Development and Validation of a Prognostic Model for Lung Adenocarcinoma Based on CAF-Related Genes: Unveiling the Role of COX6A1 in Cancer Progression and CAF Infiltration

Development and Validation of a Prognostic Model for Lung Adenocarcinoma Based on CAF-Related Genes: Unveiling the Role of COX6A1 in Cancer Progression and CAF Infiltration | 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 Development and Validation of a Prognostic Model for Lung Adenocarcinoma Based on CAF-Related Genes: Unveiling the Role of COX6A1 in Cancer Progression and CAF Infiltration Xinyu Zhu, Bo Li, Lexin Qin, Tingting Liang, Wentao Hu, Jianxiang Li, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5904445/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 Lung adenocarcinoma (LUAD), the predominant subtype of non-small cell lung cancer (NSCLC), presents significant challenges in early diagnosis and personalized treatment. Recent research has focused on the role of the tumor microenvironment, particularly tumor-associated fibroblasts (CAFs), in tumor progression. This study systematically analyzed CAF immune infiltration-related genes to construct a prognostic model for LUAD, confirming its predictive value for patient outcomes. The risk score derived from CAF-related genes (CAFRGs) was negatively correlated with immune microenvironment scores and linked to the expression of immune checkpoint genes, indicating that high-risk patients may exhibit immune escape characteristics. Analysis via the TIDE tool revealed that low-risk patients had more active T-cell immune responses. The risk score also correlated with anti-tumor drug sensitivity, particularly to doramapimod. Notably, COX6A1 emerged as a key gene in the model, with its upregulation associated with immune cell infiltration and immune escape. Further in vitro experiments demonstrated that COX6A1 regulates LUAD cell migration, proliferation, and senescence, suggesting its role in tumor immune evasion. Additionally, further co-culture studies of lung cancer cells and fibroblasts revealed that COX6A1 knockdown promotes the expression of CAF-related cytokines, enhancing CAF infiltration. Overall, this study provides a foundation for personalized treatment of LUAD and highlights COX6A1 as a promising therapeutic target within the tumor immune microenvironment, guiding future clinical research. Tumor-associated fibroblasts immune microenvironment lung adenocarcinoma prognostic model COX6A1 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer (NSCLC), accounting for approximately 40% of all lung cancer cases [ 1 ]. Despite significant advances in early detection and treatment strategies, LUAD remains a leading cause of cancer-related mortality globally [ 2 , 3 ]. Owing to its often asymptomatic nature in the early stages and the lack of effective biomarkers for prognosis, many patients are diagnosed at an advanced stage, which limits therapeutic options and impairs survival outcomes [ 4 ]. As such, there is a critical need for novel prognostic biomarkers to enhance early detection, predict patient outcomes, and guide therapeutic strategies. The tumor microenvironment (TME) plays a pivotal role in the progression and metastasis of cancer, and cancer-associated fibroblasts (CAFs) are central components of this microenvironment [ 5 ]. CAFs, which are derived from normal fibroblasts or other stromal cells, are activated in response to tumorigenic signals and contribute to various aspects of cancer biology, including tumor growth, angiogenesis, immune evasion, and resistance to therapies [ 6 , 7 ]. CAFs secrete a variety of growth factors, cytokines, and extracellular matrix proteins that facilitate tumor cell migration, invasion, and metastasis [ 8 ]. Importantly, recent studies have demonstrated that CAFs not only influence tumor progression but are also linked to poor prognosis in several cancers, including lung adenocarcinoma [ 8 – 10 ]. The interaction between CAFs and tumor cells is mediated by a complex network of signaling molecules and gene expression changes. Many studies have identified specific CAF-related genes that contribute to tumor aggressiveness and metastasis. These genes include those involved in extracellular matrix remodeling, inflammatory responses, and immune cell recruitment, all of which play critical roles in tumor progression [ 11 – 13 ]. Importantly, recent research has suggested that CAF-related genes can serve as powerful prognostic biomarkers. In the context of LUAD, the expression levels of specific CAF-associated genes have been shown to be correlated with poor survival, making them promising candidates for inclusion in prognostic models [ 14 , 15 ]. However, comprehensive models that integrate CAF-related gene signatures to predict clinical outcomes in LUAD patients are lacking. This study aimed to develop a prognostic model for lung adenocarcinoma (LUAD) patients on the basis of genes associated with tumor-associated fibroblast (CAF) infiltration scores while validating its accuracy and robustness. Key objectives include identifying CAF-related genes (CAFRGs), constructing a prognostic model, validating it in independent cohorts, assessing the clinical significance of risk scores, and conducting in vitro functional validation of key genes. These findings are intended to enhance precision in prognosis and therapeutic decision-making for LUAD patients. 2. Results 2.1 Construction and Validation of the Prognostic Model Based on CAFRGs This study utilized two LUAD cohorts from the TCGA and GEO databases, along with their corresponding clinical data. Table 1 and Table S1 summarize the demographic and clinical characteristics of the training, internal testing, and independent validation sets. After excluding samples with missing clinical information from the TCGA-LUAD dataset, a total of 504 LUAD patients were included, of whom 183 were alive and 321 had died by the end of the follow-up period (median follow-up time: 1.789 years). This dataset was randomly divided into a training set (n = 353) and an internal testing set (n = 151) at a 7:3 ratio. As expected, no significant differences were observed in the major clinicopathological characteristics between the training, testing, and entire TCGA-LUAD cohorts (Table 1 ). Additionally, the study included the GEO dataset GSE31210, which comprises 226 LUAD patients, with a mortality rate of 37.81% at the end of follow-up (median follow-up time: 4.720 years). Table 1 Demographic and Clinical Characteristics of the training, internal testing, and complete TCGA LUAD datasets Characteristics TCGA LUAD Chi-square P value Training (n = 353) Internal testing (n = 151) All (n = 504) Gender female 194 (54.96%) 76 (50.33%) 270 (53.57%) 0.634 male 159 (45.04%) 75 (49.67%) 234 (46.43%) Age ≤ 60 119 (34.59%) 39 (26.00%) 158 (31.98%) 0.170 > 60 225 (65.41%) 111 (74.00%) 336 (68.02%) M M0 231 (90.59%) 100 (95.24%) 335 (93.06%) 0.116 M1 24 (9.41%) 5 (4.76%) 25 (6.94%) N N0 225 (65.79%) 99 (66.89%) 324 (66.12%) 0.972 N1/2 117 (34.21%) 49 (33.11%) 166 (33.88%) T T1/2 306 (86.69%) 132 (87.42%) 438 (86.90%) 0.975 T3/4 47 (13.31%) 19 (12.58%) 66 (13.10%) Stage Stage I/II 272 (77.05%) 118 (78.15%) 390 (77.38%) 0.965 Stage III/IV 81 (22.95%) 33 (21.85%) 114 (22.62%) Smoke history Nonsmoke 139 (39.38%) 61 (40.40%) 200 (39.68%) 0.977 Smoke 214 (60.62%) 90 (59.60%) 304 (60.32%) time ≤ 2 204 (57.79%) 81 (53.64%) 285 (56.55%) 0.691 > 2 149 (42.21%) 70 (46.36%) 219 (43.45%) status 0 220 (62.32%) 101 (66.89%) 321 (63.69%) 0.621 1 133 (37.68%) 50 (33.11%) 183 (36.31%) Note: TCGA: The Cancer Genome Atlas; LUAD: Lung adenocarcinoma 2.2 Construction and Validation of a Prognostic Model Based on CAF-Related Genes On the basis of the TCGA LUAD dataset, CAF immune infiltration scores for each sample were obtained via the MCPCOUNTER and XCELL algorithms. Correlation analysis was performed to identify genes whose expression levels were correlated with CAF infiltration scores (CAFRGs, cancer-associated fibroblast-related genes; Figure S1 A-B ). The intersection of these genes from the two algorithmic analyses revealed that 1,154 genes were positively correlated with CAF infiltration and that 17 genes were negatively correlated ( Figure S1 C-D ). Using the training dataset, univariate Cox regression analysis identified a total of 174 CAFRGs that were associated with prognosis (Fig. 1 A). Gene selection was performed via least absolute shrinkage and selection operator (LASSO) regression, which identified 30 feature genes ( Figure S2 ). These feature genes were further incorporated into a stepwise multivariate Cox regression analysis, and the results are shown in Fig. 1 B. The final prognostic risk model based on CAFRGs was constructed as follows: risk score = COX6A1 Exp × (0.491) + ENOX1 Exp × (0.409) + FERMT2 Exp × (0.319) + NID1 Exp × (0.257) + LOX Exp × (0.223) + SNAI2 Exp × (0.148) + GLI2 Exp × (0.137) + ZNF154 Exp × (-0.136) + COX7A1 Exp × (-0.179) + NXPH3 Exp × (-0.182) + FRMD4A Exp × (-0.258) + SYT11 Exp × (-0.391) + ENTPD1 Exp × (-0.402). Receiver operating characteristic (ROC) curves demonstrated that the risk score had good predictive performance for patient prognosis, with area under the curve (AUC) values of 0.790, 0.819, and 0.839 for 1, 4, and 5 years, respectively (Fig. 1 C). High-risk patients had significantly poorer outcomes than low-risk patients did (Fig. 1 D). In the training cohort, high-risk patients had shorter overall survival and more deaths than low-risk patients did (Fig. 1 E-F). A heatmap was used to show the expression distribution of the genes in the model across high- and low-risk samples (Fig. 1 G). 2.3 Validation of the CAFRGs Risk Model Next, the robustness of the model was validated using the entire TCGA LUAD dataset and an independent dataset, GSE31210. ROC curves were plotted on the basis of the risk scores for patient prognosis in the TCGA LUAD dataset, with a 1-year AUC value of 0.786 (Fig. 2 A). After the risk scores were divided into high- and low-risk groups on the basis of the median risk score, survival curve analysis indicated that patients in the low-risk group had a significantly better prognosis than those in the high-risk group did (Fig. 2 B). The distribution of risk scores and survival outcomes revealed that low-risk patients had a lower mortality rate, whereas high-risk patients had a significantly higher mortality rate (Fig. 2 C). A heatmap revealed the expression patterns of CAFRGs across high- and low-risk samples in the TCGA LUAD dataset (Fig. 2 D). Similarly, in the GSE31210 dataset, ROC curves revealed the AUC values at 1–5 years, further validating the robustness of the model in an independent dataset (Fig. 2 E). Survival curve analysis revealed that high-risk patients had a worse prognosis (Fig. 2 F). Additionally, high-risk patients had shorter overall survival and more deaths than low-risk patients did (Fig. 2 G). A heatmap demonstrated the differential expression of CAFRGs in high- and low-risk samples in the GSE31210 dataset (Fig. 2 H). In another independent cohort, GSE13213, the 1-year AUC of the ROC curve was 0.882 ( Figure S3A ), and survival curve analysis similarly revealed poor prognosis in the high-risk group ( Figure S3B ), with high-risk patients having shorter overall survival and more deaths ( Figure S3C ). These results confirm the reliability and robustness of the CAFRG prognostic model. 2.4 CAFRG Risk Score as an Independent Prognostic Factor In the TCGA LUAD dataset, univariate Cox regression analysis revealed that the CAFRG risk score was significantly associated with key clinical and pathological features, including distant metastasis, lymph node metastasis, invasion depth, and clinical stage, all of which were identified as risk factors (HR > 0, P < 0.05; Fig. 3 A). Further multivariate Cox regression analysis confirmed that the CAFRG risk score remained an independent prognostic factor (HR = 2.37, 95% CI: 2.16–4.04, P 0, P < 0.05; Fig. 3 C). A subsequent multivariate Cox regression further validated that the CAFRG risk score remained an independent prognostic factor (HR = 2.82, 95% CI: 1.23–6.04; P = 0.014; Fig. 3 D). These consistent results across multiple datasets suggest that the CAFRG risk score is a reliable and independent prognostic biomarker with substantial clinical value. 2.5 Construction and Evaluation of a Clinical Prediction Nomogram A clinical prediction nomogram was constructed on the basis of the independent prognostic factors identified by multivariate Cox regression analysis in the TCGA LUAD dataset (Fig. 4 A). The accuracy of the nomogram was validated via calibration curves, receiver operating characteristic (ROC) curves and decision curve analysis (DCA). The calibration curve demonstrated that the predicted 1-year, 3-year, and 5-year survival rates closely matched the actual survival rates, indicating excellent predictive performance (Fig. 4 B). ROC curve analysis revealed that the nomogram predicted the 1-, 3-, and 5-year survival probabilities, with AUC values of 0.811, 0.754, and 0.787, respectively (Fig. 4 C). The DCA results demonstrated that the nomogram provided a high net benefit across various risk thresholds, supporting its value in clinical decision-making (Fig. 4 D). For the GSE31210 dataset, a corresponding clinical prediction nomogram was constructed using the independent prognostic factors from the multivariate Cox regression analysis (Fig. 4 E). The calibration curve demonstrated a good fit between the predicted and actual survival outcomes (Fig. 4 F). ROC analysis revealed the ability of the nomogram to predict the 1-, 3-, and 5-year survival probabilities, further supporting its accuracy (Fig. 4 G). DCA indicated a high net benefit across risk thresholds, highlighting its potential for clinical application (Fig. 4 H). In summary, the nomograms constructed from both the TCGA LUAD and GSE31210 datasets displayed excellent predictive performance and clinical utility, making them valuable tools for personalized treatment and prognosis in LUAD patients. 2.6 Construction of a Nomogram-Based Clinical Prediction Tool Furthermore, we developed an online clinical prediction tool based on nomograms constructed from the TCGA LUAD and GSE31210 datasets ( https://jingege.shinyapps.io/CAFRG_model/ ). This visualization tool allows users to make individualized survival predictions on the basis of various clinical features and risk scores. By adjusting clinical parameters, users can easily obtain survival curves and probabilities for individual patients. For example, when the parameters were set to T4, N0, and a risk score of 5, the predicted 1-year, 2-year, 3-year, and 5-year survival probabilities for patients were 84%, 66%, 49%, and 18%, respectively (Fig. 5 A). Figures 5 B and 5 C show the predicted survival curves for this setting, which indicate a progressively lower survival probability. On the other hand, when the parameters were set to T1, N0, and a risk score of 7, the predicted survival probabilities for 1-year, 2-year, 3-year, and 5-year survival were 47%, 17%, 5%, and 1%, respectively, suggesting poor long-term survival ( Fig. 5 D and 5 E). 