Identification of a novel nucleotide metabolism-related signature for predicting lung adenocarcinoma prognosis

Identification of a novel nucleotide metabolism-related signature for predicting lung adenocarcinoma 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 Identification of a novel nucleotide metabolism-related signature for predicting lung adenocarcinoma prognosis Yue Shi, Lin Zhao, Yali Li, Benkun Liu, Fucheng Zhou, Xue Bai, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7509527/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Lung adenocarcinoma (LUAD) accounts for 50% of lung cancer and has high mortality rate. Nucleotide metabolism exhibit crosstalk in various cancer types, which are closely associated with the progression of LUAD. The in-depth study of genes and metabolites related to nucleotide metabolism will provide new ideas for predicting the prognosis and therapeutic effect of LUAD. Methods: This study integrated transcriptomeand single-cell transcriptome datato explore the characteristics and significance of nucleotide metabolism-related genes in LUAD. We will construct a novel LUAD classifier and prognostic signature via analysis of RNA sequencing and clinical data from the TCGA and GEO databases using Cox and LASSO regression. Subsequently, we performed t-distributed Stochastic Neighbor Embedding (tSNE), estimating relative subsets of RNA transcripts (CIBERSORT), gene set enrichment analysis and other bioinformatics analyses to demonstrate correlations with clinical features, gene mutations, drug sensitivity, immune cell infiltration and the expression of immune-related factors between the stratified groups based on risk scores. Results: A total of 152 nucleotide metabolism-related genes were identified, and a prognostic signature containing 9 hub molecules was constructed. The novel signature can accurately predict LUAD prognosis and can stratify patients into high-risk and low-risk groups. Multivariate analysis indicated that the risk score is an independent prognostic factor. Functional enrichment analysis revealed that the biological functions of signature moleculeswere associated with the cellular metabolic microenvironment. Our results revealed that patients in the high-risk group had a worse prognosis, less sensitivity to chemotherapy and greater proportion of TP53 gene mutations. Then, 22 cell clusters falling within 7 cellular categories were identified from LUAD tissue. Macrophages and immune-related factor scores of cytokines and failure factors were discerned to be significantly greater in the high-risk group than low-risk group. Conclusion: This study indicated that nucleotide metabolism was correlated with LUAD progression, immunosuppression and treatment sensitivity. The developed signature can serve as a potent tailored prognostic prediction model for patients. lung adenocarcinoma (LUAD) nucleotide metabolism prognostic signature immune infiltration drug sensitivity immunotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Lung cancer is one of the most common malignant tumors in the world, and its mortality rate ranks first among cancers; it accounts for approximately 21% of all cancer-related deaths[ 1 , 2 ]. Non-small cell lung cancer (NSCLC) is the most common subtype of lung cancer and accounts for approximately 85% of all lung cancers. NSCLC can be histologically divided into lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) and large cell carcinoma, among which LUAD is the most common histological subtype[ 3 – 5 ]. Currently, the principal treatment modalities for lung cancer include surgery, chemotherapy, radiotherapy, targeted therapy and immunotherapy, but each method has its limitations[ 6 – 8 ]. Owing to the high malignant potential of lung cancer, the five-year survival rates are approximately 14–49% for stages I to IIIA and < 5% for stages IIIB to IV[ 3 ]. Cancer immunotherapy has emerged as a promising cancer treatment, but only a fraction of patients responds to treatment[ 9 – 11 ]. Therefore, the development and verification of novel markers for predicting the clinical prognosis of LUAD at an early stage are urgently needed to improve the survival rate of patients. Nucleotides are the basic building block of organisms essential raw materials for producing nucleic acids to sustain cell proliferation[ 12 , 13 ]. Nucleotide metabolism is in a state of dynamic equilibrium, which is important for maintaining the normal physiological functions of cells[ 14 , 15 ]. Recently, researchers reported that nucleotide metabolism constitutes the final and most crucial link in the chain of events that contribute to the spread of cancer[ 14 , 16 – 18 ]. To achieve uncontrolled cell proliferation, tumor cells use the nucleotide metabolism pathway to synthesize DNA and RNA[ 19 ]. Recent research has demonstrated that aberrant nucleotide metabolism can dampen the normal immune response in the tumor microenvironment (TME). Targeting nucleotide metabolism also represents a new direction for the development of novel antitumor-specific drugs[ 20 – 22 ]. Therefore, focusing on the reprogramming of nucleotide metabolism will provide new ideas for predicting prognostic outcomes in LUAD patients. Moreover, the clinical relevance of nucleotide metabolism-related genes in predicting outcomes and guiding the application of chemotherapeutic strategies for LUAD patients remains unknown. Thus, exploring the characteristics and interactions of nucleotide metabolism-related genes in LUAD is important. In the present study, we downloaded transcriptome profiles, single-cell transcriptome data and relevant clinical information for LUAD patients from the TCGA and GEO databases. We integrated the transcriptome profiles and performed screening of key molecules related to nucleotide metabolism in LUAD. Next, we employed univariate and multivariate Cox analysis and least absolute shrinkage and selection operator (LASSO) regression to construct a novel signature that included 9 hub molecules, namely SEMA3C, PTTG1, BARX1, CDCA5, TGFBI, MKI67, TSPAN7, GADD45G and IRX5, in the training cohort. The test cohort was used to further evaluate the predictive power of the signature. Subsequently, the signature was confirmed to have commendable effectiveness in forecasting the clinical attributes, TME and chemotherapeutic drug responsiveness. High-risk group patients experienced poorer survival times compared to low-risk group patients. This novel nucleotide metabolism-related signature was demonstrated to have great potential as a biomarker for predicting prognosis and offering a guideline for individual clinical intervention in LUAD patients. 2. Materials and methods 2.1 Data collection and processing The transcriptomic data and relevant clinical data related to LUAD patients were downloaded from the TCGA database ( https://portal.gdc.cancer.gov/projects/TCGA-LUAD ) and the GEO database (GSE30219, https://www.ncbi.nlm.nih.gov/gds/?term= ). We also obtained single-cell transcriptome data from the GEO database (accession number GSE149655) on 4 samples: 2 primary LUAD samples and 2 normal tissue samples. 2.2 Cluster analysis of nucleotide metabolism-related genes Nucleotide metabolism-related genes (relevance score > 20) were obtained from the GeneCards database ( https://www.genecards.org ). The “limma” package was used to identify the differentially expressed nucleotide metabolism-related genes between LUAD and normal tissues ( P value threshold of 1.5). The prognostic significance of these genes was validated via Kaplan‒Meier (K-M) analysis of 522 LUAD samples. Next, a consensus clustering procedure was conducted to determine the number and stability of each individual cluster. The clustering effect indicated that the clustering stability improved when k = 3. The samples were split into 3 distinct molecular subtypes, C1, C2 and C3. Afterward, the intersection of the 3 sets of genes was taken to obtain 152 nucleotide metabolism-related genes for subsequent analysis, as shown by the Venn diagram. 2.3 Functional enrichment analysis To investigate gene functions in each gene cluster, we used Metascape ( http://metascape.org/gp/index.html#/main/step1 ) to perform Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and gene ontology (GO) functional enrichment analyses. Additionally, gene set enrichment analysis (GSEA) was performed to assess related pathways and molecular mechanisms between low-risk and high-risk groups of LUAD patients. 