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. 2026 Jan 30;26:317. doi: 10.1186/s12885-026-15656-5

The role of CD26 in breast cancer and its pan-cancer analysis

Runyi Tao 1,2,#, Bohao Liu 1,2,#, Shan Gao 1,2, Jingyi Zhang 1,2, Jizhao Wang 1,2, Qiuyu Gong 1,2, Xingzhuo Zhu 1,2, Yilong Zhao 1,2, Jiaqi Huang 1,2, Chenrong Zhang 1,2, Yixing Li 1,2, Zhiyu Wang 1,2, Guangjian Zhang 1,2,✉
PMCID: PMC12958686  PMID: 41618209

Abstract

Background

CD26 (DPP4) is shown in literature to be implicated in multiple tumors and antitumor immunity, yet its pan-cancer and context-dependent roles remain imprecisely defined. This study comprehensively characterizes the clinical relevance of CD26 and its association with invasion-related features and immunotherapy-relevant immune infiltration and response-associated biomarkers, and to validate key findings in breast cancer cells.

Methods

The integrated pan-cancer analysis of CD26 was performed using public databases, assessing its expression patterns, associations with cancer staging and prognostic value. Co-expression and protein-interaction data were used for GO/KEGG enrichment to infer potential biological pathways. Correlations between CD26 and immune cell infiltration, cancer-associated fibroblasts (CAFs), immune checkpoints, tumor mutational burden (TMB), microsatellite instability (MSI), and neoantigen load were systematically evaluated. R software and online bioinformatics tools were employed. Additionally, qPCR, western blot and other in-vitro experiments compared CD26 expression in MCF-7 breast cancer cells and MCF-10A normal breast epithelial cells, and examined the effects of pharmacologic CD26 inhibition (alogliptin) on proliferation, invasion, and MMP9 expression in vitro.

Results

CD26 exhibited broad but low tissue specificity and was significantly upregulated in multiple malignancies compared with normal tissues, particularly in highly aggressive tumors (P < 0.01). Higher CD26 expression was associated with advanced pathological stage and adverse prognosis in several tumor types (p < 0.01), including breast cancer, while exhibiting a favorable prognostic association in a subset of tumors, indicating context-dependent relationships. Functional enrichment suggested involvement of CD26-related networks in chemokine signaling and EMT-related processes. CD26 expression correlated with estimated immune infiltration in 34 of 38 tumor types, including CD4+ and CD8+ T cells and CAFs, and was associated with multiple immune checkpoints, as well as TMB, MSI and neoantigens in selected cancers. In vitro, CD26 was elevated in MCF-7 versus MCF-10A cells (p < 0.05), and alogliptin treatment reduced MCF-7 cell proliferation and invasion (p < 0.05), accompanied by decreased MMP9 expression (p < 0.05).

Conclusions

This study presents an integrative pan-cancer framework linking CD26 expression to immune infiltration, together with in vitro observations in breast cancer cells, offering a comprehensive pan-cancer and experimental characterization of CD26. CD26 might be a novel prognostic biomarker candidate and therapeutic target to counteract tumor development in highly aggressive cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-026-15656-5.

Keywords: CD26, Prognostic biomarker, Breast cancer, Pan-cancer

Introduction

In the era of tumor immunotherapy, immune checkpoint inhibitors (ICIs) have improved survival in multiple malignancies with fewer off-target toxicities than conventional chemotherapy and targeted therapies [1]. Blockade of programmed cell death-1 (PD-1) or its ligand PD-L1 remains the most widely used approach, with additional checkpoints such as CTLA-4 and LAG-3 entering clinical development [2, 3]. However, primary and acquired resistance limits the overall benefit of ICIs, particularly in relatively cold tumors such as breast, prostate and colorectal cancers [4]. These heterogeneous responses indicate that reliance on a small set of classical checkpoints is insufficient, highlighting the need to identify novel and druggable targets for precision immunotherapy [5].

CD26 (dipeptidyl peptidase IV, DPP4) is a type II transmembrane glycoprotein with DPP4 activity and belongs to the serine protease S9B family [6]. CD26 is highly expressed on multiple immune cell populations, especially activated CD4 + and CD8 + T cells, NK cells, and dendritic cells, and also detected on various tumor cells [7–9]. As an ectopeptidase, CD26 cleaves N-terminal dipeptides from substrates with penultimate proline or alanine residues, including chemokines such as CXCL10 and CXCL12, thereby modulating chemokine gradients and immune cell activation in the tumor microenvironment [10]. CD26 also interacts with adenosine deaminase (ADA), fibronectin and other molecules, linking purine metabolism, extracellular matrix (ECM) remodeling and T cell co-stimulation signals, and placing it at the intersection of the "metabolism-immune-microenvironment" network [8]. Through these enzymatic and interaction-dependent properties, CD26 has been associated with tumor cell motility-related phenotypes and with immune infiltration and function, supporting its consideration as a non-classical, checkpoint-like immunoregulatory factor [8, 9].

Distinct from conventional inhibitory receptors such as PD-1 and CTLA-4, CD26 possesses both enzymatic activity and receptor/co-receptor functions. Its cleavage of chemokines and cytokines can effectively “switch off” multiple pro-inflammatory and anti-tumor signals while its expression on effector T cells is closely associated with differentiation states, functional capacity, and memory formation [7, 8, 10]. Preclinical studies have shown that blocking CD26 enhances T cell-mediated cytotoxicity and is synergistic or complementary with PD-1/PD-L1 and other checkpoint inhibitors across several tumor models [9, 11]. Moreover, CD26 inhibitors are widely used for diabetes with well-characterized clinical profiles, making CD26 a compelling candidate checkpoint molecule. CD26 regulates the tumor immune microenvironment through multiple immune-related pathways, and the existing drug base supports its rapid translation into tumor immunotherapy.

Despite these advantages, research on CD26 in anti-tumor immunity remains largely limited to individual tumor types and in vitro or small-sample preclinical studies, such as safety evaluations of recombinant humanized anti-CD26 monoclonal antibodies in malignant pleural mesothelioma while inhibiting CD26 suppressed the growth of lung cancer [11, 12]. Although recent bioinformatics studies have reported associations between CD26 expression and prognosis in certain cancer types and suggested CD26-related pathways as potential therapeutic targets, a comprehensive characterization across solid tumors remains limited, integrating CD26 expression with tumor immune microenvironment remodeling and broader immune checkpoint networks, especially the specific role of CD26 in breast cancer remains lacking [13–15]. As one of the most common malignant tumors in women, breast cancer has a limited response rate to PD-1/PD-L1 inhibitors. Thus, there is an urgent need to identify novel and druggable immune-related targets to improve the broad benefits of immunotherapy [4, 5].

