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. 2026 Feb 23;14(2):489. doi: 10.3390/biomedicines14020489

Identification and Functional Validation of PTH2R as a Therapeutic Target in Lung Adenocarcinoma

Changmin Liu 1,2,†, Yongfu Wang 1,2,†, Wei Liu 1,2, Yizhen Yuan 1,2, Yajing Xue 1,2, Pengzhuo Tao 1,2, Dan Sun 2, Te Kian Keong 3, Shilin Chen 1,2,*, Chi Song 1,2,*
Editor: Bo-Ying Bao
PMCID: PMC12937737  PMID: 41751388

Abstract

Background: One of the main causes of cancer-related mortality globally is lung adenocarcinoma (LUAD), necessitating the development of novel therapeutic targets. The parathyroid hormone type 2 receptor (PTH2R) exhibits differential expression across multiple cancers, yet its role in LUAD remains unclear. Methods: Through an integrated analysis of multiple public databases (including SangerBox 3.0, UALCAN, Kaplan–Meier Plotter, and TIMER), we identified PTH2R—a member of the family B1 GPCRs—as a candidate therapeutic target with significant prognostic value in LUAD. Subsequently, the antitumor effects of PTH2R knockdown and melatonin were evaluated through cell proliferation, colony formation, migration, and apoptosis assays. Transcriptome analysis revealed key biological processes and signaling pathways regulated by PTH2R, identified key genes modulated by PTH2R, and validated core gene expression via RT-qPCR. Results: PTH2R is a potential therapeutic target for lung adenocarcinoma. Both PTH2R knockdown and melatonin treatment significantly inhibited LUAD cell proliferation, colony formation, and migration capabilities while promoting apoptosis. Notably, the combination of PTH2R knockdown and melatonin treatment demonstrated synergistically enhanced antitumor effects. Transcriptome analysis revealed two key genes within the PTH2R signaling pathway, and RT-qPCR validated the expression of these two key genes. Conclusions: Our work provides the first evidence confirming the substantial value of PTH2R as a novel therapeutic target for LUAD. It preliminarily demonstrates the mechanism by which melatonin inhibits LUAD by targeting PTH2R, offering crucial experimental evidence and theoretical support for developing precision therapeutic strategies against this cancer.

Keywords: PTH2R, melatonin, lung adenocarcinoma, family B1 GPCRs, bioinformatics

1. Introduction

One of the main illnesses endangering human health globally is cancer. It imposes a heavy burden on socioeconomic development and profoundly impacts people’s daily lives [1]. Cancer comprises numerous forms, such as lung, female breast, colorectal, and prostate, with lung cancer having the highest global incidence and mortality rates [2]. Lung cancer can be broadly classified into two types based on the histology of cancer cells: small cell cancer (SCLC) and non-small cell cancer (NSCLC). NSCLC is the most common subtype of lung cancer, accounting for 85–90% of all cases. Lung adenocarcinoma (LUAD), lung squamous carcinoma, and large-cell lung cancer are among the histological subtypes of NSCLC [3]. Currently, treatment options include surgical resection, chemotherapy, targeted therapy and radiotherapy. Nevertheless, the prognosis is still quite bad, with a low five-year survival probability, even with these choices. Molecular targeted therapy has become an important treatment option for NSCLC [4]. Anaplastic lymphoma kinase, vascular endothelial growth factor, and the epidermal growth factor receptor (EGFR) are increasingly important biological targets for NSCLC [5]. Tyrosine kinase inhibitors like gefitinib and erlotinib have been developed to target EGFR mutations as therapeutic targets [6]. Despite the initial success with the molecular targeted therapy approach, acquired drug resistance is frequently unavoidable because of epigenetic changes and tumor heterogeneity, severely limiting the therapeutic efficacy of these treatments in NSCLC [7,8]. Therefore, developing novel molecular targets for therapeutic applications is of significant importance.

Seven transmembrane domains define the family of membrane proteins known as G protein-coupled receptors (GPCRs). According to relevant statistics, the human genome encodes more than 800 identified GPCRs, which comprise around 4% of all protein-coding genes in humans [9]. Based on sequence homology, GPCRs can be grouped into five families: class A (rhodopsin), class B1 (secretin), class B2 (adhesion), class C (glutamate), and class F (frizzled/smoothened) [10]. GPCRs regulate numerous cellular functions by binding to their specific ligands, hence partaking in physiological and pathological processes inside the human body. When it comes to serious illnesses like diabetes, obesity, depression, cancer, and Alzheimer’s disease, they are incredibly important. As a result, GPCRs have also emerged as one of the star targets for treating related diseases in recent years. Currently, only roughly 103 GPCRs have been identified as drug targets [11]. The features of many GPCRs are still not fully understood because there are few instruments available to probe their functions. Therefore, GPCRs may present novel opportunities as targets for drug development. Family B1 GPCRs are one of the most widely characterized neuropeptide receptor families, encompassing 15 peptide hormone receptors. Because these receptors control a variety of biological processes, such as growth and development, metabolism, and neural activity, they are significant therapeutic targets for various diseases [12]. The family includes [13] vasoactive intestinal peptide receptors 1 and 2 (VIPR1 and VIPR2), pituitary adenylate cyclase-activating peptide receptor (PAC1R/ADCYAP1R1), secretin receptor (SCTR), growth hormone-releasing factor receptor (GHRHR), glucagon receptor (GCGR), glucagon-like peptide 1 and 2 receptors (GLP1R, GLP2R), gastric inhibitory peptide receptor (GIPR), parathyroid hormone receptors 1 and 2 (PTH1R, PTH2R), calcitonin receptor (CTR/CALCR) and calcitonin receptor-like receptor (CALCRL), and corticotropin-releasing factor receptors (CRF1R/CRHR1, CRF2R/CRHR2). Family B1 GPCRs not only demonstrate remarkable therapeutic effects in several diseases such as chronic inflammation, neurodegeneration, diabetes, stress, and osteoporosis, but also possess anticancer potential. For instance, CALCRL [14] knockdown inhibits malignant acute myeloid leukemia with FMS-like tyrosine kinase 3 internal tandem duplication and DNA methyltransferase 3A-R882 double mutations. SCTR [15] reduces the proliferation of normal breast cells, while the gene enhances the proliferation and migration of cancer cells. GHRH antagonists strongly inhibited the growth, tumorigenicity, and metastasis of several human cancers, such as SCLC, NSCLC, renal, prostate, breast, ovarian, and endometrial cancer [16]. PTH2R expression knockdown can prevent tumor cell invasion, migration, and proliferation [17]. PTH2R expression in lung tissue is changed by the asbestos-induced lung cancer-associated single nucleotide polymorphism rs13383928, indicating a connection between PTH2R and lung cancer [18]. However, there has not been much research done on PTH2R and lung cancer.

