Seven-gene biomarkers reveal prognostic and immune signatures in lung adenocarcinoma

Seven-gene biomarkers reveal prognostic and immune signatures in lung adenocarcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Seven-gene biomarkers reveal prognostic and immune signatures in lung adenocarcinoma Yanhui Liu, Sisi Gong, Ruirui Tong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7512386/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Lung adenocarcinoma (LUAD) has high morbidity and mortality, with its mechanisms and treatment still under investigation. Methods Differentially expressed genes (DEGs) between LUAD and normal tissues were identified from three GEO datasets. Gene enrichment and protein-protein interaction (PPI) analyses were performed, with hub genes identified. Results Sixty-eight overlapping DEGs were found. The PPI network highlighted nine hub genes, and survival analysis linked seven of them (AGER, CAV1, EDNRB, ROBO4, EMCN, TEK, PTPRB) to LUAD prognosis. Clinical and immune analyses demonstrated these genes' significant roles in LUAD progression. ROC and PCA confirmed their diagnostic potential. Conclusions These seven genes, downregulated in LUAD, could serve as biomarkers and therapeutic targets, requiring further research. Lung adenocarcinoma differentially expressed genes bioinformatics analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Lung cancer, with incidence and mortality rates of 11.6% and 18.4%, respectively, remains a leading cause of cancer-related deaths worldwide [ 1 , 2 ]. In European countries, it claims more lives than breast, pancreatic, and prostate cancers combined [ 3 ]. In China, the increasing incidence and mortality rates of lung cancer present a significant public health challenge [ 4 ]. Lung adenocarcinoma (LUAD) is the most common histologic subtype affecting both men and women [ 5 ]. Unfortunately, LUAD is often diagnosed at an advanced stage, and despite significant advancements in treatment, the 5-year survival rate remains alarmingly low [ 1 ]. Additionally, due to tumor heterogeneity, LUAD is linked to a significant risk of recurrence following therapy [ 6 ]. Given these challenges, it is crucial to identify specific molecular markers and develop personalized therapies for LUAD. Such advancements are essential for improving early detection and enhancing patient outcomes. With the advancement of bioinformatics analysis methods, gene expression profiling has become widely used in cancer research, offering a more functional molecular understanding compared to traditional methods [ 7 ]. Numerous studies utilizing gene array-based expression analyses in lung cancer have been conducted, resulting in a diverse array of gene expression datasets. By integrating and reanalyzing these datasets, researchers can gain deeper insights into the candidate genes and molecular pathways involved in tumor progression. This integrative approach also facilitates a comprehensive examination of the tumor microenvironment, revealing the functional heterogeneity of tumor-infiltrating immune cells [ 8 ] and generating novel hypotheses relevant to cancer diagnosis, therapeutic interventions, and prognostic assessments. Currently, using gene expression profiles to define prognostic biomarkers for LUAD patients is still under investigation. Although some studies have identified genes closely associated with LUAD development [ 9 – 11 ], the clinical application of these findings remains limited, and the mechanisms underlying LUAD development are not yet fully understood. Therefore, extensive research is imperative to identify novel prognostic markers, considering the multifaceted tumor heterogeneity and complex molecular regulatory networks characteristic of LUAD. These efforts are crucial for improving the clinical management and therapeutic outcomes for this malignancy. Bioinformatic analysis serves as a robust and comprehensive approach for analyzing gene expression data across various datasets. In this study, three microarray datasets—GSE118370, GSE19188, and GSE30219—were downloaded from the GEO database to identify differentially expressed genes (DEGs) between lung adenocarcinoma (LUAD) and normal tissue samples. The DEGs underwent gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to better elucidate their roles and interactions. A protein-protein interaction (PPI) network was then constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and key hub genes were identified through the Molecular Complex Detection (MCODE) plugin in Cytoscape. To further validate the findings, additional analyses were conducted, including overall survival (OS) analysis, gene expression validation, and assessment of clinical pathological features. Receiver operating characteristic (ROC) curve analysis was also performed, along with studies on immune cell infiltration patterns. These comprehensive analyses identified critical hub genes associated with LUAD, which are expected to serve as potential biomarkers for its diagnosis, treatment, and prognosis, paving the way for more personalized and effective healthcare strategies. 2. Materials and Methods 2.1. Data processing and differentially expressed genes (DEGs) identification In this study, three gene expression datasets (GSE118370, GSE19188, and GSE30219) were obtained from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.govgeo/ ). These datasets were generated using the Affymetrix GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array). GSE118370 includes 6 LUAD tissue samples and 6 non-cancerous samples; GSE19188 comprises 45 LUAD samples and 65 non-cancerous samples; GSE30219 includes 85 LUAD samples and 14 non-cancerous samples. The GEO2R tool was subsequently employed to identify DEGs across these datasets. The criteria for significant differential expression were set at an absolute log2 fold change (FC) greater than 2 and an adjusted P-value of less than 0.05. The overlap of DEGs between the three datasets was shown using Venn diagrams ( http://www.bioinformatics.com.cn/ ). Moreover, the heat map was conducted by the Sangerbox ( http://www.sangerbox.com/tool ). 2.2. Functional and pathway enrichment analysis To investigate the biological functions associated with DEGs, GO functional analysis and KEGG pathway enrichment analysis were conducted. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) ( http://david.ncifcrf.gov ) was utilized for GO classification including biological process (GO-BP), cell component (GO-CC), and molecular function (GO-MF). Concurrently, the Sangerbox tool ( http://www.sangerbox.com/tool ) was used for KEGG analysis of the overlapping DEGs. Herein, the significance level for both GO and pathway enrichment analysis was set at P < 0.05. 2.3. Protein–protein interaction (PPI) network construction and module analysis Analyzing the functional interactions between genes can provide insights into the mechanisms underlying the generation or development of diseases. In this study, the STRING online database ( https://string-db.org ) was used to construct a PPI network of DEGs implicated in the progression of LUAD. The results were further analyzed and visualized using Cytoscape (version 3.9.1), an open-source bioinformatics software platform for visualizing molecular interaction networks. The MCODE plug-in within Cytoscape [ 12 ], which clusters networks based on topological structure to identify densely connected regions, was employed to identify key modules. The following filter criteria were applied in MCODE: degree cut-off = 2, node score cut-off = 0.2, k-core = 2, and maximum depth = 100. 2.4. The Kaplan–Meier (KM) plotter database survival analysis The Kaplan-Meier plotter database ( www.kmplot.com ) was used to validate survival analyses of LUAD patients. Overall survival (OS) was analyzed based on high and low gene expression. The statistical values, including the hazard ratio (HR) and log-rank P-value, were calculated and displayed in the graph. A log-rank P-value of less than 0.05 was set as the threshold for significance. 2.5. GEPIA database analysis The Gene Expression Profiling Interactive Analysis (GEPIA; http://gepia.cancer-pku.cn/ ) is an advanced online platform designed to analyze RNA sequencing expression data. It utilizes a comprehensive database that includes 9,736 tumor samples and 8,587 normal samples, sourced from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) projects [ 13 ]. This work analyzed the expression of hub genes across LUAD tumors. 2.6. UALCAN database analysis The University of ALabama at Birmingham CANcer data analysis Portal (UALCAN) ( http://ualcan.path.uab.edu ), stands out as a user-friendly and comprehensive online platform designed specifically for analyzing cancer OMICS data [ 14 ]. This portal makes it easier to analyze gene expression using clinical data from The Cancer Genome Atlas (TCGA). Moreover, it broadens its capabilities to include protein expression analysis, utilizing valuable data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset. Hereon, UALCAN was used to analyze key gene expression based on cancer stage, tumor grade, and other clinicopathological characteristic. 2.7. Human protein profile analysis The Human Protein Atlas ( https://www.proteinatlas.org ) is a valuable resource that provides immunohistochemistry (IHC) and immunofluorescence (IF) data. This website is an essential tool for researchers looking to study protein expression across various human tissues and cells [ 15 ]. 2.8. The receiver operating characteristic (ROC) curve analysis To evaluate the predictive ability of the hub genes, we conducted a receiver operating characteristic (ROC) curve analysis. Utilizing the area under the curve (AUC) from the corresponding ROC curves of the hub genes, we assessed the discriminative effects between LUAD tissues and healthy controls. Furthermore, the expression profiles of the hub genes in the three datasets were utilized as variables to perform principal component analysis (PCA) using SIMCA-P v14.0 software (Umetrics AB, Sweden), a multivariate pattern recognition technique. The quality of the models was evaluated using the parameters R 2 X and Q 2 . R 2 X explains the proportion of variance in the x-variables, while Q 2 indicates the models' predictive performance capability. 2.9. Immune infiltration analysis To examine the correlation between the expression of seven hub genes in LUAD and both tumor purity and immune infiltration abundance, the Tumor Immune Estimation Resource 2.0 (TIMER2.0) webserver ( http://cistrome.org/TIMER/ ) was utilized. 3. Results 3.1. Identification of DEGs in LUAD In this study, 372, 282, and 260 DEGs were extracted from GSE118370, GSE19188, and GSE30219, respectively, using the GEO2R online tool with criteria of |logFC| >2 and P-value < 0.05. Specifically, GSE118370 yielded 73 upregulated and 299 downregulated DEGs ( Fig. 1 A), GSE19188 identified 89 upregulated and 193 downregulated DEGs (Fig. 1 B), and GSE30219 showed 70 upregulated and 190 downregulated DEGs (Fig. 1 C). The Venn diagram online tool was then utilized to intersect the DEGs from the three datasets, identifying 68 common DEGs in LUAD tissues, including 8 upregulated and 60 downregulated genes (Fig. 1 D). Additionally, the overlapping DEGs in LUAD were displayed by a heat map using dataset GSE118370 as a reference. (Fig. 1 E) 3.2. Functional enrichment analysis of overlapping DEGs To explore the possible biological functions of the 68 common DEGs, GO and KEGG analyses were executed. These investigations comprised 46 GO terms including BP, CC, and MF, in addition to 8 significant pathways, as listed in Supplementary Table S1 . The GO enrichment analysis bar chart was shown in Fig. 1 A. For the BP category, the DEGs were significantly enriched in the processes of angiogenesis, receptor internalization, vasoconstriction, and so on. In the CC category, DEGs were also significantly enriched in collagen trimer, plasma membrane, extracellular region and so on. As for MF, DEGs were mainly enriched in identical protein binding, macromolecular complex binding, signaling receptor activity, and so on. In addition, KEGG pathway analysis (Fig. 2 B) was performed and the PPAR signaling pathway was found to be the most altered pathway, 3.3. Hub genes screened through the PPI network DEGs were uploaded onto the STRING database to construct the PPI network. Next, the visualization of the PPI network was performed using Cytoscape software, and the MCODE plug-in was utilized to identify the most central part of the PPI network. This analysis identified a total of 44 nodes and 45 edges in the PPI network (Fig. 3 A). Furthermore, 9 hub genes were pinpointed through MCODE, including AGER, CD36, CAV1, EDNRB, FABP4, ROBO4, EMCN, TEK and PTPRB (Fig. 3 B). 