On this basis, bioinformatics and experimental approaches were used to investigate the role of CD26. This study systematically analyzed CD26 expression and its relationship with clinical stages and prognosis in different solid tumors using across public cancer cohorts. The relationship between CD26 and diverse immune-regulatory pathways and classical immune checkpoint molecules to elucidate its potential role in reshaping the tumor immune microenvironment were then focused. Subsequently, the study concentrated on breast cancer, functionally validating the role of CD26 in breast cancer cell (MCF-7) line and demonstrating that CD26 is upregulated in breast cancer cells, while pharmacologic inhibition of CD26 significantly suppresses breast cancer cell growth. These results suggest that CD26 may serve as a novel immune checkpoint with dual enzymatic and immunoregulatory functions and a potential immunotherapeutic target breast cancer and even a variety of solid tumors.

Materials and methods

Bioinformatics analyses

Expression level and diagnostic value analyses

The CD26 (ENSG00000197635) mRNA expression map in tissues was constructed by the Human Protein Atlas (HPA) (https://www.proteinatlas.org/) database (version: 22.0, Release date: 2022.12.07).

Expression data of 34 kinds of tumors and normal tissues were investigated using the “Gene DE” module of Tumor Immune Estimation Resource 2 (TIMER 2, https://compbio.cn/timer2/), matching the Cancer Genome Atlas Program (TCGA) database (https://portal.gdc.com), to differential analysis (edgeR) the RNA-Seq raw counts.

The expressions of CD26 in Esophageal Carcinoma (ESCA), Kidney Renal Papillary Cell Carcinoma (KIRP), Acute Myeloid Leukemia (LAML), Liver Hepatocellular Carcinoma (LIHC), Lung Adenocarcinoma (LUAD), Pancreatic Adenocarcinoma (PAAD), Prostate Adenocarcinoma (PRAD), Stomach Adenocarcinoma (STAD), Thyroid Carcinoma (THCA), and Thymoma (THYM) were investigated by the “expression DIY” module of Gene Expression Profiling Interactive Analysis 2 (GEPIA 2, http://gepia2.cancer-pku.cn), matching TCGA and the Genotype-Tissue Expression project (GTEx) (https://www.gtexportal.org), with Log2FC cutoff at 1 and p-value cutoff at 0.01.

Correlation between CD26 and clinicopathology

Expression data of CD26 and clinical data were extracted from TCGA database (N = 10,535, G = 60,499). Tumors with less than three samples were eliminated. Using R (Version 4.2.1) to correlate CD26 expression with Tumor, node and metastasis (TNM) classification and the World Health Organization (WHO) stages in various types of cancer by the Wilcoxon test.

Prognostic analysis of CD26 expression

Expression data of CD26 and overall survival (OS), progression free survival (PFS) and disease-specific survival (DSS) data were extracted from TCGA database. R package tinyarray (Version 2.2.7), survival (Version 3.4–0), cutoff (Version 1.3), ggpubr (Version 0.5.0), ggplot2 (Version 3.4.0) and survminer (Version 0.4.9) were used for the Kaplan–Meier analysis, single-factor Cox analysis and visualization.

CD26-related functional enrichment analysis

The protein–protein interaction (PPI) network associated with CD26 was investigated using the STRING database (version 11.5) (https://string-db.org/). To identify genes correlated with CCDC58, we utilized the “Similar Genes Detection” module of the GEPIA2 database based on TCGA. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, Gene Ontology (GO) biological process, Reactome gene set, Canonical pathway, CORUM, Wiki pathway, and PANTHER pathway of CD26 with binding-protein genes and candidate genes were performed on the metascape website (https://metascape.org/). KEGG rest API (https://www.kegg.jp/kegg/rest/keggapi.html) was used to obtain the latest KEGG Pathway gene annotation by mapping genes to the background set. The R package cluster Profiler (version 3.14.3) was used for enrichment analysis.

Correlation between CD26 and the tumor immune microenvironment

Expression data of CD26 and coefficient of immune cells were extracted from TCGA and GTEx. After matching Gene Symbol, R package Immuno-Oncology Biological Research (IOBR, version 0.99.9) tumor and psych (version 2.1.6) were used to re-evaluate the infiltration scores and Pearson's correlation coefficient of B cell, T cell CD4, T cell CD8, Neutrophil, Macrophage and Dendritic cell (DC) for each patient in each tumor. For analyses involving multiple comparisons, p values were adjusted using the Benjamini–Hochberg method to control the false discovery rate (FDR). FDR-adjusted q < 0.05 were considered statistically significant unless otherwise stated.

Correlation between CD26 and the gene mutation landscape

By using MuTect2 software (DOI: 10.1038/nature08822) from GDC, processing of all Simple Nucleotide level4 TCGA sample Variation data set were download, after integrating mutation data of samples, protein domain information was obtained using R software package maftools (version 2.2.10). The mutation score and CD26 expression were integrated, and their association was evaluated using Spearman correlation analysis.

Correlation of CD26 expression with TMB, MSI and neoantigens

The tumor mutational burden (TMB), microsatellite instability (MSI) and neoantigens (NEO) function of the R package maftools (version 2.8.05) was used to calculate TMB, MSI and NEO Pearson's correlation coefficient of each tumor. TMB, MSI, NEO score and CD26 expression were integrated, and the associations between score and CD26 expression were evaluated using Spearman correlation analysis.

Experimental validation

Cells, cultures and reagents

The breast cancer cell line MCF-7 and the non-tumorigenic human mammary epithelial cell line MCF-10A were purchased from the Cell Bank of the Type Culture Collection of the Chinese Academy of Sciences (Shanghai, China). All cell lines were maintained in complete RPMI-1640 medium.

qRT-PCR analysis

MCF-7 and MCF-10A were used to identify the CD26 level with quantitative reverse transcription polymerase chain reaction (qRT-PCR). Each qPCR analysis was conducted in technical triplicates. The primer sequences used are as follows. GAPDH, Forward: 5′-TGTCAAGCTCATTTCCTGGTATG-3′, Reverse: 5′- TCTCTCTTCCTCTTGTGCTCTTG-3′; CD26, Forward: 5′- TACTGGCTGGGTTGGAAGATTTA-3′, Reverse: 5′- CCTTCCTCCTGGCATTCCTTTAT-3′.

