BIRC5 as a Potential Biomarker and Therapeutic Target for Lung Adenocarcinoma: A Comprehensive Bioinformatics Analysis

BIRC5 as a Potential Biomarker and Therapeutic Target for Lung Adenocarcinoma: A Comprehensive Bioinformatics Analysis | 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 BIRC5 as a Potential Biomarker and Therapeutic Target for Lung Adenocarcinoma: A Comprehensive Bioinformatics Analysis Jianliang Chang, Shuaibo Yang, Xiaocui Peng, Xue Wang, Dandan Xu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5872638/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Lung adenocarcinoma (LUAD), the most prevalent type of non-small cell lung cancer (NSCLC), has the highest incidence and mortality rates among thoracic cancers worldwide. BIRC5, a protein linked to tumor cell proliferation, differentiation, migration, and invasion, has been insufficiently studied as a potential LUAD biomarker. In this study, we used bioinformatics techniques to analyze LUAD-related data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify biomarkers associated with LUAD onset, progression, and prognosis and evaluate their clinical significance. Immune cell infiltration and single-cell RNA sequencing analyses assessed the expression of core genes in various immune environments. Drug sensitivity analysis evaluated the impact of these biomarkers on treatment response, providing a basis for early diagnosis and personalized LUAD treatment. Gene expression and clinical data from TCGA and GEO underwent weighted correlation network analysis (WGCNA), differential analysis, and univariate and multivariate Cox regression to identify prognostic core genes. High BIRC5 expression emerged as an independent risk factor for poor overall survival (OS) in LUAD, exhibiting strong diagnostic performance. Enrichment analysis indicated that BIRC5 was involved in the cell cycle and P53 signaling pathways. Single-cell RNA sequencing and immune infiltration analyses revealed that BIRC5 plays a critical role in the immune microenvironment. Drug sensitivity analysis showed that high BIRC5 expression correlated with increased sensitivity to several anticancer drugs. These findings establish BIRC5 as a promising biomarker for LUAD diagnosis and prognosis. Its role in immune regulation and drug sensitivity highlights its potential for guiding personalized treatment strategies. Lung adenocarcinoma BIRC5 Biomarker Immune microenvironment Drug sensitivity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Lung adenocarcinoma (LUAD) is the predominant pathological type of non-small cell lung cancer (NSCLC) and has the highest incidence and mortality rates among all thoracic cancers worldwide [1, 2]. Over the past two decades, the prognosis of LUAD has remained unsatisfactory, with a five-year survival rate of < 20% [3]. The main reason for this is that the early symptoms of LUAD are often subtle, with more than 70% of patients diagnosed only when the disease has progressed to an advanced stage, thus increasing the difficulty of treatment and risk of death. In recent years, patients with LUAD have had access to treatments such as surgery, chemotherapy, targeted therapy, and immunotherapy. However, the overall survival (OS) of patients with advanced disease remains low [4]. In this context, there is an urgent need for in-depth research into the molecular mechanisms underlying LUAD to better understand the disease and identify novel biomarkers for its diagnosis, prognosis, and treatment. The BIRC5 (survivin) gene, first reported in 1997, is located in the 17q25.3 region of chromosome 17 [5]. It encodes the survivin protein, which is a member of the inhibitor of apoptosis protein (IAP) family and is characterized by the presence of a baculoviral IAP repeat (BIR) domain involved in protein–protein interactions (PPIs). In addition to the BIR domain, IAPs contain other important domains, such as the C-terminal ubiquitin-binding (UBC) domain, caspase recruitment (CARD) domain, and C-terminal RING domain [6]. BIRC5 is a small protein with different isoforms that is typically expressed during fetal development and in proliferating cells in adults [7]. It plays a crucial role in cancer development by regulating cell division and proliferation while inhibiting apoptosis [8, 9]. Notably, BIRC5 is generally overexpressed in most malignant tumors [10-12], and has been shown to be associated with tumor cell proliferation, differentiation, migration, and invasion [13]. It also plays a critical role in tumor immune infiltration, chemotherapy resistance, and poor prognosis [14-16]. However, the molecular mechanisms and biological functions of BIRC5 as a novel biomarker for human cancers remain underexplored [14]. Moreover, studies on the role of BIRC5 in LUAD and its potential as a biomarker are particularly scarce. Therefore, in-depth research on the role of BIRC5 in LUAD is crucial. In this study, we aimed to screen immune-related biomarkers associated with LUAD using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. By integrating LUAD expression profile data from different databases, removing batch effects, conducting differential analysis, and performing weighted correlation network analysis (WGCNA), we performed univariate and multivariate Cox regression analyses on clinical data from TCGA LUAD dataset. Finally, we identified BIRC5 as an effective indicator of LUAD prognosis and identified its association with immunity. Subsequently, based on bioinformatics analysis, we conducted a comprehensive analysis of BIRC5 and established a predictive model for OS in LUAD. Our findings suggest that BIRC5 plays a critical role in the biological behavior of LUAD. 2. Materials And Methods 2.1 Downloading and Processing Datasets The LUAD expression profile and clinical data were downloaded from TCGA database (https://portal.gdc.cancer.gov) [17]. The GSE115002, GSE116959, and GSE140797 datasets were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo) [18] and merged for further analyses. Immune-related genes were obtained from the IMMPORT database (https://www.immport.org/home) [19]. Single-cell sequencing data were obtained from the GSE203360 dataset of the GEO database. 2.2 Differential Expression Analysis and WGCNA Differential expression analysis was performed on LUAD expression profile data from TCGA and GEO databases using the limma R package. The selection criteria were |log2FC| > 1 and an adjusted p-value < 0.05. The “sva” R package was used to merge the datasets (GSE115002, GSE116959, and GSE140797) and remove batch effects to identify characteristic genes [20]. The gene count for the smallest module was set to 30, the distance for merging similar modules was set to 0.25, and the sensitivity to module partitioning was adjusted to 2. Finally, the phenotypic information of the samples was integrated with the modules, and genes from the module with the highest correlation with the LUAD phenotype were selected for subsequent analysis. A Venn diagram was created using Xiantao Academic (https://www.xiantaozi.com), showing the intersection of TCGA differential genes, the highest correlated TCGA WGCNA module genes, the merged GEO differential genes, the highest correlated merged GEO WGCNA module genes, and immune-related genes. 2.3 Univariate and Multivariate Cox Regression Analysis and Core Gene Expression To identify prognostic genes, the expression levels of the intersecting genes in tumor samples from TCGA expression profile were extracted. Samples with a survival time of less than 30 d were removed to improve the reliability of the analysis [21]. Univariate and multivariate Cox regression analysis, along with visualization, were performed using the “survival,” “forestplot,” and “survminer” R packages to screen for core genes with prognostic value. The expression of core genes across different datasets was visualized using box plots generated using the “ggpubr” R package, and their pan-cancer expression was analyzed using Xiantao Academic. 