Integrated analysis of M2 macrophage-related gene prognostic model and single-cell sequence to predict immunotherapy response in lung adenocarcinoma

Integrated analysis of M2 macrophage-related gene prognostic model and single-cell sequence to predict immunotherapy response in lung adenocarcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrated analysis of M2 macrophage-related gene prognostic model and single-cell sequence to predict immunotherapy response in lung adenocarcinoma Meifang Li, Zhiping Wang, Bin Huang, yanyun Lai, Meng Zhang, Cheng Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5270005/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 Introduction Lung adenocarcinoma (LUAD) patients have high heterogeneity. The significance and clinical value of M2 macrophage related genes in LUAD require further exploration. We aimed to construct a prognostic signature to predict the immunotherapy efficacy and prognosis in LUAD. Methods GSE26939 and GSE19188 chips were downloaded from the Gene Expression Omnibus (GEO). Weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) analysis were used to screen M2 macrophage-related prognostic genes. A signature based on M2 macrophage-related prognostic genes was established and used to predict the prognosis and immunotherapy efficacy in LUAD. Results Twenty-two M2 macrophage-related genes associated with the prognosis of LUAD were confirmed using WGNNA, and then two molecular subtypes were identified with significant different survival, gene expressions and clinic characteristics were classified. LASSO analysis identified nine M2 macrophage-related prognostic genes to establish a risk signature, classifying patients into low- and high-risk groups. Data indicated that low-risk patients had better survival. Moreover, the signature was an independent prognostic factor for LUAD and a potential biomarker for patients receiving immunotherapy. Single-cell transcriptome analysis may provide important information on molecular subtypes and heterogeneity. Conclusions Risk signature based on M2 macrophage-related genes is a valuable tool for predicting prognosis and immunotherapy response in patients with LUAD. Lung adenocarcinoma Tumor microenvironment Macrophages Prognosis Immunotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Lung adenocarcinoma (LUAD) is the primary subtype of non-small cell lung cancer (NSCLC), and accounts for more than 50% of all NSCLC cases. The 5-year survival for patients with advanced LUAD is lower than 20%(Asamura et al., 2023 ). In recent years, the emergence of immune checkpoint inhibitors (ICIs) and targeted drugs has completely changed the outcomes of advanced LUAD(Liu et al., 2023 ; The, 2024 ). However, treatment unresponsiveness and drug resistance are common, especially in immunotherapy(Mogavero et al., 2023 ). The poor curative effects largely stem from the complicated molecular features caused by the high heterogeneity of LUAD. Therefore, exploration of meaningful “signatures” to predict the prognosis and assist the management of LUAD is urgently needed. Lots of clinical and molecular factors influence the efficacy of ICIs(Thummalapalli et al., 2023 ). Thus, exploration of cellular and molecular mechanisms, thus assisting in achieving durable responses to ICIs is essential. Tumor microenvironment (TME), including tumor cells, immune cells, stromal cells, and extracellular matrix (ECM), as well as driver genes and other genes, are involved in the treatment response and prognosis in a variety of cancers(Binnewies et al., 2018 ). At present, attention is focused on the clinical significance of T cells in TME. KEYNOTE-028 trail revealed that the T-cell-inflamed gene-expression profile (TcellinfGEP) could predict response to pembrolizumab in 20 tumor types(Ott et al., 2019 ), which was also demonstrated in advanced NSCLC in KEYNOTE-494/KeyImPaCT trail (Gutierrez et al., 2023 ). Of note, other immune cells, like cancer-associated fibroblast (CAF) and tumor-associated macrophages (TAM), were also reported to be closely associated with the development of NSCLC(Cords et al., 2024 ; Zhang et al., 2023 ). However, the values of TAM in LUAD are still unclear in clinical practice since TAM was supposed to be a double-edged sword in the TME. Macrophages can be polarized into M1 and M2 types under different microenvironments and stimulators (Funes, Rios, Escobar-Vera, & Kalergis, 2018 ). The function of TAM is similar to M2-like macrophages in cancers (Sarode, Schaefer, Grimminger, Seeger, & Savai, 2020 ; Sumitomo et al., 2019 ; Xu, Wei, Tang, Liu, & Dong, 2020 ). M2 TAMs can promote cancer proliferation, invasion, migration, angiogenesis, and multidrug resistance. More importantly, TAMs can inhibit the activation and aggregation of immune cells by secreting cytokines and chemokines, establishing suppressive TME. Therefore, in-depth research on the role of M2 macrophage in LUAD and the construction of a prognostic signature associated with M2 macrophage are necessary. In this study, we sought to screen an M2 macrophage-related signature and to predict the prognosis and immunotherapy efficacy of LUAD patients. We found that an M2 macrophage-related signature based on characteristic genes was a novel biomarker in the management of LUAD. Materials and Methods Data resource The GSE26939, GSE31210, GSE19188 and GSE135222 were downloaded from the GEO database ( https://ncbi.nlm.nih.gov/geo/ ). The immune-related profiles of LUAD were downloaded from the InnateDB database ( https://www.innatedb.ca/ ) and Immort database ( https://www.immport.org ). Immunotherapy cohorts IMvigor210 and GSE93157 were included for analysis of immune therapy response (Bhattacharya et al., 2018 ; Breuer et al., 2013 ). Acquisition of M2 macrophage-related genes We analyzed immune-related genes using the Weighted Gene Co-expression Network Analysis (WGCNA), and then constructed the network by one-step method to obtain the module genes that were most related to M2 macrophage. The module genes that were most related to M2 macrophages were identified as M2 macrophage-related hub genes. Then, univariate Cox regression analysis was carried out to confirm M2 macrophage-related prognostic genes. Prognostic genes with p < 0.05 were finally enrolled. Functional enrichment Using the “clusterprofiler” package, Gene Ontology (GO) analysis was performed on prognostic feature genes, categorizing GO functions into three parts: Cellular Component (CC), Molecular Function (MF), and Biological Process (BP). Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was conducted, with significance set at p < 0.05 for enrichment. Genotyping based on characteristic genes The “ConsensusClusterPlus” package was used to conduct consistency cluster analysis. The overall slope of the curve shows the smallest decline when K is 2, leading to the classification of patients in GSE26939 into two molecular subtypes. Then, differentially expressed genes (DEG) between two subtypes were confirmed by the “limma” package. Those genes with adj.p 1.5 were regarded as DEGs. Construction of M2 macrophage-related prognostic signature We developed a risk model based on M2 macrophage-related genes using the machine learning algorithm known as least absolute shrinkage and selection operator (LASSO) regression. The risk score model was constructed by the following formula: Risk score = ∑ Coefi * Expr i “Expr” was the expression value of signature genes in the model, and “Coef” was the regression coefficient. Then, patients were divided into high- and low- risk groups according to the optimal cutoff of risk score of all LAUD samples. Kaplan-Meier survival curves and area under curve (AUC) were used to verify the performance of the signature. Univariate and multivariate Cox were used to verify the performance of the prognostic signature. Analyses of clinical characteristics, immune cells, and immunotherapy To further explore the role of the risk signature in the immune microenvironment. Based on the core algorithm of CIBERSORT (CIBERSORT.R script analysis), we utilized the markers of 22 immune cell types provided by the CIBERSORTx website ( https://cibersortx.stanford.edu/ ) to compute the immune infiltration between high- and low-risk groups. Moreover, ImmuneScore, StromalScore, and EstimateScore were analyzed by the “ESTIMATE” package. Single-cell transcriptome database analysis The single-cell transcriptome profile (GSE131907) downloaded from the Gene Expression Omnibus (GEO) database, including 15 lung cancer samples, was selected for subsequent analyses. Firstly, quality control of single-cell profiles was done by Seurat (v4.1.0). The quality control standards were as follows: (1) Each gene was detected in more than 3 cells. (2) Features of each cell were between 500 and 6000, with 1000 ~ 20000 counts. (3) The percentage of mitochondrial genes and erythrocytes gene expression was less than 20%. We use the “NormalizeData” function for normalization and the “FindVariableFeatures” function for identifying hypervariable genes, which were with 0.1 ~ 3 average expression value and more than 0.5 dispersion. Batch correction between samples was performed by the “harmony” package. Principal component analysis (PCA) was used for dimensionality reduction, and the first 50 principal components were selected for downstream analysis. t -distributed stochastic neighbor embedding (tSNE) algorithm was used for visualization. The top 50 principal components with 0.2 resolution were used to identify subpopulations of tumor cells. The “FindAllMarkers” function was used to identify feature genes, and each model contained 10 genes. Cellscore was calculated by the “AddModuleScore” algorithm. The malignant epithelial cells were divided into high- and low-groups according to the middle value of Cellscore. The “Monocle2” package was used to analyze the single trajectory. Statistical analyses All the above statistical analysis was computed by R software (version 4.2.1, https://www.r-project.org/ ). p-value < 0.05 (two-sided) was used as the statistically significant