Bioinformatics profiling of NECTIN4 in lung cancer and comparative evaluation of NECTIN4-targeted 68Ga-N188 and 18F-FDG PET/CT

Bioinformatics profiling of NECTIN4 in lung cancer and comparative evaluation of NECTIN4-targeted 68Ga-N188 and 18F-FDG PET/CT | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Bioinformatics profiling of NECTIN4 in lung cancer and comparative evaluation of NECTIN4-targeted 68 Ga-N188 and 18 F-FDG PET/CT Yuqi Wang, Xin Zhou, Jinchuan Chen, Futao Liu, Yutao Li, Yuan Li, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8063844/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Apr, 2026 Read the published version in Journal of Translational Medicine → Version 1 posted 4 You are reading this latest preprint version Abstract Background The nectin cell adhesion molecule 4 (NECTIN4) has been implicated in tumor progression and immune evasion. However, its role and translational imaging potential in lung cancer remain unclear. Therefore, this study aims to elucidate the molecular characteristics of NECTIN4 by integrating multi-omics bioinformatics analyses with clinical PET/CT validation, and evaluate the diagnostic efficacy of the NECTIN4-targeted radiotracer ⁶⁸Ga-N188 compared to ¹⁸F-FDG. Methods Transcriptomic and proteomic datasets from The Cancer Genome Atlas, Genotype-Tissue Expression, Gene Expression Omnibus and other bioinformatic tools were used to characterize NECTIN4 expression levels, genomic alterations, associated regulatory networks and prognostic value. Subsequently, in a prospective clinical cohort study involving 20 patients with suspected primary lung cancer, paired PET/CT imaging using 68 Ga-N188 and 18 F-FDG was conducted. The diagnostic performance of imaging modalities was assessed by quantitatively comparing the tumor-to-blood pool ratio between malignant and inflammatory lesions. Results Bioinformatics analyses indicated that NECTIN4 was significantly upregulated across multiple cancer types and correlated with genomic instability and poor prognosis in non-small cell lung cancer. NECTIN4 expression was positively associated with DNA methyltransferases and RNA modifications, suggesting its regulation by epigenetic and post-transcriptional. Clinically, ⁶⁸Ga-N188 PET/CT exhibited superior specificity (100% vs. 50%) and comparable sensitivity (87.5% vs. 93.8%) to ¹⁸F-FDG PET/CT in differentiating malignant from inflammatory lung lesions, but with lower sensitivity (42.2% vs 100.0%) for detecting lymph node metastases and fewer identified distant metastatic lesions (21 vs 51). Conclusion Our findings revealed that NECTIN4 as a promising biomarker and imaging target in lung cancer. The NECTIN4-targeted ⁶⁸Ga-N188 PET/CT outperforms ¹⁸F-FDG PET/CT in diagnosing primary lung cancer, supporting its role as a promising complementary imaging modality in clinical practice. NECTIN4 bioinformatics 68Ga-N188 PET/CT genome instability lung cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Lung cancer—the most prevalent malignancy globally—remains the leading cause of cancer-related mortality, leading to about 18.4 percent of all cancer deaths. Despite advances in therapeutic strategies, the 5-year overall survival rate remains 10% to 15%, primarily due to late-stage diagnosis and the complexity of therapeutic management [ 1 , 2 ]. As of now, ¹⁸F-FDG PET/CT is a fundamental imaging instrument in the clinical assessment of lung cancer, and is extensively utilised in the diagnosis, staging, and assessment of treatment outcomes in non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) [ 3 , 4 ]. However, 18 F-FDG PET/CT exhibits limited specificity to differentiate between malignant and inflammatory lesions, and this poses significant challenges in precision medicine. This limitation demonstrates the necessity to design novel PET tracers with higher specificity that will provide more information about tumor biological characteristics to enable a personalized treatment approach and enhance patient outcomes in lung cancer. Nectin cell adhesion molecule 4 (NECTIN4) is an immunoglobulin-like cell adhesion transmembrane protein, a subgroup of nectin, a type I protein family of cell adhesion molecules. Under normal circumstances, NECTIN4 is strongly expressed in the placenta and embryo, with minimal expression observed in healthy adult tissues and organs. It controls cellular behavior by participating in signaling pathways like the PI3K-Akt signaling pathway and is essential in keeping cell-cell adhesions and tight junctions [ 5 – 7 ]. Many studies have revealed in recent years that NECTIN4 is overexpressed in about two-thirds of patients with NSCLC. It was found to be strongly associated with clinicopathological characteristics, including the size of the tumor, the disease stage, and the existence of distant metastases, and is an independent prognostic variable of overall survival (OS) in NSCLC [ 8 – 10 ]. With the approval of Enfortumab Vedotin (EV)—the first antibody-drug conjugate (ADC) targeting NECTIN4—by the U.S. Food and Drug Administration (FDA) and the European Medicines Agency for the treatment of urothelial carcinoma (UC), the clinical significance of NECTIN4 has gained increasing attention. The indication of EV is currently on the rise as several clinical trials are currently underway. At the same time, there are increasingly diverse drugs against NECTIN4 which are being developed and gradually emerging [ 11 – 15 ]. With regards to non-invasive, dynamic, whole-body NECTIN4 expression measurements, a number of NECTIN4 imaging probes, such as monoclonal antibody-derived ones, have been created and published. However, clinical translation of these probes is still challenged because of constraints like slow systemic clearance and limited tumor penetration in solid malignancies. Among such probes, 68 Ga-N188, a NECTIN4-targeted radiotracer based on a bicyclic peptide, exhibits great sensitivity and specificity in detecting NECTIN4-expressing lesions in patients with advanced UC [ 16 ]. A head-to-head comparison with 18 F-FDG PET/CT revealed that 68 Ga-N188 not only effectively detects lesions but also quantitatively assesses membranous NECTIN4 expression levels across various solid tumors, providing preliminary evidence of its clinical utility. Nevertheless, that study included only two patients with NSCLC, leaving the potential of NECTIN4-targeted PET imaging in lung cancer largely unexplored. Thus, this study conducted an integrative study combining multi-omics bioinformatics profiling with molecular imaging validation to investigate NECTIN4 expression patterns, genomic alterations, epigenetic regulation, and their associations with clinical outcomes in lung cancer. Additionally, a head-to-head comparison of NECTIN4-targeted ⁶⁸Ga-N188 and ¹⁸F-FDG PET/CT was performed in patients with suspected primary lung cancer to assess diagnostic performance and translational feasibility. Methods Bioinformatic data collection and processing Differential expression of the NECTIN4 gene across various cancer types was analyzed using the TIMER database ( http://timer.cistrome.org/ ). RNA sequencing data and corresponding clinical data were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases via UCSC Xena ( https://xenabrowser.net/datapages/ ). Cancer proteomics data were retrieved from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. The Lung Cancer Explorer (LCE) database ( https://lce.biohpc.swmed.edu/ ) was utilized to conduct a meta-analysis of OS hazard ratio in patients with lung cancer. Furthermore, single-cell RNA-sequencing (scRNA-seq) datasets for lung cancer (E-MTAB-6149, GSE127465, GSE162498, and GSE210347) were retrieved from the Gene Expression Omnibus (GEO) database. Differences in NECTIN4 mRNA expression across disease states (tumor vs. normal) and individual cancer stages were analyzed using the R package ggplot2 and visualized with box plots and paired-sample wiring diagrams. Genomic alteration and mutational burden analyses Pan-cancer analyses of genomic mutation, amplification, and deep deletion frequencies were performed using the Cancer Type Summary module of cBioPortal [ 18 ]. Tumor mutational burden (TMB) and mutant-allele tumor heterogeneity (MATH) were assessed using the R package maftools , while ploidy and microsatellite instability (MSI)-related data were obtained from previous reports [ 19 ]. Correlations between these genomic characteristics and NECTIN4 expression were subsequently analyzed. DNA methylation analyses and epigenetic modification analyses Data derived from cBioPortal was used to access the correlations between the methylation of NECTIN4 and pan-cancer. Expression data for the NECTIN4 gene and 44 tri-class RNA modifications N1-methyladenosine (m 1 A), 5-methylcytosine (m 5 C), and N6-methyladenosine (m 6 A) modifying genes from the UCSC. The Pearson correlation between NECTIN4 and the marker genes were assessed with the R package. 68 Ga-N188 production The NECTIN4-targeting ligand N188 was purchased from Shanghai Apeptide Co., Ltd (Shanghai, China) and purified using high-performance liquid chromatography (HPLC) to > 95% purity. Radiosynthesis of 68 Ga-N188 was achieved in a one-step reaction within 15 min based on a previous established protocol [ 16 ]. Radiochemical purity was evaluated using radio-HPLC and confirmed at > 95% purity. Western blot analysis Western blot analysis Total protein was extracted from tissue using prechilled radioimmunoprecipitation assay buffer containing a protease inhibitor cocktail and measured using bicinchoninic acid assay. Equal amounts of protein samples were separated on a 7.5% sodium dodecyl sulfate–polyacrylamide gel electrophoresis gel and transferred onto 0.45 µm polyvinylidene fluoride membranes. Membranes were blocked with 5% non-fat milk for 1 h, then incubated overnight at 4℃ with primary antibodies, including anti-NECTIN4 (1:2000; Proteintech, Cat. No. 21903-1-AP) and anti-rabbit β-actin (1:100000; Proteintech, Cat. No. 20536-1-AP). Immunoblots were visualized and analyzed using an automated chemiluminescence imaging system (5200 Multi, Tanon, China). The band of β-actin was used as an internal standard to normalize the results and analyzed using Image J software. Immunohistochemical analysis Paraffin-embedded lung tumor and matched adjacent non-tumorous tissues were obtained from patients of department of thoracic surgery, in accordance with institutional ethical guidelines. Tissue sections were fixed in 4% paraformaldehyde, and 4 µm thick slices were prepared from the formalin-fixed, paraffin-embedded blocks and subsequently dewaxed. Using an antigen retrieval solution, sections were pretreated in a microwave oven at medium power for 8 min until boiling, then at medium-low power for 7 min. They were then blocked with 0.3% hydrogen peroxide and goat serum, rinsed with Tris-buffered saline, and incubated with anti-Nectin4 antibody (1:1000) at 4°C overnight. The sections were subsequently incubated with a horseradish peroxidase-conjugated secondary antibody at room temperature, stained with 3,3'-diaminobenzidine, and counterstained with hematoxylin for 3 min. Images were captured using an optical microscope and analyzed using Image J software Patient information The Medical Ethics Committee of Peking University Cancer Hospital approved this prospective study (2022KT37-ZY01). Written informed consent was obtained