3.4. The survival analysis of hub genes in LUAD To determine the prognostic value of key genes, a survival analysis based on gene expression levels was performed using the KM plotter. These key genes, including AGER, CD36, CAV1, EDNRB, FABP4, ROBO4, EMCN, TEK, and PTPRB, were identified due to their association with poor OS. The results showed significant correlations for AGER (HR = 0.77 (0.69–0.87), log-rank P = 2.4e-5), CAV1 (HR = 0.82 (0.73–0.92), log-rank P = 0.00096), EDNRB (HR = 0.74 (0.66–0.84), log-rank P = 1.1e-6), ROBO4 (HR = 0.66 (0.57–0.77), log-rank P = 6.6e-8), EMCN (HR = 0.59 (0.51–0.69), log-rank P = 4.6e-12), TEK (HR = 0.62 (0.55–0.7), log-rank P = 9.6e-15), and PTPRB (HR = 0.75 (0.67–0.85), log-rank P = 2.8e-6) (Fig. 4 ). There were no statistically significant results from the survival analysis of the remaining hub genes. To summarize, our findings indicated a substantial correlation between seven genes (AGER, CAV1, EDNRB, ROBO4, EMCN, TEK, PTPRB) and patient outcomes. 3.5. Validation of seven prognostic-related hub gene expression The transcription levels of seven hub genes in 483 LUAD tissues and 347 normal lung tissues from the TCGA and GTEx databases were analyzed using GEPIA. It was found that the expression of all seven genes was significantly lower in tumor tissues compared to normal tissues ( p < 0.05, Fig. 5 ). In addition, the expression of seven hub genes was analyzed using UALCAN software, considering various clinicopathological characteristics such as age, sample types, and stage. Based on age, the transcription levels of all genes at any stage were significantly decreased in patients compared to normal samples. Moreover, CAV1 expression was significantly increased in patients aged 21–40 compared to other age groups. ROBO4 expression was significantly increased in patients aged 41–60 compared to those aged 61–80. TEK expression was significantly decreased in patients aged 21–40 compared to those aged 41–60 and 61–80 ( Supplementary Fig. 1A ). For sample types, compared to normal samples, the transcription levels of all genes decreased significantly in LUAD patients (Supplementary Fig. 1B) . When comparing stages, the transcription levels of all genes decreased significantly in stages one to four compared to normal samples. However, there were no significant differences in gene expression between stages S1, S2, S3, and S4 ( Supplementary Fig. 1C ). To validate the protein expression levels of these genes, we utilized CPTAC data from the UALCAN cancer database and staining data from tumor pathological sections obtained from HPA. Figure 6 demonstrates that all hub genes exhibited significantly lower protein expression levels in LUAD samples compared to normal samples. Additionally, leveraging the HPA datasets, we conducted further investigations into the differential expression of hub genes between tumors and normal tissues using immunohistochemistry. This analysis yielded representative staining, revealing that AGER, CAV1, EDNRB, EMCN, TEK, and PTPRB proteins were downregulated in LUCA tissues compared to normal tissues, mirroring the transcriptional patterns (Fig. 7 ). Furthermore, subcellular structural analysis utilizing the HPA database revealed that AGER is primarily localized in the nucleoli fibrillar center, while EDNRB, TEK, and PTPRB are predominantly enriched on the plasma membrane. CAV1 was primarily located in the golgi apparatus, while ROBO4 was predominantly found in the plasma membrane, although the staining for both proteins was not distinctly obvious ( Supplementary Fig. 2 ). 3.6. Diagnostic efficacy verification ROC curve analysis was conducted to evaluate the individual predictive power of the 7 hub genes. Using SPSS, the curves for these genes were drawn based on their expression in the GSE118370, GSE19188, and GSE30219 datasets to verify their discriminative effect on distinguishing LUAD patients from normal controls. The results showed that each gene had better diagnostic efficiency with AUC > 0.8 (Fig. 8 A-C). PCA was performed using the expression profiles of the hub genes in the three datasets. As illustrated in Fig. 8 D-F, the two group samples in each dataset were well separated, demonstrating the strong discriminative power of the 7 hub genes. 3.7. Relationship between infiltrating immune cells and hub gene expression Immune infiltration in the tumor microenvironment (TME) is closely related to cancer development, progression, and metastasis[ 16 ]. Using the TIMER2.0 database, the correlation between the expression levels of hub genes and 6 kinds of infiltrating immune cells, namely, CD8 + T cells, CD4 + T cells, B cells, macrophages, neutrophils and dendritic cells (DCs) within the TME in LUAD were investigated (Fig. 9 A–G). Interestingly, all genes showed a negative correlation with tumor purity. Additionally, the expression of CAV1, EDNRB, EMCN, and TEK is significantly related to the infiltration of CD8 + T cells. Apart from CAV1, the expression of all genes is significantly associated with the infiltration of CD4 + T cells. AGER and CAV1 expression is significantly related to the infiltration of B cells. Furthermore, the expression of all genes is significantly associated with macrophage infiltration. The expression of CAV1, TEK, and PTPRB is significantly related to neutrophil infiltration. The expression of AGER, CAV1, ROBO4, and TEK is significantly associated with the infiltration of DC. Discussion In recent decades, there has been a gradual increase in the incidence rate of LUAD, positioning it as one of the most prevalent forms of lung cancer. LUAD is characterized by its rapid progression, marked by the presence of micrometastatic foci, which contribute to a heightened recurrence rate and an increased propensity for metastasis[ 5 ]. Despite the availability of standard surgical interventions for localized and early-stage disease, the majority of patients receive diagnoses at advanced stages, largely owing to the presence of subtle and nonspecific early symptoms[ 17 ]. Consequently, patients often undergo conventional treatments such as combined radiotherapy and chemotherapy, which are associated with heightened mortality risks[ 18 ]. While low-dose chest computed tomography (LDCT) screening has been introduced to facilitate early detection of lung cancer, its efficacy in improving survival rates appears to be limited[ 17 , 19 ]. Unfortunately, less than 20% of LUAD patients survive for an average of five years[ 20 ]. The poor prognosis associated with LUAD is primarily attributed to the absence of specific biomarkers, which hampers timely diagnosis and the implementation of targeted therapeutic interventions. Therefore, there is a pressing need to explore novel biomarkers and associated pathways to gain insights into molecular mechanisms, thereby facilitating the development of precise medical modalities tailored to the needs of LUAD patients. Recent advancements in microarray and computational analysis methodologies have emerged as invaluable tools in this endeavor, offering promising avenues for the identification and validation of reliable biomarkers or gene signatures for LUAD diagnosis and prognosis. Despite the extensive research efforts devoted to investigating LUAD-related biomarkers, none have demonstrated efficacy thus far. Consequently, a comprehensive analysis of LUAD is warranted, aimed at identifying optimal molecular targets for therapeutic intervention, while elucidating the intricate biological pathways underlying its pathogenesis and progression. This study aimed to identify prognostic biomarkers for LUAD by analyzing three profile datasets (GSE118370, GSE19188, and GSE30219) using bioinformatic methods. The analysis included 136 LUAD specimens and 85 non-LUAD specimens. Using GEO2R, DEGs across these datasets were assessed, and their intersection was delineated using Venn diagrams, identifying 68 common DEGs (|log2FC| >2 and adjusted P value < 0.05), comprising 8 upregulated and 60 downregulated DEGs. Subsequent GO enrichment analysis highlighted significant enrichment of DEGs in pivotal biological processes such as angiogenesis, receptor internalization, and vasoconstriction; in cell components including collagen trimer, plasma membrane, and extracellular region; and in molecular functions such as identical protein binding, macromolecular complex binding, and signaling receptor activity. These findings underscored the molecular mechanisms implicated in LUAD, suggesting that tumor pathogenesis is a multifaceted biological process driven by alterations in the expression of specific genes and epigenetic modifications. Dysregulated regulation of multiple genes can facilitate the onset and progression of LUAD through diverse pathways. Specifically, DEGs showed notable enrichment in the PPAR signaling pathway through KEGG enrichment analysis. Cancer is characterized by uncontrolled cell proliferation, a process in which the PPAR signaling pathway plays a crucial role, exerting pleiotropic effects in cancer development and progression [ 21 , 22 ]. Consistent with previous studies, this investigation underscores the significant association between the PPAR signaling pathway and LUAD[ 23 ]. Furthermore, employing STRING and Cytoscape, a comprehensive DEGs PPI network was constructed, comprising 44 nodes and 45 edges. Utilizing MCODE analysis within Cytoscape identified 9 central downregulated DEGs (AGER, CD36, CAV1, EDNRB, FABP4, ROBO4, EMCN, TEK, and PTPRB), further illuminating potential key biomarkers for LUAD. To further evaluate the reliability of the hub genes and identify potential LUAD biomarkers, we conducted a Kaplan-Meier analysis on nine hub genes. Our analysis revealed that seven genes (AGER, CAV1, EDNRB, ROBO4, EMCN, TEK, PTPRB) were associated with the prognosis of LUAD. We then assessed the mRNA and protein expression levels of these seven genes. GEPIA analysis indicated that the relative expression levels of these genes in LUAD patients were significantly lower compared to normal controls. Interestingly, the transcription levels of all these genes correlated with the age, sample types, and stage of LUAD patients, suggesting their involvement in LUAD development. Furthermore, UALCAN analysis showed that the protein levels of these genes were significantly reduced in LUAD tissues compared to normal tissues. Immunohistochemistry results confirmed that AGER, CAV1, EDNRB, EMCN, TEK, and PTPRB were expressed at lower levels in LUAD tissues than in normal tissues. However, ROBO4 was undetected in both LUAD and normal lung tissues. Additionally, ROC and PCA analyses indicated that these seven genes could serve as effective biomarkers for LUAD diagnosis. In summary, we identified seven significant genes that could serve as novel molecular markers and effective therapeutic targets for LUAD in future research. Furthermore, there was some evidence suggesting that AGER, CAV1, EDNRB, ROBO4, EMCN, TEK, and PTPRB are closely associated with lung diseases and cancer. AGER also referred to as the receptor for advanced glycation end products (RAGE), is a well-established driver of inflammation[ 24 ]. It triggers pro-inflammatory pathways within cells, playing a critical role in diverse physiological and pathological processes, including autoimmune diseases and cancer[ 25 ]. AGER is notably overexpressed in various cancers such as ovarian[ 26 ], breast[ 27 ], and endometrial cancers[ 28 ]. Interestingly, studies have indicated its downregulation in lung cancer, where it exhibits tumor-suppressive properties[ 25 , 29 ]. Moreover, reducing AGER levels has been shown to diminish both the quantity and suppressive capabilities of tumor-induced myeloid-derived suppressor cells (MDSCs) [ 30 ]. In this study, we observed decreased AGER expression in LUAD, correlating with poorer OS and suggesting its potential as an independent prognostic risk factor for LUAD. Caveolin-1 (CAV1) is an integral membrane protein located in the outer cell membrane, particularly within caveolae, as well as in other intracellular membranes, including those of the endoplasmic reticulum, Golgi apparatus, and transport vesicles[ 31 ]. CAV1 functions as either an oncogenic or antineoplastic protein, influencing cell metabolism, autophagy, and senescence in cancer biology[ 32 ]. However, its role as a tumor promoter or suppressor remains controversial. For example, CAV1 has been found to promote tumors in renal cancer, prostate cancer, tongue squamous cell carcinoma (SCC), lung SCC, and bladder SCC. Conversely, it has an inhibitory role in esophageal adenocarcinoma, LUAD, and cutaneous SCC[ 33 ]. Previous research has also indicated that a reduction in CAV1 might enhance epithelial-mesenchymal transition (EMT), a crucial component in tumor metastasis[ 34 ]. In our study, we found that CAV1 expression was low in LUAD, suggesting a poor prognosis for LUAD patients. Additionally, a bioinformatics analysis indicated that CAV1 is significantly downregulated in LUAD tissues and that its expression levels are positively correlated with OS times in cancer patients[ 32 ], consistent with our findings. Additionally, our analysis of immune infiltration reveals a positive correlation between CAV1 and CD8 + T cells, macrophages, neutrophils, and dendritic cells (DC). Conversely, there is a negative correlation between CAV1 and B cells. This result indicated that CAV1 promotes antitumor immunity of immune cells. The endothelin receptor type B (EDNRB) gene, located on chromosome 13, activates multiple cancer-associated signaling pathways, such as the mitogen-activated protein kinase/Erk 2 and PI3K/AKT pathways[ 35 ]. By binding to its ligand endothelin (ET), EDNRB transmits extracellular signals that impact cell proliferation and migration. Studies indicated EDNRB’s involvement in various cellular functions linked to cancer development. Liu et al.