Abstract

   The gene Sphingomyelin phosphodiesterase 2 (SMPD2), a member of the
   SMPD family, plays crucial roles in cell cycle progression and cell
   proliferation. However, the pathogenic implications of SMPD2 across
   various cancers remain poorly understood. Its potential involvement in
   lipid metabolism and immune-related processes within the tumor
   microenvironment has not been systematically characterized. To address
   these gaps, we conducted a comprehensive pan-cancer analysis of SMPD2.
   Using a range of computational tools, we investigated its role in tumor
   immune infiltration, immune evasion, tumor progression, therapy
   response, and prognosis across various cancer types. Our findings
   suggest that SMPD2 is widely expressed across cancers in The Cancer
   Genome Atlas (TCGA) and its expression levels are associated with tumor
   stages and clinical outcomes. Additionally, SMPD2 was found to be
   involved in tumor immune evasion across different cancer types. The
   methylation status of SMPD2 was inversely correlated with its mRNA
   expression levels, which were associated with dysfunctional T cell
   phenotypes and worse prognoses in diverse cancer cohorts. Furthermore,
   SMPD2 expression was linked to heterogeneous therapeutic outcomes
   across multiple cancer types, including variable responses to immune
   checkpoint blockade. Interestingly, SMPD2 demonstrated superior
   predictive capacity for treatment response and overall survival in
   immune checkpoint blockade sub-cohorts compared to three of the seven
   established biomarkers. While functional experiments are warranted, our
   results provide a data-driven, pan-cancer landscape of SMPD2 expression
   and its potential relevance to immune modulation and clinical outcomes
   Overall, SMPD2 may serve as a candidate biomarker for cancer prognosis
   and therapeutic response, and a potential target for future mechanistic
   studies.

Supplementary Information

   The online version contains supplementary material available at
   10.1007/s12672-025-03553-5.

   Keywords: SMPD2, Biomarker, Immune cell infiltration, Pan-cancer,
   Prognosis

Introduction

   The rising incidence and mortality rates of cancer have garnered
   significant attention [[38]1]. In response, extensive research has been
   conducted to understand the mechanisms underlying cancer occurrence,
   development, and metastasis. Despite global advances in cancer therapy,
   overall survival rates remain unsatisfactory, highlighting the urgent
   need for reliable prognostic biomarkers.

   Ceramides, a class of bioactive sphingolipids, are key regulators of
   fundamental cellular processes such as cell death, proliferation,
   autophagy, and drug resistance [[39]2, [40]3]. They are also critical
   for cellular homeostasis, affecting migration, differentiation, stress
   adaptation, survival, and senescence [[41]4]. Neutral sphingomyelinases
   (nSMases) catalyze the hydrolysis of sphingomyelin to generate ceramide
   and have been implicated in diverse biological responses to
   environmental stress and inflammation [[42]5–[43]7].

   Emerging evidence suggests that sphingolipid metabolism is intricately
   linked to antitumor immunity, particularly through its influence on
   immune cell function and response to immunotherapy. Among the nSMase
   family, SMPD2 (also known as nSMase1) stands out for its subcellular
   localization and functional roles. It is predominantly found in the
   endoplasmic reticulum (ER) and Golgi apparatus [[44]5, [45]8], and is
   critical for ceramide production in response to ER stress [[46]9,
   [47]10]. Notably, SMPD2 has been shown to regulate T cell
   receptor-induced apoptosis and modulate T cell signaling [[48]11],
   distinguishing it functionally from SMPD1 (lysosomal) and SMPD3
   (exosomal/plasma membrane-associated). SMPD2 also contributes to tumor
   suppression in hepatocellular carcinoma by modulating the
   ceramide-to-sphingomyelin ratio [[49]12], while SMPD2-deficient mice
   exhibit enhanced liver tumor formation. Despite these findings, the
   role of SMPD2 in cancer immunology and its relevance across tumor types
   have not been systematically studied. These unique features make SMPD2
   a biologically plausible candidate for pan-cancer exploration,
   particularly in the context of immune regulation and prognosis.

   The tumor microenvironment (TME) is a highly complex and heterogeneous
   ecosystem comprising malignant cells, fibroblasts, vasculature, immune,
   and immunosuppressive cells. This complexity fosters interactions that
   often lead to immune dysfunction, such as T-cell anergy, and impaired
   anti-tumor immune responses [[50]13]. Components like M2 macrophages
   [[51]14], cancer-associated fibroblasts (CAFs) [[52]15], regulatory T
   cells (Tregs) [[53]16], and myeloid-derived suppressor cells (MDSCs)
   [[54]17] play key roles in suppressing cytotoxic T cell function,
   thereby promoting tumor progression, metastasis, and resistance to
   immunotherapy.

