2.1 Data source

Transcriptomic data were acquired from the TCGA Pan-cancer cohort (https//portal.gdc.cancer.gov/), the Genotype-Tissue Expression (GTEx) initiative, and the Cancer Cell Line Encyclopaedia (CCLE). The GTEx data set provided mRNA data from 31 tissues that were in a healthy state. Moreover, research was carried out on the dispersion of cancer cell line expression levels in 21 establishments utilizing information from the CCLE database. The TCGA data set provided the mRNA profiling of tumor tissues from adjacent tumor tissues and 31 tumors. Furthermore, the TCGA data set included clinical survival data.²³ To evaluate the difference in tissues, the Kruskal–Wallis test was employed.

2.2 Differential analysis

To assess the disparities between these groups, the analysis focused on the expression of CCDC25 in both healthy and tumor tissues, including tumor samples and adjacent normal tissue samples. The Wilcoxon test was employed to assess statistically significant disparities, with a p value below 0.05.

2.3 Evaluation of predictive stages among different forms of cancer

To evaluate the link between CCDC25 expression levels and different types of cancers, univariate Cox regression analyses were conducted in overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI). Significance was attributed to Cox p values that were below 0.05. Samples were divided according to the median expression of CCDC25, and prognostic analysis was determined using the Kaplan–Meier survival analysis technique. Statistical significance was assessed using the log-rank test, considering p values less than 0.05 as indicative of significant differences. Furthermore, the link between the expression of CCDC25 and various clinical parameters, such as the TNM stage, was evaluated to gain further insight into this link.

2.4 The link between the expression of CCDC25 and the infiltration of the immune system

Several research studies have indicated that tumor immune cells (TIICs), such as B cells, T cells, dendritic cells, macrophages, neutrophils, monocytes, and mast cells, can control the advancement of cancer and play a role in discovering potential treatment choices.⁶ This study examined the link between the expression of CCDC25 and the infiltration of the immune system in multiple tumor types. To assess the infiltration of immune cells in various types of cancer, the CIBERSORT method was utilized. The Spearman analysis was used to compare CCDC25 and the ratings of these immune cells in 31 different tumors. The TCGA pan-cohort utilized the ESTIMATE method to impute the level of immune cell infiltration in every tumor specimen, encompassing immune score, tumor purity, and stromal score. Moreover, the connection between immunological scores and CCDC25 was explored.

In the treatment of different types of cancers, ICI that target PD-1 or its ligand (PD-L1) or CTLA-4 have a crucial function.²⁴ This study investigated the functions of immune checkpoint-related genes, including inhibitors based on monoclonal antibodies, therapeutic antibodies, cancer vaccines, cell-based therapies, and small-molecule inhibitors. Over 40 immune checkpoint genes were analyzed in this research. The immune checkpoint genes were obtained separately, and their link with the expression of the desired gene was assessed.

Multiple research studies have indicated a link between the number of immune neoantigens linked to MHC class I and cytolytic function, which was found to be lower than anticipated in colorectal cancer and various other malignancies.²⁵ Nonetheless, the connection between immune neoantigens and CCDC25 remains ambiguous. Hence, the significance lies in investigating the link between immune neoantigens and the levels of CCDC25 expression. Furthermore, the reaction of individuals to immune checkpoint inhibitors (ICI) can be anticipated by tumor mutation burden (TMB), which serves as a unique genetic indicator.^26-28 The study aimed to assess the link between TMB and the levels of CCDC25 expression.

2.5 Investigating the link between the expression levels of CCDC25 and MMRs, DNA methyltransferase (DNMT), and MSI

Changes in DNA MMRs, such as in somatic cells or germ cell lines (Lynch syndrome), are linked to the occurrence of MSI in tumors. MSI is commonly seen in different types of tumors, such as colorectal cancer, endometrial cancer, and gastric cancer. It is characterized by a substantial presence of immune cells and a considerable quantity of tumor neoantigens.^{29, 30} This study investigated the link between CCDC25 and MSI.

