Secretory leukocyte peptidase inhibitor as a dynamic radiotherapy response biomarker in esophageal squamous cell carcinoma

Abstract

Esophageal cancer is the seventh leading cause of cancer-related deaths, with most cases diagnosed at locally advanced stages. Chemoradiotherapy is one of the most effective treatments for these patients. However, high rates of recurrence persist, highlighting the need for more reliable prognostic biomarkers. In this study, we identified immune-related prognostic genes through bulk (n = 119) and single-cell (n = 60) transcriptomic analyses. We then used Western blotting, enzyme-linked immunosorbent assay (ELISA), and immunofluorescence to preliminarily investigate the potential biological functions of the candidate biomarker. The clinical prognostic utility of the candidate biomarker was validated in an independent cohort (n = 91) via ELISA of serum samples collected before and during chemoradiotherapy. The results showed that low expression of the secretory leukocyte peptidase inhibitor (SLPI) significantly correlated with tumor progression and poor prognosis. In epithelial cells, reduced SLPI expression was associated with decreased activation of differentiation-related pathways. Furthermore, SLPI expression levels were found to influence fibroblast phenotypes, with exogenous SLPI inhibiting the NF-κB pathway and restoring fibroblast quiescence. The results of ELISA showed that dynamic changes in serum SLPI levels during chemoradiotherapy serve as an independent prognostic factor. In conclusion, serum SLPI levels represent an easy-to-detect biomarker for dynamically monitoring the efficacy of chemoradiotherapy.

Keywords

esophageal squamous cell carcinoma / secretory leukocyte peptidase inhibitor / transcriptome / radiotherapy / prognosis

1 Introduction

Esophageal cancer ranks as the 11th most common cancer globally and the 7th leading cause of cancer-related deaths [1,2]. The two major subtypes of this disease are esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC), with ESCC being predominantly observed in East and South-Central Asia [3]. In recent years, advances in the molecular subtyping of ESCC have led to significant progress in its diagnosis and treatment [4]. Today, endoscopic treatment, neoadjuvant chemoradiotherapy (nCRT), and surgical intervention are the preferred strategies for early-stage esophageal cancer [5,6]. However, due to patients often neglecting symptoms of esophageal cancer and the subtle onset of dysphagia, most cases are diagnosed at advanced stages, typically presenting as locally advanced disease [6–8]. For patients with locally advanced ESCC who are ineligible or unwilling to undergo surgery, definitive chemoradiotherapy (CRT) is considered the optimal treatment strategy [6,9].

Radiotherapy (RT) has been a cornerstone in the treatment of esophageal cancer, particularly for advanced patients who are not candidates for surgery. For instance, concurrent chemoradiotherapy (CCRT) has been proven effective in improving local tumor control, enhancing long-term survival, and reducing recurrence and metastasis [10]. However, even with such a treatment regimen, half of the patients still experience recurrence and metastasis [11–13]. Numerous studies have explored the prognostic impact of various clinical and biological factors to identify high-risk subgroups for recurrence and inform clinical decision-making. For example, studies have indicated that high levels of inflammatory factors such as the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) are associated with poor prognosis, but these studies primarily focused on populations after nCRT rather than definitive CRT patients [14,15]. Furthermore, the hemoglobin, albumin, lymphocyte, and platelet score (HALP) and the modified Gustave Roussy immune score (GRIm-Score) have been recognized for their predictive values in multiple cancer types. In ESCC, HALP and GRIm-Score have also been found to be independent predictors of overall survival (OS) in unresectable ESCC patients undergoing CCRT, but they cannot predict progression-free survival (PFS) [16]. Although these findings provide reference value, there are also limitations. Therefore, to identify and optimize stable and reliable prognostic biomarkers for RT remains crucial.