   Sphingolipids, as structural components of cell membranes, regulate
   immune cell surface dynamics and modulate lymphocyte trafficking,
   inflammatory responses, and chemotaxis [[55]18–[56]20]. Certain
   chemotherapeutic agents also induce cancer cell death via ceramide
   accumulation [[57]21–[58]23]. In melanoma, nSMase2 has been shown to
   influence immune response and modulate anti-PD-1 therapy efficacy
   [[59]24].

   However, the specific contributions of SMPD2 in shaping the immune
   landscape of tumors remain unclear. An integrated analysis of SMPD2
   expression in cancer and its associations with immune contexture,
   prognosis, and therapy response may uncover novel insights into its
   clinical and biological significance.

   Systematically profiling diverse immune and stromal cell types across
   cancers remains challenging using experimental methods alone. In this
   context, bioinformatics approaches offer efficient and scalable
   alternatives by integrating multi-omics and clinical data to extract
   immune-related features. Such computational frameworks have become
   indispensable for cancer biomarker discovery, prognosis modeling, and
   therapeutic response prediction.

   In this study, we conducted a comprehensive pan-cancer analysis of
   SMPD2 using publicly available datasets. We investigated its expression
   patterns, epigenetic regulation, associations with immune cell
   infiltration and immune checkpoint expression, prognostic implications,
   and therapeutic relevance across multiple cancer types. Our goal was to
   delineate the potential role of SMPD2 in the tumor immune
   microenvironment and evaluate its utility as a biomarker and research
   target.

Materials and methods

Data acquisition

   We downloaded the uniformly processed and batch-corrected pan-cancer
   dataset TCGA TARGET GTEx (PANCAN; N = 19,131 samples; G = 60,499 genes)
   from the UCSC Xena platform ([60]https://xenabrowser.net/). We
   extracted the expression data of SMPD2 (ENSG00000135587) across all
   available samples. Expression values were log2(x + 0.001) transformed,
   and samples with zero expression were excluded prior to downstream
   analyses. Depending on the analysis, we retained tumor-derived samples
   (e.g., Primary Solid Tumor, Primary Tumor, Bone Marrow, Peripheral
   Blood, Metastatic) and, when appropriate, included normal tissue
   samples from TCGA or GTEx cohorts for comparison.

Differential SMPD2 expression analysis

   To assess SMPD2 expression across different tumor stages, we employed
   the expression DIY module in GEPIA2 to generate pathological stage
   plots (I, II, III, IV) for TCGA cancer types. Additionally, we used the
   TNMplot module from the Kaplan-Meier (KM) plotter for differential gene
   expression analysis across tumor, normal, and metastatic tissues. The
   prognostic differences of SMPD2 expression across 38 tumor types were
   analyzed using the dichotomy method.

Differential expression and survival analysis

   Differential expression of SMPD2 between tumor and normal tissues was
   analyzed across 34 cancer types using the Wilcoxon rank-sum test in R
   (version 3.6.4). Tumor types with fewer than three samples per group
   were excluded.

   For survival analysis, patients were stratified into high- and
   low-expression groups based on median SMPD2 expression. Kaplan–Meier
   (KM) survival curves, log-rank tests, and Cox proportional hazards
   regression models were used to evaluate overall survival (OS),
   disease-specific survival (DSS), and progression-free survival (PFS).
   Hazard ratios (HR) and 95% confidence intervals (CI) were calculated
   using the R packages survival and survminer.

Immune cell infiltration analysis

   To estimate immune cell infiltration, we used multiple complementary
   methods: TIMER2.0 was applied to quantify the infiltration of six major
   immune cell types (B cells, CD4 + T cells, CD8 + T cells, neutrophils,
   macrophages, dendritic cells) across 38 tumor types. ESTIMATE scores,
   including ImmuneScore, StromalScore, and ESTIMATE Score, were computed
   using the R package estimate (v1.0.13), which infers tumor purity based
   on gene expression. xCell-based immune deconvolution was performed
   using the IOBR R package (v0.99.9), yielding enrichment scores for 67
   immune and stromal cell types, including immunosuppressive populations
   such as Tregs, M2 macrophages, and fibroblasts.

   For all immune-related scores, Pearson correlation coefficients between
   SMPD2 expression and infiltration scores were calculated using the
   psych (version 2.1.6) corr.test function in R.

