Multiple effects of immune and cuproptosis related LNCRNAs on lung adenocarcinoma and the related expression was verified by RT-PCR

Multiple effects of immune and cuproptosis related LNCRNAs on lung adenocarcinoma and the related expression was verified by RT-PCR | 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 Multiple effects of immune and cuproptosis related LNCRNAs on lung adenocarcinoma and the related expression was verified by RT-PCR Kang Sun, Zhiqiang Zhang, Dongqin Wang, Yinlong Huang, Jing Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1790514/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Lung adenocarcinoma is still a high mortality tumor disease. Immune cells play an important role in the further development of tumors, including NK and CD8 cells. Recent studies have found that cuproptosis is a new way of cell death induced by copper ions. Objective: Therefore, the purpose of this study was to screen the prognostic LNCRNAs associated with immunity and cuproptosis. Method: Based on TCGA and geo databases, LNCRNAs (IMMCusLNCRNAs) related to immunity and cuproptosis were obtained according to the grouping of NK and CD8 cells and genes related to copper filament death. Based on a variety of statistical and bioinformatics methods, an immcusLNCRNAs related risk model was established to evaluate the correlation between the model and prognosis. Using various immune cell scoring models in TIMER database, the distribution differences of immune cells in different risk groups were analyzed. Then, to explore the distribution of glycolysis and chemosensitivity in different risk groups. Finally, the expression levels of risk genes between lung epithelial cells and other lung adenocarcinoma cells were compared. Results: risk genes were closely related to prognosis. NK and CD8 cells were generally distributed in the low-risk group, while the glycolytic pathway was the opposite. Under different risk groups, the sensitivity of chemotherapy drugs is also very different. Finally, the expression of risk genes in normal lung epithelial cells is usually higher than that in other lung adenocarcinoma cells. Conclusion: the risk model related to immunity and cuproptosis and LNCRNAs provide an important idea for personalized speech therapy. Lung adenocarcinoma LNCRNAs immune Cuproptosis risk model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Lung cancer is still one of the main causes of tumor mortality, and lung adenocarcinoma is the main subtype of lung cancer, accounting for about 40% ( 1 – 2 ). At present, the conventional clinical treatment methods include surgical resection, chemotherapy, and radiotherapy. With the development and progress of treatment methods, such as molecular targeted therapy and immunotherapy ( 3 – 5 ). However, the five-year overall survival rate is still low, not more than 15% ( 6 ). At the same time, the occurrence and development of tumors are related to the clinicopathological stage and the abnormal expression of genes related to tumor cells ( 7 ). Therefore, it is necessary to develop new prognostic markers to improve the prognosis of patients. Relevant research reports pointed out that the immune cells in the tumor microenvironment (TME), play a key role in the prognosis of patients and the occurrence and development of tumor cells( 8 – 11 ). Among these immune cells, CD8 cells play the most important anti-tumor effect. However, tumor antigen can regulate the dysfunction and depletion of CD8 cells( 12 – 15 ). NK cells are toxic natural lymphoid cells (ILCs) that can target tumor cells by secreting cytolytic granules and secrete cytokines to promote an immune response. In many kinds of literature, NK cells are closely related to the prognosis of tumor patients. ( 16 – 20 ). At the same time, functionally, NK cells can complement T cells and target MHC-I deficient cells, including tumor cells ( 21 – 23 ). Therefore, immunotherapy combined with NK and CD8 cells will greatly improve the prognosis of patients. As an important cofactor, the dynamic balance of copper is important for various physiological activities. If the bioavailability of copper is maladjusted, it can cause oxidative stress and cytotoxicity ( 24 ). In recent reports, a new cell death pathway called Cuproptosis was proposed. This death occurs through the direct combination of copper and the lipoylated components of the tricarboxylic acid (TCA) cycle. The toxicity of copper is highly related to mitochondrial activity. Compared with glycolytic cells, cells with higher mitochondrial activity are more sensitive to copper ions and elesclomol treatment( 25 – 28 ). Although the specific mechanism and application of Cuproptosis need to be further explored, it also provides new thinking for the treatment of lung adenocarcinoma. Long noncoding RNAs (LNCRNAs) are composed of more than 200 nucleic acids, and most of them cannot synthesize proteins. In recent studies, LNCRNAs have an important impact on the occurrence and development of tumors, including cell proliferation, differentiation, migration, invasion, and drug resistance. At the same time, LNCRNAs play an important role in the regulation of the tumor immune microenvironment. ( 29 – 36 ). Therefore, it is necessary to find new LNCRNAs related to the prognosis of lung adenocarcinoma. In this study, we established a risk model and risk LNCRNAs related to NK, CD8, and cuproptosis, and studied the sensitivity of the model to patient prognosis, immune cells, and chemotherapeutic drugs. The risk LNCRNAs we screened can effectively inhibit the glycolytic pathway and are closely related to Cuproptosis-related genes. Methods And Materials The download of LUAD tissue and blood data Based on the TCGA database ( https://portal.gdc.cancer.gov/ ), RNA-seq data of 535 LUAD tissues were downloaded, and the corresponding clinicopathological information was also integrated, including gender, age, survival time, survival status, T, M, N, TMNstage and tumor mutation data. The gene matrix of peripheral whole blood associated with LUAD was collected from GSE20189 in the GEO database ( https://www.ncbi.nlm.nih.gov/geo/ ), a total of 82 cases. Calculate the distribution level of immune cells CIBERSOFT uses a deconvolution algorithm to calculate the relative scores of 22 kinds of immune subcellular through rich gene expression profiles. Use the R language to run the "CIBERSOFT. R" script to calculate the distribution level of immune cells in the blood and tissues of LUAD. Screening LNCRNAs closely related to NK and CD8 cells (IMMLNCRNAs) According to the best cut-off value of clinical analysis, NK and CD8 cells were divided into HIGH + HIGH, HIGH + LOW, LOW + HIGH, and LOW + LOW groups, respectively. At the same time, the "limma" package in R language, was used to analyze the difference between HIGH + HIGH and LOW + LOW groups, and the correlation analysis was made with LNCRNAs expression data (|cor| > 0.4). Finally, LNCRNAs related to NK and CD8 cells were screened. Screening of Cuproptosis related LNCRNAs (CuproptosisLNCRNAs) We collected 19 genes related to Cuproptosis from recently published articles, including NFE2L2, NLRP3, ATP7B, ATP7A, SLC31A1, FDX1, LIAS, LIPT1, LIPT2, DLD, DLAT, PDHA1, PDHB, MTF1, GLS, CDKN2A, DBT, GCSH, and DLST ( 25 – 28 ). Using the "limma" package in R language, the gene expression related to Cuproptosis in LUAD, was extracted and analyzed with LNCRNAs expression data (|cor|>0.4). LNCRNAs are associated with immunity and Cuproptosis (IMMCusLNCRNAs) Using the "Venn diagram" package in R language, IMMLNCRNAs and cuproptosis related LNCRNAs are co intersected to obtain IMMCusLNCRNAs. Establish a risk model related to immunity and Cuproptosis We used the "survival" packages in R language to conduct univariate Cox regression analysis on IMMCusLNCRNAs, to screen out prognosis-related IMMCusLNCRNAs (PMSLNCRNAs). Then, based on the "glmnet" package, we conducted the least regression analysis and absolute shrinkage and selection operator (lasso) regression analysis to select the most significant PMSLNCRNAs, The "predict" function of R language and multivariate Cox regression coefficient were used to analyze the risk score of a single sample. The "survivalroc" and "survival" packages in R language were used to calculate the clinical best cut-off value and AUC value of the three-year ROC curve (1 -, 3 -, 5-year). According to the clinical best cut-off value, they were divided into high and low-risk groups, and then the survival difference was analyzed. The correlation between various clinicopathological parameters and risk models was analyzed Integrate the clinicopathological information in the TCGA database, including age ( 65), gender (MALE, FEMALE), T (T1-2, T3-4), M (M0, M1), N (N0-1, N2-3) and onstage. Use the "limma" package in R language to analyze the expression difference of risk scores under different clinical groups. Prognostic independence of clinicopathological parameters and risk score Based on the "survival" package, univariate and multivariate Cox regression analyses were used to explore whether the predictive ability of risk score can be independent of clinicopathological parameters. Establish and evaluate the nomogram model Based on the risk score and a variety of clinicopathological parameters, we used "RMS", "replot" and "survival" packages