The pineal gland is the primary source of melatonin, an indoleamine with a variety of biological roles, including controlling circadian cycles [19]. In addition to the pineal gland, medicinal fungi such as Ganoderma lucidum (Reishi mushroom) have also been found to contain endogenous melatonin [20]. Recently, melatonin was found to inhibit cancer stem cells in NSCLC, breast, ovarian and colon cancers [21,22,23,24,25]. Additionally, melatonin exhibits anti-inflammatory effects, which are considered crucial for promoting tumor development [26].

In this study, we applied publicly available online databases—Sangerbox 3.0, UALCAN, and Kaplan–Meier Plotter—to analyze the expression level value of family B1 GPCRs in LUAD and their prognostic value. The relationship between family B1 GPCRs and invading immune cells was investigated using the TIMER. PTH2R is a possible therapeutic target for LUAD, according to bioinformatics analysis. Preliminary analyses were performed on the initial dataset generated by our research team using in-house developed high-throughput PRESTO-Salsa and CRISPRa/i platforms, and these findings revealed that melatonin directly targets PTH2R with a high likelihood. To validate its function, we further confirmed that melatonin affects PTH2R activity via activating β-arrestin protein by using PRESTO-Tango technology to establish that melatonin activates PTH2R to recruit β-arrestin protein. Here, we evaluated melatonin’s and PTH2R’s impacts on A549 cells’ capacity for migration, proliferation, and apoptosis. Transcriptomic analysis was also employed to investigate its potential pathways. Our results suggest that melatonin’s anti-tumor actions are mediated via PTH2R, which may be a therapeutic target for LUAD.

2. Materials and Methods

2.1. Family B1 GPCRs Expression Level Analysis

For pan-cancer expression analysis, data were gathered from both Sangerbox 3.0 (http://Sangerbox.com/home.html (accessed on 27 March 2025)) [27]. Gene expression data from the Cancer Genome Atlas (TCGA) can be comprehensively analyzed using UALCAN (http://ualcan.path.uab.edu (accessed on 9 April 2025)). This interactive website enables in-depth exploration of the expression patterns of members of family B1 GPCR in both tumor and normal tissues [28].

2.2. Survival Analysis

The online Kaplan–Meier Plotter (www.kmplot.com (accessed on 9 April 2025)) integrates TCGA and GEO multi-omics data to support prognostic analysis of multiple malignancies [29], including LUAD. It can also automatically generate Kaplan–Meier survival curves with statistical significance tests (p-values).

2.3. Analysis of Tumor-Infiltrating Immune Cells Analysis

TIMER (https://cistrome.shinyapps.io/timer/ (accessed on 22 April 2025)) is a web server for comprehensive analysis of tumor-infiltrating immune cells [30]. This platform was used in this investigation to examine the relationship between the expression of family B1 GPCRs and tumor-infiltrating immune cells.

2.4. Transcriptome Data Analysis

R software (version 4.4.3) and online tools were used to analyze transcriptome data. Principal component analysis (PCA) was performed using the prcomp function, and the distribution patterns of the four groups (n = 3 per group) were visualized using the ggplot2 package to reveal differences in global expression profiles; The DESeq2 or edgeR program was used to screen for differentially expressed genes (DEGs). The significance threshold was set at |log2 fold change| > 1 and adjusted p-value < 0.05. Subsequently, the EnhancedVolcano package was then used to generate volcano plots to display the DEGs in 5 comparison combinations; for the five groups of DEGs, KEGG pathway analysis and Gene Ontology (GO) enrichment analysis were performed using the Database for Annotation, Visualization and Discovery (DAVID) (https://davidbioinformatics.nih.gov/ (accessed on 15 July 2025)); visualization via the bioinformatics online platform (https://www.bioinformatics.com.cn/ (accessed on 15 July 2025)); the jvenn online [31] tool (https://www.bioinformatics.com.cn/static/others/jvenn/example.html (accessed on 16 July 2025)) was used to generate Venn diagrams to screen common differential genes among the 5 comparison combinations; finally, for the 2 obtained key genes, the bioinformatics online platform (https://www.bioinformatics.com.cn/ (accessed on 16 July 2025)) was used to generate gene expression heatmaps to analyze their expression pattern characteristics in each sample group.

2.5. Establishment of Stable Lung Adenocarcinoma Cell Lines with Knockdown of PTH2R

Human lung adenocarcinoma cell line A549 (KC0202) was obtained from Kinlogix Biotechnology Co., Ltd. (Guangzhou, China). BEAS-2B was obtained from the National Collection of Authenticated Cell Cultures. Human embryonic kidney (HEK)-293T cell line (IM-H222) was obtained from Immocell Biotechnology Co., Ltd. (Xiamen, China). HEK-293T cells were cultivated in dulbecco’s modified eagle medium (DMEM; (Gibco, Thermo Fisher Scientific, Waltham, MA, USA)) and A549 cells were cultured in RPMI-1640 medium, supplemented with 10% fetal bovine serum (VivaCell, Denzlingen, Germany) and 1% penicillin-streptomycin (Gibco, Thermo Fisher Scientific, Waltham, MA, USA). PTH2R knockdown was used to create stable transfected cell lines [32,33]. Using Lipofectamine 3000 transfection reagent (Invitrogen, Carlsbad, CA, USA), pLKO.1 empty vector and pLKO.1-PTH2R-shRNAs were co-transfected into HEK-293T cells via a co-transfection system (pMD2G:psPAX2:target plasmid = 1:3:4). Culture supernatants were collected at 48 and 72 h post-transfection. After A549 cells in 6-well plates achieved around 70% confluence, they were infected with 1 mL of lentivirus, 1 mL of new culture media, and 4 μL of polybrene (Merck, Darmstadt, Germany; 1 μg/mL). Puromycin (2 μg/μL) was used to select positive viral infection cell lines after 48 h of growth. Western blotting was used to validate PTH2R expression. After pLKO.1 transfection, A549 cells were referred to as sh-PTH2R and sh-NTC, pLKO.1-PTH2R-shRNAs and pLKO.1 empty vector, correspondingly.

