Analysis
ADCYAP1 as a pan-solid cancer biomarker: predictor of immunotherapy efficacy in bladder cancer and prognostic potential across solid tumors
Xiaoyu Jing1,2 . Ying Deng1,21D
Received: 18 December 2024 / Accepted: 16 April 2025
Published online: 23 April 2025
@ The Author(s) 2025 OPEN
Abstract
Background ADCYAP1 has been identified with potential effects ranging from tumor growth activation to inhibition. However, it remains unknown whether ADCYAP1 plays a substantial role across pan-cancer.
Methods The potential roles of ADCYAP1 in 33 different tumors were analyzed based on The Cancer Genome Atlas (TCGA). We investigated the expression levels, mutations, survival rates, DNA methylation, and immune cell infiltration associated with ADCYAP1. In addition, we analyzed immunotherapy response data from the Tumor Immunotherapy Gene Expression Resource (TIGER) database and previously reported studies.
Results In general, high expression of ADCYAP1 has been linked to poor OS in the TCGA Bladder urothelial carcinoma cohort (BLCA) (p=0.003), Stomach adenocarcinoma (STAD) cohort (p=0.002), and Uterine corpus endometrial carcinoma (UCEC) cohort (p=0.032). However, the opposite association was observed in the Adrenocortical carcinoma (ACC) cohort (p=0.034), Kidney renal clear cell carcinoma (KIRC) cohort (p <0.0001), and Liver hepatocellular carcinoma (LIHC) cohort (p=0.027). Notably, the BLCA and UCEC samples showed a higher frequency of ADCYAP1 mutations compared to oth- ers. Our results suggested that the level of ADCYAP1 methylation can serve as a prognostic factor for OS in patients with STAD and UCEC. The analysis of six cancer immunotherapy(CIT) response datasets showed that ADCYAP1 has predictive value for immunotherapy response in BLCA.
Conclusions There is a potential correlation between ADCYAP1 and tumor immunity. Consequently, we propose that ADCYAP1 could potentially serve as a valuable prognostic biomarker for BLCA.
Keywords Bioinformatics methods . ADCYAP1 . Prognosis . Immunotherapy response . Biomarker
Abbreviations
ACC Adrenocortical carcinoma
BLCA Bladder urothelial carcinoma
BRCA Breast invasive carcinoma
CESC Cervical squamous cell carcinoma and endocervical adenocarcinoma
CHOL Cholangiocarcinoma
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s12672-025- 02408-3.
☒ Ying Deng, 1262444013@qq.com | 1Department of Pediatrics, West China Second University Hospital, Sichuan University, No.20, Section 3, Renmin South Road, Chengdu 61004, Sichuan, China. 2Key Laboratory of Birth Defects and Related Disease of Women and Children, Ministry of Education, Sichuan University, No.20, Section 3, Renmin South Road, Chengdu 61004, Sichuan, China.
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(2025) 16:593 | https://doi.org/10.1007/s12672-025-02408-3
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| COAD | Colon adenocarcinoma |
| DLBC | Lymphoid neoplasm diffuse large B-cell lymphoma |
| ESCA | Esophageal carcinoma |
| GBM | Glioblastoma multiforme |
| HNSC | Head and neck squamous cell carcinoma |
| KICH | Kidney chromophobe |
| 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 |
| MESO | Mesothelioma |
| OV | Ovarian serous cystadenocarcinoma |
| PAAD | Pancreatic adenocarcinoma |
| PCPG | Pheochromocytoma and paraganglioma |
| PRAD | Prostate adenocarcinoma |
| READ | Rectum adenocarcinoma |
| SARC | Sarcoma |
| SKCM | Skin cutaneous melanoma |
| STAD | Stomach adenocarcinoma |
| TGCT | Testicular germ cell tumors |
| THCA | Thyroid carcinoma |
| THYM | Thymoma |
| UCEC | Uterine corpus endometrial carcinoma |
| UCS | Uterine carcinosarcoma |
| UVM | Uveal melanoma |
| CIT | Cancer immunotherapy |
| TCGA | Cancer genome atlas |
| GEO | Gene expression omnibus |
| OS | Overall survival |
| mOS | Median overall survival |
| DFS | Disease-free survival |
| PACAP | Adenylate cyclase-activating polypeptide |
| TMB | Tumor mutation burden |
| K-M | Kaplan-Meier |
| HR | Hazard ratios |
| CNV | Copy number variation |
1 Introduction
Cancer significantly impacts public health worldwide, with morbidity and mortality rates steadily increasing each year [1]. Despite the use of cancer immunotherapy to improve treatment outcomes in recent years, the 5-year survival rate remains unsatisfactory. A majority of patients exhibit limited responses to these therapies [2]. Therefore, there is an urgent requirement to identify predictive biomarkers for diagnosis and prognosis. Early assessment of patient benefit from these immunotherapy strategies is crucial. The application of cancer biomarkers has garnered considerable attention from researchers. Several studies have identified indicators associated with CIT response, including PD-L1 expression, tumor mutation burden (TMB), and eosinophilic count [3]. There is a need to explore novel cancer biomarkers to predict clinical outcomes and CIT responses.
The ADCYAP1 gene encodes adenylate cyclase-activating polypeptide (PACAP), a member of the secretin/glucagon/ growth releasing hormone family. PACAP is widely distributed in both the central nervous system and peripheral organs
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[4]. It exists in two isoforms derived from the same precursor: PACAP-38, consisting of 38 amino acids, and PACAP-27, consisting of 27 amino acids [5]. PACAP is known to be involved in various biological processes [6]. Its diverse effects are mediated through binding to three G-protein-coupled receptors known as PAC1, VPAC1, and VPAC2 receptors. By binding to these receptors, PACAP can initiate various signaling pathways, including downstream adenylate cyclase (AC) or phospholipase-C (PLC) activation, as well as calcium-regulated mechanisms [4]. Thus, they are involved in neuronal death, inflammatory response, cellular fission and modulation of immune system [4, 7].
