C1RL-AS1/microRNA-424-5p/adrenoceptor β2 axis: A novel regulatory mechanism in lung adenocarcinoma associated with immune infiltration and prognosis.

Introduction
Lung cancer, one of the most common and fatal cancer types worldwide, has attracted much attention due to its high morbidity and mortality [1–3]. Among lung cancer cases, ~ 85% are non-small cell lung cancer (NSCLC) cases and ~15% are small cell lung cancer cases. NSCLC is mainly divided into lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). Although the treatment of LUAD has been extensively studied, the survival rate of patients with LUAD remains poor [4,5]. Therefore, immediate action and inspection of targets and reliable prognostic markers for LUAD therapy are required.
Adrenoceptor β2 (ADRB2) expression is dysregulated in several types of cancer, including LUAD, and is closely associated with inflammatory responses. A growing number of findings suggest that ADRB2 serves an important role in the development and progression of multiple types of cancer, and is expressed differently in different types of cancer. Increasing numbers of studies have shown that the ADRB2 gene is likely to be a potential oncogene of LUAD [6]. Liang et al [7] conducted a large-scale analysis to reveal the expression patterns of ADRB2 in lung cancer and explored its interactions with the tumor microenvironment. Expression and survival analysis of ADRB2 are particularly important. A large amount of evidence has demonstrated that ADRB2 exhibits a close relationship with the emergence and development of various human diseases such as gastric cancer, breast cancer and hepatocellular carcinoma [8–11]. ADRB2 antagonists inhibit proliferation, metastasis and invasion through suppression of transcription factors and proteins involved in the ERK1/2-JNK-MAPK pathway such as activator protein 1, STAT3, cAMP-response element binding protein and NF-κB. In addition, ADRB2 is involved in immune signaling. It has been reported that systemic activation of ADRB2 in vivo can enhance the expansion and antitumor activity of T cell receptor-γδ T cells [12]. ADRB2 expression, its association with prognosis and the related mechanisms in LUAD remain unclear. To the best of our knowledge, the relationship between ADRB2 and effector immune infiltration in LUAD is unknown.
Studies have shown that non-coding RNAs (ncRNAs), including microRNAs (miRNAs/miRs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), serve important roles in the occurrence, development and treatment of lung cancer. miRNAs participate in the pathological process of lung cancer by regulating the expression of target genes, which can not only serve a tumor-suppressive role [13], but also promote tumor progression [14], and have potential in the early diagnosis and prognosis evaluation of lung cancer [15]. lncRNAs can affect lung cancer cell proliferation by regulating gene expression and chromatin cells [16]. circRNAs act as miRNA sponges to regulate gene expression [17]. At the same time, circRNAs show potential in the early diagnosis of lung cancer [18]. In addition, ncRNAs have also shown important value in lung cancer treatment [19], providing a novel strategy for the precise treatment of lung cancer.
In the present study, ADRB2 expression and overall survival were first analyzed in pan-cancer. Subsequently, the present study further explored the ncRNA-related regulation of ADRB2 in LUAD, including lncRNA- and miRNA-related regulation. A relationship was finally found between ADRB2 expression, immune cell infiltration and immune cell biomarkers. Overall, the present study demonstrated that ncRNA-mediated ADRB2 upregulation was associated with poor prognosis and immune infiltration in patients with LUAD.

Materials and methods

ADRB2 expression analysis
Data for a total of 30 cancer types including adrenocortical carcinoma, breast invasive carcinoma (BRCA), bladder urothelial carcinoma (BLCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), colon adenocarcinoma (COAD), cholangiocarcinoma (CHOL), lymphoid neoplasm diffuse large B-cell lymphoma, glioblastoma multiforme, esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal papillary cell carcinoma (KIRP), kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), acute myeloid leukemia, LUAD, LUSC, pheochromocytoma and paraganglioma (PCPG), pancreatic adenocarcinoma (PAAD), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), ovarian serous cystadenocarcinoma, sarcoma (SARC), stomach adenocarcinoma (STAD), skin cutaneous melanoma (SKCM), testicular germ cell tumors, thymoma (THYM), thyroid carcinoma (THCA), uterine corpus endometrial carcinoma (UCEC) and uterine carcinosarcoma were downloaded from UCSC Xena containing The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) datasets (https://xenabrowser.net/datapages/). The organizational correspondence of datasets between TCGA and GTEx, ADRB2 expression level in pan-cancer, and levels of lncRNAs and correlation of ADRB2 in LUAD with immune checkpoints were analyzed using Gene Expression Profiling Interactive Analysis (GEPIA) (http://gepia.cancer-pku.cn/). The correlations between the ADRB2 expression levels of immune cell infiltrated tumor tissues with immune cell infiltration levels and immune checkpoint expression levels in LUAD were analyzed using Tumor Immune Estimation Resource (https://cistrome.shinyapps.io/timer/). R 4.2 (https://www.R-project.org/) was used for data analysis and the ggplot2 package was used to plot the results. P < 0.05 was considered to indicate a statistically significant difference. R > 0.1 was set as the selection criterion.