2.4 Clinical Correlation Analysis, Survival Analysis, and Receiver Operating Characteristic (ROC) Curve Analysis Clinical data from TCGA, including patient age, sex, and tumor stage, were extracted as these factors are often linked to prognosis [21]. Analysis was performed using the “limma” and “ComplexHeatmap” R packages, and clinical correlation heatmaps and boxplots were generated. Survival analysis was conducted using the “Survival” and “SurvMiner” R packages, with Kaplan–Meier survival curves plotted. The impact of gene expression levels on the OS of patients with LUAD was assessed using the log-rank test, where a p-value < 0.05 was considered statistically significant. Additionally, the “pROC” R package was used to construct ROC curves, and the area under the curve (AUC) was calculated to evaluate the diagnostic performance of the core genes for LUAD. 2.5 Establishment of Nomogram Prognostic Model To predict the OS of patients with LUAD, a univariate Cox regression analysis was performed to identify the clinical and pathological features affecting prognosis. The “rms” R package was used to construct a nomogram prognostic model [22]. A nomogram converts complex regression models into graphical tools to predict the survival probability based on the total score (Nom score). A calibration curve was constructed to compare the consistency between the predicted survival probability and actual observed outcomes. Additionally, the “timeROC” R package was used to plot time-dependent ROC curves to evaluate the accuracy of the model in predicting 1-year, 2-year, and 3-year OS in patients with LUAD. 2.6 PPI network, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) Analyses PPI analysis of core genes was performed using the STRING database (https://string-db.org), with a confidence threshold of 0.7 and a maximum of 100 interacting genes. GO and KEGG enrichment analyses of the interacting genes were conducted using the “clusterProfiler” R package, with a significance threshold set to an adjusted p-value < 0.05. The top 15 significant terms or pathways were visualized using the “ggplot2” R package. 2.7 Gene Set Enrichment Analysis (GSEA) of Core Genes GSEA was performed to identify the potential functions and roles of core genes in different biological pathways or processes [23]. GSEA examines the entire gene expression profile, allowing the detection of smaller differences in genes. This makes it more comprehensive than GO and KEGG analyses, which focus solely on differentially expressed genes [24]. Based on TCGA expression data, genes correlated with core genes were screened using Spearman’s correlation analysis. Gene sets were downloaded from the Molecular Signatures Database (https://www.gsea-msigdb.org), including c5.go.v2024.1.Hs.symbols.gmt (GO) and c2.all.v2024.1.Hs.symbols.gmt (KEGG). GSEA was conducted on the selected related genes using the “enrichplot” and “ggplot22” R packages, and the top five enriched entries were displayed. Adjusted p-values < 0.05 were considered significantly enriched. 2.8 Immune Infiltration Analysis of Core Genes First, we used the TIMER 2.0 database (https://www.timer.cistrome.org) [25] to analyze the relationship between the core genes and the infiltration of six immune cell types: CD4 + T cells, CD8 + T cells, B cells, neutrophils, macrophages, and dendritic cells. Based on the median expression levels of the core genes, TCGA tumor samples were divided into high and low expression groups. Tumor immune infiltration analysis was performed using the “CIBERSORT” R package, and the abundance of 22 immune cell types between the high and low expression groups of core genes was visualized using the “ggpubr” R package. 2.9 Single-Cell Analysis Single-cell RNA sequencing data from GSE203360 were analyzed using the “Seurat” and “Harmony” R packages. A “Seurat” object was created, and high-quality cells were retained. The “SCTransform” method was applied for normalization, and mitochondrial gene effects were regressed out. Principal component analysis was performed for dimensionality reduction, and “Harmony” was used to correct for batch effects. Data visualization was performed with UMAP, and cells were clustered and annotated based on known marker genes. Gene expression patterns were visualized using “FeaturePlot” and “DotPlot,” and cell cluster names were validated. 2.10 Drug Sensitivity Analysis To identify drugs effective for different expression groups of LUAD core genes, Spearman correlation analysis was conducted on TCGA tumor sample expression data, selecting genes with a correlation > 0.6 with core genes. Tumor samples were divided into high and low expression groups based on the median expression levels of the core genes. Drug sensitivity analysis was conducted using the GDSC database and the “oncoPredict” R package. The IC 50 values of 198 anticancer drugs were calculated and visualized, with a p-value < 0.05 considered significant. 3. Results 3.1 Gene Selection First, three GEO datasets were merged, and batch effect correction was applied to improve the accuracy of the gene expression data analysis (Fig. 1a, b). Differential expression analysis revealed 889 upregulated genes and 1,366 downregulated genes (Fig. 1c). Subsequently, WGCNA was performed, and a soft threshold of β = 8 (R² = 0.9) was selected (Fig. 1d). Clustering trends were constructed (Fig. 1e), and 13 modules were identified (Fig. 1f). Similar processing was applied to TCGA dataset, where differential analysis identified 2,001 upregulated genes and 2,432 downregulated genes (Fig. 1g). WGCNA selected a soft threshold of β = 20 (R² = 0.9) (Fig. 1h), clustering trends were constructed (Fig. 1i), and eight modules were identified (Fig. 1j). Genes at the intersection of GEO and TCGA differential genes, GEO’s turquoise module (5,199 genes), TCGA’s brown module (348 genes), and 1,793 immune-related genes from the IMMPORT database revealed two core genes: BIRC5 and PDK1 (Fig. 1k). 3.2 Core Gene Selection and Differential Expression Based on TCGA expression profiles, the expression levels of BIRC5 and PDK1 were analyzed along with clinical information. A total of 490 tumor samples with survival times longer than 30 d were selected for univariate and multivariate Cox regression analyses. These results indicated that BIRC5 is an independent risk factor for the prognosis of patients with LUAD (Fig. 2a, b). BIRC5 showed significantly higher expression in tumor samples from both TCGA and merged GEO datasets, with a notable difference when compared to normal samples (p < 0.05) (Fig. 2c, d). Furthermore, analysis of 33 different cancers revealed that BIRC5 was highly expressed in most tumor types (Fig. 2e). 3.3 Correlation Analysis of BIRC5 Expression with LUAD Clinical Features and Prognosis Clinical correlation analysis revealed that the expression of BIRC5 was significantly associated with tumor T stage, N stage, and stage classification in the high expression group (p < 0.01) (Fig. 3a). The expression of BIRC5 in T1 was significantly different from that in T2 and T3 (p < 0.05) (Fig. 3b). A significant difference in BIRC5 expression was observed among the N0, N1, and N2 groups (p < 0.05) (Fig. 3c). There were significant differences in BIRC5 expression among stages I, II, III, and IV (p < 0.05) (Fig. 3d). No significant differences were found in BIRC5 expression among the different age, sex, and M stage groups (Fig. 3e–g). Kaplan–Meier survival curves showed that patients with high BIRC5 expression had significantly poorer OS (p = 0.002) (Fig. 3h). ROC curve analysis indicated that BIRC5 has an extremely high diagnostic value for LUAD (AUC = 0.973) (Fig. 3i). 3.4 Establishment of Nomogram Prognostic Model for Predicting Patient OS A nomogram predicted the 1-, 2-, and 3-year OS of patients with LUAD, showing that higher sample scores correlated with poorer prognosis (Fig. 4a). The calibration curve of the model demonstrated good consistency between the predicted and observed outcomes, closely aligning with the ideal 45-degree line (Fig. 4b). ROC curves based on the Nom score showed AUC values of 0.753, 0.743, and 0.734 for predicting 1-year, 2-year, and 3-year OS, respectively (Fig. 4c). These results suggest that the nomogram prognostic model has high accuracy and reliability for predicting the OS of patients with LUAD. 3.5 PPI Network and Functional Enrichment PPI analysis of BIRC5 was performed using the STRING database, and the top 100 interacting genes are shown in Fig. 5a. GO and KEGG enrichment analyses were conducted to identify the interacting genes. GO enrichment analysis revealed that in terms of biological processes (BPs), BIRC5 interacting genes were associated with chromosome segregation and cell division. In the cellular component category, genes were concentrated in the chromosome, spindle microtubules, and centromeric regions. In terms of molecular function, these genes were found to be involved in microtubule binding, cytoskeletal activity, and protein serine/threonine kinase activity (Fig. 5b). KEGG analysis indicated that BIRC5 was primarily involved in pathways related to cell cycle, P53 signaling pathway, and apoptosis (Fig. 5c). 