ABCG2 predicts the prognosis and is associated with immune infiltration in lung cancer: a bioinformatics study

ABCG2 predicts the prognosis and is associated with immune infiltration in lung cancer: a bioinformatics study | 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 ABCG2 predicts the prognosis and is associated with immune infiltration in lung cancer: a bioinformatics study Yang Zhai, XinLong Zhai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4687704/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 ATP-binding cassette superfamily G member 2 (ABCG2), a member of the ATP-binding cassette transporter family, is localized in the membrane of various human cancer cells and excludes drugs from cells in an ATP-dependent manner. Its expression is linked to numerous malignant tumors. This study focused on the expression of the ABCG2 gene in lung cancer and its association with patient prognosis. Methods The expression levels of ABCG2 between lung cancer and normal tissues were explored using The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database. The Human Protein Mapping (HPA) database was used to obtain the expression of ABCG2 protein in tissues and organs and intracellular protein expression patterns. ABCG2 was detected in the plasma membrane and nucleoplasm. University of California Santa Cruz (UCSC) and cBioPortal were used to obtain gene mapping and mutation information. The ABCG2 was significantly correlated with patient survival prognosis and immune infiltration of cancer‑associated fibroblasts in numerous types of cancer. Furthermore, Gene Ontology analysis identified that ABCG2 may be important in metabolic and cellular processes in human cancers. Results ABCG2 expression was significantly associated with multiple cancers, including lung cancer in TCGA. ABCG2 protein plays a crucial role in tumor regrowth by actively removing anticancer drugs from the cell through ABCG2-mediated efflux transport, thereby protecting against their toxic effects. The functional enrichment of ABCG2-related genes primarily involves the regulation of small GTPase-mediated signal transduction, myeloid leukocyte activation, positive regulation of cell adhesion, and endocytic vesicle localization. Additionally, it is associated with vacuolar membrane organization, lysosomal membrane organization, GTPase regulator activity, nucleoside-triphosphatase regulator activity, and small GTPase binding. Conclusion ABCG2 expression was significantly associated with poor prognosis in lung cancer patients. ABCG2 is involved in lung cancer immune infiltration and represents a suitable target for immunotherapy related to immune infiltration. ABCG2 lung cancer immune infiltration Biomarker Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction BCG2 is an ABC transporter often found in stem cell populations. The natural presence of ABCG2 in certain cancers probably demonstrates the specialized phenotype of the original cell and plays a role in drug resistance 1 .ABCG2 is predominantly located at the apical membrane of polarized cells, such as those found in the blood-brain barrier and intestinal enterocytes. Its presence in these cellular environments can significantly impact the oral absorption and pharmacokinetics of various anticancer drugs 2 . It is believed that drug-resistant cancer stem cells, which express ABCG2, play a role in tumor regrowth by effectively expelling anticancer drugs from the cell through ABCG2-mediated efflux transport, thus protecting against their cytotoxic effects 3 , 4 . Previous studies have demonstrated that a variety of naturally occurring single nucleotide polymorphisms (SNPs) within the ABCG2 gene can exert influence on the expression and functionality of the ABCG2 protein. Specifically, the nonsynonymous SNPs ABCG2 C421A (rs2231142) and ABCG2 G34A (rs2231137) have been identified as significant variants that could potentially alter the pharmacokinetics and pharmacodynamics of the drug gefitinib. The presence of these polymorphisms may confer differential susceptibility to the toxic effects of gefitinib, thereby implicating a role in genetic variability in the therapeutic response and adverse event profile associated with this targeted therapy. Gefitinib is a selective and reversible inhibitor of the epidermal growth factor receptor (EGFR) tyrosine kinase, a key enzyme that plays a pivotal role in the signal transduction pathways essential for the survival and proliferation of tumor cells. Its strong affinity for the ABCG2 transporter suggests that the expression levels of ABCG2 can significantly impact the resistance to gefitinib. This implies that the effectiveness of gefitinib as a therapeutic agent may be modulated by the presence and activity of ABCG2 in cancer cells 4 . Therefore, ABCG2, a critical player in tumor progression, facilitates multidrug resistance through efflux mechanisms, impacting therapy outcomes and necessitating further study for improved cancer treatments. Lung cancer is a prevalent malignancy, frequently diagnosed and the foremost contributor to cancer-related fatalities 5 . Despite advancements in therapeutic modalities over the past several decades, the 5-year survival rate for patients afflicted with lung cancer remains disappointingly low 6 . The disease exhibits considerable heterogeneity, encompassing both small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC). NSCLC is the predominant form, representing approximately 85% of all lung cancer diagnoses. It can be further categorized into three principal histologic subtypes: lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), and large-cell carcinoma. The heterogeneity of lung cancer underscores the complexity of the disease and the challenges it presents in terms of diagnosis, treatment, and prognosis. Understanding the molecular and cellular characteristics of each subtype is essential for the development of targeted therapies and personalized treatment strategies aimed at improving patient outcomes 7 . Furthermore, the classification of lung cancer into SCLC and NSCLC, with the latter subdivided into LUAD, LUSC, and large-cell carcinoma, reflects the diverse biological behaviors and responses to treatment among different lung cancer entities. This stratification is critical for guiding clinical management and facilitating research into the molecular underpinnings of lung cancer pathogenesis 8 . This study focused on the expression and prognostic factors of ABCG2 in lung cancer 9 . An earlier investigation has elucidated that ABCG2 possesses the capacity to safeguard cellular integrity against damage and demise mediated by reactive oxygen species (ROS) 10 . Subsequent in vitro research has further delineated that the attenuation of ABCG2 expression engenders an upsurge in ROS production, incites inflammatory responses, and concurrently suppresses the biosynthesis of antioxidant molecules 11 . It has been observed that the nuclear factor kappa B (NF-κB) signaling cascade is triggered under conditions of oxidative stress precipitated by the downregulation of ABCG2. This activation of the NF-κB pathway is suggestive of a pivotal role in the propagation of the inflammatory and oxidative stress responses observed in the context of ABCG2 suppression. In summation, the collective findings from these studies posit that ABCG2 may exert a mitigating influence on oxidative stress and inflammatory processes by dampening the activity of the NF-κB signaling pathway within cellular models. Nowadays, the advent of next-generation sequencing technologies has significantly augmented our understanding of the genomic architecture of cancer. These sophisticated methodologies have facilitated a holistic examination of the complete genome of oncogenic cells, elucidating the intricate genetic alterations that are instrumental in the etiology and progression of a diverse spectrum of neoplastic conditions 12 . The deployment of next-generation sequencing technologies has been further bolstered by concurrent enhancements in the field of bioinformatics, which are essential for the systematic interpretation and analysis of the extensive datasets procured through these sequencing endeavors. The study examined the expression levels of the ABCG2 gene in lung cancer using data from public databases. Our findings offer evidence for the involvement of ABCG2 in both the occurrence and prognosis of lung cancer and may contribute to the identification of a potential biomarker for prognosis and treatment. Methods and materials Analysis of gene expression and functions mRNA expression data and clinical information were downloaded from the TCGA database ( https://cancergenome.nih.gov/ ) and the Genotype-Tissue Expression (GTEx) database. After removing clinically uninformative and duplicate data using R software (R version 4.3.3), select appropriate statistical methods according to the data format characteristics (‘stats’ package and ‘car’ package) for statistics (statistical analysis will not be performed if the statistical requirements are not met), and visualize the data with ggplot2 package, and the R package ‘ggplot2’ was utilized to visualize the data. The Human Protein Atlas (HPA) online platform, was accessed through its website. Within the 'Tissue' module, ABCG2 was searched to determine the normalized expression (NX) levels across 55 distinct normal tissue types. Gene mapping The specific location of the ABCG2 gene on the chromosome was identified, as well as its expression in 54 tissues using RNA-seq data from the Genotype-Tissue Expression (GTEx) project; version 8, utilizing the University of California Santa Cruz (UCSC) Genome Browser Human Dec. 2013 (GRCh38/hg38) Assembly ( http://genome.ucsc.edu/ ) 13 . Genetic alteration analysis of ABCG2 Using cBioPortal, a web-based platform for cancer genomics ( https://www.cbioportal.org/ ), the genetic alterations in ABCG2 were explored via the "quick search" and "TCGA Pan-Cancer Atlas Studies." The analysis