[ 35 ] highlighted EDNRB as a promising prognostic biomarker in TNBC patients, while Zhou et al.[ 36 ] associated elevated EDNRB expression with poorer prognosis in HPV (-) HNSCC. Our research reveals diminished EDNRB expression in LUAD, which correlates with reduced overall survival (OS), underscoring its potential for clinical screening in LUAD. ROBO4 is a cell surface receptor for a secreted signaling protein, primarily expressed in endothelial cells and their progenitors, hematopoietic stem cells[ 37 ]. It plays a crucial role in both developmental and pathological angiogenesis[ 38 ]. Recent studies have found that higher levels of ROBO4 expression in gliomas[ 39 ] and acute myeloid leukemias[ 40 ] are linked to shorter overall survival. One previous study revealed that increased ROBO4 expression has been correlated with increased OS in early-stage non-small cell lung cancer. Andreas Pircher et al. [ 41 ]hypothesized that elevated levels of tumor endothelial markers (TEMs) like ROBO4 function as vascular stabilization factors, thereby reducing metastatic spread. They further suggested that higher ROBO4 expression during disease progression correlates with a longer time for antiangiogenic progression. In this study, GO analysis results showed that ROBO4 is involved in angiogenesis, is decreased in LUAD, and is associated with poor OS. Lower ROBO4 expression during disease progression carries clinical and pathophysiological implications, indicating a potential weakening of anti-angiogenic mechanisms in LUAD patients. Further development of biomarkers is needed to validate these results. EMCN is a transmembrane protein that undergoes O-sialylation and is predominantly expressed on endothelial cell surfaces[ 42 ]. It plays a critical role in mediating cellular adhesion between vascular endothelial cells and neutrophils. Under normal conditions, interference with EMCN expression on quiescent endothelial cells promotes increased neutrophil adhesion, whereas, during inflammatory events, its expression prevents excessive neutrophil binding[ 43 ]. Despite these established roles, the specific functions and mechanisms of EMCN in tumorigenesis remain incompletely understood. Recent studies have highlighted that lung neutrophils deficient in EMCN tend to proliferate and transform into N2 neutrophils under the influence of TGF-β within the tumor microenvironment, thereby facilitating tumor metastasis and growth[ 42 ]. In agreement with previous reports[ 44 ], our investigation into LUAD patients has identified a significant down-regulation of EMCN. This observation was further corroborated through analysis of mRNA and protein levels, suggesting a potential link between reduced EMCN expression and the pathogenesis of LUAD. TEK is a receptor tyrosine kinase from the Tie2 family, primarily expressed in endothelial cells where it regulates vascular regeneration and stability[ 45 ]. While the angiopoietin-Tie system is known for its roles in inflammation, metastasis, and lymphangiogenesis, TEK's specific involvement in cancer cells remains incompletely understood[ 46 ]. Our study identified significantly lower TEK expression in LUAD, correlating with advanced cancer stages and poor OS. Another study corroborated our findings, showing significant downregulation of TEK in LUAD tissues and cell lines compared to normal counterparts[ 47 ]. Importantly, they also found that this reduced expression correlated with poorer survival outcomes in LUAD patients. Recent research also suggests that reduced Tie2 signaling may promote inflammatory cell migration into the tumor microenvironment, potentially positioning TEK as a tumor suppressor in clear cell renal cell carcinoma (ccRCC)[ 48 ]. Moreover, our analysis of immune infiltration indicated a significant positive correlation between TEK expression levels and the presence of CD8 + T cells, CD4 + T cells, macrophages, neutrophils, and DCs in LUAD. These observations lead us to hypothesize that TEK likely plays a suppressive role in LUAD cells. The protein tyrosine phosphatase receptor type B (PTPRB) gene, located at 12q15, consists of 37 exons and encodes a member of the protein tyrosine phosphatase (PTP) family. This family of signaling molecules is crucial for regulating various cellular processes, including cell growth, differentiation, the mitotic cycle, and oncogenic transformation[ 49 ]. Dysregulation of PTPRB function and expression has been linked to carcinogenesis and tumor progression in several cancer types [ 50 ]. Numerous studies have shown that PTPRB has tumor-suppressive functions in NSCLC cells, both in vitro and in vivo. Specifically, overexpression of PTPRB inhibited cell growth, anchorage-independent growth, and cell invasion, while knockdown of PTPRB enhanced tumorigenic properties[ 51 ]. Our research revealed that PTPRB expression was low in LUAD and was associated with TNM stage and poor OS. Furthermore, another report also suggested that low PTPRB expression might be indicative of poor survival rates in LUAD patients[ 51 ]. In summary, this study conducted comprehensive bioinformatics analyses including GO functional annotation, KEGG enrichment analysis, PPI network construction, and identification of hub genes. It identified seven significantly downregulated DEGs (AGER, CAV1, EDNRB, ROBO4, EMCN, TEK, PTPRB) that correlate with LUAD progression and poor prognosis. However, a key limitation is the lack of experimental validation. Future research efforts should prioritize rigorous in vitro and in vivo studies to further elucidate the roles of these genes in LUAD tumorigenesis and prognosis. Declarations Acknowledgments We sincerely thank the researchers for making their GEO microarray datasets available online and acknowledge their valuable contributions. We also extend our gratitude to the support from the DAVID, STRING, and GEPIA public databases, as well as the Cytoscape and R package software. Author Contributions All the authors contributed significantly to this work. GSS designed the study. LYH and TRR contributed to the literature search. LYH performed this study and wrote the initial draft of the manuscript. GSS and LYH reviewed and edited the manuscript. All authors read and approved the manuscript. Funding This research was funded by the Startup Fund for scientific research, Fujian Medical University (Grant number:2024QH1131). Supplementary data Supplementary data associated with this article can be found in Supplementary Table S1 and Supplementary Figures. Data Availability Statement: The original datasets presented in this study are openly available in the Gene Expression Omnibus (GEO) repository. The accession numbers are GSE118370, GSE19188, and GSE30219. These datasets were used for the identification of differentially expressed genes and subsequent bioinformatics analysis in this manuscript. Ethical approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing financial interests. Clinical trial number Not applicable. References Zhang L, Liu Y, Zhuang J-G , et al. Identification of key genes and biological pathways in lung adenocarcinoma by integrated bioinformatics analysis. World Journal of Clinical Cases . 2023;11: 5504-5518. Liu B, Quan X, Xu C , et al. Lung cancer in young adults aged 35 years or younger: A full-scale analysis and review. Journal of Cancer . 2019;10: 3553-3559. M M, G C, P B , et al. European cancer mortality predictions for the year 2017, with focus on lung cancer. ANNALS OF ONCOLOGY . 2017;28. Jianjie L, Fan Y, Xiao L , et al. Characteristics, survival, and risk factors of Chinese young lung cancer patients: the experience from two institutions. Oncotarget . 2017;8. Sun LU, Tan H, Yu T , et al. Identification of lncRNAs associated with T cells as potential biomarkers and therapeutic targets in lung adenocarcinoma. Oncology Research . 2023;31: 967-988. Boone G, Avinash V, C Ryan M , et al. A clinical model to estimate recurrence risk in resected stage I non-small cell lung cancer. American journal of clinical oncology . 2008;31. Wang Y, Zhou Z, Chen L , et al. Identification of key genes and biological pathways in lung adenocarcinoma via bioinformatics analysis. Molecular and Cellular Biochemistry . 2020;476: 931-939. Xu J, Zhang C, Wang X , et al. Integrative Proteomic Characterization of Human Lung Adenocarcinoma. Cell . 2020;182: 245-261.e217. Liao Y, He D, Wen F. Analyzing the characteristics of immune cell infiltration in lung adenocarcinoma via bioinformatics to predict the effect of immunotherapy. Immunogenetics . 2021;73: 369-380. Yang X, Feng Q, Jing J , et al. Identification of differentially expressed genes associated with lung adenocarcinoma via bioinformatics analysis. GENERAL PHYSIOLOGY AND BIOPHYSICS . 2021;40: 31-48. Lu M, Fan X, Liao W , et al. Identification of significant genes as prognostic markers and potential tumor suppressors in lung adenocarcinoma via bioinformatical analysis. BMC Cancer . 2021;21. Yuan Q, Zheng L, Liao Y , et al. Overexpression of CCNE1 confers a poorer prognosis in triple-negative breast cancer identified by bioinformatic analysis. World Journal of Surgical Oncology . 2021;19. Liu X, Yuan R, Peng J , et al. SLMO2 is a potential prognostic and immunological biomarker in human pan-cancer. Sci Rep . 2024;14: 1070. Chandrashekar DS, Karthikeyan SK, Korla PK , et al. UALCAN: An update to the integrated cancer data analysis platform. Neoplasia . 2022;25: 18-27. Ying B, Xu W, Nie Y , et al. HSPA8 Is a New Biomarker of Triple Negative Breast Cancer Related to Prognosis and Immune Infiltration. DISEASE MARKERS . 2022;2022: 8446857. Kung-Chi K, Stefania V, Chin-Hsien T , et al. Metabolic communication in the tumour-immune microenvironment. NATURE CELL BIOLOGY . 2022;24. Chen Y, Ma S, Lin C , et al. Integrative analysis of DNA methylomes reveals novel cell-free biomarkers in lung adenocarcinoma. Frontiers in Genetics . 2023;14. Meina W, Roy S H, Chris BJNM. Toward personalized treatment approaches for non-small-cell lung cancer. NATURE MEDICINE . 2021;27. Peter J M, Gerard A S, Lesley H S , et al. Screening for Lung Cancer: CHEST Guideline and Expert Panel Report. Chest . 2021;160. Alesha A T, Benjamin J S, Lecia V S , et al. Lung cancer. The Lancet . 2021;398. Ma Y, Wu L. Investigation of the function of the novel tumor marker BEND5 in lung adenocarcinoma based on data mining and in vitro analysis. Journal of Thoracic Disease . 2023;15: 1749-1769. Xu F, Tian D, Shi X , et al. Analysis of the Expression and Prognostic Potential of a Novel Metabolic Regulator ANGPTL8/Betatrophin in Human Cancers. Pathology and Oncology Research . 2021;27. Hu F-F, Chen H, Duan Y , et al. CBX2 and EZH2 cooperatively promote the growth and metastasis of lung adenocarcinoma. Molecular Therapy - Nucleic Acids . 2022;27: 670-684. Liu W, Ouyang S, Zhou Z , et al. Identification of genes associated with cancer progression and prognosis in lung adenocarcinoma: Analyses based on microarray from Oncomine and The Cancer Genome Atlas databases. Molecular Genetics & Genomic Medicine . 2018;7. Zhang X, Ye T, Li M , et al. Association of Polymorphisms in Inflammation Genes With the Prognosis of Advanced Non-Small Cell Lung Cancer Patients Receiving Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors. Frontiers in Oncology . 2022;12. Farzaneh R, Jamshid K, Mohammad Taghi G , et al. Overexpression of receptor for advanced glycation end products (RAGE) in ovarian cancer. Cancer Biomarkers . 2016;18. Maryam N, Jamshid K, Mohammad T G , et al. Increased Expression of the Receptor for Advanced Glycation End-Products (RAGE) Is Associated with Advanced Breast Cancer Stage. Oncol Res Treat . 2016;39. Lu Z, Da L, Yi-Ming Z , et al. Effects of receptor for advanced glycation endproducts on microvessel formation in endometrial cancer. BMC Cancer . 2016;16. Wang Q, Zhu W, Xiao G , et al. Effect of AGER on the biological behavior of non‑small cell lung cancer H1299 cells. Mol Med Rep . 2020;22: 810-818. Tanna W, Tom H, Emily B , et al. The receptor for advanced glycation endproducts (RAGE) decreases survival of tumor-bearing mice by enhancing the generation of lung metastasis-associated myeloid-derived suppressor cells. CELLULAR IMMUNOLOGY . 2021;365. Gerstenberger W, Wrage M, Kettunen E , et al. Stromal Caveolin-1 and Caveolin-2 Expression in Primary Tumors and Lymph Node Metastases. Analytical Cellular Pathology . 2018;2018: 1-8. Yuanliang Y, Zhijie X, Long Q , et al. Identification of CAV1 and DCN as potential predictive biomarkers for lung adenocarcinoma. American journal of physiology. Lung cellular and molecular physiology . 2019;316. Fu P, Chen F-C, Pan Q , et al. The different functions and clinical significances of caveolin-1 in human adenocarcinoma and squamous cell carcinoma. OncoTargets and Therapy . 