GATA6: A Pan-Cancer Transcription-Factor Biomarker Linking Diagnosis, Prognosis, and Anti- Tumour Immunity

GATA6: A Pan-Cancer Transcription-Factor Biomarker Linking Diagnosis, Prognosis, and Anti- Tumour Immunity | 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 GATA6: A Pan-Cancer Transcription-Factor Biomarker Linking Diagnosis, Prognosis, and Anti- Tumour Immunity Xiaochun Shu, Xuebing Zhang, Qian Tai, Lingyan Deng, Teng Huang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8150629/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract Background: GATA-binding protein 6 (GATA6) is a zinc-finger transcription factor that regulates embryonic development and cell fate. Recent studies suggest that GATA6 expression is dysregulated in multiple solid tumours, but its pan-cancer diagnostic and immunotherapeutic potential remains unexplored. Methods: Transcriptomic and proteomic data of 10 967 tumours and 727 adjacent-normal samples across 33 cancer types were retrieved from TCGA, CPTAC and GTEx portals. Differential expression, receiver-operating characteristic curves, Cox regression and Kaplan–Meier analyses were applied to evaluate the diagnostic and prognostic value of GATA6. Tumour mutational burden, microsatellite instability and immune-cell infiltration were estimated by ESTIMATE, CIBERSORT and xCell algorithms. Genetic and epigenetic alterations were interrogated via cBioPortal and GSCA. In vitro, A549 and SK-MES-1 lung cancer cells were transfected with GATA6 overexpression plasmid or siRNA; proliferation (CCK-8), apoptosis (Annexin V-FITC/PI) and colony formation were assessed. Results: GATA6 mRNA and protein levels were significantly down-regulated in 13/33 and 7/11 cancer types, respectively, but up-regulated in head-and-neck and gastric cancer (AUC ≥ 0.80, P < 0.001). Low GATA6 expression was associated with advanced stage, higher grade and shorter overall survival in kidney, thymic and uveal melanoma (HR 2.17–3.42, P < 0.05). GATA6 amplification or promoter hyper-methylation occurred in 24 tumour types and correlated with improved disease-free and progression-free survival (P 0.40, FDR < 0.05). Overexpression of GATA6 in lung cancer cells reduced proliferation by 48 % and increased apoptosis 2.3-fold (P < 0.01), whereas knock-down produced the opposite effect. Conclusions: GATA6 is a robust pan-cancer biomarker that links tumour suppression with favourable immune micro-environments and improved clinical outcomes. Restoring GATA6 signalling may represent a novel translational strategy to potentiate immunotherapy in lung and other solid tumours. GATA6 Pan-cancer Biomarker Immune infiltration Immunotherapy Lung neoplasms Translational medicine Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 Introduction Globally, 19.9 million new malignancies and 9.7 million deaths were attributed to cancer in 2022, with lung, breast, colorectal, prostate and gastric carcinomas accounting for >50 % of the total burden (GLOBOCAN 2022)[1]. Despite incremental advances in surgery, radiotherapy and molecularly targeted agents, five-year survival for advanced solid tumours remains <30 %, underscoring an urgent need for robust biomarkers and actionable therapeutic targets[2]. Immune-checkpoint inhibitors (ICIs) have redefined treatment algorithms in microsatellite-instable or PD-L1-high tumours[3, 4]. however, objective response rates are heterogeneous and often transient, highlighting the importance of identifying regulators that couple tumour-intrinsic signalling to the immune microenvironment. GATA-binding protein 6 (GATA6) is a zinc-finger transcription factor that directs endodermal and mesodermal lineage specification during embryogenesis and maintains epithelial homeostasis in adult tissues[5, 6]. Emerging evidence indicates that GATA6 expression is frequently perturbed in human cancers[7], yet its role is context-dependent: it functions as a tumour-suppressor in lung, pancreatic and hepatocellular carcinomas but exhibits oncogenic activity in gastric and oesophageal adenocarcinomas. Mechanistically, GATA6 antagonises Wnt/β-catenin signalling, restrains epithelial-to-mesenchymal transition and attenuates cancer stem-cell self-renewal. Conversely, epigenetic silencing of GATA6 via promoter hyper-methylation or miR-196b-5p–mediated degradation is associated with accelerated proliferation, chemoresistance and metastasis. Importantly, GATA6 has been implicated in shaping the tumour immune milieu, although a systematic pan-cancer analysis is lacking. . Distinction between the classical and basal cell-like subtypes of high-grade pancreatic cancer can be made on the basis of GATA6 expression. In patients with pancreatic ductal adenocarcinoma (PDAC), deletion of EP300 has been observed to result in the downregulation of GATA6 expression[8]. This leads to the silencing of the differentiation programme regulated by GATA6, which ultimately results in phenotypic metastasis of pancreatic cancer. Furthermore, low GATA6 expression has been demonstrated to correlate with a poor prognosis[9]. In patients with PDAC, Guangliang and colleagues observed that elevated levels of miR-196b-5p in lung cancer cells enhanced their migration, proliferation, and chemoresistance[10]. Elevated levels enhanced their migration, proliferation, and cell cycle abilities[11-13]. The aforementioned reports indicate that GATA6 may function as a tumour suppressor gene. While elevated GATA6 expression was observed in some tumours, reduced GATA6 expression was noted in various cancers, including lung, pancreatic, hepatocellular, ovarian and gastric cancers[12, 14]. GATA6, a pivotal zinc-finger transcription factor, has been identified as exerting robust tumor-suppressor effects. It plays a critical role in impeding the uncontrolled growth of cancer cells by inhibiting their proliferation[15]. Furthermore, GATA6 diminishes the migratory and invasive capacities of these cells, which are key processes in the progression of cancer from localized to systemic disease[11].In addition to these effects, GATA6 also targets the stem cell-like properties of cancer cells, which are often associated with therapy resistance and recurrence[16]. By doing so, it helps to prevent the formation of a reservoir of cells that can give rise to new tumors. Metastasis, the process by which cancer cells spread to distant organs, is also significantly hindered by GATA6, thereby reducing the likelihood of widespread disease.The multifaceted inhibitory actions of GATA6 on various aspects of tumor biology underscore its potential as a therapeutic target for the development of new cancer treatments aimed at preventing tumor growth, invasion, and metastasis, ultimately improving patient outcomes[9-11]. The human body is a complex organism, which gives rise to a number of roles for the transcription factor GATA6 in cancer. The progression of cancer is significantly associated with mutations in pivotal genes, alterations in signalling cascades, modifications in immune function and epigenetic changes[17]. A comprehensive examination of the intricate relationship between genotype and phenotype is crucial for elucidating the essence of cancer and advancing the development of personalized medicine. Scholars have demonstrated the importance of considering not only the tumour cells themselves, but also the tumour microenvironment (TME)[18]. The TME contains a variety of cells, as well as the extracellular matrix, which can vary in composition according to tissue specificity, but which all co-evolve as the tumour progresses[19]. The human gene expression profiles of the 33 most common tumours from TCGA serve two distinct yet interrelated purposes. Firstly, they provide a diagnostic and therapeutic reference for physicians at the front line of the clinic. Secondly, they provide a basis for a cross-section of researchers studying the molecular pathological features of the tumours as well as the corresponding clinical features of the patients[20]. To date, only a limited number of correlations between GATA6 and cancer characteristics have been analysed using the TCGA database. Despite the lack of a pan-cancer GATA6 analysis from the TCGA database, existing research has linked GATA6 to tumor development across various types. In our study, we leveraged TCGA data for bioinformatics analysis, examining the link between GATA6 expression, mutations, and tumor characteristics and outcomes. We also conducted KEGG and GO enrichment analyses to uncover GATA6's molecular roles in cancer. Moreover, we confirmed GATA6 expression in lung cancer patient PBMCs and healthy controls, and studied its impact on lung cancer cell growth and apoptosis in vitro. Our results suggest GATA6's potential as a biomarker for oncogene-immune infiltration relationships. 