2.6. Western Blot

In 6-well plates, vehicle cells and sh-PTH2R cells were planted at a density of 1 × 106 cells/mL. A bicinchoninic acid kit (Thermo Scientific, Waltham, MA, USA) was used to quantify the total protein content after 24 h. Protein quantification was followed by sample denaturation and storage at −80 °C. After transferring the membrane, it was blocked at room temperature for 1 h in TBST (Servicebio, Wuhan, China) containing 5% non-fat milk. Three 5 min TBST washes were performed after the primary antibodies (including anti-PTH2R (1:1000 dilution; YN2685; Immunoway, Plano, TX, USA) and anti-GAPDH (1:200,000 dilution; 60004-1-Ig; proteintech, Wuhan, China) antibodies) were incubated for an entire night at 4 °C with mild agitation. Secondary antibodies were conjugated for one hour at room temperature (anti-rabbit IgG, HRP-linked antibody; 1:10,000 dilution; D110058; Sangon Biotech, Shanghai, China; anti-mouse IgG, HRP-linked antibody; 1:10,000 dilution; 31430; Invitrogen, Carlsbad, CA, USA). Components A and B were combined in a 1:1 ratio to create an ECL substrate (Epizyme, Shanghai, China) solution. This mixture was applied evenly onto the PVDF membranes (Merck, Darmstadt, Germany) and detected using an e-Blot Touch Imagers.

2.7. Cell Counting Kit-8 Assay

The Cell Counting Kit-8 (CCK8, TargetMol, Boston, MA, USA) was used to measure cell viability in accordance with the manufacturer’s instructions. Cells were exposed to melatonin at varying concentrations (0–4.5 mM). Fresh medium was added to the blank and control wells. Each well received 10% CCK-8 media following a 24 h treatment period. A microplate reader was used to measure absorbance at 450 nm following a 1–2 h incubation period. Cell viability was computed as a percentage in relation to the control after the data were adjusted to the untreated control group.

2.8. Cell Apoptosis Assay

Apoptosis detection was performed using flow cytometry. Two sets of logarithmic-phase A549 stable transfected cells were created: the sh-NTC group and the sh-PTH2R group. In 12-well plates, cells from both groups were planted at a density of 7 × 104 cells per well. The cells were treated with melatonin for 24 h following a 24 h culture period (until cell confluency reached 80–90%). After treatment, the cells were collected by centrifugation at 500 rpm for five minutes and washed twice with 1× phosphate buffer solution (PBS, Servicebio, China). The cells were reconstituted in 125 μL of 1× binding buffer supplemented with 1.25 μL each of APC-labeled Annexin V and DAPI (Elabscience, Wuhan, China)) added. For fifteen minutes, the mixture was incubated in the dark. Finally, a flow cytometer was used for analysis (Agilent NovoCyte Advanteon, San Diego, CA, USA).

2.9. Wound Healing Assay

The Cell Scratch Film (Beyotime, Boston, MA, USA) was used to create a standardized wound model. The inserts were sterilized with 254 nm UV irradiation for 20 min in a biosafety cabinet before the experiment. After sterilization, the adhesive backing was removed, and the insert was precisely applied to the bottom of each well of a 12-well plate, with the handle extending beyond the well edge. A sterile pipette tip was gently used to press out air bubbles, ensuring complete and bubble-free adherence. Cells were then planted at a density of 6 × 105 cells per well and grown in complete medium until they reached 90% confluence. Sterile forceps were used to carefully remove the implant, leaving a uniform scratch in each well. After gently washing twice with PBS (Servicebio, Wuhan, China) to remove unattached cells, the medium was reconstituted with 1% FBS-containing medium supplemented with the test compound or vehicle control. Phase-contrast images were captured at 0 h and 24 h using an Olympus IX73 inverted microscope at fixed fields. Wound closure was analyzed using ImageJ software (version 1.53a). To ensure reproducibility, each experiment was independently repeated three times, using three replicate wells per run.

2.10. Colony Formation Assay

Two sets of logarithmic-phase A549 stable transfected cells were created: the sh-NTC group and the sh-PTH2R group. A density of 300 cells per well was used to seed cells from both groups onto 12-well plates. Every two days, the culture medium and the appropriate medication treatments were changed. The cells were cultivated for two weeks and monitored every day. A 4% paraformaldehyde fixative solution (Biosharp, Beijing, China) was used for full fixation after PBS washing. After the cells were fixed for 30 min at room temperature, the paraformaldehyde fixative solution was aspirated out, and the cells were then given three PBS washes. For 15 min at room temperature, 0.1% crystal violet (Solarbio, Beijing, China) was added for staining. Deionized water was used to thoroughly clean the backdrop. After properly cleaning the backdrop with deionized water, the plates were allowed to air dry. The stained cells were photographed for documentation, with 3 replicates set for each group.

2.11. PRESTO-Tango Assay

When HEK-293T cells in 6 cm dishes reached approximately 80%, they were transfected with PTH2R-Tango, β-arrestin2-TEV, and TRE-Tight-Luc plasmids at a 2:1:1 ratio. Cells were seeded at 3 × 104 cells per well in 96-well plates 24 h post-transfection and then cultivated overnight. Melatonin-containing medium (final concentrations ranging from 200 μM to 0.0128 μM) was then substituted for the original medium. After 18 h of incubation, 50 μL of Bright-Glo substrate (Promega, Madison, WI, USA) was added into each well, fluorescence intensity was measured immediately after thorough mixing.

2.12. RNA Extraction and Real-Time Quantitative PCR (RT-qPCR) Analysis

Eastep® Super (Promega, USA) was used to extract the total RNA, and NanoDrop (Thermo Scientific, Waltham, MA, USA) was used to measure the RNA’s concentration and quality. Further, synthesis complementary DNA (cDNA) was performed using PrimeScript™ FAST RT reagent Kit with gDNA Eraser (Takara, Japan). RT-qPCR was then performed using TB Green Premix Ex Taq™ II (Takara, Tokyo, Japan) on a QuantStudio™ 5 system (Thermo Fisher, USA). The 2−ΔΔCt technique was used to normalize the relative expression levels of keratin 4 (KRT4) and ectonucleotide pyrophosphatase 2 (ENPP2), using GAPDH as the internal reference gene [34]. The sequence of primers used is as follows: ENPP2: (F: GGTTCCAATGTATCCTGCTTTC, R: GATGCTGTAGTAGTGAGTTGG); KRT4: (F: AGCTCAACAGGATGATCCAG, R: CATTCTCCAGACATTCTGTACTCC); GAPDH: (F: GGAGCGAGATCCCTCCAAAAT, R: GGCTGTTGTCATACTTCTCATGG).

2.13. Statistical Analysis

In this study, ImageJ software was employed to count cells in the cell cloning assay results and to calculate scratch area in the cell wound healing assay results. Statistical analysis findings were plotted using GraphPad Prism 9.5, and transcriptome data analysis was done using R software (version 4.4.3) or internet resources. When comparing two groups, the t-test was employed; when comparing several groups, one-way analysis of variance (ANOVA) was utilized. Each experiment was performed with at least three independent biological replicates. For all analyses, the criteria for statistical significance were defined as follows: *, p < 0.05; **, p < 0.01; ***, p < 0.001, and non-significant at p > 0.05.

