Comprehensive characterization of the senescence gene Klotho in lung adenocarcinoma

Comprehensive characterization of the senescence gene Klotho in lung adenocarcinoma | 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 Comprehensive characterization of the senescence gene Klotho in lung adenocarcinoma Yating Qiao, Fubin Liu, Yu Peng, Peng Wang, Changyu Si, Xixuan Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2264744/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 Aging has become an important mechanism and target for lung diseases. We aimed to explore novel aging markers for lung adenocarcinoma (LUAD) that may partially modulate immune responses. Methods Aging-related gene sets were acquired from HAGR and Ageing Atlas databases. We retrieved RNA-seq expression and clinical data of LUAD from TCGA and three GEO cohorts. Candidate differentially expressed genes (DEGs) were selected by univariate COX, LASSO COX and multivariate COX regression to construct the prognostic model and nomogram. The ssGSEA, GO terms and KEGG pathway analysis were employed for functional enrichment. The Wilcoxon test and Kaplan-Meier method were applied for differences in distribution and prognosis, respectively. The Spearman method was performed for the correlations between KL expression and CPG site methylation, m6A modifications and immunological characteristics. Results We identified a four-gene prognostic panel of LUAD to construct a nomogram with C-index of 0.721, screening KL out as one prospective senescence gene. Low-expressed KL independently contributed to a poor prognosis for LUAD patients, which may be partially mediated by hypermethylation and m6A modification. Functional enrichment revealed the involvement of immune pathways, further proved by the positive correlation between KL expression and immune scores, abundance of immune infiltrating cells, and immunological characteristics. High-expressed KL gene in decreased immune cell subgroups (CD4 + memory T cells, Eosinophils, NK cells, et al) had a better prognosis. Conclusion Immune-related KL gene was a potent predictor of LUAD, suggesting that further exploration of KL as a therapeutic agent may break the bottleneck in LUAD treatment. Aging-related gene Klotho LUAD Prognostic model Immunity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Lung cancer is one of the most prevalent cancers worldwide, with 2,206,771 new cases and 1,796,144 deaths in 2020 1 . The World Health Organization has divided it into two categories: non-small cell lung cancer (NSCLC) and small cell lung cancer, among which NSCLC includes lung adenocarcinoma (LUAD), squamous cell carcinoma and large cell lung cancer 2 . LUAD, accounting for about 40% of lung cancer, has become a great challenge to prognosis due to lacking of effective markers, strong concealment, late detection and multiple metastases 3 . Currently, surgical treatment, targeted molecular therapy, immunotherapy and their combinations coexist in LUAD, but the overall cure and survival rates of LUAD patients remain low 4 , even with the popular immune checkpoint inhibitors which only benefit few patients 5 , 6 . To address this need, we sought to explore novel biomarkers that might modulate immune responses to further treat LUAD. Aging has become a common social condition. Numerous studies indicate that senescent cells increase the probability of making mistakes in cell division and weaken the immune system, which in turn contribute to the development of aging-related diseases, especially cancer 7 , 8 . Considerable attention has been focused on slowing aging to treat cancer given the strong link between them. Previous studies have found that cell senescence could change the pericellular microenvironment by driving the release of multifaceted inflammatory factors and growth promoting factors, called senescence related secretory phenotype (SASP), which further provokes cancer 9 , 10 . Additionally, the combination of SASP with a dysfunctional immune system typically accelerates peripheral cellular senescence by inducing inflammation, further leading to negative effects 11 . Recent researches have considered cellular senescence as an important mechanism and target for lung disease 11 , 12 , and an aging-related feature has been constructed to predict favorable outcome and immunogenicity in LUAD patients 13 . However, this very limited data do not provide a reasonable interpretation for the association between aging and LUAD, suggesting a necessity of more studies to enrich the evidence. Meanwhile, the expression of aging-related genes in immune cells and whether aberrant genes affect immunity have not been studied in LUAD. Herein, to screen for key senescence factors, we systematically evaluated the correlation between cellular senescence and prognosis in LUAD, further developed a novel risk model based on aging-related genes and explored its biological functions. Subsequently, an in-depth analysis of the relationship between key aging-related gene (Klotho, aka KL) and immune function was performed, providing new insights into candidate strategies for cellular senescence and immune therapeutic possibilities in LUAD. Methods Study cohort Two independent cohorts, the Cancer Genome Atlas (TCGA) cohort and Gene Expression Omnibus (GEO) cohort were enrolled. The RNA-seq expression and clinical data of Pan-cancer and DNA methylation data of LUAD in TCGA were respectively downloaded from the GDC Hub entrance of UCSC Xena (UCSC Xena), a public database available online. Totally, 524 tumor and 59 normal samples were included in the differential gene expression analysis, and patients with the survival time or last follow-up time < 30 days were excluded for subsequent prognostic model construction (Fig. 1 ). We also downloaded gene expression profile and survival data of LUAD patients (GSE68465, GSE72094 and GSE50081) from GEO (Home - GEO - NCBI (nih.gov)) dataset adopting the same inclusion criteria with TCGA for subsequent validation of the prediction model. Acquisition Of Aging-related Gene Sets Human Ageing Genomic Resources (HAGR) (Human Ageing Genomic Resources (senescence.info)) and Ageing Atlas (Aging Atlas (cncb.ac.cn)) were combined to identify 565 aging-related genes for subsequent analysis. Differentially Expressed Genes (Degs) Analysis The DEseq2 package was used to analyze the DEGs between normal and tumor tissues in TCGA dataset. DEGs were eligible into further analysis under the following criteria: | log 2 FoldChange | > 1.5 and P -value < 0.05. Enrichment Analysis Enrichment analysis was analyzed by the R packages "DSEeq2", "clusterProfiler" and "enrichPlot", involving Single Gene Set Enrichment Analysis (ssGSEA), gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. The GO terms included biological processes (BPs), cellular components (CC) and molecular functions (MFs). The cut-off criterion was P -value < 0.05. Genomic Analysis Since tumorigenesis and progression are often accompanied by DNA methylation, gene mutations and RNA modifications, a series of genomic analyses were performed targeting the KL gene. Nineteen methylation CPG sites of KL gene were screened for their correlations with KL expression and overall survival (OS) from TCGA-LUAD DNA methylation data detected by Illumina Human Methylation 450 Beadchip. Then, we examined KL gene mutations in pan-cancer and LUAD using cBioPortal database (cBioPortal for Cancer Genomics). The m6A modification has three main elements: Writers, Erasers and Readers, which add, remove or read m6A sites respectively. We explored the association of selected m6A genes and KL using RNA-Seq expression data of TCGA-LUAD. Correlations Between Kl And Immunological Characteristics First, the immune score and the relevant proportion of 22 immune-infiltrating cell types on TCGA-LUAD were separately obtained through the "Estimate" and "Cibersort" package. Secondly, the TIMER database (TIMER (shinyapps.io)) was employed to analyze the correlation between KL gene and immune infiltrating cells in LUAD. Finally, we summarized the characteristics of several immune-related genes based on relevant literature, including chemokines and their receptors, MHC molecules, immune stimulators and immune checkpoints. Statistical Analyses Univariate COX, least absolute shrinkage and selection operator (LASSO) COX and multivariate COX regression analyses were applied to screen candidate genes to construct prognostic model for LUAD. Furthermore, the receiver operating characteristic (ROC) curve was used to test the predictive performance of the model, showing the area under the ROC curve (AUC) for survival probability at 1, 3 and 5 years. The Wilcoxon test was performed to evaluate the differences in KL distribution between normal and tumor or in immune scores between high- and low-KL groups. Kaplan-Meier (KM) method was used to investigate the correlation between KL gene and OS. The Spearman method was performed for all the correlation analysis. A P -value ≤ 0.05 was considered statistically significant. All statistical analyses were performed using R software (Version 4.0.2). Results 1. Screening and validating aging-related genes for prognosis prediction of LUAD 1.1 Identification And Functional Analysis Of Degs Of Aging-related Genes By intersecting 565 aging-related genes with TCGA-LUAD gene expression data, 406 genes were eventually included in the subsequent study. Seventy-two DEGs (Up: Down = 48:24) between tumor (n = 524) and normal (n = 59) were selected for functional analysis by GO terms and KEGG pathway (Fig. 2 A). In the GO terms, DEGs was enriched in response to peptide, regulation of receptor signaling pathway via STAT and signaling receptor activator activity. Whereas, the KEGG pathway was mainly enriched in immune pathways, such as IL-17 signaling pathway, Cytokine-Cytokine receptor interaction, et al (Fig. 2 B). Further, among 501 tumor samples with OS > 30 days, univariate COX regression analysis revealed that 24 DEGs were significantly associated with OS of LUAD (Fig. 2 C), then LASSO COX regression analysis was used to identify 12 genes with the best performance (Fig. 2 D). Finally, we used the stepwise regression method of multivariate COX regression to select 4 optimal genes and construct the prognostic model for LUAD, including CDK1, KL, TFAP2A and FBP1 (Fig. 2 E). 1.2 Construction for the four-gene prognostic model of LUAD by TCGA training cohort and GEO validation datasets The individual mRNA expression level and coefficient from multivariate regression analysis for the aforementioned four genes were employed by linear combination method to obtain the risk score formula as following: Risk Score (RS) = 0.1316*Exp (CDK1) − 0.0927*Exp (KL) + 0.0911*Exp (TFAP2A) − 0.1208*Exp (FBP1). Subsequently, the RS was calculated for each patient and divided into high- and low-risk groups based on the median. Compared to the low-risk group, the high-risk group had more fatalities, and lower FBP1 and KL expression, but higher BTFAP2A and CDK1 expression (Fig. 3 A). We further examined the effect of the four-gene prognostic model on survival, and found significantly better survival probabilities of the low-risk group in both the TCGA training cohort and externally validated GEO cohorts ( P < 0.05). The maximum AUC values in the TCGA training cohort and the external validation cohorts reached 0.70 and 0.81, respectively, displaying good sensitivity and specificity (Fig. 3 B, C, D and E). In addition, ssGSEA analysis was used to enrich the DEGs between the high- and the low-risk group, among which immune pathways, such as: B cell receptor signaling pathway, T cell receptor signaling pathway, Th17 cell differentiation and IL-17 signaling pathway, etc, were significantly accumulated in high-risk group (Supplementary Fig. 1A and B). 1.3 Development Of A Nomogram For Prediction Of Luad Outcomes To test whether RS was an independent prognostic factor, we constructed a nomogram by combination with traditional risk factors. All independent prognostic parameters were screened by univariate and multivariate Cox proportional hazards regression analysis. Finally, the AJCC-Stage and RS were used as independent risk factors to construct composite nomogram (Fig. 4 A). The point for each factor indicated its corresponding contribution to the probability of survival, and the resulting total points for each patient showed individual survival probability of 1, 3, and 5 years. The prediction efficiency was verified by C-index and calibration curve. We observed that when RS was more than 0.4, the 1 -, 3 -, and 5-year survival was approximately less than 75%, 35% and 20%, respectively (Fig. 4 B). The actual OS and the predicted OS by the nomogram matched well at 1-, 3-, and 5-year by the calibration curves (Fig. 4 C). The C-index of the nomogram was 0.721. These results suggested a good performance and reliability of nomogram based on the characteristic RS of aging-related genes for predicting the survival rates of LUAD patients. 