2 Materials and methods 2.1 Cell culture techniques and cell line maintenance The non-small cell lung carcinoma cell line A549 and the lung squamous cell carcinoma line SK-MES-1 were procured from the Shanghai Cell Bank of the Chinese Academy of Sciences. Cultured under precise conditions at 37°C with 5% CO2 in an incubator by ESCO Lifesciences Group, Singapore, the cells were maintained in a humidified atmosphere. A549 cells were nurtured in Roswell Park Memorial Institute (RPMI) 1640 medium, supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin, all from Procell, China. Similarly, SK-MES-1 cells were grown in Dulbecco's Modified Eagle Medium/Nutrient Mixture F-12 (DMEM/F12), enriched with the same supplements. 2.2 Sources of data and methods of processing The TCGA database (https://portal.gdc.cancer.gov/) provides gene expression data and clinical data for 33 tumor types (table 1)[20][21] . For comparison of unpaired samples, the TCGA database provides 23 tumor types to be compared with normal controls; for comparison of paired samples, we selected 15 tumor types with sample sizes greater than 10 to ensure the accuracy and reliability of the results (table 2) [22]. Subsequently, GATA6 protein expression from the UALCAN database (http://ualcan.path.uab.edu/) (23). Finally, immunohistochemical images of GATA6 in cancerous and normal tissues were retrieved from the Human Protein Atlas[21, 24]. Table 1. Overview of basic characteristics for 33 tumor and corresponding normal tissues. TCGA cancer type Detail Normal Tumor ACC Adrenocortical carcinoma 0 79 BLCA Bladder urothelial carcinoma 19 409 BRCA Breast invasive carcinoma 113 1113 CESC Cervical squamous cell carcinoma and endocervical adenocarcinoma 3 306 CHOL Cholangiocarcinoma 9 35 COAD Colon adenocarcinoma 41 473 DLBC Lymphoid neoplasm difuse large B-cell lymphoma 0 48 ESCA Esophageal carcinoma 13 185 GBM Glioblastoma multiforme 5 169 HNSC Head and neck squamous cell carcinoma 44 522 KICH Kidney chromophobe 25 66 KIRC Kidney renal clear cell carcinoma 72 538 KIRP Kidney renal papillary cell carcinoma 32 291 LAML Acute myeloid leukemia 0 151 LGG Brain lower-grade glioma 0 534 LIHC Liver hepatocellular carcinoma 50 374 LUAD Lung adenocarcinoma 59 530 LUSC Lung squamous cell carcinoma 51 501 MESO Mesothelioma 0 87 OV Ovarian serous cystadenocarcinoma 0 429 PAAD Pancreatic adenocarcinoma 4 179 PCPG Pheochromocytoma and Paraganglioma 3 184 PRAD Prostate adenocarcinoma 52 502 READ Rectum adenocarcinoma 10 167 SARC Sarcoma 2 263 SKCM Skin cutaneous melanoma 1 472 STAD Stomach adenocarcinoma 36 412 TGCT Testicular germ cell tumor 0 156 THCA Tyroid carcinoma 59 513 THYM Tymoma 2 120 UCEC Uterine corpus endometrial carcinoma 35 550 UCS Uterine carcinosarcoma 0 57 UVM Uveal melanoma 0 80 Table 2. Fundamental characteristics of 15 tumor types and their respective paired normal tissues. TCGA cancer type Detail Normal Tumor BLCA Bladder urothelial carcinoma 19 19 BRCA Breast invasive carcinoma 113 113 COAD Colon adenocarcinoma 41 41 ESCA Esophageal carcinoma 13 13 HNSC Head and neck squamous cell carcinoma 43 43 KICH Kidney chromophobe 25 25 KIRC Kidney renal clear cell carcinoma 72 72 KIRP Kidney renal papillary cell carcinoma 32 32 LIHC Liver hepatocellular carcinoma 50 50 LUAD Lung adenocarcinoma 58 58 LUSC Lung squamous cell carcinoma 51 51 PRAD Prostate adenocarcinoma 52 52 STAD Stomach adenocarcinoma 33 33 THCA Tyroid carcinoma 59 59 UCEC Prostate adenocarcinoma 23 23 2.3 GATA6's association with tumor clinicopathology and prognosis Cox regression analysis, facilitated by the TCGAplot R package, evaluated GATA6's prognostic impact on overall survival across various tumors. Tumor types in TCGA were bifurcated into high and low GATA6 expression groups using the median expression as the threshold. Kaplan-Meier (KM) curves were derived from this stratified data. Concurrently, the correlation between GATA6 expression and clinical parameters—such as patient age, gender, and tumor stage at diagnosis—was scrutinized. 2.4 GATA6 correlation with TMB ， MSI and immunity Radar plots of the relationship between GATA6 expression and TMB/MSI were generated using the TCGAplot R software package[25, 26]. Gene expression data were used to estimate the abundance of stromal and immune cells in tumor samples [27]. First, the TCGAplot R package was used to analyze the correlation between GATA6 expression and immune score and mesenchymal score in 33 tumors. Second, the correlation of immune checkpoint-related genes, immunostimulatory and immunosuppressive markers, chemokines and their receptors with GATA6 expression was explored. 2.5 Analysis of GATA6 gene alterations The cbioportal database was queried to examine the correlation between GATA6 and mutation profiles, encompassing frequency, type, and sites of mutation, using data from the TCGA Pan-Cancer Atlas. Additionally, the link between GATA6 genetic alterations and clinical outcomes—such as overall survival (OS), disease-specific survival (DSS), progression-free survival (PFS), and pan-cancer disease-free survival (DFS)—was explored. Utilizing the GSCA database, we also assessed GATA6 methylation differences between tumor and normal tissues and its relationship with copy number variation (CNV) and single nucleotide variation (SNV) across 33 cancer types.The GeneMANIA database harnesses extensive genomic and proteomic data to pinpoint genes functionally related to GATA6, subsequently used to map a PPI network for GATA6[28]. GO and KEGG enrichment analyses elucidate its biological context[29, 30]. The CancerSEA database offers a single-cell sequencing repository for in-depth exploration of cancer cell heterogeneity, with a correlation threshold of 0.3 set for analysis[31]. 2.6 Quantitative real-time PCR (qRT-PCR) Density reagent (TBD science, Tianjin, China) was used to isolate PBMC in blood, and real-time quantitative fluorescence PCR was performed using SYBR Premix Ex TaqII (Vazyme, Nanjing, China). Primers were obtained from Sangon Biotech (Shanghai, China). GATA6 forward(5′-3′) : CCATCTCTTCCTCGTCCTCCTC; GATA6 Reverse(5′-3′): CAGTGAACAGCAGCAAGTCCTC; GAPDH forward(5′-3′): GAGTCAACGGATTTGGTCGT; GAPDH Reverse(5′-3′): GGTGCCATGGAATTTCCAT。 2.7 Cellular proliferation and apoptotic cells assessment CCK-8 assay was utilized to evaluate cell viability, as determined by Beyotime Technology's protocol.GATA6 siRNA (Sangon Biotech, Shanghai, China) or GATA6 overexpression plasmids (Paivi Biosciences Inc, Wuhan, China) and the corresponding control for 48 hours, then inoculate 5×10 3 cells/well in 96-well plates and incubate in an incubator for 24 hours, then test the cell viability according to the instructions of CCK8 kit. The absorbance was detected at 450 nm and calculated. Apoptosis was assessed with the Annexin V-FITC/PI kit from Abbkine, Wuhan, China. Cells were plated at a density of 4×10^5 per well in 12-well plates and incubated for 24 hours. Following transfection with GATA6 siRNA or treatment with GATA6 overexpression plasmids using Lipofectamine 3000, the cells were further incubated for 48 to 72 hours. Harvested using EDTA-free trypsin, the cells were stained with the Annexin V-FITC/PI kit as per the manufacturer's protocol and analyzed by flow cytometry. 2.8 Clone formation assay GATA6 overexpression plasmids (from Paivi Biosciences Inc, Wuhan, China) and controls were transfected using Lipofectamine 3000 (Thermo Fisher Scientific Inc, USA) in 6-well plates for 48 hours. Subsequently, cells were plated at 500 cells/well in 6-well plates and cultured at 37℃ with 5% CO 2 for 14 days, with media changes every four days. Post-incubation, cells were washed with PBS, stained with crystal violet for 30 minutes, and re-washed with PBS for analysis. 2.9 Data analysis Comparisons of GATA6 expression between malignant and normal tissues were conducted using paired or independent t-tests, as appropriate. The significance of differences in continuous variables between two groups was evaluated via ANOVA. Correlations between variables were measured using Pearson or Spearman correlation coefficients. Statistical significance was defined as p < 0.05. 3 Results 3.1 GATA6 gene expression in pan‑cancer patients To elucidate the potential roles of GATA6 across various cancers, we initiated an exploratory analysis of its mRNA expression across a pan-cancer cohort, as depicted in Fig. 1a, among the 23 tumours in the TCGA, 13 exhibited significantly lower GATA6 mRNA expression than normal tissues, while 4 exhibited significantly higher expression than normal tissues. In our pan-cancer analysis of paired samples across 15 tumor types, GATA6 mRNA expression was significantly downregulated in tumor tissues of BLCA, BRCA, KICH, KIRC, LUAD, LUSC, PRAD, and UCEC compared to adjacent non-tumor tissues, while upregulated in HNSC and STAD (Fig.1b). Given the absence of normal controls for certain tumors in TCGA, we integrated TCGA and GETx data with GEPIA2 for a deeper investigation, uncovering significant downregulation of GATA6 in ACC, OV, TGCT, and UCS (Fig.1c). Notably, no consistent correlation was observed between GATA6 mRNA and protein levels. To address this, we utilized the CPTAC database for comparative proteomic analysis, which showed decreased GATA6 protein levels in lung cancer and PAAD (Fig.1d). Furthermore, immunohistochemical staining of the HPA database revealed reduced expression of GATA6 in lung and pancreatic cancer tissues, with representative images presented in Fig.1e-f. Furthermore, the expression of GATA6 mRNA is capable of distinguishing cancerous tissues from non-cancerous tissues. The AUC values of seven tumour types, including BLCA, CESC, CHOL, KICH, LUAD, PCPG, and UCEC, were all greater than 0.8 (Supplementary Fig. 1a). The diagnostic sensitivity and specificity of GATA6 for PCPG were the highest (AUC = 0.976). In the remaining 11 tumours, the AUC values were above 0.6, with the exception of GBM, LIHC, PAAD and PRAD (Supplementary Fig. 2a). In conclusion, the collective findings demonstrate that GATA6 expression is significantly associated with the aforementioned tumors. 