CNGA3, a Protective Ion Channel Gene, Is Highly Expressed in Lung Adenocarcinoma and Predicts a Favorable Prognosis

CNGA3, a Protective Ion Channel Gene, Is Highly Expressed in Lung Adenocarcinoma and Predicts a Favorable Prognosis | 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 CNGA3, a Protective Ion Channel Gene, Is Highly Expressed in Lung Adenocarcinoma and Predicts a Favorable Prognosis Jia-Jun Wu, Zhao-Yi Yue, Yu-Hui Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9214105/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer with poor prognosis. Cyclic nucleotide-gated channel subunit alpha 3 (CNGA3) regulates calcium transport and signal transduction, and is aberrantly expressed in multiple tumors, but its function and mechanism in LUAD remain unclear. Materials and Methods Based on TCGA and GTEx databases, we used bioinformatics to analyze CNGA3 expression in LUAD. GO, KEGG, GSEA and ssGSEA were performed to explore related functions and pathways. Kaplan-Meier, univariate and multivariate Cox regression and a nomogram were used to assess its prognostic value. Results CNGA3 was significantly upregulated in LUAD. It was related to ion transport, signal transduction and antitumor immune regulation, and positively correlated with Tfh and Th17 cell infiltration but negatively with activated dendritic cells in the tumor microenvironment. High CNGA3 expression predicted longer overall survival, disease-specific survival and progression-free interval, and CNGA3 was an independent prognostic factor. Conclusion CNGA3 is involved in immune cell infiltration in LUAD tumor microenvironment and serves as a key prognostic indicator. It may be a potential prognostic biomarker and therapeutic target for LUAD. Lung adenocarcinoma CNGA3 Immune infiltration Prognosis Biomarker Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Globally, lung cancer is the malignant tumor with the highest incidence and mortality rates. Among them, lung adenocarcinom1a (LUAD), as the predominant subtype, accounts for approximately 40%–50% of total lung cancer cases. Its incidence shows a continuous upward trend, with a high detection rate in both smokers and non-smokers ( 1 , 2 ). These alarming data highlight the significant threat that LUAD poses to human health and life. Prognosis varies considerably among patients with LUAD, and the 5-year overall survival rate for advanced patients is only 15%–20%. Traditional treatment regimens include surgical resection, platinum-based chemotherapy and radiotherapy. Although targeted therapies such as epidermal growth factor receptor tyrosine kinase inhibitors and anaplastic lymphoma kinase fusion gene inhibitors, as well as immune checkpoint inhibitor therapy, have significantly improved prognosis in some patients, a considerable proportion of patients still develop primary or acquired resistance ( 3 – 5 ). Despite these therapeutic interventions, more than 60% of LUAD patients experience recurrence within 3 years after diagnosis, limiting the improvement of survival rate ( 6 ). Studies have shown that after radical surgery, the 5-year survival rate of early-stage LUAD patients can reach 60%–80%, whereas that of advanced patients is merely 5%–10%. The difficulty in the treatment of LUAD lies in its concealed early symptoms and the lack of specific clinical manifestations, resulting in more than 70% of patients being diagnosed at locally advanced or distant metastatic stages ( 7 ). Although numerous advances have been achieved in precision medicine in recent years, the improvement in the 5-year survival rate of patients with advanced LUAD remains slow ( 8 , 9 ). In view of the current limitations in the treatment of LUAD, novel therapeutic targets are urgently needed to enhance clinical efficacy, and reliable prognostic models are also critically required to facilitate the development of more targeted and effective treatment strategies. Ion channel proteins play an important role in the occurrence and development of tumors. By regulating intracellular ion concentration, signal transduction and other processes, they affect the proliferation, apoptosis, invasion and metastasis of tumor cells, and have become potential candidate targets for cancer therapy ( 10 – 12 ). As a core member of the cyclic nucleotide-gated channel family, CNGA3 is a highly conserved transmembrane protein, and its roles in calcium ion transport, cellular signal transduction and immune cell activation have attracted increasing attention ( 13 – 15 ). Several studies have shown that CNGA3 is involved in a variety of physiological processes, including visual signal transduction, immune cell function regulation, cell proliferation and differentiation ( 16 ). In addition, it has been confirmed that CNGA3 participates in the maintenance of epithelial cell morphology and tissue homeostasis by regulating calcium-dependent signaling pathways. Notably, abnormal expression of CNGA3 has been found in a variety of tumor tissues, and it may be involved in tumor immune escape by affecting the function of immune cells in the tumor microenvironment ( 17 ). Previous studies have reported that CNGA3 is overexpressed in tumor tissues such as cholangiocarcinoma and is associated with poor prognosis in patients ( 18 ). CNGA3 is subject to hypermethylation and negative regulation by differentially expressed miRNAs (DEmiRNAs), which may be involved in the occurrence of rectal adenocarcinoma ( 19 ). Furthermore, Andrea Olsen et al. found that CNGA3 may serve as a potential target for the treatment of aging and glioblastoma multiforme ( 20 ). These data suggest that overexpression of CNGA3 may contribute to tumorigenesis and progression. Although previous studies have implied the potential involvement of CNGA3 in human malignancies, its precise molecular mechanism and prognostic value in LUAD remain largely underexplored. To elucidate the clinical and biological implications of upregulated CNGA3 expression in LUAD, we performed a comprehensive bioinformatics analysis using transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) database. Furthermore, we constructed a prognostic nomogram integrating CNGA3 mRNA expression levels and key clinical parameters to predict overall survival in LUAD patients. Our results demonstrated that CNGA3 exhibits multifaceted roles in the oncogenic progression of LUAD, and its elevated expression is significantly correlated with favorable clinical outcomes. Collectively, these findings indicate that CNGA3 may function as a promising novel prognostic biomarker for patients with LUAD. Materials and Methods Data Collection and Analysis CNGA3 messenger RNA (mRNA) expression data and clinical information of LUAD patients were obtained from The Cancer Genome Atlas (TCGA) project via the Genomic Data Commons (GDC) Data Portal ( https://portal.gdc.cancer.gov/ ) ( 21 ). Meanwhile, CNGA3 mRNA expression data of normal lung tissues were retrieved from the Genotype-Tissue Expression (GTEx) database( https://gtexportal.org/home/ ). The Level 3 HTSeq-FPKM data of 535 lung adenocarcinoma (LUAD) patients were converted into transcripts per million reads for subsequent analyses. For the clinical features that were unknown or unavailable among the 535 samples, they were defined as missing data. CNGA3 Expression Analysis Samples were grouped according to disease status (tumor or normal), and scatter plots and box plots were constructed to display the differential expression of CNGA3, so as to analyze the differential expression of CNGA3 between LUAD samples and normal samples. According to statistical ranking, the expression level of CNGA3 was divided into low expression (CNGA3-Low) and high expression (CNGA3-High), namely below or above the median value. Identification of Differentially Expressed Genes (DEGs) Differential expression analysis was performed on LUAD samples in the CNGA3 high-expression group and low-expression group using the edgeR package and Student’s t-test. Genes with a log fold change (FC) > 1 and adjusted P < 0.05 were regarded as statistically significant differentially expressed genes. All differentially expressed genes were visualized by volcano plots. Gene-Gene and Protein-Protein Interaction Analysis STRING ( https://cn.string-db.org ) and GeneMANIA ( http://www.genemania.org ) ( 22 , 23 ) were used to analyze the protein-protein interaction (PPI) and gene-gene interaction networks involving CNGA3. GeneMANIA integrates a variety of bioinformatics techniques, including site prediction, colocalization, co-expression, genetic interactions of physiological relationships, and gene enrichment analysis. Pairs with an interaction score > 0.90 were selected for protein-protein interaction analysis. Co-Expression Gene Analysis of CNGA3 in LUAD TCGA transcriptome sequencing data were used to screen the top 35 positively and negatively correlated co-expressed genes with CNGA3 in LUAD. The "Stats" package was used for statistical analysis, and the "ggplot2" package was used for visualization. Functional Enrichment Analysis and Tumor Microenvironment Investigation The biological effects of differentially expressed genes were evaluated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. In both analyses, the criteria for statistical significance were counts between 5 and 5000, P < 0.05, and false discovery rate (FDR) < 0.25. Gene Set Enrichment Analysis (GSEA) was applied to evaluate the biological functions and pathways in the CNGA3 high-expression and low-expression groups. Gene sets with absolute normalized enrichment score > 1, adjusted P < 0.05, and FDR < 0.25 were defined as significant gene sets. Gene sets were obtained from the Molecular Signatures Database (MSigDB) ( www.gsea-msigdb.org ). Based on gene expression profiles, the Gene Set Variation Analysis (GSVA) package and single-sample Gene Set Enrichment Analysis (ssGSEA) ( 24 ) were used to detect the infiltration levels of 24 different immune cell types. The relationship between immune cell infiltration levels and CNGA3 mRNA expression was evaluated by Wilcoxon rank-sum test and Spearman correlation analysis. Prognostic Evaluation To explore the correlation between CNGA3 expression and prognosis of LUAD, we analyzed disease-specific survival (DSS), overall survival (OS), and progression-free interval (PFI). Univariate and multivariate Cox analyses were performed on the TCGA-LUAD dataset to determine the predictive value. The median level of CNGA3 mRNA expression in LUAD tissues was used as the cutoff value and included in multivariate Cox analysis. Nomogram Construction The "rms" and "survival" packages were used for analysis and visualization to construct a nomogram for predicting 1-year, 3-year, and 5-year overall survival of LUAD patients. Calibration curves were used to graphically assess the consistency between nomogram-predicted probabilities and actual events. Statistical Analysis All statistical analyses were performed using R (v4.2.1) and RStudio software. In preliminary data analysis, two-tailed Student’s t-test and one-way analysis of variance (ANOVA) were conducted. P < 0.05 was considered the criterion for statistically significant differences. Results CNGA3 mRNA Expression