Adenylate cyclase 4 suppresses malignant progression of lung adenocarcinoma through interaction with protein kinase C beta

Introduction

Lung cancer has developed into one of the leading causes of cancer deaths worldwide, with high morbidity and mortality rates (1). Non-small cell lung cancer (NSCLC) patients account for nearly 90% of all lung cancer patients, while lung adenocarcinoma (LUAD) is the most frequent subtype of NSCLC (2,3). Currently, surgical resection, radiotherapy, immunotherapy or tyrosinase inhibitors alone or in combination for the treatment of LUAD are widely utilized in clinical practice (4). However, recurrence or metastasis of LUAD results in a poor clinical prognosis. So, it is extremely important to identify biomarkers that regulate the malignant progression of LUAD. However, the molecular mechanisms underlying LUAD progression remain incompletely understood, and effective biomarkers for prognosis and therapeutic intervention are still limited.

Adenylate cyclase 4 (ADCY4) belongs to an isoform of the adenylate cyclase family, which participates in the regulation of many different biological processes in tumors (5), such as apoptosis, proliferation, invasion, migration, and immune escape (6-8). In LUAD patients, the higher the expression of ADCY4, the better the prognosis, and it can be used as a pivotal gene to predict the occurrence of LUAD (9,10). However, whether it plays a role in the biological activity of LUAD is not known. Previous studies have shown that ADCY4 level obviously declined in breast cancer (BRCA) (11). It has also been suggested that low ADCY4 level represents worse survival of lung squamous cell carcinoma patients (12). The above evidences suggest that ADCY4 may act as a candidate anti-oncogene in malignant tumors, but its role in the biological malignant processes such as proliferation, invasion, and migration of LUAD cells deserves further investigation. Collectively, these findings suggest that ADCY4 may function as a tumor suppressor in multiple malignancies; however, its biological role and mechanistic involvement in LUAD remain largely unexplored.

Protein kinase C beta (PRKCB) is a member of the protein kinase family that exists as a potential target and prognostic factor in cancer by regulating multiple signaling pathways. Liu et al. find that PRKCB is a good predictor for clinical NSCLC patients (13). The study also presents that PRKCB expression is reduced in LUAD and is accompanied by poor prognosis (14). Besides, it is noted that high expression of both ADCY4 and PRKCB is significantly connected with longer survival in BRCA patients (15). Based on these observations, we hypothesized that ADCY4 may regulate LUAD malignant progression through interaction with PRKCB. Given that both ADCY4 and PRKCB are involved in key signaling pathways regulating tumor progression and share prognostic relevance in multiple cancers, a potential functional interaction between them is worth investigating.

In the present study, we screened the potential hub gene ADCY4 in LUAD based on the microarray datasets (GSE75037 and GSE118370) and The Cancer Genome Atlas (TCGA)-LUAD dataset in combination with TCGA database, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and the Search Tool for the Retrieval of Interacting Genes (STRING) database. We also confirmed the influence of ADCY4 expression in the malignant biological progression of LUAD and the specific role through a series of cellular and molecular biology experiments, providing important evidence to investigate the function of ADCY4 in LUAD. We present this article in accordance with the ARRIVE and MDAR reporting checklists (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-0441/rc).

Methods

Bioinformatics analysis

To identify key dysregulated genes involved in LUAD progression, we performed an integrative bioinformatics analysis using Gene Expression Omnibus (GEO) and TCGA datasets. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. GSE75037 and GSE118370 datasets were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/geo/), including 83 normal lung tissues and 83 LUAD tissues, and 6 normal lung tissues and 6 LUAD tissues, respectively, and acquired the gene expression dataset of LUAD from the TCGA database (https://cancergenome.nih.gov/). Firstly, differential genes of LUAD from the two GEO datasets were obtained using the “limma” in the R package. Then, the differential genes of LUAD from the TCGA database were acquired. Take the intersection of the differential genes of the three datasets to obtain the differential co-expressed genes, which were visualized using Venn Diagram. Pathway enrichment analysis of the differentially co-expressed genes was performed using KEGG, and protein-protein interaction (PPI) networks of genes in the enriched signaling pathways were built from the STRING database (https://string-db.org/), and these genes were visualized to obtain a key gene. Finally, the expression and prognosis of key gene in normal lung tissues and LUAD tissues were verified using the GEO dataset, the UALCAN (http://UALCAN.path.uab.edu/) database from TCGA, and the Kaplan-Meier plotter (https://kmplot.com/) database (P<0.05).