2.7 Risk score and its association with immune cell infiltration and immunotherapy Using the TCGA LUAD dataset, we analyzed the correlation between the risk score and immune cell infiltration score calculated via the xCell algorithm. The results revealed a significant correlation between the risk score and the infiltration levels of several immune cell subsets, particularly classical dendritic cells (cDCs), M2 macrophages, hematopoietic stem cells (HSCs), and mast cells (Fig. 6 A). Additionally, the risk score was strongly correlated with the immune microenvironment score and immune score, with correlation coefficients of -0.445 and − 0.435, respectively (Fig. 6 B and 6 C). Further correlation analysis revealed significant associations between the risk score and the expression levels of multiple immune checkpoint genes (Fig. 6 D), especially BTLA (r = -0.330, Fig. 6 E) and VSIR (r = -0.311, Fig. 6 F). Moreover, via the tumor immune dysfunction and exclusion (TIDE) algorithm, we obtained immune infiltration scores for cancer-associated fibroblasts (CAFs) and myeloid-derived suppressor cells (MDSCs), along with T-cell dysfunction and exclusion scores. Subsequent correlation analysis revealed that the risk score was significantly correlated with T-cell dysfunction (Fig. 6 G) and exclusion (Fig. 6 H), as were the MDSC (Fig. 6 I) and CAF (Fig. 6 J) immune infiltration scores. Similarly, in the GSE31210 dataset, correlation analysis revealed associations with multiple immune cell infiltration scores ( Figure S4A-C ) and T-cell dysfunction and exclusion scores on the basis of the TIDE algorithm ( Figure S4D-G ). These results suggest that the risk score could serve as a potential indicator of the response of LUAD patients to immunotherapy. 2.8 Risk score and its association with lung adenocarcinoma progression We further assessed the correlation between the CAFRG risk score and the biological functions of LUAD. As shown in Fig. 7 A, the heatmap displays the correlation between the CAFRG risk score and oncogenes in the TCGA LUAD and GSE31210 datasets. These results indicate that the CAFRG risk score is significantly positively correlated with several known oncogenes, such as PLK1, CDK1, and FOXM1. Further correlation analysis revealed a significant association between the CAFRG risk score and sensitivity to various anticancer drugs, as calculated by the OncoPredict algorithm, including doramapimod, axitinib, urosertib, and niraparib (Fig. 7 B). Additionally, via gene set enrichment analysis (GSEA) of the TCGA LUAD dataset, we investigated the biological functions and signaling pathways associated with the CAFRG risk score. Biological function analysis revealed that the risk score was related to functions such as DNA replication, DNA double-strand break repair, cell adhesion regulation, and immune response activation (Fig. 7 C-D). In the signaling pathway analysis, the risk score was associated with several cancer-related signaling pathways, including the cell cycle, mismatch repair, DNA replication, the JAK-STAT signaling pathway, and cell adhesion molecules (Fig. 7 E-F). 2.9 COX6A1 as a Key Gene Promoting Tumor Progression in LUAD In our analysis of the TCGA LUAD dataset, we identified COX6A1 as a key gene that promotes tumor progression within our predictive model. Scatter plot analysis revealed a strong correlation between COX6A1 expression and the risk score, suggesting that its expression is closely related to overall risk in LUAD patients (Fig. 8 A). Further validation across multiple datasets revealed that COX6A1 expression was significantly higher in tumor tissues than in normal tissues (Fig. 8 B). For patient prognosis, high expression of COX6A1 is significantly associated with poor outcomes in the TCGA LUAD dataset (Fig. 8 C). Based on the KM plotter online tool, patients with high COX6A1 expression in LUAD exhibit poor overall survival ( Figure S5A ) and first progression ( Figure S5B ). To explore the role of COX6A1 in the immune microenvironment, we evaluated the correlations between COX6A1 expression and immune checkpoint gene expression, immune cell infiltration, and antitumor drug sensitivity. The results indicated that COX6A1 expression was positively correlated with the expression of several immune checkpoint genes (Fig. 8 D). Moreover, COX6A1 was also associated with immune cell infiltration scores for various cell types, including mast cells, tumor-associated fibroblasts (Fig. 8 E, and hematopoietic stem cells, as were matrix scores, immune microenvironment scores (Fig. 8 F), and immune scores ( Figure S5C ). In terms of antitumor drug sensitivity, COX6A1 expression was linked to increased sensitivity to drugs such as doramapimod, dihydrorotenone, and docetaxel (Fig. 8 G). Additionally, COX6A1 expression was significantly positively correlated with tumor stemness, further suggesting its role in maintaining aggressive tumor characteristics (Fig. 8 H). Finally, gene set enrichment analysis (GSEA) revealed that COX6A1 expression was associated with multiple important signaling pathways, such as the TGF-β and JAK-STAT pathways, and with biological functions related to DNA replication, oxidative phosphorylation, cell adhesion molecules, and base excision repair (Fig. 8 I-K). These findings highlight COX6A1 as a potential therapeutic target and a critical regulator of the tumor immune microenvironment and drug sensitivity in LUAD. 2.10 Silencing COX6A1 Inhibits Lung Adenocarcinoma Cell Growth and Migration CCK8 assays revealed that silencing COX6A1 significantly inhibited the proliferation of the lung adenocarcinoma cell lines A549 (Fig. 9 A) and H1299 (Fig. 9 B). Figure 9 C-D presents the dose‒response curves for A549 and H1299 cells treated with various concentrations of doramapimod, demonstrating that COX6A1 knockdown increased their sensitivity to the drug, as reflected by a reduction in the IC50 values. Transwell migration assays revealed that COX6A1 knockdown significantly inhibited the migration capacity of A549 and H1299 cells (Fig. 9 E-F). Similarly, EdU assays demonstrated that COX6A1 knockdown markedly reduced cell proliferation (Fig. 9 G-H). Further analysis via β-galactosidase staining revealed that silencing COX6A1 induced senescence in lung cancer cells (Fig. 9 I ‒J ). Quantitative PCR (qPCR) analysis confirmed that COX6A1 expression was significantly reduced after COX6A1 knockdown (Fig. 9 K). Moreover, the epithelial marker CDH1 and the senescence-associated genes CDKN1A (P21) and CDKN2A (P16) were significantly upregulated, whereas the mesenchymal marker CDH2 was significantly downregulated ( Fig. 9 L ) . At the protein level, Western blot analysis revealed consistent changes in protein expression, corroborating the mRNA data and further validating the critical role of COX6A1 in regulating tumor cell migration and senescence ( Fig. 9 M ‒N) . 2.11 COX6A1 Knockdown in Lung Cancer Cells Promotes CAF Infiltration Further in vitro studies were conducted to investigate the effect of COX6A1 overexpression in lung cancer cells on the infiltration of CAFs. Analysis of the TCGA LUAD dataset (Fig. 10 A) and multiple LUAD datasets from the GEO database (Fig. 10 B) revealed a significant correlation between COX6A1 expression and the expression of CAF activation-related cytokines. To validate this finding, we used qPCR to measure the expression levels of several CAF-related cytokines in lung cancer cells with COX6A1 knockdown. The results showed a significant decrease in the expression of TGFB2 (Fig. 10 C), CXCL12 (Fig. 10 D), and FGF2 (Fig. 10 E). Additionally, ELISA further confirmed that the expression level of CXCL12 in the culture supernatant of COX6A1-knockdown lung cancer cells was significantly reduced (Fig. 10 F). To investigate the specific mechanism by which COX6A1 affects CAF infiltration, we established a co-culture system of lung cancer cells and human embryonic lung cells WI-38 (Fig. 10 G). qPCR analysis of CAF marker gene expression in co-cultured WI-38 cells showed a significant upregulation of α-SMA (Fig. 10 H), FN1 (Fig. 10 I), and VIM (Fig. 10 J) RNA levels. Immunofluorescence analysis further demonstrated a significant increase in α-SMA expression in WI-38 cells after co-culture with lung cancer cells (Fig. 10 K), with consistent results obtained through quantitative analysis (Fig. 10 L). Moreover, Transwell migration assays indicated that the migratory capacity of WI-38 cells was significantly enhanced in the co-culture system (Fig. 10 M), as confirmed by quantitative analysis (Fig. 10 N). These results suggest that COX6A1 overexpression promotes the expression of CAF-related cytokines, enhancing the attraction of lung cancer cells to CAFs, thereby facilitating CAF infiltration. 3. Discussion This study systematically analyzed tumor-associated fibroblast (CAF) immune infiltration-related genes and successfully constructed and validated a prognostic model for lung adenocarcinoma (LUAD), revealing the critical role of CAFs in the tumor immune microenvironment and providing new insights and theoretical foundations for precision medicine. We found that this model not only effectively predicts the prognosis of LUAD patients but is also closely associated with the tumor immune microenvironment, drug sensitivity, and tumor biological characteristics, such as proliferation, migration, and senescence. These findings provide important clues for a deeper understanding of the mechanisms underlying CAFs in LUAD and for the development of new therapeutic strategies. One of the key findings of this study is that the CAF-related gene (CAFRG) risk score is significantly associated with immune microenvironment scores and immune cell infiltration, particularly with the expression of immune checkpoint genes. Changes in the immune microenvironment play crucial roles in tumor immune escape [ 16 , 17 ], and CAFs, as important components of the tumor microenvironment, may influence tumor immune escape mechanisms by modulating immune cell infiltration and immune checkpoint activation [ 18 , 19 ]. We observed that the CAFRG risk score was correlated with the infiltration of various immune cells and was inversely related to the immune microenvironment score. These results suggest that high-risk patients have a lower immune microenvironment and immune activity, which may indicate immune escape features. Using the TIDE tool, we found that the risk score was negatively correlated with T-cell dysfunction and positively correlated with T-cell exclusion, suggesting that low-risk patients may have a more active T-cell immune response and an immune system capable of effectively fighting the tumor, whereas high-risk patients may face immune escape challenges, as reflected by impaired T-cell function and T-cell exclusion, leading to weakened immune surveillance [ 20 ]. Immune checkpoint genes (e.g., PD-1 and CTLA-4) are often closely associated with immune escape mechanisms, as tumor cells express immune checkpoint molecules to suppress immune system attacks, thereby evading host immune surveillance [ 21 – 23 ]. We found that the risk score was negatively correlated with the expression of these immune checkpoint genes, further suggesting that the CAFRG risk score could predict immune escape and the immune therapy response in LUAD patients. Moreover, we explored the relationship between the risk score and tumor biological functions and found that it is closely related to signaling pathways and biological functions involved in LUAD proliferation, migration, and immune system activation. Drug resistance has become a major challenge in clinical cancer therapy. Resistance not only significantly reduces treatment efficacy but also worsens patient prognosis, increasing the complexity and cost of treatment [ 24 ]. The risk score was also found to be closely associated with antitumor drug sensitivity, particularly with a significant negative correlation with the sensitivity to doramapimod (a MAPK/ERK inhibitor). Furthermore, we identified a key gene in the model, cytochrome c oxidase subunit 6A1 (COX6A1). COX6A1 is a mitochondrial membrane protein that is widely involved in cellular energy metabolism and plays an important role in oxidative phosphorylation (OXPHOS) in particular [ 25 ]. OXPHOS is the main pathway for ATP production, and tumor cells require large amounts of energy during rapid proliferation and in response to external stress; thus, OXPHOS plays a crucial role in tumor progression [ 26 , 27 ]. Although the Warburg effect dominates energy metabolism in some tumors, recent studies have shown that OXPHOS is significantly activated in chemoresistant and cancer stem cells, contributing to tumor cell survival and metastasis [ 28 ]. Therefore, COX6A1 may serve as a new target for anticancer therapies. Our study further revealed that the upregulation of COX6A1 is associated with the degree of immune cell infiltration in tumor tissues, particularly with the expression of immune checkpoint genes, suggesting that COX6A1 may affect tumor immune escape by modulating immune cell function or through immune checkpoint pathways. In vitro experiments confirmed COX6A1's role in lung cancer cells. COX6A1 Knockdown inhibited tumor cell migration and proliferation while promoting cellular senescence—a state where cells cease to divide after stress, accompanied by metabolic and gene expression changes. Tumor cell senescence can suppress tumor development but also influence metastasis potential and immune escape [ 29 – 31 ]. Knockdown of COX6A1 significantly upregulated senescence-associated genes, such as CDKN1A and CDKN2A, indicating that COX6A1 may inhibit tumor progression by inducing senescence. We conducted further in vitro studies to investigate the impact of COX6A1 knockdown on CAF infiltration in lung cancer cells. Analysis of multiple lung adenocarcinoma datasets revealed a significant correlation between COX6A1 expression and the expression of CAF activation-related cytokines. Our in vitro experiments demonstrated that COX6A1 knockdown in lung cancer cells led to the upregulation of TGFB2, CXCL12, and FGF2. Further co-culture experiments with lung cancer cells and human embryonic lung WI-38 cells showed increased expression of α-SMA, FN1, and VIM in WI-38 cells, along with significantly enhanced migration capacity. These results indicate that COX6A1 knockdown promotes the expression of CAF-related cytokines, thereby enhancing the attraction of CAFs to lung cancer cells and promoting CAF infiltration. Interestingly, CAFs typically support tumor growth and metastasis in the tumor microenvironment by secreting cytokines and matrix remodeling factors [ 5 , 8 ]. This appears to be contrary to the high expression of COX6A1 in lung cancer, prompting us to further investigate this issue through rigorous experiments. Overall, this study constructed a prognostic model based on CAF immune infiltration-related genes, providing an effective tool for prognostic evaluation in LUAD patients and opening new directions for personalized treatment of LUAD. In particular, we found that the upregulation of COX6A1 in LUAD is closely associated with immune cell infiltration, immune escape, and biological features such as tumor cell migration and senescence, suggesting its important regulatory role in the tumor microenvironment. Our study deepens the understanding of the role of CAFs in tumor progression and highlights the importance of COX6A1 in the tumor immune microenvironment, revealing its clinical application prospects as a potential therapeutic target. 4. Materials and Methods 4.1 Dataset This study utilized lung adenocarcinoma (LUAD) data from multiple public databases. Initially, the gene expression data and clinical information of LUAD patients were obtained from The Cancer Genome Atlas (TCGA) database. The TCGA dataset included gene expression data from 572 patients, comprising 513 tumor tissues and 59 adjacent normal tissues, along with corresponding clinical characteristics (e.g., age, sex, and tumor stage). To validate the model's efficacy, the GSE31210 dataset was also extracted from the Gene Expression Omnibus (GEO) database, which contains gene expression and prognosis information for 226 primary LUAD samples. 4.2 Immune infiltration analysis To evaluate the immune microenvironment in LUAD samples, particularly the infiltration level of CAFs, immune infiltration analysis was conducted via the xCell[ 32 ] and TIMER[ 33 ] algorithms. xCell Algorithm: xCell is a high-resolution immune cell infiltration analysis tool that quantitatively predicts immune cell infiltration in the TME. Through xCell analysis, infiltration scores for CAFs and other immune and stromal cells in tumor samples were obtained. TIMER Algorithm: TIMER is an online platform for immune infiltration analysis that quantifies the infiltration levels of various immune cells (including T cells, B cells, and macrophages) and provides T-cell dysfunction and exclusion scores for each sample. 