revealed a Cancer Types Summary panel indicating copy number alterations, frequencies, and mutation types affecting ABCG2 across numerous tumor samples, offering insights into its genomic variability in cancer. Immune infiltration analysis of ABCG2 To evaluate the variation in ABCG2 expression levels between various types of tumors within the TCGA cohorts and their corresponding normal tissues, the 'Exploration' feature of the Tumor Immune Estimation Resource version 2.0 (TIMER2.0) webserver was used 14 . Gene enrichment analysis Genomic ontology (GO) terminology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway studies were performed for significantly co-expressed genes using the clusterProfiler package in R language. Results ABCG2 gene expression in several tumors In the UCSC Xena database(UCSC Xena), data from various cohorts have been uniformly processed using the toil pipeline. These cohorts include the GTEx normal group with 7,568 samples, the TCGA carcinoma group with 727 samples, and the TCGA tumor group with 9,807 samples. A significant differential expression of the ABCG2 gene was observed between the normal and tumor groups. The group with low ABCG2 expression comprises the following cancer types: Bladder Urothelial Carcinoma (BLCA), Breast Invasive Carcinoma (BRCA), Cervical Endocervical Adenocarcinoma and Cervical Squamous Cell Carcinoma (CESC), Cholangiocarcinoma (CHOL), Colon Adenocarcinoma (COAD), Kidney Renal Papillary Cell Carcinoma (KIRP), Lung Adenocarcinoma (LUAD), Lung Squamous Cell Carcinoma (LUSC), Ovarian Serous Cystadenocarcinoma (OV), Prostate Adenocarcinoma (PRAD), Rectum Adenocarcinoma (READ), Thyroid Carcinoma (THCA), Uterine Corpus Endometrial Carcinoma (UCEC), and Uterine Carcinosarcoma (UCS)(Figure 1 A p < 0.001). Conversely, the group with high ABCG2 expression includes Lymphoid Neoplasm Diffuse Large B-cell lymphoma (DLBC), Esophageal Carcinoma (ESCA), Glioblastoma Multiforme (GBM), Kidney Renal Clear Cell Carcinoma (KIRC), Acute Myeloid Leukemia (LAML), Brain Lower Grade Glioma (LGG), Pancreatic Adenocarcinoma (PAAD), Skin Cutaneous Melanoma (SKCM), Stomach Adenocarcinoma (STAD), and Thymoma (THYM). (Figure 1 A p < 0.001).In the TCGA database and TCGA + GTEx database, the up-regulation and down-regulation of ABCG2 expression were different. The ABCG2 gene is underexpressed in the following cancer types: BLCA, BRCA, CESC, CHOL, COAD, KICH, KIRP, LIHC (Liver hepatocellular carcinoma), LUAD, LUSC, PRAD, READ, and UCES. But it is overexpressed in KIRC(Fig. 1 B p < 0.001). The expression profiling of APOE across various tumor types was analyzed using GEPIA2 (Figure 1 C). Overexpression of ABCG2 in some tumors may indicate this adverse outcome 15 . The human ABCG2 gene contains numerous polymorphisms and mutations that can greatly influence its expression and function 16 . Protein expression The Human Protein Atlas (HPA) aims to map the subcellular locations of all human proteins. It features an extensive collection of immunohistochemistry (IHC) images, showcasing sections from 46 different types of normal human tissues and 20 different cancer types. Explore more at ( http://www.proteinatlas.org/ ). These currently consist of the Tissue Atlas (depicting protein distribution across all major tissues), Cell Atlas (illustrating subcellular localization and heterogeneity in single cells), and Pathology Atlas (showing correlations between gene expression and patient survival in major human cancer types) 17 . The expression of ABCG2 mRNA and protein in various tissues and organs shows inconsistency. High levels of ABCG2 protein expression are observed in the duodenum, small intestine, colon, rectum, seminal vesicle, endometrium, and appendix. Moderate expression is found in the kidney, testis, placenta, and smooth muscle; while low expression is detected in the thyroid gland, lung, heart muscle, and cerebral cortex(Figure 2 A). mRNA expression in normal lung tissue compared to tumor lung tissue(Fig. 2 B-C). Protein expression patterns and immunofluorescence maps of the cells showed that ABCG2 was detected in the plasma membrane and nucleoplasm(Fig. 2 D-E). Genetic alteration analysis Based on the UCSC Genome Browser on the Human Dec.2013 (GRCh38/hg38) Assembly, ABCG2 is located on chromosome 4 at position chr4:58,500,001–65,500,000(Figure 3 A). Revised sentence: “Given the demonstrated association of gene changes with tumorigenesis, the 'TCGA Pan-Cancer Atlas Studies' module of cBioPortal was utilized for genetic analysis of ABCG2 in various TCGA-based tumors”. The UCEC exhibited the highest frequency of ABCG2 gene alterations (> 7%), predominantly characterized by mutations. In TCGA-based tumors, mutations were the predominant type of ABCG2 gene alterations, present in almost all TCGA tumors. CHOL, LUAD, UCS, KIRP, THCA, and LGG each exhibited only one type of gene alteration: mutations. Additionally, amplification was the second most commonly observed genetic alteration, with the highest frequency of alteration in DLBC and Sarcoma. Mesothelioma, Pheochromocytoma Paraganglioma, and Esophageal Adenocarcinoma each had only one type of gene alteration: amplification(Fig. 3 B). The site and number of cases of ABCG2 mutations include various types, with missense mutations being the most common. Additionally, a translocation mutation caused by the alterations K653E/Y654Ifs*21/K653Nfs*11 was found in four cases of Uterine Endometrioid Carcinoma and COAD(Fig. 3 C-D). Correlation of ABCG2 expression with immune characteristics Analysis of immune infiltration plays a crucial role in the progression of cancer, as it is influenced by the behavior of cancer cells and their interaction with the tumor microenvironment. Tumor-infiltrating immune cells also significantly impact the development and metastasis of cancer within the microenvironment. To investigate the potential correlation between ABCG2 expression and immune infiltration of cancer-associated fibroblasts, we utilized TIMER2.0, employing four algorithms (EPIC, MCPCOUNTER, XCELL, and TIDE) to generate a heatmap(Fig. 4 A). Three algorithms(MCPCOUNTER, XCELL, and TIDE) were used to study the relationship between LUAD and tumor-associated fibroblasts(Fig. 4 B-D). To investigate the correlation between ABCG2 expression levels and tumor immune response, we utilized the TCGA database to analyze immune infiltration in lung cancer across varying levels of ABCG2 expression(Fig. 5 A). The results revealed a positive association between ABCG2 expression and macrophages, immature dendritic cells (iDC), and Mast cells in lung cancer patients. Further analysis demonstrated a significant positive correlation between ABCG2 expression and levels of macrophage (r = 0.322, p < 0.001), iDC infiltration (r = 0.391, p < 0.001), and Mast cells (r = 0.373, p < 0.001)(Fig. 5 B-D). These findings prompted us to explore the relationship between ABCG2 expression level and immune infiltration, leading to significant differences (p < 0.05) in iDC infiltration levels when stratifying high versus low ABCG2 expression groups using the ssGSEA algorithm(Fig. 5 E). Functional enrichment of ABCG2-related genes We conducted differential expression analysis comparing low and high ABCG2 levels in Lung Cancer using the DESeq2 package, retaining only protein-coding genes. A total of 561 differentially expressed genes were identified based on the screening criteria of |logFC| > 1 and p.adj < 0.05(Figure 6 A). Subsequently, we investigated co-expressed genes associated with ABCG2 expression in the TCGA-LUAD and TCGA-LUSC datasets, identifying a total of 147 genes meeting the criteria of |cor spearman| > 0.3 and p < 0.05. The association between ABCG2 expression levels in lung cancer was explored(Figur 6 B). The resulting set of 1923 co-expressed genes was then subjected to GO term and KEGG pathway enrichment analyses using the clusterProfiler package in R language. The co-expressed genes related to ABCG2 were found to be involved in 1557 biological processes, 157 cellular components, 110 molecular functions, and 99 KEGGs (p.adj < 0 .05 and q value < 0 .05). Bubble plots were used to visualize the top three categories for biological processes, cellular components, molecular functions, and KEGGs respectively(Fig. 6 C).GO term annotations revealed that these genes are primarily associated with regulating small GTPase-mediated signal transduction, myeloid leukocyte activation, positive regulation of cell adhesion; as well as endocytic vesicle localization; vacuolar membrane organization; lysosomal membrane organization; GTPase regulator activity; nucleoside-triphosphatase regulator activity; small GTPase binding. KEGG pathway analysis indicated that these genes are mainly involved in signaling pathways such as Autophagy - animal, Endocytosis, and Sphingolipid signaling pathways. In summary, the results provide detailed insights into the enrichment analyses for ABCG2 co-expression involving GO terms and KEGG pathways. Table 1 Correlation between ABCG2 expression and clinicopathologic characteristics of lung cancer Characteristics Low expression of ABCG2 High expression of ABCG2 P value n 520 521 Pathologic T stage, n (%) 0.332 T1 134 (12.9%) 156 (15%) T2 297 (28.6%) 289 (27.8%) T3 67 (6.5%) 53 (5.1%) T4 21 (2%) 21 (2%) Pathologic N stage, n (%) 0.497 N0 341 (33.5%) 329 (32.3%) N1 119 (11.7%) 109 (10.7%) N2 53 (5.2%) 61 (6%) N3 2 (0.2%) 5 (0.5%) Pathologic M stage, n (%) 0.713 M0 390 (48.2%) 387 (47.8%) M1 15 (1.9%) 17 (2.1%) Pathologic stage, n (%) 0.724 Stage I 267 (25.9%) 274 (26.6%) Stage II 152 (14.8%) 135 (13.1%) Stage III 84 (8.2%) 84 (8.2%) Stage IV 15 (1.5%) 18 (1.7%) Gender, n (%) 0.046 Female 194 (18.6%) 226 (21.7%) Male 326 (31.3%) 295 (28.3%) Age, n (%) 0.097 65 297 (29.3%) 268 (26.5%) Residual tumor, n (%) 0.715 R0 395 (50.1%) 361 (45.8%) R1 11 (1.4%) 14 (1.8%) R2 4 (0.5%) 4 (0.5%) Location, n (%) 0.706 Central Lung 105 (24.4%) 105 (24.4%) Peripheral Lung 114 (26.5%) 106 (24.7%) Smoker, n (%) 0.002 No 