in R language to draw nomograms and calibration curves. Nomograms are used to predict the prognosis probability of three years (1 -, 3 -, 5-year), and calibration curves are used to evaluate whether the survival predicted by nomograms is consistent with the observed probability. The "foreign", "RMS" and "survival" packages were used to calculate the concordance index (c-index) of the nomogram model, the closer to 1, the higher the predictive value of the model. A score of tumor immune infiltrating cells Using a TIMER database, the results of four immune cell Infiltration analysis methods were downloaded, including xCell, quanTIseq, MCP-counter, and CIBERSORT-ABS, used to analyze the distribution differences of NK and CD8 cells in high and low-risk groups. Correlation between glycolytic pathway and risk model Downed the hallmark dataset, we use gene set enrichment analysis (GSEA) to study the enrichment level of glycolysis pathway in different risk groups. According to gene set variation analysis (GSVA), transcriptome data, and gene set of glycolysis pathway, we use "GSVA", "limma" and "GSEABase" in R language to calculate the glycolysis signal value in a single sample, to analyze the correlation between risk genes and models and glycolysis. Calculate the sensitivity of antitumor drugs under different risk groups We used "pRRophetic" and "limma" packages to analyze the half inhibitory concentration (IC50) of antitumor drugs in different risk groups, so as to predict the sensitivity of tumors to drugs, including bosutinib, doxorubicin, gefitinib, docetaxel, and Nilotinib. Cell culture In constant temperature incubator at 37.5 ℃ and 5% CO2, we cultured BSEA-2B, H1299, A549, PC9 and H1975 with rpim-1640 medium for 24 hours. RT-qPCR RLyse the cells with Trizol, stand still, add chloroform, shake fully, centrifuge, absorb the supernatant, add the same amount of isopropanol, add ethanol after centrifugation, centrifuge, and dissolve the RNA with DEPC water. Then reverse transcribe into cDNA by reverse transcription kit, then add the target gene primer and RT-PCR mixture respectively, operate on the computer for 1H and 30min, and finally collect the results, and repeat the results three times each time. The primer sequence of the target gene can be seen in Table 1 . Table 1 Primer sequence of risk genes Gene name Primer sequence AC011477.2 Forward: CTGCACTGAAGCACCAAAGG Reverse: ACAGCTCATGGGGTTCTGTC AL031775.2 Forward: GCATATCCTGGGGTAAGGGAC Reverse: TTGCCTTTTAGAGGTGCTAGAT AC090559.1 Forward: TCTCCGTTCTTCCTCTAGCTTC Reverse: ACCCTATGATCTCACATCAC Gene name: AC011477.2, AL031775.2 and AC090559.1. Primer sequence: the primer sequence contains the upstream and downstream primer sequence of the risk genes. Statistical analysis The relevant Cox regression, survival, difference, and correlation analysis are all using the function package of the R language, and the LNCRNAs network diagram related to immunity and Cuproptosis is drawn by Cytoscape software. In univariate Cox regression analysis, the selected P. vale < 0.01, and in other statistical analyses, the default P. vale < 0.05. Result The correlation between NK cells and CD8 cells in the tissues and blood of LUAD Based on the tissue and blood sequencing of lung adenocarcinoma, CIBERSOFT was used to calculate the correlation between immune cells. We found that the positive correlation between NK cells and CD8 cells was the highest (Fig. 1A-B), with correlation coefficients of 0.26 and 0.23, respectively (Fig. 1C-D). Screening the immune-related LNCRNAs of NK and CD8 cells (IMMLNCRNAs) Firstly, based on CIBERSOFT's score and a clinical best cut-off value of NK and CD8 cells, they will be divided into four groups, including HIGH + HIGH, HIGH + LOW, LOW + HIGH, and LOW + LOW. The pie chart shows the proportion of different groups. We found that only 60.85% of CD8 and NK cells were highly expressed (Fig. 2A). Among the four groups, the highest survival rate was in the HIGH + HIGH group, while the lowest survival rate was in the low + low group (Figue2B-C). Then, through the "limma" package in R language, the differential genes between HIGH + HIGH and LOW + LOW groups were analyzed, the screening value is | logFC | > 1, pvale < 0.05, and a total of 108 genes were significantly different (Fig. 2D). Finally, 673 immlncrnas were obtained by correlation analysis between genes with significant immune differences and LNCRNAs (Fig. 3A). Screening LNCRNAs associated with immunity and Cuproptosis (IMMCusLNCRNAs) Using the LUAD data in TCGA, the expression data of Cuproptosis-related genes were extracted and correlated with the expression data of lncRNA, 454 Cuproptosis-related LNCRNAs (CusLNCRNAs) were obtained (Fig. 3B). Finally, a total of 395 immune and Cuproptosis-related LNCRNAs (IMMCusLNCRNAs) were obtained from the intersection of IMMLNCRNAs and CusLNCRNAs (Fig. 3C). Establish a risk model related to immunity and Cuproptosis Three prognosis-related LNCRNAs were screened from 395 IMMCusLNCRNAs by univariate Cox regression analysis, and then the LNCRNAs with the best prognosis were screened by lasso Cox regression analysis. The three LNCRNAs were AL031775.2, AC011477.2, and AC090559.1 (Fig. 4A-B). Then, multivariate Cox regression was used to analyze the regression coefficients of risk genes (Fig. 4C). Based on the clinical best cutoff value of 1.312 (Fig. 4D), the risk model was divided into high and low-risk groups. Using survival analysis, it was found that the prognosis effect of the high-risk group was poor (Fig. 4E). The AUC values of ROC curve for 3 years (1-, 3-, 5-year) were 0.711, 0.626 and 0.633, respectively (Fig. 4F). A risk score is closely related to clinicopathological parameters Using the "limma" package in R language to analyze the correlation between different clinicopathological parameters and risk scores, it was found that the risk scores were higher in men, N2-3, and stage III-IV groups (Fig. 5B, C, F). Other groups had no statistical significance (Fig. 5A, D, E). Prognostic impact of risk score and clinicopathological factors Based on univariate and multivariate Cox regression analysis, it was found that only TMNstage and risk assessment could be independent prognostic factors (p.vale < 0.05) (Fig. 6A-B). Using the ROC curve analysis, it is found that the AUC values of risk score and TMNstage are high, which are 0.715 and 0.671 respectively (Fig. 6C). Establish and evaluate the predictive value of nomograms Based on age, gender, T, M, N, TMNstage, and risk score, a nomogram was established to predict the three-year survival probability of patients. The calibration curve of the nomogram is consistent with the actual prediction curve, which shows that the three-year prediction ability of patients has high accuracy. The c-index of the nomogram is 0.72 (Fig. 6D-E). Differences in the distribution of NK cells and CD8 cells in different risk groups The scoring results of various immune methods were downloaded from the TIMER database, including XCELL, QUANTISEQ, MCPCOUNTER, and CIBERSOFT-ABS. We found that among the results of the four immune cell scores, the general distribution of NK and CD8 cells was higher in the low-risk group (Fig. 7A-H). Correlation between risk genes and cuproptosis genes and their impact on the prognosis of patients. The expression of risk genes (AL031775.2, AC011477.2, and AC090559.1) were generally higher in the low-risk group and was negatively correlated with the risk score (Fig. 8A-B). Among the genes related to cuproptosis, GCSH, PDHA1, and PDHB were negatively correlated with AC011477.2. ATP7A, DBT, GLS, LIAS, LIPT1, MTF1, NFE2L2, and NLRP3 are the opposite. ATP7B, DLAT, DLD, GCSH, LIAS, LIPT2, PDHA1, and PDHB were negatively correlated with AC090559.1. ATP7A, MTF1, NLRP3, and SLC31A1 were the opposite. PDHB was negatively correlated with AL031775.2. ATP7A, DBT, GLS, LIAS, LIPT1, MTF1, and NLRP3 were the opposite (Fig. 8C). The prognosis was higher in the low expression group of risk genes (Fig. 8D-F). Glycolytic signals are closely related to risk models Through GSEA enrichment analysis, we found that the high-risk group was mainly enriched in the glycolytic signaling pathway (Fig. 9A). Then, using Gene set variation (GSVA) analysis, it was found that glycolysis signal value was also distributed in a high-risk group, and was positively correlated with a risk score, but negatively correlated with risk genes (Fig. 9B-C). The effect of the risk model on the sensitivity of antitumor drugs and Tumor burden mutation (TMB) In the high-risk group, the Tumor burden mutation was higher, which was positively correlated with the risk score (Fig. 10A-B). Among the five antitumor drugs (bosutinib, doxorubicin, gefitinib, docetaxel, and Nilotinib), the IC50 values of bosutinib, gefitinib, and Nilotinib increased significantly in the high-risk group (Fig. 10C, E, G), while the others (doxorubicin and docetaxel) were the opposite (Fig. 10D, F). Expression levels of risk genes in lung adenocarcinoma and normal cells In BEAS-2B and H1975 cells, the expression level of AC011477 2 and AL031775 was higher than that of other cells (H1299, A549 and PC9) (Fig. 11A, C). AC090559. 