ADAMDEC1 predicts prognosis and immunotherapy response in a pan-cancer study with experimental validation

Abstract

Background

ADAMDEC1 (a disintegrin and metalloproteinase domain-like protein decysin-1) is linked to immune disorders and cancers, but its pan-cancer role remains unclear.

Methods

We analyzed ADAMDEC1 using The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Gene Expression Omnibus (GEO), and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) for expression, prognosis, immune infiltration, and therapy response. Single-cell RNA sequencing (scRNA-seq) and genomic alterations were examined. Gene set enrichment analysis (GSEA) assessed pathways. Immunohistochemistry (IHC) validated ADAMDEC1 in esophageal cancer (ESCA) tissue microarray (n = 48). Immunotherapy response was predicted using Tumor Immune Dysfunction and Exclusion (TIDE) and independent cohorts.

Results

ADAMDEC1 was dysregulated across cancers with context-dependent prognosis: high expression correlated with poor survival in lower-grade glioma (LGG) and uveal melanoma (UVM), but better outcomes in ovarian cancer (OV) and skin cutaneous melanoma (SKCM). Single-cell analysis revealed a dichotomy: in UVM, ADAMDEC1 was mainly in tumor-associated macrophages; in SKCM, it was broadly expressed across malignant, immune, and stromal cells. DNA methylation inversely correlated with expression in 20 cancers. ADAMDEC1 linked to immune infiltration and immunomodulatory genes including programmed cell death protein 1 (PD-1). In ESCA, ADAMDEC1 was elevated in metastasis and positively correlated with PD-1. ADAMDEC1 predicted immunotherapy response comparably to conventional biomarkers.

Conclusions

ADAMDEC1 is a context-dependent immune regulator with prognostic direction determined by cellular origin, supporting its investigation as a therapeutic target and biomarker in precision oncology.

Supplementary Information

The online version contains supplementary material available at10.1007/s12672-026-04977-3.

Introduction

Cancer continues to be a major global source of disease and mortality, exerting substantial detrimental effects on both public health and economic stability [1]. Nowadays, surgery, chemotherapy, radiation, targeted therapy, and immunotherapy are common cancer treatments. Despite considerable advances in therapeutic modalities, 5-year survival rates continue to be discouraging [2]. Given the multifactorial nature of tumor development and the established utility of molecular biomarkers, the ongoing pursuit of new therapeutic targets and highly sensitive diagnostic markers is crucial for improving patient outcomes and the effectiveness of cancer management.

In oncology, immunotherapy has emerged as a potentially effective approach. The introduction of immunotherapy has led to notable enhancements in survival outcomes for cancers including non-small cell lung cancer (NSCLC) [3], melanoma [4], hepatocellular carcinoma [5], and renal cell carcinoma [6]. Remarkably, durable complete responses have been observed even in certain patients with metastatic cancer [7]. At present, some of the most influential immunotherapeutic approaches are immune checkpoint inhibitors (ICIs), such as monoclonal antibodies targeting PD-1, PD-L1, and CTLA-4. Nonetheless, their efficacy in solid tumors remains limited by low response rates. Recent studies have identified CD24 [8], TIGIT [9], CD276 [10], and IL-1 receptor-associated kinase-3 [11] as pivotal players in tumor immunity, implying potential utility as targets for next-generation immune checkpoint blockade. Simultaneously, the growth and improvement of extensive databases like The Cancer Genome Atlas (TCGA) have made it easier to uncover new immunotherapeutic targets via pan-cancer transcriptional profiling and comprehensive analysis of gene–prognosis relationships and relevant signaling pathways [12].

First identified by Mueller et al. [13], ADAMDEC1 has been shown to be upregulated during dendritic cells (DCs) maturation [14] and in several immune-related conditions, including pulmonary sarcoidosis, inflammatory bowel disease, and Epstein–Barr virus infection [15]. Additionally, Kang et al. [16] explored the utility of ADAMDEC1 in distinguishing rheumatoid arthritis from osteoarthritis. Through integrative analysis of TCGA and Genotype-Tissue Expression (GTEx) data, Ping et al. [17] proposed ADAMDEC1 along with PTPRC, SIGLEC10, CARD11, and LILRB1 as candidate tumor antigens, underscoring its potential role in immune regulation. Although numerous previous investigations regarding ADAMDEC1 in cancer have concentrated on specific tumor types, there has been a dearth of a comprehensive pan-cancer analysis exploring its expression and clinical implications. In this study, we performed a comprehensive pan-cancer exploration of ADAMDEC1 expression, its prognostic implications, possible biological functions, co-expressed genes, immune infiltration patterns, and associations with immunotherapy response by leveraging multiple public databases. The results suggest that ADAMDEC1 acts as a context-dependent prognostic biomarker in various cancer types and affects tumor immunity by impacting immune cell infiltration and the expression of immunoregulatory molecules. The landscape of cancer biomarkers is rapidly evolving, with numerous studies identifying novel prognostic indicators. For instance, recent research has highlighted PAK2 as a central regulator in head and neck squamous cell carcinoma, linking programmed cell death and cancer stem cell properties [18]. Similarly, LMNB2 [19] and FNDC4 [20] have been proposed as diagnostic and prognostic biomarkers in lung adenocarcinoma (LUAD), demonstrating associations with tumor stage and immune infiltration. While these studies focus on specific cancer types, our pan-cancer approach positions ADAMDEC1 as a uniquely context-dependent biomarker whose prognostic direction and functional role are fundamentally shaped by the cellular origin and immune landscape of the tumor microenvironment (TME), offering a broader and more nuanced perspective than single-cancer markers. Based on these pan-cancer findings, we selected esophageal cancer (ESCA) for experimental validation for the following reasons: (i) ADAMDEC1 was significantly upregulated in ESCA tumor tissues (Fig. 1A–C); (ii) its expression correlated with advanced tumor stage (Fig. 1D); and (iii) independent datasets (GSE161533) suggested immune-related roles in ESCA, but protein-level confirmation was lacking. These insights enhance our understanding of ADAMDEC1’s multifunctional role in oncogenesis and antitumor immunity, providing a foundation for its future application in cancer immunotherapy.

Fig. 1.

Pan-cancer patterns of ADAMDEC1 expression. A ADAMDEC1 expression levels in tumor tissues versus normal tissues according to the TIMER database. B ADAMDEC1 expression levels in different kinds of cancer based on TCGA and GTEx data. C ADAMDEC1 expression level in paired tumor–normal tissues for 20 types of cancer. *P < 0.05, **P < 0.01, ***P < 0.001, and ∗∗∗∗P < 0.001. D ADAMDEC1 expression levels at different tumor stages in various cancers determined with GEPIA2 utilizing TCGA data

Materials and methods

Tumor immune estimation resource (TIMER)

The TIMER platform (accessible at https://cistrome.shinyapps.io/timer/) offers an integrated toolkit for profiling immune infiltration, alongside features for examining gene expression, mutational profiles, somatic copy number variations, and associations with clinical prognosis [21]. In this investigation, the expression of ADAMDEC1 was first measured between malignant tissues and paired normal samples in several cancer types. Subsequently, the TIMER algorithm was utilized to evaluate potential associations between ADAMDEC1 expression and the quantity of six kinds of immune cells: B cells, CD4⁺ T cells, CD8⁺ T cells, neutrophils, macrophages, and DCs.

