Characterization of natural killer (NK) cells in lung adenocarcinoma and construction of an NK risk signature based on single-cell and macromolecular RNA-seg data

Characterization of natural killer (NK) cells in lung adenocarcinoma and construction of an NK risk signature based on single-cell and macromolecular RNA-seg data | 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 Article Characterization of natural killer (NK) cells in lung adenocarcinoma and construction of an NK risk signature based on single-cell and macromolecular RNA-seg data Chuanxi Tian, Yikun Guo, Tianyi Lv, Daowen Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4840386/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/Aims : Natural killer (NK) cells play a crucial role in tumor cell apoptosis, immune milieu regulation, and angiogenesis inhibition. This study aims to analyze the NK signature in lung adenocarcinoma (LUAD) and establish an NK cell-based risk signature for predicting the prognosis of LUAD patients. Methods : Single-cell RNA sequencing (scRNA-seq) data were obtained from the GEO database, while RNA-seq and microarray data from LUAD were simultaneously obtained from the TCGA and GEO databases. The scRNA-seq data were processed using the Seurat R package to identify NK clusters based on NK markers. Differentially expressed genes (DEGs) between normal and tumor samples were identified through differential expression analysis of LUAD-related data. Pearson correlation analysis was used to identify DEGs associated with NK clusters, followed by one-way Cox regression analysis to identify NK cell-related prognostic genes. Subsequently, Lasso regression analysis was employed to construct a risk signature based on NK cell-related prognostic genes. Finally, a column-line diagram model was constructed based on the risk signature and clinicopathological features. Results : Based on the scRNA-seq data, we identified five Natural killer (NK)cells clusters in lung adenocarcinoma (LUAD), with four of them showing associations with prognosis in LUAD. Out of 19,495 differentially expressed genes (DEGs), a total of 725 genes significantly associated with NK clusters were pinpointed and further narrowed down to form a risk profile comprising 13 genes. These 13 genes were primarily linked to 21 signaling pathways, including vascular smooth muscle contraction, RNA polymerase, and pyrimidine metabolism. Additionally, the risk profile exhibited significant associations with stromal and immune scores, as well as various immune cells. Multifactorial analysis indicated that the risk profile served as an independent prognostic factor for LUAD, and its efficacy in predicting the outcome of immunotherapy was validated. Furthermore, a novel column-line diagram integrating staging and NK-based risk profiles was developed, demonstrating strong predictability and reliability in prognostic forecasting for LUAD. Conclusion : The NK cell-based risk signature proves to be a valuable tool for predicting the prognosis of patients with lung adenocarcinoma (LUAD). Furthermore, a comprehensive understanding of NK cell characterization in LUAD could potentially unveil insights into the response of LUAD to immunotherapies and offer novel strategies for cancer treatment. Biological sciences/Immunology Health sciences/Biomarkers natural killer cells lung adenocarcinoma differentially expressed genes risk signature columnar plots Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1 Introduction Lung cancer is a lethal disease that causes the most frequent cancer-related deaths worldwide [ 1 ] , with adenocarcinoma accounting for more than half of all cases [ 2 ] . Although breakthroughs in chemotherapy, radiotherapy, surgery and other novel therapeutics, the incidence and mortality of lung adenocarcinoma (LUAD) continues to increase [ 3 ] . Therefore, it is essential to identify the relevant pathological parameters to provide foundation for clinical prognosis analysis. Omics technology is regarded as a effective tool for understanding the molecular pathogenesis of LUAD [ 4 – 6 ] . Whole-genome sequencing and transcriptome sequencing has provided valuable insights into the gene amplification of RNA methyltransferase in LUAD, suggesting the activation mechanism of tumorigenesis [ 4 ] . Single-cell RNA sequencing techniques were applied to investigate the differences in immune response between the lung squamous cell carcinoma (LUSC) and LUAD [ 5 ] . One subset of tumor immune cells called natural killer (NK) cells is well-known for its capacity to identify and then eliminate cancer cells [ 7 ] . Recent studies demonstrated that NK cells could identify and remove LUAD cells by engaging activating receptors with stress-induced ligands that are overexpressed on cancer cells' surfaces [ 8 – 9 ] . NK cells display an potent antitumor immune cytotoxicity by regulating the tumor microenvironment via MEK/ERK and PI3K/Akt/mTOR pathways [ 10 – 11 ] . NK cells have the capacity to influence the adaptive immune response to LUAD by interacting with other immune cells [ 12 ] . Growing evidence have suggested that the status of NK cells within LUAD are correlated with patient prognosis [ 13 – 15 ] . Increased NK cell infiltration and activity are frequently associated with improved survival and treatment outcomes [ 14 ] . Therefore, NK cells interact with lung cancer in a complex and dynamic manner, and understanding these interactions is critical for predicting patients’ prognosis. Despite some studies on NK cells in LUAD have been conducted [ 16 – 17 ] , the systematic characteristics of NK cells, as well as their association with LUAD prognosis and immunotherapy response, remain limited known. In this study, LUAD scRNA-seq and transcriptome data were retrieved from publicly available databases to differentiate LUAD subsets and uncover NK cell-related risk signature. The immunological environment and immunotherapy response that support the NK cell-based signature were studied, and the clinical significance of the signature was also identified. In addition, a unique nomogram that integrated the NK cell-based risk signature with clinicopathological parameters was established to make it easier to use NK cell-based factors to predict LUAD prognosis. Our study might offer researchers novel insights into the biology of LUAD, resulting in more targeted therapy and a better prognosis for those with LUAD. 2 Materials and Methods 2.1 Data Acquisition and Organization ScRNA-seq data from GSE131907 were acquired from the Gene Expression Omnibus (GEO) database, encompassing two lung adenocarcinoma (LUAD) and two normal control samples. Selection criteria included cells expressing a given gene in a minimum of three cells and cells exhibiting at least 250 genes expressed. The mitochondria to rRNA ratio was assessed utilizing the PercentageFeatureSet function within the Seurat R package. Only cells expressing over 6000 genes and with a Unique Molecular Identifier (UMI) count exceeding 100 were retained, resulting in a total of 23,673 cells. Single-cell transcriptomic and copy number variation (CNV) analyses were conducted using data from The Cancer Genome Atlas (TCGA) database, incorporating pertinent LUAD clinical data. Samples devoid of survival and outcome status were omitted, yielding 500 tumor and 59 adjacent non-tumor samples. The GSE31210 cohort, comprising 226 LUAD samples, served as a validation set; samples lacking follow-up and outcome data were excluded. Following this, ten cancer-associated pathways were identified through literature review, including WNT, PI3K, NOTCH, RAS, cell cycle, MYC, TGF-Beta, HIPPO, and NRF1 pathways [ 18 ] . 2.2 Definition of NK In this study, we conducted a re-analysis of single-cell RNA sequencing (scRNA-seq) data from LUAD using the Seurat package [ 19 ] . Our objective was to comprehensively characterize NK cells. We applied several data preprocessing steps to ensure data quality. First, we filtered out cells with over 6000 or under 250 expressed genes. Next, we performed log-normalization and removed batch effects from the four samples. To reduce the dimensionality of the data, we employed principal component analysis (PCA) with 20 principal components and a resolution of 0.25 for non-linear dimensionality reduction. Subsequently, we used the FindNeighbors and FindClusters functions (dim = 40, resolution = 0.2) to cluster cells into subgroups. To visualize the clustering results, we utilized t-distributed stochastic neighbor embedding (t-SNE) with the RunTSNE function. We then focused specifically on NK cells by identifying marker genes such as NKG7, KLRD1, KLRB1, GNLY, and GZMB. Clustering and t-SNE dimensionality reduction were performed exclusively on the NK cells. To identify marker genes specific to each NK cell cluster, we employed the FindAllMarkers function with criteria including a log fold change (logFC) threshold of 0.5, a minimum percentage threshold (minpct) of 0.35, and a corrected p-value cutoff of < 0.05. To gain further insights into the functional characteristics of the NK cell clusters, we performed gene ontology enrichment analysis using the KEGG enrichment analysis method [ 20 ] . This analysis was carried out using the clusterProfiler software package. Additionally, we analyzed copy number variation (CNV) features in the NK cell clusters to distinguish between tumor and normal cells in each sample. For this analysis, we utilized the CopyKAT R software package [ 21 ] . 2.3 Identification of NK hub genes During our analysis, we initially identified differentially expressed genes (DEGs) between tumor and normal tissues using the limma software package. We set a screening threshold of FDR 1.5 [ 22 ] to determine significant DEGs. Next, we assessed the correlation between these DEGs and the clustering of NK cells. We specifically focused on identifying key NK cell-related genes with p 0.4. For prognostic gene screening, we conducted univariate Cox regression analysis using the survival package. Genes with p < 0.05 were selected as potential prognostic markers. To further refine the gene selection, we applied the least absolute shrinkage and selection operator (lasso) Cox regression analyses. Subsequently, multivariate Cox regression analyses using stepwise regression methods were performed. Based on the results of the multivariate Cox model, we constructed a risk signature using the formula: risk score = Σβi * Expi. Here, i represents the genes included in the risk signature, expi denotes the gene expression, and βi signifies the coefficient obtained from the multivariate Cox model. We categorized patients into high-risk and low-risk groups after zero-mean normalization. To evaluate the predictive performance of the risk signatures, we conducted receiver operating characteristic curve (ROC) analyses using the timeROC software package. These analyses provided an assessment of the risk signature's ability to predict patient outcomes. Similar analyses were performed in the validation cohort to validate the findings. 