Immunotherapy has ushered in a new era in cancer treatment [17]. Over the past decade, significant achievements in cancer therapy have been attributed to the use of immune checkpoint inhibitors (ICIs) [18]. Clinical trials involving ICIs have shown significant improvements in OS and PFS for ESCC patients [19–21], particularly in those with high PD-L1 expression [6]. Additionally, a recent study employing a multi-omics approach for molecular subtyping of ESCC reported that patients in the immune modulation subtype may derive greater benefit from ICI therapy compared to the immune suppression subtype [4]. Many studies have shown that RT and immunotherapy have a synergistic effect in a variety of cancers. RT not only directly kills tumor cells but also presents neoantigens to activate immune responses, thereby reactivating the body’s anti-tumor immunity [22]. The impact of RT on the tumor microenvironment is often viewed as a double-edged sword. While RT can kill tumor cells, the DNA damage response it induces may promote the release of cytokines and chemokines, triggering inflammatory responses and altering the tumor microenvironment. On one hand, RT may induce immunogenic cell death (ICD), activate dendritic cells (DCs), promote T cell infiltration, and induce tertiary lymphoid structures (TLS) formation, thereby enhancing local antitumor immunity [23,24]. On the other hand, RT may also suppress antitumor immune function and, in certain circumstances, may even promote ESCC metastasis. This suppression may manifest as increased infiltration levels of neutrophils, M2-type tumor-associated macrophages (TAMs), myeloid-derived suppressor cells (MDSCs), and regulatory T cells (Tregs) [9,25]. Therefore, changes in immune-related factors after RT may be closely related to prognosis. As such, performing ESCC subtyping based on immune-related gene sets and constructing a RT prognostic model could offer valuable insights for identifying biomarkers, improving prognosis, and guiding clinical practice.

In this study, we focused on the potential of immune-related genes (IRGs) as prognostic biomarkers for RT in ESCC. We initially identified immune-related differentially expressed genes (IRDGs) in ESCC data set and utilized Cox regression analysis to determine the genes significantly associated with prognosis. Based on the expression of these genes, we performed consensus clustering, dividing ESCC patients into two subtypes with obvious survival differences. Through analysis and validation in multiple independent RNA sequencing (RNA-seq) and single-cell RNA-seq (scRNA-seq) data sets, we identified SLPI as a key signature gene. Subsequently, we validated the potential of SLPI as a RT prognostic biomarker in our cohort. Additionally, through biological experiments, we preliminarily investigated the mechanism underlying SLPI’s regulation of fibroblast phenotypes.

2 Materials and methods

2.1 Data acquisition and utilization overview

The RNA-seq expression data and corresponding clinical information of the ESCC data set GSE53624 were downloaded from the Gene Expression Omnibus (GEO) database. The GSE53624 data set includes tumor and paired normal tissue samples from 119 ESCC patients. Gene-specific probe and annotation information was obtained based on the method described by Alaei et al., to ensure data accuracy and consistency [26].

Additional independent ESCC data sets, including GSE20347, GSE23400, and GSE38129, were also downloaded from the GEO database. Probe information was extracted based on the respective GPL platform IDs of these data sets and mapped to Gene Symbol.

Furthermore, a list of 2483 IRGs was retrieved from The Immunology Database and Analysis Portal (ImmPort). After removing duplicate genes, a final set of 1793 unique IRGs was curated for subsequent analyses.

Single-cell transcriptome feature-barcode matrices of 208 659 cells from 60 ESCC tumor tissues and 4 adjacent normal tissues were obtained from the GSE160269 data set [27,28].

2.2 Clinical specimens and ethical approval

Serum samples were collected from 91 patients with ESCC who underwent definitive RT at the Cancer Hospital, CAMS, between January 2012 and December 2019 (NCT05543057). The clinicopathological diagnoses were verified by at least two pathologists following the American Joint Committee on Cancer (AJCC) guidelines. A total RT dose of 50.0–60.62 Gy (equivalent dose in 2-Gy fractions); median dose, 60.62 Gy) was delivered once a day for 5 days each week. Among them, 51 patients received concurrent chemotherapy (Table 1). Considering both tumor response during RT and the feasibility of clinical procedures, serum samples were collected at baseline (prior to radiotherapy) and during radiotherapy (at 40 Gy). Serum was extracted from blood samples by centrifugation at 3000 g for 10 min. The Independent Ethics Committee of CAMS approved the project (No. 22/036-3237). In addition, as the control group, we included blood samples from 40 healthy volunteers collected by the Laboratory Department at the Cancer Hospital, CAMS, in 2019, with a gender ratio of 1:1, and a median age of 46 years (range, 20–64). All patients provided informed consent before enrollment in this study.