2.4 Establishment of a nucleotide metabolism-related signature To construct the prognostic nucleotide metabolism-related signature, a univariate Cox regression model was used to identify the genes whose expression levels were significantly correlated with survival outcome. LASSO analysis was employed to select reliable predictors. The risk score of each patient was assessed via the formula risk score where n is the number of genes in the signature, Expi is the expression level of each gene, and Ci is the corresponding coefficient. K-M curves were used to evaluate the differences in overall survival (OS) between the two groups. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to evaluate the accuracy of the signature in predicting prognosis. We used the same method to validate the predictive power of the signature in the validation and GEO datasets. 2.5 Immune cell infiltration analysis CIBERSORT is a method based on the input matrix of a gene expression file to accurately estimate the relative proportions of various immune cell subtypes in tissues. Here, we used CIBERSORT analysis to assess differences in the infiltration levels of various immune cells in distinct groups. 2.6 Relationships between the prognostic signature risk score and clinical features To facilitate clinical application and provide a more convenient tool for predicting the prognosis of LUAD, we integrated gene expression data and clinical features, including age, sex, and stage, to construct a nomogram. Furthermore, a calibration curve was plotted to assess the agreement between the actual and predicted values, indicating the optimal prediction. 2.7 Single-cell transcriptome and cell‒cell crosstalk network analysis The quality control and preprocessing of the scRNA-seq data were performed via the Seurat package. The NormalizeData package was subsequently applied for data normalization. tSNE was used for unsupervised clustering and unbiased visualization of cell subpopulations on a two-dimensional map. The FindAllMarkers package was used to compare the differences in gene expression between a cluster and all other clusters. Additionally, we used the CellChat package, which is a tool for quantitatively inferring and analyzing cell-to-cell communication networks from scRNA-Seq data, to investigate the molecular interaction networks between different cell types. 2.8 Statistical analysis All the statistical analyses were conducted via R version 4.2. K-M survival curves were used for survival analysis. Unless otherwise noted, a P value < 0.05 was considered to indicate statistical significance. 3. Results 3.1 Construction of molecular subtypes and exploration of differentially expressed nucleotide metabolism-related genes associated with LUAD We extracted gene sets related to nucleotide metabolism with a relevance score > 20 using the GeneCards database ( https://www.genecards.org/ ). Next, we performed consensus clustering of these genes, with the optimal number of clusters determined on the basis of cumulative distribution function (CDF) analysis. The CDF delta area curve indicated that the clustering result was more stable when a cluster number of 3 was used (Fig. 1 A). The clustering heatmap of the three clusters was shown in Fig. 1 B. K-M survival analysis revealed a significant correlation between the three subtypes, and the C3 subtype was associated with the worst prognosis (Fig. 1 C). Then, we intersected with these differential genes, yielding 152 intersecting genes, as shown in the Venn diagram (Fig. 1 D). The detailed differentially expressed genes between subtypes were shown in Supplementary Figure S1 and Table S1 . 3.2 Construction of the nucleotide metabolism-related prognostic signature To investigate the functions of these different genes, we performed pathway enrichment analysis of these 152 differentially expressed genes in the TCGA database via the GO and KEGG methods. The result revealed that these different genes were predominantly enriched in extracellular matrix, response to nutrients and peptidase regulator activity signaling pathways (Fig. 2 A and B). Subsequently, the univariate Cox regression analysis identified 26 prognostically significant genes with a P value < 0.05 (Fig. 2 C and Supplementary Table S2 ). To further identify key molecules within the prognostic gene set, we employed a LASSO penalty with multivariate Cox regression analysis and a total of 9 hub molecules were identified (Fig. 2 D-F and Supplementary Table S3 ). Based on their correlation coefficients, a nucleotide metabolism-related genes-based risk scores were generated: Risk Score = IRX5×(-0.128648463) + GADD45G×(-0.077826735) + TSPAN7×(-0.073307189) + MKI67×0.000163894 + TGFBI×0.02372854 + CDCA5×0.035798743 + BARX1×0.089405586 + PTTG1×0.122970952 + SEMA3C×0.135467087. Then, the patients from TCGA database were randomly assigned to a training dataset and a validation dataset at a ratio of 4:1. The signature significantly stratified patients into low- and high-risk groups, with survival outcomes analyzed using K-M curves. The OS of high-risk group was significantly lower than the low-risk group in both the training and validation datasets (Fig. 3 A and B). Furthermore, the ROC curve confirmed that the nomogram performed well in predicting the prognosis of LUAD in both the training and validation datasets (Fig. 3 C and D). To validate the robustness of the novel prognostic signature, we tested it in the external validation dataset GSE30219. The results indicated that this novel signature had a strong predictive performance and good stability (Fig. 3 E and F). 3.3 Incidence risk and independent prognostic analysis Via univariate and multivariate Cox analyses, the risk score was confirmed to serve as an independent prognostic factor (hazard ratio = 4.222, P < 0.001) (Fig. 4 A-B and Supplementary Table S4 ). The results were presented in a nomogram for model visualization (Fig. 4 C). To evaluate its validation and accuracy, we generated a calibration curve to compare the predictions and actual observations and the predictive analysis revealed good prediction performance for 3-year and 5-year OS (Fig. 4 D). 3.4 Estimation of drug sensitivity and molecular pathway for patients with the risk signature Given that surgical treatment combined with chemotherapy was still effective for early- stage LUAD[ 23 ]. we employed the pRRophetic package to compare drug sensitivity between LUAD patients in the high- and low-risk groups. Significant variations in half-maximal inhibitory concentration (IC50) values were found between the high- and low-risk groups, suggesting that the low-risk group is more sensitive to AKT inhibitor VIII, Cisplatin, Dasatinib, Gefitinib and Gemcitabine (Fig. 5 A-E). Subsequently, to investigate the potential molecular mechanisms of this signature, we performed enrichment analysis using the HALLMARK gene set via the GSEA method. The enrichment analysis revealed that ALPHA LINOLENIC ACID METABOLISM, ARACHIDONIC ACID METABOLISM, FATTY ACID METABOLISM and LINOLEIC ACID METABOLISM signaling pathways were significantly enriched in the high-risk group. While CELL CYCLE and UBIQUITIN MEDIATED PROTEOLYSIS pathways were enriched in the low-risk group (Fig. 5 F). 3.5 The mutational landscape and immune features for patients with the risk signature The mutational landscape for patients within high-risk and low-risk groups was further explored. A heatmap was generated to visualize the main mutation sites and ratios between different groups. It indicated that patients in the high-risk group exhibited a significantly higher mutation ratio for genes such as TP53 compared to the low-risk group (Fig. 6 A). On the other hand, the TME plays an important role in various stages of tumor generation, metastasis, and evasion of immune monitoring and treatment. The TME primarily comprises cancer-associated fibroblasts (CAFs), extracellular matrix (ECM), tumor blood vessels and non-tumor cells[ 24 ]. We further calculated the proportions of different immune cells among the LUAD patients in both groups, the results of which are shown in Fig. 6 B. Then, we investigated the relationship between immune infiltration and risk signature in TME. As results, CD4 memory T cells, M0 macrophages and M1 macrophages were significant increase in the high-risk group. Conversely, a significant decrease was obtained for B cells naïve, Plasma cells and NK cells activated in the high-risk group (Fig. 6 C). 