Construction of a Prognostic Model for Lung Adenocarcinoma Based on Nucleotide Metabolism-Related Genes and Bioinformatics Analysis

Construction of a Prognostic Model for Lung Adenocarcinoma Based on Nucleotide Metabolism-Related Genes and Bioinformatics Analysis | 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 Construction of a Prognostic Model for Lung Adenocarcinoma Based on Nucleotide Metabolism-Related Genes and Bioinformatics Analysis Xiangyu Cui, Wenjie Han, hongyu Liu, Yongwen Li, Ruihao Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3984429/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: Metabolic reprogramming is an important hallmark of cancer. However, it is still uncertain how nucleotide metabolism-related genes (NMRGs) may affect the prognosis of Lung adenocarcinoma (LUAD). Methods: In our study, the LUAD cohorts from the bioinformatics databases were downloaded. Characteristic genes related to prognosis of LUAD patients were obtained through combining differentially expressed analysis, univariate COX analysis, least absolute shrinkage and selection operator (LASSO), and multivariate COX, and the risk model was constructed. Then, the immune infiltration, immunotherapy, and mutations analyses between high and low risk groups were conducted. Finally, drug sensitivity analysis and reverse transcription-polymerase chain reaction (RT-qPCR) was executed to validate the expression of the biomarkers. Results: Based on 4 characteristic genes (RRM2, TXNRD1, NME4, and NT5E), the risk model was established, and the patients were assigned to high/low risk groups. The survival analysis demonstrated that patients in low risk groups had higher survival. The infiltrating abundance of 11 immune cells, the expression of 25 immune checkpoints, TIDE score, Dysfunction score, Exclusion score, IPS, and IPS-CTLA4 were significantly different between two risk groups. Additionally, the survival of patients in low-risk and high-TMB group was the highest. Finally, the IC 50 of 124 drugs was considerably different between two risk groups, such as Doramapimod_1042, BMS-754807_2171, MK-2206_1053, etc. Finally, RT-qPCR results showed that RRM2 and NT5E expression was obviously up-regulated and TXNRD1 expression was obviously down-regulated in LUAD. Conclusion: Taken together, this study created a nucleotide metabolism related prognostic characteristic, which was relevant to immune microenvironment and immunotherapy. Lung adenocarcinoma Nucleotide metabolism Prognosis Immune microenvironment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Lung adenocarcinoma (LUAD) is the most common form of lung cancer, primarily originating from the epithelial cells of the small airways and type II alveolar cells, accounting for approximately 40% of all lung cancer cases [ 1 – 3 ]. Clinically, early-stage LUAD patients are often asymptomatic, but as the disease progresses, they commonly present symptoms such as shortness of breath, cough, chest pain, and hemoptysis. Despite recent advances in the diagnosis and treatment of lung adenocarcinoma, the primary treatment modalities include surgical resection, radiotherapy, chemotherapy, targeted therapy, and immunotherapy. Nevertheless, for late-stage patients, the prognosis remains unfavorable, with a 5-year overall survival rate ranging from 4–17% [ 4 , 5 ]. Therefore, the exploration of LUAD genes associated with patient survival is of paramount importance for improving prognosis and providing appropriate treatment. These genes may be involved in critical processes related to tumor initiation, development, and metastasis. Revealing the roles of these genes can provide a strong basis for personalized treatment, facilitate the development of more precise therapeutic strategies, and eventually have the potential to improve patient survival and quality of life. Nucleotide metabolism is a crucial biological process within cells, encompassing nucleotide synthesis and degradation. Serving as the fundamental building blocks of DNA and RNA, nucleotide metabolism plays a pivotal role in maintaining genome integrity, cell division, DNA repair, protein synthesis, and other essential biological processes [ 6 , 7 ]. Nucleotide synthesis is an energy-intensive process, and excessive nucleotide synthesis metabolism is closely associated with uncontrolled proliferation, immune evasion, metastasis, and drug resistance in cancer cells [ 8 ]. Research on nucleotide metabolism-related genes is gradually highlighting their significance. Some key enzymes such as ribonucleotide reductase (RR) [ 9 ], phosphoribosyl pyrophosphate synthetase 1 [ 10 ], and xanthine oxidoreductase [ 11 ] have been discovered to be closely linked to the initiation and progression of tumors, offering new opportunities for prognosis assessment and personalized treatment. However, there is currently a lack of reports on the relevance of nucleotide metabolism in the occurrence, development, and prognosis of LUAD. In this study, we obtained LUAD-related datasets from the public databases. Through a combination of univariate, Lasso and multivariate cox regression analyses, we identified nucleotide metabolism-related genes linked with the survival of LUAD patients. This research aims to supply potential targets for clinical diagnosis and patient prognosis, while also laying a theoretical foundation for a deeper understanding of the mechanisms underlying LUAD. 2. Materials and Methods 2.1. Data Acquisition From the UCSC Xena browser ( https://xenabrowser.net/datapages/ ), TCGA-LUAD cohort contains 58 control samples and 510 LUAD samples. A total of 487 LUAD samples with more than 30 days of follow-up time were utilized to establish a risk model. For further validation, through GEO database ( https://ww.ncbinlm.nih.gov/ ), GSE72094 was acquired and included 386 LUAD samples with more than 30 days of follow-up [ 12 ]. Besides, we obtained 90 nucleotide metabolism-related genes (NMRGs) from published literature [ 12 ]. 2.2. Identifying of nucleotide metabolism-related differentially expressed genes (NM-DEGs) The DEGs were screened between LUAD and control groups in TCGA-LUAD cohort via ‘DESeq’ package (v 1.36.0) with |log 2 FC|>1 and adj P < 0.05 [ 13 ]. Moreover, the DEGs were intersected to NMRGs to obtain NM-DEGs. After that, to probe the biological functions of NM-DEGs, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (adj P < 0.05) enrichment analysis were executed via ‘clusterProfiler’ package (version 4.7.1.001). Additionally, a protein-protein interaction (PPI) network for these proteins was established through STRING database. 2.3. Construction and Validation of risk model The characteristic genes were selected through combining the univariate Cox ( P < 0.05), LASSO, and multivariate COX ( P < 0.2) on the basis of the NM-DEGs in the TCGA-LUAD dataset. The risk score was computed. Therefore, each LUAD patient could obtain a risk score using this formula. Subsequently, the patients in TCGA-LUAD were assigned into high/low risk groups on the basis of the median value. The survival variance between two risk groups was analysed using Kaplan-Meier (K-M) curves. Receiver operating characteristic (ROC) curves were employed to assess the risk model's capacity for forecasting. Ultimately, the risk model was validated in GSE72094. 2.4. Independent prognostic analysis, creation of nomogram, and clinical correlation analysis The Cox analyses (including univariate Cox ( P < 0.05), and multivariate COX ( P < 0.2)) were utilized to perform the independent prognostic analysis in the TCGA-LUAD cohort to obtained independent prognostic variables. Then, a nomogram with independent prognostic variables was developed. Furthermore, a corresponding calibration curve was plotted to estimate reliability of the nomogram. In addition, the differences in risk score in different groups in each clinical characteristic were compared via Wilcoxon test (n = 2) and Kruskal test (n > 2). 2.5. Enrichment Analysis Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were executed to look into the signaling pathways and probable biological mechanisms linked to two risk groups. Firstly, the ‘c2.cp.kegg.v2023.1.Hs.symbols’ was downloaded as a background set. Then, the differentially expressed analysis was performed between two risk groups, and genes were ranked according to the log2FC. In addition, the ‘h.all.v2023.1.Hs.symbols.gmt’ was extracted in MsigDB database as reference set for GSVA. The GSVA scores for each HALLMARK pathway in the two risk groups were computed via “GSVA” package (v 1.44.5) [ 14 ], and the variations were assessed via “limma” package (v 3.52.4). 2.6. Immune infiltration analysis The CIBERSORT algorithm (v 1.03) was employed to assess the infiltrating abundance of 22 immune cells in TCGA-LUAD cohort [ 15 ], and the differences were compared via Wilcox Test ( P < 0.05). The relevance between risk score and different immune cells between two risk groups was evaluated via Spearman algorithm in “psych” package (v 2.2.9). After that, we obtained the 12 immune-related functions through the published literature [ 16 ], and ssGSEA in “GSVA” package (v 1.44.5) was employed to calculated the score of each immune-related function in LUAD patients [ 14 ]. Moreover, the differences were assessed between two risk groups. 2.7. Immunotherapy Analysis Human leukocyte antigens (HLAs) play a specific synergistic role in the immune response. Therefore, the expression of HLA related genes was measure in two risk groups. Additionally, a total of 48 immune checkpoints were discovered through published research [ 17 ], and the Wilcoxon test was employed to compare the expression of these genes in two risk groups. And the relevance between the differentially expressed immune checkpoints and characteristic genes was assessed via Spearman algorithm. TIDE algorithm was executed to assess the likelihood of tumor immune escape. Therefore, the TIDE score, Dysfunction score, and Exclusion score were evaluated [ 18 ]. And we counted LUAD patients who responded or did not respond to immunotherapy in two risk groups and compared the differences. Additionally, the immunephenoscore (IPS) of LUAD patients was gained via TCIA, and differences were measured by Wilcoxon test. 2.8. Relevance analysis between risk score and cancer stem cell index Cancer stem cells (CSCs) are cancer cells that have the capacity to transform into cancer and exhibit traits common to normal stem cells. The CSC index, which measures how closely tumor cells resemble stem cells by reflecting the stem cell's gene expression profile, could be viewed as a quantification of CSCs. The CSC indexes were computed via OCLR algorithm. Moreover, the relevance between risk score and CSC indexes was measured via Spearman algorithm. 2.9. Mutation analysis The “maftools” package (v 2.12.0) was employed to process the mutation data of LUAD patients [ 19 ]. Afterwards, in TCGA-LUAD cohort, the tumor mutation burden (TMB) scores of each LUAD patient were counted, and the Spearman algorithm was employed to assess the relevance between TMB score and risk score. LUAD patients were split into high and low TMB groups according to the median TMB score. These two groups were then joined with two risk groups to create four different groups: high risk-low TMB, high risk-high TMB, low risk-high TMB, and low risk-low TMB. The differences in survival among these four groups were compared. 