2.4 Immune landscape analysis In order to delve deeper into the tumor microenvironment (TME), we utilized the CIBERSORT algorithm to evaluate the proportions of 22 distinct immune cell subtypes within the TCGA cohort. This analysis allowed us to assess the relative abundance and composition of immune cells present in the TME. To gain additional insights into the TME, we also calculated an immunity score and stroma score using the ESTIMATE algorithm [ 23 ] . These scores provide valuable information about the immune and stromal components within the TME. The immunity score reflects the level of immune cell infiltration, while the stroma score indicates the extent of stromal cell involvement. By utilizing these scores, we can gain a better understanding of the complex interplay between the tumor and its microenvironment. 2.5 Constructing risk characteristics and column line plots To assess the prognostic significance of clinicopathological and risk characteristics, we conducted univariate and multivariate Cox regression analyses. In the multivariate Cox model, variables with a significance level of P < 0.05 were selected. Subsequently, we utilized the rms package [ 24 ] to construct column-line plots, which enabled us to predict the prognosis of LUAD patients based on these selected variables. To evaluate the predictive accuracy of the models, calibration curves were generated. These curves provide a visual representation of the agreement between the observed and predicted outcomes. Additionally, we performed decision curve analysis (DCA) to assess the reliability of the models. DCA is a useful tool for evaluating the clinical utility and net benefit of predictive models. 2.6 Responsiveness to immune checkpoint blocks We obtained transcriptomic and clinical data from the IMvigor210 cohort of LUAD patients who were treated with anti-PD-L1 drugs (atezolizumab) [ 25 ] . This cohort, which can be accessed at http://research-pub.gene.com/IMvigor210CoreBiologies , provided valuable insights into the response to anti-PD-L1 therapy in LUAD patients. Additionally, we acquired transcriptomic data from the GSE78220 cohort, which consisted of pre-existing melanomas treated with anti-PD-1 checkpoint inhibition. We utilized this data to investigate the potential value of risk marker scores in predicting the effectiveness of immune checkpoint blockade therapy (ICB) [ 26 ] . 2.7 Statistical analyses All statistical analyses were conducted using R software (v3.6.3). To assess the relationships between variables, correlation matrices were generated using either Pearson or Spearman correlation coefficients, depending on the nature of the data. The Wilcoxon test was employed to compare the two groups, providing insights into potential differences between them. Furthermore, survival differences were evaluated using Kaplan-Meier curves and the log rank test. This allowed for the examination of potential disparities in survival outcomes between different groups or conditions. A p-value of less than 0.05 was considered statistically significant, indicating a notable result. 3 Results 3.1 Screening and Identification of NK Clusters in scRNA-seq Data The flowchart depicting the design of this study is presented in Fig. 1 . After an initial screening of the scRNA-seq data, 23,673 cells were obtained. Following log-normalization and dimensionality reduction, we identified 20 subclusters. Subsequently, utilizing five marker genes (NKG7, KLRD1, KLRB1GNLY, and GZMB), we successfully identified 680 NK populations (Fig. S1 A, B). To refine our analysis, we further clustered and downscaled the cells within the 680 NK populations, ultimately identifying five distinct NK clusters (Figure S1 C, D). By observing the TSNE plots of the four sample distributions (Fig. 2 A), we were able to generate and focus on these five NK clusters for subsequent analysis (Fig. 2 B). Examining the expression profiles of the 737 differentially expressed genes (DEGs) within the five NK clusters, we identified the top five DEGs, which were considered as marker genes for the NKF clusters (Fig. 2 C). The distribution ratio of these five clusters within each cohort is illustrated in Fig. 2 D. Moreover, through KEGG analysis (Fig. 2 E), we discovered that these DEGs were significantly enriched in various pathways, including the TNF signaling pathway, T-cell receptor signaling pathway, and NK cell-mediated cytotoxicity. Additionally, based on the CNV characteristics, we observed that the five NK clusters comprised a total of 680 tumor cells and normal cells (Fig. 2 F). 3.2 Exploration of Cancer-Related Pathways in NK Clusters To gain further insights into the relationship between NK clusters and tumor progression, we conducted GSVA scoring of 10 pathways associated with tumorigenesis within the 5 NK clusters (Fig. 3A). Notably, the proportion of malignant cells in NK_0, NK_1, and NK_3 clusters was significantly higher compared to the other two clusters (Fig. 3B), and there were significant differences between each cluster. Furthermore, we analyzed the GSVA scores of the 10 tumor-associated pathways in both malignant and non-malignant cells within each NK cluster. Interestingly, we observed significant differences in the RAS pathway within the NK_0 cluster and the NOTCH pathway within the NK_1 cluster (Fig. 3C-G). To investigate the potential correlation between NK clusters and prognosis, we initially computed the ssGSEA scores of marker genes for each NK cluster utilizing the TCGA cohort. Strikingly, the results revealed that all five NK clusters exhibited higher scores in normal samples compared to tumor samples (Fig. 4 A). Next, we categorized the LUAD samples from the TCGA dataset into high and low NK score groups based on optimal cutoff values determined by the survminer R package. Intriguingly, within the high-NK score group, the samples belonging to NK_1, NK_2, NK_3, and NK_4 groups displayed a more favorable prognosis compared to the low-NK score group. However, it is noteworthy that the prognosis of LUAD was not associated with the NK_0 cluster (Fig. 4 B-F). Although there were differences in NK_0 enrichment between LUAD and normal samples, our findings suggest that the contribution of NK_0 clusters to LUAD progression is minimal. 3.3 Identification of NK cell-related core genes To identify core genes related to NK cells, we conducted a comprehensive analysis by screening differentially expressed genes (DEGs) between tumor tissues and normal tissues. Out of the 19,495 DEGs identified, 12,525 were up-regulated and 6,970 were down-regulated. Remarkably, 725 genes exhibited significant correlation with prognostically relevant NK clusters. Subsequently, we performed one-way Cox regression analysis to assess the prognostic value of each gene, resulting in the identification of 133 genes with prognostic significance (Fig. 5 A, B). Enrichment analysis of these genes revealed their involvement in various biological processes (BP), cellular components (CC), molecular functions (MF), and KEGG pathways (Fig. 5 C). However, it is important to note that the discussion section should be completed first to provide context and specific results before filling in the details of the enrichment analysis. To further refine the gene selection, we employed Lasso Cox regression analysis, which ultimately filtered down to 13 genes with a lambda value of 0.0311 (Fig. 5 D, E). These 13 genes, namely ANOS1, ADGRE3, ADAMTS8, CCT3, CD79A, CX3CR1, DPEP2, FMO3, FBP1, OASL, RRAS, SELENOP, and SFTPC, were included in the risk signature after multifactorial Cox regression analysis using stepwise regression (Fig. 5 F). The final risk signature formula consists of the following 13 genes with corresponding coefficients: RiskScore = "-0.121 * CD79A − 0.056 * DPEP2 + 0.094 * OASL + 0.363 * RRAS + 0.052 * ADAMTS8–0.282 * ADGRE3–0.039 * ANOS1 + 0.155 * CCT3 + 0.075 * CX3CR1–0.145 * FBP1–0.115 * FMO3–0.087 * SELENOP − 0.021 * SFTPC". After Z-mean normalization, we calculated the risk scores for each sample and subsequently classified them into high and low-risk groups. In the TCGA cohort, the area under the curve (AUC) values for 1- to 5-year survival ranged from 0.66 to 0.71. Similarly, in the GSE3141 cohort, the AUC values ranged from 0.64 to 0.72, while in the GSE31210 cohort, they ranged from 0.65 to 0.69. In the GSE37745 cohort, the AUC values ranged from 0.57 to 0.71, and in the GSE50081 cohort, they ranged from 0.70 to 0.77. Lastly, in the GSE68465 cohort, the AUC values ranged from 0.60 to 0.68 (Fig. 6 A-F). Furthermore, the Kaplan-Meier survival analyses demonstrated a similar pattern of survival outcomes in both the TCGA and GEO cohorts. High-risk patients exhibited significantly worse survival outcomes compared to low-risk patients (Fig. 6 A-F). 3.4 Mutation and pathway analysis of key genes In our investigation of the risk signature genes, we examined SNV mutations in these 13 genes. Interestingly, all genes except RRAS exhibited SNV mutations in a greater number of samples (Fig. 7 A). We further analyzed the probability of co-occurrence of mutations between these key genes and the 10 most frequently mutated genes. Figure 7 B demonstrates that while no significant mutation co-occurrence was observed among these 12 genes, significant co-occurrence was observed between ADGRE3 and TTN, CX3CR1 and MUC16, RYR2 and ZFHX4, ANOS1 and LRP1B, DPEP2 and CSMD3, as well as RYR2 and ZFHX4. We also used OncogenicPathways to count known oncogenic signalling pathways in the TCGA cohort (Fig. 7 C). To gain further insights into the association between these risk genes and LUAD, we investigated their correlations with various molecular features of LUAD. Remarkably, most of the genes exhibited significant negative associations with aneuploidy scores, homologous recombination defects, score alterations, fragment counts, and non-silencing mutation rates. Notably, CCTS was the only gene significantly positively associated with these molecular features (Fig. 7 D). Additionally, we explored the potential pathways associated with each risk gene. The analysis revealed significant associations between these 13 genes and a total of 21 pathways. Noteworthy pathways included the B cell receptor signaling pathway, the chemokine signaling pathway, and leukocyte migration (Fig. 7 E, 7 F). 