3.6 GSEA Results GO analysis revealed that the samples with high BIRC5 expression were significantly enriched in processes related to chromosome assembly, telomere localization, the CMG complex, the DNA replication pre-initiation complex, and the MCM complex. Samples with low expression were enriched in processes such as cerebrospinal fluid circulation, leukotriene D4 metabolism, salivary secretion, axon dynein complex formation, and adrenergic receptor activity (Fig. 5d). KEGG analysis indicated that samples with high expression were enriched in pathways associated with kinases related to breast cancer prognosis, MCC formation, the p53 signaling pathway, DNA methylation, and DNA unwinding. Low expression samples were enriched in the lectin pathway, MHC II-mediated antigen processing, invasive ovarian cancer endothelial cells, LUAD markers, and drug metabolism (Fig. 5e). 3.7. Relationship Between Core Gene Expression and Tumor Microenvironment Analysis using TIMER 2.0 revealed that the expression of BIRC5 in LUAD was significantly negatively correlated with B cells, CD4 + T cells, and dendritic cells (Fig. 6a). CIBERSORT analysis showed that in the high expression group, there was an increase in the infiltration of plasma cells, resting CD4 + memory T cells, monocytes, activated dendritic cells, and resting mast cells. Conversely, the infiltration of B cells and M0, M2, and M3 macrophages was decreased (Fig. 6b). The correlation heatmap showed a positive correlation between CD4 + memory T cells and regulatory T cells (approximately 0.8), whereas NK cells and M1 macrophages exhibited a negative correlation (approximately –0.4) (Fig. 6c). The abundance plot revealed that naïve B cells and memory B cells constituted a larger proportion, whereas activated plasma cells were significantly increased. Resting mast cells and dendritic cells exhibited low abundance in most samples (Fig. 6d). 3.8. Single-Cell Analysis of BIRC5 This study performed single-cell RNA sequencing to analyze the expression of BIRC5 in different cell types. The results showed that BIRC5 was highly expressed in proliferating cells, lymphocytes, and cancer cells, whereas its expression was low in macrophages, dendritic cells, and mast cells (Fig. 7a–d). The dot plot confirmed the high expression level and proportion of BIRC5 in proliferating cells (Fig. 7e). These findings suggest that BIRC5 is closely associated with cell proliferation, tumor formation, and immune regulation. 3.9 Potential Drugs Associated with LUAD When examining the differences in the IC 50 of chemotherapy drugs between the high and low BIRC5 expression groups, we found that 35 out of 198 anticancer drugs, including afatinib, bortezomib, and dabrafenib, had significantly lower IC 50 values in the high expression group than in the low expression group (Fig. 8). This suggests that patients with high BIRC5 expression may be more sensitive to these drugs and benefit more from treatment. 4. Discussion LUAD is the most common type of NSCLC, and its prognosis is generally poor. This is primarily because of asymptomatic early stages and frequent diagnosis at advanced stages with metastasis, complicating treatment. Recent research has focused on improving early diagnostic techniques and developing more effective treatment options. Although chemotherapy and radiotherapy are effective in some patients, their side effects, as well as the potential for developing resistance, limit their long-term use [26]. While immunotherapy has brought renewed hope for treating LUAD, its effectiveness varies owing to individual differences and may be accompanied by immune-related adverse events [27]. Therefore, comprehensive assessment and personalized treatment are essential. Thus, it is crucial to develop biomarkers for early diagnosis and treatment and to explore their roles in the immune environment. In this study, we performed bioinformatics analysis using TCGA, GEO, and IMMPORT databases to identify two immune-related differential genes in LUAD tumors and normal tissues. Subsequently, we performed univariate and multivariate Cox regression analyses to identify BIRC5 as a core prognostic gene. We found that BIRC5 was highly expressed in tumor samples from various datasets, which was confirmed via pan-cancer analysis. By analyzing clinical data from TCGA LUAD cohort, we observed that BIRC5 was associated with tumor growth and metastasis, showing higher expression in metastatic and advanced tumor stages. Survival analysis indicated that patients with high BIRC5 expression had a worse prognosis, which is consistent with previous studies [28-30]. Additionally, we validated the potential of BIRC5 as a diagnostic biomarker for LUAD using ROC curves, which yielded satisfactory results. Combining findings from multiple studies, we suggest that BIRC5 could serve as a biomarker for diagnosing LUAD [15, 31, 32]. Furthermore, we constructed a nomogram prognostic model based on significant clinicopathological features and BIRC5 from Cox regression analysis to predict patient OS. The time-dependent ROC curves demonstrated that our predictive model had excellent accuracy in forecasting patients' 1-year, 2-year, and 3-year OS. BIRC5 is an anti-apoptotic protein that is highly expressed in various cancers and is closely associated with tumorigenesis, progression, and prognosis. In LUAD, BIRC5 may promote tumor progression through multiple mechanisms. GO and KEGG enrichment analyses based on the PPI network showed that BIRC5 forms a complex interaction network with multiple key genes involved in processes such as chromosome segregation and cell division, suggesting that it may promote tumor cell proliferation and survival by regulating cell cycle-related genes [33]. GO analysis indicated that BIRC5 and its associated genes were significantly enriched in cellular components such as chromosomes and spindle microtubules, highlighting its potential critical role in cell division [9]. KEGG analysis further revealed that BIRC5 may affect LUAD progression by regulating key pathways such as the cell cycle and p53 signaling pathways [34]. Additionally, GSEA provided a more detailed perspective, showing that high expression samples of BIRC5 were significantly enriched in processes such as chromosome assembly and DNA replication, further supporting its role in cell proliferation. In contrast, samples with low expression were enriched in processes such as cerebrospinal fluid circulation and drug metabolism, suggesting a potential inhibitory role for BIRC5 in these biological processes. Notably, high BIRC5 expression is also highly associated with kinase genes related to breast cancer prognosis and regulation of the p53 signaling pathway, which could provide new therapeutic targets for LUAD treatment [35, 36]. Low expression samples were enriched in immune-related pathways such as MHC II-mediated antigen processing and presentation, suggesting that BIRC5 may play a role in tumor immune evasion [37]. In LUAD, the expression of BIRC5 and its relationship with the tumor microenvironment and immune cell infiltration have revealed its critical role in tumor biology. TIMER2.0 analysis shows that BIRC5 expression was significantly and negatively correlated with B cells, CD4 + T cells, and dendritic cells. This negative correlation suggests that BIRC5 plays a key role in immune evasion by suppressing the infiltration and function of immune cells [38]. Further CIBERSORT analysis revealed that in the BIRC5 high expression group, the infiltration levels of plasma cells, resting CD4 + memory T cells, monocytes, activated dendritic cells, and resting mast cells increased, whereas those of B cells and different types of macrophages decreased. These findings suggest that BIRC5 affects the construction of the tumor microenvironment by modulating the infiltration and activation states of immune cells [15]. Single-cell RNA sequencing revealed the expression of BIRC5 in different cell types. BIRC5 is highly expressed in proliferating cells, lymphocytes, and cancer cells, whereas its expression is low in macrophages, dendritic cells, and mast cells. This expression pattern suggests that BIRC5 plays a critical role in cell proliferation and tumor formation. Dot plot analysis confirmed this, showing that proliferating cells had the highest average expression level and ratio of BIRC5. These results suggest that BIRC5 is a key factor in tumor progression and immune system regulation [39]. In terms of treatment, the expression level of BIRC5 is closely related to sensitivity to chemotherapeutic drugs. In high BIRC5 expression groups, the IC50 of 35 anticancer drugs was significantly lower than in low expression groups, suggesting increased drug sensitivity. This indicates that BIRC5 may serve as a potential biomarker for personalized treatment [40]. This drug sensitivity may be related to BIRC5's role in cell cycle regulation and apoptosis inhibition, making it an ideal target for anticancer therapies [41, 42]. This study highlights the potential of BIRC5 as a biomarker and therapeutic target using multi-database and