Western blotting analysis

MCF-10A and MCF-7 cells seeded in 10 cm cell culture plate were washed by PBS. Extraction of cell protein by RIPA (beyotime P00138). Protein extracts were separated by 10% SDS-PAGE. Anti-CD26 (1:1000 dilution, Proteintech, Wuhan, China) and anti-GAPDH (1:1000 dilution, Proteintech) antibodies were used. Protein levels were normalized to GAPDH.

Cell viability assay

The cell viability of the breast cancer cell line MCF-7 and the non-tumorigenic mammary epithelial cell line MCF-10A was evaluated using the Cell Counting Kit-8 (CCK-8). Cells were cultured according to ATCC guidelines in a humidified incubator at 37 °C with 5% CO₂.

Briefly, cells were seeded in 96-well plates at a density of 5 × 103 cells per well in 100 µL of culture medium and allowed to adhere overnight. The following day, the medium was replaced with fresh medium containing a serial dilution of Alogliptin (SYR-322 free base, MCE) at concentrations ranging from 1 nM to 5 mM. A vehicle control group, treated with 0.1% DMSO (v/v), which corresponds to the highest solvent concentration present in the drug-treated groups, was included as the 0 µM control. After 24 h of incubation, 10 µL of CCK-8 reagent was added to each well, and the plates were further incubated for 2–4 h. The absorbance at 450 nm was then measured using a microplate reader.

Each experimental group, including all drug concentrations and controls, was assayed with six technical replicates (n = 6) within each independent experiment to ensure statistical reliability. The experiment was independently repeated three times. Cell viability was calculated as a percentage relative to the vehicle control (0.1% DMSO) group.

Invasion assays

Cells were detached and suspended at 1 × 105 cells/mL in serum-free media. The cell suspension was then divided into five experimental groups: the untreated control, vehicle control (0.1% DMSO), and groups treated with alogliptin at 500 nM, 1 μM, or 2.44 μM. Cells from each group were plated at a density of 5 × 104 cells/well in the upper chamber of 24-well plates, which were pre-coated with Matrigel (1:8 diluted with serum-free medium; BD Matrigel 356,234). The bottom chamber was filled with a culture medium containing 10% FBS as a chemoattractant. The cells were incubated for 48 h. After incubation, non-invading cells on the upper surface of the membrane were completely removed using a moist cotton swab. The invaded cells on the lower surface were fixed with 4% paraformaldehyde for 30 min and stained with 0.1% crystal violet. The cells were photographed under an inverted microscope (200 × magnification). For quantification, the number of invaded cells in five randomly selected fields per membrane was counted manually using ImageJ software. The experiment included three technical replicates per group within each independent trial and was repeated three times independently (n = 3).Data are presented as the mean ± SD.

ELISA

The secretion of MMP-9 was analyzed by Enzyme-Linked Immunosorbent Assay (ELISA). Cells were seeded in 6-well plates at a density of 1 × 10⁶ cells per well in 1 mL of culture medium and then subjected to the respective drug interventions. After 24 h, the cell culture supernatant was collected and centrifuged to remove cellular debris. The concentration of MMP-9 was quantified using a commercial ELISA kit (Proteintech, Rosemont) strictly according to the manufacturer's instructions. A standard curve was generated using the serially diluted standards provided in the kit. Each sample was assayed in duplicate, and the entire experiment was independently repeated three times (n = 3 biological replicates). Since the cells were plated at an identical density and maintained in the same volume of medium across all groups, the measured MMP-9 concentrations in the supernatant are directly comparable, and no further normalization was applied.

Results

Gene expression analysis of CD26

Based on the HPA dataset, CD26 was highly expressed in the parathyroid, placenta, kidney, and small intestine, but with low tissue specificity (Fig. 1A). Analysis of CD26 expression using TCGA and GEPIA2 databases revealed significant upregulation of CD26 in 10 tumor types, including ESCA, KIRP, LAML, LIHC, LUAD, PAAD, PRAD, STAD, THCA and THYM (Figure S1 and 1B) (*P < 0.01).

Fig. 1.

Fig. 1

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Pan-Cancer Analysis of CD26 expression. A CD26 expression in multiple tissues (HPA database). B Types of cancers with high expression (p < 0.05) of CD26 (GEPIA2 database). ESCA, Esophageal Carcinoma; KIRP, Kidney Renal Papillary Cell Carcinoma; LAML, Acute Myeloid Leukemia; LIHC, Liver Hepatocellular Carcinoma; LUAD, Lung Adenocarcinoma; PAAD, Pancreatic adenocarcinoma; PRAD, Prostate Adenocarcinoma; STAD, Stomach Adenocarcinoma; THCA, Thyroid Carcinoma; THYM, Thymoma

Relationship of CD26 with clinicopathological characteristics

We assessed the correlation between CD26 expression and WHO cancer stage in multiple tumor types according to the definition of the Union for International Cancer Control (UICC). The results showed that Breast Invasive Carcinoma (BRCA), KIRP, Pan-kidney cohort (KICH + KIRC + KIRP) (KIPAN), Kidney Renal Clear Cell Carcinoma (KIRC), THYM, THCA, and Bladder Urothelial Carcinoma (BLCA) had higher CD26 expressions at Stage III + Stage IV than at Stage I + Stage II (Fig. 2A) (*P < 0.01). Further evaluation of BRCA revealed that CD26 expression was significantly upregulated in Stage III + Stage IV compared with Stage I + Stage II (Stage I + II = 799, Stage III + IV = 268, p = 5.6e-3) (Fig. 2B).

Fig. 2.