3.5 Analysis of key genes in relation to immunity Our data analysis revealed notable findings regarding the relationship between key genes and immunity. Specifically, we observed that CCT3 exhibited a significant negative correlation with matrix score, immune score, and estimated score. Conversely, the remaining 12 genes displayed significant positive correlations with these scores (Fig. 8 A). To further explore these associations, we categorized each gene based on its median expression and compared the three scores among different expression groups. Notably, for the ANOS1 gene, the high-expression group exhibited significantly lower scores compared to the low-expression group, while the high-expression groups of the remaining genes displayed significantly higher scores compared to the low-expression group (Fig. 8 B). Furthermore, our correlation analysis demonstrated significant associations between the 13 key genes and various immune cells, including B cells, T cells, and NK cells (Fig. 8 C). Moreover, we observed significant differences in several immune cells between the high and low expression groups of the risk genes (Fig. 8 D). 3.6 Sensitivity analysis of risk signature for PD-L1 blockade immunotherapy The emergence of T-cell immunotherapy as a promising approach for cancer treatment has highlighted the importance of evaluating the prognostic value of risk characteristics in the context of immune checkpoint therapy. To assess this, we examined the IMvigor210 and GSE78220 cohorts. Within the IMvigor210 cohort, consisting of 348 patients with varying responses to anti-PD-L1 receptor blockers, including complete remission (CR), partial remission (PR), stable disease (SD), and progressive disease (PD), our analysis revealed compelling results. Patients in the low-risk group experienced a significant clinical benefit and demonstrated significantly longer overall survival compared to those in the high-risk group (Fig. 9 A, p = 0.0017). Furthermore, the risk scores were higher in patients with SD/PD compared to those with CR/PR (Fig. 9 B). Additionally, the high-risk group exhibited a higher percentage of SD/PD compared to the low-risk group (Fig. 9 C). Intriguingly, the survival analysis demonstrated a significant difference between stage I + II patients (Fig. 9 D, p = 0.041) and stage III + IV patients (Fig. 9 E, p = 0.018) within different risk groups. This suggests that risk scores were particularly sensitive in distinguishing patients with different stages of LUAD. Similarly, in the GSE78220 cohort, we observed that patients in the low-risk group had significantly longer overall survival compared to those in the high-risk group (Fig. 9 F, p = 0.0067). Notably, patients with PD exhibited higher risk scores compared to those with CR/PR (Fig. 9 G), and the high-risk group displayed a higher percentage of PD cases than the low-risk group. 3.7 Independent risk factor identification and histogram construction In order to enhance the predictive performance of risk features, we conducted univariate and multivariate COX regression analyses to integrate clinicopathological factors and risk scores. The results from the multivariate COX regression analysis revealed that the risk signature (HR = 2.69, 95% CI: 1.93–3.74, P < 0.001) and metastatic status (HR = 2.06, 95% CI: 1.39–3.05, P = 0.002) were the most crucial independent prognostic factors for LUAD. Based on these findings, we developed a histogram that incorporated both staging and risk scores (Fig. 10 A-C). This histogram demonstrated effectiveness in accurately predicting actual survival outcomes, as confirmed by the calibration curve (Fig. 10 D). Furthermore, the decision curve analysis (DCA) indicated that the discriminative power of the histogram in identifying high-risk patients surpassed that of the risk score and staging alone (Fig. 10 E). Additionally, the time-dependent receiver operating characteristic (TimeROC) analysis demonstrated that both the risk score and the histogram exhibited higher area under the curve (AUC) values compared to other metrics in the TCGA cohort (Fig. 10 F). 4 Discussion NK cells regulate tumor proliferation, angiogenesis and the immune system of LUAD [ 27 ] . Studies on the pathophysiology of NK cells in LUAD have progressed to the genetic and molecular levels [ 28 ] . Nonetheless, the characterisation of NK cells in LUAD remains unknown, and research on LUAD prognosis with respect to NK cells is comparatively limited. Our study focused on the characterization of NK cells. ScRNA-seq data were used to classify NK cells in LUAD. Eventually, we uncovered five NK cell subsets with diverse features that might regulate biology of the LUAD. Recent studies have demonstrated the prognostic significance of the NK cell-related gene signature in the assessment of LUAD outcomes [ 29 – 30 ] . Consistently, our findings revealed four distinct clusters exhibiting strong associations with LUAD prognosis, as quantified by a composite score derived from DEGs encompassing all five clusters. Moreover, our results suggested that differences in the Notch signaling pathway among the identified clusters might underlie the prognostic significance of NK cells in LUAD. Notch signaling pathway plays a vital role in regulating the progression of LUAD [ 31 ] . Suppressing the Notch signaling pathway has demonstrated efficacy in attenuating collagen synthesis and curtailing cellular invasiveness [ 32 ] . Notably, high levels of Notch-related genes have been proven to be correlated with poor survival outcomes in LUAD patients [ 33 ] . Considering the prognostic relevance of the four NK cell clusters, we constructed a 13-gene risk signature. This signature comprised nine protective genes(ANOS1, ADGRE3, CD79A, CX3CR1, DPEP2, FMO3, FBP1, SELENOP and SFTPC) and four risk genes(ADAMTS8, CCT3, RRAS and OAST). In our study, SNV mutations were detected in ANOS1, ADGRE3, ADAMTS8, CCT3, CD79A, CX3CR1, DPEP2, FMO3, FBP1, OASL, SELENOP, and SFTPC, while no statistically significant co-occurrence patterns were observed among these mutations. SNV mutations hold a significant position in the pathogenesis of LUAD, as they can manifest as "driver mutations" that bestow tumor cells with growth advantages, thereby fueling the onset and progression of the disease [ 34 – 35 ] . While no prior studies have revealed a connection between SNV mutations in these risk genes and LUAD, our findings indicated such mutations might contribute to the progression of LUAD. We also demonstrated that the 13 genes were closely associated with 21 signaling pathways. It was proven that part of these pathways were related to onset and prognosis of LUAD. Vascular smooth muscle contraction, RNA polymerase, pyrimidine metabolism and B cell receptor signaling pathway act as effective predictors for the prognosis and therapeutic responses of LUAD patients [ 36 – 38 ] . Spliceosome-related and lysosome-related genes might increase LUAD incidence rate [ 39 – 40 ] . Homologous recombination pathway is up-regulated in LUAD, and increased expression of relevant genes is associated with poor overall survival [ 41 ] . Previous research demonstrated that tumor growth could attribute to the interplay between NK cells and the tumor immune microenvironment (TIME) [ 42 – 43 ] . Our findings revealed that 9 protective genes and three risk genes were positively and significantly associated with the immunological score, whereas one risk gene exhibited a negative correlation. These findings suggested potential interactions between these genes and the TIME in LUAD, hinting at their potential as therapeutic targets. The TIME is constituted by a diverse array of immune cells within the tumor islets, collectively dictating the antitumor immunological milieu of TIME [ 44 ] . NK cells can interact in various ways with these immune cells, fostering an immunosuppressive TIME that allows tumor cells to evade immune surveillance [ 45 ] . In the risk signature, most protective genes were negatively correlated with NK cell activation and T cell activation. Meanwhile, regulatory T cells were negatively associated with protective genes and positively associated with risk genes. It was reported that NK cells could modulate T cell apoptosis in tumors [ 46 ] . T cells also could interact with NK cells and enhance their viability [ 47 ] . These suggested that NK related-genes acted not only on NK cells but also on T cells, therefore regulating the whole TIME. Despite the fact that many patients have resistance to immunotherapeutic therapy [ 48 ] , we found that relevant risk signature efficiently stratified patients based on their likelihood of benefiting from immunotherapies. Additionally, previous studies have reveal that NK cells have the potential to treat solid tumors [ 49 – 50 ] . However, the therapy was less effective for certain patients, which might be a consequence to the extremely complex immunosuppressive milieu [ 51 ] . Fortunately, our findings demonstrated that the NK cell-based signature had the capacity to predict the response to anti-PD-L1 immunotherapy. Further experimental research is warranted to elucidate the mechanisms underlying NK cell-TIME interactions in LUAD and their potential therapeutic implications in LUAD immunotherapy. There are some limitations in our study. Firstly, the retrospective use of public data to establish NK cell clusters and the NK cell-based risk signature highlights the need for validation in prospective, multi-center LUAD cohorts in future investigations. Secondly, while a predictive nomogram was developed, additional studies are required to better investigate the specific biological correlation between the included NK cell-related genes and clinicopathological characteristics. 