Abstract Objective DPH2, also known as DPH2L2, is one of two human genes similar to yeast dph2. One DPH2 variant has been linked to diphthamide syndrome, a disorder affecting ribosome function. While studies on DPH2 in a single cancer type have been documented, no comprehensive investigations of DPH2 across pan-cancer have been reported, its role in tumor pathogenesis and development remains unclear. Methods The predictive significance and immune and biological roles of DPH2 in 33 different cancer types were investigated. We conducted a comprehensive analysis of DPH2 in pan-cancer using various bioinformatics tools, including expression, prognosis, its association with immune infiltration, cell death, methylation, and many other aspects. In addition, qRT-PCR and immunohistochemistry experiments confirmed DPH2 expression in prostate adenocarcinoma (PRAD) tissues, DPH2 biological function in PRAD was assessed using in vitro experiments, and used immunofluorescence to validate the proteins associated with DPH2. Results The DPH2 expression was high in most tumors and showed significant correlations with OS and PFI. Our experimental findings confirmed that DPH2 is highly expressed in PRAD, while DPH2 knockdown inhibited prostate cancer cell proliferation, invasion, and migration. Furthermore, our data suggest that DPH2 may significantly influence immune cell infiltration. DPH2 was significantly correlated with cell death-related genes. DPH2 can influence cancer progression through changes in DNA methylation levels, or N6-methyladenosine site modification. GSEA and GSVA revealed that DPH2 levels were significantly associated with enrichment for oncogenic and immune-related pathways. Drug sensitivity analysis revealed that the elevated DPH2 expression is linked to development of resistance against numerous anticancer medications. Conclusion DPH2 has potential as a novel prognostic biomarker that may significantly impact tumor onset and progression. Consequently, DPH2 could serve as a target for new cancer treatments. Supplementary Information The online version contains supplementary material available at 10.1007/s12672-025-01924-6. Keywords: DPH2, Pan-cancer, Prognosis, PRAD, Immune microenvironment, Cell death Introduction Cancer is a global disease, but its effects vary among different countries. Approximately 1,918,030 cancer cases were detected in 2022 in the United States, amounting to a mean 5,250 new cases daily. Furthermore, it was projected that approximately 609,360 individuals would succumb to cancer in 2022, amounting to almost 1,700 fatalities each day. For men in the United States, the deadliest types of cancer are lung, prostate, and colorectal, while for women, the most fatal cancers are lung, breast, and colorectal [[32]1]. In China, 4,820,000 new cancer cases and 3,210,000 cancer deaths were estimated to have occurred in 2022, and lung cancer is the leading cause of death among both males and females, with elevated occurrence rates of colorectal and prostate cancers in males, plus an additional seven types of cancer in females [[33]2]. Worldwide, prostate cancer is a dominant form of malignant disease among men, ranking second in terms of mortality. Moreover, the incidence and mortality of black Americans was twice as high as that of white Americans, with the lowest incidence rates found in Americans with Asian and/or Pacific Islander backgrounds [[34]1]. Cancer development involves multiple layered steps, including activation of oncogenes, inhibition of tumor suppressor genes, alterations of genetic stability and epigenetic modifications, and aberrant cell signaling, causing abnormal proteins and stress signals to be produced [[35]3, [36]4]. With continuous improvement of methods used for genomic research, the molecular biology of tumors has gradually progressed into pan-cancer research, which involves analyzing genomes of several different types of tumors simultaneously and identifying their common characteristics. Pan-cancer research also incorporates data from different sources, which can help improve our understanding of tumor characteristics, as well as the roles of genes in different tumors and/or their associations with specific tumor types. Further, novel broad-spectrum targets may be identified for application in clinical diagnosis and treatment based on the information generated from pan-cancer analyses [[37]5]. The Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas (TCGA) are major cancer databases of epigenome, genome, proteome, and transcriptome level data from analyses of various human cancer tissues and cell lines, representing important resources for pan-cancer investigations [[38]6, [39]7]. Diphthamide biosynthesis 2 (DPH2), also known as DPH2L2, is closely related to the yeast gene dph2. In humans, DPH2 is located on chromosome 1p34 [[40]8], and its protein was identified by a global protein localization analysis as present in the cytoplasm [[41]9]. DPH2 encodes a 534-residue protein, which lacks any conserved domains that could indicate a specific function [[42]10]. Diphthamide, found in elongation factor 2 (EF2), is a histidine residue that undergoes post-translational modification, and serves as the target for diphtheria toxin ADP-ribosylation, an essential process in the initial stage of diphthamide biosynthesis [[43]11, [44]12]. Diphthamide modification is conserved among all eukaryotes and archaea, and has a key role in ribosomal protein synthesis [[45]11, [46]13]. Diphthamide is thought to modulate translational decisions at paused ribosomes in human cells (i.e., stall vs. continue vs. termination). Moreover, Furthermore, the absence of diphthamide impacts the tRNA production of aminoacyl synthetases and reduces the incorporation of selenocysteine at recoded SECI codons, thereby influencing selenium-containing protein synthesis. Few studies have focused on DPH2 to date. A patient with congenital heart disease, macrocephaly, delays in development, short stature, and dysmorphic craniofacial features who had a compound heterozygous DPH2 mutation was reported. The authors proposed to classify this condition as a novel ribosomal disorder, referred to as “diphthamide-deficiency syndrome” [[47]12]. The chromosome 1p34 region containing DPH2 is frequently lost or abnormally expressed in human malignancies [[48]14]. For example, cytogenetic analyses have shown underrepresentation of 1p34 in primary hepatocellular carcinoma [[49]15], while other cytogenetic and allele-type studies have revealed loss of the 1p34 region in meningiomas [[50]16], pheochromocytomas [[51]17], malignant peripheral nerve sheath tumors [[52]18], breast cancer [[53]19], and glioblastomas [[54]20]. Furthermore, human myelodysplasia, marginal zone B-cell lymphoma, and breast cancer are reported to exhibit chromosomal