Additionally, we also downloaded STAR-counts data and corresponding clinical information for LUAD tumors from the TCGA database (https://portal.gdc.cancer.gov). The GTEx data we used to be from the V8 version, detailed information can be found on the official GTEx website (https://gtexportal.org/home/datasets). We then extracted data in transcripts per million (TPM) format and performed normalization using the log₂(TPM +1) transformation. After retaining samples that included both RNAseq data and clinical information, we ultimately selected LUAD samples for further analysis.

Regarding the correlation analysis of the ADCY4 and GSEA pathways, we collected the genes included in the corresponding pathways and then analyzed them using the GSVA package in R software, choosing the parameter method = ’ssgsea’ for single-sample gene set enrichment analysis (ssGSEA). Finally, we studied the correlation between gene expression and pathway scores through Spearman correlation analysis. TIDE (tumor immune dysfunction and exclusion) algorithm was used to predict potential immune therapy responses. To conduct a reliable immune score assessment, we utilized the “immunedeconv” R package, which integrates six of the latest algorithms for evaluating immune cell infiltration. These algorithms include TIMER, xCell, MCP-counter, CIBERSORT, EPIC, and quantIseq. All these algorithms have undergone systematic comparison and validation, and each demonstrates its unique performance and advantages. In this study, we used MCP-counter to label gene sets to quantify the immune-related cell populations in tumors. Statistical analysis was conducted using R software, version v4.0.3. Results were considered statistically significant when the P value was less than 0.05.

Cell culture

Human bronchial epithelial (HBE) cells and human LUAD cell lines including A549, H1975, and H1793 were acquired from Procell (China). A549, H1975, and H1793 cells were cultured in RPMI 1640 medium (Gibco, USA) supplemented with 10% FBS (Excell, China) and 1% penicillin-streptomycin solution (Biosharp, China). HBE cells were cultured in DMEM medium (Procell, China) and added with the same reagents. All cells were grown in an incubator at 37 ℃ with 5% CO₂.

Cell transfection

Overexpression plasmids for ADCY4 (Ov-ADCY4) and PRKCB (Ov-PRKCB), ADCY4 overexpression combined with PRKCB knockdown (Ov-ADCY4 + shPRKCB) and their respective negative controls (GenePharma, China) were constructed and then transfected into H1793 cells to form overexpression of ADCY4 or PRKCB, individually. Silencing of PRKCB in H1793 cells was achieved by transfection with PRKCB siRNA (GenePharma, China). Cell transfection was executed using Liposome 2000 Transfection reagent (Invitrogen, USA).

Reverse transcription quantitative polymerase chain reaction (RT-qPCR)

Extracting total RNA from transfected H1793 cells or control cells, the TRlzol method (Qiagen, USA) was employed. cDNA was generated by reverse transcription of RNA using the Takara Reverse Transcription Kit (China). Finally, gene expression was detected using the SYBR Green reagent (Roche Diagnostics, USA). The 2^−ΔΔCt method was used to analyze all data to obtain the target mRNA expression level.