33 (3.3%) 62 (6.1%) Yes 474 (46.7%) 446 (43.9%) Number pack years smoked, n (%) 0.004 = 40 263 (33.1%) 209 (26.3%) Table 2 ABCG2 expression associated with clinicopathologic characteristics (logistic regression) in lung cancer Characteristics Total (N) OR (95% CI) P value Pathologic T stage (T3&T4 vs. T1&T2) 1,038 0.814 (0.582–1.140) 0.232 Pathologic N stage (N2&N3 vs. N0&N1) 1,019 1.260 (0.861–1.845) 0.234 Pathologic M stage (M1 vs. M0) 809 1.142 (0.562–2.319) 0.713 Pathologic stage (Stage III&Stage IV vs. Stage I&Stage II) 1,029 1.055 (0.775–1.437) 0.731 Gender (Male vs. Female) 1,041 0.777 (0.606–0.996) 0.046 Age (> 65 vs. <= 65) 1,013 0.811 (0.632–1.039) 0.097 Residual tumor (R1&R2 vs. R0) 789 1.313 (0.652–2.644) 0.446 Location (Peripheral Lung vs. Central Lung) 430 0.930 (0.637–1.357) 0.706 Smoker (Yes vs. No) 1,015 0.501 (0.322–0.779) 0.002 Number pack years smoked ( > = 40 vs. < 40) 794 0.659 (0.496–0.876) 0.004 Relationship between expression and clinicopathologic characteristics of lung cancer Baseline data with lung cancer obtained from the TCGA database were statistically analyzed. The data were divided into two groups: 520 samples from patients with lung cancer exhibiting relatively low ABCG2 expression and 521 samples from patients with relatively high ABCG2 expression. A significant difference was observed in sex (P = 0.046) and annual cigarette consumption greater than 40 packs(p = 0.04)between the two groups. Regarding different TNM stages, there was no significant difference in the number of patients with T stage (P = 0.332), N stage (P = 0.497), M stage (P = 0.713), pathologic stage (P = 0.724), residual tumor (P = 0.715), and location (P = 0.706) between the two groups (Table 1 ). A logistic regression analysis was conducted to determine the correlation between ABCG2 expression and the clinicopathologic characteristics of lung cancer(Table 2 ). Similar to the baseline statistics, logistic regression analysis revealed significant sex differences (P = 0.046) and annual cigarette consumption greater than 40 packs (P = 0.04) between the two groups. However, regarding different TNM stages, there were no significant differences in the number of patients with T stage (P = 0.232), N stage (P = 0.234), M stage (P = 0.713), pathologic stage (P = 0.731), residual tumor (P = 0.446), and location (P = 0.706) between the two groups (Table 2 ). Discussion Pan-cancer analyses serve as pivotal tools in the multi-dimensional study of various tumor types and have significant implications for the discovery of cancer biomarkers, treatment strategies, and prognostic assessments. With the advancement of human genome research, it has been recognized that abnormal gene expression, driven by factors such as gene mutations and copy number variations, is closely associated with the development and progression of cancers. Thus, the current study has focused its attention on a gene that exhibits abnormal expression across different cancerous tissues 18 , 19 . ABCG2, a member of the ATP-binding cassette (ABC) transporter family, plays a pivotal role in cancer therapy, particularly in the development of multidrug resistance (MDR). This transporter possesses the capability to efflux a wide range of compounds from the cell, posing a challenge in the treatment of chemotherapy-resistant cancers. Despite advancements in understanding the structure of ABCG2, there are still lingering questions regarding its mechanism of action 20 , 21 . ABCG2's function is not limited to promoting multidrug resistance in cancer cells, it is also expressed in normal tissues and is involved in a variety of physiological processes, including regulating intracellular cholesterol levels and participating in nutrient exchange in the placenta 22 . In addition, the expression level of ABCG2 is different in different types of tumors, and its abnormal expression is closely related to the aggressiveness of tumors, the ability to metastasize, and the prognosis of patients 23 . With the deepening of the understanding of the structure and function of ABCG2, scientists are exploring the design of drugs targeting this protein to reverse or inhibit its multi-drug resistance in tumors and improve the efficacy of chemotherapy drugs. At the same time, ABCG2 has also become a hot spot in cancer biomarker research, and its expression level may be an important indicator for diagnosis and prognosis evaluation. Therefore, ABCG2 not only has important value in basic research but also shows great application potential in clinical treatment 24 , 25 . Using bioinformatics tools, analyses of numerous cancers, including lung cancer, were performed from multiple perspectives, including gene expression and mutation, immune invasion, and survival and prognosis analyses. The expression of ABCG2 in lung cancer is associated with tumor type, differentiation level, and chemotherapy sensitivity. Specifically, ABCG2 expression in lung cancer tissues varies according to the type of lung cancer 26 , 27 . For instance, ABCG2 is more prominently expressed in squamous cell carcinoma and adenocarcinoma of the lung, while it is almost undetectable in small cell lung cancer. Additionally, the expression level of ABCG2 is correlated with the degree of differentiation in lung cancer; the higher the degree of differentiation, the higher the level of ABCG2 expression. However, there is no significant correlation between ABCG2 expression and patient gender, age, metastasis, or TNM stage 28 . The location of ABCG2 in the cell is mainly related to its function. ABCG2 can be located on the cell membrane and participate in the transport of substances inside and outside the cell 29 . However, in lung cancer cells, ABCG2 can expel chemotherapy drugs from the cell, thus reducing the toxicity of the drugs to tumor cells 30 . In addition, the bioinformatics analysis in this study identified ABCG2 gene localization, and ABCG2 mutations were found in certain cancers. By conducting an in-depth analysis of genomic data from a large number of cancer patients, the researchers were able to identify specific cancer types associated with mutations in the ABCG2 gene and assess the potential impact of these mutations on patient outcomes. The findings of this study provide new insights into understanding the role of ABCG2 in cancer development and may help in the development of targeted treatment strategies for these genetic mutations 31 . The roles of fibroblasts in the tumor microenvironment(TME) are very complex and varied. They are not only involved in the structural maintenance and repair of tissues, but also influence the behavior of tumor cells by secreting various bioactive molecules, such as cytokines, growth factors, and chemokines 32,33 . These fibroblasts, known as tumor-associated fibroblasts (CAFs), are highly heterogeneous in the tumor microenvironment and can promote tumor cell proliferation, migration, and invasion 34,35 . The interaction between fibroblasts and tumor tissues is a complex process involving a variety of biomolecules and signaling pathways. Although the detailed mechanisms of these interactions are not fully understood, studies have shown that tumor tissue can recruit fibroblasts by secreting specific chemical signals, such as C-C chemokine ligand 5 (CCL5). CCL5 binds to the C-C chemokine receptor 5 (CCR5) on the surface of fibroblasts, triggering the migration and aggregation of fibroblasts, which promotes angiogenesis and extracellular matrix remodeling in the tumor microenvironment 36 . This study showed that the expression of ABCG2 in lung cancer was positively correlated with immune infiltration of cancer-associated fibroblasts. Given the crucial role of the tumor microenvironment in facilitating cancer progression and the indispensable part played by immune cells that infiltrate the tumor, our study was designed to scrutinize the relationship between ABCG2 expression and the infiltration of immune cells in lung cancer. The analysis showed increased ABCG2 expression in lung cancer is associated with higher infiltration of macrophages, immature dendritic cells, and mast cells, highlighting a positive relationship between these immune cells and ABCG2 levels. Our study, while valuable, has its constraints. The databases utilized, like TCGA and GTEx, offer a limited cancer case range. Additionally, our focus was solely on ABCG2 expression in lung cancer, excluding other tumor types. Moreover, the bioinformatics tools we employed are somewhat limited in customization and are dependent on ever-changing external databases. Limitation Although our results may provide new insights into the correlation between ABCG2 and lung cancer, certain limitations were noted in this study. First, there may be sample bias due to data downloaded directly from public databases. Second, to increase the confidence of the results, the sample size should be further expanded. Third, further experimental validation is required to elucidate the biological functions of ABCG2 in vitro and in vivo. Conclusion Our results indicate that ABCG2 could be a predictive biomarker for lung cancer treatment efficacy and prognosis. Nevertheless, additional research is necessary to confirm its biological role and the mechanisms involved in lung cancer progression. Declarations Ethical Approval and Consent to Participate: This study's contents use a public database and do not involve human and animal experiments, so there is no need to apply for ethical review. Not applicable. Ethical Standards: This study was conducted under the relevant institutional and national guidelines and regulations. Consent to Participate: This manuscript does not involve any experiments with human participants, and therefore, no ethical approval or consent to participate is required. Consent for publication: Not applicable. Data Availability: All the data used in this the study was obtained from publicly available databases, and the data analyzed in the present study are available on request. Declaration of interest: The authors declare no competing interests. Funding: This study did not receive any specific grant from any funding agency in the public, commercial, or not-for-profit sector. Authors' contributions ： Yang Zhai and Xin Long Zhai were involved in the conception and design of the study. YZ contributed to the provision of study materials. YZ was responsible for the collection and assembly of data. YZ and XLZ performed the data analysis and interpretation. YZ and XLZ participated in the writing of the manuscript. All authors have read and approved the final manuscript. Acknowledgments: Thank yang zhai if the article is accepted. References Robey, R. W. et al. ABCG2: A perspective. Advanced Drug Delivery Reviews 61 , 3–13 (2009). S, K. et al. Multidrug efflux transporter ABCG2: expression and regulation. Cellular and molecular life sciences : CMLS 78 , (2021). Dai, Y. et al. YAP1 regulates ABCG2 and cancer cell side population in human lung cancer cells. Oncotarget 8 , 4096–4109 (2016). Tang, L. et al. 