A substantial body of literature has demonstrated PACAP’s involvement in various tumors, including testicular, lung, breast, prostate, colon, and pancreatic cancer, as well as neuroblastoma and glioblastoma [7]. The biological functions of PACAP exhibit controversy depending on the tumor phenotype and disease stage [7]. PACAP, in fact, has been shown to regulate other genes leading to the activation of tumor cell growth, survival, and hormone secretion [8]. Conversely, it is capable of impeding tumor cell progression through apoptosis by inhibiting their proliferation [9]. For instance, in glioblastoma, PACAP has demonstrated interference with the hypoxic microenvironment via modulation of hypoxia- inducible factors, achieved by inhibiting the PI3K/AKT and MAPK/ERK pathways [10]. A prior study has elucidated that PACAP stimulation prompts pro-apoptotic effects, increasing Bax expression and decreasing Bcl2 in MCF-7 breast cancer cells [11]. Overexpression of PAC1 and VPAC1 receptors has been observed in lung cancer, breast cancer, colon cancer, prostate adenocarcinoma, and pancreatic tumors [7]. Conversely, reduced expression of the VPAC2 receptor has been detected in lung adenocarcinomas and neuroendocrine tumors, and reduced expression of both PACAP and the PAC1 receptor has been described in pancreatic adenocarcinoma [7, 12]. Given the intricate and multifaceted relationships between ADCYAP1 and neoplasia, immunotherapy targeting ADCYAP1 in cancer has emerged as a promising avenue. Hence, further exploration of the role of ADCYAP1 in pan-cancer is of great significance. Currently, there exists no associated analysis between ADCYAP1 and pan-cancer.
In this study, we performed a comprehensive pan-cancer analysis of ADCYAP1 encompassing 33 different cancer types, utilizing datasets from the TCGA as well as the Gene Expression Omnibus (GEO). Our analysis incorporated various factors, including gene expression and alterations, clinical prognosis, and immune cell infiltration. The primary objective of this investigation was to assess the potential of ADCYAP1 as a predictive biomarker for diagnosis and immunotherapy response in diverse cancer types. The findings from this study hold the promise of guiding further research in this area.
2 Methods
2.1 Data collection and processing
The genomic and transcriptomic data spanning 33 distinct cancer types, involving a total of 10,251 patients, were obtained from the TCGA (https://xenabrowser.net/datapages/) (Table S1 and Table S2) [13]. The expression profiles were transformed into transcripts per kilobase million (TPM) format, and for subsequent analysis, the data were converted to log2(TPM + 1) format. The R package ‘edgeR’ (v3.40.0) was employed for data normalization and processing, utilizing R version 4.02 software. In order to assemble the therapeutic cohort, an exhaustive search was conducted to identify immunotherapy datasets that were publicly accessible and accompanied by comprehensive clinical information.Six immunotherapy datasets, namely GSE176307 (N =89) [14], GSE100797 (N =25) [15], GSE91061 (N=25) [16], phs000452. v3.p1 (N=121) [17], Motzer cohort (N=354) [18], and Braun cohort (N=86) [19] were downloaded from public resources (Table S3). The immunotherapy cohorts of BLCA was obtained from Gene Expression Omnibus (GEO) (https://www.ncbi. nlm.nih.gov/geo/query/acc.cgi?acc=GSE176307). We obtained three independent SKCM immunotherapy cohorts from the following sources: 1) Two gene expression datasets (GSE100797 and GSE91061) from the GEO database; 2) Pre- processed RNA-sequencing data (phs000452.v3.p1, N=121) of metastatic melanoma patients treated with anti-PD-1 therapy, accessed through the ZENODO repository were acquired via ZENODO repository (https://zenodo.org/record/ 4540874). Two immunotherapy cohorts of KIRC were extracted from the supplementary files of Braun’s article and Motzer’s article.
2.2 Genomic alterations analysis of ADCYAP1 in human cancers
Genetic alterations within ADCYAP1 were compiled from the TCGA tumor database, encompassing information on alteration frequency, mutation types, and total mutation count. Firstly, our analysis initially compiled mutation profiles and cohort-specific sample sizes across 32 tumor types. Subsequently, we calculated the number of ADCYAP1-mutated
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cases and copy number variation (CNV)-altered specimens within each cohort. Finally, we determined the ADCYAP1 mutation prevalence for each tumor type.The primary form of genetic alteration across the entire spectrum of TCGA tumor samples was CNV, specifically amplification,mutations and deep deletion. Differential gene expression analysis was performed using the R software (version 4.2.2), utilizing packages such as dplyr (v1.0.7), reshape (v0.8.8), tidyr (v1.1.3), stringr (v1.4.0) and pheatmap (v1.0.12), applying a fold change threshold of ≥ 2 and an adjusted p-value of < 0.05. Additionally, we proceeded to evaluate and compare the median overall survival among distinct groups based on genomic alterations in the context of pan-cancer analysis.
2.3 Cox regression analysis and Kaplan-Meier(K-M) survival analysis
The comparison of ADCYAP1 between tumor tissues and normal samples was tested by Wilcoxon rank sum test. The association between ADCYAP1 expression and some targets of interest was calculated by Pearson correlation test. The survival time distinctions among groups categorized as “high,” “median,” and “low” risk based on ADCYAP1 expression. K-M curves were generated using the R package survminer to compare the differences in survival times between these groups. For the high group, tumors with ADCYAP1 scores in the upper third of all scores within the same cancer type were considered, while tumors with ADCYAP1 scores in the lower third were classified as low group; the rest were deemed as the median group. The calculation of log-rank p-values, hazard ratios (HR), and corresponding 95% confidence intervals was carried out, with statistical significance set at a threshold of less than 0.05[20].
DNA methylation profiles extracted from the TCGA project were obtained from patients. CpGs displaying over 10% missing values were excluded from the analysis. Subsequently, K-M survival curves were constructed to elucidate the association between ADCYAP1 methylation and Overall Survival, with statistical significance set at a p-value below 0.05[21].
2.4 Immune cell infiltration analysis of ADCYAP1
The CIBERSORT-ABS algorithm was employed to assess immune infiltration levels [22]. This calculation encompassed the evaluation of twenty-two immune cell scores obtained from TIMER2.0 [23].To depict the relationship between ADCYAP1 mRNA expression and 22 distinct immune cell subsets for each cancer type, we generated a heatmap of statistical Spearman correlation coefficients. This visualization was accomplished using the R package “ggplot2”(v3.4.0).
2.5 Immunotherapy prediction analysis
As previously mentioned, six immunotherapeutic cohorts were included and analyzed in this study. Within these cohorts, patients were categorized into three groups based on ADCYAP1 expression levels: a low-expression group, a median- expression group, and a high-expression group. To evaluate potential differences in survival times, K-M curves were generated and employed for comparisons among these groups.
2.6 Statistical analyses
R software (version 4.2.2, for statistical analysis and visualization) was used for bioinformatics analysis. Survival analysis concerning the ADCYAP1 gene expression and its impact on Overall Survival (OS) and Disease-Free Survival (DFS) was conducted using the R programming language. K-M curves were employed to assess variations in survival times. The comparison of continuous variables between two groups was performed using the Wilcox test, while the calculation of Pearson coefficients was utilized to gauge correlations between two continuous variables. Hypothesis testing was carried out using the log-rank test.A p-value < 0.05 was considered statistically significant.