Immune checkpoint co-expression analysis

   We curated a List of 60 immune checkpoint–related genes (24 inhibitory
   and 36 stimulatory) based on the study The Immune Landscape of Cancer
   [[61]25]. Pearson correlation was used to evaluate co-expression
   between SMPD2 and each checkpoint gene across tumor types. The strength
   of correlation was categorized as weak (r = 0.1–0.3), moderate
   (0.3–0.6), or strong (> 0.6).

Tumor mutation burden, neoantigens, and functional enrichment

   We analyzed the correlation between SMPD2 expression and tumor mutation
   burden (TMB) using TCGA mutation data. Associations were evaluated
   using Spearman’s rank correlation.

Differential expression and functional enrichment analysis

   Gene expression and corresponding clinical data of the target cancer
   type were retrieved using the TCGAbiolinks R package. Project
   identifiers and data types were specified to batch download
   high-throughput data via the GDC API. Standardized preprocessing was
   conducted to remove batch effects. Patients were stratified into high
   and low expression groups based on SMPD2 transcript levels.

   Differential gene expression analysis between the two groups was
   performed using the DESeq2 or edgeR package in R. Genes were considered
   significantly differentially expressed if they met both the adjusted
   p-value threshold (p < 0.05) and an absolute log₂ fold change of ≥ 1.

   Functional enrichment analysis of the identified differentially
   expressed genes (DEGs) was then conducted. Gene Ontology (GO) analysis
   and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis
   were performed using the clusterProfiler and org.Hs.eg.db packages in
   R. Enrichment results for biological processes, molecular functions,
   cellular components, and signaling pathways were visualized using bar
   plots.

Epigenetic methylation analysis

   We used the TCGA methylation module of the UALCAN interactive web
   resource to analyze differential methylation levels of SMPD2 between
   tumor and paired normal tissues across TCGA cancer types. Promoter
   methylation levels were represented by beta (β) values ranging from 0
   (unmethylated) to 1 (fully methylated). Different β value cutoff points
   were considered to indicate hypomethylation (β: 0.25–0.3) and
   hypermethylation (β: 0.5–0.7). We also used the TIDE server to assess
   the effects of methylation on dysfunctional T cell phenotypes and
   prognoses.

Patient samples

   Noncancerous adjacent tissue and cancerous tissue of HCC were collected
   along with informed consent from patients under a protocol approved by
   the Medical Ethics Committee of Nanfang Hospital, Southern Medical
   University, Guangzhou, Guangdong, China (No. NEFC-2021-394). All
   samples were obtained from the Division of Hepatobiliopancreatic
   Surgery, Department of General Surgery, Nanfang Hospital, Southern
   Medical University, Guangzhou, Guangdong, China. All experiments were
   performed in accordance with relevant guidelines and regulations.

Immunohistochemistry analysis

   Formalin-fixed paraffin-embedded HCC specimens were treated with xylene
   and later rehydrated in alcohol at decreasing concentrations. Sections
   were boiled in citrate antigenic retrieval buffer (pH = 6.0) for 3 min,
   cooled to room temperature (RT), and endogenous peroxidase activity was
   quenched with 3% hydrogen peroxide for 10 min. Sections were blocked
   with 10% goat serum at RT for 30 min, then incubated with human SMPD2,
   human PD-L1, and human LAG-3 antibodies at 4 °C overnight. After
   washing, sections were incubated with a biotinylated secondary antibody
   at RT for 30 min, followed by a final wash with 1x PBS. Sections were
   then incubated with horseradish-conjugated streptavidin at RT for
   20 min, developed with 3,3-diaminobenzidine tetrahydrochloride, and
   counterstained with hematoxylin. An IHC staining without primary
   antibody served as a negative control.

Statistical analysis and multiple testing correction

   All statistical analyses were conducted using R version 3.6.4. For
   group comparisons, Wilcoxon Rank Sum tests were used. For correlation
   analyses, Pearson or Spearman methods were applied depending on data
   distribution. To account for multiple comparisons across expression,
   immune correlation, and survival analyses, we performed false discovery
   rate (FDR) correction using the Benjamini–Hochberg method. Unless
   otherwise specified, an FDR-adjusted p-value < 0.05 was considered
   statistically significant. In visualizations, significance thresholds
   were denoted as p < 0.05 (*), < 0.01 (**), and < 0.001 (***).

