Exploration of efferocytosis-related genes as potential therapeutic targets in endometrial cancer

Introduction

Endometrial cancer (EC) is one of the most common malignant tumors in women. According to statistics, there were approximately 417,000 new cases of EC globally in 2020, accounting for 4.5% of all female cancers, with an annual incidence rate increase of 1.8% (1). Currently, the main treatment options for EC include surgery and adjuvant chemotherapy, with hormone therapy, immune checkpoint inhibitors, angiogenesis inhibitors, and human epidermal growth factor receptor 2 (HER2) inhibitors also showing good efficacy in treating advanced and recurrent EC (2). Despite continuous improvements in EC treatment strategies, the mortality rate associated with EC continues to rise annually (3). Worldwide, EC remains a significant health burden, especially with a poor prognosis for advanced endometrial cancer (4). Therefore, identifying new potential therapeutic targets is crucial for improving the prognosis of EC.

Efferocytosis refers to the process by which apoptotic cells are cleared by phagocytic cells following tissue damage. This complex process plays an important role in cellular homeostasis, immune defense, tissue repair, and development (5). Efferocytosis is a highly coordinated process that can be broadly divided into five steps: (I) signal detection: chemotactic signal recognition, where apoptotic cells release “Find-Me” signals [such as adenosine triphosphate (ATP), hemolytic phosphatidylcholine] or secrete “Keep-Out” chemotactic factor gradients to recruit phagocytic cells. (II) Cell recognition: specific recognition through the “Eat-Me” signals (such as phosphatidylserine exposure) binding to phagocytic cell surface receptors (such as Tim-4, BAI1) or the suppression of “Don’t-Eat-Me” signals (such as CD47-SIRPα axis). (III) Phagocytosis: phagocytic cells engulf apoptotic cells and their fragments. (IV) Processing and digestion: phagocytic cells internalize and digest apoptotic cells and their fragments, completing the clearance process. (V) Anti-inflammatory and immune tolerance response: the release of anti-inflammatory factors [such as transforming growth factor-β (TGF-β), interleukin-10 (IL-10)] and repair mediators (such as lipoxin A4), inducing regulatory T cell (Treg) differentiation to maintain immune tolerance (6-8). In recent years, studies have found that efferocytosis is involved in the occurrence and progression of various malignant tumors in the human body (9). In the tumor microenvironment (TME), necrotic and apoptotic tumor cells are cleared through efferocytosis by phagocytes (such as macrophages) (10). After engulfing apoptotic tumor cells, macrophages, dendritic cells, and other phagocytic cells release immunosuppressive cytokines (such as TGF-β, IL-10) to inhibit the activity of effector T cells, thus helping tumors evade immune surveillance (11). In a mouse model of bladder cancer, CD276 gene knockout significantly upregulated MHC class II molecules by inhibiting tumor-associated macrophage (TAM) efferocytosis, promoting CD4⁺ and CD8⁺ T cell infiltration, and reversing the immunosuppressive microenvironment (12). In pancreatic cancer, efferocytosis-mediated clearance of apoptotic tumor cells promotes macrophage reprogramming and liver metastasis. Further blocking efferocytosis with drugs improves CD8⁺ T cell function and inhibits liver metastasis (13). Studies on colorectal cancer (CRC) have shown that activating macrophage efferocytosis promotes CRC resistance to cisplatin and tumor immune escape (14,15). In osteosarcoma, efferocytosis mediated by the Mer-TK receptor regulates macrophage phenotype through the p38/STAT3 pathway. In osteosarcoma models, inhibiting Mer-TK may increase CD8⁺ T cell infiltration and reduce T cell exhaustion, thereby inhibiting tumor growth (16). Therefore, efferocytosis-related signaling pathways not only influence tumor cell proliferation, invasion, metastasis, and angiogenesis but also regulate adaptive responses and resistance to antitumor therapy. Efferocytosis-related molecules and pathways may serve as potential targets for anticancer therapies (17). Recent studies have shown that inhibiting macrophage efferocytosis-related cancer vaccines may activate antitumor immune responses, suppressing tumor growth and metastasis (18). Other research shows that using multifunctional nanomaterials to inhibit efferocytosis can enhance antitumor immunity (19). However, there is currently little research on the role of efferocytosis in EC, and the relationship between efferocytosis-related genes or molecules and EC is not yet clear. Therefore, this study integrates single-cell RNA sequencing and spatial transcriptomics data to explore potential therapeutic targets of efferocytosis in EC, construct a prognostic model, and further conduct immune infiltration analysis and drug screening, providing new insights for the diagnosis and treatment of EC. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-488/rc).