rearrangements where 1p34 is a breakpoint [[55]21, [56]22]. Loss of heterozygosity at 1p34 was also detected in primary and metastatic cutaneous melanoma [[57]23]. Moreover, examination of chromosomal structure in over 3000 cancerous solid tumors revealed the presence of four areas of deletion on the shorter section of chromosome 1, one of which is specifically located at 1p34 [[58]24]. Collectively, the aforementioned studies indicate that DHP2 may have a significant impact on the generation and progression of diverse malignancies. No role for DPH2 in tumors has yet been demonstrated experimentally, nor has it been studied in tumors. Moreover, the mechanisms by which DPH2 may regulate tumor development and pathogenesis remain unclear. Therefore, examination of the molecular mechanism and regulatory role of DPH2 using pan-cancer datasets could offer novel approaches for the clinical management of malignancies. Through comparison and differentiation of various forms of cancer, pan-cancer analysis can offer valuable perspectives on cancer prevention and personalized treatment [[59]25]. We analyzed DPH2 expression in malignant tumors and its relationship to cancer prognosis using data from TCGA and Genotype Tissue-Expression (GTEx) databases. We also used different databases to analyze the role of DPH2 in pan-cancer in terms of DNA methylation, genomic enrichment analysis, immune infiltration, N6-methyladenosine (m6A) modification, single-cell analysis, and association with cell death-related genes, etc. Finally, to further understand the potential clinical applications of DPH2, we verified the expression and biological function of DPH2 in laboratory experiments, using prostate cancer as an example. Materials and methods Data acquisition and difference analysis TCGA database ([60]https://portal.gdc.cancer.gov) is an important database of genetic information on cancer. A pan-cancer dataset was initially created by collecting data on mRNA expression and SNPs for 33 different tumor types. We downloaded DPH2 gene expression data from GTEx ([61]http://commonfund.nih.gov/GTEx) for various groups and integrated them with TCGA data. Subsequently, variations in DPH2 expression across various cancer types were assessed. Furthermore, information regarding specific tumor cell lines was obtained from the CCLE ([62]https://portals.broadinstitute.org/ccle/) database, and DPH2 expression in different tumor cells was analyzed. In addition, we computed the correlation between the expression of DPH2 and the stage of the tumor. We validated DPH2 expression in prostate adenocarcinoma (PRAD) by Gene Expression Omnibus (GEO) databases ([63]GSE46602 and [64]GSE32571) (https: //[65]www.ncbi.nlm.nih.gov/geo/) [[66]26, [67]27]. Relationship between DPH2 and cancer prognosis We investigated the correlation between DPH2 expression and patients' overall survival (OS) and progression free interval (PFI) using data from the Xena database ([68]https://xenabrowser.net/datapages/). Survival analyses were performed using the Kaplan–Meier method and log-rank test for each type of cancer (P < 0.05). Survival curves were plotted using the R packages “survival”, “survminer”. R packages “survival” and ‘forestplot’ to plot forests. Correlations between DPH2 expression and immune cell infiltration, tumor microenvironment (TME) and cell death genes We employed Cell-Type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), a metagene tool for predicting immunocyte phenotypes, and analyzed RNA-seq data from 33 types of cancer patients in different subgroups. The purpose of this study was to determine the relative ratio of immune infiltrating cells and examine associations between DPH2 expression and immune cell content. The correlation between DPH2 levels and cancer immune cell infiltration levels was investigated in our research using R packages such as “ggplot2”, “ggpubr” and “cowplot”. P < 0.05 was deemed statistically meaningful. Moreover, We analyzed the correlation of DPH2 with cell death-related genes, including autophagy, ferroptosis and pyroptosis, hypoxia, DNA repair,etc. we used the TISIDB website ([69]http://cis.hku.hk/TISIDB) to investigate the potential relationships between DPH2 expression and immunoregulatory factors, including the major histocompatibility complex(MHC), chemokines, chemokine receptor proteins, immune checkpoint proteins, immuno-inhibitors, and immuno-stimulators. TME index values were obtained from previously published data [[70]28]. Additionally, Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data (ESTIMATE) developed by Yoshihara et al. [[71]29] was utilized to evaluate the degree of immune infiltration, the correlation between DPH2 expression and/or stromal cell scores and immune was assessed using the R packages, ‘estimate’ and ‘limma’. Analysis of drug sensitivity Through the Cellminer database ([72]https://discover.nci.nih.gov/cellminer/home.do) published by the Center for Cancer Research of the National Cancer Institute (NCI) [[73]30], we obtained drug sensitivity and RNA-seq data of gene expression for the NCI-60 cancer cell line to investigate the correlation between DPH2 levels and sensitivity to common antitumor drugs. DPH2 pathway enrichment and co-expressed genes analysis We used the STRING database to analyze DPH2-interacting proteins, with conditions set to “textmining” and “experimental”, “medium confidence (0.400)”. Meanwhile, we obtained TCGA-PRAD expression data from the TCGA database, calculated the spearman correlations of DPH2 with other molecules and combined it with the results from the STRING database, and chose the one with the highest correlation coefficient for the subsequent experiments. We further performed pathway enrichment analysis by gene set variation analysis (GSVA) and Gene set enrichment analysis (GSEA), using the R packages “limma”, “org.Hs.eg.db ‘ and’clusterprofiler” and “enrichplot”, respectively. We set significance thresholds of P < 0.05 and q < 0.25 and selected the reference gene set “c2.all.v2022.1.Hs.symbols.gmt” from the MSigDB database ([74]https://www.gsea-msigdb.org/gsea/msigdb). Methylation analysis of DPH2 We used the “Mutations” module of the Gene Set Cancer Analysis (GSCA) platform ([75]https://guolab.wchscu.cn/GSCA/#/) to analyze DPH2 methylation differences in 33 different cancer tumors versus normal samples. The connection between DPH2 methylation and mRNA expression was also analyzed, and the prognostic differences (DFI, DSS, OS, and PFS) due to DPH2 methylation levels in different cancer types were explored. m6A modification analysis: To analyze the correlation between DPH2 and N6-methyladenosine (m6A) regulators and the expression level of DPH2 when m6A regulators are mutated, we performed the above analyses by using TIMER 2.0's “Exploration-Gene_Corr” and “Gene_Mutation” panel