Expression patterns of ADAMDEC1 in diverse human cancers

To assess ADAMDEC1 expression across various malignancies and corresponding normal tissues, normal tissue datasets were retrieved from the GTEx database (https://www.genome.gov/Funded-Programs-Projects/Genotype-Tissue-Expression-Project), whereas cancer tissue data were acquired from TCGA (https://portal.gdc.cancer.gov/). RNA-seq data and clinical follow-up information for more than 30 cancer types were also provided by the TCGA repository. All the expression values underwent normalization by log2 transformation. In this investigation, TCGA data was utilized to analyze the expression of ADAMDEC1 in 20 paired tumor—normal sample sets. The results were visualized using the ggplot2 package in R software (v4.0.3). Additionally, correlations between ADAMDEC1 and various immune markers were explored based on TCGA datasets.

Gene expression profiling interactive analysis (GEPIA)

The GEPIA platform (http://gepia.cancer-pku.cn/index.html) combines RNA-seq data from both the TCGA and GTEx projects [22]. In the present research, GEPIA was employed to examine the expression pattern of ADAMDEC1 in different pathological stages of the disease.

Prognostic analysis

Forest plots and Kaplan-Meier (KM) curves were employed to analyze data from 33 cancer types in the TCGA database in order to explore the relationship between ADAMDEC1 expression and clinical outcomes such as overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI). Hazard ratios (HRs) along with their 95% confidence intervals were estimated through univariate survival analysis. Subsequently, three cancer types with consistent prognostic significance across OS, DSS and PFI were identified: lower grade glioma (LGG), uveal melanoma (UVM), and cutaneous melanoma (SKCM). For these cancers, multivariable Cox regression models were established to assess the independent prognostic value of ADAMDEC1. These models were adjusted for age, gender, race, grade, and stage. Adjusted hazard ratios with 95% confidence intervals were derived.

Association of ADAMDEC1 expression with tumor mutational burden (TMB) and microsatellite instability (MSI)

TMB and MSI data for 33 cancer types were obtained from the TCGA database. TMB was defined as the total number of non-synonymous mutations per megabase, and MSI scores were retrieved from previous publications. Spearman correlation analysis was performed to evaluate the relationships between ADAMDEC1 expression and both TMB and MSI scores across cancer types. To further investigate these associations, patients were stratified into TMB-high and TMB-low groups based on the median TMB value within each cancer type, and similarly into MSI-high and MSI-low groups using established thresholds. Differences in ADAMDEC1 expression between the high and low groups were assessed using the Wilcoxon rank-sum test. All statistical analyses were conducted using R software with the TCGAplot package utilized for data integration and visualization.

Gene set enrichment analysis (GSEA)

GSEA is a robust analytical approach that leverages predefined gene sets from the Molecular Signatures Database (MSigDB) [23]. In this investigation, transcriptomic data sourced from TCGA, Gene Expression Omnibus (GEO) (available at: https://www.ncbi.nlm.nih.gov/gds/), and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (accessible via: https://ega-archive.org/dacs/EGAC00001000484) were stratified into two cohorts according to ADAMDEC1 expression levels: a high-expression group and a low-expression group. Subsequently, we performed functional enrichment analysis using the C2 Kyoto Encyclopedia of Genes and Genomes (KEGG) gene set collection to identify signaling pathways that were significantly different between the two groups. Significance thresholds were set at P < 0.05 and an false discovery rate (FDR) q-value < 0.25. All visualizations for GSEA were created using the ggplot2 package in R software.

Pan-cancer analysis of associations between ADAMDEC1 expression and tumor immune infiltration and immunomodulatory genes

The transcriptomic data of 33 cancer types were obtained from the TCGA database via the Genomic Data Commons data portal. To ensure reliability in assessing immune infiltration, analyses were performed using both TIMER and xCell algorithms within R software. A Spearman correlation heatmap was generated with the ggplot2 package to visualize associations between ADAMDEC1 expression and immune infiltrates or immunomodulatory genes across malignancies. In these heatmaps, cancer types were displayed along the horizontal axis, while immune cell types or genes were represented on the vertical axis, with correlation strengths color-coded accordingly. The R software was employed to perform all statistical calculations. Furthermore, validation analyses utilizing the xCell package along with datasets from GEO and METABRIC were conducted to corroborate the relationships between ADAMDEC1 and immune cell infiltration or immune-related genes.

Assessment of the predictive potential of ADAMDEC1 in immunotherapy

In this study, the probability of ICI response in patients with various cancers was evaluated using the Tumor Immune Dysfunction and Exclusion (TIDE) framework (http://tide.dfci.harvard.edu/), a computational tool designed to predict tumor immune escape based on gene expression profiles [24]. The TIDE database incorporates data from multiple cancer types, including glioblastoma multiforme (glioblastoma), head and neck squamous cell carcinoma (HNSC), NSCLC, renal, bladder, gastric cancers, and 12 melanoma cohorts. Using this resource, the area under the curve (AUC) was computed to compare ADAMDEC1 expression with established markers predictive of immunotherapy response. Additionally, the correlation between ADAMDEC1 expression and immune checkpoint inhibitor (ICI) treatment outcome was further validated using the IMvigor210 cohort (via the IMvigor210CoreBiologies R package) and independent datasets from the GEO database.

Gene alteration, methylation, copy number variation (CNV) and single nucleotide variation (SNV) analysis of ADAMDEC1

To investigate the genetic alterations of ADAMDEC1 across cancer types, we queried the cBioPortal for Cancer Genomics (https://www.cbioportal.org/) and retrieved mutation data, including mutation types, frequencies, and sites, for all TCGA tumors.

CNV, SNV, and DNA methylation data for ADAMDEC1 were obtained from the GSCA database (http://bioinfo.life.hust.edu.cn/GSCA/#/mutation). CNV patterns across cancer types were categorized as heterozygous amplification or deletion, and the association between CNV and ADAMDEC1 mRNA expression was assessed by Spearman correlation. SNV frequencies were summarized by cancer type. For DNA methylation, we evaluated the correlation between ADAMDEC1 methylation and its mRNA expression, compared methylation levels between tumor and normal samples, and assessed the prognostic value of methylation for overall survival and disease-specific survival using precomputed survival data from GSCA.

Single-cell analysis of ADAMDEC1 expression

Single-cell RNA-seq data for ADAMDEC1 expression were obtained from the Tumor Immune Single-cell Hub (TISCH) database (https://tisch.compbio.cn/). For each cancer type, cell-type annotations provided by TISCH were used to classify cells into major lineages including malignant cells, immune cells, and stromal cells, as well as finer subtypes such as dendritic cells (DCs), monocytes/macrophages (Mono/Macro), B cells, endothelial cells, and fibroblasts. We focused on uveal melanoma (UVM) and skin cutaneous melanoma (SKCM) due to their contrasting prognostic associations with ADAMDEC1. Average expression levels within each cell type were calculated to identify the predominant cellular sources of ADAMDEC1 in each tumor microenvironment, aiming to provide cell-level mechanistic insights into the observed prognostic dichotomy.