threshold. The survival difference between the two groups was analyzed by Kaplan‒Meier analysis. Other statistical methods and algorithms used in this article are described in the corresponding steps. Results Screening of macrophage subtypes in LUAD The workflow of the study is shown in Fig. 1 . Immune cells were divided into three different clusters, and we evaluated the correlation of various immune cells using correlation analysis (Fig. 2 A). Macrophages are a significant constituent part of TME. To confirm the relationships between macrophages and survival in patients with LUAD, patients were divided into high- and low-macrophage groups based on macrophage infiltration level. Survival analysis suggests that patients in the high M2 group have a worse prognosis, while those in the high M1 group have a better prognosis (Fig. 2 B). Therefore, the M2 macrophage was chosen for further exploration. Screening of M2 macrophage-related hub genes Then, WGCNA was used to identify M2 macrophage-related genes in LUAD. Using the InnateDB and Immort databases, 1836 immune-related genes were obtained from the GSE26939 database (Fig. 2 C). Seven modules were identified by WGCNA, and the brown module was significantly associated with M2 macrophage (Fig. 2 D). Thus, 108 hub genes in the brown module were selected for further analysis (Fig. 2 E and Supplemental Table 1). Screening for M2 macrophage-related prognostic genes To address the critical genes involved in the biological function of M2 macrophage, univariate cox regression analysis was conducted. Twenty-two genes among 108 hub genes associated with the prognosis of LUAD were confirmed by univariate analysis. Except for BMP1 (bone morphogenetic protein 1), the remaining 21 genes were considered favorable factors in LUAD (Fig. 3 A). GO (Gene Ontology) analyses showed that the above 22 prognostic genes were significantly enriched in the activation of immune response, immune response − activating signaling pathway, immune receptor activity, etc (Fig. 3 B-D). Similarly, Immune-related pathways, like B cell and T cell receptor signaling pathways, were significantly enriched in KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses (Fig. 3 E). Molecular subtypes of LUAD As we know, LUAD is full of heterogeneity. To better identify the different populations, patients with LUAD were classified into two molecular subtypes in the GSE26939 database by consistent cluster analysis (Fig. 4 A, B). There was a significant difference in survival outcomes between the two clusters (p = 0.0025) (Fig. 4 C), with different gene expressions, clinic characteristics and profile of subtype correctness (Fig. 4 D, E). Then, differentially expressed genes (DEGs) between these two clusters was analyzed. There were 772 DEGs identified, with 75 down-regulated DEGs and 697 up-regulated DEGs. GO and KEGG analysis revealed that activation of immune response and interaction of cytokine-cytokine receptor were significantly enriched (Fig. 5 A-D). And the top 5 inhibition and activation pathways was also showed by Gene Set Enrichment Analysis (GSEA) (Fig. 5 E, F). Interestingly, immune cells, including M2 macrophage, resting CD4 memory T cells, regulatory T cells, activated NK cells, and mast cells, were lower in cluster 2, indicating a favorable prognosis (Fig. 5 G, H). Construction of an M2 macrophage-related prognostic signature To explore a simple and reliable therapy strategy, a risk prognostic model was constructed based on the 22 M2 macrophage-related prognostic genes. Nine genes were confirmed by LASSO regression analysis in GSE26939 (Fig. 6 A, B). The coefficient of each gene in the model is shown in Fig. 6 C. We divided the patients into high-risk and low-risk groups based on the median risk score (Fig. 6 D). Patients with LUAD in the low-risk group had longer overall survival (OS) than in the high-risk group (p < 0.0001) (Fig. 6 E). ROC curves were plotted to estimate the performance of the risk model. The AUC value of ROC curves at 1, 3, and 5 years was 0.787, 0.699, and 0.776, respectively, indicating this signature scoring system had a good predictive performance (Fig. 6 F). The univariate and multivariate analyses showed that the signature based on risk score was an independent prognostic factor in LUAD (Fig. 6 G, H). Moreover, the verification gene sets (GSE31210 and GSE68165) further demonstrated that the patients with the low-risk had superior survival than the high-risk group (Fig. 6 I, J). Further subgroup analysis suggested that the low-risk group aged over 60 years old and stage Ⅰ-Ⅱ had longer survival than the high-risk group, regardless of sex in GSE26939 (Fig. 7 A-F). No significant survival differences were found regarding tumor stage Ⅲ-Ⅳ, age of ≤ 60. Relationships between risk signature and immunotherapy response Since infiltrating immune cells varies in the different molecular subtypes and risk score groups, we wonder if the signature was associated with immunotherapy response. Our data revealed that the high-risk group had lower stromal score and immune score, but higher Tumor Purity, indicating patients with low-risk group had better effects of immunotherapy (Fig. 7 G). Then, the GSE93157 database, including NSCLC patients receiving PD1-targeting antibodies pembrolizumab or nivolumab, and the IMvigor210 database, including metastatic urothelial carcinoma patients receiving PD-L1-targeting antibodies atezolizumab were used. The results showed that the patients with low-risk had better immunotherapy efficacy compared to the high-risk group (Fig. 7 H, K). While the risk was not associated with the response rate (CR/PR) and nonresponse rate (SD/PD) (Fig. 7 I-J, 7 L-M). Taken together, our data suggest that the signature was a potential biomarker for NSCLC patients receiving immunotherapy. Single-cell transcriptome database analysis Fifteen primary lung cancer samples in the single-cell transcriptome profiles (GSE131907) were selected for analysis. After quality control, 27578 genes within 51935 cells were obtained. PCA (principle component analysis) results showed significant batch effects among samples (Fig. 8 A, B). After using Harmony to remove batch effects between samples (Fig. 8 C, D), UMAP (Uniform Manifold Approximation and Projection) showed seven major cell types, composed of B lymphocytes, endothelial cells, epithelial cells, fibroblasts, MAST cells, myeloid cells, and T/NK cells (Fig. 8 E). The proportion of cells in each sample was heterogeneous (Fig. 8 F). In this study, four different lung cancer subtypes were identified were defined: sftpa1 + mal, c15orf48 + mal, cxcr4 + mal, and top2a + mal, according to the high expression genes of each subtype (Fig. 9 A, B). Based on FindAllMarkers, the top 5 characteristic genes of each subtype were identified (Fig. 9 C). Sftpa1 + mal over-expressed sftpa1, sftpa2, sftpc, and other genes, and these genes were significantly enriched in biological processes such as MHC complex assembly, antigen treatment and presentation by GO and KEEP analysis (Fig. S1 ). C15orf48 + mal highly expressed c15orf48, IGFBP3, S100A4, and other genes, which was significantly enriched in the regulation of cell morphogenesis, cell-matrix adhesion, and other biological processes (Fig. S2 ). Cxcr4 + mal highly expressed SRGN, CXCR4, CD52, and other genes, which was significantly enriched in the regulation of T cell activation, T cell receptor signaling pathway, and lymphocyte differentiation (Fig. S3 ). Top2a + mal highly expressed cell cycle marker TOP2A, significantly enriched in the cell cycle, nucleus division, and DNA replication, suggesting that the tumor was in an active cell proliferation state (Fig. S4 ). To further distinguish lung cancer lineages at the single-cell level, lung cancer was divided into high- and low-cell groups according to cell scores (Fig. 9 D). Our data revealed that subtypes of sftpa1 + mal and cxcr4 + mal had higher cell scores, suggesting those two subtypes had more malignant character (Fig. 9 E). According to cell scores, malignant epithelial cells were divided into high and low cell groups (Fig. 9 F, G). In addition, DEGs of the high- and low- cell groups indicated that DEGs were significantly enriched in tumor immune-related processes, such as regulation of peptidase activity, humoral immune response, assembly of MHC class Ⅱ protein complexes, antigen processing, and presentation (Fig. S5 ). Then developmental trajectories were constructed, and three differentiation states were obtained (Fig. 10 A-C). The developmental trajectory of subtypes in state 1 to state 3 was relatively uniform. In the state 1 to state 2 developmental trajectory, the c15orf48 + mal subtype was in the early or middle stage of cell differentiation, and the sftpa1 + mal subtype was in the late stage of cell differentiation (Fig. 10 D). In these two developmental trajectories, the cell scores and the high-cell groups were increased, suggesting the malignant degree is rising (Fig. 10 E, F). Discussion Over the years, immunotherapy has significantly improved survival in LUAD without driver genes. PD-L1 expression is a currently recognized and strongly recommended tumor marker(Dora et al., 2023 ; Doroshow et al., 2021 ; Sanchez-Magraner et al., 2023 ), however, it is an imperfect biomarker. Other various biomarkers, such as neoantigens, genetic, epigenetic signatures, microbiome composition, and factors in TME, are also used to predict immunotherapy response and prognosis in LUAD (Mino-Kenudson et al., 2022 ). ICIs aim to enhance the anti-tumor effect by activating effector T cells in TME, which involves in immune escape and tumor progression by immunosuppressive cells and molecules (Binnewies et al., 2018 ; Cristescu et al., 2018 ). However, biomarkers are lacking to predict the efficacy of ICIs in clinical practice in TME. Macrophages are the most common immune cells in TME. Our study demonstrated that M2 macrophages were an unfavorable factor for patients with LUAD, and the signature based on M2 macrophages was a promising biomarker to predict the survival and immunotherapy