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Stimulus-induced expression of the ABCG2 multidrug transporter in HepG2 hepatocarcinoma model cells involves the ERK1/2 cascade and alternative promoters - ScienceDirect. https://www.sciencedirect.com/science/article/abs/pii/S0006291X12015562. Basseville, A. et al. The ABCG2 Multidrug Transporter. in ABC Transporters - 40 Years on (ed. George, A. M.) 195–226 (Springer International Publishing, Cham, 2016). doi:10.1007/978-3-319-23476-2_9. Ishikawa, T., Kajimoto, Y., Inoue, Y., Ikegami, Y. & Kuroiwa, T. Chapter Seven - Critical Role of ABCG2 in ALA-Photodynamic Diagnosis and Therapy of Human Brain Tumor. in Advances in Cancer Research (eds. Schuetz, J. D. & Ishikawa, T.) vol. 125 197–216 (Academic Press, 2015). Dai, Y. et al. YAP1 regulates ABCG2 and cancer cell side population in human lung cancer cells. Oncotarget 8 , 4096–4109 (2017). Huang, Y. et al. circSETD3 Contributes to Acquired Resistance to Gefitinib in Non-Small-Cell Lung Cancer by Targeting the miR-520h/ABCG2 Pathway. Mol Ther Nucleic Acids 21 , 885–899 (2020). Yoh, K. et al. Breast Cancer Resistance Protein Impacts Clinical Outcome in Platinum-Based Chemotherapy for Advanced Non-Small Cell Lung Cancer. Clinical Cancer Research 10 , 1691–1697 (2004). Structure of the human multidrug transporter ABCG2 | Nature. https://www.nature.com/articles/nature22345. Yu, Q. et al. Structures of ABCG2 under turnover conditions reveal a key step in the drug transport mechanism. Nat Commun 12 , 4376 (2021). Enzyme-catalysed [4+2] cycloaddition is a key step in the biosynthesis of spinosyn A | Nature. https://www.nature.com/articles/nature09981. Liu, Y.-F. & Xi, Y.-M. [Research Progress of Cancer-associated Fibroblasts in Hematolo- gic Malignancies --Review]. Zhongguo Shi Yan Xue Ye Xue Za Zhi 31 , 1885–1889 (2023). Koliaraki, V., Prados, A., Armaka, M. & Kollias, G. The mesenchymal context in inflammation, immunity and cancer. Nat Immunol 21 , 974–982 (2020). Zeng, F. et al. Role and mechanism of CD90+ fibroblasts in inflammatory diseases and malignant tumors. Mol Med 29 , 20 (2023). Zhou, J., Wei, T. & He, Z. ADSCs enhance VEGFR3-mediated lymphangiogenesis via METTL3-mediated VEGF-C m6A modification to improve wound healing of diabetic foot ulcers. Mol Med 27 , 146 (2021). Full article: The regulatory network of the chemokine CCL5 in colorectal cancer. https://www.tandfonline.com/doi/full/10.1080/07853890.2023.2205168. Additional Declarations No competing interests reported. Supplementary Files Baselinedatasheet.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4687704","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":333249082,"identity":"e06b848b-2c67-4c0a-9580-970a333eea65","order_by":0,"name":"Yang Zhai","email":"data:image/png;base64,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","orcid":"","institution":"Taihe Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yang","middleName":"","lastName":"Zhai","suffix":""},{"id":333249083,"identity":"69cf3c74-2908-4c0b-8f7e-3c9e1da03240","order_by":1,"name":"XinLong Zhai","email":"","orcid":"","institution":"Taihe Hospital","correspondingAuthor":false,"prefix":"","firstName":"XinLong","middleName":"","lastName":"Zhai","suffix":""}],"badges":[],"createdAt":"2024-07-04 15:57:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4687704/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4687704/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62186834,"identity":"9f68351e-5deb-4e4a-9576-eb2d5aa9524c","added_by":"auto","created_at":"2024-08-10 12:08:02","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":933338,"visible":true,"origin":"","legend":"\u003cp\u003eThe mRNA expression patterns of ABCG2 have been analyzed in various studies that combine data from The Cancer Genome Atlas (TCGA) and the Genome Tissue Expression Consortium Project (GTEx). (A) The expression of ABCG2 was analyzed in TCGA and GTEx databases.. with the p-value cut-offs of *p \u0026lt; 0.05; **p \u0026lt; 0.01; and ***p \u0026lt; 0.001. (B) Only the TCGA database was used to analyze the expression of ABCG2. (C) ABCG2 expression profiling across various tumor types was analyzed using GEPIA2. Red indicates tumor groups, while blue represents their corresponding normal controls.\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4687704/v1/0af7fe4c85194a02525e4478.jpg"},{"id":62188401,"identity":"6c7d292e-2663-4d93-9ae0-36f35ff65842","added_by":"auto","created_at":"2024-08-10 12:16:02","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1818800,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eData obtained from the hpa database \u003c/strong\u003e（A）Protein expression of ABCG2 in different tissues and organs （B）The protein expression level of ABCG2 in normal lung tissue. \u0026nbsp;(C) The protein expression level of ABCG2 in lung cancer tissue. (D) Protein expression patterns in cells, ABCG2 detected in Plasma membrane and Nucleoplasm. (E) Immunofluorescence showed that ABCG2 was detected in the Plasma membrane and Nucleoplasm.\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4687704/v1/1bb6751b0e9188cd0095091c.jpg"},{"id":62188404,"identity":"a9c3fc99-b2c9-4bc9-9224-62b07aff1fc9","added_by":"auto","created_at":"2024-08-10 12:16:03","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":948013,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMutation features of the ABCG2 gene in different tumors based on data from TCGA, obtained from cBioPortal\u003c/strong\u003e. （A）Genetic location of human ABCG2 using data from The University of California Santa Cruz Genome Browser Genome Browser on Human Dec. 2013 (GRCh38/hg38) Assembly (B) Genetic variation of ABCG2 in different types of tumors through the cBioPortal site. There are 32 studies with a total of 10,953 patients with 10,967 samples. (C-D) The most observed mutation was K653E/Y654Ifs*21/K653Nfs*11 in the TCGA cohort.\u003c/p\u003e","description":"","filename":"figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4687704/v1/0d0602a3e7f507f857cf52e2.jpg"},{"id":62186837,"identity":"6eecc00e-9094-437a-a66a-4547b67ee4c2","added_by":"auto","created_at":"2024-08-10 12:08:03","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":400476,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between ABCG2 expression and immune infiltration, based on data from four algorithms: EPIC, MCPCOUNTER, XCELL, and TIDE. (A) Immune infiltration of cancer‑associated fibroblasts has a significant positive correlation with diverse TCGA cancer types. (B-D) Relationship between tumor-associated fibroblasts and lung cancer based on three different algorithms(MCPCOUNTER, XCELL, and TIDE).\u003c/p\u003e","description":"","filename":"figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4687704/v1/822c8a735c14920c4c27ee3e.jpg"},{"id":62186839,"identity":"068af73d-5608-4bff-b11f-31ff7c004a5a","added_by":"auto","created_at":"2024-08-10 12:08:03","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1568148,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis of ABCG2 expression and immune infiltration in lung cancer. \u003c/strong\u003e\u0026nbsp;(A) The correlation between ABCG2 and immune infiltrating cells in lung cancer. (B) Scatter plot showing the correlation between the expression of ABCG2 and the infiltration level of macrophages. (C) Scatter plot showing the correlation between the expression of ABCG2 and the infiltration level of Mast cells. (D) Scatter plot showing the correlation between the expression of ABCG2 and the infiltration level of iDC. (E) Differential distribution of immune cells in patients with high ABCG2 expression and low ABCG2 expression. expression groups. *, ** and *** indicate p \u0026lt; 0.05, p \u0026lt; 0.01 and p \u0026lt; 0.001, respectively.\u003c/p\u003e","description":"","filename":"figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4687704/v1/5ee8c020809c35310865c697.jpg"},{"id":62188403,"identity":"80e5387b-fdd2-4389-a8d1-887084d0e8ad","added_by":"auto","created_at":"2024-08-10 12:16:03","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":791828,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional clustering and interaction network analyses of ABCG2-related genes. \u003c/strong\u003e(A) Volcano map of differential genes. (B) Heat map showing the top 20 genes positively associated. (C) Enrichment analyses of GO and KEGG of ABCG2 co-expressed genes.