multi-method analyses. These findings provide new insights into personalized treatments for LUAD. However, this study had some limitations. Although the bioinformatics analysis offered compelling evidence, the lack of experimental validation may have affected the accuracy of the results. Future studies should utilize in vitro and in vivo experiments to validate the functions and mechanisms of BIRC5. Nevertheless, this study lays a solid foundation for understanding the role of BIRC5 in LUAD and offers a roadmap for future investigations. In conclusion, this study revealed a complex relationship between high BIRC5 expression in LUAD and tumor proliferation, metastasis, and the immune microenvironment, underscoring the potential of BIRC5 as a biomarker and therapeutic target. These findings provide important insights into the role of BIRC5 in LUAD and offer new perspectives for the development of personalized treatment strategies. Overall, this study provides novel approaches for the diagnosis and treatment of LUAD, with significant clinical implications. Declarations Acknowledgements We would like to express our sincere gratitude to TCGA, GEO, and IMMPORT databases for providing the data used in this study. We also appreciate the visualization tools provided by Xiantao Academic, the STRING database, and TIMER 2.0. We would also like to thank Editage (www.editage.cn) for their translation and editing services. Author Contributions Jianliang Chang wrote the manuscript. Shuai Bo Yang was responsible for data processing. Dan Dan Xu designed the study. Xiao Cui Peng and Xue Wang performed the software operations. Zhi Hua Zhang provided funding for this study. Data and Materials Availability The data supporting the results of this study and the codes used are available from the corresponding author upon reasonable request. Ethics and Informed Consent Not applicable. Conflict of Interest The authors involved in this study declare no conflicts of interest. 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Ma, Q., et al., microRNA-16 represses colorectal cancer cell growth in vitro by regulating the p53/survivin signaling pathway. Oncol Rep, 2013. 29 (4): p. 1652-8. Li, F., et al., Kidney cancer biomarkers and targets for therapeutics: survivin (BIRC5), XIAP, MCL-1, HIF1α, HIF2α, NRF2, MDM2, MDM4, p53, KRAS and AKT in renal cell carcinoma. J Exp Clin Cancer Res, 2021. 40 (1): p. 254. Kanwar, J.R., S.K. Kamalapuram, and R.K. Kanwar, Targeting survivin in cancer: the cell-signalling perspective. Drug Discov Today, 2011. 16 (11-12): p. 485-94. Leisegang, M., et al., MHC-restricted fratricide of human lymphocytes expressing survivin-specific transgenic T cell receptors. J Clin Invest, 2010. 120 (11): p. 3869-77. Xu, L., et al., BIRC5 is a prognostic biomarker associated with tumor immune cell infiltration. Sci Rep, 2021. 11 (1): p. 390. Zhang, S., et al., Longitudinal single-cell profiling reveals molecular heterogeneity and tumor-immune evolution in refractory mantle cell lymphoma. Nat Commun, 2021. 12 (1): p. 2877. Sanchon-Sanchez, P., et al., Evaluation of potential targets to enhance the sensitivity of cholangiocarcinoma cells to anticancer drugs. Biomed Pharmacother, 2023. 168 : p. 115658. Kahm, Y.J. and R.K. Kim, BIRC5: A novel therapeutic target for lung cancer stem cells and glioma stem cells. Biochem Biophys Res Commun, 2023. 682 : p. 141-147. Zafar, A., et al., Molecular targeting therapies for neuroblastoma: Progress and challenges. Med Res Rev, 2021. 41 (2): p. 961-1021. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5872638","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":405584785,"identity":"54798cd6-e15f-4e79-a7a5-95ef12fcbb54","order_by":0,"name":"Jianliang Chang","email":"","orcid":"","institution":"Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Jianliang","middleName":"","lastName":"Chang","suffix":""},{"id":405584786,"identity":"d9d72070-6758-434b-b6e6-b82908d3d17e","order_by":1,"name":"Shuaibo Yang","email":"","orcid":"","institution":"Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Shuaibo","middleName":"","lastName":"Yang","suffix":""},{"id":405584787,"identity":"fc7459d7-5e4f-46a6-bc50-de3305eb2f85","order_by":2,"name":"Xiaocui Peng","email":"","orcid":"","institution":"Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Xiaocui","middleName":"","lastName":"Peng","suffix":""},{"id":405584788,"identity":"dd261328-c9dd-46d5-8d35-82a897e2ba9b","order_by":3,"name":"Xue Wang","email":"","orcid":"","institution":"Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Wang","suffix":""},{"id":405584789,"identity":"ed54a147-ef35-432e-b532-8b2f339dbf7a","order_by":4,"name":"Dandan Xu","email":"","orcid":"","institution":"Central Laboratory of the First Affiliated Hospital of Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Dandan","middleName":"","lastName":"Xu","suffix":""},{"id":405584790,"identity":"4254f9fd-7732-48ac-95b0-8f813a48a137","order_by":5,"name":"Zhihua Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACeWbm47//GNQwM7Y3HyBOi2E7W4IET8ExduaeYwlEWnOeR0GC5wMzP/uMHAPidDA28zAYSBiwSfPOyPl44w2DnZxuAwEt7My8BxIMDGSMJXvebracw5BsbHaAoC18CUA9bMmG7bnbpHkYDiRuI6SF4TCPYcMBA+b6/QdynhGtxZixwYCZmbEjh404LYbNbGnMDAbHmBl7jhlbzjEgwi/y/IePMTP8AUflwxtvKuzkCGpBARI8REYNshZSdYyCUTAKRsGIAADqbD4D65ZkcgAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Hebei North University","correspondingAuthor":true,"prefix":"","firstName":"Zhihua","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-01-21 10:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5872638/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5872638/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74695040,"identity":"d096ffaf-7068-434d-9aaf-4713ff0c6e3c","added_by":"auto","created_at":"2025-01-24 20:00:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3057849,"visible":true,"origin":"","legend":"\u003cp\u003eData processing, differential analysis, and WGCNA. (a) Before batch effect removal of the three GEO datasets. (b) After batch effect removal of the three GEO datasets. (c) Volcano plot of differential analysis for TCGA dataset. (d) Selection of soft threshold for WGCNA in TCGA dataset. (e) Clustering trend of WGCNA in TCGA dataset. (f) Correlation heatmap of WGCNA in TCGA dataset. (g) Volcano plot of differential analysis after merging GEO datasets. (h) Soft threshold selection in merged GEO WGCNA. (i) Clustering trend in merged GEO WGCNA. (j) Correlation heatmap of merged GEO WGCNA. (k) Venn diagram of gene intersection\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5872638/v1/8106ab10a985604b9914de1e.png"},{"id":74694588,"identity":"eac97e24-8d53-4994-b4b7-c50aec65cf07","added_by":"auto","created_at":"2025-01-24 19:52:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2135290,"visible":true,"origin":"","legend":"\u003cp\u003eScreening of prognostic genes and their expression in different datasets. (a, b) Forest plots of univariate and multivariate Cox regression analysis showing the prognostic value of clinical features and intersecting genes. (c, d) Expression differences of BIRC5 between LUAD tissues and adjacent normal tissues in TCGA and GEO datasets. (e) Expression differences of BIRC5 across various cancers (*p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001)\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5872638/v1/c1c8bb15b28e3da42b21fbb5.png"},{"id":74694590,"identity":"d8ccf583-f569-44a4-ade4-dfce3aec9f07","added_by":"auto","created_at":"2025-01-24 19:52:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1575581,"visible":true,"origin":"","legend":"\u003cp\u003eClinical correlation and prognosis analysis of BIRC5 in TCGA dataset. (a) Correlation heatmap of BIRC5 in TCGA database, showing significant correlations between high BIRC5 expression and T, N, and stage classifications (**p \u0026lt; 0.01; ***p \u0026lt; 0.001). (b–g) Differential expression of BIRC5 across different T, N, stage, age, gender, and M classifications. (h) Kaplan–Meier survival analysis of BIRC5 expression in TCGA dataset. (i) ROC curve\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5872638/v1/909a375f49c7b3b74eb40e69.png"},{"id":74694592,"identity":"db9524ca-95c4-44fd-a50a-58a9544a3101","added_by":"auto","created_at":"2025-01-24 19:52:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1781928,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of the Nomogram prognostic model to predict patient OS. (a) Nomogram predicting 1-year, 2-year, and 3-year OS in patients with LUAD based on TCGA data, incorporating BIRC5. (b) Calibration curves showing the agreement between predicted and actual OS at 1, 2, and 3 years. (c) ROC curves illustrating the predictive accuracy of the nomogram for 1-year, 2-year, and 3-year