response in LUAD. Single-cell transcriptome analysis is a useful tool to predict molecular heterogeneity and give a highlight to a more precise classification of lung cancer. M1/M2 polarization is dynamic to adapt tumor progression (Yang & Zhang, 2017 ). Emerging reports have shown a positive correlation between macrophage density and poor survival (Festekdjian & Bonavida, 2024 ). Consistent with the reports, we found that patients with high-M2 macrophage had worse prognosis compared to those patients with low-M2 macrophage. The underlying mechanisms lie in that cancer cells can secrete cytokines, such as IL10, IL12, IL 6, and TNF, facilitating M2-like polarization, then exerting immunosuppressive effects, and finally accelerating cancer progression (Sarode et al., 2020 ). In lung cancer, transforming growth factor beta (TGF- β), IL-10, cytokines, and chemokines released by M2 macrophages can promote tumor growth and infiltration (Wang, Li, Cang, & Guo, 2019 ; Yang et al., 2019 ). In addition, M2 macrophages (CD163+) could promote angiogenesis by releasing angiogenic growth factors such as vascular endothelial growth factor A (VEGF-A) and VEGF-C in NSCLC (Hwang et al., 2020 ). However, LUAD has great heterogeneity, especially in patients with different driver genes, which may affect the roles of macrophages. Therefore, more research is needed to explore the potential mechanisms and clinical implications. Nine macrophage-related prognostic genes (TLR10, PSTPIP1, FYN, IL22RA2, LY9, CD79B, BMP1, TNFRSF13C, ICOS) were screened for construction of prognostic signature in LUAD. Nishikawa S et al. found phosphorylated FYN expression was associated with poor relapse-free survival and overall survival in patients with LUAD after lung resection (Nishikawa et al., 2019 ). In line with FYN, LUAD patients with high expression of TNFRSF13C (BAFFR) had worse survival (Dimitrakopoulos et al., 2019 ). Rochigneux P reported that ICOS + CD4 + T cells were closely associated with better survival for patients receiving pembrolizumab in NSCLC (Rochigneux et al., 2022 ). Moreover, Wu G et al. suggested that ICOS was closely correlated with poor outcomes in multiple cancers, especially LUAD, and was a good biomarker of OS in LUAD (G. Wu, He, Ren, Ma, & Xue, 2022 ). Our data suggested that BMP1 plays the opposite role compared to the other eight genes in the prognostic signature. X Wu reported that downregulation of BMP1 leads to suppression of TGFβ and matrix metalloproteinases 2 (MMP2) and MMP9, and finally decreased tumor invasion in NSCLC (X. Wu et al., 2014 ). In addition, different BMP1 isoforms may impact NSCLC disease progression (Donovan et al., 2023 ), however, insights into the mechanisms remain unclear. ICIs have demonstrated improved OS compared with chemotherapy in non-oncogene-addicted metastatic NSCLC (Hendriks et al., 2023), while immunotherapy biomarkers are lacking. Our study revealed that the signature based on M2 macrophage-related prognostic genes was a potential biomarker for NSCLC patients receiving immunotherapy. Our study found that patients with high-risk tended to have a ‘cold’ tumor phenotype, with a lower proportion of activated T cells and a higher proportion of M2 macrophage, indicating poor response to immunotherapy. Thus, integral evaluation of Tumor microenvironment, including M2 macrophage and PD-L1, is essential before immunotherapy in lung cancer. Of note, Mechanical studies are also necessary. M2 macrophages could release immunosuppressive cytokines in tumors to weaken the function of T cells, leading to an immunosuppressive TME (Bui & Bonavida, 2024 ). However, the relationships between efficacy of ICIs and different subtype of M2 macrophages were unclear. Yamaguchi, Y et al. reported that PD-L1 blockade could restore CAR T cell activity through IFN-gamma-regulation of CD163 + M2 macrophages, suggesting the potential value of the combination of CAR T cells and ICIs in solid tumors to enhance therapeutic efficacy (Yamaguchi et al., 2022 ). Besides, the interaction and mechanism between PD-L1 expression and M2 macrophages worthy of further study, which could provide promising strategy n cancer immunotherapy (Zhao et al., 2024 ). More importantly, the signature needs to be confirmed in multicenter clinical trials. Single-cell sequencing analysis is being more and more used in exploring the heterogeneity of tumor cells in TME. Our data found that subtypes of sftpa1 + mal and cxcr4 + mal in LUAD were with worse biological behavior. Of note, the result was different in other studies. Sorin M et al. reported that TAM was the most common cell in LUAD patients, accounting for 34.1% of immune cells, and CD163 + TAM (M2-like) was the most invasive structure (Sorin et al., 2023 ). Thus, basic, and translational research were wanted in the future. This study has some limitations worth mentioning. Firstly, in vivo and in vitro validation were lacking to explore the underlying mechanisms of immune efficacy affected by M2 macrophage-related prognostic genes in LUAD. Secondly, relationships between driving genes (EGFR and ALK) and M2 macrophage-associated immune response in LUAD were not further analyzed. Last, the clinical significance of different lung cancer subtypes of single-cell sequencing in the managing immunotherapy remains explored. Conclusion In summary, M2 macrophages were significantly associated with worse survival in LUAD. A risk signature based on M2 macrophage-related genes was a promising independent prognostic factor for patients with LUAD. More importantly, the signature was a potential biomarker for NSCLC patients receiving immunotherapy. Single-cell transcriptome analysis was a valuable tool for defining molecular subtypes and malignant degree. In the further, the necessity for more extensive translational research on M2 macrophage or M2 macrophage-related genes was needed to enable individual therapies for patients with LUAD. Declarations Acknowledgements We acknowledge GSE and GEO database for providing their platforms and contributors for uploading their meaningful datasets. Authors' contributions All authors helped to perform the research. ML and ZW: manuscript writing and data analysis. ML, ZW and CL: study concept and study design. ML, ZW, BH, YL, MZ and CL: data collection. All authors reviewed the manuscript. All authors approved the final manuscript. Funding This study was supported by the High-level Talent Development Program (Grant Number: 2024YNG11), the Fujian Provincial Natural Science Foundation of China (Grant Number: 2024J), the National Clinical Key Specialty Construction Program and Fujian Provincial Clinical Research Center for Cancer Radiotherapy and Immunotherapy (Grant Number: 2020Y2012), Fujian Clinical Research Center for Radiation and Therapy of Digestive, Respiratory and Genitourinary Malignancies, (Grant Number: 2021Y2014). Availability of data and materials The datasets generated or analysed during this study are freely available in the Cancer Genome Atlas (https://tcga.xenahubs.net) and the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/). Conflict of interest The authors declare no competing interests. Consent to participate Not applicable. Consent for publication Not applicable. 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H., Leggett, N., . . . Priceman, S. J. (2022). PD-L1 blockade restores CAR T cell activity through IFN-gamma-regulation of CD163+ M2 macrophages. J Immunother Cancer, 10 (6). doi:10.1136/jitc-2021-004400 Yang, L., Dong, Y., Li, Y., Wang, D., Liu, S., Wang, D., . . . Zhang, Y. (2019). IL-10 derived from M2 macrophage promotes cancer stemness via JAK1/STAT1/NF-kappaB/Notch1 pathway in non-small cell lung cancer. Int J Cancer, 145 (4), 1099-1110. doi:10.1002/ijc.32151 Yang, L., & Zhang, Y. (2017). Tumor-associated macrophages: from basic research to clinical application. J Hematol Oncol, 10 (1), 58. doi:10.1186/s13045-017-0430-2 Zhang, H., Wang, Y., Wang, K., Ding, Y., Li, X., Zhao, S., . . . Sun, D. (2023). Prognostic analysis of lung adenocarcinoma based on cancer-associated fibroblasts genes using scRNA-sequencing. Aging (Albany NY), 15 (14), 6774-6797. doi:10.18632/aging.204838 Zhao, C., Pan, Y., Liu, L., Zhang, J., Wu, X., Liu, Y., . . . Rao, L. (2024). Hybrid Cellular Nanovesicles Block PD-L1 Signal and Repolarize M2 Macrophages for Cancer Immunotherapy. Small, 20 (31), e2311702. doi:10.1002/smll.202311702 Additional Declarations No competing interests reported. Supplementary Files SupplementalFig.S1.pdf Supplementary Figures Fig. S1 Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Ontology (GO) analysis of over-expressed genes in the Sftpa1+mal subtype of lung cancer. Biological process (BP), molecular function (MF), and cellular component. SupplementalFig.S2.pdf Fig. S2 Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Ontology (GO) analysis of over-expressed genes in the C15orf48+malsubtype of lung cancer. Biological process (BP), molecular function (MF), and cellular component. SupplementalFig.S3.pdf Fig. S3 Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Ontology (GO) analysis of over-expressed genes in the Cxcr4+malsubtype of lung cancer. Biological process (BP), molecular function (MF), and cellular component. SupplementalFig.S4.pdf Fig. S4 Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Ontology (GO) analysis of over-expressed genes in the Top2a+malsubtype of lung cancer. Biological process (BP), molecular function (MF), and cellular component. SupplementalFig.S5.pdf Fig. S5 Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Ontology (GO) analysis of differentially expressed genes (DEG) between high- and low- cell groups. Biological process (BP), molecular function (MF), and cellular component. SupplementaryTable1.xls Supplementary Table Supplementary Table 1. A total of 108 hub genes in the brown module in the database GSE26939. 