in Human Cancers First, TCGA and GTEx data were used to compare CNGA3 mRNA expression between human cancer tissues and normal tissues. The results showed that among the 33 cancer types studied, CNGA3 expression levels were significantly different in 14 cancers, among which CNGA3 expression was significantly elevated in 3 cancers and significantly decreased in 11 cancers. Comprehensive analysis of CNGA3 in various cancer tissues suggested that CNGA3 may act as a potential tumor suppressor-related gene involved in tumorigenesis and progression. Specifically, CNGA3 was significantly overexpressed in LUAD, glioblastoma, thyroid cancer and other cancers compared with normal tissues. In contrast, CNGA3 mRNA expression was low in bladder urothelial carcinoma, colorectal cancer, esophageal cancer, head and neck squamous cell carcinoma, chromophobe renal cell carcinoma, clear cell renal cell carcinoma, papillary renal cell carcinoma, lung squamous cell carcinoma, rectal cancer, gastric cancer and endometrial cancer (P < 0.05) (Fig. 1 A). Identification and Analysis of Differentially Expressed Genes in LUAD To explore gene expression differences between 267 CNGA3 low-expression samples and 268 CNGA3 high-expression samples in LUAD, a total of 553 differentially expressed genes were identified, including 221 upregulated and 332 downregulated genes. Figure 1 B shows the volcano plot of these differentially expressed genes. Functional Enrichment and Mechanistic Investigation in LUAD To further understand the biological functions and mechanisms of CNGA3 in LUAD, we performed GO and KEGG enrichment analyses on the differentially expressed genes associated with this protein. Figure 2 A illustrates the roles of these differentially expressed genes in various biological processes, cellular components and molecular functions, including epidermal development, negative regulation of peptidase activity, ion channel activity, neuroactive ligand-receptor interaction and other processes. GSEA was conducted by comparing CNGA3 high-expression and low-expression samples to further clarify CNGA3-related pathways. Notably, the CNGA3 high-expression phenotype was significantly correlated with the pancreatic beta cell pathway (Fig. 2 B). On the other hand, the CNGA3 low-expression phenotype was significantly associated with allograft rejection pathway, G2/M checkpoint pathway, interferon-gamma response pathway, E2F target pathway, KRAS signaling up, MYC target pathway V1, apoptosis pathway, glycolysis pathway, mitotic spindle pathway and hypoxia pathway (Fig. 2 C–L). These results provide clues for understanding the potential functions of CNGA3 in LUAD and its effects on related pathways and processes. Correlation with Immune Infiltration Using ssGSEA, Spearman correlation analysis was performed to demonstrate the relationship between CNGA3 expression and immune cell infiltration levels in the tumor microenvironment of LUAD (Fig. 3 A). As shown in Figs. 3 B–E (P < 0.05), CNGA3 expression was significantly positively correlated with the counts of T follicular helper cells (R = 0.285, P < 0.001, Fig. 3 B), T helper 17 cells (R = 0.205, P < 0.001, Fig. 3 C), and effector memory T cells (R = 0.156, P < 0.001, Fig. 3 D). CNGA3 expression was negatively correlated with activated dendritic cells (R=-0.157, P < 0.001, Fig. 3 F), T helper 2 cells (R=-0.152, P < 0.001, Fig. 3 G), and natural killer cells with low CD56 expression (R=-0.131, P = 0.003, Fig. 3 G). CNGA3 Expression Is Associated with Clinical Pathological Variables in LUAD Patients Gene expression and clinical information of 516 LUAD patients were obtained from the TCGA database. According to the mean value of CNGA3 expression, these patients were divided into high-expression and low-expression groups (Table 1 ), and the potential correlation between CNGA3 expression and clinical characteristics was evaluated. Logistic regression analysis showed that CNGA3 mRNA expression was significantly negatively correlated with tumor T stage, primary therapeutic outcome, and pack-years of smoking (P < 0.05, Table 2 ). Table 1 Relationship between CNGA3 mRNA expression and clinical characteristics in LUAD characteristics Low expression of CNGA3 High expression of CNGA3 n 258 258 Race, n (%) Asian 4 (0.9%) 4 (0.9%) Black or African American 22 (4.9%) 30 (6.7%) White 198 (44.1%) 191 (42.5%) Gender, n (%) Female 139 (26.9%) 139 (26.9%) Male 119 (23.1%) 119 (23.1%) Age, n (%) 65 134 (27%) 124 (24.9%) Smoker, n (%) No 40 (8%) 35 (7%) Yes 207 (41.2%) 220 (43.8%) Number pack years smoked, n (%) = 40 99 (28.2%) 78 (22.2%) Pathologic T stage, n (%) T1 73 (14.2%) 96 (18.7%) T2 143 (27.9%) 135 (26.3%) T3 31 (6%) 16 (3.1%) T4 8 (1.6%) 11 (2.1%) Pathologic N stage, n (%) N0 162 (32.1%) 170 (33.7%) N1 50 (9.9%) 46 (9.1%) N2 37 (7.3%) 37 (7.3%) N3 1 (0.2%) 1 (0.2%) Pathologic M stage, n (%) M0 172 (46.2%) 175 (47%) M1 17 (4.6%) 8 (2.2%) Pathologic stage, n (%) Stage I 129 (25.4%) 147 (28.9%) Stage II 65 (12.8%) 57 (11.2%) Stage III 42 (8.3%) 42 (8.3%) Stage IV 18 (3.5%) 8 (1.6%) Anatomic neoplasm subdivision, n (%) Left 104 (20.8%) 97 (19.4%) Right 145 (28.9%) 155 (30.9%) Location, n (%) Central Lung 23 (12.1%) 40 (21.1%) Peripheral Lung 66 (34.7%) 61 (32.1%) Residual tumor, n (%) R0 167 (46.1%) 178 (49.2%) R1 8 (2.2%) 5 (1.4%) R2 4 (1.1%) 0 (0%) Primary therapy outcome, n (%) PD 43 (10%) 25 (5.8%) SD 19 (4.4%) 18 (4.2%) PR 4 (0.9%) 2 (0.5%) CR 139 (32.5%) 178 (41.6%) TNM tumor, node and metastasis, R0 resection margin 0, R1 resection margin 1, R2 resection margin 2, PD progressive disease, SD stable disease, PR partial response, CR complete response Table 2 CNGA3 mRNA expression association with clinical pathological characteristics (logistic regression) Characteristics Total (N) OR (95% CI) P value Pathologic T stage (T3&T4&T2 vs. T1) 513 0.677 (0.467–0.981) 0.039 Pathologic N stage (N2&N3&N1 vs. N0) 504 0.910 (0.629–1.315) 0.614 Pathologic M stage (M1 vs. M0) 372 0.463 (0.195–1.100) 0.081 Pathologic stage (Stage III&Stage IV&Stage II vs. Stage I) 508 0.751 (0.529–1.066) 0.109 Primary therapy outcome (PD&SD&PR vs. CR) 428 0.532 (0.343–0.826) 0.005 Gender (Male vs. Female) 516 1.000 (0.707–1.414) 1.000 Race (White vs. Asian&Black or African American) 449 0.738 (0.426–1.276) 0.276 Age (> 65 vs. <= 65) 497 0.858 (0.603–1.221) 0.395 Residual tumor (R1&R2 vs. R0) 362 0.391 (0.135–1.133) 0.084 Anatomic neoplasm subdivision (Right vs. Left) 501 1.146 (0.802–1.639) 0.455 Location (Peripheral Lung vs. Central Lung) 190 0.531 (0.286–0.988) 0.046 Number pack years smoked ( > = 40 vs. < 40) 351 0.597 (0.391–0.910) 0.017 Smoker (Yes vs. No) 502 1.215 (0.743–1.986) 0.438 OR odds ratio, CI confidence interval, TNM tumor, node and metastasis, PD progressive disease, SD stable disease, PR partial response, CR complete response, R0 resection margin 0, R1 resection margin 1, R2 resection margin 2 CNGA3 Is an Independent Prognostic Factor for LUAD Patients Survival analysis showed that high CNGA3 expression was associated with better overall survival, disease-specific survival and progression-free interval (Fig. 4 A–C). Log-rank regression analysis indicated that LUAD patients with high CNGA3 expression had favorable overall survival (HR = 0.611 [0.459–0.814], P < 0.001), disease-specific survival (HR = 0.557 [0.387–0.801], P = 0.002) and progression-free interval (HR = 0.751 [0.578–0.978], P = 0.032). Multivariate Cox regression analysis revealed that high CNGA3 expression was independently associated with improved overall survival (HR = 0.686 [0.489–0.964], P = 0.030) (Table 3 ). Table 3 Univariate and multivariate analyses (overall survival) for prognostic factors in ovarian cancer Characteristics Total(N) Univariate analysis Multivariate analysis HR(95% CI) P value HR(95% CI) P value Pathologic stage 522 Stage I&Stage II 415 Reference Reference Stage III&Stage IV 107 2.710 (1.994–3.685) < 0.001 1.639 (1.069–2.514) 0.023 Primary therapy outcome 442 CR 328 Reference Reference PD&SD&PR 114 2.673 (1.906–3.749) < 0.001 2.174 (1.523–3.104) < 0.001 Gender 530 Female 283 Reference Male 247 1.087 (0.816–1.448) 0.569 Race 472 Asian&Black or African American 63 Reference White 409 1.493 (0.913–2.440) 0.110 Age 520 65 263 1.216 (0.910–1.625) 0.186 Anatomic neoplasm subdivision 516 Left 202 Reference Right 314 1.040 (0.772–1.401) 0.797 Location 183 Central Lung 63 Reference Peripheral Lung 120 0.949 (0.593–1.520) 0.829 Number pack years smoked 363 = 40 180 1.073 (0.753–1.528) 0.697 Pathologic T stage 527 T1 176 Reference Reference T3&T4&T2 351 1.717 (1.221–2.415) 0.002 1.247 (0.849–1.832) 0.261 Pathologic N stage 514 N0 345 Reference Reference N1&N2&N3 169 2.547 (1.904–3.407) < 0.001 1.770 (1.199–2.612) 0.004 CNGA3 530 Low 263 Reference Reference High 267 0.610 (0.456–0.816) < 0.001 0.686 (0.489–0.964) 0.030 HR hazard ratio, CI confidence interval, PD progressive disease, SD stable disease, PR partial response, CR complete response, TNM tumor, node and metastasis Construction and Validation of a CNGA3-Based Nomogram for Predicting LUAD Patient Survival Based on CNGA3 expression and other independent clinical variables, we developed a nomogram to predict the prognosis of patients with LUAD. This nomogram was used to predict 1-year, 3-year, and 5-year overall survival, disease-specific survival, and progression-free interval in LUAD patients (Fig. 5 A). In addition, calibration curves were constructed to evaluate the efficacy of the nomogram (Fig. 5 B). The predicted lines for 1-year, 3-year, and 5-year overall survival, disease-specific survival, and progression-free interval were close to the ideal lines, indicating that the nomogram model had satisfactory accuracy. Discussion CNGA3 is a highly conserved transmembrane protein that functions as a core member of the cyclic nucleotide-gated channel family ( 25 – 27 ). Previous studies have reported that CNGA3 is upregulated in xenografts and various cancer tissues. CNGA3 participates in diverse physiological processes, including ion transport, signal transduction, and immune cell function regulation ( 13 , 28 ). However, little is known about the underlying mechanism or prognostic significance of CNGA3 in LUAD. Despite numerous studies, the prognosis of LUAD remains poor, with a 5-year overall survival rate of only 15%–20% in patients with advanced disease ( 29 ). Therefore, it is crucial to identify effective and convincing prognostic and therapeutic targets for LUAD patients. Accordingly, in the present study, we explored the mRNA expression of CNGA3 and its prognostic significance in LUAD using public datasets. The present study found that mRNA expression of CNGA3 was significantly higher in tumor tissues than in normal tissues in 3 types of cancer, whereas it was significantly lower in 11 types of cancer. CNGA3 may act as a tumor suppressor in tumorigenesis and progression, with potential value as a tumor molecular marker. To further understand the biological functions and processes of CNGA3 in LUAD, we performed GSEA, GO, and KEGG analyses. The results of KEGG enrichment and GO analyses indicated that the differentially expressed genes were involved in epidermal development, negative regulation of peptidase activity, ion