from all participants. The trial was registered at www.clinicaltrialsregister.eu (Trial Identifier: NCT06648317). Patients with lung lesions were consecutively recruited between September 2024 and December 2024. Inclusion criteria were: (1) presence of lung lesions; (2) normal kidney, liver, and bone marrow hemopoietic function; and (3) an ECOG performance status of 0–1. Exclusion criteria included: (1) prior chemotherapy or radiotherapy; (2) refusal to undergo paired baseline 68 Ga-N188 PET/CT and 18 F-FDG PET/CT within 1 week. Clinical and pathological characteristics of the patients were recorded. Position emission tomography/computed tomography acquisition Patients received an intravenous injection of 68 Ga-N188 (1.9–3.7 MBq/kg) and were instructed to drink 800–1500 mL of water. PET/CT was conducted 1 h post-injection using a 194-cm-long axial field of view (FOV) total-body PET/CT (uEXPLORER, United Imaging Healthcare, Shanghai, China). Acquisition time was 5 min. Image reconstruction was conducted using the ordered subset expectation maximization algorithm with two iterations, 20 subsets, a 192×192 matrix, and a 600 mm FOV. The slice thickness was 2.886 mm. Attenuation and scattering corrections were applied, along with point spread function and time-of-flight reconstruction. No post-filtering was applied. The attenuation corrected CT was acquired using 120 kV with a modulated current of approximately 75 mA. Vital signs were recorded before injection, throughout the screening period, and 2 h after the PET/CT scan. Patients were required to fast for at least 6 h before the 18 F-FDG PET/CT scans to maintain normal blood glucose levels (4.4–9.3 mmol/L). The intravenous dose was body weight-based (3.7 MBq/kg), followed by a 1 h rest before imaging. Acquisition conditions were consistent with those used for the 68 Ga-N188 PET/CT. PET/CT image analysis Post-processing of images was conducted using a vendor-provided software (Multi-Modality Workplace, United Imaging, China). Two nuclear medicine physicians with 5–10 years of diagnostic experience, blinded to prior imaging and pathological findings, independently reviewed all images. Discrepancies were resolved by a third physician with 15–20 years of diagnostic experience. The volume of interest for the primary pulmonary tumor on 18 F-FDG PET/CT and 68 Ga-N188 PET/CT images was manually delineated to include the entire target lesion while excluding surrounding tissues and organs. The maximum standardized uptake value (SUV max ) of the lesions and the mean SUV (SUV mean ) of the blood pool—derived from uptake in the descending aorta (used as background) were recorded. The tumor-to- blood-pool ratio (TBR) was calculated as tumor SUV max /BP SUV mean . Metastatic lesions were confirmed via pathology or follow-up imaging. A lesion was considered positive for metastasis if its uptake exceeded the physical uptake of surrounding tissues. Statistical analysis R software (version 4.2.1) was utilized for statistical analyses of the bioinformatics data. Group comparisons were conducted using one-way analysis of variance (ANOVA) or Student's t-test, as appropriate. Survival analyses were conducted using Kaplan–Meier curves with log-rank tests or Cox proportional hazards regression models. Correlations between variables were assessed using Pearson or Spearman correlation coefficients. Statistical analyses of the data of the patients were conducted using IBM SPSS Statistics (version 25.0) and GraphPad Prism (version 8.0). Measurement data were expressed as mean ± standard deviation, while categorical variables were presented as frequencies and percentages. Differences in SUV max values among lesions with different pathological types on 68 Ga-N188 PET/CT were analyzed using one-way ANOVA. The normality of continuous variables was assessed using the Shapiro–Wilk test. For non-normally distributed data, the Mann–Whitney U test was utilized. Data distributions were visualized using histograms. The predictive value of the uptake parameters for differentiating malignant and benign lesions was analyzed using the area under the receiver operating characteristic (ROC) curve, with the cut-off value determined based on the Youden index. The diagnostic performance metric, positive predictive value (PPV), was calculated. A p-value of < 0.05 was considered statistically significant. Results Expression of NECTIN4 in multiple cancers and lung cancer Using RNA sequencing data from the TCGA and GTEx databases for systematic analysis, we investigated the expression of NECTIN4 mRNA across multiple cancer types. Differential expression analysis revealed significantly dysregulation of NECTIN4 mRNA in 16 tumor types, including lung cancer, compared with adjacent normal tissues (Fig. 1 A and B). NECTIN4 was significantly overexpressed in lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) in the TCGA dataset, with expression progressively increasing across disease stages (Additional file1). Consistent with the above findings, NECTIN4 protein was overexpressed in lung cancer, breast invasive carcinoma (BRCA), uterine corpus endometrial carcinoma (UCEC), pancreatic adenocarcinoma (PAAD), with particularly significant in NSCLC (LUAD and LUSC; Fig. 1 D and 1 E). Further analysis of 196 lung cancer cell lines revealed that NECTIN4 expression significantly elevated expression of NECTIN4 in non-small cell lung cancer (NSCLC) cell lines, encompassing both LUAD and LUSC (Fig. 1 C). Single-Cell landscape of NECTIN4 expression in lung cancer By analyzing scRNA-seq transcriptomic data obtained from lung tumor specimens and adjacent non-tumor tissues across multiple datasets (E-TMAB-6169, GSE127465, GSE162498, and GSE210347), we further elucidated the role of NECTIN4 in lung cancer (Fig. 2 A and 2 B). The cells were categorized into distinct clusters, including cancer cells, epithelial cells, alveolar cells, and immune cells based on established cell-type markers. NECTIN4 was detected in all clusters, showing highest expression levels observed in cancer cells and lower expression levels in lymphocytes and fibroblasts. Molecular validation of NECTIN4 overexpression and correlation with poorer prognosis To confirm NECTIN4 protein expression in NSCLC, immunohistochemistry (IHC) and western blot analysis were conducted. The results showed that NECTIN4 protein was localized in membranous, and its elevated expression was observed in tumor sections compared to inflammatory and normal lung tissues (Fig. 2 D). The findings of the western blot analysis were in agreement with the findings described above (Fig. 2 C). We analyzed the data that were obtained from GSE11969, GSE41271, GSE81089, and GSE47115 and realized that increased NECTIN4 expression had a negative relationship with the OS of patients with NSCLC (Fig. 2 E). This result was also confirmed by a meta-analysis of the Lung Cancer Explorer (LCE) database, which revealed a stable correlation between high NECTIN4 expression and poor outcomes in NSCLC (Additional file 2). Genomic alterations and genomic instability in NECTIN4 expression To assess potential genome-level alterations of NECTIN4 in cancers, we conducted a pan-cancer analysis of NECTIN4 copy number variations (CNVs) and single nucleotide variants (SNVs). The genomic mutation landscape showed that NECTIN4 exhibits frequent genetic alterations across various types of tumors and, most notably, in NSCLC (Fig. 3 A, B). NECTIN4 amplification was most frequently detected in multiple cancers, followed by mutation and deep deletion. It is also interesting to note that the frequency of NECTIN4 alteration in LUSC and LUAD is above 10%. Additionally, Also, NECTIN4-gained samples were characterised by elevated NECTIN4 mRNA, in comparison with control samples with copy number neutral samples, indicating that copy number variations might be used to regulate its expression ( P < 0.0001) (Fig. 3 C-D). Further to simplify the genomic background of NECTIN4 expression, we analysed patterns of co-occurring mutations in LUAD and LUSC (Fig. 3 E). Results revealed that high expression of NECTIN4 was frequently co-expressed with mutations in established cancer-associated genes including TP53, TTN, RYR1, NFE2L2. The NECTIN4 high-expression and low-expression groups exhibited significant differences in mutation profiles and transcriptomic characteristics, showing a different pattern of clustering. On the other hand, correlations between NECTIN4 and TMB, MSI, MATH and ploidy were also evaluated given the abundance of such mutations in pan-cancer (Fig. 3 F, Additional file 3). NECTIN4 was positively correlated with MSI, and this was significant in both LUAD and LUSC. Correlation analysis further indicated that NECTIN4 expression was positively correlated with TMB, ploidy, and MATH, albeit with modest correlation coefficients. Analyses of NECTIN4 with DNA methylation and cancer cell stem-like characteristics DNA methylation is one of the most critical epigenetic modifications in cancer initiation and progression [ 20 , 21 ]. As key enzymes catalyzing DNA methylation, DNA methyltransferases (DNMTs) were responsible to modulate tumor invasion, proliferation, metastasis, diagnosis, and prognosis [ 22 – 27 ]. The expression of NECTIN4 in LUAD had significant positive correlations with both DNMT1 and DNMT3B ( P < 0.05), which demonstrated that the expression of NECTIN4 was affected by DNA methylation (Fig. 4 A). In order to explore potential epigenetic regulatory pathways of NECTIN4, we examined the correlations of its expression with the genes related to different RNA modifications (Fig. 4 B). Results indicated that NECTIN4 expression positively correlated with numerous key writers (e.g., METTL14), readers (e.g., ALYREF), and erasers (e.g., ALKBH1) in both LUAD and LUSC. Among these, the associations with METTL3, YTHDF1, and IGF2BP3 were particularly notable, although these did not reach statistical significance. Furthermore, we evaluated the relationship between cancer stemness scores and NECTIN4 expression in LUAD and LUSC (Fig. 4 C). NECTIN4 expression was positive correlated with DNA stemness score (DNAss), RNA stemness score (RNAss), differentially methylated probes signature score (DMPss), enhancer methylation signature score (ENHss) in LUSC. In contrast, NECTIN4 expression in LUAD showed a negative correlation with those scores. Patient Characteristics Twenty patients (6 women and 14 men; mean age 60.5 ± 8.1 years) presenting with lung lesions were enrolled. Patients diagnosed with NSCLC, SCLC, and inflammatory lesions numbered 13, 3, and 4, respectively. Table 1 presents the clinicopathological characteristics of the patients. All patients underwent paired 18 F-FDG PET/CT and 68 Ga-N188 PET/CT. Table 1 Clinicopathological characteristics of patients Information Patients n (%) Age (y) Median (range) 62 (35–72) Sex Female 6 (40.0) Male 14 (60.0) Smoking No 4 (20.0) Yes 16 (80.0) Family history of malignancy No 18 (90.0) Yes 2 (10.0) Pathology Adenocarcinoma 9 (45.0) Squamous carcinoma 4 (20.0) Small cell lung cancer 3 (15.0) Inflammation 4 (20.0) Clinical Staging I 3 (18.8) II 4 (25.0) III 2 (12.5) IV 7 (43.7) PET/CT uptake of lesions with different pathology Using 18 F-FDG PET/CT imaging, TBRs for NSCLC, SCLC, and inflammatory lesions were 7.7 ± 3.2, 5.4 ± 1.3, and 4.9 ± 3.0, respectively; no significant differences were observed among these groups. In 68 