4.3 Prognostic Model Construction and Validation All genes associated with CAF scores (|correlation coefficient|>0.25, P < 0.05) in the TCGA LUAD dataset were analyzed via the Spearman method and defined as CAF-related genes (CAFRGs). The CAFRG prognostic model was constructed and validated via dataset splitting. The TCGA LUAD dataset was split into a training set and an internal test set at a 7:3 ratio. Clinical characteristic analysis: Chi-square tests were used to analyze the differences in the distributions of clinical characteristics (e.g., sex, age, tumor stage, and lymph node metastasis) between the training and validation sets. Univariate Cox regression analysis: Each CAFRG was evaluated for its relationship with overall survival (OS), and the hazard ratio (HR) and corresponding p value were calculated. CAFRGs with p values less than 0.05 were selected as candidate genes. LASSO regression analysis: To reduce model complexity and select the best prognostic factors, least absolute shrinkage and selection operator (LASSO) regression analysis was conducted to screen the most OS-related CAFRGs from the univariate Cox regression. Multivariate Cox regression analysis: Based on the LASSO regression results, multivariate Cox stepwise regression analysis was further performed to establish a prognostic model for CAF-related genes. Each patient's risk score was calculated from the Cox regression results via the formula \(\:\:\text{R}\text{i}\text{s}\text{k}\:\text{s}\text{c}\text{o}\text{r}\text{e}\hspace{0.17em}={\sum\:}_{i=1}^{n}{Exp}_{i}\times\:{coef}_{i}\) , where Exp_i is the expression value of the ith ARG and coef_i is the Cox regression coefficient of that gene. Model validation: The predictive performance of the prognostic model was evaluated via Kaplan‒Meier survival analysis and time‒dependent receiver operating characteristic (ROC) curves (using the "TimeROC" package) in the training set, internal test set, and validation set. 4.4 Nomogram Construction and Evaluation On the basis of the results of multivariate Cox regression analysis, a nomogram was constructed to provide an individualized survival prediction tool for patients. The nomogram combined the CAFRG risk score with independent prognostic factors identified from the multivariate Cox analysis to generate a survival probability prediction for patients. Nomogram Construction: Using the "rms" package, a comprehensive model was constructed that visually represents the impact of risk scores and clinical characteristics on patient survival. Model evaluation: The nomogram's predictive performance was assessed by calculating the C-index (concordance index). Calibration curves were used to evaluate the model's applicability in clinical practice. Decision curve analysis (DCA) was employed to assess the clinical decision value of the nomogram. 4.5 Clinical prediction tool development On the basis of the prognostic model and nomogram, a clinical prediction tool was developed to assist clinicians in predicting patient survival risk on the basis of clinical characteristics and CAFRG risk scores. This tool can generate survival curves and predict 1-year, 3-year, and 5-year survival probabilities on the basis of patient clinical information and risk scores. When implemented via the R Shiny package, the tool allows clinicians to easily utilize it, aiding in the formulation of more personalized treatment plans. This tool can improve the accuracy of prognosis assessment for cancer patients and guide clinical decision-making. 4.6 Drug sensitivity analysis The purpose of drug sensitivity analysis is to evaluate the sensitivity of various tumor samples to multiple antitumor drugs, providing a basis for clinical treatment. Drug sensitivity data were obtained from the GDSC database ( https://www.cancerrxgene.org/ ), which offers cell line sensitivity data for a range of drugs, including chemotherapeutic and targeted agents [ 34 ]. Drug sensitivity prediction for each sample was conducted via the OncoPredict package [ 35 ]. 4.7 GSEA enrichment analysis Gene set enrichment analysis (GSEA) was employed to investigate signaling pathways and biological functions associated with risk scores. Initially, all genes correlated with the risk scores were identified through correlation analysis and ranked according to their correlation coefficients. GSEA was conducted via the "ClusterProfiler" R package, which is based on predefined C2 (curated gene sets) and C5 (GO gene sets) [ 36 ]. Pathways significantly associated with risk scores were identified on the basis of the normalized enrichment score (NES) and false discovery rate (FDR). 4.8 Cell Culture The cell lines used in this study included the human non-small cell lung cancer (NSCLC) cell lines A549 and H1299. All the cell lines were purchased from the American Type Culture Collection (ATCC) and cultured under standard conditions. A549 and H1299 cells were cultured in RPMI-1640 medium supplemented with 10% fetal bovine serum (FBS). The cells were maintained at 37°C in a humidified incubator with 5% CO₂ and passaged when they reached 70–80% confluence. Morphological examination confirmed the absence of contamination in all the cell lines before use. 4.9 shRNA Construction and Transfection To investigate the function of the key gene COX6A1 in the model, we designed shRNA expression vectors targeting COX6A1. The specific steps are as follows: shRNA Design: Specific shRNA sequences targeting COX6A1 were designed via siRNA design software (e.g., BLOCK-iT™ RNAi Designer). Vector construction: The shRNA sequences were subsequently cloned and inserted into the pGreen vector, and successful cloning was confirmed via agarose gel electrophoresis. Cell Transfection: A549 or H1299 cells at 70–80% confluence were cotransfected with the shRNA vector via the Lipofectamine 3000 transfection reagent. Subsequent experimental analyses were conducted 48 hours posttransfection. Transfection efficiency: Transfection efficiency was assessed via qPCR and Western blot analysis. 4.10 CCK-8 Assay Cell proliferation was assessed via the Cell Counting Kit-8 (CCK-8) assay (Beyotime). The experimental steps were as follows: transfected A549 or H1299 cells were seeded into 96-well plates at a density of 2 × 10³ cells per well. After 24 hours of culture, various concentrations of drugs or media were added. At 24, 48, and 72 hours posttreatment, 10 µL of CCK-8 solution was added to each well, and the cells were incubated for an additional 2 hours. The optical density (OD) at 450 nm was measured via a microplate reader (BioTek). The cell proliferation inhibition rate was calculated on the basis of the OD values to generate proliferation curves for different time points and drug concentrations. 4.11 Doramapimod Dose‒Response Curve To evaluate the inhibitory effect of Doramapimod on cells, a dose‒response curve was constructed. Transfected A549 or H1299 cells were seeded into 96-well plates, with drug concentrations ranging from 0 to 512 µM. The effects of various drug concentrations on cell proliferation were assessed 24 hours posttreatment via the CCK-8 assay. Dose‒Response Analysis: Dose‒response curves were generated via Grap 4.12 Transwell Cell Migration Assay Cell migration ability was assessed via a Transwell chamber assay. The experimental procedure was as follows: transfected A549 or H1299 cells were seeded into the upper chamber with serum-free medium, and the lower chamber contained medium supplemented with 20% FBS as a chemoattractant. After 24 hours of incubation, nonmigrated cells in the upper chamber were removed with a sterile cotton swab. The migrated cells in the lower chamber were fixed and stained with crystal violet. The stained cells were observed and counted under a microscope. The results are expressed as the number of migrated cells, and differences between groups were compared. 4.13 EdU Cell Proliferation Assay Cell proliferation was evaluated via an EdU (5-ethynyl-2'-deoxyuridine) assay kit. The experimental steps were as follows: transfected A549 or H1299 cells were seeded into 96-well plates, treated with drugs, and then incubated with EdU labeling solution for 2 hours. After fixation, the cells were stained via an EdU staining kit. EdU-positive cells were counted under a fluorescence microscope, and the proliferation index was calculated. 4.14 β-Galactosidase Assay To assess cellular senescence, a β-galactosidase staining kit was used. The experimental procedure was as follows: Transfected cells were seeded in culture dishes and treated with specific drugs or stimuli. β-Galactosidase staining was performed according to the kit instructions. Senescent cells, which appeared blue under a microscope, were observed and quantified. The proportion of senescent cells was calculated and compared between groups. 4.15 Quantitative PCR (qPCR) Quantitative PCR (qPCR) was used to measure the relative expression levels of the CDKN1A, CDKN2A, CDH1, and CDH2 genes, with GAPDH serving as the internal control. Total RNA was extracted and reverse-transcribed into cDNA via reverse transcriptase. Each qPCR mixture included SYBR Green Master Mix, gene-specific primers (CDKN1A, CDKN2A, CDH1, CDH2, and GAPDH), and a cDNA template. The PCR program was set as follows: initial denaturation at 95°C for 30 seconds, followed by 40 cycles of 95°C for 5 seconds and 60°C for 30 seconds. Relative expression levels were calculated via the 2^(-ΔΔCt) method, where ΔCt represents the difference in Ct values between the target gene and ACTB and where ΔΔCt represents the difference between the experimental and control groups. 4.16 Western blotting Western blotting was used to detect the protein expression of CDKN1A, CDKN2A, CDH1, and CDH2, with GAPDH serving as the internal control. The protein concentration in the cell lysates was determined via the BCA method, and 30 µg of protein was loaded for SDS‒PAGE. After transfer, the membranes were blocked with 5% nonfat milk for 1 hour and incubated with specific primary antibodies (CDKN1A, CDKN2A, CDH1, CDH2, and GAPDH) overnight. The membranes were subsequently incubated with HRP-conjugated secondary antibodies and developed via enhanced chemiluminescence (ECL) reagents. The signals were captured via a chemiluminescence imaging system, and protein expression levels were quantified via ImageJ software, with GAPDH used as the normalization control. 4.17 ELISA (Enzyme-Linked Immunosorbent Assay) In this study, we used ELISA to quantify the levels of CXCL12 in the culture supernatant of COX6A1 knockdown lung cancer cells. We collected the culture supernatant from COX6A1 knockdown-treated lung cancer cells and processed it according to the manufacturer’s instructions. ELISA plates were coated with CXCL12-specific antibodies. After washing, samples were added and incubated at appropriate temperatures and times to ensure binding. Subsequently, an enzyme-labeled secondary antibody was added, followed by another wash and the addition of a substrate solution to generate a measurable signal. The concentration of CXCL12 in the samples was calculated by comparing the amount of product from the enzymatic reaction to a standard curve. 4.18 Co-Culture System To explore the impact of COX6A1 on lung cancer cell-induced CAF infiltration, we established a co-culture system with lung cancer cells and human embryonic lung WI-38 cells. Lung cancer cells were first seeded in appropriate media to reach 70–80% confluence. WI-38 cells were then introduced at a suitable ratio to ensure effective contact and interaction between the cells. The media were regularly changed to remove metabolic waste and maintain optimal growth conditions. 4.19 Immunofluorescence Analysis We used immunofluorescence to detect α-SMA expression in WI-38 cells. Co-cultured WI-38 cells were fixed with 4% paraformaldehyde and permeabilized to allow antibody access. Cells were incubated with an α-SMA-specific primary antibody, followed by PBS washing to remove unbound antibodies. A fluorescently labeled secondary antibody was then added, incubated, and washed to remove excess secondary antibody. Cells were observed using a fluorescence microscope, and images were captured for quantitative analysis of α-SMA expression. 4.20 Statistical analysis Statistical analyses were conducted via GraphPad V8.3.0 software (GraphPad Software, LLC). The data are presented as the means ± standard deviations. Differences between two groups were assessed via Student’s t test, whereas comparisons among multiple groups were made via analysis of variance (ANOVA). All the statistical tests were two-tailed, with a P value of < 0.05 considered statistically significant. Abbreviations AUC Area Under the Curve CAF Cancer-Associated Fibroblast CAFRG Cancer-Associated Fibroblast-Related Genes COX Cox regression analysis DCA Decision Curve Analysis GEO Gene Expression Omnibus GSEA Gene Set Enrichment Analysis HR Hazard Ratio LASSO Least Absolute Shrinkage and Selection Operator LUAD Lung Adenocarcinoma NES Normalized Enrichment Score OXPHOS Oxidative Phosphorylation qPCR Quantitative Polymerase Chain Reaction ROC Receiver Operating Characteristic TCGA The Cancer Genome Atlas TIDE Tumor Immune Dysfunction and Exclusion TIMER Tumor Immune Estimation Resource Declarations Conflicts of interest: The authors declare that they have no conflicts of interest. Funding: This research was funded by the National Natural Science Foundation of China (Project No. 82373613 and 82304184) and the China Postdoctoral Science Foundation (Project No. 2023M732527). The study was also supported by the Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). Author Contribution X.Z. and L.B. conceptualized the study; X.Z., L.B., and L.Q. developed the methodology; X.Z. conducted the software analysis; T.L., L.Q., and W.H. performed the validation; X.Z. carried out the formal analysis; T.L. con-ducted the investigation; L.B. provided resources; J.L. handled data curation; X.Z. and L.B. prepared the original draft of the manuscript; J.L. and J.W. reviewed and edited the manuscript; L.Q. created the visualizations; J.W. supervised the study; J.W. administered the project; and J.L. and J.W. acquired funding. All authors have read and agreed to the published version of the manuscript. Data Availability The gene expression data and clinical information for lung adenocarcinoma (LUAD) patients were sourced from The Cancer Genome Atlas (TCGA) database, accessible via the TCGA portal at https://portal.gdc.cancer.gov/. Addi-tionally, the GSE31210 dataset was obtained from the Gene Expression Omnibus (GEO) database, available under accession number GSE31210 at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE31210. References Thai AA, Solomon BJ, Sequist LV, Gainor JF, Heist RS: Lung cancer. Lancet 2021, 398: 535-554. Chen P, Liu Y, Wen Y, Zhou C: Non-small cell lung cancer in China. Cancer Commun (Lond) 2022, 42: 937-970. Li Y, Yan B, He S: Advances and challenges in the treatment of lung cancer. Biomed Pharmacother 2023, 169: 115891. Sen T, Takahashi N, Chakraborty S, Takebe N, Nassar AH, Karim NA, Puri S, Naqash AR: Emerging advances in defining the molecular and therapeutic landscape of small-cell lung cancer. Nat Rev Clin Oncol 2024, 21: 610-627. 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Feng S, Xu Y, Dai Z, Yin H, Zhang K, Shen Y: Integrative Analysis From Multicenter Studies Identifies a WGCNA-Derived Cancer-Associated Fibroblast Signature for Ovarian Cancer. Front Immunol 2022, 13: 951582. Herrera M, Berral-González A, López-Cade I, Galindo-Pumariño C, Bueno-Fortes S, Martín-Merino M, Carrato A, Ocaña A, De La Pinta C, López-Alfonso A, et al: Cancer-associated fibroblast-derived gene signatures determine prognosis in colon cancer patients. Mol Cancer 2021, 20: 73. Rui R, Zhou L, He S: Cancer immunotherapies: advances and bottlenecks. Front Immunol 2023, 14: 1212476. Gajewski TF, Schreiber H, Fu YX: Innate and adaptive immune cells in the tumor microenvironment. Nat Immunol 2013, 14: 1014-1022. Mao X, Xu J, Wang W, Liang C, Hua J, Liu J, Zhang B, Meng Q, Yu X, Shi S: Crosstalk between cancer-associated fibroblasts and immune cells in the tumor microenvironment: new findings and future perspectives. Mol Cancer 2021, 20: 131. 