Results

Expression levels of SMPD2

   Analysis of the GTEx dataset revealed that SMPD2 is expressed at low
   levels in muscle tissues but is most highly expressed in the testis
   (Fig. [62]1A). In the CCLE database, SMPD2 expression was lowest in
   kidney cell lines and highest in intestinal cell lines (Fig. [63]1B).
   We observed significant upregulation in 18 types of tumors, including
   GBM glioblastoma multiforme (p = 4.2 × 10^−37), GBMLGG glioblastoma
   multiforme and lower-grade glioma (p = 7.4 × 10^−51), LGG brain
   lower-grade glioma (p = 1.5 × 10^−31), UCEC uterine corpus endometrial
   carcinoma (p = 6.6 × 10^−8), BRCA breast invasive carcinoma
   (p = 1.2 × 10^−40), ESCA esophageal carcinoma (p = 1.3 × 10^−3), STES
   stomach and esophageal carcinoma (p = 7.9 × 10^−13), KIRP kidney renal
   papillary cell carcinoma (p = 2.1 × 10^−14), COAD colon adenocarcinoma
   (p = 3.7 × 10^−51), COADREAD colorectal adenocarcinoma
   (p = 7.4 × 10^−60), PRAD prostate adenocarcinoma (p = 1.7 × 10^−8),
   STAD stomach adenocarcinoma (p = 3.3 × 10^−16), LIHC liver
   hepatocellular carcinoma (p = 2.0 × 10^−30), BLCA bladder carcinoma
   (p = 4.6 × 10^−4), PAAD pancreatic adenocarcinoma (p = 7.5 × 10^−22),
   ALL acute lymphoblastic leukemia (p = 1.6 × 10^−20), LAML acute myeloid
   leukemia (p = 4.3 × 10^−47), CHOL cholangiocarcinoma (p = 4.6 × 10^−6).
   We also observed significant downregulation in 9 types of tumors,
   including LUAD lung adenocarcinoma (p = 6.0 × 10^−5), HNSC head and
   neck squamous cell carcinoma (p = 3.6 × 10^−8), KIRC kidney renal clear
   cell carcinoma (p = 7.8 × 10^−3), WT Wilms tumor (p = 6.8 × 10^−21),
   SKCM skin cutaneous melanoma (p = 6.0 × 10^−6), THCA thyroid carcinoma
   (p = 5.1 × 10^−60), TGCT testicular germ cell tumor (p = 1.1 × 10^−49),
   ACC adrenocortical carcinoma (p = 4.0 × 10^−8), and KICH kidney
   chromophobe (p = 5.6 × 10^−13) (Fig. [64]1C). We further analyzed the
   relationship between SMPD2 expression and tumor staging, and found that
   in ESCA esophageal carcinoma, SMPD2 is highly expressed in both early
   (Stage 1) and late (Stage 4) stages of the tumor. In KIRC kidney renal
   clear cell carcinoma and LIHC liver hepatocellular carcinoma, SMPD2
   expression is slightly increased in the mid and late stages of the
   tumor (Fig. [65]1D). These findings suggest that SMPD2 expression may
   play a role in promoting tumor metastasis and enhancing the malignant
   phenotype of tumors.

Fig. 1.

   [66]Fig. 1
   [67]Open in a new tab

   SMPD2 expression in different tissue types; a SMPD2 gene expression
   levels analyzed based on the GTEx dataset; b SMPD2 gene expression
   levels analyzed based on the CCLE dataset; c Tumor vs. normal SMPD2
   gene expression comparison; d the relationship between SMPD2 expression
   and tumor staging

Prognostic relevance of SMPD2 expression in tumor

   To assess the correlation between SMPD2 expression and patient
   prognosis across the 38 tumor types, with results compiled as forest
   plots (p < 0.05).

   For Disease-specific survival (DSS), we observed that high expression
   of SMPD2 was associated with poor prognosis in five tumor types:
   TCGA-GBMLGG glioblastoma multiforme and lower-grade glioma (n = 619,
   p = 4.2 × 10^−12, HR = 2.34 [1.84, 2.97]), TCGA-LIHC (n = 341,
   p = 1.6 × 10^−4, HR = 1.62 [1.26, 2.08]), TCGA-SKCM skin cutaneous
   melanoma (n = 444, p = 0.01, HR = 1.22 [1.05, 1.42]), TCGA-SKCM
   metastatic skin cutaneous melanoma (n = 347, p = 0.02, HR = 1.21 [1.03,
   1.44]), and TCGA-LAML acute myeloid leukemia (n = 209, p = 6.0 × 10^−3,
   HR = 1.47 [1.12, 1.93]). In contrast, low expression was associated
   with poor prognosis in one tumor type: TCGA-PAAD (n = 172,
   p = 2.4 × 10^−3, HR = 0.56 [0.38, 0.81]), as shown in Fig. [68]2A.

Fig. 2.