ADRB2 drug sensitivity analysis
To determine the relationship between ADRB2 expression and drug sensitivity (IC50), ADRB2 expression data and drug sensitivity data were merged using the Genomics of Drug Sensitivity in Cancer (GDSC) database (https://www.cancerrxgene.org/). Using Pearson correlation analysis, the correlation between ADRB2 expression and drug sensitivity was determined. P < 0.05 was considered to indicate a statistically significant difference.

Copy number variation (CNV) and DNA methylation analysis
ADRB2 copy number data, DNA methylation data and corresponding expression data were obtained from UCSC Xena (https://xenabrowser.net/). CNV classification was performed based on the TCGA gene copy data The Genomic Identification of Significant Targets In Cancer 2 threshold was set as follows: A copy number of −1 or −2 was defined as a missing copy number, a copy number of 0 was defined as a normal copy number, and a copy number of 1 or 2 was defined as a copy number gain. P < 0.05 was considered to indicate a statistically significant difference.

Candidate miRNA prediction and analysis
The miRNA prediction tools RNA22 (https://cm.jefferson.edu/rna22v2/), PITA (http://genie.weizmann.ac.il/pubs/mir07/mir07 _data.html), miRmap (https://mirmap.ezlab.org/), miRanda (www.microrna.org/), microT (http://www.microrna.gr/microT-CDS), PicTar (https://pictar.mdc-berlin.de/) and TargetScan (https://www.targetscan.org/vert_80/) were used for the prediction of ADRB2 gene interactions with miRNAs. miRNAs that were predicted by more than two tools were considered as the candidate miRNAs.
The information of the aforementioned candidate miRNAs was obtained from the StarBase database (http://starbase.sysu.edu.cn/), including the expression levels of miRNAs in LUAD and normal tissues, the correlation analysis of miRNA and ADRB2 expression, and the predicted candidate interacting lncRNAs.

Cell culture
Lewis lung carcinoma (LLC) cells (Cat. No.: BNCC338433, Onco biomedical technology Co., Ltd.), human normal lung epithelial (BEAS-2B) cells (Cat. No.: ZQ0381, Shanghai Zhongqiaoxinzhou Biotechnology Co., Ltd.), human non-small cell lung cancer (A549) cells (Cat. No.: ZQ0003, Shanghai Zhongqiaoxinzhou Biotechnology Co., Ltd.), human non-small cell lung cancer (HCC827) cells (Cat. No.: ZQ0386, Shanghai Zhongqiaoxinzhou Biotechnology Co., Ltd.), human lung squamous cell carcinoma (H520) cells (Cat. No.: ZQ0014, Shanghai Zhongqiaoxinzhou Biotechnology Co., Ltd.) and human lung squamous cell carcinoma (H1703) cells (Cat. No.: ZQ0015, Shanghai Zhongqiaoxinzhou Biotechnology Co., Ltd.) were cultured in DMEM (Gibco; Thermo Fisher Scientific, Inc.) supplemented with 10% FBS (Gibco; Thermo Fisher Scientific, Inc.) and 1% penicillin-streptomycin in a humidified atmosphere with 5% CO2 at 37˚C.

Dual luciferase reporter gene experiment
A dual luciferase assay was performed. Approximately 200 bp sequences near the binding sites of ADRB2 and C1RL-AS1-wild-type (WT)/mutant (MT) （S1 Table）were synthesized and inserted into the pmirGLO vector, which contained firefly luciferase and Renilla luciferase genes. The Renilla luciferase activity was used as a control. The mimics of hsa-miR-424-5p and NC were synthesized. Plasmid transfection into 293T cells (SCC-120511, Beijing Solarbio Technology Co., Ltd) was performed in the following combinations to detect the interaction of hsa-miR-424-5p with ADRB2 and C1RL-AS1: i) NC-mimics + ADRB2-WT; ii) hsa-miR-424-5p-mimics + ADRB2-WT; iii) NC-mimics + ADRB2-MT; iv) hsa-miR-424-5p-mimics +ADRB2-MT; v) NC-mimics + C1RL-AS1-WT; vi) hsa-miR-424-5p-mimics + C1RL-AS1-WT; vii) NC-mimics + C1RL-AS1-MT; and viii) hsa-miR-424-5p-mimics + C1RL-AS1-MT. The luciferase activity was measured using the dual luciferase reporter assay kit (R41128).