Methods

Data acquisition

The transcriptomic data of EC and corresponding clinical data were downloaded from the TCGA database. A total of 167 efferocytosis-related genes were obtained from published literature (20,21) (as shown in Table S1). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Identification of key efferocytosis genes in EC

The R package “limma” was used to extract efferocytosis-related genes, and differential analysis was performed. The STRING online tool was used to create a gene interaction network for the differentially expressed genes. The R package “survival” was used to select differential genes via random forest, and least absolute shrinkage and selection operator (LASSO) regression was applied to select the final hub genes.

Prognostic model construction and validation

The merged data were randomly divided into training and validation groups using R packages “survival” “caret” “glmnet” “survminer” and “time ROC”. Based on this, a LASSO regression model was constructed using gene expression levels, survival time, and survival status, and cross-validation was performed. Genes corresponding to the minimum cross-validation error were selected as significant genes. A Cox regression model was then constructed using the significant genes and their expression levels. In the training group, risk scores were calculated based on the results of the Cox regression analysis, with the formula: risk score = expression of gene 1 × β1 coefficient of gene 1 + expression of gene 2 × β2 coefficient of gene 2 + … + expression of gene n × βn coefficient of gene n, where the β coefficients were directly obtained from the multivariate Cox regression analysis in R software. Patients were divided into high- and low-risk groups based on the median risk score. The same formula was applied to the validation group, and the high- and low-risk groups were distinguished based on the median risk score of the training group. Finally, survival analysis was performed, with P values calculated and reported, and receiver operating characteristic (ROC) curves, risk curves, and nomograms were drawn to assess the prognostic predictive ability of the model.

Gene Ontology (GO) enrichment and gene set enrichment analysis (GSEA) analysis of hub genes

GO enrichment analysis was conducted for the hub genes using the R package “GO plot” with biological process (BP), cellular component (CC), and molecular function (MF) categories. GSEA was performed for the hub genes using the R package “DOSE”.

Immune infiltration analysis of hub genes

Immune cell infiltration and tumor micro environment analysis in endometrial cancer were performed using the R packages “CIBERSORT” and “estimate”. Correlation analysis between immune cell infiltration and hub gene expression was conducted using the R package “limma”, and differential analysis of immune-related functions was performed using the R packages “GSEA Base” and “limma”. Immune micro environment differences were analyzed using the R package “ggpubr”.

Expression of hub genes in normal and tumor tissues

Immunohistochemistry data of lysosomal-associated protein transmembrane 5 (LAPTM5), sodium-dependent sulfate (SDS), and Src-like adaptor (SLA) in normal endometrial tissue and endometrial cancer tissue were obtained from the Human Protein Atlas (HPA) website: https://www.proteinatlas.org/.

Drug sensitivity analysis

The “pRRophetic” package was used to calculate the half-maximal inhibitory concentration (IC50) of drugs. Differential analysis of IC50 between high-risk and low-risk groups was performed and visualized.

Statistical analysis

Rv.4.4.0 software was used for statistical analysis. Independent sample comparisons between two groups were conducted using a t-test. For three or more groups, one-way ANOVA and Kruskal-Wallis rank-sum test were used. Spearman correlation analysis was performed for correlation analysis.

Results

Data acquisition results

A total of 589 gene expression data samples were obtained from the TCGA database, including 554 tumor samples and 35 normal samples, as well as corresponding clinical data for 548 cases of EC. Additionally, 167 efferocytosis-related genes were acquired from published literature. Data analysis flowchart is shown in Figure 1.