2017;Volume 10: 819-835. Yao J, Zhang Y, Li M , et al. Single-Cell RNA-Seq Reveals the Promoting Role of Ferroptosis Tendency During Lung Adenocarcinoma EMT Progression. Frontiers in Cell and Developmental Biology . 2022;9. Liu S, Zhang J, Zhu J , et al. Prognostic values of EDNRB in triple‑negative breast cancer. Oncology Letters . 2020;20: 1-1. Zhou D, Wang J, Wang J , et al. Profiles of immune cell infiltration and immune-related genes in the tumor microenvironment of HNSCC with or without HPV infection. American Journal of Translational Research . 2021;13: 2163-2180. Zhao H, Ahirwar DK, Oghumu S , et al. Endothelial Robo4 suppresses breast cancer growth and metastasis through regulation of tumor angiogenesis. Molecular Oncology . 2015;10: 272-281. Krafft U, Reis H, Ingenwerth M , et al. Nuclear Localization of Robo is Associated with Better Survival in Bladder Cancer. PATHOLOGY & ONCOLOGY RESEARCH . 2018;26: 253-261. Heng C, Yixue X, Wenjing L , et al. Overexpression of Roundabout4 predicts poor prognosis of primary glioma patients via correlating with microvessel density. JOURNAL OF NEURO-ONCOLOGY . 2015;123. Yin-Kai C, Hsin-An H, Jih-Luh T , et al. Clinical and prognostic implications of Roundabout 4 (robo4) in adult patients with acute myeloid leukemia. PLoS One . 2015;10. Andreas P, Karin J, Florian K , et al. Biomarkers of evasive resistance predict disease progression in cancer patients treated with antiangiogenic therapies. Oncotarget . 2016;7. Zhang G, Li M, Zhou D , et al. Loss of endothelial EMCN drives tumor lung metastasis through the premetastatic niche. Journal of Translational Medicine . 2022;20. Huang Q, Li Xm, Sun Jp , et al. Tumor‐derived endomucin promotes colorectal cancer proliferation and metastasis. Cancer Medicine . 2022;12: 3222-3236. Yin D, Qiu J, Hu S , et al. CAV1 is a prognostic predictor for patients with idiopathic pulmonary fibrosis and lung cancer. Journal of biosciences . 2022;47. M H, Y R S, J K , et al. TEK is a novel prognostic marker for clear cell renal cell carcinoma. European Review for Medical and Pharmacological Sciences . 2019;23. Kim GH, Heo HJ, Kang JW , et al. Multi-omics analysis revealed TEK and AXIN2 are potential biomarkers in multifocal papillary thyroid cancer. Cancer Cell International . 2022;22. Peng T, Yang F, Sun Z , et al. miR-19a-3p Facilitates Lung Adenocarcinoma Cell Phenotypes by Inhibiting TEK. Cancer Biotherapy and Radiopharmaceuticals . 2022;37: 589-601. Liao Z, Yao H, Wei J , et al. Development and validation of the prognostic value of the immune-related genes in clear cell renal cell carcinoma. Translational Andrology and Urology . 2021;10: 1607-1619. Xia X, Fan J, Fan Z. Hsa_circ_0129047 sponges miR-665 to attenuate lung adenocarcinoma progression by upregulating protein tyrosine phosphatase receptor type B. The Korean Journal of Physiology & Pharmacology . 2023;27: 131-141. Weng X, Chen W, Hu W , et al. PTPRB promotes metastasis of colorectal carcinoma via inducing epithelial-mesenchymal transition. Cell Death & Disease . 2019;10. Qi Y, Dai Y, Gui S. Protein tyrosine phosphatase PTPRB regulates Src phosphorylation and tumour progression in NSCLC. Clinical and Experimental Pharmacology and Physiology . 2016;43: 1004-1012. 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04:06:21","extension":"html","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":122181,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7512386/v1/d314ca5f7706b632ba578662.html"},{"id":95799429,"identity":"b6743613-c8ec-461c-854c-a906918a12b5","added_by":"auto","created_at":"2025-11-13 08:19:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":378235,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of overlapping DEGs. Volcano plots for DEGs in LUAD and normal tissues based on data from GEO datasets\u003cstrong\u003e (A) \u003c/strong\u003eGSE118370,\u003cstrong\u003e(B) \u003c/strong\u003eGSE19188 and \u003cstrong\u003e(C) \u003c/strong\u003eGSE30219. Green: downregulated genes; Black: no differentially expressed genes; Red: upregulated genes. \u003cstrong\u003e(D)\u003c/strong\u003e Venn diagrams of the DEGs from the three data sets. Different colors in the figure mean different data sets. \u003cstrong\u003e(E) \u003c/strong\u003eA representative heat map of the 68 genes in the GSE118370 dataset that overlap between LUAD and normal samples.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7512386/v1/a863b6e9db3e2b7b1965a745.png"},{"id":95697630,"identity":"c4569949-e519-4e42-abf8-cc469ac250c4","added_by":"auto","created_at":"2025-11-12 04:06:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":171199,"visible":true,"origin":"","legend":"\u003cp\u003eFunction enrichment analysis. \u003cstrong\u003e(A) \u003c/strong\u003eGO functional analysis and\u003cstrong\u003e(B)\u003c/strong\u003e KEGG pathway analysis of the overlapping DEGs between LUAD and normal. The dot color represents the p-value and the dot size represents the number of differential genes.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7512386/v1/679cebeac91e691a26612e00.png"},{"id":95799433,"identity":"3501de8d-4207-4349-83fa-43e26ad75316","added_by":"auto","created_at":"2025-11-13 08:19:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":142540,"visible":true,"origin":"","legend":"\u003cp\u003ePPI network analysis and hub genes identification.\u003cstrong\u003e (A) \u003c/strong\u003eThe PPI network of 68 DEGs. \u003cstrong\u003e(B)\u003c/strong\u003e Nine hub genes were screened using the Cytoscape software plugin MCODE.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7512386/v1/15b98373aa86ef2544fde94e.png"},{"id":95697636,"identity":"69d97583-540b-452c-aec1-bc62dc713442","added_by":"auto","created_at":"2025-11-12 04:06:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":197451,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier overall survival analyses for the seven key genes\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7512386/v1/2a4950b1c1aaf7597b3e0052.png"},{"id":95697641,"identity":"ea3c510d-661f-4e97-b728-be8c3cfa714c","added_by":"auto","created_at":"2025-11-12 04:06:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":135183,"visible":true,"origin":"","legend":"\u003cp\u003eSignificantly expressed seven genes including AGER, CAV1, EDNRB, ROBO4, EMCN, TEK, and PTPRB in LUAD samples compared to normal samples. All genes have notably low mRNA expression in the LUAD specimen compared to the normal specimen (*p \u0026lt; 0.05). Red color refers to tumor tissues and grey color refers to normal samples\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7512386/v1/8d195f6314c30ce179b2665f.png"},{"id":95697638,"identity":"615d0fa9-6f83-4d60-9e45-1355b39c06be","added_by":"auto","created_at":"2025-11-12 04:06:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":149169,"visible":true,"origin":"","legend":"\u003cp\u003eThe protein expression of AGER, CAV1, EDNRB, ROBO4, EMCN, TEK, and PTPRB in normal tissues and lung cancer tissues based on subclasses analyzed by the UALCAN cancer database. Z-values show standard deviations for the specified cancer type from the median across samples. Values for the Log2 Spectral count ratio obtained from CPTAC were first normalized within each sample profile and then across samples.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7512386/v1/4fe08f72e4e0a422e82f7069.png"},{"id":95799856,"identity":"79033b6f-1cae-4b3a-b19c-957a6d70edaf","added_by":"auto","created_at":"2025-11-13 08:21:00","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":227549,"visible":true,"origin":"","legend":"\u003cp\u003eThe representative immunohistochemistry (IHC) images of AGER, CAV1, EDNRB, EMCN, TEK, and PTPRB in normal and LUAD tissues were extracted from the HPA database. In each set, tumor tissue sections were displayed on the upper side, and normal tissue sections were displayed on the lower side\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7512386/v1/75be912b0cc59b0c03f6b1fb.jpeg"},{"id":95800387,"identity":"5e54c3fd-a1fe-4b1d-9ebe-22af30308943","added_by":"auto","created_at":"2025-11-13 08:22:35","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":282010,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curves and PCA 3D score plots of hub genes. ROC curve analysis of hub genes including AGER, CAV1, EDNRB, ROBO4, EMCN, TEK and PTPRB in the \u003cstrong\u003e(A) \u003c/strong\u003eGSE118370 dataset, \u003cstrong\u003e(B)\u003c/strong\u003e GSE19188 dataset and \u003cstrong\u003e(C)\u003c/strong\u003e GSE30219 dataset;\u003cstrong\u003e \u003c/strong\u003ePCA models from \u003cstrong\u003e(D) \u003c/strong\u003eGSE118370 dataset, \u003cstrong\u003e(E)\u003c/strong\u003eGSE19188 dataset and \u003cstrong\u003e(F)\u003c/strong\u003e GSE30219 dataset. Red and green colors represent LUAD and normal samples, respectively.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7512386/v1/b74d947b2db872a6221d61da.png"},{"id":95697642,"identity":"c88c57a1-de82-4e6a-ad33-604ec23b11d5","added_by":"auto","created_at":"2025-11-12 04:06:21","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":380398,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between expression of the seven hub genes and immune infiltration of LUAD. \u003cstrong\u003e(A–G) \u003c/strong\u003eCorrelation of genes including seven hub genes with tumor purity and infiltration of CD8 + T cells, CD4 + T cells, B cells, macrophages, neutrophils, and dendritic cells.\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7512386/v1/765fe84e35fbf9e71db0a7f9.jpeg"},{"id":100787593,"identity":"35752be8-b05b-4cfc-ad01-4117d3460d92","added_by":"auto","created_at":"2026-01-21 12:02:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2997350,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7512386/v1/5b4ee461-e846-4764-a3b9-5bdde480b891.pdf"},{"id":95697628,"identity":"a7c9f6e8-30e2-44ce-98d1-c81ad005066f","added_by":"auto","created_at":"2025-11-12 04:06:20","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":11919,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7512386/v1/c14e5780c065a51734521ff1.xlsx"},{"id":95697634,"identity":"024aa803-af9b-469e-8ee5-1e3abe18a125","added_by":"auto","created_at":"2025-11-12 04:06:20","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":162497,"visible":true,"origin":"","legend":"","description":"","filename":"SupplyFigure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7512386/v1/5217a4511d1fd88478a04d3e.jpg"},{"id":95798768,"identity":"37625bca-3514-4404-a8b8-d42c3bf57d9e","added_by":"auto","created_at":"2025-11-13 08:17:45","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":176398,"visible":true,"origin":"","legend":"","description":"","filename":"SupplyFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7512386/v1/602923b16d4af3383b099acf.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Seven-gene biomarkers reveal prognostic and immune signatures in lung adenocarcinoma","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLung cancer, with incidence and mortality rates of 11.6% and 18.4%, respectively, remains a leading cause of cancer-related deaths worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In European countries, it claims more lives than breast, pancreatic, and prostate cancers combined [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In China, the increasing incidence and mortality rates of lung cancer present a significant public health challenge [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Lung adenocarcinoma (LUAD) is the most common histologic subtype affecting both men and women [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Unfortunately, LUAD is often diagnosed at an advanced stage, and despite significant advancements in treatment, the 5-year survival rate remains alarmingly low [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Additionally, due to tumor heterogeneity, LUAD is linked to a significant risk of recurrence following therapy [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Given these challenges, it is crucial to identify specific molecular markers and develop personalized therapies for LUAD. Such advancements are essential for improving early detection and enhancing patient outcomes.