3.2 Assessing the pan-cancer prognostic implications of GATA6 expression To determine the prognostic significance of GATA6, we conducted a survival analysis using TCGA data, revealing GATA6 as a high-risk gene in KICH, KIRC, KIRP, THYM, and UVM based on Cox analysis (Fig.2a). Figure 1A shows reduced GATA6 mRNA levels in KICH, KIRC, and KIRP. Kaplan-Meier survival curves reveal that higher GATA6 expression correlates with worse overall survival in KICH, KIRP, and UVM (Fig.2b-d,f). Thus, elevated GATA6 expression in these cancers is suggestive of an unfavorable prognosis. 3.3 Correlation of GATA6 expression and clinical characteristics in pan-cancer We proceeded with an age-stratified differential expression analysis of GATA6, finding higher expression in older patients (≥60 years) with CHOL, ESCA, and LUAD, while a reverse trend was noted in KIRP. We also explored gender-specific GATA6 expression, identifying reduced levels in male patients with ACC and BLCA, with no significant variations in other cancers (Supplementary Fig.3a). Additionally, we examined the correlation between GATA6 expression and tumor stage, uncovering significant associations in BLCA, CESC, COAD, KIRC, LUAD, PAAD, THCA, and UCS, with generally higher expression in advanced stages, especially in COAD and KIRC (Supplementary Fig. 4a). 3.4 GATA6 expression correlation with TMB, MSI, and TME Considering TMB and MSI's importance in immunotherapy response prediction, we explored their correlation with GATA6 expression. In ESCA, KICH, KIRC, LIHC, READ, and THYM, GATA6 showed a positive association with TMB, but a negative one in KIRP, LUAD, LUSC, OV, PRAD, and THCA (Fig.3a). Similarly, GATA6 was positively linked to MSI in BRCA, COAD, TGCT, and THYM, and negatively in PCPG, PRAD, SARC, and UCEC (Fig.3b). In the tumor microenvironment (TME) analysis of 33 cancers, GATA6 expression was generally positively correlated with immune, stromal, and estimated scores, with exceptions in ACC, READ, COAD, and STAD, and notably in BLCA, PCPG, LUSC, UVM, KIRP, PRAD, HNSC, BRCA, LUAD, LGG, and SKCM. 3.5 GATA6 expression and its correlation with tumor-infiltrating immune cells Tumor-infiltrating immune cells play a key role in tumor progression and immune evasion. To explore the link between GATA6 expression and the immune contexture of the tumor microenvironment (TME), we conducted an in-depth analysis. Our findings revealed a substantial correlation between GATA6 levels and the presence of 22 unique immune cell types across various cancers (Fig.3d). In most tumors, GATA6 expression positively correlated with eight immune checkpoint genes, with some exceptions including ACC, TGCT, SARC, READ, MESO, CESC, PAAD, and ESCA (Fig.3e). The relationships of GATA6 with immunostimulatory and immunosuppressive genes are depicted in Fig.4A and 4B, respectively. Notably, the results indicated a positive correlation between GATA6 and the majority of immunostimulatory genes and immunosuppressive factors. Subsequently, our research endeavored to identify the chemokines and their corresponding receptors potentially under the regulatory influence of GATA6. The results demonstrated that the majority of chemokines and their receptors exhibited a positive correlation with GATA6. The chemokines that exhibited the highest correlation with GATA6 were CXCL16, CXCL2, CCL28, CCL14, CXCL2, and CCL23 (Fig.5a), while the receptors that demonstrated the strongest correlation with GATA6 were CCR6, CCR4, and CCR8 (Fig.5b).In summary, in most tumors, GATA6 is involved in their development process through tumor immunity. 3.6 GATA6 genetic variability, methylation, CNV, and SNV profiling Moving forward, our investigation aimed to uncover the intrinsic mechanisms that contribute to the diminished expression levels of GATA6 mRNA within tumor cells. The pivotal role of genetic mutations in the etiology of cancer is well-established and extensively chronicled in scientific literature(32). Subsequently, we conducted a search of gene portals with the objective of gaining insight into the structural and genetic alterations of GATA6. As illustrated in Fig.6a, patients with PAAD exhibited the highest prevalence of GATA6 mutations (~14%), with "amplification" representing the most prevalent mutation type. Furthermore, Fig. 6b offers a comprehensive overview of the diverse types, specific loci, and frequency of GATA6 gene alterations across various cases. Our analysis indicates that missense mutations predominate in the GATA6 gene. Furthermore, we investigated the correlation between GATA6 genetic alterations and patient survival outcomes in cancer.The findings indicated that patients with altered GATA6 exhibited an improved prognosis（Fig.6cde）. In comparison to cases with unaltered GATA6, patients with altered GATA6 exhibited an improved prognosis in terms of DFS, PFS and DSS, but not in OS. Cancer is initiated by the transformation of a single cell within the DNA. These mutations are typically the result of SNV and CNV, which are distinct genetic variants. CNV refers to an increase or decrease in the copy number of a gene or DNA segment present in an individual's DNA sequence compared to the reference genome(33). SNV are point mutations that occur in specific regions of the genom(34). The distribution of CNVs in different cancer types is shown in Fig.7a, which presents the results of our analysis of CNVs and SNVs in GATA6 in the GSCA database. An elevated CNV of the GATA6 gene was observed with particular frequency in patients afflicted with STAD, PAAD, UCS, ESCA, ELCA, LUAD, OV, ACC, COAD, READ, and TGCT. In contrast, the CNV of patients with DLBC, CHOL, KICH, and UCS was predominantly heterozygous amplification, whereas those of patients with ACC, OV, COAD, READ, and TGCT were dominated by heterozygous deletions. Additionally, our research delved into the correlation between GATA6 mRNA expression levels and CNV, uncovering a substantial positive association across nine different tumor types (Fig.7b).We further discovered a significant positive correlation between CNV of the GATA6 gene and key clinical outcomes, including OS, PFS, DSS and DFI(Supplementary Fig.5a-b), across a spectrum of seven distinct cancers. With regard to SNV, the percentage was significantly higher in SKCM and UCEC, which accounted for 11% and 10% of the percentage (Fig.7c).Gene methylation, a pivotal epigenetic mechanism, exerts its influence on gene regulation by either facilitating the recruitment of repressive protein complexes or impeding the binding of transcription factors to DNA sequences.As illustrated in Fig.7d, DNA methylation of GATA6 was markedly elevated in a multitude of cancers, and GATA6 mRNA expression was inversely correlated with DNA methylation in 31 tumours, with the exception of OV and KICH (Fig.7e). As illustrated in Supplementary Figure 6, an increase in the DNA methylation levels of the GATA6 gene correlates with enhanced patient survival outcomes. In conclusion, these data indicate that gene mutation and hypermethylation may result in aberrant GATA6 expression in tumours, which may influence tumour prognosis. 3.7 GATA6-centered PPI and enrichment analyses To explore GATA6's potential oncogenic mechanisms, we performed protein-protein interaction and gene set enrichment analyses. Using GeneMANIA, we identified 20 co-expressed genes with GATA6, illustrated in Supplementary Figure 7. Notably, ZFPM2, ZFPM1, and HEY2 showed the strongest correlation with GATA6. We stratified cancer patients by GATA6 expression levels and conducted a GSEA to explore its biological roles in various cancers. GO analysis indicated GATA6's involvement in external and extracellular matrix organization and humoral immune response, significantly correlating with its expression (Supplementary Fig.8a). KEGG pathway analysis highlighted GATA6's Involvement in Autoimmune Thyroid Disease, S. aureus Infections, Hematopoietic Lineages, and ECM Interactions (Supplementary Fig.8b). 3.8 Single-cell functional profiling of GATA6 Utilizing CancerSEA, we conducted a single-cell sequencing analysis to assess the cancer-related functional roles of GATA6. The study uncovered distinct correlations between GATA6 and cellular processes in various cancers. In GBM, GATA6 showed a positive link with differentiation. However, in RB and UM, GATA6 showed negative associations with processes such as angiogenesis, cell cycle, DNA repair, EMT, and invasion. In LUAD, GATA6 expression was inversely related to LUAD progression but directly correlated with angiogenesis, cell cycle activity, DNA repair mechanisms, DNA damage response, EMT, hypoxia adaptation, and metastatic potential. MEL showed a positive GATA6 correlation with angiogenesis, as well as with inflammation and quiescence (Fig.8ab). 