   [69]Fig. 2
   [70]Open in a new tab

   Prognostic relevance of SMPD2 expression in 38 tumor types. a, b A
   forest plot of hazard ratios of SMPD2 in 38 types of tumors

   Overall survival (OS) analysis revealed that high expression was
   associated with poor prognosis in five tumor types: TCGA-GBMLGG
   glioblastoma multiforme and lower-grade glioma (n = 619,
   p = 4.2 × 10^−12, HR = 2.34 [1.84, 2.97]), TCGA-LIHC liver
   hepatocellular carcinoma (n = 341, p = 1.6 × 10^−4, HR = 1.62 [1.26,
   2.08]), TCGA-SKCM skin cutaneous melanoma (n = 444, p = 0.01, HR = 1.22
   [1.05, 1.42]), TCGA-SKCM-M metastatic skin cutaneous melanoma (n = 347,
   p = 0.02, HR = 1.21 [1.03, 1.44]), and TCGA-LAML acute myeloid leukemia
   (n = 209, p = 6.0 × 10^−3, HR = 1.47 [1.12, 1.93]). In contrast, low
   expression was associated with poor prognosis in one tumor type:
   TCGA-PAAD pancreatic adenocarcinoma (n = 172, p = 2.4 × 10^−3,
   HR = 0.56 [0.38, 0.81]), as shown in Fig. [71]2B. To further illustrate
   the impact of SMPD2 expression levels on survival in different tumors,
   survival curves was plotted for high and low SMPD2 expression in the
   aforementioned cancers (Fig [72]S1). The data reveal that high SMPD2
   expression is significantly associated with reduced survival rates in
   LIHC liver hepatocellular carcinoma (p < 0.01), SKCM skin cutaneous
   melanoma (p < 0.01), while low SMPD2 expression is related to reduced
   survival rates in PAAD pancreatic adenocarcinoma (p < 0.05). These
   results suggest that high SMPD2 expression may be a risk factor for
   adverse prognosis in ACC adrenocortical carcinoma, KIRC kidney renal
   clear cell carcinoma, LGG brain lower-grade glioma, LIHC liver
   hepatocellular carcinoma, and SKCM skin cutaneous melanoma, whereas low
   SMPD2 expression may indicate worse prognosis in UCEC uterine corpus
   endometrial carcinoma and PAAD pancreatic adenocarcinoma.

Relationship between SMPD2 expression and immune cell infiltration

   To investigate the immunological relevance of SMPD2 in the tumor
   microenvironment, we comprehensively analyzed its association with
   immune cell infiltration, immune checkpoint gene expression, and tumor
   mutational characteristics across cancer types. SMPD2 expression
   exhibited diverse correlations with immune cell infiltration levels
   across cancers. Using the TIMER2.0 algorithm, we observed both positive
   and negative correlations between SMPD2 and the infiltration of B
   cells, CD4⁺ and CD8⁺ T cells, neutrophils, macrophages, and dendritic
   cells, indicating a heterogeneous immune regulatory landscape
   associated with SMPD2 (Fig. [73]3A). We further examined the
   relationship between SMPD2 and immune and stromal content using the
   ESTIMATE algorithm. SMPD2 expression showed significant correlations
   with ImmuneScore and StromalScore in 27 out of 44 cancer types, with
   the strongest associations found in cervical squamous cell carcinoma
   (CESC), thyroid carcinoma (THCA), and stomach adenocarcinoma (STAD),
   suggesting a link between SMPD2 and the tumor immune microenvironment
   (Fig. [74]3B). To assess the potential role of SMPD2 in immune escape,
   we analyzed its co-expression with immune checkpoint genes across
   cancers. SMPD2 was positively correlated with multiple checkpoint
   molecules, especially in cancers with unfavorable prognosis such as
   adrenocortical carcinoma (ACC), kidney renal clear cell carcinoma
   (KIRC), lower-grade glioma (LGG), liver hepatocellular carcinoma
   (LIHC), and skin cutaneous melanoma (SKCM) (Fig. [75]3C). In addition,
   we found that SMPD2 expression was significantly associated with tumor
   mutation burden (TMB) in several cancer types, including STAD, THCA,
   THYM, ESCA, LGG, LUAD, and PRAD, indicating that SMPD2 may reflect
   underlying genomic instability or immunogenicity (Fig. [76]3D). To
   further characterize its immunoregulatory profile, we examined the
   correlation between SMPD2 and 60 immune checkpoint–related genes (24
   inhibitory, 36 stimulatory). The analysis revealed a predominant
   positive correlation pattern, particularly with inhibitory checkpoint
   genes such as PDCD1, CTLA4, and LAG3, implying a possible role of SMPD2
   in promoting immune suppression (Fig. [77]3E). Finally, using
   xCell-based deconvolution, we observed that SMPD2 expression was
   positively correlated with regulatory T cells (Tregs), M2 macrophages,
   and fibroblasts in multiple cancer types, especially LIHC, LGG, KIRC,
   and SKCM. Conversely, it was negatively correlated with antitumor
   immune populations such as CD8⁺ T cells and Th1 cells in several other
   cancers (Fig. [78]3F). Together, these findings suggest that SMPD2 is
   closely associated with an immunosuppressive tumor microenvironment and
   may serve as a marker of immune dysfunction and evasion across multiple
   cancer types.