channel activity, neuroactive ligand-receptor interaction, and other processes. Recent studies have shown that signaling pathways mediated by ion channel proteins are critical for tumor formation and provide potential targets for anticancer drugs ( 30 , 31 ). These findings provide insights for future research on the role of CNGA3 in LUAD. GSEA results revealed that CNGA3 was associated with the “pancreatic beta cell pathway”, “hypoxia response pathway”, “interferon-gamma signaling pathway”, “apoptosis pathway”, and others. These pathways are related to the invasion, metastasis, and proliferation of LUAD cells. The pattern of immune cell infiltration in the tumor microenvironment (TME) is closely associated with LUAD progression and prognosis. In this study, ssGSEA combined with Spearman correlation analysis was used to investigate the correlation between CNGA3 and immune cell infiltration in LUAD, providing evidence for its protective mechanism. The results showed that CNGA3 expression was significantly positively correlated with T follicular helper (Tfh) cells, Th17 cells, and effector memory T (Tem) cells (all P < 0.001). Tfh cells regulate B-cell maturation and cytotoxic T-cell activation, and Tem cells can directly kill tumor cells; increased infiltration of both cell types contributes to improved prognosis ( 32 , 33 ). Th17 cells recruit immune cells to participate in antitumor responses ( 34 , 35 ), suggesting that CNGA3 may enhance antitumor immunity by promoting the infiltration of these cells. Meanwhile, CNGA3 was negatively correlated with activated dendritic cells (aDCs), Th2 cells, and low-CD56 NK cells (P < 0.05). Abnormal aDCs, Th2 cells, and low-function NK cells promote tumor immune escape ( 36 – 38 ), and we speculate that CNGA3 may improve the immune status of the TME by inhibiting their infiltration. In summary, CNGA3 can positively regulate the infiltration of antitumor immune cells and negatively regulate immunosuppression-related cells, which may be the core mechanism underlying its ability to improve patient prognosis. This study is only a bioinformatics analysis, and its specific regulatory pathways still require experimental verification, but it may provide a new direction for immunotherapy and prognostic stratification of LUAD. To date, no study has specifically reported the protective effect of CNGA3 in LUAD, and this study aimed to evaluate the correlation between CNGA3 mRNA expression and prognosis in LUAD patients. Kaplan-Meier survival analysis revealed that low CNGA3 mRNA expression was significantly associated with longer overall survival, disease-specific survival, and progression-free interval. Both univariate and multivariate Cox regression analyses indicated that CNGA3 mRNA expression is an independent and reliable predictor for LUAD patients. Furthermore, we developed a predictive nomogram combining clinicopathological variables and CNGA3 mRNA expression to predict survival in LUAD patients. This study is the first to systematically reveal the expression pattern and prognostic value of CNGA3 as a protective gene in LUAD, which may represent a novel perspective for ion biology research in lung cancer. Although we conducted a comprehensive analysis of the association between LUAD and CNGA3 mRNA expression, this study has several limitations. A larger clinical sample size is needed to confirm the association between CNGA3 expression and prognosis in LUAD patients. Second, since our raw data were obtained from public databases, further studies are required to fully understand the molecular and functional pathways related to CNGA3. Meanwhile, further research on the clinical significance of CNGA3 in LUAD is urgently needed. Conclusion In conclusion, the present study represents the first systematic investigation into the expression profile, biological functions, and clinical prognostic significance of CNGA3 in LUAD. We demonstrate that CNGA3 is markedly upregulated in LUAD tissues, and its high expression serves as an independent favorable prognostic factor for patients with LUAD. Furthermore, CNGA3 is closely correlated with immune cell infiltration within the LUAD tumor microenvironment (TME) and may participate in shaping antitumor immunity by regulating the immune cell landscape. We also established a CNGA3-integrated nomogram that exhibits favorable predictive performance for survival in LUAD patients. Collectively, these findings identify CNGA3 as a novel prognostic biomarker and potential therapeutic target for LUAD, offering new insights into the roles of ion channel proteins in LUAD pathogenesis and supporting the development of personalized therapeutic strategies for affected individuals. Declarations Funding The authors declare that no funds, grants, or other financial support were received for the conduct of this study. Ethics Approval Ethical approval was not applicable as this study only used publicly available datasets. Author Contributions (I) Conception and design:Yu-Hui Zhang. (II) Data collection: Jia-Jun Wu and Zhao-Yi Yue. (III) Data analysis and interpretation: Jia-Jun Wu and Zhao-Yi Yue. (IV) Manuscript writing: Jia-Jun Wu and Yu-Hui Zhang. (V) Final approval of manuscript: All authors. Disclosures The authors have no relevant financial or nonfinancial interests to disclose. Data Availability The analytical approach of this study was ethically sound, as all data utilized had obtained prior approval and informed consent in their original studies.The cyclic nucleotide-gated channel protein 3 (CNGA3) messenger RNA (mRNA) expression data and corresponding clinical information of LUAD patients were retrieved from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). The CNGA3 mRNA expression data of normal lung tissues were obtained from the Genotype-Tissue Expression (GTEx) database(https://gtexportal.org/home/).The basic annotation data required for Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were derived from the standard annotation sets of the corresponding databases. The gene sets used for Gene Set Enrichment Analysis (GSEA) and single-sample Gene Set Enrichment Analysis (ssGSEA) were all obtained from the Molecular Signatures Database (MSigDB, www.gsea-msigdb.org).The data for protein-protein interaction (PPI) and gene-gene interaction network analyses were acquired from the STRING database (https://cn.string-db.org) and GeneMANIA database (http://www.genemania.org). Pairs with an interaction score > 0.90 were selected for subsequent analyses to ensure high confidence. References Bade BC, Dela Cruz CS. Lung Cancer. 2020: Epidemiology, Etiology, and Prevention[J]. Clin Chest Med, 2020, 41(1):1–24. Ren C, Li J, Zhou Y, et al. Typical tumor immune microenvironment status determine prognosis in lung adenocarcinoma[J]. Transl Oncol. 2022;18:101367. Yue P, He Y, Zuo R, et al. CCDC34 maintains stemness phenotype through beta-catenin-mediated autophagy and promotes EGFR-TKI resistance in lung adenocarcinoma[J]. Cancer Gene Ther. 2025;32(1):104–21. Dela Cruz MC, Yao X, Roberts N, et al. TLE1 drives EGFR-TKI resistance in EGFR-mutant lung adenocarcinoma through epithelial to mesenchymal transition[J]. Biochem Biophys Res Commun. 2025;775:152146. Zhang H, Wang SQ, Wang L, et al. m6A methyltransferase METTL3-induced lncRNA SNHG17 promotes lung adenocarcinoma gefitinib resistance by epigenetically repressing LATS2 expression[J]. Cell Death Dis. 2022;13(7):657. Wang C, Wu Y, Shao J, et al. Clinicopathological variables influencing overall survival, recurrence and post-recurrence survival in resected stage I non-small-cell lung cancer[J]. BMC Cancer. 2020;20(1):150. Lai HC, Liu JF, Chang TM, et al. MCM4 as Potential Metastatic Biomarker in Lung Adenocarcinoma[J]. Diagnostics. 2025;15(12):3567. Dang HX, White NM, Rozycki EB, et al. Long non-coding RNA LCAL62 / LINC00261 is associated with lung adenocarcinoma prognosis[J]. Heliyon. 2020;6(3):e03521. Souza VGP, Telkar N, Lam WL, et al. Comprehensive Analysis of Lung Adenocarcinoma and Brain Metastasis through Integrated Single-Cell Transcriptomics[J]. Int J Mol Sci. 2024;25(7):3892. Leanza L, Zoratti M, Gulbins E, et al. Mitochondrial ion channels as oncological targets[J]. Oncogene. 2014;33(49):5569–81. Arcangeli A. Ion channels and transporters in cancer. 3. Ion channels in the tumor cell-microenvironment cross talk[J]. Am J Physiol Cell Physiol. 2011;301(4):C762–71. Becchetti A. Ion channels and transporters in cancer. 1. Ion channels and cell proliferation in cancer[J]. Am J Physiol Cell Physiol. 2011;301(2):C255–65. Jacobson MA, Jones LJ, Colussi DJ, et al. High-Throughput Ca(2+) Flux Assay To Monitor Cyclic Nucleotide-Gated Channel Activity and Characterize Achromatopsia Mutant Channel Function[J]. ACS Chem Neurosci. 2019;10(8):3662–70. Koeppen K, Reuter P, Ladewig T, et al. Dissecting the pathogenic mechanisms of mutations in the pore region of the human cone photoreceptor cyclic nucleotide-gated channel[J]. Hum Mutat. 2010;31(7):830–9. Reuter P, Koeppen K, Ladewig T, et al. Mutations in CNGA3 impair trafficking or function of cone cyclic nucleotide-gated channels, resulting in achromatopsia[J]. Hum Mutat. 2008;29(10):1228–36. Chao YC, Cheng CJ, Hsieh HT, et al. Guanylate cyclase-G, expressed in the Grueneberg ganglion olfactory subsystem, is activated by bicarbonate[J]. Biochem J. 2010;432(2):267–73. Cassar SC, Chen J, Zhang D, et al. Tissue specific expression of alternative splice forms of human cyclic nucleotide gated channel subunit CNGA3[J]. Mol Vis. 2004;10:808–13. Grimsrud MM, Forster M, Goeppert B, et al. Whole-exome sequencing reveals novel cancer genes and actionable targets in biliary tract cancers in primary sclerosing cholangitis[J]. Hepatol Commun. 2024;8(7):e02654. Hua Y, Ma X, Liu X, et al. Abnormal expression of mRNA, microRNA alteration and aberrant DNA methylation patterns in rectal adenocarcinoma[J]. PLoS ONE. 2017;12(3):e0174461. Olsen A, Harpaz Z, Ren C, et al. Identification of dual-purpose therapeutic targets implicated in aging and glioblastoma multiforme using PandaOmics - an AI-enabled biological target discovery platform[J]. Aging. 2023;15(8):2863–76. Warde-Farley D, Donaldson SL, Comes O et al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function[J]. Nucleic Acids Res, 2010, 38(Web Server issue):W214–20. Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets[J]. Nucleic Acids Res. 2019;47(D1):D607–13. Franz M, Rodriguez H, Lopes C, et al. GeneMANIA update 2018[J]. Nucleic Acids Res. 2018;46(W1):W60–4. Bindea G, Mlecnik B, Tosolini M, et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer[J]. Immunity. 2013;39(4):782–95. Dai G, Peng C, Liu C, et al. Two structural components in CNGA3 support regulation of cone CNG channels by phosphoinositides[J]. J Gen Physiol. 