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Wong-Riley M, Guo A, Bachman NJ, Lomax MI: Human COX6A1 gene: promoter analysis, cDNA isolation and expression in the monkey brain. Gene 2000, 247: 63-75. Sica V, Bravo-San Pedro JM, Stoll G, Kroemer G: Oxidative phosphorylation as a potential therapeutic target for cancer therapy. International Journal of Cancer 2020, 146: 10-17. Ashton TM, McKenna WG, Kunz-Schughart LA, Higgins GS: Oxidative Phosphorylation as an Emerging Target in Cancer Therapy. Clinical Cancer Research 2018, 24: 2482-2490. Uslu C, Kapan E, Lyakhovich A: Cancer resistance and metastasis are maintained through oxidative phosphorylation. Cancer Letters 2024, 587: 216705. Wang L, Lankhorst L, Bernards R: Exploiting senescence for the treatment of cancer. Nat Rev Cancer 2022, 22: 340-355. Calcinotto A, Kohli J, Zagato E, Pellegrini L, Demaria M, Alimonti A: Cellular Senescence: Aging, Cancer, and Injury. Physiol Rev 2019, 99: 1047-1078. Lunin SM, Novoselova EG, Glushkova OV, Parfenyuk SB, Novoselova TV, Khrenov MO: Cell Senescence and Central Regulators of Immune Response. Int J Mol Sci 2022, 23 . Aran D, Hu Z, Butte AJ: xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol 2017, 18: 220. Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, Li B, Liu XS: TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res 2017, 77: e108-e110. Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, Bindal N, Beare D, Smith JA, Thompson IR, et al: Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res 2013, 41: D955-961. Maeser D, Gruener RF, Huang RS: oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data. Brief Bioinform 2021, 22 . Yu G, Wang LG, Han Y, He QY: clusterProfiler: an R package for comparing biological themes among gene clusters. Omics 2012, 16: 284-287. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5904445","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":407661406,"identity":"c29a29f7-b2aa-4005-9f6d-2fd2d16fa0c8","order_by":0,"name":"Xinyu Zhu","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Xinyu","middleName":"","lastName":"Zhu","suffix":""},{"id":407661407,"identity":"b041c078-cfb1-44b5-a7b1-a2da7c17873c","order_by":1,"name":"Bo Li","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Li","suffix":""},{"id":407661408,"identity":"0a8c4789-e7cc-4364-9f4a-0fecac4d2607","order_by":2,"name":"Lexin Qin","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Lexin","middleName":"","lastName":"Qin","suffix":""},{"id":407661409,"identity":"214b00ec-aa6d-40dc-9087-4f7c6ab163bb","order_by":3,"name":"Tingting Liang","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Liang","suffix":""},{"id":407661410,"identity":"bb1996ff-b3f1-40ca-bdb6-69ef6fd7f0a3","order_by":4,"name":"Wentao Hu","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Wentao","middleName":"","lastName":"Hu","suffix":""},{"id":407661411,"identity":"78944ff2-7ae2-40ca-8781-e8af5a452958","order_by":5,"name":"Jianxiang Li","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Jianxiang","middleName":"","lastName":"Li","suffix":""},{"id":407661412,"identity":"edab5d86-4b22-45bd-b8a9-8b380f14e650","order_by":6,"name":"Jin Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYBACxmYILSfP3sDAwANiHiBSi7FhzwEitcBAYsONBCK1MLfzHpP4uaM2sXHm82cSb2oY5PhuJDB+LsDrML40yd4zx43bpXPMJOccYzCWvJHALD0DrxYesxu8bcdkG2fnsEnzsDEkbriRwMbMQ0DLzb9txxgbbh5/Js3zj6GeKC23edtqFBtuMJhJ87YxJBgQocX8t2zbAWAg5xhbzu2TMJx55mGzND4thv1njA3fttUBo/L4wxtvvtnI8x1PPvgZr5YGMHUYxpcA2dyARwMDgzyEqsOraBSMglEwCkY4AACDlEwG9Ts8nQAAAABJRU5ErkJggg==","orcid":"","institution":"Soochow University","correspondingAuthor":true,"prefix":"","firstName":"Jin","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-01-26 04:23:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5904445/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5904445/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75086399,"identity":"ed11eba9-7d03-46fd-adcf-876bcd8dd58b","added_by":"auto","created_at":"2025-01-30 10:09:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":824719,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and performance analysis of the CAFRG prognostic model. \u003c/strong\u003e(A) Univariate Cox regression analysis identified CAFRGs associated with prognosis in the training cohort. (B) Multivariate Cox regression analysis was used to construct the prognostic model. (C) Time-dependent ROC curve showing the ROC and AUC values for 1–5 years in the training cohort. (D) Survival curves of high-risk and low-risk patients in the training cohort. (E) Distribution of risk scores in high- and low-risk patients and their survival outcomes and times in the training cohort. (F) Distribution of survival outcomes and times for high- and low-risk patients. (G) Heatmap showing the differential expression of CAFRGs in high- and low-risk samples in the training cohort. ROC: receiver operating characteristic; CAFRG: cancer-associated fibroblast-related genes; AUC: area under the curve.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5904445/v1/5c99bc67d9800df4de751230.png"},{"id":75085545,"identity":"b4e11e78-63ae-4d61-a33b-fa35cae19db6","added_by":"auto","created_at":"2025-01-30 10:01:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":793149,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of the model's robustness in the TCGA LUAD and GSE31210 datasets.\u003c/strong\u003e (A) Time-dependent ROC curve showing ROC and AUC values for 1–5 years in the TCGA LUAD dataset; (B) survival curves comparing high-risk and low-risk patients in the TCGA LUAD dataset; (C) distribution of risk scores and survival outcomes in high- and low-risk samples in the TCGA LUAD dataset; (D) heatmap showing the differential expression of CAFRGs in high- and low-risk samples in the TCGA LUAD dataset; (E) time-dependent ROC curve showing ROC and AUC values for 1–5 years in the GSE31210 dataset; (F) survival curve comparison between high-risk and low-risk patients in the GSE31210 dataset; (G) distribution of risk scores and survival outcomes in high- and low-risk patients in the GSE31210 dataset; (H) heatmap showing the expression patterns of CAFRGs in high- and low-risk samples in the GSE31210 dataset. TCGA: The Cancer Genome Atlas; LUAD: Lung adenocarcinoma; ROC: Receiver operating characteristic; CAFRG: Cancer-associated fibroblast-related genes; AUC: Area under the curve.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5904445/v1/dabbb66d4fedec4233a3c2fa.png"},{"id":75085513,"identity":"88133d76-196b-41c0-bd24-fb1b4d6a8309","added_by":"auto","created_at":"2025-01-30 10:01:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":377605,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCAFRG risk score as an independent prognostic factor. \u003c/strong\u003e(A) Forest plot showing univariate Cox analysis results of the CAFRG risk score and major clinicopathological features for patient prognosis in the TCGA LUAD dataset. (B) Forest plot showing the multivariate Cox analysis results of significant factors from univariate analysis for patient prognosis in the TCGA LUAD dataset. (C) Forest plot showing univariate Cox analysis results of the CAFRG risk score and major clinicopathological features for patient prognosis in the GSE31210 dataset. (D) Forest plot showing the multivariate Cox analysis results of significant factors from univariate analysis for patient prognosis in the GSE31210 dataset. TCGA: The Cancer Genome Atlas; LUAD: Lung adenocarcinoma; CAFRG: Cancer-associated fibroblast-related genes.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5904445/v1/b2e1bdd60677646e2c9ea651.png"},{"id":75085523,"identity":"88ae617e-32a4-4c2e-9d9f-7efcfd9fe984","added_by":"auto","created_at":"2025-01-30 10:01:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":535348,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and evaluation of the clinical prediction nomogram. \u003c/strong\u003e(A) Clinical prediction nomogram constructed on the basis of independent prognostic factors identified by multivariate Cox analysis in the TCGA LUAD dataset; (B) Calibration curve showing the agreement between the predicted and actual 1-, 3-, and 5-year survival rates for the TCGA LUAD nomogram; (C) ROC curve evaluating the accuracy of the TCGA LUAD nomogram for predicting 1-, 3-, and 5-year survival; (D) DCA curve for the TCGA LUAD nomogram showing net benefit across different risk thresholds; (E) Clinical prediction nomogram constructed for the GSE31210 dataset on the basis of independent prognostic factors from multivariate Cox analysis; (F) Calibration curve for the GSE31210 nomogram; (G) Kaplan‒Meier survival curve for the GSE31210 nomogram; (H) Decision curve analysis for the GSE31210 nomogram. TCGA: The Cancer Genome Atlas; LUAD: Lung adenocarcinoma; ROC: Receiver operating characteristic; CAFRG: Cancer-associated fibroblast-related genes; AUC: Area under the curve; DCA: Decision curve analysis; Cox: Cox regression analysis; HR: Hazard ratio.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5904445/v1/5a62e19271cee9f6f7d595b9.png"},{"id":75085515,"identity":"21ec82ff-62c7-41aa-9fa8-67b8a57c8667","added_by":"auto","created_at":"2025-01-30 10:01:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":586392,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of the nomogram-based clinical prediction tool. \u003c/strong\u003e(A) Online tool designed for visual prediction based on the TCGA LUAD and GSE31210 data; predicted survival probabilities and curves for parameters T4, N0, and risk score of 5 (B and C); (D) Predicted survival probabilities and curves for parameters T1, N0, and risk score of 7 (D and E).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5904445/v1/8903df79aac7aa4e95b8fe52.png"},{"id":75085521,"identity":"aec99271-adea-4ea9-bcd0-84c65ee55b24","added_by":"auto","created_at":"2025-01-30 10:01:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":669614,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation of the risk score with immune cell infiltration and immunotherapy in the TCGA LUAD dataset. \u003c/strong\u003e(A) Lollipop plot showing the correlation between the risk score and immune cell infiltration score calculated via the xCell algorithm. Scatter plots depicting the correlation between the risk score and immune microenvironment score (B) and immune score (C). (D) Lollipop plot illustrating the correlation between the risk score and expression of immune checkpoint genes. Scatter plots showing the correlation between the risk score and the expression of BTLA (E) and VSIR (F). Scatter plots and box plots demonstrating the correlation between the risk score and immune infiltration score of T-cell dysfunction (G), exclusion (H), MDSC (I), and CAF (J) based on the TIDE algorithm. TCGA: The Cancer Genome Atlas; LUAD: Lung adenocarcinoma; TIDE: Tumor immune dysfunction and exclusion; CAF: Cancer-associated fibroblasts; MDSC: Myeloid-derived suppressor cells.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5904445/v1/d5732c34d15efddd46a0da69.png"},{"id":75085534,"identity":"4f348c5c-c266-492f-b294-56c17258ac3b","added_by":"auto","created_at":"2025-01-30 10:01:38","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":808883,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between the CAFRG risk score and progression of lung adenocarcinoma. \u003c/strong\u003eHeatmap displaying the correlation between the CAFRG risk score and oncogenes in the TCGA LUAD and GSE31210 datasets (A) and the association with anticancer drug sensitivity calculated via OncoPredict (B). Lollipop plots showing the results of GSEA enrichment analysis for biological functions (C) and signaling pathways (E) in the TCGA LUAD dataset. GSEA plots showing enrichment of the CAFRG risk score in key biological functions (D) and signaling pathways (F). TCGA: The Cancer Genome Atlas; LUAD: Lung adenocarcinoma; CAFRG: Cancer-associated fibroblast-related genes; GSEA: Gene set enrichment analysis.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5904445/v1/e0225f0edc758cf05bd5a0e2.png"},{"id":75086395,"identity":"c9b6b5a7-a298-416b-b9d1-953a92bcea20","added_by":"auto","created_at":"2025-01-30 10:09:37","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":708268,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCOX6A1 is a gene that promotes tumor progression in the CAFRG prognostic model.\u003c/strong\u003e (A) The scatter plot illustrates the correlation analysis between CAFRGs and the risk score in the model, revealing a strong association between COX6A1 and the risk score. (B) The heatmap illustrates the expression variation of COX6A1 in tumor tissues compared with normal tissues across multiple LUAD datasets. (C) The KM survival curve for patients with high and low expression of COX6A1 in the TCGA LUAD dataset. (D) The lollipop plot shows the correlation between COX6A1 expression and immune checkpoint gene expression in the TCGA LUAD dataset. (E) The scatter plot shows the correlation between COX6A1 expression and microenvironment score. (F) The scatter plot shows the correlation between COX6A1 expression and the infiltration score of cancer associated fibroblast. (G) The scatter plot displays the correlation between COX6A1 expression and antitumor drug sensitivity scores on the basis of OncoPredict, suggesting enhanced drug sensitivity. (H) The scatter plot shows the correlation between COX6A1 expression and tumor stemness assessed by the RNA stemness score (RNAs). (I) The lollipop plot depicts signaling pathways associated with COX6A1 identified by GSEA. (J) The lollipop plot reveals biological functions related to COX6A1 expression, including DNA replication, oxidative phosphorylation, and base excision repair. (K) The GSEA plot highlights the TCGA: The Cancer Genome Atlas; LUAD: Lung adenocarcinoma; CAFRG: Cancer-associated fibroblast-related gene; GSEA: Gene set enrichment analysis.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-5904445/v1/9551f1c695f202d36b47181f.png"},{"id":75085536,"identity":"d527615a-d0e8-4dbf-a0b2-85f4be172247","added_by":"auto","created_at":"2025-01-30 10:01:38","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":733506,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSilencing COX6A1 Inhibits Lung Adenocarcinoma Cell Growth and Migration. \u003c/strong\u003eCCK8 assay for analyzing the effects of COX6A1 knockdown on the proliferation of A549 (A) and H1299 (B) lung adenocarcinoma cells; CCK8 assay dose‒response curves of A549 (C) and H1299 (D) cells treated with different concentrations of Doramapimod; EdU proliferation assay images (E) and quantification (F) showing the impact of COX6A1 knockdown on cell proliferation; Transwell migration assay images (G) and quantification (H) showing the effects of COX6A1 knockdown on cell migration; β-galactosidase staining images (I) and quantification (J) showing the induction of cellular senescence following COX6A1 knockdown; qPCR analysis showing changes in COX6A1 (K) and migration and senescence-related genes (L) after knockdown; Western blot analysis images (M) and quantification (N) demonstrating changes in migration and senescence-related proteins. This section may be divided into subheadings. It should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn. Compare with shNC, * P \u0026lt; 0.05, ** P \u0026lt; 0.01, *** P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-5904445/v1/b818804184c9a29279d148b8.png"},{"id":75085530,"identity":"b0110cee-75f2-41e2-9c9d-d1d8b40d209d","added_by":"auto","created_at":"2025-01-30 10:01:38","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":522783,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCOX6A1 overexpression in lung cancer cells promotes CAF infiltration. \u003c/strong\u003eHeat maps show the correlation between COX6A1 and the expression of CAF activation-related cytokines in the TCGA LUAD dataset (A) and multiple LUAD datasets from the GEO database (B). qPCR analysis of COX6A1-knockdown lung cancer cells shows changes in the expression of TGFB2 (C), CXCL12 (D), and FGF2 (E). (F) ELISA analysis of CXCL12 levels in the culture supernatant of COX6A1-knockdown lung cancer cells. (G) Co-culture system of lung cancer cells and human embryonic lung cells WI-38. qPCR analysis of RNA expression of α-SMA (H), FN1 (I), and VIM (J) in WI-38 cells co-cultured with lung cancer cells. Immunofluorescence images (K) and quantitative results (L) showing α-SMA expression in WI-38 cells co-cultured with lung cancer cells. Transwell migration assay images (M) and quantitative results (N) showing the migratory capacity of WI-38 cells co-cultured with lung cancer cells. TCGA, The Cancer Genome Atlas; LUAD, Lung Adenocarcinoma; GEO, Gene Expression Omnibus.