Secretogranin II serves as a potential prognostic biomarker and correlates with the immune microenvironment in pancreatic neuroendocrine tumors

Introduction

Pancreatic neuroendocrine tumors (pNETs) originate from pancreatic neuroendocrine cells and account for approximately 3–5% of all pancreatic tumors, representing a rare and heterogeneously dispersed tumor type. Recent advancements in diagnostic capabilities have led to a substantial increase in the incidence of pNETs (1,2). The heterogeneous biological behaviors of pNETs result in varied prognoses and therapeutic efficacies among patients. To address the limitations of the 8th edition of the American Joint Committee on Cancer (AJCC) standards (3), which rely on a limited set of clinical variables for pNET staging and result in suboptimal patient prognostic stratification (4,5), researchers have developed various nomograms (6,7). However, the heterogeneity of pNETs indicates that patients with identical clinicopathological features may experience substantially different outcomes, thereby challenging the credibility and predictive value of these models. Currently, surgery is the primary treatment for pNETs. However, some patients experience early postoperative recurrence and metastasis, thereby deriving no benefit from the procedure. Drug treatments, including somatostatin analogs, interferons, systemic chemotherapy, targeted therapy, and immunotherapy, have been used. However, due to the high heterogeneity of pNETs, differences in drug sensitivity and tolerance among patients highlight the importance of individualized and precision treatments (8). Identifying genes crucial for the development and progression of pNETs, to utilize them as effective prognostic markers and therapeutic targets, is crucial for optimizing treatment strategies.

Secretogranin II (SCG2), a member of the secretogranin family found in the endocrine and neural tissues, is involved in neurotransmission, inflammation, cell proliferation, and angiogenesis (9,10). SCG2 has been identified as a potent biological marker for predicting the prognosis of various malignancies, including lung (11), colon (12), and renal cancers (13). It can also promote tumor progression through multiple pathways, such as activating signaling pathways, inducing immunosuppression, and enhancing tumor drug resistance (14-16). However, the mechanism of action and prognostic value of SCG2 in pNET remain unclear.

Advances in high-throughput sequencing have revealed the genomic landscape of pNETs. By analyzing the gene expression profiles of pNETs and normal pancreatic tissues, we found that SCG2 messenger RNA (mRNA) expression was elevated in tumors compared to normal tissues and identified SCG2 as a key gene in pNETs. By analyzing immune infiltration and employing weighted gene co-expression network analysis (WGCNA) and enrichment analysis, we identified SCG2-related immune cells, genes, and pathways and screened potential drugs for pNET treatment. A clinical cohort analysis further explored the prognostic value and mechanism of action of SCG2 in pNETs. The detailed analysis process is illustrated in Figure 1. We present this article in accordance with the STREGA reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1550/rc).

Figure 1 The workflow chart of the analysis process conducted in this study. DEG, differentially expressed gene; GEO, Gene Expression Omnibus; GSEA, gene set enrichment analysis; PPI, protein-protein interaction; RT-PCR, reverse transcription polymerase chain reaction; WGCNA, weighted gene co-expression network analysis.

Methods

Data source and preprocessing

We obtained the mRNA expression profile data for pNET tissues from the Gene Expression Omnibus (GEO) database. The datasets included GSE73338 on the GPL20945 platform (82 pNET samples and 8 normal tissues), GSE117851 on GPL571 (47 pNET samples), and GSE43795 on GPL10558 (6 pNET samples and 5 normal tissues). Gene annotation and data normalization for these datasets were conducted using the “Affy” package in R software (version 4.1.2). The “ComBat” function from the “sva” package removed batch effects, merging datasets into a combined dataset with 135 pNET and 13 normal tissues. Uniform manifold approximation and projection (UMAP) analysis, both before and after data preprocessing, was performed using the “UMAP” package to further confirm the elimination of batch effects. The results were visualized using the “ggplot2” package.

Screening of the key gene SCG2

The “limma” package was employed to identify differentially expressed genes (DEGs) between pNET and normal tissues, setting |log₂ fold change (FC)| >2 and P.adjust <0.05 as thresholds. Volcano plots and heatmaps for upregulated genes were generated using “ggplot2” and “ComplexHeatmap”, respectively. The STRING database facilitated protein-protein interaction (PPI) analysis of the upregulated DEGs, with an interaction score of 0.4 (17). Visualization using Cytoscape (v3.8.2) identified SCG2 as a key gene upregulated in pNETs (18).