to perform the above analysis. Furthermore, in full transcript mode, the sequence-based RNA adenosine methylation site predictor (SRAMP) network tool was utilized to predict m6A modification sites in DPH2 [[76]31]. Single-cell analysis of DPH2 To analyze the correlation between DPH2 and the functional states of 14 types of cancer, we employed single-cell sequencing data from the CancerSEA dataset ([77]http://biocc.hrbmu.edu.cn/CancerSEA/). Furthermore, we applied TISCH ([78]http://tisch1.comp-genomics.org/) to analyze the DPH2 mRNA expression levels in immune cells of different cancer types to explore the correlation between DPH2 and immune cells and stromal cells in the microenvironments of various cancer cells,, aiming at elucidating the complex components of the TME at the single-cell level [[79]32]. Correlations of DPH2 expression levels with tumour mutation burden (TMB) and microsatellite instability (MSI) TMB refers to the total number of somatic gene coding errors, base substitutions, and insertions or deletions detected per megabase [[80]33, [81]34]. Patient outcomes [[82]35] are associated with mismatch repair deficiency, the underlying cause of MSI. TMB and MSI scores were calculated from TCGA and previously published results [[83]36]. To generate the results, we used the R packages “Fmsb”, “limma” and “dplyr”. Genetic alteration analysis Through the online cBioPortal database ([84]http://www.cbioportal.org/), we obtained alterations in DPH2 states in various types of cancer patients [[85]37]. The frequency and type of mutations, as well as copy number alterations (CNA), were assessed in TCGA malignancies utilizing the “Cancer Types of Summar” module. Additionally, the association between DPH2 expression and copy number variation (CNV) in 26 cancers was investigated using datasets obtained from the UCSC database and the GDC database and processed through the GISTIC software [[86]38]. Nomogram model construction Following multivariate regression analysis, a nomogram was generated by incorporating the expression of DPH2 and observed clinical symptoms. The relationships among variables in the prediction model were represented by scaled line segments on the same plane and specific ratios. Values were determined by assigning scores, based on the outcome variable is affected by each of the influencing factor using a multifactor regression model. After adding individual scores, prediction values were calculated. Sample preparation and cell culture Specimens were collected from 30 individuals diagnosed with prostate cancer and matched normal tissue, obtained from the Department of Urology of the First Affiliated Hospital of Chongqing Medical University. Tissue samples were rapidly frozen in liquid nitrogen, then stored at −80 °C. In addition, tissue sections from 75 cases of prostate cancer and 40 cases of benign prostatic hyperplasia were collected from the Department of Pathology of Chongqing Medical University. Demographic and clinical characteristics, such as tumor stage and Gleason score, were also collected. This study is in accordance with the Declaration of Helsinki. The Ethics Committee of the First Affiliated Hospital of Chongqing Medical University reviewed and approved all experimental procedures (Research ethics approval number: 2021-660). Furthermore, all participants read and signed informed consent prior to participation. The human prostate cancer cell line, 22RV1, was obtained from American Type Culture Collection (ATCC; Manassas, VA). Cells were cultured in RPMI 1640 medium (Gibco; Thermo Fisher Scientific, USA) containing 10% fetal bovine serum (FBS) (Gibco; Thermo Fisher Scientific, USA) at 37°C and 5% CO[2]. Cell transfection DPH2-targeting short interfering RNA (siRNA) and control siRNA were purchased from GenePharma (Shanghai, China). The transfection process was carried out by utilizing Lipofectamine 2000 reagent (Invitrogen, Carlsbad, CA, USA), following the guidelines provided by the manufacturer. Cell function experiment Cell counting Kit-8 (CCK-8) to verify cell proliferation capacity. After resuspending and counting the cells using a cell counter, cell suspensions were adjusted to contain 2 × 10^3 cells per well seeded in 96-well culture plates (n = 5 wells per group) and analyzed at five time points (0, 24, 48, 72, and 96 h). Once every 24 h, CCK-8 reagent (10 μL) (Beyotime, Jiangsu, China) was introduced into each well and incubated for 1 h. The absorbance at 450 nm was then measured in each well using a microplate reader. Transwell assay for cell invasion assays, Matrigel (ABWbio, Shanghai, China) and all related consumables need to be pre- chilled at 4 °C before use. Matrigel (60 μL) was added to the upper wells of 8 μm Transwell chambers (Biosharp, Shanghai, China). Once the Matrigel solidified, the upper chambers were added with 200 μL serum-free medium, containing a cell count of 2 × 10^4, and the lower chambers were added with 600 μL medium containing 10% FBS. Cells were wiped from the upper membrane using a cotton swab after 24 h, and those on the lower membrane were fixed using methanol and stained with 0.1% crystal violet (Beyotime, Jiangsu, China). Photographs were taken of five randomized areas in each chamber and cell counts conducted in parallel. Scratch test to verify cell migration ability. Cells (2 × 10^5) were added into each well of 6-well plates. Once the cells reached > 95% confluence, a scratch was made through them using the end of a 10-µl pipette tip. Following three washes with PBS, serum-free medium was added to the cells. Images of three areas were captured in each replicate well at two time points (0 and 24 h) and relative cell migration rates calculated. Quantitative real-time polymerase chain reaction (qRT-PCR) Total RNA samples were extracted using M5 SuperPure Total RNA Extraction Reagent (Thermo Fisher Scientific), following the manufacturer’s protocol. A reverse transcription kit (TaKaRa, Dalian, China) was used to generate cDNA. PCR assays included 5 μL TB Green^® Fast qPCR Mix, 0.2 μL forward and reverse primers (100 nM), 2 μL cDNA template (500 ng), and 2.6 μL nuclease-free water and were conducted using a program consisting of 95 °C, 30 s, followed by 40 cycles of 95 °C, 5 s and 60 °C, 10 s. Relative mRNA expression levels were calculated using the 2^−ΔΔCt approach. GAPDH served as an internal control. Primer sequences were as follows: DPH2, forward 5'-ACCTCACACATTATGCGGACTTA-3', reverse 5'-GCCAAGCTTCCATGATCATTTGA-3'; GAPDH, forward 5'-GGAGTCCACTGGCGTCTTCA-3', reverse 5'-GTCATGAGTCCTTCCACGATACC-3'. Immunohistochemistry (IHC) A PV-9000 kit (Beijing Zhongshan Golden Bridge Biotechnology, Co., Ltd., Beijing, China) was used for IHC staining. First, tissue slides were baked at 55 °C for 2 h. Then, they were submerged in xylene to remove wax for 1 h, followed