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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-5270005","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":367761564,"identity":"11698e95-bcae-49a4-a12d-20d822323dad","order_by":0,"name":"Meifang Li","email":"","orcid":"","institution":"Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Meifang","middleName":"","lastName":"Li","suffix":""},{"id":367761565,"identity":"d1d5b476-76e4-4a65-b14a-17391dd712ed","order_by":1,"name":"Zhiping Wang","email":"","orcid":"","institution":"Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhiping","middleName":"","lastName":"Wang","suffix":""},{"id":367761566,"identity":"a2d30a91-9345-4f78-8c39-d6fc701a1991","order_by":2,"name":"Bin Huang","email":"","orcid":"","institution":"Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Huang","suffix":""},{"id":367761567,"identity":"821060e5-4422-4005-823f-8d458938e52a","order_by":3,"name":"yanyun Lai","email":"","orcid":"","institution":"Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"yanyun","middleName":"","lastName":"Lai","suffix":""},{"id":367761568,"identity":"9bd6e7b9-dcb6-4315-af05-c29448551349","order_by":4,"name":"Meng Zhang","email":"","orcid":"","institution":"Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Meng","middleName":"","lastName":"Zhang","suffix":""},{"id":367761569,"identity":"b031a224-8a25-47c3-8a74-5c56047481ff","order_by":5,"name":"Cheng Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYBACAyjNw8/efPABnEuMFhnJnmPJBiRpsTG4kWMmQZTDzNnPHnvM88uGx+DMsbTKHwV35BnYDx/dgE+LZU9eujFvXxqP5PHmY7d5DJ4ZNvCkpd3A67ADOWbSvD2HefiAttxmMDjM2CDBY4Zfy/k3EC0MQL8U/jA4bE9YC1ClNM+PwzwCQAYDj8HhRCK0vEs3nNsA9AswkKWBWpLbCPrlfO6xB2/+2NiDovLjjz+HbfvZDx/DqwUY72xMvG1IfDb8yiFaGH/8IaxsFIyCUTAKRjAAAJ2mUC1kgV8DAAAAAElFTkSuQmCC","orcid":"","institution":"Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital","correspondingAuthor":true,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2024-10-15 15:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5270005/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5270005/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67356894,"identity":"68e2b260-cfec-4aca-870c-65780c85ba79","added_by":"auto","created_at":"2024-10-24 05:22:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":620657,"visible":true,"origin":"","legend":"\u003cp\u003eComplete workflow of our research. “n” denotes sample size. “p \u0026lt; 0.05” denotes the statistically significant threshold.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5270005/v1/ee6f64b9b0f2c5e157ab980c.png"},{"id":67356896,"identity":"2a9631e1-2461-4643-8fa2-a17f1961c076","added_by":"auto","created_at":"2024-10-24 05:22:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":527381,"visible":true,"origin":"","legend":"\u003cp\u003eScreening of macrophage subtypes in LUAD. (\u003cstrong\u003eA\u003c/strong\u003e) Network diagram of infiltrating immune cells in lung adenocarcinomasamples. (\u003cstrong\u003eB\u003c/strong\u003e) Comparison of survival between high or low M0, M1, and M2 macrophage in GSE26939 (upper column) and GSE19188 databases (lower column). (\u003cstrong\u003eC\u003c/strong\u003e) Hierarchical clustering tree view by weighted gene co-expression network analysis. (\u003cstrong\u003eD\u003c/strong\u003e) Heat map of module phenotypic correlation. \u003cstrong\u003eE\u003c/strong\u003e Internal gene scatter map in brown module.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5270005/v1/6eadcb4537fcc4410c6dc03f.png"},{"id":67358746,"identity":"ae890f66-8559-47f6-bdff-e5e037488ac4","added_by":"auto","created_at":"2024-10-24 05:38:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":519646,"visible":true,"origin":"","legend":"\u003cp\u003eScreening of M2 macrophage-related hub genes. (\u003cstrong\u003eA\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eUnivariate Cox analysis of 22 prognostic genes in lung adenocarcinoma. (\u003cstrong\u003eB-E\u003c/strong\u003e) Pathways enriched by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses based on 22 prognostic genes. Biological process (BP), molecular function (MF), and cellular component.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5270005/v1/dedfb9c4ebf2743f8aa3cdea.png"},{"id":67355845,"identity":"48749679-776f-4c4d-984a-930b71de9873","added_by":"auto","created_at":"2024-10-24 05:06:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":357049,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular subtypes based on M2 macrophage-related prognostic genes. (\u003cstrong\u003eA\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eMatrix heat map when k = 2. (\u003cstrong\u003eB\u003c/strong\u003e) consistent cumulative distribution function (CDF) diagram, which shows the cumulative distribution function when k takes different values.\u003cstrong\u003e (C\u003c/strong\u003e) Survival curve between subtypes in GSE26939.\u003cstrong\u003e (D\u003c/strong\u003e) Heat map of correlation between subtypes and clinical features.\u003cstrong\u003e (E\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eProfile of subtype correctness.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5270005/v1/227952cab25c6121b7ba7bfc.png"},{"id":67355862,"identity":"65d3175d-d5d3-45da-be46-b32b995b1365","added_by":"auto","created_at":"2024-10-24 05:06:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1004701,"visible":true,"origin":"","legend":"\u003cp\u003ePathways enrichment and immune cells between molecular subtypes. (\u003cstrong\u003eA-D\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eThe\u003cstrong\u003e \u003c/strong\u003etop 20 pathways enriched by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis based on differentially expressed genes (DEGs) between molecular subtypes. Biological process (BP), molecular function (MF), and cellular component. (\u003cstrong\u003eE\u003c/strong\u003e) The top 5 inhibition and activation (\u003cstrong\u003eF\u003c/strong\u003e) pathways enriched by Gene Set Enrichment Analysis (GSEA). (\u003cstrong\u003eG, H\u003c/strong\u003e) Different expression of immune cells between molecular subtypes.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5270005/v1/9a719fbca9d810cf273b2d03.png"},{"id":67355855,"identity":"219d8bd0-d6f4-4eb2-98f4-caf54cef1a30","added_by":"auto","created_at":"2024-10-24 05:06:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":528487,"visible":true,"origin":"","legend":"\u003cp\u003eUsing Lasso regression analysis to construct the risk signature. (\u003cstrong\u003eA\u003c/strong\u003e)Lasso coefficient distribution of 22 M2 macrophage-related prognostic genes. (\u003cstrong\u003eB\u003c/strong\u003e) Tuning parameter (λ) selection cross‐validation error curve. Vertical lines were drawn at the optimal values. (\u003cstrong\u003eC\u003c/strong\u003e) Regression coefficient corresponding to the 9 M2 macrophage-related prognostic genes screened. A larger absolute value of the coefficient represents a higher correlation. (\u003cstrong\u003eD\u003c/strong\u003e) Survival status of patients with high- and low-risk scores. (\u003cstrong\u003eE\u003c/strong\u003e) Survival curves for LUADs with high- and low-risk scores. (\u003cstrong\u003eF\u003c/strong\u003e) Receiver operating characteristic (ROC) curves for 1-, 3- and 5-year overall survival in LUAD cohort. (\u003cstrong\u003eG\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eUnivariate and multivariate (\u003cstrong\u003eH\u003c/strong\u003e) analyses of clinical features and risk signature. (\u003cstrong\u003eI, J\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eValidation of the risk signature in the database of GSE31210 and GSE68165 with high- or low-risk scores.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5270005/v1/fd0a2b1a6820f473ca658718.png"},{"id":67355859,"identity":"b24fd726-8475-484b-8ec7-6d0640262d33","added_by":"auto","created_at":"2024-10-24 05:06:50","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1252613,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between risk score and clinical features and immune response. (\u003cstrong\u003eA-F\u003c/strong\u003e) Survival difference between age, sex, and clinical stage of the high- or low-risk score groups. (\u003cstrong\u003eG\u003c/strong\u003e) Correlation analysis between risk score and stromal score, immune score, and tumor Purity. (\u003cstrong\u003eH, K)\u003c/strong\u003eKaplan–Meier curves of overall survival time of the high- and low-risk score groups in the metastatic non-small cell lung cancer (NSCLC) sample and in the metastatic urothelial carcinoma (mUC) sample. (\u003cstrong\u003eI, L\u003c/strong\u003e) Correlations of response (complete response / partial response) and nonresponse (stable disease / progressive disease) to immunotherapy in different risk score groups. (\u003cstrong\u003eJ, M\u003c/strong\u003e) Relative percent of response and nonresponse to immunotherapy in the high- or low-risk score groups in the metastatic NSCLC sample and the mUC sample. not significant.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-5270005/v1/935fd6c9ed8d605946cbd4ee.png"},{"id":67355858,"identity":"2769c8a3-5c38-4fd6-9d74-be27b7750ba1","added_by":"auto","created_at":"2024-10-24 05:06:50","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":5042289,"visible":true,"origin":"","legend":"\u003cp\u003eMicroenvironment cell landscape. (\u003cstrong\u003eA\u003c/strong\u003e) ElbowPlot of principal component analysis (PCA). (\u003cstrong\u003eB\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003ePCA of 15 lung cancer samples. \u003cstrong\u003eC\u003c/strong\u003e Sample cell distribution before de-batch effect. (\u003cstrong\u003eD\u003c/strong\u003e) Sample cell distribution after de-batch effect. (\u003cstrong\u003eE\u003c/strong\u003e) t-Distributed Stochastic Neighbor Embedding (tSNE) distribution of different cell types. (\u003cstrong\u003eF\u003c/strong\u003e)Percentage of cell types.