OS\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5872638/v1/8e9b419c013338720a2f3786.png"},{"id":74694594,"identity":"cd653a85-6e5f-4d18-832e-fb5a5bf61846","added_by":"auto","created_at":"2025-01-24 19:52:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":5653489,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional analysis of BIRC5 based on TCGA dataset. (a) Top 100 PPIs network. (b, c) GO and KEGG enrichment analysis of the top 100 genes from the PPI network. (d, e) GSEA of BIRC5-related genes selected by Spearman correlation analysis\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5872638/v1/2aef0761aaaad33b2fb4f990.png"},{"id":74694599,"identity":"3aaa8158-4cd9-4d16-8702-0b203ac62ee0","added_by":"auto","created_at":"2025-01-24 19:52:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":4120643,"visible":true,"origin":"","legend":"\u003cp\u003eImmune infiltration analysis of BIRC5 in TCGA dataset. (a) Correlation between BIRC5 expression and CD4\u003csup\u003e+\u003c/sup\u003e T cells, CD8\u003csup\u003e+\u003c/sup\u003e T cells, B cells, neutrophils, macrophages, and dendritic cells using the TIMER2.0 database. (b) Differences in the infiltration levels of 22 immune cell types between the high and low BIRC5 expression groups (**p \u0026lt; 0.01; ***p \u0026lt; 0.001; ****p \u0026lt; 0.0001). (c) Correlation heatmap of 22 immune cell types. (d) Abundance map of BIRC5 in 22 immune cell types\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5872638/v1/78fb8174b1a1d99c9933554c.png"},{"id":74694597,"identity":"0400d96d-0a05-4601-b218-ce4e124153b0","added_by":"auto","created_at":"2025-01-24 19:52:16","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":4546552,"visible":true,"origin":"","legend":"\u003cp\u003eSingle-cell sequencing analysis of BIRC5 expression in different cell types. (a–d) UMAP plots showing the expression levels of BIRC5 in different cell types. (e) Dot plot displaying the expression levels of BIRC5 in proliferating cells and lymphocytes\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-5872638/v1/0d5579951bc8ff13f90dc2a2.png"},{"id":74694602,"identity":"55534633-bb08-4cda-9e3a-e30906901d6b","added_by":"auto","created_at":"2025-01-24 19:52:16","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":4436455,"visible":true,"origin":"","legend":"\u003cp\u003eThe difference in IC\u003csub\u003e50\u003c/sub\u003e of 35 chemotherapeutic drugs between the high and low expression groups of BIRC5\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-5872638/v1/39a9c28db62a3007e7fcc1b4.png"},{"id":74702268,"identity":"b8f45076-4446-4ebd-a678-9392e4b44e9a","added_by":"auto","created_at":"2025-01-25 01:01:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":28890223,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5872638/v1/b69983cc-8255-41f7-899a-966ab0e49dcb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"BIRC5 as a Potential Biomarker and Therapeutic Target for Lung Adenocarcinoma: A Comprehensive Bioinformatics Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLung adenocarcinoma (LUAD) is the predominant pathological type of non-small cell lung cancer (NSCLC) and has the highest incidence and mortality rates among all thoracic cancers worldwide [1, 2]. Over the past two decades, the prognosis of LUAD has remained unsatisfactory, with a five-year survival rate of \u0026lt; 20% [3]. The main reason for this is that the early symptoms of LUAD are often subtle, with more than 70% of patients diagnosed only when the disease has progressed to an advanced stage, thus increasing the difficulty of treatment and risk of death. In recent years, patients with LUAD have had access to treatments such as surgery, chemotherapy, targeted therapy, and immunotherapy. However, the overall survival (OS) of patients with advanced disease remains low [4]. In this context, there is an urgent need for in-depth research into the molecular mechanisms underlying LUAD to better understand the disease and identify novel biomarkers for its diagnosis, prognosis, and treatment.\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eBIRC5\u003c/em\u003e (survivin) gene, first reported in 1997, is located in the 17q25.3 region of chromosome 17 [5]. It encodes the survivin protein, which is a member of the inhibitor of apoptosis protein (IAP) family and is characterized by the presence of a baculoviral IAP repeat (BIR) domain involved in protein\u0026ndash;protein interactions (PPIs). In addition to the BIR domain, IAPs contain other important domains, such as the C-terminal ubiquitin-binding (UBC) domain, caspase recruitment (CARD) domain, and C-terminal RING domain [6]. BIRC5 is a small protein with different isoforms that is typically expressed during fetal development and in proliferating cells in adults [7]. It plays a crucial role in cancer development by regulating cell division and proliferation while inhibiting apoptosis [8, 9]. Notably, BIRC5 is generally overexpressed in most malignant tumors [10-12], and has been shown to be associated with tumor cell proliferation, differentiation, migration, and invasion [13]. It also plays a critical role in tumor immune infiltration, chemotherapy resistance, and poor prognosis [14-16]. However, the molecular mechanisms and biological functions of BIRC5 as a novel biomarker for human cancers remain underexplored [14]. Moreover, studies on the role of BIRC5 in LUAD and its potential as a biomarker are particularly scarce. Therefore, in-depth research on the role of BIRC5 in LUAD is crucial.\u003c/p\u003e\n\u003cp\u003eIn this study, we aimed to screen immune-related biomarkers associated with LUAD using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. By integrating LUAD expression profile data from different databases, removing batch effects, conducting differential analysis, and performing weighted correlation network analysis (WGCNA), we performed univariate and multivariate Cox regression analyses on clinical data from TCGA LUAD dataset. Finally, we identified BIRC5 as an effective indicator of LUAD prognosis and identified its association with immunity. Subsequently, based on bioinformatics analysis, we conducted a comprehensive analysis of BIRC5 and established a predictive model for OS in LUAD. Our findings suggest that BIRC5 plays a critical role in the biological behavior of LUAD.\u003c/p\u003e"},{"header":"2. Materials And Methods","content":"\u003cp\u003e2.1 Downloading and Processing Datasets\u003c/p\u003e\n\u003cp\u003eThe LUAD expression profile and clinical data were downloaded from TCGA database (https://portal.gdc.cancer.gov) [17]. The GSE115002, GSE116959, and GSE140797 datasets were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo) [18] and merged for further analyses. Immune-related genes were obtained from the IMMPORT database (https://www.immport.org/home) [19]. Single-cell sequencing data were obtained from the GSE203360 dataset of the GEO database.\u003c/p\u003e\n\u003cp\u003e2.2 Differential Expression Analysis and WGCNA\u003c/p\u003e\n\u003cp\u003eDifferential expression analysis was performed on LUAD expression profile data from TCGA and GEO databases using the limma R package. The selection criteria were |log2FC| \u0026gt; 1 and an adjusted p-value \u0026lt; 0.05. The \u0026ldquo;sva\u0026rdquo; R package was used to merge the datasets (GSE115002, GSE116959, and GSE140797) and remove batch effects to identify characteristic genes [20]. The gene count for the smallest module was set to 30, the distance for merging similar modules was set to 0.25, and the sensitivity to module partitioning was adjusted to 2. Finally, the phenotypic information of the samples was integrated with the modules, and genes from the module with the highest correlation with the LUAD phenotype were selected for subsequent analysis. A Venn diagram was created using Xiantao Academic (https://www.xiantaozi.com), showing the intersection of TCGA differential genes, the highest correlated TCGA WGCNA module genes, the merged GEO differential genes, the highest correlated merged GEO WGCNA module genes, and immune-related genes.\u003c/p\u003e\n\u003cp\u003e2.3 Univariate and Multivariate Cox Regression Analysis and Core Gene Expression\u003c/p\u003e\n\u003cp\u003eTo identify prognostic genes, the expression levels of the intersecting genes in tumor samples from TCGA expression profile were extracted. Samples with a survival time of less than 30 d were removed to improve the reliability of the analysis [21]. Univariate and multivariate Cox regression analysis, along with visualization, were performed using the \u0026ldquo;survival,\u0026rdquo; \u0026ldquo;forestplot,\u0026rdquo; and \u0026ldquo;survminer\u0026rdquo; R packages to screen for core genes with prognostic value. The expression of core genes across different datasets was visualized using box plots generated using the \u0026ldquo;ggpubr\u0026rdquo; R package, and their pan-cancer expression was analyzed using Xiantao Academic.