1 was highly expressed in BEAS-2B cells compared with other cells (H1299, A549, PC9 and H1975) (Fig. 11B). Discussion Lung adenocarcinoma is the subtype of lung cancer with the highest incidence rate and mortality in the world. After diagnosis, the five-year survival rate is still low. The main reason is that lung adenocarcinoma is late diagnosed and widely metastasized ( 37 – 38 ). In recent years, tumor treatment methods have made significant progress, such as using the immune escape of lung cancer to develop immunosuppressants targeting programmed death 1 (PD1) / programmed death ligand 1 (PDL1). Although good results have been achieved, most patients still can not benefit from it ( 39 – 40 ). Therefore, we urgently need to find new prognostic markers to improve the prognosis of patients. In this study, we used the sequencing data of blood and tissue to analyze the distribution level of immune cells and found that NK cells and CD8 cells were closely related, at the same time, the highly expressed NK cells and CD8 cells have the best prognosis. In recent studies, NK cells stimulate the toxic effect of CD8 cells by producing proinflammatory cytokines ( 41 ). At the same time, tumor cells can down-regulate the surface expression of the MHC-I molecule, to bypass the killing of CD8 cells, but NK cells can be effectively activated, which can assist CD8 cells in killing tumors. Therefore, the combination of CD8 and NK cells is the main immune defense line against tumors ( 42 – 45 ). The combination of NK and CD8 cells to kill tumors and improve the prognosis of patients will be a new treatment scheme (46). Then, we performed differential expression analysis on high and low-low groups of NK and CD8 cells, obtained significant genes of NK and CD8 cells, and performed correlation analysis with lncRNA expression data to obtain immune related LNCRNAs (IMMLNCRNAs). In the human genome, 93% of genes can be transcribed into RNA, but only 2% of them can encode proteins, and more than 200 nucleic acids are long noncoding RNA (lncRNA) ( 47 – 49 ). More and more evidence shows that LNCRNA is closely related to the development of immune cells in the tumor environment, including tumor-associated macrophages (TAMs) and tumor-associated neutrophils (TANs) ( 50 – 53 ). Therefore, it is necessary to study new LNCRNAs that can activate NK and CD8 cells, to improve the prognosis of patients. Recently, cuproptosis, a new mode of death has been reported, which uses the combination of copper and the lipoylated components of the tricarboxylic acid cycle (TCA) to lead to the aggregation of copper bound lipoylated mitochondrial proteins, followed by the deletion of iron-sulfur cluster proteins, which eventually leads to cell death. However, this mode of death is highly dependent on mitochondrial activity and is less sensitive to cells with high glycolytic activity ( 25 – 28 ). 19 genes that may be related to cuproptosis were collected, including NFE2L2, NLRP3, ATP7B, ATP7A, SLC31A1, FDX1, LIAS, LIPT1, LIPT2, DLD, DLAT, PDHA1, PDHB, MTF1, GLS, CDKN2A, DBT, GCSH, and DLST ( 25 – 28 ). We integrated the expression data of these genes and analyzed the correlation with LNCRNAs, respectively, and finally obtained LNCRNAs related to cuproptosis (CusLNCRNAs). This discovery will bring new hope for the treatment of tumors. We obtained 395 LNCRNAs related to immunity and cuproptosis (IMMCusLNCRNAs) by taking the intersection of IMMLNCRNAs and CusLNCRNAs through "Venn diagram" in the R language. Through univariate, multivariate, and lasso cox regression analysis, the prognosis-related genes were selected, and the risk model related to immunity and cuproptosis was established. Using the best clinical cut-off value to divide into high and low-risk groups, we found that the prognosis of patients in the low-risk group was better, and the AUC values of the ROC curve for 3 years (1 -, 3 -, 5-year) were more than 0.6, indicating that the model has good prediction ability. Subsequently, we analyzed the correlation between the model and clinicopathological parameters, and found that the risk score was highly distributed in Male, N2-3, and stageIII-IV, which showed that the risk model was closely related to the further development of tumor and gender. Then, we used univariate and multivariate Cox regression analysis and ROC curve to compare the impact of risk score and various clinicopathological factors on the prognosis of patients, and found that risk score can be the best independent prognostic factor. We established a nomogram to predict the three-year survival probability of patients. Through the test of the calibration chart and C index, we found that the prediction ability of the nomogram has high consistency and accuracy. Based on the above analysis, the model has an important impact on the prognosis of patients. Using a variety of immune cell scoring methods in the time database, it is found that NK and CD8 cells are highly distributed in the low-risk group, which further verifies that the model is closely related to immune regulation, which is likely to promote the joint use of NK and CD8 cells in anti-tumor. Risk genes and risk scores were negatively correlated and highly expressed in the low-risk group. Then, correlation analysis with CusLNCRNAs showed that GCSH, PDHA1, and PDHB were negatively correlated with AC011477.2. ATP7A, DBT, GLS, LIAS, LIPT1, MTF1, NFE2L2, and NLRP3 are the opposite. ATP7B, DLAT, DLD, GCSH, LIAS, LIPT2, PDHA1, and PDHB were negatively correlated with AC090559.1. ATP7A, MTF1, NLRP3, and SLC31A1 were the opposite. PDHB was negatively correlated with AL031775.2. ATP7A, DBT, GLS, LIAS, LIPT1, MTF1, and NLRP3 were the opposite. Among them, SLC31A1, FDX1, LIAS, LIPT1, LIPT2, DLD, DLAT, PDHA1, and PDHB will contribute to the occurrence of cuproptosis, while NFE2L2, ATP7B, ATP7A, MTF1, GLS, and CDKN2A may avoid the toxicity of copper to tumor cells, and the inflammatory body will rise naturally after the toxic effect of copper on cells (NLRP3). Interestingly, risk genes (AL031775.2, AC011477.2, and AC090559.1) cannot single regulate the genes that promote or inhibit cuproptosis, But what can be determined is that risk genes are closely related to cuproptosis genes. In later experiments, we will mainly explore the specific regulation of risk genes on cuproptosis. Then, what is more interesting is that the risk model is related to glycolysis. The enrichment level of the glycolysis pathway is higher in the high-risk group, and the risk gene is significantly negatively correlated with the value range of the glycolysis signal. Risk genes can significantly promote the prognosis of patients. In recent studies, AC090559.1 and AL031775.2 are beneficial to the prognosis of patients with osteosarcoma and lung adenocarcinoma, respectively, while there are few reports of AC011477.2 in cancer ( 54 – 55 ). Glycolysis is a biological characteristic of most tumor cells, which is closely related to the progression of advanced tumors, drug resistance, and adverse clinical results ( 56 – 57 ). Under glycolysis, cuproptosis is difficult to occur ( 25 – 26 ). Finally, the expression of risk genes in normal lung epithelial cells (BEAS-2B) is generally higher than that in other lung adenocarcinoma cells. Therefore, this means that risk genes can inhibit glycolytic signals and promote the occurrence of cuproptosis. Based on the package of "pRRophetic" in R language, we analyzed the IC50 value of antitumor drugs in high-risk and low-risk groups and found that bosutinib, gefitinib, and nilotinib had higher IC50 in a high-risk group, resulting in obvious drug resistance, while doxorubicin and docetaxel had higher IC50 in a low-risk group. This discovery provides new thinking for patients' combined multi-means treatment. In conclusion, the risk model related to immunity and cuproptosis established by us has an important impact on the prognosis of patients and the activation of immune cells, and the model is closely related to the regulation of cuproptosis. This series of findings will contribute to the personalized treatment and prognosis improvement of patients. Declarations Ethics approval The data used in this study were obtained from the publicly available datasets, such as GEO database (https:// www. ncbi. nlm. nih. gov/ geo), and The Cancer Genome Atlas (https:// portal.gdc. cancer. gov). Consent for publication All relevant authors agree to publish Author's contribution KS, ZZ, and DW provide the idea and design of this article. Clinical data were collected and analyzed by KS, ZZ, and DW. KS and YH drafted the first draft of the article and the drawing of charts. CL and JZ reviewed the revised paper. All authors read and approved the final manuscript Competing interests The authors declare that they have no competing interests. Availability of data and materials All data generated or analyzed during this study are included in this published article. FUNDING This study was supported by grants from the Key Natural Science Project of Anhui Provincial Education Department (No.KJ2015A289, KJ2018A0221, and KJ2020A0578) and National Innovation Program for College Students (Nos. 201910367041 and 202010367008). Key projects of Anhui Provincial Department of Education (KJ2021A0773). 