Cell Transfection and Gene Modulation Assays
A549 cells were cultured in high-glucose DMEM medium supplemented with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin under the condition of 37 °C and 5% CO₂. When the cell confluency reached 60%–70%, C1RL-AS1 expression was inhibited via siRNA-C1RL-AS1, while hsa-miR-424-5p was overexpressed using its mimic (GenePharma, S1 Table); cell transfection was performed with Lipofectamine 3000 reagent. At 48 h post-transfection, cells were lysed with RIPA lysis buffer containing protease and phosphatase inhibitors to extract total cellular protein, and western blotting analysis were performed as the aforementioned method.
The lentiviral vector used for ADRB2 overexpression was pLV17-EF1α-ADRB2-Luciferase17-Puro (GenePharma, Shanghai GenePharma Co., Ltd.). The negative control group was transduced with an empty vector construct (pLV17-EF1α-Luciferase17-Puro; lacking ADRB2). When the cell confluence reached 70–80%, lentiviral transduction was performed to establish stable ADRB2 expression as follows: The cell culture medium was replaced with fresh DMEM, and lentivirus carrying the ADRB2 expression construct, which had been thawed on ice beforehand, was added to the cells at an appropriate ratio. To enhance the transduction efficiency, 5 µg/ml polybrene (GenePharma) was added. The mixture was gently agitated to ensure even distribution and incubated in a humidified incubator at 37˚C for 6 h. Following incubation, the lentivirus-containing medium was removed and replaced with fresh complete DMEM. The cells were further cultured for 48 h to allow for sufficient transgene expression. To establish a stable transfected cell line, 3 μg/ml puromycin selection was initiated, and the surviving cells were continuously passaged for at least three generations under selection pressure. The transfection efficiency was confirmed by western blotting, ensuring stable ADRB2 expression.

Western blotting analysis
Total protein was extracted from the cells of BEAS-2B (RRID: CVCL_0168), A549 (RRID: CVCL_0023), HCC827 (RRID: CVCL_2063), H520 ((RRID: CVCL_1566) and H1703 (RRID: CVCL_1490) after the treatment with RIPA lysis buffer () supplemented with 1% protease and 1% phosphatase inhibitors on ice. Proteins were quantified by the BCA assay and then the proteins were separated with 10% SDS-PAGE gel and electrophoretically transferred onto nitrocellulose membranes (Bio-Rad). ADRB2 was determined with the primary anti-ADRB2 antibody (29864–1-AP, Wuhan Proteintech Group, Inc., Ltd) and secondary antibody (SA00001–2, Wuhan Proteintech Group, Inc., Ltd). Images were quantified with Image J software and normalized to HSP90.

CCK-8 Assay for Cell Proliferation Detection
100 μL of cell suspension (approximately 1 × 10⁴ cells per well) was added to each well of a 96-well plate, which was then cultured in a 37 °C incubator with 5% CO₂ for 24 h to allow cell attachment. After cell attachment, the cells were transfected with the ADRB2 overexpression plasmid using Lipofectamine 3000 transfection reagent. Meanwhile, the detection time points were set at 0, 12, 24 and 48 h post-transfection. At each preset time point, the culture medium was discarded, and 100 μL of basal medium and 10 μL of CCK-8 solution were added to each well, followed by incubation in a 37 °C incubator for 2 h. Finally, the absorbance was measured at a wavelength of 450 nm using a microplate reader.

Animal model construction
C57BL/6 mice (male, 6-week-old) were randomly allocated into two groups (n = 5 per group) and maintained under specific pathogen-free (SPF) conditions. ADRB2-overexpressing LLC cells and empty vector-transduced control cells were harvested at 80% confluence, and resuspended in ice-cold PBS (pH 7.4; Gibco; Thermo Fisher Scientific, Inc.). A suspension containing 4 × 105 viable cells in 100 μl was subcutaneously injected into the left side of the mice using a 27-gauge insulin syringe (BD Biosciences, Charles River Laboratories, Inc.). Mice were anesthetized with 4% (v/v) isoflurane (Pfizer, Inc.) in oxygen for induction, followed by 1.5–2% (v/v) for maintenance via a nose cone, with the respiratory rate monitored throughout the procedure. The study was approved by the Institutional Animal Care and Use Committee of Hunan University of Chinese Medicine (approval no. A11051, Hunan, China).
Tumor size measurements were initiated on day 7 post-inoculation and were conducted every 2 days using digital vernier calipers, with volumes calculated as follows: V = 0.5 x length x width2 (mm3). When tumors reached 2,000 mm3 or at the study endpoint (day 22), mice were deeply anesthetized with 5% isoflurane and euthanized by cervical dislocation. Death was confirmed by absence of spontaneous breathing for >3 min, loss of pedal reflex and fixed/dilated pupils. Humane endpoints were implemented if the mice developed symptoms of other health issues, including rapid weight loss, severe debilitating diarrhea, labored breathing, bleeding from any body orifice, self-inflicted trauma, and impaired mobility.
All animal procedures were approved by the Institutional Animal Care and Use Committee of Hunan University of Chinese Medicine (approval no. A11051, Hunan, China) and performed in accordance with the American Veterinary Medical Association Guidelines for the Euthanasia of Animals.

Flow cytometry
A single-cell suspension was prepared from fresh tumor tissues immediately after sacrifice. Briefly, tumor tissues were minced and enzymatically digested at 37˚C for 30 min using collagenase IV (MilliporeSigma). The resulting cell suspension was filtered through a 70-µm cell strainer, washed twice with PBS and subjected to surface staining at 4˚C for 30 min. CD4+T cells (CD45+CD11b-C45R-CD3+CD4+) and dendritic cells (DCs; CD45+CD11c+MHCII+) were stained with fluorescently labeled antibodies (S2 Table). Immunophenotyping was conducted utilizing a Cytoflex LX flow cytometer (Beckman Coulter, Inc.), and the data on immune cell population frequencies were analyzed using FlowJo X (10.0.7 R2 x64) software.

