function analysis of IPA suggested that these DEGs were associated with ‘cell-to-cell signaling and interaction’ and ‘immune cell trafficking’.

Figure 3

Identification of key module genes associated with LN. (A) Analysis of the scale-free fit index and mean connectivity for various soft-thresholding powers (β). (B) Gene dendrogram obtained by hierarchical clustering. A total of 10 modules were obtained by the Dynamic Tree Cut algorithm and similar merging. (C) Heatmap suggested that the MEturquoise, MEsalmon, MEtan, MEgreenyellow, MEmagenta, and MEblack modules (|cor|>0.3, P<0.05) were markedly correlated with LN. (D) Scatterplots of gene significance (GS) versus module membership (MM) showed that 3800 key module genes related to LN were obtained.

Figure 4

The acquisition and function analysis of LR-DEGs. (A) Venn diagram showing 19 LR-DEGs in LN that overlapped DEGs, key module genes, and LRGs. (B) Chromosome localization circles of LR-DEGs. (C) PPI network of LR-DEGs. (D–F) Chord diagrams obtained from the functional enrichment analysis of LR-DEGs. TOP10 GO results indicated that these LR-DEGs were principally involved in the ‘glucose metabolic process’ and ‘small molecule catabolic process’. (G) KEGG analysis implied that these LR-DEGs were mainly enriched in the ‘Propanoate metabolism’ and ‘Fructose and mannose metabolism’.

Figure 5

Screening lactated-related biomarkers of LN. (A, B) SVM-RFE analysis of 19 LR-DEGs ultimately obtained 13 feature genes. (C) RF algorithm showing the TOP10 feature genes. (D) Venn diagram identified 8 key LR-DEGs via the intersection of two machine-learning algorithms. (E, F) ROC curves of the 8 key genes in the training set and the external validation set. COQ2, COQ4, and NDUFV1 demonstrated strong diagnostic values for LN in the external validation set (AUC> 0.7). (G, H) The expression pattern of COQ2, COQ4, and NDUFV1 in the external validation set was entirely consistent with the training set. ns, not significant. *p < 0.05, **p < 0.01, ****P < 0.0001.

Figure 6

Clinical and functional enrichment analysis of biomarkers with LN. (A) Nomogram containing biomarkers. (B, C) Calibration and ROC curves proved the feasibility of the nomogram. (D–F) Single-gene GSEA of COQ2, COQ4, and NDUFV1 showed that these three biomarkers were related to ‘antigen processing and presentation’ and ‘NOD-like receptor signaling pathway’. (G) Classical pathway analysis of IPA.

Figure 7

Immune infiltration analysis of lactate-related biomarkers in LN. (A) Relative proportions of immune infiltration in LN. (B) Abundances of 13 immune cells differed significantly in LN. (C–F) Correlation analysis of biomarkers and twenty-one kinds of immune cells showed that Macrophages M0 and T cells regulatory (Tregs) were positively associated with COQ4 and NDUFV1; they were negatively associated with COQ2. ns, not significant. *p < 0.05, **p < 0.01, ***p < 0.001, ****P < 0.0001.

Figure 8

Analysis of the role of biomarkers in LN immune microenvironment. (A) The Stromal, Immunological, and ESTIMATE scores were all higher in LN compared to the control group. (B) Correlation analysis indicated that these three scores were negatively correlated with COQ4 and NDUFV1, but positively correlated with COQ2. ****P < 0.0001.

Figure 9

Analysis of the role of biomarkers in LN immune microenvironment. (A) ‘TF-miRNA-gene’ network presenting the regulatory mechanisms of COQ2, COQ4, and NDUFV1, which had 46 nodes and 45 edges. (B) The relationship between biomarkers and drugs predicted from the DrugBank database.