2013;141(4):413–30. Tanaka N, Delemotte L, Klein ML, et al. A cyclic nucleotide-gated channel mutation associated with canine daylight blindness provides insight into a role for the S2 segment tri-Asp motif in channel biogenesis[J]. PLoS ONE. 2014;9(2):e88768. Zelman AK, Dawe A, Berkowitz GA. Identification of cyclic nucleotide gated channels using regular expressions[M]//Methods in Molecular Biology. Volume 1016. Totowa: Humana; 2013. pp. 207–24. Sun R, Wang Y, Zhou W, et al. Novel compound heterozygous CNGA3 mutation associated with retinal cone dystrophy[J]. Exp Ther Med. 2025;30(2):155. Liu CH, Liu SH, Lai YL, et al. Using bioinformatics approaches to identify survival-related oncomiRs as potential targets of miRNA-based treatments for lung adenocarcinoma[J]. Comput Struct Biotechnol J. 2022;20:4626–35. Shi Q, Yang Z, Yang H, et al. Targeting ion channels: innovative approaches to combat cancer drug resistance[J]. Theranostics. 2025;15(2):521–45. Zhou S, Song X, Zeng W, et al. Targeting Ion Channels for Cancer Therapy: From Pathophysiological Mechanisms to Clinical Translation[J]. Pharmaceuticals. 2025;18(10):568. Ren HM, Lukacher AE. IL-21 in Homeostasis of Resident Memory and Exhausted CD8 T Cells during Persistent Infection[J]. Int J Mol Sci. 2020;21(18):6792. Zhou T, Zhang Y, Cao J, et al. Identification of immune infiltration-related ZNF480 for predicting prognosis in breast cancer[J]. Am J Clin Exp Immunol. 2025;14(1):1–13. Najafi S, Mirshafiey A. The role of T helper 17 and regulatory T cells in tumor microenvironment[J]. Immunopharmacol Immunotoxicol. 2019;41(1):16–24. Su X, Ye J, Hsueh EC, et al. Tumor microenvironments direct the recruitment and expansion of human Th17 cells[J]. J Immunol. 2010;184(3):1630–41. Song D, Li H, Li H, et al. Effect of human papillomavirus infection on the immune system and its role in the course of cervical cancer[J]. Oncol Lett. 2015;10(2):600–6. Matsuo K, Yoshie O, Nakayama T. Multifaceted Roles of Chemokines and Chemokine Receptors in Tumor Immunity[J]. Cancers (Basel). 2021;13(23):5907. Luo L, Zhu J, Guo Y, et al. Mitophagy and immune infiltration in vitiligo: evidence from bioinformatics analysis[J]. Front Immunol. 2023;14:1164124. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 07 May, 2026 Reviews received at journal 27 Apr, 2026 Reviewers agreed at journal 26 Apr, 2026 Reviewers agreed at journal 26 Apr, 2026 Reviewers agreed at journal 25 Apr, 2026 Reviewers invited by journal 21 Apr, 2026 Editor invited by journal 03 Apr, 2026 Editor assigned by journal 25 Mar, 2026 Submission checks completed at journal 25 Mar, 2026 First submitted to journal 24 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9214105","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":631804492,"identity":"64c09880-10d7-4d5b-8044-35bf6a95b690","order_by":0,"name":"Jia-Jun Wu","email":"","orcid":"","institution":"Ningxia Hui Autonomous Region Peoples Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jia-Jun","middleName":"","lastName":"Wu","suffix":""},{"id":631804493,"identity":"f2c8732e-0c46-4940-a608-d0e407935b0e","order_by":1,"name":"Zhao-Yi Yue","email":"","orcid":"","institution":"Ningxia Hui Autonomous Region Peoples Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhao-Yi","middleName":"","lastName":"Yue","suffix":""},{"id":631804494,"identity":"cd1bffee-1d01-4d0c-92e3-d3236385ef76","order_by":2,"name":"Yu-Hui Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYDAC/uYDBxLYGOQYmInWInEs8cAHNgZjErQw5CgfnMHGkNhAtAZ5hzMMh3nKtqXPb+c9+IGhxiaaoBbDw70HDvOcu5274TBfsgTDsbRcgtYZNpxLOMzbBtTCzGMgwdhwmBgtOQYgLenyzTzGP4jSIs+QY3BwZtvtBKCPzIizxUDiWMKBD+duG24AarFIIMYv8v3Nhz8klN2Wl+8/Y3zjQ40NEbYcQOYlEFIOtoWgoaNgFIyCUTAKAFPyRTblBOUaAAAAAElFTkSuQmCC","orcid":"","institution":"Ningxia Hui Autonomous Region Peoples Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yu-Hui","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-03-24 15:53:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9214105/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9214105/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108247309,"identity":"2dfd9174-0db4-42a3-8988-e2159273c88f","added_by":"auto","created_at":"2026-05-01 00:41:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":330259,"visible":true,"origin":"","legend":"\u003cp\u003eExpression levels of CNGA3 mRNA across pan-cancer. (A) ression of CNGA3 in normal (unpaired) and cancerous samples from the TCGA and GTEx databases. (B) olcano plot of differentially expressed genes.*P\u0026lt;0.05, **P\u0026lt;0.01, ***P\u0026lt;0.001.GTEx: Genotype-Tissue Expression; TCGA: The Cancer Genome Atlas\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9214105/v1/7f9d35539a2c5f6401a5ca06.png"},{"id":108491886,"identity":"a1a3986c-29f9-46d3-9221-2c621a64d1a1","added_by":"auto","created_at":"2026-05-05 09:56:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":583009,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of CNGA3 in LUAD. (A) GO and KEGG enrichment analyses of differentially expressed genes in LUAD. (B-L) GSEA revealed CNGA3-related signaling pathways in h.all.v2022.1.Hs.symbols.gmt. (B) Pancreatic beta cell pathway; (C) Allograft rejection pathway; (D) G2/M checkpoint pathway; (E) Interferon-gamma response pathway; (F) E2f target pathway; (G) KRAS signaling up pathway; (H) MYC target pathway V1; (I) Apoptosis pathway; (J) Glycolysis pathway; (K) Mitotic spindle pathway; (L) Hypoxia pathway\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9214105/v1/aa7dbb848b5fa4ed7d00c5e2.png"},{"id":108492442,"identity":"7c73dcff-7ddd-47a6-a821-39adf384673b","added_by":"auto","created_at":"2026-05-05 09:57:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":392230,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between CNGA3 expression and immune cell infiltration in the tumor microenvironment of LUAD.(A) Relationship between immune cell levels and CNGA3 mRNA expression.Scatter plots show the correlation between CNGA3 expression and abundance of (B) T follicular helper cells, (C) T helper 17 cells, (D) effector memory T cells, (E) activated dendritic cells, (F) T helper 2 cells, and (G) natural killer cells with low CD56 expression\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9214105/v1/7f3b2472e5c0ab1e09c4cd21.png"},{"id":108247312,"identity":"46d58592-7353-424d-807a-6fdcde9ca7de","added_by":"auto","created_at":"2026-05-01 00:41:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":178099,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival analysis of CNGA3 in LUAD patients. (A) Overall survival, (B) Disease-specific survival, (C) Progression-free interval of LUAD\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9214105/v1/b234580ee248d676b600eabc.png"},{"id":108247313,"identity":"b9ad7088-b136-42aa-8aa3-017a10e12152","added_by":"auto","created_at":"2026-05-01 00:41:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":280981,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction and validation of a CNGA3-based nomogram for LUAD patients. (A) Nomogram for predicting 1-year, 3-year, and 5-year OS of LUAD patients integrating CNGA3 expression and clinical pathological variables. (B) Calibration curves of the nomogram for evaluating the consistency between predicted and actual 1-year, 3-year, and 5-year OS probabilities\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9214105/v1/47fff69503f1e458e0598fd5.png"},{"id":108495304,"identity":"417e19dc-6579-4b58-b95f-9b6f3e810a37","added_by":"auto","created_at":"2026-05-05 10:09:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2216882,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9214105/v1/0a859232-6d45-4eea-a1f2-d28d30a23c48.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CNGA3, a Protective Ion Channel Gene, Is Highly Expressed in Lung Adenocarcinoma and Predicts a Favorable Prognosis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobally, lung cancer is the malignant tumor with the highest incidence and mortality rates. Among them, lung adenocarcinom1a (LUAD), as the predominant subtype, accounts for approximately 40%\u0026ndash;50% of total lung cancer cases. Its incidence shows a continuous upward trend, with a high detection rate in both smokers and non-smokers (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). These alarming data highlight the significant threat that LUAD poses to human health and life.\u003c/p\u003e \u003cp\u003ePrognosis varies considerably among patients with LUAD, and the 5-year overall survival rate for advanced patients is only 15%\u0026ndash;20%. Traditional treatment regimens include surgical resection, platinum-based chemotherapy and radiotherapy. Although targeted therapies such as epidermal growth factor receptor tyrosine kinase inhibitors and anaplastic lymphoma kinase fusion gene inhibitors, as well as immune checkpoint inhibitor therapy, have significantly improved prognosis in some patients, a considerable proportion of patients still develop primary or acquired resistance (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Despite these therapeutic interventions, more than 60% of LUAD patients experience recurrence within 3 years after diagnosis, limiting the improvement of survival rate (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Studies have shown that after radical surgery, the 5-year survival rate of early-stage LUAD patients can reach 60%\u0026ndash;80%, whereas that of advanced patients is merely 5%\u0026ndash;10%. The difficulty in the treatment of LUAD lies in its concealed early symptoms and the lack of specific clinical manifestations, resulting in more than 70% of patients being diagnosed at locally advanced or distant metastatic stages (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Although numerous advances have been achieved in precision medicine in recent years, the improvement in the 5-year survival rate of patients with advanced LUAD remains slow (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In view of the current limitations in the treatment of LUAD, novel therapeutic targets are urgently needed to enhance clinical efficacy, and reliable prognostic models are also critically required to facilitate the development of more targeted and effective treatment strategies.\u003c/p\u003e \u003cp\u003eIon channel proteins play an important role in the occurrence and development of tumors. By regulating intracellular ion concentration, signal transduction and other processes, they affect the proliferation, apoptosis, invasion and metastasis of tumor cells, and have become potential candidate targets for cancer therapy (\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). As a core member of the cyclic nucleotide-gated channel family, CNGA3 is a highly conserved transmembrane protein, and its roles in calcium ion transport, cellular signal transduction and immune cell activation have attracted increasing attention (\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Several studies have shown that CNGA3 is involved in a variety of physiological processes, including visual signal transduction, immune cell function regulation, cell proliferation and differentiation (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In addition, it has been confirmed that CNGA3 participates in the maintenance of epithelial cell morphology and tissue homeostasis by regulating calcium-dependent signaling pathways. Notably, abnormal expression of CNGA3 has been found in a variety of tumor tissues, and it may be involved in tumor immune escape by affecting the function of immune cells in the tumor microenvironment (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious studies have reported that CNGA3 is overexpressed in tumor tissues such as cholangiocarcinoma and is associated with poor prognosis in patients (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). CNGA3 is subject to hypermethylation and negative regulation by differentially expressed miRNAs (DEmiRNAs), which may be involved in the occurrence of rectal adenocarcinoma (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Furthermore, Andrea Olsen et al. found that CNGA3 may serve as a potential target for the treatment of aging and glioblastoma multiforme (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). These data suggest that overexpression of CNGA3 may contribute to tumorigenesis and progression.