Western blot

Transfected H1793 cells or control cells were lysed with RIPA protein extraction reagent (Beyotime, China). Total protein samples were separated by 10% SDS-PAGE and then transferred to PVDF membranes. Five percent skimmed milk was prepared, after which the membrane was closed for 1 h. Later, primary antibodies were added for the reaction (4 ℃, overnight), and the following day, an HPR-coupled rabbit second antibody (1:3,000, RGAR001, Proteintech) was added and reacted for 1–2 hours. Protein bands were detected using the ECL system, and the bands were scanned with image J for analysis of protein expression. Primary antibodies used included ADCY4 (1:1,000, Invitrogen, PA5-101283), MMP9 (1:1,000, ab76003), MMP14 (1:5,000, ab51074), PRKCB (1:2,000, ab181558) (all from Abcam except ADCY4 antibody).

Cell counting kit-8 (CCK-8)

Transfected H1793 cells or control cells were inoculated into 96-well plates. The medium in the wells was replaced with RPIM1640 medium containing 10% CCK-8 reagent (APE Bio, China) at different time points, and after half an hour of reaction at 37 ℃, the absorbance values were detected under a microplate reader.

5-ethynyl-2'-deoxyuridine assay (EDU) staining

Treated H1793 cells were planted into 96-well plates and stained using the EDU assay kit (Beyotime, China) according to the user’s guide. Images were captured under a fluorescence microscope by selecting random areas.

Wound healing

Transfected H1793 cells or control cells with 5×10⁵ cells per well were grown at 6-well plates. When the cells were in a good state of growth, the cell monolayers were scratched to form a wound with a sterile 200 µL pipette tip. The floating cells were rinsed using sterile PBS, and the remaining cells in the wells were incubated with serum-free RPIM 1640 medium for 24 hours. The wound area reflects the migratory capacity of the cells. Wound images were captured with a microscope at 0 and 24 hours.

Transwell

The invasion assay was implemented using a transwell insert (Nest, China) in 24-well plates. 100 µL of Matrix (Med Chem Express, China) was spread in the upper chamber to await membrane formation. H1793 cells mixed with serum-free medium were injected into the upper chamber, and 600 µL of medium with normal concentration of serum was injected into the lower chamber. After 24 hours, the upper cells were wiped with a cotton swab, and the lower cells were treated with 4% paraformaldehyde and then stained with crystal violet for 15 minutes. Random area images were acquired by a microscope (×200 magnification).

Co-immunoprecipitation (Co-IP)

The experiment was applied using the Co-IP kit (Bersinbio, China). Briefly, the IP lysate lysed the cells and later centrifuged to gain the cell lysate. Appropriate antibodies and agarose beads were added for immunoprecipitation and protein-protein complexes were analyzed using western blot and quantified by Image J software.

Statistical analysis

In this study, all biological experiments were conducted with at least three biological replicates, and each replicate consisted of three technical replicates. Statistical analysis was performed using GraphPad Prism 8 software, and significance analysis between different groups were achieved by one-way analysis of variance (ANOVA) or t-test. The results of the analysis were expressed as mean ± standard deviation (SD). P<0.05 indicates that the results are statistically significant.

Xenograft tumor model

BALB/c nude mice (4 weeks old, female) were purchased from GemPharmatech and maintained under specific pathogen-free (SPF) conditions. All animal experiments were conducted in accordance with the institutional guidelines for the care and use of animals and were carried out under the project approval [No. IACUC-FPH-SL-20260605 (0409)] granted by the Ethics Committee of Fuzhou University Affiliated Provincial Hospital.The A549 cells with overexpression of ADCY4 (Ov-ADCY4) or with overexpression of ADCY4 combined with knockout of PRKCB (Ov-ADCY4 + shPRKCB), as well as their control group of A549 cells, were collected and resuspended in PBS. A total of 5×10⁶ cells in 100 µL were subcutaneously injected into the right flank of each mouse (n=5 per group). Tumor size was measured every 3 days using a micrometer, and tumor volume was calculated using the formula:

Tumor volume = 1/2 × length × width²

After 28 days, mice were sacrificed, and tumors were excised, photographed, and weighed. Tumor growth curves were plotted based on tumor volume measurements.