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-5270005/v1/52e2fa238ef9efb75b1812f2.png"},{"id":67355852,"identity":"d4743eba-232d-480e-b20b-4908b1655a70","added_by":"auto","created_at":"2024-10-24 05:06:48","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":3845548,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of tumor cell subsets. (\u003cstrong\u003eA\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eDistribution of each cell subgroup. (\u003cstrong\u003eB\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003et-Distributed Stochastic Neighbor Embedding (tSNE) map of highly expressed genes. (\u003cstrong\u003eC\u003c/strong\u003e) Bubble map of the top 5 characteristic gene expressions in each subgroup. (\u003cstrong\u003eD\u003c/strong\u003e) tSNE map of cellscore. (\u003cstrong\u003eE\u003c/strong\u003e) Violin distribution map of different cell subsets of cellscore. (\u003cstrong\u003eF\u003c/strong\u003e)tSNE map of high or low cellgroup. (\u003cstrong\u003eG\u003c/strong\u003e) Proportion of high or low cellgroup in different subgroups.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-5270005/v1/fdd52cc8e7a1c8b2049b25b4.png"},{"id":67356901,"identity":"b0df0339-1f1f-435b-b3c3-75a6a5d1345c","added_by":"auto","created_at":"2024-10-24 05:22:50","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":976737,"visible":true,"origin":"","legend":"\u003cp\u003eTrajectory analysis of malignant epithelial cells. (\u003cstrong\u003eA\u003c/strong\u003e) Trajectory distribution of state. (\u003cstrong\u003eB\u003c/strong\u003e) Trajectory distribution of cellscore. (\u003cstrong\u003eC\u003c/strong\u003e) Violin distribution of cellscore in different states. (\u003cstrong\u003eD\u003c/strong\u003e)Trajectory distribution of subsets. (\u003cstrong\u003eE\u003c/strong\u003e) Trajectory distribution of cellgroup. (\u003cstrong\u003eF\u003c/strong\u003e) Proportion of cellgroup in different state.\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-5270005/v1/e258ca07ec7f8416237f1ec7.png"},{"id":67358901,"identity":"557a7d29-1525-49cf-a41d-527480bf7e5f","added_by":"auto","created_at":"2024-10-24 05:39:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":16271656,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5270005/v1/370c31c7-112e-4552-bd75-11c0c318b182.pdf"},{"id":67358201,"identity":"c54ddaf6-5691-42dc-956e-1fd4253ff80a","added_by":"auto","created_at":"2024-10-24 05:30:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16971,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. S1 \u003c/strong\u003eKyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Ontology (GO) analysis of over-expressed genes in the Sftpa1+mal subtype of lung cancer. Biological process (BP), molecular function (MF), and cellular component.\u003c/p\u003e","description":"","filename":"SupplementalFig.S1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5270005/v1/f38ff4ba38c499518152878c.pdf"},{"id":67355853,"identity":"70f210cd-a7c3-42ba-bc32-593ba0a8e9a8","added_by":"auto","created_at":"2024-10-24 05:06:50","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16623,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S2\u003c/strong\u003e Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Ontology (GO) analysis of over-expressed genes in the C15orf48+malsubtype of lung cancer. Biological process (BP), molecular function (MF), and cellular component.\u003c/p\u003e","description":"","filename":"SupplementalFig.S2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5270005/v1/50ba6992fd5c0f874d178cd9.pdf"},{"id":67355844,"identity":"f571eaab-56ca-47c6-8763-1d9728a52003","added_by":"auto","created_at":"2024-10-24 05:06:47","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":16490,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S3\u003c/strong\u003e Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Ontology (GO) analysis of over-expressed genes in the Cxcr4+malsubtype of lung cancer. Biological process (BP), molecular function (MF), and cellular component.\u003c/p\u003e","description":"","filename":"SupplementalFig.S3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5270005/v1/c94d8124852c73317cab1a73.pdf"},{"id":67355851,"identity":"f57fc472-fae0-46da-bd48-8f33b0e259e7","added_by":"auto","created_at":"2024-10-24 05:06:48","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":16476,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S4\u003c/strong\u003e Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Ontology (GO) analysis of over-expressed genes in the Top2a+malsubtype of lung cancer. Biological process (BP), molecular function (MF), and cellular component.\u003c/p\u003e","description":"","filename":"SupplementalFig.S4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5270005/v1/65c67372409eb673ab03f3c2.pdf"},{"id":67356900,"identity":"7ad9f2d3-ac99-4acc-b632-d487dcc6fb8f","added_by":"auto","created_at":"2024-10-24 05:22:50","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":16507,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S5\u003c/strong\u003e Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Ontology (GO) analysis of differentially expressed genes (DEG) between high- and low- cell groups. Biological process (BP), molecular function (MF), and cellular component.\u003c/p\u003e","description":"","filename":"SupplementalFig.S5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5270005/v1/008c4fd6e6673e0b8ffca088.pdf"},{"id":67356899,"identity":"2a8db550-fb73-42be-b31f-3979b06a1ead","added_by":"auto","created_at":"2024-10-24 05:22:50","extension":"xls","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1249,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 1.\u003c/strong\u003e A total of 108 hub genes in the brown module in the database GSE26939.\u003c/p\u003e","description":"","filename":"SupplementaryTable1.xls","url":"https://assets-eu.researchsquare.com/files/rs-5270005/v1/5b806b6b7c4107d1336b81b4.xls"},{"id":67356898,"identity":"550ffd05-9774-4dea-a3ed-c30e8c750d17","added_by":"auto","created_at":"2024-10-24 05:22:50","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":23179,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-5270005/v1/4ef58e3daa63e46b9cf029a7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated analysis of M2 macrophage-related gene prognostic model and single-cell sequence to predict immunotherapy response in lung adenocarcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung adenocarcinoma (LUAD) is the primary subtype of non-small cell lung cancer (NSCLC), and accounts for more than 50% of all NSCLC cases. The 5-year survival for patients with advanced LUAD is lower than 20%(Asamura et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In recent years, the emergence of immune checkpoint inhibitors (ICIs) and targeted drugs has completely changed the outcomes of advanced LUAD(Liu et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; The, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, treatment unresponsiveness and drug resistance are common, especially in immunotherapy(Mogavero et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The poor curative effects largely stem from the complicated molecular features caused by the high heterogeneity of LUAD. Therefore, exploration of meaningful \u0026ldquo;signatures\u0026rdquo; to predict the prognosis and assist the management of LUAD is urgently needed.\u003c/p\u003e \u003cp\u003eLots of clinical and molecular factors influence the efficacy of ICIs(Thummalapalli et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thus, exploration of cellular and molecular mechanisms, thus assisting in achieving durable responses to ICIs is essential. Tumor microenvironment (TME), including tumor cells, immune cells, stromal cells, and extracellular matrix (ECM), as well as driver genes and other genes, are involved in the treatment response and prognosis in a variety of cancers(Binnewies et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). At present, attention is focused on the clinical significance of T cells in TME. KEYNOTE-028 trail revealed that the T-cell-inflamed gene-expression profile (TcellinfGEP) could predict response to pembrolizumab in 20 tumor types(Ott et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which was also demonstrated in advanced NSCLC in KEYNOTE-494/KeyImPaCT trail (Gutierrez et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Of note, other immune cells, like cancer-associated fibroblast (CAF) and tumor-associated macrophages (TAM), were also reported to be closely associated with the development of NSCLC(Cords et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the values of TAM in LUAD are still unclear in clinical practice since TAM was supposed to be a double-edged sword in the TME.\u003c/p\u003e \u003cp\u003eMacrophages can be polarized into M1 and M2 types under different microenvironments and stimulators (Funes, Rios, Escobar-Vera, \u0026amp; Kalergis, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The function of TAM is similar to M2-like macrophages in cancers (Sarode, Schaefer, Grimminger, Seeger, \u0026amp; Savai, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sumitomo et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Xu, Wei, Tang, Liu, \u0026amp; Dong, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). M2 TAMs can promote cancer proliferation, invasion, migration, angiogenesis, and multidrug resistance. More importantly, TAMs can inhibit the activation and aggregation of immune cells by secreting cytokines and chemokines, establishing suppressive TME. Therefore, in-depth research on the role of M2 macrophage in LUAD and the construction of a prognostic signature associated with M2 macrophage are necessary.\u003c/p\u003e \u003cp\u003eIn this study, we sought to screen an M2 macrophage-related signature and to predict the prognosis and immunotherapy efficacy of LUAD patients. We found that an M2 macrophage-related signature based on characteristic genes was a novel biomarker in the management of LUAD.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData resource\u003c/h2\u003e \u003cp\u003eThe GSE26939, GSE31210, GSE19188 and GSE135222 were downloaded from the GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The immune-related profiles of LUAD were downloaded from the InnateDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.innatedb.ca/\u003c/span\u003e\u003cspan address=\"https://www.innatedb.