3. Results

3.1. Expression of Family B1 GPCRs in LUAD

To examine the differential expression of family B1 GPCRs across different cancer types, we analyzed mRNA expression levels using Sangerbox 3.0. The results revealed remarkable differences in the expression of multiple family B1 GPCRs in various cancers (Figure 1 and Figure S1), suggesting a potential involvement of this receptor family in tumor development and progression. We also assessed the differential expression of family B1 GPCRs in LUAD using the UALCAN database. Analysis revealed that compared to normal tissue, PTH1R (p = 1.62 × 10−12), CALCRL (p = 1.62 × 10−12), ADCYAP1R1 (p = 1.11 × 10−7), and VIPR1 (p = 3.36 × 10−12) were significantly downregulated in LUAD tissues. In contrast, GIPR (p = 2.46 × 10−5), CALCR (p = 7.55 × 10−5), PTH2R (p = 1.87 × 10−6), CRHR1 (p = 2.89 × 10−7), CRHR2 (p = 8.04 × 10−4), and GCGR (p = 1.19 × 10−4) were significantly upregulated. The remaining family B1 GPCRs did not show significant differential expression in LUAD (Figure 2).

Figure 1.

Figure 1

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The expression levels of family B1 GPCRs in different tumor types in the TCGA database were determined. Sangerbox 3.0 was used to evaluate the expression levels of CALCR, PTH2R, GHRHR, GIPR, GLP1R, GLP2R, SCTR and PTH1R in various tumor types from the TCGA database. Red indicates high expression, blue indicates low expression, and LUAD is marked with a black box. (* p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001).

Figure 2.

Figure 2

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The expression of family B1 GPCRs in LUAD tissues and normal tissues is derived from UALCAN. Box plots display mRNA expression levels of family B1 GPCRs genes in LUAD tumor tissues (n = 515) and adjacent normal tissues (n = 59), generated using the UALCAN platform. The black dashed line indicates the median expression of all samples.

3.2. Association Between Clinical Features and Family B1 GPCRs

We then looked into the relationship between clinical traits and the expression of family B1 GPCRs. We evaluated the relationship between family B1 GPCR expression and tumor pathological stage in LUAD patients, as well as its effect on survival rates, using UALCAN and Kaplan–Meier plotting techniques. The association between tumor pathological stage and family B1 GPCRs was examined using the UALCAN database (Figure 3). Expression levels of several family B1 GPCRs, including PTH2R, VIPR2, CRHR1, CRHR2, and GCGR, were significantly correlated with tumor stage and showed marked upregulation during disease progression, suggesting their potential roles in promoting LUAD development and their promise as therapeutic targets. Kaplan–Meier survival analysis of family B1 GPCRs expression in 2166 LUAD patients revealed significant associations with OS, indicating these receptors play a crucial role in LUAD prognosis. As depicted in Figure 4, high expression of CALCR, SCTR, VIPR1, CALCRL, and GCGR correlated with prolonged OS. Conversely, low expression of PTH2R, GHRHR, GLP2R, and ADCYAP1R1 was significantly associated with extended OS. Notably, the consistent upregulation of PTH2R expression with tumor staging progression further supports its potential role as a key oncogenic factor.

Figure 3.

Figure 3

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Association between family B1 GPCRs and tumor stage in LUAD. Expression of family B1 GPCRs in LUAD, based on samples from the TCGA database, grouped by clinical stage (from stage I to IV). A box-and-line plot was used to show the distribution of gene expression in different stages. The vertical axis indicates the expression per million transcripts, and the horizontal axis indicates the different clinical stages. The median, upper and lower quartiles, and possible outliers in the box-and-line plots provide information on the centralized trends and variability of gene expression in each stage. The black dashed line indicates the median expression of all samples.

Figure 4.

Figure 4

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The prognostic value of family B1 GPCRs in LUAD. Demonstrates the impact of family B1 GPCRs expression levels in LUAD on overall patient survival. Survival probabilities for the high-expression group (red) and low-expression group (black) were compared using Kaplan–Meier survival curves. The vertical axis indicates the survival probability, and the horizontal axis indicates the follow-up time (months).

3.3. The Correlation Between Family B1 GPCRs Expression and Immune Infiltration

Tumor-associated immune cells comprise tumor-antagonistic and tumor-promoting immune cells, representing a significant focus in cancer research. We conducted an in-depth analysis using the TIMER database to investigate the association between family B1 GPCRs expression and immune cell infiltration in LUAD. The findings show that the expression of family B1 GPCRs typically shows a positive connection with the levels of CD4+ T cell infiltration. CD8+ T cell infiltration levels showed a moderate positive correlation with CALCRL but no correlation with other family B1 GPCRs. High expression of CALCRL was associated with favorable prognosis, suggesting they may enhance antitumor immune responses by recruiting and activating cytotoxic T cells. Collectively, family B1 GPCRs may profoundly influence the immune status of the tumor microenvironment and patient prognosis in LUAD by differentially regulating immune cell infiltration (Figure 5 and Figure S2).

Figure 5.

Figure 5

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Correlation of family B1 GPCRs expression with immune infiltration levels in LUAD. (A) CALCR expression was positively correlated with infiltration levels of tumor-infiltrating lymphocytes. (B) VIPR1 was negatively correlated with infiltration levels of neutrophils. (C) CALCRL expression was favorably linked with infiltration levels of tumor-infiltrating lymphocytes. (D) GCGR expression was favorably correlated with infiltration levels of tumor-infiltrating lymphocytes. (E) PTH2R was negatively correlated with infiltration levels of neutrophils. (F) SCTR was negatively correlated with infiltration levels of neutrophils. (G) VIPR2 was positively correlated with infiltration levels of B cells and CD4+ T cells. Each point represents a sample; the blue, smoothed curve indicates the trend line fitted by the local regression (LOESS) method, and the gray area represents the 95% confidence interval.