Figure 1 Data analysis flowchart. DEGs, differentially expressed genes; GO, Gene Ontology; LASSO, least absolute shrinkage and selection operator; RF, random forest; TCGA, The Cancer Genome Atlas; UCEC, Uterine Corpus Endometrial Carcinoma.

Differential expression analysis of efferocytosis-related genes

By analyzing the differential expression of efferocytosis-related genes between normal endometrial samples and EC samples, 35 differentially expressed genes were identified. The STRING online tool was then used to construct a protein-protein interaction network of these differentially expressed genes. Furthermore, using the random forest method, 13 prognosis-related genes were selected, including 4 high-risk genes and 9 low-risk genes (as shown in Figure 2). LASSO regression analysis further identified oxidized low-density lipoprotein receptor 1 (OLR1), SDS, LAPTM5, and SLA as Hub genes. The results indicated that OLR1 and SDS functioned as high-risk genes in EC, suggesting that their high expression might be associated with poor prognosis, while LAPTM5 and SLA were identified as low-risk genes, indicating that high expression of these two genes might be associated with better prognosis. GO enrichment analysis of the Hub genes showed that, in terms of BP, they were mainly involved in the regulation of receptor catabolic processes, Golgi to lysosome transport, and defense response to tumor cells, suggesting that these genes may play important roles in regulating tumor cell metabolism, immune response, and intracellular transport. In terms of CC, they were associated with the COP9 signalosome, tertiary granule membrane, and specific granule membrane, suggesting that these Hub genes may be closely related to the formation of immune cell granules and signal transduction. In terms of MF, they were mainly involved in carbon-nitrogen lyase activity, low-density lipoprotein receptor activity, and lipoprotein particle receptor activity, indicating that these genes may play important roles in lipid metabolism and intercellular signal transduction (as shown in Figure 3).

Figure 2 Selection of Hub genes of efferocytosis-related genes in EC. (A) Apoptosis-related gene differential heatmap; (B) differential gene PPI network; (C) prognostic apoptosis gene random forest plot. *, P<0.05; **, P<0.01. CI, confidence interval; EC, endometrial cancer; PPI, protein-protein interaction.

Figure 3 Selection of Hub genes and GO enrichment analysis. (A) Hub gene GO enrichment bar plot; (B) Hub gene GO enrichment circle plot; (C) LASSO regression coefficient path plot; (D) LASSO regression cross-validation plot. BP, biological process; CC, cellular component; GO, Gene Ontology; LASSO, least absolute shrinkage and selection operator; MF, molecular function.

Prognostic model construction and validation

Based on the expression levels of four genes—OLR1, SDS, LAPTM5, and SLA—the risk score for each patient developing EC was calculated using the following formula: Risk score = OLR1 × (0.257) + SDS × (0.401) + LAPTM5 × (−1.249) + SLA × (−0.555). Survival curve analysis revealed significant differences in survival times between high-risk and low-risk groups in the training, validation, and overall cohorts, indicating that the constructed prognostic model effectively differentiates between high-risk and low-risk patient populations. According to ROC curve analysis, the area under the curve (AUC) for 1-, 3-, and 5-year survival rates in the overall, training, and validation cohorts was greater than 0.5, demonstrating the model’s high accuracy in predicting survival duration (as shown in Figure 4). Additionally, a nomogram was used to predict survival rates at different time points based on the clinical features of the patients. The results showed 1-, 3-, and 5-year survival rates of 97.3%, 88.0%, and 83.2%, respectively (as shown in Figure 4). The risk curve, based on the risk ranking of the patients, showed that the number of deaths increased with the rising risk scores in the training, validation, and overall groups, as expected. The risk heatmap further indicated that OLR1 and SDS were high-risk genes in all three groups, while LAPTM5 and SLA were low-risk genes (as shown in Figure 5).

Figure 4 Construction and validation of the nomogram model. (A) Overall group survival curve; (B) training group survival curve; (C) validation group survival curve; (D) overall group ROC curve; (E) training group ROC curve; (F) validation group ROC curve; (G) nomogram model calibration curve; (H) survival prediction nomogram. *, P<0.05; ***, P<0.001. AUC, area under the curve; OS, overall survival; ROC, receiver operating characteristic.