5 Conclusion To sum up, we comprehensively categorized NK cell in LUAD, identifying five distinct clusters with different characteristics. The DEGs in the five clusters were abundant in vascular smooth muscle contraction, RNA polymerase, pyrimidine metabolism, the B cell receptor signaling pathway, etc. Four of the clusters showed significant correlations with LUAD prognosis and. An NK cell-based prognostic risk signature was created by 13 key genes from the four clusters. This signature appeared to be associated with the immune milieu and might be used to predict responsiveness to anti-PD-L1 therapy. Finally, we constructed a reliable nomogram incorporating the clinicopathological parameters and NK cell-related risk signature for predicting the clinical prognosis of patients with LUAD. Declarations Conflicts of Interest: Chuanxi Tian, Yikun Guo, Tianyi Lv, Daowen Yang, and Min Li declare that they have no competing interests. Funding: This work was supported by Ministry of Science and Technology of the People´s Republic of China (2022YFC3500801). Author Contribution Conceptualization: C.X.T., and D.W.Y.; Formal analysis and Data Curation: Y.K.G.; Writing - Original Draft: C.X.T., Y.K.G., T.Y.L.; Writing - Review & Editing: C.X.T., Y.K.G., T.Y.L. All authors read and approved the final manuscript. 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Spatial landscape of the tumor immune microenvironment. Trends Cancer. 2023;9(6):459–460. doi: 10.1016/j.trecan.2023.03.006 Wang M, Zhou Z, Wang X, Zhang C, Jiang X. Natural killer cell awakening: unleash cancer-immunity cycle against glioblastoma. Cell Death Dis. 2022;13(7):588. doi: 10.1038/s41419-022-05041-y Jin WJ, Jagodinsky JC, Vera JM, Clark PA, Zuleger CL, Erbe AK, Ong IM, Le T, Tetreault K, Berg T, Rakhmilevich AL, Kim K, Newton MA, Albertini MR, Sondel PM, Morris ZS. NK cells propagate T cell immunity following in situ tumor vaccination. Cell Rep. 2023;42(12):113556. doi: 10.1016/j.celrep.2023.113556 . Besla R, Penuel E, Del Rosario G, et al. T cell-Dependent Bispecific Therapy Enhances Innate Immune Activation and Antibody-Mediated Killing. Cancer Immunol Res. 2024;12(1):60–71. doi: 10.1158/2326-6066.CIR-23-0072 Desbois M, Wang Y. Cancer-associated fibroblasts: Key players in shaping the tumor immune microenvironment. 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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-4840386","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":352281122,"identity":"49c1278e-a095-464c-9913-d7f1d5f50f9d","order_by":0,"name":"Chuanxi Tian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIie2RP0vDQBTA7wgkywM7XijETyBcCbSD2n6VHAGzOknHOwLn0upqv0WmolvCDVnyAVK6JIuumcQu1WuHosNF3BzuBw/e8H68fwhZLP8RRwfmhwyLJppfwZknRNv1GPBNSWlX3QT+QqUh6VPQSUGuv5IqpHUiB9CjzDzntdk9q/MJifkQXIdlm1YigqbBBTcN5k5Gy0qNXhYFDwFcttoy2dyiOBznxl3GQywVzkrBYyDAHrbsnhKUs7VR8d4PyixTmCughPFNIQn0KnDswnQXIZ4iGg5q/Iui4M5fyiTOqiJFXR7pIzN9ZGrexXss12QnL6+zOnn7YPtP/cqybbv5NDApJujfyi0Wi8Xyky/r0GIntyX0RwAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Chuanxi","middleName":"","lastName":"Tian","suffix":""},{"id":352281125,"identity":"bbda18ab-4d8f-4278-b960-5db98f424a88","order_by":1,"name":"Yikun Guo","email":"","orcid":"","institution":"Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yikun","middleName":"","lastName":"Guo","suffix":""},{"id":352281129,"identity":"dde67e3e-8789-4dda-b6b8-dc8fc9d4f902","order_by":2,"name":"Tianyi Lv","email":"","orcid":"","institution":"Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Tianyi","middleName":"","lastName":"Lv","suffix":""},{"id":352281133,"identity":"2f8c771d-f533-4c85-ab1a-f700df315d00","order_by":3,"name":"Daowen Yang","email":"","orcid":"","institution":"China-Japan Friendship Hospital","correspondingAuthor":false,"prefix":"","firstName":"Daowen","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2024-08-01 08:11:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4840386/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4840386/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66540850,"identity":"ae705f12-8acb-4e01-8a69-4dc8efbd6176","added_by":"auto","created_at":"2024-10-14 07:40:00","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86401,"visible":true,"origin":"","legend":"\u003cp\u003eResearch Design Process Diagram\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4840386/v1/f5e8916bbcb7804b64c72298.jpeg"},{"id":66539441,"identity":"34dab668-3ec5-4bfb-bd75-0a104cbaff0b","added_by":"auto","created_at":"2024-10-14 07:32:00","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":107931,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of NK clusters based on scRNA seq data fromLUAD patients. (A)21 samples distribution map; (B) 5 natural killer cells Distribution map; (C) The top 5 marker genes expression point map; (D) Proportion and cell number of the subgroups and adjacent tissues in cancerous tissues; (E)5 natural killer cells KEGG enrichment analysis; (F) malignant and non-malignant tsne cells predicted Distribution map.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4840386/v1/d855ca5843929cf2bdf2e9e3.jpeg"},{"id":66539442,"identity":"31cde15d-b0e1-4089-adeb-4edb20658a40","added_by":"auto","created_at":"2024-10-14 07:32:00","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":186552,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterisation of tumour-associated pathways in NK clusters. (A) Heatmap of the scores of 10 tumour-associated pathways enriched by NK cells; (B) Comparison of NK clusters in malignant and non-malignant cells; Comparison of GSVA scores of each pathway between malignant and non-malignant cells (C) NK_0; (D) NK_1; (E) NK_2; (F) NK_3; (G) NK_4;(wilcox.test, *P \u0026lt; 0.05; **P \u0026lt; 0.01; ***P \u0026lt; 0.001; and ****P \u0026lt; 0.0001). ns, not significant.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4840386/v1/480bb13aa644fde578323995.jpeg"},{"id":66542948,"identity":"845ea7c4-f716-4b0e-8137-d879c51da8f6","added_by":"auto","created_at":"2024-10-14 07:56:00","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":50846,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of 5 NK clusters with prognosis of LUAD patients:(A) Comparison of 5 NK scores in cancerous and normal tissues; K-M curves of high and low NK score groups (B) NK_0 cluster; (C) NK_1 cluster; (D) NK_2 cluster; (E) NK_3 cluster; (F) NK_4 cluster. P \u0026lt; 0.01, ****P \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4840386/v1/d34973db4b847c95e7a3c3cd.jpeg"},{"id":66539447,"identity":"35e0fab6-a7df-4da3-bf54-6b6c80a3f9ff","added_by":"auto","created_at":"2024-10-14 07:32:00","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":78080,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of pivotal predictive genes to construct risk profiles. (A) Volcano plot of genes differentially expressed in cancer and normal tissues in the TCGA cohort; (B) Volcano plot of prognostic-related genes by one-way Cox regression analysis; (C) Bar chart of prognostic gene enrichment analysis; (D) Lasso trajectory plot; (E) coefficient distribution plot; (F) Cox coefficients of prognostic genes.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4840386/v1/9cf0c469c822b54864b4cf99.jpeg"},{"id":66539451,"identity":"a3730ad5-26b8-4f0c-8a76-d706af654e2d","added_by":"auto","created_at":"2024-10-14 07:32:00","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":68257,"visible":true,"origin":"","legend":"\u003cp\u003eThe cohort risk model ROC curve and K-M curve. (A) TCGA; (B) GSE3141; (C) GSE31210; (D) GSE37745; (E) GSE50081; (F) GSE68465.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4840386/v1/9ea422f3dba59e8991dc8bbe.jpeg"},{"id":66539452,"identity":"c7abc1cb-915f-4f22-b7ea-6a066aa3b380","added_by":"auto","created_at":"2024-10-14 07:32:00","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":174762,"visible":true,"origin":"","legend":"\u003cp\u003eRisk gene-associated pathways (A) SNV mutation map of key genes; (B) mutation cosegregation probability map; (C)Fraction of pathway and samples affected ; (D) molecular characterisation map of key genes; (E) gene-pathway correlation heatmap; (F) heatmap of enriched scores of key pathways. *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4840386/v1/29c51974053d3b755be73ba7.jpeg"},{"id":66539444,"identity":"b3c8217d-91d4-4a4a-9d7c-abfb1154a808","added_by":"auto","created_at":"2024-10-14 07:32:00","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":147209,"visible":true,"origin":"","legend":"\u003cp\u003eKey gene immune relationship analysis (A) Heatmap of key gene immune infiltration algorithm (B) Key gene immune score box plot (C) Heatmap of key immune cell relationship (D) Heatmap of key gene immune cell correlation analysis\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4840386/v1/c1ba6af0f089c13916e9d3d7.jpeg"},{"id":66540851,"identity":"4f6a9ddc-7328-4e53-9061-6ca55acbbfaa","added_by":"auto","created_at":"2024-10-14 07:40:00","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":103147,"visible":true,"origin":"","legend":"\u003cp\u003eRisk score responsiveness to PD-L1 blockade immunotherapy in the IMvigor210 cohort. (A) Prognostic differences between risk score groups in the IMvigor210 cohort; (B) Differences in risk scores for response to immunotherapy in the IMvigor210 cohort; (C) Distribution of response to immunotherapy in risk score groups in the IMvigor210 cohort; (D) Prognostic differences between risk score groups for early patients in the IMvigor210 cohort; (E) Prognostic difference between risk scoring groups of late-stage patients in the IMvigor210 cohort; (F) Prognostic difference between risk scoring groups in the GSE78220 cohort GSE78220 cohort; (G) Difference in risk scoring of immunotherapy responses in the GSE78220 cohort; (H) Distribution of immunotherapy responses in risk scoring groups in the GSE78220 cohort. p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4840386/v1/39ee472acdc1f69e241296e6.jpeg"},{"id":66541452,"identity":"34581fc8-d74f-497e-ab0b-cb05b5c2e872","added_by":"auto","created_at":"2024-10-14 07:48:00","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":106746,"visible":true,"origin":"","legend":"\u003cp\u003eColumn line graphs predicting prognosis for LUAD (A, B) Univariate and multivariate Cox analysis of risk score and clinicopathological characteristics; (C) Nomogram of predictive models combining risk score and staging; (D) 1-, 3-, and 5-year calibration curves of the Nomogram; (E) Decision curves of the Nomogram; (F) analysis using Time-ROC Comparison of clinicopathological characteristics and predictive ability of Nomogram. ***P \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4840386/v1/5c4b9febdca78d3c4d2759ea.jpeg"},{"id":72435294,"identity":"ef7a6873-087c-49a6-8b49-75bc3180b8c1","added_by":"auto","created_at":"2024-12-27 05:32:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1737993,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4840386/v1/ffdf693e-e21e-4122-9e55-e15b9bf34a39.pdf"},{"id":66541453,"identity":"63947995-6c84-476b-bbe1-29ce298de359","added_by":"auto","created_at":"2024-10-14 07:48:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":144832,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4840386/v1/7d9a387a7ae4afe739d92574.