2. KL gene acting as an effective biomarker for LUAD 2.1 Expression And Prognostic Analysis Of Kl Gene In Tcga-luad Among the aforementioned four-gene predicting LUAD prognosis, we focused on the KL gene for two reasons: (1) although discovered earlier, current studies had shown KL gene 14 , with anti-aging 15 and anti-tumor effects 16 , only limited to mice and a few epidemiological studies; (2) more interestingly, there have been two publications glimpsing a subtle link between KL gene and immunity 17 , 18 , suggesting a worthwhile to explore their relationship in cancer patients. The RNA-seq expression data of TCGA was transformed as log 2 (count + 1) for downstream analysis. KL gene was low-expressed in tumor tissues compared to normal tissues for TCGA-LUAD ( P < 2.2e-16) (Fig. 5 A), which were also observed for most cancers in pan-cancer analysis (Supplementary Fig. 2). LUAD patients with high-KL expression have a better prognosis than those with low-KL expression grouped by median ( P = 0.0018) (Fig. 5 B), especially in individuals aged > = 60 years ( P = 0.0043), male ( P = 0.0081), or with pathological stage Ⅲ-Ⅳ ( P = 0.035) (Supplementary Fig. 3). Univariate and multivariate COX regression analysis showed that AJCC-Stage and KL were independent prognostic factors for LUAD (Fig. 5 C). 2.2 Gene Enrichment Analysis By Kl Gene Expression Subsequently, we revealed 1023 DEGs (Down: Up = 535:488) between high- and low-KL expression groups (Supplementary Fig. 4A). Further GO terms for DEGs showed an enrichment in calcium ion homeostasis, humoral immune response, collagen-containing extracellular matrix, signaling receptor activator activity and so on (Supplementary Fig. 4B). The KEGG pathway involved neuroactive ligand-receptor interaction, Camp-pkg signaling pathway, cAMP signaling pathway, etc (Supplementary Fig. 4C). In summary, these enriched pathways were common in aging, cancer and immunity. 2.3 Multidimensional Analysis For Kl Gene DNA methylation, gene mutation and RNA modification were all important factors regulating the KL expression level and leading to the carcinogenesis and progression of LUAD. We observed that KL gene expression was negatively correlated with the mean methylation level [ P = 7.94e-11, r=-0.29, 95%CI (-0.38, -0.21)] (Supplementary Fig. 5A). Each of the 19 CPG sites located in KL gene was individually negatively correlated with KL expression except cg23584087 and cg20672059 (Supplementary Fig. 5B). Further, most sites exhibited higher methylation levels in tumor tissues than normal tissues (Supplementary Fig. 5C). After classified into hypermethylation and hypomethylation groups according to the best cut-off value, most CPG sites with hypermethylation level had non-protective effect of the survival of LUAD patients compared to hypomethylation level (Supplementary Fig. 5D). These results suggested the abnormal methylation of KL gene might partly affect the development of LUAD. Next, KL gene mutation analysis revealed a mutation rate of only 7.46% in NSCLC and even less than 3% in LUAD (Supplementary Fig. 6), implying KL gene mutations may not be directly involved in the progression of LUAD since the low mutation rate. Alternatively, KL gene was positively correlated with 12 selected m6A genes (Supplementary Fig. 7A). All of them were highly expressed in high-KL expression group (Supplementary Fig. 7B), and higher expression of most of them were protective against OS compared to lower expression group divided by optimal cutoff values (Supplementary Fig. 7C), implying m6A modification of KL probably as another transcriptional regulatory mechanism in the development of LUAD. 3. KL gene might affect the development of LUAD by regulating immune cells 3.1 Correlation analysis of KL gene with immune score, immune infiltration cells and immunological characteristics All the immune scores ( P = 1.8e-05), stromal scores ( P = 6.8e-10) and ESTIMATE scores ( P = 6.9e-08) were higher in the high-KL expression group than those in the low-KL expression group (Fig. 6 A). The distribution ratio of resting memory CD4 T cells, M0 and M2 macrophages ranked highest in 22 kinds of immune infiltrating cells in LUAD patients (Fig. 6 B). Meanwhile, we observed a negative correlation between KL expression level and the infiltrating abundance of dendritic cells resting, macrophages M1, plasma cells, T cells CD4 memory activated, T cells CD4 naive, T cells follicular helper and T cells regulatory Tregs. By contrast, there was a positive association of KL expression with eosinophils, mast cells resting, monocytes, neutrophils, NK cells resting, and T cells CD4 memory resting infiltration (Fig. 6 C). These findings were further supported by the box diagram depicting the difference of immune cell distribution between high- and low-KL expression groups. Consistently, higher expression of inactive CD4 memory T cells, resting mast cells, monocytes and naive B cells, but lower expression of helper follicular T cells, M1 macrophages and regulatory T cells were presented in the high-KL expression group than the low-KL expression group, respectively (Fig. 6 D). Concomitantly, KL gene expression level were positively correlated with B cells (r = 0.241, P = 2.04e-06) and macrophage (r = 0.203, P = 6.79e-06) in TIMER database (Fig. 6 E). Furthermore, we found that the expression level of KL gene was positively correlated with most of genes from immune characteristics (MHC, chemokine receptors, chemokines, Immune-stimulatory factors and immune checkpoint molecules) in the LUAD (Fig. 6 F), which were also observed for most cancers in pan-cancer analysis (Supplementary Fig. 8A-E). Thereafter, the positive correlation of KL gene with most immune features could be speculated that overexpressed KL gene may enhance the effect of immune cells in tumor cells, which further consolidated the evidence of linking KL and immunity. 3.2 Prognostic analysis of KL gene based on immune cell in LUAD patients Since KL gene expression was significantly associated with immune cell infiltration and prognosis of LUAD, we investigated whether KL gene expression affected the prognosis of LUAD mediated by immune cell infiltration in KM Plotter (Kaplan-Meier plotter (kmplot.com)) database. Prognostic analysis was further performed based on the immune cell subgroups. LUAD patients with high-expression KL gene and decreased B cells, CD4 + memory T cells, Eosinophils, Mesenchymal stem cells, NK cells and macrophages had a better prognosis (Fig. 7 ). It is worth emphasizing that KL gene influence on OS was only significant with decreased Natural killer T-cells [HR (95%CI): 0.41 (0.28, 0.6), P -value = 2.80e-06]. These results suggested that the KL gene might affect the prognosis of LUAD patients in part because of immune infiltration. Discussion In summary, we performed a complex analysis to explore new biomarker that may regulate immune process derived from aging data pooled in LUAD. We constructed and validated a prognostic model of four aging-related genes for LUAD. Further we revealed KL as a key regulator of most immune features to affect the prognosis of LUAD partly mediated by methylation and RNA modification. This report for the first time elucidated the significance of prognosis prediction and immunological regulation of KL in LUAD. Aging has a dual role in cancer, including (1) aging as a powerful barrier to prevent tumorigenesis and (2) a favorable partner to promote tumor progression 9 , 19 . There is a consensus that delayed aging can prevent and slow down tumorigenesis and progression. However, rare studies have addressed aging in LUAD, especially aging-related biomarkers. It is well established that aging negatively affects the function of immune cells 20 . Immunotherapy has become popular in LUAD treatment because of its irreplaceable role in tumor progression, but only few people benefit. Therefore, we endeavored to identify key aging-related genes in LUAD, and explore their relevance to immunity. Four aging-related genes were identified to constructed prognostic model for LUAD, including CDK1, FBP1, TFAP2A and KL. CDK1 could promote the development of lung cancer cells by interacting with Sox2, activating GP130/STAT3 signaling pathway or being targeted by miR-34c-3p 21 – 23 . FBP1 affected lung cancer through inhibition of the Warburg effect 24 , NK cell dysfunction 25 , and promoter methylation 26 . TFAP2A has a clear anti-LUAD effect proved by multiple ways, such as through Mir-16 family/TFAP2A/PSG9/TGF-β signaling pathway or inducing ITPKA 27 , 28 . Klotho inhibits the insulin and insulin-like growth factor 1 signaling pathway as potential treatments for lung cancer 29 , unveiling one tip of the iceberg between them. Rab8 GTPase can indirectly inhibit Klotho-mediated Wnt signaling activity, which in turn affects the progression of NSCLC 30 . Additionally, SHANK1 regulates ubiquitination of Klotho by interacting with MDM2, thereby promoting NSCLC process 31 . The above findings have not yet focused on Klotho in LUAD, therefore, we focused on KL gene for the following analysis. Consistent with most studies 32 , the KL gene was down-expressed which leaded to a poorer prognosis for LUAD patients, as was first determined. It is common knowledge that the aging and cancer process is usually accompanied by abnormal DNA methylation. Promoter hypermethylation could modulate KL gene silencing in gastric, ovarian and renal cancers 33 . Unsurprisingly, KL gene expression was negatively correlated with the majority of hypermethylated CPG sites resulting in a poorer prognosis in this analysis. In addition, we found that KL was positively associated with 12 selected m6A modifications among which higher expression contributed a better survival than lower expression. Interestingly, methyltransferase-like 3 (METTL3) acts as an oncogene for NSCLC, by depositing m6A modifications on key transcripts 34 where it can also participate in tumor immunotherapy through multiple pathways, such as NF-kB and STAT3 phosphorylation, AKT and MAPK pathways 35 . Next, the functional analysis of the DEGs after median grouping by KL expression revealed multiple pathways involved in immunity, such as humoral immune response, PI3K-Akt signaling pathway, complement and coagulation cascades, cAMP signaling pathway, etc. Therefore, KL might affect the occurrence and development of LUAD by regulating immune cells. Tumor microenvironment (TME) mainly consists of two non-tumor components: immune cells and stromal cells, whose scores are combined to form ESTIMATE scores, which are valuable for tumor diagnosis and prognosis prediction 36 . Not only did the corresponding scores indicate a greater auto component in TME, but also the proportion of the immune component positively correlated with OS 36 . The higher immune scores, stromal scores and ESTIMATE scores in high-KL expression group, suggested that immunity played an important role in the process of KL affecting LUAD. B cells, T cells and NK cells are important immune substances involved in antitumor effects 37 , 38 . A study has shown that B and T cells are positively associated with prognosis in LUAD 39 . There are two types of macrophages, M1 and M2 40 , among which M1 type is mainly involved in inflammatory reaction and mediates anti-tumor immune response, while M2 has the opposite role 41 . We found that KL were positively correlated with macrophages, further revealed a tendency of higher expressed proportion of M0 and M2 macrophages but significant lower M1 macrophages in the high-KL group. The prognostic analysis of KL gene in LUAD patients based on immune cells also confirmed more evident OS protection of KL gene in LUAD patients when B cells, CD4 + memory T cells, Eosinophils, Mesenchymal stem cells and macrophages were in the decreased state, especially in the natural killer T-cell reduction group. These suggested that immune infiltration may