Fig. 2

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The Association between CD26 Expression and WHO cancer stages. A Pan-cancer. B BRCA. *p < 0.05. CESC, Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma; LUAD, Lung Adenocarcinoma; COAD, Colon Adenocarcinoma; COADREAD, Colon adenocarcinoma/Rectum adenocarcinoma Esophageal carcinoma; BRCA, Breast Invasive Carcinoma; ESCA, Esophageal Carcinoma; STES, Stomach and Esophageal carcinoma; KIRP, Kidney Renal Papillary Cell Carcinoma; KIPAN, Pan-kidney cohort (KICH + KIRC + KIRP); STAD, Stomach Adenocarcinoma; UCEC, Uterine Corpus Endometrial Carcinoma; HNSC, Head and Neck Squamous Cell Carcinoma; KIRC, Kidney Renal Clear Cell Carcinoma; LUSC, Lung Squamous Cell Carcinoma; THYM, Thymoma; LIHC, Liver Hepatocellular Carcinoma; THCA, Thyroid Carcinoma; MESO, Mesothelioma; READ, Rectum Adenocarcinoma; PAAD, Pancreatic adenocarcinoma; OV, Ovarian Serous Cystadenocarcinoma; TGCT, Testicular Germ Cell Tumors; SKCM, Skin Cutaneous Melanoma; UVM, Uveal Melanoma; UCS, Uterine Carcinosarcoma; BLCA, Bladder Urothelial Carcinoma; ACC, Adrenocortical Carcinoma; KICH, Kidney Chromophobe; CHOL, Cholangiocarcinoma; DLBC, Lymphoid Neoplasm Diffuse Large B-cell Lymphoma

Analysis of CD26 expression across TNM stages demonstrated that CD26 expression varied significantly at all T, N, and M stages in KIRP (T stage P = 3.6e-4, N stage P = 3.4e-6, M stage P = 0.04), PRAD (T stage P = 6.6e-6, N stage P = 1.3e-4, M stage P = 0.04) and KIRC (T stage P = 3.6e-7, N stage p = 0.01, M stage P = 3.3e-4) (Figure S2). All these findings suggested thatCD26 expression is associated with tumor progression markers.

Relationship between CD26 expression and prognosis

Overall survival (OS), progression-free survival (PFS), and disease-specific survival (DSS) were chosen to evaluate CD26 expression impact on prognosis. The results of OS showed that overexpression of CD26 was a risk indicator in BRCA (P = 0.046), LAML (P = 0.041), Lung Squamous Cell Carcinoma (LUSC) (P = 0.0072), PRAD (P = 0.0023), and STAD (P = 0.0017) (Fig. 3A and S3). Conversely, in KIRP (P = 0.0054), LUAD (P = 0.037), Skin Cutaneous Melanoma (SKCM) (P = 0.0065) and THCA (P = 0.0028), patients with elevated CD26 had longer OS (Fig. 3B). All hazard ratios (HR) values can be presented in Figure S3.

Fig. 3.

Fig. 3

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K-M analysis of the association between CD26 expression and OS in different cancers. A Cancers with high CD26 expression for good prognosis. B Cancers with high CD26 expression for poor prognosis. BRCA, Breast Invasive Carcinoma; LAML, Acute Myeloid Leukemia; LUSC, Lung Squamous Cell Carcinoma; PRAD, Prostate Adenocarcinoma; STAD, Stomach Adenocarcinoma; KIRP, Kidney Renal Papillary Cell Carcinoma; LUAD, Lung Adenocarcinoma; SKCM, Skin Cutaneous Melanoma; THCA, Thyroid Carcinoma

CD26 expression and breast cancer cell phenotype

To further validate the impact of CD26 on breast cancer cells, qPCR and Western blotting in breast cancer cells (MCF-7) and normal breast cells (MCF-10A) were performed. CD26 expression in MCF-7 cells was higher than in MCF10A cells (P < 0.05) (Fig. 4A-C). To investigate the effect of CD26 on breast cancer cell proliferation, this study used the CD26 inhibitor, Alogliptin (1–5 nM), to inhibit CD26 enzymatic activity. The growth of MCF-7, but not MCF-10A, was significantly inhibited by the addition of alogliptin (Fig. 4D-F), indicating the potential value of CD26 in targeted therapy in breast cancer. The effect of CD26 on breast cancer cell invasion was examined using a Transwell invasion assay in the MCF-7 cell line. Fewer cells passed through the chamber in the CD26 inhibitor group than the control group (Fig. 4G, 4H). The decrease of matrix metallopeptidase 9 (MMP9), an index for regulating invasion, was found decreased concomitantly with CD26 downregulation (Fig. 4I).

Fig. 4.

Fig. 4

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CD26 expression and the effect of CD26 inhibitor Alogliptin on BRCA cell lines MCF-7 and MCF-10A. A RT-PCR validation of CD26 expression. B-C Western blot analysis to verify CD26 expression in MCF-7 and MCF-10A cells. D-E Western blot analysis to measure functional inhibition of CD26 in MCF-7 and MCF-10A cells. F IC50 assay to analyze the effects of Alogliptin on MCF-7 and MCF-10A cells. G-H Transwell assay to analyze the impact of Alogliptin on MCF-7 cells migration (Scale bar: 100 μm). I ELISA assay to analyze the impact of Alogliptin on MMP9 level of MCF-7 cells. *p < 0.05. IC50: Median Inhibition Concentration, ELISA: Enzyme-Linked Immunosorbent Assay; MMP9: Matrix metallopeptidase 9

Biological functional analysis of CD26 in pan-cancer

100 genes with expression patterns similar to CD26 and 10 proteins known to bind CD26 as candidate genes were extracted to explore the potential mechanisms of CD26 in cancer. After excluding six genes without official gene symbols, GO and KEGG functional enrichment analyses were performed. The result indicated that CD26 and its associated genes were mainly involved in 7 significantly related biology pathways and processes, including the chemokine receptors bind chemokines, the drug ADME, the xenobiotic transport, the export from cell, the monocarboxylic acid metabolic process, the cellular chemical homeostasis and the regulation of cell–cell adhesion mediated by integrin (Fig. 5A, B). These findings suggest that CD26 was mainly involved in tumor progression through the above-mentioned pathways.

Fig. 5.

Fig. 5

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Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of the top 100 genes co-expressed with CD26 and 10 genes with the proteins bound to CD26. A GO analysis. B KEGG analysis

Relationship between CD26 expression, immune cell infiltration and immune checkpoints

The immune-cell infiltration score was evaluated with CD26 expression in 38 tumors, 34 of which were significantly correlated with CD26 expression (R value and P value are shown in Fig. 6) (*P < 0.01). CD26 expression was positively and negatively correlated with CD4+, CD8+ cells and cancer-associated fibroblasts (CAF) subtypes in different tumors (Figure S4), suggesting a key role for CD26 in shaping the tumor immune microenvironment. Immune checkpoints are the main targets of immunotherapy. To further understand how CD26 affects the occurrence and development of immune processes, the correlation between CD26 and immune checkpoints was further analyzed. CD26 expression was positively or negatively correlated with multiple immune checkpoints and immunomodulatory molecules, including LAG-3, CTLA-4, TIGIT and IFN-α,, in various tumor types (Fig. 7).

Fig. 6.