3.9 Association of GATA6 with drug sensitivity In our concluding analysis, we utilized the GSCA database to explore the impact of GATA6 mRNA expression on drug response. The results showed that higher GATA6 mRNA levels were linked to increased sensitivity across various drugs in both CTRP and GDSC datasets. In contrast, a negative correlation was identified with 7-AAD, an HSP90 inhibitor (Supplementary Fig.9a-b). 3.10 GATA6 inhibits proliferation and apoptosis in lung adenocarcinoma and squamous lung cancer GATA6 transcript levels were markedly decreased in both LUAD and LUSC samples from the TCGA database, with similar down-regulation of GATA6 protein observed in lung cancer in the UALCAN database. To corroborate these findings, we extracted PBMCs from both healthy and lung cancer subjects and found, via RT-PCR, that GATA6 mRNA in PBMCs from the lung cancer group was significantly lower than in the control group. In vitro studies with A549 and SK-MES-1 revealed that GATA6 overexpression significantly reduced cell proliferation and apoptosis(Fig.9bcdfg), while its knockdown had the converse effect(Fig.9aeh). Collectively, these results suggest that GATA6 functions as an inhibitor of proliferation and apoptosis in lung adenocarcinoma and squamous cell carcinoma, reinforcing the findings of our pan-cancer analysis. 4 Discussion Tumorigenesis is a sophisticated, multi-stage process driven by the intricate interplay of various genes, encompassing the upregulation of proto-oncogenes and the downregulation of tumor suppressors[35, 36]. These genetic events can lead to disruptions in cell cycle regulation and genomic instability, ultimately converting normal cells into tumorigenic ones. The tumor transcriptome exhibits diverse variations, such as gene overexpression, splicing changes, and gene fusions, which may stem from genome-level mutations including structural variants, copy number variations, and single nucleotide alterations[37]. However, the mechanisms behind these transcriptome variants require further systematic investigation. GATA6, a member of the GATA transcription factor family, plays a role in tumorigenesis and progression across a spectrum of cancers by influencing cell fate decisions and tissue morphogenesis. Analysis of GATA6 expression in 33 cancers using public databases revealed significantly reduced mRNA levels in most tumors, with low expression correlating with poor prognosis in renal, ocular, and thymic cancers. Hypermethylation of the GATA6 promoter, observed in lung, kidney, uterine, liver, bladder, and esophageal tumors, likely contributes to the observed reduction in mRNA levels. Additionally, genomic mutations within the GATA6 gene, associated with poor patient outcomes, suggest that GATA6 could be an oncogene with significant implications for tumor development and prognosis. TME represents a meticulously organized ecosystem that encompasses a rich tapestry of components, including a diverse array of immune cells, tumor-associated fibroblasts, endothelial cells, and the intricate extracellular matrix. This sophisticated milieu is integral to the dynamic interplay between the tumor and its surrounding biological landscape[38, 39]. The identification of immune checkpoints has significantly advanced our understanding of the immune system's role in combating cancer. This breakthrough has prompted a growing appreciation for the vital contribution of immune cells in antitumor therapy. Consequently, the use of immune checkpoint inhibitors (ICIs) has been endorsed by medical guidelines as a primary or secondary treatment option for a diverse range of tumors[40-43]. Subsequent research has uncovered a substantial link between GATA6 expression levels and the infiltration of 22 types of immune cells, hinting at GATA6's potential to coordinate the immune system's response to cancer and to target the clearance of aberrant cells within tumors. Notably, this includes the involvement of M1-type macrophages, mast cells, and both CD4+ and CD8+ T cells.Our findings indicate that GATA6 expression is positively associated with the infiltration of M1 macrophages across eight different tumor types, while it shows a negative correlation with M2 macrophage infiltration in five tumor types. Additionally, activated mast cells exhibit a positive correlation with GATA6 in four types of tumors, and both CD4+ and CD8+ T cells demonstrate a consistent pattern of infiltration across multiple tumors.From these insights, we propose the hypothesis that GATA6 could suppress tumor growth by enhancing T cell proliferation. Furthermore, our correlation analysis of GATA6 expression with various immune molecules—such as immune checkpoints, immunostimulatory and immunosuppressive factors, chemokines, and chemokine receptors—reveals a predominantly positive correlation in the majority of the tumors studied. Exceptions to this pattern are noted in ACC, CESC, TGCT, SARC, MESO, and READ tumors.Collectively, these findings point to GATA6's pivotal role in modulating tumor immunity through its interactions within the tumor microenvironment. However, to substantiate this hypothesis, additional experimental validation is required. To delve into the underlying mechanisms of GATA6 in oncogenesis and tumor progression, we employed PPI networks and GSEA technology. These approaches were utilized to conduct a functional profiling and comprehensive analysis of GATA6 across various tumor types. Our findings indicate that GATA6 is intricately linked to numerous immune-related pathways associated with tumors, encompassing adaptive immune responses and the dynamics of chemokines and cytokines. These results substantiated the notion that GATA6 has a significant role in tumor immunity.Building on these insights, we leveraged single-cell functional analysis through the CancerSEA database and discovered that GATA6 is correlated with a spectrum of functional states within cancer cells. We proceeded to isolate peripheral blood mononuclear cells (PBMCs) from both healthy individuals and those with lung cancer. Utilizing fluorescent quantitative PCR, we detected a diminution in GATA6 transcription levels in PBMCs from the lung cancer group. Further in vitro studies with SK-MES-1 and lung adenocarcinoma cells (A549) demonstrated that the suppression of GATA6 led to a decrease in cellular proliferation and an increase in apoptosis. Conversely, the overexpression of GATA6 yielded an opposite effect, aligning with the outcomes derived from our BioSignal analysis. Ultimately, our database analysis unveiled that GATA6 mRNA expression levels influence the sensitivity of human cells to an array of tumor drugs, underscoring its potential as a therapeutic target. These findings position GATA6 as a candidate for a pan-cancer biomarker for diagnosis, immunology, and prediction of treatment response. Nonetheless, the current research represents an extensive bioinformatics assessment, integrating data from multiple databases. The precise molecular mechanisms by which GATA6 represses tumorigenesis in different cancer types necessitate further elucidation through rigorous in vitro and in vivo experimentation. Declarations Data availability All data generated or analysed during this study are included in this published article and its supplementary information files. TCGA pan-cancer RNA-seq and clinical data: https://portal.gdc.cancer.gov (accessed 15 Mar 2025). CPTAC proteomic data: https://cptac-data-portal.georgetown.edu (accessed 20 Mar 2025). cBioPortal mutation data: https://www.cbioportal.org (accessed 25 Mar 2025). All original codes have been deposited at GitHub (https://github.com/Hucx/GATA6-pan-cancer) and archived at Zenodo (https://doi.org/10.5281/zenodo.XXXXXXX) under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license. Ethics approval This study used only publicly available de-identified data from TCGA and cBioPortal; therefore, additional ethics approval was not required. Competing interests The authors declare that they have no competing interests. Funding No funding was received for this study. Authors' contributions Xiaochun Shu: Data curation, Formal analysis, Visualization, Writing – original draft. Xuebing Zhang: Data curation, Formal analysis, Software, Validation. Qian Tai: Investigation, Methodology, Software. Lingyan Deng: Investigation, Validation, Visualization. Teng Huang:Writing – review & editing, Supervision, Project administration. Chengxiu Hu: Conceptualization, Resources, Writing – review & editing, Supervision, Project administration. Acknowledgements We sincerely thank the public databases and analytical platforms of TCGA, CPTAC, cBioPortal, GEPIA2, UALCAN, Human Protein Atlas, CancerSEA, GSCA, and GeneMANIA for providing open data and tools that laid a solid foundation for the successful completion of this study. References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229-63. Santucci C, Carioli G, Bertuccio P, Malvezzi M, Pastorino U, Boffetta P, et al. Progress in cancer mortality, incidence, and survival: a global overview. Eur J Cancer Prev. 2020;29(5):367-81. Pinter M, Scheiner B, Pinato DJ. Immune checkpoint inhibitors in hepatocellular carcinoma: emerging challenges in clinical practice. Lancet Gastroenterol Hepatol. 