Ga-N188 PET/CT imaging, tracer uptake in NSCLC was significantly higher than in SCLC (2.1 ± 0.6 vs. 1.3 ± 0.5, P = 0.046), and also significantly higher than in inflammatory lesions (TBR: 1.1 ± 0.1; P = 0.009). Figure 5 A and 5 B illustrate uptake patterns in inflammatory and malignant lung lesions, respectively, while Fig. 6 presents representative imaging examples. Figure 5 C shows the ROC curves of 18 F-FDG PET/CT and 68 Ga-N188 PET/CT for differentiating between inflammation and cancerous lesions. The area under the curve (AUC) for 18 F-FDG PET/CT and 68 Ga-N188 PET/CT was 0.703 ( P = 0.219) and 0.906 ( P = 0.014), respectively. The diagnostic efficacy of 68 Ga-N188 PET/CT was overall superior to it of 18 F-FDG PET/CT (the sensitivity and specificity was 87.5% and 100.0% vs. 93.8% and 50.0%, respectively). The detection efficacy of PET/CT for metastatic lesions in patients with lung cancer A total of 103 lymph nodes (45 metastatic, 58 non-metastatic) were confirmed by histopathology and follow-up imaging. Using 18 F-FDG PET/CT, 45, 15, 0, and 43 lymph nodes were classified as true positive (TP), false positive (FP), false negative (FN), and true negative (TN), respectively. In contrast, ⁶⁸Ga-N188 PET/CT identified 19 TP, 0 FP, 26 FN, and 58 TN lymph nodes. In 18 F-FDG PET/CT, SUV max values for true positive and true negative lymph nodes were 11.5 ± 3.2 and 1.9 ± 0.8, respectively ( P < 0.001); in 68 Ga-N188 PET/CT, the corresponding values were 4.1 ± 0.9 and 1.4 ± 0.6 ( P < 0.001). For lymph node differential diagnosis, 68 Ga-N188 PET/CT demonstrated higher specificity and positive predictive value (PPV) (both 100%), but lower sensitivity (42.2%). Conversely, 18 F-FDG PET/CT showed sensitivity of 100%, specificity of 74.1%, and PPV of 75%. Detailed diagnostic efficacy metrics are summarized in Table 2 . Table 2 Efficacy of PET/CT in identifying metastatic lymph nodes Sensitivity Specificity Accuracy PPV NPV 18 F-FDG PET/CT 100.0% 74.1% 85.4% 75.0% 100.0% 68 Ga-N188 PET/CT 42.2% 100.0% 74.8% 100.0% 69.0% PPV: Positive Predictive Value; NPV: Negative Predictive Value. 18 F-FDG PET/CT identified significantly more distant metastatic lesions than 68 Ga-N188 PET/CT (51 vs. 21; mean SUV max 9.2 ± 2.8 vs. 3.2 ± 0.7; P < 0.001) (Table 3 ). However, in organs characterized by high physiological 18 F-FDG uptake—such as the brain— 68 Ga-N188 PET/CT provided superior lesion delineation compared to 18 F-FDG PET/CT, as demonstrated by a higher TBR of 0.83 vs. 0.55. Table 3 Efficacy of PET/CT in detecting metastatic lesions Brain Lung Pleura Bone Adrenal Gland Others Total (N) SUV max 18 F-FDG PET/CT 1 13 12 14 4 8 51 9.2 ± 2.8 68 Ga-N188 PET/CT 1 / 12 5 3 / 21 3.2 ± 0.7 Discussion NECTIN4 has emerged as a valuable target for cancer diagnosis and therapy. Previous studies have extensively characterized its roles in various solid tumors, demonstrating its involvement in cell proliferation, migration, and angiogenesis [ 28 – 33 ]. Our findings on bioinformatic studies revealed NECTIN4 to be a biomarker of lung cancer and its overexpression was associated with poor prognosis in NSCLC, with specific focus on the correlation between NECTIN4 expression and genomic instability as well as epigenetic regulation. Clinically, 68 Ga-N188 PET/CT demonstrated superior specificity over 18 F-FDG in differentiating malignant from inflammatory lesions, reflecting the biological relevance of NECTIN4 expression in tumor tissue. An integrated analyses of the TCGA, GTEx, and CPTAC datasets denoted notable upregulation of NECTIN4 at the protein level and mRNA level in various cancers such as lung cancer. NECTIN4 overexpression was more pronounced in NSCLC compared to SCLC, consistent with the differing uptake patterns of NECTIN4-targeted tracers, with NSCLC lesions typically showing higher accumulation than those of SCLC. Such results highlight the clinical usefulness of NECTIN4 as a biomarker that can be used to differentiate between primary lung cancer and inflammatory lesions. Studies on cancer genomics aims to identify recurrent abnormalities in certain cancer types and elucidate their pathogenesis based on a systematic study of genomic changes, including nucleotide substitutions, copy number variations, and DNA rearrangements [ 34 , 35 ]. In the present study, NECTIN4 expression was associated positively with the mutation landscape, with copy number amplification being predominant, which means its upregulation is accompanied by increased genomic instability, particularly in NSCLC. This is in line with previous studies that NECTIN4 amplifications is evident in several solid tumors, most commonly in BLCA, BRCA, and LUAD, accounting for 5%–10% of all cases [ 36 ]. Thus, we conclude that its amplification was associating with increased NECTIN4 mRNA expression and elevated NECTIN4 protein levels in lung cancer. Moreover, in LUAD and LUSC, NECTIN4 alterations were often present in association with mutations in tumour-related genes (TP53, TTN, RYR1, and NFE2L2). It is important to state that the expression of NECTIN4 and the mutation profile co-occurring in these two subtypes are not the same, which could indicate that it may possess cell-specific functions and highlighting its role within complex oncogenic networks. In addition to genomic alterations, epigenetic modifications are also are also instrumental in regulating the progression of cancer [ 37 ]. DNA methylation, as one of the most critical epigenetic mechanisms, can alter gene expression. This possess is primarily catalyzed by DNMTs, thereby regulating the production of proteins encoded by genes [ 27 ]. Previous researches have demonstrated that DNMT1 has the capacity to increase the methylation of the hMLH1 and hMSH2 gene promoter regions, in turn, inhibiting their activities and finally encouraging the growth of epidermal growth factor receptor-mutated NSCLC cells [ 38 ]. It is also observed that DNMT3B overexpression is closely correlated with reduced OS in lung cancer patients [ 39 ]. This study further supports the aforementioned mechanisms, reveling that NECTIN4 expression exhibits significant positive correlations with DNMT1 and DNMT3B ( P < 0.05), suggesting that DNA methylation-related regulation may influence NECTIN4 transcription. Along with DNA methylation, NECTIN4 was also positively associated with several RNA modification regulators, such as METTL3, YTHDF1, and IGF2BP3, which implies the cross-regulatory relation between NECTIN4 and post-transcriptional m6A-mediated modifications [ 40 ]. Additionally, the correlations between NECTIN4 expression and tumor stemness scores further emphasize its potential role in maintaining cancer stem-like characteristics. Interestingly, NECTIN4 showed different trends of stemness in LUSC and LUAD, suggesting heterogeneous regulation of NECTIN4 across distinct histological subtypes. 68 Ga-N188, an innovative PET imaging probe targeting NECTIN4, demonstrates high sensitivity and specificity for NECTIN4 detection in prior clinical studies. Its primary tumor identification rate is comparable to that of 18 F-FDG PET/CT, along with improved specificity for detecting lymph node metastases across multiple cancer types [ 17 ]. To directly assess their diagnostic performance in lung cancer, a head-to-head comparison between 68 Ga-N188 and 18 F-FDG was conducted in this study. Our findings indicate that 68 Ga-N188 PET/CT exhibits significantly variable uptake across diagnostic groups, with sensitivity and specificity values of 100% and 87.5%, respectively, in distinguishing malignant tumors from inflammatory lesions. In contrast, 18 F-FDG PET/CT produced overlapping SUV max and TBR measurements for malignant and inflammatory lesions, limiting its diagnostic accuracy. Although 18F-FDG PET/CT can be used with high sensitivity and specificity of 96% and 79% to distinguish between benign and malignant lung lesions, this test is prone to false-positive outcomes, especially when inflammation or granuloma exists [ 4 , 41 ]. Collectively, these findings highlight 68 Ga-N188 as a promising alternative for enhancing diagnostic precision in clinically equivocal scenarios. Although both NSCLC and SCLC lesions exhibit higher 68 Ga-N188 uptake than inflammatory lesions, uptake in NSCLC is significantly greater than in SCLC. This finding is consistent with previous reports showing that NECTIN4 expression is predominantly upregulated in NSCLC but comparatively lower in SCLC [ 9 ]. This differential uptake suggests that 68 Ga-N188 PET/CT may aid in molecular subtyping of lung cancer by identifying NECTIN4-enriched tumors. Regarding lymph node metastasis, 68 Ga-N188 PET/CT exhibits higher specificity and positive predictive value than 18 F-FDG PET/CT, indicating its potential to reduce false-positive results. However, 18 F-FDG PET/CT detected a greater number of metastatic lymph nodes overall, reflecting its superior sensitivity, accuracy and negative predictive value in this context. Regarding distant metastasis, 58.8% of lesions were positive on 18 F-FDG PET/CT but negative on 68 Ga-N188 PET/CT. However, 68 Ga-N188 shows comparable detection performance for brain metastases. Given the well-documented limitation of 18 F-FDG PET/CT in brain imaging—attributable to high physiological uptake in normal brain tissue—the low background signal of 68 Ga-N188 in the brain may provide a distinct advantage for detecting intracranial lesions [ 42 ]. This study has some limitations. First, NECTIN4 expression analysis was primarily based on NSCLC data from the TCGA database, without including SCLC datasets for comparison or validation. Second, although the 68 Ga-N188 uptake has demonstrated clinical relevance for lung cancer diagnosis, its absolute tumor uptake values were suboptimal, suggesting future optimization of probe structure to enhance imaging performance. Third, the absence of long-term clinical follow-up limited the evaluation of the prognostic value of 68 Ga-N188 uptake. Future studies incorporating extended follow-up and larger patient cohorts are necessary to validate the potential of NECTIN4-targeted PET/CT as a prognostic imaging biomarker. Conclusions In conclusion, this study establishes NECTIN4 as a clinically molecular target in lung cancer and validates NECTIN4-targeted ⁶⁸Ga-N188 PET/CT as a feasible imaging approach for its noninvasive visualization. Integrative bioinformatic analyses confirmed that NECTIN4 overexpression, genomic amplification, and epigenetic activation are correlated with poor prognosis in lung cancer. In addition, 68 Ga-N188 PET/CT demonstrates superior performance over 18 F-FDG PET/CT in distinguishing primary lung cancer from inflammatory lesions, and may serve as a valuable complementary imaging modality following initial 18 F-FDG PET/CT in the diagnostic workup of lung cancer. Abbreviations PET/CT, positron emission tomography/computed tomography NECTIN4, nectin cell adhesion molecule 4 NSCLC, non-small cell lung cancer SCLC, small cell lung cancers OS, overall survival ADC, antibody-drug conjugate UC, urothelial carcinoma TCGA, The Cancer Genome Atlas GTEx, Genotype-Tissue Expression CPTAC, Clinical Proteomic Tumor Analysis Consortium scRNA-seq, single-cell RNA-sequencing GEO, Gene Expression Omnibus HPLC, high-performance liquid chromatography PPV, positive predictive value FOV, field of view SUVmax, maximum standardized uptake value SUVmean, mean SUV