Fig. 6

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Correlation analysis between CD26 expression and immune cell infiltration levels in pan-cancer. THYM, Thymoma; SKCM, Skin Cutaneous Melanoma; KIRC, Kidney Renal Clear Cell Carcinoma; LGG, Brain Lower Grade Glioma; PCPG: Pheochromocytoma and Paraganglioma; THCA, Thyroid Carcinoma; BRCA, Breast Invasive Carcinoma; LUSC, Lung Squamous Cell Carcinoma; LIHC, Liver Hepatocellular Carcinoma; KICH, Kidney Chromophobe; LUAD, Lung Adenocarcinoma; TGCT, Testicular Germ Cell Tumors; UCS, Uterine Carcinosarcoma; GBMLGG, Glioma; KIPAN, Pan-kidney cohort (KICH + KIRC + KIRP); READ, Rectum Adenocarcinoma; COADREAD, Colon adenocarcinoma/Rectum adenocarcinoma Esophageal carcinoma; STAD, Stomach Adenocarcinoma; PRAD: Prostate Adenocarcinoma; CESC, Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma; PAAD, Pancreatic adenocarcinoma; KIRP, Kidney Renal Papillary Cell Carcinoma; STES, Stomach and Esophageal carcinoma; COAD, Colon Adenocarcinoma; ACC, Adrenocortical Carcinoma; SARC, Sarcoma; GBM, Glioblastoma Multiforme; HNCA, Head and Neck Squamous Cell Carcinoma; BLCA, Bladder Urothelial Carcinoma; UVM, Uveal Melanoma; CHOL, Cholangiocarcinoma; MESO, Mesothelioma; OV, Ovarian Serous Cystadenocarcinoma; ESCA, Esophageal Carcinoma; UCEC, Uterine Corpus Endometrial Carcinoma; DLBC, Lymphoid Neoplasm Diffuse Large B-cell Lymphoma

Fig. 7.

Fig. 7

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Correlation analysis between CD26 expression and immune checkpoints in pan-cancer. LUAD, Lung Adenocarcinoma; WT, High-Risk Wilms Tumor; PAAD, Pancreatic adenocarcinoma; READ, Rectum Adenocarcinoma; HNSC, Head and Neck Squamous Cell Carcinoma; OV, Ovarian Serous Cystadenocarcinoma; KICH, Kidney Chromophobe; KIPAN, Pan-kidney cohort (KICH + KIRC + KIRP); KIRC, Kidney Renal Clear Cell Carcinoma; CHOL, Cholangiocarcinoma; UCS, Uterine Carcinosarcoma; COAD, Colon Adenocarcinoma; COADREAD, Colon adenocarcinoma/Rectum adenocarcinoma Esophageal carcinoma; SKCM, Skin Cutaneous Melanoma; UCEC, Uterine Corpus Endometrial Carcinoma; THCA, Thyroid Carcinoma; GBM, Glioblastoma Multiforme; LGG, Brain Lower Grade Glioma; SARC, Sarcoma; PCPG: Pheochromocytoma and Paraganglioma; BRCA, Breast Invasive Carcinoma; LUSC, Lung Squamous Cell Carcinoma; ALL, Acute Lymphoblastic Leukemia; LAML: Acute Myeloid Leukemia; DLBC, Lymphoid Neoplasm Diffuse Large B-cell Lymphoma; NB, Neuroblastoma; BLCA, Bladder Urothelial Carcinoma; UVM, Uveal Melanoma; THYM, Thymoma; TGCT, Testicular Germ Cell Tumors; KIRP, Kidney Renal Papillary Cell Carcinoma; ACC, Adrenocortical Carcinoma; CESC, Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma; STAD, Stomach Adenocarcinoma; STES, Stomach and Esophageal carcinoma; ESCA, Esophageal Carcinoma; MESO, Mesothelioma; LIHC, Liver Hepatocellular Carcinoma; PRAD: Prostate Adenocarcinoma

Mutational analysis of CD26 in pan-cancer

The mutations of CD26 gene in different tumors were manifested as mutation, structural variation, amplification, deep deletion and multiple variation, with missense mutation being the most common (Fig. 8). The highest mutation frequency was observed in Glioma (GBMLGG), followed by Brain Lower Grade Glioma (LGG) and Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (CESC).

Fig. 8.

Fig. 8

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CD26 Gene mutation information in pan-cancer. TMB: Tumor Mutational Burden, MSI: Microsatellite Instability; NEO: Neoantigen. GBMLGG, Glioma; LGG, Brain Lower Grade Glioma; CESC, Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma; LUAD, Lung Adenocarcinoma; COAD, Colon Adenocarcinoma; COADREAD, Colon adenocarcinoma/Rectum adenocarcinoma Esophageal carcinoma; LAML: Acute Myeloid Leukemia; BRCA, Breast Invasive Carcinoma; ESCA, Esophageal Carcinoma; STES, Stomach and Esophageal carcinoma; SARC, Sarcoma; KIRP, Kidney Renal Papillary Cell Carcinoma; KIPAN, Pan-kidney cohort (KICH + KIRC + KIRP); STAD, Stomach Adenocarcinoma; UCEC, Uterine Corpus Endometrial Carcinoma; HNSC, Head and Neck Squamous Cell Carcinoma; KIRC, Kidney Renal Clear Cell Carcinoma; LUSC, Lung Squamous Cell Carcinoma; THYM, Thymoma; LIHC, Liver Hepatocellular Carcinoma; PAAD, Pancreatic adenocarcinoma; OV, Ovarian Serous Cystadenocarcinoma; TGCT, Testicular Germ Cell Tumors; SKCM, Skin Cutaneous Melanoma; UCS, Uterine Carcinosarcoma; BLCA, Bladder Urothelial Carcinoma

Relationship between CD26 expression and TMB, MSI, and Neoantigens

TMB was significantly correlated in 10 tumors, with significant positive correlations in 6 tumors: Colon Adenocarcinoma (COAD), Colon adenocarcinoma/Rectum adenocarcinoma Esophageal carcinoma (COAD-READ), ESCA, KIRP, Uterine Corpus Endometrial Carcinoma (UCEC) and Ovarian Serous Cystadenocarcinoma (OV), and negative correlations in 4 tumors: LUAD, STAD, PRAD, and THYM (Fig. 9A). Microsatellite instability (MSI) was significantly associated in 9 tumors, with positively correlated in COAD, COAD-READ, and Testicular Germ Cell Tumors (TGCT), and negatively correlated in GBMLGG, KIPAN, PRAD, Head and Neck Squamous Cell Carcinoma (HNSC), LUSC, and DLBCL (Fig. 9B). Neoantigens was significantly correlated in 5 tumors, four of which were significantly positively correlated: COAD, COAD-READ, UCEC, and Uterine Carcinosarcoma (UCS), and significantly negatively correlated in PRAD (Fig. 9C). Notably, CD26 expression was positively correlated with TMB, MSI, and Neoantigens in both COAD and COAD-READ, suggesting that these two tumor types respond favorably to immunotherapy and may benefit from CD26-related immunotherapy.

