scRNA-Seq Combined with scPagwas Analysis Identifies GNG7 as a Core Gene in Lung Adenocarcinoma

scRNA-Seq Combined with scPagwas Analysis Identifies GNG7 as a Core Gene 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 scRNA-Seq Combined with scPagwas Analysis Identifies GNG7 as a Core Gene in Lung Adenocarcinoma Hanyang Liu, Beibei Han, Guiting Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7388573/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 Objective: Lung cancer is a leading cause of cancer-related deaths globally, with lung adenocarcinoma (LUAD) showing high incidence in non-smoking cases. Over one million die annually from lung cancer, and LUAD presents treatment challenges due to complex biological behavior, variability in therapeutic responses, and pronounced heterogeneity (within tumor cells and the microenvironment). This study integrates single-cell RNA sequencing (scRNA-Seq) and genome-wide association study (GWAS) data to identify LUAD-associated cell subpopulations and core genes, offering insights into pathogenesis and potential therapeutic targets/prognostic biomarkers. Methods: The GSE196303 single-cell transcriptomic dataset (3 adjacent normal, 3 tumor tissues) was analyzed via Seurat and Harmony for clustering, annotation, and batch correction. Combined with GWAS summary data (1002 cases, 462008 controls), scPagwas identified trait-associated subpopulations. Using TCGA-LUAD bulk RNA-seq (541 tumors, 59 adjacent tissues) and clinical data, BayesPrism quantified cell subtype abundance. Limma, WGCNA, somatic mutation analysis, and immune infiltration assessment (ssGSEA, CIBERSORT) explored clinical associations. Drug sensitivity was predicted via oncoPredict, with survival analysis and Cox regression screening prognostic markers. Results: Eleven cell subpopulations were identified; CD8+ T cells had significantly higher trait-related scores (TRS, P<0.05). BayesPrism showed CD8+ T cell abundance was upregulated in LUAD and linked to favorable prognosis (P<0.05). Effector CD8+ T cell abundance correlated with gender, TNM stage, and survival, with high-abundance patients more sensitive to drugs like Savolitinib (P<0.05). TP53 and STK11 mutations associated with effector CD8+ T cell abundance. WGCNA and differential expression screening identified GNG7 as a core gene linked to effector CD8+ T cells, with high expression significantly improving survival (P=0.004). Conclusion: CD8+ T cells are a core LUAD subpopulation, with abundance correlating with clinical characteristics and the immune microenvironment. GNG7 is a core gene associated with effector CD8+ T cells. Lung adenocarcinoma CD8+ T cells GNG7 Prognostic biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Introduction Lung adenocarcinoma (LUAD) has emerged as the most common subtype of non-small cell lung cancer (NSCLC), accounting for 40% of global lung cancer cases, with over 2 million new cases reported annually. Despite significant advancements in targeted therapy and immunotherapy, the overall survival rate for LUAD patients remains concerning, with a 5-year survival rate still below 20% [ 1 ] . This poor prognosis is primarily attributed to treatment resistance caused by tumor heterogeneity, as well as the low response rate to immunotherapy (only 20%-30%) [ 2 , 3 ] . This pressing clinical situation underscores the urgency of delving deeper into the regulatory mechanisms of the immune microenvironment in lung adenocarcinoma, particularly in exploring novel biomarkers and therapeutic targets with clinical translational potential. CD8 + T cells play a pivotal role in anti-tumor immunity, especially in LUAD. They exert their cytotoxic effects by releasing cytotoxic molecules such as perforin and granzymes, thereby inducing apoptosis in cancer cells [ 4 ] . However, the functional state and efficacy of CD8 + T cells are significantly influenced by the tumor microenvironment. In LUAD, CD8 + T cells often exhibit dysfunction and heterogeneity, complicating their role in anti-tumor immunity [ 5 ] . Recent advancements in single-cell sequencing technology have provided crucial insights into the cellular composition and functional states of immune cells, particularly CD8 + T cells, within this microenvironment. Research indicates that the phenotypic differentiation of CD8 + T cells is closely associated with patient prognosis, suggesting that they may serve as key targets for therapeutic interventions aimed at addressing immune evasion in LUAD [ 6 , 7 ] . Current research faces several limitations. Firstly, traditional immune infiltration analysis methods struggle to overcome the impact of tissue heterogeneity on the precise quantification of cellular subpopulations. Secondly, the mechanistic links between genetic risk loci identified by GWAS and the functions of specific immune cells have not been established. Lastly, there is a lack of an analytical framework that integrates multi-omics data to systematically evaluate the clinical value of immune cells. To address these challenges, this study innovatively adopts a multi-omics integration strategy. By employing the scPagwas algorithm to correlate single-cell transcriptomics (scRNA-seq) with GWAS data, the study aims to dissect the associations between cell subtypes and disease. Additionally, the application of BayesPrism technology enables the precise mapping of single-cell characteristics to large samples. This approach not only identifies disease-related cell subpopulations but also elucidates their molecular regulatory foundations. Compared to traditional immune infiltration analysis, this method overcomes the limitations imposed by tissue heterogeneity and clarifies the clinical value of specific cell subpopulations. This study is expected to provide a new theoretical foundation and intervention strategies for the personalized treatment of LUAD, particularly by offering molecular targets to improve the efficacy of immunotherapy. The innovative analytical framework employed in this research also serves as a methodological reference for studying the immune microenvironment in other malignancies. Materials and Methods 1.Data Sources The dataset with the accession number GSE196303 was downloaded from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ). This dataset includes single-cell transcriptomic data from 3 adjacent normal tissues and 3 lung adenocarcinoma (LUAD) tissues. Transcriptomic data, somatic mutation data, and clinical data from the TCGA-LUAD project were downloaded from the TCGA database, including 541 LUAD samples and 59 adjacent normal tissue samples. Samples with incomplete survival information or survival days < 30 were excluded during survival analysis and model construction. Genome-wide association study (GWAS) summary data for LUAD were downloaded from the IEU-Open-GWAS database ( https://gwas.mrcieu.ac.uk/ ), including 1002 LUAD cases and 462008 healthy controls. 2. scRNA-seq Data Analysis The scRNA-seq dataset GSE196303 was analyzed using the standard workflow in the "Seurat" R package. Cells with fewer than 200 genes or mitochondrial gene content exceeding 15% were filtered out. The "harmony" R package was used to reduce batch effects between samples. The "FindVariableFeatures" function identified the top 2000 variable genes, and principal component analysis (PCA) was used for dimensionality reduction. Marker genes were identified using the default parameters of the FindMarkers function. Cell subpopulations were annotated using the CellMarker2.0 database ( http://117.50.127.228/CellMarker/ ). 3.scPagwas scPagwas employs a polygenic regression model to prioritize trait-associated genes and identify trait-relevant cell subpopulations by integrating pathway activity-transformed scRNA-seq data with GWAS summary data. In this study, the "scPagwas" R package was used to identify key cell subpopulations in lung adenocarcinoma. 4.BayesPrism BayesPrism, a cutting-edge Bayesian model-based technique, ingeniously integrates scRNA-seq as a reference profile into Bulk RNA-seq, enabling cell type scoring and inference of posterior distributions of gene expression. In this study, the "BayesPrism" R package was utilized to project the cell subtypes identified from lung adenocarcinoma scRNA-seq data onto the Bulk RNA-seq data from the TCGA-LUAD project, and to score each cell subtype. 5.Bulk RNA-seq Differential Expression Analysis Limma (Linear Models for Microarray Data, DOI: 10.1093/nar/gkv007 ) is a differential expression screening method based on generalized linear models. Here, we used the R package limma (version 3.40.6) to perform differential analysis to identify differentially expressed genes (DEGs) between different comparison groups and the control group. DEGs were defined using a threshold of fold change > 2 and adjusted P-value < 0.05. The results were visualized using the "ggplot" R package, presented as volcano plots and heatmaps. 6. Weighted Gene Co-expression Network Analysis (WGCNA) We used the WGCNA package in R to construct a co-expression network. First, sample clustering was performed to assess the presence of significant outliers. Second, the automatic network construction function was employed to build the co-expression network. The R function `pickSoftThreshold` was used to calculate the soft thresholding power β, which was then applied to compute the adjacency matrix based on co-expression similarity. Third, hierarchical clustering and the dynamic tree cut function were utilized to detect modules. Fourth, gene significance and module membership were calculated, and modules were correlated with trait cell content. The gene information from the relevant modules was extracted for further analysis. 7. Somatic Mutation Analysis Somatic mutation analysis was performed using the R package maftools. The `read.maf` function was used to read the Mutation Annotation Format (MAF) file and construct a MAF object. Statistical information such as mutation burden, variant type classification, and frequently mutated genes was extracted using `getSampleSummary` and `getGeneSummary`. The mutation landscape was visualized using oncoplots to display the distribution of frequently mutated genes and their variant types. The mutation frequencies of genes were compared between the two groups. 8. Immune Infiltration Analysis Gene expression data from lung adenocarcinoma tissues were normalized using log2(TPM + 1) and analyzed to decipher the immune microenvironment using the following methods: 1) Single-sample Gene Set Enrichment Analysis (ssGSEA): The R package GSVA (v1.48.1) was employed based on immune-related gene sets from the MSigDB database (C7 category, containing 4879 immune signatures). Sample-specific enrichment scores were calculated with parameters set as `method = "ssgsea"` and `tau = 0.25`. 2) Immune Cell Proportion Quantification: The CIBERSORT algorithm was applied using the LM22 signature matrix (22 immune cell marker genes) via an R script. A permutation test with 1000 iterations and quantile normalization was performed, and samples with p < 0.05 were selected to exclude low-quality deconvolution results. Both methods were combined to evaluate the immune phenotype of lung adenocarcinoma, and the enrichment scores and immune cell proportions were output for further analysis. Additionally, the immune score in the tumor microenvironment of lung adenocarcinoma was assessed using the ESTIMATE algorithm. 9. Chemotherapy Sensitivity Analysis Based on TCGA-LUAD gene expression data, the R package oncoPredict was used to evaluate the sensitivity of tumor samples to chemotherapy drugs. The `calcPhenotype` function was employed to load the GDSC (Genomics of Drug Sensitivity in Cancer) training set model, and the Drug Sensitivity Score (DSS) was calculated for each sample. 10. Immunohistochemistry Cancer and adjacent tissues from three lung adenocarcinoma cases were obtained from the First Affiliated Hospital of Guangzhou Medical University. Freshly collected lung adenocarcinoma and corresponding adjacent tissues were fixed in 4% paraformaldehyde overnight, followed by dehydration and embedding in paraffin. Paraffin-embedded samples were sectioned into 4 µm thick slices. Antigen retrieval was performed in a pressure cooker using Tris-EDTA buffer (pH = 8.0) for 2 minutes. Endogenous peroxidase activity was blocked with 3% hydrogen peroxide. The sections were incubated with 10% goat serum at 37°C for 30 minutes to block nonspecific binding. Subsequently, the sections were incubated with the primary antibody GNG7 (Affinity; DF9562; 1:200) at 4°C overnight. After washing, the sections were incubated with a horseradish peroxidase (HRP)-labeled secondary antibody at 37°C for 30 minutes. Finally, the sections were incubated with diaminobenzidine (DAB) for 5–10 minutes for color development. 