SCG2 functional enrichment analysis

We used the “clusterProfiler” package to conduct SCG2 functional enrichment analysis. We conducted gene set enrichment analysis (GSEA) on 135 pNET tissues against the MSigDB Hallmark gene set, considering pathways with P.adjust <0.05 and false discovery rate (FDR) <0.25 as significant. The top pathways were visualized based on their enrichment scores. By dividing the samples into high and low SCG2 expression groups by the median SCG2 expression levels, we analyzed DEGs for Gene Ontology (GO) analysis [biological processes (BP), cellular components (CC), and molecular functions (MF)] and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The P.adjust <0.05 were considered significant, visualized using the GOplot package.

Immune infiltration analysis

The CIBERSORT package, based on 22 immune cell markers provided by the CIBERSORTx platform (https://cibersortx.stanford.edu/), was used to analyze tumor-infiltrating immune cells (TIICs) in the pNET samples (19,20). Differences in TIIC between the high and low SCG2 expression groups were assessed using the Wilcox test. The relationship between SCG2 and TIICs subgroups was examined through the Spearman correlation coefficient, with a lollipop plot illustrating this correlation. Additionally, the Stromal Score, Immune Score, and ESTIMATE Score were calculated for each sample using the “estimate” package, providing a comprehensive evaluation of the pNET tissue microenvironment (21). The correlation between SCG2 expression and immune regulatory genes (including immunosuppressive genes, immunostimulant genes, and chemokines) was also analyzed (22).

WGCNA

The “WGCNA” package facilitated gene co-expression network analysis in 135 pNET tissues to identify SCG2-associated key genes (23). This process involved selecting a soft thresholding power of 12 (for an R² of 0.85) to achieve a scale-free network and employing hierarchical clustering and dynamic tree cutting to organize the network into 31 modules. The module most significantly associated with SCG2 expression was further analyzed, and its gene information was visualized in a PPI network using the Cytoscape software. The top 20 genes identified by the cytoHubba plugin’s top 10 algorithms [Maximal Clique Centrality (MCC), Density of Maximum Neighborhood Component (DMNC), Maximum Neighborhood Component (MNC), degree, Edge Percolated Component (EPC), bottleneck, closeness, radiality, and betweenness] underwent intersection analysis via the “UpSet” package to pinpoint reliable hub genes and their SCG2 correlation, showcased through heatmaps and radar charts.

Identification of potential therapeutic compounds

The Connectivity Map (CMap) database search identified potential therapeutic compounds against pNETs by analyzing DEGs between SCG2 expression extremes and prioritizing them based on |log₂FC| (24). The top 30 upregulated and downregulated genes were processed using the L1000 platform, highlighting five compounds with the lowest enrichment scores and scores <0 as potential inhibitors.

Collection of clinical case data

Clinical and pathological data were retrospectively collected from patients with pNETs who underwent curative surgery at The Fourth Hospital of Hebei Medical University between August 2013 and December 2021. The inclusion criteria were as follows: (I) pathological diagnosis confirmed as pNET; (II) complete surgical resection of the tumors; and (III) postoperative survival time of >6 months. The exclusion criteria were as follows: (I) coexistence of other malignant tumors; and (II) incomplete clinical and pathological data. After screening, 130 patients were eligible for inclusion in the study. Between January 2020 and January 2021, fresh tumor tissues and adjacent pancreatic tissues from 12 patients with pNETs were analyzed using reverse transcription polymerase chain reaction (RT-PCR) and paired immunohistochemistry (IHC) staining. The collected data included variables such as sex, age, primary tumor location and size, lymph node and liver metastases, invasion of surrounding tissues, tumor-node-metastasis (TNM) staging (3), histological grading, CA-199 levels, disease-free survival (DFS), and overall survival (OS). This study was conducted in accordance with Declaration of Helsinki and its subsequent amendments. This study was approved by the Institutional Research Ethics Committee of The Fourth Hospital of Hebei Medical University (2022KY234), and informed consent was obtained from all patients.

Total RNA extraction and quantitative RT-PCR (qRT-PCR) detection of SCG2 expression

Total RNA was extracted from pNETs and adjacent pancreatic tissues using the TRIzol reagent according to the manufacturer’s instructions. The RNA was then reverse transcribed into complementary DNA (cDNA) following the protocol provided in the reverse transcription kit (Table S1). SCG2 expression was quantified using the ABI7500 quantitative polymerase chain reaction (qPCR) instrument with glyceraldehyde 3-phosphate dehydrogenase as an internal reference. The primer concentration for qRT-PCR was 10 µm, with primer sequences detailed in Table S1. The relative expression levels of the target gene were calculated using the 2^−ΔΔCT method.