TBR, tumor-to-blood-pool ratio ANOVA, analysis of variance ROC, receiver operating characteristic LUSC, lung squamous cell carcinoma LUAD, lung adenocarcinoma BRCA, breast invasive carcinoma UCEC, uterine corpus endometrial carcinoma PAAD, pancreatic adenocarcinoma LCE, Lung Cancer Explorer TP, true positive FP, false positive FN, false negative TN, true negative Declarations Funding This work was partially supported by the National Natural Science Foundation of China (grant numbers: 82472015), Beijing Hospitals Authority's Ascent Plan (grant numbers: DFL20241103), Beijing Research Ward Excellence Program (grant numbers: BRWEP2024W032150102), National Key R&D Program of China (grant numbers: 2022YFC2409405), Beijing Physician Scientist Training Program (BJPSTP-2025-21), National Natural Science Foundation of China (grant numbers: 82402318), Beijing Natural Science Foundation (grant numbers: L252055), CAMS Medical and Health Science and Technology Innovation (grant numbers: 2021-I2M-5-002), and CAMS Innovation Fund for Medical Sciences (grant numbers: 2022-I2M-C&T-B-1202). No other potential conflict of interest relevant to this article was reported. Competing interests The authors have no relevant financial or non-financial interests to disclose. Ethics approval This study was conducted following the principles of the Declaration of Helsinki. Approval was granted by the Medical Ethics Committee of Peking University Cancer Hospital (2022KT37-ZY01). This article does not contain any experiments with animals. Consent to participate Written informed consent was obtained from participants included in the study. Data availability The datasets generated during and/or analyzed during the current study are available from the corresponding authors on reasonable request Consent to publish The authors affirm that human research patients provided informed consent for publication of the images in Figures. 3 and 4. Author contributions All authors contributed to the study's conception, design, and data analysis or interpretation. 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14:27:41","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":124825,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8063844/v1/1a7608aa3b07e41288b57dda.png"},{"id":97265649,"identity":"ebb81127-b564-4eec-bb97-70f72c6504ef","added_by":"auto","created_at":"2025-12-02 14:27:41","extension":"xml","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":129333,"visible":true,"origin":"","legend":"","description":"","filename":"JTRMD25197650structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8063844/v1/4b7d4ae7891b6b4a386d6d17.xml"},{"id":97265646,"identity":"4b045768-f8e5-4368-9961-c7466042decf","added_by":"auto","created_at":"2025-12-02 14:27:41","extension":"html","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":142071,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8063844/v1/8d7ae6e7ff00ede271135a8c.html"},{"id":97367414,"identity":"b49e82ea-b567-4219-9455-007a20db7bd2","added_by":"auto","created_at":"2025-12-03 16:18:25","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":656723,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVariation in NECTIN4 expression between tumor and normal samples. \u003c/strong\u003e(A, B) Comparison of NECTIN4 mRNA expression in tumors versus normal tissues using TCGA and GTEx datasets. (C) Expression profiles across lung cancer cell lines, showing elevated levels in NSCLC than in SCLC. (D, E) NECTIN4 protein levels in primary tumors and normal tissues, analyzed using the UALCAN database. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, and ***\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8063844/v1/09c87829d09a80b1441aa807.jpeg"},{"id":97367448,"identity":"be236853-933c-4c15-9fc0-9611ca7025cf","added_by":"auto","created_at":"2025-12-03 16:18:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1107433,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrognostic relevance and cellular distribution of NECTIN4 in lung cancer. \u003c/strong\u003e(A, B) Expression pattern of NECTIN4 in human NSCLC scRNA-seq datasets. (C) Western blot image of the tumor tissues in patients with lung cancer. (D) IHC staining of the tumor slices in patients with lung cancer. (E) Relationship between NECTIN4 expression and OS in patients with NSCLC.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8063844/v1/7f5970526542343a8cd4c67b.png"},{"id":97265622,"identity":"73345ee9-9d16-415e-bdc2-1ffd004e3f80","added_by":"auto","created_at":"2025-12-02 14:27:41","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1301312,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between genomic alterations and NECTIN4 expression. \u0026nbsp;\u003c/strong\u003e(A) Different alteration types of NECTIN4 alterations of pan-cancer in TCGA datasets, including mutation, structural variant, amplification, deep deletion, and multiple alterations. (B) \u0026nbsp;The pan-cancer NECTIN4 mutational landscape, including missense, frameshift deletion, and splice site mutations. (C, D) Comparison of NECTIN4 mRNA expression levels according to SNV and CNV in LUAD and LUSC. (E) Mutation co-occurrence patterns associated with NECTIN4 in LUAD and LUSC. (F) Correlation analyses of NECTIN4 expression with TMB, MSI, ploidy, and MATH.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8063844/v1/e59d7ef08aeb941dffd6a048.jpeg"},{"id":97265628,"identity":"5e289348-a54d-4669-a0f1-f1c1b96bafa9","added_by":"auto","created_at":"2025-12-02 14:27:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":777299,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation of NECTIN4 expression with DNA methylation, RNA modification, and cancer stemness. \u0026nbsp;\u003c/strong\u003e(A) The correlation between NECTIN4 expression and DNA methyltransferases in pan-cancer. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(B) The correlation analysis between NECTIN4 expression and RNA modification–related genes in LUAD and LUSC. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(C) The relationships between NECTIN4 expression and cancer stemness scores, including RNAss, DNAss, DMPss, and ENHss in LUAD and LUSC.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8063844/v1/275cd88207a99f6a9f1593b2.png"},{"id":97367977,"identity":"076cc867-e183-411c-9dce-144530956e0d","added_by":"auto","created_at":"2025-12-03 16:21:10","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":218791,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of tumor-to-blood pool ratio and its diagnostic efficacy in \u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eF-FDG PET/CT and \u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e68\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eGa-N188 PET/CT\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8063844/v1/83d57bc30fab2dad1c31cf69.jpeg"},{"id":97367673,"identity":"49419c5c-d3d1-46dc-a6fa-2a5e968f640a","added_by":"auto","created_at":"2025-12-03 16:20:08","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":487424,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePatients with different pulmonary lesions in \u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eF-FDG PET/CT and \u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e68\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eGa-N188 PET/CT.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8063844/v1/afbe34515410789e2a5473be.jpeg"},{"id":108437905,"identity":"4f2ccf01-f64c-447a-bbce-cdb24ccdfd7a","added_by":"auto","created_at":"2026-05-04 16:04:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4638648,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8063844/v1/7757a02a-6e26-4447-81b1-ed75eee19e62.pdf"},{"id":97367305,"identity":"a06cecb4-a033-490a-8e39-ea638560f230","added_by":"auto","created_at":"2025-12-03 16:18:08","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":1815107,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-8063844/v1/11aab5c26a847828d68a56f1.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eBioinformatics profiling of NECTIN4 in lung cancer and comparative evaluation of NECTIN4-targeted \u003csup\u003e68\u003c/sup\u003eGa-N188 and \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eLung cancer\u0026mdash;the most prevalent malignancy globally\u0026mdash;remains the leading cause of cancer-related mortality, leading to about 18.4 percent of all cancer deaths. Despite advances in therapeutic strategies, the 5-year overall survival rate remains 10% to 15%, primarily due to late-stage diagnosis and the complexity of therapeutic management [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As of now, \u0026sup1;⁸F-FDG PET/CT is a fundamental imaging instrument in the clinical assessment of lung cancer, and is extensively utilised in the diagnosis, staging, and assessment of treatment outcomes in non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT exhibits limited specificity to differentiate between malignant and inflammatory lesions, and this poses significant challenges in precision medicine. This limitation demonstrates the necessity to design novel PET tracers with higher specificity that will provide more information about tumor biological characteristics to enable a personalized treatment approach and enhance patient outcomes in lung cancer.\u003c/p\u003e\u003cp\u003eNectin cell adhesion molecule 4 (NECTIN4) is an immunoglobulin-like cell adhesion transmembrane protein, a subgroup of nectin, a type I protein family of cell adhesion molecules. Under normal circumstances, NECTIN4 is strongly expressed in the placenta and embryo, with minimal expression observed in healthy adult tissues and organs. It controls cellular behavior by participating in signaling pathways like the PI3K-Akt signaling pathway and is essential in keeping cell-cell adhesions and tight junctions [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Many studies have revealed in recent years that NECTIN4 is overexpressed in about two-thirds of patients with NSCLC. It was found to be strongly associated with clinicopathological characteristics, including the size of the tumor, the disease stage, and the existence of distant metastases, and is an independent prognostic variable of overall survival (OS) in NSCLC [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e With the approval of Enfortumab Vedotin (EV)\u0026mdash;the first antibody-drug conjugate (ADC) targeting NECTIN4\u0026mdash;by the U.S. Food and Drug Administration (FDA) and the European Medicines Agency for the treatment of urothelial carcinoma (UC), the clinical significance of NECTIN4 has gained increasing attention. The indication of EV is currently on the rise as several clinical trials are currently underway. At the same time, there are increasingly diverse drugs against NECTIN4 which are being developed and gradually emerging [\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWith regards to non-invasive, dynamic, whole-body NECTIN4 expression measurements, a number of NECTIN4 imaging probes, such as monoclonal antibody-derived ones, have been created and published. However, clinical translation of these probes is still challenged because of constraints like slow systemic clearance and limited tumor penetration in solid malignancies. Among such probes, \u003csup\u003e68\u003c/sup\u003eGa-N188, a NECTIN4-targeted radiotracer based on a bicyclic peptide, exhibits great sensitivity and specificity in detecting NECTIN4-expressing lesions in patients with advanced UC [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A head-to-head comparison with \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT revealed that \u003csup\u003e68\u003c/sup\u003eGa-N188 not only effectively detects lesions but also quantitatively assesses membranous NECTIN4 expression levels across various solid tumors, providing preliminary evidence of its clinical utility. Nevertheless, that study included only two patients with NSCLC, leaving the potential of NECTIN4-targeted PET imaging in lung cancer largely unexplored.