\u003c/p\u003e\n\u003cp\u003e2.4 Clinical Correlation Analysis, Survival Analysis, and Receiver Operating Characteristic (ROC) Curve Analysis\u003c/p\u003e\n\u003cp\u003eClinical data from TCGA, including patient age, sex, and tumor stage, were extracted as these factors are often linked to prognosis [21]. Analysis was performed using the \u0026ldquo;limma\u0026rdquo; and \u0026ldquo;ComplexHeatmap\u0026rdquo; R packages, and clinical correlation heatmaps and boxplots were generated. Survival analysis was conducted using the \u0026ldquo;Survival\u0026rdquo; and \u0026ldquo;SurvMiner\u0026rdquo; R packages, with Kaplan\u0026ndash;Meier survival curves plotted. The impact of gene expression levels on the OS of patients with LUAD was assessed using the log-rank test, where a p-value \u0026lt; 0.05 was considered statistically significant. Additionally, the \u0026ldquo;pROC\u0026rdquo; R package was used to construct ROC curves, and the area under the curve (AUC) was calculated to evaluate the diagnostic performance of the core genes for LUAD.\u003c/p\u003e\n\u003cp\u003e2.5 Establishment of Nomogram Prognostic Model\u003c/p\u003e\n\u003cp\u003eTo predict the OS of patients with LUAD, a univariate Cox regression analysis was performed to identify the clinical and pathological features affecting prognosis. The \u0026ldquo;rms\u0026rdquo; R package was used to construct a nomogram prognostic model [22]. A nomogram converts complex regression models into graphical tools to predict the survival probability based on the total score (Nom score). A calibration curve was constructed to compare the consistency between the predicted survival probability and actual observed outcomes. Additionally, the \u0026ldquo;timeROC\u0026rdquo; R package was used to plot time-dependent ROC curves to evaluate the accuracy of the model in predicting 1-year, 2-year, and 3-year OS in patients with LUAD.\u003c/p\u003e\n\u003cp\u003e2.6 PPI network, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) Analyses\u003c/p\u003e\n\u003cp\u003ePPI analysis of core genes was performed using the STRING database (https://string-db.org), with a confidence threshold of 0.7 and a maximum of 100 interacting genes. GO and KEGG enrichment analyses of the interacting genes were conducted using the \u0026ldquo;clusterProfiler\u0026rdquo; R package, with a significance threshold set to an adjusted p-value \u0026lt; 0.05. The top 15 significant terms or pathways were visualized using the \u0026ldquo;ggplot2\u0026rdquo; R package.\u003c/p\u003e\n\u003cp\u003e2.7 Gene Set Enrichment Analysis (GSEA) of Core Genes\u003c/p\u003e\n\u003cp\u003eGSEA was performed to identify the potential functions and roles of core genes in different biological pathways or processes [23]. GSEA examines the entire gene expression profile, allowing the detection of smaller differences in genes. This makes it more comprehensive than GO and KEGG analyses, which focus solely on differentially expressed genes [24]. Based on TCGA expression data, genes correlated with core genes were screened using Spearman\u0026rsquo;s correlation analysis. Gene sets were downloaded from the Molecular Signatures Database (https://www.gsea-msigdb.org), including c5.go.v2024.1.Hs.symbols.gmt (GO) and c2.all.v2024.1.Hs.symbols.gmt (KEGG). GSEA was conducted on the selected related genes using the \u0026ldquo;enrichplot\u0026rdquo; and \u0026ldquo;ggplot22\u0026rdquo; R packages, and the top five enriched entries were displayed. Adjusted p-values \u0026lt; 0.05 were considered significantly enriched.\u003c/p\u003e\n\u003cp\u003e2.8 Immune Infiltration Analysis of Core Genes\u003c/p\u003e\n\u003cp\u003eFirst, we used the TIMER 2.0 database (https://www.timer.cistrome.org) [25] to analyze the relationship between the core genes and the infiltration of six immune cell types: CD4\u003csup\u003e+\u003c/sup\u003e T cells, CD8\u003csup\u003e+\u003c/sup\u003e T cells, B cells, neutrophils, macrophages, and dendritic cells. Based on the median expression levels of the core genes, TCGA tumor samples were divided into high and low expression groups. Tumor immune infiltration analysis was performed using the \u0026ldquo;CIBERSORT\u0026rdquo; R package, and the abundance of 22 immune cell types between the high and low expression groups of core genes was visualized using the \u0026ldquo;ggpubr\u0026rdquo; R package.\u003c/p\u003e\n\u003cp\u003e2.9 Single-Cell Analysis\u003c/p\u003e\n\u003cp\u003eSingle-cell RNA sequencing data from GSE203360 were analyzed using the \u0026ldquo;Seurat\u0026rdquo; and \u0026ldquo;Harmony\u0026rdquo; R packages. A \u0026ldquo;Seurat\u0026rdquo; object was created, and high-quality cells were retained. The \u0026ldquo;SCTransform\u0026rdquo; method was applied for normalization, and mitochondrial gene effects were regressed out. Principal component analysis was performed for dimensionality reduction, and \u0026ldquo;Harmony\u0026rdquo; was used to correct for batch effects. Data visualization was performed with UMAP, and cells were clustered and annotated based on known marker genes. Gene expression patterns were visualized using \u0026ldquo;FeaturePlot\u0026rdquo; and \u0026ldquo;DotPlot,\u0026rdquo; and cell cluster names were validated.\u003c/p\u003e\n\u003cp\u003e2.10 Drug Sensitivity Analysis\u003c/p\u003e\n\u003cp\u003eTo identify drugs effective for different expression groups of LUAD core genes, Spearman correlation analysis was conducted on TCGA tumor sample expression data, selecting genes with a correlation \u0026gt; 0.6 with core genes. Tumor samples were divided into high and low expression groups based on the median expression levels of the core genes. Drug sensitivity analysis was conducted using the GDSC database and the \u0026ldquo;oncoPredict\u0026rdquo; R package. The IC\u003csub\u003e50\u003c/sub\u003e values of 198 anticancer drugs were calculated and visualized, with a p-value \u0026lt; 0.05 considered significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1 Gene Selection\u003c/p\u003e\n\u003cp\u003eFirst, three GEO datasets were merged, and batch effect correction was applied to improve the accuracy of the gene expression data analysis (Fig. 1a, b). Differential expression analysis revealed 889 upregulated genes and 1,366 downregulated genes (Fig. 1c). Subsequently, WGCNA was performed, and a soft threshold of β = 8 (R² = 0.9) was selected (Fig. 1d). Clustering trends were constructed (Fig. 1e), and 13 modules were identified (Fig. 1f). Similar processing was applied to TCGA dataset, where differential analysis identified 2,001 upregulated genes and 2,432 downregulated genes (Fig. 1g). WGCNA selected a soft threshold of β = 20 (R² = 0.9) (Fig. 1h), clustering trends were constructed (Fig. 1i), and eight modules were identified (Fig. 1j). Genes at the intersection of GEO and TCGA differential genes, GEO’s turquoise module (5,199 genes), TCGA’s brown module (348 genes), and 1,793 immune-related genes from the IMMPORT database revealed two core genes: \u003cem\u003eBIRC5\u0026nbsp;\u003c/em\u003eand \u003cem\u003ePDK1\u003c/em\u003e (Fig. 1k).\u003c/p\u003e\n\u003cp\u003e3.2 Core Gene Selection and Differential Expression\u003c/p\u003e\n\u003cp\u003eBased on TCGA expression profiles, the expression levels of BIRC5 and PDK1 were analyzed along with clinical information. A total of 490 tumor samples with survival times longer than 30 d were selected for univariate and multivariate Cox regression analyses. These results indicated that BIRC5 is an independent risk factor for the prognosis of patients with LUAD (Fig. 2a, b). BIRC5 showed significantly higher expression in tumor samples from both TCGA and merged GEO datasets, with a notable difference when compared to normal samples (p \u0026lt; 0.05) (Fig. 2c, d). Furthermore, analysis of 33 different cancers revealed that BIRC5 was highly expressed in most tumor types (Fig. 2e).\u003c/p\u003e\n\u003cp\u003e3.3 Correlation Analysis of BIRC5 Expression with LUAD Clinical Features and Prognosis\u003c/p\u003e\n\u003cp\u003eClinical correlation analysis revealed that the expression of BIRC5 was significantly associated with tumor T stage, N stage, and stage classification in the high expression group (p \u0026lt; 0.01) (Fig. 3a). The expression of BIRC5 in T1 was significantly different from that in T2 and T3 (p \u0026lt; 0.05) (Fig. 3b). A significant difference in BIRC5 expression was observed among the N0, N1, and N2 groups (p \u0026lt; 0.05) (Fig. 3c). There were significant differences in BIRC5 expression among stages I, II, III, and IV (p \u0026lt; 0.05) (Fig. 3d). No significant differences were found in BIRC5 expression among the different age, sex, and M stage groups (Fig. 3e–g). Kaplan–Meier survival curves showed that patients with high BIRC5 expression had significantly poorer OS (p = 0.002) (Fig. 3h). ROC curve analysis indicated that BIRC5 has an extremely high diagnostic value for LUAD (AUC = 0.973) (Fig. 3i).