MMRs are linked to intracellular processes associated with the correction of mismatches. Therefore, the absence of genes essential to this process may lead to irreversible errors during DNA replication, ultimately increasing somatic mutations.^{31, 32} The main cause of colorectal cancer, endometrial cancer, and gastric cancer is the lack of MMRs, which results in MSI. The present study utilized TCGA gene expression analysis to assess the link between alterations in MMRs (MLH1, MSH2, MSH6, PMS2, and EPCAM) and CCDC25.

Regulation of gene expression can occur through DNA methylation, leading to alterations in chromatin organization, DNA shape, stability, and protein binding. DNA methylation is the process of adding a methyl group to the carbon located at the 5′ position of the cytosine residue within the genomic CpG dinucleotide. This process is catalyzed by DNMT.³³ To establish their link, the connection between CCDC25 and four methyltransferases (DNMT1, DNMT2, DNMT3A, and DNMT3B) was examined in this investigation.

2.6 Performing GSEA analysis on different cancer tissue types obtained from the TCGA data set

To assess the impact of CCDC25 expression on tumors, the present investigation classified the specimens into high and low categories according to the levels of gene expression. To evaluate the impact of CCDC25 expression on tumors, we utilized Gene Set Enrichment Analysis (GSEA) to investigate the enrichment of KEGG pathways.³⁴ The curated signatures from the Molecular Signature Database (MSigDB) of Broad Institute were acquired by utilizing the c5 gene sets. In addition, the KEGG terms were identified for both the high and low CCDC25 groups. With a threshold of a false discovery rate (FDR) below 0.05, the obvious pathway of enrichment was determined.

2.7 Verification of the human protein atlas (HPA)

The HPA database (https//www.proteinatlas.org/) was used to determine the immunohistochemistry (IHC) images of CCDC25 protein expression levels in carcinoma and normal para-carcinoma tissues.

3.1 The presence of CCDC25 in healthy populations and cancerous cell lines

In the healthy population, CCDC25 exhibited high expression in most organs, with the nerves, bone marrow, and fallopian tubes showing the highest levels. Conversely, the blood, pancreas, and heart displayed the lowest expression of CCDC25 (Figure 1A). CCDC25 expression in different organs was observed to be moderate to low in cancer cell lines. The highest expression was observed in autonomic ganglia, hematopoietic and lymphoid tissues, and prostate, whereas the kidney, biliary tract, and small intestine showed the lowest expression (Figure 1B). The Kruskal–Wallis test showed a significant difference in the expression of CCDC25 across the different organs.

3.2 The levels of CCDC25 mRNA expression in various human tumor types

A notable variation in CCDC25 expression levels was observed between adjacent tumor samples and tumor samples in various cancer types, as indicated by TCGA pan-cancer data (Figure 2A). The expression of CCDC25 was increased in CHOL, HNSC, and STAD tumors, whereas it was decreased in BLCA, BRCA, COAD, KICH, KIRC, KIRP, LIHC, LUAD, PRAD, READ, THCA, and UCEC tumors. Nevertheless, there were no notable alterations observed in the expression of CCDC25 in ESCA, GBM, LGG, LUSC, and PAAD.

Analyzing the expression of CCDC25 in 31 various cancer types and one blood cancer (acute myeloid leukemia), data from normal tissues in the GTEx database and tumor tissues from the TCGA data set were integrated. There was notable variation in the levels of CCDC25 expression across various types of cancer (Figure 2B). CCDC25 expression was significantly elevated in breast, lung, prostate, brain, and pancreatic tumors in comparison to normal tissues. In certain tissues, there was a decrease in CCDC25 expression observed in kidney, rectum, and biliary tract cancers.

3.3 Examining predictive elements across multiple types of cancer in TCGA

By analyzing 31 tumors in the TCGA data set, a thorough examination was conducted to systematically analyze the predictive landscape. The objective of this study was to investigate the potential link between the expression of CCDC25 and the outcomes of survival in different types of cancer. Figure 3A and Table 1 showcase the forest plot and univariate Cox regression analysis for different types of cancers. Except for KIRC (OS HR = 0.41, CI = 0.30-0.58, p < 0.001), THYM (OS HR = 11.80, CI = 1.11 to 125.12, p < 0.001), and SKCM (OS HR = 0.77, CI = 0.62-0.96, p < 0.001), CCDC25 had minimal impact on most cancers. A Kaplan–Meier survival analysis was conducted to determine prognostic tumor classifications. Figure 3B exhibits the Kaplan–Meier survival graph. ESCA and KIRC showed a positive link between increased levels of CCDC25 and improved overall survival, whereas LAML exhibited a negative link between higher levels of CCDC25 and overall survival. Furthermore, the COX findings demonstrated a lack of link between CCDC25 expression and prognosis in most tumors.