10. Statistical Analysis For comparisons between two groups, a t-test or Mann-Whitney U test was selected based on whether the data followed a normal distribution. Comparisons among more than two groups were performed using the Kruskal-Wallis test. Correlation analysis was conducted using Spearman's method. Survival analysis was performed using Kaplan-Meier survival analysis and log-rank test, as well as univariate Cox analysis. All statistical analyses were conducted in R software, and p < 0.05 was considered statistically significant (*p < 0.05, p < 0.005, *p 0.05). Results 1. scRNA-Seq Analysis Combined with scPagwas Identifies Core Cells in Lung Adenocarcinoma To better understand the immune microenvironment landscape of lung adenocarcinoma at the single-cell level, we performed scRNA-seq analysis using public data (Fig. 2 ). A total of 25 cell clusters were identified in both the control and lung adenocarcinoma groups (Figs. 3 A- 3 B). After annotation, these clusters were classified into 11 cell subtypes: CD8 + T cells, CD163 + macrophages, NK cells, monocytes, epithelial cells, CD4 + T cells, CD163- macrophages, endothelial cells, B cells, fibroblasts, and smooth muscle cells (Figs. 3 C- 3 D). Subsequently, we conducted scPagwas analysis combined with GWAS summary data to calculate the trait-related score (TRS) for each cell subtype. The results showed that among these 11 cell types, CD8 + T cells had significantly higher TRS scores compared to other cells, followed by NK cells and monocytes (Fig. 3 C). Bootstrap analysis revealed that CD8 + T cells were negatively associated with lung adenocarcinoma, while CD4 + T cells and smooth muscle cells were also negatively correlated with lung adenocarcinoma (Figs. 3 F and 3 G, P < 0.05). These findings suggest that CD8 + T cells, CD4 + T cells, and smooth muscle cells are potential core cell subpopulations in lung adenocarcinoma. 2. CD8 + T Cells as Core Cells Influencing Lung Adenocarcinoma Are Associated with Prognosis Subsequently, in the TCGA-LUAD dataset, the 11 cell subtypes identified in the scRNA-seq data were quantified using the BayesPrism algorithm (Fig. 4 A), and their differences between tumor and control tissues were explored. The results showed that compared to the control group, the abundance of CD8 + T cells, epithelial cells, CD163- macrophages, and fibroblasts was upregulated in the lung adenocarcinoma group, while the abundance of NK cells, monocytes, CD4 + T cells, endothelial cells, B cells, and smooth muscle cells was downregulated (Fig. 4 B, P < 0.05). Prognostic analysis revealed that B cells, smooth muscle cells, NK cells, epithelial cells, monocytes, CD163- macrophages, CD4 + T cells, and CD8 + T cells were significantly associated with the prognosis of lung adenocarcinoma patients. Specifically, higher abundances of B cells, smooth muscle cells, NK cells, monocytes, CD4 + T cells, and CD8 + T cells were associated with better prognosis, while higher abundances of epithelial cells and CD163- macrophages were associated with worse prognosis (Fig. 4 C, P > 0.05). Based on the scPagwas analysis and BayesPrism algorithm, we found that CD8 + T cells significantly contributed to lung adenocarcinoma and were negatively correlated with its occurrence and prognosis. Therefore, we selected CD8 + T cells for further analysis. 3. Identification of Effector CD8 + T Cells as Core Cells Influencing Clinical Characteristics of Lung Adenocarcinoma Subsequently, we extracted CD8 + T cells for further clustering analysis. A total of 14 clusters of CD8 + T cell subtypes were identified in both groups, and these clusters were classified into three subtypes based on marker genes: Cytotoxic T cells, Effector CD8 + T cells, and Exhausted CD8 + T cells (Figs. 5 A- 5 C). The scPagwas analysis results showed that among these three types of CD8 + T cells, Effector CD8 + T cells had significantly higher TRS scores compared to the other two subtypes and were associated with lung adenocarcinoma (Figs. 5 D- 5 E, P < 0.05). In the TCGA-LUAD dataset, the three CD8 + T cell subtypes were quantified using the BayesPrism algorithm, and survival analysis was performed. The results indicated that a higher abundance of Effector CD8 + T cells was associated with better prognosis in lung adenocarcinoma patients, while the abundance of Cytotoxic T cells and Exhausted CD8 + T cells was not correlated with prognosis (Fig. 6 A, P < 0.05). Compared to female lung adenocarcinoma patients, male patients exhibited lower abundance of Effector CD8 + T cells. Compared to T1 stage lung adenocarcinoma patients, T2 and T3 stage patients had lower abundance of Effector CD8 + T cells. Compared to N0 stage patients, N2 stage patients showed lower abundance of Effector CD8 + T cells. Additionally, compared to TNM stage I patients, stage III patients had lower abundance of Effector CD8 + T cells (Fig. 6 B). 4. Infiltration of Effector CD8 + T Cells Is Associated with Drug Sensitivity and Somatic Mutations in Lung Adenocarcinoma Given the association between the infiltration of Effector CD8 + T cells and the clinical characteristics of lung adenocarcinoma, we indirectly explored whether the infiltration of Effector CD8 + T cells is related to drug sensitivity in lung adenocarcinoma patients. Compared to the low infiltration group, patients in the high infiltration group showed greater sensitivity to drugs such as Savolitinib, Lapatinib, Staurosporine, Gefitinib, and Dasatinib (Fig. 7 A), while exhibiting higher tolerance to drugs such as Ribociclib, Doramapimod, Dihydrorotenone, Uprosertib, and Oxaliplatin (Fig. 7 B). Somatic mutation analysis revealed that among the 557 samples with available somatic mutation information, 513 patients had somatic mutations. TP53 mutations were the most common in lung adenocarcinoma, with a mutation rate of 42%, predominantly as Multi Hit mutations. This was followed by TTN mutations (38%), mainly Missense Mutations and Multi_Hit mutations, and MUC16 mutations (35%), also primarily Missense Mutations and Multi_Hit mutations (Figs. 8 A- 8 B). Both the low and high Effector CD8 + T cell infiltration groups showed TP53 as the most frequent mutation (Fig. 8 C). COL5A2 was the second most common mutation gene in the low infiltration group (14%), while STK11 was the second most common mutation gene in the high infiltration group (19%) (Fig. 8 C). Figure 8 D illustrates the differences in gene mutation frequencies between the high and low Effector CD8 + T cell infiltration groups. 5. Identification of GNG7 as a Core Gene Associated with Effector CD8 + T Cells Given the potential critical role of Effector CD8 + T cells in lung adenocarcinoma, this study aimed to identify core genes associated with Effector CD8 + T cells. WGCNA analysis divided the genes from TCGA-LUAD into 17 co-expression modules based on gene expression profiles (Figs. 9 A and 9 B). Correlation analysis revealed that the MEred module (correlation = 0.38) was significantly positively correlated with the infiltration score of Effector CD8 + T cells (Fig. 9 B, p < 0.05). By intersecting the 1307 genes in the MEtan module, the 14,637 genes obtained from bulk transcriptome differential expression analysis, and the marker genes of Effector CD8 + T cells from single-cell transcriptome data, 28 genes were identified (Fig. 9 C). Univariate Cox analysis showed that among these 28 genes, PHACTR1, ID3, SH3BP5, PIK3R1, IL7R, JUNB, GNG7, and GIMAP7 were associated with the prognosis of lung adenocarcinoma patients. Specifically, higher expression levels of GNG7, SH3BP5, PHACTR1, GIMAP7, PIK3R1, and IL7R were associated with better overall survival (OS), while higher expression levels of JUNB and ID3 were associated with worse OS (Fig. 10 A). Multivariate Cox analysis, incorporating the genes identified in the univariate Cox analysis, revealed that GNG7, SH3BP5, JUNB, and ID3 remained significantly associated with the prognosis of lung adenocarcinoma patients (Fig. 10 B). Compared to control tissues, the expression levels of GNG7, SH3BP5, JUNB, and ID3 were significantly lower in lung adenocarcinoma tissues (Fig. 10 C). When genes were divided into high and low expression groups based on median expression levels, survival analysis indicated that the high GNG7 expression group had better OS, while the other genes showed no significant association with OS (Fig. 10 D). Additionally, the high GNG7 expression group also exhibited better disease-specific survival (DSS) and progression-free interval (PFI) (Fig. 10 E). Logistic regression analysis demonstrated that higher GNG7 expression was associated with lower T stage and TNM stage (Table 1). Furthermore, GNG7 expression was negatively correlated with male gender and smoking status (Table 1). 6. GNG7 Is Associated with the Immune Microenvironment of Lung Adenocarcinoma Subsequently, we conducted a correlation analysis between the expression level of GNG7 and the abundance of various cell types identified through scRNA-seq. The results showed that GNG7 expression was positively correlated with the abundance of Effector CD8 + T cells, B cells, Smooth muscle cells, CD4 + T cells, Monocytes, NK cells, CD163 + Macrophages, and Endothelial cells (Fig. 11 ), while it was negatively correlated with the abundance of CD163- Macrophages, Exhausted CD8 + T cells, and Epithelial cells (Fig. 11 ). To further explore the relationship between GNG7 and the immune microenvironment of lung adenocarcinoma, we employed the Estimate, ssGSEA, and Cibersort algorithms to assess the abundance of immune cells in the TCGA-LUAD dataset and performed correlation analysis with GNG7. The results from the Estimate algorithm indicated that GNG7 expression was positively correlated with the Estimate score, immune score, and stromal score (Fig. 12 A). The ssGSEA results showed that GNG7 expression was positively correlated with the infiltration scores of Mast cells, TFH, iDC, DC, NK cells, B cells, CD8 T cells, Eosinophils, T cells, pDC, Th1 cells, Th17 cells, Macrophages, Cytotoxic cells, Treg, aDC, Tcm, Neutrophils, and NK CD56bright cells, while it was negatively correlated with Tgd cells and Th2 cells (Fig. 12 B). The Cibersort results demonstrated that GNG7 expression was positively correlated with Mast cells resting, Dendritic cells resting, T cells, CD4 memory resting, T cells regulatory (Tregs), B cells memory, Monocytes, NK cells activated, Plasma cells, and B cells naïve, while it was negatively correlated with T cells gamma delta, Macrophages M1, Mast cells activated, Eosinophils, Macrophages M0, NK cells resting, and T cells CD4 memory activated (Fig. 12 C). 