influence the LUAD prognosis in part through KL. These significant correlations between KL gene and most immune traits further enhanced the probability of the hypothesis being valid. Tumor cells and most immune cells recruit large amounts of chemokines, which not only play an important role in TME but are also associated with multiple tumor prognosis 42 . For example, high expression of CXCL17 and XCL2 genes, which are positively correlated with KL expression in the herein study, favors lung cancer prognosis 43 . In addition, immune checkpoints or co-receptors play a key role in immune tolerance and regulation of homeostasis in vivo, especially in regulating T-cell responses 44 . Activation of T cells requires stimulatory signals such as CD28, CD80 and CD86, which were positively correlated with the KL gene, while CD80 and CD86 bind to CD28 protein to provide signals for the initial activation of T cells and produce stimulatory effects 45 . All of the above suggested that there might be a strong link between KL gene and immunity. In this study, we systemically used the aging gene sets to construct a prognostic model and screen out one key gene KL, and analyzed for the first time its expression, prognostic prediction, epigenetic and immunological features in LUAD. Imperfections must be mentioned. The limited and incomplete clinical characteristics of the TCGA and GEO databases prompted the inability to adjust for multiple confounding factors to mitigate study bias. In addition, suitable real-world immunotherapy data were unavailable to determine whether KL could be a drug target for LUAD. More importantly, this was only a bioinformatics analysis and further wet-lab evidence was necessary to confirm these findings. Conclusions In conclusion, the senescence gene KL, acted as a potent prognostic factor for LUAD, possibly mediated by promoter hypermethylation and RNA modification. The multi-dimensional associations between KL and immunological features provided important insights into the development of KL-targeted immunotherapies for LUAD treatment. Abbreviations Abbreviations The full names AUC Area Under the ROC curve (AUC) BPs Biological Processes CC Cellular Components CI Confidence Interval DEGs Differentially Expressed genes GO Gene Ontology GEO Gene Expression Omnibus data base HR Hazard Ratio HAGR Human Ageing Genomic Resources KL Klotho gene KEGG Kyoto Encyclopedia of Genes and Genomes KM Kaplan-Meier LASSO Least Absolute Shrinkage and Selection Operator LUAD Lung Adenocarcinoma METTL3 methyltransferase-like 3 MFs Molecular Functions NSCLC non-small cell lung cancer OS Overall Survival RS Risk Score ROC Receiver Operating Characteristic ssGSEA Single Gene Set Enrichment Analysis SASP Senescence related secretory phenotype TME tumor microenvironment TCGA The Cancer Genome Atlas Declarations Ethics Statement: All data obtained in this study are publicly available in TCGA and GEO at GSE68465, GSE72094 and GSE50081, and all participants signed informed consent. Consent for publication: Not applicable. Availability of data and materials: Not applicable. Competing interests: The authors declare that they have no competing interests. Funding: This study was funded by Tianjin Key Medical Discipline (Specialty) Construction Project (TJYXZDXK-009A) and Guangdong Basic and Applied Basic Research Foundation (2022A1515010436). Author Contributions: YTQ, FBL and YP provided the idea and designed the research. YTQ, PW, CYS and XXW analyzed the data. YTQ was the major contributor in writing the manuscript. MZ and FFS supervised the study and edited the manuscript. Acknowledgements: Not applicable. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin May. 2021;71(3):209–49. doi: 10.3322/caac.21660 . Travis WD, Brambilla E, Nicholson AG, Yatabe Y, Austin JHM, Beasley MB, et al. The 2015 World Health Organization Classification of Lung Tumors: Impact of Genetic, Clinical and Radiologic Advances Since the 2004 Classification. J Thorac Oncol Sep. 2015;10(9):1243–60. doi: 10.1097/JTO.0000000000000630 . Cagle PT, Allen TC, Olsen RJ. Lung cancer biomarkers: present status and future developments. Arch Pathol Lab Med. Sep 2013;137(9):1191–8. doi: 10.5858/arpa.2013-0319-CR . Herbst RS, Morgensztern D, Boshoff C. The biology and management of non-small cell lung cancer. Nat Jan. 2018;24(7689):446–54. doi: 10.1038/nature25183 . 553 ) . Osmani L, Askin F, Gabrielson E, Li QK. Current WHO guidelines and the critical role of immunohistochemical markers in the subclassification of non-small cell lung carcinoma (NSCLC): Moving from targeted therapy to immunotherapy. Semin Cancer Biol Oct. 2018;52(Pt 1):103–9. doi: 10.1016/j.semcancer.2017.11.019 . Doroshow DB, Herbst RS. Treatment of Advanced Non-Small Cell Lung Cancer in 2018. JAMA Oncol Apr. 2018;1(4):569–70. doi: 10.1001/jamaoncol.2017.5190 . 4 ) . Childs BG, Durik M, Baker DJ, van Deursen JM. Cellular senescence in aging and age-related disease: from mechanisms to therapy. Nat Med Dec. 2015;21(12):1424–35. doi: 10.1038/nm.4000 . Di Micco R, Krizhanovsky V, Baker D. d'Adda di Fagagna F. Cellular senescence in ageing: from mechanisms to therapeutic opportunities. Nat Rev Mol Cell Biol Feb. 2021;22(2):75–95. doi: 10.1038/s41580-020-00314-w . Campisi J. Aging, cellular senescence, and cancer. Annu Rev Physiol. 2013;75:685–705. doi: 10.1146/annurev-physiol-030212-183653 . Yasuda T, Koiwa M, Yonemura A, Miyake K, Kariya R, Kubota S, et al. Inflammation-driven senescence-associated secretory phenotype in cancer-associated fibroblasts enhances peritoneal dissemination. Cell Rep Feb. 2021;23(8):108779. doi: 10.1016/j.celrep.2021.108779 . 34 ) . Barnes PJ. Senescence in COPD and Its Comorbidities. Annu Rev Physiol. 2017;79:517–39. doi: 10.1146/annurev-physiol-022516-034314 . Barnes PJ, Baker J, Donnelly LE. Cellular Senescence as a Mechanism and Target in Chronic Lung Diseases. Am J Resp Crit Care Sep. 2019;1(5):556–64. doi: 10.1164/rccm.201810-1975TR . 200 ) . Zhang WJ, Li YT, Lyu JC, Shi FY, Kong YJ, Sheng C, et al. An aging-related signature predicts favorable outcome and immunogenicity in lung adenocarcinoma. Cancer Sci Mar. 2022;113(3):891–903. doi: 10.1111/cas.15254 . Kuro-o M, Matsumura Y, Aizawa H, Kawaguchi H, Suga T, Utsugi T, et al. Mutation of the mouse klotho gene leads to a syndrome resembling ageing. Nat Nov. 1997;6(6655):45–51. doi: 10.1038/36285 . 390 ) . Buchanan S, Combet E, Stenvinkel P, Shiels PG. Klotho, Aging, and the Failing Kidney. Front Endocrinol (Lausanne). 2020;11:560. doi: 10.3389/fendo.2020.00560 . Zhou X, Wang X. Klotho: a novel biomarker for cancer. J Cancer Res Clin Oncol Jun. 2015;141(6):961–9. doi: 10.1007/s00432-014-1788-y . Lai L, Li Y, Liu J, Luo L, Tang J, Xue J, et al. Bovine serum albumin aggravates macrophage M1 activation and kidney injury in heterozygous Klotho-deficient mice via the gut microbiota-immune axis. Int J Biol Sci. 2021;17(3):742–55. doi: 10.7150/ijbs.56424 . Zhu L, Stein LR, Kim D, Ho K, Yu GQ, Zhan L, et al. Klotho controls the brain-immune system interface in the choroid plexus. Proc Natl Acad Sci U S A Nov. 2018;27(48):E11388–96. doi: 10.1073/pnas.1808609115 . 115 ) . Calcinotto A, Kohli J, Zagato E, Pellegrini L, Demaria M, Alimonti A. Cellular Senescence: Aging, Cancer, and Injury. Physiol Rev Apr. 2019;1(2):1047–78. doi: 10.1152/physrev.00020.2018 . 99 ) . Jackaman C, Tomay F, Duong L, Abdol Razak NB, Pixley FJ, Metharom P, et al. Aging and cancer: The role of macrophages and neutrophils. Ageing Res Rev Jul. 2017;36:105–16. doi: 10.1016/j.arr.2017.03.008 . Huang Z, Shen G, Gao J. CDK1 promotes the stemness of lung cancer cells through interacting with Sox2. Clin Transl Oncol Sep. 2021;23(9):1743–51. doi: 10.1007/s12094-021-02575-z . Palma F, Affinito A, Nuzzo S, Roscigno G, Scognamiglio I, Ingenito F, et al. miR-34c-3p targets CDK1 a synthetic lethality partner of KRAS in non-small cell lung cancer. Cancer Gene Ther May. 2021;28(5):413–26. doi: 10.1038/s41417-020-00224-1 . Kuang YB, Guo WZ, Ling J, Xu DL, Liao YL, Zhao H, et al. Iron-dependent CDK1 activity promotes lung carcinogenesis via activation of the GP130/STAT3 signaling pathway. Cell Death Dis. Apr 1 2019;10doi:ARTN 297. 1038/s41419-019-1528-y. Dong CF, Yuan TT, Wu YD, Wang YF, Fan TWM, Miriyala S, et al. Loss of FBP1 by Snail-Mediated Repression Provides Metabolic Advantages in Basal-like Breast Cancer. Cancer Cell Mar. 2013;18(3):316–31. doi: 10.1016/j.ccr.2013.01.022 . 23 ) . Cong JJ, Wang XW, Zheng XH, Wang D, Fu BQ, Sun R, et al. Dysfunction of Natural Killer Cells by FBP1-Induced Inhibition of Glycolysis during Lung Cancer Progression. Cell Metab Aug. 2018;7(2):243-+. doi: 10.1016/j.cmet.2018.06.021 . 28 ) . Li LF, Yang L, Fan ZR, Xue WH, Shen ZB, Yuan YL, et al. Hypoxia-induced GBE1 expression promotes tumor progression through metabolic reprogramming in lung adenocarcinoma. Signal Transduct Tar May 22 2020;5(1)doi: 10.1038/s41392-020-0152-8 . Xiong Y, Feng Y, Zhao J, Lei J, Qiao T, Zhou Y, et al. TFAP2A potentiates lung adenocarcinoma metastasis by a novel miR-16 family/TFAP2A/PSG9/TGF-beta signaling pathway. Cell Death Dis Apr. 2021;6(4):352. doi: 10.1038/s41419-021-03606-x . 12 ) . Zhou GR, Fan ZH, Zhu W, Wang M, Wu Y, Shi L, et al. TFAP2A Induced ITPKA Serves as an Oncogene and Interacts with DBN1 in Lung Adenocarcinoma. Int J Biol Sci. 2020;16(3):504–14. doi: 10.7150/ijbs.40435 . Ma Z, Dong A, Kong M, Qian J. Silencing of the type 1 insulin-like growth factor receptor increases the sensitivity to apoptosis and inhibits invasion in human lung adenocarcinoma A549 cells. Cell Mol Biol Lett. 2007;12(4):556–72. doi: 10.2478/s11658-007-0022-1 . Chen B, Huang S, Pisanic Ii TR, Stark A, Tao Y, Cheng B, et al. Rab8 GTPase regulates Klotho-mediated inhibition of cell growth and progression by directly modulating its surface expression in human non-small cell lung cancer. EBioMedicine Nov. 2019;49:118–32. doi: 10.1016/j.ebiom.2019.10.040 . Chen B, Zhao H, Li M, She Q, Liu W, Zhang J, et al. SHANK1 facilitates non-small cell lung cancer processes through modulating the ubiquitination of Klotho by interacting with MDM2. Cell Death Dis Apr. 2022;25(4):403. doi: 10.1038/s41419-022-04860-3 . 13 ) . Chen B, Wang X, Zhao W, Wu J. Klotho inhibits growth and promotes apoptosis in human lung cancer cell line A549. J Exp Clin Cancer Res Jul. 2010;19:29:99. doi: 10.1186/1756-9966-29-99 . Xie B, Chen J, Liu B, Zhan J. Klotho acts as a tumor suppressor in cancers. Pathol Oncol Res Oct. 2013;19(4):611–7. doi: 10.1007/s12253-013-9663-8 . Zeng C, Huang W, Li Y, Weng H. Roles of METTL3 in cancer: mechanisms and therapeutic targeting. J Hematol Oncol Aug. 2020;27(1):117. doi: 10.1186/s13045-020-00951-w . 13 ) . Hu C, Liu J, Li Y, Jiang W, Ji D, Liu W, et al. Multifaceted Roles of the N(6)-Methyladenosine RNA Methyltransferase METTL3 in Cancer and Immune Microenvironment. Biomolecules Jul 28 2022;12(8)doi: 10.3390/biom12081042 . Bi KW, Wei XG, Qin XX, Li B. BTK Has Potential to Be a Prognostic Factor for Lung Adenocarcinoma and an Indicator for Tumor Microenvironment Remodeling: A Study Based on TCGA Data Mining. Front Oncol. Apr 15 2020;10doi:ARTN 424.3389/fonc.2020.00424. Shevtsov M, Multhoff G. Immunological and Translational Aspects of NK Cell-Based Antitumor Immunotherapies. Front Immunol. 2016;7:492. doi: 10.3389/fimmu.2016.00492 . Yu SF, Zhang YN, Yang BY, Wu CY. Human memory, but not naive, CD4 + T cells expressing transcription factor T-bet might drive rapid cytokine production. J Biol Chem Dec. 2014;19(51):35561–9. doi: 10.1074/jbc.M114.608745 . 289 ) . Iglesia MD, Parker JS, Hoadley KA, Serody JS, Perou CM, Vincent BG. Genomic Analysis of Immune Cell Infiltrates Across 11 Tumor Types. J Natl Cancer Inst Nov 2016;108(11)doi: 10.1093/jnci/djw144 . Mantovani A, Sozzani S, Locati M, Allavena P, Sica A. Macrophage polarization: tumor-associated macrophages as a paradigm for polarized M2 mononuclear phagocytes. Trends Immunol Nov. 2002;23(11):549–55. Doi 10.1016/S1471-4906(02)02302-5 . doi:Pii S1471-4906(02)02302-5. Sica A, Mantovani A. Macrophage plasticity and polarization: in vivo veritas. J Clin Invest Mar. 2012;122(3):787–95. doi: 10.1172/Jci59643 . Vilgelm AE, Richmond A. Chemokines Modulate Immune Surveillance in Tumorigenesis, Metastasis, and Response to Immunotherapy. Front Immunol. 