Immunohistochemical staining and assessment

Paraffin-embedded tissue sections from 130 pNET samples and 12 matched non-tumorous pancreatic tissues were prepared for immunohistochemical staining. Tumor samples were fixed in 3.7% paraformaldehyde, dehydrated, and embedded in paraffin. Sections of 4 µm were heated at 75 °C for 2 h, treated with xylene and 95% ethanol, then rinsed with distilled water. For antigen retrieval, the sections were heated in an ethylenediaminetetraacetic acid solution (MaxVision, 1:50, Fuzhou, China) and then cooled. Endogenous peroxidase activity was blocked using 3% H₂O₂. The sections were washed in phosphate buffered saline (PBS), and non-specific binding was blocked with goat serum. The SCG2 antibody (clone OTI3F12, ORIGENE, Rockville, USA, at a dilution of 1:500) application followed, with incubations at room temperature and then 4 ℃. After washing with PBS, the secondary antibody was applied. A 3,3'-diaminobenzidine chromogenic solution was used for visualization and hematoxylin counterstaining was performed using a LEICA auto STAINER XL (CV5030). The sections were then dehydrated, cleared, and sealed with neutral balsam. The immunohistochemical staining results were evaluated by two pathologists in a double-blind manner and graded according to the staining intensity as follows: 0= negative staining (negative), 1= weak staining (weak yellow), 2= moderate staining (yellow), and 3= strong staining (brown). The percentage of positive cells was graded on a four-point scale: 1: ≤25%; 2: 26–50%; 3: 51–75%; 4: 76–100%. The final immunohistochemical score was calculated by multiplying the two scores. Based on receiver operating characteristic curve analysis, a score >6 was defined as high SCG2 expression, while scores ≤6 were classified as low expression.

Statistical analysis

All data processing and analyses were performed using R software (version 4.1.2). Multiple hypothesis testing for differential gene expression and enrichment analyses was performed using the Benjamini-Hochberg procedure for FDR control. Adjusted P values were computed and consistently reported throughout the analysis. Continuous variables with a normal distribution are presented as mean ± standard deviation (SD), while those with a non-normal distribution are represented as median (interquartile range). For normally distributed variables, differences between the two groups were analyzed using an independent Student’s t-test. Wilcoxon test was applied to non-normally distributed variables. Categorical differences were assessed using the Chi-squared test or Fisher’s exact test, as appropriate. Spearman’s correlation test was used to examine the relationships between continuous variables. Survival analysis was performed using the Kaplan-Meier method, with the log-rank test used to compare survival rates between the groups. The Cox proportional hazards model was used to analyze prognostic factors in pNET patients, incorporating variables with P<0.10 from univariate Cox regression into the multivariate analysis. The results were visualized using the “ggplot2” package. Significance levels were defined as P<0.05 (*), P<0.01 (**), and P<0.001 (***).

Results

Data merging and batch effect removing.

We analyzed the GSE73338, GSE117851, and GSE43795 datasets comprising 135 pNET samples and 13 normal pancreatic tissues. After preprocessing and removing the batch effects, we combined the datasets. Substantial improvements in batch effects were observed before and after pre-processing (Figure 2A,2B). The UMAP analysis further confirmed the effectiveness of batch-effect removal (Figure 2C,2D).

Figure 2 Merging Gene Expression Omnibus datasets and removing batch effects. (A) Boxplot of pNETs dataset before batch effect removal. (B) Boxplot of pNETs dataset after batch effect removal. (C) UMAP plot of pNETs dataset before batch effect removal. (D) UMAP plot of pNETs dataset after batch effect removal. pNET, pancreatic neuroendocrine tumor; UMAP, uniform manifold approximation and projection.

Identification and screening of SCG2

Differential expression analysis identified 41 upregulated and 92 downregulated DEGs in pNET versus normal pancreatic tissue (|log₂FC| >2, P.adjust <0.05). A volcano plot (Figure 3A) and heatmap of the upregulated DEGs (Figure 3B) were created. A PPI network featuring 19 nodes and 29 edges from the STRING database highlighted SCG2 as a key gene for further analysis in pNETs (Figure 3C) and was significantly upregulated in pNET samples (P<0.001; Figure 3D).

Figure 3 Selection of SCG2. (A) Volcano plot of differential expression between pNETs and normal pancreatic tissues, a total of 41 genes were significantly upregulated and 92 genes were downregulated (|log₂FC| >2, adjusted P<0.05). (B) Heatmap illustrating the expression profiles of the 41 upregulated DEGs in pNET and normal pancreatic sample. The color scale represents normalized gene expression values, with red indicating high expression and blue indicating low expression. (C) PPI network constructed from upregulated DEGs, with SCG2 identified as a key gene in pNETs. (D) SCG2 expression was significantly upregulated in pNET tissues compared to normal pancreatic tissues in the merged dataset. ***, P<0.001. DEGs, differentially expressed genes; FC, fold change; pNET, pancreatic neuroendocrine tumor; PPI, protein-protein interaction; TPM, transcripts per million.