Results

Differential gene acquisition and enrichment analysis in LUAD

Differentially expressed genes in LUAD tissues and normal lung tissue samples were compared based on GSE75027, GSE118370, and TCGA-LUAD dataset. The results proved that 2,552 genes were remarkably dysregulated in GSE75027, 2,365 genes were dysregulated in GSE118370 (Figure 1A,1B), and 4,235 genes were differentially expressed in the TCGA-LUAD dataset. The Venn Diagram clearly suggested that there were 665 genes differentially co-expressed in the three datasets (Figure 1C). To understand the possible biological effects of these differentially co-expressed genes, we performed KEGG pathway analysis, which was seen to be predominantly enriched in ‘Pathways in cancer’ and ‘Rap1 signaling Pathway’ in Figure 1D. The STRING database predicted that ADCY4 belongs to the co-critical genes in the two signaling pathways (Figure 1E,1F). To further explore the expression pattern of ADCY4 across different cancer types, we performed a pan-cancer analysis using TCGA and GTEx datasets. As shown in the violin plot (Figure S1), the expression level of ADCY4 was significantly lower in multiple tumor types compared to normal tissues, including LUAD, BRCA, and colon adenocarcinoma (COAD), etc. These results suggest that ADCY4 may play a central role in LUAD-related oncogenic signaling pathways. But whether ADCY4 works in the same way is unclear.

Figure 1 Differential gene acquisition and functional enrichment analysis in LUAD. Differential genes for LUAD in the (A) GSE75037 and (B) GSE118370 datasets. The blue dots indicate down-regulated genes and red indicate up-regulated genes. (C) Co-expressed differential genes in the two GSE datasets and a TCGA-LUAD dataset. (D) Enriched KEGG pathway of co-expressed differential genes. Enriched genes of (E) ‘Pathway in cancer’ and (F) ‘RAP1 signaling pathway’. FDR, false discovery rate; KEGG, Kyoto Encyclopedia of Genes and Genomes; LUAD, lung adenocarcinoma; TCGA, The Cancer Genome Atlas.

Validation of the significance of ADCY4 in LUAD

To confirmed the role of ADCY4 in LUAD, we validated ADCY4 expression in the GSE dataset as well as the UALCAN database from TCGA. The results supported that ADCY4 was clearly lower in LUAD tissues compared with normal lung tissues (Figure 2A,2B). Prognostic analysis performed with the Kaplan-Meier plotter database presented that low expression of ADCY4 was obviously correlated with poorer patient prognosis (Figure 2C). Additionally, our analysis of LUAD patient data from TCGA revealed that patients with high ADCY4 expression had longer disease-free survival periods and a higher proportion of early-stage lung cancer patients (Figure 2D,2E). Further analysis indicated that compared to patients with advanced-stage disease, patients with T1 stage had higher levels of ADCY4 expression (Figure 2F, Figure S2A,S2B). By analyzing the correlation of ADCY4 with various signaling pathways in LUAD and GSEA, we found that the expression level of ADCY4 was significantly negatively correlated with tumor proliferation and metabolic-related pathways (Figure 2G,2H, Figure S2C-S2I). Additionally, immune-related analysis also revealed a significant association between ADCY4 expression and immune checkpoints and immune checkpoint blockade therapies. Moreover, the immune score showed that various immune-related cells in LUAD had significant differences in ADCY4 expression between the differential expression groups (Figure S3). These findings suggest that ADCY4 may play a dual role in LUAD by regulating both tumor-intrinsic signaling and tumor-immune interactions.