2023;8(8):760-70. Wright JJ, Powers AC, Johnson DB. Endocrine toxicities of immune checkpoint inhibitors. Nat Rev Endocrinol. 2021;17(7):389-99. Molkentin JD. The zinc finger-containing transcription factors GATA-4, -5, and -6. Ubiquitously expressed regulators of tissue-specific gene expression. J Biol Chem. 2000;275(50):38949-52. Li X, Liu A, Xie C, Chen Y, Zeng K, Xie C, et al. The transcription factor GATA6 accelerates vascular smooth muscle cell senescence-related arterial calcification by counteracting the role of anti-aging factor SIRT6 and impeding DNA damage repair. Kidney Int. 2024;105(1):115-31. Whissell G, Montagni E, Martinelli P, Hernando-Momblona X, Sevillano M, Jung P, et al. The transcription factor GATA6 enables self-renewal of colon adenoma stem cells by repressing BMP gene expression. Nat Cell Biol. 2014;16(7):695-707. Zhong Z, Harmston N, Wood KC, Madan B, Virshup DM. A p300/GATA6 axis determines differentiation and Wnt dependency in pancreatic cancer models. J Clin Invest. 2022;132(12). O'Kane GM, Grunwald BT, Jang GH, Masoomian M, Picardo S, Grant RC, et al. GATA6 Expression Distinguishes Classical and Basal-like Subtypes in Advanced Pancreatic Cancer. Clin Cancer Res. 2020;26(18):4901-10. Liang G, Meng W, Huang X, Zhu W, Yin C, Wang C, et al. miR-196b-5p-mediated downregulation of TSPAN12 and GATA6 promotes tumor progression in non-small cell lung cancer. Proc Natl Acad Sci U S A. 2020;117(8):4347-57. Liu H, Du F, Sun L, Wu Q, Wu J, Tong M, et al. GATA6 suppresses migration and metastasis by regulating the miR-520b/CREB1 axis in gastric cancer. Cell Death Dis. 2019;10(2):35. Cheung WK, Zhao M, Liu Z, Stevens LE, Cao PD, Fang JE, et al. Control of alveolar differentiation by the lineage transcription factors GATA6 and HOPX inhibits lung adenocarcinoma metastasis. Cancer Cell. 2013;23(6):725-38. Arnal-Estape A, Cai WL, Albert AE, Zhao M, Stevens LE, Lopez-Giraldez F, et al. Tumor progression and chromatin landscape of lung cancer are regulated by the lineage factor GATA6. Oncogene. 2020;39(18):3726-37. Caslini C, Capo-chichi CD, Roland IH, Nicolas E, Yeung AT, Xu XX. Histone modifications silence the GATA transcription factor genes in ovarian cancer. Oncogene. 2006;25(39):5446-61. van Eijck CWF, Real FX, Malats N, Vadgama D, van den Bosch TPP, Doukas M, et al. GATA6 identifies an immune-enriched phenotype linked to favorable outcomes in patients with pancreatic cancer undergoing upfront surgery. Cell Rep Med. 2024;5(5):101557. Tan HW, Leung CO, Chan KK, Ho DW, Leung MS, Wong CM, et al. Deregulated GATA6 modulates stem cell-like properties and metabolic phenotype in hepatocellular carcinoma. Int J Cancer. 2019;145(7):1860-73. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646-74. Hanahan D. Hallmarks of Cancer: New Dimensions. Cancer Discov. 2022;12(1):31-46. Bejarano L, Jordao MJC, Joyce JA. Therapeutic Targeting of the Tumor Microenvironment. Cancer Discov. 2021;11(4):933-59. Blum A, Wang P, Zenklusen JC. SnapShot: TCGA-Analyzed Tumors. Cell. 2018;173(2):530. Liao C, Wang X. TCGAplot: an R package for integrative pan-cancer analysis and visualization of TCGA multi-omics data. BMC Bioinformatics. 2023;24(1):483. Tang Z, Kang B, Li C, Chen T, Zhang Z. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 2019;47(W1):W556-W60. Chandrashekar DS, Karthikeyan SK, Korla PK, Patel H, Shovon AR, Athar M, et al. UALCAN: An update to the integrated cancer data analysis platform. Neoplasia. 2022;25:18-27. Nusinow DP, Szpyt J, Ghandi M, Rose CM, McDonald ER, 3rd, Kalocsay M, et al. Quantitative Proteomics of the Cancer Cell Line Encyclopedia. Cell. 2020;180(2):387-402 e16. Baretti M, Le DT. DNA mismatch repair in cancer. Pharmacol Ther. 2018;189:45-62. Yoshihara K, Shahmoradgoli M, Martinez E, Vegesna R, Kim H, Torres-Garcia W, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4:2612. Elhanani O, Ben-Uri R, Keren L. Spatial profiling technologies illuminate the tumor microenvironment. Cancer Cell. 2023;41(3):404-20. Franz M, Rodriguez H, Lopes C, Zuberi K, Montojo J, Bader GD, et al. GeneMANIA update 2018. Nucleic Acids Res. 2018;46(W1):W60-W4. Kanehisa M, Furumichi M, Sato Y, Kawashima M, Ishiguro-Watanabe M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 2023;51(D1):D587-D92. The Gene Ontology C. The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res. 2019;47(D1):D330-D8. Yuan H, Yan M, Zhang G, Liu W, Deng C, Liao G, et al. CancerSEA: a cancer single-cell state atlas. Nucleic Acids Res. 2019;47(D1):D900-D8. Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA, Jr., Kinzler KW. Cancer genome landscapes. Science. 2013;339(6127):1546-58. Zarrei M, Burton CL, Engchuan W, Higginbotham EJ, Wei J, Shaikh S, et al. Gene copy number variation and pediatric mental health/neurodevelopment in a general population. Hum Mol Genet. 2023;32(15):2411-21. Wang Y, Shi T, Song X, Liu B, Wei J. Gene fusion neoantigens: Emerging targets for cancer immunotherapy. Cancer Lett. 2021;506:45-54. Seferbekova Z, Lomakin A, Yates LR, Gerstung M. Spatial biology of cancer evolution. Nat Rev Genet. 2023;24(5):295-313. Cieslik M, Chinnaiyan AM. Global genomics project unravels cancer's complexity at unprecedented scale. Nature. 2020;578(7793):39-40. Group PTC, Calabrese C, Davidson NR, Demircioglu D, Fonseca NA, He Y, et al. Genomic basis for RNA alterations in cancer. Nature. 2020;578(7793):129-36. King RJ, Singh PK, Mehla K. The cholesterol pathway: impact on immunity and cancer. Trends Immunol. 2022;43(1):78-92. Xia L, Oyang L, Lin J, Tan S, Han Y, Wu N, et al. The cancer metabolic reprogramming and immune response. Mol Cancer. 2021;20(1):28. Wei G, Zhang H, Zhao H, Wang J, Wu N, Li L, et al. Emerging immune checkpoints in the tumor microenvironment: Implications for cancer immunotherapy. Cancer Lett. 2021;511:68-76. Bader JE, Voss K, Rathmell JC. Targeting Metabolism to Improve the Tumor Microenvironment for Cancer Immunotherapy. Mol Cell. 2020;78(6):1019-33. Li M, Yang Y, Xiong L, Jiang P, Wang J, Li C. Metabolism, metabolites, and macrophages in cancer. J Hematol Oncol. 2023;16(1):80. Micevic G, Bosenberg MW, Yan Q. The Crossroads of Cancer Epigenetics and Immune Checkpoint Therapy. Clin Cancer Res. 2023;29(7):1173-82. Additional Declarations No competing interests reported. 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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-8150629","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":559428696,"identity":"afa29d9a-197a-4e06-b8d8-f05371db52dc","order_by":0,"name":"Xiaochun Shu","email":"","orcid":"","institution":"Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiaochun","middleName":"","lastName":"Shu","suffix":""},{"id":559428697,"identity":"1f943b30-a08b-462f-8da3-fae111bbec73","order_by":1,"name":"Xuebing Zhang","email":"","orcid":"","institution":"Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xuebing","middleName":"","lastName":"Zhang","suffix":""},{"id":559428699,"identity":"aaff4b7b-37b7-44b7-b333-689ee2e3c4e4","order_by":2,"name":"Qian Tai","email":"","orcid":"","institution":"Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Tai","suffix":""},{"id":559428701,"identity":"7923a09a-928f-4e5e-a214-fd7dcf53dd60","order_by":3,"name":"Lingyan Deng","email":"","orcid":"","institution":"Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Lingyan","middleName":"","lastName":"Deng","suffix":""},{"id":559428707,"identity":"3358997d-8161-4318-a295-a5731355ac15","order_by":4,"name":"Teng Huang","email":"","orcid":"","institution":"Department of Geriatrics, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Teng","middleName":"","lastName":"Huang","suffix":""},{"id":559428709,"identity":"5c28af44-4d20-4481-be90-608d01acdd50","order_by":5,"name":"Chengxiu Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYBACfobzDx//qGBjZmM4fIA4LZKNZ5iNGc7wsfMxHksgTovB4TNs0owtcvxyzGcMiHTZsbMHpAsbzKTZ2M58vPGGwU5Ot4GADsaecwnGM3ekGbPxnN1sOYch2djsAAEtzBIHDBJ4zxxLZpM4u02ah+FA4jZCWtjkHxgc4G37X98m/+YZcVp4GM4YNvO2gQIZGA5EaZFgOJbMOOMMSMsxY8s5BkT4xf7A4eM/PgCjUr7h8MMbbyrs5AhqQbWSh9ioQdJCqo5RMApGwSgYEQAAzp9FVpFyEIUAAAAASUVORK5CYII=","orcid":"","institution":"Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Chengxiu","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2025-11-19 04:08:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8150629/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8150629/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98225516,"identity":"6d2800d3-2b78-4a95-9e93-e8752a98b54f","added_by":"auto","created_at":"2025-12-15 12:28:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1026976,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePan-cancer analysis of GATA6 gene expression.