by 5 min in each step of a gradient alcohol hydration process. Antigen was then retrieved by boiling in sodium citrate buffer (pH 6.0) for 15 min. Next, the slides were treated with an endogenous peroxidase blocker for 10 min at room temperature, followed by incubation with DPH2 antibody (1:100) (Proteintech, 12367-1-AP) for 16 h at 4 °C. Incubate at 37 °C for 20 min with reaction enhancement solution. Subsequently, they were incubated with secondary antibodies for 20 min at 37 °C. Diaminobenzidine (Beijing Zhongshan Golden Bridge Biotechnology) was used to develop sections, while Mayer’s modified hematoxylin was used to counterstain the nuclei. The results were analyzed using Image J software, and GraphPad Prism version 8.0 was used for statistical analysis of the results. Immunofluorescence Cells were seeded into petri dishes and incubated at 37 °C for 48 h. Cells were washed three times with PBS for 3 min each. 4% paraformaldehyde was used to fix the cells for 30 min, and the cells were permeabilized with 0.5% Triton X-100 at room temperature for 20 min. Cells were then closed with 5% bovine serum albumin (BSA) for 1 h. DPH2 primary antibody (1:50) (Proteintech, 12367-1-AP) was added and incubated at 4°C overnight. The cells were rinsed 3 times using PBS, fluorescent secondary antibody was added and incubated at room temperature for 1h. Repeat the steps for appeal incubation of primary and secondary antibodies, the primary antibody being EEF2 (20107-1-AP). DAPI was added dropwise to re-stain the nuclei of the specimen, and the cells were protected from light for 5 min at room temperature. After rinsing the cells with PBS and drying naturally, The cells were observed with a fluorescence microscope (OLYMPUS, Japan) and photographed. Statistical analysis We used two-sided t-tests for analyzing the differential expression of DPH2 in pan-cancer, as well as the use of Wilcoxon's rank-sum test to analyze the expression levels of DPH2 in prostate cancer tissues compared to normal tissues in the GEO data. In analyzing the differences in DPH2 expression across samples from various clinical stages of each tumor, unpaired Student’s t-tests were employed for two comparisons of significance, while ANOVA was used to assess differences among multiple groups of samples. A Cox proportional hazard regression model was used to calculate hazard ratio and corresponding 95% confidence interval values. Kaplan–Meier analysis was employed to examine associations between DPH2 expression and patient survival rates. The Pearson correlation coefficient was used to assess the correlation between two variables, including the relationship between DPH2 and immune-related genes, as well as cell death-related genes. Additionally, Pearson correlation coefficients were used to analyze the immune cell infiltration scores of DPH2 in each tumor. The results of IHC and cellular experiments were analyzed using ImageJ software, cell function experiments were performed with valid biological replicates, Student's t-test was used to analyze the data between groups, and error bars are expressed as median ± standard deviation. All statistical analyses or graphs were conducted using R version 4.0 and GraphPad Prism version 8.0.2. P < 0.05 was considered significant. Results Pan-cancer expression analysis of DPH2 DPH2 expression analysis was initially conducted in 33 types of human cancer using TCGA and GTEx datasets. DPH2 exhibited high expression in 22 distinct tumor types, encompassing: BLCA, CESC, PRAD, etc. (Fig. [87]1A). Next, we arranged the levels of DPH2 expression in different cancer cells from lowest to highest. As illustrated in Fig. [88]1B, DPH2 expression were highest in myeloma, neuroblastoma, lung cancer, thyroid cancer, and bladder cancer. We also investigated the relationships between DPH2 expression and tumor stage and observed strong associations in ACC, LIHC, and READ (Fig. [89]1C). Fig. 1. [90]Fig. 1 [91]Open in a new tab Pan-cancer analysis of DPH2 expression. A Comparison of DPH2 expression in tumor and normal samples. B DPH2 expression levels in various cancer cell lines, ranked low to high. C Relationship between DPH2 expression and tumor stage. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 qRT-PCR and IHC show that DPH2 is highly expressed in PRAD Next, we examined the expression levels of DPH2 in PRAD and noncancerous tissues using qRT-PCR and IHC. Significantly higher DPH2 levels were detected in PRAD tissues than in noncancerous tissues (Fig. [92]2A–C). Our experimental results were confirmed by data from the GEO database, which also showed that DPH2 is highly expressed in PRAD (Fig. [93]2D). We assessed the correlation between DPH2 expression levels and various clinical features by IHC results and found that DPH2 expression levels were significantly correlated only with Gleason score (Table [94]1). Fig. 2. [95]Fig. 2 [96]Open in a new tab DPH2 expression in prostate adenocarcinoma (PRAD) and benign tissues. A–C Immunohistochemistry suggesting that DPH2 is highly expressed in prostate cancer. D Data from the GEO database showing that DPH2 expression is higher in PRAD than that in normal tissue. **P < 0.01, ****P < 0.0001 Table 1. Analysis of correlations between DPH2 expression levels and clinicopathological variables in patients with prostate cancer Characteristics Low DPH2 expression High DPH2 expression P value n 37 38 T.stage, n (%) 0.174 2 26 (34.7%) 21 (28%) 3 6 (8%) 5 (6.7%) 4 5 (6.7%) 12 (16%) N.stage, n (%) 0.330 N0 34 (45.3%) 31 (41.3%) N1&N2 3 (4%) 7 (9.3%) M.stage, n (%) 0.069 M0 37 (49.3%) 33 (44%) M1 0 (0%) 5 (6.7%) Gleason score, n (%) 0.001 7 18 (24%) 19 (25.3%)  ≤ 6 10 (13.3%) 0 (0%)  ≥ 8 9 (12%) 19 (25.3%) Age, n (%) 0.738  > 60 34 (45.3%) 33 (44%)  ≤ 60 3 (4%) 5 (6.7%) PSA (ng/ml), n (%) 0.563  > 20 20 (26.7%) 18 (24%)  ≤ 20 17 (22.7%) 20 (26.7%) [97]Open in a new tab PSA prostate-specific antige Biological function of DPH2 in prostate cancer To determine whether DPH2 expression affects cancer cell proliferation and metastasis, we selected the prostate cancer cell line, 22RV1, which expressed DPH2 at the highest level, based on bioinformatics analysis (Fig. [98]3A). We knocked down DPH2 in 22RV1 cells using siRNA and verified the effect by qRT-PCR 48 h after siRNA transfection (Fig. [99]3B). After DPH2 knockdown, cell proliferation assays showed significant inhibition of 22RV1 cell growth after 48 h and longer cultivation (Fig. [100]3C). Further, we investigated the invasion of 22RV1 cells with DPH2 knocked down using transwell assays. Compared with control 22RV1 cells, 22RV1 cells with DPH2 knocked down showed significantly inhibited cell invasion (Fig. [101]3D). In addition, the migration ability of 22RV1 cells was significantly inhibited after DPH2 knockdown, relative to that of control cells (Fig. [102]3E). Fig. 