Statistical analysis
All quantitative data was expressed as mean ± standard deviation (SD). Statistical analyses were conducted using IBM SPSS Statistics software (version 22). Graphs were conducted using GraphPad Prism software (version 8.0.1). Intergroup comparisons between two groups were performed with two-tailed unpaired Student’s t-tests, while those among multiple groups were analyzed using one-way ANOVA followed by LSD post hoc test. The statistical significance was defined as P < 0.05 (*: P ＜ 0.05, **: P ＜ 0.01, ***: P ＜ 0.001).

Results

Analysis of ADRB2 expression in pan-cancer
ADRB2 was markedly downregulated in seventeen cancer types (ACC, BLCA, BRCA, CHOL, COAD, DLBC, ESCA, HNSC, LUAD, LUSC, OV, READ, SKCM, STAD, THCA, UCEC, and UCS) compared with normal tissue samples, and markedly upregulated in GBM, KICH, KIRP, LAML, LGG, LIHC, PAAD, PCPG, PRAD, and TGCT, whereas in CESC, KIRC, PCPG, SARC, and THYM, no significant differences were observed (Fig 1A). Further investigation of ADBR2 gene expression in cancer using the GEPIA database suggested that ADRB2 expression in BRCA, BLCA, CHOL, LUSC, LUAD, STAD and UCEC was decreased (Fig 1B), No significant expression differences were found in other types of cancers (S1A Fig).
In summary, ADRB2 was downregulated in BRCA, BLCA, CHOL, LUSC, LUAD, STAD and UCEC, which indicated that ADRB2 might serve a role in the inhibition of these seven types of cancer.
Survival analysis was performed in BRCA, BLCA, CHOL, LUSC, LUAD, STAD and UCEC for the ADRB2 gene. Patients with LUAD with high ADRB2 expression exhibited an improved prognosis (Fig 1C), but no prognostic value was observed in patients with BLCA, BRCA, CHOL, LUSC, STAD and UCEC (S1B Fig). This result suggested that ADRB2 might be a potential prognostic biomarker in patients with LUAD. Besides, experimental studies revealed that ADRB2 expression was significantly downregulated in LUAD cells, whereas no significant changes were observed in LUSC cells (Fig 1D).

Biological function of ADRB2
To understand the biological function of ADRB2 in LUAD, the correlation between protein-coding genes in LUAD and ADRB2 was analyzed using the R (4.2) function cor. test (). All gene correlation analysis results are presented in S3 Table, and these genes were subjected to gene set enrichment analysis Kyoto Encyclopedia of Genes and Genomes (GSEAKEGG) analysis. S2 Fig shows the top 30 enriched GSEAKEGG pathways. The enrichment results suggested that the co-expressed genes of ADRB2 were mainly involved in the chemokine signaling pathway, calcium signaling pathway, regulation of actin cytoskeleton, cell adhesion molecules, Jak stat signaling pathway, B cell receptor signaling pathway and natural killer cell-mediated cytotoxicity. The complete enrichment results are presented in S4 Table.

Multidimensional mechanism of ADRB2 dysregulation and its upstream miRNA prediction
To clarify the cause of ADRB2 dysregulation, factors associated with ADRB2 non-expression, including genetic variation, DNA methylation and associated miRNAs, were comprehensively analyzed. CNV of ADRB2 is usually detected in patients with LUAD with a deletion rate of 34.81% and an expansion rate of 19.96% (Fig 2A). Among them, loss of copy number was the genetic variant most closely associated with ADRB2 expression (R = 0.29; P = 2x10-4), while copy number was the most closely related genetic variant. There was no significant correlation between the number of expansions and the expression levels. (Figs 2B-2D). ADRB2 has no introns, and its different polymorphic forms, mutations and downregulation are associated with a variety of diseases. Besides copy numbers, DNA methylation also affects gene expression (Fig 2E). ADRB2 expression was negatively correlated with DNA methylation (R = −0.49; P = 1.2x10-6). In addition to CNV and DNA methylation, miRNAs are of great importance in regulating mRNA expression.
It is well-known that ncRNAs are responsible for modulating the expression of genes. To better understand whether the ADRB2 gene is regulated or controlled by ncRNA, the upstream miRNAs that may target ADRB2 were first predicted, and 15 possible miRNAs were identified (let-7a-5p, let-7b-5p, let-7c-5p, let-7i-5p, let-7e-5p, let-7g-5p, let-7f-5p, let-7d-5p, miR-15a-5p, miR-15b-5p, miR-16-5p, miR-98-5p, miR-195-5p, miR-424-5p and miR-497-5p). Cytoscape software was used to build a miRNA-ADRB2 regulatory network (Fig 2F). Fig 2G shows that miR-424-5p was markedly upregulated in LUAD. Based on the regulatory mechanism of miRNAs and target genes, a negative correlation between miRNAs and ADRB2 was shown. Subsequently, expression correlation analysis was performed. As shown in Table 1, ADRB2 exhibited a negative correlation with miR-424-5p (R = −0.108; P = 1.41x10-2), and a positive correlation with let-7a-5p (R = 0.219; P = 5.51x10-7), let-7b-5p (R = 0.269; P = 6.60x10-10), let-7c-5p (R = 0.392; P = 2.66x10-20), let-7f-5p (R = 0.142; P = 1.32x10-3), let-7g-5p (R = 0.149; P = 7.03x10-4), miR-195-5p (R = 0.352; P = 2.43x10-16) and miR-497-5p (R = 0.337; P = 5.05x10-15). However, no statistically significant correlation was observed between ADRB2 and the other seven predicted miRNAs. Finally, miR-424-5p expression in LUAD was determined (Fig 2G). The dual luciferase reporter gene experiment results revealed that the relative luciferase activity was markedly decreased in the miR-424-5p-mimics + ADRB2-WT transfection group compared with the NC-mimics + ADRB2-WT group (P < 0.01; Fig 2H). No significant difference was observed in the ADRB2-MT groups. Further overexpression of miR-424-5p in A549 cells showed a significant reduction in ADRB2 expression (Fig 2I). These results indicate that miR-424-5p may serve as the core miRNA that represses ADRB2 expression in LUAD.