Enrichment analysis of SCG2

In total, 99 enriched terms were identified using GO and KEGG analyses. The top two entries from CC, BP, MF, and KEGG were visualized (Figure 4A). KEGG analysis revealed that SCG2-related DEGs were enriched in pathways such as neuroactive ligand-receptor interactions. GO analysis associated SCG2 with functions, such as positive regulation of synapse assembly (BP), postsynaptic specialization membrane (CC), and receptor ligand activity (MF). GSEA showed that 28 pathways were markedly enriched, linking low SCG2 expression with inflammatory, interferon-α, and interferon-γ response (Figure 4B), while high SCG2 expression was mainly related to processes in pancreatic beta cells, protein secretion, and oxidative phosphorylation (Figure 4C).

Figure 4 Enrichment analysis associated with SCG2 expression in pNETs. (A) GO and KEGG enrichment analysis. (B) Top three negatively enriched pathways in the low SCG2 expression group identified by GSEA, include interferon-γ response, interferon-α response, and inflammatory response. (C) Top three positively enriched pathways in the high SCG2 expression group were identified by GSEA. BP, biological processes; CC, cellular components; FC, fold change; pNET; GO, Gene Ontology; GSEA, gene set enrichment analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes; MF, molecular functions.

Immune infiltration analysis

The CIBERSORT algorithm analyzed the TIICs composition of 135 pNET tumor samples (Figure 5A), revealing considerable decreases in Tregs, γδ T cells, macrophages M1, and neutrophils in the SCG2 high expression group; while activated NK cells and dendritic cells showed substantial increases (Figure 5B,5C). The ESTIMATE algorithm showed lower Stromal Score, Immune Score, and ESTIMATE Score in the high SCG2 group (Figure 5D-5F). SCG2 expression was positively correlated with activated dendritic cells, M2 macrophages, and activated NK cells, whereas it showed significant negative correlations with M1 macrophages, γδ T cells, and regulatory T cells (Figure 6). Negative correlations between SCG2 and various immune regulatory genes were also significant, such as VTCN1, IL10, LAG3 and L10RB in immunosuppressive genes; CD70, IL6, ICOS, CXCL12, TNFRSF17, CD86, CD48, IL6R, CD27, CXCR4, TNFRSF14 in immune-stimulant genes; and CCL19, CCL2, CCL5, CXCL11, CXCL12, CXCL8, CXCL9, CXCL2, and XCL in chemokines (Figure 7). High SCG2 expression was accompanied by pronounced downregulation of immune-related genes, potentially affecting TIICs and immune responses in the tumor microenvironment (TME), offering a new perspective on the role of SCG2 in tumor immune evasion (Table S2).

Figure 5 Analysis of SCG2-related immune infiltration in pNET patients. (A) A bar chart showing the difference in the proportion of 22 types of TIICs in the TME of pNETs. (B) Boxplot showing the difference in the proportion of 22 types of TIICs in pNET tissue samples between high and low SCG2 expression groups. SCG2-high pNETs exhibited significantly reduced infiltration of regulatory T cells and M1 macrophages, and increased infiltration of activated natural killer cells and dendritic cells. (C) Correlation of the relative abundance of 22 TIICs with SCG2 expression level. (D) Boxplot showing significantly lower ESTIMATE Scores in SCG2 high-expression patients. (E) Boxplot showing a significant decrease in Immune Score in the SCG2 high-expression group. (F) Stromal Score was significantly lower in the SCG2 high-expression group. ns, not significant; *, P<0.05; **, P<0.01; ***, P<0.001. pNET, pancreatic neuroendocrine tumor; TIIC, tumor-infiltrating immune cells; TME, tumor microenvironment.

Figure 6 Scatter plots showing significant associations between SCG2 expression and selected infiltrating immune cells. (A) Activated dendritic cells. (B) Macrophage M1. (C) Macrophage M2. (D) NK cells. (E) γδ T cells. (F) Treg cells. NK cells, activated natural killer cells; TPM, transcripts per million; Treg cells, regulatory T cells.

Figure 7 Co-expression heatmap of SCG2 with immune-related genes. (A) Immunosuppressive-related genes. (B) Immune stimulants. (C) Chemokines. The color scale represents normalized gene expression values, with red indicating high expression and blue indicating low expression. ns, not significant; *, P<0.05; **, P<0.01; ***, P<0.001. TPM, transcripts per million.