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-5904445/v1/af2a1fe669938d5a92304ebf.png"},{"id":75088941,"identity":"f0cbbdc1-1577-450c-981a-ffb5c50b06ab","added_by":"auto","created_at":"2025-01-30 10:33:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8461219,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5904445/v1/154decd3-1ccd-4c36-9956-572e22462def.pdf"},{"id":75085525,"identity":"9bcc06c0-2c0d-4828-a3cf-0371a0943f24","added_by":"auto","created_at":"2025-01-30 10:01:38","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1495366,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-5904445/v1/3536367f1805d5c84d150229.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Prognostic Model for Lung Adenocarcinoma Based on CAF-Related Genes: Unveiling the Role of COX6A1 in Cancer Progression and CAF Infiltration","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer (NSCLC), accounting for approximately 40% of all lung cancer cases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite significant advances in early detection and treatment strategies, LUAD remains a leading cause of cancer-related mortality globally [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Owing to its often asymptomatic nature in the early stages and the lack of effective biomarkers for prognosis, many patients are diagnosed at an advanced stage, which limits therapeutic options and impairs survival outcomes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As such, there is a critical need for novel prognostic biomarkers to enhance early detection, predict patient outcomes, and guide therapeutic strategies.\u003c/p\u003e \u003cp\u003eThe tumor microenvironment (TME) plays a pivotal role in the progression and metastasis of cancer, and cancer-associated fibroblasts (CAFs) are central components of this microenvironment [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. CAFs, which are derived from normal fibroblasts or other stromal cells, are activated in response to tumorigenic signals and contribute to various aspects of cancer biology, including tumor growth, angiogenesis, immune evasion, and resistance to therapies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. CAFs secrete a variety of growth factors, cytokines, and extracellular matrix proteins that facilitate tumor cell migration, invasion, and metastasis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Importantly, recent studies have demonstrated that CAFs not only influence tumor progression but are also linked to poor prognosis in several cancers, including lung adenocarcinoma [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe interaction between CAFs and tumor cells is mediated by a complex network of signaling molecules and gene expression changes. Many studies have identified specific CAF-related genes that contribute to tumor aggressiveness and metastasis. These genes include those involved in extracellular matrix remodeling, inflammatory responses, and immune cell recruitment, all of which play critical roles in tumor progression [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Importantly, recent research has suggested that CAF-related genes can serve as powerful prognostic biomarkers. In the context of LUAD, the expression levels of specific CAF-associated genes have been shown to be correlated with poor survival, making them promising candidates for inclusion in prognostic models [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, comprehensive models that integrate CAF-related gene signatures to predict clinical outcomes in LUAD patients are lacking.\u003c/p\u003e \u003cp\u003eThis study aimed to develop a prognostic model for lung adenocarcinoma (LUAD) patients on the basis of genes associated with tumor-associated fibroblast (CAF) infiltration scores while validating its accuracy and robustness. Key objectives include identifying CAF-related genes (CAFRGs), constructing a prognostic model, validating it in independent cohorts, assessing the clinical significance of risk scores, and conducting in vitro functional validation of key genes. These findings are intended to enhance precision in prognosis and therapeutic decision-making for LUAD patients.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Construction and Validation of the Prognostic Model Based on CAFRGs\u003c/h2\u003e \u003cp\u003eThis study utilized two LUAD cohorts from the TCGA and GEO databases, along with their corresponding clinical data. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e summarize the demographic and clinical characteristics of the training, internal testing, and independent validation sets. After excluding samples with missing clinical information from the TCGA-LUAD dataset, a total of 504 LUAD patients were included, of whom 183 were alive and 321 had died by the end of the follow-up period (median follow-up time: 1.789 years). This dataset was randomly divided into a training set (n\u0026thinsp;=\u0026thinsp;353) and an internal testing set (n\u0026thinsp;=\u0026thinsp;151) at a 7:3 ratio. As expected, no significant differences were observed in the major clinicopathological characteristics between the training, testing, and entire TCGA-LUAD cohorts (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, the study included the GEO dataset GSE31210, which comprises 226 LUAD patients, with a mortality rate of 37.81% at the end of follow-up (median follow-up time: 4.720 years).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and Clinical Characteristics of the training, internal testing, and complete TCGA LUAD datasets\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eTCGA LUAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;353)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInternal testing\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;151)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;504)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e194 (54.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76 (50.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e270 (53.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e159 (45.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (49.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e234 (46.43%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119 (34.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (26.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e158 (31.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e225 (65.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111 (74.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e336 (68.02%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e231 (90.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100 (95.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e335 (93.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (9.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (4.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (6.94%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e225 (65.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99 (66.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e324 (66.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117 (34.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (33.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e166 (33.88%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e306 (86.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132 (87.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e438 (86.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT3/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (13.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (12.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66 (13.10%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage I/II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e272 (77.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e118 (78.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e390 (77.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage III/IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81 (22.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (21.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e114 (22.62%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSmoke history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNonsmoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139 (39.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61 (40.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e200 (39.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e214 (60.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90 (59.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e304 (60.32%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003etime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e204 (57.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (53.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e285 (56.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149 (42.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70 (46.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e219 (43.45%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003estatus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e220 (62.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 (66.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e321 (63.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133 (37.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 (33.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e183 (36.31%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: TCGA: The Cancer Genome Atlas; LUAD: Lung adenocarcinoma\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Construction and Validation of a Prognostic Model Based on CAF-Related Genes\u003c/h2\u003e \u003cp\u003eOn the basis of the TCGA LUAD dataset, CAF immune infiltration scores for each sample were obtained via the MCPCOUNTER and XCELL algorithms. Correlation analysis was performed to identify genes whose expression levels were correlated with CAF infiltration scores (CAFRGs, cancer-associated fibroblast-related genes; \u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA-B\u003c/b\u003e). The intersection of these genes from the two algorithmic analyses revealed that 1,154 genes were positively correlated with CAF infiltration and that 17 genes were negatively correlated (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC-D\u003c/b\u003e). Using the training dataset, univariate Cox regression analysis identified a total of 174 CAFRGs that were associated with prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Gene selection was performed via least absolute shrinkage and selection operator (LASSO) regression, which identified 30 feature genes (\u003cb\u003eFigure S2\u003c/b\u003e). These feature genes were further incorporated into a stepwise multivariate Cox regression analysis, and the results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB. The final prognostic risk model based on CAFRGs was constructed as follows: risk score\u0026thinsp;=\u0026thinsp;COX6A1 Exp \u0026times; (0.491)\u0026thinsp;+\u0026thinsp;ENOX1 Exp \u0026times; (0.409)\u0026thinsp;+\u0026thinsp;FERMT2 Exp \u0026times; (0.319)\u0026thinsp;+\u0026thinsp;NID1 Exp \u0026times; (0.257)\u0026thinsp;+\u0026thinsp;LOX Exp \u0026times; (0.223)\u0026thinsp;+\u0026thinsp;SNAI2 Exp \u0026times; (0.148)\u0026thinsp;+\u0026thinsp;GLI2 Exp \u0026times; (0.137)\u0026thinsp;+\u0026thinsp;ZNF154 Exp \u0026times; (-0.136)\u0026thinsp;+\u0026thinsp;COX7A1 Exp \u0026times; (-0.179)\u0026thinsp;+\u0026thinsp;NXPH3 Exp \u0026times; (-0.182)\u0026thinsp;+\u0026thinsp;FRMD4A Exp \u0026times; (-0.258)\u0026thinsp;+\u0026thinsp;SYT11 Exp \u0026times; (-0.391)\u0026thinsp;+\u0026thinsp;ENTPD1 Exp \u0026times; (-0.402). Receiver operating characteristic (ROC) curves demonstrated that the risk score had good predictive performance for patient prognosis, with area under the curve (AUC) values of 0.790, 0.819, and 0.839 for 1, 4, and 5 years, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). High-risk patients had significantly poorer outcomes than low-risk patients did (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). In the training cohort, high-risk patients had shorter overall survival and more deaths than low-risk patients did (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE-F). A heatmap was used to show the expression distribution of the genes in the model across high- and low-risk samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Validation of the CAFRGs Risk Model\u003c/h2\u003e \u003cp\u003eNext, the robustness of the model was validated using the entire TCGA LUAD dataset and an independent dataset, GSE31210. ROC curves were plotted on the basis of the risk scores for patient prognosis in the TCGA LUAD dataset, with a 1-year AUC value of 0.786 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). After the risk scores were divided into high- and low-risk groups on the basis of the median risk score, survival curve analysis indicated that patients in the low-risk group had a significantly better prognosis than those in the high-risk group did (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The distribution of risk scores and survival outcomes revealed that low-risk patients had a lower mortality rate, whereas high-risk patients had a significantly higher mortality rate (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). A heatmap revealed the expression patterns of CAFRGs across high- and low-risk samples in the TCGA LUAD dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Similarly, in the GSE31210 dataset, ROC curves revealed the AUC values at 1\u0026ndash;5 years, further validating the robustness of the model in an independent dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Survival curve analysis revealed that high-risk patients had a worse prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). Additionally, high-risk patients had shorter overall survival and more deaths than low-risk patients did (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). A heatmap demonstrated the differential expression of CAFRGs in high- and low-risk samples in the GSE31210 dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). In another independent cohort, GSE13213, the 1-year AUC of the ROC curve was 0.882 (\u003cb\u003eFigure S3A\u003c/b\u003e), and survival curve analysis similarly revealed poor prognosis in the high-risk group (\u003cb\u003eFigure S3B\u003c/b\u003e), with high-risk patients having shorter overall survival and more deaths (\u003cb\u003eFigure S3C\u003c/b\u003e). These results confirm the reliability and robustness of the CAFRG prognostic model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 CAFRG Risk Score as an Independent Prognostic Factor\u003c/h2\u003e \u003cp\u003eIn the TCGA LUAD dataset, univariate Cox regression analysis revealed that the CAFRG risk score was significantly associated with key clinical and pathological features, including distant metastasis, lymph node metastasis, invasion depth, and clinical stage, all of which were identified as risk factors (HR\u0026thinsp;\u0026gt;\u0026thinsp;0, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Further multivariate Cox regression analysis confirmed that the CAFRG risk score remained an independent prognostic factor (HR\u0026thinsp;=\u0026thinsp;2.37, 95% CI: 2.16\u0026ndash;4.04, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). In the GSE31210 dataset, univariate Cox analysis also revealed that the CAFRG risk score and clinical stage were significant factors affecting patient prognosis (HR\u0026thinsp;\u0026gt;\u0026thinsp;0, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). A subsequent multivariate Cox regression further validated that the CAFRG risk score remained an independent prognostic factor (HR\u0026thinsp;=\u0026thinsp;2.82, 95% CI: 1.23\u0026ndash;6.04; P\u0026thinsp;=\u0026thinsp;0.014; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). These consistent results across multiple datasets suggest that the CAFRG risk score is a reliable and independent prognostic biomarker with substantial clinical value.