Frontiers in immunology · 2026
Shared immune dysregulation in systemic lupus erythematosus and colorectal cancer: a multi-omics guided discovery of DNASE1L3-centric efferocytosis deficiency.
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Guan Hui, Tian Chengzi, Zhong Ming, Zhang Lihuan, Wang Wenjing, Huang Mingcheng, Chen Duo
Аннотация
BACKGROUND: Although immune dysregulation is implicated in both autoimmune diseases and cancer, comparative pathogenesis and immune response mechanisms between systemic lupus erythematosus (SLE) and colorectal cancer (CRC) remain elusive. This study identifies common molecular biomarkers and pathogenic pathways shared between SLE and CRC via multi-omics analysis.
METHODS: Integrated datasets including SLE (GSE61635, GSE50772, GSE142016) and CRC cohorts (GSE39582, GSE17536, E-MTAB-8107, COAD, READ), were analyzed. Differential expression analysis identified shared genes, and machine learning screened four hub prognostic genes. A risk model based on these genes was constructed to evaluate SLE screening and CRC prognosis. Multi-omics approaches explored mutational profiles, immune infiltration, drug sensitivity, and signaling pathways. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR), staining were performed to confirmed the expression levels of the hub genes. The impact of DNASE1L3 knockdown in the human monocyte cell line THP-1 on phagocytosis of apoptotic intestinal epithelial cell line NCM460 was evaluated using RT-qPCR and flow cytometry.
RESULTS: We identified 58 shared differentially expressed genes (DEGs) between SLE and CRC, enriched in apoptosis, oxidative phosphorylation, and immune infiltration. Machine learning highlighted four hub genes (DNASE1L3, PTPN14, SELENBP1, ECRG4), forming a robust predictive model for SLE occurrence and CRC prognosis. Shared immune infiltration patterns and small-molecule drug candidates were observed. Single-cell and spatial transcriptomic analyses revealed DNASE1L3 predominantly in myeloid cells. Cellular experiments revealed that reduced DNASE1L3 levels significantly compromised macrophage efferocytosis of apoptotic cells, accompanied by a decrease in M2 macrophage proportion, an increase in M1 macrophage proportion, diminished LOX enzyme activity, and elevated levels of interleukin-1 β and tumor necrosis factor-α.
CONCLUSION: SLE and CRC exhibit overlapping DEGs, immune profiles, and signaling pathways. The model based on the shared genes-DNASE1L3, PTPN14, SELENBP1, and ECRG4-offers novel insights for precise intervention in both diseases.
METHODS: Integrated datasets including SLE (GSE61635, GSE50772, GSE142016) and CRC cohorts (GSE39582, GSE17536, E-MTAB-8107, COAD, READ), were analyzed. Differential expression analysis identified shared genes, and machine learning screened four hub prognostic genes. A risk model based on these genes was constructed to evaluate SLE screening and CRC prognosis. Multi-omics approaches explored mutational profiles, immune infiltration, drug sensitivity, and signaling pathways. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR), staining were performed to confirmed the expression levels of the hub genes. The impact of DNASE1L3 knockdown in the human monocyte cell line THP-1 on phagocytosis of apoptotic intestinal epithelial cell line NCM460 was evaluated using RT-qPCR and flow cytometry.
RESULTS: We identified 58 shared differentially expressed genes (DEGs) between SLE and CRC, enriched in apoptosis, oxidative phosphorylation, and immune infiltration. Machine learning highlighted four hub genes (DNASE1L3, PTPN14, SELENBP1, ECRG4), forming a robust predictive model for SLE occurrence and CRC prognosis. Shared immune infiltration patterns and small-molecule drug candidates were observed. Single-cell and spatial transcriptomic analyses revealed DNASE1L3 predominantly in myeloid cells. Cellular experiments revealed that reduced DNASE1L3 levels significantly compromised macrophage efferocytosis of apoptotic cells, accompanied by a decrease in M2 macrophage proportion, an increase in M1 macrophage proportion, diminished LOX enzyme activity, and elevated levels of interleukin-1 β and tumor necrosis factor-α.
CONCLUSION: SLE and CRC exhibit overlapping DEGs, immune profiles, and signaling pathways. The model based on the shared genes-DNASE1L3, PTPN14, SELENBP1, and ECRG4-offers novel insights for precise intervention in both diseases.