3 Results
3.1 Genetic alteration analysis
In this phase of the study, the genetic alteration status of ADCYAP1 within the TCGA tumor cohorts was meticulously examined. The complete list of tumor-specific enumeration of ADCYAP1-altered samples (including single nucleotide
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variants and CNA) and corresponding mutation frequencies across all cancer types are detailed in Table S4 and Table S5. The results revealed a considerable prevalence of ADCYAP1 variations in six distinct tumor types: BLCA, HNSC, LUSC, OA, SKCM, and UCEC (as depicted in Fig. 1A). Notably, the most pronounced alteration frequency (8%) was observed in patients afflicted with UCS (Uterine Carcinosarcoma), as illustrated in Fig. 1B. Importantly, all genetic alterations wit- nessed within UCS tumor samples were characterized by copy number amplification, which stood as the primary form of genetic alteration across the entire spectrum of TCGA tumor samples. Beyond UCS, the genetic alteration of ADCYAP1 was also prevalent in more than 5% of BLCA, ESCA, HNSC, LUSC, OA, SKCM, and UCEC samples. Subsequently, a thorough exploration was undertaken to ascertain the potential correlation between ADCYAP1 genetic alterations and the clinical outcomes of individuals with cancer. The results highlighted a significant association between ADCYAP1 amplification and unfavorable prognosis in tumor patients, particularly concerning median overall survival (mOS) (56 months vs. 81.8 months, HR = 1.31, 95% CI= 1-1.71, p=0.027) (as depicted in Fig. 1D). There were no significant association between ADCYAP1 mutation and deep deletion and prognosis in tumor patients(P> 0.05) (Fig. 1C and 1E).
A
ADCYAP1_Variant
0
1-10
11-20
21-40
33
33
Numbers of ADCYAP1 variant patients
31
30
24
21
22
20
16
17
14
10
9
9
9
6
5
6
6
6
6
2
3
3
4
0
0
0
1
1
0
1
0
0
1
0
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
B
8
Mutation
0.7
0.1
0.3
1.5
1.1
0.7
1.4
0.2
0.3
1.2
1.9
1.1
0.4
1.3
3.6
0.9
0.8
3.8
Amplication
4.4
1.0
2.7
0.2
5.9
5.1
1.5
0.4
0.4
1.4
1.6
4.3
4.4
2.2
0.5
0.2
0.7
2.1
1.5
1.1
2.3
8.0
Deep_deletion
0.5
0.5
2.7
1.1
0.7
0.2
0.4
0.2
0.2
1.0
0.5
0.6
0.4
2.0
0.2
4
All_variant
1.6
3.1
2.2
2.7
1.5
1.5
0.4
1.0
1.6
3.0
6.4
5.3
3.4
1.1
1.2
2.0
2.5
5.1
2.1
2.0
0.8
6.2
8.0
2
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA THYM
UCEC
UCS
UVM
C
Strata
Wild Mutation
D
Strata
Wild
Amplication
E
Strata
Wild
Deep_deletion
1.00
1.00
1.00
ADCYAP1 Mutation
ADCYAP1 Amplication
ADCYAP1 Deep_deletion
Survival probability
0.75
Survival probability
0.75
Survival probability
0.75
p = 0.206
p = 0.027
p = 0.826
0.50
0.50
0.50
mOS: 62.1 vs 81.8
mOS: 56.0 vs 81.8
mOS: 87.4 vs 81.8
0.25
HR,1.23; 95% CI 0.86-1.76
0.25
HR,1.31; 95% CI 1.00-1.71
0.25
HR,0.93; 95% CI 0.49-1.75
0.00
0.00
0.00
0
24
48
72
96
120
0
24
48
72
96
120
0
24
48
72
96
120
months
months
months
Note the risk set sizes
Note the risk set sizes
Note the risk set sizes
Strata
10808
5383
2541
1288
650
332
Strata
10808
5383
2541
1288
650
332
Strata
10808
5383
2541
1288
650
332
96
49
18
8
3
2
171
83
37
16
8
5
30
16
5
5
2
1
0
24
48
72
96
120
0
24
48
72
96
120
0
24
48
72
96
120
months
months
months
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6
5.1
7.6
6.5
Analysis
cancer types. High ADCYAP1 mRNA levels were significantly correlated with worse OS in patients with BLCA (HR= 1.77,
Patients were stratified into high, median, and low ADCYAP1 score groups, respectively.
The findings of our analysis revealed noteworthy associations between ADCYAP1 expression and OS in several
categorizing tumors into three groups based on their ADCYAP1 score distribution: high, median, and low.
To investigate the potential relationship between ADCYAP1 expression and cancer survival, we conducted OS analysis.
To quantitatively assess the expression levels of ADCYAP1 in various cancers, we employed single-sample gene-set enrichment analysis to calculate the ADCYAP1 score for each cancer type. This score allowed us to compare ADCYAP1 expression between tumor and normal samples across 33 cancers within the TCGA dataset. Our methodology involved
3.3 Analysis of survival prognosis
control tissues in KICH (p<0.01) (Fig. 2B).
ESCA, KIRC, KIRP, LIHC, LUSC, READ, STAD, and UCEC, the expression level of ADCYAP1 was consistently lower in tumor tissues (all p<0.05). Notably, the expression level of ADCYAP1 was significantly higher in tumor tissues compared to
types, MESO exhibited the highest average expression level of ADCYAP1, whereas UVM demonstrated the lowest expres- sion level on average. When comparing tumor tissues to their corresponding control tissues, such as BLCA, BRCA, COAD,
We conducted an in-depth analysis of ADCYAP1 mRNA expression in a total of 10,152 tumor samples. The results of this analysis demonstrated a wide spectrum of ADCYAP1 mRNA expression levels (as depicted in Fig. 2A). Among all cancer
3.2 Analysis of gene expression
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A
ADCYAP1 log2(TPM+1)
15
10
10152 tumors in TCGA
(2025) 16:593
01
B
0
ACC
ADCYAP1 log2(TPM+1)
BLCA
5
708 normals vs 7078 tumors in TCGA
BRCA
CESC
| https://doi.org/10.1007/s12672-025-02408-3
0
CHOL
:
0
COAD
adjacent normal tissues based on the TCGA project. * p<0.05; ** p<0.01; *** p <0.001, **** p <0.0001
Fig. 2 The ADCYAP1 expression status in different tumors and normal tissues. 2A The mRNA expression of ADCYAP1 across 33 cancer types from TCGA data. 2B Pan-cancer differential expression analysis revealed significant ADCYAP1 difference across 16 tumor types compared to
DLBC
BLCA
ESCA
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GBM
BRCA
HNSC
KICH
ns
COAD
KIRC
KIRP
b
:
ESCA
LAML
LGG
HNSC
LIHC
LUAD
**
KICH
Normal
LUSC
MESO
KIRC
Tumor
OV
PAAD
ns
KIRP
PCPG
PRAD
READ
LIHC
SARC
ns
SKCM
LUAD
STAD
:
TGCT
LUSC
THCA
THYM
PRAD
UCEC
ns
UCS
READ
UVM
STAD
THCA
UCEC
95% CI= 1.22-2.56, p=0.003), STAD (HR= 1.91, 95% CI = 1.29-2.83, p= 0.002), and UCEC (HR = 1.73, 95% CI = 1.06-2.83, p=0.032). In contrast, high ADCYAP1 expression in ACC (HR=0.37, 95% CI = 0.15-0.89, p= 0.034), KIRC (HR= 0.51, 95% CI=0.36-0.73, p <0.0001), and LIHC (HR= 0.62, 95% CI = 0.40-0.94, p=0.027) was associated with longer OS.