7. Experimental Validation of Upregulated GNG7 Expression in Lung Adenocarcinoma Tissues To further validate the expression of GNG7 at the tissue level, we performed immunohistochemical staining on lung adenocarcinoma tissues and their corresponding adjacent tissues obtained from patients. The results demonstrated that the expression level of GNG7 was significantly higher in lung adenocarcinoma tissues compared to adjacent tissues (Fig. 13 ). Discussion LUAD is one of the most common subtypes of lung cancer and has a significant impact on cancer-related morbidity and mortality worldwide. It accounts for approximately 40% of all lung cancer cases and is associated with a poor prognosis, with a 5-year survival rate of only about 16% for patients in advanced stages [ 8 , 9 ] . The epidemiological characteristics of lung adenocarcinoma reveal a concerning prevalence, which is closely associated with factors such as age, smoking status, and exposure to environmental pollutants [ 10 – 12 ] . Extensive research has been dedicated to understanding its molecular basis, as the tumor microenvironment plays a critical role in tumor progression and treatment response. The high heterogeneity and complex tumor microenvironment of lung adenocarcinoma make it an urgent research priority to deeply dissect its immune regulatory mechanisms and develop novel therapeutic targets. Emerging studies have emphasized the importance of immune infiltration patterns, particularly tumor-associated immune cells, which are linked to both tumor progression and treatment response [ 8 , 13 ] . The complexity of the immune microenvironment in lung adenocarcinoma necessitates high-resolution analytical tools. In this study, we systematically dissected the immune microenvironment characteristics of lung adenocarcinoma by integrating multi-omics data, including single-cell transcriptomics, GWAS, and bulk RNA-seq. For the first time, we dynamically linked genetic risk with single-cell phenotypes, identifying effector CD8 + T cells as a key protective subset in lung adenocarcinoma, with their infiltration levels significantly correlated with clinical stage, gender, and prognosis. More importantly, we identified GNG7 as the core regulatory gene of this cell subset, whose expression level not only predicts patient treatment response but also reshapes the immune microenvironment. These findings provide new insights into the immune evasion mechanisms of lung adenocarcinoma and lay a theoretical foundation for developing precision treatment strategies based on immune cell subsets. This study, through multi-omics integrative analysis, reveals the pivotal role of effector CD8 + T cells and their core regulatory gene GNG7 in the immune microenvironment of lung adenocarcinoma. The scPagwas algorithm identified effector CD8 + T cells as the protective subset with the highest disease association score, a finding consistent with previous studies demonstrating that CD8 + T cells inhibit tumor progression through IFN-γ secretion and granzyme B-mediated cytotoxicity [ 14 , 15 ] . Notably, the abundance of effector CD8 + T cells was significantly correlated with sensitivity to specific targeted therapies, such as Savolitinib (a MET inhibitor), likely due to its regulation of the HGF-MET pathway crosstalk [ 16 ] . Somatic mutation analysis highlighted the particularly noteworthy differences in STK11 mutations, which were the second most common mutations (19%) in the high-abundance effector CD8 + T cell group. The AMPK pathway activated by STK11 plays a crucial role in T cell metabolism and function. AMPK activation is linked to the regulation of the mTOR complex, which integrates signals from nutrients and growth factors, thereby influencing cell metabolism and growth. When STK11 is lost or mutated, AMPK activity decreases, leading to enhanced mTOR signaling. Excessive mTOR activation creates a metabolic environment that may be insufficient to support effector T cell differentiation and function, further limiting T cell activity and tumor infiltration [ 17 , 18 ] . The interplay between STK11, AMPK, and mTOR also impacts T cell heterogeneity. The variability in T cell metabolic states shaped by AMPK and mTOR signaling can influence the phenotype and function of effector T cells, which are critical during T cell activation and differentiation [ 19 ] . The G protein γ subunit 7 (GNG7) has been observed to be downregulated in various tumor types, suggesting its potential role as a tumor suppressor and its candidacy as a biomarker in multiple cancers, including LUAD [ 20 , 21 ] . In the context of LUAD, GNG7 expression is associated with patient prognosis, making it an important factor in outcome assessment and treatment planning. Previous studies have shown that GNG7 expression is significantly lower in LUAD tissues compared to healthy tissues, while higher GNG7 levels correlate with better patient outcomes [ 22 ] . Importantly, this study identifies GNG7 as a novel biomarker whose high expression is not only associated with significantly prolonged overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) but also exhibits unique immunomodulatory features—positively correlating with anti-tumor immune cells (e.g., effector CD8 + T cells, B cells, and NK cells) and negatively correlating with pro-inflammatory CD163 + macrophages and exhausted T cells.Further analysis, from a clinical translation perspective, reveals that effector CD8 + T cell infiltration characteristics have significant prognostic stratification value. High infiltration of effector CD8 + T cells is associated with better prognosis, while high infiltration of CD163 + macrophages and epithelial cells correlates with poorer prognosis. This bidirectional regulatory pattern suggests that GNG7 may reshape the immune microenvironment by maintaining the functional integrity of effector CD8 + T cells, though its specific mechanisms require validation through gene knockout experiments to determine whether it affects T cell receptor signaling pathways or metabolic reprogramming. This indicates that GNG7 may influence the tumor microenvironment and immune response, which are key components of immunotherapy efficacy. The complex relationship between GNG7 and immune regulatory processes in the tumor microenvironment warrants further investigation, as it may provide insights into the mechanisms of LUAD progression and treatment response. Multivariate Cox analysis confirms that GNG7 is an independent prognostic factor, and its expression level is associated with lower T stage and TNM stage, providing an important supplement to the existing staging system. Risk factor analysis shows that GNG7 expression is significantly reduced in male smokers, aligning with epidemiological data indicating a higher incidence of lung adenocarcinoma in smoking males [ 10 , 23 ] .The relationship between tobacco carcinogens and GNG7 may be part of a complex network involving epigenetic modifications, immune responses, and dynamic gene expression, ultimately influencing cancer susceptibility and progression, highlighting its potential importance in future cancer research and therapeutic development. In summary, this study systematically elucidates the clinical significance of the effector CD8 + T cell subset and its key gene, GNG7, providing new insights for optimizing immunotherapy strategies in lung adenocarcinoma. Although our multi-omics integrative analysis revealed the critical roles of CD8 + T cells and GNG7 in lung adenocarcinoma, several limitations remain to be addressed. First, while retrospective analysis based on public databases can identify potential biomarkers, it lacks functional experiments to validate the molecular mechanisms by which GNG7 regulates T cell activity. Second, the relatively small sample size of scRNA-seq (n = 6) may limit the accuracy of cell subset annotation, particularly in identifying rare cell populations. Technically, although the BayesPrism algorithm effectively mapped single-cell features to larger samples, batch effects between GEO and TCGA platforms could affect quantitative accuracy, and standardized methods should be adopted in the future to reduce technical variability. Finally, the clinical translational value of GNG7 requires validation through prospective cohorts, including the establishment of standardized detection protocols and analysis of its association with immunotherapy response. These limitations suggest that follow-up studies should combine organoid models and gene-editing technologies to further explore the underlying mechanisms and expand sample sizes through multicenter collaborations. Conclusion This study systematically elucidates the protective role of CD8 + T cells and their core regulatory gene, GNG7, in the immune microenvironment of lung adenocarcinoma. Multi-omics data confirm that GNG7 is not only significantly associated with patient prognosis but also predicts drug sensitivity and reshapes immune cell composition. These findings provide new insights for immune subtyping in lung adenocarcinoma, particularly revealing potential pathways through which smoking and gender differences influence T cell function. Future research should focus on developing combination therapeutic strategies targeting GNG7 and exploring its feasibility for integration into the existing TNM staging system, ultimately translating mechanistic discoveries into clinical applications. Abbreviations Abbreviation Full Name LUAD Lung Adenocarcinoma NSCLC Non-Small Cell Lung Cancer scRNA-Seq single-cell RNA sequencing GWAS Genome-Wide Association Study DEGs Differentially Expressed Genes WGCNA Weighted Gene Co-expression Network Analysis TCGA The Cancer Genome Atlas GEO Gene Expression Omnibus MAF Mutation Annotation Format ssGSEA Single-sample Gene Set Enrichment Analysis OS Overall Survival DSS Disease-Specific Survival PFI Progression-Free Interval HRP Horseradish Peroxidase DAB Diaminobenzidine TPM Transcripts Per Kilobase Million GDSC Genomics of Drug Sensitivity in Cancer TRS Trait-Related Scores IFN- Interferon-Gamma EGFR Epidermal Growth Factor Receptor MET Mesenchymal-Epithelial Transition HGF Hepatocyte Growth Factor AMPK Adenosine Monophosphate-Activated Protein Kinase mTOR Mechanistic Target of Rapamycin PD-1 Programmed Cell Death Protein 1 PD-L1 Programmed Death-Ligand 1 TNF Tumor Necrosis Factor IL Interleukin NK Natural Killer DC Dendritic Cell Tregs Regulatory T Cells FDR False Discovery Rate PCA Principal Component Analysis UMAP Uniform Manifold Approximation and Projection t-SNE t-Distributed Stochastic Neighbor Embedding Declarations Ethics approval and consent to participate The use of human lung adenocarcinoma and adjacent normal tissues for immunohistochemistry in this study was approved by the Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University(NO.PJ[IIT-2025028-02]）. Written informed consent was obtained from all participants prior to sample collection, in compliance with the Declaration of Helsinki. Consent for publication All participants provided written consent for the publication of their de-identified clinical and experimental data included in this study. Availability of data and materials The single-cell transcriptomic dataset (GSE196303) is available from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). Transcriptomic, somatic mutation, and clinical data of TCGA-LUAD were retrieved from The Cancer Genome Atlas (TCGA) database. Lung adenocarcinoma GWAS summary data were obtained from the IEU-Open-GWAS database (https://gwas.mrcieu.ac.uk/). All data used in this study are publicly available, and no restrictions apply to their use. Competing interests The authors declare no competing interests. Funding This study received no specific funding. Authors' contributions Hanyang Liu designed the study, supervised data analysis, and drafted the manuscript. Beibei Han and Guiting Liu contributed to data collection, statistical analysis, and manuscript revision. All authors read and approved the final manuscript. Hanyang Liu is the corresponding author responsible for the overall content. Acknowledgements The authors thank the First Affiliated Hospital of Guangzhou Medical University for providing tissue samples used in immunohistochemistry experiments. We also appreciate the public databases (GEO, TCGA, IEU-Open-GWAS) for making the datasets available. References Luo Z, Ye X, Shou F, et al. RNF115-mediated ubiquitination of p53 regulates lung adenocarcinoma proliferation [J]. Biochem Biophys Res Commun, 2020, 530(2): 425–431. Wang Y, Yang X, Tian X, et al. Neoadjuvant immunotherapy plus chemotherapy achieved pathologic complete response in stage IIIB lung adenocarcinoma harbored EGFR G779F: a case report [J]. 