2.3 RNA-seq data analysis and establishment of prognostic model

Principal component analysis (PCA) was performed using the R package “factoextra” (version 1.0.7). The R package “limma” was used to analyze differential gene expression between tumor and normal tissues in the GSE53624 data set, using |log₂FoldChange| > 1 and P < 0.05 as the criteria for determining differentially expressed genes (DEGs). Kaplan–Meier survival analysis, univariate Cox regression analysis, multivariate Cox regression analysis and establishment of a prognostic model were performed using the “survival” package (version 3.6.4), “survminer” package (version 0.4.9), “forestplot” package (version 3.1.3). Receiver operating characteristic (ROC) curve plotting were performed by using the ROC Plot tool in Hiplot (ORG) [29]. Consensus clustering was performed using the “ConsensusClusterPlus” package (version 1.68.0) on the GSE53624 cohort. To ensure the robustness of the results, the clustering was repeated 500 times.

2.4 scRNA-seq data analysis

Single-cell data was processed using the Seurat pipeline (version 4.4.0) [30] in R (version 4.4.2), and downstream analysis was performed using the SCP pipeline (version 0.5.1). Quality control parameters were consistent with the original study [27]. After normalization, high-variance feature definition and scaling, uniform manifold approximation and projection (UMAP) graph-based dimensionality reduction was performed based on the top 30 principal components [31]. For major cell types such as epithelial cell, fibroblast, fibroblastic reticular cell (FRC), pericyte, endothelial cell, myeloid cell, T cell and B cell, annotations consistent with the original study were used. For cell subtypes of interest, UMAP was performed again based on the top 15 principal components. Cell clustering was performed using the Louvain algorithm. Cell clusters were annotated according to their tissue origin and marker genes from a previous study [32]. Patients were equally divided into high SLPI expression group (SLPI-high) and low SLPI expression group (SLPI-low) based on the average SLPI expression level of the tumor samples. DEGs were found using the Wilcoxon test, and gene set enrichment analysis (GSEA) [33] was performed on biological process (BP) gene sets in the Gene Ontology (GO) [34,35]. Cell trajectory inference was performed using partition-based graph abstraction (PAGA) [36] and Slingshot (version 2.14.0) [37].

2.5 Cell line, cell culture, and reagents

Normal human fibroblast (NHF) cells were obtained from the Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences (Beijing). The cell line is typically maintained in dulbecco’s modified eagle’s medium (DMEM) with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin (P/S). These cultures were incubated at 37 °C in a 5% CO₂ atmosphere.

The recombinant SLPI protein was purchased from MCE (HY-P73414, USA), while the NF-κB agonist TNFα (31–45), were bought from Selleck (E7641, USA).

2.6 Western blotting analysis

The NHF cells were lysed in RIPA lysis buffer (Solarbio, Beijing, China). Western blotting analysis was performed as described previously [38]. Primary Antibodies used for western blotting were p-IκBα (CST 2859s, 1:1000), IκBα (CST 4812s, 1:1000), p65 (Bioworld BS1253P, 1:1000), p-p65 (ABclonal AP1294, 1:1000), α-SMA (ABclonal A17910, 1:10 000), and β-actin (ABclonal AC038, 1:10 000).

2.7 Immunofluorescence (IF)

NHF cells were fixed in 4% formalin at room temperature for a duration of 15 min. The cells underwent a 10-min incubation with 0.5% Triton X-100. After blocking with goat serum for 1 h, the cells were exposed to primary antibodies (Ki-67, sc-15402, Santa cruz, USA) overnight at 4 °C. Subsequently, the cells were incubated with either Fluorescein-conjugated Goat Anti-Rabbit IgG (H+L) (SA00003-2, Proteintech, Wuhan, China) for 1 h at room temperature. The final step involved staining the cells with 4,6-diamidino-2-phenylindole (DAPI), and visualization was performed by using a fluorescence microscope.