Moreover, for certain common tumors like OA and THCA, although the association between ADCYAP1 expression and OS was not statistically significant, the p-values were in close proximity to 0.05 (Fig. 3A).
To further investigate the relationship between ADCYAP1 expression and OS across various cancers, we conducted K-M analysis using data from the TCGA database. This analysis allowed us to assess the mOS in different cancer types based on ADCYAP1 expression levels. Our findings from this analysis yielded the following results: Increased ADCYAP1 expression was associated with better prognosis in patients with ACC, KIRC and LIHC (Fig. 3B, 3C, and 3D). In contrast, patients with high ADCYAP1 expression exhibited significantly shorter mOS in BLCA, UCEC and STAD ( Fig. 3E, 3F, and 3G).
3.4 Analysis of DNA methylation and survival prognosis
We analyzed the association between OS and cg07376535 expression, a marker of DNA methylation. We further conducted univariate Cox regression analysis for overall survival and generated forest plots to assess the impact of cg07376535 expression on clinical outcomes (as depicted in Fig. 4A). Our results demonstrated that different cg07376535 expression levels were associated with heterogeneous prognosis across various tumor types. High cg07376535 expres- sion was significantly associated with adverse OS in ACC (HR=4.20, 95% CI = 1.82-9.66, p <0.001), KIRC (HR= 2.52, 95% CI=1.77-3.59, p<0.001), KIRP (HR=4.04, 95% CI=2.06-7.92, p<0.001), LGG (HR = 2.68, 95% CI= 1.76-4.08, p < 0.001), and THCA (HR=8.14, 95% CI =2.35-28.15, p=0.024). Conversely, high cg07376535 expression was observed as a favorable OS in STAD (HR= 0.56, 95% CI =0.37-0.83, p=0.007) and UCEC (HR = 0.44, 95% CI = 0.27-0.71, p=0.001).
We divided patients into high, median, and low expression groups and examined the relationship between cg07376535 expression and OS. Our analysis yielded the following key findings. The cg07376535 expression level served as a prognostic factor for OS in patients with several cancer types, including ACC, KIRC, KIRP, LGG, THCA, STAD, and UCEC (p <0.05). In the cases of ACC and KIRC, patients with high cg07376535 expression exhibited a poorer prognosis (mOS: 39.9 months vs. not available (NA) months, p <0.001; mOS: 54.2 months vs. 118.5 months, p <0.001, respectively). On the other hand, for STAD and UCEC, patients with high cg07376535 expression were associated with a better prognosis (mOS: 73.2 months vs. 26 months, p= 0.007; mOS: NA months vs. 108.4 months, p < 0.001, respectively) (Fig. 4B-4H).
3.5 Analysis of immune cell infiltration
While the existing findings suggest a potential prognostic role of ADCYAP1 expression in different cancer types, further investigation is required to better understand its underlying mechanisms. The immune microenvironment is recognized to significantly influence cancer prognosis, with immune cells playing a crucial role. Tumor-infiltrating immune cells can contribute to immune evasion, tumor progression, and overall patient outcomes.
A clustering heatmap analysis displayed a positive correlation between ADCYAP1 expression and various immune cell types, including B cells, CD8+T cells, macrophages (M0, M1, M2), activated NK cells, and monocytes (Fig. 5A). This positive association indicated that higher ADCYAP1 expression levels were linked to increased immune cell infiltration in the majority of the 33 cancer types studied. Notably, a negative correlation was observed between ADCYAP1 expres- sion and immune cell infiltration specifically in KICH. Moreover, we conducted a comparison of CD8 +T cell infiltrations across different cancer types within high, middle, and low ADCYAP1 score groups. The results consistently demonstrated a significant relationship. The elevated ADCYAP1 scores were positively associated with increased CD8 +T cell ratios in several cancer types, including ACC, BLCA, BRCA, CESC, CHOL, COAD, ESCA, HNSC, LGG, LUAD, LUSC, OV, pancreatic adenocarcinoma (PAAD), pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), STAD, THCA, and UCEC (Fig. 5B).