2.10. Chemotherapy drug sensitivity analysis The GDSC database was employed to gain anti-cancer drugs. The half-maximal inhibitory concentration (IC50) in anti-cancer drugs was estimated via the "oncoPredict" package (v 0.2), and the differences of IC50 were estimated using Wilcoxon test ( P < 0.05), in order to evaluate the sensitivity of anti-cancer drugs in the two risk groups of the training set. Additionally, since the NCI-60 cell line was used for screening new anticancer drugs, we further analyzed drug sensitivity of the NCI-60 cell line. The drug sensitivity data were extracted from CellMiner database. The correlation between characteristic genes and drug sensitivity was computed via Spearman algorithm with |cor| > 0.3 and P < 0.05. 2.11. Expression analysis of characterized genes First, we examined the expression of characterized genes in single cells in order to evaluate the connection between single cells and characterized genes via the TISCH database ( http://tisch1.comp-genomics.org/home/ ) [ 20 ]. The HPA online database was executed to measure the protein expression levels of the characterized genes in tissue samples from LUAD and healthy individuals. Subsequently, the characterized genes expression in various LUAD cell lines was then further examined after the RNA-seq profiles of various LUAD cell lines were retrieved from the cell database ( http://www.broadinstitute.org/ccle ). 2.12. Real time quantitative polymerase chain reaction (RT-qPCR) First, 20 frozen tissue samples were collected from Qingdao Central Hospital, of which 10 were control and 10 were LUAD. This study was approved by Qingdao Central Medical Group Medical Ethics Committee. All patients had signed an informed consent form. Total RNA was then obtained from a 50 mg tissue sample by TRIzol and converted to cDNA using a reverse transcription reaction (SureScript-First-strand-cDNA-synthesis-kit). The characteristic genes were then identified by bioinformatics analysis and specific primers were designed for RT-qPCR amplification. Next, the RT-qPCR reaction mixture was set up, including the cDNA template, primer and appropriate RT-qPCR master reagent mixture (2xUniversal Blue SYBR Green qPCR Master Mix) (Table S1 ). Subsequently, RT-qPCR amplification was performed on a CFX Connect real-time quantitative fluorescent PCR instrument, and amplification curves and fluorescence signals were recorded. Finally, the 2 −△△CT value was counted, and the P-value was counted by Graphpad Prism 5 [ 21 ]. 3. Results 3.1. There were 25 NM-DEGs between LUAD and control groups Between LUAD and control groups, a total of 3,229 DEGs were obtained (Figure S1 a-b). These genes interacted with NMRGs to obtain 25 NM-DEGs (Figure S1 c). Of the GO results, top 8 items were demonstrated. a total of 211 items (including 164 GO BP items, 8 GO CC items, and 39 GO MF items) were significantly enriched (Table S2 ), such as ‘nucleoside monophosphate metabolic process’, ‘ficolin-1-rich granule lumen’, ‘nucleobase-containing compound kinase activity’ (Figure S1 d). Of the KEGG results, there were 10 KEGG pathways markedly enriched (Table S3 ). For instance, NM-DEGs were involved in ‘Nucleotide metabolism’, ‘Pyrimidine metabolism’, ‘Purine metabolism’, etc. (Figure S1 e). The PPI network contained 25 nodes and 104 edges. For instance, DTYMK protein interacted with multiple proteins, including NME4, NT5E, TK1, etc. (Figure S1 f). 3.2. Construction of risk model incorporating NM-DEGs Through univariate Cox, 17 NM-DEGs with prognostic values were selected (Fig. 1 a). Using LASSO analysis, there were 6 NM-DEGs were identified, namely RRM2, TYMS, ATIC, TXNRD1, NME4, and NT5E (Fig. 1 b). After that, in total, 4 characteristic genes, RRM2, TXNRD1, NME4, and NT5E, were selected by multivariate COX (Fig. 1 c). The LUAD patients were split into high/low risk groups according to the medium risk score (Fig. 1 d), and those in the high risk group had a decreased chance of survival (Fig. 1 e). The AUC values were greater than 0.6 in 1-, 3-, 5-year, demonstrating that the risk model had well performance of the prognostic prediction of LUAD (Fig. 1 f). Additionally, we verified processing in GSE72094, and the outcomes matched the TCGA-LUAD cohort (Figure S2 a-c). 3.3. Establishment and calibration of the nomogram that combined clinical characteristics and risk score Through univariate Cox and multivariate COX, there were three independent prognostic factors, namely stage (P = 6.1e-06), risk score (P = 2.7e-06), and pathologic-T (P = 0.15) (Fig. 2 a-b). Thereafter, the nomogram was created, and the slopes of the calibration curves for 1-, 3-, and 5- years were close to 1, especially for 1- and 3- years (Fig. 2 c-d). These findings revealed that the nomogram was an excellent predictor of LUAD patient prognosis. Additionally, the variations in risk score between various groups for each clinical characteristic were examined to look into the relevance between risk score and clinical characteristics. The results demonstrated that risk score was considerably different in pathologic-T (T1/T2/T3/T4) (P = 5e-04), age (< 60/ ≥ 60) (P = 0.016), stage (stage Ⅰ/Ⅱ/Ⅲ/Ⅳ) (P = 1.9e-07), and pathologic-N (N0/N1/N2/N3) (P = 8.7e-08) (Fig. 2 e-i). 3.4. GSEA and GSVA of two risk groups For GSEA results, a total of 52 KEGG pathways were considerably enriched (Table S4 ), and top5 pathways were displayed based on significance ranking (Figure S3 a). Among these, ‘cell cycle’, ‘dna replication’, ‘proteasome’, and ‘spliceosome’ were enriched in high risk group, and ‘vascular smooth muscle contraction’ was enriched in low risk group. For GSVA results, the score of each pathway in two risk groups was demonstrated via heap map (Figure S3 b). The scores of 32 HALLMARK pathways were significantly different between two risk groups (Table S5 ). Top 10 pathways in two risk groups were shown according to |t| ranking (Figure S3 c). For instance, ‘glycolysis’, ‘E2F targets’, ‘MTORC1 signaling’, and so on were activated in the high risk groups. However, the ‘KRAS signaling DN’, ‘bile acid metabolism’, ‘hedgehog signaling’ and etc. were activated in low risk groups. 3.5. Evaluation immune microenvironment and Immunotherapy between two risk groups After excluding immune cells with 0 immune infiltration (T cells CD4 naive), the infiltration abundance of the remaining 21 immune cells was displayed by heatmap (Figure S4 a). In total, the infiltration abundance of 11 immune cells were markedly different between two risk groups, such as plasma cells, monocytes, macrophages, etc. (Figure S4 b). Relevant analysis results showed that risk score was most obviously positively relevant to activated memory CD4 T cells (R = 0.34, P = 3e-14), and was most considerably negatively relevant to resting memory CD4 T cells (R = -0.25, P = 6.5e-08) (Figure S4 c-d). After that, among 12 immune-related functions, there were 4 immune-related functions markedly different between two risk groups, namely APC co-stimulation, MHC class Ⅰ, parainflammation, and Type Ⅱ IFN response (Figure S4 e). Additionally, the results of relevance between risk score and CSC indexes (mDNAsi and mRNAsi) suggested that risk score was markedly positively relevant to mRNAsi (R = 0.47, P < 2.2e-16) (Figure S4 f). However, the relevant between risk score and mDNAsi was not significant (R = 0.077, P = 0.11) (Figure S4 g). We further explored the response to immunotherapy of the patients in the two risk groups. Firstly, a total of 14 HLA related genes (including HLA-DMA, HLA-DMB, HLA-DOA) and 25 immune checkpoints (including ADORA2A, BTLA, BTNL2) were markedly differentially expressed between two risk groups (Fig. 3 a-b). The relevant analysis suggested that RRM2 was most significantly positively correlated with CD276 (R = 0.34, P < 0,001), and NME4 was most considerably negatively relevant to CD200R1 (R = -0.37, P < 0.001) (Fig. 3 c). Then, through TIDE algorithm, the percentage of LUAD patients who responded to immunotherapy in low risk group was 43.9%, and the percentage was 28.4% in the high risk group (Fig. 3 d). There were considerably different between two risk groups (P = 0.00038). Afterwards, the TIDE score was obviously higher in high risk group, and IPS score and IPS-CTLA4 were markedly lower in the high risk group (Fig. 3 e-f). These finding indicated that the patients benefited more from immunotherapy in low risk group. 3.6. Mutation analysis between two clusters Somatic mutations in two risk groups were demonstrated in Figure S5 a-b. The genes with the highest rates of mutation in the high and low risk groups, respectively, were MUC16 and TTN. Additionally, the TMB score was markedly positively relevant to risk score (R = 0.3, P = 1.2e-11) (Figure S5 c). Moreover, the survival of patients in low-risk and high-TMB group was the highest (Figure S5 d). 3.7. Drug sensitivity analysis A total of 198 anti-cancer drugs were obtained via GDSC database. Among these drugs, the IC50 of 124 drugs were considerably different between two risk groups. The top 8 drugs according to significance ranking were shown via box plot, and the patients in the low-risk group were more sensitive to these eight drugs (Fig. 4 a). Additionally, the relevant analysis demonstrated that NT5E was most relevant to AFP464 (R = -0.54, P < 0.0001); RRM2 was most relevant to Crizotinib (R = 0.34, P < 0.01); NME4 was most relevant to Cladribine (R = 0.59, P < 0.0001); and TXNRD1 was most relevant to Tamoxifen (R = -0.46, P < 0.001) (Fig. 4 b-e). 3.8. Expression analysis for characteristic genes In TCGA-LUAD cohort, all characteristic genes were up-regulated in LUAD group (Fig. 5 a). Four proteins were discovered to be highly expressed in LUAD tissue using the HPA database (Fig. 5 b). Moreover, all characteristic genes were expressed in all cell lines (Fig. 5 c-f). In six single-cell databases, we also examined the expression of characteristic genes in various cell types via the TISCH database, and the expression of characteristic genes was higher in each cell type in GSE131907 (Fig. 6 a-d). In addition, we selected GSE131907 for further study. In GSE131907, the percentage of each cell type was shown, and the percentage of CD4 T conv was highest (Fig. 6 e-f). Among these cell types, NME4 was mainly expressed in epithelial and oligodendrocyte (Fig. 6 g). NT5E was mainly expressed in epithelial and B cells (Fig. 6 h). RRM2 was mainly expressed in plasma and DC cells (Fig. 6 i). TXNRD1 was mainly expressed in epithelial and mono/macro cells (Fig. 6 j). 