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The screening of immune-related biomarkers for prognosis of lung adenocarcinoma [J]. Bioengineered, 2021, 12(1): 1273–1285. Zheng C, Li X, Ren Y, et al. Long Noncoding RNA RAET1K Enhances CCNE1 Expression and Cell Cycle Arrest of Lung Adenocarcinoma Cell by Sponging miRNA-135a-5p [J]. Front Genet, 2019, 10:1348. Abdul Wahab S, Hassan A, Latif M T, et al. Cluster Analysis Evaluating PM2.5, Occupation Risk and Mode of Transportation as Surrogates for Air-pollution and the Impact on Lung Cancer Diagnosis and 1-Year Mortality [J]. Asian Pac J Cancer Prev, 2019, 20(7): 1959–1965. Abdennadher M, Dahmane M H, Zair S, et al. Sex-specificity in Surgical Stages of Lung Cancer in Young Adults [J]. Open Respir Med J, 2023, 17:e187430642307140. Chien L H, Jiang H F, Tsai F Y, et al. Incidence of Lung Adenocarcinoma by Age, Sex, and Smoking Status in Taiwan [J]. JAMA Netw Open, 2023, 6(11): e2340704. Sui P, Liu X, Zhong C, et al. Integrated single-cell and bulk RNA-Seq analysis enhances prognostic accuracy of PD-1/PD-L1 immunotherapy response in lung adenocarcinoma through necroptotic anoikis gene signatures [J]. Sci Rep, 2024, 14(1): 10873. Li Z, Wu Y, Wang C, et al. Mouse CD8(+)NKT-like cells exert dual cytotoxicity against mouse tumor cells and myeloid-derived suppressor cells [J]. Cancer Immunol Immunother, 2019, 68(8): 1303–1315. Lin L, Rayman P, Pavicic P G, Jr., et al. Ex vivo conditioning with IL-12 protects tumor-infiltrating CD8(+) T cells from negative regulation by local IFN-γ [J]. Cancer Immunol Immunother, 2019, 68(3): 395–405. Moosavi F, Giovannetti E, Saso L, et al. HGF/MET pathway aberrations as diagnostic, prognostic, and predictive biomarkers in human cancers [J]. Crit Rev Clin Lab Sci, 2019, 56(8): 533–566. Zi Z, Zhang Z, Feng Q, et al. Quantitative phosphoproteomic analyses identify STK11IP as a lysosome-specific substrate of mTORC1 that regulates lysosomal acidification [J]. Nat Commun, 2022, 13(1): 1760. Monlish D A, Beezhold K J, Chiaranunt P, et al. Deletion of AMPK minimizes graft-versus-host disease through an early impact on effector donor T cells [J]. JCI Insight, 2021, 6(14): e143811. Kashiwakura J I, Saitoh K, Ihara T, et al. Expression of signal-transducing adaptor protein-1 attenuates experimental autoimmune hepatitis via down-regulating activation and homeostasis of invariant natural killer T cells [J]. PLoS One, 2020, 15(11): e0241440. Xu S, Zhang H, Liu T, et al. G Protein γ subunit 7 loss contributes to progression of clear cell renal cell carcinoma [J]. J Cell Physiol, 2019, 234(11): 20002–20012. Zheng J, Zhang W, Zhang J. Establishment of a new prognostic risk model of GNG7 pathway-related molecules in clear cell renal cell carcinoma based on immunomodulators [J]. BMC Cancer, 2023, 23(1): 864. Wei Q, Miao T, Zhang P, et al. Comprehensive analysis to identify GNG7 as a prognostic biomarker in lung adenocarcinoma correlating with immune infiltrates [J]. Front Genet, 2022, 13:984575. Wu Z, Tan F, Yang Z, et al. Sex disparity of lung cancer risk in non-smokers: a multicenter population-based prospective study based on China National Lung Cancer Screening Program [J]. Chin Med J (Engl), 2022, 135(11): 1331–1339. Table 1 Table 1 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":1438882,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of the study.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7388573/v1/bf61c8ae92d59f16f033d2c4.jpg"},{"id":92009039,"identity":"57c0e502-66a5-4998-95e6-beabc3c46a2f","added_by":"auto","created_at":"2025-09-23 15:33:40","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":413441,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell transcriptome sequencing analysis. \u003c/strong\u003eA. Transcriptomic information of each sample. B. Dendrogram for resolution selection in dimensionality reduction.\u003c/p\u003e","description":"","filename":"Figure201.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7388573/v1/c272cc92c7df6cea21028ef1.jpg"},{"id":92011815,"identity":"fb7e2988-2cff-48ea-863d-44dcd2534d03","added_by":"auto","created_at":"2025-09-23 15:49:40","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":433062,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell transcriptome sequencing combined with scPagwas analysis identifies CD8+ T cells as the core cell subset in lung adenocarcinoma. \u003c/strong\u003eA. t-SNE plot of single-cell transcriptome clustering and dimensionality reduction. B. UMAP plot of single-cell transcriptome clustering and dimensionality reduction. C. t-SNE plot of cell subset annotation based on marker genes. D. UMAP plot of cell subset annotation based on marker genes. E. TRS scores for each cell subset from scPagwas analysis. F. P-values of Bootstrap results for each cell subset in scPagwas analysis. G. Effect size estimates of each CD8+ T cell subset on lung adenocarcinoma in scPagwas analysis.\u003c/p\u003e","description":"","filename":"Figure301.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7388573/v1/71eb8bbc9578f7dda6c6997d.jpg"},{"id":92009049,"identity":"0adab304-ac44-4e82-858e-718e1ecbce14","added_by":"auto","created_at":"2025-09-23 15:33:40","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":739704,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBayesPrism algorithm. \u003c/strong\u003eA. BayesPrism algorithm evaluates the abundance of each CD8+ T cell subset in the TCGA-LUAD cohort. B. Differences in cell subset abundance between tumor and adjacent normal tissues. C. Correlation between cell subset abundance and overall survival (OS) in the TCGA-LUAD cohort.\u003c/p\u003e","description":"","filename":"Figure401.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7388573/v1/1fa6714e911f868905f9e230.jpg"},{"id":92009051,"identity":"0964487f-2701-4cb8-882a-3d6ce70b845e","added_by":"auto","created_at":"2025-09-23 15:33:40","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":305375,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell transcriptome sequencing combined with scPagwas analysis identifies effector CD8+ T cells as the core cell subset in lung adenocarcinoma. \u003c/strong\u003eA. Marker genes for each CD8+ T cell subset. B. t-SNE plot of re-clustering and dimensionality reduction of CD8+ T cells.\u003c/p\u003e\n\u003cp\u003eC. t-SNE plot of CD8+ T cell subset annotation based on marker genes. D. P-values of Bootstrap results for each CD8+ T cell subset in scPagwas analysis. E. Effect size estimates of each CD8+ T cell subset on lung adenocarcinoma in scPagwas analysis.\u003c/p\u003e","description":"","filename":"Figure501.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7388573/v1/35b580193c2bd2e1e04b1cba.jpg"},{"id":92011008,"identity":"68ffae77-8fb8-4dd5-8952-c252d8b6bff2","added_by":"auto","created_at":"2025-09-23 15:41:40","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":291475,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffector CD8+ T cells are associated with clinical features of lung adenocarcinoma.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Correlation between CD8+ T cell subsets and OS in lung adenocarcinoma. B. Correlation between CD8+ T cell subsets and clinical features of lung adenocarcinoma.\u003c/p\u003e","description":"","filename":"Figure601.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7388573/v1/687a47f2732c5afbe55f2b5d.jpg"},{"id":92009044,"identity":"76dcf6cf-3ada-41f8-b0a2-58366b8be1c3","added_by":"auto","created_at":"2025-09-23 15:33:40","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":185496,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffector CD8+ T cells are associated with chemotherapy sensitivity. \u003c/strong\u003eA. Top 5 chemotherapy drugs sensitive to the high-abundance effector CD8+ T cell group. B. Top 5 chemotherapy drugs sensitive to the low-abundance effector CD8+ T cell group.\u003c/p\u003e","description":"","filename":"Figure701.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7388573/v1/ed509303996566a4bf736102.jpg"},{"id":92009046,"identity":"bf8a3861-b2d1-48f7-83e3-5df91bb5f40d","added_by":"auto","created_at":"2025-09-23 15:33:40","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":285475,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffector CD8+ T cells are associated with somatic mutations. \u003c/strong\u003eA-B. Somatic mutation landscape in the TCGA-LUAD cohort. C-D. Differences in somatic mutations between high and low effector CD8+ T cell groups in the TCGA-LUAD cohort.\u003c/p\u003e","description":"","filename":"Figure801.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7388573/v1/04402179c2e6d34b58d317c6.jpg"},{"id":92009041,"identity":"00b99949-c243-4ed2-9e8f-98f820cbacb8","added_by":"auto","created_at":"2025-09-23 15:33:40","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":465508,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWGCNA analysis identifies genes associated with effector CD8+ T cells. \u003c/strong\u003eA. WGCNA analysis dendrogram. B. WGCNA correlation heatmap. C. Intersection of core genes, differentially expressed genes between lung adenocarcinoma and normal tissues, and effector CD8+ T cell marker genes.\u003c/p\u003e","description":"","filename":"Figure901.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7388573/v1/4e1b047be4db0bba38fc477a.jpg"},{"id":92011814,"identity":"a81e5524-7acb-481d-a501-8c8a5a6f5ad8","added_by":"auto","created_at":"2025-09-23 15:49:40","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":231115,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of GNG7 as a core gene associated with effector CD8+ T cells. \u003c/strong\u003eA. Univariate Cox analysis of genes associated with effector CD8+ T cells. B. Multivariate Cox analysis of genes associated with effector CD8+ T cells. C. Differential expression of ID3, SH3BP5, JUNB, and GNG7 between lung adenocarcinoma and adjacent normal tissues. D. KM survival analysis based on OS for high and low expression groups of ID3, SH3BP5, JUNB, and GNG7. E. KM survival analysis based on DSS and PFI for high and low expression groups of GNG7.\u003c/p\u003e","description":"","filename":"Figure1001.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7388573/v1/a0d4ec5535cd14bf21eaf9da.jpg"},{"id":92011013,"identity":"9d8ed96a-7d7e-4238-a199-fb2b931d214d","added_by":"auto","created_at":"2025-09-23 15:41:40","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":303697,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis between GNG7 expression and cell subset abundance based on the BayesPrism algorithm.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1101.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7388573/v1/9891303aa94d499daf3063d3.jpg"},{"id":92012969,"identity":"94720177-7ab1-4403-b031-446b2c110d37","added_by":"auto","created_at":"2025-09-23 15:57:40","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":295746,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between GNG7 expression and immune cell infiltration. \u003c/strong\u003eA. Correlation between GNG7 expression and ESTIMATE scores. B. Correlation between GNG7 expression and immune cell scores calculated using ssGSEA. C. Correlation between GNG7 expression and immune cell scores calculated using CIBERSORT.\u003c/p\u003e","description":"","filename":"Figure1201.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7388573/v1/d8e66ce2cd6366725a658f9e.jpg"},{"id":92011007,"identity":"17818bd6-291e-44cd-9e4a-d9ca35064dda","added_by":"auto","created_at":"2025-09-23 15:41:40","extension":"jpg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":122728,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmunohistochemistry showing GNG7 expression in lung adenocarcinoma and adjacent normal tissues.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1301.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7388573/v1/9c63bd78571845015b822c3f.jpg"},{"id":95655205,"identity":"570da2bb-e54b-43e3-9715-9e330f99c2a8","added_by":"auto","created_at":"2025-11-11 16:14:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6906343,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7388573/v1/8d747399-c1f0-4149-a8c7-cd73608e5381.