Prediction and analysis of upstream lncRNAs of miR-424-5p
In the following steps, the upstream lncRNAs of miR-424-5p were predicted using StarBase, which resulted in a total of 73 potential lncRNAs (S5 Table). The present study further analyzed the expression levels of the 73 lncRNAs in LUAD. Among all 73 lncRNAs, only STAG3L5P-PVRIG2P-PILRB (ENSG00000272752), C1RL-AS1 (ENSG00000205885) and GABPB1-AS1 (ENSG00000244879) were significantly upregulated in LUAD (Figs 3A-3C). miRNA and lncRNA should be negatively correlated, or mRNA and lncRNA should be positively correlated, according to the competing endogenous RNA (ceRNA) hypothesis. As shown in Table 2, the correlation between the expression of STAG3L5P-PVRIG2P-PILRB, C1RL-AS1, GABPB1-AS1 and/or miR-424-5p/ADRB2 in LUAD was examined using StarBase. The expression levels of STAG3L5P-PVRIG2P-PILRB (R = −0.15; P = 5.33x10-4), C1RL-AS1 (R = −0.22; P = 4.99x10-7) and GABPB1-AS1 (R = −0.15; P = 5.97x10-4) were all negatively correlated with miR-424-5p. However, ADRB2 expression was positively correlated only with C1RL-AS1 (R = 0.112; P = 1.05x10-2). The dual luciferase reporter gene experiment results also revealed that the relative luciferase activity was markedly decreased in the miR-424-5p-mimics + C1RL-AS1-WT transfection group compared with the NC-mimics + C1RL-AS1-WT group (P < 0.01; Fig 3D). No significant difference was found in the ADRB2-MT groups. Further knockdown of C1RL-AS1 in A549 cells revealed a significant decrease in ADRB2 expression (Fig 3E). Combined expression and correlation analyses suggested that C1RL-AS1 may be the upstream lncRNA for the miR-424-5p/ADRB2 axis in LUAD.

Association of ADRB2 with immune cell infiltration in LUAD
ADRB2 encodes the β-2-adrenoceptor, a member of the G protein-coupled receptor superfamily, which mediates the production of anti-inflammatory cytokines. We hypothesized that it may be vital in the immune system. Analysis of ADRB2 expression levels in tumor tissues infiltrated by immune cells revealed that the level of immune cell infiltration in LUAD was significantly affected by multiple copies of ADRB2 (Figs 4A and 4B). ADRB2 expression was positive in B cells, CD8+T cells, CD4+T cells, macrophages, neutrophils and DCs in LUAD. Single-cell RNA-sequencing (scRNA-seq) analysis reinforced these findings by identifying ADRB2 expression specifically within these immune cell types at the single-cell level (Figs 4C and 4D). The data from GSM3516676 and GSE127465 showed that ADRB2 was broadly expressed in a variety of immune cells, with notable enrichment in CD8+T cells and macrophages, consistently aligning with its functional role identified in bulk RNA-seq analysis. A detailed comparison revealed higher expression in activated macrophages, neutrophils and DCs, pointing to its involvement in both innate and adaptive immune responses. Furthermore, the scRNA-seq data highlighted a more specific role of ADRB2 in modulating immune cell activity within the LUAD tumor microenvironment. This included significant upregulation in CD4+T cell subtypes, specifically regulatory and helper T cells, and in antigen-presenting cells, such as DCs and activated macrophages, suggesting a dynamic role of ADRB2 in shaping the immune landscape of LUAD. The integrated results from scRNA-seq and bulk RNA-seq confirmed the original observations regarding the influence of ADRB2 on immune cell infiltration, while providing additional cellular resolution, enabling a more detailed assessment of its role in the LUAD tumor microenvironment. Afterwards, the correlations of expression levels between ADRB2 and immune-related genes, including chemokine, chemokine receptor proteins, immune-activating or suppressive, were investigated across LUAD. Analysis result representation and co-expression of chemokine and chemokine-receptor genes were observable with ADRB2 (Figs 4E and 4F). For example, C-C motif chemokine receptor (CCR) 4, CCR6, C-X3-C motif chemokine receptor 1, C-C motif chemokine ligand (CCL)14 and CCL23 were strongly and positively correlated with the expression levels of ADRB2. ADRB2 expression was significantly correlated with the expression of a majority of immune-activating and immune-suppressing genes (Figs 4G and 4H). In the tumor immune microenvironment in LUAD, ADRB2 might regulate immune cell infiltration and immune-related gene function.