3.9. The Expression Levels of the Biomarkers The RT-qPCR results displayed that the expression of the RRM2, NT5E and TXNRD1 between LUAD and control groups were markedly different. The TXNRD1 was lowly expressed and RRM2 and NT5E were highly expressed in LUAD (Figure S6 a-d). In summary, the results of RT-qPCR suggested that RRM2 and NT5E have good diagnostic value for LUAD. 4. Discussion Early-stage LUAD patients can achieve curative results through surgical intervention. However, due to the lack of obvious clinical symptoms in the early-stage of LUAD patients and limitations in screening methods, most patients are found to have already progressed to the middle and late stages of the disease [ 22 ]. Among the factors influencing tumorigenesis, alterations in nucleotide metabolism play a pivotal role, with enhanced synthesis of nucleoside triphosphates being crucial for the development of LUAD [ 23 ]. A deeper understanding of metabolism in cancer can assist in the identification of valuable diagnostic biomarkers [ 24 ]. In this study, we utilized data from the public datasets and employed a combination of univariate cox regression, Lasso cox regression, and multivariate cox regression analysis to identify four nucleotide metabolism-related genes: RRM2, TXNRD1, NME4, and NT5E, as potential biomarkers. Based on the expression levels of these four prognostic genes and overall survival (OS) data, we established a risk scoring system that categorizes LUAD patients into low and high risk groups. In this context, we have identified key Nucleotide Metabolism-Related Genes (NMRGs) relevant to LUAD progression and prognosis and have analyzed their potential functional mechanisms. To understand the molecular and immune-related differences between high/low-risk subgroups, we investigated the functions of the four biomarkers. Ribonucleotide reductase M2 subunit (RRM2) is a rate-limiting enzyme in the nucleotide synthesis pathway, and increased RRM2 expression and activity have been associated with various cancer types. It has been overexpressed and linked to tumor progression, invasion, metastasis, and lower patient survival rates in cancers such as gastric, ovarian, colorectal, brain, and breast cancer [ 25 – 29 ]. Consequently, RRM2 has long been considered an important drug target for various proliferative diseases, including cancer [ 30 ]. In LUAD cells, RRM2 overexpression enhances tumor cell proliferation and invasion, and it is considered an independent risk factor for the overall survival (OS) of LUAD patients [ 31 ]. Our experiments also confirm that RRM2 expression is significantly higher in LUAD tissues compared to adjacent normal tissues. Cytoplasmic selenoprotein thioredoxin reductase 1 (TXNRD1) plays multiple roles associated with malignant tumors, as it can protect normal cells from malignant transformation [ 32 ]. However, in liver cancer, upregulated TXNRD1 expression promotes hepatocellular carcinoma progression through the activation of the Akt/mTOR signaling pathway and is associated with lower patient survival rates [ 33 ]. There is limited research on TXNRD1 in LUAD, but a study by Jin X suggests that TXNRD1 expression is reduced in LUAD patients who are female, have not received radiotherapy, and have no distant metastases, which aligns with our verification results [ 34 ]. Nucleoside Diphosphate Kinase 4 (NME4) is a critical rate-limiting enzyme that regulates nucleotide metabolism and ATP/ITP metabolism [ 35 ]. Aberrant overexpression of the NME4 gene in gastric and colon cancer may lead to an imbalance in nucleotide pools in mitochondria, resulting in checkpoint regulation failure and the accumulation of genetic changes, ultimately leading to tumorigenesis [ 36 ]. As an oncogenic promoter, NME4 can promote the progression of NSCLC by inhibiting cell cycle arrest and stimulating tumor cell proliferation [ 37 ]. NT5E encodes the ecto-5’-nucleotidase (CD73) is a critical rate-limiting enzyme in the extracellular purine metabolism pathway. It plays a crucial role in generating and maintaining adenosine concentrations, thereby influencing tumor cell neovascularization, immune evasion, and immune response [ 38 , 39 ]. CD73 is highly expressed in most cancers, but its expression levels are lower than adjacent normal tissues in appendiceal adenocarcinoma and ovarian cancer. Moreover, high CD73 expression is closely associated with lower overall survival (OS) but not with recurrence-free survival (RFS) [ 40 ]. In vitro experiments, NT5E may promote LUAD proliferation and metastasis through the EGFR/AKT/mTOR pathway [ 38 ]. Through GSEA enrichment analysis, in addition to cell cycle, DNA replication, proteasome, and vascular smooth muscle contraction pathways associated with tumor proliferation and invasion, spliceosome-related signaling pathways were also enriched. These findings suggest that differences in survival may be driven by different immune status in LUAD patients [ 41 ]. Vascular smooth muscle contraction plays a crucial role in controlling blood flow and the delivery of oxygen and nutrients to tissues. Tumors often possess a higher density of microvasculature than normal tissues. However, these pathological vessels are often less elastic and function differently from normal vessels, often promoting tumor growth through autocrine signaling [ 42 ]. The specific mechanisms underlying this phenomenon in LUAD are not yet clear. GSVA analysis indicates a significant upregulation of the glycolysis signaling pathway in the high-risk group. Elevated glycolysis signaling pathways are closely associated with immune therapy resistance and poor prognosis [ 43 ]. Studies have shown that chenodeoxycholic acid (CDCA), acting as an integrin α5β1 inhibitor, can inhibit LUAD cell proliferation, migration, and invasion and induce apoptosis through the α5β1/FAK/p53 signaling pathway [ 44 ]. Our research suggests that the downregulation of bile acid metabolism pathways is more pronounced in the high-risk group, possibly contributing to the poor prognosis. Immunotherapy, as an emerging treatment approach, has shown promising results. However, it is not universally effective for all LUAD patients, as its efficacy varies depending on the patient's immune system status and cancer characteristics. Immune evasion is a significant hallmark of cancer progression, and the downregulation of HLA can reduce antigen presentation, thereby promoting immune escape [ 45 ]. Our results indicate that 14 HLA family genes are expressed at higher levels in low-risk group patients, suggesting that low-risk patients may benefit from immunotherapy and consequently achieve longer overall survival (OS). We conducted a differential expression analysis of 48 immune checkpoint molecules between high and low-risk groups in LUAD. We found significant differences in the expression of 25 immune checkpoint molecules between the high and low-risk groups. Our study demonstrated that RRM2 has the highest positive correlation with CD276, an immune checkpoint molecule. Aberrant expression of CD276 upregulates the epithelial-mesenchymal transition (EMT) of LUAD cells, promoting LUAD development [ 46 ]. However, further studies are needed to understand the interaction between RRM2 and CD276. TIDE, as an algorithm for predicting patient response to immune checkpoint inhibitor (ICI) therapy, suggests that higher scores indicate a higher likelihood of significant immune escape and a lower likelihood of benefiting from ICI treatment [ 47 ]. We used the TIDE algorithm, IPS algorithm, and TMB analysis to assess their responses to immunotherapy, and our research suggests that low-risk patients are more likely to benefit from immunotherapy. Furthermore, understanding the composition of immune cells in tumor tissue can help identify new cancer treatment approaches and enhance the efficiency of ICI therapy. Tumor-associated macrophages can promote tumor cell proliferation, migration, and tumor angiogenesis. M0 macrophage high-density infiltration is closely associated with poor clinical outcomes in early-stage LUAD, which is consistent with our research [ 48 , 49 ]. Many studies suggest that increased infiltration of plasma cells in tumors can significantly prolong the OS of NSCLC patients receiving PD-L1 treatment [ 50 ]. Our research suggests a significant downregulation of plasma cell infiltration in the high-risk group, indicating a worse prognosis. High infiltration density of mast cells can extend the survival of early-stage LUAD patients [ 51 ] and lead to a higher recurrence-free survival (RFS) for stage I and II postoperative patients [ 52 ]. However, another study suggests that mast cells promote LUAD cell metastasis through the release of proteases via exosomes [ 53 ]. Therefore, the relationship between mast cells and LUAD remains controversial and requires further investigation. We assessed the sensitivity of high/low-risk group patients to drugs currently used for the treatment of LUAD. High-risk group patients had obviously higher IC50 values for Doramapimod_1042, BMS-754807_2171, MK-2206_1053, and Nutlin-3a (-)_1047 compared to low-risk group patients, suggesting that the low-risk group may have a better response to chemotherapy and targeted therapy. Doramapimod_1042, BMS-754807_2171, MK-2206_1053 are inhibitors of the p38-MARK, PI3K/AKT, and AKT/PKB pathways, respectively, and they can significantly inhibit the proliferation of lung adenocarcinoma cells and increase apoptosis [ 54 – 56 ]. On the other hand, Nutlin-3a (-)_1047 activates p53 in normal lung epithelial cells and induces apoptosis in lung adenocarcinoma cells [ 57 ]. The validation results of gene expression showed consistency with our bioinformatics analysis for RRM2 and NT5E. We speculate that RRM2 inhibitors can activate the cGAS/STING signaling pathway, increase CD8 + T cell infiltration, and have an anti-tumor effect [ 58 ]. When given EGFR, AKT, or mTOR inhibitors, the function of NT5E can be significantly inhibited, indicating that NT5E may be involved in the pathogenesis of LUAD through the EGFR/AKT/mTOR axis [ 38 ]. The differences in NME4 and TXNRD1 expression may be attributed to sample variations. We have constructed a novel model based on nucleotide metabolism that underwent comprehensive analysis and validation across multiple databases, showing significant potential for LUAD prognosis prediction and providing new insights into LUAD treatment. However, this model still has its limitations. Firstly, immunotherapy has emerged as a viable treatment option for LUAD, but the selection and specific mechanisms of combination therapy remain to be further explored. Additionally, before the application of NMRGs in predicting LUAD treatment responses in clinical settings, further validation and support from real-world and basic research data are required. Abbreviations LUAD, Lung adenocarcinoma; RR, ribonucleotide reductase; NMRGs, nucleotide metabolism-related genes; NM-DEGs, nucleotide metabolism-related differentially expressed genes; DEGs, Diferentially expressed genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; PPI, protein-protein interaction; LASSO, Least absolute shrinkage and selection operator; ROC, receiver operating characteristic; K-M, Kaplan-Meier; GSEA, gene set enrichment analysis; GSVA, gene set variation analysis; HHLAs, uman leukocyte antigens; IPS, immunephenoscore; CSCs, Cancer stem cells; TMB, tumor mutation burden; IC50, half-maximal inhibitory concentration; RT-qPCR, real time quantitative polymerase chain reaction Declarations Conflict of interest Statement None. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. CRediT authorship contribution statement Xiangyu Cui: Data curation, Methodology, Formal analysis, Writing - review & editing, Writing-original draft. Wenjie Han: Data curation, Methodology, Formal analysis, Writing - review & editing, Writing - original draft. Ruihao Zhang: Data curation, Writing - review & editing, Writing - original draft. Guangsheng Zhu: Formal analysis, Writing - review & editing, Writing-original draft. Hua Huang: Formal analysis, Writing - review & editing, Writing-original draft. Yongwen Li: Methodology, Writing - review & editing, Supervision. Hongyu Liu: Conceptualization, Writing - review & editing, Supervision. Jun Chen: Conceptualization, Writing - review & editing, Supervision. Data Availability The datasets analysed during the current study are available in UCSC Xena browser [https://xenabrowser.net/datapages/] TCGA-LUAD, and the GEO repository, [https://www.ncbi.nlm.nih.gov/geo/] GSE72094. Ethics approval This study was in accordance with the principles laid down in the Declaration of Helsinki; and was approved by Qingdao Central Medical Group Medical Ethics Committee. Consent to participate Informed consent was obtained from all individual participants included in the study. Consent to publish Not applicable. Acknowledgements Writing assistance was provided by zhixue Zhang, PhD, of Qingdao Central Hospital (Qingdao, Shandong, China) ; and Language assistance was provided by zhanqing LI, PhD, of The Second Affiliated Hospital of Xiamen Medical College (Xiamen, Fujian, China). References A.C. Borczuk. Updates in grading and invasion assessment in lung adenocarcinoma. Modern Pathol . Inc, 2022; 35; 28-35. https://doi.org/10.1038/s41379-021-00934-3. T.V. Denisenko, I.N. Budkevich, B. Zhivotovsky. Cell death-based treatment of lung adenocarcinoma. Cell Death Dis . 