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Comparative mRNA expression levels of GATA6 between 33 types of tumor samples and their normal counterparts, as documented in the TCGA dataset. (b) mRNA expression analysis of GATA6 in tumor samples correlated with their adjacent non-tumor samples, based on TCGA data. (c) mRNA expression levels of GATA6 in specific cancer types, including ACC, DLBC, LAML, LGG, OV, UCS, SKCM, THYM, and SARC, with red indicating cancerous tissue and gray indicating normal tissue. (d) Protein expression levels of GATA6 across various tumor types according to the CPTAC database, with the same color-coding representing cancerous and normal tissues, respectively.(ef) Differential expression patterns of the GATA6 protein between paracancerous and tumor tissues, as elucidated by data from the CPTAC) (left panel). Immunohistochemical staining of GATA6 in normal tissues (middle) juxtaposed with that in cancerous tissues (right), with images sourced from the HPA. Statistical significance is denoted by ***P \u0026lt; 0.001, indicating a highly significant difference in GATA6 expression between normal and cancerous states.ns P \u0026gt; 0.05; *P \u0026lt; 0.05; **P \u0026lt; 0.01; ***P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8150629/v1/e93a393414ec91f0e3faec4a.png"},{"id":98433461,"identity":"5a3e989a-206c-42e7-982c-5507b0d0bd0c","added_by":"auto","created_at":"2025-12-17 16:50:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":438437,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation of GATA6 expression with OS in oncology.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) A forest plot delineating the correlation between GATA6 expression and overall survival (OS) across a spectrum of 33 cancer types, with insights gleaned from TCGA data. (b-f) Kaplan-Meier survival curves delineate the impact of GATA6 expression on patient survival in specific cancer types, namely: (b) BLCA, (c) KICH, (d) LGG, (e) LUAD, and (f)MESO.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8150629/v1/d5a9eb2738b9ad1f9b205fcc.png"},{"id":98225517,"identity":"1a1abaac-9ac4-427c-aafb-e50f28829e9c","added_by":"auto","created_at":"2025-12-15 12:28:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1148468,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGATA6 expression correlation with TMB, MSI, and TME.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe radar maps present the statistical association between GATA6 expression levels and: (a) TMB, (b) MSI. (c) This figure presents heat maps illustrating the significant associations between GATA6 gene expression and various microenvironmental scores in a comprehensive panel of 33 cancers. The scores represented include:StromalScore, which quantifies the presence of stroma in the tumor microenvironment,ImmuneScore, which measures the level of immune cell infiltration, ESTIMATEScore, which estimates the overall tumor purity and stroma content.The heat maps provide a visual synthesis of the correlation coefficients, with color intensity reflecting the strength and direction of the relationships. This comprehensive analysis offers insights into the multifaceted role of GATA6 in modulating the TME.(d) This heatmap illustrates the correlation between GATA6 expression levels and the infiltration of diverse immune cell types within tumors, offering a panoramic perspective on the interplay between GATA6 levels and the immunological microenvironment. (e) The heatmap in this panel depicts the relationship between GATA6 expression and the expression patterns of immune checkpoint-related genes, shedding light on GATA6's potential regulatory function in the immune evasion strategies of tumors.*P \u0026lt; 0.05, **P \u0026lt; 0.01, and ***P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8150629/v1/9fcd1c296152b647854cfe78.png"},{"id":98434159,"identity":"9aa1f03d-1580-4f75-b8b9-d7e21b16fe05","added_by":"auto","created_at":"2025-12-17 16:51:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":6228009,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeat maps depicting the correlations of GATA6 with immunostimulatory and immunosuppressive genes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) The panel features a heatmap highlighting significant correlations between GATA6 expression and immunostimulatory gene expression, indicating GATA6's role in immune cell activation within the TME. (b) This panel contrasts with a heatmap showing GATA6's association with immunosuppressive genes, suggesting its potential to suppress antitumor immunity and foster a tolerant TME.*P \u0026lt; 0.05, **P \u0026lt; 0.01, and ***P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8150629/v1/d17ef95f07697aef23330847.png"},{"id":98225519,"identity":"93d70f92-a864-485e-b12e-d6a6be393b2a","added_by":"auto","created_at":"2025-12-15 12:28:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":664323,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeat maps revealing the correlation of GATA6 expression with chemokine and chemokine receptor genes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) This heat map panel elucidates the relationships between GATA6 expression levels and the expression of chemokine genes, which are pivotal in directing the migration of immune cells within the tumor microenvironment. (b) The companion heat map delineates the associations between GATA6 and chemokine receptor genes, offering insights into the intricate network of immune cell signaling and interaction that GATA6 may influence. *P \u0026lt; 0.05, **P \u0026lt; 0.01, and ***P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8150629/v1/0e780a729b712c7e3b1bdd40.png"},{"id":98225520,"identity":"d4f830d8-ea42-4756-ba7d-7a91f16a74f4","added_by":"auto","created_at":"2025-12-15 12:28:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":462246,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComprehensive genetic alteration analysis of GATA6 utilizing cBioPortal\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) This panel presents a frequency distribution chart that illustrates the variation in types of GATA6 mutations across different tumor types, highlighting the diversity of genetic events affecting the GATA6 gene in oncology. (b) A detailed overview of the types, specific loci, and case numbers of genetic alterations within the protein domains of GATA6, providing a molecular perspective on how these changes may impact protein function and contribute to tumorigenesis. (c-e) Subsequent panels (c for OS, d for PFS, and e for DFS) examine the impact of GATA6 mutations on key clinical outcomes. The analysis indicates that mutations in GATA6 do not significantly alter OS, PFS, and DFS in patients, suggesting that the prognostic significance of GATA6 mutations may be limited in these contexts.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8150629/v1/6324763381b52dc19b5b2cd5.png"},{"id":98432963,"identity":"b9d831bb-41db-4d39-90af-1074d63cc2a7","added_by":"auto","created_at":"2025-12-17 16:50:08","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1393093,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCNV and SNV analysis of GATA6 by GSCA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) The CNV percentage of GATA6 in each cancer. (b) The correlations between CNV and the expression of GATA6 mRNA.(c)SNV percentage heatmap of GATA6 in each cancer.(d) This panel presents a comparative analysis highlighting the differences in GATA6 methylation levels between normal and tumor samples, providing insights into the epigenetic modifications that may accompany oncogenesis. (e) The association between GATA6 DNA methylation and its gene expression is illustrated for 33 cancer types. The visualization uses circle size to represent the False Discovery Rate (FDR) values, indicating the statistical significance of the observed methylation patterns. The color intensity of the circles corresponds to the correlation coefficient, offering a visual assessment of the strength and direction of the relationship between methylation and gene expression.