2.8 Enzyme-linked immunosorbent assay (ELISA)

The concentration of SLPI in serum or cell culture supernatants was determined by ELISA using a commercial SLPI detection kit (EIAab, E1312h, Wuhan, China) strictly following the manufacturer’s protocols. Similarly, collagen type I levels in culture media were quantified using a dedicated collagen I ELISA kit (CUSABIO, CSB-E08082h, Wuhan, China) according to the vendor’s instructions. In addition, the dilution factor is determined by pre-experiment before the experiment to ensure that the experimental results meet the standard curve interval.

2.9 Statistical analysis

Statistical analyses for this study were performed using R (version 4.4.0) and GraphPad Prism (version 10.1.2). Detailed statistical methods are described in the figure legends. For serum sample ELISA quantification, one biological replicate was performed per experimental group, with each 96-well plate containing independent standard curves generated from serially diluted recombinant protein standards. A P-value < 0.05 was considered statistically significant.

3 Results

3.1 The identification and analysis of immune-related prognostic genes (IRPGs) in ESCC

The analysis strategy and research workflow of this study are illustrated in Fig. 1. Based on the GEO data set GSE53624, we performed PCA. The PCA results demonstrated that the data set effectively distinguishes between the tumor and normal groups (Fig. S1A). Next, we utilized the R package “limma” to conduct differential expression analysis on the tumor and normal samples in the GSE53624. According to the screening criteria (|log₂FoldChange| > 1, P < 0.05), a total of 2622 DEGs were identified, including 1095 upregulated genes and 1527 downregulated genes (Fig. 2A). Subsequently, by accessing ImmPort, we intersected the curated 1793 IRGs with the DEGs and ultimately identified 207 IRDGs in ESCC (Fig. 2B).

A univariate Cox regression analysis was performed on these 207 IRDGs. Based on the P-values, we selected the top 10 genes with the smallest P-values and visualized them using a forest plot (Fig. 2C). These genes were designated as IRPGs.

Furthermore, based on the expression of these IRPGs, we performed Consensus Clustering on the GSE53624. The analysis successfully divided the samples into two clusters. The optimal clustering variable was 2 (Fig. S1B). Survival analysis was conducted for the two clusters. The results indicated that Cluster 1 exhibited better prognosis compared to Cluster 2. Thus, we designated Cluster 1 as “Low Risk” and Cluster 2 as “High Risk” (Fig. 2D).

Subsequently, we visualized the expression levels of these IRPGs across the two clusters (Figs. 2E and S1C). After multiple testing correction, we found that S100A7, SLPI, S100A7A, and IL36RN were significantly differentially expressed between the two clusters, suggesting that these four genes may be important factors contributing to the better prognosis observed in the Low Risk cluster compared to the High Risk cluster.

3.2 ScRNA-seq results suggest that SLPI exhibits more distinctive characteristics among the IRPGs in ESCC

To further analyze the expression of the above four genes in different cell populations in ESCC and normal esophagus, we analyzed scRNA-seq data of 208 659 cells from 60 tumor tissues and 4 adjacent normal tissues of ESCC patients (Fig. 3A). The results showed that the expression level of SLPI was significantly higher in adjacent normal tissues than in tumor tissues, but S100A7, S100A7A, and IL36RN were expressed at lower levels in tumor tissues and adjacent normal tissues of all cell types (Fig. 3B). SLPI was predominantly expressed in fibroblasts and epithelial cells, whereas S100A7, S100A7A, and IL36RN were almost exclusively expressed in epithelial cells (Fig. 3C). Therefore, we focused our interest on SLPI.