Identification of key genes related to SCG2 by WGCNA

To explore SCG2-related genes in pNETs, we conducted a WGCNA analysis on 135 pNET samples using the “WGCNA” package, including 6,339 genes after filtering out those with a SD ≤0.5. By constructing a topology overlap matrix, we ensured that the gene distribution conformed to scale-free network properties by selecting a soft-thresholding power of 12 (R² value of 0.85) for a scale-free network. We identified 31 different modules and assessed their correlation with SCG2 expression using the Spearman’s test. The skyblue module containing 297 genes showed the highest correlation with SCG2 high expression, with a correlation coefficient of 0.47 (Figure 8). Subsequently, we constructed a scale-free network of genes from the skyblue module and imported it into Cytoscape software. Using the “Upset” package combined with the top 10 algorithms from the cytoHubba plugin, we cross-selected the top 20 genes ranked by each algorithm, ultimately identifying 8 core genes, including PRR4, PTPRN, SCG3, ZNF248, BEX1, DFCAM, DZIP3, and KIAA1324, which were visualized along with their chromosomal positions and showed significant positive correlations to SCG2 expression detailed in radar charts and heatmaps (Figure 9).

Figure 8 Identification of SCG2 high expression-related gene modules by WGCNA. (A) Weighted gene co-expression network of 6,339 genes, clustered into 31 modules using a soft-thresholding power of 12 to achieve scale-free topology. (B) Construction of a topological overlap matrix of all gene correlations after weighting. (C) Heatmap of module correlation with high SCG2 expression. Blue represents negative correlation, and red represents positive correlation. (D) The skyblue module showed the highest correlation with SCG2 expression (Spearman R=0.47, P<0.001). *, P<0.05; **, P<0.01; ***, P<0.001. Cor, correlation; WGCNA, weighted gene co-expression network analysis.

Figure 9 Identification of key genes in the skyblue module. (A) Key genes selected using the “Upset” package. (B) Visualization of the interaction between 8 key genes using Cytoscape software. (C) Chromosomal localization of key genes. (D) Heatmap displaying the expression levels of 8 key genes in pNETs. Red indicates high and blue indicates low normalized gene expression levels. (E) All 8 key genes show significant positive correlation with SCG2 expression. ***, P<0.001. DMNC, Density of Maximum Neighborhood Component; EPC, Edge Percolated Component; MCC, Maximal Clique Centrality; MNC, Maximum Neighborhood Component; pNET, pancreatic neuroendocrine tumor.

Screening of potential molecule drugs related to SCG2

Using the CMap database, we identified the top potential small-molecule drugs targeting SCG2 in pNET treatment, including telomerase inhibitors, caspase activators, histamine receptor antagonists, histone lysine methyltransferase inhibitors, and estrogen receptor antagonists (Table 1). The molecular structures of these compounds are shown in Figure 10.

Figure 10 Molecular formulas of the top five compounds with negative enrichment scores. (A) Telomerase inhibitor. (B) Caspase activator. (C) Histone lysine methyltransferase inhibitor. (D) Histamine receptor antagonist. (E) Estrogen receptor antagonist.

Expression of SCG2 mRNA in pNET tissues

To validate SCG2 mRNA expression, 12 pairs of pNETs and adjacent tissue samples were selected for RT-PCR. The results showed that the mRNA expression level of SCG2 in the pNET tissues was significantly higher than in the adjacent pancreatic tissues, with a statistically significant difference (P<0.001) (Figure 11A).

Figure 11 Immunohistochemical validation and expression patterns of SCG2 in pNET. (A) SCG2 mRNA expression is significantly elevated in pNET tissues. (B) Representative IHC images of SCG2 in pNETs and peritumoral tissues. Scale bar: 50 µm. (C) Expression level of SCG2 in pNETs and peritumoral tissues, showing significantly higher expression in pNETs. (D) Representative IHC staining images of pNETs showing negative, weak, moderate, and strong staining (Scale bar: 100 µm). ***, P<0.001. IHC, immunohistochemistry; mRNA, messenger RNA; pNET, pancreatic neuroendocrine tumor.

Immunohistochemical validation

Immunohistochemical staining of 130 pNETs and their adjacent tissues showed predominantly cytoplasmic SCG2 staining (Figure 11B). In paired sample IHC, tumor tissue staining was significantly higher than that in adjacent pancreatic tissues (P<0.001; Figure 11C). Among the 130 pNETs, 79 showed high SCG2 expression and 51 showed low expression. Representative IHC images showing negative, weakly positive, moderately positive, and strongly positive SCG2 expression in pNETs are shown in Figure 11D.