2018; 9; 117. https://doi.org/10.1038/s41419-017-0063-y. T. Liu, L. Guo, G. Liu, Z. Dai, L. Wang, B. Lin, X. 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RRM2 silencing suppresses malignant phenotype and enhances radiosensitivity via activating cGAS/STING signaling pathway in lung adenocarcinoma. Cell Biosci. 2022;12(1):149. doi:10.1186/s13578-022-00882-8. Additional Declarations No competing interests reported. Supplementary Files FigureS1.tif FigureS2.tif FigureS3.tif FigureS4.tif FigureS5.tif FigureS6.tif TableS1.xlsx TableS2.xlsx TableS3.xlsx TableS4.xlsx TableS5.xlsx Supplementarymaterial.docx Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-3984429","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276294832,"identity":"462c9694-2b3c-4909-9270-a4bcb94edc0e","order_by":0,"name":"Xiangyu Cui","email":"","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiangyu","middleName":"","lastName":"Cui","suffix":""},{"id":276294833,"identity":"a2e9189b-a87d-4c18-8fb4-24f66cd44c72","order_by":1,"name":"Wenjie Han","email":"","orcid":"","institution":"Qingdao Women and Children’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenjie","middleName":"","lastName":"Han","suffix":""},{"id":276294834,"identity":"7d023a31-d8d5-4155-b107-7605c0adcc3e","order_by":2,"name":"hongyu Liu","email":"","orcid":"","institution":"Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment","correspondingAuthor":false,"prefix":"","firstName":"hongyu","middleName":"","lastName":"Liu","suffix":""},{"id":276294835,"identity":"a7306184-641f-464c-9bde-f41c58ce878b","order_by":3,"name":"Yongwen Li","email":"","orcid":"","institution":"Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment","correspondingAuthor":false,"prefix":"","firstName":"Yongwen","middleName":"","lastName":"Li","suffix":""},{"id":276294836,"identity":"dc021948-42e2-477c-a6e4-b684e6169f95","order_by":4,"name":"Ruihao Zhang","email":"","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ruihao","middleName":"","lastName":"Zhang","suffix":""},{"id":276294837,"identity":"452109f6-81c7-45fc-836c-3de0908e1d01","order_by":5,"name":"Guangsheng Zhu","email":"","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Guangsheng","middleName":"","lastName":"Zhu","suffix":""},{"id":276294838,"identity":"347fef93-acf2-44f3-9361-dd48eac9fef0","order_by":6,"name":"Hua Huang","email":"","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hua","middleName":"","lastName":"Huang","suffix":""},{"id":276294839,"identity":"9c0f9cca-01b7-4119-822a-40ef8d7c14f9","order_by":7,"name":"Jun Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACAyjNzM/M2GDwgUgtjA0gLZLtzQcKZ5CihcHgzLGEzzzEaDGXyD3+4OMeO3aGGzmGm23KrBn427sT8GqxnJGX2DjjWTIz44wcY+Occ+kMEmfObsDvMKDhzTwHDjAzS+SYGee2HWYwkMglQssfoBY2iRzz35ZEa2EAauHhOZZgzEiUljNvDGf2HEhmlmBvPmDYcy6dh7BfjucYfPhxwC7Z/jAwKn+UWcvxt/fi1wIDyRCKjZmoqAEDO5gWonWMglEwCkbByAEAZvhJmmgvHZgAAAAASUVORK5CYII=","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jun","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-02-24 08:46:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3984429/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3984429/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52105348,"identity":"7f14b036-0263-4011-a299-d26149b1d5f4","added_by":"auto","created_at":"2024-03-06 19:27:23","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1345794,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of the risk model in TCGA-LUAD dataset based on 4 prognostic genes. (a) Forest map of 17 NM-DEGs with prognostic values selected by univariate Cox analysis. (b) Least absolute shrinkage and selection operator (LASSO) model for screening 6 prognostic NM-DEGs. (c) Forest map of 4 characteristic genes selected by multivariate Cox regression. (d) Distribution of risk scores and survival status of patients with different risk score, and the heat map of the four survival-associated model genes expression in the TCGA-LUAD dataset. (e) Kaplan-Meier (K-M) survival analysis between the high- and low-risk groups in the TCGA-LUAD dataset. (f) Receiver operating characteristic (ROC) curve of 1, 3, 5 years for predicting survival in the TCGA-LUAD dataset.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3984429/v1/20a82aff3aa940a111f435e1.jpg"},{"id":52105346,"identity":"5ccf9cad-c63c-46d7-9640-f70652368e45","added_by":"auto","created_at":"2024-03-06 19:27:23","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1374451,"visible":true,"origin":"","legend":"\u003cp\u003eIndependent prognostic factors to construct a nomogram for predicting LUAD. (a) The univariate and (b) multivariate Cox regression analyses to screen independent prognostic factors. (c) Nomogram containing stage, risk score, and pathologic-T for the prediction of 1-, 3-, and 5-year survival of TCGA-LUAD cohorts. (d) Calibration curves of nomogram. The x-axis and y-axis represent the predicted survival rates of the nomogram and actual outcomes, respectively. The vertical line represents the 95% confidence interval. (e-i) Difference of risk scores in the TCGA-LUAD cohort with different clinical characteristics, including (e) age (\u0026lt; 60/ ≥ 60), (f) gender (female/male), (g) stage (stage Ⅰ/Ⅱ/Ⅲ/Ⅳ), (h) pathologic-T (T1/T2/T3/T4), and (i) pathologic-N (N0/N1/N2/N3).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3984429/v1/ccb2e2360b6c586524080503.jpg"},{"id":52105347,"identity":"3ffea2e8-9cbd-42a3-942f-5f06335ddad6","added_by":"auto","created_at":"2024-03-06 19:27:23","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1456368,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction for the response to immunotherapy of the patients in the two risk groups. (a,b) The boxplots for the differential expression of (a) 14 HLA related genes and (b) 25 immune checkpoints between the high and low risk groups. (c) Scatter plots for the Spearman correlations of 4 characteristic genes and the 25 significant immune checkpoints. (d) Bar chart for the percents of who responded or did not respond to immunotherapy in two risk groups. (e) Difference of the Dysfunction score, Exclusion score, and TIDE score between the high and low risk groups. (f) Difference of the immunephenoscore (IPS) (including IPS values, IPS-PD1, IPS-CTLA4, IPS-CTLA4-PD1) of LUAD patients between the high and low risk groups. *\u003cem\u003eP \u0026lt;\u003c/em\u003e 0.05; **\u003cem\u003eP \u0026lt;\u003c/em\u003e 0.01; ***\u003cem\u003eP \u0026lt;\u003c/em\u003e 0.001; ****\u003cem\u003eP \u0026lt;\u003c/em\u003e0.0001; ns, no significance.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3984429/v1/2e1e4c19fe971c719053976f.jpg"},{"id":52106014,"identity":"4635c944-6313-4f58-9e81-35e74d251012","added_by":"auto","created_at":"2024-03-06 19:35:24","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1105150,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis for drug sensitivity of cohorts with different risk scores. (a) Differences in the half-maximal inhibitory concentration (IC50) of the top 8 drugs (according to significance ranking) between the two risk groups from the TCGA-LUAD dataset. (b-e) Scatter plot for the Spearman correlation of 4 characteristic genes and corresponding most relevant drugs.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3984429/v1/943d6f0ddd375e490b9eb9a7.jpg"},{"id":52105351,"identity":"82ec33b6-5511-4068-b7a1-d257399905ba","added_by":"auto","created_at":"2024-03-06 19:27:24","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2023422,"visible":true,"origin":"","legend":"\u003cp\u003eVerifying of the expression level of 4 characteristic genes in samples from different sources. (a) TCGA-LUAD cohorts. (b) HPA online database (image form immunohistochemically stained tissue microarrays). (c-f) Various LUAD cell lines from the the cell database for (c) RRM2, (d) TXNRD1, (e) NME4, (f) NT5E.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3984429/v1/6a74b28dbe443b0a021ac70f.jpg"},{"id":52106013,"identity":"8505b039-2a9c-4881-8102-273f85dd1d21","added_by":"auto","created_at":"2024-03-06 19:35:24","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1718519,"visible":true,"origin":"","legend":"\u003cp\u003eVerifying of the expression level of 4 characteristic genes in single cells data from the TISCH database. The heat maps of correlation of the single cells and characterized genes: (a) NME4, (b) NT5E, (c) RRM2, (d) TXNRD1. (e) Pie chart for the percentage of each cell type in GSE131907 datasets. (f) Cellular components based on GSE131907. (g-j) Uniform manifold approximation and projection (UMAP) plots for the expression of (g) NME4, (h) NT5E, (i) RRM2, (j) TXNRD1 based on GSE131907.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3984429/v1/f3d43565f3e209b4f35f4741.jpg"},{"id":52417599,"identity":"a609276e-e5b1-47a1-b6a6-af068c6c6763","added_by":"auto","created_at":"2024-03-11 12:08:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1586260,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3984429/v1/796f5fcc-ff22-471e-9bf8-81860098edf2.pdf"},{"id":52105352,"identity":"102dc943-c24b-42bb-844b-5e24d4066860","added_by":"auto","created_at":"2024-03-06 19:27:24","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7183316,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-3984429/v1/5876a7f013a12b24352bae2a.tif"},{"id":52105353,"identity":"ee2c5222-c714-403d-bc57-b830f12eb4f0","added_by":"auto","created_at":"2024-03-06 19:27:24","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2874148,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-3984429/v1/dee46b608cae26583de1e154.tif"},{"id":52105360,"identity":"972ae071-b0d8-4985-bd01-24123c3c5541","added_by":"auto","created_at":"2024-03-06 19:27:24","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":5849296,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-3984429/v1/6ec667d8d9b0da0c26e0845d.tif"},{"id":52106623,"identity":"0539709f-4b21-469d-97ed-a3233541f1d1","added_by":"auto","created_at":"2024-03-06 19:43:24","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":4493772,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS4.tif","url":"https://assets-eu.researchsquare.com/files/rs-3984429/v1/afc984786a93908f7b01161d.tif"},{"id":52105356,"identity":"13be85c3-2b56-4e0f-a629-6fd742a0119c","added_by":"auto","created_at":"2024-03-06 19:27:24","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":7124332,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS5.tif","url":"https://assets-eu.researchsquare.com/files/rs-3984429/v1/467fd58611d17a65cb38d883.tif"},{"id":52105363,"identity":"05cdd2b7-eb53-4299-a5ab-6ba293661b21","added_by":"auto","created_at":"2024-03-06 