3.6 Potential of ADCYAP1 score in predicting immunotherapy response
Building upon our earlier findings that established a close link between ADCYAP1 expression and the degree of immune infiltration within tumors, we sought to investigate the potential relationship between ADCYAP1 expression and the response to immunotherapy. Given the connection we identified between ADCYAP1 and tumor immunity, we postulated that the ADCYAP1 score might have implications for immunotherapeutic outcomes. To explore this hypothesis, we conducted an in-depth analysis of the association between ADCYAP1 expression and tumor immunotherapy response. We accomplished
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A
ADCYAP1 correlated with overall survival across 33 cancers
B
Strata
ADCYAP1-Low
ADCYAP1-Intermedian
ADCYAP1-High
Tumor type
p for trend
HR (95% CI)
1.00
ACC
0.034
0.37 (0.15 - 0.89)
ACC
BLCA
0.003
1.77 (1.22 - 2.56)
BRCA
0.683
0.95 (0.63 - 1.43)
0.75
CESC
0.196
0.68 (0.39 - 1.19)
Survival probability
CHOL
0.886
0.93 (0.30 - 2.90)
p = 0.034
0.50
COAD
0.444
1.20 (0.76 - 1.91)
DLBC
0.773
0.72 (0.10 - 5.40)
mOS: NA vs 58.3
ESCA
0.203
0.69 (0.41 - 1.18)
0.25
HR,0.37; 95% CI 0.15-0.89
GBM
0.910
1.02 (0.67 - 1.56)
HNSC
0.207
0.82 (0.60 - 1.13)
0.00
KICH
0.093
0.20 (0.03 - 1.16)
0
24
48
72
96
120
months
KIRC
0.000
0.51 (0.36 - 0.73)
Note the risk set sizes
KIRP
0.675
1.18 (0.55 - 2.51)
LAML
0.557
0.88 (0.59 - 1.32)
Strata
26
19
11
5
2
2
27
18
8
3
3
2
LGG
0.265
1.27 (0.82 - 1.99)
26
22
11
8
4
1
0
24
48
72
96
120
LIHC
0.027
0.62 (0.40 - 0.94)
months
LUAD
0.107
0.74 (0.52 - 1.06)
C
Strata
ADCYAPI-Low - ADCYAP1-Intermedian
ADCYAP1-High
LUSC
0.127
1.29 (0.92 - 1.80)
MESO
0.511
0.78 (0.45 - 1.34)
1.00
OV
0.053
1.42 (1.00 - 2.02)
KIRC
PAAD
0.595
0.86 (0.52 - 1.42)
0.75
PCPG
0.975
0.93 (0.13 - 6.61)
Survival probability
PRAD
0.615
1.29 (0.32 - 5.20)
p < 0.001
READ
0.514
1.39 (0.51 - 3.81)
0.50
SARC
0.091
0.66 (0.40 - 1.07)
SKCM
0.274
1.18 (0.85 - 1.64)
mOS: NA vs 71.5
0.25
STAD
0.002
1.91 (1.29 - 2.83)
HR,0.51; 95% CI 0.36-0.73
THCA
0.791
0.83 (0.24 - 2.88)
THYM
0.062
5.95 (1.19 -29.65)
0.00
0
24
48
72
96
UCEC
120
0.032
1.73 (1.06 - 2.84)
months
UCS
0.596
1.19 (0.51 - 2.76)
Note the risk set sizes
UVM
0.544
1.31 (0.53 - 3.22)
Strata
178
114
63
42
14
4
177
129
80
25
11
6
178
119
81
39
17
6
1/32
1/16
1/8
1/4
1/2
1
2
4
8
16
32
0
24
48
72
96
120
months
Strata
ADCYAP1-Low — ADCYAP1-Intermedian
ADCYAP1-High
E
Strata
ADCYAP1-Low
ADCYAP1-Intermedian
ADCYAP1-High
F
Strata
ADCYAP1-Low
ADCYAP1-Intermedian
ADCYAPI-High
D
1.00
1.00
00
LIHC
BLCA
STAD
Survival probability
0,75
Survival probability
0.75
.75
p = 0.027
0.50
p = 0.003
p = 0.002
0.50
.50
0.25
mOS: 81.9 vs 45.7
0.25
mOS: 28.6 vs NA
25
mOS: 25.5 vs 58.2
HR,0.62; 95% CI 0.40-0.94
HR,1.77; 95% C| 1.22-2.56
HR,1.91; 95% CI 1.29-2.83
0.00
0.00
00
0
24
48
72
96
120
0
24
48
72
96
120
months
months
0
24
48
72
96
120
months
Note the risk set sizes
Note the risk set sizes
Note the risk set sizes
Strata
123
46
19
5
1
1
Strata
136
56
29
9
6
3
136
40
8
1
0
0
124
52
23
6
1
0
135
42
21
12
5
1
137
36
8
2
1
0
123
47
23
16
4
0
136
42
19
7
4
2
136
34
8
5
3
1
0
24
48
72
96
120
0
24
48
72
96
120
months
months
0
24
48
72
96
120
G
months
Strata
ADCYAP1-Low - ADCYAPI-Intermedian
ADCYAP1-High
1.00
UCEC
Survival probability
0.75
p = 0.032
0.50
mOS: 111.6 vs NA
0.25
HR,1.73; 95% CI 1.06-2.84
0.00
0
24
48
72
96
120
months
Note the risk set sizes
Strata
209
132
75
40
8
1
145
81
37
20
10
4
177
104
42
20
7
2
0
24
48
72
96
120
months
Discover
A
cg07376535 correlated with overall survival across 33 cancers
B
Strata
og07376535-Low
og07376535-Intermedian
og07376535-High
Tumor type
p for trend
HR (95% CI)
1.00
ACC
0.000
4.20 (1.82 - 9.66)
ACC
BLCA
0.242
1.27 (0.88 - 1.83)
BRCA
0.029
1.55 (1.04 - 2.31)
Survival probability
0.75
CESC
0.959
1.00 (0.56 - 1.79)
CHOL
0.832
1.02 (0.35 - 2.93)
p < 0.001
0.50
COAD
0.672
1.08 (0.68 - 1.73)
DLBC
0.945
0.88 (0.18 - 4.39)
0.25
mOS: 39.9 vs NA
ESCA
0.828
0.96 (0.56 - 1.66)
HR,4.20; 95% CI 1.82-9.66
GBM
0.451
1.17 (0.72 - 1.90)
HNSC
0.512
1.13 (0.81 - 1.56)
0.00
KICH
0.154
2.35 (0.63 - 8.70)
0
24
48
72
96
120
KIRC
2.52 (1.77 - 3.59)
months
0.000
Note the risk set sizes
KIRP
0.000
4.04 (2.06 - 7.92)
Strata
26
24
14
9
6
4
LAML
0.940
1.01 (0.62 - 1.65)
26
21
8
3
2
0
LGG
0.000
2.68 (1.76 - 4.08)
26
14
8
4
1
1
1.10 (0.73 - 1.65)
0
24
48
LIHC
72
96
120
0.642
months
LUAD
0.272
0.82 (0.58 - 1.16)
LUSC
0.336
0.85 (0.61 - 1.18)
C
Strata + og07376535-Low - og07376535-Intermedian
c007376535-High
MESO
0.925
0.92 (0.53 - 1.58)
1.00
OV
0.238
0.80 (0.56 - 1.14)
KIRC
PAAD
0.266
1.30 (0.78 - 2.15)
PCPG
0.609
1.99 (0.21 -19.15)
0.75
PRAD
0.506
1.87 (0.38 - 9.27)
Survival probability
READ
0.603
1.33 (0.48 - 3.66)
p < 0.001
SARC
0.957
0.99 (0.62 - 1.58)