Correlation between SCG2 expression and clinical outcomes

Baseline characteristics, as presented in Table 2, revealed critical insights into the impact of SCG2 expression on patient outcomes. We found that patients with high SCG2 expression had significantly shorter DFS compared to those with low expression [hazard ratio (HR) =3.07, 95% confidence interval (CI): 1.41–6.69, P=0.005, Figure 12A]. Furthermore, high SCG2 expression was associated with a considerably shorter OS (HR =4.01, 95% CI: 1.17–13.79, P=0.03, Figure 12B). Additional analysis showed that SCG2 expression levels were significantly higher in female patients and in those with late-stage pNETs (Figure 12C-12F). Univariate and multivariate Cox regression analyses confirmed that SCG2 expression is an independent predictor of DFS and OS in patients with pNETs (Tables 3,4). These results emphasize the effectiveness of SCG2 expression as a prognostic marker in patients with pNETs.

Figure 12 Association of SCG2 expression with clinicopathologic parameters. (A) Kaplan-Meier analysis showing significantly shorter DFS in patients with high SCG2 expression. (B) Kaplan-Meier analysis showing significantly poorer OS in patients with high SCG2 expression. Association between SCG2 expression and clinicopathologic characteristics, including (C) sex, (D) stage, (E) CA19-9, (F) grade. CA-199, carbohydrate antigen-199; DFS, disease-free survival; HR, hazard ratio; IHC, immunohistochemistry; OS, overall survival.

Discussion

A previous study reported a 5-year progression-free survival (PFS) rate of approximately 66.5% and a 5-year OS rate of approximately 85.4% in patients with pNETs (1,25). In this study, the 5-year PFS rate was 75.6%, and the 5-year OS rate was 89.1%, which is consistent with previous findings. The high heterogeneity of pNETs challenges the accuracy of the traditional AJCC staging in predicting patient prognosis. Novel treatments such as biological therapy, chemotherapy, and targeted therapy have improved outcomes for some patients. However, the heterogeneity of tumors leaves some patients with no appreciable benefits. This necessitates the identification of new prognostic markers and therapeutic targets for pNETs. We validated SCG2 as a key gene in pNETs by analyzing mRNA expression data from the GEO database. SCG2 expression levels were significantly upregulated in pNETs compared to those in normal pancreatic tissue, as confirmed by RT-PCR and IHC analyses of our clinical samples. SCG2, secreted by neuroendocrine cells, plays a crucial role in the formation of secretory granules and regulation of secretory pathways (26,27). Its upregulation suggests a significant role in pNET progression and development. Further analysis showed that high SCG2 expression was correlated with advanced stages and poorer differentiation, indicating a worse prognosis. Cox multivariate regression analysis suggested that SCG2 might be an independent risk factor for pNET prognosis. Prognostic models based on clinical data do not fully capture the molecular characteristics of various diseases. Advances in technology have allowed us to explore pNETs’ heterogeneity and pathogenesis in greater depth, providing a more reliable prognostic value through molecular profiling (28). Currently, targeted sequencing and genetic mutation analyses can specifically identify high-risk patients and formulate personalized treatment strategies; however, their high costs limit their widespread use (29,30). This study identified and validated SCG2 as a novel prognostic marker for pNETs, offering cost-effectiveness with greater credibility than traditional prediction models.

SCG2 is a neuroendocrine secretory protein that is involved in hormone regulation and secretory granule formation. Emerging evidence indicates that SCG2 contributes to tumor progression through diverse molecular mechanisms. In bladder cancer, SCG2 activates oncogenic pathways, including mitogen-activated protein kinase and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), promoting cell proliferation and invasion (15). In renal cancer, SCG2 enhances angiogenesis through interaction with hypoxia-inducible factor 1-alpha (HIF-1α), subsequently modulating vascular endothelial growth factor signaling pathways (13). Furthermore, SCG2 facilitates immune evasion in colorectal cancer by fostering an immunosuppressive microenvironment and reducing T cell-mediated cytotoxicity in melanoma by downregulating major histocompatibility complex class I (MHC-I) molecules (31). Moreover, SCG2 has been implicated in resistance to sunitinib, a therapeutic agent known to prolong DFS in patients with advanced pNET (13). Collectively, these findings emphasize SCG2’s potential role as an oncogenic factor and therapeutic target in various malignancies, although its specific molecular function in pNETs remains inadequately characterized.