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characterization of natural killer (NK) cells in lung adenocarcinoma and construction of an NK risk signature based on single-cell and macromolecular RNA-seg data","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eLung cancer is a lethal disease that causes the most frequent cancer-related deaths worldwide\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e, with adenocarcinoma accounting for more than half of all cases\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Although breakthroughs in chemotherapy, radiotherapy, surgery and other novel therapeutics, the incidence and mortality of lung adenocarcinoma (LUAD) continues to increase\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Therefore, it is essential to identify the relevant pathological parameters to provide foundation for clinical prognosis analysis. Omics technology is regarded as a effective tool for understanding the molecular pathogenesis of LUAD\u003csup\u003e[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Whole-genome sequencing and transcriptome sequencing has provided valuable insights into the gene amplification of RNA methyltransferase in LUAD, suggesting the activation mechanism of tumorigenesis\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Single-cell RNA sequencing techniques were applied to investigate the differences in immune response between the lung squamous cell carcinoma (LUSC) and LUAD\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOne subset of tumor immune cells called natural killer (NK) cells is well-known for its capacity to identify and then eliminate cancer cells\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Recent studies demonstrated that NK cells could identify and remove LUAD cells by engaging activating receptors with stress-induced ligands that are overexpressed on cancer cells' surfaces\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. NK cells display an potent antitumor immune cytotoxicity by regulating the tumor microenvironment via MEK/ERK and PI3K/Akt/mTOR pathways\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. NK cells have the capacity to influence the adaptive immune response to LUAD by interacting with other immune cells\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Growing evidence have suggested that the status of NK cells within LUAD are correlated with patient prognosis\u003csup\u003e[\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Increased NK cell infiltration and activity are frequently associated with improved survival and treatment outcomes\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Therefore, NK cells interact with lung cancer in a complex and dynamic manner, and understanding these interactions is critical for predicting patients\u0026rsquo; prognosis.\u003c/p\u003e \u003cp\u003eDespite some studies on NK cells in LUAD have been conducted\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, the systematic characteristics of NK cells, as well as their association with LUAD prognosis and immunotherapy response, remain limited known. In this study, LUAD scRNA-seq and transcriptome data were retrieved from publicly available databases to differentiate LUAD subsets and uncover NK cell-related risk signature. The immunological environment and immunotherapy response that support the NK cell-based signature were studied, and the clinical significance of the signature was also identified. In addition, a unique nomogram that integrated the NK cell-based risk signature with clinicopathological parameters was established to make it easier to use NK cell-based factors to predict LUAD prognosis. Our study might offer researchers novel insights into the biology of LUAD, resulting in more targeted therapy and a better prognosis for those with LUAD.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Acquisition and Organization\u003c/h2\u003e \u003cp\u003eScRNA-seq data from GSE131907 were acquired from the Gene Expression Omnibus (GEO) database, encompassing two lung adenocarcinoma (LUAD) and two normal control samples. Selection criteria included cells expressing a given gene in a minimum of three cells and cells exhibiting at least 250 genes expressed. The mitochondria to rRNA ratio was assessed utilizing the PercentageFeatureSet function within the Seurat R package. Only cells expressing over 6000 genes and with a Unique Molecular Identifier (UMI) count exceeding 100 were retained, resulting in a total of 23,673 cells.\u003c/p\u003e \u003cp\u003eSingle-cell transcriptomic and copy number variation (CNV) analyses were conducted using data from The Cancer Genome Atlas (TCGA) database, incorporating pertinent LUAD clinical data. Samples devoid of survival and outcome status were omitted, yielding 500 tumor and 59 adjacent non-tumor samples. The GSE31210 cohort, comprising 226 LUAD samples, served as a validation set; samples lacking follow-up and outcome data were excluded. Following this, ten cancer-associated pathways were identified through literature review, including WNT, PI3K, NOTCH, RAS, cell cycle, MYC, TGF-Beta, HIPPO, and NRF1 pathways\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Definition of NK\u003c/h2\u003e \u003cp\u003eIn this study, we conducted a re-analysis of single-cell RNA sequencing (scRNA-seq) data from LUAD using the Seurat package\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Our objective was to comprehensively characterize NK cells. We applied several data preprocessing steps to ensure data quality. First, we filtered out cells with over 6000 or under 250 expressed genes. Next, we performed log-normalization and removed batch effects from the four samples.\u003c/p\u003e \u003cp\u003eTo reduce the dimensionality of the data, we employed principal component analysis (PCA) with 20 principal components and a resolution of 0.25 for non-linear dimensionality reduction. Subsequently, we used the FindNeighbors and FindClusters functions (dim\u0026thinsp;=\u0026thinsp;40, resolution\u0026thinsp;=\u0026thinsp;0.2) to cluster cells into subgroups. To visualize the clustering results, we utilized t-distributed stochastic neighbor embedding (t-SNE) with the RunTSNE function.\u003c/p\u003e \u003cp\u003eWe then focused specifically on NK cells by identifying marker genes such as NKG7, KLRD1, KLRB1, GNLY, and GZMB. Clustering and t-SNE dimensionality reduction were performed exclusively on the NK cells. To identify marker genes specific to each NK cell cluster, we employed the FindAllMarkers function with criteria including a log fold change (logFC) threshold of 0.5, a minimum percentage threshold (minpct) of 0.35, and a corrected p-value cutoff of \u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eTo gain further insights into the functional characteristics of the NK cell clusters, we performed gene ontology enrichment analysis using the KEGG enrichment analysis method\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. This analysis was carried out using the clusterProfiler software package.\u003c/p\u003e \u003cp\u003eAdditionally, we analyzed copy number variation (CNV) features in the NK cell clusters to distinguish between tumor and normal cells in each sample. For this analysis, we utilized the CopyKAT R software package\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Identification of NK hub genes\u003c/h2\u003e \u003cp\u003eDuring our analysis, we initially identified differentially expressed genes (DEGs) between tumor and normal tissues using the limma software package. We set a screening threshold of FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2(Fold Change)| \u0026gt; 1.5\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e to determine significant DEGs.\u003c/p\u003e \u003cp\u003eNext, we assessed the correlation between these DEGs and the clustering of NK cells. We specifically focused on identifying key NK cell-related genes with p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and a correlation coefficient (cor)\u0026thinsp;\u0026gt;\u0026thinsp;0.4.\u003c/p\u003e \u003cp\u003eFor prognostic gene screening, we conducted univariate Cox regression analysis using the survival package. Genes with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were selected as potential prognostic markers. To further refine the gene selection, we applied the least absolute shrinkage and selection operator (lasso) Cox regression analyses. Subsequently, multivariate Cox regression analyses using stepwise regression methods were performed.\u003c/p\u003e \u003cp\u003eBased on the results of the multivariate Cox model, we constructed a risk signature using the formula: risk score\u0026thinsp;=\u0026thinsp;Σβi * Expi. Here, i represents the genes included in the risk signature, expi denotes the gene expression, and βi signifies the coefficient obtained from the multivariate Cox model. We categorized patients into high-risk and low-risk groups after zero-mean normalization.