2019;10:333. doi: 10.3389/fimmu.2019.00333 . Karin N. Chemokines and cancer: new immune checkpoints for cancer therapy. Curr Opin Immunol Apr. 2018;51:140–5. doi: 10.1016/j.coi.2018.03.004 . Tsai HF, Hsu PN. Cancer immunotherapy by targeting immune checkpoints: mechanism of T cell dysfunction in cancer immunity and new therapeutic targets. J Biomed Sci. May 25 2017;24doi:ARTN 35.1186/s12929-017-0341-0. Chen LP, Flies DB. Molecular mechanisms of T cell co-stimulation and co-inhibition. Nat Rev Immunol Apr. 2013;13(4):227–42. doi: 10.1038/nri3405 . Additional Declarations No competing interests reported. Supplementary Files SupplementalInformation.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. 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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-2264744","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":152682553,"identity":"0d5fc4ff-c995-4de5-969c-5292fb4dcf8a","order_by":0,"name":"Yating Qiao","email":"","orcid":"","institution":"National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yating","middleName":"","lastName":"Qiao","suffix":""},{"id":152682555,"identity":"20e8c049-4757-4df5-b58f-c0599984dec0","order_by":1,"name":"Fubin Liu","email":"","orcid":"","institution":"National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fubin","middleName":"","lastName":"Liu","suffix":""},{"id":152682556,"identity":"b52545f4-4339-4022-ad58-86fecdcbe36f","order_by":2,"name":"Yu Peng","email":"","orcid":"","institution":"National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Peng","suffix":""},{"id":152682558,"identity":"3acce545-cca7-4a18-a3bf-46854dabf000","order_by":3,"name":"Peng Wang","email":"","orcid":"","institution":"National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Wang","suffix":""},{"id":152682560,"identity":"c3550956-7867-4d47-b429-b98f5538a31d","order_by":4,"name":"Changyu Si","email":"","orcid":"","institution":"National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Changyu","middleName":"","lastName":"Si","suffix":""},{"id":152682561,"identity":"a779e2fd-5ae2-4a5e-8311-54be187062ff","order_by":5,"name":"Xixuan Wang","email":"","orcid":"","institution":"National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xixuan","middleName":"","lastName":"Wang","suffix":""},{"id":152682562,"identity":"88f6679c-039d-465f-8e97-3390bc28d458","order_by":6,"name":"Ming Zhang","email":"","orcid":"","institution":"Comprehensive Management Department of Occupational Health, Shenzhen Prevention and Treatment Center for Occupational Diseases","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Zhang","suffix":""},{"id":152682563,"identity":"7960f4b4-b988-4da8-a423-e6ad0ce31c42","order_by":7,"name":"Fangfang Song","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYLCCCiBmbwASHyB8A8JazgAxzwEGBsYZCaRqYeYhRot8e+/hFwdq7tj1sJ89/Nr2x7bEBvbmbRIMNXdwajE4cy7N4sCxZ8k9PHlp1jkJtxMbeI6VSTAce4Zbi0SOmfEHtsPJ9gxABlgLUESCseEwbofNyDEzOPDvcDIP/xszYwuQFvk3+LUw3MgxfnCw7bAdj0SO8WMGsC08+LUYnDljxnCw73ACj8QbM8aetNvGbTxpxRYJx/A4rL3H+MOBb4ftefhzjD/8sLkt289+eOONDzV4HMbAwCYBJBIboAwGNhCRgE8DMAJBycQexhgFo2AUjIJRgAEA0qZb96flTHoAAAAASUVORK5CYII=","orcid":"","institution":"National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Fangfang","middleName":"","lastName":"Song","suffix":""}],"badges":[],"createdAt":"2022-11-11 23:59:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2264744/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2264744/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":29355194,"identity":"8af01b10-9694-4358-bf83-5e425be0acde","added_by":"auto","created_at":"2022-11-21 21:41:23","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1032313,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart for the comprehensive characterization of aging related genes in LUAD\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1FlowchartforthecomprehensivecharacterizationofagingrelatedgenesinLUAD..jpg","url":"https://assets-eu.researchsquare.com/files/rs-2264744/v1/358f24b78f12834261f68d03.jpg"},{"id":29355192,"identity":"bfdc565a-55d5-4f3b-b5f4-cc26991431b9","added_by":"auto","created_at":"2022-11-21 21:41:23","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4147242,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScreening aging-related genes for prognosis prediction of LUAD. (A)\u003c/strong\u003e The volcano map indicating differential expressed genes (DEGs) between the normal and tumor in TCGA-LUAD.\u003cstrong\u003e(B)\u003c/strong\u003e Enrichment analysis of gene ontology (GO) terms and Kyoto Encyclopedia of genes Number of Genomes (KEGG) pathway for different genes. \u003cstrong\u003e(C)\u003c/strong\u003e Forest plots of univariate COX regression analysis of 24 DEGs. \u003cstrong\u003e(D) \u003c/strong\u003eLASSO coefficient maps and coefficient profiles of 24 survival-related DEGs. The first black dotted line of the figure was selected as the optimal parameter (lambda).\u003cstrong\u003e (E)\u003c/strong\u003e Forest plot of the four optimal genes screened by multivariate COX regression analysis.\u003c/p\u003e","description":"","filename":"Figure2ScreeningagingrelatedgenesforprognosispredictionofLUAD..jpg","url":"https://assets-eu.researchsquare.com/files/rs-2264744/v1/d15671964b8c522495b842c1.jpg"},{"id":29355191,"identity":"368b34cd-7cc0-49e4-b269-c96b12e4a4b0","added_by":"auto","created_at":"2022-11-21 21:41:23","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2342741,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction for the four-gene prognostic model of LUAD by TCGA training cohort and GEO validation datasets.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Distribution of risk scores, survival times and survival status of patients, as well as heatmap of the four-gene-expression used to construct prognostic model in the TCGA training cohort.\u003cstrong\u003e (B, C, D and E) \u003c/strong\u003eKaplan-Meier and Time-dependent ROC analysis of four prognostic genes signature in TCGA training set and GEO external validation sets.\u003c/p\u003e","description":"","filename":"Figure3ConstructionforthefourgeneprognosticmodelofLUADbyTCGAtrainingcohortandGEOvalidationdatasets..jpg","url":"https://assets-eu.researchsquare.com/files/rs-2264744/v1/7b5bb82d7c0e9b7836fb0480.jpg"},{"id":29355771,"identity":"1f5c652f-da81-4c59-8b7b-3931f684ee16","added_by":"auto","created_at":"2022-11-21 21:49:23","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1178937,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression and prognostic analysis of KL gene in TCGA-LUAD. (A) \u003c/strong\u003eExpression levels of KL gene in normal and LUAD tumor tissues. \u003cstrong\u003e(B)\u003c/strong\u003e The effect of KL gene on LUADOS in high-KL and low-KL subgroups. \u003cstrong\u003e(C) \u003c/strong\u003eForest plots from univariate and multivariate COX regression analyses in TCGA-LUAD.\u003c/p\u003e","description":"","filename":"Figure5ExpressionandprognosticanalysisofKLgeneinTCGALUAD..jpg","url":"https://assets-eu.researchsquare.com/files/rs-2264744/v1/5055077dca91ce12fc747aa1.jpg"},{"id":29355793,"identity":"368bd179-6de0-4ab2-977a-fc9ef8d9a8f0","added_by":"auto","created_at":"2022-11-21 21:49:23","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":14825166,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis of KL gene with immunological characteristics. \u003c/strong\u003eBox diagram evaluating the distribution of immune score, stromal score, ESTIMATE score \u003cstrong\u003e(A)\u003c/strong\u003e and 22 immune infiltrating cells \u003cstrong\u003e(D) \u003c/strong\u003ein the KL high-low subgroup. \u003cstrong\u003e(B)\u003c/strong\u003e The distribution ratio of 22 immune infiltrating cells in LUAD. The correlations between KL gene expression and 22 immune infiltrating cells in LUAD \u003cstrong\u003e(C), \u003c/strong\u003eimmune infiltrating cells in the TIMER database \u003cstrong\u003e(E)\u003c/strong\u003e, and\u003cstrong\u003e \u003c/strong\u003eimmune characteristics\u003cstrong\u003e (F)\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"Figure6CorrelationanalysisofKLgenewithimmunologicalcharacteristics..jpg","url":"https://assets-eu.researchsquare.com/files/rs-2264744/v1/0e6bbb3ed6f145bf76684e71.jpg"},{"id":29355196,"identity":"e675828e-9361-491e-84e5-a647358b3384","added_by":"auto","created_at":"2022-11-21 21:41:23","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":903308,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot for prognostic value of KL gene expression by different immune cell subsets in LUAD patients.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure7ForestplotforprognosticvalueofKLgeneexpressionbydifferentimmunecellsubsetsinLUADpatients..jpg","url":"https://assets-eu.researchsquare.com/files/rs-2264744/v1/1d7e409eac5dd595d786f210.jpg"},{"id":29355911,"identity":"f4567271-c656-4d68-854d-d2931acc02f1","added_by":"auto","created_at":"2022-11-21 21:49:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1414753,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2264744/v1/3118d219-c886-4252-813a-ff570b2a1cc5.pdf"},{"id":29355197,"identity":"f6a4d5b3-83a8-41b6-876c-00a5acdfc7e5","added_by":"auto","created_at":"2022-11-21 21:41:23","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":1954801,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-2264744/v1/b49f33f4fca38930e318b479.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comprehensive characterization of the senescence gene Klotho in lung adenocarcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer is one of the most prevalent cancers worldwide, with 2,206,771 new cases and 1,796,144 deaths in 2020\u003csup\u003e1\u003c/sup\u003e. The World Health Organization has divided it into two categories: non-small cell lung cancer (NSCLC) and small cell lung cancer, among which NSCLC includes lung adenocarcinoma (LUAD), squamous cell carcinoma and large cell lung cancer\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. LUAD, accounting for about 40% of lung cancer, has become a great challenge to prognosis due to lacking of effective markers, strong concealment, late detection and multiple metastases\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Currently, surgical treatment, targeted molecular therapy, immunotherapy and their combinations coexist in LUAD, but the overall cure and survival rates of LUAD patients remain low\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, even with the popular immune checkpoint inhibitors which only benefit few patients\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. To address this need, we sought to explore novel biomarkers that might modulate immune responses to further treat LUAD.\u003c/p\u003e \u003cp\u003eAging has become a common social condition. Numerous studies indicate that senescent cells increase the probability of making mistakes in cell division and weaken the immune system, which in turn contribute to the development of aging-related diseases, especially cancer\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Considerable attention has been focused on slowing aging to treat cancer given the strong link between them. Previous studies have found that cell senescence could change the pericellular microenvironment by driving the release of multifaceted inflammatory factors and growth promoting factors, called senescence related secretory phenotype (SASP), which further provokes cancer\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Additionally, the combination of SASP with a dysfunctional immune system typically accelerates peripheral cellular senescence by inducing inflammation, further leading to negative effects\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Recent researches have considered cellular senescence as an important mechanism and target for lung disease\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, and an aging-related feature has been constructed to predict favorable outcome and immunogenicity in LUAD patients\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. However, this very limited data do not provide a reasonable interpretation for the association between aging and LUAD, suggesting a necessity of more studies to enrich the evidence. Meanwhile, the expression of aging-related genes in immune cells and whether aberrant genes affect immunity have not been studied in LUAD.