Fig. 3.

   [79]Fig. 3
   [80]Open in a new tab

   The SMPD2 expression correlated with immune infiltration. a The SMPD2
   expression significantly correlated with the infiltration levels of
   various immune cells in the TIMER database. b Correlation analyses of
   the SMPD2 expression with ESTIMATE score in CESC, THCA, and STAD. c
   Correlation analyses of the SMPD2 expression with immune checkpoint
   genes in pan-cancer. d The relationship between the SMPD2 gene
   expression and TMB in diverse tumors e Co-expression analysis between
   SMPD2 and 60 immune checkpoint-related genes (24 inhibitory, 36
   stimulatory) across pan-cancer, using Pearson correlation based on
   TCGA-TARGET datasets. f Correlation between SMPD2 expression and immune
   infiltration scores of 67 immune/stromal cell types calculated via
   xCell deconvolution (IOBR package), across 44 cancer types

Transcriptomic and functional pathway alterations associated with SMPD2
overexpression

   Comparison of gene expression profiles between SMPD2-high and -low
   tumors revealed marked transcriptional differences. A volcano plot
   illustrates the global DEG distribution (Fig. [81]3A), and a heatmap of
   the top 50 genes highlights consistent differential expression patterns
   (Fig. [82]S2A).

   Functional enrichment analysis of the upregulated genes revealed strong
   associations with key metabolic and immune-related pathways. KEGG
   analysis identified significant enrichment in complement and
   coagulation cascades, cholesterol metabolism, steroid hormone
   biosynthesis, bile secretion, and drug metabolism via cytochrome P450
   (Fig. [83]3B), indicating potential involvement of SMPD2 in both lipid
   metabolic reprogramming and immune modulation.

   In line with this, GO biological process analysis further highlighted
   enrichment in organic acid biosynthetic process, steroid metabolic
   process, and small molecule catabolic process, supporting a role for
   SMPD2 in shaping the metabolic landscape of tumors (Fig. [84]S2B).

   Collectively, these findings suggest that SMPD2 may contribute to tumor
   progression by orchestrating transcriptomic programs linked to
   metabolic adaptation and immunosuppressive microenvironmental
   remodeling.

Fig. 4.

   [85]Fig. 4
   [86]Open in a new tab

   Transcriptomic and pathway alterations associated with high SMPD2
   expression. A Volcano plot showing differentially expressed genes
   (DEGs) between SMPD2-high and -low expression groups. Significantly
   upregulated and downregulated genes are highlighted in red and blue,
   respectively (|log₂FC| ≥ 1, adjusted p < 0.05). B KEGG pathway
   enrichment analysis of DEGs in the SMPD2-high group. The bar length
   represents gene count per pathway, and the color indicates the adjusted
   p-value

Epigenetic modifications of SMPD2 is associated with dysfunctional T cell
phenotypes and poor prognoses in cancer cohorts

   Promoter methylation status was analysed and it was found that SMPD2 is
   hypermethylated in PRAD, while hypomethylated in cancers such as BRCA,
   BLCA, LUAD, LUSC, THCA, UCEC, and READ (Fig. [87]5A). These findings
   indicate an inverse relationship between SMPD2 methylation and mRNA
   expression levels. Hypomethylation of SMPD2 was associated with
   dysfunctional T cell phenotypes (Fig. [88]5B) and shorter survival
   durations in brain, melanoma, metastatic melanoma, breast, kidney, and
   endometrium (Fig. [89]5C). Overall, these results suggest that SMPD2
   methylation affects T cell function and prognosis through different
   mechanisms in various cancer types.

Fig. 5.