\u003c/p\u003e \u003cp\u003eAlthough previous studies have implied the potential involvement of CNGA3 in human malignancies, its precise molecular mechanism and prognostic value in LUAD remain largely underexplored. To elucidate the clinical and biological implications of upregulated CNGA3 expression in LUAD, we performed a comprehensive bioinformatics analysis using transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) database. Furthermore, we constructed a prognostic nomogram integrating CNGA3 mRNA expression levels and key clinical parameters to predict overall survival in LUAD patients. Our results demonstrated that CNGA3 exhibits multifaceted roles in the oncogenic progression of LUAD, and its elevated expression is significantly correlated with favorable clinical outcomes. Collectively, these findings indicate that CNGA3 may function as a promising novel prognostic biomarker for patients with LUAD.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Collection and Analysis\u003c/h2\u003e \u003cp\u003eCNGA3 messenger RNA (mRNA) expression data and clinical information of LUAD patients were obtained from \u003cb\u003eThe Cancer Genome Atlas (TCGA)\u003c/b\u003e project via the Genomic Data Commons (GDC) Data Portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Meanwhile, CNGA3 mRNA expression data of normal lung tissues were retrieved from the \u003cb\u003eGenotype-Tissue Expression (GTEx)\u003c/b\u003e database(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gtexportal.org/home/\u003c/span\u003e\u003cspan address=\"https://gtexportal.org/home/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The Level 3 HTSeq-FPKM data of 535 lung adenocarcinoma (LUAD) patients were converted into transcripts per million reads for subsequent analyses. For the clinical features that were unknown or unavailable among the 535 samples, they were defined as missing data.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCNGA3 Expression Analysis\u003c/h3\u003e\n\u003cp\u003eSamples were grouped according to disease status (tumor or normal), and scatter plots and box plots were constructed to display the differential expression of CNGA3, so as to analyze the differential expression of CNGA3 between LUAD samples and normal samples. According to statistical ranking, the expression level of CNGA3 was divided into low expression (CNGA3-Low) and high expression (CNGA3-High), namely below or above the median value.\u003c/p\u003e\n\u003ch3\u003eIdentification of Differentially Expressed Genes (DEGs)\u003c/h3\u003e\n\u003cp\u003eDifferential expression analysis was performed on LUAD samples in the CNGA3 high-expression group and low-expression group using the edgeR package and Student\u0026rsquo;s t-test. Genes with a log fold change (FC)\u0026thinsp;\u0026gt;\u0026thinsp;1 and adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were regarded as statistically significant differentially expressed genes. All differentially expressed genes were visualized by volcano plots.\u003c/p\u003e\n\u003ch3\u003eGene-Gene and Protein-Protein Interaction Analysis\u003c/h3\u003e\n\u003cp\u003eSTRING (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and GeneMANIA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genemania.org\u003c/span\u003e\u003cspan address=\"http://www.genemania.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) were used to analyze the protein-protein interaction (PPI) and gene-gene interaction networks involving CNGA3. GeneMANIA integrates a variety of bioinformatics techniques, including site prediction, colocalization, co-expression, genetic interactions of physiological relationships, and gene enrichment analysis. Pairs with an interaction score\u0026thinsp;\u0026gt;\u0026thinsp;0.90 were selected for protein-protein interaction analysis.\u003c/p\u003e\n\u003ch3\u003eCo-Expression Gene Analysis of CNGA3 in LUAD\u003c/h3\u003e\n\u003cp\u003eTCGA transcriptome sequencing data were used to screen the top 35 positively and negatively correlated co-expressed genes with CNGA3 in LUAD. The \"Stats\" package was used for statistical analysis, and the \"ggplot2\" package was used for visualization.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Enrichment Analysis and Tumor Microenvironment Investigation\u003c/h2\u003e \u003cp\u003eThe biological effects of differentially expressed genes were evaluated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. In both analyses, the criteria for statistical significance were counts between 5 and 5000, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.25. Gene Set Enrichment Analysis (GSEA) was applied to evaluate the biological functions and pathways in the CNGA3 high-expression and low-expression groups. Gene sets with absolute normalized enrichment score\u0026thinsp;\u0026gt;\u0026thinsp;1, adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.25 were defined as significant gene sets. Gene sets were obtained from the Molecular Signatures Database (MSigDB) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://portal.gdc.cancer.gov/\" target=\"_blank\"\u003ewww.gsea-msigdb.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.gsea-msigdb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Based on gene expression profiles, the Gene Set Variation Analysis (GSVA) package and single-sample Gene Set Enrichment Analysis (ssGSEA) (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) were used to detect the infiltration levels of 24 different immune cell types. The relationship between immune cell infiltration levels and CNGA3 mRNA expression was evaluated by Wilcoxon rank-sum test and Spearman correlation analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePrognostic Evaluation\u003c/h3\u003e\n\u003cp\u003eTo explore the correlation between CNGA3 expression and prognosis of LUAD, we analyzed disease-specific survival (DSS), overall survival (OS), and progression-free interval (PFI). Univariate and multivariate Cox analyses were performed on the TCGA-LUAD dataset to determine the predictive value. The median level of CNGA3 mRNA expression in LUAD tissues was used as the cutoff value and included in multivariate Cox analysis.\u003c/p\u003e\n\u003ch3\u003eNomogram Construction\u003c/h3\u003e\n\u003cp\u003eThe \"rms\" and \"survival\" packages were used for analysis and visualization to construct a nomogram for predicting 1-year, 3-year, and 5-year overall survival of LUAD patients. Calibration curves were used to graphically assess the consistency between nomogram-predicted probabilities and actual events.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using R (v4.2.1) and RStudio software. In preliminary data analysis, two-tailed Student\u0026rsquo;s t-test and one-way analysis of variance (ANOVA) were conducted. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered the criterion for statistically significant differences.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCNGA3 mRNA Expression in Human Cancers\u003c/h2\u003e \u003cp\u003eFirst, TCGA and GTEx data were used to compare CNGA3 mRNA expression between human cancer tissues and normal tissues. The results showed that among the 33 cancer types studied, CNGA3 expression levels were significantly different in 14 cancers, among which CNGA3 expression was significantly elevated in 3 cancers and significantly decreased in 11 cancers. Comprehensive analysis of CNGA3 in various cancer tissues suggested that CNGA3 may act as a potential tumor suppressor-related gene involved in tumorigenesis and progression. Specifically, CNGA3 was significantly overexpressed in LUAD, glioblastoma, thyroid cancer and other cancers compared with normal tissues. In contrast, CNGA3 mRNA expression was low in bladder urothelial carcinoma, colorectal cancer, esophageal cancer, head and neck squamous cell carcinoma, chromophobe renal cell carcinoma, clear cell renal cell carcinoma, papillary renal cell carcinoma, lung squamous cell carcinoma, rectal cancer, gastric cancer and endometrial cancer (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIdentification and Analysis of Differentially Expressed Genes in LUAD\u003c/h2\u003e \u003cp\u003eTo explore gene expression differences between 267 CNGA3 low-expression samples and 268 CNGA3 high-expression samples in LUAD, a total of 553 differentially expressed genes were identified, including 221 upregulated and 332 downregulated genes. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB shows the volcano plot of these differentially expressed genes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Enrichment and Mechanistic Investigation in LUAD\u003c/h2\u003e \u003cp\u003eTo further understand the biological functions and mechanisms of CNGA3 in LUAD, we performed GO and KEGG enrichment analyses on the differentially expressed genes associated with this protein. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA illustrates the roles of these differentially expressed genes in various biological processes, cellular components and molecular functions, including epidermal development, negative regulation of peptidase activity, ion channel activity, neuroactive ligand-receptor interaction and other processes. GSEA was conducted by comparing CNGA3 high-expression and low-expression samples to further clarify CNGA3-related pathways. Notably, the CNGA3 high-expression phenotype was significantly correlated with the pancreatic beta cell pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). On the other hand, the CNGA3 low-expression phenotype was significantly associated with allograft rejection pathway, G2/M checkpoint pathway, interferon-gamma response pathway, E2F target pathway, KRAS signaling up, MYC target pathway V1, apoptosis pathway, glycolysis pathway, mitotic spindle pathway and hypoxia pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC\u0026ndash;L). These results provide clues for understanding the potential functions of CNGA3 in LUAD and its effects on related pathways and processes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation with Immune Infiltration\u003c/h2\u003e \u003cp\u003eUsing ssGSEA, Spearman correlation analysis was performed to demonstrate the relationship between CNGA3 expression and immune cell infiltration levels in the tumor microenvironment of LUAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). As shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB\u0026ndash;E (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), CNGA3 expression was significantly positively correlated with the counts of T follicular helper cells (R\u0026thinsp;=\u0026thinsp;0.285, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), T helper 17 cells (R\u0026thinsp;=\u0026thinsp;0.205, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), and effector memory T cells (R\u0026thinsp;=\u0026thinsp;0.156, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). CNGA3 expression was negatively correlated with activated dendritic cells (R=-0.157, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF), T helper 2 cells (R=-0.152, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG), and natural killer cells with low CD56 expression (R=-0.131, P\u0026thinsp;=\u0026thinsp;0.003, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCNGA3 Expression Is Associated with Clinical Pathological Variables in LUAD Patients\u003c/h2\u003e \u003cp\u003eGene expression and clinical information of 516 LUAD patients were obtained from the TCGA database. According to the mean value of CNGA3 expression, these patients were divided into high-expression and low-expression groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and the potential correlation between CNGA3 expression and clinical characteristics was evaluated. Logistic regression analysis showed that CNGA3 mRNA expression was significantly negatively correlated with tumor T stage, primary therapeutic outcome, and pack-years of smoking (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003e Relationship between CNGA3 mRNA expression and clinical characteristics in LUAD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003echaracteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow expression of CNGA3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh expression of CNGA3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack or African American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e198 (44.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191 (42.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\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\u003e139 (26.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139 (26.9%)\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\u003e119 (23.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119 (23.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;= 65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115 (23.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124 (24.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124 (24.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\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\u003e40 (8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (7%)\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\u003e207 (41.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e220 (43.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber pack years smoked, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; 40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75 (21.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99 (28.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;= 40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99 (28.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic T stage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (14.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96 (18.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143 (27.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135 (26.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic N stage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162 (32.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170 (33.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (9.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic M stage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e172 (46.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e175 (47%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic stage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129 (25.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147 (28.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (12.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (11.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnatomic neoplasm subdivision, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104 (20.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97 (19.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e145 (28.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155 (30.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Lung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (21.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral Lung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (34.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (32.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual tumor, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167 (46.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178 (49.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary therapy outcome, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (5.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139 (32.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178 (41.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTNM tumor, node and metastasis, R0 resection margin 0, R1 resection margin 1, R2 resection margin 2, PD progressive disease, SD stable disease, PR partial response, CR complete response\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\u003e CNGA3 mRNA expression association with clinical pathological characteristics (logistic regression)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic T stage (T3\u0026amp;T4\u0026amp;T2 vs. T1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.677 (0.467\u0026ndash;0.981)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic N stage (N2\u0026amp;N3\u0026amp;N1 vs. N0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.910 (0.629\u0026ndash;1.315)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic M stage (M1 vs. M0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.463 (0.195\u0026ndash;1.100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic stage (Stage III\u0026amp;Stage IV\u0026amp;Stage II vs. Stage I)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.751 (0.529\u0026ndash;1.066)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary therapy outcome (PD\u0026amp;SD\u0026amp;PR vs. CR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.532 (0.343\u0026ndash;0.826)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Male vs. Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000 (0.707\u0026ndash;1.414)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace (White vs. Asian\u0026amp;Black or African American)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.738 (0.426\u0026ndash;1.276)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (\u0026gt;\u0026thinsp;65 vs. \u0026lt;= 65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.858 (0.603\u0026ndash;1.221)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual tumor (R1\u0026amp;R2 vs. R0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.391 (0.135\u0026ndash;1.133)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnatomic neoplasm subdivision (Right vs. Left)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.146 (0.802\u0026ndash;1.639)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation (Peripheral Lung vs. Central Lung)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.531 (0.286\u0026ndash;0.988)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber pack years smoked (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;40 vs. \u0026lt; 40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.597 (0.391\u0026ndash;0.910)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker (Yes vs. No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.215 (0.743\u0026ndash;1.986)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOR odds ratio, CI confidence interval, TNM tumor, node and metastasis, PD progressive disease, SD stable disease, PR partial response, CR complete response, R0 resection margin 0, R1 resection margin 1, R2 resection margin 2\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eCNGA3 Is an Independent Prognostic Factor for LUAD Patients\u003c/h2\u003e \u003cp\u003eSurvival analysis showed that high CNGA3 expression was associated with better overall survival, disease-specific survival and progression-free interval (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u0026ndash;C). Log-rank regression analysis indicated that LUAD patients with high CNGA3 expression had favorable overall survival (HR\u0026thinsp;=\u0026thinsp;0.611 [0.459\u0026ndash;0.814], P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), disease-specific survival (HR\u0026thinsp;=\u0026thinsp;0.557 [0.387\u0026ndash;0.801], P\u0026thinsp;=\u0026thinsp;0.002) and progression-free interval (HR\u0026thinsp;=\u0026thinsp;0.751 [0.578\u0026ndash;0.978], P\u0026thinsp;=\u0026thinsp;0.032). Multivariate Cox regression analysis revealed that high CNGA3 expression was independently associated with improved overall survival (HR\u0026thinsp;=\u0026thinsp;0.686 [0.489\u0026ndash;0.964], P\u0026thinsp;=\u0026thinsp;0.030) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \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\u003eUnivariate and multivariate analyses (overall survival) for prognostic factors in ovarian cancer\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=\"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=\"left\" 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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal(N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage I\u0026amp;Stage II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage III\u0026amp;Stage IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.710 (1.994\u0026ndash;3.685)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.639 (1.069\u0026ndash;2.514)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary therapy outcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u0026amp;SD\u0026amp;PR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.673 (1.906\u0026ndash;3.749)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.174 (1.523\u0026ndash;3.104)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.087 (0.816\u0026ndash;1.448)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u0026amp;Black or African American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.493 (0.913\u0026ndash;2.440)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;= 65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.216 (0.910\u0026ndash;1.625)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnatomic neoplasm subdivision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.040 (0.772\u0026ndash;1.401)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Lung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral Lung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.949 (0.593\u0026ndash;1.520)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber pack years smoked\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;= 40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.073 (0.753\u0026ndash;1.528)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic T stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u0026amp;T4\u0026amp;T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.717 (1.221\u0026ndash;2.415)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.247 (0.849\u0026ndash;1.832)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic N stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u0026amp;N2\u0026amp;N3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.547 (1.904\u0026ndash;3.407)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.770 (1.199\u0026ndash;2.612)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNGA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.610 (0.456\u0026ndash;0.816)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.686 (0.489\u0026ndash;0.964)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHR hazard ratio, CI confidence interval, PD progressive disease, SD stable disease, PR partial response, CR complete response, TNM tumor, node and metastasis\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and Validation of a CNGA3-Based Nomogram for Predicting LUAD Patient Survival\u003c/h2\u003e \u003cp\u003eBased on CNGA3 expression and other independent clinical variables, we developed a nomogram to predict the prognosis of patients with LUAD. This nomogram was used to predict 1-year, 3-year, and 5-year overall survival, disease-specific survival, and progression-free interval in LUAD patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). In addition, calibration curves were constructed to evaluate the efficacy of the nomogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The predicted lines for 1-year, 3-year, and 5-year overall survival, disease-specific survival, and progression-free interval were close to the ideal lines, indicating that the nomogram model had satisfactory accuracy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCNGA3 is a highly conserved transmembrane protein that functions as a core member of the cyclic nucleotide-gated channel family (\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Previous studies have reported that CNGA3 is upregulated in xenografts and various cancer tissues. CNGA3 participates in diverse physiological processes, including ion transport, signal transduction, and immune cell function regulation (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). However, little is known about the underlying mechanism or prognostic significance of CNGA3 in LUAD.\u003c/p\u003e \u003cp\u003eDespite numerous studies, the prognosis of LUAD remains poor, with a 5-year overall survival rate of only 15%\u0026ndash;20% in patients with advanced disease (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Therefore, it is crucial to identify effective and convincing prognostic and therapeutic targets for LUAD patients. Accordingly, in the present study, we explored the mRNA expression of CNGA3 and its prognostic significance in LUAD using public datasets.\u003c/p\u003e \u003cp\u003eThe present study found that mRNA expression of CNGA3 was significantly higher in tumor tissues than in normal tissues in 3 types of cancer, whereas it was significantly lower in 11 types of cancer. CNGA3 may act as a tumor suppressor in tumorigenesis and progression, with potential value as a tumor molecular marker. To further understand the biological functions and processes of CNGA3 in LUAD, we performed GSEA, GO, and KEGG analyses. The results of KEGG enrichment and GO analyses indicated that the differentially expressed genes were involved in epidermal development, negative regulation of peptidase activity, ion channel activity, neuroactive ligand-receptor interaction, and other processes. Recent studies have shown that signaling pathways mediated by ion channel proteins are critical for tumor formation and provide potential targets for anticancer drugs (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). These findings provide insights for future research on the role of CNGA3 in LUAD. GSEA results revealed that CNGA3 was associated with the \u0026ldquo;pancreatic beta cell pathway\u0026rdquo;, \u0026ldquo;hypoxia response pathway\u0026rdquo;, \u0026ldquo;interferon-gamma signaling pathway\u0026rdquo;, \u0026ldquo;apoptosis pathway\u0026rdquo;, and others. These pathways are related to the invasion, metastasis, and proliferation of LUAD cells.\u003c/p\u003e \u003cp\u003eThe pattern of immune cell infiltration in the tumor microenvironment (TME) is closely associated with LUAD progression and prognosis. In this study, ssGSEA combined with Spearman correlation analysis was used to investigate the correlation between CNGA3 and immune cell infiltration in LUAD, providing evidence for its protective mechanism. The results showed that CNGA3 expression was significantly positively correlated with T follicular helper (Tfh) cells, Th17 cells, and effector memory T (Tem) cells (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Tfh cells regulate B-cell maturation and cytotoxic T-cell activation, and Tem cells can directly kill tumor cells; increased infiltration of both cell types contributes to improved prognosis (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Th17 cells recruit immune cells to participate in antitumor responses (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), suggesting that CNGA3 may enhance antitumor immunity by promoting the infiltration of these cells. Meanwhile, CNGA3 was negatively correlated with activated dendritic cells (aDCs), Th2 cells, and low-CD56 NK cells (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Abnormal aDCs, Th2 cells, and low-function NK cells promote tumor immune escape (\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), and we speculate that CNGA3 may improve the immune status of the TME by inhibiting their infiltration. In summary, CNGA3 can positively regulate the infiltration of antitumor immune cells and negatively regulate immunosuppression-related cells, which may be the core mechanism underlying its ability to improve patient prognosis. This study is only a bioinformatics analysis, and its specific regulatory pathways still require experimental verification, but it may provide a new direction for immunotherapy and prognostic stratification of LUAD.\u003c/p\u003e \u003cp\u003eTo date, no study has specifically reported the protective effect of CNGA3 in LUAD, and this study aimed to evaluate the correlation between CNGA3 mRNA expression and prognosis in LUAD patients. Kaplan-Meier survival analysis revealed that low CNGA3 mRNA expression was significantly associated with longer overall survival, disease-specific survival, and progression-free interval. Both univariate and multivariate Cox regression analyses indicated that CNGA3 mRNA expression is an independent and reliable predictor for LUAD patients. Furthermore, we developed a predictive nomogram combining clinicopathological variables and CNGA3 mRNA expression to predict survival in LUAD patients. This study is the first to systematically reveal the expression pattern and prognostic value of CNGA3 as a protective gene in LUAD, which may represent a novel perspective for ion biology research in lung cancer.\u003c/p\u003e \u003cp\u003eAlthough we conducted a comprehensive analysis of the association between LUAD and CNGA3 mRNA expression, this study has several limitations. A larger clinical sample size is needed to confirm the association between CNGA3 expression and prognosis in LUAD patients. Second, since our raw data were obtained from public databases, further studies are required to fully understand the molecular and functional pathways related to CNGA3. Meanwhile, further research on the clinical significance of CNGA3 in LUAD is urgently needed.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the present study represents the first systematic investigation into the expression profile, biological functions, and clinical prognostic significance of CNGA3 in LUAD. We demonstrate that CNGA3 is markedly upregulated in LUAD tissues, and its high expression serves as an independent favorable prognostic factor for patients with LUAD. Furthermore, CNGA3 is closely correlated with immune cell infiltration within the LUAD tumor microenvironment (TME) and may participate in shaping antitumor immunity by regulating the immune cell landscape. We also established a CNGA3-integrated nomogram that exhibits favorable predictive performance for survival in LUAD patients. Collectively, these findings identify CNGA3 as a novel prognostic biomarker and potential therapeutic target for LUAD, offering new insights into the roles of ion channel proteins in LUAD pathogenesis and supporting the development of personalized therapeutic strategies for affected individuals.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other financial support were received for the conduct of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was not applicable as this study only used publicly available datasets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(I) Conception and design:Yu-Hui Zhang. (II) Data collection: Jia-Jun Wu and Zhao-Yi Yue. (III) Data analysis and interpretation: Jia-Jun Wu and Zhao-Yi Yue. (IV) Manuscript writing: Jia-Jun Wu and Yu-Hui Zhang. (V) Final approval of manuscript: All authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or nonfinancial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analytical approach of this study was ethically sound, as all data utilized had obtained prior approval and informed consent in their original studies.The cyclic nucleotide-gated channel protein 3 (CNGA3) messenger RNA (mRNA) expression data and corresponding clinical information of LUAD patients were retrieved from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). The CNGA3 mRNA expression data of normal lung tissues were obtained from the Genotype-Tissue Expression (GTEx) database(https://gtexportal.org/home/).The basic annotation data required for Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were derived from the standard annotation sets of the corresponding databases. The gene sets used for Gene Set Enrichment Analysis (GSEA) and single-sample Gene Set Enrichment Analysis (ssGSEA) were all obtained from the Molecular Signatures Database (MSigDB, www.gsea-msigdb.org).The data for protein-protein interaction (PPI) and gene-gene interaction network analyses were acquired from the STRING database (https://cn.string-db.org) and GeneMANIA database (http://www.genemania.org). Pairs with an interaction score \u0026gt; 0.90 were selected for subsequent analyses to ensure high confidence.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBade BC, Dela Cruz CS. Lung Cancer. 2020: Epidemiology, Etiology, and Prevention[J]. Clin Chest Med, 2020, 41(1):1\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRen C, Li J, Zhou Y, et al. Typical tumor immune microenvironment status determine prognosis in lung adenocarcinoma[J]. Transl Oncol. 2022;18:101367.