\u003c/p\u003e\u003cp\u003eThus, this study conducted an integrative study combining multi-omics bioinformatics profiling with molecular imaging validation to investigate NECTIN4 expression patterns, genomic alterations, epigenetic regulation, and their associations with clinical outcomes in lung cancer. Additionally, a head-to-head comparison of NECTIN4-targeted ⁶⁸Ga-N188 and \u0026sup1;⁸F-FDG PET/CT was performed in patients with suspected primary lung cancer to assess diagnostic performance and translational feasibility.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eBioinformatic data collection and processing\u003c/h2\u003e\u003cp\u003eDifferential expression of the NECTIN4 gene across various cancer types was analyzed using the TIMER database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.cistrome.org/\u003c/span\u003e\u003cspan address=\"http://timer.cistrome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). RNA sequencing data and corresponding clinical data were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases via UCSC Xena (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/datapages/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/datapages/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Cancer proteomics data were retrieved from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. The Lung Cancer Explorer (LCE) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://lce.biohpc.swmed.edu/\u003c/span\u003e\u003cspan address=\"https://lce.biohpc.swmed.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized to conduct a meta-analysis of OS hazard ratio in patients with lung cancer. Furthermore, single-cell RNA-sequencing (scRNA-seq) datasets for lung cancer (E-MTAB-6149, GSE127465, GSE162498, and GSE210347) were retrieved from the Gene Expression Omnibus (GEO) database. Differences in NECTIN4 mRNA expression across disease states (tumor vs. normal) and individual cancer stages were analyzed using the R package \u003cem\u003eggplot2\u003c/em\u003e and visualized with box plots and paired-sample wiring diagrams.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eGenomic alteration and mutational burden analyses\u003c/h3\u003e\n\u003cp\u003ePan-cancer analyses of genomic mutation, amplification, and deep deletion frequencies were performed using the Cancer Type Summary module of cBioPortal [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Tumor mutational burden (TMB) and mutant-allele tumor heterogeneity (MATH) were assessed using the R package \u003cem\u003emaftools\u003c/em\u003e, while ploidy and microsatellite instability (MSI)-related data were obtained from previous reports [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Correlations between these genomic characteristics and NECTIN4 expression were subsequently analyzed.\u003c/p\u003e\n\u003ch3\u003eDNA methylation analyses and epigenetic modification analyses\u003c/h3\u003e\n\u003cp\u003eData derived from cBioPortal was used to access the correlations between the methylation of NECTIN4 and pan-cancer. Expression data for the NECTIN4 gene and 44 tri-class RNA modifications N1-methyladenosine (m\u003csup\u003e1\u003c/sup\u003eA), 5-methylcytosine (m\u003csup\u003e5\u003c/sup\u003eC), and N6-methyladenosine (m\u003csup\u003e6\u003c/sup\u003eA) modifying genes from the UCSC. The Pearson correlation between NECTIN4 and the marker genes were assessed with the R package.\u003c/p\u003e\u003cp\u003e\u003csup\u003e\u003cb\u003e68\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eGa-N188 production\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe NECTIN4-targeting ligand N188 was purchased from Shanghai Apeptide Co., Ltd (Shanghai, China) and purified using high-performance liquid chromatography (HPLC) to \u0026gt;\u0026thinsp;95% purity. Radiosynthesis of \u003csup\u003e68\u003c/sup\u003eGa-N188 was achieved in a one-step reaction within 15 min based on a previous established protocol [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Radiochemical purity was evaluated using radio-HPLC and confirmed at \u0026gt;\u0026thinsp;95% purity.\u003c/p\u003e\n\u003ch3\u003eWestern blot analysis\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eWestern blot analysis\u003c/div\u003e\u003cp\u003eTotal protein was extracted from tissue using prechilled radioimmunoprecipitation assay buffer containing a protease inhibitor cocktail and measured using bicinchoninic acid assay. Equal amounts of protein samples were separated on a 7.5% sodium dodecyl sulfate\u0026ndash;polyacrylamide gel electrophoresis gel and transferred onto 0.45 \u0026micro;m polyvinylidene fluoride membranes. Membranes were blocked with 5% non-fat milk for 1 h, then incubated overnight at 4℃ with primary antibodies, including anti-NECTIN4 (1:2000; Proteintech, Cat. No. 21903-1-AP) and anti-rabbit β-actin (1:100000; Proteintech, Cat. No. 20536-1-AP). Immunoblots were visualized and analyzed using an automated chemiluminescence imaging system (5200 Multi, Tanon, China). The band of β-actin was used as an internal standard to normalize the results and analyzed using Image J software.\u003c/p\u003e\n\u003ch3\u003eImmunohistochemical analysis\u003c/h3\u003e\n\u003cp\u003eParaffin-embedded lung tumor and matched adjacent non-tumorous tissues were obtained from patients of department of thoracic surgery, in accordance with institutional ethical guidelines. Tissue sections were fixed in 4% paraformaldehyde, and 4 \u0026micro;m thick slices were prepared from the formalin-fixed, paraffin-embedded blocks and subsequently dewaxed. Using an antigen retrieval solution, sections were pretreated in a microwave oven at medium power for 8 min until boiling, then at medium-low power for 7 min. They were then blocked with 0.3% hydrogen peroxide and goat serum, rinsed with Tris-buffered saline, and incubated with anti-Nectin4 antibody (1:1000) at 4\u0026deg;C overnight. The sections were subsequently incubated with a horseradish peroxidase-conjugated secondary antibody at room temperature, stained with 3,3'-diaminobenzidine, and counterstained with hematoxylin for 3 min. Images were captured using an optical microscope and analyzed using Image J software\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003ePatient information\u003c/h2\u003e\u003cp\u003e The Medical Ethics Committee of Peking University Cancer Hospital approved this prospective study (2022KT37-ZY01). Written informed consent was obtained from all participants. The trial was registered at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://timer.cistrome.org/\" target=\"_blank\"\u003ewww.clinicaltrialsregister.eu\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.clinicaltrialsregister.eu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (Trial Identifier: NCT06648317).\u003c/p\u003e\u003cp\u003ePatients with lung lesions were consecutively recruited between September 2024 and December 2024. Inclusion criteria were: (1) presence of lung lesions; (2) normal kidney, liver, and bone marrow hemopoietic function; and (3) an ECOG performance status of 0\u0026ndash;1. Exclusion criteria included: (1) prior chemotherapy or radiotherapy; (2) refusal to undergo paired baseline \u003csup\u003e68\u003c/sup\u003eGa-N188 PET/CT and \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT within 1 week. Clinical and pathological characteristics of the patients were recorded.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePosition emission tomography/computed tomography acquisition\u003c/h3\u003e\n\u003cp\u003ePatients received an intravenous injection of \u003csup\u003e68\u003c/sup\u003eGa-N188 (1.9\u0026ndash;3.7 MBq/kg) and were instructed to drink 800\u0026ndash;1500 mL of water. PET/CT was conducted 1 h post-injection using a 194-cm-long axial field of view (FOV) total-body PET/CT (uEXPLORER, United Imaging Healthcare, Shanghai, China). Acquisition time was 5 min. Image reconstruction was conducted using the ordered subset expectation maximization algorithm with two iterations, 20 subsets, a 192\u0026times;192 matrix, and a 600 mm FOV. The slice thickness was 2.886 mm. Attenuation and scattering corrections were applied, along with point spread function and time-of-flight reconstruction. No post-filtering was applied. The attenuation corrected CT was acquired using 120 kV with a modulated current of approximately 75 mA. Vital signs were recorded before injection, throughout the screening period, and 2 h after the PET/CT scan.\u003c/p\u003e\u003cp\u003ePatients were required to fast for at least 6 h before the \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT scans to maintain normal blood glucose levels (4.4\u0026ndash;9.3 mmol/L). The intravenous dose was body weight-based (3.7 MBq/kg), followed by a 1 h rest before imaging. Acquisition conditions were consistent with those used for the \u003csup\u003e68\u003c/sup\u003eGa-N188 PET/CT.\u003c/p\u003e\n\u003ch3\u003ePET/CT image analysis\u003c/h3\u003e\n\u003cp\u003ePost-processing of images was conducted using a vendor-provided software (Multi-Modality Workplace, United Imaging, China). Two nuclear medicine physicians with 5\u0026ndash;10 years of diagnostic experience, blinded to prior imaging and pathological findings, independently reviewed all images. Discrepancies were resolved by a third physician with 15\u0026ndash;20 years of diagnostic experience.\u003c/p\u003e\u003cp\u003eThe volume of interest for the primary pulmonary tumor on \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT and \u003csup\u003e68\u003c/sup\u003eGa-N188 PET/CT images was manually delineated to include the entire target lesion while excluding surrounding tissues and organs. The maximum standardized uptake value (SUV\u003csub\u003emax\u003c/sub\u003e) of the lesions and the mean SUV (SUV\u003csub\u003emean\u003c/sub\u003e) of the blood pool\u0026mdash;derived from uptake in the descending aorta (used as background) were recorded. The tumor-to- blood-pool ratio (TBR) was calculated as tumor SUV\u003csub\u003emax\u003c/sub\u003e/BP SUV\u003csub\u003emean\u003c/sub\u003e. Metastatic lesions were confirmed via pathology or follow-up imaging. A lesion was considered positive for metastasis if its uptake exceeded the physical uptake of surrounding tissues.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eR software (version 4.2.1) was utilized for statistical analyses of the bioinformatics data. Group comparisons were conducted using one-way analysis of variance (ANOVA) or Student's t-test, as appropriate. Survival analyses were conducted using Kaplan\u0026ndash;Meier curves with log-rank tests or Cox proportional hazards regression models. Correlations between variables were assessed using Pearson or Spearman correlation coefficients.