   [90]Fig. 5
   [91]Open in a new tab

   Methylation-mediated T cell phenotype dysregulation is associated with
   poor prognosis. A Boxplots showing differential SMPD2 methylation
   levels (beta values) between tumor and adjacent normal tissues across
   TCGA database. B Heatmap showing the roles of SMPD2 methylation in
   cytotoxic T-cell levels (CTLs), dysfunctional T-cell phenotypes, and
   risk factors of TCGA cancer cohorts. C Kaplan–Meier curves of overall
   survival differences between TCGA cancer cohorts with high methylation
   levels and those with low methylation levels of SMPD2

Association of SMPD2 expression with therapeutic responses

   The impact of SMPD2 expression on chemotherapeutic responses was
   investigated and it was found that SMPD2 expression did not
   significantly affect chemotherapy responses in ovarian cancer and
   colorectal cancer patients. However, higher SMPD2 expression was
   associated with favorable trends in treatment response in breast cancer
   and glioblastoma patients (Fig. [92]6A). Additionally, elevated SMPD2
   expression was associated with clinical benefits from PD-L1 immune
   checkpoint blockade (ICB) in ovarian cancer and colorectal cancer
   patients (Fig. [93]6B).

Fig. 6.

   [94]Fig. 6
   [95]Open in a new tab

   SMPD2 expression associated with therapeutic responses. A The receiver
   operating characteristic (ROC) curve plot of the association between
   SMPD2 expression and responses to chemotherapy in breast, brain,
   colorectal, and ovarian cancer cohorts. B Kaplan–Meier curves (upper
   panel) of survival ratios as a measure of the immunotherapeutic
   response (immune checkpoint blockade) between cancer cohorts with high
   and those with low expression levels of SMPD2

Verification of SMPD2 expression in vitro and in vivo

   To further illustrate the expression pattern of SMPD2 in HCC, we
   investigated SMPD2 expression in HCC tissues collected from 24 cases in
   our center as stated above. SMPD2 expression in liver tissues from HCC
   patients was also visualized through immunostaining. We found that the
   protein level of SMPD2 was overexpressed in HCC tissues in majority of
   the enrolled patients, thus it was significantly overexpressed in HCC
   tissues than in general (Figs. [96]7). Moreover, our data also showed
   that the expression levels of SMPD2 was positively correlated with the
   expression levels of LAG-3, CTLA-4 and PDL-1 in HCC (Fig. [97]7). These
   findings provide preliminary in situ evidence suggesting a potential
   association between SMPD2 expression and an immunosuppressive tumor
   microenvironment in HCC. Further functional studies are needed to
   validate these observations.

Fig. 7.

   [98]Fig. 7
   [99]Open in a new tab

   Verification of SMPD2 expression in vitro and in vivo. a, b The
   expression of SMPD2, LAG3,CTLA-4, and PD-L1 protein was higher in liver
   cancer than normal liver tissue

Conclusion

   In summary, our pan-cancer analysis revealed that SMPD2 is
   differentially expressed across multiple cancer types and is
   significantly associated with patient prognosis, immune features, and
   therapeutic responses. High SMPD2 expression predicted poorer survival
   outcomes in cancers such as ACC, KIRC, LGG, LIHC, and SKCM, while lower
   expression was unfavorable in UCEC and PAAD.

   SMPD2 expression was positively correlated with immune checkpoint
   molecules, immune cell infiltration, and tumor mutational burden,
   suggesting a potential role in immune evasion. Additionally, SMPD2
   methylation status was linked to dysfunctional T cell phenotypes and
   clinical outcomes, further supporting its involvement in immune
   regulation.

   Importantly, transcriptomic and enrichment analyses revealed that SMPD2
   may contribute to tumor progression by modulating key metabolic and
   immunosuppressive pathways, including lipid metabolism, steroid
   biosynthesis, and complement activation. These mechanisms may underlie
   its impact on therapeutic responses, especially in the context of
   chemotherapy and immune checkpoint blockade.

   Overall, SMPD2 emerges as a promising biomarker and potential
   therapeutic target in cancer. Future studies should aim to
   experimentally validate its mechanistic roles and assess its clinical
   utility in precision oncology.

Discussion

   Our pan-cancer integrative analysis revealed that SMPD2 is aberrantly
   expressed across multiple cancer types and is closely associated with
   patient prognosis, tumor heterogeneity, and the tumor microenvironment.
   In several cancers, SMPD2 may act as a tumor-promoting factor,
   potentially through its roles in immune modulation and genomic
   instability.

Expression patterns of SMPD2

   Our analysis of the GTEx and CCLE databases demonstrated that SMPD2 is
   predominantly expressed in the testis and shows variable expression in
   different cancer cell lines. Notably, high SMPD2 expression was
   observed in several cancers, including BLCA, BRCA, CHOL, GBM, KIRP,
   LIHC, LUSC, PRAD, READ, THCA, and UCEC. This suggests that SMPD2 may
   play diverse roles depending on the tissue context. The observed low
   expression in certain cancers, such as HNSC and KICH, further highlight
   the tissue-specific regulation of SMPD2.