3.4. Melatonin-PTH2R Axis Activation Functional Study of Melatonin on A549 Cells

Bioinformatic analysis indicated that PTH2R exhibits significant correlations with the clinical pathological features and prognosis of LUAD patients. Therefore, we proceeded to further analyze this result through in vitro experiments. Preliminary analyses were performed on the initial dataset generated by our research team using in-house developed high-throughput PRESTO-Salsa and CRISPRa/i platforms, and these findings revealed that melatonin directly targets PTH2R with high likelihood. We validated this finding using PRESTO-Tango, which revealed that melatonin recruits β-arrestin in a concentration-dependent manner as melatonin levels increase, suggesting that PTH2R may serve as a potential target for melatonin (Figure 6A). To explore the functional significance and mechanism of action of PTH2R in LUAD, we utilized the A549 cells line as a model system. A sh-PTH2R cell line was established via lentiviral infection. Western blot analysis indicated efficient PTH2R knockdown, confirming the successful establishment of this cell line (Figure 6B). We evaluated changes in the viability of BEAS-2B cells following treatment with varying concentrations of melatonin. The results show that no significant toxicity was observed in the cells treated with melatonin (Figure 6C).

Figure 6.

Figure 6

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Melatonin activates PTH2R signaling and exerts differential effects on viability and proliferation in normal lung epithelial (BEAS-2B) and A549 cells. (A) Detection of melatonin binding to PTH2R recruiting β-arrestin through PRESTO-Tango. (B) Western blot investigation of sh-PTH2R efficiency and GAPDH protein expression in A549 cells. (C) Cell viability of BEAS-2B exposed to different concentrations of melatonin for 24 h. Cells were treated with melatonin at concentrations ranging from 0.5 to 2.5 mM. The treated groups showed no significant differences from the control group per t-test. (D) The CCK-8 test was used to measure the viability of A549 cells cultured in melatonin at doses ranging from 0.5 mM to 4.5 mM for 24 h. (E) A549 cells were cultured in melatonin at specified concentrations for 24–96 h to assess cell viability. All experiments were performed with 3 independent biological replicates (n = 3). (ns p ≥ 0.05, **** p < 0.0001).

We evaluated the anticancer potential of melatonin against A549 cells through the CCK-8 assay. The results indicate that melatonin effectively reduced A549 cell viability in a concentration-dependent manner, significantly inhibiting cell proliferation with a half-maximal inhibition (IC50) value of 1.847 mM (~2 mM) (Figure 6D). Based on the findings, 2 mM melatonin was chosen as the optimal concentration for subsequent experiments. Simultaneously, we evaluated the inhibitory effect of melatonin treatment duration on cells. The results demonstrate that both PTH2R knockdown and melatonin treatment alone effectively reduced the cell viability of A549 cells (Figure 6E).

3.5. Melatonin Activates the PTH2R Receptor to Inhibit Apoptosis in A549 Cells

We then assessed how the sh-NTC, sh-PTH2R-NTC, sh-PTH2R-melatonin, and sh-melatonin groups affected the ability of A549 cells to form colonies, migrate, and undergo apoptosis. A wound healing assay was conducted, and after 24 h, the sh-PTH2R-NTC group exhibited significantly suppressed cell migration compared to the sh-NTC group, while 2 mM melatonin treatment further enhanced this inhibitory effect. The migration ability of the sh-PTH2R-melatonin group was significantly lower than that of both the sh-PTH2R-NTC group and the sh-melatonin group (Figure 7A). T A colony formation assay was then carried out to confirm this outcome. The findings showed that the sh-PTH2R-NTC group’s ability to establish colonies was considerably lower than that of the sh-NTC group. Both the sh-melatonin group and the sh-PTH2R-melatonin group showed noticeably reduced colony formation rates than the sh-NTC group after treatment with 2 mM melatonin. Furthermore, the sh-PTH2R-melatonin group showed a considerably lower colony formation rate than both the sh-PTH2R-NTC group and the sh-melatonin group (Figure 7B). In addition, flow cytometry experiments demonstrated that melatonin effectively induces apoptosis in A549 cells. In simple terms, Annexin V-DAPI/APC dual staining apoptosis assays revealed a higher apoptosis rate in the sh-PTH2R-NTC group compared to the sh-NTC group. Compared with the sh-NTC group and the sh-PTH2R-NTC group, in A549 cells treated with 2 mM melatonin, the promotion of apoptosis was significantly more pronounced (p < 0.01); the apoptosis rate in the sh-PTH2R-melatonin group was significantly higher than that in the sh-PTH2R-NTC group (Figure 7C). These results indicate that both sh-PTH2R and melatonin treatment inhibit LUAD cell, and melatonin synergistically enhances the inhibitory effect of sh-PTH2R on cell.

Figure 7.

Figure 7

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PTH2R and melatonin inhibit cell proliferation, migration, and apoptosis. (A) Cell migration ability after melatonin treatment was assessed using a wound healing assay. (B) Cell proliferation capacity following melatonin treatment was determined via colony formation assays. (C) Percentage of apoptotic cells after melatonin treatment, determined by fluorescence-enhanced flow cytometry. All experiments were performed with 3 independent biological replicates (n = 3). (* p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001).

3.6. Differentially Expressed Gene Analysis After PTH2R Knockdown and Melatonin Treatment

To investigate the mechanism of sh-PTH2R in LUAD, we performed transcriptome analysis on the following groups: sh-NTC group, sh-PTH2R-NTC group, sh-melatonin group and sh-PTH2R-melatonin group. After performing principal component analysis (PCA) on the complete transcript abundance data (expressed as TPM) across all experimental conditions and biological replicates, the results revealed that the separation trend observed in PC1 (82.9%) was primarily attributable to the treatment effect of PTH2R knockdown. The close grouping of sh-PTH2R-melatonin with sh-PTH2R-NTC suggests that PTH2R deficiency is the primary determinant of transcriptional variation in this dataset. Concurrently, PC2 (8.9%) distinguishes between sh-melatonin and sh-PTH2R-melatonin, indicating that melatonin exerts secondary transcriptional effects independent of PTH2R. Batch corrections were performed during data processing, and experimental samples were randomly assigned across batches to minimize technical bias, supporting that the separation on PC2 reflects a biological response to melatonin (Figure 8A). This confirms that both PTH2R knockdown and melatonin treatment influence the transcriptome profile.

Figure 8.

Figure 8

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Differential expression of sh-NTC, sh-melatonin, sh-PTH2R-melatonin, sh-PTH2R-NTC. (A) Principal component analysis of sh-NTC, sh-melatonin, sh-PTH2R-melatonin, sh-PTH2R-NTC. (B) Volcano plots showing DEGs that were upregulated and downregulated in the sh-melatonin and sh-PTH2R-melatonin groups. (C) Volcano plots showing DEGs that are upregulated and downregulated between sh-NTC and sh-PTH2R-NTC. (D) Volcano plots of DEGs that are upregulated and downregulated in relation to sh-melatonin and sh-NTC. (E) Volcano plots of DEGs that are upregulated and downregulated in relation to sh-PTH2R-NTC and sh-PTH2R-melatonin. (F) Volcano plots of DEGs that are upregulated and downregulated in relation to sh-NTC and sh-PTH2R-melatonin. Red/blue: significantly up/downregulated genes. Criteria: (two-sided Wald test, FDR < 0.05 (Benjamini–Hochberg), |log2FC| ≥ 1. Gray: non-significant). (G) Venn plot of genes from these four comparisons of sh-NTC, sh-melatonin, sh-PTH2R-melatonin, sh-PTH2R-NTC. (H) Heatmap visualization of core genes.