0.50
SKCM
0.070
1.32 (0.96 - 1.82)
STAD
0.007
0.56 (0.37 - 0.83)
0.25
mOS: 54.2 vs 118.5
TGCT
HR,2.52; 95% CI 1.77-3.59
0.673
0.44 (0.05 - 4.24)
THCA
0.024
8.14 (2.35 -28.15)
THYM
0.690
0.81 (0.20 - 3.27)
0.00
0
24
48
72
96
120
UCEC
0.001
0.44 (0.27 - 0.71)
months
UCS
0.524
1.38 (0.60 - 3.14)
Note the risk set sizes
UVM
0.523
1.27 (0.47 - 3.41)
Strata
171
124
77
34
14
2
171
116
76
37
12
8
172
107
64
31
14
6
1/32
1/16
1/8
1/4
1/2
1
2
4
8
16
32
0
24
48
72
96
120
months
D
E
F
Strata + 0907376535-Low == c907376535-intermedian + c907376535-High
Strata
og07376535-Low
og07376535-Intermedian
og 07376535-High
Strata
c907376535-Low
cg07376535-Intermedian
cg07376535-High
1.00
THCA
1.00
1.00
LGG
KIRP
Survival probability
0.75
Survival probability
0.75
Survival probability
0.75
p = 0.024
0.50
p < 0.0001
p < 0.0001
0.50
0.50
0.25
mOS: NA vs NA
mOS: 52.8 vs 106.7
HR,8.14; 95% CI 2.35-28.15
0.25
HR,2.68; 95% CI 1.76-4.08
0.25
mOS: 87.0 vs NA
HR,4.04; 95% CI 2.06-7.92
0.00
0.00
0.00
0
24
48
72
96
120
months
0
24
48
72
96
120
0
24
48
72
months
96
120
Note the risk set sizes
months
Note the risk set sizes
Note the risk set sizes
Strata
167
93
43
25
11
4
Strata
171
85
30
18
27
12
7
167
101
45
14
5
95
58
31
12
4
1
170
91
33
20
11
7
Strata
167
54
118
50
22
13
28
8
94
17
10
3
171
68
28
14
5
2
24
72
95
36
96
120
19
10
0
0
0
48
months
0
24
48
72
95
120
months
0
24
48
72
96
120
months
G
H
Strata
og07376535-Low == cg07376535-Intermedian + 0007376535-High
Strata
og07376535-Low - og07376535-Intermedian
og07376535-High
1.00
1.00
STAD
UCEC
Survival probability
0.75
Survival probability
0.75
EN
p = 0.007
p < 0.001
0.50
0.50
0.25
mOS: 73.2 vs 26.0
HR,0.56; 95% CI 0.37-0.83
0.25
mOS: NA vs 108.4
HR,0.44; 95% CI 0.27-0.71
0.00
~
0.00
0
24
48
72
96
120
0
24
48
72
96
120
months
months
Note the risk set sizes
Note the risk set sizes
Strata
136
31
7
2
1
0
Strata
176
94
44
24
6
3
135
41
9
5
3
1
176
103
48
24
10
1
136
38
8
1
0
0
176
119
62
32
9
3
0
24
48
72
96
120
0
24
48
72
96
120
months
months
Discover
Analysis
A
Discover Oncology
B
ACC
T cell CD8 by CIBERSORT.ABS
0.5
BLCA
0.4
BRCA
CESC
0.3
NA
0.2
E
Ja
CHOL
Jen
1*
1:
COAD
0.1
IS
15
E
DLBC
1*
E
ESCA
the potential existence of tumor lineage-dependent molecular determinants of treatment response.
may require comprehensive biomarker signatures rather than relying on single-gene expression thresholds. Furthermore, cohorts, cutaneous melanoma cohorts receiving adoptive T cell therapy with tumor-infiltrating lymphocytes, and anti-PD-1
the observed cancer type-specific variations in predictive biomarkers for identical immunotherapeutic regimens underscore inhibitor cohorts. This cross-therapeutic validation suggests that combination immunotherapies and cellular immunotherapy
including renal cell carcinoma cohorts treated with avelumab plus axitinib combination therapy, nivolumab monotherapy
vs. 1.9 months, p=0.007). This observation suggests that ADCYAP1 may serve as an indicator of favorable anti-PD-L1/PD-1 therapy in BLCA(Fig. 6).However, ADCYAP1 wasn’t a significant predictive biomarker across multiple immunotherapy cohorts,
this by examining six datasets that are correlated with CIT. Our analysis revealed a significant finding in patients with BLCA: those with high ADCYAP1 expression had a notably extended mPFS compared to those with low expression (3.5 months
middle and low expression groups
CIBERSORT Absolute algorithm. Green represents positive correlation, grey represents negative correlation, and the darker the color, the stronger the correlation. * P<0.05, ** P<0.01, *** P<0.001, **** P<0.0001. B: Comparisons of CD8+T cell infiltration among ADCYAP1 high,
Fig. 5 ADCYAP1 in tumor immunity. A: Correlation of ADCYAP1 expression with twenty-two immune infiltrating cells in pan-cancer by
0.0
(2025) 16:593
GBM
ACC
+
Ja
1:
HNSC
1*
1*
KICH
BLCA
KIRC
BRCA
KIRP
CESC
1:
: is
LAML
NA
J:
11
LGG
CHOL
Jan
LIHC
COAD
LUAD
Group
| https://doi.org/10.1007/s12672-025-02408-3
DLBC
LUSC
MESO
ESCA
OV
NA
GBM
High
PAAD
HNSC
PCPG
NA
M
PRAD
KICH
Discover
KIRC
12
*
J:
Intermediate
READ
14
]*
SARC
NA
KIRP
Jo
NK cell resting
Myeloid dendritic cell activated
Eosinophil
T cell CD4 naive
SKCM
LAML
JE
1:
S
STAD
1*
TGCT
LGG
Low
THCA
LIHC
Ja * Ja
THYM
T cell follicular helper
Macrophage M1
B cell memory
Macrophage MO
Neutrophil
Mast cell resting
T cell gamma delta
T cell CD4 memory activated Myeloid dendritic cell resting
LUAD
Jo
UCEC
J: 11
T cell CD8
NK cell activated
T cell regulatory Tregs
Monocyte
B cell plasma
B cell naive
UCS
LUSC
UVM
MESO
JE
J:
Ji
1*
T cell CD4 memory resting
Macrophage M2 Mast cell activated
0.4
0.6
OV
0.2
0
PAAD
-0.6
-0.4
-0.2
PCPG
1:
PRAD
]*
READ
SARC
SKCM
12
STAD
ns
m
TGCT
THCA
THYM
UCEC
UCS
UVM
A
Strata + ADCYAP1-Low --- ADCYAP1-Intermedian + ADCYAP1-High