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Immort database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.immport.org\u003c/span\u003e\u003cspan address=\"https://www.immport.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Immunotherapy cohorts IMvigor210 and GSE93157 were included for analysis of immune therapy response (Bhattacharya et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Breuer et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAcquisition of M2 macrophage-related genes\u003c/h3\u003e\n\u003cp\u003eWe analyzed immune-related genes using the Weighted Gene Co-expression Network Analysis (WGCNA), and then constructed the network by one-step method to obtain the module genes that were most related to M2 macrophage. The module genes that were most related to M2 macrophages were identified as M2 macrophage-related hub genes. Then, univariate Cox regression analysis was carried out to confirm M2 macrophage-related prognostic genes. Prognostic genes with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were finally enrolled.\u003c/p\u003e\n\u003ch3\u003eFunctional enrichment\u003c/h3\u003e\n\u003cp\u003eUsing the \u0026ldquo;clusterprofiler\u0026rdquo; package, Gene Ontology (GO) analysis was performed on prognostic feature genes, categorizing GO functions into three parts: Cellular Component (CC), Molecular Function (MF), and Biological Process (BP). Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was conducted, with significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for enrichment.\u003c/p\u003e\n\u003ch3\u003eGenotyping based on characteristic genes\u003c/h3\u003e\n\u003cp\u003eThe \u0026ldquo;ConsensusClusterPlus\u0026rdquo; package was used to conduct consistency cluster analysis. The overall slope of the curve shows the smallest decline when K is 2, leading to the classification of patients in GSE26939 into two molecular subtypes. Then, differentially expressed genes (DEG) between two subtypes were confirmed by the \u0026ldquo;limma\u0026rdquo; package. Those genes with adj.p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |logFC| \u0026gt; 1.5 were regarded as DEGs.\u003c/p\u003e\n\u003ch3\u003eConstruction of M2 macrophage-related prognostic signature\u003c/h3\u003e\n\u003cp\u003eWe developed a risk model based on M2 macrophage-related genes using the machine learning algorithm known as least absolute shrinkage and selection operator (LASSO) regression. The risk score model was constructed by the following formula:\u003c/p\u003e \u003cp\u003e \u003cb\u003eRisk score\u003c/b\u003e \u003cem\u003e= \u0026sum; Coefi * Expr i\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u0026ldquo;Expr\u0026rdquo; was the expression value of signature genes in the model, and \u0026ldquo;Coef\u0026rdquo; was the regression coefficient. Then, patients were divided into high- and low- risk groups according to the optimal cutoff of risk score of all LAUD samples. Kaplan-Meier survival curves and area under curve (AUC) were used to verify the performance of the signature. Univariate and multivariate Cox were used to verify the performance of the prognostic signature.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnalyses of clinical characteristics, immune cells, and immunotherapy\u003c/h2\u003e \u003cp\u003eTo further explore the role of the risk signature in the immune microenvironment. Based on the core algorithm of CIBERSORT (CIBERSORT.R script analysis), we utilized the markers of 22 immune cell types provided by the CIBERSORTx website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cibersortx.stanford.edu/\u003c/span\u003e\u003cspan address=\"https://cibersortx.stanford.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to compute the immune infiltration between high- and low-risk groups. Moreover, ImmuneScore, StromalScore, and EstimateScore were analyzed by the \u0026ldquo;ESTIMATE\u0026rdquo; package.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSingle-cell transcriptome database analysis\u003c/h3\u003e\n\u003cp\u003eThe single-cell transcriptome profile (GSE131907) downloaded from the Gene Expression Omnibus (GEO) database, including 15 lung cancer samples, was selected for subsequent analyses.\u003c/p\u003e \u003cp\u003eFirstly, quality control of single-cell profiles was done by Seurat (v4.1.0). The quality control standards were as follows: (1) Each gene was detected in more than 3 cells. (2) Features of each cell were between 500 and 6000, with 1000\u0026thinsp;~\u0026thinsp;20000 counts. (3) The percentage of mitochondrial genes and erythrocytes gene expression was less than 20%. We use the \u0026ldquo;NormalizeData\u0026rdquo; function for normalization and the \u0026ldquo;FindVariableFeatures\u0026rdquo; function for identifying hypervariable genes, which were with 0.1\u0026thinsp;~\u0026thinsp;3 average expression value and more than 0.5 dispersion. Batch correction between samples was performed by the \u0026ldquo;harmony\u0026rdquo; package. Principal component analysis (PCA) was used for dimensionality reduction, and the first 50 principal components were selected for downstream analysis. \u003cem\u003et\u003c/em\u003e-distributed stochastic neighbor embedding (tSNE) algorithm was used for visualization.\u003c/p\u003e \u003cp\u003eThe top 50 principal components with 0.2 resolution were used to identify subpopulations of tumor cells. The \u0026ldquo;FindAllMarkers\u0026rdquo; function was used to identify feature genes, and each model contained 10 genes. Cellscore was calculated by the \u0026ldquo;AddModuleScore\u0026rdquo; algorithm. The malignant epithelial cells were divided into high- and low-groups according to the middle value of Cellscore. The \u0026ldquo;Monocle2\u0026rdquo; package was used to analyze the single trajectory.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eAll the above statistical analysis was computed by R software (version 4.2.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-sided) was used as the statistically significant threshold. The survival difference between the two groups was analyzed by Kaplan‒Meier analysis. Other statistical methods and algorithms used in this article are described in the corresponding steps.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eScreening of macrophage subtypes in LUAD\u003c/h2\u003e \u003cp\u003eThe workflow of the study is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Immune cells were divided into three different clusters, and we evaluated the correlation of various immune cells using correlation analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Macrophages are a significant constituent part of TME. To confirm the relationships between macrophages and survival in patients with LUAD, patients were divided into high- and low-macrophage groups based on macrophage infiltration level. Survival analysis suggests that patients in the high M2 group have a worse prognosis, while those in the high M1 group have a better prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Therefore, the M2 macrophage was chosen for further exploration.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eScreening of M2 macrophage-related hub genes\u003c/h2\u003e \u003cp\u003eThen, WGCNA was used to identify M2 macrophage-related genes in LUAD. Using the InnateDB and Immort databases, 1836 immune-related genes were obtained from the GSE26939 database (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Seven modules were identified by WGCNA, and the brown module was significantly associated with M2 macrophage (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Thus, 108 hub genes in the brown module were selected for further analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE and Supplemental Table\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eScreening for M2 macrophage-related prognostic genes\u003c/h2\u003e \u003cp\u003eTo address the critical genes involved in the biological function of M2 macrophage, univariate cox regression analysis was conducted. Twenty-two genes among 108 hub genes associated with the prognosis of LUAD were confirmed by univariate analysis. Except for BMP1 (bone morphogenetic protein 1), the remaining 21 genes were considered favorable factors in LUAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). GO (Gene Ontology) analyses showed that the above 22 prognostic genes were significantly enriched in the activation of immune response, immune response\u0026thinsp;\u0026minus;\u0026thinsp;activating signaling pathway, immune receptor activity, etc (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-D). Similarly, Immune-related pathways, like B cell and T cell receptor signaling pathways, were significantly enriched in KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMolecular subtypes of LUAD\u003c/h2\u003e \u003cp\u003eAs we know, LUAD is full of heterogeneity. To better identify the different populations, patients with LUAD were classified into two molecular subtypes in the GSE26939 database by consistent cluster analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). There was a significant difference in survival outcomes between the two clusters (p\u0026thinsp;=\u0026thinsp;0.0025) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), with different gene expressions, clinic characteristics and profile of subtype correctness (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, E).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThen, differentially expressed genes (DEGs) between these two clusters was analyzed. There were 772 DEGs identified, with 75 down-regulated DEGs and 697 up-regulated DEGs. GO and KEGG analysis revealed that activation of immune response and interaction of cytokine-cytokine receptor were significantly enriched (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-D). And the top 5 inhibition and activation pathways was also showed by Gene Set Enrichment Analysis (GSEA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE, F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInterestingly, immune cells, including M2 macrophage, resting CD4 memory T cells, regulatory T cells, activated NK cells, and mast cells, were lower in cluster 2, indicating a favorable prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG, H).