\u003c/p\u003e \u003cp\u003eTo evaluate the predictive performance of the risk signatures, we conducted receiver operating characteristic curve (ROC) analyses using the timeROC software package. These analyses provided an assessment of the risk signature's ability to predict patient outcomes. Similar analyses were performed in the validation cohort to validate the findings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Immune landscape analysis\u003c/h2\u003e \u003cp\u003eIn order to delve deeper into the tumor microenvironment (TME), we utilized the CIBERSORT algorithm to evaluate the proportions of 22 distinct immune cell subtypes within the TCGA cohort. This analysis allowed us to assess the relative abundance and composition of immune cells present in the TME.\u003c/p\u003e \u003cp\u003eTo gain additional insights into the TME, we also calculated an immunity score and stroma score using the ESTIMATE algorithm\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. These scores provide valuable information about the immune and stromal components within the TME. The immunity score reflects the level of immune cell infiltration, while the stroma score indicates the extent of stromal cell involvement. By utilizing these scores, we can gain a better understanding of the complex interplay between the tumor and its microenvironment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Constructing risk characteristics and column line plots\u003c/h2\u003e \u003cp\u003eTo assess the prognostic significance of clinicopathological and risk characteristics, we conducted univariate and multivariate Cox regression analyses. In the multivariate Cox model, variables with a significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were selected. Subsequently, we utilized the rms package\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e to construct column-line plots, which enabled us to predict the prognosis of LUAD patients based on these selected variables.\u003c/p\u003e \u003cp\u003eTo evaluate the predictive accuracy of the models, calibration curves were generated. These curves provide a visual representation of the agreement between the observed and predicted outcomes. Additionally, we performed decision curve analysis (DCA) to assess the reliability of the models. DCA is a useful tool for evaluating the clinical utility and net benefit of predictive models.\u003c/p\u003e \u003ch2\u003e2.6 Responsiveness to immune checkpoint blocks\u003c/b\u003e \u003c/h2\u003e \u003cp\u003eWe obtained transcriptomic and clinical data from the IMvigor210 cohort of LUAD patients who were treated with anti-PD-L1 drugs (atezolizumab)\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. This cohort, which can be accessed at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://research-pub.gene.com/IMvigor210CoreBiologies\u003c/span\u003e\u003cspan address=\"http://research-pub.gene.com/IMvigor210CoreBiologies\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, provided valuable insights into the response to anti-PD-L1 therapy in LUAD patients.\u003c/p\u003e \u003cp\u003eAdditionally, we acquired transcriptomic data from the GSE78220 cohort, which consisted of pre-existing melanomas treated with anti-PD-1 checkpoint inhibition. We utilized this data to investigate the potential value of risk marker scores in predicting the effectiveness of immune checkpoint blockade therapy (ICB)\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Statistical analyses\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using R software (v3.6.3). To assess the relationships between variables, correlation matrices were generated using either Pearson or Spearman correlation coefficients, depending on the nature of the data. The Wilcoxon test was employed to compare the two groups, providing insights into potential differences between them.\u003c/p\u003e \u003cp\u003eFurthermore, survival differences were evaluated using Kaplan-Meier curves and the log rank test. This allowed for the examination of potential disparities in survival outcomes between different groups or conditions. A p-value of less than 0.05 was considered statistically significant, indicating a notable result.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Screening and Identification of NK Clusters in scRNA-seq Data\u003c/h2\u003e \u003cp\u003eThe flowchart depicting the design of this study is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. After an initial screening of the scRNA-seq data, 23,673 cells were obtained. Following log-normalization and dimensionality reduction, we identified 20 subclusters. Subsequently, utilizing five marker genes (NKG7, KLRD1, KLRB1GNLY, and GZMB), we successfully identified 680 NK populations (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA, B).\u003c/p\u003e \u003cp\u003eTo refine our analysis, we further clustered and downscaled the cells within the 680 NK populations, ultimately identifying five distinct NK clusters (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC, D). By observing the TSNE plots of the four sample distributions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), we were able to generate and focus on these five NK clusters for subsequent analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eExamining the expression profiles of the 737 differentially expressed genes (DEGs) within the five NK clusters, we identified the top five DEGs, which were considered as marker genes for the NKF clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The distribution ratio of these five clusters within each cohort is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD.\u003c/p\u003e \u003cp\u003eMoreover, through KEGG analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE), we discovered that these DEGs were significantly enriched in various pathways, including the TNF signaling pathway, T-cell receptor signaling pathway, and NK cell-mediated cytotoxicity. Additionally, based on the CNV characteristics, we observed that the five NK clusters comprised a total of 680 tumor cells and normal cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Exploration of Cancer-Related Pathways in NK Clusters\u003c/h2\u003e \u003cp\u003eTo gain further insights into the relationship between NK clusters and tumor progression, we conducted GSVA scoring of 10 pathways associated with tumorigenesis within the 5 NK clusters (Fig.\u0026nbsp;3A). Notably, the proportion of malignant cells in NK_0, NK_1, and NK_3 clusters was significantly higher compared to the other two clusters (Fig.\u0026nbsp;3B), and there were significant differences between each cluster.\u003c/p\u003e \u003cp\u003eFurthermore, we analyzed the GSVA scores of the 10 tumor-associated pathways in both malignant and non-malignant cells within each NK cluster. Interestingly, we observed significant differences in the RAS pathway within the NK_0 cluster and the NOTCH pathway within the NK_1 cluster (Fig.\u0026nbsp;3C-G).\u003c/p\u003e \u003cp\u003eTo investigate the potential correlation between NK clusters and prognosis, we initially computed the ssGSEA scores of marker genes for each NK cluster utilizing the TCGA cohort. Strikingly, the results revealed that all five NK clusters exhibited higher scores in normal samples compared to tumor samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eNext, we categorized the LUAD samples from the TCGA dataset into high and low NK score groups based on optimal cutoff values determined by the survminer R package. Intriguingly, within the high-NK score group, the samples belonging to NK_1, NK_2, NK_3, and NK_4 groups displayed a more favorable prognosis compared to the low-NK score group. However, it is noteworthy that the prognosis of LUAD was not associated with the NK_0 cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-F). Although there were differences in NK_0 enrichment between LUAD and normal samples, our findings suggest that the contribution of NK_0 clusters to LUAD progression is minimal.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Identification of NK cell-related core genes\u003c/h2\u003e \u003cp\u003eTo identify core genes related to NK cells, we conducted a comprehensive analysis by screening differentially expressed genes (DEGs) between tumor tissues and normal tissues. Out of the 19,495 DEGs identified, 12,525 were up-regulated and 6,970 were down-regulated. Remarkably, 725 genes exhibited significant correlation with prognostically relevant NK clusters. Subsequently, we performed one-way Cox regression analysis to assess the prognostic value of each gene, resulting in the identification of 133 genes with prognostic significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B).\u003c/p\u003e \u003cp\u003eEnrichment analysis of these genes revealed their involvement in various biological processes (BP), cellular components (CC), molecular functions (MF), and KEGG pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). However, it is important to note that the \u003cspan refid=\"Sec17\" class=\"InternalRef\"\u003ediscussion\u003c/span\u003e section should be completed first to provide context and specific results before filling in the details of the enrichment analysis.\u003c/p\u003e \u003cp\u003eTo further refine the gene selection, we employed Lasso Cox regression analysis, which ultimately filtered down to 13 genes with a lambda value of 0.0311 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, E). These 13 genes, namely ANOS1, ADGRE3, ADAMTS8, CCT3, CD79A, CX3CR1, DPEP2, FMO3, FBP1, OASL, RRAS, SELENOP, and SFTPC, were included in the risk signature after multifactorial Cox regression analysis using stepwise regression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). The final risk signature formula consists of the following 13 genes with corresponding coefficients: RiskScore = \"-0.121 * CD79A \u0026minus;\u0026thinsp;0.056 * DPEP2\u0026thinsp;+\u0026thinsp;0.094 * OASL\u0026thinsp;+\u0026thinsp;0.363 * RRAS\u0026thinsp;+\u0026thinsp;0.052 * ADAMTS8\u0026ndash;0.282 * ADGRE3\u0026ndash;0.039 * ANOS1\u0026thinsp;+\u0026thinsp;0.155 * CCT3\u0026thinsp;+\u0026thinsp;0.075 * CX3CR1\u0026ndash;0.145 * FBP1\u0026ndash;0.115 * FMO3\u0026ndash;0.087 * SELENOP \u0026minus;\u0026thinsp;0.021 * SFTPC\".