Figure 5 Prognostic-related cellular burial gene risk curve. (A) Overall group risk scatter plot; (B) training group risk scatter plot; (C) validation group risk scatter plot; (D) overall group risk curve; (E) training group risk curve; (F) validation group risk curve; (G) overall group risk heatmap; (H) training group risk heatmap; (I) validation group risk heatmap.

GSEA enrichment analysis and immune infiltration analysis of high- and low-risk groups

GSEA enrichment analysis revealed that the low-risk score group was primarily enriched in the following pathways: allograft rejection, chemokine signaling pathway, cytokine-cytokine receptor interaction, etc. In contrast, the high-risk score group was mainly enriched in the following pathways: drug metabolism cytochrome P450 (CYP450) pathway, metabolism of xenobiotics by cytochrome P450, neuroactive ligand-receptor interaction, etc. These results suggest that the low-risk group has active immune responses, which may be related to the immune system’s effective recognition and rejection of foreign substances, while the high-risk group involves changes in drug metabolism and neuro-receptor pathways, possibly associated with tumor drug resistance or abnormal activity of the nervous system. In immune cell analysis, 22 types of immune cells, except for activated dendritic cells and Parainflammation, showed significant differences between the high- and low-risk groups. This indicates that the infiltration of immune cells in the TME may be associated with the prognosis of the tumor. Furthermore, the differences in stromal score, immune score, and ESTIMATE score between the high- and low-risk groups suggest that the composition of the TME and the immune environment may play an important role in the formation of risk scores. Finally, the correlation analysis between hub genes and immune cell infiltration showed that SDS was positively correlated with the infiltration of resting Mast cells, activated Mast cells, and Eosinophils, suggesting that SDS may influence the immune microenvironment of EC by regulating the infiltration of these immune cells (as shown in Figure 6).

Figure 6 GSEA enrichment analysis and immune infiltration analysis of Hub genes. (A) GSEA enrichment analysis of the low-risk group; (B) GSEA enrichment analysis of the high-risk group; (C) immune microenvironment difference analysis between high- and low-risk groups; (D) correlation analysis between Hub genes and immune cell infiltration; (E) immune cell infiltration difference analysis between high- and low-risk groups. *, P<0.05; **, P<0.01; ***, P<0.001. GSEA, gene set enrichment analysis; TME, tumor microenvironment.

Drug sensitivity analysis results

Using P<0.001 as the screening criterion, a total of 46 drugs sensitive to EC were selected. After excluding drugs that are still under research and not yet on the market, 12 drugs sensitive to EC were finally selected. The sensitive drugs for the high-risk group are: doxorubicin, embelin, etoposide, gemcitabine, nilotinib, pyrimethamine, shikonin, sorafenib, tipifarnib, and vinorelbine. The sensitive drugs for the low-risk group are: gefitinib and temsirolimus (as shown in Figure 7). For detailed information on drug grouping, please refer to Table S2.

Figure 7 Drug screening for prognostic model. IC50, half-maximal inhibitory concentration.

Hub gene immunohistochemical results

The expression of LAPTM5, SDS, and SLA in normal endometrial tissue and EC tissue was obtained from the HPA database. The results showed that LAPTM5 and SLA were lowly expressed in endometrial cancer tissue, while SDS was highly expressed in EC tissue (as shown in Figure 8).

Figure 8 Hub gene immunohistochemical results (3,3'-diaminobenzidine staining, ×40). (A) Expression of LAPTM5 in normal endometrial tissue; (B) expression of SDS in normal endometrial tissue; (C) expression of SLA in normal endometrial tissue; (D) expression of LAPTM5 in EC tissue; (E) expression of SDS in EC tissue; (F) expression of SLA in EC tissue. EC, endometrial cancer.