Tissue microlarray (TMA) and immunohistochemistry (IHC)

Shanghai Zhuoli Biotechnology Co., Ltd (Shanghai, China) provided a TMA which included 48 primary esophageal carcinoma (ESCA) samples with complete clinicopathological data. This study was approved by the Ethics Committee of Shanghai Zhuoli Biotechnology Company (Approval number: LLSM-15-01). The Ethics Committee waived the requirement for informed consent, as all the samples used in this study were archival paraffin-embedded cancer samples that had been fully anonymized prior to access by the researcher. IHC staining was performed using antibodies targeting ADAMDEC1 (17899-1-AP; Proteintech), PD-1 (18106-1-AP; Proteintech), and CTLA-4 (YT5933-AP; Immunoway). Staining evaluation was carried out using the VIS DIA VisioMorph automated platform (Visiopharm^®, Denmark). Briefly, whole-slide images of the TMA were acquired at 20× magnification with a Leica SCN400 scanner (Leica Biosystems), and then analyzed using Visiopharm^® software to obtain digital images of each core. The staining intensity was classified into four levels: 0 (negative), 1 (weak), 2 (moderate), and 3 (strong). The percentage of positively stained cells was measured from 0% to 100%. For each sample, an H-score (ranging from 0 to 300) was calculated with the following formula: (1 multiplied by the percentage of weak staining) + (2 multiplied by the percentage of moderate staining) + (3 multiplied by the percentage of strong staining). To evaluate the associations among the three markers, Spearman correlation analysis was employed, and the Wilcoxon–Mann–Whitney test was utilized to explore the relationships between ADAMDEC1 expression and clinicopathological variables.

Statistical analyses

The analysis of all data and the generation of figures were carried out by making use of GraphPad Prism 6.0 and the ggplot2 package in R software. Differences in ADAMDEC1 expression between tumor and normal tissues were evaluated using t-tests. Survival outcomes were analyzed by univariate Cox regression to derive HR and corresponding P-values. KM analysis was applied to compare survival times between patients with high versus low ADAMDEC1 expression. Multivariable Cox regression was performed for LGG, UVM, and SKCM adjusting for age, gender, race, grade, and stage to assess independent prognostic value. A significance threshold of P < 0.05 was used for all statistical tests.

Results

The patterns of ADAMDEC1 expression levels across various cancers

Investigation of the TIMER database indicated heterogeneous mRNA expression profiles of ADAMDEC1 across 16 common human cancers. More precisely, in comparison to adjacent normal samples, ADAMDEC1 was notably upregulated in tumor tissues of various cancer types. These cancers include breast cancer (BRCA), cholangiocellular carcinoma (CHOL), ESCA, HNSC, kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), LUAD, lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), stomach adenocarcinoma (STAD), and uterine corpus endometrial carcinoma (UCEC). Conversely, its expression was downregulated in colon adenocarcinoma (COAD), rectum adenocarcinoma (READ), and thyroid carcinoma (THCA) (Fig. 1A).

Through additional research utilizing combined TCGA and GTEx datasets, it was shown that ADAMDEC1 mRNA was consistently upregulated in tumor tissues of 22 cancer types. These included BRCA, cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), CHOL, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LGG, LIHC, LUAD, LUSC, ovarian serous cystadenocarcinoma (OV), pancreatic adenocarcinoma (PAAD), PRAD, SKCM, STAD, testicular germ cell tumors (TGCT), THCA, UCEC, and uterine carcinosarcoma (UCS). On the contrary, a decrease in expression was seen in adrenocortical carcinoma (ACC) and READ (Fig. 1B).

Paired tumor-normal comparisons based on TCGA data further confirmed elevated ADAMDEC1 expression in tumors from BRCA, CHOL, ESCA, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, PRAD, STAD, and UCEC. Conversely, reduced expression was detected in COAD, PAAD, and READ (Fig. 1C). Together, these findings indicate a context-dependent dysregulation of ADAMDEC1 expression in cancer.

Moreover, an assessment was made of the connection between the expression of ADAMDEC1 and the stage of the tumor. In bladder urothelial carcinoma (BLCA), ESCA, KICH, and OV, notable correlations with tumor advancement were detected (Fig. 1D).

Multifaceted prognostic capability of ADAMDEC1 across different cancers

To evaluate the prognostic relevance of ADAMDEC1 expression in cancer, survival analyses were performed using TCGA data, focusing on OS, DSS, and PFI. Cox regression identified significant associations between ADAMDEC1 expression and OS in ACC (P = 0.038), acute myeloid leukemia (LAML) (P = 0.017), LGG (P < 0.001), OV (P = 0.017), READ (P = 0.023), SKCM (P < 0.001), thymoma (THYM) (P = 0.005), and UVM (P < 0.001) (Fig. 2A). Kaplan–Meier analysis further indicated that high ADAMDEC1 expression was linked to worse OS in ACC (P = 0.03), LAML (P = 0.015), LGG (P < 0.0001), and UVM (P < 0.0001), but correlated with better OS in OV (P = 0.047), READ (P = 0.044), and SKCM (P = 0.00014). No significant association was observed in THYM (P = 0.05) (Fig. 2B).

Fig. 2.

Relationship between ADAMDEC1 expression and cancer patient survival. A Forest plot of the hazard ratios of overall survival (OS) for ADAMDEC1 in 33 cancer types. B Kaplan–Meier OS curves of patients with different ADAMDEC1 expression levels in adrenocortical carcinoma (ACC), acute myeloid leukemia (LAML), uveal melanoma (UVM), ovarian serous cystadenocarcinoma (OV), brain lower grade glioma (LGG), thymoma (THYM), rectum adenocarcinoma (READ), and skin cutaneous melanoma (SKCM). C Forest plot of the hazard ratios of disease-specific survival (DSS) for ADAMDEC1 in 33 cancer types. D Kaplan–Meier DSS curves of patients with different ADAMDEC1 expression levels in LGG, THYM, UVM, cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), OV, SKCM, and thyroid carcinoma (THCA). E Forest plot of the hazard ratios of progression-free interval (PFI) for ADAMDEC1 in 33 cancer types. F) aplan–Meier PFI curves of patients with different ADAMDEC1 expression levels in glioblastoma multiforme (GBM), LGG, UVM, CESC, OV, and SKCM

For DSS, Cox modeling showed that high ADAMDEC1 expression was associated with poor outcomes in LGG (P < 0.001), UVM (P < 0.001), and THYM (P = 0.040), whereas it correlated with favorable prognosis in CESC (P = 0.013), OV (P = 0.008), SKCM (P < 0.001), and THCA (P = 0.016) (Fig. 2C). KM analysis confirmed adverse DSS with high ADAMDEC1 in LGG (P < 0.0001) and UVM (P < 0.0001), but improved DSS in OV (P = 0.028) and SKCM (P = 0.0001). For THYM, CESC, or THCA, no notable correlation was detected (Fig. 2D).