Moreover, the link between the expression of CCDC25 and DSS was examined in 31 tumors. Except for KIRC (DDS HR = 0.29, CI = 0.19–0.43, p < 0.001), COAD (DDS HR = 0.60, CI = 0.39–0.92, p < 0.001), SKCM (DDS HR = 0.75, CI = 0.58–0.95, p < 0.001), READ (DDS HR = 0.35, CI = 0.14–0.84, p < 0.001), and KIRP (DDS HR = 0.38, CI = 0.17-0.87, p < 0.001) (Figure 4A and Table 2), most tumors did not exhibit a link between the expression of CCDC25 and DSS. According to the Kaplan–Meier survival analysis (Figure 4B), there was notable diversity in CCDC25 expression among the COAD, KICH, KIRC, LUSC, and READ. Furthermore, elevated levels of CCDC25 were linked to enhanced DSS in the above-mentioned cancer types, except for KICH.

Afterward, an examination was performed to uncover the connection between CCDC25 and DFI in 31 tumors. Figure 5A and Table 3 display the forest plot. A significant link was observed between CCDC25 expression and DFI in KIRC (DFI HR = 0.29, CI = 0.19-0.43, p < 0.001), COAD (DFI HR = 0.60, CI = 0.39-0.92, p < 0.001), SKCM (DFI HR = 0.74, CI = 0.58-0.95, p < 0.001), READ (DFI HR = 0.35, CI = 0.14-0.84, p < 0.001), and KIRP (DFI HR = 0.38, CI = 0.17-0.87, p < 0.001) among the assessed tumors. Figure 5B displayed differences in survival rates among DLBC, UCS, and LUSC according to the Kaplan–Meier survival analysis.

Moreover, the link between the manifestation of CCDC25 and PFI was evaluated in various forms of malignancies. The forest plot is presented in Figure 5A and Table 4. Except for PRAD (PFI HR = 0.32, CI = 0.12-0.89, p < 0.001), there was no link between CCDC25 and PFI in the evaluated tumors. Nevertheless, DLBC, LUSC, and UCS exhibited improved rates of survival according to the Kaplan–Meier survival analysis (Figure 6B).

Furthermore, the link between CCDC25 and the TNM stage was investigated in 31 different types of tumors. No apparent link between the TNM stage and the expression of CCDC25 was observed in the majority of tumors (Supporting Information: Figure 1).

3.4 The link between CCDC25 and immune infiltration in pan-cancer analysis

In multiple types of tumors, the CIBERSORT technique was used to estimate the ratios of immune cell infiltration. The link between the expression of CCDC25 and the levels of immune infiltration in different malignancies indicated a link with the infiltration level of macrophage Mo in 17 types of cancer. In multiple cancers, Figure 7 shows a strong link between CCDC25 expression and the infiltration levels of activated memory CD4 T cells, resting mast cells, activated dendritic cells, follicular helper T cells, and macrophages M2. This link is observed in 16 cases for CD4 T cells and mast cells, 13 cases for dendritic cells and helper T cells, and 12 cases for M2 macrophages. Furthermore, the extent of CCDC25 expression correlated with the presence of neutrophil infiltration in MESO, LGG, KIRP, HNSC, GBM, UCEC, LUSC, and BLCA. In HNSC, the infiltration level of quiescent dendritic cells (r = −0.10, p = 0.02), M0 macrophages (r = −0.15, p = 0.00088), macrophages M2 (r = −0.11, p = 0.015), and activated mast cells (r = −0.14, p = 0.0015) showed a significant inverse link with CCDC25 expression.