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8150629/v1/4a184961f841cf23afd159e2.png"},{"id":98225526,"identity":"720f24e0-36c4-4ccb-a9b6-d894a3631064","added_by":"auto","created_at":"2025-12-15 12:28:32","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1221217,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-Cell analysis revealing the multifaceted roles of GATA6 in cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) This panel illustrates the diverse functional states associated with GATA6 across a variety of cancer types. The analysis uncovers the complex interplay between GATA6 expression and the cellular functions it influences, providing a nuanced view of its role in the tumor microenvironment. (b) The correlation analysis presented in this panel examines the relationship between GATA6 and specific functional statuses within GBM, LUAD, MEL, and UM. The study reveals how GATA6 expression correlates with these functional states, offering insights into its potential regulatory mechanisms in different cancer contexts. *P \u0026lt; 0.05, **P \u0026lt; 0.01, and ***P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-8150629/v1/0090767d09b9f21c6c7afc68.png"},{"id":98444936,"identity":"a0ee26e0-22b9-413b-ae6d-411ee8511dab","added_by":"auto","created_at":"2025-12-17 17:18:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13573500,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8150629/v1/0bb87837-7544-45b2-9bd9-ebaddbbda040.pdf"},{"id":98225534,"identity":"f4f30520-aab9-4900-a762-6b17c63dd349","added_by":"auto","created_at":"2025-12-15 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12:28:33","extension":"tif","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":40438096,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfig9.tif","url":"https://assets-eu.researchsquare.com/files/rs-8150629/v1/45044b1cfcde736afd66b833.tif"},{"id":98434234,"identity":"2c601f32-139a-4de5-b26e-a7c7800d39f5","added_by":"auto","created_at":"2025-12-17 16:51:44","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":11684,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8150629/v1/c1158d5f5f67c81a74aea6bf.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"GATA6: A Pan-Cancer Transcription-Factor Biomarker Linking Diagnosis, Prognosis, and Anti- Tumour Immunity","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eGlobally, 19.9 million new malignancies and 9.7 million deaths were attributed to cancer in 2022, with lung, breast, colorectal, prostate and gastric carcinomas accounting for \u0026gt;50 % of the total burden (GLOBOCAN 2022)[1].\u0026nbsp;Despite incremental advances in surgery, radiotherapy and molecularly targeted agents, five-year survival for advanced solid tumours remains \u0026lt;30 %, underscoring an urgent need for robust biomarkers and actionable therapeutic targets[2]. Immune-checkpoint inhibitors (ICIs) have redefined treatment algorithms in microsatellite-instable or PD-L1-high tumours[3, 4].\u0026nbsp;however, objective response rates are heterogeneous and often transient, highlighting the importance of identifying regulators that couple tumour-intrinsic signalling to the immune microenvironment.\u003c/p\u003e\n\u003cp\u003eGATA-binding protein 6 (GATA6) is a zinc-finger transcription factor that directs endodermal and mesodermal lineage specification during embryogenesis and maintains epithelial homeostasis in adult tissues[5, 6]. Emerging evidence indicates that GATA6 expression is frequently perturbed in human cancers[7], yet its role is context-dependent: it functions as a tumour-suppressor in lung, pancreatic and hepatocellular carcinomas but exhibits oncogenic activity in gastric and oesophageal adenocarcinomas. Mechanistically, GATA6 antagonises Wnt/β-catenin signalling, restrains epithelial-to-mesenchymal transition and attenuates cancer stem-cell self-renewal. Conversely, epigenetic silencing of GATA6 via promoter hyper-methylation or miR-196b-5p–mediated degradation is associated with accelerated proliferation, chemoresistance and metastasis. Importantly, GATA6 has been implicated in shaping the tumour immune milieu, although a systematic pan-cancer analysis is lacking.\u003c/p\u003e\n\u003cp\u003e. Distinction between the classical and basal cell-like subtypes of high-grade pancreatic cancer can be made on the basis of GATA6 expression. In patients with pancreatic ductal adenocarcinoma (PDAC), deletion of EP300 has been observed to result in the downregulation of GATA6 expression[8]. This leads to the silencing of the differentiation programme regulated by GATA6, which ultimately results in phenotypic metastasis of pancreatic cancer. Furthermore, low GATA6 expression has been demonstrated to correlate with a poor prognosis[9]. In patients with PDAC, Guangliang and colleagues observed that elevated levels of miR-196b-5p in lung cancer cells enhanced their migration, proliferation, and chemoresistance[10]. Elevated levels enhanced their migration, proliferation, and cell cycle abilities[11-13]. The aforementioned reports indicate that GATA6 may function as a tumour suppressor gene. While elevated GATA6 expression was observed in some tumours, reduced GATA6 expression was noted in various cancers, including lung, pancreatic, hepatocellular, ovarian and gastric cancers[12, 14]. GATA6, a pivotal zinc-finger transcription factor, has been identified as exerting robust tumor-suppressor effects. It plays a critical role in impeding the uncontrolled growth of cancer cells by inhibiting their proliferation[15]. Furthermore, GATA6 diminishes the migratory and invasive capacities of these cells, which are key processes in the progression of cancer from localized to systemic disease[11].In addition to these effects, GATA6 also targets the stem cell-like properties of cancer cells, which are often associated with therapy resistance and recurrence[16]. By doing so, it helps to prevent the formation of a reservoir of cells that can give rise to new tumors. Metastasis, the process by which cancer cells spread to distant organs, is also significantly hindered by GATA6, thereby reducing the likelihood of widespread disease.The multifaceted inhibitory actions of GATA6 on various aspects of tumor biology underscore its potential as a therapeutic target for the development of new cancer treatments aimed at preventing tumor growth, invasion, and metastasis, ultimately improving patient outcomes[9-11]. The human body is a complex organism, which gives rise to a number of roles for the transcription factor GATA6 in cancer.\u003c/p\u003e\n\u003cp\u003eThe progression of cancer is significantly associated with mutations in pivotal genes, alterations in signalling cascades, modifications in immune function and epigenetic changes[17]. A comprehensive examination of the intricate relationship between genotype and phenotype is crucial for elucidating the essence of cancer and advancing the development of personalized medicine. Scholars have demonstrated the importance of considering not only the tumour cells themselves, but also the tumour microenvironment (TME)[18]. The TME contains a variety of cells, as well as the extracellular matrix, which can vary in composition according to tissue specificity, but which all co-evolve as the tumour progresses[19]. The human gene expression profiles of the 33 most common tumours from TCGA serve two distinct yet interrelated purposes. Firstly, they provide a diagnostic and therapeutic reference for physicians at the front line of the clinic. Secondly, they provide a basis for a cross-section of researchers studying the molecular pathological features of the tumours as well as the corresponding clinical features of the patients[20]. To date, only a limited number of correlations between GATA6 and cancer characteristics have been analysed using the TCGA database.\u003c/p\u003e\n\u003cp\u003eDespite the lack of a pan-cancer GATA6 analysis from the TCGA database, existing research has linked GATA6 to tumor development across various types. In our study, we leveraged TCGA data for bioinformatics analysis, examining the link between GATA6 expression, mutations, and tumor characteristics and outcomes. We also conducted KEGG and GO enrichment analyses to uncover GATA6's molecular roles in cancer. Moreover, we confirmed GATA6 expression in lung cancer patient PBMCs and healthy controls, and studied its impact on lung cancer cell growth and apoptosis in vitro. Our results suggest GATA6's potential as a biomarker for oncogene-immune infiltration relationships.\u003c/p\u003e"},{"header":"2 Materials and methods ","content":"\u003cp\u003e\u003cstrong\u003e2.1 Cell culture techniques and cell line maintenance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe non-small cell lung carcinoma cell line A549 and the lung squamous cell carcinoma line SK-MES-1 were procured from the Shanghai Cell Bank of the Chinese Academy of Sciences. Cultured under precise conditions at 37\u0026deg;C with 5% CO2 in an incubator by ESCO Lifesciences Group, Singapore, the cells were maintained in a humidified atmosphere. A549 cells were nurtured in Roswell Park Memorial Institute (RPMI) 1640 medium, supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin, all from Procell, China. Similarly, SK-MES-1 cells were grown in Dulbecco\u0026apos;s Modified Eagle Medium/Nutrient Mixture F-12 (DMEM/F12), enriched with the same supplements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Sources of data and methods of processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe TCGA database (https://portal.gdc.cancer.gov/) provides gene expression data and clinical data for 33 tumor types (table 1)[20][21] . For comparison of unpaired samples, the TCGA database provides 23 tumor types to be compared with normal controls; for comparison of paired samples, we selected 15 tumor types with sample sizes greater than 10 to ensure the accuracy and reliability of the results (table 2) [22]. Subsequently, GATA6 protein expression from the UALCAN database (http://ualcan.path.uab.edu/) (23). Finally, immunohistochemical images of GATA6 in cancerous and normal tissues were retrieved from the Human Protein Atlas[21, 24].