\u003c/p\u003e\u003cp\u003eStatistical analyses of the data of the patients were conducted using IBM SPSS Statistics (version 25.0) and GraphPad Prism (version 8.0). Measurement data were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, while categorical variables were presented as frequencies and percentages. Differences in SUV\u003csub\u003emax\u003c/sub\u003e values among lesions with different pathological types on \u003csup\u003e68\u003c/sup\u003eGa-N188 PET/CT were analyzed using one-way ANOVA. The normality of continuous variables was assessed using the Shapiro\u0026ndash;Wilk test. For non-normally distributed data, the Mann\u0026ndash;Whitney U test was utilized. Data distributions were visualized using histograms. The predictive value of the uptake parameters for differentiating malignant and benign lesions was analyzed using the area under the receiver operating characteristic (ROC) curve, with the cut-off value determined based on the Youden index. The diagnostic performance metric, positive predictive value (PPV), was calculated. A \u003cem\u003ep-value of\u003c/em\u003e \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eExpression of NECTIN4 in multiple cancers and lung cancer\u003c/h2\u003e\u003cp\u003eUsing RNA sequencing data from the TCGA and GTEx databases for systematic analysis, we investigated the expression of NECTIN4 mRNA across multiple cancer types. Differential expression analysis revealed significantly dysregulation of NECTIN4 mRNA in 16 tumor types, including lung cancer, compared with adjacent normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and B). NECTIN4 was significantly overexpressed in lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) in the TCGA dataset, with expression progressively increasing across disease stages (Additional file1).\u003c/p\u003e\u003cp\u003eConsistent with the above findings, NECTIN4 protein was overexpressed in lung cancer, breast invasive carcinoma (BRCA), uterine corpus endometrial carcinoma (UCEC), pancreatic adenocarcinoma (PAAD), with particularly significant in NSCLC (LUAD and LUSC; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Further analysis of 196 lung cancer cell lines revealed that NECTIN4 expression significantly elevated expression of NECTIN4 in non-small cell lung cancer (NSCLC) cell lines, encompassing both LUAD and LUSC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eSingle-Cell landscape of NECTIN4 expression in lung cancer\u003c/h2\u003e\u003cp\u003e By analyzing scRNA-seq transcriptomic data obtained from lung tumor specimens and adjacent non-tumor tissues across multiple datasets (E-TMAB-6169, GSE127465, GSE162498, and GSE210347), we further elucidated the role of NECTIN4 in lung cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The cells were categorized into distinct clusters, including cancer cells, epithelial cells, alveolar cells, and immune cells based on established cell-type markers. NECTIN4 was detected in all clusters, showing highest expression levels observed in cancer cells and lower expression levels in lymphocytes and fibroblasts.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eMolecular validation of NECTIN4 overexpression and correlation with poorer prognosis\u003c/h2\u003e\u003cp\u003eTo confirm NECTIN4 protein expression in NSCLC, immunohistochemistry (IHC) and western blot analysis were conducted. The results showed that NECTIN4 protein was localized in membranous, and its elevated expression was observed in tumor sections compared to inflammatory and normal lung tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). The findings of the western blot analysis were in agreement with the findings described above (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eWe analyzed the data that were obtained from GSE11969, GSE41271, GSE81089, and GSE47115 and realized that increased NECTIN4 expression had a negative relationship with the OS of patients with NSCLC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). This result was also confirmed by a meta-analysis of the Lung Cancer Explorer (LCE) database, which revealed a stable correlation between high NECTIN4 expression and poor outcomes in NSCLC (Additional file 2).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eGenomic alterations and genomic instability in NECTIN4 expression\u003c/h2\u003e\u003cp\u003eTo assess potential genome-level alterations of NECTIN4 in cancers, we conducted a pan-cancer analysis of NECTIN4 copy number variations (CNVs) and single nucleotide variants (SNVs). The genomic mutation landscape showed that NECTIN4 exhibits frequent genetic alterations across various types of tumors and, most notably, in NSCLC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B). NECTIN4 amplification was most frequently detected in multiple cancers, followed by mutation and deep deletion. It is also interesting to note that the frequency of NECTIN4 alteration in LUSC and LUAD is above 10%. Additionally, Also, NECTIN4-gained samples were characterised by elevated NECTIN4 mRNA, in comparison with control samples with copy number neutral samples, indicating that copy number variations might be used to regulate its expression (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-D).\u003c/p\u003e\u003cp\u003eFurther to simplify the genomic background of NECTIN4 expression, we analysed patterns of co-occurring mutations in LUAD and LUSC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Results revealed that high expression of NECTIN4 was frequently co-expressed with mutations in established cancer-associated genes including TP53, TTN, RYR1, NFE2L2. The NECTIN4 high-expression and low-expression groups exhibited significant differences in mutation profiles and transcriptomic characteristics, showing a different pattern of clustering. On the other hand, correlations between NECTIN4 and TMB, MSI, MATH and ploidy were also evaluated given the abundance of such mutations in pan-cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF, Additional file 3). NECTIN4 was positively correlated with MSI, and this was significant in both LUAD and LUSC. Correlation analysis further indicated that NECTIN4 expression was positively correlated with TMB, ploidy, and MATH, albeit with modest correlation coefficients.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eAnalyses of NECTIN4 with DNA methylation and cancer cell stem-like characteristics\u003c/h2\u003e\u003cp\u003eDNA methylation is one of the most critical epigenetic modifications in cancer initiation and progression [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. As key enzymes catalyzing DNA methylation, DNA methyltransferases (DNMTs) were responsible to modulate tumor invasion, proliferation, metastasis, diagnosis, and prognosis [\u003cspan additionalcitationids=\"CR23 CR24 CR25 CR26\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The expression of NECTIN4 in LUAD had significant positive correlations with both DNMT1 and DNMT3B (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which demonstrated that the expression of NECTIN4 was affected by DNA methylation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003eIn order to explore potential epigenetic regulatory pathways of NECTIN4, we examined the correlations of its expression with the genes related to different RNA modifications (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Results indicated that NECTIN4 expression positively correlated with numerous key writers (e.g., METTL14), readers (e.g., ALYREF), and erasers (e.g., ALKBH1) in both LUAD and LUSC. Among these, the associations with METTL3, YTHDF1, and IGF2BP3 were particularly notable, although these did not reach statistical significance.\u003c/p\u003e\u003cp\u003eFurthermore, we evaluated the relationship between cancer stemness scores and NECTIN4 expression in LUAD and LUSC (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). NECTIN4 expression was positive correlated with DNA stemness score (DNAss), RNA stemness score (RNAss), differentially methylated probes signature score (DMPss), enhancer methylation signature score (ENHss) in LUSC. In contrast, NECTIN4 expression in LUAD showed a negative correlation with those scores.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003ePatient Characteristics\u003c/h2\u003e\u003cp\u003eTwenty patients (6 women and 14 men; mean age 60.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1 years) presenting with lung lesions were enrolled. Patients diagnosed with NSCLC, SCLC, and inflammatory lesions numbered 13, 3, and 4, respectively. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the clinicopathological characteristics of the patients. All patients underwent paired \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT and \u003csup\u003e68\u003c/sup\u003eGa-N188 PET/CT.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClinicopathological characteristics of patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInformation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatients n (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (y)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62 (35\u0026ndash;72)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (40.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14 (60.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (20.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 (80.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily history of malignancy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (90.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (10.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdenocarcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (45.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSquamous carcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (20.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmall cell lung cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (15.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInflammation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (20.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinical Staging\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (18.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (25.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (12.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (43.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003ePET/CT uptake of lesions with different pathology\u003c/h2\u003e\u003cp\u003eUsing \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT imaging, TBRs for NSCLC, SCLC, and inflammatory lesions were 7.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2, 5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3, and 4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0, respectively; no significant differences were observed among these groups. In \u003csup\u003e68\u003c/sup\u003eGa-N188 PET/CT imaging, tracer uptake in NSCLC was significantly higher than in SCLC (2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 vs. 1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046), and also significantly higher than in inflammatory lesions (TBR: 1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009). Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB illustrate uptake patterns in inflammatory and malignant lung lesions, respectively, while Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents representative imaging examples. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC shows the ROC curves of \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT and \u003csup\u003e68\u003c/sup\u003eGa-N188 PET/CT for differentiating between inflammation and cancerous lesions. The area under the curve (AUC) for \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT and \u003csup\u003e68\u003c/sup\u003eGa-N188 PET/CT was 0.703 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.219) and 0.906 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014), respectively. The diagnostic efficacy of \u003csup\u003e68\u003c/sup\u003eGa-N188 PET/CT was overall superior to it of \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT (the sensitivity and specificity was 87.5% and 100.0% vs. 93.8% and 50.0%, respectively).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eThe detection efficacy of PET/CT for metastatic lesions in patients with lung cancer\u003c/h2\u003e\u003cp\u003eA total of 103 lymph nodes (45 metastatic, 58 non-metastatic) were confirmed by histopathology and follow-up imaging. Using \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT, 45, 15, 0, and 43 lymph nodes were classified as true positive (TP), false positive (FP), false negative (FN), and true negative (TN), respectively. In contrast, ⁶⁸Ga-N188 PET/CT identified 19 TP, 0 FP, 26 FN, and 58 TN lymph nodes. In \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT, SUV\u003csub\u003emax\u003c/sub\u003e values for true positive and true negative lymph nodes were 11.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2 and 1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8, respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); in \u003csup\u003e68\u003c/sup\u003eGa-N188 PET/CT, the corresponding values were 4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 and 1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For lymph node differential diagnosis, \u003csup\u003e68\u003c/sup\u003eGa-N188 PET/CT demonstrated higher specificity and positive predictive value (PPV) (both 100%), but lower sensitivity (42.2%). Conversely, \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT showed sensitivity of 100%, specificity of 74.1%, and PPV of 75%. Detailed diagnostic efficacy metrics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEfficacy of PET/CT in identifying metastatic lymph nodes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePPV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNPV\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e100.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e74.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e75.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e100.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003csup\u003e68\u003c/sup\u003eGa-N188 PET/CT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e74.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e100.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e69.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003ePPV: Positive Predictive Value; NPV: Negative Predictive Value.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT identified significantly more distant metastatic lesions than \u003csup\u003e68\u003c/sup\u003eGa-N188 PET/CT (51 vs. 21; mean SUV\u003csub\u003emax\u003c/sub\u003e 9.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8 vs. 3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, in organs characterized by high physiological \u003csup\u003e18\u003c/sup\u003eF-FDG uptake\u0026mdash;such as the brain\u0026mdash;\u003csup\u003e68\u003c/sup\u003eGa-N188 PET/CT provided superior lesion delineation compared to \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT, as demonstrated by a higher TBR of 0.83 vs. 0.55.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEfficacy of PET/CT in detecting metastatic lesions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBrain\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLung\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePleura\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBone\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAdrenal Gland\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTotal (N)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSUV\u003csub\u003emax\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e\u003cp\u003e9.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003csup\u003e68\u003c/sup\u003eGa-N188 PET/CT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c9\"\u003e\u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eNECTIN4 has emerged as a valuable target for cancer diagnosis and therapy. Previous studies have extensively characterized its roles in various solid tumors, demonstrating its involvement in cell proliferation, migration, and angiogenesis [\u003cspan additionalcitationids=\"CR29 CR30 CR31 CR32\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Our findings on bioinformatic studies revealed NECTIN4 to be a biomarker of lung cancer and its overexpression was associated with poor prognosis in NSCLC, with specific focus on the correlation between NECTIN4 expression and genomic instability as well as epigenetic regulation. Clinically, \u003csup\u003e68\u003c/sup\u003eGa-N188 PET/CT demonstrated superior specificity over \u003csup\u003e18\u003c/sup\u003eF-FDG in differentiating malignant from inflammatory lesions, reflecting the biological relevance of NECTIN4 expression in tumor tissue.\u003c/p\u003e\u003cp\u003eAn integrated analyses of the TCGA, GTEx, and CPTAC datasets denoted notable upregulation of NECTIN4 at the protein level and mRNA level in various cancers such as lung cancer. NECTIN4 overexpression was more pronounced in NSCLC compared to SCLC, consistent with the differing uptake patterns of NECTIN4-targeted tracers, with NSCLC lesions typically showing higher accumulation than those of SCLC. Such results highlight the clinical usefulness of NECTIN4 as a biomarker that can be used to differentiate between primary lung cancer and inflammatory lesions.\u003c/p\u003e\u003cp\u003eStudies on cancer genomics aims to identify recurrent abnormalities in certain cancer types and elucidate their pathogenesis based on a systematic study of genomic changes, including nucleotide substitutions, copy number variations, and DNA rearrangements [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In the present study, NECTIN4 expression was associated positively with the mutation landscape, with copy number amplification being predominant, which means its upregulation is accompanied by increased genomic instability, particularly in NSCLC. This is in line with previous studies that NECTIN4 amplifications is evident in several solid tumors, most commonly in BLCA, BRCA, and LUAD, accounting for 5%\u0026ndash;10% of all cases [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Thus, we conclude that its amplification was associating with increased NECTIN4 mRNA expression and elevated NECTIN4 protein levels in lung cancer. Moreover, in LUAD and LUSC, NECTIN4 alterations were often present in association with mutations in tumour-related genes (TP53, TTN, RYR1, and NFE2L2). It is important to state that the expression of NECTIN4 and the mutation profile co-occurring in these two subtypes are not the same, which could indicate that it may possess cell-specific functions and highlighting its role within complex oncogenic networks.\u003c/p\u003e\u003cp\u003eIn addition to genomic alterations, epigenetic modifications are also are also instrumental in regulating the progression of cancer [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. DNA methylation, as one of the most critical epigenetic mechanisms, can alter gene expression. This possess is primarily catalyzed by DNMTs, thereby regulating the production of proteins encoded by genes [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Previous researches have demonstrated that DNMT1 has the capacity to increase the methylation of the hMLH1 and hMSH2 gene promoter regions, in turn, inhibiting their activities and finally encouraging the growth of epidermal growth factor receptor-mutated NSCLC cells [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. It is also observed that DNMT3B overexpression is closely correlated with reduced OS in lung cancer patients [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. This study further supports the aforementioned mechanisms, reveling that NECTIN4 expression exhibits significant positive correlations with DNMT1 and DNMT3B (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that DNA methylation-related regulation may influence NECTIN4 transcription.\u003c/p\u003e\u003cp\u003eAlong with DNA methylation, NECTIN4 was also positively associated with several RNA modification regulators, such as METTL3, YTHDF1, and IGF2BP3, which implies the cross-regulatory relation between NECTIN4 and post-transcriptional m6A-mediated modifications [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Additionally, the correlations between NECTIN4 expression and tumor stemness scores further emphasize its potential role in maintaining cancer stem-like characteristics. Interestingly, NECTIN4 showed different trends of stemness in LUSC and LUAD, suggesting heterogeneous regulation of NECTIN4 across distinct histological subtypes.\u003c/p\u003e\u003cp\u003e\u003csup\u003e68\u003c/sup\u003eGa-N188, an innovative PET imaging probe targeting NECTIN4, demonstrates high sensitivity and specificity for NECTIN4 detection in prior clinical studies. Its primary tumor identification rate is comparable to that of \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT, along with improved specificity for detecting lymph node metastases across multiple cancer types [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. To directly assess their diagnostic performance in lung cancer, a head-to-head comparison between \u003csup\u003e68\u003c/sup\u003eGa-N188 and \u003csup\u003e18\u003c/sup\u003eF-FDG was conducted in this study. Our findings indicate that \u003csup\u003e68\u003c/sup\u003eGa-N188 PET/CT exhibits significantly variable uptake across diagnostic groups, with sensitivity and specificity values of 100% and 87.5%, respectively, in distinguishing malignant tumors from inflammatory lesions. In contrast, \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT produced overlapping SUV\u003csub\u003emax\u003c/sub\u003e and TBR measurements for malignant and inflammatory lesions, limiting its diagnostic accuracy. Although 18F-FDG PET/CT can be used with high sensitivity and specificity of 96% and 79% to distinguish between benign and malignant lung lesions, this test is prone to false-positive outcomes, especially when inflammation or granuloma exists [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Collectively, these findings highlight \u003csup\u003e68\u003c/sup\u003eGa-N188 as a promising alternative for enhancing diagnostic precision in clinically equivocal scenarios. Although both NSCLC and SCLC lesions exhibit higher \u003csup\u003e68\u003c/sup\u003eGa-N188 uptake than inflammatory lesions, uptake in NSCLC is significantly greater than in SCLC. This finding is consistent with previous reports showing that NECTIN4 expression is predominantly upregulated in NSCLC but comparatively lower in SCLC [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This differential uptake suggests that \u003csup\u003e68\u003c/sup\u003eGa-N188 PET/CT may aid in molecular subtyping of lung cancer by identifying NECTIN4-enriched tumors.