Subsequent differential expression analysis (|log2FC| ≥ 1, padj < 0.05, n = 3 biological replicates) identified the following changes: Comparison of sh-melatonin versus sh-PTH2R-melatonin groups revealed 4342 DEGs, comprising 2426 upregulated and 1916 downregulated genes (Figure 8B). Comparison of sh-NTC versus sh-PTH2R-NTC groups yielded 4385 DEGs, including 2379 upregulated and 2006 downregulated genes (Figure 8C), highlighting extensive transcriptional reprogramming induced by sh-PTH2R alone. Comparison of sh-NTC with sh-melatonin group revealed 313 DEGs, encompassing 116 upregulated and 197 downregulated genes (Figure 8D). Comparing sh-PTH2R-NTC with sh-PTH2R-melatonin yielded 494 DEGs, comprising 240 upregulated and 254 downregulated genes (Figure 8E), indicating that melatonin induces a significant transcriptional response. Comparing sh-NTC with sh-PTH2R-melatonin revealed 4653 DEGs, encompassing 2381 upregulated and 2272 downregulated genes (Figure 8F). These results indicate that co-administering drug treatment with sh-PTH2R may enhance transcriptional regulation of tumor-related pathways through synergistic or additive mechanisms, thereby jointly inhibiting LUAD progression. In summary, sh-PTH2R and melatonin independently or synergistically regulate gene expression to inhibit LUAD progression. Through Venn diagram analysis, two genes were identified as core DEGs co-present under both sh-PTH2R and melatonin treatment conditions, these genes exhibited highly consistent regulatory responses to both treatments (Figure 8G). KRT4 was significantly expressed in the sh-NTC group and lowly expressed in the sh-PTH2R-NTC and sh-PTH2R-melatonin groups, according to a heatmap visualization created for these two important target genes. The sh-NTC and sh-melatonin groups had low expression of ENPP2, while the sh-PTH2R-NTC group had strong expression. These results imply that PTH2R may influence the development of LUAD by controlling the expression of KRT4 and ENPP2 (Figure 8H).

3.7. Expression Validation of Core Genes

Then, RT-qPCR validated the expression of two core genes identified by transcriptomic analysis. Following melatonin treatment, mRNA levels of ENPP2 (p < 0.05) and KRT4 were significantly reduced in line with sequencing results, and while KRT4 displayed a downregulation trend consistent with transcriptomic data in our independent validation, this difference did not reach statistical significance due to within-group variation (Figure 9A). To assess the clinical relevance of melatonin-mediated regulation of ENPP2 and KRT4, we analyzed their expression patterns using the UALCAN database, which integrates RNA-seq data from TCGA, including 515 LUAD samples and 59 normal tissues. Results showed ENPP2 and KRT4 was significantly downregulated in LUAD tissues (p < 0.05, Figure 9B). The expression levels of ENPP2 and KRT4 were found to be highly connected with tumor pathological staging and to show a pronounced downregulation trend throughout disease progression, according to an analysis of their expression in relation to tumor staging using the UALCAN database (Figure 9C). Promoter methylation analysis revealed significantly elevated methylation levels for ENPP2. KRT4 methylation levels were slightly lower than in normal tissue but exhibited markedly higher internal heterogeneity, with extremely wide fluctuations in Beta values, indicating high methylation in some tumor samples and low methylation in others (Figure 9D). Results in Figure 9B,C indicate markedly reduced ENPP2 expression compared to normal tissue, likely due to silencing caused by high promoter methylation. Furthermore, survival analysis conducted using the Kaplan–Meier Plotter platform revealed that low expression levels of ENPP2 (p < 0.0001) and KRT4 (p < 0.05) were associated with improved OS in LUAD patients (n = 2166, Figure 9E).

Figure 9.

Figure 9

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Transcriptome-based validation and public-database mining identify potential clinical targets in LUAD. (A) Two key genes (ENPP2 and KRT4) were identified by transcriptome analysis and confirmed by RT-qPCR after total mRNA was extracted. (B) Online gene expression databases were used to examine the global expression of melatonin-regulated genes. Gene expression in LUAD (red boxplot) and normal (gray boxplot) samples is displayed in the UALCAN dataset. (C) ENPP2 and KRT4 of tumor stage in LUAD. (D) Promoter methylation levels of ENPP2 and KRT4 in LUAD. (E) Kaplan–Meier Plotter analysis demonstrated the connection between the OS rate of LUAD patients and the decreased expression of ENPP2 and CYK4/KRT4. All experiments were performed with 3 independent biological replicates (n = 3). (ns p ≥ 0.05, * p < 0.05, ** p < 0.01, **** p < 0.0001).

3.8. GO and KEGG Enrichment Analysis of PTH2R Knockdown and Melatonin Treatment

Next, we performed systematic GO analysis on the DEGs identified across groups to reveal their potential biological functions and pathways. For the comparisons of sh-NTC vs. sh-melatonin and sh-PTH2R-NTC vs. sh-PTH2R-melatonin (Figure 10A,B), DEGs in both groups were enriched for shared cellular components (CC) categories, including the collagen-containing extracellular matrix and intermediate filament. For the sh-NTC vs. sh-melatonin group, DEGs were primarily enriched for biological processes (BP) terms such as cell adhesion, positive regulation of axon extension involved in axon guidance, axon guidance, negative chemotaxis, and nervous system development, while their molecular functions (MF) enrichment was mainly concentrated in integrin binding and signaling receptor binding. In contrast, DEGs in the sh-PTH2R-NTC vs. sh-PTH2R-melatonin group were predominantly enriched for the tetrahydrobiopterin metabolic process in BP, with MF enrichment focused on transmembrane transporter activity. For the PTH2R knockdown group comparisons (Figure 10C,D), DEGs in the sh-NTC vs. sh-PTH2R-NTC group were enriched for BP terms including the regulation of transcription by RNA polymerase II, cell adhesion, and extracellular matrix organization; CC categories included the collagen-containing extracellular matrix and plasma membrane; and MF terms encompassed DNA-binding transcription factor activity, RNA polymerase II-specific, and RNA polymerase II cis-regulatory region sequence-specific DNA binding. DEGs in the sh-melatonin vs. sh-PTH2R-melatonin group showed further enhanced enrichment of these aforementioned functional categories. To elucidate the specific regulatory patterns of melatonin treatment under PTH2R-deficient conditions, we conducted a comparative DEG analysis for the sh-NTC vs. sh-PTH2R-melatonin comparison (Figure 10E). The enriched BP, CC, and MF categories in this group showed a high degree of consistency with those in the PTH2R knockdown group. This result indicates that PTH2R knockdown is the primary driver of transcriptomic functional regulation in LUAD cells, while melatonin serves to potentiate this regulatory effect. Collectively, these enrichment results indicate that sh-PTH2R and melatonin treatment jointly influence multiple biological processes closely associated with tumor progression, particularly cell adhesion, extracellular matrix remodeling, and transcriptional regulation pathways. Notably, the combined sh-PTH2R-melatonin treatment exhibited a more pronounced enrichment trend in transcriptional regulation and cell adhesion, suggesting that sh-PTH2R may enhance melatonin’s regulatory effects on certain anticancer pathways. This provides functional genomic support for their synergistic effects in inhibiting LUAD progression.