\u003c/p\u003e \u003cp\u003eHerein, to screen for key senescence factors, we systematically evaluated the correlation between cellular senescence and prognosis in LUAD, further developed a novel risk model based on aging-related genes and explored its biological functions. Subsequently, an in-depth analysis of the relationship between key aging-related gene (Klotho, aka KL) and immune function was performed, providing new insights into candidate strategies for cellular senescence and immune therapeutic possibilities in LUAD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy cohort\u003c/h2\u003e \u003cp\u003eTwo independent cohorts, the Cancer Genome Atlas (TCGA) cohort and Gene Expression Omnibus (GEO) cohort were enrolled. The RNA-seq expression and clinical data of Pan-cancer and DNA methylation data of LUAD in TCGA were respectively downloaded from the GDC Hub entrance of UCSC Xena (UCSC Xena), a public database available online. Totally, 524 tumor and 59 normal samples were included in the differential gene expression analysis, and patients with the survival time or last follow-up time\u0026thinsp;\u0026lt;\u0026thinsp;30 days were excluded for subsequent prognostic model construction (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe also downloaded gene expression profile and survival data of LUAD patients (GSE68465, GSE72094 and GSE50081) from GEO (Home - GEO - NCBI (nih.gov)) dataset adopting the same inclusion criteria with TCGA for subsequent validation of the prediction model.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAcquisition Of Aging-related Gene Sets\u003c/h3\u003e\n\u003cp\u003eHuman Ageing Genomic Resources (HAGR) (Human Ageing Genomic Resources (senescence.info)) and Ageing Atlas (Aging Atlas (cncb.ac.cn)) were combined to identify 565 aging-related genes for subsequent analysis.\u003c/p\u003e\n\u003ch3\u003eDifferentially Expressed Genes (Degs) Analysis\u003c/h3\u003e\n\u003cp\u003eThe DEseq2 package was used to analyze the DEGs between normal and tumor tissues in TCGA dataset. DEGs were eligible into further analysis under the following criteria: | log\u003csub\u003e2\u003c/sub\u003eFoldChange | \u0026gt; 1.5 and \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003ch3\u003eEnrichment Analysis\u003c/h3\u003e\n\u003cp\u003eEnrichment analysis was analyzed by the \u003cem\u003eR\u003c/em\u003e packages \"DSEeq2\", \"clusterProfiler\" and \"enrichPlot\", involving Single Gene Set Enrichment Analysis (ssGSEA), gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. The GO terms included biological processes (BPs), cellular components (CC) and molecular functions (MFs). The cut-off criterion was \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003ch3\u003eGenomic Analysis\u003c/h3\u003e\n\u003cp\u003eSince tumorigenesis and progression are often accompanied by DNA methylation, gene mutations and RNA modifications, a series of genomic analyses were performed targeting the KL gene. Nineteen methylation CPG sites of KL gene were screened for their correlations with KL expression and overall survival (OS) from TCGA-LUAD DNA methylation data detected by Illumina Human Methylation 450 Beadchip. Then, we examined KL gene mutations in pan-cancer and LUAD using cBioPortal database (cBioPortal for Cancer Genomics). The m6A modification has three main elements: Writers, Erasers and Readers, which add, remove or read m6A sites respectively. We explored the association of selected m6A genes and KL using RNA-Seq expression data of TCGA-LUAD.\u003c/p\u003e\n\u003ch3\u003eCorrelations Between Kl And Immunological Characteristics\u003c/h3\u003e\n\u003cp\u003eFirst, the immune score and the relevant proportion of 22 immune-infiltrating cell types on TCGA-LUAD were separately obtained through the \"Estimate\" and \"Cibersort\" package. Secondly, the TIMER database (TIMER (shinyapps.io)) was employed to analyze the correlation between KL gene and immune infiltrating cells in LUAD. Finally, we summarized the characteristics of several immune-related genes based on relevant literature, including chemokines and their receptors, MHC molecules, immune stimulators and immune checkpoints.\u003c/p\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003eUnivariate COX, least absolute shrinkage and selection operator (LASSO) COX and multivariate COX regression analyses were applied to screen candidate genes to construct prognostic model for LUAD. Furthermore, the receiver operating characteristic (ROC) curve was used to test the predictive performance of the model, showing the area under the ROC curve (AUC) for survival probability at 1, 3 and 5 years. The Wilcoxon test was performed to evaluate the differences in KL distribution between normal and tumor or in immune scores between high- and low-KL groups. Kaplan-Meier (KM) method was used to investigate the correlation between KL gene and OS. The Spearman method was performed for all the correlation analysis. A \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026le;\u0026thinsp;0.05 was considered statistically significant. All statistical analyses were performed using \u003cem\u003eR\u003c/em\u003e software (Version 4.0.2).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e1. Screening and validating aging-related genes for prognosis prediction of LUAD\u003c/h2\u003e \u003c/div\u003e\n\u003ch3\u003e1.1 Identification And Functional Analysis Of Degs Of Aging-related Genes\u003c/h3\u003e\n\u003cp\u003eBy intersecting 565 aging-related genes with TCGA-LUAD gene expression data, 406 genes were eventually included in the subsequent study. Seventy-two DEGs (Up: Down =\u0026thinsp;48:24) between tumor (n\u0026thinsp;=\u0026thinsp;524) and normal (n\u0026thinsp;=\u0026thinsp;59) were selected for functional analysis by GO terms and KEGG pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In the GO terms, DEGs was enriched in response to peptide, regulation of receptor signaling pathway via STAT and signaling receptor activator activity. Whereas, the KEGG pathway was mainly enriched in immune pathways, such as IL-17 signaling pathway, Cytokine-Cytokine receptor interaction, et al (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Further, among 501 tumor samples with OS\u0026thinsp;\u0026gt;\u0026thinsp;30 days, univariate COX regression analysis revealed that 24 DEGs were significantly associated with OS of LUAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), then LASSO COX regression analysis was used to identify 12 genes with the best performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Finally, we used the stepwise regression method of multivariate COX regression to select 4 optimal genes and construct the prognostic model for LUAD, including CDK1, KL, TFAP2A and FBP1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e1.2 Construction for the four-gene prognostic model of LUAD by TCGA training cohort and GEO validation datasets\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe individual mRNA expression level and coefficient from multivariate regression analysis for the aforementioned four genes were employed by linear combination method to obtain the risk score formula as following: Risk Score (RS)\u0026thinsp;=\u0026thinsp;0.1316*Exp (CDK1) \u0026minus;\u0026thinsp;0.0927*Exp (KL)\u0026thinsp;+\u0026thinsp;0.0911*Exp (TFAP2A) \u0026minus;\u0026thinsp;0.1208*Exp (FBP1). Subsequently, the RS was calculated for each patient and divided into high- and low-risk groups based on the median. Compared to the low-risk group, the high-risk group had more fatalities, and lower FBP1 and KL expression, but higher BTFAP2A and CDK1 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). We further examined the effect of the four-gene prognostic model on survival, and found significantly better survival probabilities of the low-risk group in both the TCGA training cohort and externally validated GEO cohorts (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The maximum AUC values in the TCGA training cohort and the external validation cohorts reached 0.70 and 0.81, respectively, displaying good sensitivity and specificity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, C, D and E).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, ssGSEA analysis was used to enrich the DEGs between the high- and the low-risk group, among which immune pathways, such as: B cell receptor signaling pathway, T cell receptor signaling pathway, Th17 cell differentiation and IL-17 signaling pathway, etc, were significantly accumulated in high-risk group (Supplementary Fig.\u0026nbsp;1A and B).\u003c/p\u003e\n\u003ch3\u003e1.3 Development Of A Nomogram For Prediction Of Luad Outcomes\u003c/h3\u003e\n\u003cp\u003eTo test whether RS was an independent prognostic factor, we constructed a nomogram by combination with traditional risk factors. All independent prognostic parameters were screened by univariate and multivariate Cox proportional hazards regression analysis. Finally, the AJCC-Stage and RS were used as independent risk factors to construct composite nomogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The point for each factor indicated its corresponding contribution to the probability of survival, and the resulting total points for each patient showed individual survival probability of 1, 3, and 5 years. The prediction efficiency was verified by C-index and calibration curve. We observed that when RS was more than 0.4, the 1 -, 3 -, and 5-year survival was approximately less than 75%, 35% and 20%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The actual OS and the predicted OS by the nomogram matched well at 1-, 3-, and 5-year by the calibration curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). The C-index of the nomogram was 0.721. These results suggested a good performance and reliability of nomogram based on the characteristic RS of aging-related genes for predicting the survival rates of LUAD patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2. KL gene acting as an effective biomarker for LUAD\u003c/h2\u003e \u003c/div\u003e\n\u003ch3\u003e2.1 Expression And Prognostic Analysis Of Kl Gene In Tcga-luad\u003c/h3\u003e\n\u003cp\u003eAmong the aforementioned four-gene predicting LUAD prognosis, we focused on the KL gene for two reasons: (1) although discovered earlier, current studies had shown KL gene\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, with anti-aging\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e and anti-tumor effects \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, only limited to mice and a few epidemiological studies; (2) more interestingly, there have been two publications glimpsing a subtle link between KL gene and immunity\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, suggesting a worthwhile to explore their relationship in cancer patients.\u003c/p\u003e \u003cp\u003eThe RNA-seq expression data of TCGA was transformed as log\u003csub\u003e2\u003c/sub\u003e(count\u0026thinsp;+\u0026thinsp;1) for downstream analysis. KL gene was low-expressed in tumor tissues compared to normal tissues for TCGA-LUAD (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;2.2e-16) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), which were also observed for most cancers in pan-cancer analysis (Supplementary Fig.\u0026nbsp;2). LUAD patients with high-KL expression have a better prognosis than those with low-KL expression grouped by median (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0018) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), especially in individuals aged\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;60 years (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0043), male (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0081), or with pathological stage Ⅲ-Ⅳ (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035) (Supplementary Fig.\u0026nbsp;3). Univariate and multivariate COX regression analysis showed that AJCC-Stage and KL were independent prognostic factors for LUAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e2.2 Gene Enrichment Analysis By Kl Gene Expression\u003c/h3\u003e\n\u003cp\u003eSubsequently, we revealed 1023 DEGs (Down: Up =\u0026thinsp;535:488) between high- and low-KL expression groups (Supplementary Fig.\u0026nbsp;4A). Further GO terms for DEGs showed an enrichment in calcium ion homeostasis, humoral immune response, collagen-containing extracellular matrix, signaling receptor activator activity and so on (Supplementary Fig.\u0026nbsp;4B). The KEGG pathway involved neuroactive ligand-receptor interaction, Camp-pkg signaling pathway, cAMP signaling pathway, etc (Supplementary Fig.