\u003c/p\u003e","description":"","filename":"figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4687704/v1/f342ec0925d4630fa2fa1f8f.jpg"},{"id":70184610,"identity":"3eb8efa6-9af3-4d18-b4bf-37fe553261af","added_by":"auto","created_at":"2024-11-29 09:09:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7240169,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4687704/v1/b291de0a-9b89-4ffd-9a70-fc86ae68ca8f.pdf"},{"id":62186841,"identity":"e29f80af-f47f-4f80-a130-70e861ef5214","added_by":"auto","created_at":"2024-08-10 12:08:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18471,"visible":true,"origin":"","legend":"","description":"","filename":"Baselinedatasheet.docx","url":"https://assets-eu.researchsquare.com/files/rs-4687704/v1/4d76717f1e463ed0df3da96c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"ABCG2 predicts the prognosis and is associated with immune infiltration in lung cancer: a bioinformatics study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBCG2 is an ABC transporter often found in stem cell populations. The natural presence of ABCG2 in certain cancers\u003c/p\u003e \u003cp\u003eprobably demonstrates the specialized phenotype of the original cell and plays a role in drug resistance\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e .ABCG2 is predominantly located at the apical membrane of polarized cells, such as those found in the blood-brain barrier and intestinal enterocytes. Its presence in these cellular environments can\u003c/p\u003e \u003cp\u003esignificantly impact the oral absorption and pharmacokinetics of various anticancer drugs \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. It is believed that drug-resistant cancer stem cells, which express ABCG2, play a role in tumor regrowth by effectively expelling anticancer drugs from the cell through ABCG2-mediated efflux transport, thus protecting against their cytotoxic effects\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Previous studies have demonstrated that a variety of naturally occurring single\u003c/p\u003e \u003cp\u003enucleotide polymorphisms (SNPs) within the ABCG2 gene can exert influence on the expression and functionality of the ABCG2 protein. Specifically, the nonsynonymous SNPs ABCG2 C421A (rs2231142) and ABCG2 G34A (rs2231137) have been identified as significant variants that could potentially alter the pharmacokinetics and pharmacodynamics of the drug gefitinib.\u003c/p\u003e \u003cp\u003eThe presence of these polymorphisms may confer differential susceptibility to the toxic effects of gefitinib, thereby implicating a role in genetic variability in the therapeutic response and adverse event profile associated with this targeted therapy. Gefitinib is a selective and reversible inhibitor of the epidermal growth factor receptor (EGFR) tyrosine kinase, a key enzyme that plays a pivotal role in the signal transduction pathways essential for the survival and proliferation of tumor cells. Its strong affinity for the ABCG2 transporter suggests that the expression levels of ABCG2 can significantly impact the resistance to gefitinib. This implies that the effectiveness of gefitinib as a therapeutic agent may be modulated by the presence and activity of ABCG2 in cancer cells\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Therefore, ABCG2, a critical player in tumor progression, facilitates multidrug resistance through efflux mechanisms, impacting therapy outcomes and necessitating further study for improved cancer treatments.\u003c/p\u003e \u003cp\u003eLung cancer is a prevalent malignancy, frequently diagnosed and the foremost contributor to cancer-related fatalities\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Despite advancements in therapeutic modalities over the past several decades, the 5-year survival rate for patients afflicted with lung cancer remains disappointingly low\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The disease exhibits considerable heterogeneity, encompassing both small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC). NSCLC is the predominant form, representing approximately 85% of all lung cancer diagnoses. It can be further categorized into three principal histologic subtypes: lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), and large-cell carcinoma. The heterogeneity of lung cancer underscores the complexity of the disease and the challenges it presents in terms of diagnosis, treatment, and prognosis. Understanding the molecular and cellular characteristics of each subtype is essential for the development of targeted therapies and personalized treatment strategies aimed at improving patient outcomes\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Furthermore, the classification of lung cancer into SCLC and NSCLC, with the latter subdivided into LUAD, LUSC, and large-cell carcinoma, reflects the diverse biological behaviors and responses to treatment among different lung cancer entities. This stratification is critical for guiding clinical management and facilitating research into the molecular underpinnings of lung cancer pathogenesis\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. This study focused on the expression and prognostic factors of ABCG2 in lung cancer\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAn earlier investigation has elucidated that ABCG2 possesses the capacity to safeguard cellular integrity against damage and demise mediated by reactive oxygen species (ROS) \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Subsequent in vitro research has further delineated that the attenuation of ABCG2 expression engenders an upsurge in ROS production, incites inflammatory responses, and concurrently suppresses the biosynthesis of antioxidant molecules\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. It has been observed that the nuclear factor kappa B (NF-κB) signaling cascade is triggered under conditions of oxidative stress precipitated by the downregulation of ABCG2. This activation of the NF-κB pathway is suggestive of a pivotal role in the propagation of the inflammatory and oxidative stress responses observed in the context of ABCG2 suppression. In summation, the collective findings from these studies posit that ABCG2 may exert a mitigating influence on oxidative stress and inflammatory processes by dampening the activity of the NF-κB signaling pathway within cellular models.\u003c/p\u003e \u003cp\u003eNowadays, the advent of next-generation sequencing technologies has significantly augmented our understanding of the genomic architecture of cancer. These sophisticated methodologies have facilitated a holistic examination of the complete genome of oncogenic cells, elucidating the intricate genetic alterations that are instrumental in the etiology and progression of a diverse spectrum of neoplastic conditions\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The deployment of next-generation sequencing technologies has been further bolstered by concurrent enhancements in the field of bioinformatics, which are essential for the systematic interpretation and analysis of the extensive datasets procured through these sequencing endeavors. The study examined the expression levels of the ABCG2 gene in lung cancer using data from public databases. Our findings offer evidence for the involvement of ABCG2 in both the occurrence and prognosis of lung cancer and may contribute to the identification of a potential biomarker for prognosis and treatment.\u003c/p\u003e"},{"header":"Methods and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of gene expression and functions\u003c/h2\u003e \u003cp\u003emRNA expression data and clinical information were downloaded from the TCGA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cancergenome.nih.gov/\u003c/span\u003e\u003cspan address=\"https://cancergenome.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the Genotype-Tissue Expression (GTEx) database. After removing clinically uninformative and duplicate data using R software (R version 4.3.3), select appropriate statistical methods according to the data format characteristics (\u0026lsquo;stats\u0026rsquo; package and \u0026lsquo;car\u0026rsquo; package) for statistics (statistical analysis will not be performed if the statistical requirements are not met), and visualize the data with ggplot2 package, and the R package \u0026lsquo;ggplot2\u0026rsquo; was utilized to visualize the data. The Human Protein Atlas (HPA) online platform, was accessed through its website. Within the 'Tissue' module, ABCG2 was searched to determine the normalized expression (NX) levels across 55 distinct normal tissue types.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGene mapping\u003c/h2\u003e \u003cp\u003eThe specific location of the ABCG2 gene on the chromosome was identified, as well as its expression in 54 tissues using RNA-seq data from the Genotype-Tissue Expression (GTEx) project; version 8, utilizing the University of California Santa Cruz (UCSC) Genome Browser Human Dec. 2013 (GRCh38/hg38) Assembly (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://genome.ucsc.edu/\u003c/span\u003e\u003cspan address=\"http://genome.ucsc.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e13\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGenetic alteration analysis of ABCG2\u003c/h2\u003e \u003cp\u003eUsing cBioPortal, a web-based platform for cancer genomics (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbioportal.org/\u003c/span\u003e\u003cspan address=\"https://www.cbioportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the genetic alterations in ABCG2 were explored via the \"quick search\" and \"TCGA Pan-Cancer Atlas Studies.\" The analysis revealed a Cancer Types Summary panel indicating copy number alterations, frequencies, and mutation types affecting ABCG2 across numerous tumor samples, offering insights into its genomic variability in cancer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eImmune infiltration analysis of ABCG2\u003c/h2\u003e \u003cp\u003eTo evaluate the variation in ABCG2 expression levels between various types of tumors within the TCGA cohorts and their corresponding normal tissues, the 'Exploration' feature of the Tumor Immune Estimation Resource version 2.0 (TIMER2.0) webserver was used\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGene enrichment analysis\u003c/h2\u003e \u003cp\u003eGenomic ontology (GO) terminology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway studies were performed for significantly co-expressed genes using the clusterProfiler package in R language.