3.3 SLPI is correlated with the behavior of epithelial cells in ESCC tissues

Since SLPI was mainly expressed in epithelial cells and fibroblasts, we performed further analyses in these two cell groups. Based on the SLPI mean expression level of the tumor tissue, we divided the patients equally into two groups, SLPI-high (n = 30) and SLPI-low (n = 30) (Fig. 4A). Epithelial cells from these two groups of patients can be distinguished by UMAP (Fig. 4B). Apoptosis-related genes (e.g., ANXA1, GSTM1, and CLU), small proline-rich proteins (SPRRs)-related genes (e.g., SPRR2A, SPRR2D, SPRR2E, and SPRR3), and genes related to metal sequestration by antimicrobial proteins (e.g., LCN2, S100A8, and S100A9) were most highly expressed in the epithelial cells of SLPI-high tumors, while A2M, CRYAB, and the epithelial-mesenchymal transition-related genes (e.g., SFRP1 and IGFBP2) were highly expressed in that of SLPI-low tumors (Fig. 4C). Epithelial cells in the SLPI-high group showed significant activation of differentiation-related gene sets (Fig. 4D and 4E). In addition, epithelial cells in the SLPI-high group showed significantly higher innate immune response, whereas epithelial cells in the SLPI-low group showed higher activation of the protein synthesis-related gene sets (Fig. 4D and 4E). These findings suggest that the expression level of SLPI may influence the biological functions of epithelial cell differentiation, innate immunity, and protein synthesis in tumor tissues.

3.4 Changes in SLPI expression are associated with the phenotypic evolution of fibroblasts during ESCC development

Besides epithelial cells, the other major SLPI-expressing cell population is fibroblasts. Following the aforementioned grouping strategy, we analyzed fibroblasts in tumor samples from both SLPI-high and SLPI-low groups as well as adjacent normal tissues. Based on the tissue of origin and marker genes, fibroblasts were divided into three categories, i.e., normal fibroblasts (NF), inflammatory cancer-associated fibroblast (iCAF) and matrix cancer-associated fibroblast (mCAF) (Fig. 5A). Further annotation was performed based on the top-expressed genes for each cell cluster. The representative marker genes are shown in Fig. 5B. Interestingly, a group of iCAFs with high expression of APOD, C7 and IGFBP7 genes (iCAF-APOD) was specifically present in SLPI-high tumor tissues. Moreover, the proportion of iCAF expressing interleukins and chemokines such as IL24, IL11, IL6, CXCL8, and CXCL1 (iCAF-IL24) was higher in SLPI-high tumor tissues. In contrast, mCAFs expressing extracellular matrix formation-related genes (e.g., CTHRC1, COL1A1, COL3A1, and FN1) and tumor progression-related genes (e.g., MMP11 and POSTN) (mCAF-MMP11) had a higher percentage in SLPI-low tumor tissues. In addition, a few NF distributions were observed in SLPI-high tumor tissues, but almost none in SLPI-low tumor tissues. In adjacent normal tissue, we also observed a minority distribution of iCAF-PTGDS in addition to NF (Fig. 5C). Cell trajectory analyses based on PAGA and Slingshot consistently suggest that a group of NFs highly expressing SLPI (NF-SLPI) may be the starting point of cellular evolution of fibroblasts during ESCC development. In SLPI-low tumor tissues, the majority of fibroblasts eventually differentiate into mCAF-MMP11 (lineage 1). In contrast, in SLPI-high tumor tissues, fibroblasts were more prone to differentiate into iCAF-APOD (lineage 3) or to reside in iCAF-IL24 (lineage 2) (Fig. 5D). Thus, differences in SLPI expression levels may play an important role in determining the evolutionary trajectory of fibroblasts during ESCC development.

3.5 SLPI influences fibroblast phenotypes via NF-κB signaling pathway

SLPI is recognized as an anti-inflammatory modulator that functions by inhibiting the NF-κB signaling pathway [39,40]. To investigate whether the relationship between SLPI and fibroblast phenotypes depends on NF-κB signaling pathway, we performed single-cell subpopulation analysis using the PROGENy package [41]. The results revealed NF-κB pathway activity was significantly suppressed under high SLPI expression (Fig. 6A), suggesting that SLPI indeed negatively regulates NF-κB signaling in fibroblasts. When NHF cells were treated with recombinant SLPI protein, the levels of p-p65 and p-IκBα were downregulated, and total IκBα levels increased (Fig. 6B).