Association between ADRB2 expression and immune cell markers in LUAD
To further explore the function of ADRB2 in tumor immunity, TCGA datasets were used to determine the expression correlation of ADRB2 with biomarkers of some immune cells in LUAD. Table 3 shows that ADRB2 exhibited a significant correlation with B cell biomarkers (CD19: R = 0.23, P = 5.5x10-7; CD79A: R = 0.19, P = 2.4x10-5), CD8+T cell biomarkers (CD8A: R = 0.14, P = 1.4x10-4; CD8B: R = 0.14, P = 2.4x10-4), a CD4+T cell biomarker (CD4: R = 0.54, P = 2.7x10-38), M1 macrophage biomarkers (NOS2: R = 0.17, P = 2.5x10-5; IRF5: R = 0.26, P = 4.8x10-9; PTGS2: R = 0.15, P = 9.4x10-5), M2 macrophage biomarkers (CD163: R = 0.35, P = 8.7x10-16; VSIG4: R = 0.39, P = 3.1x10-19; MS4A4A: R = 0.43, P = 3.6x10-23), neutrophil biomarkers (CEACAM8: R = 0.42, P = 4.5x10-22; ITGAM: R = 0.49, P = 1.5x10-30; CCR7: R = 0.45, P = 8.3x10-26) and DC biomarkers (HLA-DPB1: R = 0.56, P = 7.6x10-41; HLA-DQB1: R = 0.36, P = 1.6x10-16; HLA-DRA: R = 0.52, P = 3.9x10-34; HLA-DPA1: R = 0.54, P = 2.4x10-37; CD1C: R = 0.57, P = 3x10-42; NRP1: R = 0.29, P = 1.1x10-10; ITGAX: R = 0.39, P = 1x10-18) in LUAD. ADRB2 expression is positively correlated with the infiltration of neutrophils and dendritic cells (Fig 4 and Table 3). To some extent, these findings provided strong evidence that the ADRB2 gene was positively correlated with immune cell infiltration. There was some evidence that low ADRB2 expression might be associated with increased levels of tumor-infiltrating neutrophils and DCs in LUAD.

ADRB2 overexpression inhibits tumor growth and modulates immune infiltration in a LUAD mouse model
To investigate the effect of ADRB2 on tumor cell proliferation, we determined the growth curves of A549 and LLC cells with ADRB2 overexpression using the CCK-8 assay. The results showed that ADRB2 overexpression significantly inhibited the proliferation of both cell lines compared with the empty vector control group (Fig 5A). To further investigate the role of ADRB2 in tumor growth and its impact on immune cell infiltration, a tumor model was established in C57BL/6 mice and stable transfection of LUAD cells (LLC) with overexpression of ADRB2 was performed (Fig 5B). The experimental results demonstrated that, compared with the control group mice that received empty vector-transfected cells, mice transplanted with ADRB2-overexpressing LLC cells exhibited a significant reduction in tumor volume (Figs 5C-5E), indicating that the overexpression of ADRB2 significantly inhibited tumor growth (P < 0.05).
To assess the influence of ADRB2 on the immune microenvironment within the tumor, flow cytometry analysis of CD4+T cells and DCs within the mouse tumor tissues was conducted. The analysis revealed a significant increase in the proportion of CD4+T cells in the tumor tissues of mice with ADRB2-overexpressing tumors (Fig 5F), and a similar trend of increase was observed for the proportion of DCs (Fig 5G). This phenomenon was consistent with the results from public databases, suggesting that the level of ADRB2 expression may modulate the infiltration of immune cells, thereby affecting the tumor microenvironment. These findings indicated that ADRB2 serves a crucial role not only in the regulation of tumor cell proliferation but may also alter the tumor microenvironment by affecting the infiltration of immune cells, thus having a positive impact on the prognosis of LUAD.
Relationship between ADRB2 and immune checkpoints in LUAD. In immune checkpoint therapy, the main targets are programmed cell death protein 1 (PD-1), programmed death-ligand 1 (PD-L1) and cytotoxic T-lymphocyte associated protein 4 (CTLA-4). Therefore, the present study evaluated the relationship of ADRB2 with PD-1, PD-L1 and CTLA-4 using Tumor Immune Estimation Resource (Figs 6A-6C). PD-L1 and ADRB2 exhibited a significant positive correlation (R = 0.215; P = 1.45x10-6), and CTLA-4 and ADRB2 also exhibited a significant positive correlation (R = 0.152; P = 7.21x10-4) after adjusting for purity. After verification using TCGA data, it was found that ADRB2 exhibited a significant positive correlation with PD-1 (R = 0.13; P = 5.7x10-3), PD-L1 (R = 0.28; P = 3.6x10-10) and CTLA-4 (R = 0.27; P = 2.1x10-9) (Figs 6D-6F). The results suggested that immunotherapy might be associated with the involvement of ADRB2 in LUAD.