In the PFI analysis, elevated ADAMDEC1 expression was significantly associated with worse outcomes in GBM (P = 0.026), LGG (P = 0.001), and UVM (P = 0.001) by Cox regression, whereas lower expression correlated with poor PFI in CESC (P = 0.004), OV (P = 0.032), and SKCM (P = 0.004) (Fig. 2E). KM curves indicated longer PFI in CESC (P = 0.032) and SKCM (P = 0.0044) patients with high ADAMDEC1 expression, but shorter PFI in LGG (P = 0.031) and UVM (P < 0.0001) cases with elevated expression (Fig. 2F).

Multivariable cox regression analysis for LGG, UVM, and SKCM

Three cancer types—LGG, UVM, and SKCM—drew our attention because ADAMDEC1 exhibited consistent trends across OS, DSS, and PFI in these tumors. To determine whether ADAMDEC1 is an independent prognostic factor, we subsequently performed multivariable Cox regression analyses for these three cancer types, adjusting for age, gender, race, grade, and stage. ADAMDEC1 remained a significant independent prognostic factor in all three cancers. For LGG, the hazard ratios (HRs) for OS, DSS, and PFI were 1.13 (95% confidence interval [CI] 1.05–1.21; p = 0.001), 1.13 (95% CI 1.05–1.22; p = 0.001), and 1.06 (95% CI 1.00–1.13; p = 0.044), respectively (Fig. 3A). For UVM, the HRs were 1.43 (95% CI 1.22–1.67; p < 0.001), 1.39 (95% CI 1.18–1.63; p < 0.001), and 1.26 (95% CI 1.11–1.43; p < 0.001), respectively (Fig. 3B). For SKCM, the HRs were 0.88 (95% CI 0.83–0.92; p < 0.001), 0.87 (95% CI 0.82–0.93; p < 0.001), and 0.93 (95% CI 0.89–0.97; p = 0.002), respectively (Fig. 3C). These results establish ADAMDEC1 as an independent prognostic biomarker in these cancers.

Fig. 3.

Multivariable Cox regression analysis of ADAMDEC1 for overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) in three cancer types. Forest plots display hazard ratios (HRs) and 95% confidence intervals (CIs) for ADAMDEC1 expression after adjusting for age, gender, race, grade, and stage. A Lower-grade glioma (LGG). B Uveal melanoma (UVM). C Skin cutaneous melanoma (SKCM)

Association of ADAMDEC1 expression with TMB and MSI

Given the significance of TMB and MSI in predicting immunotherapy outcomes, we inspected the relationships between ADAMDEC1 expression and these two indicators. ADAMDEC1 expression was positively correlated with TMB in BLCA, BRCA, LGG, LUAD, OV, sarcoma, and UCEC, whereas it exhibited an inverse correlation in LIHC and THCA (Fig. 4A). Additionally, ADAMDEC1 expression was positively correlated with MSI in BRCA and THYM, but negatively in HNSC, KIRP, LUSC, OV, SKCM, and TGCT (Fig. 4B).

Fig. 4.

Association of ADAMDEC1 expression with tumor mutational burden (TMB) and microsatellite instability (MSI) across cancer types. A Radar plot depicting the correlation between ADAMDEC1 expression and TMB in various cancer types from the TCGA cohort. B Radar plot showing the correlation between ADAMDEC1 expression and MSI across cancer types. C Box plots comparing ADAMDEC1 expression between TMB-high and TMB-low groups across cancer types. D Box plots comparing ADAMDEC1 expression between MSI-H and MSI-L groups across cancer types. Statistical significance is indicated by asterisks (*P < 0.05, **P < 0.01, ***P < 0.001)

We further compared ADAMDEC1 expression between TMB-high and TMB-low groups (Fig. 4C). The findings indicated that ADAMDEC1 expression was higher in the TMB-high group in BRCA, LGG, LUAD, OV, PRAD, STAD, THYM, and UCEC. Conversely, in LAML, LIHC, and THCA, the opposite pattern was observed. Regarding MSI, ADAMDEC1 expression was higher in the MSI-H group in STAD, while HNSC, KIRC, LIHC, LUSC, OV, TGCT, and UCS showed higher ADAMDEC1 expression in the MSI-low group (Fig. 4D).

Pan-cancer functional enrichment analysis regarding ADAMDEC1 expression

To further explore the molecular mechanisms through which ADAMDEC1 influences various tumors, GSEA was conducted using C2 KEGG gene sets across 33 cancer types. The analysis of the KEGG pathway revealed that the expression of ADAMDEC1 was generally linked to numerous oncogenic pathways in the majority of cancers. These cancers included acute myeloid leukemia, chronic myeloid leukemia, colorectal cancer, endometrial cancer, glioma, non - small cell lung cancer, small cell lung cancer, pancreatic cancer, along with general cancer pathways and renal cell carcinoma.

The analysis also revealed positive associations between ADAMDEC1 expression and immune-related pathways, such as antigen processing and presentation, B cell receptor signaling, complement and coagulation cascades, Fc epsilon RI and Fc gamma R-mediated phagocytosis, intestinal immune network for IgA production, natural killer cell-mediated cytotoxicity, NOD-like receptor signaling, T cell receptor signaling, and Toll-like receptor signaling. Additionally, ADAMDEC1 expression correlated with pathways involved in autoimmune and immunodeficiency conditions, including allograft rejection, systemic lupus erythematosus, asthma, autoimmune thyroid disease, graft-versus-host disease, primary immunodeficiency, and type I diabetes mellitus.

Furthermore, ADAMDEC1 expression was linked to several processes implicated in tumor progression, such as cell adhesion molecules, chemokine signaling, cytokine-cytokine receptor interaction, JAK-STAT, MAPK, mTOR, and VEGF signaling pathways (Fig. 5A). It can be inferred from these findings that ADAMDEC1 might have various functions in tumorigenesis, development, and immune regulation among different cancer types.

Fig. 5.

Relationship between the expression of ADAMDEC1 and signaling pathways, infiltrating immune cells, and immunotherapy modulators in TCGA datasets. A Gene set enrichment analysis of ADAMDEC1 expression in diverse tumor types in TCGA datasets. B ADAMDEC1 expression levels were significantly associated with the infiltration levels of different immune cells according to the TIMER database. C ADAMDEC1 expression levels were significantly associated with the infiltration levels of different immune cells according to the xCell algorithm. D Pan-cancer analysis of associations between the expression level of ADAMDEC1 and different immunotherapy modulators based on TCGA data. NES normalized enrichment score. Asterisks indicate significant differences as follows: *P < 0.05, **P < 0.01, ***P < 0.001, and ∗∗∗∗P < 0.001

Correlations between ADAMDEC1 expression and infiltrating immune cells

Considering the substantial connection between ADAMDEC1 expression and the immune response, a pan-cancer analysis was carried out to assess its relationship with the levels of immune cell infiltration by utilizing TIMER. The findings indicated that the expression of ADAMDEC1 had a notable connection with the quantity of various infiltrating immune cells. To be more precise, it had a correlation with B cells in 28 cancer types, CD4⁺ T cells in 26, CD8⁺ T cells in 24, macrophages in 26, neutrophils in 26, and DCs in 29 types (Fig. 5B) Among these, DC infiltration showed the strongest correlation with ADAMDEC1 expression.