19:27:25","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":697116,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS6.tif","url":"https://assets-eu.researchsquare.com/files/rs-3984429/v1/70c6d39ea29feb3338a0ca34.tif"},{"id":52106018,"identity":"bf6381bf-18b2-4ebc-b19d-ede31c92b908","added_by":"auto","created_at":"2024-03-06 19:35:25","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":9462,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3984429/v1/2c73b736d7a047c73ec1e4de.xlsx"},{"id":52105361,"identity":"db3b6c80-d950-4aa4-b369-cfbf3dcf2ddc","added_by":"auto","created_at":"2024-03-06 19:27:24","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":31805,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3984429/v1/7d54e56e76235c86e4915be5.xlsx"},{"id":52105354,"identity":"5a950f95-4cf4-4b08-bac0-46f175f565af","added_by":"auto","created_at":"2024-03-06 19:27:24","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":10936,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3984429/v1/dcae5bfaf44f3274d1d929f3.xlsx"},{"id":52105358,"identity":"d50882d7-215b-4692-847f-e5353a7fd1e7","added_by":"auto","created_at":"2024-03-06 19:27:24","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":20829,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3984429/v1/eb11d443a1a056bac5732588.xlsx"},{"id":52106016,"identity":"e6a51c33-5016-48ae-a1cb-ac3f04231955","added_by":"auto","created_at":"2024-03-06 19:35:24","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":13405,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3984429/v1/45eb9be80b85bf77e4c531ab.xlsx"},{"id":52105364,"identity":"24040981-ee59-4566-9ba9-09deddbbb998","added_by":"auto","created_at":"2024-03-06 19:27:25","extension":"docx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":4748506,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-3984429/v1/a172c46ea29e17f2e1044888.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Construction of a Prognostic Model for Lung Adenocarcinoma Based on Nucleotide Metabolism-Related Genes and Bioinformatics Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLung adenocarcinoma (LUAD) is the most common form of lung cancer, primarily originating from the epithelial cells of the small airways and type II alveolar cells, accounting for approximately 40% of all lung cancer cases [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Clinically, early-stage LUAD patients are often asymptomatic, but as the disease progresses, they commonly present symptoms such as shortness of breath, cough, chest pain, and hemoptysis. Despite recent advances in the diagnosis and treatment of lung adenocarcinoma, the primary treatment modalities include surgical resection, radiotherapy, chemotherapy, targeted therapy, and immunotherapy. Nevertheless, for late-stage patients, the prognosis remains unfavorable, with a 5-year overall survival rate ranging from 4\u0026ndash;17% [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, the exploration of LUAD genes associated with patient survival is of paramount importance for improving prognosis and providing appropriate treatment. These genes may be involved in critical processes related to tumor initiation, development, and metastasis. Revealing the roles of these genes can provide a strong basis for personalized treatment, facilitate the development of more precise therapeutic strategies, and eventually have the potential to improve patient survival and quality of life.\u003c/p\u003e \u003cp\u003eNucleotide metabolism is a crucial biological process within cells, encompassing nucleotide synthesis and degradation. Serving as the fundamental building blocks of DNA and RNA, nucleotide metabolism plays a pivotal role in maintaining genome integrity, cell division, DNA repair, protein synthesis, and other essential biological processes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Nucleotide synthesis is an energy-intensive process, and excessive nucleotide synthesis metabolism is closely associated with uncontrolled proliferation, immune evasion, metastasis, and drug resistance in cancer cells [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Research on nucleotide metabolism-related genes is gradually highlighting their significance. Some key enzymes such as ribonucleotide reductase (RR) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], phosphoribosyl pyrophosphate synthetase 1 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and xanthine oxidoreductase [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] have been discovered to be closely linked to the initiation and progression of tumors, offering new opportunities for prognosis assessment and personalized treatment. However, there is currently a lack of reports on the relevance of nucleotide metabolism in the occurrence, development, and prognosis of LUAD.\u003c/p\u003e \u003cp\u003eIn this study, we obtained LUAD-related datasets from the public databases. Through a combination of univariate, Lasso and multivariate cox regression analyses, we identified nucleotide metabolism-related genes linked with the survival of LUAD patients. This research aims to supply potential targets for clinical diagnosis and patient prognosis, while also laying a theoretical foundation for a deeper understanding of the mechanisms underlying LUAD.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data Acquisition\u003c/h2\u003e \u003cp\u003eFrom the UCSC Xena browser (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/datapages/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/datapages/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), TCGA-LUAD cohort contains 58 control samples and 510 LUAD samples. A total of 487 LUAD samples with more than 30 days of follow-up time were utilized to establish a risk model. For further validation, through GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ww.ncbinlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://ww.ncbinlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), GSE72094 was acquired and included 386 LUAD samples with more than 30 days of follow-up [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Besides, we obtained 90 nucleotide metabolism-related genes (NMRGs) from published literature [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Identifying of nucleotide metabolism-related differentially expressed genes (NM-DEGs)\u003c/h2\u003e \u003cp\u003eThe DEGs were screened between LUAD and control groups in TCGA-LUAD cohort via \u0026lsquo;DESeq\u0026rsquo; package (v 1.36.0) with |log\u003csub\u003e2\u003c/sub\u003eFC|\u0026gt;1 and adj \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Moreover, the DEGs were intersected to NMRGs to obtain NM-DEGs. After that, to probe the biological functions of NM-DEGs, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (adj \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05) enrichment analysis were executed via \u0026lsquo;clusterProfiler\u0026rsquo; package (version 4.7.1.001). Additionally, a protein-protein interaction (PPI) network for these proteins was established through STRING database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Construction and Validation of risk model\u003c/h2\u003e \u003cp\u003eThe characteristic genes were selected through combining the univariate Cox (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05), LASSO, and multivariate COX (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.2) on the basis of the NM-DEGs in the TCGA-LUAD dataset. The risk score was computed. Therefore, each LUAD patient could obtain a risk score using this formula. Subsequently, the patients in TCGA-LUAD were assigned into high/low risk groups on the basis of the median value. The survival variance between two risk groups was analysed using Kaplan-Meier (K-M) curves. Receiver operating characteristic (ROC) curves were employed to assess the risk model's capacity for forecasting. Ultimately, the risk model was validated in GSE72094.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Independent prognostic analysis, creation of nomogram, and clinical correlation analysis\u003c/h2\u003e \u003cp\u003eThe Cox analyses (including univariate Cox (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05), and multivariate COX (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.2)) were utilized to perform the independent prognostic analysis in the TCGA-LUAD cohort to obtained independent prognostic variables. Then, a nomogram with independent prognostic variables was developed. Furthermore, a corresponding calibration curve was plotted to estimate reliability of the nomogram. In addition, the differences in risk score in different groups in each clinical characteristic were compared via Wilcoxon test (n\u0026thinsp;=\u0026thinsp;2) and Kruskal test (n\u0026thinsp;\u0026gt;\u0026thinsp;2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Enrichment Analysis\u003c/h2\u003e \u003cp\u003eGene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were executed to look into the signaling pathways and probable biological mechanisms linked to two risk groups. Firstly, the \u0026lsquo;c2.cp.kegg.v2023.1.Hs.symbols\u0026rsquo; was downloaded as a background set. Then, the differentially expressed analysis was performed between two risk groups, and genes were ranked according to the log2FC. In addition, the \u0026lsquo;h.all.v2023.1.Hs.symbols.gmt\u0026rsquo; was extracted in MsigDB database as reference set for GSVA. The GSVA scores for each HALLMARK pathway in the two risk groups were computed via \u0026ldquo;GSVA\u0026rdquo; package (v 1.44.5) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and the variations were assessed via \u0026ldquo;limma\u0026rdquo; package (v 3.52.4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Immune infiltration analysis\u003c/h2\u003e \u003cp\u003eThe CIBERSORT algorithm (v 1.03) was employed to assess the infiltrating abundance of 22 immune cells in TCGA-LUAD cohort [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and the differences were compared via Wilcox Test (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05). The relevance between risk score and different immune cells between two risk groups was evaluated via Spearman algorithm in \u0026ldquo;psych\u0026rdquo; package (v 2.2.9). After that, we obtained the 12 immune-related functions through the published literature [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and ssGSEA in \u0026ldquo;GSVA\u0026rdquo; package (v 1.44.5) was employed to calculated the score of each immune-related function in LUAD patients [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Moreover, the differences were assessed between two risk groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Immunotherapy Analysis\u003c/h2\u003e \u003cp\u003eHuman leukocyte antigens (HLAs) play a specific synergistic role in the immune response. Therefore, the expression of HLA related genes was measure in two risk groups. Additionally, a total of 48 immune checkpoints were discovered through published research [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and the Wilcoxon test was employed to compare the expression of these genes in two risk groups. And the relevance between the differentially expressed immune checkpoints and characteristic genes was assessed via Spearman algorithm. TIDE algorithm was executed to assess the likelihood of tumor immune escape. Therefore, the TIDE score, Dysfunction score, and Exclusion score were evaluated [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. And we counted LUAD patients who responded or did not respond to immunotherapy in two risk groups and compared the differences. Additionally, the immunephenoscore (IPS) of LUAD patients was gained via TCIA, and differences were measured by Wilcoxon test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Relevance analysis between risk score and cancer stem cell index\u003c/h2\u003e \u003cp\u003eCancer stem cells (CSCs) are cancer cells that have the capacity to transform into cancer and exhibit traits common to normal stem cells. The CSC index, which measures how closely tumor cells resemble stem cells by reflecting the stem cell's gene expression profile, could be viewed as a quantification of CSCs. The CSC indexes were computed via OCLR algorithm. Moreover, the relevance between risk score and CSC indexes was measured via Spearman algorithm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9. Mutation analysis\u003c/h2\u003e \u003cp\u003eThe \u0026ldquo;maftools\u0026rdquo; package (v 2.12.0) was employed to process the mutation data of LUAD patients [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Afterwards, in TCGA-LUAD cohort, the tumor mutation burden (TMB) scores of each LUAD patient were counted, and the Spearman algorithm was employed to assess the relevance between TMB score and risk score. LUAD patients were split into high and low TMB groups according to the median TMB score. These two groups were then joined with two risk groups to create four different groups: high risk-low TMB, high risk-high TMB, low risk-high TMB, and low risk-low TMB. The differences in survival among these four groups were compared.