To further confirm whether SLPI inhibits fibroblast activation via NF-κB pathway, we assessed the activation markers of fibroblasts, such as collagen type I [42,43], α-SMA [44,45] and the proliferation marker Ki-67 [46], respectively. The results showed that SLPI significantly suppressed α-SMA expression, collagen I secretion and Ki-67 expression in NHF cells, and these effects could be reversed by treatment with NF-κB agonists TNFα (Fig. 6C–6E).

3.6 SLPI is downregulated in ESCC and associated with poor prognosis

Given that in epithelial cells, the gene set of the SLPI-high group is enriched in differentiation-related biological processes (Fig. 4D). And during the progression of fibroblasts into CAFs, SLPI expression gradually decreases (Fig. 5B). Therefore, we hypothesize that SLPI expression is associated with tumor differentiation. Subsequently, we found in the data set GSE53624 with clinical information that SLPI expression correlates with tumor grade, with expression levels decreasing as tumor differentiation worsens (Fig. 7A). By dividing the SLPI into SLPI-high and SLPI-low groups based on median expression in the GSE53624 data set, the Kaplan–Meier survival analysis shows that the SLPI-high group exhibited a better prognosis compared to the SLPI-low group (Fig. 7B). In addition to using the GSE53624 data set (119 cases), we further validated the expression of SLPI in the tumor-normal paired ESCC data sets GSE20347 (17 cases), GSE23400 (53 cases), and GSE38129 (30 cases). We found that SLPI expression was consistently downregulated in tumor tissues across all data sets (Fig. 7C–7F). All these results suggest that SLPI expression is associated with the malignancy of ESCC, and lower SLPI expression is correlated with a poorer prognosis. These findings highlight the potential of SLPI as a prognostic biomarker for ESCC.

3.7 Changes in serum SLPI levels can serve as a prognostic marker for RT in ESCC

We next investigated the relationship between SLPI expression during RT and disease recurrence or progression. SLPI, being a secreted protein, is present in the plasma of patients [39]. Therefore, we measured SLPI concentrations in 182 serum samples collected before and after RT from a cohort of 91 ESCC patients, as well as in serum from 40 healthy volunteers, which served as controls. The median follow-up time of the RT group was 12 (6–61) months. The results revealed that serum SLPI concentrations, both before and after RT, were significantly lower in ESCC patients compared to healthy controls (Fig. 8A). This finding is consistent with previous scRNA-seq and RNA-seq analyses in ESCC. The clinical characteristics of the ESCC patients are summarized in Table 1. Patients were categorized into two groups based on the changes in serum SLPI levels before and after RT, following the analysis of the cut-off values (0.5, 1.0, 1.5, and 2.0). Ultimately, a 1.5-fold change (post-RT vs. pre-RT) was determined as the critical threshold. Survival analysis demonstrated that the ratio ≥ 1.5-fold group exhibited significantly better OS and PFS compared to the ratio < 1.5-fold group (Figs. 8B and S2). Univariate Cox regression analysis of clinical variables in the cohort identified SLPI variation, treatment approach, and tumor stage as significant prognostic factors (Table 1). Multivariate Cox regression analysis further confirmed that SLPI variation, treatment approach, and tumor stage were independent prognostic factors for both OS and PFS (Fig. 8C). Based on these factors, we constructed a prognostic model to predict patient outcomes. ROC curve analysis showed that the area under the ROC curve (AUC) was 0.740 for predicting OS and 0.761 for predicting PFS (Fig. 8D and 8E), indicating that our model has good predictive capability for RT outcomes in ESCC patients.