3.6 Single-cell analysis for the immune landscape in the LUAD Afterward, using the tSNE algorithm 22 cell clusters were identified and visualized in Fig. 7 A. Using the “singleR” algorithm to annotate cell subpopulations, we labeled these identified clusters as 7 cellular categories, such as epithelial cells, endothelial cells, T cells, macrophages, tissue stem cells, monocytes and B cells (Fig. 7 B). The expression profiles of selected key molecules across these cell types are present in Fig. 7 C and Figure S2 . To clarify the underlying intercellular communications, we analyzed the intercellular communication network from the scRNA-seq data via the “CellChat” algorithm. We detected many significant ligand‒receptor pairs among the 7 cell types. In the high-risk group, macrophages were found to have the most potential interactions with other cells, as epithelial cells in the low-risk group (Fig. 7 D and E, Supplementary Fig. 3A and B). Following this, we measured cytokine and exhaustion factor scores using the GeneCards database and compared it between groups. The results showed that elevated scores of cytokine and exhaustion scores were detected in the high-risk group (Fig. 7 F and G). Meanwhile, the associations of signature hub molecules and the scores were analyzed and depicted in the Supplementary Figure S4 A and B. 4. Discussion Lung cancer is extremely aggressive and can be divided into small cell lung cancer and non-small cell lung cancer; LUAD represents the most common histological subtype of NSCLC, so exploring effective prognostic indicators for this disease is clinically important[ 25 , 26 ]. With the advancements in sequencing technology, the traditional prognostic assessment system has failed to facilitate precision medicine[ 27 , 28 ]. There is an urgent need to identify sensitive and specific biomarkers to help with clinical diagnosis[ 29 , 30 ]. Recent research has demonstrated that metabolic aberration is an important feature of tumors and that aberrant nucleotide metabolism can suppress the TME immune response to promote the progression of tumors[ 31 – 33 ]. In addition, the altered metabolic environment can also affect the traditional therapeutic and immune responses of tumors and may cause immune escape[ 34 – 36 ]. Therefore, an in-depth study of the potential value of metabolism-related genes in cancer is of clinical significance. In our study, we analyzed the transcriptome sequencing data of LUAD patients via a bioinformatics approach. Potential prognostic genes were mined, and a novel prognostic signature was constructed. The novel signature consisted of 9 hub molecules, including SEMA3C, PTTG1, BARX1, CDCA5, TGFBI, MKI67, TSPAN7, GADD45G and IRX5. Several hub molecules have been proven to be associated with the development and progression of LUAD. For example, PTTG1 was one type of DNA repair-related gene and might promote progression of LUAD via the P53 signaling pathway[ 37 ]. BARX1 could repressed FOXF1 expression and activated Wnt/β-catenin signaling pathway to drive lung adenocarcinoma[ 38 ]. CDCA5 regulated the cell cycle of NSCLC cells by mediating the P53-P21 signaling pathway, participating in the development and progression of NSCLC patients[ 39 ]. These studies highlight the significance of these molecules in LUAD and their potential as prognostic factors. Furthermore, the risk signature has been proven to exhibit good prediction efficiency in both TCGA and GEO cohorts. We found that integrating these 9 molecules into one parameter could significantly improve the accuracy of prognosis prediction. The risk score calculated according to this signature could distinguish patients well according to the prognosis of LUAD. Chemotherapy remains the primary and effective treatment for LUAD patients[ 40 , 41 ]. However, the efficacy of chemotherapy is variable. We then assessed the sensitivity of patients stratified into two groups according to the novel signature risk score to commonly used chemotherapeutic agents. We found that the low-risk group might benefit more from AKT inhibitor VIII, Cisplatin, Dasatinib, Gefitinib and Gemcitabine treatment than the high-risk group. These findings demonstrated that this signature may be helpful in improving the accuracy of individualized treatment. To further investigate the potential molecular mechanisms, we employed GSEA analysis. The pathways of ALPHA LINOLENIC ACID METABOLISM, ARACHIDONIC ACID METABOLISM, FATTY ACID METABOLISM and LINOLEIC ACID METABOLISM were notably enriched in the high-risk group, as CELL CYCLE and UBIQUITIN MEDIATED PROTEOLYSIS in the low-risk group. The current studies revealed that these pathways play crucial roles in the development and metastasis of LUAD[ 42 – 46 ]. In addition, the TME plays a crucial role in the antitumor response and can significantly affect patient prognosis[ 47 – 49 ]. We investigated the relationship between the signature and the TME. We observed a significant increase in the infiltration of CD4 memory T cells, M0 macrophages and M1 macrophages in the high-risk group, as well as a decrease in B cells naïve, Plasma cells and NK cells. This pattern suggests an overall trend toward immune suppression. On the other hand, single-cell analysis indicated that strong macrophage communication in the high-risk group, suggesting that these patients may be in a relatively active state of antitumor immune response. Meanwhile elevated scores of cytokines and exhaustion factor scores were detected, which is related to tumor progression, abnormal activation of the immune system and inflammatory responses[ 50 ]. However, there are some limitations to our study: all the RNA sequencing data and clinical information were obtained from public databases, such as the TCGA and GEO databases. We plan to perform prospective and genetic functional research in the future to further verify the predictive significance of this nucleotide metabolism-related signature. 5. Conclusion In summary, by integrating bulk RNA-seq and scRNA-seq data, we constructed a novel nucleotide metabolism-related signature that accurately predicted the survival outcome and clinical response of LUAD patients. Our signature is an efficient prognostic indicator that can offer novel insights for clinical decision making and facilitate individualized treatment for LUAD patients. Abbreviations LUAD Lung Adenocarcinoma LUSC Lung Squamous Cell Carcinoma tSNE t-distributed Stochastic Neighbor Embedding TME Tumor Microenvironment LASSO Least Absolute Shrinkage and Selection Operator KEGG Kyoto Encyclopedia of Genes and Genomes GO Gene Ontology GSEA Gene Set Enrichment Analysis OS Overall Survival ROC Receiver Operating Characteristic AUC Area Under the Curve CDF Cumulative Distribution Function CAFs Cancer-associated Fibroblasts ECM Extracellular Matrix. Declarations Ethics approval and consent to participate: The ethical approval did not refer to this study and this study did not need the informed consent. Consent for publication: Not applicable. Availability of data and material: All data generated or analyzed during this study are included in this published article and its supplementary information files. Competing interests: The authors declare no potential conflicts of interest. Funding: This study was supported by National Natural Science Foundation of China (Grant Number 82303616), Postdoctoral Science Foundation of China (Grant Number 2021MD703830), Natural Science Foundation of Heilongjiang Province (Grant Number PL2024H171), Haiyan Science Foundation of Harbin Medical University Cancer Hospital (Grant Number JJYQ2024-07), Climbing Fund of Harbin Medical University Cancer Hospital (PDTS2024A-05), and Individualized and precise treatment of lung cancer (Nn10py2017-04). Authors' contributions: Conception and design: Jian Zhang and Xue Bai. Data acquisition, assembly and experiments: Yue Shi, Lin Zhao and Yali Li. Data analyse and interpretation: Benkun Liu and Fucheng Zhou. Writing-original draft preparation: Yue Shi. Acknowledgements: Not applicable. References Siegel, R.L., et al., Cancer statistics, 2022. CA Cancer J Clin, 2022. 72 (1): p. 7-33. 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Ann Oncol, 2016. 27 (8): p. 1482-92. Jin, H., et al., A Novel Lipid Metabolism and Endoplasmic Reticulum Stress-Related Risk Model for Predicting Immune Infiltration and Prognosis in Colorectal Cancer. Int J Mol Sci, 2023. 24 (18). Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigureS1.tiff Supplementary Figure S1: (A) Heatmap showing 726 genes differentially expressed between the C1 and C2 subtypes. (B) Heatmap showing 726 genes differentially expressed between the C2 and C3 subtypes. (C) Heatmap showing 2315 differentially expressed genes between the C1 and C3 subtypes. SupplementaryFigureS2.tiff Supplementary Figure S2: The hub molecule expressions in the 7 cell types were examined. SupplementaryFigureS3.tiff Supplementary Figure S3: (A-B) Intercellular ligand‒receptor prediction in the high-risk and low-risk groups. Ligand–receptor pairs are linked with solid lines. SupplementaryFigureS4.tiff Supplementary Figure S4: (A) The expression of hub molecules (including PTTG1, TGFBI, MKI67, TSPAN7 and IRX5) with cytokine scores in the single-cell dataset for LUAD. (B) The expression of hub molecules (including SEMA3C, PTTG1, TGFBI and TSPAN7) with exhaustion factor scores in the single-cell dataset for LUAD. SupplementaryTableS1.xlsx Supplementary Table S1: The detail differentially expressed nucleotide metabolism-related genes among the C1, C2 and C3 subtypes. SupplementaryTableS2.xlsx Supplementary Table S2: The detail information of 26 prognostically significant genes. SupplementaryTableS3.xlsx Supplementary Table S3: The detail information of 9 hub molecules in the signature. SupplementaryTableS4.xlsx Supplementary Table S4: The table of univariate and multivariate Cox regression analyses. 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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-7509527","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":524578332,"identity":"705b3443-6f6d-42f4-883e-e38c75024a49","order_by":0,"name":"Yue Shi","email":"","orcid":"","institution":"Harbin Medical University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Shi","suffix":""},{"id":524578333,"identity":"67f144eb-999d-4e79-aba2-c87d6a334e36","order_by":1,"name":"Lin Zhao","email":"","orcid":"","institution":"Harbin Medical University Cancer 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The delta area curves show that the clustering results were relatively stable when the number of clusters was 3. (B) The sample clustering heatmap with a consensus number=3. (C) Kaplan‒Meier survival analysis of the 3 subtypes in the LUAD cohort from the TCGA database. (D) Venn diagram depicting the intersection of 152 genes among the C1, C2 and C3 subtypes.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7509527/v1/806330698a6c346db6453cee.png"},{"id":92859230,"identity":"0e7fad6a-d157-4727-a582-3fd2118d3dbd","added_by":"auto","created_at":"2025-10-06 12:00:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3060593,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of the nucleotide metabolism-related prognostic signature for LUAD patients.\u003c/p\u003e\n\u003cp\u003e(A-B) GO and KEGG enrichment analyses of the candidate nucleotide metabolism-related genes. (C) Univariate Cox regression analysis of the nucleotide metabolism-related genes associated with overall survival (OS) in LUAD patients is shown in forest plots of hazard ratios. (D-E) LASSO regression analysis of OS- and nucleotide metabolism-related prognostic events. (F) The coefficient profile plot of 9 hub prognostic molecules included in the signature (including SEMA3C, PTTG1, BARX1, CDCA5, TGFBI, MKI67, TSPAN7, GADD45G and IRX5).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7509527/v1/a952bab2e4a21013dfc01f3a.png"},{"id":92859232,"identity":"71bf3bd5-335f-440d-a124-bc0d09419819","added_by":"auto","created_at":"2025-10-06 12:00:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1946585,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation of the prognostic signature in predicting LUAD patients’ survival.\u003c/p\u003e\n\u003cp\u003e(A-F) Kaplan‒Meier curves for patients, as well as the time-dependent ROC curves for the nomogram at different time points in the TCGA training, TCGA validation and external GSE30219 validation datasets.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7509527/v1/e0a2e312583dff760c52585b.png"},{"id":92860201,"identity":"508c2d2b-492f-459c-80ec-544f9a88d7c7","added_by":"auto","created_at":"2025-10-06 12:08:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1467016,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction and evaluation of the predictive nomogram for LUAD patients.\u003c/p\u003e\n\u003cp\u003e(A-B) Univariate and multivariate Cox proportional hazard regression analyses of nucleotide metabolism-related signatures and clinical characteristics. (C) Construction of the nomogram. (D) Time‒dependent ROC curves for the nomogram at different time points.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7509527/v1/d5af83a375ed194ab408ee47.png"},{"id":92859239,"identity":"6e935199-c784-42d0-bf0c-1493a5a44028","added_by":"auto","created_at":"2025-10-06 12:00:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2386314,"visible":true,"origin":"","legend":"\u003cp\u003eDrug sensitivity and molecular pathway for the risk signature.\u003c/p\u003e\n\u003cp\u003e(A-E) Chemotherapeutic agents with the greatest sensitivity in patients in the high- and low-risk groups in the TCGA cohort. (F) GSEA analysis based on the KEGG pathway revealed biological differences between the high- and low-risk groups.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7509527/v1/c935d3b992fb90aec64b52ce.png"},{"id":92860211,"identity":"296d8714-2259-441a-a209-8fd41eb179f3","added_by":"auto","created_at":"2025-10-06 12:08:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":4494571,"visible":true,"origin":"","legend":"\u003cp\u003eThe mutational and immune landscapes of patients with the risk signature\u003c/p\u003e\n\u003cp\u003e(A) The mutation frequency of genes in the high- and low-risk groups from TCGA dataset. (B) Proportion of immune cells in LUAD patients in the high- and low-risk groups. (C) A thorough analysis revealed variations in the expression levels of diverse immune cells across the high- and low-risk groups. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; **\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01; ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7509527/v1/a4e8cd9b155da02ecf6c52e9.png"},{"id":92860212,"identity":"f8c27d59-8636-444a-a1cb-05720ab57631","added_by":"auto","created_at":"2025-10-06 12:08:50","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":4570381,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the immune landscape of LUAD based on single-cell transcriptional profiles. (A) 22 cell clusters were visualized via the tSNE algorithm. (B) Cell subpopulations were visualized on the basis of marker genes. (C) Dot plot depicting the expression of 9 hub molecules among the 7 identified cell subpopulations. (D-E) Analysis of the number of interactions and interaction strength among different cell types in the high-risk and low-risk groups. (F-G) The cytokine and exhaustion factor scores were compared between the high-risk and low-risk groups. ****\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.0001\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-7509527/v1/beb4cbe89fe602dee85da402.png"},{"id":95228028,"identity":"6cae7244-76a9-49bf-bc04-3d72bb238753","added_by":"auto","created_at":"2025-11-05 16:33:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":18422300,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7509527/v1/47ca51e4-4157-40c1-a488-8972d1753313.pdf"},{"id":92860921,"identity":"20d04235-ff4c-4c88-a037-378c8fcb60ef","added_by":"auto","created_at":"2025-10-06 12:16:50","extension":"tiff","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2959890,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Figure S1: (A) Heatmap showing 726 genes differentially expressed between the C1 and C2 subtypes. (B) Heatmap showing 726 genes differentially expressed between the C2 and C3 subtypes. (C) Heatmap showing 2315 differentially expressed genes between the C1 and C3 subtypes.\u003c/p\u003e","description":"","filename":"SupplementaryFigureS1.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7509527/v1/4d1203f303327748a615b115.tiff"},{"id":92860202,"identity":"5d61a3fe-a845-4072-8a90-a19d2396174c","added_by":"auto","created_at":"2025-10-06 12:08:50","extension":"tiff","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1675000,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Figure S2: The hub molecule expressions in the 7 cell types were examined.\u003c/p\u003e","description":"","filename":"SupplementaryFigureS2.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7509527/v1/b77ab102d7f0dc1cc956e170.tiff"},{"id":92859235,"identity":"92917e88-5202-442d-b1a5-31a54435b1f8","added_by":"auto","created_at":"2025-10-06 12:00:50","extension":"tiff","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1759512,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Figure S3: (A-B) Intercellular ligand‒receptor prediction in the high-risk and low-risk groups. Ligand–receptor pairs are linked with solid lines.\u003c/p\u003e","description":"","filename":"SupplementaryFigureS3.