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10. Chemotherapy drug sensitivity analysis\u003c/h2\u003e \u003cp\u003eThe GDSC database was employed to gain anti-cancer drugs. The half-maximal inhibitory concentration (IC50) in anti-cancer drugs was estimated via the \"oncoPredict\" package (v 0.2), and the differences of IC50 were estimated using Wilcoxon test (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05), in order to evaluate the sensitivity of anti-cancer drugs in the two risk groups of the training set. Additionally, since the NCI-60 cell line was used for screening new anticancer drugs, we further analyzed drug sensitivity of the NCI-60 cell line. The drug sensitivity data were extracted from CellMiner database. The correlation between characteristic genes and drug sensitivity was computed via Spearman algorithm with |cor| \u0026gt; 0.3 and \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11. Expression analysis of characterized genes\u003c/h2\u003e \u003cp\u003eFirst, we examined the expression of characterized genes in single cells in order to evaluate the connection between single cells and characterized genes via the TISCH database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tisch1.comp-genomics.org/home/\u003c/span\u003e\u003cspan address=\"http://tisch1.comp-genomics.org/home/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The HPA online database was executed to measure the protein expression levels of the characterized genes in tissue samples from LUAD and healthy individuals. Subsequently, the characterized genes expression in various LUAD cell lines was then further examined after the RNA-seq profiles of various LUAD cell lines were retrieved from the cell database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.broadinstitute.org/ccle\u003c/span\u003e\u003cspan address=\"http://www.broadinstitute.org/ccle\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12. Real time quantitative polymerase chain reaction (RT-qPCR)\u003c/h2\u003e \u003cp\u003eFirst, 20 frozen tissue samples were collected from Qingdao Central Hospital, of which 10 were control and 10 were LUAD. This study was approved by Qingdao Central Medical Group Medical Ethics Committee. All patients had signed an informed consent form. Total RNA was then obtained from a 50 mg tissue sample by TRIzol and converted to cDNA using a reverse transcription reaction (SureScript-First-strand-cDNA-synthesis-kit). The characteristic genes were then identified by bioinformatics analysis and specific primers were designed for RT-qPCR amplification. Next, the RT-qPCR reaction mixture was set up, including the cDNA template, primer and appropriate RT-qPCR master reagent mixture (2xUniversal Blue SYBR Green qPCR Master Mix) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Subsequently, RT-qPCR amplification was performed on a CFX Connect real-time quantitative fluorescent PCR instrument, and amplification curves and fluorescence signals were recorded. Finally, the 2\u003csup\u003e\u0026minus;△△CT\u003c/sup\u003e value was counted, and the P-value was counted by Graphpad Prism 5 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1. There were 25 NM-DEGs between LUAD and control groups\u003c/h2\u003e \u003cp\u003eBetween LUAD and control groups, a total of 3,229 DEGs were obtained (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea-b). These genes interacted with NMRGs to obtain 25 NM-DEGs (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ec). Of the GO results, top 8 items were demonstrated. a total of 211 items (including 164 GO BP items, 8 GO CC items, and 39 GO MF items) were significantly enriched (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e), such as \u0026lsquo;nucleoside monophosphate metabolic process\u0026rsquo;, \u0026lsquo;ficolin-1-rich granule lumen\u0026rsquo;, \u0026lsquo;nucleobase-containing compound kinase activity\u0026rsquo; (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ed). Of the KEGG results, there were 10 KEGG pathways markedly enriched (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). For instance, NM-DEGs were involved in \u0026lsquo;Nucleotide metabolism\u0026rsquo;, \u0026lsquo;Pyrimidine metabolism\u0026rsquo;, \u0026lsquo;Purine metabolism\u0026rsquo;, etc. (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ee). The PPI network contained 25 nodes and 104 edges. For instance, DTYMK protein interacted with multiple proteins, including NME4, NT5E, TK1, etc. (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ef).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Construction of risk model incorporating NM-DEGs\u003c/h2\u003e \u003cp\u003eThrough univariate Cox, 17 NM-DEGs with prognostic values were selected (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Using LASSO analysis, there were 6 NM-DEGs were identified, namely RRM2, TYMS, ATIC, TXNRD1, NME4, and NT5E (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). After that, in total, 4 characteristic genes, RRM2, TXNRD1, NME4, and NT5E, were selected by multivariate COX (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). The LUAD patients were split into high/low risk groups according to the medium risk score (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed), and those in the high risk group had a decreased chance of survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). The AUC values were greater than 0.6 in 1-, 3-, 5-year, demonstrating that the risk model had well performance of the prognostic prediction of LUAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef). Additionally, we verified processing in GSE72094, and the outcomes matched the TCGA-LUAD cohort (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ea-c).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Establishment and calibration of the nomogram that combined clinical characteristics and risk score\u003c/h2\u003e \u003cp\u003eThrough univariate Cox and multivariate COX, there were three independent prognostic factors, namely stage (P\u0026thinsp;=\u0026thinsp;6.1e-06), risk score (P\u0026thinsp;=\u0026thinsp;2.7e-06), and pathologic-T (P\u0026thinsp;=\u0026thinsp;0.15) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-b). Thereafter, the nomogram was created, and the slopes of the calibration curves for 1-, 3-, and 5- years were close to 1, especially for 1- and 3- years (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec-d). These findings revealed that the nomogram was an excellent predictor of LUAD patient prognosis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, the variations in risk score between various groups for each clinical characteristic were examined to look into the relevance between risk score and clinical characteristics. The results demonstrated that risk score was considerably different in pathologic-T (T1/T2/T3/T4) (P\u0026thinsp;=\u0026thinsp;5e-04), age (\u0026lt;\u0026thinsp;60/ \u0026ge; 60) (P\u0026thinsp;=\u0026thinsp;0.016), stage (stage Ⅰ/Ⅱ/Ⅲ/Ⅳ) (P\u0026thinsp;=\u0026thinsp;1.9e-07), and pathologic-N (N0/N1/N2/N3) (P\u0026thinsp;=\u0026thinsp;8.7e-08) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee-i).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.4. GSEA and GSVA of two risk groups\u003c/h2\u003e \u003cp\u003eFor GSEA results, a total of 52 KEGG pathways were considerably enriched (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e), and top5 pathways were displayed based on significance ranking (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003ea). Among these, \u0026lsquo;cell cycle\u0026rsquo;, \u0026lsquo;dna replication\u0026rsquo;, \u0026lsquo;proteasome\u0026rsquo;, and \u0026lsquo;spliceosome\u0026rsquo; were enriched in high risk group, and \u0026lsquo;vascular smooth muscle contraction\u0026rsquo; was enriched in low risk group.\u003c/p\u003e \u003cp\u003eFor GSVA results, the score of each pathway in two risk groups was demonstrated via heap map (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eb). The scores of 32 HALLMARK pathways were significantly different between two risk groups (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). Top 10 pathways in two risk groups were shown according to |t| ranking (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003ec). For instance, \u0026lsquo;glycolysis\u0026rsquo;, \u0026lsquo;E2F targets\u0026rsquo;, \u0026lsquo;MTORC1 signaling\u0026rsquo;, and so on were activated in the high risk groups. However, the \u0026lsquo;KRAS signaling DN\u0026rsquo;, \u0026lsquo;bile acid metabolism\u0026rsquo;, \u0026lsquo;hedgehog signaling\u0026rsquo; and etc. were activated in low risk groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Evaluation immune microenvironment and Immunotherapy between two risk groups\u003c/h2\u003e \u003cp\u003eAfter excluding immune cells with 0 immune infiltration (T cells CD4 naive), the infiltration abundance of the remaining 21 immune cells was displayed by heatmap (Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003ea). In total, the infiltration abundance of 11 immune cells were markedly different between two risk groups, such as plasma cells, monocytes, macrophages, etc. (Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eb). Relevant analysis results showed that risk score was most obviously positively relevant to activated memory CD4 T cells (R\u0026thinsp;=\u0026thinsp;0.34, P\u0026thinsp;=\u0026thinsp;3e-14), and was most considerably negatively relevant to resting memory CD4 T cells (R = -0.25, P\u0026thinsp;=\u0026thinsp;6.5e-08) (Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003ec-d). After that, among 12 immune-related functions, there were 4 immune-related functions markedly different between two risk groups, namely APC co-stimulation, MHC class Ⅰ, parainflammation, and Type Ⅱ IFN response (Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003ee). Additionally, the results of relevance between risk score and CSC indexes (mDNAsi and mRNAsi) suggested that risk score was markedly positively relevant to mRNAsi (R\u0026thinsp;=\u0026thinsp;0.47, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;2.2e-16) (Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003ef). However, the relevant between risk score and mDNAsi was not significant (R\u0026thinsp;=\u0026thinsp;0.077, P\u0026thinsp;=\u0026thinsp;0.11) (Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eg).