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"scRNA-Seq Combined with scPagwas Analysis Identifies GNG7 as a Core Gene in Lung Adenocarcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung adenocarcinoma (LUAD) has emerged as the most common subtype of non-small cell lung cancer (NSCLC), accounting for 40% of global lung cancer cases, with over 2\u0026nbsp;million new cases reported annually. Despite significant advancements in targeted therapy and immunotherapy, the overall survival rate for LUAD patients remains concerning, with a 5-year survival rate still below 20%\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. This poor prognosis is primarily attributed to treatment resistance caused by tumor heterogeneity, as well as the low response rate to immunotherapy (only 20%-30%)\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. This pressing clinical situation underscores the urgency of delving deeper into the regulatory mechanisms of the immune microenvironment in lung adenocarcinoma, particularly in exploring novel biomarkers and therapeutic targets with clinical translational potential. CD8\u0026thinsp;+\u0026thinsp;T cells play a pivotal role in anti-tumor immunity, especially in LUAD. They exert their cytotoxic effects by releasing cytotoxic molecules such as perforin and granzymes, thereby inducing apoptosis in cancer cells\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. However, the functional state and efficacy of CD8\u0026thinsp;+\u0026thinsp;T cells are significantly influenced by the tumor microenvironment. In LUAD, CD8\u0026thinsp;+\u0026thinsp;T cells often exhibit dysfunction and heterogeneity, complicating their role in anti-tumor immunity\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Recent advancements in single-cell sequencing technology have provided crucial insights into the cellular composition and functional states of immune cells, particularly CD8\u0026thinsp;+\u0026thinsp;T cells, within this microenvironment. Research indicates that the phenotypic differentiation of CD8\u0026thinsp;+\u0026thinsp;T cells is closely associated with patient prognosis, suggesting that they may serve as key targets for therapeutic interventions aimed at addressing immune evasion in LUAD\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Current research faces several limitations. Firstly, traditional immune infiltration analysis methods struggle to overcome the impact of tissue heterogeneity on the precise quantification of cellular subpopulations. Secondly, the mechanistic links between genetic risk loci identified by GWAS and the functions of specific immune cells have not been established. Lastly, there is a lack of an analytical framework that integrates multi-omics data to systematically evaluate the clinical value of immune cells. To address these challenges, this study innovatively adopts a multi-omics integration strategy. By employing the scPagwas algorithm to correlate single-cell transcriptomics (scRNA-seq) with GWAS data, the study aims to dissect the associations between cell subtypes and disease. Additionally, the application of BayesPrism technology enables the precise mapping of single-cell characteristics to large samples. This approach not only identifies disease-related cell subpopulations but also elucidates their molecular regulatory foundations. Compared to traditional immune infiltration analysis, this method overcomes the limitations imposed by tissue heterogeneity and clarifies the clinical value of specific cell subpopulations. This study is expected to provide a new theoretical foundation and intervention strategies for the personalized treatment of LUAD, particularly by offering molecular targets to improve the efficacy of immunotherapy. The innovative analytical framework employed in this research also serves as a methodological reference for studying the immune microenvironment in other malignancies.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003ch3\u003e1.Data Sources\u003c/h3\u003e\n\u003cp\u003eThe dataset with the accession number GSE196303 was downloaded from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This dataset includes single-cell transcriptomic data from 3 adjacent normal tissues and 3 lung adenocarcinoma (LUAD) tissues. Transcriptomic data, somatic mutation data, and clinical data from the TCGA-LUAD project were downloaded from the TCGA database, including 541 LUAD samples and 59 adjacent normal tissue samples. Samples with incomplete survival information or survival days\u0026thinsp;\u0026lt;\u0026thinsp;30 were excluded during survival analysis and model construction. Genome-wide association study (GWAS) summary data for LUAD were downloaded from the IEU-Open-GWAS database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), including 1002 LUAD cases and 462008 healthy controls.\u003c/p\u003e\n\u003ch3\u003e2. scRNA-seq Data Analysis\u003c/h3\u003e\n\u003cp\u003eThe scRNA-seq dataset GSE196303 was analyzed using the standard workflow in the \"Seurat\" R package. Cells with fewer than 200 genes or mitochondrial gene content exceeding 15% were filtered out. The \"harmony\" R package was used to reduce batch effects between samples. The \"FindVariableFeatures\" function identified the top 2000 variable genes, and principal component analysis (PCA) was used for dimensionality reduction. Marker genes were identified using the default parameters of the FindMarkers function. Cell subpopulations were annotated using the CellMarker2.0 database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://117.50.127.228/CellMarker/\u003c/span\u003e\u003cspan address=\"http://117.50.127.228/CellMarker/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003e3.scPagwas\u003c/h3\u003e\n\u003cp\u003escPagwas employs a polygenic regression model to prioritize trait-associated genes and identify trait-relevant cell subpopulations by integrating pathway activity-transformed scRNA-seq data with GWAS summary data. In this study, the \"scPagwas\" R package was used to identify key cell subpopulations in lung adenocarcinoma.\u003c/p\u003e\n\u003ch3\u003e4.BayesPrism\u003c/h3\u003e\n\u003cp\u003eBayesPrism, a cutting-edge Bayesian model-based technique, ingeniously integrates scRNA-seq as a reference profile into Bulk RNA-seq, enabling cell type scoring and inference of posterior distributions of gene expression. In this study, the \"BayesPrism\" R package was utilized to project the cell subtypes identified from lung adenocarcinoma scRNA-seq data onto the Bulk RNA-seq data from the TCGA-LUAD project, and to score each cell subtype.\u003c/p\u003e\n\u003ch3\u003e5.Bulk RNA-seq Differential Expression Analysis\u003c/h3\u003e\n\u003cp\u003eLimma (Linear Models for Microarray Data, DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkv007\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkv007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a differential expression screening method based on generalized linear models. Here, we used the R package limma (version 3.40.6) to perform differential analysis to identify differentially expressed genes (DEGs) between different comparison groups and the control group. DEGs were defined using a threshold of fold change\u0026thinsp;\u0026gt;\u0026thinsp;2 and adjusted P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The results were visualized using the \"ggplot\" R package, presented as volcano plots and heatmaps.\u003c/p\u003e\n\u003ch3\u003e6. Weighted Gene Co-expression Network Analysis (WGCNA)\u003c/h3\u003e\n\u003cp\u003eWe used the WGCNA package in R to construct a co-expression network. First, sample clustering was performed to assess the presence of significant outliers. Second, the automatic network construction function was employed to build the co-expression network. The R function `pickSoftThreshold` was used to calculate the soft thresholding power β, which was then applied to compute the adjacency matrix based on co-expression similarity. Third, hierarchical clustering and the dynamic tree cut function were utilized to detect modules. Fourth, gene significance and module membership were calculated, and modules were correlated with trait cell content. The gene information from the relevant modules was extracted for further analysis.\u003c/p\u003e\n\u003ch3\u003e7. Somatic Mutation Analysis\u003c/h3\u003e\n\u003cp\u003eSomatic mutation analysis was performed using the R package maftools. The `read.maf` function was used to read the Mutation Annotation Format (MAF) file and construct a MAF object. Statistical information such as mutation burden, variant type classification, and frequently mutated genes was extracted using `getSampleSummary` and `getGeneSummary`. The mutation landscape was visualized using oncoplots to display the distribution of frequently mutated genes and their variant types. The mutation frequencies of genes were compared between the two groups.\u003c/p\u003e\n\u003ch3\u003e8. Immune Infiltration Analysis\u003c/h3\u003e\n\u003cp\u003eGene expression data from lung adenocarcinoma tissues were normalized using log2(TPM\u0026thinsp;+\u0026thinsp;1) and analyzed to decipher the immune microenvironment using the following methods: 1) Single-sample Gene Set Enrichment Analysis (ssGSEA): The R package GSVA (v1.48.1) was employed based on immune-related gene sets from the MSigDB database (C7 category, containing 4879 immune signatures). Sample-specific enrichment scores were calculated with parameters set as `method = \"ssgsea\"` and `tau\u0026thinsp;=\u0026thinsp;0.25`. 2) Immune Cell Proportion Quantification: The CIBERSORT algorithm was applied using the LM22 signature matrix (22 immune cell marker genes) via an R script. A permutation test with 1000 iterations and quantile normalization was performed, and samples with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were selected to exclude low-quality deconvolution results. Both methods were combined to evaluate the immune phenotype of lung adenocarcinoma, and the enrichment scores and immune cell proportions were output for further analysis. Additionally, the immune score in the tumor microenvironment of lung adenocarcinoma was assessed using the ESTIMATE algorithm.\u003c/p\u003e\n\u003ch3\u003e9. Chemotherapy Sensitivity Analysis\u003c/h3\u003e\n\u003cp\u003eBased on TCGA-LUAD gene expression data, the R package oncoPredict was used to evaluate the sensitivity of tumor samples to chemotherapy drugs. The `calcPhenotype` function was employed to load the GDSC (Genomics of Drug Sensitivity in Cancer) training set model, and the Drug Sensitivity Score (DSS) was calculated for each sample.\u003c/p\u003e\n\u003ch3\u003e10. Immunohistochemistry\u003c/h3\u003e\n\u003cp\u003eCancer and adjacent tissues from three lung adenocarcinoma cases were obtained from the First Affiliated Hospital of Guangzhou Medical University. Freshly collected lung adenocarcinoma and corresponding adjacent tissues were fixed in 4% paraformaldehyde overnight, followed by dehydration and embedding in paraffin. Paraffin-embedded samples were sectioned into 4 \u0026micro;m thick slices. Antigen retrieval was performed in a pressure cooker using Tris-EDTA buffer (pH\u0026thinsp;=\u0026thinsp;8.0) for 2 minutes. Endogenous peroxidase activity was blocked with 3% hydrogen peroxide. The sections were incubated with 10% goat serum at 37\u0026deg;C for 30 minutes to block nonspecific binding. Subsequently, the sections were incubated with the primary antibody GNG7 (Affinity; DF9562; 1:200) at 4\u0026deg;C overnight. After washing, the sections were incubated with a horseradish peroxidase (HRP)-labeled secondary antibody at 37\u0026deg;C for 30 minutes. Finally, the sections were incubated with diaminobenzidine (DAB) for 5\u0026ndash;10 minutes for color development.\u003c/p\u003e\n\u003ch3\u003e10. Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eFor comparisons between two groups, a t-test or Mann-Whitney U test was selected based on whether the data followed a normal distribution. Comparisons among more than two groups were performed using the Kruskal-Wallis test. Correlation analysis was conducted using Spearman's method. Survival analysis was performed using Kaplan-Meier survival analysis and log-rank test, as well as univariate Cox analysis. All statistical analyses were conducted in R software, and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant (*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.005, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ns: p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003e1. scRNA-Seq Analysis Combined with scPagwas Identifies Core Cells in Lung Adenocarcinoma\u003c/h3\u003e\n\u003cp\u003eTo better understand the immune microenvironment landscape of lung adenocarcinoma at the single-cell level, we performed scRNA-seq analysis using public data (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A total of 25 cell clusters were identified in both the control and lung adenocarcinoma groups (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). After annotation, these clusters were classified into 11 cell subtypes: CD8\u0026thinsp;+\u0026thinsp;T cells, CD163\u0026thinsp;+\u0026thinsp;macrophages, NK cells, monocytes, epithelial cells, CD4\u0026thinsp;+\u0026thinsp;T cells, CD163- macrophages, endothelial cells, B cells, fibroblasts, and smooth muscle cells (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Subsequently, we conducted scPagwas analysis combined with GWAS summary data to calculate the trait-related score (TRS) for each cell subtype. The results showed that among these 11 cell types, CD8\u0026thinsp;+\u0026thinsp;T cells had significantly higher TRS scores compared to other cells, followed by NK cells and monocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Bootstrap analysis revealed that CD8\u0026thinsp;+\u0026thinsp;T cells were negatively associated with lung adenocarcinoma, while CD4\u0026thinsp;+\u0026thinsp;T cells and smooth muscle cells were also negatively correlated with lung adenocarcinoma (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These findings suggest that CD8\u0026thinsp;+\u0026thinsp;T cells, CD4\u0026thinsp;+\u0026thinsp;T cells, and smooth muscle cells are potential core cell subpopulations in lung adenocarcinoma.