\u0026nbsp;4C). In summary, these enriched pathways were common in aging, cancer and immunity.\u003c/p\u003e\n\u003ch3\u003e2.3 Multidimensional Analysis For Kl Gene\u003c/h3\u003e\n\u003cp\u003eDNA methylation, gene mutation and RNA modification were all important factors regulating the KL expression level and leading to the carcinogenesis and progression of LUAD. We observed that KL gene expression was negatively correlated with the mean methylation level [\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.94e-11, r=-0.29, 95%CI (-0.38, -0.21)] (Supplementary Fig.\u0026nbsp;5A). Each of the 19 CPG sites located in KL gene was individually negatively correlated with KL expression except cg23584087 and cg20672059 (Supplementary Fig.\u0026nbsp;5B). Further, most sites exhibited higher methylation levels in tumor tissues than normal tissues (Supplementary Fig.\u0026nbsp;5C). After classified into hypermethylation and hypomethylation groups according to the best cut-off value, most CPG sites with hypermethylation level had non-protective effect of the survival of LUAD patients compared to hypomethylation level (Supplementary Fig.\u0026nbsp;5D). These results suggested the abnormal methylation of KL gene might partly affect the development of LUAD.\u003c/p\u003e \u003cp\u003eNext, KL gene mutation analysis revealed a mutation rate of only 7.46% in NSCLC and even less than 3% in LUAD (Supplementary Fig.\u0026nbsp;6), implying KL gene mutations may not be directly involved in the progression of LUAD since the low mutation rate.\u003c/p\u003e \u003cp\u003eAlternatively, KL gene was positively correlated with 12 selected m6A genes (Supplementary Fig.\u0026nbsp;7A). All of them were highly expressed in high-KL expression group (Supplementary Fig.\u0026nbsp;7B), and higher expression of most of them were protective against OS compared to lower expression group divided by optimal cutoff values (Supplementary Fig.\u0026nbsp;7C), implying m6A modification of KL probably as another transcriptional regulatory mechanism in the development of LUAD.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3. KL gene might affect the development of LUAD by regulating immune cells\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.1 Correlation analysis of KL gene with immune score, immune infiltration cells and immunological characteristics\u003c/h2\u003e \u003cp\u003eAll the immune scores (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.8e-05), stromal scores (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.8e-10) and ESTIMATE scores (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.9e-08) were higher in the high-KL expression group than those in the low-KL expression group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The distribution ratio of resting memory CD4 T cells, M0 and M2 macrophages ranked highest in 22 kinds of immune infiltrating cells in LUAD patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Meanwhile, we observed a negative correlation between KL expression level and the infiltrating abundance of dendritic cells resting, macrophages M1, plasma cells, T cells CD4 memory activated, T cells CD4 naive, T cells follicular helper and T cells regulatory Tregs. By contrast, there was a positive association of KL expression with eosinophils, mast cells resting, monocytes, neutrophils, NK cells resting, and T cells CD4 memory resting infiltration (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). These findings were further supported by the box diagram depicting the difference of immune cell distribution between high- and low-KL expression groups. Consistently, higher expression of inactive CD4 memory T cells, resting mast cells, monocytes and naive B cells, but lower expression of helper follicular T cells, M1 macrophages and regulatory T cells were presented in the high-KL expression group than the low-KL expression group, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Concomitantly, KL gene expression level were positively correlated with B cells (r\u0026thinsp;=\u0026thinsp;0.241, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.04e-06) and macrophage (r\u0026thinsp;=\u0026thinsp;0.203, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.79e-06) in TIMER database (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, we found that the expression level of KL gene was positively correlated with most of genes from immune characteristics (MHC, chemokine receptors, chemokines, Immune-stimulatory factors and immune checkpoint molecules) in the LUAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF), which were also observed for most cancers in pan-cancer analysis (Supplementary Fig.\u0026nbsp;8A-E).\u003c/p\u003e \u003cp\u003eThereafter, the positive correlation of KL gene with most immune features could be speculated that overexpressed KL gene may enhance the effect of immune cells in tumor cells, which further consolidated the evidence of linking KL and immunity.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Prognostic analysis of KL gene based on immune cell in LUAD patients\u003c/h2\u003e \u003cp\u003eSince KL gene expression was significantly associated with immune cell infiltration and prognosis of LUAD, we investigated whether KL gene expression affected the prognosis of LUAD mediated by immune cell infiltration in KM Plotter (Kaplan-Meier plotter (kmplot.com)) database. Prognostic analysis was further performed based on the immune cell subgroups. LUAD patients with high-expression KL gene and decreased B cells, CD4\u0026thinsp;+\u0026thinsp;memory T cells, Eosinophils, Mesenchymal stem cells, NK cells and macrophages had a better prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). It is worth emphasizing that KL gene influence on OS was only significant with decreased Natural killer T-cells [HR (95%CI): 0.41 (0.28, 0.6), \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;2.80e-06]. These results suggested that the KL gene might affect the prognosis of LUAD patients in part because of immune infiltration.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn summary, we performed a complex analysis to explore new biomarker that may regulate immune process derived from aging data pooled in LUAD. We constructed and validated a prognostic model of four aging-related genes for LUAD. Further we revealed KL as a key regulator of most immune features to affect the prognosis of LUAD partly mediated by methylation and RNA modification. This report for the first time elucidated the significance of prognosis prediction and immunological regulation of KL in LUAD.\u003c/p\u003e \u003cp\u003eAging has a dual role in cancer, including (1) aging as a powerful barrier to prevent tumorigenesis and (2) a favorable partner to promote tumor progression\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. There is a consensus that delayed aging can prevent and slow down tumorigenesis and progression. However, rare studies have addressed aging in LUAD, especially aging-related biomarkers. It is well established that aging negatively affects the function of immune cells\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Immunotherapy has become popular in LUAD treatment because of its irreplaceable role in tumor progression, but only few people benefit. Therefore, we endeavored to identify key aging-related genes in LUAD, and explore their relevance to immunity.\u003c/p\u003e \u003cp\u003eFour aging-related genes were identified to constructed prognostic model for LUAD, including CDK1, FBP1, TFAP2A and KL. CDK1 could promote the development of lung cancer cells by interacting with Sox2, activating GP130/STAT3 signaling pathway or being targeted by miR-34c-3p\u003csup\u003e\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. FBP1 affected lung cancer through inhibition of the Warburg effect\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, NK cell dysfunction\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, and promoter methylation\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. TFAP2A has a clear anti-LUAD effect proved by multiple ways, such as through Mir-16 family/TFAP2A/PSG9/TGF-β signaling pathway or inducing ITPKA\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Klotho inhibits the insulin and insulin-like growth factor 1 signaling pathway as potential treatments for lung cancer\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, unveiling one tip of the iceberg between them. Rab8 GTPase can indirectly inhibit Klotho-mediated Wnt signaling activity, which in turn affects the progression of NSCLC\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Additionally, SHANK1 regulates ubiquitination of Klotho by interacting with MDM2, thereby promoting NSCLC process\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The above findings have not yet focused on Klotho in LUAD, therefore, we focused on KL gene for the following analysis.\u003c/p\u003e \u003cp\u003eConsistent with most studies\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, the KL gene was down-expressed which leaded to a poorer prognosis for LUAD patients, as was first determined. It is common knowledge that the aging and cancer process is usually accompanied by abnormal DNA methylation. Promoter hypermethylation could modulate KL gene silencing in gastric, ovarian and renal cancers\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Unsurprisingly, KL gene expression was negatively correlated with the majority of hypermethylated CPG sites resulting in a poorer prognosis in this analysis. In addition, we found that KL was positively associated with 12 selected m6A modifications among which higher expression contributed a better survival than lower expression. Interestingly, methyltransferase-like 3 (METTL3) acts as an oncogene for NSCLC, by depositing m6A modifications on key transcripts\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e where it can also participate in tumor immunotherapy through multiple pathways, such as NF-kB and STAT3 phosphorylation, AKT and MAPK pathways\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Next, the functional analysis of the DEGs after median grouping by KL expression revealed multiple pathways involved in immunity, such as humoral immune response, PI3K-Akt signaling pathway, complement and coagulation cascades, cAMP signaling pathway, etc. Therefore, KL might affect the occurrence and development of LUAD by regulating immune cells.\u003c/p\u003e \u003cp\u003eTumor microenvironment (TME) mainly consists of two non-tumor components: immune cells and stromal cells, whose scores are combined to form ESTIMATE scores, which are valuable for tumor diagnosis and prognosis prediction\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Not only did the corresponding scores indicate a greater auto component in TME, but also the proportion of the immune component positively correlated with OS\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. The higher immune scores, stromal scores and ESTIMATE scores in high-KL expression group, suggested that immunity played an important role in the process of KL affecting LUAD. B cells, T cells and NK cells are important immune substances involved in antitumor effects\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. A study has shown that B and T cells are positively associated with prognosis in LUAD\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. There are two types of macrophages, M1 and M2\u003csup\u003e40\u003c/sup\u003e, among which M1 type is mainly involved in inflammatory reaction and mediates anti-tumor immune response, while M2 has the opposite role\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. We found that KL were positively correlated with macrophages, further revealed a tendency of higher expressed proportion of M0 and M2 macrophages but significant lower M1 macrophages in the high-KL group. The prognostic analysis of KL gene in LUAD patients based on immune cells also confirmed more evident OS protection of KL gene in LUAD patients when B cells, CD4\u0026thinsp;+\u0026thinsp;memory T cells, Eosinophils, Mesenchymal stem cells and macrophages were in the decreased state, especially in the natural killer T-cell reduction group. These suggested that immune infiltration may influence the LUAD prognosis in part through KL.