\u003c/p\u003e \u003cp\u003eWe further explored the response to immunotherapy of the patients in the two risk groups. Firstly, a total of 14 HLA related genes (including HLA-DMA, HLA-DMB, HLA-DOA) and 25 immune checkpoints (including ADORA2A, BTLA, BTNL2) were markedly differentially expressed between two risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-b). The relevant analysis suggested that RRM2 was most significantly positively correlated with CD276 (R\u0026thinsp;=\u0026thinsp;0.34, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0,001), and NME4 was most considerably negatively relevant to CD200R1 (R = -0.37, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Then, through TIDE algorithm, the percentage of LUAD patients who responded to immunotherapy in low risk group was 43.9%, and the percentage was 28.4% in the high risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). There were considerably different between two risk groups (P\u0026thinsp;=\u0026thinsp;0.00038). Afterwards, the TIDE score was obviously higher in high risk group, and IPS score and IPS-CTLA4 were markedly lower in the high risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee-f). These finding indicated that the patients benefited more from immunotherapy in low risk group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Mutation analysis between two clusters\u003c/h2\u003e \u003cp\u003eSomatic mutations in two risk groups were demonstrated in Figure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003ea-b. The genes with the highest rates of mutation in the high and low risk groups, respectively, were MUC16 and TTN. Additionally, the TMB score was markedly positively relevant to risk score (R\u0026thinsp;=\u0026thinsp;0.3, P\u0026thinsp;=\u0026thinsp;1.2e-11) (Figure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003ec). Moreover, the survival of patients in low-risk and high-TMB group was the highest (Figure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003ed).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Drug sensitivity analysis\u003c/h2\u003e \u003cp\u003eA total of 198 anti-cancer drugs were obtained via GDSC database. Among these drugs, the IC50 of 124 drugs were considerably different between two risk groups. The top 8 drugs according to significance ranking were shown via box plot, and the patients in the low-risk group were more sensitive to these eight drugs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Additionally, the relevant analysis demonstrated that NT5E was most relevant to AFP464 (R = -0.54, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.0001); RRM2 was most relevant to Crizotinib (R\u0026thinsp;=\u0026thinsp;0.34, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.01); NME4 was most relevant to Cladribine (R\u0026thinsp;=\u0026thinsp;0.59, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.0001); and TXNRD1 was most relevant to Tamoxifen (R = -0.46, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb-e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.8. Expression analysis for characteristic genes\u003c/h2\u003e \u003cp\u003eIn TCGA-LUAD cohort, all characteristic genes were up-regulated in LUAD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Four proteins were discovered to be highly expressed in LUAD tissue using the HPA database (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Moreover, all characteristic genes were expressed in all cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec-f). In six single-cell databases, we also examined the expression of characteristic genes in various cell types via the TISCH database, and the expression of characteristic genes was higher in each cell type in GSE131907 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea-d). In addition, we selected GSE131907 for further study. In GSE131907, the percentage of each cell type was shown, and the percentage of CD4 T conv was highest (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee-f). Among these cell types, NME4 was mainly expressed in epithelial and oligodendrocyte (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eg). NT5E was mainly expressed in epithelial and B cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eh). RRM2 was mainly expressed in plasma and DC cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ei). TXNRD1 was mainly expressed in epithelial and mono/macro cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ej).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.9. The Expression Levels of the Biomarkers\u003c/h2\u003e \u003cp\u003eThe RT-qPCR results displayed that the expression of the RRM2, NT5E and TXNRD1 between LUAD and control groups were markedly different. The TXNRD1 was lowly expressed and RRM2 and NT5E were highly expressed in LUAD (Figure \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003ea-d). In summary, the results of RT-qPCR suggested that RRM2 and NT5E have good diagnostic value for LUAD.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eEarly-stage LUAD patients can achieve curative results through surgical intervention. However, due to the lack of obvious clinical symptoms in the early-stage of LUAD patients and limitations in screening methods, most patients are found to have already progressed to the middle and late stages of the disease [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Among the factors influencing tumorigenesis, alterations in nucleotide metabolism play a pivotal role, with enhanced synthesis of nucleoside triphosphates being crucial for the development of LUAD [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. A deeper understanding of metabolism in cancer can assist in the identification of valuable diagnostic biomarkers [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In this study, we utilized data from the public datasets and employed a combination of univariate cox regression, Lasso cox regression, and multivariate cox regression analysis to identify four nucleotide metabolism-related genes: RRM2, TXNRD1, NME4, and NT5E, as potential biomarkers. Based on the expression levels of these four prognostic genes and overall survival (OS) data, we established a risk scoring system that categorizes LUAD patients into low and high risk groups. In this context, we have identified key Nucleotide Metabolism-Related Genes (NMRGs) relevant to LUAD progression and prognosis and have analyzed their potential functional mechanisms.\u003c/p\u003e \u003cp\u003eTo understand the molecular and immune-related differences between high/low-risk subgroups, we investigated the functions of the four biomarkers. Ribonucleotide reductase M2 subunit (RRM2) is a rate-limiting enzyme in the nucleotide synthesis pathway, and increased RRM2 expression and activity have been associated with various cancer types. It has been overexpressed and linked to tumor progression, invasion, metastasis, and lower patient survival rates in cancers such as gastric, ovarian, colorectal, brain, and breast cancer [\u003cspan additionalcitationids=\"CR26 CR27 CR28\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Consequently, RRM2 has long been considered an important drug target for various proliferative diseases, including cancer [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In LUAD cells, RRM2 overexpression enhances tumor cell proliferation and invasion, and it is considered an independent risk factor for the overall survival (OS) of LUAD patients [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Our experiments also confirm that RRM2 expression is significantly higher in LUAD tissues compared to adjacent normal tissues. Cytoplasmic selenoprotein thioredoxin reductase 1 (TXNRD1) plays multiple roles associated with malignant tumors, as it can protect normal cells from malignant transformation [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, in liver cancer, upregulated TXNRD1 expression promotes hepatocellular carcinoma progression through the activation of the Akt/mTOR signaling pathway and is associated with lower patient survival rates [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. There is limited research on TXNRD1 in LUAD, but a study by Jin X suggests that TXNRD1 expression is reduced in LUAD patients who are female, have not received radiotherapy, and have no distant metastases, which aligns with our verification results [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Nucleoside Diphosphate Kinase 4 (NME4) is a critical rate-limiting enzyme that regulates nucleotide metabolism and ATP/ITP metabolism [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Aberrant overexpression of the NME4 gene in gastric and colon cancer may lead to an imbalance in nucleotide pools in mitochondria, resulting in checkpoint regulation failure and the accumulation of genetic changes, ultimately leading to tumorigenesis [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. As an oncogenic promoter, NME4 can promote the progression of NSCLC by inhibiting cell cycle arrest and stimulating tumor cell proliferation [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. NT5E encodes the ecto-5\u0026rsquo;-nucleotidase (CD73) is a critical rate-limiting enzyme in the extracellular purine metabolism pathway. It plays a crucial role in generating and maintaining adenosine concentrations, thereby influencing tumor cell neovascularization, immune evasion, and immune response [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. CD73 is highly expressed in most cancers, but its expression levels are lower than adjacent normal tissues in appendiceal adenocarcinoma and ovarian cancer. Moreover, high CD73 expression is closely associated with lower overall survival (OS) but not with recurrence-free survival (RFS) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In vitro experiments, NT5E may promote LUAD proliferation and metastasis through the EGFR/AKT/mTOR pathway [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThrough GSEA enrichment analysis, in addition to cell cycle, DNA replication, proteasome, and vascular smooth muscle contraction pathways associated with tumor proliferation and invasion, spliceosome-related signaling pathways were also enriched. These findings suggest that differences in survival may be driven by different immune status in LUAD patients [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Vascular smooth muscle contraction plays a crucial role in controlling blood flow and the delivery of oxygen and nutrients to tissues. Tumors often possess a higher density of microvasculature than normal tissues. However, these pathological vessels are often less elastic and function differently from normal vessels, often promoting tumor growth through autocrine signaling [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The specific mechanisms underlying this phenomenon in LUAD are not yet clear. GSVA analysis indicates a significant upregulation of the glycolysis signaling pathway in the high-risk group. Elevated glycolysis signaling pathways are closely associated with immune therapy resistance and poor prognosis [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Studies have shown that chenodeoxycholic acid (CDCA), acting as an integrin α5β1 inhibitor, can inhibit LUAD cell proliferation, migration, and invasion and induce apoptosis through the α5β1/FAK/p53 signaling pathway [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Our research suggests that the downregulation of bile acid metabolism pathways is more pronounced in the high-risk group, possibly contributing to the poor prognosis.