\u003c/p\u003e\u003cp\u003eWith the advancement of bioinformatics analysis methods, gene expression profiling has become widely used in cancer research, offering a more functional molecular understanding compared to traditional methods [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Numerous studies utilizing gene array-based expression analyses in lung cancer have been conducted, resulting in a diverse array of gene expression datasets. By integrating and reanalyzing these datasets, researchers can gain deeper insights into the candidate genes and molecular pathways involved in tumor progression. This integrative approach also facilitates a comprehensive examination of the tumor microenvironment, revealing the functional heterogeneity of tumor-infiltrating immune cells [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and generating novel hypotheses relevant to cancer diagnosis, therapeutic interventions, and prognostic assessments. Currently, using gene expression profiles to define prognostic biomarkers for LUAD patients is still under investigation. Although some studies have identified genes closely associated with LUAD development [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], the clinical application of these findings remains limited, and the mechanisms underlying LUAD development are not yet fully understood. Therefore, extensive research is imperative to identify novel prognostic markers, considering the multifaceted tumor heterogeneity and complex molecular regulatory networks characteristic of LUAD. These efforts are crucial for improving the clinical management and therapeutic outcomes for this malignancy.\u003c/p\u003e\u003cp\u003eBioinformatic analysis serves as a robust and comprehensive approach for analyzing gene expression data across various datasets. In this study, three microarray datasets\u0026mdash;GSE118370, GSE19188, and GSE30219\u0026mdash;were downloaded from the GEO database to identify differentially expressed genes (DEGs) between lung adenocarcinoma (LUAD) and normal tissue samples. The DEGs underwent gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to better elucidate their roles and interactions. A protein-protein interaction (PPI) network was then constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and key hub genes were identified through the Molecular Complex Detection (MCODE) plugin in Cytoscape. To further validate the findings, additional analyses were conducted, including overall survival (OS) analysis, gene expression validation, and assessment of clinical pathological features. Receiver operating characteristic (ROC) curve analysis was also performed, along with studies on immune cell infiltration patterns. These comprehensive analyses identified critical hub genes associated with LUAD, which are expected to serve as potential biomarkers for its diagnosis, treatment, and prognosis, paving the way for more personalized and effective healthcare strategies.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Data processing and differentially expressed genes (DEGs) identification\u003c/h2\u003e\u003cp\u003eIn this study, three gene expression datasets (GSE118370, GSE19188, and GSE30219) were obtained from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.govgeo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.govgeo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). These datasets were generated using the Affymetrix GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array). GSE118370 includes 6 LUAD tissue samples and 6 non-cancerous samples; GSE19188 comprises 45 LUAD samples and 65 non-cancerous samples; GSE30219 includes 85 LUAD samples and 14 non-cancerous samples. The GEO2R tool was subsequently employed to identify DEGs across these datasets. The criteria for significant differential expression were set at an absolute log2 fold change (FC) greater than 2 and an adjusted P-value of less than 0.05. The overlap of DEGs between the three datasets was shown using Venn diagrams (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioinformatics.com.cn/\u003c/span\u003e\u003cspan address=\"http://www.bioinformatics.com.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Moreover, the heat map was conducted by the Sangerbox (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.sangerbox.com/tool\u003c/span\u003e\u003cspan address=\"http://www.sangerbox.com/tool\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Functional and pathway enrichment analysis\u003c/h2\u003e\u003cp\u003eTo investigate the biological functions associated with DEGs, GO functional analysis and KEGG pathway enrichment analysis were conducted. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://david.ncifcrf.gov\u003c/span\u003e\u003cspan address=\"http://david.ncifcrf.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized for GO classification including biological process (GO-BP), cell component (GO-CC), and molecular function (GO-MF). Concurrently, the Sangerbox tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.sangerbox.com/tool\u003c/span\u003e\u003cspan address=\"http://www.sangerbox.com/tool\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used for KEGG analysis of the overlapping DEGs. Herein, the significance level for both GO and pathway enrichment analysis was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Protein\u0026ndash;protein interaction (PPI) network construction and module analysis\u003c/h2\u003e\u003cp\u003eAnalyzing the functional interactions between genes can provide insights into the mechanisms underlying the generation or development of diseases. In this study, the STRING online database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org\u003c/span\u003e\u003cspan address=\"https://string-db.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to construct a PPI network of DEGs implicated in the progression of LUAD. The results were further analyzed and visualized using Cytoscape (version 3.9.1), an open-source bioinformatics software platform for visualizing molecular interaction networks. The MCODE plug-in within Cytoscape [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], which clusters networks based on topological structure to identify densely connected regions, was employed to identify key modules. The following filter criteria were applied in MCODE: degree cut-off =\u0026thinsp;2, node score cut-off =\u0026thinsp;0.2, k-core\u0026thinsp;=\u0026thinsp;2, and maximum depth\u0026thinsp;=\u0026thinsp;100.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. The Kaplan\u0026ndash;Meier (KM) plotter database survival analysis\u003c/h2\u003e\u003cp\u003eThe Kaplan-Meier plotter database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://www.ncbi.nlm.nih.govgeo/\" target=\"_blank\"\u003ewww.kmplot.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.kmplot.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to validate survival analyses of LUAD patients. Overall survival (OS) was analyzed based on high and low gene expression. The statistical values, including the hazard ratio (HR) and log-rank P-value, were calculated and displayed in the graph. A log-rank P-value of less than 0.05 was set as the threshold for significance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. GEPIA database analysis\u003c/h2\u003e\u003cp\u003eThe Gene Expression Profiling Interactive Analysis (GEPIA; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia.cancer-pku.cn/\u003c/span\u003e\u003cspan address=\"http://gepia.cancer-pku.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is an advanced online platform designed to analyze RNA sequencing expression data. It utilizes a comprehensive database that includes 9,736 tumor samples and 8,587 normal samples, sourced from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) projects [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This work analyzed the expression of hub genes across LUAD tumors.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6. UALCAN database analysis\u003c/h2\u003e\u003cp\u003eThe University of ALabama at Birmingham CANcer data analysis Portal (UALCAN) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ualcan.path.uab.edu\u003c/span\u003e\u003cspan address=\"http://ualcan.path.uab.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), stands out as a user-friendly and comprehensive online platform designed specifically for analyzing cancer OMICS data [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This portal makes it easier to analyze gene expression using clinical data from The Cancer Genome Atlas (TCGA). Moreover, it broadens its capabilities to include protein expression analysis, utilizing valuable data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset. Hereon, UALCAN was used to analyze key gene expression based on cancer stage, tumor grade, and other clinicopathological characteristic.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7. Human protein profile analysis\u003c/h2\u003e\u003cp\u003eThe Human Protein Atlas (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a valuable resource that provides immunohistochemistry (IHC) and immunofluorescence (IF) data. This website is an essential tool for researchers looking to study protein expression across various human tissues and cells [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8. The receiver operating characteristic (ROC) curve analysis\u003c/h2\u003e\u003cp\u003eTo evaluate the predictive ability of the hub genes, we conducted a receiver operating characteristic (ROC) curve analysis. Utilizing the area under the curve (AUC) from the corresponding ROC curves of the hub genes, we assessed the discriminative effects between LUAD tissues and healthy controls. Furthermore, the expression profiles of the hub genes in the three datasets were utilized as variables to perform principal component analysis (PCA) using SIMCA-P v14.0 software (Umetrics AB, Sweden), a multivariate pattern recognition technique. The quality of the models was evaluated using the parameters \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eX\u003c/em\u003e and \u003cem\u003eQ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e. \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eX\u003c/em\u003e explains the proportion of variance in the x-variables, while \u003cem\u003eQ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e indicates the models' predictive performance capability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9. Immune infiltration analysis\u003c/h2\u003e\u003cp\u003eTo examine the correlation between the expression of seven hub genes in LUAD and both tumor purity and immune infiltration abundance, the Tumor Immune Estimation Resource 2.0 (TIMER2.0) webserver (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cistrome.org/TIMER/\u003c/span\u003e\u003cspan address=\"http://cistrome.org/TIMER/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Identification of DEGs in LUAD\u003c/h2\u003e\u003cp\u003eIn this study, 372, 282, and 260 DEGs were extracted from GSE118370, GSE19188, and GSE30219, respectively, using the GEO2R online tool with criteria of |logFC| \u0026gt;2 and P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Specifically, GSE118370 yielded 73 upregulated and 299 downregulated DEGs \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), GSE19188 identified 89 upregulated and 193 downregulated DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), and GSE30219 showed 70 upregulated and 190 downregulated DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). The Venn diagram online tool was then utilized to intersect the DEGs from the three datasets, identifying 68 common DEGs in LUAD tissues, including 8 upregulated and 60 downregulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Additionally, the overlapping DEGs in LUAD were displayed by a heat map using dataset GSE118370 as a reference. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Functional enrichment analysis of overlapping DEGs\u003c/h2\u003e\u003cp\u003eTo explore the possible biological functions of the 68 common DEGs, GO and KEGG analyses were executed. These investigations comprised 46 GO terms including BP, CC, and MF, in addition to 8 significant pathways, as listed in \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/b\u003e The GO enrichment analysis bar chart was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. For the BP category, the DEGs were significantly enriched in the processes of angiogenesis, receptor internalization, vasoconstriction, and so on. In the CC category, DEGs were also significantly enriched in collagen trimer, plasma membrane, extracellular region and so on. As for MF, DEGs were mainly enriched in identical protein binding, macromolecular complex binding, signaling receptor activity, and so on. In addition, KEGG pathway analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) was performed and the PPAR signaling pathway was found to be the most altered pathway,\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Hub genes screened through the PPI network\u003c/h2\u003e\u003cp\u003eDEGs were uploaded onto the STRING database to construct the PPI network. Next, the visualization of the PPI network was performed using Cytoscape software, and the MCODE plug-in was utilized to identify the most central part of the PPI network. This analysis identified a total of 44 nodes and 45 edges in the PPI network (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Furthermore, 9 hub genes were pinpointed through MCODE, including AGER, CD36, CAV1, EDNRB, FABP4, ROBO4, EMCN, TEK and PTPRB (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.4. The survival analysis of hub genes in LUAD\u003c/h2\u003e\u003cp\u003eTo determine the prognostic value of key genes, a survival analysis based on gene expression levels was performed using the KM