Drug sensitivity analysis of ADRB2
To identify the relationship between ADRB2 and drug sensitivity, the drug sensitivity of ADRB2 across different types of cancer was analyzed using the GDSC drug database. The results suggested that ADRB2 was associated with multiple drug responses. Patients with higher ADRB2 expression are probably more sensitive to afatinib, docetaxel, gefitinib and bleomycin, and resistant to navitoclax, SB52334, NPK76-II-72–1, YM201636 and KIN001–102. Afatinib, docetaxel, gefitinib, WZ-1–84 and bleomycin are all components of the classical chemotherapy regimen for LUAD. Patients with high ADRB2 expression were likely to be sensitive to 21 of the top 30 drugs and resistant to 9 (Fig 7). This finding suggested that the association between decreased ADRB2 expression and drug sensitivity may be closely correlated with the poor prognosis of LUAD.

Discussion
LUAD is the most common subtype of lung cancer. There has been an increasing number of studies showing that different subtypes of lung cancer have different clinical features and require different treatment options. Demonstrating the molecular mechanism of tumor suppression in LUAD may serve as an important clue for the evolution of valid therapeutic targets. It is becoming increasingly clear that ADRB2 serves a critical role in cancer incidence and development. Nevertheless, the functions and mechanisms of ADRB2 in LUAD remain unclear. In this study, we identified a competitive endogenous RNA (ceRNA) network where lncRNA C1RL-AS1 functions as a molecular sponge for miR-424-5p, thereby alleviating its post-transcriptional repression of ADRB2. Mechanistically, dual luciferase reporter gene experiment confirmed direct binding between lncRNA C1RL-AS1 and miR-424-5p, and also demonstrated miR-424–5 p’s specific targeting of the ADRB2 3’UTR. Further experiments involving C1RL-AS1 knockdown and miR-424-5p overexpression also successfully demonstrated the regulatory role of the C1RL-AS1/miR-424-5p/ADRB2 axis.
The first step was to analyze ADRB2 expression in pan-cancer using TCGA data and to validate it using GEPIA data. Furthermore, the results of prognostic value analysis showed that ADRB2 expressed at a low level in LUAD, which was associated with poor prognosis of LUAD. According to the present findings, ADRB2 serves a tumor suppressor role in LUAD.
To gain insights into the biological functions of ADRB2 in LUAD, explore its pathogenesis, and provide favorable reference values for the prevention and diagnosis of LUAD, the present study investigated the biological functions of ADRB2 in LUAD, and the enrichment results suggested that the co-expressed genes of ADRB2 were mainly involved in the Jak stat signaling pathway, calcium signaling pathway, cell adhesion molecules, regulation of actin cytoskeleton and natural killer cell-mediated cytotoxicity, and B cell receptor signaling pathway. The results suggested that ADRB2 was involved in multiple immune-related pathways. To further explore the regulatory mechanism of low ADRB2 expression in LUAD, the present study analyzed for the first time the effect of ADRB2 CNV and DNA methylation levels on its expression. The results suggested that low ADRB2 expression in LUAD was regulated by copy number deletion and hypermethylation, and detection of ADRB2 deletion and methylation levels may be a novel method to predict the occurrence of LUAD.
According to research, ncRNAs such as lncRNAs, miRNAs and circRNAs are important regulators of gene expression, react to each other through the ceRNA mechanism, and participate in the control of mRNA expression [20–23]. To investigate the upstream regulatory relationships of ADRB2, multiple prediction programs were used to predict miRNAs that may target ADRB2. After correlation and expression analysis, it was finally determined that miR-424-5p was the most likely upstream cancer-promoting miRNA of ADRB2. miRNAs have a central position in several biological processes [24]. Research has shown that miR-424-5p is involved in the regulation of vascular endothelial cell migration and cell proliferation, is downregulated in pulmonary arterial hypertension, and serves an antiproliferative role in pulmonary artery endothelial cells [25,26].
According to the ceRNA hypothesis, a tumor-suppressive lncRNA may sponge miR-424-5p to derepress ADRB2 expression, providing a potential therapeutic strategy for LUAD [27]. The present study further predicted the upstream lncRNAs of the miR-424-5p/ADRB2 axis and identified 73 possible lncRNAs. Based on expression and correlation analysis, it was finally determined that C1RL-AS1 was the most likely downregulated lncRNA. Studies have shown that C1RL-AS1 can activate the AKT/β-catenin/c-Myc pathway to promote the carcinogenesis of gastric adenocarcinoma cells [28]. There is also an association between it and the prognosis and microenvironment of colon cancer and HNSC [28,29]. In conclusion, the C1RL-AS1/miR-424-5p/ADRB2 axis was identified as a potential regulatory pathway for LUAD.