\u003c/p\u003e\n\u003cp\u003e3.4 Establishment of Nomogram Prognostic Model for Predicting Patient OS\u003c/p\u003e\n\u003cp\u003eA nomogram predicted the 1-, 2-, and 3-year OS of patients with LUAD, showing that higher sample scores correlated with poorer prognosis (Fig. 4a). The calibration curve of the model demonstrated good consistency between the predicted and observed outcomes, closely aligning with the ideal 45-degree line (Fig. 4b). ROC curves based on the Nom score showed AUC values of 0.753, 0.743, and 0.734 for predicting 1-year, 2-year, and 3-year OS, respectively (Fig. 4c). These results suggest that the nomogram prognostic model has high accuracy and reliability for predicting the OS of patients with LUAD.\u003c/p\u003e\n\u003cp\u003e3.5 PPI Network and Functional Enrichment\u003c/p\u003e\n\u003cp\u003ePPI analysis of BIRC5 was performed using the STRING database, and the top 100 interacting genes are shown in Fig. 5a. GO and KEGG enrichment analyses were conducted to identify the interacting genes. GO enrichment analysis revealed that in terms of biological processes (BPs), BIRC5 interacting genes were associated with chromosome segregation and cell division. In the cellular component category, genes were concentrated in the chromosome, spindle microtubules, and centromeric regions. In terms of molecular function, these genes were found to be involved in microtubule binding, cytoskeletal activity, and protein serine/threonine kinase activity (Fig. 5b). KEGG analysis indicated that BIRC5 was primarily involved in pathways related to cell cycle, P53 signaling pathway, and apoptosis (Fig. 5c).\u003c/p\u003e\n\u003cp\u003e3.6 GSEA Results\u003c/p\u003e\n\u003cp\u003eGO analysis revealed that the samples with high BIRC5 expression were significantly enriched in processes related to chromosome assembly, telomere localization, the CMG complex, the DNA replication pre-initiation complex, and the MCM complex. Samples with low expression were enriched in processes such as cerebrospinal fluid circulation, leukotriene D4 metabolism, salivary secretion, axon dynein complex formation, and adrenergic receptor activity (Fig. 5d).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKEGG analysis indicated that samples with high expression were enriched in pathways associated with kinases related to breast cancer prognosis, MCC formation, the p53 signaling pathway, DNA methylation, and DNA unwinding. Low expression samples were enriched in the lectin pathway, MHC II-mediated antigen processing, invasive ovarian cancer endothelial cells, LUAD markers, and drug metabolism (Fig. 5e).\u003c/p\u003e\n\u003cp\u003e3.7. Relationship Between Core Gene Expression and Tumor Microenvironment\u003c/p\u003e\n\u003cp\u003eAnalysis using TIMER 2.0 revealed that the expression of \u003cem\u003eBIRC5\u003c/em\u003e in LUAD was significantly negatively correlated with B cells, CD4\u003csup\u003e+\u003c/sup\u003e T cells, and dendritic cells (Fig. 6a). CIBERSORT analysis showed that in the high expression group, there was an increase in the infiltration of plasma cells, resting CD4\u003csup\u003e+\u003c/sup\u003e memory T cells, monocytes, activated dendritic cells, and resting mast cells. Conversely, the infiltration of B cells and M0, M2, and M3 macrophages was decreased (Fig. 6b). The correlation heatmap showed a positive correlation between CD4\u003csup\u003e+\u003c/sup\u003e memory T cells and regulatory T cells (approximately 0.8), whereas NK cells and M1 macrophages exhibited a negative correlation (approximately\u0026nbsp;–0.4) (Fig. 6c). The abundance plot revealed that naïve B cells and memory B cells constituted a larger proportion, whereas activated plasma cells\u0026nbsp;were significantly increased.\u0026nbsp;Resting mast cells and dendritic cells exhibited low abundance in most samples (Fig. 6d).\u003c/p\u003e\n\u003cp\u003e3.8. Single-Cell Analysis of BIRC5\u003c/p\u003e\n\u003cp\u003eThis study performed single-cell RNA sequencing to analyze the expression of BIRC5 in different cell types. The results showed that BIRC5 was highly expressed in proliferating cells, lymphocytes, and cancer cells, whereas its expression was low in macrophages, dendritic cells, and mast cells (Fig. 7a–d). The dot plot confirmed the high expression level and proportion of BIRC5 in proliferating cells (Fig. 7e). These findings suggest that BIRC5 is closely associated with cell proliferation, tumor formation, and immune regulation.\u003c/p\u003e\n\u003cp\u003e3.9 Potential Drugs Associated with LUAD\u003c/p\u003e\n\u003cp\u003eWhen examining the differences in the IC\u003csub\u003e50\u003c/sub\u003e of chemotherapy drugs between the high and low BIRC5 expression groups, we found that 35 out of 198 anticancer drugs, including afatinib, bortezomib, and dabrafenib, had significantly lower IC\u003csub\u003e50\u003c/sub\u003e values in the high expression group than in the low expression group (Fig. 8). This suggests that patients with high BIRC5 expression may be more sensitive to these drugs and benefit more from treatment.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eLUAD is the most common type of NSCLC, and its prognosis is generally poor. This is primarily because of asymptomatic early stages and frequent diagnosis at advanced stages with metastasis, complicating treatment. Recent research has focused on improving early diagnostic techniques and developing more effective treatment options. Although chemotherapy and radiotherapy are effective in some patients, their side effects, as well as the potential for developing resistance, limit their long-term use [26]. While immunotherapy has brought renewed hope for treating LUAD, its effectiveness varies owing to individual differences and may be accompanied by immune-related adverse events [27]. Therefore, comprehensive assessment and personalized treatment are essential. Thus, it is crucial to develop biomarkers for early diagnosis and treatment and to explore their roles in the immune environment.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;In this study, we performed bioinformatics analysis using TCGA, GEO, and IMMPORT databases to identify two immune-related differential genes in LUAD tumors and normal tissues. Subsequently, we performed univariate and multivariate Cox regression analyses to identify \u003cem\u003eBIRC5\u003c/em\u003e as a core prognostic gene. We found that \u003cem\u003eBIRC5\u003c/em\u003e was highly expressed in tumor samples from various datasets, which was confirmed via pan-cancer analysis. By analyzing clinical data from TCGA LUAD cohort, we observed that \u003cem\u003eBIRC5\u003c/em\u003e was associated with tumor growth and metastasis, showing higher expression in metastatic and advanced tumor stages. Survival analysis indicated that patients with high \u003cem\u003eBIRC5\u003c/em\u003e expression had a worse prognosis, which is consistent with previous studies [28-30]. Additionally, we validated the potential of \u003cem\u003eBIRC5\u003c/em\u003e as a diagnostic biomarker for LUAD using ROC curves, which yielded satisfactory results. Combining findings from multiple studies, we suggest that \u003cem\u003eBIRC5\u003c/em\u003e could serve as a biomarker for diagnosing LUAD [15, 31, 32]. Furthermore, we constructed a nomogram prognostic model based on significant clinicopathological features and \u003cem\u003eBIRC5\u003c/em\u003e from Cox regression analysis to predict patient OS. The time-dependent ROC curves demonstrated that our predictive model had excellent accuracy in forecasting patients' 1-year, 2-year, and 3-year OS.