\u003c/p\u003e \u003cp\u003eAfter Z-mean normalization, we calculated the risk scores for each sample and subsequently classified them into high and low-risk groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the TCGA cohort, the area under the curve (AUC) values for 1- to 5-year survival ranged from 0.66 to 0.71. Similarly, in the GSE3141 cohort, the AUC values ranged from 0.64 to 0.72, while in the GSE31210 cohort, they ranged from 0.65 to 0.69. In the GSE37745 cohort, the AUC values ranged from 0.57 to 0.71, and in the GSE50081 cohort, they ranged from 0.70 to 0.77. Lastly, in the GSE68465 cohort, the AUC values ranged from 0.60 to 0.68 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-F).\u003c/p\u003e \u003cp\u003eFurthermore, the Kaplan-Meier survival analyses demonstrated a similar pattern of survival outcomes in both the TCGA and GEO cohorts. High-risk patients exhibited significantly worse survival outcomes compared to low-risk patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Mutation and pathway analysis of key genes\u003c/h2\u003e \u003cp\u003eIn our investigation of the risk signature genes, we examined SNV mutations in these 13 genes. Interestingly, all genes except RRAS exhibited SNV mutations in a greater number of samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). We further analyzed the probability of co-occurrence of mutations between these key genes and the 10 most frequently mutated genes. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eB demonstrates that while no significant mutation co-occurrence was observed among these 12 genes, significant co-occurrence was observed between ADGRE3 and TTN, CX3CR1 and MUC16, RYR2 and ZFHX4, ANOS1 and LRP1B, DPEP2 and CSMD3, as well as RYR2 and ZFHX4. We also used OncogenicPathways to count known oncogenic signalling pathways in the TCGA cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eTo gain further insights into the association between these risk genes and LUAD, we investigated their correlations with various molecular features of LUAD. Remarkably, most of the genes exhibited significant negative associations with aneuploidy scores, homologous recombination defects, score alterations, fragment counts, and non-silencing mutation rates. Notably, CCTS was the only gene significantly positively associated with these molecular features (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eAdditionally, we explored the potential pathways associated with each risk gene. The analysis revealed significant associations between these 13 genes and a total of 21 pathways. Noteworthy pathways included the B cell receptor signaling pathway, the chemokine signaling pathway, and leukocyte migration (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eE, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Analysis of key genes in relation to immunity\u003c/h2\u003e \u003cp\u003eOur data analysis revealed notable findings regarding the relationship between key genes and immunity. Specifically, we observed that CCT3 exhibited a significant negative correlation with matrix score, immune score, and estimated score. Conversely, the remaining 12 genes displayed significant positive correlations with these scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eTo further explore these associations, we categorized each gene based on its median expression and compared the three scores among different expression groups. Notably, for the ANOS1 gene, the high-expression group exhibited significantly lower scores compared to the low-expression group, while the high-expression groups of the remaining genes displayed significantly higher scores compared to the low-expression group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eFurthermore, our correlation analysis demonstrated significant associations between the 13 key genes and various immune cells, including B cells, T cells, and NK cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). Moreover, we observed significant differences in several immune cells between the high and low expression groups of the risk genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Sensitivity analysis of risk signature for PD-L1 blockade immunotherapy\u003c/h2\u003e \u003cp\u003eThe emergence of T-cell immunotherapy as a promising approach for cancer treatment has highlighted the importance of evaluating the prognostic value of risk characteristics in the context of immune checkpoint therapy. To assess this, we examined the IMvigor210 and GSE78220 cohorts.\u003c/p\u003e \u003cp\u003eWithin the IMvigor210 cohort, consisting of 348 patients with varying responses to anti-PD-L1 receptor blockers, including complete remission (CR), partial remission (PR), stable disease (SD), and progressive disease (PD), our analysis revealed compelling results. Patients in the low-risk group experienced a significant clinical benefit and demonstrated significantly longer overall survival compared to those in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003eA, p\u0026thinsp;=\u0026thinsp;0.0017). Furthermore, the risk scores were higher in patients with SD/PD compared to those with CR/PR (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). Additionally, the high-risk group exhibited a higher percentage of SD/PD compared to the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eIntriguingly, the survival analysis demonstrated a significant difference between stage I\u0026thinsp;+\u0026thinsp;II patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003eD, p\u0026thinsp;=\u0026thinsp;0.041) and stage III\u0026thinsp;+\u0026thinsp;IV patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003eE, p\u0026thinsp;=\u0026thinsp;0.018) within different risk groups. This suggests that risk scores were particularly sensitive in distinguishing patients with different stages of LUAD.\u003c/p\u003e \u003cp\u003eSimilarly, in the GSE78220 cohort, we observed that patients in the low-risk group had significantly longer overall survival compared to those in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003eF, p\u0026thinsp;=\u0026thinsp;0.0067). Notably, patients with PD exhibited higher risk scores compared to those with CR/PR (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003eG), and the high-risk group displayed a higher percentage of PD cases than the low-risk group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Independent risk factor identification and histogram construction\u003c/h2\u003e \u003cp\u003eIn order to enhance the predictive performance of risk features, we conducted univariate and multivariate COX regression analyses to integrate clinicopathological factors and risk scores. The results from the multivariate COX regression analysis revealed that the risk signature (HR\u0026thinsp;=\u0026thinsp;2.69, 95% CI: 1.93\u0026ndash;3.74, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and metastatic status (HR\u0026thinsp;=\u0026thinsp;2.06, 95% CI: 1.39\u0026ndash;3.05, P\u0026thinsp;=\u0026thinsp;0.002) were the most crucial independent prognostic factors for LUAD.\u003c/p\u003e \u003cp\u003eBased on these findings, we developed a histogram that incorporated both staging and risk scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003eA-C). This histogram demonstrated effectiveness in accurately predicting actual survival outcomes, as confirmed by the calibration curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003eD). Furthermore, the decision curve analysis (DCA) indicated that the discriminative power of the histogram in identifying high-risk patients surpassed that of the risk score and staging alone (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003eE). Additionally, the time-dependent receiver operating characteristic (TimeROC) analysis demonstrated that both the risk score and the histogram exhibited higher area under the curve (AUC) values compared to other metrics in the TCGA cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eNK cells regulate tumor proliferation, angiogenesis and the immune system of LUAD\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Studies on the pathophysiology of NK cells in LUAD have progressed to the genetic and molecular levels\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Nonetheless, the characterisation of NK cells in LUAD remains unknown, and research on LUAD prognosis with respect to NK cells is comparatively limited. Our study focused on the characterization of NK cells. ScRNA-seq data were used to classify NK cells in LUAD. Eventually, we uncovered five NK cell subsets with diverse features that might regulate biology of the LUAD. Recent studies have demonstrated the prognostic significance of the NK cell-related gene signature in the assessment of LUAD outcomes\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Consistently, our findings revealed four distinct clusters exhibiting strong associations with LUAD prognosis, as quantified by a composite score derived from DEGs encompassing all five clusters. Moreover, our results suggested that differences in the Notch signaling pathway among the identified clusters might underlie the prognostic significance of NK cells in LUAD. Notch signaling pathway plays a vital role in regulating the progression of LUAD\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Suppressing the Notch signaling pathway has demonstrated efficacy in attenuating collagen synthesis and curtailing cellular invasiveness\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Notably, high levels of Notch-related genes have been proven to be correlated with poor survival outcomes in LUAD patients\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eConsidering the prognostic relevance of the four NK cell clusters, we constructed a 13-gene risk signature. This signature comprised nine protective genes(ANOS1, ADGRE3, CD79A, CX3CR1, DPEP2, FMO3, FBP1, SELENOP and SFTPC) and four risk genes(ADAMTS8, CCT3, RRAS and OAST). In our study, SNV mutations were detected in ANOS1, ADGRE3, ADAMTS8, CCT3, CD79A, CX3CR1, DPEP2, FMO3, FBP1, OASL, SELENOP, and SFTPC, while no statistically significant co-occurrence patterns were observed among these mutations. SNV mutations hold a significant position in the pathogenesis of LUAD, as they can manifest as \"driver mutations\" that bestow tumor cells with growth advantages, thereby fueling the onset and progression of the disease\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. While no prior