Figure 10.

Figure 10

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GO enrichment of DEGs between sh-NTC, sh-melatonin, sh-PTH2R-melatonin and sh-PTH2R-NTC. (A) sh-NTC and sh-melatonin. (B) sh-NTC and sh-PTH2R-NTC. (C) sh-NTC and sh-PTH2R-melatonin. (D) sh-melatonin and sh-PTH2R-melatonin. (E) sh-PTH2R-NTC and sh-PTH2R-melatonin.

The comprehensive results of KEGG pathway enrichment analysis indicate that sh-NTC vs. sh-melatonin primarily enriched metabolic pathways including glycolysis/gluconeogenesis, drug metabolism−cytochrome P450, and metabolism of xenobiotics by cytochrome P450 (Figure 11A), in the sh-PTH2R-NTC vs. sh-PTH2R-melatonin group, melatonin further regulated apoptosis, platelet activation, and colorectal cancer processes (Figure 9B). sh-NTC vs. sh-PTH2R-NTC and sh-melatonin vs. sh-PTH2R-melatonin significantly impacted cytoskeleton in muscle cells, focal adhesion, and extracellular matrix (ECM) -receptor interaction (Figure 11C,D). Crucially, in the combined treatment groups (sh-NTC vs. sh-PTH2R-melatonin), PTH2R deficiency enhanced melatonin’s inhibitory effects on signaling pathways (e.g., calcium signaling pathway and AGE-RAGE signaling pathway in diabetic complications) and arrhythmogenic right ventricular cardiomyopathy, revealing a synergistic mechanism regulating tumor malignancy (Figure 11E).

Figure 11.

Figure 11

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Top 10 enriched KEGG pathways of DEGs between sh-NTC, sh-melatonin, sh-PTH2R-melatonin and sh-PTH2R-NTC. (A) sh-NTC and sh-melatonin. (B) sh-NTC and sh-PTH2R-NTC. (C) sh-NTC and sh-PTH2R-melatonin. (D) sh-melatonin and sh-PTH2R-melatonin. (E) sh-PTH2R-NTC and sh-PTH2R-melatonin.

4. Discussion

Lung cancer, particularly its most common type LUAD, is a leading malignant tumor worldwide [35]. Although there have been advances in the treatment of LUAD, patient prognosis remains challenging. Molecular targeted therapy enables precise intervention against tumors, blocking excessive proliferation, invasion, and metastasis of tumor cells at the molecular level [36]. GPCRs are important targets for drug development since they are found throughout the body and are involved in many physiological functions. PTH2R is a member of family B1 GPCRs and participates in a wide range of physiological functions [37,38]. PTH2R is primarily expressed in normal tissues such as the pancreas, kidneys, testes, skin, and central nervous system. Its expression in lung tissue is extremely low under physiological conditions, making its induced expression in tumor states more specific [39]. In progressive lung cancer, PTH2R is classified as one of the neuroendocrine markers. Together with genes such as TPH2 and RIT2, it constitutes the cAMP and Ca2+ signaling pathways, suggesting its specific role in the neuroendocrine transformation of LUAD [40]. Although PTH2R has been demonstrated to be a prognostic indicator for papillary thyroid carcinoma [41], breast cancer bone metastases [42], and ovarian cancer [17], current research on its association with LUAD remains limited.

Through bioinformatics analysis, we have provided important insights into PTH2R research and preliminarily validated its expression and function. Comprehensive tumor cell functional assays, including CCK-8, colony formation, wound healing, and apoptosis assays, demonstrated that PTH2R knockdown significantly suppressed tumor cell growth and migration whilst inducing apoptosis. This finding is consistent with previous studies. This fully demonstrates the potential of PTH2R as a novel molecular biomarker for LUAD. Additionally, our research team’s previous research indicated that melatonin is a novel ligand for PTH2R. We preliminarily validated this finding using PRESTO-Tango, though the evidence chain remains incomplete, it provides initial support. Earlier studies revealed that melatonin exhibits antitumor properties against multiple human malignancies at both physiological and pharmacological doses [43]. Our experiments also confirm melatonin’s inhibitory effects on tumor cells. Combined drug treatment and knockdown experiments yielded more pronounced results, potentially indicating that melatonin, acting as a PTH2R antagonist, further suppresses related signaling pathways under knockdown conditions.