We performed enrichment analyses to explore the biological functions of SCG2. SCG2 is involved in neuroactive ligand-receptor interactions (CC), positive regulation of synapse assembly (BP), and postsynaptic specialization membranes (MF), emphasizing its role in regulating neural activities. GSEA revealed that low SCG2 expression correlates with interferon-α, interferon-γ, and inflammatory responses. Interferons produced by the innate immune system play important roles in inflammatory and antitumor responses (32). Interferon-α induces apoptosis through regulating mitochondrial and kinase pathways, exerting antitumor effects (33). Given its antitumor properties, interferon-α has been widely used in the clinical treatment of various malignancies (34,35). Interferon-γ plays a crucial role in immune responses, immune surveillance, antibacterial defense, and regulating cell proliferation and apoptosis (36,37). A previous study has shown that interferon-γ can induce apoptosis in NET cells, inhibit tumor growth, and exert antitumor effects against NETs (38). Currently, interferons are used to treat pNETs, often in combination with other treatments. Clinical reports indicate that interferon treatment achieves a partial response rate of 11% and a disease stability rate of 35% in patients with pNETs (39). Substantial enrichment of genes related to low SCG2 expression were observed in pathways involving interferon and inflammatory responses. This suggests that inhibiting SCG2 could potentially enhance antitumor effects by activating interferon pathways. These findings highlight the potential of SCG2 as a novel target for enhancing the efficacy of interferon-based therapies for pNETs.

Tumor development often involves evasion of the immune system, which employs various mechanisms to avoid immune-mediated antitumor responses (40). Cancer immunotherapy strategies have been designed to enhance host antitumor immune responses and promote tumor clearance (41). The degree of immune cell infiltration within tumors significantly affects patient prognosis (42). This study specifically investigated the relationship between SCG2 expression and immune cell infiltration using CIBERSORT. The results indicated a significant negative correlation between SCG2 expression and γδ T cell infiltration, which are known to mediate tumor immune responses by secreting interferon-γ and directly killing tumor cells through various pathways such as perforin-granzyme pathways or binding with tumor necrosis factor (TNF)-related apoptosis-inducing ligand and Fas ligand (43). Furthermore, our immune infiltration analysis revealed a complex interplay between SCG2 expression and macrophage type, with a positive correlation with macrophages M2 and a negative correlation with macrophages M1. Co-expression analysis also revealed a significant decrease in the expression of CD86, an activated M1 macrophage marker, in patients with high SCG2 expression. Macrophages M1 are known to produce pro-inflammatory cytokines like interferon-γ and enhance the TME by activating other immune cells and promoting immune phagocytosis and cytotoxicity (44,45). Conversely, macrophages M2 generally support tumor growth and spread by contributing to immune suppression, tissue remodeling, and angiogenesis (46). These findings highlight the role of SCG2 in potentially promoting pNET progression by influencing macrophage polarization, γδ T cell activity, and the overall inflammatory response, as evidenced by the enrichment of inflammatory and interferon-γ receptor-related genes in conditions of low SCG2 expression. However, the precise molecular pathways through which SCG2 influences M1/M2 macrophage polarization and T cell recruitment remain unclear. A study has demonstrated that activation of the NF-κB signaling pathway is crucial for promoting M1 macrophage polarization and enhancing pro-inflammatory functions (47). Notably, NF-κB signaling plays a pivotal role in macrophage phenotypic switching and cytokine-mediated immune regulation (48). SCG2 has been reported to activate the NF-κB pathway and promote tumor progression in bladder cancer, suggesting a potential mechanistic link between SCG2 and immune modulation (15). Consequently, we hypothesized that SCG2 modulates the immune microenvironment via the NF-κB pathway. Future studies should focus on clarifying the interactions between SCG2 and NF-κB signaling to further understand their contributions to immune modulation and pNET progression.

Interestingly, patients with high SCG2 expression exhibited a marked increase in activated dendritic cells, accompanied by a reduction in Tregs, presenting a seemingly “immune-activated” phenotype (49). However, the overall Immune Score analyses suggested a persistently immunosuppressed TME. This apparent paradox may reflect a state of immunological tolerance, wherein qualitative dysfunction of immune cells, despite quantitative increases in dendritic cells, prevents effective immune surveillance and antitumor responses. These findings highlight the complexity of SCG2-driven immune modulation in pNETs and emphasize the need for comprehensive mechanistic studies to delineate the functional status of immune cells affected by SCG2 dysregulation.