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7509527/v1/a051b56b56564f3cc77bcdfb.tiff"},{"id":92860922,"identity":"1b297ffa-ca50-4ba4-8bb2-995fd7e99452","added_by":"auto","created_at":"2025-10-06 12:16:51","extension":"tiff","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":2436106,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Figure S4: (A) The expression of hub molecules (including PTTG1, TGFBI, MKI67, TSPAN7 and IRX5) with cytokine scores in the single-cell dataset for LUAD. (B) The expression of hub molecules (including SEMA3C, PTTG1, TGFBI and TSPAN7) with exhaustion factor scores in the single-cell dataset for LUAD.\u003c/p\u003e","description":"","filename":"SupplementaryFigureS4.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7509527/v1/20f2e3ad67c638b6145c6281.tiff"},{"id":92859245,"identity":"ba80af35-64b6-47fe-9fbb-4676397008cb","added_by":"auto","created_at":"2025-10-06 12:00:50","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":285056,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table S1: The detail differentially expressed nucleotide metabolism-related genes among the C1, C2 and C3 subtypes.\u003c/p\u003e","description":"","filename":"SupplementaryTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7509527/v1/2fb7f1b3a34cdc8c008ad563.xlsx"},{"id":92860213,"identity":"cb08bb95-6d1d-46b1-83ac-5232f975b873","added_by":"auto","created_at":"2025-10-06 12:08:50","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":12598,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table S2: The detail information of 26 prognostically significant genes.\u003c/p\u003e","description":"","filename":"SupplementaryTableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7509527/v1/d88ead0029bf52158f916086.xlsx"},{"id":92859237,"identity":"5dcda534-a3d5-4c6c-a4f2-f7e636066419","added_by":"auto","created_at":"2025-10-06 12:00:50","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":10978,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table S3: The detail information of 9 hub molecules in the signature.\u003c/p\u003e","description":"","filename":"SupplementaryTableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7509527/v1/c60e14ed1267a50c27b4c0db.xlsx"},{"id":92860923,"identity":"9785da6e-224f-4fc7-8604-918295e59b3b","added_by":"auto","created_at":"2025-10-06 12:16:51","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":12127,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table S4: The table of univariate and multivariate Cox regression analyses.\u003c/p\u003e","description":"","filename":"SupplementaryTableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7509527/v1/b82d39771b160de3c4d16b69.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of a novel nucleotide metabolism-related signature for predicting lung adenocarcinoma prognosis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLung cancer is one of the most common malignant tumors in the world, and its mortality rate ranks first among cancers; it accounts for approximately 21% of all cancer-related deaths[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Non-small cell lung cancer (NSCLC) is the most common subtype of lung cancer and accounts for approximately 85% of all lung cancers. NSCLC can be histologically divided into lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) and large cell carcinoma, among which LUAD is the most common histological subtype[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Currently, the principal treatment modalities for lung cancer include surgery, chemotherapy, radiotherapy, targeted therapy and immunotherapy, but each method has its limitations[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Owing to the high malignant potential of lung cancer, the five-year survival rates are approximately 14\u0026ndash;49% for stages I to IIIA and \u0026lt;\u0026thinsp;5% for stages IIIB to IV[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Cancer immunotherapy has emerged as a promising cancer treatment, but only a fraction of patients responds to treatment[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, the development and verification of novel markers for predicting the clinical prognosis of LUAD at an early stage are urgently needed to improve the survival rate of patients.\u003c/p\u003e\u003cp\u003eNucleotides are the basic building block of organisms essential raw materials for producing nucleic acids to sustain cell proliferation[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Nucleotide metabolism is in a state of dynamic equilibrium, which is important for maintaining the normal physiological functions of cells[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Recently, researchers reported that nucleotide metabolism constitutes the final and most crucial link in the chain of events that contribute to the spread of cancer[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. To achieve uncontrolled cell proliferation, tumor cells use the nucleotide metabolism pathway to synthesize DNA and RNA[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Recent research has demonstrated that aberrant nucleotide metabolism can dampen the normal immune response in the tumor microenvironment (TME). Targeting nucleotide metabolism also represents a new direction for the development of novel antitumor-specific drugs[\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Therefore, focusing on the reprogramming of nucleotide metabolism will provide new ideas for predicting prognostic outcomes in LUAD patients. Moreover, the clinical relevance of nucleotide metabolism-related genes in predicting outcomes and guiding the application of chemotherapeutic strategies for LUAD patients remains unknown. Thus, exploring the characteristics and interactions of nucleotide metabolism-related genes in LUAD is important.\u003c/p\u003e\u003cp\u003eIn the present study, we downloaded transcriptome profiles, single-cell transcriptome data and relevant clinical information for LUAD patients from the TCGA and GEO databases. We integrated the transcriptome profiles and performed screening of key molecules related to nucleotide metabolism in LUAD. Next, we employed univariate and multivariate Cox analysis and least absolute shrinkage and selection operator (LASSO) regression to construct a novel signature that included 9 hub molecules, namely SEMA3C, PTTG1, BARX1, CDCA5, TGFBI, MKI67, TSPAN7, GADD45G and IRX5, in the training cohort. The test cohort was used to further evaluate the predictive power of the signature. Subsequently, the signature was confirmed to have commendable effectiveness in forecasting the clinical attributes, TME and chemotherapeutic drug responsiveness. High-risk group patients experienced poorer survival times compared to low-risk group patients. This novel nucleotide metabolism-related signature was demonstrated to have great potential as a biomarker for predicting prognosis and offering a guideline for individual clinical intervention in LUAD patients.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Data collection and processing\u003c/h2\u003e\n \u003cp\u003eThe transcriptomic data and relevant clinical data related to LUAD patients were downloaded from the TCGA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/projects/TCGA-LUAD\u003c/span\u003e\u003c/span\u003e) and the GEO database (GSE30219, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/gds/?term=\u003c/span\u003e\u003c/span\u003e). We also obtained single-cell transcriptome data from the GEO database (accession number GSE149655) on 4 samples: 2 primary LUAD samples and 2 normal tissue samples.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Cluster analysis of nucleotide metabolism-related genes\u003c/h2\u003e\n \u003cp\u003eNucleotide metabolism-related genes (relevance score\u0026thinsp;\u0026gt;\u0026thinsp;20) were obtained from the GeneCards database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org\u003c/span\u003e\u003c/span\u003e). The \u0026ldquo;limma\u0026rdquo; package was used to identify the differentially expressed nucleotide metabolism-related genes between LUAD and normal tissues (\u003cem\u003eP\u003c/em\u003e value threshold of \u0026lt;\u0026thinsp;0.05, fold change\u0026thinsp;\u0026gt;\u0026thinsp;1.5). The prognostic significance of these genes was validated via Kaplan‒Meier (K-M) analysis of 522 LUAD samples. Next, a consensus clustering procedure was conducted to determine the number and stability of each individual cluster. The clustering effect indicated that the clustering stability improved when k\u0026thinsp;=\u0026thinsp;3. The samples were split into 3 distinct molecular subtypes, C1, C2 and C3. Afterward, the intersection of the 3 sets of genes was taken to obtain 152 nucleotide metabolism-related genes for subsequent analysis, as shown by the Venn diagram.