Figure 2 Validation of the significance of ADCY4 in LUAD. (A,B) The expression of ADCY4 in normal and LUAD tissues in the GSE and TCGA-LUAD datasets. Data are shown as means ± SD. *, P<0.05 and ***, P<0.001. (C) Kaplan-Meier plotter database demonstrated the prognosis of ADCY4. (D) The KM survival curve of the ADCY4 gene in LUAD from TCGA data, where different groups are tested using the log-rank test. HR represents the hazard ratio of the ADCY4 high expression group relative to the ADCY4 low expression group. The 95% CI represents the confidence interval for the HR. (E) The proportion of patients with different clinical stages in the LUAD group with high or low expression of ADCY4 in the TCGA database was analyzed using the chi-square test. Group I, II, III, and IV respectively represent the first, second, third, and fourth stages of the clinical TNM staging of LUAD. The significant P value between the two groups was determined. Here, n(obs) represents the number of samples used for the analysis. (F) This figure presents a boxplot of the expression distribution of the ADCY4 gene in T1 and Tx (x>1) stage LUAD patients in the TCGA database, as well as in normal lung tissues from the GTEx. The significance P values were calculated using the Kruskal-Wallis, where *** indicates P<0.001 and **** indicates P<0.0001. (G,H)This is a Spearman correlation analysis plot, which is used to show the correlation between the pathway score and the expression of gene ADCY4. In this plot, the x-axis represents the distribution of the expression of gene ADCY4, and the y-axis represents the distribution of the pathway score. The values at the top represent the results of the Spearman correlation analysis, including the p-value, correlation coefficient. ADCY4, adenylyl cyclase 4; CI, confidence interval; GSE, Gene Expression Omnibus Series; GTEx, genotype-tissue expression; HR, hazard ratio; KM, Kaplan-Meier; LUAD, lung adenocarcinoma; SD, standard deviation; TCGA, The Cancer Genome Atlas; TNM, tumor-node-metastasis; TPM, transcripts per million.

ADCY4 overexpression inhibits LUAD cell proliferation

Western blot assay tested the ADCY4 expression in HBE, A59, H1975, and H1793. The results expounded that ADCY4 level was reduced in LUAD cells compared with the HBE cells, especially the lowest level in H1793 cells, which were used for subsequent experiments (Figure 3A). Therefore, we overexpressed ADCY4 in H1793 cells and verified the transfection efficiency (Figure 3B,3C). The results of CCK-8 and EDU assays showed that ADCY4 upregulation greatly inhibited the proliferation of LUAD cells (Figure 3D,3E).

Figure 3 ADCY4 overexpression inhibits LUAD cell proliferation. (A) Western blot was used to detect the ADCY4 expression in LUAD cell lines. (B,C) The overexpression efficiency of ADCY4 was detected by RT-qPCR and western blot. (D,E) Cell proliferation was tested by CCK-8 and EDU staining. Data are shown as means ± SD. **, P<0.01; ***, P<0.001. ADCY4, adenylyl cyclase 4; CCK-8, cell counting kit-8; DAPI, 4',6-diamidino-2-phenylindole; EDU, 5-ethynyl-2'-deoxyuridine; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; HBE, human bronchial epithelial; LUAD, lung adenocarcinoma; mRNA, messenger RNA; Ov-ADCY4, overexpression-ADCY4; Ov-NC, overexpression-negative control; RT-qPCR, reverse transcription quantitative polymerase chain reaction; SD, standard deviation.

ADCY4 upregulation impedes invasion and migration of LUAD cells

In the wound healing and transwell assays, we examined the invasive and migratory abilities of H1793 cells after ADCY4 overexpression. As shown in Figure 4A,4B, the cell migration ability was notably impaired and the number of invaded cells was reduced in the Ov-ADCY4 group compared with the Ov-NC group. Western blot results similarly confirmed that the upregulation of ADCY4 suppressed the levels of the migration-associated proteins MMP9 and MMP14 (Figure 4C).