Prognostic significance

   Our survival analysis indicates that SMPD2 expression significantly
   impacts prognosis in various cancers. High SMPD2 expression was
   associated with poor survival in ACC, KIRC, LGG, LIHC, and SKCM,
   suggesting that SMPD2 may act as an oncogene in these cases.
   Conversely, low SMPD2 expression was linked to poorer outcomes in UCEC
   and PAAD. These differential effects highlight the complex role of
   SMPD2 in cancer biology and its potential utility as a prognostic
   biomarker.

Role in immune microenvironment

   We observed significant positive correlations between SMPD2 expression
   and key immune checkpoint molecules, including PD-1, PD-L1, and CTLA4,
   across multiple tumor types. This suggests that SMPD2 may contribute to
   immune evasion mechanisms and could represent a potential target for
   immunotherapy [[100]26]. Moreover, distinct immune checkpoint
   expression patterns were found between tumors with high and low SMPD2
   expression, further supporting its potential role in shaping immune
   phenotypes [[101]27, [102]28]. The association between SMPD2 expression
   and immune subtypes also indicates its possible utility in stratifying
   patients for immune-based therapies.

Transcriptomic and pathway mechanisms

   Transcriptome-wide comparisons revealed that high SMPD2 expression is
   associated with the upregulation of genes involved in lipid metabolism,
   steroid biosynthesis, and xenobiotic detoxification. These findings
   suggest that SMPD2 may promote tumor adaptation by enhancing metabolic
   plasticity, a hallmark of cancer cells that supports survival under
   stress and therapeutic pressure [[103]29, [104]30]. In parallel, the
   enrichment of complement and coagulation cascades implies a link to
   immunosuppressive microenvironment remodeling. Together, these results
   point to a potential role of SMPD2 in coordinating metabolic and
   immune-related programs that facilitate tumor progression.

Epigenetic modifications

   Abnormal DNA methylation is an important epigenetic regulator involving
   tumorigenesis [[105]31], And lead to atypical T cell generation. Our
   analysis of SMPD2 promoter methylation demonstrated that
   hypermethylation and hypomethylation are associated with differential
   expression patterns and prognostic outcomes [[106]25, [107]32].
   Specifically, hypomethylation of SMPD2 was linked to dysfunctional T
   cell phenotypes and poor survival in several cancers, while
   hypermethylation in breast cancer correlated with longer survival.
   These findings suggest that epigenetic regulation of SMPD2 may
   contribute to its diverse functional effects and highlight the need for
   further investigation into the underlying mechanisms.

Therapeutic implications

   The impact of SMPD2 on therapeutic responses was also explored. While
   SMPD2 expression did not significantly affect chemotherapeutic
   responses in glioblastoma, ovarian, and colorectal cancers, it was
   associated with improved outcomes in breast cancer patients undergoing
   chemotherapy. Furthermore, higher SMPD2 expression correlated with
   better responses to PD-L1 immune checkpoint blockade in melanoma and
   GBM patients. PD-1 (PD) pathway blockade is a highly promising therapy
   [[108]33, [109]34], These results suggest that SMPD2 could be a
   valuable biomarker for predicting response to specific therapies,
   particularly immunotherapy.

Validation and prospects

   In vitro validation of SMPD2 expression in various cancer tissues
   confirmed its differential expression, with overexpression observed in
   several tumor types. Notably, SMPD2 was highly expressed in HCC
   tissues, and its levels positively correlated with PD-L1 and LAG-3,
   indicating its potential role in creating an immunosuppressive
   microenvironment.

   Overall, our study highlights the multifaceted role of SMPD2 in cancer,
   from influencing tumor progression and immune interactions to affecting
   therapeutic responses. Future research should focus on elucidating the
   precise mechanisms by which SMPD2 regulates these processes and explore
   its potential as a therapeutic target or biomarker in cancer treatment.

   While our findings provide novel insights into the potential roles of
   SMPD2 in cancer, several limitations should be considered. First, our
   analyses were primarily based on large-scale transcriptomic and
   clinical data from public databases, which may introduce bias due to
   data heterogeneity. Second, although we identified significant
   correlations between SMPD2 and immune checkpoint molecules, functional
   validation through in vitro or in vivo experiments was not feasible in
   the current study due to experimental constraints. Further experimental
   studies are warranted to validate these associations and clarify the
   mechanistic roles of SMPD2 in tumor immunity.

Supplementary Information

   Below is the link to the electronic supplementary material.
   [110]Supplementary Material 1.^ (179.6KB, jpg)
   [111]Supplementary Material 2.^ (96.4KB, jpg)

Acknowledgements