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;BIRC5 is an anti-apoptotic protein that is highly expressed in various cancers and is closely associated with tumorigenesis, progression, and prognosis. In LUAD, BIRC5 may promote tumor progression through multiple mechanisms. GO and KEGG enrichment analyses based on the PPI network showed that BIRC5 forms a complex interaction network with multiple key genes involved in processes such as chromosome segregation and cell division, suggesting that it may promote tumor cell proliferation and survival by regulating cell cycle-related genes [33]. GO analysis indicated that BIRC5 and its associated genes were significantly enriched in cellular components such as chromosomes and spindle microtubules, highlighting its potential critical role in cell division [9]. KEGG analysis further revealed that BIRC5 may affect LUAD progression by regulating key pathways such as the cell cycle and p53 signaling pathways [34]. Additionally, GSEA provided a more detailed perspective, showing that high expression samples of BIRC5 were significantly enriched in processes such as chromosome assembly and DNA replication, further supporting its role in cell proliferation. In contrast, samples with low expression were enriched in processes such as cerebrospinal fluid circulation and drug metabolism, suggesting a potential inhibitory role for BIRC5 in these biological processes. Notably, high BIRC5 expression is also highly associated with kinase genes related to breast cancer prognosis and regulation of the p53 signaling pathway, which could provide new therapeutic targets for LUAD treatment [35, 36]. Low expression samples were enriched in immune-related pathways such as MHC II-mediated antigen processing and presentation, suggesting that BIRC5 may play a role in tumor immune evasion [37].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;In LUAD, the expression of BIRC5 and its relationship with the tumor microenvironment and immune cell infiltration have revealed its critical role in tumor biology. TIMER2.0 analysis shows that BIRC5 expression was significantly and negatively correlated with B cells, CD4\u003csup\u003e+\u003c/sup\u003e T cells, and dendritic cells. This negative correlation suggests that BIRC5 plays a key role in immune evasion by suppressing the infiltration and function of immune cells [38]. Further CIBERSORT analysis revealed that in the BIRC5 high expression group, the infiltration levels of plasma cells, resting CD4\u003csup\u003e+\u003c/sup\u003e memory T cells, monocytes, activated dendritic cells, and resting mast cells increased, whereas those of B cells and different types of macrophages decreased. These findings suggest that BIRC5 affects the construction of the tumor microenvironment by modulating the infiltration and activation states of immune cells [15].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Single-cell RNA sequencing revealed the expression of BIRC5 in different cell types. BIRC5 is highly expressed in proliferating cells, lymphocytes, and cancer cells, whereas its expression is low in macrophages, dendritic cells, and mast cells. This expression pattern suggests that BIRC5 plays a critical role in cell proliferation and tumor formation. Dot plot analysis confirmed this, showing that proliferating cells had the highest average expression level and ratio of BIRC5. These results suggest that BIRC5 is a key factor in tumor progression and immune system regulation [39].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;In terms of treatment, the expression level of BIRC5 is closely related to sensitivity to chemotherapeutic drugs. In high BIRC5 expression groups, the IC50 of 35 anticancer drugs was significantly lower than in low expression groups, suggesting increased drug sensitivity. This indicates that BIRC5 may serve as a potential biomarker for personalized treatment [40]. This drug sensitivity may be related to BIRC5's role in cell cycle regulation and apoptosis inhibition, making it an ideal target for anticancer therapies [41, 42].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;This study highlights the potential of BIRC5 as a biomarker and therapeutic target using multi-database and multi-method analyses. These findings provide new insights into personalized treatments for LUAD. However, this study had some limitations. Although the bioinformatics analysis offered compelling evidence, the lack of experimental validation may have affected the accuracy of the results. Future studies should utilize in vitro and in vivo experiments to validate the functions and mechanisms of BIRC5. Nevertheless, this study lays a solid foundation for understanding the role of BIRC5 in LUAD and offers a roadmap for future investigations.\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study revealed a complex relationship between high BIRC5 expression in LUAD and tumor proliferation, metastasis, and the immune microenvironment, underscoring the potential of BIRC5 as a biomarker and therapeutic target. These findings provide important insights into the role of BIRC5 in LUAD and offer new perspectives for the development of personalized treatment strategies. Overall, this study provides novel approaches for the diagnosis and treatment of LUAD, with significant clinical implications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to TCGA, GEO, and IMMPORT databases for providing the data used in this study. We also appreciate the visualization tools provided by Xiantao Academic, the STRING database, and TIMER 2.0. We would also like to thank Editage (www.editage.cn) for their translation and editing services.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJianliang Chang wrote the manuscript. Shuai Bo Yang was responsible for data processing. Dan Dan Xu designed the study. Xiao Cui Peng and Xue Wang performed the software operations. Zhi Hua Zhang provided funding for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and Materials Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the results of this study and the codes used are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics and Informed Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors involved in this study declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Support\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eScientific Research Projects on Traditional Chinese Medicine in 2023 (ID 2023097) by the Hebei Provincial Administration of Traditional Chinese Medicine .\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMa, T., et al., BIRC5 Modulates PD-L1 Expression and Immune Infiltration in Lung Adenocarcinoma. 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Med Res Rev, 2021. \u003cstrong\u003e41\u003c/strong\u003e(2): p. 961-1021.\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, BIRC5, Biomarker, Immune microenvironment, Drug sensitivity","lastPublishedDoi":"10.21203/rs.3.rs-5872638/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5872638/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Lung adenocarcinoma (LUAD), the most prevalent type of non-small cell lung cancer (NSCLC), has the highest incidence and mortality rates among thoracic cancers worldwide. BIRC5, a protein linked to tumor cell proliferation, differentiation, migration, and invasion, has been insufficiently studied as a potential LUAD biomarker. In this study, we used bioinformatics techniques to analyze LUAD-related data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify biomarkers associated with LUAD onset, progression, and prognosis and evaluate their clinical significance. Immune cell infiltration and single-cell RNA sequencing analyses assessed the expression of core genes in various immune environments. Drug sensitivity analysis evaluated the impact of these biomarkers on treatment response, providing a basis for early diagnosis and personalized LUAD treatment. Gene expression and clinical data from TCGA and GEO underwent weighted correlation network analysis (WGCNA), differential analysis, and univariate and multivariate Cox regression to identify prognostic core genes. High BIRC5 expression emerged as an independent risk factor for poor overall survival (OS) in LUAD, exhibiting strong diagnostic performance. Enrichment analysis indicated that BIRC5 was involved in the cell cycle and P53 signaling pathways. Single-cell RNA sequencing and immune infiltration analyses revealed that BIRC5 plays a critical role in the immune microenvironment. Drug sensitivity analysis showed that high BIRC5 expression correlated with increased sensitivity to several anticancer drugs. These findings establish BIRC5 as a promising biomarker for LUAD diagnosis and prognosis. Its role in immune regulation and drug sensitivity highlights its potential for guiding personalized treatment strategies.","manuscriptTitle":"BIRC5 as a Potential Biomarker and Therapeutic Target for Lung Adenocarcinoma: A Comprehensive Bioinformatics Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-24 19:52:11","doi":"10.21203/rs.3.rs-5872638/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":"9c4477d1-b1ec-4507-8a06-7cb3e5aa15fd","owner":[],"postedDate":"January 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-28T07:23:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-24 19:52:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5872638","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5872638","identity":"rs-5872638","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}