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e2. CD8 + T Cells as Core Cells Influencing Lung Adenocarcinoma Are Associated with Prognosis\u003c/h3\u003e\n\u003cp\u003eSubsequently, in the TCGA-LUAD dataset, the 11 cell subtypes identified in the scRNA-seq data were quantified using the BayesPrism algorithm (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), and their differences between tumor and control tissues were explored. The results showed that compared to the control group, the abundance of CD8\u0026thinsp;+\u0026thinsp;T cells, epithelial cells, CD163- macrophages, and fibroblasts was upregulated in the lung adenocarcinoma group, while the abundance of NK cells, monocytes, CD4\u0026thinsp;+\u0026thinsp;T cells, endothelial cells, B cells, and smooth muscle cells was downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Prognostic analysis revealed that B cells, smooth muscle cells, NK cells, epithelial cells, monocytes, CD163- macrophages, CD4\u0026thinsp;+\u0026thinsp;T cells, and CD8\u0026thinsp;+\u0026thinsp;T cells were significantly associated with the prognosis of lung adenocarcinoma patients. Specifically, higher abundances of B cells, smooth muscle cells, NK cells, monocytes, CD4\u0026thinsp;+\u0026thinsp;T cells, and CD8\u0026thinsp;+\u0026thinsp;T cells were associated with better prognosis, while higher abundances of epithelial cells and CD163- macrophages were associated with worse prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Based on the scPagwas analysis and BayesPrism algorithm, we found that CD8\u0026thinsp;+\u0026thinsp;T cells significantly contributed to lung adenocarcinoma and were negatively correlated with its occurrence and prognosis. Therefore, we selected CD8\u0026thinsp;+\u0026thinsp;T cells for further analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e3. Identification of Effector CD8 + T Cells as Core Cells Influencing Clinical Characteristics of Lung Adenocarcinoma\u003c/h3\u003e\n\u003cp\u003eSubsequently, we extracted CD8\u0026thinsp;+\u0026thinsp;T cells for further clustering analysis. A total of 14 clusters of CD8\u0026thinsp;+\u0026thinsp;T cell subtypes were identified in both groups, and these clusters were classified into three subtypes based on marker genes: Cytotoxic T cells, Effector CD8\u0026thinsp;+\u0026thinsp;T cells, and Exhausted CD8\u0026thinsp;+\u0026thinsp;T cells (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). The scPagwas analysis results showed that among these three types of CD8\u0026thinsp;+\u0026thinsp;T cells, Effector CD8\u0026thinsp;+\u0026thinsp;T cells had significantly higher TRS scores compared to the other two subtypes and were associated with lung adenocarcinoma (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the TCGA-LUAD dataset, the three CD8\u0026thinsp;+\u0026thinsp;T cell subtypes were quantified using the BayesPrism algorithm, and survival analysis was performed. The results indicated that a higher abundance of Effector CD8\u0026thinsp;+\u0026thinsp;T cells was associated with better prognosis in lung adenocarcinoma patients, while the abundance of Cytotoxic T cells and Exhausted CD8\u0026thinsp;+\u0026thinsp;T cells was not correlated with prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Compared to female lung adenocarcinoma patients, male patients exhibited lower abundance of Effector CD8\u0026thinsp;+\u0026thinsp;T cells. Compared to T1 stage lung adenocarcinoma patients, T2 and T3 stage patients had lower abundance of Effector CD8\u0026thinsp;+\u0026thinsp;T cells. Compared to N0 stage patients, N2 stage patients showed lower abundance of Effector CD8\u0026thinsp;+\u0026thinsp;T cells. Additionally, compared to TNM stage I patients, stage III patients had lower abundance of Effector CD8\u0026thinsp;+\u0026thinsp;T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e4. Infiltration of Effector CD8\u0026thinsp;+\u0026thinsp;T Cells Is Associated with Drug Sensitivity and Somatic Mutations in Lung Adenocarcinoma\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGiven the association between the infiltration of Effector CD8\u0026thinsp;+\u0026thinsp;T cells and the clinical characteristics of lung adenocarcinoma, we indirectly explored whether the infiltration of Effector CD8\u0026thinsp;+\u0026thinsp;T cells is related to drug sensitivity in lung adenocarcinoma patients. Compared to the low infiltration group, patients in the high infiltration group showed greater sensitivity to drugs such as Savolitinib, Lapatinib, Staurosporine, Gefitinib, and Dasatinib (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA), while exhibiting higher tolerance to drugs such as Ribociclib, Doramapimod, Dihydrorotenone, Uprosertib, and Oxaliplatin (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSomatic mutation analysis revealed that among the 557 samples with available somatic mutation information, 513 patients had somatic mutations. TP53 mutations were the most common in lung adenocarcinoma, with a mutation rate of 42%, predominantly as Multi Hit mutations. This was followed by TTN mutations (38%), mainly Missense Mutations and Multi_Hit mutations, and MUC16 mutations (35%), also primarily Missense Mutations and Multi_Hit mutations (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). Both the low and high Effector CD8\u0026thinsp;+\u0026thinsp;T cell infiltration groups showed TP53 as the most frequent mutation (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). COL5A2 was the second most common mutation gene in the low infiltration group (14%), while STK11 was the second most common mutation gene in the high infiltration group (19%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD illustrates the differences in gene mutation frequencies between the high and low Effector CD8\u0026thinsp;+\u0026thinsp;T cell infiltration groups.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e5. Identification of GNG7 as a Core Gene Associated with Effector CD8 + T Cells\u003c/h3\u003e\n\u003cp\u003eGiven the potential critical role of Effector CD8\u0026thinsp;+\u0026thinsp;T cells in lung adenocarcinoma, this study aimed to identify core genes associated with Effector CD8\u0026thinsp;+\u0026thinsp;T cells. WGCNA analysis divided the genes from TCGA-LUAD into 17 co-expression modules based on gene expression profiles (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). Correlation analysis revealed that the MEred module (correlation\u0026thinsp;=\u0026thinsp;0.38) was significantly positively correlated with the infiltration score of Effector CD8\u0026thinsp;+\u0026thinsp;T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). By intersecting the 1307 genes in the MEtan module, the 14,637 genes obtained from bulk transcriptome differential expression analysis, and the marker genes of Effector CD8\u0026thinsp;+\u0026thinsp;T cells from single-cell transcriptome data, 28 genes were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eUnivariate Cox analysis showed that among these 28 genes, PHACTR1, ID3, SH3BP5, PIK3R1, IL7R, JUNB, GNG7, and GIMAP7 were associated with the prognosis of lung adenocarcinoma patients. Specifically, higher expression levels of GNG7, SH3BP5, PHACTR1, GIMAP7, PIK3R1, and IL7R were associated with better overall survival (OS), while higher expression levels of JUNB and ID3 were associated with worse OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA). Multivariate Cox analysis, incorporating the genes identified in the univariate Cox analysis, revealed that GNG7, SH3BP5, JUNB, and ID3 remained significantly associated with the prognosis of lung adenocarcinoma patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB). Compared to control tissues, the expression levels of GNG7, SH3BP5, JUNB, and ID3 were significantly lower in lung adenocarcinoma tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eC). When genes were divided into high and low expression groups based on median expression levels, survival analysis indicated that the high GNG7 expression group had better OS, while the other genes showed no significant association with OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eD). Additionally, the high GNG7 expression group also exhibited better disease-specific survival (DSS) and progression-free interval (PFI) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eE). Logistic regression analysis demonstrated that higher GNG7 expression was associated with lower T stage and TNM stage (Table\u0026nbsp;1). Furthermore, GNG7 expression was negatively correlated with male gender and smoking status (Table\u0026nbsp;1).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e6. GNG7 Is Associated with the Immune Microenvironment of Lung Adenocarcinoma\u003c/h3\u003e\n\u003cp\u003eSubsequently, we conducted a correlation analysis between the expression level of GNG7 and the abundance of various cell types identified through scRNA-seq.\u0026nbsp;The results showed that GNG7 expression was positively correlated with the abundance of Effector CD8\u0026thinsp;+\u0026thinsp;T cells, B cells, Smooth muscle cells, CD4\u0026thinsp;+\u0026thinsp;T cells, Monocytes, NK cells, CD163\u0026thinsp;+\u0026thinsp;Macrophages, and Endothelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e), while it was negatively correlated with the abundance of CD163- Macrophages, Exhausted CD8\u0026thinsp;+\u0026thinsp;T cells, and Epithelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo further explore the relationship between GNG7 and the immune microenvironment of lung adenocarcinoma, we employed the Estimate, ssGSEA, and Cibersort algorithms to assess the abundance of immune cells in the TCGA-LUAD dataset and performed correlation analysis with GNG7. The results from the Estimate algorithm indicated that GNG7 expression was positively correlated with the Estimate score, immune score, and stromal score (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eA). The ssGSEA results showed that GNG7 expression was positively correlated with the infiltration scores of Mast cells, TFH, iDC, DC, NK cells, B cells, CD8 T cells, Eosinophils, T cells, pDC, Th1 cells, Th17 cells, Macrophages, Cytotoxic cells, Treg, aDC, Tcm, Neutrophils, and NK CD56bright cells, while it was negatively correlated with Tgd cells and Th2 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eB). The Cibersort results demonstrated that GNG7 expression was positively correlated with Mast cells resting, Dendritic cells resting, T cells, CD4 memory resting, T cells regulatory (Tregs), B cells memory, Monocytes, NK cells activated, Plasma cells, and B cells na\u0026iuml;ve, while it was negatively correlated with T cells gamma delta, Macrophages M1, Mast cells activated, Eosinophils, Macrophages M0, NK cells resting, and T cells CD4 memory activated (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e7. Experimental Validation of Upregulated GNG7 Expression in Lung Adenocarcinoma Tissues\u003c/h3\u003e\n\u003cp\u003eTo further validate the expression of GNG7 at the tissue level, we performed immunohistochemical staining on lung adenocarcinoma tissues and their corresponding adjacent tissues obtained from patients. The results demonstrated that the expression level of GNG7 was significantly higher in lung adenocarcinoma tissues compared to adjacent tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eLUAD is one of the most common subtypes of lung cancer and has a significant impact on cancer-related morbidity and mortality worldwide. It accounts for approximately 40% of all lung cancer cases and is associated with a poor prognosis, with a 5-year survival rate of only about 16% for patients in advanced stages\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. The epidemiological characteristics of lung adenocarcinoma reveal a concerning prevalence, which is closely associated with factors such as age, smoking status, and exposure to environmental pollutants\u003csup\u003e[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Extensive research has been dedicated to understanding its molecular basis, as the tumor microenvironment plays a critical role in tumor progression and treatment response. The high heterogeneity and complex tumor microenvironment of lung adenocarcinoma make it an urgent research priority to deeply dissect its immune regulatory mechanisms and develop novel therapeutic targets. Emerging studies have emphasized the importance of immune infiltration patterns, particularly tumor-associated immune cells, which are linked to both tumor progression and treatment response\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. The complexity of the immune microenvironment in lung adenocarcinoma necessitates high-resolution analytical tools. In this study, we systematically dissected the immune microenvironment characteristics of lung adenocarcinoma by integrating multi-omics data, including single-cell transcriptomics, GWAS, and bulk RNA-seq.