Fig. 9.

Fig. 9

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Spearman correlation between CD26 expression and (A)Tumor Mutational Burden (TMB), (B) microsatellite instability (MSI) and (C) Neoantigen (NEO) in pan-cancer. THYM, Thymoma; MESO, Mesothelioma; PRAD: Prostate Adenocarcinoma; UVM, Uveal Melanoma; KICH, Kidney Chromophobe; STAD, Stomach Adenocarcinoma; DLBC, Lymphoid Neoplasm Diffuse Large B-cell Lymphoma; LUAD, Lung Adenocarcinoma; CESC, Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma; KIRC, Kidney Renal Clear Cell Carcinoma; STES, Stomach and Esophageal carcinoma; LAML: Acute Myeloid Leukemia; PAAD, Pancreatic adenocarcinoma; ACC, Adrenocortical Carcinoma; LGG, Brain Lower Grade Glioma; BRCA, Breast Invasive Carcinoma; BLCA, Bladder Urothelial Carcinoma; GBM, Glioblastoma Multiforme; THCA, Thyroid Carcinoma; HNSC, Head and Neck Squamous Cell Carcinoma; GBMLGG, Glioma; LUSC, Lung Squamous Cell Carcinoma; TGCT, Testicular Germ Cell Tumors; KIPAN, Pan-kidney cohort (KICH + KIRC + KIRP); READ, Rectum Adenocarcinoma; LIHC, Liver Hepatocellular Carcinoma; PCPG: Pheochromocytoma and Paraganglioma; UCS, Uterine Carcinosarcoma; SARC, Sarcoma; SKCM, Skin Cutaneous Melanoma; COADREAD, Colon adenocarcinoma/Rectum adenocarcinoma Esophageal carcinoma; COAD, Colon Adenocarcinoma; CHOL, Cholangiocarcinoma; UCEC, Uterine Corpus Endometrial Carcinoma; OV, Ovarian Serous Cystadenocarcinoma; KIRP, Kidney Renal Papillary Cell Carcinoma; ESCA, Esophageal Carcinoma

Discussion

In this study, we integrated pan-cancer multi-omics analyses with in vitro experiments in breast cancer to evaluate the expression patterns, clinical associations, and potential biological relevance of CD26 across multiple tumor types. Overall, CD26 was upregulated in multiple highly aggressive solid tumors, associated with advanced stage and poor prognosis, and was highly expressed in invasive breast cancer cells. Functionally, pharmacologic inhibition of CD26 (alogliptin) suppressed breast cancer cell proliferation and invasion with minimal effects on normal breast epithelial cells, supporting CD26 may be associated with invasion-related phenotypes, tumor microenvironment features, and prognosis. Therefore, we summarized a potential mechanism by which CD26 may be involved in tumors (Fig. 10).

Fig. 10.

Fig. 10

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The potential mechanisms by which CD26 may be involved in tumors. M1, M1 macrophage; M2, M2 macrophage; ADA, Adenosine deaminase; FAPα, Fibroblast activation protein α; MMPs, Matrix metalloproteinases; EGF, Epidermal growth factor; ERK, Extracellular-signal regulated kinase; bFGF, Basic fibroblast growth factor; MEK, Mitogen-activated protein kinase

Previous studies suggest that CD26 exerts complex and context-dependent roles in different tumor types. CD26 has been linked to tumor growth and metastasis in several solid tumors through chemokine- and ECM-related pathways, and CD26 inhibition has been reported to impede tumor progression in certain models, including via immune effector activation [16–21]. In contrast, higher CD26 expression has also been associated with favorable outcomes in selected tumors, implying that its functional impact varies with tumor lineage and microenvironmental context [22, 23].

Our pan-cancer results integrate and extend these findings. Based on the TCGA database, CD26 was widely expressed in most hematopoietic tissues and multiple solid organs with low tissue specificity, and was aberrantly expressed in a variety of malignancies of the digestive, endocrine, and genitourinary systems. CD26 was significantly up-regulated in most invasive tumors, including invasive breast cancer, and was associated with more advanced tumor stage and a higher degree of malignancy. However, CD26 was down-regulated in Kidney Chromophobe (KICH) and other less invasive or indolent tumors (p < 0.01), consistent with differential membranous CD26 expression reported across renal tumor subtypes [24]. We further confirmed that CD26 expression was relatively low in KICH, LUSC and SKCM (p < 0.01), but increased in most of the other solid tumors. We also found that CD26 expression was significantly correlated with the TNM stage of THCA, KIRP and PRAD, which was consistent with the reports that CD26 was included in the three-gene panel to distinguish benign and malignant thyroid nodules [25]. Survival and Cox analyses showed that high CD26 expression was significantly associated with shorter survival in reproductive system-related malignant tumors such as breast cancer and Uveal Melanoma (UVM), supporting the conclusions of Choi and Zhang et al. [26, 27]. In some hematological malignancies, CD26 was also an independent risk factor (p < 0.01). Notably, CD26 can arise from multiple cellular sources, and cross-lineage heterogeneity in the stromal context and immune architecture may modulate CD26-associated functions. Overall, this study indicates that CD26 tends to function as a protumor-genic, poor-prognosis factor in most tumor types, but its functional impact is clearly dependent on tumor type and the surrounding tissue microenvironment.