3. [103]Fig. 3 [104]Open in a new tab Knockdown of DPH2 inhibits cell proliferation, migration and invasion in vitro. A Analysis of the CELL database showing that DPH2 was most highly expressed in 22RV1 among various prostate cancer cell lines. B qRT-PCR validation of DPH2 knockdown efficiency in 22RV1 cells. C CCK-8 assay showing that DPH2 knockdown inhibits 22RV1 cell proliferation. D Transwell assay revealing significant effects of DPH2 knockdown on 22RV1 cell invasion. E DPH2 knockdown significantly inhibited 22RV1 cell migration. **P < 0.01, ***P < 0.001, ****P < 0.0001 In addition, we also analyzed the genes that might be related to DPH2 through the STRING database (Fig. [105]4A), and combined with the correlation analysis of DPH2 with other molecules in prostate cancer (Table [106]2), we found that EEF2 had the highest correlation with DPH2 (Fig. [107]4B, C), and in order to further validate the correlation between DPH2 and EEF2, we used the immunofluorescence method to analyze the localization of DPH2 and EEF2. We observed the colocalization of DPH2 and EEF2 and found that they were both predominantly localized in the cytoplasm, further illustrating their relationship (Fig. [108]4D).Pan-cancer analysis of DPH2 expression and patient prognosis. Fig. 4. [109]Fig. 4 [110]Open in a new tab Analysis of genes associated with DPH2 in prostate cancer. A Proteins that interact with DPH2 analyzed by STRING. B Correlation of DPH2 with appeal protein within prostate cancer. C Correlation of DPH2 and EEF2 in prostate cancer. D Immunofluorescence detection of co-localization of DPH2 (green) and EEF2 (red) Table 2. Analysis of molecules associated with DPH2 in PRAD by the STRING database and the TCGA database (Spearman correlation analysis) Gene Cor P DPH1 0.429330221 6.914E-24 DPH5 0.505347962 7.8487E-34 DPH6 0.433536506 2.2509E-24 DPH7 0.265892597 1.7231E-09 EEF2 0.583033423 0 DNAJC24 0.299041494 8.2737E-12 GFM1 0.548045041 0 ATP6V0B 0.21228857 1.7258E-06 [111]Open in a new tab We also examined whether DPH2 expression was correlated with cancer prognosis by analyzing survival outcomes, including OS and PFI. The results showed that DPH2 expression was significantly associated with OS in ACC, LAML, PRAD and so on (Fig. [112]5A), high DPH2 expression were associated with poor OS ACC, LIHC, PRAD, etc. (Fig. [113]5B). Furthermore, DPH2 expression was strongly correlated with PFI in ACC, GBM, LGG, etc. (Fig. [114]5C), high DPH2 expression were linked to unfavorable patient prognosis in ACC, LGG, LIHC, and PRAD (Fig. [115]5D). Fig. 5. [116]Fig. 5 [117]Open in a new tab Association between DPH2 expression and cancer prognosis outcomes, including OS and PFI. A Forest plot of hazard ratio values for DPH2 associations with OS in 33 different tumor types. B Kaplan–Meier survival curves of OS of patients with ACC, LIHC, LGG, MESO, PRAD, and UCEC stratified according to DPH2 expression level. C Forest plot of hazard ratio values for DPH2 associations with PFI in 33 different tumor types. D Kaplan–Meier survival curves of PFI in patients with ACC, LGG, LIHC, and PRAD stratified according to DPH2 expression level Pan-cancer analysis of the relationship between DPH2 expression and immune cell infiltration In the CIBERSORT analysis, DPH2 expression was notably and positively correlated with follicular helper T cell infiltration in BRCA, THYM, GBM, PRAD, LIHC, UCEC, THCA, STAD, LUSC, SKCM, and significantly correlated with that of M0 macrophages in TGCT, BRCA, SARC, ACC, CESC, LUAD, BCLA, LIHC, STAD, SKCM. Additionally, DPH2 expression was significantly associated with activated CD4 memory T cells in TGCT, BRCA, THYM, SARC, PRAD, LUAD, THCA, COAD, SKCM, and significantly negatively associated with mast cells in TGCT, BRCA, THYM, SARC, ACC, PRAD, CESC, ESCA, LUAD, BLCA, LIHC, STAD (Fig. [118]6A). Fig. 6. [119]Fig. 6 [120]Open in a new tab Pan-cancer analysis of DPH2 expression and immune cell infiltration. A CIBERSORT analysis revealed correlations between DPH2 expression and infiltration of 22 different immune cell subtypes. B Pan-cancer analysis of relationships between DPH2 expression and the TME. C Relationship between TME and DPH2 in PRAD samples grouped according to high and low DPH2 expression. D Correlations between DPH2 expression and ESTIMATE scores, immune scores, and stromal scores, determined using the ESTIMATE algorithm. Positive relationships are indicated by red, while negative relationships are indicated by blue. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 The TME plays a significant role in tumorigenesis and cancer progression [[121]39, [122]40]. Thus, pan-cancer associations between DPH2 expression and changes in the TME were assessed by analyzing DPH2 expression and the TME in 33 cancers (Fig. [123]6B). DPH2 expression in PRAD was significantly correlated with TMEscore, CD_8_T_effector, Immune_Checkpoint, etc. (Fig. [124]6C). Moreover, DPH2 expression was significantly negatively correlated with the immune scores of malignancies such as ACC, CESC and PRAD according to the ESTIMATE algorithm, while in BRCA, LGG, and THYM, it was positively correlated with immune scores. Furthermore, DPH2 expression was negatively correlated with stromal scores in ESCS, LIHC, TGCT, etc.; conversely, it was positively associated with stromal scores in PCPG. Additionally, we discovered a negative correlation between DPH2 expression and ESTIMATE scores in GBM, LAML, STAD and so on, while it was positively associated with ESTIMATE scores in LGG alone. Finally, we examined associations between DPH2 expression and tumor purity. Our findings revealed that, of the 21 types of cancer, DPH2 expression was negatively correlated with tumor purity in PCPG, whereas it was positively correlated in ACC, BRCA, CESC, etc. (Fig. [125]6D). Pan-cancer analysis of DPH2 expression and cell death and tumor-regulated genes Subsequently, we investigated the correlation between DPH2 expression and genes involved in cell death-related genes in 33 tumors. This analysis encompassed the ferroptosis, pyroptosis, autophagy, hypoxia response, DNA repair, TGF-β and TNFA signaling (Fig. [126]7A–G). DPH2 levels were also significantly correlated with multiple immune-related genes, including the MHC, immune checkpoint regulation, immuno-inhibitors, immuno-stimulators, chemokines, and chemokine receptor proteins (Supplementary Fig. 1). Our findings demonstrated significant correlations between DPH2 expression and those genes. Fig. 7. [127]Fig. 7 [128]Open in a new tab DPH2 co-expression with A autophagy genes, B DNA repair genes, C ferroptosis genes, D hypoxia genes, E pyroptosis genes, F TGF-β signaling genes, and G TNFA signaling genes. Red indicates a positive relationship, and blue indicates a negative relationship. * P < 0.05, ** P < 0.01, *** P < 0.001 Pan-cancer analysis