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Construction and Evaluation of a Clinical Prediction Nomogram\u003c/h2\u003e \u003cp\u003eA clinical prediction nomogram was constructed on the basis of the independent prognostic factors identified by multivariate Cox regression analysis in the TCGA LUAD dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The accuracy of the nomogram was validated via calibration curves, receiver operating characteristic (ROC) curves and decision curve analysis (DCA). The calibration curve demonstrated that the predicted 1-year, 3-year, and 5-year survival rates closely matched the actual survival rates, indicating excellent predictive performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). ROC curve analysis revealed that the nomogram predicted the 1-, 3-, and 5-year survival probabilities, with AUC values of 0.811, 0.754, and 0.787, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). The DCA results demonstrated that the nomogram provided a high net benefit across various risk thresholds, supporting its value in clinical decision-making (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). For the GSE31210 dataset, a corresponding clinical prediction nomogram was constructed using the independent prognostic factors from the multivariate Cox regression analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). The calibration curve demonstrated a good fit between the predicted and actual survival outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). ROC analysis revealed the ability of the nomogram to predict the 1-, 3-, and 5-year survival probabilities, further supporting its accuracy (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG). DCA indicated a high net benefit across risk thresholds, highlighting its potential for clinical application (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH). In summary, the nomograms constructed from both the TCGA LUAD and GSE31210 datasets displayed excellent predictive performance and clinical utility, making them valuable tools for personalized treatment and prognosis in LUAD patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Construction of a Nomogram-Based Clinical Prediction Tool\u003c/h2\u003e \u003cp\u003eFurthermore, we developed an online clinical prediction tool based on nomograms constructed from the TCGA LUAD and GSE31210 datasets (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://jingege.shinyapps.io/CAFRG_model/\u003c/span\u003e\u003cspan address=\"https://jingege.shinyapps.io/CAFRG_model/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This visualization tool allows users to make individualized survival predictions on the basis of various clinical features and risk scores. By adjusting clinical parameters, users can easily obtain survival curves and probabilities for individual patients. For example, when the parameters were set to T4, N0, and a risk score of 5, the predicted 1-year, 2-year, 3-year, and 5-year survival probabilities for patients were 84%, 66%, 49%, and 18%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Figures\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC show the predicted survival curves for this setting, which indicate a progressively lower survival probability. On the other hand, when the parameters were set to T1, N0, and a risk score of 7, the predicted survival probabilities for 1-year, 2-year, 3-year, and 5-year survival were 47%, 17%, 5%, and 1%, respectively, suggesting poor long-term survival \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Risk score and its association with immune cell infiltration and immunotherapy\u003c/h2\u003e \u003cp\u003eUsing the TCGA LUAD dataset, we analyzed the correlation between the risk score and immune cell infiltration score calculated via the xCell algorithm. The results revealed a significant correlation between the risk score and the infiltration levels of several immune cell subsets, particularly classical dendritic cells (cDCs), M2 macrophages, hematopoietic stem cells (HSCs), and mast cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Additionally, the risk score was strongly correlated with the immune microenvironment score and immune score, with correlation coefficients of -0.445 and \u0026minus;\u0026thinsp;0.435, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Further correlation analysis revealed significant associations between the risk score and the expression levels of multiple immune checkpoint genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD), especially BTLA (r = -0.330, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE) and VSIR (r = -0.311, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). Moreover, via the tumor immune dysfunction and exclusion (TIDE) algorithm, we obtained immune infiltration scores for cancer-associated fibroblasts (CAFs) and myeloid-derived suppressor cells (MDSCs), along with T-cell dysfunction and exclusion scores. Subsequent correlation analysis revealed that the risk score was significantly correlated with T-cell dysfunction (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG) and exclusion (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH), as were the MDSC (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI) and CAF (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ) immune infiltration scores. Similarly, in the GSE31210 dataset, correlation analysis revealed associations with multiple immune cell infiltration scores (\u003cb\u003eFigure S4A-C\u003c/b\u003e) and T-cell dysfunction and exclusion scores on the basis of the TIDE algorithm (\u003cb\u003eFigure S4D-G\u003c/b\u003e). These results suggest that the risk score could serve as a potential indicator of the response of LUAD patients to immunotherapy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Risk score and its association with lung adenocarcinoma progression\u003c/h2\u003e \u003cp\u003eWe further assessed the correlation between the CAFRG risk score and the biological functions of LUAD. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, the heatmap displays the correlation between the CAFRG risk score and oncogenes in the TCGA LUAD and GSE31210 datasets. These results indicate that the CAFRG risk score is significantly positively correlated with several known oncogenes, such as PLK1, CDK1, and FOXM1. Further correlation analysis revealed a significant association between the CAFRG risk score and sensitivity to various anticancer drugs, as calculated by the OncoPredict algorithm, including doramapimod, axitinib, urosertib, and niraparib (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Additionally, via gene set enrichment analysis (GSEA) of the TCGA LUAD dataset, we investigated the biological functions and signaling pathways associated with the CAFRG risk score. Biological function analysis revealed that the risk score was related to functions such as DNA replication, DNA double-strand break repair, cell adhesion regulation, and immune response activation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC-D). In the signaling pathway analysis, the risk score was associated with several cancer-related signaling pathways, including the cell cycle, mismatch repair, DNA replication, the JAK-STAT signaling pathway, and cell adhesion molecules (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE-F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 COX6A1 as a Key Gene Promoting Tumor Progression in LUAD\u003c/h2\u003e \u003cp\u003eIn our analysis of the TCGA LUAD dataset, we identified COX6A1 as a key gene that promotes tumor progression within our predictive model. Scatter plot analysis revealed a strong correlation between COX6A1 expression and the risk score, suggesting that its expression is closely related to overall risk in LUAD patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Further validation across multiple datasets revealed that COX6A1 expression was significantly higher in tumor tissues than in normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). For patient prognosis, high expression of COX6A1 is significantly associated with poor outcomes in the TCGA LUAD dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). Based on the KM plotter online tool, patients with high COX6A1 expression in LUAD exhibit poor overall survival (\u003cb\u003eFigure S5A\u003c/b\u003e) and first progression (\u003cb\u003eFigure S5B\u003c/b\u003e). To explore the role of COX6A1 in the immune microenvironment, we evaluated the correlations between COX6A1 expression and immune checkpoint gene expression, immune cell infiltration, and antitumor drug sensitivity. The results indicated that COX6A1 expression was positively correlated with the expression of several immune checkpoint genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). Moreover, COX6A1 was also associated with immune cell infiltration scores for various cell types, including mast cells, tumor-associated fibroblasts (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE, and hematopoietic stem cells, as were matrix scores, immune microenvironment scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eF), and immune scores (\u003cb\u003eFigure S5C\u003c/b\u003e). In terms of antitumor drug sensitivity, COX6A1 expression was linked to increased sensitivity to drugs such as doramapimod, dihydrorotenone, and docetaxel (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eG). Additionally, COX6A1 expression was significantly positively correlated with tumor stemness, further suggesting its role in maintaining aggressive tumor characteristics (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eH). Finally, gene set enrichment analysis (GSEA) revealed that COX6A1 expression was associated with multiple important signaling pathways, such as the TGF-β and JAK-STAT pathways, and with biological functions related to DNA replication, oxidative phosphorylation, cell adhesion molecules, and base excision repair (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eI-K). These findings highlight COX6A1 as a potential therapeutic target and a critical regulator of the tumor immune microenvironment and drug sensitivity in LUAD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Silencing COX6A1 Inhibits Lung Adenocarcinoma Cell Growth and Migration\u003c/h2\u003e \u003cp\u003eCCK8 assays revealed that silencing COX6A1 significantly inhibited the proliferation of the lung adenocarcinoma cell lines A549 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA) and H1299 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). Figure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC-D presents the dose‒response curves for A549 and H1299 cells treated with various concentrations of doramapimod, demonstrating that COX6A1 knockdown increased their sensitivity to the drug, as reflected by a reduction in the IC50 values. Transwell migration assays revealed that COX6A1 knockdown significantly inhibited the migration capacity of A549 and H1299 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE-F). Similarly, EdU assays demonstrated that COX6A1 knockdown markedly reduced cell proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eG-H). Further analysis via β-galactosidase staining revealed that silencing COX6A1 induced senescence in lung cancer cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eI\u003cb\u003e‒J\u003c/b\u003e). Quantitative PCR (qPCR) analysis confirmed that COX6A1 expression was significantly reduced after COX6A1 knockdown (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eK). Moreover, the epithelial marker CDH1 and the senescence-associated genes CDKN1A (P21) and CDKN2A (P16) were significantly upregulated, whereas the mesenchymal marker CDH2 was significantly downregulated \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eL\u003cb\u003e)\u003c/b\u003e. At the protein level, Western blot analysis revealed consistent changes in protein expression, corroborating the mRNA data and further validating the critical role of COX6A1 in regulating tumor cell migration and senescence \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eM\u003cb\u003e‒N)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 COX6A1 Knockdown in Lung Cancer Cells Promotes CAF Infiltration\u003c/h2\u003e \u003cp\u003eFurther \u003cem\u003ein vitro\u003c/em\u003e studies were conducted to investigate the effect of COX6A1 overexpression in lung cancer cells on the infiltration of CAFs. Analysis of the TCGA LUAD dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA) and multiple LUAD datasets from the GEO database (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB) revealed a significant correlation between COX6A1 expression and the expression of CAF activation-related cytokines. To validate this finding, we used qPCR to measure the expression levels of several CAF-related cytokines in lung cancer cells with COX6A1 knockdown. The results showed a significant decrease in the expression of TGFB2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eC), CXCL12 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eD), and FGF2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eE). Additionally, ELISA further confirmed that the expression level of CXCL12 in the culture supernatant of COX6A1-knockdown lung cancer cells was significantly reduced (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eF). To investigate the specific mechanism by which COX6A1 affects CAF infiltration, we established a co-culture system of lung cancer cells and human embryonic lung cells WI-38 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eG). qPCR analysis of CAF marker gene expression in co-cultured WI-38 cells showed a significant upregulation of α-SMA (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eH), FN1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eI), and VIM (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eJ) RNA levels. Immunofluorescence analysis further demonstrated a significant increase in α-SMA expression in WI-38 cells after co-culture with lung cancer cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eK), with consistent results obtained through quantitative analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eL). Moreover, Transwell migration assays indicated that the migratory capacity of WI-38 cells was significantly enhanced in the co-culture system (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eM), as confirmed by quantitative analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eN). These results suggest that COX6A1 overexpression promotes the expression of CAF-related cytokines, enhancing the attraction of lung cancer cells to CAFs, thereby facilitating CAF infiltration.