4 Discussion

In this study, we first characterized 207 IRDGs in ESCC and identified the top 10 genes most strongly associated with prognosis. These genes are CSRP1, OBP2A, ANGPTL7, TPM2, LCN1, GHRHR, S100A7, SLPI, S100A7A, and IL36RN. Based on the expression of these genes, we stratified the ESCC cohort into two clusters with significantly different survival outcomes. Subsequently, by using multiple data sets to filter and analyze, we focused on SLPI. Single-cell data analysis showed that patients with high SLPI expression had better innate immune response and higher cell differentiation. In fibroblasts, cell trajectory analyses showed that SLPI expression was inhibited during the differentiation of NF to CAF. And RNA-seq data set showed that patients in SLPI-high group had a better prognosis. Therefore, we found the potential of SLPI as a prognostic marker. Notably, the potential of SLPI as a RT prognostic biomarker for ESCC has not been previously reported. Here, we present SLPI as a promising candidate for predicting RT outcomes in ESCC.

SLPI, also known as Antileukoproteinase, is a small secreted protein with a molecular weight of 11.4 kDa. It is widely distributed in various bodily fluids, including serum and saliva [47,48]. Initially discovered in epithelial cells [49], it was subsequently identified in macrophages [50], neutrophils [51], mast cells [52], and stromal cells [53]. SLPI has been shown to inhibit serine proteases produced by immune cells [54,55] and exhibits antimicrobial and antiviral properties [56,57]. In addition, it plays a role in immune modulation and anti-inflammatory processes by binding to specific receptors. For example, SLPI interferes with the interaction between ANXA2 and phosphatidylserine by binding to ANXA2. It also blocks the interaction between lipopolysaccharide and CD14 by binding to the latter. Furthermore, SLPI binds to PLSCR1 and PLSCR4, thereby participating in the regulation of antiviral responses (Fig. S3). Research has demonstrated that various molecules, including IRF-1 [58], IFNγ [59], IL-10, IL-6 [60], progesterone, IL-1β, and TNFα [61], can regulate the expression of SLPI. Furthermore, SLPI itself can modulate the NF-κB signaling pathway by inhibiting IκBα degradation or competing for NF-κB binding sites [40,62,63].

In cancer research, the expression of SLPI varies across different cancer types. Studies have reported that SLPI is upregulated in colorectal cancer [64], ovarian cancer [65], and gastric cancer [66], while it is downregulated in oral squamous cell carcinoma (OSCC) [67–69], lung cancer [70], and liver cancer [71]. These differences suggest that the role of SLPI may depend on specific tumor microenvironments and underlying molecular mechanisms. Recently, Chang Cui et al., demonstrated that SLPI inhibits the cytotoxic activity of mouse-derived ELANE (elastase) against tumor cells. The deletion of Slpi in Slpi^−/− mice restored ELANE’s tumor-killing capacity [72]. Another study [73] revealed that SLPI protects progranulin (PGRN), an epithelial growth factor critical for wound healing, from degradation by elastase into granulin, which inhibits epithelial cell proliferation. Based on this mechanism, the upregulation of SLPI in ovarian and prostate cancers has been shown to preserve PGRN and promote tumor cell proliferation [74–76]. Interestingly, both functional SLPI and mutant forms such as F-SLPI (L72F) and R-SLPI (L72R) exhibit tumor-promoting effects [77,78], indicating that SLPI may facilitate tumor proliferation through additional pathways [65,79,80]. Furthermore, SLPI has been implicated in tumor invasion and metastasis through its regulation of matrix metalloproteinase (MMP) expression [69,81,82].

An alternative perspective suggests that SLPI may inhibit tumor initiation and progression in cancers with aberrant activation of the NF-κB pathway by suppressing its activation. For instance, in OSCC, proteomic analyses revealed that SLPI expression gradually decreases with increasing malignancy of the lesions, while exogenous SLPI protein significantly inhibits NF-κB pathway activity [67,68]. A recent study further identified SLPI as a marker of quiescent fibroblasts (QFs), a subtype of NFs, in ESCC. The homeostasis of QFs is maintained by crosstalk between normal esophageal epithelial cells and fibroblasts. However, during epithelial malignant transformation, QFs transition into cancer-associated fibroblasts (CAFs), accompanied by suppressed SLPI expression [83]. This finding aligns with our observations, where scRNA-seq analysis showed significantly reduced SLPI expression in CAFs compared to NFs. However, the aforementioned study did not elucidate the mechanisms through which SLPI maintains fibroblasts quiescence. Our study further demonstrated that in addition to CAFs, malignant epithelial cells also express SLPI, but at significantly lower levels than normal epithelial cells. Given the aberrant activation of the NF-κB pathway and the suppression of SLPI expression in ESCC, this imbalance may hinder the effective regulation of NF-κB activity by SLPI, potentially serving as a driving factor in ESCC initiation and progression.