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Functional enrichment analysis\u003c/h2\u003e\n \u003cp\u003eTo investigate gene functions in each gene cluster, we used Metascape (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://metascape.org/gp/index.html#/main/step1\u003c/span\u003e\u003c/span\u003e) to perform Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and gene ontology (GO) functional enrichment analyses. Additionally, gene set enrichment analysis (GSEA) was performed to assess related pathways and molecular mechanisms between low-risk and high-risk groups of LUAD patients.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Establishment of a nucleotide metabolism-related signature\u003c/h2\u003e\n \u003cp\u003eTo construct the prognostic nucleotide metabolism-related signature, a univariate Cox regression model was used to identify the genes whose expression levels were significantly correlated with survival outcome. LASSO analysis was employed to select reliable predictors. The risk score of each patient was assessed via the formula risk score \u003cimg src=\"data:image/png;base64,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\"\u003e\u0026nbsp;where n is the number of genes in the signature, Expi is the expression level of each gene, and Ci is the corresponding coefficient. K-M curves were used to evaluate the differences in overall survival (OS) between the two groups. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to evaluate the accuracy of the signature in predicting prognosis. We used the same method to validate the predictive power of the signature in the validation and GEO datasets.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Immune cell infiltration analysis\u003c/h2\u003e\n \u003cp\u003eCIBERSORT is a method based on the input matrix of a gene expression file to accurately estimate the relative proportions of various immune cell subtypes in tissues. Here, we used CIBERSORT analysis to assess differences in the infiltration levels of various immune cells in distinct groups.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6 Relationships between the prognostic signature risk score and clinical features\u003c/h2\u003e\n \u003cp\u003eTo facilitate clinical application and provide a more convenient tool for predicting the prognosis of LUAD, we integrated gene expression data and clinical features, including age, sex, and stage, to construct a nomogram. Furthermore, a calibration curve was plotted to assess the agreement between the actual and predicted values, indicating the optimal prediction.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.7 Single-cell transcriptome and cell‒cell crosstalk network analysis\u003c/h2\u003e\n \u003cp\u003eThe quality control and preprocessing of the scRNA-seq data were performed via the Seurat package. The NormalizeData package was subsequently applied for data normalization. tSNE was used for unsupervised clustering and unbiased visualization of cell subpopulations on a two-dimensional map. The FindAllMarkers package was used to compare the differences in gene expression between a cluster and all other clusters. Additionally, we used the CellChat package, which is a tool for quantitatively inferring and analyzing cell-to-cell communication networks from scRNA-Seq data, to investigate the molecular interaction networks between different cell types.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e2.8 Statistical analysis\u003c/h2\u003e\n \u003cp\u003eAll the statistical analyses were conducted via R version 4.2. K-M survival curves were used for survival analysis. Unless otherwise noted, a \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to indicate statistical significance.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Construction of molecular subtypes and exploration of differentially expressed nucleotide metabolism-related genes associated with LUAD\u003c/h2\u003e\u003cp\u003eWe extracted gene sets related to nucleotide metabolism with a relevance score\u0026thinsp;\u0026gt;\u0026thinsp;20 using the GeneCards database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Next, we performed consensus clustering of these genes, with the optimal number of clusters determined on the basis of cumulative distribution function (CDF) analysis. The CDF delta area curve indicated that the clustering result was more stable when a cluster number of 3 was used (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The clustering heatmap of the three clusters was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB. K-M survival analysis revealed a significant correlation between the three subtypes, and the C3 subtype was associated with the worst prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Then, we intersected with these differential genes, yielding 152 intersecting genes, as shown in the Venn diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). The detailed differentially expressed genes between subtypes were shown in Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Construction of the nucleotide metabolism-related prognostic signature\u003c/h2\u003e\u003cp\u003eTo investigate the functions of these different genes, we performed pathway enrichment analysis of these 152 differentially expressed genes in the TCGA database via the GO and KEGG methods. The result revealed that these different genes were predominantly enriched in extracellular matrix, response to nutrients and peptidase regulator activity signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and B). Subsequently, the univariate Cox regression analysis identified 26 prognostically significant genes with a \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). To further identify key molecules within the prognostic gene set, we employed a LASSO penalty with multivariate Cox regression analysis and a total of 9 hub molecules were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-F and Supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Based on their correlation coefficients, a nucleotide metabolism-related genes-based risk scores were generated: Risk Score\u0026thinsp;=\u0026thinsp;IRX5\u0026times;(-0.128648463)\u0026thinsp;+\u0026thinsp;GADD45G\u0026times;(-0.077826735)\u0026thinsp;+\u0026thinsp;TSPAN7\u0026times;(-0.073307189)\u0026thinsp;+\u0026thinsp;MKI67\u0026times;0.000163894\u0026thinsp;+\u0026thinsp;TGFBI\u0026times;0.02372854\u0026thinsp;+\u0026thinsp;CDCA5\u0026times;0.035798743\u0026thinsp;+\u0026thinsp;BARX1\u0026times;0.089405586\u0026thinsp;+\u0026thinsp;PTTG1\u0026times;0.122970952\u0026thinsp;+\u0026thinsp;SEMA3C\u0026times;0.135467087. Then, the patients from TCGA database were randomly assigned to a training dataset and a validation dataset at a ratio of 4:1. The signature significantly stratified patients into low- and high-risk groups, with survival outcomes analyzed using K-M curves. The OS of high-risk group was significantly lower than the low-risk group in both the training and validation datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and B). Furthermore, the ROC curve confirmed that the nomogram performed well in predicting the prognosis of LUAD in both the training and validation datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC and D). To validate the robustness of the novel prognostic signature, we tested it in the external validation dataset GSE30219. The results indicated that this novel signature had a strong predictive performance and good stability (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE and F).