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Induction of Split Anergy Conditions Natural Killer Cells to Promote Differentiation of Stem Cells through Cell-Cell Contact and Secreted Factors. Frontiers in immunology 2014, 5:269. Teng M W, Galon J, Fridman W H et al . From mice to humans: developments in cancer immunoediting. The Journal of clinical investigation 2015, 125(9):3338-3346. Maskalenko N A, Zhigarev D, Campbell K S. Harnessing natural killer cells for cancer immunotherapy: dispatching the first responders. Nature reviews Drug discovery 2022. Clark M B, Amaral P P, Schlesinger F J et al . The reality of pervasive transcription. PLoS biology 2011, 9(7):e1000625; discussion e1001102. Parasramka M A, Maji S, Matsuda A et al . Long non-coding RNAs as novel targets for therapy in hepatocellular carcinoma. Pharmacology & therapeutics 2016, 161:67-78. Ulitsky I, Bartel D P. lincRNAs: genomics, evolution, and mechanisms. Cell 2013, 154(1):26-46. Recalcati S, Locati M, Marini A et al . Differential regulation of iron homeostasis during human macrophage polarized activation. European journal of immunology 2010, 40(3):824-835. Carpenter S, Aiello D, Atianand M K et al . A long noncoding RNA mediates both activation and repression of immune response genes. Science (New York, NY) 2013, 341(6147):789-792. Zhang Y, Li Z, Chen M et al . lncRNA TCL6 correlates with immune cell infiltration and indicates worse survival in breast cancer. Breast cancer (Tokyo, Japan) 2020, 27(4):573-585. Shang A, Wang W, Gu C et al . Long non-coding RNA HOTTIP enhances IL-6 expression to potentiate immune escape of ovarian cancer cells by upregulating the expression of PD-L1 in neutrophils. Journal of experimental & clinical cancer research : CR 2019, 38(1):411. Zhang J, Ding R, Wu T et al . Autophagy-Related Genes and Long Noncoding RNAs Signatures as Predictive Biomarkers for Osteosarcoma Survival. Frontiers in cell and developmental biology 2021, 9:705291. Li A, Yu W H, Hsu C L et al . Modular signature of long non-coding RNA association networks as a prognostic biomarker in lung cancer. BMC medical genomics 2021, 14(Suppl 3):290. Yu M, Chen S, Hong W et al . Prognostic role of glycolysis for cancer outcome: evidence from 86 studies. Journal of cancer research and clinical oncology 2019, 145(4):967-999. Kim H S, Choi J Y, Choi D W et al . Prognostic Value of Volume-Based Metabolic Parameters Measured by (18)F-FDG PET/CT of Pancreatic Neuroendocrine Tumors. Nuclear medicine and molecular imaging 2014, 48(3):180-186. Additional Declarations No competing interests reported. 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. 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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-1790514","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":116920155,"identity":"ed9fe2c5-2b17-4964-8049-56fe12ab45f9","order_by":0,"name":"Kang Sun","email":"","orcid":"","institution":"Bengbu Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kang","middleName":"","lastName":"Sun","suffix":""},{"id":116920156,"identity":"8efc05f6-1c38-4f48-8470-66778e790f34","order_by":1,"name":"Zhiqiang Zhang","email":"","orcid":"","institution":"Bengbu Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhiqiang","middleName":"","lastName":"Zhang","suffix":""},{"id":116920157,"identity":"b15c7313-0f3e-4077-8ef0-9b304ee1e858","order_by":2,"name":"Dongqin Wang","email":"","orcid":"","institution":"Bengbu Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dongqin","middleName":"","lastName":"Wang","suffix":""},{"id":116920158,"identity":"8f06679b-07cb-4de2-a195-bbbf3af0f9f8","order_by":3,"name":"Yinlong Huang","email":"","orcid":"","institution":"Bengbu Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yinlong","middleName":"","lastName":"Huang","suffix":""},{"id":116920159,"identity":"a10543f7-6f6c-4c58-aa4e-eab72e9b4ffa","order_by":4,"name":"Jing Zhang","email":"","orcid":"","institution":"Bengbu Medical College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Zhang","suffix":""},{"id":116920160,"identity":"61e12371-3ef7-4342-8d5a-d5beb1dd518d","order_by":5,"name":"Chaoqun Lian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYBACPmYgwdgAZjM+hjIM8GphQ9LCbEycFgaEFjZp4rSwMz978HOHnRzfjdxj1YU7tskzsDdvk2CouYPHYWzmhr1nko0lb+Sl3Z555rZhA8+xMgmGY8/w+cVMmrGNOXHDjRyz27xttxMYJHLMJBgbDuPRwv4NqKUerKUYrEX+DSEtPCBbDoO1MENs4SGopUyyt+24seSZN8bSIL+08aQVWyQcw62Fn//4NomfbdVyfMdzDD8X7rgtz89+eOONDzW4tSDAAZi9ICKBCA0ILaNgFIyCUTAK0AEAzpZOMb+bQpQAAAAASUVORK5CYII=","orcid":"","institution":"Bengbu Medical College","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Chaoqun","middleName":"","lastName":"Lian","suffix":""}],"badges":[],"createdAt":"2022-06-24 06:14:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1790514/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1790514/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":23541818,"identity":"eccc61fc-f781-42f6-96db-48532124c18e","added_by":"auto","created_at":"2022-07-06 16:58:45","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1911786,"visible":true,"origin":"","legend":"\u003cp\u003eTCGA and GEO databases were used to analyze the correlation between NK cells and other immune cells. A. Correlation between NK cells and other immune cells in the tissue of lung adenocarcinoma. B. Correlation line between NK cells and CD8 cells in the tissue of lung adenocarcinoma. C. Correlation heat map between NK cells and immune cells in peripheral blood of lung adenocarcinoma. D. Correlation line between NK cells and CD8 cells in peripheral blood of lung adenocarcinoma\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1790514/v1/eba259b85569bb25c3d5426e.jpg"},{"id":23541265,"identity":"ff12e3e9-0aa1-4b22-9836-efb6c926b368","added_by":"auto","created_at":"2022-07-06 16:53:45","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1105120,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic effects of NK cells and CD8 cells on patients and related genes. A. The pie chart shows the comparison of NK cells and CD8 cells under different groups. B-C. Survival deterioration and analysis chart of NK and CD8 cells in different groups. D. Differential volcanic map of NK and CD8 cells in different groups\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1790514/v1/3f1d4eb9a6be5af0a2ac37ba.jpg"},{"id":23541264,"identity":"7834ab1d-d414-48cd-b690-01b8be6cbd53","added_by":"auto","created_at":"2022-07-06 16:53:45","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2193525,"visible":true,"origin":"","legend":"\u003cp\u003eScreening of copper death and immune related LncRNA. A-B. The network diagram shows lncrnas associated with immunity and cuproptosis, respectively. C. Wayne diagram shows lncrna associated with cuproptosis and immunity.\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1790514/v1/657c343dc7431e9e0defe5b3.jpg"},{"id":23542146,"identity":"6e1fd2ce-baca-4092-a169-64f0ad9f34a6","added_by":"auto","created_at":"2022-07-06 17:03:45","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1502669,"visible":true,"origin":"","legend":"\u003cp\u003eEstablish a risk model related to cuproptosisand immunity. A. Forest map of single factor Cox regression analysis. B. Lasso regression analysis. C. Multivariate Cox regression analysis forest map. D. The reduction of risk score is determined by ROC curve. E. Survival analysis under high and low-risk groups. F. AUC value of three-year (1, -3, -5 year) ROC curve.\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1790514/v1/111625b787a3544d645c8e76.jpg"},{"id":23541271,"identity":"e7452453-ce0c-43c0-b75b-015093e58ab6","added_by":"auto","created_at":"2022-07-06 16:53:46","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1750005,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between risk score and clinicopathological parameters. A. age. B. gender. C. N stage (N). D. T stage (T). E. M stage (M). F. TMN Stage.\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1790514/v1/9d5865f364a40f928b5f9143.jpg"},{"id":23542600,"identity":"46943e94-ae12-420d-822d-933ec3aa9579","added_by":"auto","created_at":"2022-07-06 17:08:46","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1747376,"visible":true,"origin":"","legend":"\u003cp\u003eTo evaluate the predictive ability and accuracy of the risk model for the prognosis of patients. A-B. Univariate and multivariate Cox regression analysis of risk score and clinicopathological parameters. C. ROC curve analysis of risk score and various clinicopathological parameters. D. Nomograms of prognostic factors were used to analyze the three-year (1, -3, -5 year) prognostic probability of patients. E. The calibration chart reveals the prediction accuracy of nomogram.\u003c/p\u003e","description":"","filename":"Fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1790514/v1/20a9f4f07685411beec06810.jpg"},{"id":23541266,"identity":"1408b2ec-77d1-483a-a384-4a6cbdc8ca51","added_by":"auto","created_at":"2022-07-06 16:53:45","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1148432,"visible":true,"origin":"","legend":"\u003cp\u003eBased on different methods, the distribution of NK and CD8 cells under high and low-risk resistance was analyzed. A-D. Nk cell. E-H. CD8 cell ( xCell, MCP-counter, quanTIseq, CIBERSORT-ABS).