In order to first clarify the chemical mechanism by which PTH2R stimulates LUAD progression, we performed transcriptome sequencing on four groups: the sh-NTC group, sh-PTH2R-NTC group, sh-melatonin group, and sh-PTH2R-melatonin group. The sh-NTC vs. sh-melatonin groups and sh-PTH2R-NTC vs. sh-PTH2R-melatonin groups were found to co-enrich the cornified envelope formation pathway. Cornified envelope formation signifies the production of keratinocytes—the final differentiated product of keratinocyte differentiation [44,45]. The endogenous ligand for PTH2R is the tuberoinfundibular peptide of 39 residues (TIP39). Studies indicate that TIP39-PTH2R signaling regulates keratinocyte differentiation [45], suggesting that melatonin may share similar functional properties with TIP39. Knockdown of sh-NTC versus sh-PTH2R-NTC and sh-melatonin versus sh-PTH2R-melatonin jointly enriched multiple signaling pathways, including cytoskeleton in muscle cells, focal adhesion, and ECM-receptor interaction pathways. Research indicates that excessive ECM deposition leads to poor prognosis in pulmonary diseases, promotes cancer cell proliferation, metastasis, and immune evasion [46,47,48]. This indicates that PTH2R knockdown may suppress cancer growth and migration by regulating ECM homeostasis. The combined treatment groups (sh-NTC vs. sh-PTH2R-melatonin) exhibited highly similar activated signaling pathways to the knockdown group, indicating that PTH2R knockdown significantly impacts the progression of LUAD. Notably, the proteoglycan pathway in cancer and the AGE-RAGE signaling pathway in diabetic complications were only affected in the combination therapy group (sh-NTC vs. sh-PTH2R-melatonin). Alterations in proteoglycans (PGs) genes can lead to abnormal protein expression, which is associated with cancer invasion and metastasis [49]. Studies indicate that PGs are overexpressed in LUAD patients and correlate with aggressive phenotypes and poor prognosis [50]. Most PGs genes exhibit significantly lower expression in lung cancer tissues compared to normal lung tissue, while PGs protein levels are markedly reduced in both tumor cells and the stroma [51]. The binding of AGEs and RAGE triggers multiple signal transduction cascades, thereby contributing to the development of various diseases. For instance, AGEs increase the carcinogenicity of cancer-associated fibroblasts via mediating RAGE activation of prostate cancer epithelial cells [52]. In human LUAD cells, overexpressed RAGE stimulates cell motility, invasion, and the epithelial-to-mesenchymal transition through ERK signaling [53]. Therefore, combined treatment with melatonin and PTH2R knockdown may inhibit the development of LUAD by affecting the AGE-RAGE signaling pathway.

We also identified two core genes—ENPP2 and KRT4—that exhibited highly consistent regulatory responses across different treatments. ENPP2 is an enzyme belonging to the nucleoside pyrophosphatase/phosphodiesterase family. Numerous studies indicate that ENPP2 plays a crucial role in tumor cell proliferation, metastasis, invasion, and angiogenesis [54,55]. Aberrant ENPP2 overexpression has been reported in lung cancer [56,57,58,59], and is correlated with adverse clinical outcomes. KRT4 is a member of the type II keratin family, participates in key aspects of cancer, including responding to mechanical forces, evading the immune system, reprogramming metabolism, promoting angiogenesis, and resisting apoptosis [60]. Abnormal keratin expression is commonly observed in various malignant epithelial tumors. It is closely linked to the development of tumors and functions as a diagnostic marker [61,62,63]. Our data indicate that ENPP2 and KRT4 are closely associated with the development of LUAD, further demonstrating that PTH2R serves as a significant therapeutic target for LUAD.

In the future, we will collect tissue samples from LUAD patients and analyze PTH2R protein expression in these tissues via immunohistochemistry (IHC). We will investigate the association between PTH2R protein expression and clinical prognosis in LUAD patients, providing more compelling clinical evidence for PTH2R as a potential biomarker for prognosis assessment in LUAD patients. This will advance PTH2R-guided precision treatment strategies toward clinical application. Concurrently, we should evaluate melatonin’s differential efficacy across LUAD molecular subtypes. Additionally, we will investigate the functional interplay among PTH2R, ENPP2, and KRT4 in lung cancer to elucidate their functional associations in LUAD progression.

5. Conclusions

In conclusion, through systematic bioinformatics analysis, this study identified PTH2R, a member of family B1 GPCRs, as a key prognostic-associated gene in LUAD. In vitro functional experiments confirmed that targeting PTH2R via either gene knockdown or pharmacological inhibition significantly suppressed malignant phenotypes in LUAD cells, including proliferation, colony formation, and migration, while inducing apoptosis. At the mechanistic level, transcriptome sequencing analysis revealed core downstream regulatory genes and associated signaling pathways of PTH2R, thereby providing preliminary insights into the molecular basis of PTH2R-mediated LUAD progression. Finally, we validated that the low expression of these two key genes was significantly associated with favorable OS in patients, further solidifying the clinical significance of the PTH2R signaling axis in LUAD.

Acknowledgments

We are grateful to all the laboratory members for their technical advice and helpful discussions.

Abbreviations

The following abbreviations are used in this manuscript:

LUAD Lung adenocarcinoma
PTH2R Parathyroid hormone 2 receptor
OS Overall survival
SCLC Small cell cancer
NSCLC Non-small cell cancer
EGFR Epidermal growth factor receptor
GPCRs G protein-coupled receptors
VIPR1 Vasoactive intestinal peptide receptors 1 and 2
VIPR2 Vasoactive intestinal peptide receptors 2
PAC1R/ADCYAP1R1 Pituitary adenylate cyclase-activating peptide receptor
SCTR Secretin receptor
GHRHR Growth hormone-releasing factor receptor
GCGR Glucagon receptor
GLP1R Glucagon-like peptide 1 receptor
GLP2R Glucagon-like peptide 2 receptor
GIPR Gastric inhibitory peptide receptor
PTH1R Parathyroid hormone receptors 1
CTR/CALCR Calcitonin receptor
CALCRL Calcitonin receptor-like receptor
CRF1R/CRHR1 Corticotropin-releasing factor 1 receptor
CRF2R/CRHR2 Corticotropin-releasing factor 2 receptor
TCGA The Cancer Genome Atlas
KEGG Kyoto Encyclopedia of Genes and Genomes
GO Gene Ontology
DAVID Database for Annotation, Visualization and Integrated Discovery
ATCC American Type Culture Collection
OD Optical density
PBS Phosphate buffer solution
IC50 A half-maximal inhibition
DEGs Differentially expressed genes
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Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biomedicines14020489/s1, Figure S1: Sangerbox 3.0 was used to evaluate the expression levels of VIPR1, VIPR2, CALRL, CRHR1, CRHR2, GCGR and ADCYAP1 in various tumor types from the TCGA database. Figure S2: Correlation of family B1 GPCRs expression with immune infiltration levels in LUAD.

biomedicines-14-00489-s001.zip (1.1MB, zip)

Author Contributions

Conceptualization, W.L. and Y.W.; methodology, W.L. and Y.W.; validation, C.L., Y.Y., Y.X. and P.T.; formal analysis, C.L.; data curation, C.L.; writing—original draft preparation, C.L.; writing—review and editing, D.S., T.K.K., S.C. and C.S.; visualization, C.L.; supervision, C.S.; funding acquisition, C.S. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original data presented in this study are openly available from the National Genomics Data Center at reference number PRJNA1357987.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding Statement

This research was funded and supported by the National Key Research and Development Program (NO. 2024YFD2100700).

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

biomedicines-14-00489-s001.zip (1.1MB, zip)

Data Availability Statement

The original data presented in this study are openly available from the National Genomics Data Center at reference number PRJNA1357987.

Articles from Biomedicines are provided here courtesy of Multidisciplinary Digital Publishing Institute (MDPI)

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