of the relationship of DPH2 expression with TMB and MSI The novel biomarkers, TMB and MSI, can be associated with response to immune checkpoint inhibitors (ICIs) [[129]41, [130]42]. Here, we examined associations between DPH2 and TMB levels and found that they were significantly associated in ACC, LGG, lung adenocarcinoma (LUAD), PRAD, sarcoma(SARC), skin cutaneous melanoma (SKCM), THYM, and uterine corpus endometrial carcinoma (UCEC) (Fig. [131]8A). A comparable examination of MSI revealed notable associations between DPH2 expression and MSI in BRCA, CESC, COAD, KIRC, LIHC, SARC, THCA and THYM (Fig. [132]8B). Fig. 8. [133]Fig. 8 [134]Open in a new tab Correlations of DPH2 expression with TMB and MSI in pan-cancer. A Correlation between DPH2 expression and TMB. B Correlation between DPH2 expression and MSI. *P < 0.05, **P < 0.01, ***P < 0.001 Pan-cancer analysis of DPH2’s m6A modifications m6A Modifications Linked to Multiple Diseases, Including Obesity, Infertility and Cancer [[135]43]. And in tumors, it is also strongly associated with it's proliferation, differentiation, invasion, and metastasis [[136]44]. It was shown that DPH2 expression levels were positively correlated with 19 m6A regulators of BLCA, HNSC, human papillomavirus (HPV)-negative HNSC (HNSC-HPV-), KIRP, LIHC, PAAD, STAD, and THCA by heatmap (Fig. [137]9A). When m6A writers METTL3 and readers YTHDF3 were mutated, DPH2 expression was decreased in HNSC, HNSC-HPV- and LUSC (Fig. [138]9B). Conversely, when m6A writers WATP, m6A erasers FTO or readers HNRNPA2B1, HNRNPC, IGF2BP1, IGF2BP3, RBMX, YTHDF1, YTHDF2, YTHDC1 were mutated, DPH2 was expressed at a significantly high level in most tumors (Fig. [139]9C). We also identified m6A modification sites in the DPH2 gene sequence by SRAMP (Fig. [140]9D). There were three m6A sites of very high confidence, 2050, 2549, and 2559, suggesting that DPH2 expression may be regulated by m6A regulators, which in turn affects tumor progression. Fig. 9. [141]Fig. 9 [142]Open in a new tab Pan-cancer analysis of DPH2 and (m6A) modification. A Heatmap of the correlation between DPH2 and different m6A regulators in different cancer types. B DPH2 expression was lower than that of the wild type in HNSC and LUSC when METTL3/YTHDF3 were mutated. C DPH2 expression was higher than wild type in most tumors when 10 m6A regulators were mutated. D Computational identification of m6A modification sites of DPH2 sequences Pan-cancer analysis of DPH2 methylation We analyzed the DPH2 methylation levels in different cancers. Our results revealed that DPH2 methylation levels differed between nine cancers and their normal samples, in which DPH2 methylation levels were elevated in ESCA and KIRC whereas in BLCA, HNSC, LIHC, LUAD, PRAD, THCA, and UCEC, the DPH2 methylation levels were reduced (Fig. [143]10A). Meanwhile, we observed a negative correlation between DNA methylation of DPH2 and mRNA expression in most tumors, especially PCPG, TGCT, and LGG (Fig. [144]10B). In the prognostic analysis of DPH2 methylation, LUAD and OV patients with the presence of DPH2 methylation had worse OS, PFS, DFS, and DSI, while LGG patients with DPH2 methylation had longer OS, PFS, and DFS (Fig. [145]10C). Fig. 10. [146]Fig. 10 [147]Open in a new tab Methylation analysis of DPH2 in pan-cancer. A DPH2 methylation levels differ in 9 tumor and normal samples. B Correlation between methylation of DPH2 and mRNA expression in different cancers. C Differences in survival between groups with higher and lower levels of DPH2 methylation in each cancers Pan-cancer single-cell sequencing analysis Next, an analysis was performed to investigate the relationship between DPH2 expression and the functional states of 14 different types of cancer using single-cell sequencing data sourced from CancerSEA. The results indicated a notable association between DPH2 expression levels and the functional states across all 14 cancer types. Notably, the differentiation in 2 or 3 cancer types showed a positive correlation with DPH2 expression, while the DNA repair in 3 cancer types exhibited a negative correlation with DPH2 expression (Fig. [148]11A). Subsequently, we conducted an analysis of the association between DPH2 and the microenvironment of different types of various cancer cell types using single-cell RNA sequencing data obtained from TISCH. In most tumors, DPH2 was expressed in immune cells including B cells, CD4 T cells, CD8 T cells, Mono/Macro, natural killer (NK) cells and regulatory T cells (Tregs). Additionally, DPH2 expression was observed in malignant cells across various cancer types, particularly in Glioma and MCC, and for stromal cells, DPH2 expression was higher in CRC and SKCM than in other tumors (Fig. [149]11B). Fig. 11. [150]Fig. 11 [151]Open in a new tab Single-cell sequencing analysis of DPH2. A Correlation between DPH2 expression in pan-cancer and functional states of 14 cancers. B Expression of DPH2 in different cells, and DPH2 expression based on malignancy classification Pan-cancer genomic alterations of DPH2 Genomic mutations play a significant role in tumorigenesis. We examined mutations in DPH2 in different tumor tissues using the cBioportal database. Our findings revealed that amplification and mutation were the predominant types of genetic variation, with the highest frequency of DPH2 genetic alteration observed in OV patients and the type of mutation was found to be a major factor in CHOL patients (Fig. [152]12A). Figure [153]12B depicts the types, locations and numbers of DPH2 genetic alterations, with missense alterations being the most prevalent. E213K/Q was caused by a frame shift mutations that translate from E (glutamic acid) to K (lysine) or Q (glutamine) at the site of 213 in the DPH2 protein, was identified in 2 cases of BLCA and 1 case of OV. Additionally, we investigated the relationship between DPH2 expression and CNV states in 26 cancers and observed significant differences in 18 cancers (Fig. [154]12C). Remarkably, the loss of DPH2 in the genome occurred infrequently, increasing the copy number of DPH2 in the tumors, aligning with the high expression of DPH2 in the vast majority of tumors, which were high expression in the vast majority of tumors. Figure [155]12D illustrates the association between DPH2 copy number and mRNA expression through a dot plot. Our analysis revealed that samples with DPH2 deletion exhibited lower mRNA expression levels compared to those with DPH2 amplification. In addition, there was a significant positive correlation between DPH2 mRNA expression and copy number in TCGA pan-cancer samples (Fig. [156]12E). Fig. 12. [157]Fig. 12 [158]Open in a new tab Mutational characterization of DPH2 in pan-cancer. A Types and frequency of genetic alterations of DPH2 in various cancers. B Mutation site of DPH2. C The relationship between DPH2 expression and CNV states in 26 cancers. D, E Dot plot and correlation plot of the relationship between DPH2 copy number and mRNA expression. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 Pan-cancer analysis of the relationship of DPH2 expression with drug sensitivity Combination surgery and chemotherapy has definite curative effect on early-stage tumors. To investigate correlations between DPH2 expression and commonly used antineoplastic drugs, we used information extracted from the Cellminer database (Table [159]3). We discovered that high DPH2 expression levels were associated with resistance to multiple types of antineoplastic drugs. As illustrated in Fig. [160]13, positive correlations were detected between the effectiveness of cyclophosphamide, carmustine, ethinyl estradiol, parthenolide, and asparaginase, with DPH2 expression levels. Conversely, negative correlations with DPH2 expression were observed for kahalide f, dasatinib, and LY-294002. Table 3. Correlation analysis between DPH2 expression and drug IC50 Drug Cor P Cyclophosphamide 0.362398166 0.004434272 Kahalide f −0.35717099 0.005086753 Carmustine 0.355911831 0.005256022 Ethinyl estradiol 0.345678641 0.006825599 Parthenolide 0.345374301 0.006877971 Asparaginase 0.338626643 0.008132983 Vismodegib 0.315246968 0.014149796 Imexon 0.308303231 0.016549454 Cladribine 0.305361252 0.017666564 Allopurinol 0.302853653 0.018668933 Curcumin 0.301165162 0.019370838 Estramustine 0.296384747 0.021480991 Nelarabine 0.295670259 0.021812532 Dimethylaminoparthenolide 0.293283362 0.022951726 Vorinostat 0.291362493 0.023904675 Fostamatinib 0.283846121 0.027961027 Buthionine sulphoximine 0.282317634 0.028852942 Fludarabine 0.277041703 0.032116906 Lomustine 0.276536812 0.032444801 Dasatinib −0.269622271 0.037223493 Carboplatin 0.263225744 0.042150064 LY-294002 −0.259451582 0.045300762 Chelerythrine 0.255001395 0.049261528 [161]Open in a new tab On the left is the drug name, and r is the correlation coefficient Fig. 13. [162]Fig. 13 [163]Open in a new tab Drug sensitivity analysis of DPH2 Pan-cancer GSVA/GSEA according to DPH2 expression level Furthermore, to gain deeper understanding of pan-cancer molecular mechanisms involving DPH2, we performed GSEA and GSVA. Functional enrichment associated with high and low levels of DPH2 expression was identified using GSEA. High DPH2 levels were primarily linked to the cell cycle and purine metabolism signaling pathways, while low DPH2 expression was mainly associated with MAPK signaling, cytokine_cytokine receptor interaction, oxidative phosphorylation, and chemokine signaling pathways (Fig. [164]14A, B). We then scored all tumors using GSVA and found that DPH2 expression levels were high in PRAD, leading to enrichment in terms including unfolded protein response, G2M checkpoint, MYC target v1, MYC target v2, and mTORC1 signaling (Fig. [165]14C). Fig. 14. [166]Fig. 14 [167]Open in a new tab GSEA and GSVA results according to DPH2 expression. A Pan-cancer GSEA showing pathways enriched in patients with high DPH2 expression. B Pan-cancer GSEA showing pathways enriched in patients with low DPH2 expression. C Significant DPH2-related biological pathways in PRAD determined by GSVA Risk analysis and independent prognostic value of DPH2 Finally, by using DPH2 expression and clinical symptom data, we constructed a nomogram prediction model, examined their correlation by regression analysis, and illustrated the outcomes on a line chart (Fig. [168]15). Overall, the regression model showed that DPH2 gene expression could predict survival rates of patients with PRAD using a linear model. Fig. 15. [169]Fig. 15 [170]Open in a new tab Nomogram for prediction of 3- and 5-year survival of patients with PRAD Discussion DPH2 is one of two human genes that resemble yeast dph2, the other being DPH1, which has been reported to be associated with human tumors and is a tumor suppressor gene [[171]8]. The yeast diphthamide biosynthesis process occurs in four sequential stages, requiring a minimum of seven gene products (Dph1–Dph7), along with S-adenosyl methionine, which serves as a co-factor [[172]45]. Recent studies have shown that DPH2 plays a role in the diagnosis and assessment of prognosis in hepatocellular carcinoma (HCC) and may be a potential target for therapy [[173]46, [174]47]. Meanwhile, a study demonstrated that the knockdown of genes related to diphthamide synthesis, such as DPH2, in breast cancer cell lines revealed the key role of diphthamide in cell biology, particularly in regulating cellular sensitivity to tumor necrosis factor. This finding provides new perspectives for further understanding the survival mechanisms of cancer cells [[175]48]. The aim of this study was to comprehensively explore the expression, prognostic value, immune infiltration, and potential function of DPH2 in pan-cancer, with experimental validation conducted in prostate cancer tissues and cells. Here, we analyzed the expression of DPH2 in pan-cancer. High DPH2 expression levels were detected in most tumors. Moreover, poor OS or PFI were associated with DPH2 upregulation in several tumors. In some cancers (e.g., ACC), DPH2 expression was also related to tumor stage. Based on these discoveries, DPH2 has potential to serve as a biomarker for predicting cancer prognosis. Although high DPH2 expression can predict poor OS in some tumors, additional experiments are needed to determine whether our database-oriented analyses accurately reflect the prognostic significance of DPH2 in other tumor types. Simultaneously, we performed pan-cancer cell analyses using CCLE expression profiles to evaluate DPH2 expression in various cancer cell lines and the resulting data may provide a useful reference for future cell culture experiments. In addition, we collected samples from patients with prostate cancer and verified DPH2 is highly expressed in PRAD by qRT-PCR and IHC analysis, which was further validated by the GEO dataset. Next, we constructed a nomogram prediction model that further confirmed the prognostic value of DPH2 for PRAD. Meanwhile, our in vitro experiments revealed that altered DPH2 expression could affect prostate cancer cell function. Therefore, DPH2 has potential to serve as a treatment target in prostate cancer. Meanwhile, it has been demonstrated that in hepatocellular carcinoma, the mRNA expression level of DPH2 differs from its protein expression level, with the protein expression level being elevated during the progression of the intercellular phase. This suggests that there may be a regulatory relationship between DPH2 and cell proliferation, providing insights for subsequent experimental studies [[176]47]. The TME is mainly composed of tumor-associated fibroblasts, immune cells, extracellular matrix, various growth factors, inflammatory factors, and cancer