The TME, composed of immune cells, extracellular matrix, and inflammatory mediators, plays a pivotal role in promoting tumor cell growth, survival, and metastasis (50). The activity and composition of immune cells within the TME are crucial for determining the response to tumor immunotherapy (51,52). IHC analysis of tumor tissues from 183 patients with pNETs for nine immune cell markers demonstrated widespread immune cell infiltration, indicating the ‘immune-hot’ nature of pNETs (53). Our analysis using the ESTIMATE score to assess tumor purity and the presence of stromal and immune cells revealed that high SCG2 expression correlated with a significantly lower Immune Score, Stromal Score, and ESTIMATE Score. This suggests that elevated SCG2 expression contributes to an immunosuppressive environment that facilitates tumor progression. Chemokine systems also play a vital role in the TME by mediating the directed migration of immune cells and influencing tumor cell activities (54). Notably, the CCL2/CCR2 axis is involved in these processes along with the CCL5/CCR5 and CCL19/CCL21/CCR7 axes, which are critical for T cell-mediated antigen presentation and immune response enhancement against tumors (55). Co-expression analysis of SCG2 and various immune-related genes showed a significant negative correlation with key chemokines, including CCL2, CCL5, and CCL19, indicating that SCG2 modulates the TME of pNETs. Previous studies have demonstrated that SCG2 can foster an immunosuppressive environment conducive to tumor progression, as observed in colorectal cancer and melanoma, where SCG2 overexpression leads to reduced MHC-I complex expression in tumor cells, facilitating immune escape (16,31). In head and neck squamous cell carcinoma, SCG2 has been shown to promote tumor proliferation and migration by facilitating M2 macrophage infiltration through activation of the CCL2/TGF-β1 signaling axis (27). These findings suggest that SCG2 is instrumental in regulating the tumor immune environment and affecting pNET development through various pathways, including immune cells, chemokines, and immune response-related mechanisms. Therefore, targeting SCG2 could offer a novel approach for pNET immunotherapy, potentially making it a valuable immune marker.

Using the cMAP database, we identified small-molecule compounds that are potentially beneficial for pNET treatment, with telomerase inhibitors emerging as particularly promising candidates. Telomerase is utilized in most cancers to maintain telomere length, enabling limitless replication and preventing tumor cell senescence (56). In pNET, genomic landscape analyses have indicated that alterations in the telomere maintenance mechanism and the mechanistic target of rapamycin (mTOR) pathway are markers of aggressive behavior. In particular, functional silencing of the DAXX or ATRX genes can activate alternative lengthening of the telomere pathway, leading to distant metastasis (29,57). MST-312, a telomerase inhibitor, exerts its antitumor effects through multiple mechanisms. In an acute lymphoblastic leukemia study, MST-312 was shown to induce apoptosis in Jurkat cells by downregulating the expression of genes such as CCND1 (58). Additionally, when combined with imatinib, MST-312 exhibits synergistic effects in chronic myeloid leukemia cells, significantly enhancing apoptosis (59). It has also been reported to suppress the expression of pro-inflammatory cytokines, including interleukin-6 (IL-6) and TNF-α, and to prolong DFS in multiple myeloma (60). These findings highlight the broad therapeutic potential of telomerase inhibitors in various cancers. In our analysis, MST-312 was a compelling candidate for pNET treatment, warranting further clinical evaluation.

Despite the in-depth analysis of SCG2’s role and applicability in pNETs, there are some limitations in this study. First, although the clinical data significantly associated SCG2 with poor prognosis in pNETs, the results of this retrospective study need to be validated through prospective research. Second, the rarity of pNETs resulted in a small clinical and bioinformatics analysis sample size. To address these limitations, we plan to expand our sample size and establish multicenter clinical cohorts to facilitate more robust validation in future studies. Additionally, although we have uncovered the molecular mechanisms of SCG2 in pNETs and its association with immune infiltration pathways through transcriptome-based computational analyses, direct experimental validation is currently lacking. In future studies, we plan to perform immunohistochemical analyses targeting markers of M1 and M2 macrophages as well as T cell subpopulations to further substantiate our findings and enhance the reliability of the results.

Conclusions

In summary, our study indicates that SCG2 expression is significantly increased in pNET tissues and is closely associated with a poor prognosis. Thus, SCG2 may be involved in pNET development by influencing immune cell infiltration and related pathways in the TME. This suggests that SCG2 is not only a potential prognostic marker for pNETs but also a promising target for future strategies, providing a new perspective for the prognostic monitoring, management, and clinical treatment of pNETs.

Acknowledgments

We would like to thank Dr. Huichai Yang (pathologist in The Fourth Hospital of Hebei Medical University) for the technical guidance on immunohistochemical staining.

Footnote

Reporting Checklist: The authors have completed the STREGA reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1550/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1550/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1550/prf

Funding: This work was supported by Hebei Natural Science Foundation National (No. H2022206335) and the Medical Science Research Project of Hebei (No. 20230756).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1550/coif). All authors report that this work was supported by Hebei Natural Science Foundation National (No. H2022206335) and the Medical Science Research Project of Hebei (No. 20230756). The authors have no other 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. This study was conducted in accordance with Declaration of Helsinki and its subsequent amendments. This study was approved by the Institutional Research Ethics Committee of The Fourth Hospital of Hebei Medical University (No. 2022KY234), and informed consent was obtained from all patients.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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