In addition, the xCell algorithm was utilized to explore the connection between the expression of ADAMDEC1 and the infiltration degrees of immune cell subtypes in diverse cancers. Of the 38 immune cell subtypes that were assessed, the majority had a positive correlation with the expression of ADAMDEC1. Conversely, negative correlations were noted for CD4⁺ central memory T cells (Tcm) and Th1 cells. Only weak correlations were observed with natural killer (NK) cells and natural killer T (NKT) cells. Positive and significant correlations were found between the expression of ADAMDEC1 and activated dendritic cells (aDCs), traditional dendritic cells, macrophages, and M1 macrophages (Fig. 5C).

Pan-cancer analysis of associations between ADAMDEC1 expression levels and immunoregulatory modulators

The interplay between cancer cells and the tumor microenvironment is of great significance in the biology of solid tumors. To assess the influence of ADAMDEC1 within this context, we analyzed TCGA data to evaluate its association with key immune modulators. ADAMDEC1 expression was positively correlated with numerous immunoinhibitory molecules, including classical immune checkpoints such as CTLA-4, PD-L1, and PD-1, as well as other inhibitory regulators like VSIG4, TIGIT, SIGLEC15, SELPLG, PVRIG, PDCD1LG2, LILRB2, LAG3, KLRB1, HAVCR2, CD96, CD47, CD200R1, CCR8, and BTN3A1, indicating a potential immunosuppressive role. Conversely, ADAMDEC1 also exhibited positive correlations with several immunostimulators, including TNFRSF9, TNFRSF4, TNFRSF18, TMEM173, CD40, and CD27. Additionally, negative correlations were observed between ADAMDEC1 and the immunoinhibitors SOX4, SETDB1, KDM5B, and DDR1 in certain cancers (Fig. 5D). Together, these findings imply that ADAMDEC1 may exert a complex and context-dependent role in immune regulation.

Validation of the role of ADAMDEC1 using additional independent datasets

To further investigate the immunoregulatory function of ADAMDEC1, independent validation was carried out using the METABRIC cohort, which includes 1992 primary breast tumors, along with multiple GEO datasets comprising breast cancer (GSE167213, GSE196723), esophageal carcinoma (GSE161533), and melanoma (GSE22138, GSE3189) cohorts. These analyses confirmed that ADAMDEC1 expression was associated with cancer-associated signaling pathways, immune response pathways, and pathways associated with autoimmune disorders (Fig. 6A). Furthermore, immune infiltration analyses carried out using the TIMER and xCell algorithms showed notable correlations between the expression of ADAMDEC1 and diverse tumor-infiltrating immune cells (Fig. 6B-C). Additionally, ADAMDEC1 was found to be positively correlated with several immunoinhibitory molecules, including PD-L1, VSIG4, SELPLG, PVRIG, LILRB2, KLRB1, and BTN3A1, as well as immunostimulators such as TNFRSF9, CD40, and CD27 (Fig. 6D).

Fig. 6.

Correlations between ADAMDEC1 expression and signaling pathways, infiltrating immune cells, and immunotherapy modulators according to GEO and METABRIC datasets. A Gene set enrichment analysis of ADAMDEC1 expression levels in diverse tumor types based on GEO and METABRIC data. B Significant correlations between the expression of ADAMDEC1 and the infiltration levels of different immune cells according to TIMER data. C Significant correlations between the expression of ADAMDEC1 and the infiltration levels of different immune cells according to the xCell algorithm. D Pan-cancer analysis of the relationships between the expression of ADAMDEC1 and immunotherapy modulators based on the GEO and METABRIC datasets. NES normalized enrichment score, GEO Gene Expression Omnibus, METABRIC Molecular Taxonomy of Breast Cancer International Consortium. Asterisks indicate significant differences as follows: *P < 0.05, **P < 0.01, ***P < 0.001, and ∗∗∗∗P < 0.001

Single-cell analysis reveals distinct cellular origins of ADAMDEC1 in UVM and SKCM

To investigate the cellular basis for ADAMDEC1’s opposite prognostic effects in UVM and SKCM, we analyzed single-cell RNA-seq data from the TISCH database. In UVM, ADAMDEC1 expression was almost exclusively restricted to monocytes/macrophages (Mono/Macro, average expression 0.38), with negligible expression in malignant cells (0) or other immune subsets (Fig. 7). This indicates that in UVM, the prognostic signal associated with ADAMDEC1 primarily reflects tumor-associated macrophages rather than the tumor cells themselves.

Fig. 7.

Single-cell RNA sequencing reveals distinct cellular origins of ADAMDEC1 in UVM and SKCM. A Heatmap of ADAMDEC1 expression across major cell lineages (immune, malignant, stromal) in multiple SKCM datasets and one UVM dataset. B Heatmap showing ADAMDEC1 expression in immune and stromal subpopulations

In contrast, SKCM showed broad expression across multiple cell types: ADAMDEC1 was detected across multiple cell types, including malignant cells, dendritic cells (DCs), monocytes/macrophages, B cells, and stromal cells, with expression levels ranging from 0.05 to 0.14 across datasets (Fig. 7). This multi-cellular origin suggests that in SKCM, ADAMDEC1 expression reflects a more generalized immune-active tumor microenvironment.

These single-cell findings provide a mechanistic explanation for the prognostic dichotomy: in “cold” tumors like UVM, ADAMDEC1 likely marks immunosuppressive macrophages driving poor outcomes; in “hot” tumors like SKCM, its broad expression across immune effector cells may signify active antitumor immunity associated with favorable prognosis.

Genomic and epigenetic alterations underlying ADAMDEC1 dysregulation

To explore the mechanisms driving ADAMDEC1 dysregulation, we analyzed its genomic and epigenetic alterations. cBioPortal analysis revealed that SKCM exhibited the highest mutation frequency (~ 12%), with missense mutations being far more common than truncating mutations, suggesting that functional modulation through missense changes, rather than complete inactivation, may be the predominant mechanism (Supplementary Fig. 1).

Copy number variation (CNV) analysis using GSCA showed that ADAMDEC1 CNV was particularly frequent in UCS, TGCT, OV, LIHC, LUSC, and BLCA (Supplementary Fig. 2A). The CNV patterns were context-dependent: TGCT, ACC, STAD, SKCM, UVM, and KICH predominantly showed heterozygous amplification, while LIHC, OV, LUSC, BLCA, READ, UCS, HNSC, BRCA, LUAD, and ESCA were characterized by heterozygous deletion (Supplementary Fig. 2A). Significant positive correlations between CNV and mRNA expression were observed in 4 tumor types (Supplementary Fig. 2B). In terms of SNV, the proportion was notably higher in SKCM and UCEC, accounting for the percentages of 52% and 21%, respectively (Supplementary Fig. 2C).