Discussion

EC is one of the most common malignant tumors of the female reproductive system, with its incidence showing a rising trend year by year. Currently, the prognosis of advanced EC remains poor, and it continues to pose a significant disease burden globally (22). A previous study has indicated that efferocytosis may play a critical role in immune evasion, tumor progression, metastasis, and chemotherapy resistance in malignant tumors (23). Targeted drugs aimed at efferocytosis may offer a new therapeutic approach for malignant tumors (24). However, the specific role and mechanism of efferocytosis in EC remain unclear. In this study, we identified four efferocytosis-related genes, OLR1, SDS, LAPTM5, and SLA, and further constructed a prognostic model. Internal validation showed that the model’s predictive performance was good. We also performed immune infiltration analysis and drug screening, providing new ideas and potential targets for the diagnosis and treatment of endometrial cancer.

OLR1, as a member of the C-type lectin receptor family, is widely distributed in cells such as macrophages, smooth muscle cells, and endothelial cells. It primarily participates in BPs such as lipid metabolism, inflammatory response, immune regulation, and the development of atherosclerosis (25). OLR1 is highly expressed in various types of tumors, including liver cancer, breast cancer, lung cancer, and CRC, where it is involved in immune evasion, tumor cell metastasis, chemotherapy resistance, and other processes. It is also closely related to tumor proliferation, metastasis, and poor prognosis (26-29). OLR1 is involved in processes such as immune evasion in tumors, tumor cell metastasis, and drug resistance, and is closely related to tumor proliferation, metastasis, and poor prognosis (26-30). However, the specific role of OLR1 in EC is still unclear. In this study, we found that OLR1 is highly expressed in EC and is associated with poor prognosis, which is similar to its role in other malignant tumors. Nevertheless, further experiments are needed to explore the molecular mechanisms of OLR1 in EC. SDS is a protein related to sulfate transport, primarily involved in regulating the transport and metabolism of sulfates in the body (31). Abnormal expression of SDS is mainly associated with diseases involving sulfate metabolism and transport (32). A study on breast cancer showed that the expression level of SDS can influence the growth and metabolism of cancer cells by regulating intracellular sulfate balance (33). It is overexpressed on the surface of tumor cells, affecting the remodeling of the extracellular matrix and promoting the invasion and metastasis of cancer cells. Hormone-dependent breast cancers (such as estrogen receptor-positive breast cancer) are often influenced by hormones and hormone metabolism-related molecules. Sulfate transporters can affect the hormonal responsiveness of tumors by participating in the metabolism, conversion, and clearance of hormones, especially the impact of sulfation in hormone metabolism, which may affect the growth of breast cancer cells (34,35). However, the specific role of SDS in EC is still unclear. In this study, we found that SDS is highly expressed in EC, and future experiments can further verify the specific mechanistic role of SDS in EC.

LAPTM5 is located on chromosome 19 in humans and is primarily expressed in immune system cells, such as T cells and B cells. It is a member of the lysosome-associated protein family (33,36). Its abnormal expression is mainly associated with cardiovascular diseases, immune system disorders, viral infections, and cancers (37). LAPTM5 expression is found in all neuroblastoma (NB) cell lines and primary NB tumor samples, where it is epigenetically downregulated through DNA methylation. These conditions are related to tumor differentiation (38). LAPTM5 promotes tumor progression in most malignant tumors, although in some malignancies, it may inhibit tumor progression. For example, LAPTM5 drives tumor cell resistance to lenvatinib treatment in liver cancer (39), promotes lung metastasis in renal cancer (40), and enhances cancer cell proliferation, migration, invasion, and epithelial-to-mesenchymal transition in breast cancer (41). However, low expression of LAPTM5 in B-cell lymphoma, esophageal squamous cell carcinoma (ESCC), and non-small cell lung cancer (NSCLC) is significantly associated with poor prognosis (42,43). The relationship between LAPTM5 and EC is still unclear. In this study, LAPTM5 was found to be lowly expressed in EC, and further experiments are needed to explore its specific role and molecular mechanisms in EC. The SLA gene is an adaptor protein associated with the Src tyrosine kinase family (44), involved in regulating the signal transduction of various immune cell surface receptors, including B cell receptors, T cell receptors, cytokine receptors, and receptor tyrosine kinases, all of which are important regulators of immune and cancer cell signaling (45). A previous study has shown that SLA may participate in the development of cancer by regulating the ubiquitination process (46). The expression of SLA protein on the surface of tumor cells may help create an immune-tolerant environment between tumor cells and immune cells, thereby promoting tumor growth and metastasis. SLA protein may regulate local immune responses by affecting the function of immune cells in the TME, such as natural killer (NK) cells and dendritic cells, thereby facilitating immune evasion of tumor cells (47). EC cells may escape immune surveillance by altering the expression or function of SLA protein. The TME in EC is a critical factor influencing cancer progression (48). SLA protein may participate in the malignant process of EC by regulating tumor cell proliferation, migration, and invasion abilities (49). The specific mechanism may be related to the role of SLA protein in cell signaling pathways, further affecting the growth and invasion characteristics of tumor cells (48). However, the relationship between SLA and EC remains unclear. In this study, SLA was found to be lowly expressed in EC, and its specific mechanisms still need further investigation.