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eThis study systematically analyzed tumor-associated fibroblast (CAF) immune infiltration-related genes and successfully constructed and validated a prognostic model for lung adenocarcinoma (LUAD), revealing the critical role of CAFs in the tumor immune microenvironment and providing new insights and theoretical foundations for precision medicine. We found that this model not only effectively predicts the prognosis of LUAD patients but is also closely associated with the tumor immune microenvironment, drug sensitivity, and tumor biological characteristics, such as proliferation, migration, and senescence. These findings provide important clues for a deeper understanding of the mechanisms underlying CAFs in LUAD and for the development of new therapeutic strategies.\u003c/p\u003e \u003cp\u003eOne of the key findings of this study is that the CAF-related gene (CAFRG) risk score is significantly associated with immune microenvironment scores and immune cell infiltration, particularly with the expression of immune checkpoint genes. Changes in the immune microenvironment play crucial roles in tumor immune escape [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and CAFs, as important components of the tumor microenvironment, may influence tumor immune escape mechanisms by modulating immune cell infiltration and immune checkpoint activation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. We observed that the CAFRG risk score was correlated with the infiltration of various immune cells and was inversely related to the immune microenvironment score. These results suggest that high-risk patients have a lower immune microenvironment and immune activity, which may indicate immune escape features. Using the TIDE tool, we found that the risk score was negatively correlated with T-cell dysfunction and positively correlated with T-cell exclusion, suggesting that low-risk patients may have a more active T-cell immune response and an immune system capable of effectively fighting the tumor, whereas high-risk patients may face immune escape challenges, as reflected by impaired T-cell function and T-cell exclusion, leading to weakened immune surveillance [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Immune checkpoint genes (e.g., PD-1 and CTLA-4) are often closely associated with immune escape mechanisms, as tumor cells express immune checkpoint molecules to suppress immune system attacks, thereby evading host immune surveillance [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. We found that the risk score was negatively correlated with the expression of these immune checkpoint genes, further suggesting that the CAFRG risk score could predict immune escape and the immune therapy response in LUAD patients. Moreover, we explored the relationship between the risk score and tumor biological functions and found that it is closely related to signaling pathways and biological functions involved in LUAD proliferation, migration, and immune system activation. Drug resistance has become a major challenge in clinical cancer therapy. Resistance not only significantly reduces treatment efficacy but also worsens patient prognosis, increasing the complexity and cost of treatment [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The risk score was also found to be closely associated with antitumor drug sensitivity, particularly with a significant negative correlation with the sensitivity to doramapimod (a MAPK/ERK inhibitor).\u003c/p\u003e \u003cp\u003eFurthermore, we identified a key gene in the model, cytochrome c oxidase subunit 6A1 (COX6A1). COX6A1 is a mitochondrial membrane protein that is widely involved in cellular energy metabolism and plays an important role in oxidative phosphorylation (OXPHOS) in particular [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. OXPHOS is the main pathway for ATP production, and tumor cells require large amounts of energy during rapid proliferation and in response to external stress; thus, OXPHOS plays a crucial role in tumor progression [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Although the Warburg effect dominates energy metabolism in some tumors, recent studies have shown that OXPHOS is significantly activated in chemoresistant and cancer stem cells, contributing to tumor cell survival and metastasis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Therefore, COX6A1 may serve as a new target for anticancer therapies. Our study further revealed that the upregulation of COX6A1 is associated with the degree of immune cell infiltration in tumor tissues, particularly with the expression of immune checkpoint genes, suggesting that COX6A1 may affect tumor immune escape by modulating immune cell function or through immune checkpoint pathways. \u003cem\u003eIn vitro\u003c/em\u003e experiments confirmed COX6A1's role in lung cancer cells. COX6A1 Knockdown inhibited tumor cell migration and proliferation while promoting cellular senescence\u0026mdash;a state where cells cease to divide after stress, accompanied by metabolic and gene expression changes. Tumor cell senescence can suppress tumor development but also influence metastasis potential and immune escape [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Knockdown of COX6A1 significantly upregulated senescence-associated genes, such as CDKN1A and CDKN2A, indicating that COX6A1 may inhibit tumor progression by inducing senescence.\u003c/p\u003e \u003cp\u003eWe conducted further \u003cem\u003ein vitro\u003c/em\u003e studies to investigate the impact of COX6A1 knockdown on CAF infiltration in lung cancer cells. Analysis of multiple lung adenocarcinoma datasets revealed a significant correlation between COX6A1 expression and the expression of CAF activation-related cytokines. Our in vitro experiments demonstrated that COX6A1 knockdown in lung cancer cells led to the upregulation of TGFB2, CXCL12, and FGF2. Further co-culture experiments with lung cancer cells and human embryonic lung WI-38 cells showed increased expression of α-SMA, FN1, and VIM in WI-38 cells, along with significantly enhanced migration capacity. These results indicate that COX6A1 knockdown promotes the expression of CAF-related cytokines, thereby enhancing the attraction of CAFs to lung cancer cells and promoting CAF infiltration. Interestingly, CAFs typically support tumor growth and metastasis in the tumor microenvironment by secreting cytokines and matrix remodeling factors [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This appears to be contrary to the high expression of COX6A1 in lung cancer, prompting us to further investigate this issue through rigorous experiments.\u003c/p\u003e \u003cp\u003eOverall, this study constructed a prognostic model based on CAF immune infiltration-related genes, providing an effective tool for prognostic evaluation in LUAD patients and opening new directions for personalized treatment of LUAD. In particular, we found that the upregulation of COX6A1 in LUAD is closely associated with immune cell infiltration, immune escape, and biological features such as tumor cell migration and senescence, suggesting its important regulatory role in the tumor microenvironment. Our study deepens the understanding of the role of CAFs in tumor progression and highlights the importance of COX6A1 in the tumor immune microenvironment, revealing its clinical application prospects as a potential therapeutic target.\u003c/p\u003e"},{"header":"4. Materials and Methods","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Dataset\u003c/h2\u003e \u003cp\u003eThis study utilized lung adenocarcinoma (LUAD) data from multiple public databases. Initially, the gene expression data and clinical information of LUAD patients were obtained from The Cancer Genome Atlas (TCGA) database. The TCGA dataset included gene expression data from 572 patients, comprising 513 tumor tissues and 59 adjacent normal tissues, along with corresponding clinical characteristics (e.g., age, sex, and tumor stage). To validate the model's efficacy, the GSE31210 dataset was also extracted from the Gene Expression Omnibus (GEO) database, which contains gene expression and prognosis information for 226 primary LUAD samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Immune infiltration analysis\u003c/h2\u003e \u003cp\u003eTo evaluate the immune microenvironment in LUAD samples, particularly the infiltration level of CAFs, immune infiltration analysis was conducted via the xCell[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and TIMER[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] algorithms. xCell Algorithm: xCell is a high-resolution immune cell infiltration analysis tool that quantitatively predicts immune cell infiltration in the TME. Through xCell analysis, infiltration scores for CAFs and other immune and stromal cells in tumor samples were obtained. TIMER Algorithm: TIMER is an online platform for immune infiltration analysis that quantifies the infiltration levels of various immune cells (including T cells, B cells, and macrophages) and provides T-cell dysfunction and exclusion scores for each sample.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Prognostic Model Construction and Validation\u003c/h2\u003e \u003cp\u003eAll genes associated with CAF scores (|correlation coefficient|\u0026gt;0.25, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the TCGA LUAD dataset were analyzed via the Spearman method and defined as CAF-related genes (CAFRGs). The CAFRG prognostic model was constructed and validated via dataset splitting. The TCGA LUAD dataset was split into a training set and an internal test set at a 7:3 ratio. Clinical characteristic analysis: Chi-square tests were used to analyze the differences in the distributions of clinical characteristics (e.g., sex, age, tumor stage, and lymph node metastasis) between the training and validation sets. Univariate Cox regression analysis: Each CAFRG was evaluated for its relationship with overall survival (OS), and the hazard ratio (HR) and corresponding p value were calculated. CAFRGs with p values less than 0.05 were selected as candidate genes. LASSO regression analysis: To reduce model complexity and select the best prognostic factors, least absolute shrinkage and selection operator (LASSO) regression analysis was conducted to screen the most OS-related CAFRGs from the univariate Cox regression. Multivariate Cox regression analysis: Based on the LASSO regression results, multivariate Cox stepwise regression analysis was further performed to establish a prognostic model for CAF-related genes. Each patient's risk score was calculated from the Cox regression results via the formula \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\text{R}\\text{i}\\text{s}\\text{k}\\:\\text{s}\\text{c}\\text{o}\\text{r}\\text{e}\\hspace{0.17em}={\\sum\\:}_{i=1}^{n}{Exp}_{i}\\times\\:{coef}_{i}\\)\u003c/span\u003e\u003c/span\u003e, where Exp_i is the expression value of the ith ARG and coef_i is the Cox regression coefficient of that gene. Model validation: The predictive performance of the prognostic model was evaluated via Kaplan‒Meier survival analysis and time‒dependent receiver operating characteristic (ROC) curves (using the \"TimeROC\" package) in the training set, internal test set, and validation set.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Nomogram Construction and Evaluation\u003c/h2\u003e \u003cp\u003eOn the basis of the results of multivariate Cox regression analysis, a nomogram was constructed to provide an individualized survival prediction tool for patients. The nomogram combined the CAFRG risk score with independent prognostic factors identified from the multivariate Cox analysis to generate a survival probability prediction for patients. Nomogram Construction: Using the \"rms\" package, a comprehensive model was constructed that visually represents the impact of risk scores and clinical characteristics on patient survival. Model evaluation: The nomogram's predictive performance was assessed by calculating the C-index (concordance index). Calibration curves were used to evaluate the model's applicability in clinical practice. Decision curve analysis (DCA) was employed to assess the clinical decision value of the nomogram.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Clinical prediction tool development\u003c/h2\u003e \u003cp\u003eOn the basis of the prognostic model and nomogram, a clinical prediction tool was developed to assist clinicians in predicting patient survival risk on the basis of clinical characteristics and CAFRG risk scores. This tool can generate survival curves and predict 1-year, 3-year, and 5-year survival probabilities on the basis of patient clinical information and risk scores. When implemented via the R Shiny package, the tool allows clinicians to easily utilize it, aiding in the formulation of more personalized treatment plans. This tool can improve the accuracy of prognosis assessment for cancer patients and guide clinical decision-making.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Drug sensitivity analysis\u003c/h2\u003e \u003cp\u003eThe purpose of drug sensitivity analysis is to evaluate the sensitivity of various tumor samples to multiple antitumor drugs, providing a basis for clinical treatment. Drug sensitivity data were obtained from the GDSC database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancerrxgene.org/\u003c/span\u003e\u003cspan address=\"https://www.cancerrxgene.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which offers cell line sensitivity data for a range of drugs, including chemotherapeutic and targeted agents [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Drug sensitivity prediction for each sample was conducted via the OncoPredict package [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.7 GSEA enrichment analysis\u003c/h2\u003e \u003cp\u003eGene set enrichment analysis (GSEA) was employed to investigate signaling pathways and biological functions associated with risk scores. Initially, all genes correlated with the risk scores were identified through correlation analysis and ranked according to their correlation coefficients. GSEA was conducted via the \"ClusterProfiler\" R package, which is based on predefined C2 (curated gene sets) and C5 (GO gene sets) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Pathways significantly associated with risk scores were identified on the basis of the normalized enrichment score (NES) and false discovery rate (FDR).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.8 Cell Culture\u003c/h2\u003e \u003cp\u003eThe cell lines used in this study included the human non-small cell lung cancer (NSCLC) cell lines A549 and H1299. All the cell lines were purchased from the American Type Culture Collection (ATCC) and cultured under standard conditions. A549 and H1299 cells were cultured in RPMI-1640 medium supplemented with 10% fetal bovine serum (FBS). The cells were maintained at 37\u0026deg;C in a humidified incubator with 5% CO₂ and passaged when they reached 70\u0026ndash;80% confluence. Morphological examination confirmed the absence of contamination in all the cell lines before use.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.9 shRNA Construction and Transfection\u003c/h2\u003e \u003cp\u003eTo investigate the function of the key gene COX6A1 in the model, we designed shRNA expression vectors targeting COX6A1. The specific steps are as follows:\u003c/p\u003e \u003cp\u003eshRNA Design: Specific shRNA sequences targeting COX6A1 were designed via siRNA design software (e.g., BLOCK-iT\u0026trade; RNAi Designer).