DNA methylation analysis revealed a critical epigenetic regulatory mechanism: ADAMDEC1 exhibited hypomethylation in 11 tumors including BRCA, BLCA, ESCA, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, PRAD, and UCEC (Fig. 8A). Specifically, ADAMDEC1 expression was significantly inversely correlated with its methylation level in 20 cancer types, and positively correlated in only 2 (LIHC and SKCM) (Fig. 8B). Survival analysis further showed that higher ADAMDEC1 methylation was a protective factor, associated with better OS and DSS in LGG, KIRC, and KIRP (Fig. 8C). These findings implicate hypomethylation as a key driver of ADAMDEC1 upregulation in multiple cancers, with direct prognostic consequences.

Fig. 8.

Relationship between ADAMDEC1 methylation, gene expression, and patient survival across cancers. A Differences in ADAMDEC1 methylation levels between tumor and matched normal tissues for multiple cancer types. B Correlation of ADAMDEC1 DNA methylation with its mRNA expression. C Survival analysis (DSS and OS) based on high versus low ADAMDEC1 methylation in specific cancers such as LGG, KIRC, and KIRP

TMA validation of ADAMDEC1 in esophageal carcinoma

To validate the correlations between ADAMDEC1 and immunoregulatory factors at the protein level, we selected esophageal carcinoma (ESCA) for immunohistochemical analysis based on our predefined criteria: (i) significant upregulation in ESCA tumor tissues (Fig. 1A-C); (ii) correlation with advanced tumor stage (Fig. 1D); and (iii) prior independent datasets suggesting immune-related roles lacking protein-level confirmation. An ESCA tissue microarray (TMA) containing 48 primary tumors with complete clinicopathological data was available for this purpose. As illustrated in Fig. 9A, ADAMDEC1 expression was localized primarily in the cytoplasm, CTLA-4 was detected in the cytoplasm, nucleus, and cell membrane, and PD-1 was observed in both the cytoplasm and membrane. Based on H-score evaluation, PD-1 expression showed a significant positive correlation with ADAMDEC1 (Fig. 9B, R = 0.42, P = 0.0027), while a moderate yet non-significant positive trend was noted between CTLA-4 and ADAMDEC1 (R = 0.26, P = 0.08).

Fig. 9.

ADAMDEC1 as an immunological and prognostic biomarker. A Immunohistochemistry (IHC) analysis of ADAMDEC1, CTLA-4, and PD-1 expression in an esophageal carcinoma (ESCA) patient tissue array (n = 48). Shown are representative images of low, moderate, and strong expression in cancer tissues. Scale bars = 100 μm. B Correlation analysis of the relationship between the expression of ADAMDEC1 and the expression of PD-1 and CTLA-4 in ESCA. C Correlation analysis of the relationship between the expression of ADAMDEC1 and clinicopathologic characteristics. D Predictive value of ADAMDEC1 for immunotherapy in the IMvigor210 cohort and NSCLC cohort (GSE135222)

Furthermore, the relationship between ADAMDEC1 expression and clinicopathological features was assessed. Results indicated that ADAMDEC1 levels were significantly elevated in patients with distant metastasis compared to those without (P = 0.0316, Wilcoxon test). A trend toward higher ADAMDEC1 expression was also observed in patients with T3–T4 stage tumors relative to T1–T2 stages (P = 0.0564, Wilcoxon test) (Fig. 9C).

Predictive value of ADAMDEC1 in immunotherapy

Given the emerging role of ADAMDEC1 in immunoregulation, we next assessed its potential as a predictor of response to immunotherapy. Using the TIDE framework, the predictive performance of ADAMDEC1—measured by AUC—was compared against established biomarkers across multiple cancer cohorts. ADAMDEC1 achieved an AUC > 0.7 in over half of the melanoma cohorts (4 out of 7) and in one glioblastoma cohort, showing predictive performance comparable to, or in some cases exceeding, conventional biomarkers. In comparison, tumor mutational burden (TMB) reached this threshold in only one bladder cancer and one melanoma cohort, while PD-L1 showed AUC > 0.7 in two melanoma, one gastric cancer, one NSCLC, and one HNSC cohort. MSI score exceeded AUC 0.7 in three melanoma and one gastric cancer cohort (Table 1). These results suggest that ADAMDEC1 may serve as a complementary biomarker to existing predictors, potentially capturing aspects of tumor immunogenicity not fully reflected by mutation burden or single checkpoint molecule expression.

Table 1.

Evaluation of ADAMDEC1 as a biomarker of immunotherapy response based on the TIDE database

Study	Cancer type	Treatment	Pos/neg cases	ADAMDEC1	TMB	PD-L1	MSI. score
Zhao_2019	Glioblastoma	PD-1_Pre	8/7	0.38	NA	0.68	0.41
	Glioblastoma	PD-1_Post	6/3	0.94	NA	0.61	0.67
VanAllen_2015	Melanoma	CTLA-4	19/23	0.73	0.67	0.64	0.74
Uppaluri_2020	HNSC	PD-1_Pre	8/15	0.5	NA	0.69	0.63
	HNSC	PD-1_Post	9/13	0.38	NA	0.7	0.45
Riaz_2018	Melanoma	PD-1_Prog	4/22	0.82	0.47	0.52	0.69
	Melanoma	PD-1_Naive	6/19	0.47	0.62	0.27	0.4
Ruppin_2021	NSCLC	PD-1	7/15	0.61	NA	0.7	0.46
Prat_2017	NSCLC/HNSC/Melanoma	PD-1	21/12	NA	NA	0.58	0.41
Nathansom_2017	Melanoma	CTLA-4_Pre	4/5	0.55	NA	0.65	0.95
	Melanoma	CTLA-4_Post	4/11	0.73	NA	0.66	0.52
Miao_2018	ccRCC	PD-1 or PD-L1 ± CTLA-4	20/13	0.48	0.65	0.42	0.25
McDemott_2018	ccRCC	PD-L1	20/61	0.57	0.54	0.62	0.55
Mariathasan _2018	Bladder_mUC	PD-L1	68/230	0.56	0.73	0.58	0.56
Liu_2020	Melanoma	PD-1_Prog	16/31	0.63	NA	0.56	0.45
	Melanoma	PD-1_Naive	33/41	0.47	NA	0.51	0.5
Lauss_2017	Melanoma	ACT	10/15	0.6	0.76	0.73	0.49
Kim_2018	Gastric	PD-1	12/33	0.54	NA	0.88	0.75
Hugo_2016	Melanoma	PD-1	14/12	0.35	0.63	0.6	0.69
Hee_2020	NSCLC	PD-1	9/12	NA	na	0.45	0.54
Gide_2019	Melanoma	PD-1	19/22	0.73	NA	0.83	0.43
	Melanoma	PD-1 + CTLA-4	21/11	0.65	NA	0.79	0.7
Braun_2020	ccRCC	PD-1	201/94	0.46	0.56	0.56	0.53

We additionally assessed the usefulness of ADAMDEC1 in predicting the results of anti-PD-1/PD-L1 treatment in NSCLC and urothelial cancer. A phase II trial of atezolizumab in advanced urothelial cancer, the IMvigor210 cohort, showed that elevated ADAMDEC1 expression was linked to better prognosis and higher objective response rates (ORR: 26% vs. 13%, P = 0.0354; Fig. 9D). In GSE135222, a separate NSCLC cohort, although elevated ADAMDEC1 expression showed only a non-significant trend toward prolonged survival (P = 0.087), it was strongly linked to durable clinical benefit (DCB), with DCB rates of 80% in the high-expression group compared to 18% in the low-expression group (P = 0.0285; Fig. 9D).

