Exploration of shared biomarkers and mechanisms in systemic lupus erythematous and lung cancer via bioinformatics analysis

Abstract

Systemic lupus erythematosus (SLE) patients exhibit a heightened risk of developing lung cancer, yet the underlying molecular mechanisms remain poorly understood. This study aimed to identify shared genetic factors linking SLE and LC using publicly available transcriptomic data from the Gene Expression Omnibus (GEO). Through integrated differentially expressed gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA), we identified five genes consistently upregulated in both SLE and lung cancer. Gene set enrichment analysis (GSEA) revealed that these shared genes were enriched in inflammatory pathways, particularly those involving interferon-alpha, interferon-gamma, and general inflammatory responses. We applied least absolute shrinkage and selection operator (LASSO) regression to pinpoint potential diagnostic biomarkers and identified two key candidates: AIM2 and SLC26A8. These biomarkers demonstrated robust diagnostic performance with area under the ROC curve (AUC) values exceeding 0.75 in both training and validation cohorts. Immune infiltration and survival analyses using The Cancer Genome Atlas (TCGA) further supported their clinical relevance. Notably, high AIM2 expression was significantly associated with poorer overall survival in female lung adenocarcinoma patients (P = 0.03), and SLC26A8 expression was significantly linked to survival outcomes only in patients with a history of smoking (P = 0.01). These findings are particularly meaningful in SLE, where most patients are female and smoking is a known risk factor. Our study enhances the understanding of autoimmune-driven carcinogenesis and opens new avenues for precision medicine strategies in managing patients with SLE at risk for lung cancer.

Keywords: AIM2; Bioinformatics; Lung cancer; SLC26A8; Systemic lupus erythematosus; Tumor immune microenvironment; Tumorigenesis.

Fig. 1

Flow chart.

Fig. 2

Identification and analyses of DEGs from the SLE and lung cancer datasets. A The volcano plot presenting DEGs in GSE72509. B The volcano plot presenting DEGs in GSE42830. C Venn diagram showing the intersected DEGs of SLE and lung cancer. D GO analysis for shared DEGs.

Fig. 3

WGCNA for identification of genes and modules positively correlated with SLE and lung cancer. A The scale-free topology model for identifying optimal soft power threshold in SLE dataset. B The heatmap illustrating the module-traits relationship of SLE. C The scale-free topology model for identifying optimal soft power threshold in lung cancer dataset. D The heatmap illustrating the module-traits relationship of lung cancer.

Fig. 4

Identification and functional enrichment analysis of key modules genes in SLE and lung cancer. A The Venn diagram presenting the intersection of most positively correlated genes of SLE and lung cancer. B GO and KEGG enrichment analysis of shared modules genes.

Fig. 5

Five hub genes were obtained by intersecting the shared DEGs and key module genes of SLE and lung cancer.

Fig. 6

GSEA of core shared genes in SLE (A) and lung cancer(B).

Fig. 7

ROC curves of AIM2 and SLC26A8 on training and validating datasets to evaluate and validate the diagnostic values for SLE and lung cancer.

Fig. 8

The UMAP visualization, integrating SingleR-predicted cell types with AIM2 gene expression, reveals a distinct enrichment of AIM2 expression within B cell populations.

Fig. 9

Immune infiltration analysis of core shared genes in LUAD and LUSC. A Correlation between the expression of AIM2 and tumor immune microenvironment. B Correlation between the expression of SLC26A8 and tumor immune microenvironment.

Fig. 10

Kaplan‒Meier analysis of overall survival based on AIM2 and SLC26A8 expression. A Higher AIM2 expression was associated with poorer overall survival in the LUAD subtype. B Elevated AIM2 expression is linked to poorer overall survival exclusively in female lung cancer patients. C Data stratified by smoking history indicated that lower SLC26A8 expression is significantly associated with better survival outcomes only in patients with tobacco exposure.