\u003c/p\u003e","description":"","filename":"Fig7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1790514/v1/9cb9d72d78d8f1c3e6dc6f38.jpg"},{"id":23541831,"identity":"4ebf2ab7-8413-4bc7-9dac-ee94a5a1c77b","added_by":"auto","created_at":"2022-07-06 16:58:45","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1697967,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between risk genes and copper death and its impact on the prognosis of patients. A. Expression levels of risk genes in different risk groups. B. Correlation between risk score and risk gene. C. Related levels of risk genes and cuproptosis genes. D-F. Analysis chart of risk genes on patient survival, including AC011477.2, AC090559.1 and AL031775.2.\u003c/p\u003e","description":"","filename":"Fig8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1790514/v1/8e330301971679ebc4f917f2.jpg"},{"id":23541273,"identity":"c45e884e-0923-4981-b454-f57288f2c638","added_by":"auto","created_at":"2022-07-06 16:53:46","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1676493,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis of GSEA under different risk groups. A. The glycolytic pathway in the hallmark data set is mainly concentrated in the high-risk group. B. Based on GSVA analysis, glycolysis pathway scored higher in high-risk group. C. Circle chart of correlation between risk genes, scores and and glycolysis.\u003c/p\u003e","description":"","filename":"Fig9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1790514/v1/579dfed6039cf1895dcec723.jpg"},{"id":23542148,"identity":"72e37820-3b64-4a53-aa1c-0ed5dfd67de1","added_by":"auto","created_at":"2022-07-06 17:03:46","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1327167,"visible":true,"origin":"","legend":"\u003cp\u003eThe sensitivity and effect of different risk groups to chemotherapy, molecular targeted drugs and gene mutation was analyzed. A. Difference of tumor mutation load in risk groups. B. Correlation linear graph between risk score and tumor mutation load. C-G. Differences in sensitivity of different drugs under risk groups, including Bosutinib, Docetaxel, Gefitinib, Doxorubicin and Nilotinib.\u003c/p\u003e","description":"","filename":"Fig10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1790514/v1/d633578dcff1a8a60f2a673d.jpg"},{"id":23541838,"identity":"f768c952-0cb4-44dd-8204-110e2be24a6a","added_by":"auto","created_at":"2022-07-06 16:58:46","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":940603,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential expression of risk genes in lung adenocarcinoma and normal cells. A. AC011477.2. B. AC090559.1. C. AL031775.2.\u003c/p\u003e","description":"","filename":"Fig11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1790514/v1/dc1bb3e35172f569c530bb9f.jpg"},{"id":27275933,"identity":"757677bc-5f50-4d1e-9c9a-f1ff691e033a","added_by":"auto","created_at":"2022-10-03 14:29:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1396539,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1790514/v1/1cc8c620-fbac-4b27-91c6-0952a961bae2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multiple effects of immune and cuproptosis related LNCRNAs on lung adenocarcinoma and the related expression was verified by RT-PCR","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer is still one of the main causes of tumor mortality, and lung adenocarcinoma is the main subtype of lung cancer, accounting for about 40% (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). At present, the conventional clinical treatment methods include surgical resection, chemotherapy, and radiotherapy. With the development and progress of treatment methods, such as molecular targeted therapy and immunotherapy (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). However, the five-year overall survival rate is still low, not more than 15% (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). At the same time, the occurrence and development of tumors are related to the clinicopathological stage and the abnormal expression of genes related to tumor cells (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Therefore, it is necessary to develop new prognostic markers to improve the prognosis of patients.\u003c/p\u003e \u003cp\u003eRelevant research reports pointed out that the immune cells in the tumor microenvironment (TME), play a key role in the prognosis of patients and the occurrence and development of tumor cells(\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Among these immune cells, CD8 cells play the most important anti-tumor effect. However, tumor antigen can regulate the dysfunction and depletion of CD8 cells(\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). NK cells are toxic natural lymphoid cells (ILCs) that can target tumor cells by secreting cytolytic granules and secrete cytokines to promote an immune response. In many kinds of literature, NK cells are closely related to the prognosis of tumor patients. (\u003cspan additionalcitationids=\"CR17 CR18 CR19\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). At the same time, functionally, NK cells can complement T cells and target MHC-I deficient cells, including tumor cells (\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Therefore, immunotherapy combined with NK and CD8 cells will greatly improve the prognosis of patients.\u003c/p\u003e \u003cp\u003eAs an important cofactor, the dynamic balance of copper is important for various physiological activities. If the bioavailability of copper is maladjusted, it can cause oxidative stress and cytotoxicity (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). In recent reports, a new cell death pathway called Cuproptosis was proposed. This death occurs through the direct combination of copper and the lipoylated components of the tricarboxylic acid (TCA) cycle. The toxicity of copper is highly related to mitochondrial activity. Compared with glycolytic cells, cells with higher mitochondrial activity are more sensitive to copper ions and elesclomol treatment(\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Although the specific mechanism and application of Cuproptosis need to be further explored, it also provides new thinking for the treatment of lung adenocarcinoma.\u003c/p\u003e \u003cp\u003eLong noncoding RNAs (LNCRNAs) are composed of more than 200 nucleic acids, and most of them cannot synthesize proteins. In recent studies, LNCRNAs have an important impact on the occurrence and development of tumors, including cell proliferation, differentiation, migration, invasion, and drug resistance. At the same time, LNCRNAs play an important role in the regulation of the tumor immune microenvironment. (\u003cspan additionalcitationids=\"CR30 CR31 CR32 CR33 CR34 CR35\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Therefore, it is necessary to find new LNCRNAs related to the prognosis of lung adenocarcinoma.\u003c/p\u003e \u003cp\u003eIn this study, we established a risk model and risk LNCRNAs related to NK, CD8, and cuproptosis, and studied the sensitivity of the model to patient prognosis, immune cells, and chemotherapeutic drugs. The risk LNCRNAs we screened can effectively inhibit the glycolytic pathway and are closely related to Cuproptosis-related genes.\u003c/p\u003e"},{"header":"Methods And Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eThe download of LUAD tissue and blood data\u003c/h2\u003e\n\u003cp\u003eBased on the TCGA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003c/span\u003e), RNA-seq data of 535 LUAD tissues were downloaded, and the corresponding clinicopathological information was also integrated, including gender, age, survival time, survival status, T, M, N, TMNstage and tumor mutation data. The gene matrix of peripheral whole blood associated with LUAD was collected from GSE20189 in the GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003c/span\u003e), a total of 82 cases.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003eCalculate the distribution level of immune cells\u003c/h2\u003e\n\u003cp\u003eCIBERSOFT uses a deconvolution algorithm to calculate the relative scores of 22 kinds of immune subcellular through rich gene expression profiles. Use the R language to run the \"CIBERSOFT. R\" script to calculate the distribution level of immune cells in the blood and tissues of LUAD.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003eScreening LNCRNAs closely related to NK and CD8 cells (IMMLNCRNAs)\u003c/h2\u003e\n\u003cp\u003eAccording to the best cut-off value of clinical analysis, NK and CD8 cells were divided into HIGH\u0026thinsp;+\u0026thinsp;HIGH, HIGH\u0026thinsp;+\u0026thinsp;LOW, LOW\u0026thinsp;+\u0026thinsp;HIGH, and LOW\u0026thinsp;+\u0026thinsp;LOW groups, respectively. At the same time, the \"limma\" package in R language, was used to analyze the difference between HIGH\u0026thinsp;+\u0026thinsp;HIGH and LOW\u0026thinsp;+\u0026thinsp;LOW groups, and the correlation analysis was made with LNCRNAs expression data (|cor| \u0026gt; 0.4). Finally, LNCRNAs related to NK and CD8 cells were screened.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003eScreening of Cuproptosis related LNCRNAs (CuproptosisLNCRNAs)\u003c/h2\u003e\n\u003cp\u003eWe collected 19 genes related to Cuproptosis from recently published articles, including NFE2L2, NLRP3, ATP7B, ATP7A, SLC31A1, FDX1, LIAS, LIPT1, LIPT2, DLD, DLAT, PDHA1, PDHB, MTF1, GLS, CDKN2A, DBT, GCSH, and DLST (\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e). Using the \"limma\" package in R language, the gene expression related to Cuproptosis in LUAD, was extracted and analyzed with LNCRNAs expression data (|cor|\u0026gt;0.4).