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eABCG2 gene expression in several tumors\u003c/h2\u003e \u003cp\u003eIn the UCSC Xena database(UCSC Xena), data from various cohorts have been uniformly processed using the toil pipeline. These cohorts include the GTEx normal group with 7,568 samples, the TCGA carcinoma group with 727 samples, and the TCGA tumor group with 9,807 samples.\u003c/p\u003e \u003cp\u003eA significant differential expression of the ABCG2 gene was observed between the normal and tumor groups. The group with low ABCG2 expression comprises the following cancer types: Bladder Urothelial Carcinoma (BLCA), Breast Invasive Carcinoma (BRCA), Cervical Endocervical Adenocarcinoma and Cervical Squamous Cell Carcinoma (CESC), Cholangiocarcinoma (CHOL), Colon Adenocarcinoma (COAD), Kidney Renal Papillary Cell Carcinoma (KIRP), Lung Adenocarcinoma (LUAD), Lung Squamous Cell Carcinoma (LUSC), Ovarian Serous Cystadenocarcinoma (OV), Prostate Adenocarcinoma (PRAD), Rectum Adenocarcinoma (READ), Thyroid Carcinoma (THCA), Uterine Corpus Endometrial Carcinoma (UCEC), and Uterine Carcinosarcoma (UCS)(Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, the group with high ABCG2 expression includes Lymphoid Neoplasm Diffuse Large B-cell lymphoma (DLBC), Esophageal Carcinoma (ESCA), Glioblastoma Multiforme (GBM), Kidney Renal Clear Cell Carcinoma (KIRC), Acute Myeloid Leukemia (LAML), Brain Lower Grade Glioma (LGG), Pancreatic Adenocarcinoma (PAAD), Skin Cutaneous Melanoma (SKCM), Stomach Adenocarcinoma (STAD), and Thymoma (THYM). (Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).In the TCGA database and TCGA\u0026thinsp;+\u0026thinsp;GTEx database, the up-regulation and down-regulation of ABCG2 expression were different. The ABCG2 gene is underexpressed in the following cancer types: BLCA, BRCA, CESC, CHOL, COAD, KICH, KIRP, LIHC (Liver hepatocellular carcinoma), LUAD, LUSC, PRAD, READ, and UCES. But it is overexpressed in KIRC(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The expression profiling of APOE across various tumor types was analyzed using GEPIA2 (Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Overexpression of ABCG2 in some tumors may indicate this adverse outcome\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The human ABCG2 gene contains numerous polymorphisms and mutations that can greatly influence its expression and function\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eProtein expression\u003c/h2\u003e \u003cp\u003eThe Human Protein Atlas (HPA) aims to map the subcellular locations of all human proteins. It features an extensive collection of immunohistochemistry (IHC) images, showcasing sections from 46 different types of normal human tissues and 20 different cancer types. Explore more at (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.proteinatlas.org/\u003c/span\u003e\u003cspan address=\"http://www.proteinatlas.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese currently consist of the Tissue Atlas (depicting protein distribution across all major tissues), Cell Atlas (illustrating subcellular localization and heterogeneity in single cells), and Pathology Atlas (showing correlations between gene expression and patient survival in major human cancer types) \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe expression of ABCG2 mRNA and protein in various tissues and organs shows inconsistency. High levels of ABCG2 protein expression are observed in the duodenum, small intestine, colon, rectum, seminal vesicle, endometrium, and appendix. Moderate expression is found in the kidney, testis, placenta, and smooth muscle; while low expression is detected in the thyroid gland, lung, heart muscle, and cerebral cortex(Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). mRNA expression in normal lung tissue compared to tumor lung tissue(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-C). Protein expression patterns and immunofluorescence maps of the cells showed that ABCG2 was detected in the plasma membrane and nucleoplasm(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-E).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGenetic alteration analysis\u003c/h2\u003e \u003cp\u003eBased on the UCSC Genome Browser on the Human Dec.2013 (GRCh38/hg38) Assembly, ABCG2 is located on chromosome 4 at position chr4:58,500,001\u0026ndash;65,500,000(Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Revised sentence: \u0026ldquo;Given the demonstrated association of gene changes with tumorigenesis, the 'TCGA Pan-Cancer Atlas Studies' module of cBioPortal was utilized for genetic analysis of ABCG2 in various TCGA-based tumors\u0026rdquo;. The UCEC exhibited the highest frequency of ABCG2 gene alterations (\u0026gt;\u0026thinsp;7%), predominantly characterized by mutations. In TCGA-based tumors, mutations were the predominant type of ABCG2 gene alterations, present in almost all TCGA tumors. CHOL, LUAD, UCS, KIRP, THCA, and LGG each exhibited only one type of gene alteration: mutations. Additionally, amplification was the second most commonly observed genetic alteration, with the highest frequency of alteration in DLBC and Sarcoma. Mesothelioma, Pheochromocytoma Paraganglioma, and Esophageal Adenocarcinoma each had only one type of gene alteration: amplification(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The site and number of cases of ABCG2 mutations include various types, with missense mutations being the most common. Additionally, a translocation mutation caused by the alterations K653E/Y654Ifs*21/K653Nfs*11 was found in four cases of Uterine Endometrioid Carcinoma and COAD(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-D).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation of ABCG2 expression with immune characteristics\u003c/h2\u003e \u003cp\u003eAnalysis of immune infiltration plays a crucial role in the progression of cancer, as it is influenced by the behavior of cancer cells and their interaction with the tumor microenvironment. Tumor-infiltrating immune cells also significantly impact the development and metastasis of cancer within the microenvironment. To investigate the potential correlation between ABCG2 expression and immune infiltration of cancer-associated fibroblasts, we utilized TIMER2.0, employing four algorithms (EPIC, MCPCOUNTER, XCELL, and TIDE) to generate a heatmap(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Three algorithms(MCPCOUNTER, XCELL, and TIDE) were used to study the relationship between LUAD and tumor-associated fibroblasts(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-D).\u003c/p\u003e \u003cp\u003eTo investigate the correlation between ABCG2 expression levels and tumor immune response, we utilized the TCGA database to analyze immune infiltration in lung cancer across varying levels of ABCG2 expression(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The results revealed a positive association between ABCG2 expression and macrophages, immature dendritic cells (iDC), and Mast cells in lung cancer patients. Further analysis demonstrated a significant positive correlation between ABCG2 expression and levels of macrophage (r\u0026thinsp;=\u0026thinsp;0.322, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), iDC infiltration (r\u0026thinsp;=\u0026thinsp;0.391, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Mast cells (r\u0026thinsp;=\u0026thinsp;0.373, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB-D). These findings prompted us to explore the relationship between ABCG2 expression level and immune infiltration, leading to significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in iDC infiltration levels when stratifying high versus low ABCG2 expression groups using the ssGSEA algorithm(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment of ABCG2-related genes\u003c/h2\u003e \u003cp\u003eWe conducted differential expression analysis comparing low and high ABCG2 levels in Lung Cancer using the DESeq2 package, retaining only protein-coding genes. A total of 561 differentially expressed genes were identified based on the screening criteria of |logFC| \u0026gt; 1 and p.adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05(Figure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Subsequently, we investigated co-expressed genes associated with ABCG2 expression in the TCGA-LUAD and TCGA-LUSC datasets, identifying a total of 147 genes meeting the criteria of |cor spearman| \u0026gt; 0.3 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The association\u003c/p\u003e \u003cp\u003ebetween ABCG2 expression levels in lung cancer was explored(Figur 6 B). The resulting set of 1923 co-expressed genes was then subjected to GO term and KEGG pathway enrichment analyses using the clusterProfiler package in R language. The co-expressed genes related to ABCG2 were found to be involved in 1557 biological processes, 157 cellular components, 110 molecular functions, and 99 KEGGs (p.adj\u0026thinsp;\u0026lt;\u0026thinsp;0 .05 and q value\u0026thinsp;\u0026lt;\u0026thinsp;0 .05). Bubble plots were used to visualize the top three categories for biological processes, cellular components, molecular functions, and KEGGs respectively(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).GO term annotations revealed that these genes are primarily associated with regulating small GTPase-mediated signal transduction, myeloid leukocyte activation, positive regulation of cell adhesion; as well as endocytic vesicle localization; vacuolar membrane organization; lysosomal membrane organization; GTPase regulator activity; nucleoside-triphosphatase regulator activity; small GTPase binding. KEGG pathway analysis indicated that these genes are mainly involved in signaling pathways such as Autophagy - animal, Endocytosis, and Sphingolipid signaling pathways. In summary, the results provide detailed insights into the enrichment analyses for ABCG2 co-expression involving GO terms and KEGG pathways.