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of an M2 macrophage-related prognostic signature\u003c/h2\u003e \u003cp\u003eTo explore a simple and reliable therapy strategy, a risk prognostic model was constructed based on the 22 M2 macrophage-related prognostic genes. Nine genes were confirmed by LASSO regression analysis in GSE26939 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, B). The coefficient of each gene in the model is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC. We divided the patients into high-risk and low-risk groups based on the median risk score (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Patients with LUAD in the low-risk group had longer overall survival (OS) than in the high-risk group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). ROC curves were plotted to estimate the performance of the risk model. The AUC value of ROC curves at 1, 3, and 5 years was 0.787, 0.699, and 0.776, respectively, indicating this signature scoring system had a good predictive performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). The univariate and multivariate analyses showed that the signature based on risk score was an independent prognostic factor in LUAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG, H). Moreover, the verification gene sets (GSE31210 and GSE68165) further demonstrated that the patients with the low-risk had superior survival than the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI, J).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurther subgroup analysis suggested that the low-risk group aged over 60 years old and stage Ⅰ-Ⅱ had longer survival than the high-risk group, regardless of sex in GSE26939 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-F). No significant survival differences were found regarding tumor stage Ⅲ-Ⅳ, age of \u0026le;\u0026thinsp;60.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eRelationships between risk signature and immunotherapy response\u003c/h2\u003e \u003cp\u003eSince infiltrating immune cells varies in the different molecular subtypes and risk score groups, we wonder if the signature was associated with immunotherapy response. Our data revealed that the high-risk group had lower stromal score and immune score, but higher Tumor Purity, indicating patients with low-risk group had better effects of immunotherapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG). Then, the GSE93157 database, including NSCLC patients receiving PD1-targeting antibodies pembrolizumab or nivolumab, and the IMvigor210 database, including metastatic urothelial carcinoma patients receiving PD-L1-targeting antibodies atezolizumab were used. The results showed that the patients with low-risk had better immunotherapy efficacy compared to the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eH, K). While the risk was not associated with the response rate (CR/PR) and nonresponse rate (SD/PD) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eI-J, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eL-M). Taken together, our data suggest that the signature was a potential biomarker for NSCLC patients receiving immunotherapy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSingle-cell transcriptome database analysis\u003c/h2\u003e \u003cp\u003eFifteen primary lung cancer samples in the single-cell transcriptome profiles (GSE131907) were selected for analysis. After quality control, 27578 genes within 51935 cells were obtained. PCA (principle component analysis) results showed significant batch effects among samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, B). After using Harmony to remove batch effects between samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC, D), UMAP (Uniform Manifold Approximation and Projection) showed seven major cell types, composed of B lymphocytes, endothelial cells, epithelial cells, fibroblasts, MAST cells, myeloid cells, and T/NK cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE). The proportion of cells in each sample was heterogeneous (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn this study, four different lung cancer subtypes were identified were defined: sftpa1\u0026thinsp;+\u0026thinsp;mal, c15orf48\u0026thinsp;+\u0026thinsp;mal, cxcr4\u0026thinsp;+\u0026thinsp;mal, and top2a\u0026thinsp;+\u0026thinsp;mal, according to the high expression genes of each subtype (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA, B). Based on FindAllMarkers, the top 5 characteristic genes of each subtype were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC). Sftpa1\u0026thinsp;+\u0026thinsp;mal over-expressed sftpa1, sftpa2, sftpc, and other genes, and these genes were significantly enriched in biological processes such as MHC complex assembly, antigen treatment and presentation by GO and KEEP analysis (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). C15orf48\u0026thinsp;+\u0026thinsp;mal highly expressed c15orf48, IGFBP3, S100A4, and other genes, which was significantly enriched in the regulation of cell morphogenesis, cell-matrix adhesion, and other biological processes (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Cxcr4\u0026thinsp;+\u0026thinsp;mal highly expressed SRGN, CXCR4, CD52, and other genes, which was significantly enriched in the regulation of T cell activation, T cell receptor signaling pathway, and lymphocyte differentiation (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Top2a\u0026thinsp;+\u0026thinsp;mal highly expressed cell cycle marker TOP2A, significantly enriched in the cell cycle, nucleus division, and DNA replication, suggesting that the tumor was in an active cell proliferation state (Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further distinguish lung cancer lineages at the single-cell level, lung cancer was divided into high- and low-cell groups according to cell scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD). Our data revealed that subtypes of sftpa1\u0026thinsp;+\u0026thinsp;mal and cxcr4\u0026thinsp;+\u0026thinsp;mal had higher cell scores, suggesting those two subtypes had more malignant character (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE). According to cell scores, malignant epithelial cells were divided into high and low cell groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eF, G). In addition, DEGs of the high- and low- cell groups indicated that DEGs were significantly enriched in tumor immune-related processes, such as regulation of peptidase activity, humoral immune response, assembly of MHC class Ⅱ protein complexes, antigen processing, and presentation (Fig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThen developmental trajectories were constructed, and three differentiation states were obtained (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e10\u003c/span\u003eA-C). The developmental trajectory of subtypes in state 1 to state 3 was relatively uniform. In the state 1 to state 2 developmental trajectory, the c15orf48\u0026thinsp;+\u0026thinsp;mal subtype was in the early or middle stage of cell differentiation, and the sftpa1\u0026thinsp;+\u0026thinsp;mal subtype was in the late stage of cell differentiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e10\u003c/span\u003eD). In these two developmental trajectories, the cell scores and the high-cell groups were increased, suggesting the malignant degree is rising (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e10\u003c/span\u003eE, F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOver the years, immunotherapy has significantly improved survival in LUAD without driver genes. PD-L1 expression is a currently recognized and strongly recommended tumor marker(Dora et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Doroshow et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sanchez-Magraner et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), however, it is an imperfect biomarker. Other various biomarkers, such as neoantigens, genetic, epigenetic signatures, microbiome composition, and factors in TME, are also used to predict immunotherapy response and prognosis in LUAD (Mino-Kenudson et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). ICIs aim to enhance the anti-tumor effect by activating effector T cells in TME, which involves in immune escape and tumor progression by immunosuppressive cells and molecules (Binnewies et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Cristescu et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, biomarkers are lacking to predict the efficacy of ICIs in clinical practice in TME. Macrophages are the most common immune cells in TME. Our study demonstrated that M2 macrophages were an unfavorable factor for patients with LUAD, and the signature based on M2 macrophages was a promising biomarker to predict the survival and immunotherapy response in LUAD. Single-cell transcriptome analysis is a useful tool to predict molecular heterogeneity and give a highlight to a more precise classification of lung cancer.\u003c/p\u003e \u003cp\u003eM1/M2 polarization is dynamic to adapt tumor progression (Yang \u0026amp; Zhang, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Emerging reports have shown a positive correlation between macrophage density and poor survival (Festekdjian \u0026amp; Bonavida, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Consistent with the reports, we found that patients with high-M2 macrophage had worse prognosis compared to those patients with low-M2 macrophage. The underlying mechanisms lie in that cancer cells can secrete cytokines, such as IL10, IL12, IL 6, and TNF, facilitating M2-like polarization, then exerting immunosuppressive effects, and finally accelerating cancer progression (Sarode et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In lung cancer, transforming growth factor beta (TGF- β), IL-10, cytokines, and chemokines released by M2 macrophages can promote tumor growth and infiltration (Wang, Li, Cang, \u0026amp; Guo, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, M2 macrophages (CD163+) could promote angiogenesis by releasing angiogenic growth factors such as vascular endothelial growth factor A (VEGF-A) and VEGF-C in NSCLC (Hwang et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, LUAD has great heterogeneity, especially in patients with different driver genes, which may affect the roles of macrophages. Therefore, more research is needed to explore the potential mechanisms and clinical implications.