\u0026nbsp;For the first time, we dynamically linked genetic risk with single-cell phenotypes, identifying effector CD8\u0026thinsp;+\u0026thinsp;T cells as a key protective subset in lung adenocarcinoma, with their infiltration levels significantly correlated with clinical stage, gender, and prognosis. More importantly, we identified GNG7 as the core regulatory gene of this cell subset, whose expression level not only predicts patient treatment response but also reshapes the immune microenvironment. These findings provide new insights into the immune evasion mechanisms of lung adenocarcinoma and lay a theoretical foundation for developing precision treatment strategies based on immune cell subsets.\u003c/p\u003e\u003cp\u003eThis study, through multi-omics integrative analysis, reveals the pivotal role of effector CD8\u0026thinsp;+\u0026thinsp;T cells and their core regulatory gene GNG7 in the immune microenvironment of lung adenocarcinoma. The scPagwas algorithm identified effector CD8\u0026thinsp;+\u0026thinsp;T cells as the protective subset with the highest disease association score, a finding consistent with previous studies demonstrating that CD8\u0026thinsp;+\u0026thinsp;T cells inhibit tumor progression through IFN-γ secretion and granzyme B-mediated cytotoxicity\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Notably, the abundance of effector CD8\u0026thinsp;+\u0026thinsp;T cells was significantly correlated with sensitivity to specific targeted therapies, such as Savolitinib (a MET inhibitor), likely due to its regulation of the HGF-MET pathway crosstalk\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Somatic mutation analysis highlighted the particularly noteworthy differences in STK11 mutations, which were the second most common mutations (19%) in the high-abundance effector CD8\u0026thinsp;+\u0026thinsp;T cell group. The AMPK pathway activated by STK11 plays a crucial role in T cell metabolism and function. AMPK activation is linked to the regulation of the mTOR complex, which integrates signals from nutrients and growth factors, thereby influencing cell metabolism and growth. When STK11 is lost or mutated, AMPK activity decreases, leading to enhanced mTOR signaling. Excessive mTOR activation creates a metabolic environment that may be insufficient to support effector T cell differentiation and function, further limiting T cell activity and tumor infiltration\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. The interplay between STK11, AMPK, and mTOR also impacts T cell heterogeneity. The variability in T cell metabolic states shaped by AMPK and mTOR signaling can influence the phenotype and function of effector T cells, which are critical during T cell activation and differentiation\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. The G protein γ subunit 7 (GNG7) has been observed to be downregulated in various tumor types, suggesting its potential role as a tumor suppressor and its candidacy as a biomarker in multiple cancers, including LUAD\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. In the context of LUAD, GNG7 expression is associated with patient prognosis, making it an important factor in outcome assessment and treatment planning. Previous studies have shown that GNG7 expression is significantly lower in LUAD tissues compared to healthy tissues, while higher GNG7 levels correlate with better patient outcomes\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Importantly, this study identifies GNG7 as a novel biomarker whose high expression is not only associated with significantly prolonged overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) but also exhibits unique immunomodulatory features\u0026mdash;positively correlating with anti-tumor immune cells (e.g., effector CD8\u0026thinsp;+\u0026thinsp;T cells, B cells, and NK cells) and negatively correlating with pro-inflammatory CD163\u0026thinsp;+\u0026thinsp;macrophages and exhausted T cells.Further analysis, from a clinical translation perspective, reveals that effector CD8\u0026thinsp;+\u0026thinsp;T cell infiltration characteristics have significant prognostic stratification value. High infiltration of effector CD8\u0026thinsp;+\u0026thinsp;T cells is associated with better prognosis, while high infiltration of CD163\u0026thinsp;+\u0026thinsp;macrophages and epithelial cells correlates with poorer prognosis. This bidirectional regulatory pattern suggests that GNG7 may reshape the immune microenvironment by maintaining the functional integrity of effector CD8\u0026thinsp;+\u0026thinsp;T cells, though its specific mechanisms require validation through gene knockout experiments to determine whether it affects T cell receptor signaling pathways or metabolic reprogramming. This indicates that GNG7 may influence the tumor microenvironment and immune response, which are key components of immunotherapy efficacy. The complex relationship between GNG7 and immune regulatory processes in the tumor microenvironment warrants further investigation, as it may provide insights into the mechanisms of LUAD progression and treatment response. Multivariate Cox analysis confirms that GNG7 is an independent prognostic factor, and its expression level is associated with lower T stage and TNM stage, providing an important supplement to the existing staging system. Risk factor analysis shows that GNG7 expression is significantly reduced in male smokers, aligning with epidemiological data indicating a higher incidence of lung adenocarcinoma in smoking males\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.The relationship between tobacco carcinogens and GNG7 may be part of a complex network involving epigenetic modifications, immune responses, and dynamic gene expression, ultimately influencing cancer susceptibility and progression, highlighting its potential importance in future cancer research and therapeutic development.\u003c/p\u003e\u003cp\u003eIn summary, this study systematically elucidates the clinical significance of the effector CD8\u0026thinsp;+\u0026thinsp;T cell subset and its key gene, GNG7, providing new insights for optimizing immunotherapy strategies in lung adenocarcinoma. Although our multi-omics integrative analysis revealed the critical roles of CD8\u0026thinsp;+\u0026thinsp;T cells and GNG7 in lung adenocarcinoma, several limitations remain to be addressed. First, while retrospective analysis based on public databases can identify potential biomarkers, it lacks functional experiments to validate the molecular mechanisms by which GNG7 regulates T cell activity. Second, the relatively small sample size of scRNA-seq (n\u0026thinsp;=\u0026thinsp;6) may limit the accuracy of cell subset annotation, particularly in identifying rare cell populations. Technically, although the BayesPrism algorithm effectively mapped single-cell features to larger samples, batch effects between GEO and TCGA platforms could affect quantitative accuracy, and standardized methods should be adopted in the future to reduce technical variability. Finally, the clinical translational value of GNG7 requires validation through prospective cohorts, including the establishment of standardized detection protocols and analysis of its association with immunotherapy response. These limitations suggest that follow-up studies should combine organoid models and gene-editing technologies to further explore the underlying mechanisms and expand sample sizes through multicenter collaborations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study systematically elucidates the protective role of CD8\u0026thinsp;+\u0026thinsp;T cells and their core regulatory gene, GNG7, in the immune microenvironment of lung adenocarcinoma. Multi-omics data confirm that GNG7 is not only significantly associated with patient prognosis but also predicts drug sensitivity and reshapes immune cell composition. These findings provide new insights for immune subtyping in lung adenocarcinoma, particularly revealing potential pathways through which smoking and gender differences influence T cell function. Future research should focus on developing combination therapeutic strategies targeting GNG7 and exploring its feasibility for integration into the existing TNM staging system, ultimately translating mechanistic discoveries into clinical applications.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAbbreviation\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFull Name\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLUAD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLung Adenocarcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNSCLC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNon-Small Cell Lung Cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003escRNA-Seq\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003esingle-cell RNA sequencing\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGWAS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGenome-Wide Association Study\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDifferentially Expressed Genes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWGCNA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWeighted Gene Co-expression Network Analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTCGA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eThe Cancer Genome Atlas\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGEO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGene Expression Omnibus\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMAF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMutation Annotation Format\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003essGSEA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSingle-sample Gene Set Enrichment Analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOverall Survival\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDSS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDisease-Specific Survival\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePFI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eProgression-Free Interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHRP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHorseradish Peroxidase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDAB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDiaminobenzidine\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTPM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTranscripts Per Kilobase Million\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGDSC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGenomics of Drug Sensitivity in Cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTRS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTrait-Related Scores\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIFN-\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInterferon-Gamma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEGFR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEpidermal Growth Factor Receptor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMET\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMesenchymal-Epithelial Transition\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHGF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHepatocyte Growth Factor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAMPK\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAdenosine Monophosphate-Activated Protein Kinase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003emTOR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMechanistic Target of Rapamycin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePD-1\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eProgrammed Cell Death Protein 1\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePD-L1\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eProgrammed Death-Ligand 1\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTNF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTumor Necrosis Factor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInterleukin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNK\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNatural Killer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDendritic Cell\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTregs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRegulatory T Cells\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFalse Discovery Rate\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePrincipal Component Analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUMAP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUniform Manifold Approximation and Projection\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003et-SNE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003et-Distributed Stochastic Neighbor Embedding\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe use of human lung adenocarcinoma and adjacent normal tissues for immunohistochemistry in this study was approved by the Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University(NO.PJ[IIT-2025028-02]）. Written informed consent was obtained from all participants prior to sample collection, in compliance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided written consent for the publication of their de-identified clinical and experimental data included in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe single-cell transcriptomic dataset (GSE196303) is available from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). Transcriptomic, somatic mutation, and clinical data of TCGA-LUAD were retrieved from The Cancer Genome Atlas (TCGA) database. Lung adenocarcinoma GWAS summary data were obtained from the IEU-Open-GWAS database (https://gwas.mrcieu.ac.uk/). All data used in this study are publicly available, and no restrictions apply to their use.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received no specific funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHanyang Liu designed the study, supervised data analysis, and drafted the manuscript. Beibei Han and Guiting Liu contributed to data collection, statistical analysis, and manuscript revision. All authors read and approved the final manuscript. Hanyang Liu is the corresponding author responsible for the overall content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the First Affiliated Hospital of Guangzhou Medical University for providing tissue samples used in immunohistochemistry experiments. We also appreciate the public databases (GEO, TCGA, IEU-Open-GWAS) for making the datasets available.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLuo Z, Ye X, Shou F, et al. RNF115-mediated ubiquitination of p53 regulates lung adenocarcinoma proliferation [J]. Biochem Biophys Res Commun, 2020, 530(2): 425\u0026ndash;431.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Y, Yang X, Tian X, et al. Neoadjuvant immunotherapy plus chemotherapy achieved pathologic complete response in stage IIIB lung adenocarcinoma harbored EGFR G779F: a case report [J]. Ann Palliat Med, 2020, 9(6): 4339\u0026ndash;4345.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKolb T, M\u0026uuml;ller S, M\u0026ouml;ller P, et al. Molecular heterogeneity in histomorphologic subtypes of lung adeno carcinoma represents a challenge for treatment decision [J]. Neoplasia, 2024, 49:100955.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSandoz P A, Kuhnigk K, Szabo E K, et al. Modulation of lytic molecules restrain serial killing in γδ T lymphocytes [J]. Nat Commun, 2023, 14(1): 6035.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChang C Y, Chang S C, Wei Y F, et al. Exploring the evolution of T cell function and diversity across different stages of non-small cell lung cancer [J]. Am J Cancer Res, 2024, 14(3): 1243\u0026ndash;1257.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSong X, Zhao G, Wang G, et al. Heterogeneity and Differentiation Trajectories of Infiltrating CD8\u0026thinsp;+\u0026thinsp;T Cells in Lung Adenocarcinoma [J]. Cancers (Basel), 2022, 14(21): 5183.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang M, Ma J, Guo Q, et al. CD8(+) T Cell-Associated Gene Signature Correlates With Prognosis Risk and Immunotherapy Response in Patients With Lung Adenocarcinoma [J]. Front Immunol, 2022, 13:806877.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu Z, Sun D, Zhu Q, et al. The screening of immune-related biomarkers for prognosis of lung adenocarcinoma [J]. Bioengineered, 2021, 12(1): 1273\u0026ndash;1285.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZheng C, Li X, Ren Y, et al. Long Noncoding RNA RAET1K Enhances CCNE1 Expression and Cell Cycle Arrest of Lung Adenocarcinoma Cell by Sponging miRNA-135a-5p [J]. Front Genet, 2019, 10:1348.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbdul Wahab S, Hassan A, Latif M T, et al. Cluster Analysis Evaluating PM2.5, Occupation Risk and Mode of Transportation as Surrogates for Air-pollution and the Impact on Lung Cancer Diagnosis and 1-Year Mortality [J]. Asian Pac J Cancer Prev, 2019, 20(7): 1959\u0026ndash;1965.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbdennadher M, Dahmane M H, Zair S, et al. Sex-specificity in Surgical Stages of Lung Cancer in Young Adults [J]. Open Respir Med J, 2023, 17:e187430642307140.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChien L H, Jiang H F, Tsai F Y, et al. Incidence of Lung Adenocarcinoma by Age, Sex, and Smoking Status in Taiwan [J]. JAMA Netw Open, 2023, 6(11): e2340704.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSui P, Liu X, Zhong C, et al. Integrated single-cell and bulk RNA-Seq analysis enhances prognostic accuracy of PD-1/PD-L1 immunotherapy response in lung adenocarcinoma through necroptotic anoikis gene signatures [J]. Sci Rep, 2024, 14(1): 10873.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Z, Wu Y, Wang C, et al. Mouse CD8(+)NKT-like cells exert dual cytotoxicity against mouse tumor cells and myeloid-derived suppressor cells [J]. Cancer Immunol Immunother, 2019, 68(8): 1303\u0026ndash;1315.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin L, Rayman P, Pavicic P G, Jr., et al. Ex vivo conditioning with IL-12 protects tumor-infiltrating CD8(+) T cells from negative regulation by local IFN-γ [J]. Cancer Immunol Immunother, 2019, 68(3): 395\u0026ndash;405.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoosavi F, Giovannetti E, Saso L, et al. HGF/MET pathway aberrations as diagnostic, prognostic, and predictive biomarkers in human cancers [J]. Crit Rev Clin Lab Sci, 2019, 56(8): 533\u0026ndash;566.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZi Z, Zhang Z, Feng Q, et al. Quantitative phosphoproteomic analyses identify STK11IP as a lysosome-specific substrate of mTORC1 that regulates lysosomal acidification [J]. Nat Commun, 2022, 13(1): 1760.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMonlish D A, Beezhold K J, Chiaranunt P, et al. Deletion of AMPK minimizes graft-versus-host disease through an early impact on effector donor T cells [J]. JCI Insight, 2021, 6(14): e143811.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKashiwakura J I, Saitoh K, Ihara T, et al. Expression of signal-transducing adaptor protein-1 attenuates experimental autoimmune hepatitis via down-regulating activation and homeostasis of invariant natural killer T cells [J]. PLoS One, 2020, 15(11): e0241440.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu S, Zhang H, Liu T, et al. G Protein γ subunit 7 loss contributes to progression of clear cell renal cell carcinoma [J]. J Cell Physiol, 2019, 234(11): 20002\u0026ndash;20012.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZheng J, Zhang W, Zhang J. Establishment of a new prognostic risk model of GNG7 pathway-related molecules in clear cell renal cell carcinoma based on immunomodulators [J]. BMC Cancer, 2023, 23(1): 864.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWei Q, Miao T, Zhang P, et al. Comprehensive analysis to identify GNG7 as a prognostic biomarker in lung adenocarcinoma correlating with immune infiltrates [J]. Front Genet, 2022, 13:984575.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu Z, Tan F, Yang Z, et al. Sex disparity of lung cancer risk in non-smokers: a multicenter population-based prospective study based on China National Lung Cancer Screening Program [J]. Chin Med J (Engl), 2022, 135(11): 1331\u0026ndash;1339.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1\u003c/p\u003e\n\u003cp\u003e\u003cimg 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Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lung adenocarcinoma, CD8+ T cells, GNG7, Prognostic biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-7388573/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7388573/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eLung cancer is a leading cause of cancer-related deaths globally, with lung adenocarcinoma (LUAD) showing high incidence in non-smoking cases. Over one million die annually from lung cancer, and LUAD presents treatment challenges due to complex biological behavior, variability in therapeutic responses, and pronounced heterogeneity (within tumor cells and the microenvironment). This study integrates single-cell RNA sequencing (scRNA-Seq) and genome-wide association study (GWAS) data to identify LUAD-associated cell subpopulations and core genes, offering insights into pathogenesis and potential therapeutic targets/prognostic biomarkers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe GSE196303 single-cell transcriptomic dataset (3 adjacent normal, 3 tumor tissues) was analyzed via Seurat and Harmony for clustering, annotation, and batch correction. Combined with GWAS summary data (1002 cases, 462008 controls), scPagwas identified trait-associated subpopulations. Using TCGA-LUAD bulk RNA-seq (541 tumors, 59 adjacent tissues) and clinical data, BayesPrism quantified cell subtype abundance. Limma, WGCNA, somatic mutation analysis, and immune infiltration assessment (ssGSEA, CIBERSORT) explored clinical associations. Drug sensitivity was predicted via oncoPredict, with survival analysis and Cox regression screening prognostic markers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eEleven cell subpopulations were identified; CD8+ T cells had significantly higher trait-related scores (TRS, P\u0026lt;0.05). BayesPrism showed CD8+ T cell abundance was upregulated in LUAD and linked to favorable prognosis (P\u0026lt;0.05). Effector CD8+ T cell abundance correlated with gender, TNM stage, and survival, with high-abundance patients more sensitive to drugs like Savolitinib (P\u0026lt;0.05). TP53 and STK11 mutations associated with effector CD8+ T cell abundance. WGCNA and differential expression screening identified GNG7 as a core gene linked to effector CD8+ T cells, with high expression significantly improving survival (P=0.004).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eCD8+ T cells are a core LUAD subpopulation, with abundance correlating with clinical characteristics and the immune microenvironment. GNG7 is a core gene associated with effector CD8+ T cells.\u003c/p\u003e","manuscriptTitle":"scRNA-Seq Combined with scPagwas Analysis Identifies GNG7 as a Core Gene in Lung Adenocarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 15:33:35","doi":"10.21203/rs.3.rs-7388573/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":"43fa78f2-5cab-4461-930c-f1f8d47cbea6","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-11T01:38:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 15:33:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7388573","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7388573","identity":"rs-7388573","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}