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation between ABCG2 expression and clinicopathologic characteristics of lung cancer\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow expression of ABCG2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh expression of ABCG2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic T stage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134 (12.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e297 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e289 (27.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic N stage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e341 (33.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e329 (32.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119 (11.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109 (10.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (5.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic M stage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e390 (48.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e387 (47.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic stage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e267 (25.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e274 (26.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e152 (14.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135 (13.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84 (8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e194 (18.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e226 (21.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e326 (31.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e295 (28.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;= 65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e212 (20.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e236 (23.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e297 (29.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e268 (26.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual tumor, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e395 (50.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e361 (45.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Lung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105 (24.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105 (24.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral Lung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114 (26.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106 (24.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e474 (46.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e446 (43.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber pack years smoked, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146 (18.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e176 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;= 40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e263 (33.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e209 (26.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eABCG2 expression associated with clinicopathologic characteristics (logistic regression) in lung cancer\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic T stage (T3\u0026amp;T4 vs. T1\u0026amp;T2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.814 (0.582\u0026ndash;1.140)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic N stage (N2\u0026amp;N3 vs. N0\u0026amp;N1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.260 (0.861\u0026ndash;1.845)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic M stage (M1 vs. M0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.142 (0.562\u0026ndash;2.319)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic stage (Stage III\u0026amp;Stage IV vs. Stage I\u0026amp;Stage II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.055 (0.775\u0026ndash;1.437)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Male vs. Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.777 (0.606\u0026ndash;0.996)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.046\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (\u0026gt;\u0026thinsp;65 vs. \u0026lt;= 65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.811 (0.632\u0026ndash;1.039)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual tumor (R1\u0026amp;R2 vs. R0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.313 (0.652\u0026ndash;2.644)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation (Peripheral Lung vs. Central Lung)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.930 (0.637\u0026ndash;1.357)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker (Yes vs. No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.501 (0.322\u0026ndash;0.779)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber pack years smoked (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;40 vs. \u0026lt; 40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.659 (0.496\u0026ndash;0.876)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between expression and clinicopathologic characteristics of lung cancer\u003c/h2\u003e \u003cp\u003eBaseline data with lung cancer obtained from the TCGA database were statistically analyzed. The data were divided into two groups: 520 samples from patients with lung cancer exhibiting relatively low ABCG2 expression and 521 samples from patients with relatively high ABCG2 expression. A significant difference was observed in sex (P\u0026thinsp;=\u0026thinsp;0.046) and annual cigarette consumption greater than 40 packs(p\u0026thinsp;=\u0026thinsp;0.04)between the two groups. Regarding different TNM stages, there was no significant difference in the number of patients with T stage (P\u0026thinsp;=\u0026thinsp;0.332), N stage (P\u0026thinsp;=\u0026thinsp;0.497), M stage (P\u0026thinsp;=\u0026thinsp;0.713), pathologic stage (P\u0026thinsp;=\u0026thinsp;0.724), residual tumor (P\u0026thinsp;=\u0026thinsp;0.715), and location (P\u0026thinsp;=\u0026thinsp;0.706) between the two groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA logistic regression analysis was conducted to determine the correlation between ABCG2 expression and the clinicopathologic characteristics of lung cancer(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Similar to the baseline statistics, logistic regression analysis revealed significant sex differences (P\u0026thinsp;=\u0026thinsp;0.046) and annual cigarette consumption greater than 40 packs (P\u0026thinsp;=\u0026thinsp;0.04) between the two groups. However, regarding different TNM stages, there were no significant differences in the number of patients with T stage (P\u0026thinsp;=\u0026thinsp;0.232), N stage (P\u0026thinsp;=\u0026thinsp;0.234), M stage (P\u0026thinsp;=\u0026thinsp;0.713), pathologic stage (P\u0026thinsp;=\u0026thinsp;0.731), residual tumor (P\u0026thinsp;=\u0026thinsp;0.446), and location (P\u0026thinsp;=\u0026thinsp;0.706) between the two groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePan-cancer analyses serve as pivotal tools in the multi-dimensional study of various tumor types and have significant implications for the discovery of cancer biomarkers, treatment strategies, and prognostic assessments. With the advancement of human genome research, it has been recognized that abnormal gene expression, driven by factors such as gene mutations and copy number variations, is closely associated with the development and progression of cancers. Thus, the current study has focused its attention on a gene that exhibits abnormal expression across different cancerous tissues\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eABCG2, a member of the ATP-binding cassette (ABC) transporter family, plays a pivotal role in cancer therapy, particularly in the development of multidrug resistance (MDR). This transporter possesses the capability to efflux a wide range of compounds from the cell, posing a challenge in the treatment of chemotherapy-resistant cancers. Despite advancements in understanding the structure of ABCG2, there are still lingering questions regarding its mechanism of action\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eABCG2's function is not limited to promoting multidrug resistance in cancer cells, it is also expressed in normal tissues and is involved in a variety of physiological processes, including regulating intracellular cholesterol levels and participating in nutrient exchange in the placenta\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. In addition, the expression level of ABCG2 is different in different types of tumors, and its abnormal expression is closely related to the aggressiveness of tumors, the ability to metastasize, and the prognosis of patients\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWith the deepening of the understanding of the structure and function of ABCG2, scientists are exploring the design of drugs targeting this protein to reverse or inhibit its multi-drug resistance in tumors and improve the efficacy of chemotherapy drugs. At the same time, ABCG2 has also become a hot spot in cancer biomarker research, and its expression level may be an important indicator for diagnosis and prognosis evaluation. Therefore, ABCG2 not only has important value in basic research but also shows great application potential in clinical treatment\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eUsing bioinformatics tools, analyses of numerous cancers, including lung cancer, were performed from multiple perspectives, including gene expression and mutation, immune invasion, and survival and prognosis analyses.