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003eLNCRNAs are associated with immunity and Cuproptosis (IMMCusLNCRNAs)\u003c/h2\u003e\n\u003cp\u003eUsing the \"Venn diagram\" package in R language, IMMLNCRNAs and cuproptosis related LNCRNAs are co intersected to obtain IMMCusLNCRNAs.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eEstablish a risk model related to immunity and Cuproptosis\u003c/h2\u003e\n\u003cp\u003eWe used the \"survival\" packages in R language to conduct univariate Cox regression analysis on IMMCusLNCRNAs, to screen out prognosis-related IMMCusLNCRNAs (PMSLNCRNAs). Then, based on the \"glmnet\" package, we conducted the least regression analysis and absolute shrinkage and selection operator (lasso) regression analysis to select the most significant PMSLNCRNAs, The \"predict\" function of R language and multivariate Cox regression coefficient were used to analyze the risk score of a single sample. The \"survivalroc\" and \"survival\" packages in R language were used to calculate the clinical best cut-off value and AUC value of the three-year ROC curve (1 -, 3 -, 5-year). According to the clinical best cut-off value, they were divided into high and low-risk groups, and then the survival difference was analyzed.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eThe correlation between various clinicopathological parameters and risk models was analyzed\u003c/h2\u003e\n\u003cp\u003eIntegrate the clinicopathological information in the TCGA database, including age (\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;65, \u0026gt;\u0026thinsp;65), gender (MALE, FEMALE), T (T1-2, T3-4), M (M0, M1), N (N0-1, N2-3) and onstage. Use the \"limma\" package in R language to analyze the expression difference of risk scores under different clinical groups.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003ePrognostic independence of clinicopathological parameters and risk score\u003c/h2\u003e\n\u003cp\u003eBased on the \"survival\" package, univariate and multivariate Cox regression analyses were used to explore whether the predictive ability of risk score can be independent of clinicopathological parameters.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eEstablish and evaluate the nomogram model\u003c/h2\u003e\n\u003cp\u003eBased on the risk score and a variety of clinicopathological parameters, we used \"RMS\", \"replot\" and \"survival\" packages in R language to draw nomograms and calibration curves. Nomograms are used to predict the prognosis probability of three years (1 -, 3 -, 5-year), and calibration curves are used to evaluate whether the survival predicted by nomograms is consistent with the observed probability. The \"foreign\", \"RMS\" and \"survival\" packages were used to calculate the concordance index (c-index) of the nomogram model, the closer to 1, the higher the predictive value of the model.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eA score of tumor immune infiltrating cells\u003c/h2\u003e\n\u003cp\u003eUsing a TIMER database, the results of four immune cell Infiltration analysis methods were downloaded, including xCell, quanTIseq, MCP-counter, and CIBERSORT-ABS, used to analyze the distribution differences of NK and CD8 cells in high and low-risk groups.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003eCorrelation between glycolytic pathway and risk model\u003c/h2\u003e\n\u003cp\u003eDowned the hallmark dataset, we use gene set enrichment analysis (GSEA) to study the enrichment level of glycolysis pathway in different risk groups. According to gene set variation analysis (GSVA), transcriptome data, and gene set of glycolysis pathway, we use \"GSVA\", \"limma\" and \"GSEABase\" in R language to calculate the glycolysis signal value in a single sample, to analyze the correlation between risk genes and models and glycolysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003eCalculate the sensitivity of antitumor drugs under different risk groups\u003c/h2\u003e\n\u003cp\u003eWe used \"pRRophetic\" and \"limma\" packages to analyze the half inhibitory concentration (IC50) of antitumor drugs in different risk groups, so as to predict the sensitivity of tumors to drugs, including bosutinib, doxorubicin, gefitinib, docetaxel, and Nilotinib.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch2\u003eCell culture\u003c/h2\u003e\n\u003cp\u003eIn constant temperature incubator at 37.5 ℃ and 5% CO2, we cultured BSEA-2B, H1299, A549, PC9 and H1975 with rpim-1640 medium for 24 hours.\u003c/p\u003e\n\u003ch2\u003eRT-qPCR\u003c/h2\u003e\n\u003cp\u003eRLyse the cells with Trizol, stand still, add chloroform, shake fully, centrifuge, absorb the supernatant, add the same amount of isopropanol, add ethanol after centrifugation, centrifuge, and dissolve the RNA with DEPC water. Then reverse transcribe into cDNA by reverse transcription kit, then add the target gene primer and RT-PCR mixture respectively, operate on the computer for 1H and 30min, and finally collect the results, and repeat the results three times each time. The primer sequence of the target gene can be seen in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePrimer sequence of risk genes\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGene name\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePrimer sequence\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAC011477.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eForward: CTGCACTGAAGCACCAAAGG\u003c/p\u003e\n\u003cp\u003eReverse: ACAGCTCATGGGGTTCTGTC\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAL031775.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eForward: GCATATCCTGGGGTAAGGGAC\u003c/p\u003e\n\u003cp\u003eReverse: TTGCCTTTTAGAGGTGCTAGAT\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAC090559.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eForward: TCTCCGTTCTTCCTCTAGCTTC\u003c/p\u003e\n\u003cp\u003eReverse: ACCCTATGATCTCACATCAC\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\"\u003eGene name: AC011477.2, AL031775.2 and AC090559.1. Primer sequence: the primer sequence contains the upstream and downstream primer sequence of the risk genes.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eThe relevant Cox regression, survival, difference, and correlation analysis are all using the function package of the R language, and the LNCRNAs network diagram related to immunity and Cuproptosis is drawn by Cytoscape software. In univariate Cox regression analysis, the selected P. vale\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and in other statistical analyses, the default P. vale\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n\u003ch2\u003eThe correlation between NK cells and CD8 cells in the tissues and blood of LUAD\u003c/h2\u003e\n\u003cp\u003eBased on the tissue and blood sequencing of lung adenocarcinoma, CIBERSOFT was used to calculate the correlation between immune cells. We found that the positive correlation between NK cells and CD8 cells was the highest (Fig.\u0026nbsp;1A-B), with correlation coefficients of 0.26 and 0.23, respectively (Fig.\u0026nbsp;1C-D).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n\u003ch2\u003eScreening the immune-related LNCRNAs of NK and CD8 cells (IMMLNCRNAs)\u003c/h2\u003e\n\u003cp\u003eFirstly, based on CIBERSOFT's score and a clinical best cut-off value of NK and CD8 cells, they will be divided into four groups, including HIGH\u0026thinsp;+\u0026thinsp;HIGH, HIGH\u0026thinsp;+\u0026thinsp;LOW, LOW\u0026thinsp;+\u0026thinsp;HIGH, and LOW\u0026thinsp;+\u0026thinsp;LOW. The pie chart shows the proportion of different groups. We found that only 60.85% of CD8 and NK cells were highly expressed (Fig.\u0026nbsp;2A). Among the four groups, the highest survival rate was in the HIGH\u0026thinsp;+\u0026thinsp;HIGH group, while the lowest survival rate was in the low\u0026thinsp;+\u0026thinsp;low group (Figue2B-C). Then, through the \"limma\" package in R language, the differential genes between HIGH\u0026thinsp;+\u0026thinsp;HIGH and LOW\u0026thinsp;+\u0026thinsp;LOW groups were analyzed, the screening value is | logFC | \u0026gt; 1, pvale\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and a total of 108 genes were significantly different (Fig.\u0026nbsp;2D). Finally, 673 immlncrnas were obtained by correlation analysis between genes with significant immune differences and LNCRNAs (Fig.\u0026nbsp;3A).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n\u003ch2\u003eScreening LNCRNAs associated with immunity and Cuproptosis (IMMCusLNCRNAs)\u003c/h2\u003e\n\u003cp\u003eUsing the LUAD data in TCGA, the expression data of Cuproptosis-related genes were extracted and correlated with the expression data of lncRNA, 454 Cuproptosis-related LNCRNAs (CusLNCRNAs) were obtained (Fig.\u0026nbsp;3B). Finally, a total of 395 immune and Cuproptosis-related LNCRNAs (IMMCusLNCRNAs) were obtained from the intersection of IMMLNCRNAs and CusLNCRNAs (Fig.\u0026nbsp;3C).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n\u003ch2\u003eEstablish a risk model related to immunity and Cuproptosis\u003c/h2\u003e\n\u003cp\u003eThree prognosis-related LNCRNAs were screened from 395 IMMCusLNCRNAs by univariate Cox regression analysis, and then the LNCRNAs with the best prognosis were screened by lasso Cox regression analysis. The three LNCRNAs were AL031775.2, AC011477.2, and AC090559.1 (Fig.