plotter. These key genes, including AGER, CD36, CAV1, EDNRB, FABP4, ROBO4, EMCN, TEK, and PTPRB, were identified due to their association with poor OS. The results showed significant correlations for AGER (HR\u0026thinsp;=\u0026thinsp;0.77 (0.69\u0026ndash;0.87), log-rank P\u0026thinsp;=\u0026thinsp;2.4e-5), CAV1 (HR\u0026thinsp;=\u0026thinsp;0.82 (0.73\u0026ndash;0.92), log-rank P\u0026thinsp;=\u0026thinsp;0.00096), EDNRB (HR\u0026thinsp;=\u0026thinsp;0.74 (0.66\u0026ndash;0.84), log-rank P\u0026thinsp;=\u0026thinsp;1.1e-6), ROBO4 (HR\u0026thinsp;=\u0026thinsp;0.66 (0.57\u0026ndash;0.77), log-rank P\u0026thinsp;=\u0026thinsp;6.6e-8), EMCN (HR\u0026thinsp;=\u0026thinsp;0.59 (0.51\u0026ndash;0.69), log-rank P\u0026thinsp;=\u0026thinsp;4.6e-12), TEK (HR\u0026thinsp;=\u0026thinsp;0.62 (0.55\u0026ndash;0.7), log-rank P\u0026thinsp;=\u0026thinsp;9.6e-15), and PTPRB (HR\u0026thinsp;=\u0026thinsp;0.75 (0.67\u0026ndash;0.85), log-rank P\u0026thinsp;=\u0026thinsp;2.8e-6) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). There were no statistically significant results from the survival analysis of the remaining hub genes. To summarize, our findings indicated a substantial correlation between seven genes (AGER, CAV1, EDNRB, ROBO4, EMCN, TEK, PTPRB) and patient outcomes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Validation of seven prognostic-related hub gene expression\u003c/h2\u003e\u003cp\u003eThe transcription levels of seven hub genes in 483 LUAD tissues and 347 normal lung tissues from the TCGA and GTEx databases were analyzed using GEPIA. It was found that the expression of all seven genes was significantly lower in tumor tissues compared to normal tissues (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn addition, the expression of seven hub genes was analyzed using UALCAN software, considering various clinicopathological characteristics such as age, sample types, and stage. Based on age, the transcription levels of all genes at any stage were significantly decreased in patients compared to normal samples. Moreover, CAV1 expression was significantly increased in patients aged 21\u0026ndash;40 compared to other age groups. ROBO4 expression was significantly increased in patients aged 41\u0026ndash;60 compared to those aged 61\u0026ndash;80. TEK expression was significantly decreased in patients aged 21\u0026ndash;40 compared to those aged 41\u0026ndash;60 and 61\u0026ndash;80 (\u003cb\u003eSupplementary Fig.\u0026nbsp;1A\u003c/b\u003e). For sample types, compared to normal samples, the transcription levels of all genes decreased significantly in LUAD patients \u003cb\u003e(Supplementary Fig.\u0026nbsp;1B)\u003c/b\u003e. When comparing stages, the transcription levels of all genes decreased significantly in stages one to four compared to normal samples. However, there were no significant differences in gene expression between stages S1, S2, S3, and S4 (\u003cb\u003eSupplementary Fig.\u0026nbsp;1C\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eTo validate the protein expression levels of these genes, we utilized CPTAC data from the UALCAN cancer database and staining data from tumor pathological sections obtained from HPA. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e demonstrates that all hub genes exhibited significantly lower protein expression levels in LUAD samples compared to normal samples. Additionally, leveraging the HPA datasets, we conducted further investigations into the differential expression of hub genes between tumors and normal tissues using immunohistochemistry. This analysis yielded representative staining, revealing that AGER, CAV1, EDNRB, EMCN, TEK, and PTPRB proteins were downregulated in LUCA tissues compared to normal tissues, mirroring the transcriptional patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Furthermore, subcellular structural analysis utilizing the HPA database revealed that AGER is primarily localized in the nucleoli fibrillar center, while EDNRB, TEK, and PTPRB are predominantly enriched on the plasma membrane. CAV1 was primarily located in the golgi apparatus, while ROBO4 was predominantly found in the plasma membrane, although the staining for both proteins was not distinctly obvious (\u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.6. Diagnostic efficacy verification\u003c/h2\u003e\u003cp\u003eROC curve analysis was conducted to evaluate the individual predictive power of the 7 hub genes. Using SPSS, the curves for these genes were drawn based on their expression in the GSE118370, GSE19188, and GSE30219 datasets to verify their discriminative effect on distinguishing LUAD patients from normal controls. The results showed that each gene had better diagnostic efficiency with AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-C). PCA was performed using the expression profiles of the hub genes in the three datasets. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD-F, the two group samples in each dataset were well separated, demonstrating the strong discriminative power of the 7 hub genes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.7. Relationship between infiltrating immune cells and hub gene expression\u003c/h2\u003e\u003cp\u003eImmune infiltration in the tumor microenvironment (TME) is closely related to cancer development, progression, and metastasis[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Using the TIMER2.0 database, the correlation between the expression levels of hub genes and 6 kinds of infiltrating immune cells, namely, CD8\u0026thinsp;+\u0026thinsp;T cells, CD4\u0026thinsp;+\u0026thinsp;T cells, B cells, macrophages, neutrophils and dendritic cells (DCs) within the TME in LUAD were investigated (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA\u0026ndash;G). Interestingly, all genes showed a negative correlation with tumor purity. Additionally, the expression of CAV1, EDNRB, EMCN, and TEK is significantly related to the infiltration of CD8\u0026thinsp;+\u0026thinsp;T cells. Apart from CAV1, the expression of all genes is significantly associated with the infiltration of CD4\u0026thinsp;+\u0026thinsp;T cells. AGER and CAV1 expression is significantly related to the infiltration of B cells. Furthermore, the expression of all genes is significantly associated with macrophage infiltration. The expression of CAV1, TEK, and PTPRB is significantly related to neutrophil infiltration. The expression of AGER, CAV1, ROBO4, and TEK is significantly associated with the infiltration of DC.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn recent decades, there has been a gradual increase in the incidence rate of LUAD, positioning it as one of the most prevalent forms of lung cancer. LUAD is characterized by its rapid progression, marked by the presence of micrometastatic foci, which contribute to a heightened recurrence rate and an increased propensity for metastasis[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Despite the availability of standard surgical interventions for localized and early-stage disease, the majority of patients receive diagnoses at advanced stages, largely owing to the presence of subtle and nonspecific early symptoms[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Consequently, patients often undergo conventional treatments such as combined radiotherapy and chemotherapy, which are associated with heightened mortality risks[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. While low-dose chest computed tomography (LDCT) screening has been introduced to facilitate early detection of lung cancer, its efficacy in improving survival rates appears to be limited[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Unfortunately, less than 20% of LUAD patients survive for an average of five years[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The poor prognosis associated with LUAD is primarily attributed to the absence of specific biomarkers, which hampers timely diagnosis and the implementation of targeted therapeutic interventions. Therefore, there is a pressing need to explore novel biomarkers and associated pathways to gain insights into molecular mechanisms, thereby facilitating the development of precise medical modalities tailored to the needs of LUAD patients. Recent advancements in microarray and computational analysis methodologies have emerged as invaluable tools in this endeavor, offering promising avenues for the identification and validation of reliable biomarkers or gene signatures for LUAD diagnosis and prognosis. Despite the extensive research efforts devoted to investigating LUAD-related biomarkers, none have demonstrated efficacy thus far. Consequently, a comprehensive analysis of LUAD is warranted, aimed at identifying optimal molecular targets for therapeutic intervention, while elucidating the intricate biological pathways underlying its pathogenesis and progression.\u003c/p\u003e\u003cp\u003eThis study aimed to identify prognostic biomarkers for LUAD by analyzing three profile datasets (GSE118370, GSE19188, and GSE30219) using bioinformatic methods. The analysis included 136 LUAD specimens and 85 non-LUAD specimens. Using GEO2R, DEGs across these datasets were assessed, and their intersection was delineated using Venn diagrams, identifying 68 common DEGs (|log2FC| \u0026gt;2 and adjusted P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05), comprising 8 upregulated and 60 downregulated DEGs. Subsequent GO enrichment analysis highlighted significant enrichment of DEGs in pivotal biological processes such as angiogenesis, receptor internalization, and vasoconstriction; in cell components including collagen trimer, plasma membrane, and extracellular region; and in molecular functions such as identical protein binding, macromolecular complex binding, and signaling receptor activity. These findings underscored the molecular mechanisms implicated in LUAD, suggesting that tumor pathogenesis is a multifaceted biological process driven by alterations in the expression of specific genes and epigenetic modifications. Dysregulated regulation of multiple genes can facilitate the onset and progression of LUAD through diverse pathways. Specifically, DEGs showed notable enrichment in the PPAR signaling pathway through KEGG enrichment analysis. Cancer is characterized by uncontrolled cell proliferation, a process in which the PPAR signaling pathway plays a crucial role, exerting pleiotropic effects in cancer development and progression [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Consistent with previous studies, this investigation underscores the significant association between the PPAR signaling pathway and LUAD[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Furthermore, employing STRING and Cytoscape, a comprehensive DEGs PPI network was constructed, comprising 44 nodes and 45 edges. Utilizing MCODE analysis within Cytoscape identified 9 central downregulated DEGs (AGER, CD36, CAV1, EDNRB, FABP4, ROBO4, EMCN, TEK, and PTPRB), further illuminating potential key biomarkers for LUAD.\u003c/p\u003e\u003cp\u003eTo further evaluate the reliability of the hub genes and identify potential LUAD biomarkers, we conducted a Kaplan-Meier analysis on nine hub genes. Our analysis revealed that seven genes (AGER, CAV1, EDNRB, ROBO4, EMCN, TEK, PTPRB) were associated with the prognosis of LUAD. We then assessed the mRNA and protein expression levels of these seven genes. GEPIA analysis indicated that the relative expression levels of these genes in LUAD patients were significantly lower compared to normal controls. Interestingly, the transcription levels of all these genes correlated with the age, sample types, and stage of LUAD patients, suggesting their involvement in LUAD development. Furthermore, UALCAN analysis showed that the protein levels of these genes were significantly reduced in LUAD tissues compared to normal tissues. Immunohistochemistry results confirmed that AGER, CAV1, EDNRB, EMCN, TEK, and PTPRB were expressed at lower levels in LUAD tissues than in normal tissues. However, ROBO4 was undetected in both LUAD and normal lung tissues. Additionally, ROC and PCA analyses indicated that these seven genes could serve as effective biomarkers for LUAD diagnosis. In summary, we identified seven significant genes that could serve as novel molecular markers and effective therapeutic targets for LUAD in future research. Furthermore, there was some evidence suggesting that AGER, CAV1, EDNRB, ROBO4, EMCN, TEK, and PTPRB are closely associated with lung diseases and cancer.