\u003c/p\u003e\u003cp\u003eRegarding lymph node metastasis, \u003csup\u003e68\u003c/sup\u003eGa-N188 PET/CT exhibits higher specificity and positive predictive value than \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT, indicating its potential to reduce false-positive results. However, \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT detected a greater number of metastatic lymph nodes overall, reflecting its superior sensitivity, accuracy and negative predictive value in this context. Regarding distant metastasis, 58.8% of lesions were positive on \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT but negative on \u003csup\u003e68\u003c/sup\u003eGa-N188 PET/CT. However, \u003csup\u003e68\u003c/sup\u003eGa-N188 shows comparable detection performance for brain metastases. Given the well-documented limitation of \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT in brain imaging\u0026mdash;attributable to high physiological uptake in normal brain tissue\u0026mdash;the low background signal of \u003csup\u003e68\u003c/sup\u003eGa-N188 in the brain may provide a distinct advantage for detecting intracranial lesions [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study has some limitations. First, NECTIN4 expression analysis was primarily based on NSCLC data from the TCGA database, without including SCLC datasets for comparison or validation. Second, although the \u003csup\u003e68\u003c/sup\u003eGa-N188 uptake has demonstrated clinical relevance for lung cancer diagnosis, its absolute tumor uptake values were suboptimal, suggesting future optimization of probe structure to enhance imaging performance. Third, the absence of long-term clinical follow-up limited the evaluation of the prognostic value of \u003csup\u003e68\u003c/sup\u003eGa-N188 uptake. Future studies incorporating extended follow-up and larger patient cohorts are necessary to validate the potential of NECTIN4-targeted PET/CT as a prognostic imaging biomarker.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this study establishes NECTIN4 as a clinically molecular target in lung cancer and validates NECTIN4-targeted ⁶⁸Ga-N188 PET/CT as a feasible imaging approach for its noninvasive visualization. Integrative bioinformatic analyses confirmed that NECTIN4 overexpression, genomic amplification, and epigenetic activation are correlated with poor prognosis in lung cancer. In addition, \u003csup\u003e68\u003c/sup\u003eGa-N188 PET/CT demonstrates superior performance over \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT in distinguishing primary lung cancer from inflammatory lesions, and may serve as a valuable complementary imaging modality following initial \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT in the diagnostic workup of lung cancer.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePET/CT, positron emission tomography/computed tomography\u003c/p\u003e\u003cp\u003eNECTIN4, nectin cell adhesion molecule 4\u003c/p\u003e\u003cp\u003eNSCLC, non-small cell lung cancer\u003c/p\u003e\u003cp\u003eSCLC, small cell lung cancers\u003c/p\u003e\u003cp\u003eOS, overall survival\u003c/p\u003e\u003cp\u003eADC, antibody-drug conjugate\u003c/p\u003e\u003cp\u003eUC, urothelial carcinoma\u003c/p\u003e\u003cp\u003eTCGA, The Cancer Genome Atlas\u003c/p\u003e\u003cp\u003eGTEx, Genotype-Tissue Expression\u003c/p\u003e\u003cp\u003eCPTAC, Clinical Proteomic Tumor Analysis Consortium\u003c/p\u003e\u003cp\u003escRNA-seq, single-cell RNA-sequencing\u003c/p\u003e\u003cp\u003eGEO, Gene Expression Omnibus\u003c/p\u003e\u003cp\u003eHPLC, high-performance liquid chromatography\u003c/p\u003e\u003cp\u003ePPV, positive predictive value\u003c/p\u003e\u003cp\u003eFOV, field of view\u003c/p\u003e\u003cp\u003eSUVmax, maximum standardized uptake value\u003c/p\u003e\u003cp\u003eSUVmean, mean SUV\u003c/p\u003e\u003cp\u003eTBR, tumor-to-blood-pool ratio\u003c/p\u003e\u003cp\u003eANOVA, analysis of variance\u003c/p\u003e\u003cp\u003eROC, receiver operating characteristic\u003c/p\u003e\u003cp\u003eLUSC, lung squamous cell carcinoma\u003c/p\u003e\u003cp\u003eLUAD, lung adenocarcinoma\u003c/p\u003e\u003cp\u003eBRCA, breast invasive carcinoma\u003c/p\u003e\u003cp\u003eUCEC, uterine corpus endometrial carcinoma\u003c/p\u003e\u003cp\u003ePAAD, pancreatic adenocarcinoma\u003c/p\u003e\u003cp\u003eLCE, Lung Cancer Explorer\u003c/p\u003e\u003cp\u003eTP, true positive\u003c/p\u003e\u003cp\u003eFP, false positive\u003c/p\u003e\u003cp\u003eFN, false negative\u003c/p\u003e\u003cp\u003eTN, true negative\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was partially supported by the National Natural Science Foundation of China (grant numbers: 82472015),\u0026nbsp;Beijing Hospitals Authority\u0026apos;s Ascent Plan (grant numbers: DFL20241103), Beijing Research Ward Excellence Program (grant numbers: BRWEP2024W032150102), National Key R\u0026amp;D Program of China (grant numbers: 2022YFC2409405), Beijing Physician Scientist Training Program (BJPSTP-2025-21),\u0026nbsp;National Natural Science Foundation of China (grant numbers: 82402318), Beijing Natural Science Foundation (grant numbers: L252055), CAMS Medical and Health Science and Technology Innovation (grant numbers: 2021-I2M-5-002), and CAMS Innovation Fund for Medical Sciences (grant numbers: 2022-I2M-C\u0026amp;T-B-1202). No other potential conflict of interest relevant to this article was reported.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted following the principles of the Declaration of Helsinki. Approval was granted by the Medical Ethics Committee of Peking University Cancer Hospital (2022KT37-ZY01). This article does not contain any experiments with animals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding authors on reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors affirm that human research patients provided informed consent for publication of the images in Figures. 3 and 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study\u0026apos;s conception, design, and data analysis or interpretation. Material preparation, data collection, and analysis were performed by Yuqi Wang, Xin Zhou, Jinchuan Chen, and Futao Liu. Yutao Li performed experiment. Yuan Li and Kezhong Chen contributed to project administration and resource allocation of the study. The first draft of the manuscript was written by Yuqi Wang, Xin Zhou and Jinchuan Chen. Jun Wang, Xing Yang and Nan Li Chen designed the research program and were involved in reviewing and revising the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFreddie B. Jacques, Ferlay, Isabelle, Soerjomataram, Rebecca, Siegel, Lindsey. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394\u0026ndash;424.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSpiro SG, Silvestri GA. One hundred years of lung cancer. 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PET scan in lung cancer: current recommendations and innovation. J Thorac Oncol. 2006;1:71\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYi CA, Shin KM, Lee KS, Kim BT, Kim H, Kwon OJ, Choi JY, Chung MJ. Non-small cell lung cancer staging: efficacy comparison of integrated PET/CT versus 3.0-T whole-body MR imaging. Radiology. 2008;248:632\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"NECTIN4, bioinformatics, 68Ga-N188, PET/CT, genome instability, lung cancer","lastPublishedDoi":"10.21203/rs.3.rs-8063844/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8063844/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe nectin cell adhesion molecule 4 (NECTIN4) has been implicated in tumor progression and immune evasion. However, its role and translational imaging potential in lung cancer remain unclear. Therefore, this study aims to elucidate the molecular characteristics of NECTIN4 by integrating multi-omics bioinformatics analyses with clinical PET/CT validation, and evaluate the diagnostic efficacy of the NECTIN4-targeted radiotracer ⁶⁸Ga-N188 compared to ¹⁸F-FDG.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTranscriptomic and proteomic datasets from The Cancer Genome Atlas, Genotype-Tissue Expression, Gene Expression Omnibus and other bioinformatic tools were used to characterize NECTIN4 expression levels, genomic alterations, associated regulatory networks and prognostic value. Subsequently, in a prospective clinical cohort study involving 20 patients with suspected primary lung cancer, paired PET/CT imaging using \u003csup\u003e68\u003c/sup\u003eGa-N188 and \u003csup\u003e18\u003c/sup\u003eF-FDG was conducted. The diagnostic performance of imaging modalities was assessed by quantitatively comparing the tumor-to-blood pool ratio between malignant and inflammatory lesions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBioinformatics analyses indicated that NECTIN4 was significantly upregulated across multiple cancer types and correlated with genomic instability and poor prognosis in non-small cell lung cancer. NECTIN4 expression was positively associated with DNA methyltransferases and RNA modifications, suggesting its regulation by epigenetic and post-transcriptional. Clinically, ⁶⁸Ga-N188 PET/CT exhibited superior specificity (100% vs. 50%) and comparable sensitivity (87.5% vs. 93.8%) to ¹⁸F-FDG PET/CT in differentiating malignant from inflammatory lung lesions, but with lower sensitivity (42.2% vs 100.0%) for detecting lymph node metastases and fewer identified distant metastatic lesions (21 vs 51).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur findings revealed that NECTIN4 as a promising biomarker and imaging target in lung cancer. The NECTIN4-targeted ⁶⁸Ga-N188 PET/CT outperforms ¹⁸F-FDG PET/CT in diagnosing primary lung cancer, supporting its role as a promising complementary imaging modality in clinical practice.\u003c/p\u003e","manuscriptTitle":"Bioinformatics profiling of NECTIN4 in lung cancer and comparative evaluation of NECTIN4-targeted 68Ga-N188 and 18F-FDG PET/CT","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-02 14:27:36","doi":"10.21203/rs.3.rs-8063844/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-12-05T08:20:11+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-27T14:39:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-10T14:49:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Translational Medicine","date":"2025-11-08T07:08:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"10642d42-90b8-4782-88e0-32d767550a1a","owner":[],"postedDate":"December 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T16:03:37+00:00","versionOfRecord":{"articleIdentity":"rs-8063844","link":"https://doi.org/10.1186/s12967-026-08152-8","journal":{"identity":"journal-of-translational-medicine","isVorOnly":false,"title":"Journal of Translational Medicine"},"publishedOn":"2026-04-27 15:58:07","publishedOnDateReadable":"April 27th, 2026"},"versionCreatedAt":"2025-12-02 14:27:36","video":"","vorDoi":"10.1186/s12967-026-08152-8","vorDoiUrl":"https://doi.org/10.1186/s12967-026-08152-8","workflowStages":[]},"version":"v1","identity":"rs-8063844","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8063844","identity":"rs-8063844","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}