The immunological landscape of tumor microenvironment exhibits a dualistic role in shaping therapeutic outcomes, where the net effect of immune infiltration is governed by the functional polarization of infiltrating immune subsets. While immunosuppressive cell populations (e.g., regulatory T cells, M2-polarized tumor-associated macrophages) frequently correlate with therapy resistance and adverse prognosis, robust infiltration of effector immune cells (e.g., CD8 + cytotoxic T lymphocytes, M1-polarized macrophages) has been consistently associated with enhanced treatment responsiveness and favorable clinical outcomes across multiple malignancies [30,31]. The present findings demonstrated that ADRB2 was significantly associated with various immune cells, and the biomarkers of these immune cells were positively correlated with ADRB2, and the results are consistent with those of Ji et al [32]. The addition of scRNA-seq datasets (GSM3516676 and GSE127465) provided a more granular understanding of how ADRB2 regulates the immune cell landscape in LUAD. As previously reported, ADRB2 expression was positively associated with a broad range of immune cell populations, highlighting its general role in immune modulation. The scRNA-seq data refined this understanding, showing that ADRB2 expression was not only widespread across immune cells but also exhibited cell type-specific enrichment patterns that varied across the tumor microenvironment. In both scRNA-seq datasets, ADRB2 was highly expressed in various adaptive and innate immune cells, including B cells, CD8+T cells, CD4+T cells, macrophages and DCs, consistently supporting its contribution to immune cell infiltration levels in LUAD. However, the single-cell resolution data further revealed its preferential upregulation in effector immune cells during infiltration and in immunosuppressive subtypes within the tumor environment. For example, ADRB2 enrichment in regulatory T cells and macrophage subtypes (such as M2 macrophages) underscores its potential involvement in immune evasion mechanisms. These cell type-specific patterns suggest a dual role for ADRB2, supporting both immune activation and immune suppression in a context-dependent manner. These findings position ADRB2 as a critical player in shaping the immune microenvironment of LUAD. Its expression appears to enhance immune cell infiltration and activation in certain contexts, but it may also facilitate immunosuppressive pathways that contribute to tumor progression. These results highlighted ADRB2 as a promising therapeutic target in LUAD, offering potential for novel immunomodulation strategies by disrupting its role in immune evasion and restoring effective antitumor immune responses.
Combined with the enrichment analysis results, the present study further revealed that ADRB2 expression was associated with chemokines, chemokine receptors, and genes involved in immune system activation and suppression. The efficacy of immunotherapy not only requires a tumor microenvironment infiltrated by immune cells but also relies on the adequate expression of immune checkpoints. There was a significant positive correlation between PD-L1 and CTLA-4 immune checkpoints and ADRB2, suggesting ADRB2 targeting could enhance the efficacy of immunotherapy in LUAD. Meanwhile, the present findings suggested that patients with elevated ADRB2 gene expression may respond poorly to treatment with navitoclax, SB52334, NPK76-II-72–1, YM201636 and KIN001–102, while responding to treatment with afatinib, docetaxel, gefitinib, WZ-1–84 and bleomycin.
There are some limitations in this study. In our initial study, our flow cytometry analysis, which was used to assess the influence of ADRB2 on the immune microenvironment within the tumor, focused solely on the overall dynamic changes in CD4+T cells. We did not conduct further analysis of their functional subsets, such as Th or Treg cells. While the results achieved the objectives of this study, they did not provide detailed insights into the specific changes in these subsets. Moreover, we focused solely on the impact of ADRB2 expression levels on tumor growth, without examining the mechanism underlying the role of the C1RL-AS1/miR-424-5p/ADRB2 axis in tumor formation through intervention. Moving forward, we plan to explore the mechanism of this regulatory axis and its application potential in LUAD treatment by intervening with miR-454-5p and C1RL-AS1.
In conclusion, this study provides compelling theoretical underpinnings for the expression and function of ADRB2 in LUAD. The present results demonstrated that ADRB2 was downregulated in an array of cancer types, including LUAD, and positively associated with poor prognosis in LUAD. Dysregulation of ADRB2 involves multidimensional mechanisms, including genetic alterations, DNA methylation, miRNAs and lncRNAs. Meanwhile, the present study also suggested that ADRB2 may modulate immune cell infiltration and immune checkpoint expression by exerting its tumor suppressor effect. Our study revealed that the C1RL-AS1/miR-424-5p/ADRB2 axis in LUAD was a potential upstream regulatory mechanism of ADRB2 (Fig 8). Overall, these findings indicated that ADRB2 could be regulated by C1RL-AS1/miR-424-5p in LUAD and provide guidance for the treatment of LUAD.

Supporting information
S1 Fig
(A) ADRB2 expression in 16 types of cancer based on GEPIA database cancer and normal sample data.
(B) Association between overall survival and ADRB2 expression in 6 types of cancers.
(TIF)

S2 Fig
Gene set enrichment analysis Kyoto Encyclopedia of Genes and Genomes analysis of the genes co-expressed with ADRB2.
The x-axis shows the normalized enrichment score.
(TIF)

S1 Table
Sequence information of genes used in gene functional verification and manipulation experiments.
(DOCX)

S2 Table
List of antibodies used for multiparameter flow cytometry staining.
(DOCX)

S3 Table
Correlation of genes with ADRB2.
(DOCX)

S4 Table
Results of genes enrichment analysis for co-expression with ADRB2.
(DOCX)

S5 Table
Predicted 73 potential lncRNAs of mir-424-5p.
(DOCX)