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Incidence risk and independent prognostic analysis\u003c/h2\u003e\u003cp\u003eVia univariate and multivariate Cox analyses, the risk score was confirmed to serve as an independent prognostic factor (hazard ratio\u0026thinsp;=\u0026thinsp;4.222, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-B and Supplementary Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). The results were presented in a nomogram for model visualization (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). To evaluate its validation and accuracy, we generated a calibration curve to compare the predictions and actual observations and the predictive analysis revealed good prediction performance for 3-year and 5-year OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Estimation of drug sensitivity and molecular pathway for patients with the risk signature\u003c/h2\u003e\u003cp\u003eGiven that surgical treatment combined with chemotherapy was still effective for early- stage LUAD[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. we employed the pRRophetic package to compare drug sensitivity between LUAD patients in the high- and low-risk groups. Significant variations in half-maximal inhibitory concentration (IC50) values were found between the high- and low-risk groups, suggesting that the low-risk group is more sensitive to AKT inhibitor VIII, Cisplatin, Dasatinib, Gefitinib and Gemcitabine (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-E). Subsequently, to investigate the potential molecular mechanisms of this signature, we performed enrichment analysis using the HALLMARK gene set via the GSEA method. The enrichment analysis revealed that ALPHA LINOLENIC ACID METABOLISM, ARACHIDONIC ACID METABOLISM, FATTY ACID METABOLISM and LINOLEIC ACID METABOLISM signaling pathways were significantly enriched in the high-risk group. While CELL CYCLE and UBIQUITIN MEDIATED PROTEOLYSIS pathways were enriched in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.5 The mutational landscape and immune features for patients with the risk signature\u003c/h2\u003e\u003cp\u003eThe mutational landscape for patients within high-risk and low-risk groups was further explored. A heatmap was generated to visualize the main mutation sites and ratios between different groups. It indicated that patients in the high-risk group exhibited a significantly higher mutation ratio for genes such as TP53 compared to the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). On the other hand, the TME plays an important role in various stages of tumor generation, metastasis, and evasion of immune monitoring and treatment. The TME primarily comprises cancer-associated fibroblasts (CAFs), extracellular matrix (ECM), tumor blood vessels and non-tumor cells[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. We further calculated the proportions of different immune cells among the LUAD patients in both groups, the results of which are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB. Then, we investigated the relationship between immune infiltration and risk signature in TME. As results, CD4 memory T cells, M0 macrophages and M1 macrophages were significant increase in the high-risk group. Conversely, a significant decrease was obtained for B cells na\u0026iuml;ve, Plasma cells and NK cells activated in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Single-cell analysis for the immune landscape in the LUAD\u003c/h2\u003e\u003cp\u003eAfterward, using the tSNE algorithm 22 cell clusters were identified and visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA. Using the \u0026ldquo;singleR\u0026rdquo; algorithm to annotate cell subpopulations, we labeled these identified clusters as 7 cellular categories, such as epithelial cells, endothelial cells, T cells, macrophages, tissue stem cells, monocytes and B cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). The expression profiles of selected key molecules across these cell types are present in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC and Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. To clarify the underlying intercellular communications, we analyzed the intercellular communication network from the scRNA-seq data via the \u0026ldquo;CellChat\u0026rdquo; algorithm. We detected many significant ligand‒receptor pairs among the 7 cell types. In the high-risk group, macrophages were found to have the most potential interactions with other cells, as epithelial cells in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD and E, Supplementary Fig.\u0026nbsp;3A and B). Following this, we measured cytokine and exhaustion factor scores using the GeneCards database and compared it between groups. The results showed that elevated scores of cytokine and exhaustion scores were detected in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF and G). Meanwhile, the associations of signature hub molecules and the scores were analyzed and depicted in the Supplementary Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eA and B.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eLung cancer is extremely aggressive and can be divided into small cell lung cancer and non-small cell lung cancer; LUAD represents the most common histological subtype of NSCLC, so exploring effective prognostic indicators for this disease is clinically important[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. With the advancements in sequencing technology, the traditional prognostic assessment system has failed to facilitate precision medicine[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. There is an urgent need to identify sensitive and specific biomarkers to help with clinical diagnosis[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Recent research has demonstrated that metabolic aberration is an important feature of tumors and that aberrant nucleotide metabolism can suppress the TME immune response to promote the progression of tumors[\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In addition, the altered metabolic environment can also affect the traditional therapeutic and immune responses of tumors and may cause immune escape[\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Therefore, an in-depth study of the potential value of metabolism-related genes in cancer is of clinical significance.\u003c/p\u003e\u003cp\u003eIn our study, we analyzed the transcriptome sequencing data of LUAD patients via a bioinformatics approach. Potential prognostic genes were mined, and a novel prognostic signature was constructed. The novel signature consisted of 9 hub molecules, including SEMA3C, PTTG1, BARX1, CDCA5, TGFBI, MKI67, TSPAN7, GADD45G and IRX5. Several hub molecules have been proven to be associated with the development and progression of LUAD. For example, PTTG1 was one type of DNA repair-related gene and might promote progression of LUAD via the P53 signaling pathway[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. BARX1 could repressed FOXF1 expression and activated Wnt/β-catenin signaling pathway to drive lung adenocarcinoma[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. CDCA5 regulated the cell cycle of NSCLC cells by mediating the P53-P21 signaling pathway, participating in the development and progression of NSCLC patients[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. These studies highlight the significance of these molecules in LUAD and their potential as prognostic factors. Furthermore, the risk signature has been proven to exhibit good prediction efficiency in both TCGA and GEO cohorts. We found that integrating these 9 molecules into one parameter could significantly improve the accuracy of prognosis prediction. The risk score calculated according to this signature could distinguish patients well according to the prognosis of LUAD.\u003c/p\u003e\u003cp\u003eChemotherapy remains the primary and effective treatment for LUAD patients[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. However, the efficacy of chemotherapy is variable. We then assessed the sensitivity of patients stratified into two groups according to the novel signature risk score to commonly used chemotherapeutic agents. We found that the low-risk group might benefit more from AKT inhibitor VIII, Cisplatin, Dasatinib, Gefitinib and Gemcitabine treatment than the high-risk group. These findings demonstrated that this signature may be helpful in improving the accuracy of individualized treatment. To further investigate the potential molecular mechanisms, we employed GSEA analysis. The pathways of ALPHA LINOLENIC ACID METABOLISM, ARACHIDONIC ACID METABOLISM, FATTY ACID METABOLISM and LINOLEIC ACID METABOLISM were notably enriched in the high-risk group, as CELL CYCLE and UBIQUITIN MEDIATED PROTEOLYSIS in the low-risk group. The current studies revealed that these pathways play crucial roles in the development and metastasis of LUAD[\u003cspan additionalcitationids=\"CR43 CR44 CR45\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In addition, the TME plays a crucial role in the antitumor response and can significantly affect patient prognosis[\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. We investigated the relationship between the signature and the TME. We observed a significant increase in the infiltration of CD4 memory T cells, M0 macrophages and M1 macrophages in the high-risk group, as well as a decrease in B cells na\u0026iuml;ve, Plasma cells and NK cells. This pattern suggests an overall trend toward immune suppression. On the other hand, single-cell analysis indicated that strong macrophage communication in the high-risk group, suggesting that these patients may be in a relatively active state of antitumor immune response. Meanwhile elevated scores of cytokines and exhaustion factor scores were detected, which is related to tumor progression, abnormal activation of the immune system and inflammatory responses[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHowever, there are some limitations to our study: all the RNA sequencing data and clinical information were obtained from public databases, such as the TCGA and GEO databases. We plan to perform prospective and genetic functional research in the future to further verify the predictive significance of this nucleotide metabolism-related signature.