\u003c/p\u003e \u003cp\u003eVector construction: The shRNA sequences were subsequently cloned and inserted into the pGreen vector, and successful cloning was confirmed via agarose gel electrophoresis.\u003c/p\u003e \u003cp\u003eCell Transfection: A549 or H1299 cells at 70\u0026ndash;80% confluence were cotransfected with the shRNA vector via the Lipofectamine 3000 transfection reagent. Subsequent experimental analyses were conducted 48 hours posttransfection.\u003c/p\u003e \u003cp\u003eTransfection efficiency: Transfection efficiency was assessed via qPCR and Western blot analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.10 CCK-8 Assay\u003c/h2\u003e \u003cp\u003eCell proliferation was assessed via the Cell Counting Kit-8 (CCK-8) assay (Beyotime). The experimental steps were as follows: transfected A549 or H1299 cells were seeded into 96-well plates at a density of 2 \u0026times; 10\u0026sup3; cells per well. After 24 hours of culture, various concentrations of drugs or media were added. At 24, 48, and 72 hours posttreatment, 10 \u0026micro;L of CCK-8 solution was added to each well, and the cells were incubated for an additional 2 hours. The optical density (OD) at 450 nm was measured via a microplate reader (BioTek). The cell proliferation inhibition rate was calculated on the basis of the OD values to generate proliferation curves for different time points and drug concentrations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.11 Doramapimod Dose‒Response Curve\u003c/h2\u003e \u003cp\u003eTo evaluate the inhibitory effect of Doramapimod on cells, a dose‒response curve was constructed.\u003c/p\u003e \u003cp\u003eTransfected A549 or H1299 cells were seeded into 96-well plates, with drug concentrations ranging from 0 to 512 \u0026micro;M. The effects of various drug concentrations on cell proliferation were assessed 24 hours posttreatment via the CCK-8 assay. Dose‒Response Analysis: Dose‒response curves were generated via Grap\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e4.12 Transwell Cell Migration Assay\u003c/h2\u003e \u003cp\u003eCell migration ability was assessed via a Transwell chamber assay. The experimental procedure was as follows: transfected A549 or H1299 cells were seeded into the upper chamber with serum-free medium, and the lower chamber contained medium supplemented with 20% FBS as a chemoattractant. After 24 hours of incubation, nonmigrated cells in the upper chamber were removed with a sterile cotton swab. The migrated cells in the lower chamber were fixed and stained with crystal violet. The stained cells were observed and counted under a microscope. The results are expressed as the number of migrated cells, and differences between groups were compared.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e4.13 EdU Cell Proliferation Assay\u003c/h2\u003e \u003cp\u003eCell proliferation was evaluated via an EdU (5-ethynyl-2'-deoxyuridine) assay kit. The experimental steps were as follows: transfected A549 or H1299 cells were seeded into 96-well plates, treated with drugs, and then incubated with EdU labeling solution for 2 hours. After fixation, the cells were stained via an EdU staining kit. EdU-positive cells were counted under a fluorescence microscope, and the proliferation index was calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e4.14 β-Galactosidase Assay\u003c/h2\u003e \u003cp\u003eTo assess cellular senescence, a β-galactosidase staining kit was used. The experimental procedure was as follows: Transfected cells were seeded in culture dishes and treated with specific drugs or stimuli. β-Galactosidase staining was performed according to the kit instructions. Senescent cells, which appeared blue under a microscope, were observed and quantified. The proportion of senescent cells was calculated and compared between groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e4.15 Quantitative PCR (qPCR)\u003c/h2\u003e \u003cp\u003eQuantitative PCR (qPCR) was used to measure the relative expression levels of the CDKN1A, CDKN2A, CDH1, and CDH2 genes, with GAPDH serving as the internal control. Total RNA was extracted and reverse-transcribed into cDNA via reverse transcriptase. Each qPCR mixture included SYBR Green Master Mix, gene-specific primers (CDKN1A, CDKN2A, CDH1, CDH2, and GAPDH), and a cDNA template. The PCR program was set as follows: initial denaturation at 95\u0026deg;C for 30 seconds, followed by 40 cycles of 95\u0026deg;C for 5 seconds and 60\u0026deg;C for 30 seconds. Relative expression levels were calculated via the 2^(-ΔΔCt) method, where ΔCt represents the difference in Ct values between the target gene and ACTB and where ΔΔCt represents the difference between the experimental and control groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e4.16 Western blotting\u003c/h2\u003e \u003cp\u003eWestern blotting was used to detect the protein expression of CDKN1A, CDKN2A, CDH1, and CDH2, with GAPDH serving as the internal control. The protein concentration in the cell lysates was determined via the BCA method, and 30 \u0026micro;g of protein was loaded for SDS‒PAGE. After transfer, the membranes were blocked with 5% nonfat milk for 1 hour and incubated with specific primary antibodies (CDKN1A, CDKN2A, CDH1, CDH2, and GAPDH) overnight. The membranes were subsequently incubated with HRP-conjugated secondary antibodies and developed via enhanced chemiluminescence (ECL) reagents. The signals were captured via a chemiluminescence imaging system, and protein expression levels were quantified via ImageJ software, with GAPDH used as the normalization control.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e4.17 ELISA (Enzyme-Linked Immunosorbent Assay)\u003c/h2\u003e \u003cp\u003eIn this study, we used ELISA to quantify the levels of CXCL12 in the culture supernatant of COX6A1 knockdown lung cancer cells. We collected the culture supernatant from COX6A1 knockdown-treated lung cancer cells and processed it according to the manufacturer\u0026rsquo;s instructions. ELISA plates were coated with CXCL12-specific antibodies. After washing, samples were added and incubated at appropriate temperatures and times to ensure binding. Subsequently, an enzyme-labeled secondary antibody was added, followed by another wash and the addition of a substrate solution to generate a measurable signal. The concentration of CXCL12 in the samples was calculated by comparing the amount of product from the enzymatic reaction to a standard curve.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003e4.18 Co-Culture System\u003c/h2\u003e \u003cp\u003eTo explore the impact of COX6A1 on lung cancer cell-induced CAF infiltration, we established a co-culture system with lung cancer cells and human embryonic lung WI-38 cells. Lung cancer cells were first seeded in appropriate media to reach 70\u0026ndash;80% confluence. WI-38 cells were then introduced at a suitable ratio to ensure effective contact and interaction between the cells. The media were regularly changed to remove metabolic waste and maintain optimal growth conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003ch2\u003e4.19 Immunofluorescence Analysis\u003c/h2\u003e \u003cp\u003eWe used immunofluorescence to detect α-SMA expression in WI-38 cells. Co-cultured WI-38 cells were fixed with 4% paraformaldehyde and permeabilized to allow antibody access. Cells were incubated with an α-SMA-specific primary antibody, followed by PBS washing to remove unbound antibodies. A fluorescently labeled secondary antibody was then added, incubated, and washed to remove excess secondary antibody. Cells were observed using a fluorescence microscope, and images were captured for quantitative analysis of α-SMA expression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section2\"\u003e \u003ch2\u003e4.20 Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted via GraphPad V8.3.0 software (GraphPad Software, LLC). The data are presented as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations. Differences between two groups were assessed via Student\u0026rsquo;s t test, whereas comparisons among multiple groups were made via analysis of variance (ANOVA). All the statistical tests were two-tailed, with a P value of \u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"524\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 461px;\"\u003e\n \u003cp\u003eArea Under the Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eCAF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 461px;\"\u003e\n \u003cp\u003eCancer-Associated Fibroblast\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eCAFRG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 461px;\"\u003e\n \u003cp\u003eCancer-Associated Fibroblast-Related Genes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eCOX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 461px;\"\u003e\n \u003cp\u003eCox regression analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 461px;\"\u003e\n \u003cp\u003eDecision Curve Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eGEO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 461px;\"\u003e\n \u003cp\u003eGene Expression Omnibus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eGSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 461px;\"\u003e\n \u003cp\u003eGene Set Enrichment Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 461px;\"\u003e\n \u003cp\u003eHazard Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eLASSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 461px;\"\u003e\n \u003cp\u003eLeast Absolute Shrinkage and Selection Operator\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eLUAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 461px;\"\u003e\n \u003cp\u003eLung Adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eNES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 461px;\"\u003e\n \u003cp\u003eNormalized Enrichment Score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eOXPHOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 461px;\"\u003e\n \u003cp\u003eOxidative Phosphorylation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eqPCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 461px;\"\u003e\n \u003cp\u003eQuantitative Polymerase Chain Reaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 461px;\"\u003e\n \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eTCGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 461px;\"\u003e\n \u003cp\u003eThe Cancer Genome Atlas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eTIDE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 461px;\"\u003e\n \u003cp\u003eTumor Immune Dysfunction and Exclusion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eTIMER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 461px;\"\u003e\n \u003cp\u003eTumor Immune Estimation Resource\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of interest:\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research was funded by the National Natural Science Foundation of China (Project No. 82373613 and 82304184) and the China Postdoctoral Science Foundation (Project No. 2023M732527). The study was also supported by the Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eX.Z. and L.B. conceptualized the study; X.Z., L.B., and L.Q. developed the methodology; X.Z. conducted the software analysis; T.L., L.Q., and W.H. performed the validation; X.Z. carried out the formal analysis; T.L. con-ducted the investigation; L.B. provided resources; J.L. handled data curation; X.Z. and L.B. prepared the original draft of the manuscript; J.L. and J.W. reviewed and edited the manuscript; L.Q. created the visualizations; J.W. supervised the study; J.W. administered the project; and J.L. and J.W. acquired funding. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe gene expression data and clinical information for lung adenocarcinoma (LUAD) patients were sourced from The Cancer Genome Atlas (TCGA) database, accessible via the TCGA portal at https://portal.gdc.cancer.gov/. Addi-tionally, the GSE31210 dataset was obtained from the Gene Expression Omnibus (GEO) database, available under accession number GSE31210 at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE31210.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eThai AA, Solomon BJ, Sequist LV, Gainor JF, Heist RS: \u003cstrong\u003eLung cancer.\u003c/strong\u003e \u003cem\u003eLancet \u003c/em\u003e2021, \u003cstrong\u003e398:\u003c/strong\u003e535-554.\u003c/li\u003e\n\u003cli\u003eChen P, Liu Y, Wen Y, Zhou C: \u003cstrong\u003eNon-small cell lung cancer in China.\u003c/strong\u003e \u003cem\u003eCancer Commun (Lond) \u003c/em\u003e2022, \u003cstrong\u003e42:\u003c/strong\u003e937-970.\u003c/li\u003e\n\u003cli\u003eLi Y, Yan B, He S: \u003cstrong\u003eAdvances and challenges in the treatment of lung cancer.\u003c/strong\u003e \u003cem\u003eBiomed Pharmacother \u003c/em\u003e2023, \u003cstrong\u003e169:\u003c/strong\u003e115891.\u003c/li\u003e\n\u003cli\u003eSen 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Bioinform \u003c/em\u003e2021, \u003cstrong\u003e22\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eYu G, Wang LG, Han Y, He QY: \u003cstrong\u003eclusterProfiler: an R package for comparing biological themes among gene clusters.\u003c/strong\u003e \u003cem\u003eOmics \u003c/em\u003e2012, \u003cstrong\u003e16:\u003c/strong\u003e284-287.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Tumor-associated fibroblasts, immune microenvironment, lung adenocarcinoma, prognostic model, COX6A1","lastPublishedDoi":"10.21203/rs.3.rs-5904445/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5904445/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLung adenocarcinoma (LUAD), the predominant subtype of non-small cell lung cancer (NSCLC), presents significant challenges in early diagnosis and personalized treatment. Recent research has focused on the role of the tumor microenvironment, particularly tumor-associated fibroblasts (CAFs), in tumor progression. This study systematically analyzed CAF immune infiltration-related genes to construct a prognostic model for LUAD, confirming its predictive value for patient outcomes. The risk score derived from CAF-related genes (CAFRGs) was negatively correlated with immune microenvironment scores and linked to the expression of immune checkpoint genes, indicating that high-risk patients may exhibit immune escape characteristics. Analysis via the TIDE tool revealed that low-risk patients had more active T-cell immune responses. The risk score also correlated with anti-tumor drug sensitivity, particularly to doramapimod. Notably, COX6A1 emerged as a key gene in the model, with its upregulation associated with immune cell infiltration and immune escape. Further in vitro experiments demonstrated that COX6A1 regulates LUAD cell migration, proliferation, and senescence, suggesting its role in tumor immune evasion. Additionally, further co-culture studies of lung cancer cells and fibroblasts revealed that COX6A1 knockdown promotes the expression of CAF-related cytokines, enhancing CAF infiltration. Overall, this study provides a foundation for personalized treatment of LUAD and highlights COX6A1 as a promising therapeutic target within the tumor immune microenvironment, guiding future clinical research.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Prognostic Model for Lung Adenocarcinoma Based on CAF-Related Genes: Unveiling the Role of COX6A1 in Cancer Progression and CAF Infiltration","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-30 10:01:30","doi":"10.21203/rs.3.rs-5904445/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":"02a9211f-0baa-4100-aca0-575ec2c57f60","owner":[],"postedDate":"January 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-30T10:09:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-30 10:01:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5904445","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5904445","identity":"rs-5904445","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}