Identification of a disulfidptosis-related genes signature for diagnostic and immune infiltration characteristics in endometriosis

Abstract

Endometriosis (EMs) is the prevalent gynecological disease with the typical features of intricate pathogenesis and immune-related factors. Currently, there is no effective therapeutic intervention for EMs. Disulfidptosis, the cell death pattern discovered recently, may show close relationships to immunity and EMs. In this study, bioinformatics analysis was used to investigate the role of disulfide breakdown related genes (DRGs) in EMs. The EMs gene expression matrix was subjected to differential analysis for identifying overlap between differentially expressed genes (DEGs) in EMs and genes associated with disulfide poisoning. Immunoinfiltration analysis was performed. In addition, the association of hub genes with immune cells was examined. Multiple machine learning methods were employed to identify hub genes, construction of predictive models, and validation using external datasets and clinical samples. Totally 15 overlapping genes were identified. Immune-correlation analysis showed that NK cells played a vital role, and these 15 genes were closely related to NK cells. PDLIM1 was further determined as the hub gene through machine learning techniques. Clinical samples and external datasets were adopted for validating the performance in diagnosis. According to the above findings, we built the predictive model, and calculated the AUCs obtained from three external validation datasets to demonstrate the model accuracy. RT-qPCR and IHC analyses were applied to confirm the results. Colony formation was used to verify the effect of PDLIM1 on the proliferation of primary EMs cells. A strong correlation between disulfidptosis and EMs was identified in this study, highlighting its close correlation with the immune microenvironment. Moreover, our results shed new lights on exploring biomarkers and potential therapeutic targets for EMs.

Keywords: Biomarkers; Disulfidptosis; Endometriosis; Immune.

Fig. 1

The study flow chart.

Fig. 2

DEG detection. (A) Heatmap displays the expression levels of EMs-related DEGs in GSE7305. (B) volcano plot showing significant DEGs of GSE7305. (C) GO annotation displays enriched terms associated with EMs. (D) KEGG analysis suggests pathways enriched into EMs. GO gene ontology, BP biological process, CC cellular component, MF molecular function, KEGG Kyoto Encyclopedia of Genes and Genomes.

Fig. 3

GSEA on pathways in EMs and normal groups (GSE7305).

Fig. 4

DRGs levels and immune cell infiltrating degrees within EMs. (A) Overlap of genes between DEGs and DRGs. (B) Boxplot showing DRGs expression profiles. (C) Heatmap displaying DRGs expression. (D) Heatmap representing percentages of 22 infiltrating immune cells within EMs and healthy controls. (E) Boxplots exhibiting diverse immune infiltration degrees in EMs compared with normal samples. (F) Infiltrating immune cell percentages within EMs and healthy samples were compared. (G) Correlation analysis of 15 key genes. (H) Correlation analysis of 15 key genes and infiltrated immune cells.

Fig. 5

Disulfidptosis-signature construction based on machine learning. (A,B) LASSO algorithm adopted for the construction of disulfidptosis-signatures. (C) RF algorithm applied in ranking genes according to their importance. (D) The Burota algorithm. Tentative is yellow, Rejected is red, Accepted is green, and Shadow is blue. (E) Venn diagram displaying overlap of possible genes obtained from five algorithms.

Fig. 6

Diagnostic significance validated using hub genes. (A) A nomogram constructed for predicting EMs with hub gene. (B) Calibration curve plotted for evaluating the nomogram prediction value. (C–E) ROC curves of GSE23339, GSE51981, and GSE6364.

Fig. 7

Results of RT-qPCR and IHC. (A) The results of RT-qPCR illustrated PDLIM1 expression in EMs tissues (n = 3) and healthy endometrium (n = 3). *p < 0.05. (B) IHC analysis revealed PDLIM1 expression in EMs tissues and normal endometrium. (C) The Colony formation assay to determine the effect of PDLIM1 knockdown on the proliferation of EMs primary cells.