The molecular and cellular mechanisms by which CD26 modulates prognosis in solid tumors remain incompletely understood. Our multidimensional analyses and in vitro experiments suggest that CD26 may operate along a unified axis of invasion remodeling–immune regulation–prognostic effect. In terms of invasion and epithelial-mesenchymal transition (EMT), CD26 expression was associated with higher tumor grade stage, more invasive tumors and was highly expressed in invasive breast cancer cells. CD26 inhibitors significantly suppressed breast cancer cell proliferation and invasion while sparing MCF-10A cells, indicating that breast cancer cells may be more dependent on CD26-related activity for these phenotypes. Notably, our validation relied on an inhibitor, these data mainly inform potential contribution of CD26 catalytic activity to tumor cells, while non-enzymatic functions of CD26 and off-target effects cannot be excluded. Pan-cancer correlation analysis revealed a strong association between CD26 and MMP9, a key EMT-related factor, suggesting that CD26 may promote cell migration and invasion by regulating MMP-mediated matrix degradation and ECM remodeling. This is consistent with prior studies implicating CD26 in malignant transformation and invasion [24, 28], such as in malignant pleural mesothelioma where CD26 promotes cell motility and invasion by upregulating membrane-associated components [29].

At the immune microenvironment level, CD26 expression was associated with immune infiltration in approximately 89.47% of tumor types and was significantly correlated with CD4+ and CD8+ T cells as well as CAFs. CAFs secrete multiple pro-inflammatory cytokines that promote tumor cell proliferation, resist apoptosis, and induce EMT, which is considered an important mediator to enhance tumor invasion and immune evasion [30]. Our findings indicate that CD26 may drive EMT and MMP9 upregulation by promoting CAF enrichment and activation, supporting a putative "CD26-CAF-MMP9-EMT" pro-invasive axis. In parallel, CD26 as a surface protease may modulate the activity and gradient distribution of various chemokines, including CXCL12 [28, 31], and affect the recruitment and positioning of lymphocytes within the tumor milieu. CD26 high expression T cells have been reported to show higher chemokine receptor expression, stronger cytotoxicity and resistance to apoptosis [32]. This dynamic balance between "highly responsive CD26hi T cells" and "highly invasive CD26 high tumor cells" may determine the prognostic direction of CD26 in different tumors. When CD26-driven effects from tumor cells and CAFs dominate, tumors exhibit increased invasiveness and poorer outcomes. When the CD26 effector cells are high and can effectively infiltrate the tumor, the "tumor suppressor" phenotype with relatively improved prognosis may appear.

Based on these results, we propose a putative unified model in which CD26, through its protease activity and signal functions, drives ECM remodeling and EMT, reshapes chemokine networks and immune cell infiltration patterns, and forms an interaction network with CAFs and T cell subsets. The balance of this “CD26-invasion-immunity” axis in different tumor types and microenvironmental contexts may underlie the bidirectional prognostic associations observed across cancers. This model provides a biological framework for understanding the context-dependent bidirectional roles of CD26 in pan-cancer.

From a clinical perspective, this study suggests that CD26 has potential diagnostic and prognostic value across multiple tumor types. CD26 expressions in most solid tumors are significantly higher than that in paired normal tissues, indicating that CD26 may be helpful to distinguish tumors from normal tissues, particularly in cancers where the expression difference is pronounced. The association between CD26 expression and TNM stage in THCA, KIRP, and PRAD, together with its inclusion in a gene panel for differentiating benign and malignant thyroid nodules [25], supports its potential as a complementary marker in risk stratification. The experimental results of CD26 in breast cancer show that CD26 inhibition significantly reduced the proliferation and invasion of invasive breast cancer cells while sparing normal mammary epithelial cells, highlighting its potential as a therapeutic target. Given that CD26 inhibitors are already in clinical use for metabolic diseases, drug repurposing and combination therapies might be considered. Moreover, the close associations between CD26 and various immune cell infiltrates and T-cell subsets suggest that, in an era where immune checkpoint inhibitors face efficacy ceilings and resistance issues [3, 33–39], CD26 may represent a promising immunoregulatory target. Therapeutically, CD26 modulation might simultaneously attenuate tumor cell and CAF-mediated immunosuppression and invasion, and augment the antitumor activity of CD26hi effector T cells, thereby improving the overall effectiveness of cancer immunotherapy.

Several limitations of the study need to be emphasized. First, the pan-cancer analyses relied on transcriptome and clinical data from public databases, which is a retrospective study, and it is difficult to completely avoid dataset bias such as unbalanced sample size, different inclusion criteria, different sequencing platforms, and batch effect. The sample size of some tumor subtypes is limited, which may affect the statistical power and robustness of the conclusions. Second, bulk RNA-seq cannot precisely distinguish the origin of CD26 in tumor cells, immune cells and stromal cells, and it is also difficult to resolve the spatial structure information, which limits the fine understanding of the role of CD26 in different cell populations and the heterogeneity of the tumor microenvironment. Third, although we observed significant correlations between CD26 and MMP9, of EMT markers and immune infiltration, and preliminarily confirmed the regulatory role of CD26 on proliferation and invasion in breast cancer cells, the current results are mainly correlation and in vitro phenomena, which are not enough to prove causality and CD26-CAF-MMP9-EMT axis. The enzymatic activity of CD26 was not directly measured, we cannot conclusively determine whether the observed in vitro effects are strictly dependent on CD26 enzymatic activity, while the dual function of CD26 as a protease and a receptor have not been systematically verified at the experimental level. Its enzyme activity dependence and direct interaction with chemokines, CAFs and T cell subsets have not been systematically verified. Our study primarily utilized the luminal breast cancer model MCF-7, which was selected based on its high basal CD26 expression, to establish a clear proof-of-concept for the effects of alogliptin. It is important to note that the significant molecular heterogeneity across breast cancer subtypes may limit the direct generalizability of our findings to other subtypes, such as triple-negative breast cancer. Future studies are warranted to validate the therapeutic potential of CD26 inhibition in a broader panel of cell lines representing different subtypes and in relevant in vivo models.

Future studies should incorporate larger, multi-center prospective cohorts and independent validation sets to confirm the diagnostic and prognostic value of CD26. Single-cell transcriptome, spatial transcriptome, multiple immunohistochemistry or imaging mass spectrometry technology is more likely to systematically describe CD26+ cell population and its interaction network at the cellular and spatial scales. Co-culture systems and in vivo models will be critical for dissecting the mechanisms of CD26–immune cell interactions and for comprehensively evaluating the impact of CD26-targeted therapies on tumor burden, metastasis, immune infiltration, and systemic safety.

In summary, this study provides a pan-cancer overview of CD26 expressions, clinical association, immunological features and functional assays in breast cancer cells, and proposed a unified model linking CD26 to tumor invasion, immune microenvironment and prognosis. Although further mechanistic and clinical studies are required, our findings provide a conceptual and experimental basis for considering CD26 as a diagnostic and therapeutic target in breast cancer and other CD26-high malignancies and suggest new avenues for refining current immunotherapy strategies and developing novel combination regimens.