Figure 4 ADCY4 upregulation impedes invasion and migration of LUAD cells. (A) The ability of cell migration was analyzed using the wound healing assay. (B) The number of invaded cells was measured using the transwell assay. (C) Detection of MMP14 and MMP9 protein expressions through western blot assay. Data are shown as means ± SD. ***, P<0.001. ADCY4, adenylyl cyclase 4; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; LUAD, lung adenocarcinoma; MMP14, matrix metalloproteinase 14; MMP9, matrix metalloproteinase 9; Ov-ADCY4, overexpression-ADCY4; Ov-NC, overexpression-negative control; SD, standard deviation.

ADCY4 binds to PRKCB in LUAD cells

An interaction between ADCY4 and PRKCB was found in the PPI network (Figure 1E,1F), so we analyzed the relationship between ADCY4 and PRKCB (Figure 1E,1F). The results of western blot revealed that the expression of PRKCB was declined in LUAD cell lines and was also at the lowest level in H1793 cells (Figure 5A), and further assay revealed that PRKCB expression was elevated after overexpression of ADCY4 (Figure 5B). Finally, the results of Co-IP assay verified that ADCY4 could bind to PRKCB (Figure 5C,5D). Above evidences suggests that the ADCY4 and PRKCB are positively correlated in LUAD and ADCY4 is likely to regulate LUAD progression through PRKCB. These findings indicate that PRKCB is a direct downstream effector of ADCY4 in LUAD cells.

Figure 5 ADCY4 binds to PRKCB in LUAD cells. (A) The expression of PRKCB in LUAD cell lines was assessed by western blot. (B) The level of PRKCB was detected in H1793 cells overexpressing ADCY4. (C,D) Co-IP assay was performed to verify the binding of ADCY4 and PRKCB. Data are shown as means ± SD. **, P<0.01; ***, P<0.001. ADCY4, adenylyl cyclase 4; Co-IP, co-immunoprecipitation; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; HBE, human bronchial epithelial; IgG, immunoglobulin G; LUAD, lung adenocarcinoma; Ov-ADCY4, overexpression-ADCY4; Ov-NC, overexpression-negative control; PRKCB, protein kinase C beta; SD, standard deviation.

Elevated PRKCB inhibits LUAD cell proliferation, invasion and migration

Next, we made PRKCB overexpressed in H1793 cells using the same cellular experiments to verify its impact in LUAD (Figure 6A). The results implied that PRKCB elevated notably suppressed the proliferation, invasion, and migration of H1793 cells (Figure 6B-6E), as well as the levels of migration-associated proteins, but did not raise the level of ADCY4 in the cells (Figure 6F).

Figure 6 Elevated PRKCB inhibits LUAD cell proliferation, invasion and migration. (A) The overexpression efficiency of PRKCB was evaluated by western blot. (B,C) Cell proliferation was tested by CCK-8 and EDU staining. (D) The ability of cell migration was analyzed using the wound healing assay. (E) The number of invaded cells was measured using the transwell assay, crystal violet staining, ×200 magnification. (F) Detection of MMP14 and MMP9 expressions through western blot assay. Data are shown as means ± SD. ***, P<0.001. ADCY4, adenylyl cyclase 4; CCK-8, cell counting kit-8; DAPI, 4',6-diamidino-2-phenylindole; EDU, 5-ethynyl-2'-deoxyuridine; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; LUAD, lung adenocarcinoma; MMP14, matrix metalloproteinase 14; MMP9, matrix metalloproteinase 9; Ov, overexpression; Ov-NC, overexpression-negative control; PRKCB, protein kinase C beta; SD, standard deviation.