\u003c/p\u003e \u003cp\u003eThese significant correlations between KL gene and most immune traits further enhanced the probability of the hypothesis being valid. Tumor cells and most immune cells recruit large amounts of chemokines, which not only play an important role in TME but are also associated with multiple tumor prognosis\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. For example, high expression of CXCL17 and XCL2 genes, which are positively correlated with KL expression in the herein study, favors lung cancer prognosis\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. In addition, immune checkpoints or co-receptors play a key role in immune tolerance and regulation of homeostasis in vivo, especially in regulating T-cell responses\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Activation of T cells requires stimulatory signals such as CD28, CD80 and CD86, which were positively correlated with the KL gene, while CD80 and CD86 bind to CD28 protein to provide signals for the initial activation of T cells and produce stimulatory effects\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. All of the above suggested that there might be a strong link between KL gene and immunity.\u003c/p\u003e \u003cp\u003eIn this study, we systemically used the aging gene sets to construct a prognostic model and screen out one key gene KL, and analyzed for the first time its expression, prognostic prediction, epigenetic and immunological features in LUAD. Imperfections must be mentioned. The limited and incomplete clinical characteristics of the TCGA and GEO databases prompted the inability to adjust for multiple confounding factors to mitigate study bias. In addition, suitable real-world immunotherapy data were unavailable to determine whether KL could be a drug target for LUAD. More importantly, this was only a bioinformatics analysis and further wet-lab evidence was necessary to confirm these findings.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, the senescence gene KL, acted as a potent prognostic factor for LUAD, possibly mediated by promoter hypermethylation and RNA modification. The multi-dimensional associations between KL and immunological features provided important insights into the development of KL-targeted immunotherapies for LUAD treatment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"567\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.67844522968198%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"73.32155477031802%\"\u003e\n \u003cp\u003e\u003cstrong\u003eThe full names\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.67844522968198%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"73.32155477031802%\"\u003e\n \u003cp\u003eArea Under the ROC curve (AUC)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.67844522968198%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBPs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"73.32155477031802%\"\u003e\n \u003cp\u003eBiological Processes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.67844522968198%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"73.32155477031802%\"\u003e\n \u003cp\u003eCellular Components\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.67844522968198%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"73.32155477031802%\"\u003e\n \u003cp\u003eConfidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.67844522968198%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDEGs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"73.32155477031802%\"\u003e\n \u003cp\u003eDifferentially Expressed genes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.67844522968198%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"73.32155477031802%\"\u003e\n \u003cp\u003eGene Ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.67844522968198%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGEO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"73.32155477031802%\"\u003e\n \u003cp\u003eGene Expression Omnibus data base\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.67844522968198%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"73.32155477031802%\"\u003e\n \u003cp\u003eHazard Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.67844522968198%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHAGR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"73.32155477031802%\"\u003e\n \u003cp\u003eHuman Ageing Genomic Resources\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.67844522968198%\"\u003e\n \u003cp\u003e\u003cstrong\u003eKL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"73.32155477031802%\"\u003e\n \u003cp\u003eKlotho gene\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.67844522968198%\"\u003e\n \u003cp\u003e\u003cstrong\u003eKEGG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"73.32155477031802%\"\u003e\n \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.67844522968198%\"\u003e\n \u003cp\u003e\u003cstrong\u003eKM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"73.32155477031802%\"\u003e\n \u003cp\u003eKaplan-Meier\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.67844522968198%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLASSO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"73.32155477031802%\"\u003e\n \u003cp\u003eLeast Absolute Shrinkage and Selection Operator\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.67844522968198%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLUAD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"73.32155477031802%\"\u003e\n \u003cp\u003eLung Adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.67844522968198%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMETTL3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"73.32155477031802%\"\u003e\n \u003cp\u003emethyltransferase-like 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.67844522968198%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMFs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"73.32155477031802%\"\u003e\n \u003cp\u003eMolecular Functions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.67844522968198%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSCLC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"73.32155477031802%\"\u003e\n \u003cp\u003enon-small cell lung cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.67844522968198%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"73.32155477031802%\"\u003e\n \u003cp\u003eOverall Survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.67844522968198%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"73.32155477031802%\"\u003e\n \u003cp\u003eRisk Score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.67844522968198%\"\u003e\n \u003cp\u003e\u003cstrong\u003eROC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"73.32155477031802%\"\u003e\n \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.67844522968198%\"\u003e\n \u003cp\u003e\u003cstrong\u003essGSEA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"73.32155477031802%\"\u003e\n \u003cp\u003eSingle Gene Set Enrichment Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.67844522968198%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSASP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"73.32155477031802%\"\u003e\n \u003cp\u003eSenescence related secretory phenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.67844522968198%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTME\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"73.32155477031802%\"\u003e\n \u003cp\u003etumor microenvironment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.67844522968198%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTCGA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"73.32155477031802%\"\u003e\n \u003cp\u003eThe Cancer Genome Atlas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Statement:\u0026nbsp;\u003c/strong\u003eAll data obtained in this study are publicly available in TCGA and GEO at GSE68465, GSE72094 and GSE50081, and all participants signed informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This study was funded by Tianjin Key Medical Discipline (Specialty) Construction Project (TJYXZDXK-009A)\u0026nbsp;and Guangdong Basic and Applied Basic Research Foundation (2022A1515010436).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e YTQ, FBL and YP provided the idea and designed the research. YTQ, PW, CYS and XXW analyzed the data. YTQ was the major contributor in writing the manuscript. MZ and FFS supervised the study and edited the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin May. 2021;71(3):209\u0026ndash;49. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3322/caac.21660\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eTravis WD, Brambilla E, Nicholson AG, Yatabe Y, Austin JHM, Beasley MB, et al. The 2015 World Health Organization Classification of Lung Tumors: Impact of Genetic, Clinical and Radiologic Advances Since the 2004 Classification. J Thorac Oncol Sep. 2015;10(9):1243\u0026ndash;60. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/JTO.0000000000000630\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eCagle PT, Allen TC, Olsen RJ. Lung cancer biomarkers: present status and future developments. Arch Pathol Lab Med. Sep 2013;137(9):1191\u0026ndash;8. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5858/arpa.2013-0319-CR\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eHerbst RS, Morgensztern D, Boshoff C. The biology and management of non-small cell lung cancer. Nat Jan. 2018;24(7689):446\u0026ndash;54. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nature25183\u003c/span\u003e\u003c/span\u003e. 553 ) .\u003c/li\u003e\n\u003cli\u003eOsmani L, Askin F, Gabrielson E, Li QK. Current WHO guidelines and the critical role of immunohistochemical markers in the subclassification of non-small cell lung carcinoma (NSCLC): Moving from targeted therapy to immunotherapy. Semin Cancer Biol Oct. 2018;52(Pt 1):103\u0026ndash;9. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.semcancer.2017.11.019\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eDoroshow DB, Herbst RS. Treatment of Advanced Non-Small Cell Lung Cancer in 2018. JAMA Oncol Apr. 2018;1(4):569\u0026ndash;70. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamaoncol.2017.5190\u003c/span\u003e\u003c/span\u003e. 4 ) .\u003c/li\u003e\n\u003cli\u003eChilds BG, Durik M, Baker DJ, van Deursen JM. Cellular senescence in aging and age-related disease: from mechanisms to therapy. Nat Med Dec. 2015;21(12):1424\u0026ndash;35. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nm.4000\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eDi Micco R, Krizhanovsky V, Baker D. d'Adda di Fagagna F. Cellular senescence in ageing: from mechanisms to therapeutic opportunities. Nat Rev Mol Cell Biol Feb. 2021;22(2):75\u0026ndash;95. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41580-020-00314-w\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eCampisi J. Aging, cellular senescence, and cancer. Annu Rev Physiol. 2013;75:685\u0026ndash;705. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1146/annurev-physiol-030212-183653\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eYasuda T, Koiwa M, Yonemura A, Miyake K, Kariya R, Kubota S, et al. Inflammation-driven senescence-associated secretory phenotype in cancer-associated fibroblasts enhances peritoneal dissemination. Cell Rep Feb. 2021;23(8):108779. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.celrep.2021.108779\u003c/span\u003e\u003c/span\u003e. 34 ) .\u003c/li\u003e\n\u003cli\u003eBarnes PJ. Senescence in COPD and Its Comorbidities. Annu Rev Physiol. 2017;79:517\u0026ndash;39. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1146/annurev-physiol-022516-034314\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eBarnes PJ, Baker J, Donnelly LE. Cellular Senescence as a Mechanism and Target in Chronic Lung Diseases. Am J Resp Crit Care Sep. 2019;1(5):556\u0026ndash;64. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1164/rccm.201810-1975TR\u003c/span\u003e\u003c/span\u003e. 200 ) .\u003c/li\u003e\n\u003cli\u003eZhang WJ, Li YT, Lyu JC, Shi FY, Kong YJ, Sheng C, et al. An aging-related signature predicts favorable outcome and immunogenicity in lung adenocarcinoma. Cancer Sci Mar. 2022;113(3):891\u0026ndash;903. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/cas.15254\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eKuro-o M, Matsumura Y, Aizawa H, Kawaguchi H, Suga T, Utsugi T, et al. Mutation of the mouse klotho gene leads to a syndrome resembling ageing. Nat Nov. 1997;6(6655):45\u0026ndash;51. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/36285\u003c/span\u003e\u003c/span\u003e. 390 ) .\u003c/li\u003e\n\u003cli\u003eBuchanan S, Combet E, Stenvinkel P, Shiels PG. Klotho, Aging, and the Failing Kidney. Front Endocrinol (Lausanne). 2020;11:560. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fendo.2020.00560\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eZhou X, Wang X. Klotho: a novel biomarker for cancer. J Cancer Res Clin Oncol Jun. 2015;141(6):961\u0026ndash;9. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00432-014-1788-y\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eLai L, Li Y, Liu J, Luo L, Tang J, Xue J, et al. Bovine serum albumin aggravates macrophage M1 activation and kidney injury in heterozygous Klotho-deficient mice via the gut microbiota-immune axis. Int J Biol Sci. 2021;17(3):742\u0026ndash;55. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7150/ijbs.56424\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eZhu L, Stein LR, Kim D, Ho K, Yu GQ, Zhan L, et al. Klotho controls the brain-immune system interface in the choroid plexus. Proc Natl Acad Sci U S A Nov. 2018;27(48):E11388\u0026ndash;96. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.1808609115\u003c/span\u003e\u003c/span\u003e. 115 ) .\u003c/li\u003e\n\u003cli\u003eCalcinotto A, Kohli J, Zagato E, Pellegrini L, Demaria M, Alimonti A. Cellular Senescence: Aging, Cancer, and Injury. Physiol Rev Apr. 2019;1(2):1047\u0026ndash;78. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1152/physrev.00020.2018\u003c/span\u003e\u003c/span\u003e. 99 ) .\u003c/li\u003e\n\u003cli\u003eJackaman C, Tomay F, Duong L, Abdol Razak NB, Pixley FJ, Metharom P, et al. Aging and cancer: The role of macrophages and neutrophils. Ageing Res Rev Jul. 2017;36:105\u0026ndash;16. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.arr.2017.03.008\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eHuang Z, Shen G, Gao J. CDK1 promotes the stemness of lung cancer cells through interacting with Sox2. Clin Transl Oncol Sep. 2021;23(9):1743\u0026ndash;51. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12094-021-02575-z\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003ePalma F, Affinito A, Nuzzo S, Roscigno G, Scognamiglio I, Ingenito F, et al. miR-34c-3p targets CDK1 a synthetic lethality partner of KRAS in non-small cell lung cancer. Cancer Gene Ther May. 2021;28(5):413\u0026ndash;26. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41417-020-00224-1\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eKuang YB, Guo WZ, Ling J, Xu DL, Liao YL, Zhao H, et al. Iron-dependent CDK1 activity promotes lung carcinogenesis via activation of the GP130/STAT3 signaling pathway. Cell Death Dis. Apr 1 2019;10doi:ARTN 297. 1038/s41419-019-1528-y.\u003c/li\u003e\n\u003cli\u003eDong CF, Yuan TT, Wu YD, Wang YF, Fan TWM, Miriyala S, et al. Loss of FBP1 by Snail-Mediated Repression Provides Metabolic Advantages in Basal-like Breast Cancer. Cancer Cell Mar. 2013;18(3):316\u0026ndash;31. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ccr.2013.01.022\u003c/span\u003e\u003c/span\u003e. 23 ) .\u003c/li\u003e\n\u003cli\u003eCong JJ, Wang XW, Zheng XH, Wang D, Fu BQ, Sun R, et al. Dysfunction of Natural Killer Cells by FBP1-Induced Inhibition of Glycolysis during Lung Cancer Progression. Cell Metab Aug. 2018;7(2):243-+. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cmet.2018.06.021\u003c/span\u003e\u003c/span\u003e. 28 ) .\u003c/li\u003e\n\u003cli\u003eLi LF, Yang L, Fan ZR, Xue WH, Shen ZB, Yuan YL, et al. Hypoxia-induced GBE1 expression promotes tumor progression through metabolic reprogramming in lung adenocarcinoma. Signal Transduct Tar May 22 2020;5(1)doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41392-020-0152-8\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eXiong Y, Feng Y, Zhao J, Lei J, Qiao T, Zhou Y, et al. TFAP2A potentiates lung adenocarcinoma metastasis by a novel miR-16 family/TFAP2A/PSG9/TGF-beta signaling pathway. Cell Death Dis Apr. 2021;6(4):352. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41419-021-03606-x\u003c/span\u003e\u003c/span\u003e. 12 ) .\u003c/li\u003e\n\u003cli\u003eZhou GR, Fan ZH, Zhu W, Wang M, Wu Y, Shi L, et al. TFAP2A Induced ITPKA Serves as an Oncogene and Interacts with DBN1 in Lung Adenocarcinoma. Int J Biol Sci. 2020;16(3):504\u0026ndash;14. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7150/ijbs.40435\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eMa Z, Dong A, Kong M, Qian J. Silencing of the type 1 insulin-like growth factor receptor increases the sensitivity to apoptosis and inhibits invasion in human lung adenocarcinoma A549 cells. Cell Mol Biol Lett. 2007;12(4):556\u0026ndash;72. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2478/s11658-007-0022-1\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eChen B, Huang S, Pisanic Ii TR, Stark A, Tao Y, Cheng B, et al. Rab8 GTPase regulates Klotho-mediated inhibition of cell growth and progression by directly modulating its surface expression in human non-small cell lung cancer. EBioMedicine Nov. 2019;49:118\u0026ndash;32. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ebiom.2019.10.040\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eChen B, Zhao H, Li M, She Q, Liu W, Zhang J, et al. SHANK1 facilitates non-small cell lung cancer processes through modulating the ubiquitination of Klotho by interacting with MDM2. Cell Death Dis Apr. 2022;25(4):403. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41419-022-04860-3\u003c/span\u003e\u003c/span\u003e. 13 ) .\u003c/li\u003e\n\u003cli\u003eChen B, Wang X, Zhao W, Wu J. Klotho inhibits growth and promotes apoptosis in human lung cancer cell line A549. J Exp Clin Cancer Res Jul. 2010;19:29:99. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1756-9966-29-99\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eXie B, Chen J, Liu B, Zhan J. Klotho acts as a tumor suppressor in cancers. Pathol Oncol Res Oct. 2013;19(4):611\u0026ndash;7. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12253-013-9663-8\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eZeng C, Huang W, Li Y, Weng H. Roles of METTL3 in cancer: mechanisms and therapeutic targeting. J Hematol Oncol Aug. 2020;27(1):117. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13045-020-00951-w\u003c/span\u003e\u003c/span\u003e. 13 ) .\u003c/li\u003e\n\u003cli\u003eHu C, Liu J, Li Y, Jiang W, Ji D, Liu W, et al. Multifaceted Roles of the N(6)-Methyladenosine RNA Methyltransferase METTL3 in Cancer and Immune Microenvironment. Biomolecules Jul 28 2022;12(8)doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/biom12081042\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eBi KW, Wei XG, Qin XX, Li B. BTK Has Potential to Be a Prognostic Factor for Lung Adenocarcinoma and an Indicator for Tumor Microenvironment Remodeling: A Study Based on TCGA Data Mining. Front Oncol. Apr 15 2020;10doi:ARTN 424.3389/fonc.2020.00424.\u003c/li\u003e\n\u003cli\u003eShevtsov M, Multhoff G. Immunological and Translational Aspects of NK Cell-Based Antitumor Immunotherapies. Front Immunol. 2016;7:492. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2016.00492\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eYu SF, Zhang YN, Yang BY, Wu CY. Human memory, but not naive, CD4 + T cells expressing transcription factor T-bet might drive rapid cytokine production. J Biol Chem Dec. 2014;19(51):35561\u0026ndash;9. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1074/jbc.M114.608745\u003c/span\u003e\u003c/span\u003e. 289 ) .\u003c/li\u003e\n\u003cli\u003eIglesia MD, Parker JS, Hoadley KA, Serody JS, Perou CM, Vincent BG. Genomic Analysis of Immune Cell Infiltrates Across 11 Tumor Types. J Natl Cancer Inst Nov 2016;108(11)doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/jnci/djw144\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eMantovani A, Sozzani S, Locati M, Allavena P, Sica A. Macrophage polarization: tumor-associated macrophages as a paradigm for polarized M2 mononuclear phagocytes. Trends Immunol Nov. 2002;23(11):549\u0026ndash;55. Doi \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S1471-4906(02)02302-5\u003c/span\u003e\u003c/span\u003e. doi:Pii S1471-4906(02)02302-5.\u003c/li\u003e\n\u003cli\u003eSica A, Mantovani A. Macrophage plasticity and polarization: in vivo veritas. J Clin Invest Mar. 2012;122(3):787\u0026ndash;95. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1172/Jci59643\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eVilgelm AE, Richmond A. Chemokines Modulate Immune Surveillance in Tumorigenesis, Metastasis, and Response to Immunotherapy. Front Immunol. 2019;10:333. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2019.00333\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eKarin N. Chemokines and cancer: new immune checkpoints for cancer therapy. Curr Opin Immunol Apr. 2018;51:140\u0026ndash;5. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.coi.2018.03.004\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n\u003cli\u003eTsai HF, Hsu PN. Cancer immunotherapy by targeting immune checkpoints: mechanism of T cell dysfunction in cancer immunity and new therapeutic targets. J Biomed Sci. May 25 2017;24doi:ARTN 35.1186/s12929-017-0341-0.\u003c/li\u003e\n\u003cli\u003eChen LP, Flies DB. Molecular mechanisms of T cell co-stimulation and co-inhibition. Nat Rev Immunol Apr. 2013;13(4):227\u0026ndash;42. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nri3405\u003c/span\u003e\u003c/span\u003e.\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":"Aging-related gene, Klotho, LUAD, Prognostic model, Immunity","lastPublishedDoi":"10.21203/rs.3.rs-2264744/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2264744/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAging has become an important mechanism and target for lung diseases. We aimed to explore novel aging markers for lung adenocarcinoma (LUAD) that may partially modulate immune responses.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAging-related gene sets were acquired from HAGR and Ageing Atlas databases. We retrieved RNA-seq expression and clinical data of LUAD from TCGA and three GEO cohorts. Candidate differentially expressed genes (DEGs) were selected by univariate COX, LASSO COX and multivariate COX regression to construct the prognostic model and nomogram. The ssGSEA, GO terms and KEGG pathway analysis were employed for functional enrichment. The Wilcoxon test and Kaplan-Meier method were applied for differences in distribution and prognosis, respectively. The Spearman method was performed for the correlations between KL expression and CPG site methylation, m6A modifications and immunological characteristics.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe identified a four-gene prognostic panel of LUAD to construct a nomogram with C-index of 0.721, screening KL out as one prospective senescence gene. Low-expressed KL independently contributed to a poor prognosis for LUAD patients, which may be partially mediated by hypermethylation and m6A modification. Functional enrichment revealed the involvement of immune pathways, further proved by the positive correlation between KL expression and immune scores, abundance of immune infiltrating cells, and immunological characteristics. High-expressed KL gene in decreased immune cell subgroups (CD4\u0026thinsp;+\u0026thinsp;memory T cells, Eosinophils, NK cells, et al) had a better prognosis.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eImmune-related KL gene was a potent predictor of LUAD, suggesting that further exploration of KL as a therapeutic agent may break the bottleneck in LUAD treatment.\u003c/p\u003e","manuscriptTitle":"Comprehensive characterization of the senescence gene Klotho in lung adenocarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-11-21 21:41:18","doi":"10.21203/rs.3.rs-2264744/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":"d5a25c3a-64f0-4a83-8222-fc3fc382aa9a","owner":[],"postedDate":"November 21st, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-11-21T21:41:21+00:00","versionOfRecord":[],"versionCreatedAt":"2022-11-21 21:41:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2264744","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2264744","identity":"rs-2264744","version":["v1"]},"buildId":"ApUGefWb6u5IBVtyqm6d5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}