Conclusions

This study provides a comprehensive multi-omics and experimental characterization of CD26 across cancers. CD26 is broadly upregulated in highly aggressive tumors, and is associated with changes in breast cancer cell proliferation and invasion in vitro, with limited effects observed in normal mammary epithelial cells. Furthermore, CD26 expression was associated with advanced-stage tumors and poorer clinical outcomes. This study supports a potential model linking CD26 expression with immune infiltration patterns involving invasion, immune contexture and clinical outcome. These findings highlight CD26 may represent a putative, context-dependent biomarker candidate that holds promise for tumor risk stratification and as a candidate target for therapeutic intervention, particularly in invasive breast cancer and other CD26-overexpressing malignancies.

Supplementary Information

12885_2026_15656_MOESM1_ESM.pdf (22MB, pdf)

Supplementary Material 1: Supplementary Figures. Figure S1-S5.

Acknowledgements

Not applicable.

Abbreviations

ACC

Adrenocortical Carcinoma

ADA

Adenosine deaminase

BLCA

Bladder Urothelial Carcinoma

BRCA

Breast Invasive Carcinoma

CAF

Cancer-associated fibroblast

CCK8

Cell Counting Kit-8

CESC

Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma

CHOL

Cholangiocarcinoma

COAD

Colon Adenocarcinoma

COAD-READ

Colon adenocarcinoma/Rectum adenocarcinoma Esophageal carcinoma

DC

Dendritic cell

DLBCL

Lymphoid Neoplasm Diffuse Large B-cell Lymphoma

EGF

Epidermal growth factor

ELISA

Enzyme-Linked Immunosorbent Assay

EMT

Epithelial-to-mesenchymal transition

ERK

Extracellular-signal regulated kinase

ESCA

Esophageal Carcinoma

FAPα

Fibroblast activation protein α

bFGF

Basic fibroblast growth factor

GAPDH

Phosphoglyceraldehyde dehydrogenase

GBM

Glioblastoma Multiforme

GBMLGG

Glioma

GEPIA2

Gene Expression Profiling Interactive Analysis 2

GO

Gene Ontology

GTEx

The Genotype-Tissue Expression project

HNSC

Head and Neck Squamous Cell Carcinoma

HPA

The Human Protein Atlas

IOBR

Immuno-Oncology Biological Research

KEGG

Kyoto Encyclopedia of Genes and Genomes

KICH

Kidney Chromophobe

KIPAN

Pan-kidney cohort (KICH + KIRC + KIRP)

KIRC

Kidney Renal Clear Cell Carcinoma

KIRP

Kidney Renal Papillary Cell Carcinoma

LAML

Acute Myeloid Leukemia

LGG

Brain Lower Grade Glioma

LIHC

Liver Hepatocellular Carcinoma

LUAD

Lung Adenocarcinoma

LUSC

Lung Squamous Cell Carcinoma

MEK

Mitogen-activated protein kinase

MESO

Mesothelioma

MMP9

Matrix metallopeptidase 9

MMPs

Matrix metalloproteinases

MSI

Microsatellite instability

NB

Neuroblastoma

NEO

Neoantigen

OS

Overall survival

OV

Ovarian Serous Cystadenocarcinoma

PAAD

Pancreatic adenocarcinoma

PCPG

Pheochromocytoma and Paraganglioma

PRAD

Prostate Adenocarcinoma

PD-1

Programmed cell death 1

PD-L1

Programmed Cell Death Ligand 1

qRT-PCR

Quantitative reverse transcription polymerase chain reaction

READ

Rectum Adenocarcinoma

SARC

Sarcoma

SKCM

Skin Cutaneous Melanoma

STAD

Stomach Adenocarcinoma

STES

Stomach and Esophageal carcinoma

STRING

Search Tool for the Retrieval of Interacting Genes/Proteins

SUM159PT

Human SUM-159PT Breast Cancer Cell Line

TCGA

The Cancer Genome Atlas

TGCT

Testicular Germ Cell Tumors

THCA

Thyroid Carcinoma

THYM

Thymoma

TIMER

Tumor Immune Estimation Resource

TMB

Tumor mutational burden

TNM

Tumor, node and metastasis

UCEC

Uterine Corpus Endometrial Carcinoma

UCS

Uterine Carcinosarcoma

UVM

Uveal Melanoma

WB

Western Blot

WHO

World Health Organization

WT

High-Risk Wilms Tumor

Authors’ contributions

Conceptualization, validation, visualization and writing—original draft: Runyi Tao and Bohao Liu; Methodology: Jizhao Wang and Qiuyu Gong; Data curation: Jingyi Zhang, Xingzhuo Zhu and Yixing Li; Software and formal analysis: Yilong Zhao and Zhiyu Wang; Investigation: Jiaqi Huang and Chenrong Zhang; Writing—review and editing: Shan Gao. Funding acquisition, project administration, resources and supervision: Guangjian Zhang. All authors have read and approved the final manuscript. All authors have read and approved the final manuscript.

Funding

This work was supported by grants from Noncommunicable Chronic Diseases-National Science and Technology Major Project (2025ZD0548503), The Key Research and Development Program of Shaanxi (2022ZDLSF01-04), the Research of the Shaanxi Administration of Traditional Chinese Medicine (2022-SLRH-LJ-009), and the Clinical Research Award of the First Affiliated Hospital of Xi'an Jiaotong University, China (No. XJTU1AF-CRF-2023–006).

Data availability

The data we used from the HPA dataset (https://www.proteinatlas.org/) and the TCGA program (https://portal.gdc.cancer.gov/) are publicly available. Any additional information required to reanalyze the data reported in this paper is available from the corresponding author, Dr. Guangjian Zhang (michael8039@xjtu.edu.cn) upon reasonable request.

Declarations

Ethics approval and consent to participate

Data from the HPA dataset and the TCGA program are publicly available, so we did not require informed patient consent.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Runyi Tao and Bohao Liu have contributed equally to this study.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

12885_2026_15656_MOESM1_ESM.pdf (22MB, pdf)

Supplementary Material 1: Supplementary Figures. Figure S1-S5.

Data Availability Statement

The data we used from the HPA dataset (https://www.proteinatlas.org/) and the TCGA program (https://portal.gdc.cancer.gov/) are publicly available. Any additional information required to reanalyze the data reported in this paper is available from the corresponding author, Dr. Guangjian Zhang (michael8039@xjtu.edu.cn) upon reasonable request.

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