\u0026nbsp;4A-B). Then, multivariate Cox regression was used to analyze the regression coefficients of risk genes (Fig.\u0026nbsp;4C). Based on the clinical best cutoff value of 1.312 (Fig.\u0026nbsp;4D), the risk model was divided into high and low-risk groups. Using survival analysis, it was found that the prognosis effect of the high-risk group was poor (Fig.\u0026nbsp;4E). The AUC values of ROC curve for 3 years (1-, 3-, 5-year) were 0.711, 0.626 and 0.633, respectively (Fig.\u0026nbsp;4F).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\n\u003ch2\u003eA risk score is closely related to clinicopathological parameters\u003c/h2\u003e\n\u003cp\u003eUsing the \"limma\" package in R language to analyze the correlation between different clinicopathological parameters and risk scores, it was found that the risk scores were higher in men, N2-3, and stage III-IV groups (Fig.\u0026nbsp;5B, C, F). Other groups had no statistical significance (Fig.\u0026nbsp;5A, D, E).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n\u003ch2\u003ePrognostic impact of risk score and clinicopathological factors\u003c/h2\u003e\n\u003cp\u003eBased on univariate and multivariate Cox regression analysis, it was found that only TMNstage and risk assessment could be independent prognostic factors (p.vale\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;6A-B). Using the ROC curve analysis, it is found that the AUC values of risk score and TMNstage are high, which are 0.715 and 0.671 respectively (Fig.\u0026nbsp;6C).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\n\u003ch2\u003eEstablish and evaluate the predictive value of nomograms\u003c/h2\u003e\n\u003cp\u003eBased on age, gender, T, M, N, TMNstage, and risk score, a nomogram was established to predict the three-year survival probability of patients. The calibration curve of the nomogram is consistent with the actual prediction curve, which shows that the three-year prediction ability of patients has high accuracy. The c-index of the nomogram is 0.72 (Fig.\u0026nbsp;6D-E).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\n\u003ch2\u003eDifferences in the distribution of NK cells and CD8 cells in different risk groups\u003c/h2\u003e\n\u003cp\u003eThe scoring results of various immune methods were downloaded from the TIMER database, including XCELL, QUANTISEQ, MCPCOUNTER, and CIBERSOFT-ABS. We found that among the results of the four immune cell scores, the general distribution of NK and CD8 cells was higher in the low-risk group (Fig.\u0026nbsp;7A-H).\u003c/p\u003e\n\u003ch2\u003eCorrelation between risk genes and cuproptosis genes and their impact on the prognosis of patients.\u003c/h2\u003e\n\u003cp\u003eThe expression of risk genes (AL031775.2, AC011477.2, and AC090559.1) were generally higher in the low-risk group and was negatively correlated with the risk score (Fig.\u0026nbsp;8A-B). Among the genes related to cuproptosis, GCSH, PDHA1, and PDHB were negatively correlated with AC011477.2. ATP7A, DBT, GLS, LIAS, LIPT1, MTF1, NFE2L2, and NLRP3 are the opposite. ATP7B, DLAT, DLD, GCSH, LIAS, LIPT2, PDHA1, and PDHB were negatively correlated with AC090559.1. ATP7A, MTF1, NLRP3, and SLC31A1 were the opposite. PDHB was negatively correlated with AL031775.2. ATP7A, DBT, GLS, LIAS, LIPT1, MTF1, and NLRP3 were the opposite (Fig.\u0026nbsp;8C). The prognosis was higher in the low expression group of risk genes (Fig.\u0026nbsp;8D-F).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\n\u003ch2\u003eGlycolytic signals are closely related to risk models\u003c/h2\u003e\n\u003cp\u003eThrough GSEA enrichment analysis, we found that the high-risk group was mainly enriched in the glycolytic signaling pathway (Fig.\u0026nbsp;9A). Then, using Gene set variation (GSVA) analysis, it was found that glycolysis signal value was also distributed in a high-risk group, and was positively correlated with a risk score, but negatively correlated with risk genes (Fig.\u0026nbsp;9B-C).\u003c/p\u003e\n\u003ch2\u003eThe effect of the risk model on the sensitivity of antitumor drugs and Tumor burden mutation (TMB)\u003c/h2\u003e\n\u003cp\u003eIn the high-risk group, the Tumor burden mutation was higher, which was positively correlated with the risk score (Fig.\u0026nbsp;10A-B). Among the five antitumor drugs (bosutinib, doxorubicin, gefitinib, docetaxel, and Nilotinib), the IC50 values of bosutinib, gefitinib, and Nilotinib increased significantly in the high-risk group (Fig.\u0026nbsp;10C, E, G), while the others (doxorubicin and docetaxel) were the opposite (Fig.\u0026nbsp;10D, F).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\n\u003ch2\u003eExpression levels of risk genes in lung adenocarcinoma and normal cells\u003c/h2\u003e\n\u003cp\u003eIn BEAS-2B and H1975 cells, the expression level of AC011477 2 and AL031775 was higher than that of other cells (H1299, A549 and PC9) (Fig.\u0026nbsp;11A, C). AC090559. 1 was highly expressed in BEAS-2B cells compared with other cells (H1299, A549, PC9 and H1975) (Fig.\u0026nbsp;11B).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eLung adenocarcinoma is the subtype of lung cancer with the highest incidence rate and mortality in the world. After diagnosis, the five-year survival rate is still low. The main reason is that lung adenocarcinoma is late diagnosed and widely metastasized (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). In recent years, tumor treatment methods have made significant progress, such as using the immune escape of lung cancer to develop immunosuppressants targeting programmed death 1 (PD1) / programmed death ligand 1 (PDL1). Although good results have been achieved, most patients still can not benefit from it (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Therefore, we urgently need to find new prognostic markers to improve the prognosis of patients.\u003c/p\u003e \u003cp\u003eIn this study, we used the sequencing data of blood and tissue to analyze the distribution level of immune cells and found that NK cells and CD8 cells were closely related, at the same time, the highly expressed NK cells and CD8 cells have the best prognosis. In recent studies, NK cells stimulate the toxic effect of CD8 cells by producing proinflammatory cytokines (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). At the same time, tumor cells can down-regulate the surface expression of the MHC-I molecule, to bypass the killing of CD8 cells, but NK cells can be effectively activated, which can assist CD8 cells in killing tumors. Therefore, the combination of CD8 and NK cells is the main immune defense line against tumors (\u003cspan additionalcitationids=\"CR43 CR44\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). The combination of NK and CD8 cells to kill tumors and improve the prognosis of patients will be a new treatment scheme (46). Then, we performed differential expression analysis on high and low-low groups of NK and CD8 cells, obtained significant genes of NK and CD8 cells, and performed correlation analysis with lncRNA expression data to obtain immune related LNCRNAs (IMMLNCRNAs). In the human genome, 93% of genes can be transcribed into RNA, but only 2% of them can encode proteins, and more than 200 nucleic acids are long noncoding RNA (lncRNA) (\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). More and more evidence shows that LNCRNA is closely related to the development of immune cells in the tumor environment, including tumor-associated macrophages (TAMs) and tumor-associated neutrophils (TANs) (\u003cspan additionalcitationids=\"CR51 CR52\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Therefore, it is necessary to study new LNCRNAs that can activate NK and CD8 cells, to improve the prognosis of patients.\u003c/p\u003e \u003cp\u003eRecently, cuproptosis, a new mode of death has been reported, which uses the combination of copper and the lipoylated components of the tricarboxylic acid cycle (TCA) to lead to the aggregation of copper bound lipoylated mitochondrial proteins, followed by the deletion of iron-sulfur cluster proteins, which eventually leads to cell death. However, this mode of death is highly dependent on mitochondrial activity and is less sensitive to cells with high glycolytic activity (\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). 19 genes that may be related to cuproptosis were collected, including NFE2L2, NLRP3, ATP7B, ATP7A, SLC31A1, FDX1, LIAS, LIPT1, LIPT2, DLD, DLAT, PDHA1, PDHB, MTF1, GLS, CDKN2A, DBT, GCSH, and DLST (\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). We integrated the expression data of these genes and analyzed the correlation with LNCRNAs, respectively, and finally obtained LNCRNAs related to cuproptosis (CusLNCRNAs). This discovery will bring new hope for the treatment of tumors.