ADCY4 suppresses malignant progression of LUAD through PRKCB

To further investigate the interaction between ADCY4 and PRKC, we established overexpression of ADCY4 and knockdown of PRKCB in H1793 cells. As shown in Figure 7A, siRNA-PRKCB-1 had better interference efficiency and was therefore used for subsequent experiments. The results of a series of cellular phenotyping assays indicated that PRKCB knockdown reversed the suppression effect of ADCY4 upregulation on the proliferation, invasion, and migration abilities of LUAD cells (Figure 7B-7F). To further validate the tumor-suppressive role of ADCY4 in vivo, a xenograft model was established using LUAD cells. As shown in Figure 8A, tumor growth was significantly inhibited in the ADCY4 overexpression group compared with the control group. Notably, knockdown of PRKCB in ADCY4-overexpressing cells partially restored tumor growth, indicating that the inhibitory effect of ADCY4 is at least partially dependent on PRKCB. Consistently, tumor weight measurements at the endpoint showed that ADCY4 overexpression significantly reduced tumor mass, whereas PRKCB knockdown partially reversed this effect (Figure 8B). These findings demonstrate that ADCY4 suppresses LUAD tumor growth through regulation of PRKCB both in vitro and vivo.

Figure 7 ADCY4 suppresses malignant progression of LUAD through PRKCB. (A) The interference level of PRKCB was estimated by western blot. (B,C) Cell proliferation was tested by CCK-8 and EDU staining. (D,E) The abilities of cell migration and invasion were analyzed using the wound healing assay and transwell assay, crystal violet staining, ×200 magnification. (F) Detection of MMP14 and MMP9 expressions through western blot assay. Data are shown as means ± SD. ***, P<0.001. ADCY4, adenylyl cyclase 4; CCK-8, cell counting kit-8; DAPI, 4',6-diamidino-2-phenylindole; EDU, 5-ethynyl-2'-deoxyuridine; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; LUAD, lung adenocarcinoma; MMP14, matrix metalloproteinase 14; MMP9, matrix metalloproteinase 9; NC, negative control; Ov, overexpression; PRKCB, protein kinase C beta; SD, standard deviation; siRNA, small interfering RNA.

Figure 8 ADCY4 suppresses tumor growth in vivo through PRKCB. (A) Tumor growth curves of xenograft tumors derived from LUAD cells with control vector, ADCY4 overexpression, or ADCY4 overexpression combined with PRKCB knockdown. Tumor volume was measured at indicated time points. (B) Representative images of xenograft tumors and tumor weight analysis at the endpoint. Data are presented as mean ± SD. *, P<0.05; ****, P<0.0001. ADCY4, adenylyl cyclase 4; LUAD, lung adenocarcinoma; Ov, overexpression; PRKCB, protein kinase C beta; SD, standard deviation; shRNA, short hairpin RNA.

Discussion

LUAD remains a major cause of cancer-related mortality, and the identification of novel molecular regulators is critical for improving prognosis and therapeutic strategies (1). In the present study, we identified ADCY4 as a key tumor suppressor in LUAD through integrative bioinformatics analysis and experimental validation. Our findings demonstrate that ADCY4 is significantly downregulated in LUAD tissues and cell lines and is associated with poor patient prognosis, highlighting its potential as a prognostic biomarker.

Previous studies have shown that ADCY4 exhibits tumor-type specific functions. In BRCA, ADCY4 inhibits proliferation, migration, and metastasis through cAMP/PKA-mediated suppression of FAK/AKT and ERK signaling (11,16). In contrast, in small cell lung cancer, ADCY4 has been reported to promote brain metastasis through energy metabolism-related pathways (17). In colorectal cancer, ADCY4 regulates ferroptosis through the ALOX12B/ADCY4 axis by stabilizing downstream proteins (18). These findings suggest that ADCY4 functions in a context-dependent manner across different tumor types. In line with this, our pan-cancer analysis shows that ADCY4 expression varies significantly across cancers, supporting its tumor-type-specific role.

Mechanistically, we identified a novel regulatory axis in which ADCY4 directly interacts with PRKCB and positively regulates its expression. Functional assays demonstrated that PRKCB overexpression phenocopied the tumor-suppressive effects of ADCY4, whereas PRKCB knockdown significantly attenuated the inhibitory effects of ADCY4 on LUAD cell proliferation, migration, and invasion. Importantly, our in vivo xenograft experiments further confirmed that ADCY4 suppresses tumor growth, and that PRKCB knockdown partially rescues this effect. These findings establish the ADCY4-PRKCB axis as a functional regulator of LUAD progression.