\u003c/p\u003e \u003cp\u003eImmunotherapy, as an emerging treatment approach, has shown promising results. However, it is not universally effective for all LUAD patients, as its efficacy varies depending on the patient's immune system status and cancer characteristics. Immune evasion is a significant hallmark of cancer progression, and the downregulation of HLA can reduce antigen presentation, thereby promoting immune escape [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Our results indicate that 14 HLA family genes are expressed at higher levels in low-risk group patients, suggesting that low-risk patients may benefit from immunotherapy and consequently achieve longer overall survival (OS). We conducted a differential expression analysis of 48 immune checkpoint molecules between high and low-risk groups in LUAD. We found significant differences in the expression of 25 immune checkpoint molecules between the high and low-risk groups. Our study demonstrated that RRM2 has the highest positive correlation with CD276, an immune checkpoint molecule. Aberrant expression of CD276 upregulates the epithelial-mesenchymal transition (EMT) of LUAD cells, promoting LUAD development [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. However, further studies are needed to understand the interaction between RRM2 and CD276. TIDE, as an algorithm for predicting patient response to immune checkpoint inhibitor (ICI) therapy, suggests that higher scores indicate a higher likelihood of significant immune escape and a lower likelihood of benefiting from ICI treatment [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. We used the TIDE algorithm, IPS algorithm, and TMB analysis to assess their responses to immunotherapy, and our research suggests that low-risk patients are more likely to benefit from immunotherapy. Furthermore, understanding the composition of immune cells in tumor tissue can help identify new cancer treatment approaches and enhance the efficiency of ICI therapy. Tumor-associated macrophages can promote tumor cell proliferation, migration, and tumor angiogenesis. M0 macrophage high-density infiltration is closely associated with poor clinical outcomes in early-stage LUAD, which is consistent with our research [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Many studies suggest that increased infiltration of plasma cells in tumors can significantly prolong the OS of NSCLC patients receiving PD-L1 treatment [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Our research suggests a significant downregulation of plasma cell infiltration in the high-risk group, indicating a worse prognosis. High infiltration density of mast cells can extend the survival of early-stage LUAD patients [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] and lead to a higher recurrence-free survival (RFS) for stage I and II postoperative patients [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. However, another study suggests that mast cells promote LUAD cell metastasis through the release of proteases via exosomes [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Therefore, the relationship between mast cells and LUAD remains controversial and requires further investigation.\u003c/p\u003e \u003cp\u003eWe assessed the sensitivity of high/low-risk group patients to drugs currently used for the treatment of LUAD. High-risk group patients had obviously higher IC50 values for Doramapimod_1042, BMS-754807_2171, MK-2206_1053, and Nutlin-3a (-)_1047 compared to low-risk group patients, suggesting that the low-risk group may have a better response to chemotherapy and targeted therapy. Doramapimod_1042, BMS-754807_2171, MK-2206_1053 are inhibitors of the p38-MARK, PI3K/AKT, and AKT/PKB pathways, respectively, and they can significantly inhibit the proliferation of lung adenocarcinoma cells and increase apoptosis [\u003cspan additionalcitationids=\"CR55\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. On the other hand, Nutlin-3a (-)_1047 activates p53 in normal lung epithelial cells and induces apoptosis in lung adenocarcinoma cells [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. The validation results of gene expression showed consistency with our bioinformatics analysis for RRM2 and NT5E. We speculate that RRM2 inhibitors can activate the cGAS/STING signaling pathway, increase CD8\u0026thinsp;+\u0026thinsp;T cell infiltration, and have an anti-tumor effect [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. When given EGFR, AKT, or mTOR inhibitors, the function of NT5E can be significantly inhibited, indicating that NT5E may be involved in the pathogenesis of LUAD through the EGFR/AKT/mTOR axis [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The differences in NME4 and TXNRD1 expression may be attributed to sample variations.\u003c/p\u003e \u003cp\u003eWe have constructed a novel model based on nucleotide metabolism that underwent comprehensive analysis and validation across multiple databases, showing significant potential for LUAD prognosis prediction and providing new insights into LUAD treatment. However, this model still has its limitations. Firstly, immunotherapy has emerged as a viable treatment option for LUAD, but the selection and specific mechanisms of combination therapy remain to be further explored. Additionally, before the application of NMRGs in predicting LUAD treatment responses in clinical settings, further validation and support from real-world and basic research data are required.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eLUAD, Lung adenocarcinoma; RR, ribonucleotide reductase; NMRGs, nucleotide metabolism-related genes; NM-DEGs, nucleotide metabolism-related differentially expressed genes; DEGs, Diferentially expressed genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; PPI, protein-protein interaction; LASSO, Least absolute shrinkage and selection operator; ROC, receiver operating characteristic; K-M, Kaplan-Meier; GSEA, gene set enrichment analysis; GSVA, gene set variation analysis; HHLAs, uman leukocyte antigens; IPS, immunephenoscore; CSCs, Cancer stem cells; TMB, tumor mutation burden; IC50, half-maximal inhibitory concentration; RT-qPCR, real time quantitative polymerase chain reaction\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiangyu Cui: Data curation, Methodology, Formal analysis, Writing - review \u0026amp; editing, Writing-original draft. Wenjie Han: Data curation, Methodology, Formal analysis, Writing - review \u0026amp; editing, Writing - original draft. Ruihao Zhang: Data curation, Writing - review \u0026amp; editing, Writing - original draft. Guangsheng Zhu: Formal analysis, Writing - review \u0026amp; editing, Writing-original draft. Hua Huang: Formal analysis, Writing - review \u0026amp; editing, Writing-original draft. Yongwen Li: Methodology, Writing - review \u0026amp; editing, Supervision. Hongyu Liu: Conceptualization, Writing - review \u0026amp; editing, Supervision. Jun Chen: Conceptualization, Writing - review \u0026amp; editing, Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the current study are available in UCSC Xena browser [https://xenabrowser.net/datapages/] TCGA-LUAD, and the GEO repository, [https://www.ncbi.nlm.nih.gov/geo/] GSE72094.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was in accordance with the principles laid down in the Declaration of Helsinki; and was approved by Qingdao Central Medical Group Medical Ethics Committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWriting assistance was provided by zhixue Zhang, PhD, of Qingdao Central Hospital (Qingdao, Shandong, China) ; and Language assistance was provided by zhanqing LI, PhD, of The Second Affiliated Hospital of Xiamen Medical College (Xiamen, Fujian, China).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eA.C. 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Cell Biosci. 2022;12(1):149. doi:10.1186/s13578-022-00882-8.\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, Nucleotide metabolism, Prognosis, Immune microenvironment","lastPublishedDoi":"10.21203/rs.3.rs-3984429/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3984429/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Metabolic reprogramming is an important hallmark of cancer. However, it is still uncertain how nucleotide metabolism-related genes (NMRGs) may affect the prognosis of Lung adenocarcinoma (LUAD).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eIn our study, the LUAD cohorts from the bioinformatics databases were downloaded. Characteristic genes related to prognosis of LUAD patients were obtained through combining differentially expressed analysis, univariate COX analysis, least absolute shrinkage and selection operator (LASSO), and multivariate COX, and the risk model was constructed. Then, the immune infiltration, immunotherapy, and mutations analyses between high and low risk groups were conducted. Finally, drug sensitivity analysis and reverse transcription-polymerase chain reaction (RT-qPCR) was executed to validate the expression of the biomarkers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eBased on 4 characteristic genes (RRM2, TXNRD1, NME4, and NT5E), the risk model was established, and the patients were assigned to high/low risk groups. The survival analysis demonstrated that patients in low risk groups had higher survival. The infiltrating abundance of 11 immune cells, the expression of 25 immune checkpoints, TIDE score, Dysfunction score, Exclusion score, IPS, and IPS-CTLA4 were significantly different between two risk groups. Additionally, the survival of patients in low-risk and high-TMB group was the highest. Finally, the IC\u003csub\u003e50 \u003c/sub\u003eof 124 drugs was considerably different between two risk groups, such as Doramapimod_1042, BMS-754807_2171, MK-2206_1053, etc. Finally, RT-qPCR results showed that RRM2 and NT5E expression was obviously up-regulated and TXNRD1 expression was obviously down-regulated in LUAD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eTaken together, this study created a nucleotide metabolism related prognostic characteristic, which was relevant to immune microenvironment and immunotherapy.\u003c/p\u003e","manuscriptTitle":"Construction of a Prognostic Model for Lung Adenocarcinoma Based on Nucleotide Metabolism-Related Genes and Bioinformatics Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-06 19:27:18","doi":"10.21203/rs.3.rs-3984429/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":"a50f5563-0d95-4597-a146-f830f1d029a1","owner":[],"postedDate":"March 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-11T03:14:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-06 19:27:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3984429","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3984429","identity":"rs-3984429","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}