B
Strata + ADCYAPI-Low --- ADCYAP1-Intermedian + ADCYAP1-High
C
Strata + ADCYAP1-Low — ADCYAP1-Intermedian + ADCYAP1-High
1.00
1.00
1.00
BLCA GSE176307
KIRC PMID32472114
KIRC PMID32948859
Survival probability
0.75
Survival probability
0.75
Survival probability
0.75
p = 0.007
p = 0.148
0.50
mPFS: 3.5 vs 1.9
0.50
mPFS: 5.8 vs 3.6
p = 0.456
HR,0.46; 95% CI 0.26-0.81
0.50
HR,0.69; 95% CI 0.41-1.17
0.25
0.25
0.25
mPFS: 10.9 vs 11.4
HR,1.09; 95% CI 0.80-1.50
0.00
0.00
0.00
0
6
12
18
24
30
0
12
24
36
48
60
0
6
12
18
24
30
months
months
months
Note the risk set sizes
Note the risk set sizes
Note the risk set sizes
Strata
30
4
0
0
0
0
Strata
35
5
2
1
0
0
Strata
195
121
48
14
0
0
29
6
2
2
1
0
22
3
0
0
0
0
41
24
10
2
0
0
29
10
4
1
1
0
29
8
4
2
1
0
118
70
26
5
0
0
0
6
12
18
24
30
0
12
24
36
48
60
0
6
12
18
24
30
months
months
months
D
Strata
ADCYAPI-Low - ADCYAP1-Intermedian
ADCYAP1-High
E
Strata
ADCYAP1-Low - ADCYAP1-Intermedian
ADCYAPI-High
F
Strata
ADCYAP1-Low
ADCYAP1-Intermedian
ADCYAP1-High
1.00
1.00
1.00
SKCM GSE100797
SKCM GSE91061
SKCM phs000452
Survival probability
0.75
0.75
0.75
p = 0.096
Survival probability
Survival probability
p = 0.659
mPFS: 28.2 vs 3.4
p = 0.957
mPFS: 3.9 vs 5.9
0.50
HR,0.42; 95% CI 0.13-1.36
0.50
0.50
mPFS: 3.7 vs 1.9
HR,1.12; 95% CI 0.71-1.78
HR,1.11; 95% CI 0.42-2.97
0.25
0.25
0.25
0.00
0.00
0.00
0
12
24
36
48
60
0
6
12
18
24
30
months
months
0
12
24
36
48
60
months
Note the risk set sizes
Note the risk set sizes
Note the risk set sizes
Strata
8
1
1
1
1
1
Strata
8
2
2
2
0
0
Strata
73
28
19
4
1
0
9
2
1
1
1
0
9
2
1
1
1
0
8
3
1
0
0
0
8
4
4
3
2
1
8
3
2
1
0
0
40
13
7
1
1
0
0
12
24
36
48
60
0
6
12
18
24
30
months
months
0
12
24
36
48
60
months
4 Discussion
A significant amount of evidence has focused on analyzing the role of ADCYAP1 in physiological conditions such as aging, as well as in diseases, including cancer [4, 7]. However, it remains unclear whether ADCYAP1 is involved in the oncogenesis of certain tumor types or if it is more commonly involved in pathways promoting tumor pathogenesis. While many studies have indicated the involvement of ADCYAP1 in different cancers, its controversial function has been highlighted due to its diverse effects depending on the histopathological hallmarks of the tumor, disease stage, peptide concentration, and treatment duration [4, 7, 24, 25]. In some cases, it has been found to promote cell proliferation, while in others, it has promoted apoptotic cell death [9, 26]. Therefore, the aim of this study was to gain more insight into the potential role of ADCYAP1. We conducted a pan-cancer analysis of ADCYAP1 in 33 different cancer types to explore their prognostic value, relationship with the tumor microenvironment, and immunotherapeutic responses.
Firstly, we observed a high prevalence of CNV in cancers, including BLCA, HNSC, LUSC, OA, SKCM, and UCEC. Through mutation feature analysis of ADCYAP1 in pan-cancer, we found that the highest alteration frequency (> 4%) appeared in patients with BLCA, ESCA, HNSC, LUSC, OA, SKCM, UCEC, and UCS. Amplification, a type of CNV, was associated with poor prognosis.
Furthermore, the mRNA expression level of ADCYAP1 was relatively overexpressed in KICH, MESO, PAAD, and PCPG, but was found to be lowly expressed in DLBC, LAML, and UVM. To further confirm the expression of ADCYAP1, we compared it in 16 tumor types with normal tissues using an online database. ADCYAP1 was significantly downregulated in most types of cancer, such as BLCA, BRCA, COAD, ESCA, KIRC, KIRP, LIHC, LUSC, READ, STAD, and UCEC. However, it was only found to be upregulated in KICH. These results suggest a potential involvement of ADCYAP1 in cancer inhibition, consistent with previous findings [9].
Discover
Moreover, a gene methylation study reported the repression of ADCYAP1 expression in cervical cancer cell lines [24]. Another study indicated that ADCYAP1 can suppress the proliferation of human k and 1 light chain-secreting multiple myeloma-derived cells, demonstrating its potential as an antitumor agent that directly inhibits myeloma cell growth and indirectly affects tumor cell growth [27]. Conversely, other studies have shown that ADCYAP1 plays a neurotrophic role in human neuroblastoma SH-SY5Y tumor cells and exhibits anti-apoptotic effects in schwannoma cell lines [28]. Similar results were found in breast cancer and lung cancer [4, 7]. These findings contradict our results. The discrepancy can be attributed to the fact that ADCYAP1 has both proliferative and anti-proliferative effects on cancer cell growth. It is possible that ADCYAP1 may serve different functions in different types of cancers. Further research should be conducted to deepen our knowledge of the role of ADCYAP1.