As a member of the adenylate cyclase family, ADCY4 is known to catalyze the conversion of adenosine triphosphate (ATP) into cyclic AMP (cAMP), which serves as a key second messenger in multiple signaling pathways. Previous studies have demonstrated that ADCY4 exerts tumor-suppressive effects through cAMP-dependent mechanisms (19). However, in the present study, we uncovered a direct PPI between ADCY4 and PRKCB, suggesting a cAMP-independent regulatory mechanism. It is possible that ADCY4 regulates PRKCB through both cAMP-dependent and cAMP-independent pathways, representing a dual regulatory model. Further studies are needed to clarify the relative contribution of these mechanisms.

In addition to its tumor-intrinsic role, our bioinformatics analysis indicates that ADCY4 may also participate in the regulation of the tumor microenvironment. ADCY4 expression was significantly associated with immune cell infiltration and immune checkpoint-related genes, suggesting a potential role in tumor-immune interactions. These findings extend the functional scope of ADCY4 from a tumor suppressor to a potential modulator of the tumor microenvironment, highlighting its relevance for immunotherapy.

Despite these findings, several limitations should be noted. First, although we demonstrated the interaction between ADCY4 and PRKCB, the precise molecular mechanism remains to be fully elucidated. Second, the involvement of cAMP signaling in ADCY4-mediated regulation was not directly investigated. Third, although bioinformatics analyses suggest a role of ADCY4 in immune regulation, further experimental validation is required.

Hu et al. point that PRKCB expression is obviously correlated with the tumor-node-metastasis (TNM) stage of the tumor, and lower PRKCB shows poorer overall survival of patients, which can be seen as an independent predictor of prognosis in NSCLC patients (20). Likewise, PRKCB is significantly correlated with the survival of LUAD patients (21). The study also indicates that high levels of ADCY4 and PRKCB are significantly correlated with longer survival of BRCA patients (22). In present study, the PPI network predicted that ADCY4 could interact with PRKCB, and further Co-IP assay confirmed their mutual binding. With the validation of a series of cellular experiments, it was found that overexpression of PRKCB promoted LUAD cell progression, on the basis of which silencing of PRKCB could attenuate the suppression of malignant phenotype of LUAD cells by ADCY4 upregulation. Thus, in LUAD, low levels of ADCY4 inhibit PRKCB expression, which in turn promotes tumor cell proliferation, invasion, and migration.

Conclusions

In summary, this study identifies ADCY4 as a tumor suppressor in LUAD and reveals a novel ADCY4-PRKCB regulatory axis that inhibits malignant progression. Our findings provide new insights into LUAD biology and suggest that ADCY4 may serve as a promising biomarker and therapeutic target.

Acknowledgments

We would like to express our gratitude to the sources of all the public databases, algorithms and tools mentioned in the article.

Footnote

Reporting Checklist: The authors have completed the ARRIVE and MDAR reporting checklists. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-0441/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-0441/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-0441/prf

Funding: This work was supported by the Natural Science Foundation of Fujian Province, China (No. 2026J01632, to L.G.) and the Innovation of Science and Technology, Fujian Province (No. 2023Y9168, to L.G.).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-0441/coif). L.G. reports receiving research funding from the Natural Science Foundation of Fujian Province, China (No. 2026J01632) and the Innovation of Science and Technology, Fujian Province (No. 2023Y9168). The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. All animal experiments were performed under a project license [No. IACUC-FPH-SL-20260605 (0409)] granted by the Ethics Committee of Fuzhou University Affiliated Provincial Hospital, in compliance with Fuzhou University guidelines for the care and use of animals.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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