\u003c/p\u003e \u003cp\u003eNine macrophage-related prognostic genes (TLR10, PSTPIP1, FYN, IL22RA2, LY9, CD79B, BMP1, TNFRSF13C, ICOS) were screened for construction of prognostic signature in LUAD. Nishikawa S et al. found phosphorylated FYN expression was associated with poor relapse-free survival and overall survival in patients with LUAD after lung resection (Nishikawa et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In line with FYN, LUAD patients with high expression of TNFRSF13C (BAFFR) had worse survival (Dimitrakopoulos et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Rochigneux P reported that ICOS\u003csup\u003e+\u003c/sup\u003e CD4\u003csup\u003e+\u003c/sup\u003e T cells were closely associated with better survival for patients receiving pembrolizumab in NSCLC (Rochigneux et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Moreover, Wu G et al. suggested that ICOS was closely correlated with poor outcomes in multiple cancers, especially LUAD, and was a good biomarker of OS in LUAD (G. Wu, He, Ren, Ma, \u0026amp; Xue, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Our data suggested that BMP1 plays the opposite role compared to the other eight genes in the prognostic signature. X Wu reported that downregulation of BMP1 leads to suppression of TGFβ and matrix metalloproteinases 2 (MMP2) and MMP9, and finally decreased tumor invasion in NSCLC (X. Wu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In addition, different BMP1 isoforms may impact NSCLC disease progression (Donovan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), however, insights into the mechanisms remain unclear.\u003c/p\u003e \u003cp\u003eICIs have demonstrated improved OS compared with chemotherapy in non-oncogene-addicted metastatic NSCLC (Hendriks et al., 2023), while immunotherapy biomarkers are lacking. Our study revealed that the signature based on M2 macrophage-related prognostic genes was a potential biomarker for NSCLC patients receiving immunotherapy. Our study found that patients with high-risk tended to have a \u0026lsquo;cold\u0026rsquo; tumor phenotype, with a lower proportion of activated T cells and a higher proportion of M2 macrophage, indicating poor response to immunotherapy. Thus, integral evaluation of Tumor microenvironment, including M2 macrophage and PD-L1, is essential before immunotherapy in lung cancer. Of note, Mechanical studies are also necessary. M2 macrophages could release immunosuppressive cytokines in tumors to weaken the function of T cells, leading to an immunosuppressive TME (Bui \u0026amp; Bonavida, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, the relationships between efficacy of ICIs and different subtype of M2 macrophages were unclear. Yamaguchi, Y et al. reported that PD-L1 blockade could restore CAR T cell activity through IFN-gamma-regulation of CD163\u0026thinsp;+\u0026thinsp;M2 macrophages, suggesting the potential value of the combination of CAR T cells and ICIs in solid tumors to enhance therapeutic efficacy (Yamaguchi et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Besides, the interaction and mechanism between PD-L1 expression and M2 macrophages worthy of further study, which could provide promising strategy n cancer immunotherapy (Zhao et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). More importantly, the signature needs to be confirmed in multicenter clinical trials.\u003c/p\u003e \u003cp\u003eSingle-cell sequencing analysis is being more and more used in exploring the heterogeneity of tumor cells in TME. Our data found that subtypes of sftpa1\u0026thinsp;+\u0026thinsp;mal and cxcr4\u0026thinsp;+\u0026thinsp;mal in LUAD were with worse biological behavior. Of note, the result was different in other studies. Sorin M et al. reported that TAM was the most common cell in LUAD patients, accounting for 34.1% of immune cells, and CD163\u0026thinsp;+\u0026thinsp;TAM (M2-like) was the most invasive structure (Sorin et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thus, basic, and translational research were wanted in the future.\u003c/p\u003e \u003cp\u003eThis study has some limitations worth mentioning. Firstly, in vivo and in vitro validation were lacking to explore the underlying mechanisms of immune efficacy affected by M2 macrophage-related prognostic genes in LUAD. Secondly, relationships between driving genes (EGFR and ALK) and M2 macrophage-associated immune response in LUAD were not further analyzed. Last, the clinical significance of different lung cancer subtypes of single-cell sequencing in the managing immunotherapy remains explored.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, M2 macrophages were significantly associated with worse survival in LUAD. A risk signature based on M2 macrophage-related genes was a promising independent prognostic factor for patients with LUAD. More importantly, the signature was a potential biomarker for NSCLC patients receiving immunotherapy. Single-cell transcriptome analysis was a valuable tool for defining molecular subtypes and malignant degree. In the further, the necessity for more extensive translational research on M2 macrophage or M2 macrophage-related genes was needed to enable individual therapies for patients with LUAD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge GSE and GEO database for providing their platforms and contributors for uploading their meaningful datasets.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors helped to perform the research. ML and ZW: manuscript writing and data analysis. ML, ZW and CL: study concept and study design. ML, ZW, BH, YL, MZ and CL: data collection. All authors reviewed the manuscript. All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the High-level Talent Development Program (Grant Number: 2024YNG11), the Fujian Provincial Natural Science Foundation of China (Grant Number: 2024J), the National Clinical Key Specialty Construction Program and Fujian Provincial Clinical Research Center for Cancer Radiotherapy and Immunotherapy (Grant Number: 2020Y2012), Fujian Clinical Research Center for Radiation and Therapy of Digestive, Respiratory and Genitourinary Malignancies, (Grant Number: 2021Y2014).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated or analysed during this study are freely available in the Cancer Genome Atlas (https://tcga.xenahubs.net) and the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAsamura, H., Nishimura, K. 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Hybrid Cellular Nanovesicles Block PD-L1 Signal and Repolarize M2 Macrophages for Cancer Immunotherapy. \u003cem\u003eSmall, 20\u003c/em\u003e(31), e2311702. doi:10.1002/smll.202311702\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lung adenocarcinoma, Tumor microenvironment, Macrophages, Prognosis, Immunotherapy","lastPublishedDoi":"10.21203/rs.3.rs-5270005/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5270005/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction\u003c/h2\u003e \u003cp\u003eLung adenocarcinoma (LUAD) patients have high heterogeneity. The significance and clinical value of M2 macrophage related genes in LUAD require further exploration. We aimed to construct a prognostic signature to predict the immunotherapy efficacy and prognosis in LUAD.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eGSE26939 and GSE19188 chips were downloaded from the Gene Expression Omnibus (GEO). Weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) analysis were used to screen M2 macrophage-related prognostic genes. A signature based on M2 macrophage-related prognostic genes was established and used to predict the prognosis and immunotherapy efficacy in LUAD.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTwenty-two M2 macrophage-related genes associated with the prognosis of LUAD were confirmed using WGNNA, and then two molecular subtypes were identified with significant different survival, gene expressions and clinic characteristics were classified. LASSO analysis identified nine M2 macrophage-related prognostic genes to establish a risk signature, classifying patients into low- and high-risk groups. Data indicated that low-risk patients had better survival. Moreover, the signature was an independent prognostic factor for LUAD and a potential biomarker for patients receiving immunotherapy. Single-cell transcriptome analysis may provide important information on molecular subtypes and heterogeneity.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eRisk signature based on M2 macrophage-related genes is a valuable tool for predicting prognosis and immunotherapy response in patients with LUAD.\u003c/p\u003e","manuscriptTitle":"Integrated analysis of M2 macrophage-related gene prognostic model and single-cell sequence to predict immunotherapy response in lung adenocarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-24 05:06:42","doi":"10.21203/rs.3.rs-5270005/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":"b4f7b329-4916-4e51-86c3-a2a2b83d853b","owner":[],"postedDate":"October 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-24T05:06:45+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-24 05:06:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5270005","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5270005","identity":"rs-5270005","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}