\u003c/p\u003e \u003cp\u003eWe obtained 395 LNCRNAs related to immunity and cuproptosis (IMMCusLNCRNAs) by taking the intersection of IMMLNCRNAs and CusLNCRNAs through \"Venn diagram\" in the R language. Through univariate, multivariate, and lasso cox regression analysis, the prognosis-related genes were selected, and the risk model related to immunity and cuproptosis was established. Using the best clinical cut-off value to divide into high and low-risk groups, we found that the prognosis of patients in the low-risk group was better, and the AUC values of the ROC curve for 3 years (1 -, 3 -, 5-year) were more than 0.6, indicating that the model has good prediction ability. Subsequently, we analyzed the correlation between the model and clinicopathological parameters, and found that the risk score was highly distributed in Male, N2-3, and stageIII-IV, which showed that the risk model was closely related to the further development of tumor and gender. Then, we used univariate and multivariate Cox regression analysis and ROC curve to compare the impact of risk score and various clinicopathological factors on the prognosis of patients, and found that risk score can be the best independent prognostic factor. We established a nomogram to predict the three-year survival probability of patients. Through the test of the calibration chart and C index, we found that the prediction ability of the nomogram has high consistency and accuracy. Based on the above analysis, the model has an important impact on the prognosis of patients.\u003c/p\u003e \u003cp\u003eUsing a variety of immune cell scoring methods in the time database, it is found that NK and CD8 cells are highly distributed in the low-risk group, which further verifies that the model is closely related to immune regulation, which is likely to promote the joint use of NK and CD8 cells in anti-tumor. Risk genes and risk scores were negatively correlated and highly expressed in the low-risk group. Then, correlation analysis with CusLNCRNAs showed that GCSH, PDHA1, and PDHB were negatively correlated with AC011477.2. ATP7A, DBT, GLS, LIAS, LIPT1, MTF1, NFE2L2, and NLRP3 are the opposite. ATP7B, DLAT, DLD, GCSH, LIAS, LIPT2, PDHA1, and PDHB were negatively correlated with AC090559.1. ATP7A, MTF1, NLRP3, and SLC31A1 were the opposite. PDHB was negatively correlated with AL031775.2. ATP7A, DBT, GLS, LIAS, LIPT1, MTF1, and NLRP3 were the opposite. Among them, SLC31A1, FDX1, LIAS, LIPT1, LIPT2, DLD, DLAT, PDHA1, and PDHB will contribute to the occurrence of cuproptosis, while NFE2L2, ATP7B, ATP7A, MTF1, GLS, and CDKN2A may avoid the toxicity of copper to tumor cells, and the inflammatory body will rise naturally after the toxic effect of copper on cells (NLRP3). Interestingly, risk genes (AL031775.2, AC011477.2, and AC090559.1) cannot single regulate the genes that promote or inhibit cuproptosis, But what can be determined is that risk genes are closely related to cuproptosis genes. In later experiments, we will mainly explore the specific regulation of risk genes on cuproptosis. Then, what is more interesting is that the risk model is related to glycolysis. The enrichment level of the glycolysis pathway is higher in the high-risk group, and the risk gene is significantly negatively correlated with the value range of the glycolysis signal. Risk genes can significantly promote the prognosis of patients. In recent studies, AC090559.1 and AL031775.2 are beneficial to the prognosis of patients with osteosarcoma and lung adenocarcinoma, respectively, while there are few reports of AC011477.2 in cancer (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Glycolysis is a biological characteristic of most tumor cells, which is closely related to the progression of advanced tumors, drug resistance, and adverse clinical results (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). Under glycolysis, cuproptosis is difficult to occur (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Finally, the expression of risk genes in normal lung epithelial cells (BEAS-2B) is generally higher than that in other lung adenocarcinoma cells. Therefore, this means that risk genes can inhibit glycolytic signals and promote the occurrence of cuproptosis.\u003c/p\u003e \u003cp\u003eBased on the package of \"pRRophetic\" in R language, we analyzed the IC50 value of antitumor drugs in high-risk and low-risk groups and found that bosutinib, gefitinib, and nilotinib had higher IC50 in a high-risk group, resulting in obvious drug resistance, while doxorubicin and docetaxel had higher IC50 in a low-risk group. This discovery provides new thinking for patients' combined multi-means treatment.\u003c/p\u003e \u003cp\u003eIn conclusion, the risk model related to immunity and cuproptosis established by us has an important impact on the prognosis of patients and the activation of immune cells, and the model is closely related to the regulation of cuproptosis. This series of findings will contribute to the personalized treatment and prognosis improvement of patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study were obtained from the publicly available datasets, such as GEO database (https:// www. ncbi. nlm. nih. gov/ geo), and The Cancer Genome Atlas (https:// portal.gdc. cancer. gov).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll relevant authors agree to publish\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026apos;s contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKS, ZZ, and DW provide the idea and design of this article. Clinical data were collected and analyzed by KS, ZZ, and DW. KS and YH drafted the first draft of the article and the drawing of charts. CL and JZ reviewed the revised paper. All authors read and approved the final manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from the Key Natural Science Project of Anhui Provincial Education Department (No.KJ2015A289, KJ2018A0221, and KJ2020A0578) and National Innovation Program for College Students (Nos. 201910367041 and 202010367008). Key projects of Anhui Provincial Department of Education (KJ2021A0773).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePart of the results of this study are based on TCGA (\u003ca href=\"https://portal.gdc.cancer.gov/\"\u003ehttps://portal.gdc.cancer.gov/\u003c/a\u003e) and GEO (\u003ca href=\"https://www.ncbi.nlm.nih.gov/geo/\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/a\u003e) databases.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel R L, Miller K D, Fuchs H E\u003cem\u003e et al\u003c/em\u003e. Cancer Statistics, 2021. CA: a cancer journal for clinicians 2021, 71(1):7-33.\u003c/li\u003e\n\u003cli\u003eBade B C, Dela Cruz C S. Lung Cancer 2020: Epidemiology, Etiology, and Prevention. Clinics in chest medicine 2020, 41(1):1-24.\u003c/li\u003e\n\u003cli\u003eHirsch F R, Scagliotti G V, Mulshine J L\u003cem\u003e et al\u003c/em\u003e. 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Nuclear medicine and molecular imaging 2014, 48(3):180-186.\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, LNCRNAs, immune, Cuproptosis, risk model","lastPublishedDoi":"10.21203/rs.3.rs-1790514/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1790514/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Lung adenocarcinoma is still a high mortality tumor disease. Immune cells play an important role in the further development of tumors, including NK and CD8 cells. Recent studies have found that cuproptosis is a new way of cell death induced by copper ions.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTherefore, the purpose of this study was to screen the prognostic LNCRNAs associated with immunity and cuproptosis.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethod: \u003c/strong\u003eBased on TCGA and geo databases, LNCRNAs (IMMCusLNCRNAs) related to immunity and cuproptosis were obtained according to the grouping of NK and CD8 cells and genes related to copper filament death. Based on a variety of statistical and bioinformatics methods, an immcusLNCRNAs related risk model was established to evaluate the correlation between the model and prognosis. Using various immune cell scoring models in TIMER database, the distribution differences of immune cells in different risk groups were analyzed. Then, to explore the distribution of glycolysis and chemosensitivity in different risk groups. Finally, the expression levels of risk genes between lung epithelial cells and other lung adenocarcinoma cells were compared.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e risk genes were closely related to prognosis. NK and CD8 cells were generally distributed in the low-risk group, while the glycolytic pathway was the opposite. Under different risk groups, the sensitivity of chemotherapy drugs is also very different. Finally, the expression of risk genes in normal lung epithelial cells is usually higher than that in other lung adenocarcinoma cells.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003ethe risk model related to immunity and cuproptosis and LNCRNAs provide an important idea for personalized speech therapy.\u003c/p\u003e","manuscriptTitle":"Multiple effects of immune and cuproptosis related LNCRNAs on lung adenocarcinoma and the related expression was verified by RT-PCR","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-07-06 16:53:44","doi":"10.21203/rs.3.rs-1790514/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":"518ed818-0dfa-4c7b-9cb9-c073cd04404b","owner":[],"postedDate":"July 6th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-10-03T14:29:20+00:00","versionOfRecord":[],"versionCreatedAt":"2022-07-06 16:53:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1790514","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1790514","identity":"rs-1790514","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}