\u003c/p\u003e\u003cp\u003eAGER also referred to as the receptor for advanced glycation end products (RAGE), is a well-established driver of inflammation[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. It triggers pro-inflammatory pathways within cells, playing a critical role in diverse physiological and pathological processes, including autoimmune diseases and cancer[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. AGER is notably overexpressed in various cancers such as ovarian[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], breast[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and endometrial cancers[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Interestingly, studies have indicated its downregulation in lung cancer, where it exhibits tumor-suppressive properties[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Moreover, reducing AGER levels has been shown to diminish both the quantity and suppressive capabilities of tumor-induced myeloid-derived suppressor cells (MDSCs) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In this study, we observed decreased AGER expression in LUAD, correlating with poorer OS and suggesting its potential as an independent prognostic risk factor for LUAD.\u003c/p\u003e\u003cp\u003eCaveolin-1 (CAV1) is an integral membrane protein located in the outer cell membrane, particularly within caveolae, as well as in other intracellular membranes, including those of the endoplasmic reticulum, Golgi apparatus, and transport vesicles[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. CAV1 functions as either an oncogenic or antineoplastic protein, influencing cell metabolism, autophagy, and senescence in cancer biology[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, its role as a tumor promoter or suppressor remains controversial. For example, CAV1 has been found to promote tumors in renal cancer, prostate cancer, tongue squamous cell carcinoma (SCC), lung SCC, and bladder SCC. Conversely, it has an inhibitory role in esophageal adenocarcinoma, LUAD, and cutaneous SCC[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Previous research has also indicated that a reduction in CAV1 might enhance epithelial-mesenchymal transition (EMT), a crucial component in tumor metastasis[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In our study, we found that CAV1 expression was low in LUAD, suggesting a poor prognosis for LUAD patients. Additionally, a bioinformatics analysis indicated that CAV1 is significantly downregulated in LUAD tissues and that its expression levels are positively correlated with OS times in cancer patients[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], consistent with our findings. Additionally, our analysis of immune infiltration reveals a positive correlation between CAV1 and CD8\u0026thinsp;+\u0026thinsp;T cells, macrophages, neutrophils, and dendritic cells (DC). Conversely, there is a negative correlation between CAV1 and B cells. This result indicated that CAV1 promotes antitumor immunity of immune cells.\u003c/p\u003e\u003cp\u003eThe endothelin receptor type B (EDNRB) gene, located on chromosome 13, activates multiple cancer-associated signaling pathways, such as the mitogen-activated protein kinase/Erk 2 and PI3K/AKT pathways[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. By binding to its ligand endothelin (ET), EDNRB transmits extracellular signals that impact cell proliferation and migration. Studies indicated EDNRB\u0026rsquo;s involvement in various cellular functions linked to cancer development. Liu et al.[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] highlighted EDNRB as a promising prognostic biomarker in TNBC patients, while Zhou et al.[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] associated elevated EDNRB expression with poorer prognosis in HPV (-) HNSCC. Our research reveals diminished EDNRB expression in LUAD, which correlates with reduced overall survival (OS), underscoring its potential for clinical screening in LUAD.\u003c/p\u003e\u003cp\u003eROBO4 is a cell surface receptor for a secreted signaling protein, primarily expressed in endothelial cells and their progenitors, hematopoietic stem cells[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. It plays a crucial role in both developmental and pathological angiogenesis[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Recent studies have found that higher levels of ROBO4 expression in gliomas[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and acute myeloid leukemias[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] are linked to shorter overall survival. One previous study revealed that increased ROBO4 expression has been correlated with increased OS in early-stage non-small cell lung cancer. Andreas Pircher et al. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]hypothesized that elevated levels of tumor endothelial markers (TEMs) like ROBO4 function as vascular stabilization factors, thereby reducing metastatic spread. They further suggested that higher ROBO4 expression during disease progression correlates with a longer time for antiangiogenic progression. In this study, GO analysis results showed that ROBO4 is involved in angiogenesis, is decreased in LUAD, and is associated with poor OS. Lower ROBO4 expression during disease progression carries clinical and pathophysiological implications, indicating a potential weakening of anti-angiogenic mechanisms in LUAD patients. Further development of biomarkers is needed to validate these results.\u003c/p\u003e\u003cp\u003eEMCN is a transmembrane protein that undergoes O-sialylation and is predominantly expressed on endothelial cell surfaces[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. It plays a critical role in mediating cellular adhesion between vascular endothelial cells and neutrophils. Under normal conditions, interference with EMCN expression on quiescent endothelial cells promotes increased neutrophil adhesion, whereas, during inflammatory events, its expression prevents excessive neutrophil binding[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Despite these established roles, the specific functions and mechanisms of EMCN in tumorigenesis remain incompletely understood. Recent studies have highlighted that lung neutrophils deficient in EMCN tend to proliferate and transform into N2 neutrophils under the influence of TGF-β within the tumor microenvironment, thereby facilitating tumor metastasis and growth[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In agreement with previous reports[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], our investigation into LUAD patients has identified a significant down-regulation of EMCN. This observation was further corroborated through analysis of mRNA and protein levels, suggesting a potential link between reduced EMCN expression and the pathogenesis of LUAD.\u003c/p\u003e\u003cp\u003eTEK is a receptor tyrosine kinase from the Tie2 family, primarily expressed in endothelial cells where it regulates vascular regeneration and stability[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. While the angiopoietin-Tie system is known for its roles in inflammation, metastasis, and lymphangiogenesis, TEK's specific involvement in cancer cells remains incompletely understood[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Our study identified significantly lower TEK expression in LUAD, correlating with advanced cancer stages and poor OS. Another study corroborated our findings, showing significant downregulation of TEK in LUAD tissues and cell lines compared to normal counterparts[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Importantly, they also found that this reduced expression correlated with poorer survival outcomes in LUAD patients. Recent research also suggests that reduced Tie2 signaling may promote inflammatory cell migration into the tumor microenvironment, potentially positioning TEK as a tumor suppressor in clear cell renal cell carcinoma (ccRCC)[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Moreover, our analysis of immune infiltration indicated a significant positive correlation between TEK expression levels and the presence of CD8\u0026thinsp;+\u0026thinsp;T cells, CD4\u0026thinsp;+\u0026thinsp;T cells, macrophages, neutrophils, and DCs in LUAD. These observations lead us to hypothesize that TEK likely plays a suppressive role in LUAD cells.\u003c/p\u003e\u003cp\u003eThe protein tyrosine phosphatase receptor type B (PTPRB) gene, located at 12q15, consists of 37 exons and encodes a member of the protein tyrosine phosphatase (PTP) family. This family of signaling molecules is crucial for regulating various cellular processes, including cell growth, differentiation, the mitotic cycle, and oncogenic transformation[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Dysregulation of PTPRB function and expression has been linked to carcinogenesis and tumor progression in several cancer types [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Numerous studies have shown that PTPRB has tumor-suppressive functions in NSCLC cells, both in vitro and in vivo. Specifically, overexpression of PTPRB inhibited cell growth, anchorage-independent growth, and cell invasion, while knockdown of PTPRB enhanced tumorigenic properties[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Our research revealed that PTPRB expression was low in LUAD and was associated with TNM stage and poor OS. Furthermore, another report also suggested that low PTPRB expression might be indicative of poor survival rates in LUAD patients[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn summary, this study conducted comprehensive bioinformatics analyses including GO functional annotation, KEGG enrichment analysis, PPI network construction, and identification of hub genes. It identified seven significantly downregulated DEGs (AGER, CAV1, EDNRB, ROBO4, EMCN, TEK, PTPRB) that correlate with LUAD progression and poor prognosis. However, a key limitation is the lack of experimental validation. Future research efforts should prioritize rigorous in vitro and in vivo studies to further elucidate the roles of these genes in LUAD tumorigenesis and prognosis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank the researchers for making their GEO microarray datasets available online and acknowledge their valuable contributions. We also extend our gratitude to the support from the DAVID, STRING, and GEPIA public databases, as well as the Cytoscape and R package software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors contributed significantly to this work. GSS designed the study. LYH and TRR contributed to the literature search. LYH performed this study and wrote the initial draft of the manuscript. GSS and LYH reviewed and edited the manuscript. All authors read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Startup Fund for scientific research, Fujian Medical University (Grant number:2024QH1131).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary data\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary data associated with this article can be found in Supplementary Table S1 and Supplementary Figures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e The original datasets presented in this study are openly available in the Gene Expression Omnibus (GEO) repository. The accession numbers are GSE118370, GSE19188, and GSE30219. These datasets were used for the identification of differentially expressed genes and subsequent bioinformatics analysis in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing financial interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhang L, Liu Y, Zhuang J-G\u003cem\u003e, et al.\u003c/em\u003e Identification of key genes and biological pathways in lung adenocarcinoma by integrated bioinformatics analysis. \u003cem\u003eWorld Journal of Clinical Cases\u003c/em\u003e. 2023;11: 5504-5518.\u003c/li\u003e\n\u003cli\u003eLiu B, Quan X, Xu C\u003cem\u003e, et al.\u003c/em\u003e Lung cancer in young adults aged 35 years or younger: A full-scale analysis and review. \u003cem\u003eJournal of Cancer\u003c/em\u003e. 2019;10: 3553-3559.\u003c/li\u003e\n\u003cli\u003eM M, G C, P B\u003cem\u003e, et al.\u003c/em\u003e European cancer mortality predictions for the year 2017, with focus on lung cancer. \u003cem\u003eANNALS OF ONCOLOGY\u003c/em\u003e. 2017;28.\u003c/li\u003e\n\u003cli\u003eJianjie L, Fan Y, Xiao L\u003cem\u003e, et al.\u003c/em\u003e Characteristics, survival, and risk factors of Chinese young lung cancer patients: the experience from two institutions. \u003cem\u003eOncotarget\u003c/em\u003e. 2017;8.\u003c/li\u003e\n\u003cli\u003eSun LU, Tan H, Yu T\u003cem\u003e, et al.\u003c/em\u003e Identification of lncRNAs associated with T cells as potential biomarkers and therapeutic targets in lung adenocarcinoma. \u003cem\u003eOncology Research\u003c/em\u003e. 2023;31: 967-988.\u003c/li\u003e\n\u003cli\u003eBoone G, Avinash V, C Ryan M\u003cem\u003e, et al.\u003c/em\u003e A clinical model to estimate recurrence risk in resected stage I non-small cell lung cancer. \u003cem\u003eAmerican journal of clinical oncology\u003c/em\u003e. 2008;31.\u003c/li\u003e\n\u003cli\u003eWang Y, Zhou Z, Chen L\u003cem\u003e, et al.\u003c/em\u003e Identification of key genes and biological pathways in lung adenocarcinoma via bioinformatics analysis. \u003cem\u003eMolecular and Cellular Biochemistry\u003c/em\u003e. 2020;476: 931-939.\u003c/li\u003e\n\u003cli\u003eXu J, Zhang C, Wang X\u003cem\u003e, et al.\u003c/em\u003e Integrative Proteomic Characterization of Human Lung Adenocarcinoma. \u003cem\u003eCell\u003c/em\u003e. 2020;182: 245-261.e217.