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn summary, by integrating bulk RNA-seq and scRNA-seq data, we constructed a novel nucleotide metabolism-related signature that accurately predicted the survival outcome and clinical response of LUAD patients. Our signature is an efficient prognostic indicator that can offer novel insights for clinical decision making and facilitate individualized treatment for LUAD patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLUAD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLung Adenocarcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLUSC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLung Squamous Cell Carcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003etSNE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003et-distributed Stochastic Neighbor Embedding\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTME\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTumor Microenvironment\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLASSO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLeast Absolute Shrinkage and Selection Operator\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGene Ontology\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGSEA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGene Set Enrichment Analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOverall Survival\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArea Under the Curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCDF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCumulative Distribution Function\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCAFs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCancer-associated Fibroblasts\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eECM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eExtracellular Matrix.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ethical approval did not refer to this study and this study did not need the informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no potential conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by National Natural Science Foundation of China (Grant Number 82303616), Postdoctoral Science Foundation of China (Grant Number 2021MD703830), Natural Science Foundation of Heilongjiang Province (Grant Number PL2024H171), Haiyan Science Foundation of Harbin Medical University Cancer Hospital (Grant Number JJYQ2024-07), Climbing Fund of Harbin Medical University Cancer Hospital (PDTS2024A-05), and Individualized and precise treatment of lung cancer (Nn10py2017-04).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design: Jian Zhang and Xue Bai. Data acquisition, assembly and experiments: Yue Shi, Lin Zhao and Yali Li. Data analyse and interpretation: Benkun Liu and Fucheng Zhou. Writing-original draft preparation: Yue Shi.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSiegel, R.L., et al., \u003cem\u003eCancer statistics, 2022.\u003c/em\u003e CA Cancer J Clin, 2022. \u003cstrong\u003e72\u003c/strong\u003e(1): p. 7-33.\u003c/li\u003e\n \u003cli\u003eLi, W., et al., \u003cem\u003eLiquid biopsy in lung cancer: significance in diagnostics, prediction, and treatment monitoring.\u003c/em\u003e Mol Cancer, 2022. \u003cstrong\u003e21\u003c/strong\u003e(1): p. 25.\u003c/li\u003e\n \u003cli\u003eKo, E.C., D. Raben, and S.C. 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Lujambio, \u003cem\u003eThe liver cancer immune microenvironment: Therapeutic implications for hepatocellular carcinoma.\u003c/em\u003e Hepatology, 2023. \u003cstrong\u003e77\u003c/strong\u003e(5): p. 1773-1796.\u003c/li\u003e\n \u003cli\u003ePitt, J.M., et al., \u003cem\u003eTargeting the tumor microenvironment: removing obstruction to anticancer immune responses and immunotherapy.\u003c/em\u003e Ann Oncol, 2016. \u003cstrong\u003e27\u003c/strong\u003e(8): p. 1482-92.\u003c/li\u003e\n \u003cli\u003eJin, H., et al., \u003cem\u003eA Novel Lipid Metabolism and Endoplasmic Reticulum Stress-Related Risk Model for Predicting Immune Infiltration and Prognosis in Colorectal Cancer.\u003c/em\u003e Int J Mol Sci, 2023. \u003cstrong\u003e24\u003c/strong\u003e(18).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"lung adenocarcinoma (LUAD), nucleotide metabolism, prognostic signature, immune infiltration, drug sensitivity, immunotherapy","lastPublishedDoi":"10.21203/rs.3.rs-7509527/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7509527/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Lung adenocarcinoma (LUAD) accounts for 50% of lung cancer and has high mortality rate. Nucleotide metabolism exhibit crosstalk in various cancer types, which are closely associated with the progression of LUAD. The in-depth study of genes and metabolites related to nucleotide metabolism will provide new ideas for predicting the prognosis and therapeutic effect of LUAD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This study integrated transcriptomeand single-cell transcriptome datato explore the characteristics and significance of nucleotide metabolism-related genes in LUAD. We will construct a novel LUAD classifier and prognostic signature via analysis of RNA sequencing and clinical data from the TCGA and GEO databases using Cox and LASSO regression. Subsequently, we performed t-distributed Stochastic Neighbor Embedding (tSNE), estimating relative subsets of RNA transcripts (CIBERSORT), gene set enrichment analysis and other bioinformatics analyses to demonstrate correlations with clinical features, gene mutations, drug sensitivity, immune cell infiltration and the expression of immune-related factors between the stratified groups based on risk scores.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of 152 nucleotide metabolism-related genes were identified, and a prognostic signature containing 9 hub molecules was constructed. The novel signature can accurately predict LUAD prognosis and can stratify patients into high-risk and low-risk groups. Multivariate analysis indicated that the risk score is an independent prognostic factor. Functional enrichment analysis revealed that the biological functions of signature moleculeswere associated with the cellular metabolic microenvironment. Our results revealed that patients in the high-risk group had a worse prognosis, less sensitivity to chemotherapy and greater proportion of TP53 gene mutations. Then, 22 cell clusters falling within 7 cellular categories were identified from LUAD tissue. Macrophages and immune-related factor scores of cytokines and failure factors were discerned to be significantly greater in the high-risk group than low-risk group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e This study indicated that nucleotide metabolism was correlated with LUAD progression, immunosuppression and treatment sensitivity. The developed signature can serve as a potent tailored prognostic prediction model for patients.\u003c/p\u003e","manuscriptTitle":"Identification of a novel nucleotide metabolism-related signature for predicting lung adenocarcinoma prognosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-06 12:00:45","doi":"10.21203/rs.3.rs-7509527/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5a993afd-71b9-4c50-8a08-2b33a3918df2","owner":[],"postedDate":"October 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-02T09:53:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-06 12:00:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7509527","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7509527","identity":"rs-7509527","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}