Our survival analysis revealed a clear link between high expression of ADCYAP1 and poor prognosis in BLCA, STAD, and UCEC. However, ACC, KIRC, and LIHC showed an exception to this trend. DNA methylation alterations are frequently observed in various cancers and are believed to play a critical role in carcinogenesis and epigenetics. Specifically, hypermethylation of the promoter region is known to inhibit the expression of downstream genes [29]. There has been limited research on ADCYAP1 methylation in cancers. In this study, we performed DNA methylation analysis of ADCYAP1 in pan-cancer samples. High expression of cg07376535 was observed as a favorable survival factor in STAD, whereas adverse overall survival was observed in ACC, KIRP, and THCA. Many researchers are focusing on developing diagnostic markers for the early detection of various cancers using epigenetic alterations in oncogenes and tumor suppressor genes. DNA methylation has been considered a useful tool for cancer diagnosis. Hypermethylation in the promoter region of potential tumor suppressor genes often leads to gene inactivation and loss of normal function. Our findings suggest ADCYAP1 as a potential prognostic marker in the indicated tumor types. Previous studies have shown a high correlation between ADCYAP1 methylation and the development of cervical cancer, with gene expression being suppressed by gene promoter hypermethylation [30]. Another study revealed the impact of promoter region methylation on ADCYAP1 expression in bladder cancer [31]. However, limited research exists regarding the relationship between ADCYAP1 methylation level and prognosis in ACC and KIRC. Our findings indicate a correlation between ADCYAP1 methylation and prognosis in certain tumors. Nonetheless, additional evidence is required to fully evaluate the multiple functions of ADCYAP1 methylation in tumorigenesis.
The tumor microenvironment refers to the environment in which tumor cells grow and interact. Immune cells within the tumor microenvironment play a crucial role in the prognosis and survival outcomes of cancer patients. The infiltration of immune cells is a common feature in most types of cancer [32-34]. Immune cells such as CD8 +T cells, B cells, CD4+T cells, neutrophils, and macrophages secrete various factors that influence the tumor microenvironment, regulate tumorigenesis, and possess anti-cancer abilities [35]. Previous studies have found a correlation between tumor immune infiltration and cancer prognosis [36]. In our study, we aimed to investigate the potential association between ADCYAP1 expression and the tumor microenvironment. Interestingly, we found that ADCYAP1 expression was significantly correlated with various immune-associated cells in different cancer types. Of particular note, we observed a negative correlation between ADCYAP1 expression and the infiltration level of different immune cells in KICH. This finding supports our previous conclusion that ADCYAP1 has contrasting effects in different tumor types. Additionally, our results revealed a strong link between ADCYAP1 levels and CD8 +T cells. Using the CIBERSORT ABS algorithm, we determined that 18 cancers (ACC, BLCA, BRCA, CESC, CHOL, COAD, ESCA, HNSC, LGG, LUAD, LUSC, OV, PAAD, PCPG, PRAD, STAD, THCA, and UCEC) showed significantly elevated CD8 +T cell infiltration in the high ADCYAP1 expression group compared to the low ADCYAP1 expression group. These results suggest that ADCYAP1 expression may influence the response of cancer patients to immune checkpoint therapy, contributing to a better understanding of the mechanism of immunotherapy for treating cancers. However, there is still limited research on the function of ADCYAP1 in the human immune system. The roles of ADCYAP1 in the tumor immune microenvironment and immune-associated cells remain areas of research gap and should be investigated further.
CIT has emerged as a new method for cancer therapy, with immune checkpoint genes like CTLA-4, PD-1, and PD-L1 being important predictive biomarkers in various cancer treatments [17, 37]. Immune checkpoint inhibitors are currently a highly effective anticancer immunotherapy [38]. The question arises: can ADCYAP1 act as an immune checkpoint gene? A study by Xu et al. [31] uncovered a significant correlation between ADCYAP1 expression and immune-infiltrated microenvironment as well as responses to immunotherapy in bladder cancer. Considering our previous findings, it suggests that ADCYAP1 may serve as a potential prognostic biomarker and have an influence on immune cell infiltration. To explore this further, we examined the predictive value of ADCYAP1 in six immunotherapy datasets. We analyzed the relationship between patients’ response to immunotherapy and ADCYAP1 expression in three cancers: BLCA, KIRC, and SKCM. Surprisingly, we discovered that ADCYAP1 expression in BLCA affected
Discover
the effectiveness of immunotherapy, with higher ADCYAP1 expression associated with improved progression-free survival. Furthermore, comparing the gene expression levels between BLCA and normal tissues, we found that ADCYAP1 was relatively less expressed in BLCA. Consequently, the results of the immunotherapy study suggested that patients with high ADCYAP1 expression experienced better favorable immunotherapy effects. However, it is important to note that these findings need clinical confirmation to establish a definitive correlation between ADCYAP1 expression and immunotherapy response.
To the best of our knowledge, this is the first pan-cancer analysis focused on the roles of ADCYAP1 in tumors. Our comprehensive pan-cancer analysis of ADCYAP1 has shed light on its features in multiple cancer types. We observed that ADCYAP1 was predominantly downregulated in most cancers. Interestingly, the correlation of ADCYAP1 with overall survival varied across different cancer types. Similarly, the correlation between ADCYAP1 methylation and overall survival was also inconsistent across various cancers. Together, these results suggest that ADCYAP1 could serve as a potential biomarker for immunotherapy response and may act as an immune checkpoint gene.
However, there are several limitations to our study. Firstly, our study relied primarily on bulk RNA sequencing data from public databases, which may obscure cell-type-specific expression patterns due to the averaging of signals across heterogeneous cell populations within tumors. Single-cell RNA sequencing could provide a more granular understanding of ADCYAP1’s spatial and temporal expression dynamics within the tumor microenvironment. By resolving expression at the single-cell level, future studies could identify whether ADCYAP1 is predominantly expressed in malignant cells, infiltrating immune cells, or other stromal components, thereby clarifying its context-dependent mechanisms. Additionally, it might reveal how ADCYAP1 expression correlates with functional states of immune cells or tumor cell subpopulations with varying metastatic or therapeutic resistance potentials. Such insights could refine its utility as a prognostic or predictive biomarker and inform targeted therapeutic strategies. Our survival analyses primarily relied on treatment-naive TCGA data and on-treatment ICB cohorts due to the lack of pre-treatment transcriptomic profiles in public datasets. Future studies with baseline samples from ICB trials are critical to confirm ADCYAP1’s prognostic utility.
In summary, our study provides a comprehensive analysis of ADCYAP1 across a wide range of 33 cancer types. The results highlight its prognostic value, its influence on immune infiltration, and its potential correlation with immunotherapy response in diverse cancer types. These findings offer new insights into the prognostic significance of ADCYAP1 and open avenues for further exploration of the interactions between ADCYAP1 and the tumor microenvironment, as well as its implications for immunotherapy response in BLCA.
Acknowledgements None.
Author contributions XY.J and Y.D wrote the main manuscript text. All authors reviewed the manuscript.
Funding This work isn’t supported by any fund.
Data availability The datasets for this study can be found in the following website: https://xenabrowser.net/datapages/.
Declarations
Ethics approval and consent to participate Not applicable.
Consent for publication Not applicable.
Competing interests The authors declare no competing interests.
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