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. 2023 Nov 16:14:1288263.
doi: 10.3389/fimmu.2023.1288263. eCollection 2023.

Unraveling immunotherapeutic targets for endometriosis: a transcriptomic and single-cell analysis

Cankun Zhou #  1 , Minqing Feng #  1 , Yonglian Chen #  1 , Side Lv  2 , Yifan Zhang  3 , Jiebo Chen  1 , Rujian Zhang  1 , Xiaobin Huang  1
Affiliations

Unraveling immunotherapeutic targets for endometriosis: a transcriptomic and single-cell analysis

Cankun Zhou et al. Front Immunol. .

Abstract

Background: Endometriosis (EMs), a common gynecological disorder, adversely affects the quality of life of females. The pathogenesis of EMs has not been elucidated and the diagnostic methods for EMs have limitations. This study aimed to identify potential molecular biomarkers for the diagnosis and treatment of EMs.

Methods: Differential gene expression (DEG) and functional enrichment analyses were performed using the R language. WGCNA, Random Forest, SVM-REF and LASSO methods were used to identify core immune genes. The CIBERSORT algorithm was then used to analyse the differences in immune cell infiltration and to explore the correlation between immune cells and core genes. In addition, the extent of immune cell infiltration and the expression of immune core genes were investigated using single-cell RNA (scRNA) sequencing data. Finally, we performed molecular docking of three core genes with dienogest and goserelin to screen for potential drug targets.

Results: DEGs enriched in immune response, angiogenesis and estrogen processes. CXCL12, ROBO3 and SCG2 were identified as core immune genes. RT-PCR confirmed that the expression of CXCL12 and SCG2 was significantly upregulated in 12Z cells compared to hESCs cells. ROC curves showed high diagnostic value for these genes. Abnormal immune cell distribution, particularly increased macrophages, was observed in endometriosis. CXCL12, ROBO3 and SCG2 correlated with immune cell levels. Molecular docking suggested their potential as drug targets.

Conclusion: This study investigated the correlation between EMs and the immune system and identified potential immune-related biomarkers. These findings provided valuable insights for developing clinically relevant diagnostic and therapeutic strategies for EMs.

Keywords: endometriosis; CIBERSORT; immune; molecular docking; single-cell RNA sequencing.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Screening of differentially expressed genes (DEGs) and functional enrichment analysis. (A) Heatmap of DEGs. (B) Volcano plot of DEGs. (C) Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DEGs. (D) Gene Ontology (GO) enrichment analysis of DEGs.
Figure 2
Figure 2
Construction of weighted gene co-expression (WGCNA) network. (A) Screening the co -expression module. (B) Heatmap of module-trait correlations (red and blue indicate positive and negative correlations, respectively). (C) Venn plot of key module genes versus DEGs and immune genes.
Figure 3
Figure 3
Multiple machine learning models were used to identify core immune genes (CXCL12, ROBO3, and SCG2) in the GSE141549 dataset. (A, B) The genes were selected based on the random forest (RF) model. (C) The support vector machine (SVM) algorithm was used to select the genes. (D, E) Least absolute shrinkage and selection operator (LASSO) regression analysis was used to select the genes. (F) Three key immune genes identified from the Venn diagram.
Figure 4
Figure 4
Validating the expression and diagnostic value of core immune genes. (A, C) The expression levels of CXCL12, ROBO3, and SCG2 in the test (GSE141549) and validation cohorts (GSE7305). (B, D) Receiver operating characteristic (ROC) curves for evaluating the diagnostic values of CXCL12, ROBO3, and SCG2 in the test (GSE141549) and validation cohorts (GSE7305). (E, F) The expression levels of CXCL12 and SCG2 in 12Z cells and hESCs cells.
Figure 5
Figure 5
Construction and validation of the endometriosis diagnostic column line graph. (A) Diagnostic column line graph was used to predict the occurrence of endometriosis. (B) Calibration curve to examine the predictive power of the diagnostic column line graph. (C) Decision curve analysis (DCA) to evaluate the predictive power of the diagnostic column line graph. (D) Clinical impact curve to assess the predictive power of the diagnostic column line graph. (E, F) Receiver operating characteristic (ROC) curves to assess the clinical value of the diagnostic column line graph in the test (GSE141549) and validation cohorts (GSE7305).
Figure 6
Figure 6
Analysis of immune landscape related to endometriosis. Heatmap (A) and violin plot (B) showing the distribution of 22 immune cells in endometrium samples and endometriosis samples of the GSE141549 dataset. *P < 0.05; **P < 0.01; ***P < 0.001;****P < 0.0001; -, non-significant (P > 0.05).
Figure 7
Figure 7
Correlation between infiltrating immune cells and core immune genes (CXCL12 (A), ROBO3 (B), and SCG2 (C)).
Figure 8
Figure 8
Visualization plots of single-cell RNA sequencing (scRNA-seq) data of endometriosis samples. t-Distributed stochastic neighbor embedding (tSNE) plots showing integrated analysis of eutopic endometrium and endometriosis. Cells were colored according to cell types (A) or tissue types (B). (C) Violin plot of marker genes in different cell type. (D) Proportions of different T cell subtypes in each sample (left) or different tissue types (right). (E) The expression pattern of CXCL12, ROBO3, and SCG2 were representedd in the t-SNE plot. (F) The gene expression levels of CXCL12, ROBO3, and SCG2 in the scRNA-seq dataset (GSE179640). ****P < 0.0001; ns, non-significant (P > 0.05).
Figure 9
Figure 9
Molecular docking patterns of dienogest or goserelin with target proteins. ALA, alanine; ASP, asparticacid; ARG, arginine; GLN, glutarnine; GLU, glutamicacid; GLY, glycine; PHE, phenylalanine; RPO, proline; SER, serine; THR, threonine.
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