Collectively, these results suggest that ADAMDEC1 can serve as a potential complementary predictive biomarker for the response to immunotherapy in multiple cancer types, such as melanoma, glioma, urothelial carcinoma, and NSCLC.

Discussion

Initially identified as a member of the ADAM family due to sequence homology, ADAMDEC1 is now recognized as an atypical representative within this group of type I transmembrane metzincin metalloproteases. Typical ADAM proteins contain multiple C-terminal domains—such as disintegrin-like, cysteine-rich, EGF-like, transmembrane, and cytoplasmic regions—that enable diverse functions including proteolytic shedding of membrane-bound proteins, growth factors, and cytokines, thereby influencing cell adhesion, integrin binding, intracellular signaling, proliferation, migration, and angiogenesis [15, 25]. In contrast, ADAMDEC1 retains only a metalloprotease domain and a disintegrin-like domain, lacking most structural extensions, which suggests it may function as a soluble metalloprotease [26]. Its exact biological roles, however, require further investigation.

In this study, pan-cancer analyses indicated a multifaceted and context-dependent role for ADAMDEC1 in tumor development. We observed significant upregulation of ADAMDEC1 in tumor tissues from multiple cancer types including BRCA, CHOL, ESCA, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, PRAD, STAD, and UCEC. Conversely, its expression decreased in COAD and READ. Furthermore, survival analyses indicated that an increase in ADAMDEC1 was linked to unfavorable outcomes (such as OS, DSS, or PFI) in ACC, LAML, LGG, and UVM. Conversely, it was associated with a better prognosis in OV, READ, and SKCM. Importantly, multivariable Cox regression confirmed that ADAMDEC1 is an independent prognostic factor in LGG, UVM, and SKCM after adjusting for clinical covariates. These findings align with previous studies in glioma, renal cancer, and melanoma [17, 27, 28], underscoring the gene’s complex and cancer-specific behavior.

Emerging evidence indicates that ADAMDEC1 plays divergent roles across cancer types. In glioma, it promotes cancer stem cell self-renewal via FGF2 solubilization and FGFR1 activation [29], and its knockdown suppresses proliferation and induces apoptosis [30]. In NSCLC, ADAMDEC1 enhances tumor progression through PI3K/AKT signaling and correlates with poor survival [31]. It also facilitates proliferation, migration, and invasion in colorectal cancer via the Wnt/β-catenin pathway [32]. Conversely, some studies propose tumor-suppressive functions for ADAMDEC1 in gastric and colorectal cancers [33, 34]. Our data in ESCA show higher ADAMDEC1 expression in metastatic and advanced-stage tumors, suggesting an oncogenic role in this malignancy—a finding that warrants further mechanistic exploration. GSEA of TCGA data indicated involvement of ADAMDEC1 in tumorigenic, pro-metastatic, and immune-related pathways, a result corroborated by GEO and METABRIC datasets.

A key finding of our study is the prognostic dichotomy of ADAMDEC1: a risk factor in UVM, but a protective factor in SKCM. Our integrated single-cell analysis provides a cell-level mechanistic explanation for this paradox. In UVM, a prototypical ‘cold’ tumor with limited immune infiltration, ADAMDEC1 expression was almost exclusively restricted to tumor-associated macrophages (TAMs). This finding directly implicates ADAMDEC1 as a marker of TAMs in this context. Given the established role of immunosuppressive M2-like macrophages in promoting tumor progression, the poor prognosis associated with high ADAMDEC1 expression in UVM is likely driven by its expression in these pro-tumorigenic macrophages. Conversely, in SKCM, a classic “hot” tumor characterized by robust immune infiltration, ADAMDEC1 was broadly expressed across multiple immune cell types including dendritic cells, macrophages, and T cells, as well as malignant cells. This broad expression pattern aligns with the concept that an immune-active tumor microenvironment is associated with favorable prognosis in various cancers, as exemplified by the lung progression associated signature (LPAS) in lung adenocarcinoma, where low LPAS scores (indicating high immune activity) correlate with better patient outcomes [35]. In this setting, ADAMDEC1 expression may signify an active, ongoing antitumor immune response—potentially reflecting dendritic cell maturation and antigen presentation—thus correlating with favorable prognosis. This interpretation is supported by our GSEA findings showing ADAMDEC1’s association with both immune activation (antigen processing, T cell signaling) and immune suppression pathways, suggesting it may function as a context-dependent rheostat of immune responses within the TME.

The therapeutic efficacy of immunotherapy is profoundly influenced by the complex interactions within the tumor microenvironment (TME), which encompasses a diverse array of immune cells, stromal components, cytokines, and metabolic factors. Our findings position ADAMDEC1 as a key player within this intricate network. The observed correlation between ADAMDEC1 and immune cell infiltration, particularly dendritic cells, aligns with the concept that the TME’s cellular composition dictates immunotherapy outcomes [36]. Furthermore, ADAMDEC1’s association with multiple immunomodulatory molecules (PD-1, CTLA-4, TIGIT) suggests it may be part of a broader regulatory circuit that tumors exploit for immune evasion. Recent studies have illuminated specific TME-driven mechanisms of resistance and response. For instance, in lung adenocarcinoma, EGFR mutations have been shown to reshape the immune microenvironment, fostering an immunosuppressive state characterized by enrichment of TIGIT+ regulatory T cells and depletion of active NK cells [37]. This aligns with our observation of ADAMDEC1’s correlation with TIGIT and its role in “cold” tumors like UVM. Conversely, in “hot” tumors, signatures of immune activity, such as a low lung progression associated signature (LPAS) score, which reflects robust T cell infiltration and an immune-active microenvironment [35], or the presence of specific NK cell subsets [37], are associated with favorable prognosis and immunotherapy response. Additionally, metabolic reprogramming within the TME, as highlighted by studies on SLC25A1 [38], can lead to immune exclusion. SLC25A1-mediated metabolic changes create a “cold” tumor phenotype, mirroring the immune-excluded state that we observed in ADAMDEC1-high ESCA tumors. The development of glycosylation-related prognostic models [39] further underscores the diverse molecular pathways within the TME that can be targeted or used for patient stratification. By integrating ADAMDEC1 expression with these multifaceted TME components—immune cell subsets, metabolic states, and genetic drivers—we can move towards a more holistic understanding of its function. Our data suggest that ADAMDEC1 is not merely a passive marker but an active participant in the TME crosstalk, potentially influencing the cytokine milieu and immune cell recruitment, which ultimately determines the success or failure of immunotherapies like checkpoint inhibitors.