\u003c/p\u003e \u003cp\u003eThe expression of ABCG2 in lung cancer is associated with tumor type, differentiation level, and chemotherapy sensitivity. Specifically, ABCG2 expression in lung cancer tissues varies according to the type of lung cancer\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. For instance, ABCG2 is more prominently expressed in squamous cell carcinoma and adenocarcinoma of the lung, while it is almost undetectable in small cell lung cancer. Additionally, the expression level of ABCG2 is correlated with the degree of differentiation in lung cancer; the higher the degree of differentiation, the higher the level of ABCG2 expression. However, there is no significant correlation between ABCG2 expression and patient gender, age, metastasis, or TNM stage\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe location of ABCG2 in the cell is mainly related to its function. ABCG2 can be located on the cell membrane and participate in the transport of substances inside and outside the cell\u003csup\u003e29\u003c/sup\u003e. However, in lung cancer cells, ABCG2 can expel chemotherapy drugs from the cell, thus reducing the toxicity of the drugs to tumor cells\u003csup\u003e30\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition, the bioinformatics analysis in this study identified ABCG2 gene localization, and ABCG2 mutations were found in certain cancers. By conducting an in-depth analysis of genomic data from a large number of cancer patients, the researchers were able to identify specific cancer types associated with mutations in the ABCG2 gene and assess the potential impact of these mutations on patient outcomes. The findings of this study provide new insights into understanding the role of ABCG2 in cancer development and may help in the development of targeted treatment strategies for these genetic mutations\u003csup\u003e31\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe roles of fibroblasts in the tumor microenvironment(TME) are very complex and varied. They are not only involved in the structural maintenance and repair of tissues, but also influence the behavior of tumor cells by secreting various bioactive molecules, such as cytokines, growth factors, and chemokines\u003csup\u003e32,33\u003c/sup\u003e. These fibroblasts, known as tumor-associated fibroblasts (CAFs), are highly heterogeneous in the tumor microenvironment and can promote tumor cell proliferation, migration, and invasion\u003csup\u003e34,35\u003c/sup\u003e. The interaction between fibroblasts and tumor tissues is a complex process involving a variety of biomolecules and signaling pathways. Although the detailed mechanisms of these interactions are not fully understood, studies have shown that tumor tissue can recruit fibroblasts by secreting specific chemical signals, such as C-C chemokine ligand 5 (CCL5). CCL5 binds to the C-C chemokine receptor 5 (CCR5) on the surface of fibroblasts, triggering the migration and aggregation of fibroblasts, which promotes angiogenesis and extracellular matrix remodeling in the tumor microenvironment\u003csup\u003e36\u003c/sup\u003e. This study showed that the expression of ABCG2 in lung cancer was positively correlated with immune infiltration of cancer-associated fibroblasts.\u003c/p\u003e \u003cp\u003eGiven the crucial role of the tumor microenvironment in facilitating cancer progression and the indispensable part played by immune cells that infiltrate the tumor, our study was designed to scrutinize the relationship between ABCG2 expression and the infiltration of immune cells in lung cancer.\u003c/p\u003e \u003cp\u003eThe analysis showed increased ABCG2 expression in lung cancer is associated with higher infiltration of macrophages, immature dendritic cells, and mast cells, highlighting a positive relationship between these immune cells and ABCG2 levels.\u003c/p\u003e \u003cp\u003eOur study, while valuable, has its constraints. The databases utilized, like TCGA and GTEx, offer a limited cancer case range. Additionally, our focus was solely on ABCG2 expression in lung cancer, excluding other tumor types. Moreover, the bioinformatics tools we employed are somewhat limited in customization and are dependent on ever-changing external databases.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitation\u003c/h2\u003e \u003cp\u003eAlthough our results may provide new insights into the correlation between ABCG2 and lung cancer, certain limitations were noted in this study. First, there may be sample bias due to data downloaded directly from public databases. Second, to increase the confidence of the results, the sample size should be further expanded. Third, further experimental validation is required to elucidate the biological functions of ABCG2 in vitro and in vivo.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur results indicate that ABCG2 could be a predictive biomarker for lung cancer treatment efficacy and prognosis. Nevertheless, additional research is necessary to confirm its biological role and the mechanisms involved in lung cancer progression.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to Participate:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study\u0026apos;s contents use a public database and do not involve human and animal experiments, so there is no need to apply for ethical review. Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Standards:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted under the relevant institutional and national guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript does not involve any experiments with human participants, and therefore, no ethical approval or consent to participate is required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data used in this the study was obtained from publicly available databases, and the data analyzed in the present study are available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not receive any specific grant from any funding agency in the public, commercial, or not-for-profit sector.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Yang Zhai and \u0026nbsp;Xin Long Zhai were involved in the conception and design of the study. \u0026nbsp;YZ contributed to the provision of study materials. YZ was responsible for the collection and assembly of data. YZ and XLZ performed the data analysis and interpretation. YZ and XLZ participated in the writing of the manuscript. \u0026nbsp;All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThank yang zhai if the article is accepted.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRobey, R. 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ADSCs enhance VEGFR3-mediated lymphangiogenesis via METTL3-mediated VEGF-C m6A modification to improve wound healing of diabetic foot ulcers. \u003cem\u003eMol Med\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 146 (2021).\u003c/li\u003e\n\u003cli\u003eFull article: The regulatory network of the chemokine CCL5 in colorectal cancer. https://www.tandfonline.com/doi/full/10.1080/07853890.2023.2205168.\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":"ABCG2, lung cancer, immune infiltration, Biomarker","lastPublishedDoi":"10.21203/rs.3.rs-4687704/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4687704/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eATP-binding cassette superfamily G member 2 (ABCG2), a member of the ATP-binding cassette transporter family, is localized in the membrane of various human cancer cells and excludes drugs from cells in an ATP-dependent manner. Its expression is linked to numerous malignant tumors. This study focused on the expression of the ABCG2 gene in lung cancer and its association with patient prognosis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe expression levels of ABCG2 between lung cancer and normal tissues were explored using The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database. The Human Protein Mapping (HPA) database was used to obtain the expression of ABCG2 protein in tissues and organs and intracellular protein expression patterns. ABCG2 was detected in the plasma membrane and nucleoplasm. University of California Santa Cruz (UCSC) and cBioPortal were used to obtain gene mapping and mutation information. The ABCG2 was significantly correlated with patient survival prognosis and immune infiltration of cancer‑associated fibroblasts in numerous types of cancer. Furthermore, Gene Ontology analysis identified that ABCG2 may be important in metabolic and cellular processes in human cancers.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eABCG2 expression was significantly associated with multiple cancers, including lung cancer in TCGA. ABCG2 protein plays a crucial role in tumor regrowth by actively removing anticancer drugs from the cell through ABCG2-mediated efflux transport, thereby protecting against their toxic effects. The functional enrichment of ABCG2-related genes primarily involves the regulation of small GTPase-mediated signal transduction, myeloid leukocyte activation, positive regulation of cell adhesion, and endocytic vesicle localization. Additionally, it is associated with vacuolar membrane organization, lysosomal membrane organization, GTPase regulator activity, nucleoside-triphosphatase regulator activity, and small GTPase binding.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eABCG2 expression was significantly associated with poor prognosis in lung cancer patients. ABCG2 is involved in lung cancer immune infiltration and represents a suitable target for immunotherapy related to immune infiltration.\u003c/p\u003e","manuscriptTitle":"ABCG2 predicts the prognosis and is associated with immune infiltration in lung cancer: a bioinformatics study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-10 12:07:58","doi":"10.21203/rs.3.rs-4687704/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":"bc226027-a1fd-4c86-9c96-d339e26f9da7","owner":[],"postedDate":"August 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-29T09:08:38+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-10 12:07:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4687704","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4687704","identity":"rs-4687704","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}