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYue P, He Y, Zuo R, et al. CCDC34 maintains stemness phenotype through beta-catenin-mediated autophagy and promotes EGFR-TKI resistance in lung adenocarcinoma[J]. Cancer Gene Ther. 2025;32(1):104\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDela Cruz MC, Yao X, Roberts N, et al. TLE1 drives EGFR-TKI resistance in EGFR-mutant lung adenocarcinoma through epithelial to mesenchymal transition[J]. Biochem Biophys Res Commun. 2025;775:152146.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang H, Wang SQ, Wang L, et al. m6A methyltransferase METTL3-induced lncRNA SNHG17 promotes lung adenocarcinoma gefitinib resistance by epigenetically repressing LATS2 expression[J]. Cell Death Dis. 2022;13(7):657.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang C, Wu Y, Shao J, et al. Clinicopathological variables influencing overall survival, recurrence and post-recurrence survival in resected stage I non-small-cell lung cancer[J]. BMC Cancer. 2020;20(1):150.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLai HC, Liu JF, Chang TM, et al. MCM4 as Potential Metastatic Biomarker in Lung Adenocarcinoma[J]. Diagnostics. 2025;15(12):3567.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDang HX, White NM, Rozycki EB, et al. Long non-coding RNA LCAL62 / LINC00261 is associated with lung adenocarcinoma prognosis[J]. Heliyon. 2020;6(3):e03521.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSouza VGP, Telkar N, Lam WL, et al. Comprehensive Analysis of Lung Adenocarcinoma and Brain Metastasis through Integrated Single-Cell Transcriptomics[J]. Int J Mol Sci. 2024;25(7):3892.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeanza L, Zoratti M, Gulbins E, et al. Mitochondrial ion channels as oncological targets[J]. Oncogene. 2014;33(49):5569\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArcangeli A. Ion channels and transporters in cancer. 3. Ion channels in the tumor cell-microenvironment cross talk[J]. Am J Physiol Cell Physiol. 2011;301(4):C762\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBecchetti A. Ion channels and transporters in cancer. 1. Ion channels and cell proliferation in cancer[J]. Am J Physiol Cell Physiol. 2011;301(2):C255\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJacobson MA, Jones LJ, Colussi DJ, et al. High-Throughput Ca(2+) Flux Assay To Monitor Cyclic Nucleotide-Gated Channel Activity and Characterize Achromatopsia Mutant Channel Function[J]. ACS Chem Neurosci. 2019;10(8):3662\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoeppen K, Reuter P, Ladewig T, et al. Dissecting the pathogenic mechanisms of mutations in the pore region of the human cone photoreceptor cyclic nucleotide-gated channel[J]. Hum Mutat. 2010;31(7):830\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReuter P, Koeppen K, Ladewig T, et al. Mutations in CNGA3 impair trafficking or function of cone cyclic nucleotide-gated channels, resulting in achromatopsia[J]. Hum Mutat. 2008;29(10):1228\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChao YC, Cheng CJ, Hsieh HT, et al. Guanylate cyclase-G, expressed in the Grueneberg ganglion olfactory subsystem, is activated by bicarbonate[J]. Biochem J. 2010;432(2):267\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCassar SC, Chen J, Zhang D, et al. Tissue specific expression of alternative splice forms of human cyclic nucleotide gated channel subunit CNGA3[J]. Mol Vis. 2004;10:808\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrimsrud MM, Forster M, Goeppert B, et al. Whole-exome sequencing reveals novel cancer genes and actionable targets in biliary tract cancers in primary sclerosing cholangitis[J]. Hepatol Commun. 2024;8(7):e02654.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHua Y, Ma X, Liu X, et al. Abnormal expression of mRNA, microRNA alteration and aberrant DNA methylation patterns in rectal adenocarcinoma[J]. PLoS ONE. 2017;12(3):e0174461.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlsen A, Harpaz Z, Ren C, et al. Identification of dual-purpose therapeutic targets implicated in aging and glioblastoma multiforme using PandaOmics - an AI-enabled biological target discovery platform[J]. Aging. 2023;15(8):2863\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWarde-Farley D, Donaldson SL, Comes O et al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function[J]. Nucleic Acids Res, 2010, 38(Web Server issue):W214\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSzklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets[J]. Nucleic Acids Res. 2019;47(D1):D607\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFranz M, Rodriguez H, Lopes C, et al. GeneMANIA update 2018[J]. Nucleic Acids Res. 2018;46(W1):W60\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBindea G, Mlecnik B, Tosolini M, et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer[J]. Immunity. 2013;39(4):782\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDai G, Peng C, Liu C, et al. Two structural components in CNGA3 support regulation of cone CNG channels by phosphoinositides[J]. J Gen Physiol. 2013;141(4):413\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanaka N, Delemotte L, Klein ML, et al. A cyclic nucleotide-gated channel mutation associated with canine daylight blindness provides insight into a role for the S2 segment tri-Asp motif in channel biogenesis[J]. PLoS ONE. 2014;9(2):e88768.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZelman AK, Dawe A, Berkowitz GA. Identification of cyclic nucleotide gated channels using regular expressions[M]//Methods in Molecular Biology. Volume 1016. Totowa: Humana; 2013. pp. 207\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun R, Wang Y, Zhou W, et al. Novel compound heterozygous CNGA3 mutation associated with retinal cone dystrophy[J]. Exp Ther Med. 2025;30(2):155.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu CH, Liu SH, Lai YL, et al. Using bioinformatics approaches to identify survival-related oncomiRs as potential targets of miRNA-based treatments for lung adenocarcinoma[J]. Comput Struct Biotechnol J. 2022;20:4626\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi Q, Yang Z, Yang H, et al. Targeting ion channels: innovative approaches to combat cancer drug resistance[J]. Theranostics. 2025;15(2):521\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou S, Song X, Zeng W, et al. Targeting Ion Channels for Cancer Therapy: From Pathophysiological Mechanisms to Clinical Translation[J]. Pharmaceuticals. 2025;18(10):568.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRen HM, Lukacher AE. IL-21 in Homeostasis of Resident Memory and Exhausted CD8 T Cells during Persistent Infection[J]. Int J Mol Sci. 2020;21(18):6792.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou T, Zhang Y, Cao J, et al. Identification of immune infiltration-related ZNF480 for predicting prognosis in breast cancer[J]. Am J Clin Exp Immunol. 2025;14(1):1\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNajafi S, Mirshafiey A. The role of T helper 17 and regulatory T cells in tumor microenvironment[J]. Immunopharmacol Immunotoxicol. 2019;41(1):16\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu X, Ye J, Hsueh EC, et al. Tumor microenvironments direct the recruitment and expansion of human Th17 cells[J]. J Immunol. 2010;184(3):1630\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong D, Li H, Li H, et al. Effect of human papillomavirus infection on the immune system and its role in the course of cervical cancer[J]. Oncol Lett. 2015;10(2):600\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsuo K, Yoshie O, Nakayama T. Multifaceted Roles of Chemokines and Chemokine Receptors in Tumor Immunity[J]. Cancers (Basel). 2021;13(23):5907.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo L, Zhu J, Guo Y, et al. Mitophagy and immune infiltration in vitiligo: evidence from bioinformatics analysis[J]. Front Immunol. 2023;14:1164124.\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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Lung adenocarcinoma, CNGA3, Immune infiltration, Prognosis, Biomarker","lastPublishedDoi":"10.21203/rs.3.rs-9214105/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9214105/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eLung adenocarcinoma (LUAD) is the most common subtype of lung cancer with poor prognosis. Cyclic nucleotide-gated channel subunit alpha 3 (CNGA3) regulates calcium transport and signal transduction, and is aberrantly expressed in multiple tumors, but its function and mechanism in LUAD remain unclear.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eBased on TCGA and GTEx databases, we used bioinformatics to analyze CNGA3 expression in LUAD. GO, KEGG, GSEA and ssGSEA were performed to explore related functions and pathways. Kaplan-Meier, univariate and multivariate Cox regression and a nomogram were used to assess its prognostic value.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCNGA3 was significantly upregulated in LUAD. It was related to ion transport, signal transduction and antitumor immune regulation, and positively correlated with Tfh and Th17 cell infiltration but negatively with activated dendritic cells in the tumor microenvironment. High CNGA3 expression predicted longer overall survival, disease-specific survival and progression-free interval, and CNGA3 was an independent prognostic factor.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eCNGA3 is involved in immune cell infiltration in LUAD tumor microenvironment and serves as a key prognostic indicator. It may be a potential prognostic biomarker and therapeutic target for LUAD.\u003c/p\u003e","manuscriptTitle":"CNGA3, a Protective Ion Channel Gene, Is Highly Expressed in Lung Adenocarcinoma and Predicts a Favorable Prognosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-01 00:41:27","doi":"10.21203/rs.3.rs-9214105/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-08T01:11:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T10:02:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"47940594154549357129452594686417620387","date":"2026-04-27T03:32:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"106957201624932275517767074334209414446","date":"2026-04-27T01:39:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"94859736790460254054455918945691799313","date":"2026-04-25T08:36:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-21T16:43:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-03T07:45:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-25T13:51:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-25T13:51:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2026-03-24T15:37:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6fb9666f-91e8-40f8-b783-07546532895c","owner":[],"postedDate":"May 1st, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-08T01:11:57+00:00","index":66,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-01T00:41:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-01 00:41:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9214105","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9214105","identity":"rs-9214105","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}