studies have revealed a connection between SNV mutations in these risk genes and LUAD, our findings indicated such mutations might contribute to the progression of LUAD. We also demonstrated that the 13 genes were closely associated with 21 signaling pathways. It was proven that part of these pathways were related to onset and prognosis of LUAD. Vascular smooth muscle contraction, RNA polymerase, pyrimidine metabolism and B cell receptor signaling pathway act as effective predictors for the prognosis and therapeutic responses of LUAD patients\u003csup\u003e[\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Spliceosome-related and lysosome-related genes might increase LUAD incidence rate\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. Homologous recombination pathway is up-regulated in LUAD, and increased expression of relevant genes is associated with poor overall survival\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrevious research demonstrated that tumor growth could attribute to the interplay between NK cells and the tumor immune microenvironment (TIME)\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. Our findings revealed that 9 protective genes and three risk genes were positively and significantly associated with the immunological score, whereas one risk gene exhibited a negative correlation. These findings suggested potential interactions between these genes and the TIME in LUAD, hinting at their potential as therapeutic targets. The TIME is constituted by a diverse array of immune cells within the tumor islets, collectively dictating the antitumor immunological milieu of TIME\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. NK cells can interact in various ways with these immune cells, fostering an immunosuppressive TIME that allows tumor cells to evade immune surveillance\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. In the risk signature, most protective genes were negatively correlated with NK cell activation and T cell activation. Meanwhile, regulatory T cells were negatively associated with protective genes and positively associated with risk genes. It was reported that NK cells could modulate T cell apoptosis in tumors\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. T cells also could interact with NK cells and enhance their viability\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. These suggested that NK related-genes acted not only on NK cells but also on T cells, therefore regulating the whole TIME.\u003c/p\u003e \u003cp\u003eDespite the fact that many patients have resistance to immunotherapeutic therapy\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e, we found that relevant risk signature efficiently stratified patients based on their likelihood of benefiting from immunotherapies. Additionally, previous studies have reveal that NK cells have the potential to treat solid tumors\u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e. However, the therapy was less effective for certain patients, which might be a consequence to the extremely complex immunosuppressive milieu\u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. Fortunately, our findings demonstrated that the NK cell-based signature had the capacity to predict the response to anti-PD-L1 immunotherapy. Further experimental research is warranted to elucidate the mechanisms underlying NK cell-TIME interactions in LUAD and their potential therapeutic implications in LUAD immunotherapy.\u003c/p\u003e \u003cp\u003eThere are some limitations in our study. Firstly, the retrospective use of public data to establish NK cell clusters and the NK cell-based risk signature highlights the need for validation in prospective, multi-center LUAD cohorts in future investigations. Secondly, while a predictive nomogram was developed, additional studies are required to better investigate the specific biological correlation between the included NK cell-related genes and clinicopathological characteristics.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eTo sum up, we comprehensively categorized NK cell in LUAD, identifying five distinct clusters with different characteristics. The DEGs in the five clusters were abundant in vascular smooth muscle contraction, RNA polymerase, pyrimidine metabolism, the B cell receptor signaling pathway, etc. Four of the clusters showed significant correlations with LUAD prognosis and. An NK cell-based prognostic risk signature was created by 13 key genes from the four clusters. This signature appeared to be associated with the immune milieu and might be used to predict responsiveness to anti-PD-L1 therapy. Finally, we constructed a reliable nomogram incorporating the clinicopathological parameters and NK cell-related risk signature for predicting the clinical prognosis of patients with LUAD.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of Interest:\u003c/h2\u003e \u003cp\u003eChuanxi Tian, Yikun Guo, Tianyi Lv, Daowen Yang, and Min Li declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis work was supported by Ministry of Science and Technology of the People\u0026acute;s Republic of China (2022YFC3500801).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: C.X.T., and D.W.Y.; Formal analysis and Data Curation: Y.K.G.; Writing - Original Draft: C.X.T., Y.K.G., T.Y.L.; Writing - Review \u0026amp; Editing: C.X.T., Y.K.G., T.Y.L. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analysed during the current study are available in the Gene Expression Omnibus (GEO) database(GSE131907) and the Cancer Genome Atlas (TCGA) database(GSE31210).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChen J, Yang H, Teo ASM, et al. Genomic landscape of lung adenocarcinoma in East Asians. Nat Genet. 2020;52(2):177\u0026ndash;186. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41588-019-0569-6\u003c/span\u003e\u003cspan address=\"10.1038/s41588-019-0569-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang J, Bi G, Huang Y, et al. 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Successful targeting of PD-1/PD-L1 with chimeric antigen receptor-natural killer cells and nivolumab in a humanized mouse cancer model. Sci Adv. 2022;8(47):eadd1187. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/sciadv.add1187\u003c/span\u003e\u003cspan address=\"10.1126/sciadv.add1187\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\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":"natural killer cells, lung adenocarcinoma, differentially expressed genes, risk signature, columnar plots","lastPublishedDoi":"10.21203/rs.3.rs-4840386/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4840386/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eBackground/Aims\u003c/b\u003e: Natural killer (NK) cells play a crucial role in tumor cell apoptosis, immune milieu regulation, and angiogenesis inhibition. This study aims to analyze the NK signature in lung adenocarcinoma (LUAD) and establish an NK cell-based risk signature for predicting the prognosis of LUAD patients.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e: Single-cell RNA sequencing (scRNA-seq) data were obtained from the GEO database, while RNA-seq and microarray data from LUAD were simultaneously obtained from the TCGA and GEO databases. The scRNA-seq data were processed using the Seurat R package to identify NK clusters based on NK markers. Differentially expressed genes (DEGs) between normal and tumor samples were identified through differential expression analysis of LUAD-related data. Pearson correlation analysis was used to identify DEGs associated with NK clusters, followed by one-way Cox regression analysis to identify NK cell-related prognostic genes. Subsequently, Lasso regression analysis was employed to construct a risk signature based on NK cell-related prognostic genes. Finally, a column-line diagram model was constructed based on the risk signature and clinicopathological features.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e: Based on the scRNA-seq data, we identified five Natural killer (NK)cells clusters in lung adenocarcinoma (LUAD), with four of them showing associations with prognosis in LUAD. Out of 19,495 differentially expressed genes (DEGs), a total of 725 genes significantly associated with NK clusters were pinpointed and further narrowed down to form a risk profile comprising 13 genes. These 13 genes were primarily linked to 21 signaling pathways, including vascular smooth muscle contraction, RNA polymerase, and pyrimidine metabolism. Additionally, the risk profile exhibited significant associations with stromal and immune scores, as well as various immune cells. Multifactorial analysis indicated that the risk profile served as an independent prognostic factor for LUAD, and its efficacy in predicting the outcome of immunotherapy was validated. Furthermore, a novel column-line diagram integrating staging and NK-based risk profiles was developed, demonstrating strong predictability and reliability in prognostic forecasting for LUAD.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusion\u003c/b\u003e: The NK cell-based risk signature proves to be a valuable tool for predicting the prognosis of patients with lung adenocarcinoma (LUAD). Furthermore, a comprehensive understanding of NK cell characterization in LUAD could potentially unveil insights into the response of LUAD to immunotherapies and offer novel strategies for cancer treatment.\u003c/p\u003e","manuscriptTitle":"Characterization of natural killer (NK) cells in lung adenocarcinoma and construction of an NK risk signature based on single-cell and macromolecular RNA-seg data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-14 07:31:55","doi":"10.21203/rs.3.rs-4840386/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":"16251b88-8ae5-47fc-a9fc-130d20eb194b","owner":[],"postedDate":"October 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":37412452,"name":"Biological sciences/Immunology"},{"id":37412453,"name":"Health sciences/Biomarkers"}],"tags":[],"updatedAt":"2024-12-27T05:08:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-14 07:31:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4840386","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4840386","identity":"rs-4840386","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}