The TME is composed of immune cells, stromal cells, and a variety of cytokines. Immune cells and the cytokines they secrete within the TME may promote tumor growth, immune evasion, and drug resistance (50). The immune cells in the TME mainly include CD8⁺ T cells, CD4⁺ T cells, dendritic cells, macrophages, and others. Some immune cells may play an anti-tumor role, while others may promote tumor immune evasion (51). The TME often exhibits immunosuppressive characteristics, with common immune checkpoint molecules such as programmed cell death protein 1 (PD-1)/programmed cell death ligand 1 (PD-L1). Their overexpression leads to limited T cell function, allowing tumor cells to escape immune system surveillance (52). Currently, immunotherapy for endometrial cancer is rapidly developing. Immune checkpoint inhibitors such as pembrolizumab, lenvatinib, and nivolumab have shown positive effects in some clinical trials (53-55). Immunotherapy holds great potential in EC, and the immune microenvironment plays a role in determining the sensitivity to immunotherapy (36). In this study, there were significant differences in immune cell infiltration between the high- and low-risk groups. Immune cells in the low-risk group were more active, and SDS was positively correlated with the infiltration of resting mast cells, activated mast cells, and eosinophils. This suggests that SDS may influence the immune microenvironment of EC by regulating the infiltration of these immune cells. Further experiments are needed to validate the specific molecular mechanisms of SDS expression and its regulation of the immune microenvironment in EC. Currently, common drug treatments for EC include chemotherapy drugs, hormonal drugs, and targeted drugs (56). Common chemotherapy drugs include paclitaxel, cisplatin, and carboplatin (57). Hormonal therapy is generally used for hormone receptor-positive (such as estrogen receptor-positive) EC patients (58). Targeted therapies include bevacizumab (VEGF inhibitor), olaparib (PARP inhibitor), and others (59). In this study, a differential analysis based on the IC50 values of drugs was conducted for high- and low-risk groups, and 12 sensitivity drugs were selected, providing ideas for clinical treatment of EC.

Conclusions

This study identified efferocytosis-related genes OLR1, SDS, LAPTM5, and SLA in EC, and established a risk score that can differentiate between high-risk and low-risk patient groups, with the low-risk group showing significantly better prognosis than the high-risk group. The further developed prediction nomogram model can predict the 1-, 3-, and 5-year survival rates for endometrial cancer, and the model demonstrated good predictive performance, providing clinical guidance for the assessment of prognosis in EC to some extent. Additional immune infiltration analysis revealed that immune cells in the low-risk group were more active, providing a theoretical basis for immune therapy in EC. Moreover, 12 sensitivity drugs were screened, offering clinical guidance for drug treatment in EC. Our study identified efferocytosis-related genes, which may serve as potential therapeutic targets for endometrial cancer. In the future, experimental research can be conducted to further explore the specific mechanisms of these genes in endometrial cancer, thereby providing new insights and strategies for the treatment of this disease.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-488/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-488/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-488/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

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