Our genomic and epigenetic analyses further illuminate the mechanisms underlying ADAMDEC1 dysregulation. The significant inverse correlation between ADAMDEC1 expression and its DNA methylation level in 20 cancer types, coupled with its hypomethylation in 11 tumor types, strongly implicates epigenetic activation as a key driver of ADAMDEC1 upregulation. The prognostic protection conferred by ADAMDEC1 hypermethylation in LGG, KIRC, and KIRP—cancers where high ADAMDEC1 expression is a risk factor—provides convergent evidence linking epigenetic status, expression, and clinical outcome. These findings position ADAMDEC1 as an epigenetically regulated gene whose expression is modulated by tumor-type-specific methylation patterns.

Given its pervasive role in immune signaling and regulation, we evaluated ADAMDEC1’s potential as a predictor of immunotherapy response. Our analysis suggests that ADAMDEC1 demonstrates predictive performance comparable to established biomarkers such as TMB, MSI, and PD-L1 in multiple cohorts. Rather than claiming superiority, we propose that ADAMDEC1 may serve as a complementary functional biomarker, potentially capturing aspects of tumor immunogenicity—such as dendritic cell infiltration and T cell activation—that are not fully reflected by genomic instability metrics or single checkpoint molecule expression. In the IMvigor210 and GSE135222 cohorts, ADAMDEC1 high expression was significantly associated with improved response and clinical benefit, supporting its utility in urothelial carcinoma and NSCLC.

Nevertheless, this study has certain limitations. The heterogeneity of the integrated multi-omics datasets (TCGA, GTEx, GEO, METABRIC) may introduce batch effects and confounding biases. Moreover, our single-cell RNA sequencing analysis for UVM was derived from only one public database, limiting the generalizability of its conclusions regarding the cellular origin of ADAMDEC1 in “cold” tumors. To address these gaps, future studies should incorporate additional single-cell datasets from diverse platforms to robustly validate the cell-type-specific expression patterns of ADAMDEC1. Furthermore, functional experiments using gain- and loss-of-function models in relevant cellular contexts (e.g., macrophages, dendritic cells, and tumor cells), combined with in vivo models, are essential to dissect the causal role of ADAMDEC1 in immune regulation, therapy response, and its functional heterogeneity across different tumor microenvironments.

Conclusions

In summary, our comprehensive pan-cancer analysis reveals that ADAMDEC1 is not only dysregulated across multiple cancer types but also serves as a robust prognostic and immunological biomarker. Single-cell resolution analysis reveals that its prognostic direction—risk versus protective—is determined by its cellular origin within the tumor microenvironment: predominantly macrophage-derived in “cold” tumors like UVM, versus multi-lineage immune expression in “hot” tumors like SKCM. It exhibits strong associations with immune cell infiltration—particularly dendritic cells—and the expression of key immunomodulatory molecules, including PD-1 and CTLA-4. Genomic and epigenetic analyses implicate DNA hypomethylation as a key driver of ADAMDEC1 upregulation in multiple cancers, with methylation status directly linked to prognosis. Importantly, ADAMDEC1 demonstrates significant potential in predicting response to immunotherapy, comparable to or complementing conventional biomarkers such as TMB, MSI, and PD-L1 in some cancers. These findings underscore its dual role in tumor progression and immune regulation, positioning ADAMDEC1 as a promising target for future immunotherapeutic strategies and a valuable functional biomarker for treatment personalization.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

ACC

Adrenocortical carcinoma

AUC

Area under the ROC curve

BLCA

Bladder urothelial carcinoma

BRCA

Breast cancer

CESC

Cervical squamous cell carcinoma and endocervical adenocarcinoma

CHOL

Cholangiocellular carcinoma

COAD

Colon adenocarcinoma

DCs

Dendritic cells

DSS

Disease-specific survival

ESCA

Esophageal carcinoma

GBM

Glioblastoma multiforme

GEO

Gene Expression Omnibus

GEPIA

Gene Expression Profiling Interactive Analysis

GSEA

Gene Set Enrichment Analysis

GTEx

Genotype-Tissue Expression

HNSC

Head and neck squamous cell carcinoma

ICI

Immune checkpoint inhibitors

IHC

Immunohistochemistry

KEGG

Kyoto Encyclopedia of Genes and Genomes

KICH

Kidney chromophobe

KIRC

Kidney renal clear cell carcinoma

KIRP

Kidney renal papillary cell carcinoma

LAML

Acute myeloid leukemia

LGG

Lower grade glioma

LIHC

Liver hepatocellular carcinoma

LUAD

Lung adenocarcinoma

LUSC

Lung squamous cell carcinoma

METABRIC

Molecular Taxonomy of Breast Cancer International Consortium

MSI

Microsatellite instability

NSCLC

Non-small-cell lung carcinoma

OS

Overall survival

OV

Ovarian serous cystadenocarcinoma

PAAD

Pancreatic adenocarcinoma

PFI

Progression-free interval

PRAD

Prostate adenocarcinoma

READ

Rectum adenocarcinoma

SKCM

Skin cutaneous melanoma

STAD

Stomach adenocarcinoma

TCGA

The Cancer Genome Atlas

TGCT

Testicular germ cell tumors

THCA

Thyroid carcinoma

THYM

Thymoma

TIDE

The Tumor Immune Dysfunction and Exclusion

TIMER

Tumor Immune Estimation Resource

TMA

Tissue Microarray

TMB

Tumor mutational burden

TME

Tumor microenvironment

UCEC

Uterine corpus endometrial carcinoma

UCS

Uterine carcinosarcoma

UVM

Uveal melanoma

Author contributions

Shuchen Lin: Writing—original draft, formal analysis, data curation, conceptualization. Hui Cong: methodology, investigation, formal analysis, data curation. Zhen Li: formal analysis. Lihua Zhang: formal analysis. Xinmiao Yang: formal analysis. Jie Fu: writing—review & editing, supervision, conceptualization.

Funding

None.

Data availability

The datasets analyzed in this study are publicly available from the following repositories: The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov), the Gene Expression Omnibus (GEO) under accession numbers GSE167213, GSE196723, GSE161533, GSE22138, and GSE3189 (https://www.ncbi.nlm.nih.gov/geo/), the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (https://ega-archive.org/dacs/EGAC00001000484), the Tumor Immune Estimation Resource (TIMER) (https://cistrome.shinyapps.io/timer/), the Genotype-Tissue Expression (GTEx) project (https://gtexportal.org/home/), and the Gene Expression Profiling Interactive Analysis (GEPIA) platform (http://gepia.cancer-pku.cn/index.html).

Declarations

Ethics approval and consent to participate

This study was approved by the Ethics Committee of Shanghai Zhuoli Biotechnology Company (Approval number: LLSM-15-01). All research procedures were conducted in accordance with this committee’s ethical standards and the principles of the Declaration of Helsinki. The Ethics Committee waived the requirement for informed consent, as all samples used were archival paraffin-embedded cancer tissues that had been fully anonymized prior to researcher access.

Competing interests

The authors declare no competing interests.