CCDC25 controls the levels of immune infiltration in different types of cancer. Genomics methods have been utilized to determine the particular forms of cancers in which CCDC25 is linked to tumor purity. Tumor purity had a significant impact on the assessment of immune infiltration in clinical tumor specimens. CCDC25 expression is positively linked to immune infiltration in LGG and PAAD, while it is negatively linked to immune infiltration in ACC, GBM, HNSC, KIRC, KIRP, LAMY, MESO, OV, STAD, TGCT, THCA, THYM, UCEC, and UCS (Figure 8). The findings suggest that CCDC25 controls the infiltration of immune cells in different types of tumors. Various forms of tumor immunotherapy have been employed for the treatment of numerous tumor varieties. The analysis of over 40 genes related to immune checkpoint was conducted in this study to investigate the relationship between CCDC25 and the expression of immune checkpoint genes. Figure 9 displays the link heatmap. The link between CCDC25 expression and immune checkpoint genes was detected in different types of tumors, including TGCT, KIRC, LGG, and THCA.

3.5 HLA, TMB, and immune neoantigen

The purpose of developing neoantigen vaccines is to regulate the response of the immune system and attain the intended therapeutic outcome. Immunization was carried out by administering vaccines formulated with altered tumor cells. The count of novel antigens in every tumor sample was established, and an analysis was performed to investigate the link between CCDC25 expression and the count of antigens. A positive link was observed between the expression of CCDC25 and the presence of immune neoantigens in THCA, STAD, READ, UCEC, BRCA, and OV (Figure 10A).

The cancer cell mutations were measured using the biomarker TMB. The TMB for each tumor sample was computed separately, and the Spearman rank link coefficient was utilized to establish the link between the expression level of CCDC25 and TMB. Figure 10B illustrates a significant link between TMB and the manifestation of CCDC25 across multiple cancer categories, such as BRCA, STAD, SKCM, COAD, SARC, HNSC, KIRC, KIRP, LAML, LGG, LUAD, and PAAD. The results demonstrated a notable link between TMB and CCDC25 expression in the majority of tumors. In multiple tumors, Figure 10C demonstrates a significant link between CCDC25 and most HLA genes.

3.6 MSI, MMRs, and DNA methyl transferases

A strong link is observed in most tumors between the expression of CCDC25 and MSI. The link between CCDC25 expression and MSI was assessed in this study using the Spearman rank correlation coefficient. Figure 11A revealed a notable link between TMB and CCDC25 expression in BLCA, BRCA, DLBC, HNSC, LUAD, LUSC, OV, READ, SARC, THCA, and UCEC. The results indicate a robust link between the expression of CCDC25 and MSI in the majority of tumors.

The absence of mismatch repair in certain tumors was revealed by establishing a link between intracellular mechanisms associated with mismatch repairs and MMRs. An analysis of TCGA gene expression was conducted to assess the link between the expression of MMRs (MLH1, MSH2, MSH6, PMS2, EPCAM) and CCDC25 gene expression, as depicted in Figure 11B. A positive link was found between CCDC25 expression and the five MMRs in the majority of cancer cases. This study examined the link between CCDC25 expression and four crucial DNMTs (Figure 11C). The findings indicated a significant statistical link in THCA, THYM, STAD, PRAD, LUSC, LGG, KIRP, and COAD.

3.7 GSEA analysis

To evaluate the involvement of CCDC25 in tumors, the samples were categorized into two groups based on the CCDC25 expression levels, utilizing the median value as the threshold. Next, both sets of groups underwent KEGG pathway analysis utilizing GSEA.

Figure 12 displays the three most notably enriched pathways. The analysis of GSEA showed heightened activity in several pathways associated with cancer and the immune system among individuals with high CCDC25 expression. ACC, BLCA, GBM, LUAD, PCPG, READ, and THYM exhibited activation of the RIG-I-like receptor signaling pathway and the Toll-like receptor signaling pathway. Moreover, the control of autophagy was detected in ACC, BLCA, BRCA, CHOL, ESCA, GBM, LUAD, LUSC, and PCPG. Finally, primary immunodeficiency was found in THCA and UVM, while BRCA tumors exhibited cytotoxicity mediated by natural killer cells.

3.8 Validation of CCDC25 using immunohistochemistry

The immunohistochemical staining of CCDC25 was obtained from the HPA database. In comparison to the regular samples, the LIHC, THCA, COAD, OV, PRAD, and BRCA samples exhibited elevated protein expression levels of CCDC25 (Figure 13).