cells themselves, and has distinct physical and chemical characteristics [[177]49, [178]50]. Variations in the TME can impact the identification, responsiveness to clinical treatment, and OS associated with tumors [[179]39, [180]51]. The tumor immune microenvironment (TIME) is a novel idea that is strongly correlated with the clinical prognosis of individuals with tumors [[181]52]. To enhance the efficacy of immunotherapy, it is crucial to discover novel targets and biomarkers. Additionally, elucidating the characteristics of immune infiltration in individual patients with cancer holds significant importance for immunotherapy strategy optimization [[182]52–[183]54]. Our results suggest that DPH2 expression is significantly correlated with immune infiltration in a variety of cancers. In previous studies, it was shown that DPH2 was significantly associated with immune infiltration in HCC, DPH2 could alter the immune status of the TME by adjusting the ratio and activity of immune cells such as Th2 cells, helper T cells, and macrophages, which in turn could affect the progression of HCC. Additionally, high expression of DPH2 was found to predict poor prognosis in patients [[184]46]. These findings are consistent with some of our results; however, these results are still at the theoretical stage, including the mechanisms by which DPH2 affects the immune microenvironment and therapeutic progression in other tumors, which need to be validated in further studies and experiments. Cell death plays an important role in tumor progression, and our analysis revealed that DPH2 and the expression of cell death-related genes are closely related. Apoptosis, autophagy-dependent cell death, ferroptosis and pyroptosis belongs to regulated cell death (RCD). Growing evidence that autophagy, ferroptosis and pyroptosis can influence human cancer progression and play a role in immunotherapy [[185]55–[186]57]. In a study based on the breast cancer cell line MCF7, deletion of diphthamide was found to activate the NF-κB or the death receptor pathway, and diphthamide synthesis-deficient cells were hypersensitive to TNF-induced apoptosis [[187]48]. In contrast, in our study, DPH2 was found to be significantly correlated with TNFA_SIGNALING_VIA_ NF-κB -related genes, suggesting that DPH2 expression is closely related to tumor cell death and immunity, and may contribute to the development of novel immunosuppressive drugs through further mechanistic studies. Recent years have seen a rise in the popularity of cancer immunotherapy, particularly treatments focused on ICI therapy [[188]58]. Studies have demonstrated that T-lymphocyte infiltration is key to evaluating the efficacy of immune checkpoint inhibitors (ICIs) [[189]59]. Solid tumors that respond to immunotherapy are characterized by T-lymphocyte infiltration, as well as high TMB, referred to as “hot tumors” (or immune-inflamed tumors). In contrast, non-responsive tumors may exhibit a lack of T cells in the tumor parenchyma and low TMB, referred to as “cold tumors” (including immune-excluded tumors and immune-desert tumors) [[190]60, [191]61]. The conversion of “cold” tumors into “hot” tumors, thereby making malignant tumors responsive to subsequent ICI therapy, is a hot topic of current research, and angiogenesis-mediated immunosuppression reversal has proven to be successful in treating kidney cancer and hepatocellular carcinoma [[192]62–[193]64]. Prostate cancer is considered a “cold” tumor, in which immune-based therapies have limited efficacy [[194]65–[195]67]. Currently, sipuleucel-T is the only immunotherapy option for prostate cancer approved by the FDA, and provided improvements in progression-free survival or OS in clinical trials [[196]68]. Our results showed that the expression of DPH2 in PRAD, SARC, and LUAD was positively correlated with TMB and CD8 T-cell infiltration. We hypothesize that in cancers, including prostate cancer, the level of DPH2 may affect T-cell infiltration and influence the levels of TMB and/or MSI, potentially playing a positive role in ICI treatment and helping patients benefit from longer OS. We also conducted GSEA and GSVA to further elucidate the molecular pathways and biological processes linked to DPH2 expression. Our GSVA findings in the context of prostate cancer indicated that high DPH2 expression is l is associated with multiple tumor-related signaling pathways. The unfolded protein response is a component of the endoplasmic reticulum stress response [[197]69], where tumor growth and invasion are significantly influenced by endoplasmic reticulum stress [[198]70]. Further, the initiation of unfolded protein response signaling is associated with prostate cancer progression and prognosis [[199]71–[200]73]. Our GSEA findings indicated that DPH2 expression was associated with enrichment for oxidative phosphorylation, MAPK signaling, and cytokine–cytokine receptor interaction, and chemokine signaling pathways. The MAPK signaling pathway is widely involved in tumor proliferation, apoptosis, tumor drug resistance, and related functions [[201]74–[202]76]. Furthermore, we detected strong correlations between immune activity and DPH2 expression. Collectively, our enrichment analyses suggest that DPH2 may have significant impacts in numerous types of cancer, such as prostate cancer, and could influence patient prognosis. Moreover, by improving our understanding of the specific mechanism by which DPH2 affects pathways linked to cancer, we may identify new ways to treat prostate cancer. Although this study integrates multiple datasets, it still has its limitations. First, although we performed more bioinformatic analyses, only limited experimental validation was performed, and more empirical studies are necessary. Second, despite correlations between DPH2 expression and immune infiltration, it cannot be shown DPH2 modulates the immune system to affect patient outcomes, and further evaluation in patients with cancer is needed. Third, we demonstrated that DPH2 is highly expressed in prostate cancer by RT-qPCR and IHC, DPH2 may influence prostate cancer progression; however, DPH2 expression may also affect cancer prognosis in other tumors. Moreover, the specific mechanism of action of DPH2 in all tumors remains unclear, and further research to elucidate it is warranted. As a result, our findings suggest DPH2 plays a crucial role in the initiation and development of tumors and could serve as a prognostic marker as well as a new cancer therapeutic target。 Supplementary Information [203]12672_2025_1924_MOESM1_ESM.docx^ (514.3KB, docx) Supplementary material 1: Figure 1. DPH2 co-expression with (A) immune checkpoint molecules, (B) immuno-stimulators, (C) immuno-inhibitors, (D) MHC genes, (E) chemokines, and (F) chemokine receptors. *P < 0.05, **P <0.01, ***P <0.001. Acknowledgements