\u003c/li\u003e\n\u003cli\u003eLiao Y, He D, Wen F. Analyzing the characteristics of immune cell infiltration in lung adenocarcinoma via bioinformatics to predict the effect of immunotherapy. \u003cem\u003eImmunogenetics\u003c/em\u003e. 2021;73: 369-380.\u003c/li\u003e\n\u003cli\u003eYang X, Feng Q, Jing J\u003cem\u003e, et al.\u003c/em\u003e Identification of differentially expressed genes associated with lung adenocarcinoma via bioinformatics analysis. \u003cem\u003eGENERAL PHYSIOLOGY AND BIOPHYSICS\u003c/em\u003e. 2021;40: 31-48.\u003c/li\u003e\n\u003cli\u003eLu M, Fan X, Liao W\u003cem\u003e, et al.\u003c/em\u003e Identification of significant genes as prognostic markers and potential tumor suppressors in lung adenocarcinoma via bioinformatical analysis. \u003cem\u003eBMC Cancer\u003c/em\u003e. 2021;21.\u003c/li\u003e\n\u003cli\u003eYuan Q, Zheng L, Liao Y\u003cem\u003e, et al.\u003c/em\u003e Overexpression of CCNE1 confers a poorer prognosis in triple-negative breast cancer identified by bioinformatic analysis. \u003cem\u003eWorld Journal of Surgical Oncology\u003c/em\u003e. 2021;19.\u003c/li\u003e\n\u003cli\u003eLiu X, Yuan R, Peng J\u003cem\u003e, et al.\u003c/em\u003e SLMO2 is a potential prognostic and immunological biomarker in human pan-cancer. \u003cem\u003eSci Rep\u003c/em\u003e. 2024;14: 1070.\u003c/li\u003e\n\u003cli\u003eChandrashekar DS, Karthikeyan SK, Korla PK\u003cem\u003e, et al.\u003c/em\u003e UALCAN: An update to the integrated cancer data analysis platform. \u003cem\u003eNeoplasia\u003c/em\u003e. 2022;25: 18-27.\u003c/li\u003e\n\u003cli\u003eYing B, Xu W, Nie Y\u003cem\u003e, et al.\u003c/em\u003e HSPA8 Is a New Biomarker of Triple Negative Breast Cancer Related to Prognosis and Immune Infiltration. \u003cem\u003eDISEASE MARKERS\u003c/em\u003e. 2022;2022: 8446857.\u003c/li\u003e\n\u003cli\u003eKung-Chi K, Stefania V, Chin-Hsien T\u003cem\u003e, et al.\u003c/em\u003e Metabolic communication in the tumour-immune microenvironment. \u003cem\u003eNATURE CELL BIOLOGY\u003c/em\u003e. 2022;24.\u003c/li\u003e\n\u003cli\u003eChen Y, Ma S, Lin C\u003cem\u003e, et al.\u003c/em\u003e Integrative analysis of DNA methylomes reveals novel cell-free biomarkers in lung adenocarcinoma. \u003cem\u003eFrontiers in Genetics\u003c/em\u003e. 2023;14.\u003c/li\u003e\n\u003cli\u003eMeina W, Roy S H, Chris BJNM. Toward personalized treatment approaches for non-small-cell lung cancer. \u003cem\u003eNATURE MEDICINE\u003c/em\u003e. 2021;27.\u003c/li\u003e\n\u003cli\u003ePeter J M, Gerard A S, Lesley H S\u003cem\u003e, et al.\u003c/em\u003e Screening for Lung Cancer: CHEST Guideline and Expert Panel Report. \u003cem\u003eChest\u003c/em\u003e. 2021;160.\u003c/li\u003e\n\u003cli\u003eAlesha A T, Benjamin J S, Lecia V S\u003cem\u003e, et al.\u003c/em\u003e Lung cancer. \u003cem\u003eThe Lancet\u003c/em\u003e. 2021;398.\u003c/li\u003e\n\u003cli\u003eMa Y, Wu L. Investigation of the function of the novel tumor marker BEND5 in lung adenocarcinoma based on data mining and in vitro analysis. \u003cem\u003eJournal of Thoracic Disease\u003c/em\u003e. 2023;15: 1749-1769.\u003c/li\u003e\n\u003cli\u003eXu F, Tian D, Shi X\u003cem\u003e, et al.\u003c/em\u003e Analysis of the Expression and Prognostic Potential of a Novel Metabolic Regulator ANGPTL8/Betatrophin in Human Cancers. \u003cem\u003ePathology and Oncology Research\u003c/em\u003e. 2021;27.\u003c/li\u003e\n\u003cli\u003eHu F-F, Chen H, Duan Y\u003cem\u003e, et al.\u003c/em\u003e CBX2 and EZH2 cooperatively promote the growth and metastasis of lung adenocarcinoma. \u003cem\u003eMolecular Therapy - Nucleic Acids\u003c/em\u003e. 2022;27: 670-684.\u003c/li\u003e\n\u003cli\u003eLiu W, Ouyang S, Zhou Z\u003cem\u003e, et al.\u003c/em\u003e Identification of genes associated with cancer progression and prognosis in lung adenocarcinoma: Analyses based on microarray from Oncomine and The Cancer Genome Atlas databases. \u003cem\u003eMolecular Genetics \u0026amp; Genomic Medicine\u003c/em\u003e. 2018;7.\u003c/li\u003e\n\u003cli\u003eZhang X, Ye T, Li M\u003cem\u003e, et al.\u003c/em\u003e Association of Polymorphisms in Inflammation Genes With the Prognosis of Advanced Non-Small Cell Lung Cancer Patients Receiving Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors. \u003cem\u003eFrontiers in Oncology\u003c/em\u003e. 2022;12.\u003c/li\u003e\n\u003cli\u003eFarzaneh R, Jamshid K, Mohammad Taghi G\u003cem\u003e, et al.\u003c/em\u003e Overexpression of receptor for advanced glycation end products (RAGE) in ovarian cancer. \u003cem\u003eCancer Biomarkers\u003c/em\u003e. 2016;18.\u003c/li\u003e\n\u003cli\u003eMaryam N, Jamshid K, Mohammad T G\u003cem\u003e, et al.\u003c/em\u003e Increased Expression of the Receptor for Advanced Glycation End-Products (RAGE) Is Associated with Advanced Breast Cancer Stage. \u003cem\u003eOncol Res Treat\u003c/em\u003e. 2016;39.\u003c/li\u003e\n\u003cli\u003eLu Z, Da L, Yi-Ming Z\u003cem\u003e, et al.\u003c/em\u003e Effects of receptor for advanced glycation endproducts on microvessel formation in endometrial cancer. \u003cem\u003eBMC Cancer\u003c/em\u003e. 2016;16.\u003c/li\u003e\n\u003cli\u003eWang Q, Zhu W, Xiao G\u003cem\u003e, et al.\u003c/em\u003e Effect of AGER on the biological behavior of non‑small cell lung cancer H1299 cells. \u003cem\u003eMol Med Rep\u003c/em\u003e. 2020;22: 810-818.\u003c/li\u003e\n\u003cli\u003eTanna W, Tom H, Emily B\u003cem\u003e, et al.\u003c/em\u003e The receptor for advanced glycation endproducts (RAGE) decreases survival of tumor-bearing mice by enhancing the generation of lung metastasis-associated myeloid-derived suppressor cells. \u003cem\u003eCELLULAR IMMUNOLOGY\u003c/em\u003e. 2021;365.\u003c/li\u003e\n\u003cli\u003eGerstenberger W, Wrage M, Kettunen E\u003cem\u003e, et al.\u003c/em\u003e Stromal Caveolin-1 and Caveolin-2 Expression in Primary Tumors and Lymph Node Metastases. \u003cem\u003eAnalytical Cellular Pathology\u003c/em\u003e. 2018;2018: 1-8.\u003c/li\u003e\n\u003cli\u003eYuanliang Y, Zhijie X, Long Q\u003cem\u003e, et al.\u003c/em\u003e Identification of CAV1 and DCN as potential predictive biomarkers for lung adenocarcinoma. \u003cem\u003eAmerican journal of physiology. Lung cellular and molecular physiology\u003c/em\u003e. 2019;316.\u003c/li\u003e\n\u003cli\u003eFu P, Chen F-C, Pan Q\u003cem\u003e, et al.\u003c/em\u003e The different functions and clinical significances of caveolin-1 in human adenocarcinoma and squamous cell carcinoma. \u003cem\u003eOncoTargets and Therapy\u003c/em\u003e. 2017;Volume 10: 819-835.\u003c/li\u003e\n\u003cli\u003eYao J, Zhang Y, Li M\u003cem\u003e, et al.\u003c/em\u003e Single-Cell RNA-Seq Reveals the Promoting Role of Ferroptosis Tendency During Lung Adenocarcinoma EMT Progression. \u003cem\u003eFrontiers in Cell and Developmental Biology\u003c/em\u003e. 2022;9.\u003c/li\u003e\n\u003cli\u003eLiu S, Zhang J, Zhu J\u003cem\u003e, et al.\u003c/em\u003e Prognostic values of EDNRB in triple‑negative breast cancer. \u003cem\u003eOncology Letters\u003c/em\u003e. 2020;20: 1-1.\u003c/li\u003e\n\u003cli\u003eZhou D, Wang J, Wang J\u003cem\u003e, et al.\u003c/em\u003e Profiles of immune cell infiltration and immune-related genes in the tumor microenvironment of HNSCC with or without HPV infection. \u003cem\u003eAmerican Journal of Translational Research\u003c/em\u003e. 2021;13: 2163-2180.\u003c/li\u003e\n\u003cli\u003eZhao H, Ahirwar DK, Oghumu S\u003cem\u003e, et al.\u003c/em\u003e Endothelial Robo4 suppresses breast cancer growth and metastasis through regulation of tumor angiogenesis. \u003cem\u003eMolecular Oncology\u003c/em\u003e. 2015;10: 272-281.\u003c/li\u003e\n\u003cli\u003eKrafft U, Reis H, Ingenwerth M\u003cem\u003e, et al.\u003c/em\u003e Nuclear Localization of Robo is Associated with Better Survival in Bladder Cancer. \u003cem\u003ePATHOLOGY \u0026amp; ONCOLOGY RESEARCH\u003c/em\u003e. 2018;26: 253-261.\u003c/li\u003e\n\u003cli\u003eHeng C, Yixue X, Wenjing L\u003cem\u003e, et al.\u003c/em\u003e Overexpression of Roundabout4 predicts poor prognosis of primary glioma patients via correlating with microvessel density. \u003cem\u003eJOURNAL OF NEURO-ONCOLOGY\u003c/em\u003e. 2015;123.\u003c/li\u003e\n\u003cli\u003eYin-Kai C, Hsin-An H, Jih-Luh T\u003cem\u003e, et al.\u003c/em\u003e Clinical and prognostic implications of Roundabout 4 (robo4) in adult patients with acute myeloid leukemia. \u003cem\u003ePLoS One\u003c/em\u003e. 2015;10.\u003c/li\u003e\n\u003cli\u003eAndreas P, Karin J, Florian K\u003cem\u003e, et al.\u003c/em\u003e Biomarkers of evasive resistance predict disease progression in cancer patients treated with antiangiogenic therapies. \u003cem\u003eOncotarget\u003c/em\u003e. 2016;7.\u003c/li\u003e\n\u003cli\u003eZhang G, Li M, Zhou D\u003cem\u003e, et al.\u003c/em\u003e Loss of endothelial EMCN drives tumor lung metastasis through the premetastatic niche. \u003cem\u003eJournal of Translational Medicine\u003c/em\u003e. 2022;20.\u003c/li\u003e\n\u003cli\u003eHuang Q, Li Xm, Sun Jp\u003cem\u003e, et al.\u003c/em\u003e Tumor‐derived endomucin promotes colorectal cancer proliferation and metastasis. \u003cem\u003eCancer Medicine\u003c/em\u003e. 2022;12: 3222-3236.\u003c/li\u003e\n\u003cli\u003eYin D, Qiu J, Hu S\u003cem\u003e, et al.\u003c/em\u003e CAV1 is a prognostic predictor for patients with idiopathic pulmonary fibrosis and lung cancer. \u003cem\u003eJournal of biosciences\u003c/em\u003e. 2022;47.\u003c/li\u003e\n\u003cli\u003eM H, Y R S, J K\u003cem\u003e, et al.\u003c/em\u003e TEK is a novel prognostic marker for clear cell renal cell carcinoma. \u003cem\u003eEuropean Review for Medical and Pharmacological Sciences\u003c/em\u003e. 2019;23.\u003c/li\u003e\n\u003cli\u003eKim GH, Heo HJ, Kang JW\u003cem\u003e, et al.\u003c/em\u003e Multi-omics analysis revealed TEK and AXIN2 are potential biomarkers in multifocal papillary thyroid cancer. \u003cem\u003eCancer Cell International\u003c/em\u003e. 2022;22.\u003c/li\u003e\n\u003cli\u003ePeng T, Yang F, Sun Z\u003cem\u003e, et al.\u003c/em\u003e miR-19a-3p Facilitates Lung Adenocarcinoma Cell Phenotypes by Inhibiting TEK. \u003cem\u003eCancer Biotherapy and Radiopharmaceuticals\u003c/em\u003e. 2022;37: 589-601.\u003c/li\u003e\n\u003cli\u003eLiao Z, Yao H, Wei J\u003cem\u003e, et al.\u003c/em\u003e Development and validation of the prognostic value of the immune-related genes in clear cell renal cell carcinoma. \u003cem\u003eTranslational Andrology and Urology\u003c/em\u003e. 2021;10: 1607-1619.\u003c/li\u003e\n\u003cli\u003eXia X, Fan J, Fan Z. Hsa_circ_0129047 sponges miR-665 to attenuate lung adenocarcinoma progression by upregulating protein tyrosine phosphatase receptor type B. \u003cem\u003eThe Korean Journal of Physiology \u0026amp; Pharmacology\u003c/em\u003e. 2023;27: 131-141.\u003c/li\u003e\n\u003cli\u003eWeng X, Chen W, Hu W\u003cem\u003e, et al.\u003c/em\u003e PTPRB promotes metastasis of colorectal carcinoma via inducing epithelial-mesenchymal transition. \u003cem\u003eCell Death \u0026amp; Disease\u003c/em\u003e. 2019;10.\u003c/li\u003e\n\u003cli\u003eQi Y, Dai Y, Gui S. Protein tyrosine phosphatase PTPRB regulates Src phosphorylation and tumour progression in NSCLC. \u003cem\u003eClinical and Experimental Pharmacology and Physiology\u003c/em\u003e. 2016;43: 1004-1012.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lung adenocarcinoma, differentially expressed genes, bioinformatics analysis","lastPublishedDoi":"10.21203/rs.3.rs-7512386/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7512386/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eLung adenocarcinoma (LUAD) has high morbidity and mortality, with its mechanisms and treatment still under investigation.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eDifferentially expressed genes (DEGs) between LUAD and normal tissues were identified from three GEO datasets. Gene enrichment and protein-protein interaction (PPI) analyses were performed, with hub genes identified.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eSixty-eight overlapping DEGs were found. The PPI network highlighted nine hub genes, and survival analysis linked seven of them (AGER, CAV1, EDNRB, ROBO4, EMCN, TEK, PTPRB) to LUAD prognosis. Clinical and immune analyses demonstrated these genes' significant roles in LUAD progression. ROC and PCA confirmed their diagnostic potential.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThese seven genes, downregulated in LUAD, could serve as biomarkers and therapeutic targets, requiring further research.\u003c/p\u003e","manuscriptTitle":"Seven-gene biomarkers reveal prognostic and immune signatures in lung adenocarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-12 04:06:15","doi":"10.21203/rs.3.rs-7512386/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":"6b0ca8f0-aa84-437f-8591-90323690441c","owner":[],"postedDate":"November 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-12T09:54:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-12 04:06:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7512386","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7512386","identity":"rs-7512386","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}