In our study, through univariate Cox regression analysis of 207 IRDGs, we identified other molecules significantly associated with the prognosis of ESCC, such as S100A7, S100A7A, and IL36RN. Both S100A7 and S100A7A belong to the S100 protein family. Previous research has shown differential expression of several S100 family members in ESCC, with only S100A7 being upregulated [84]. However, our study also revealed the upregulation of S100A7A. Recently, the role of S100A7 in ESCC has been elucidated: its overexpression promotes tumor proliferation, and secreted S100A7 reshapes the immune microenvironment by facilitating M2 macrophage infiltration and promoting tumor angiogenesis [85,86]. Notably, S100A7 has demonstrated potential as a non-invasive diagnostic biomarker in these studies. This supports the reliability of our method for identifying prognostic markers in ESCC.

Although we also identified other candidate genes with potential research value, we consider SLPI more significant in this study due to its ability to be secreted into the bloodstream, allowing detection in serum and enabling non-invasive monitoring of patients. This is particularly advantageous since the collection of pre- and post-radiotherapy blood samples has become a standard clinical procedure. In contrast, studies on S100A7A and IL36RN in cancer are relatively limited. While the role of SLPI in tumors has yet to be fully elucidated, prior studies have highlighted its potential as a biomarker in ovarian cancer [65], liver cancer [71], clear cell renal cell carcinoma [87], and OSCC [88]. Furthermore, considering that RT is a primary treatment modality for ESCC, the lack of convenient and reliable prognostic markers complicates the assessment of disease progression and survival outcomes in post-radiotherapy patients. Here, we report for the first time the systemic downregulation of SLPI in ESCC, which correlates with tumor grade and patient prognosis. By validating its expression in pre- and post-radiotherapy blood samples, we further demonstrate the potential of SLPI as a prognostic biomarker for RT outcomes in ESCC. Currently, no SLPI-based drugs have been approved for market release, but SLPI nanodelivery systems may hold potential [89]. To overcome short half-life of SLPI in vivo and susceptibility to protease degradation, researchers have explored utilizing nanocarriers (such as human serum albumin nanoparticles) to deliver recombinant human secretory leukocyte protease inhibitor (rhSLPI), thereby enhancing its stability and targeting capabilities. All these findings provide a rationale for pharmaceutical intervention targeting SLPI might improve the prognosis for ESCC patients with CRT resistance.

However, our study has several limitations. First, as a single-center study, it may have limited external validity. Thus, multi-center studies with larger sample sizes are needed to enhance the reliability of our conclusions. Second, although we identified suppressed SLPI expression in malignant epithelial cells and CAFs within ESCC, the underlying mechanisms driving this phenomenon remain unclear. Most importantly, given the diverse roles of SLPI across different cancer types, a deeper investigation into its functional mechanisms in ESCC, particularly its role within the immune system, will be critical for advancing our understanding of disease progression.

In summary, our results indicated SLPI may involve in the regulation of epithelial differentiation and maintenance of fibroblast quiescence, and serum SLPI levels may serve as a potential biomarker for prognostic evaluation in ESCC patients undergoing RT.

4.0.0.0.1 Acknowledgements

This work was supported by the National Key R&D Program of China (No. 2023YFC3503205) and the Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (CIFMS) (Nos. 2021-I2M-1-018 and 2023-I2M-2-004). The funders had no role in study design, data collection and analysis, interpretation of data, or preparation of the manuscript.

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