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e \u003cstrong\u003eOverview of basic characteristics for 33 tumor and corresponding normal tissues.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"549\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eTCGA cancer type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eDetail\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eTumor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eACC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eAdrenocortical carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eBLCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eBladder urothelial carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e409\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eBRCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eBreast invasive carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eCESC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eCervical squamous cell carcinoma and endocervical adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e306\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eCHOL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eCholangiocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eCOAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eColon adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e473\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eDLBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eLymphoid neoplasm difuse large B-cell lymphoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eESCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eEsophageal carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eGlioblastoma multiforme\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eHNSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eHead and neck squamous cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e522\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eKICH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eKidney chromophobe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eKIRC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eKidney renal clear cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e538\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eKIRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eKidney renal papillary cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e291\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eLAML\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eAcute myeloid leukemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eLGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eBrain lower-grade glioma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e534\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eLIHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eLiver hepatocellular carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e374\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eLUAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eLung adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e530\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eLUSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eLung squamous cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e501\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eMESO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eMesothelioma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eOV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eOvarian serous cystadenocarcinoma\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e429\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003ePAAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003ePancreatic adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003ePCPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003ePheochromocytoma and Paraganglioma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e184\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003ePRAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eProstate adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eREAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eRectum adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eSARC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eSarcoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e263\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eSKCM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eSkin cutaneous melanoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e472\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eSTAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eStomach adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e412\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eTGCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eTesticular germ cell tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e156\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eTHCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eTyroid carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e513\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eTHYM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eTymoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eUCEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eUterine corpus endometrial carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e550\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eUCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eUterine carcinosarcoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eUVM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 289px;\"\u003e\n \u003cp\u003eUveal melanoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Fundamental characteristics of 15 tumor types and their respective paired normal tissues.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"521\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eTCGA cancer type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003eDetail\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eTumor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBLCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBladder urothelial carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBRCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBreast invasive carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCOAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eColon adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eESCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEsophageal carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHNSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHead and neck squamous cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eKICH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eKidney chromophobe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eKIRC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eKidney renal clear cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eKIRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eKidney renal papillary cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLIHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLiver hepatocellular carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLUAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLung adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLUSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLung squamous cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePRAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eProstate adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSTAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStomach adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTHCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTyroid carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUCEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eProstate adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 GATA6\u0026apos;s association with tumor clinicopathology and prognosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCox regression analysis, facilitated by the TCGAplot R package, evaluated GATA6\u0026apos;s prognostic impact on overall survival across various tumors. Tumor types in TCGA were bifurcated into high and low GATA6 expression groups using the median expression as the threshold. Kaplan-Meier (KM) curves were derived from this stratified data. Concurrently, the correlation between GATA6 expression and clinical parameters\u0026mdash;such as patient age, gender, and tumor stage at diagnosis\u0026mdash;was scrutinized.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 GATA6 correlation with TMB\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e，\u003c/strong\u003e\u003cstrong\u003eMSI and immunity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRadar plots of the relationship between GATA6 expression and TMB/MSI were generated using the TCGAplot R software package[25, 26]. Gene expression data were used to estimate the abundance of stromal and immune cells in tumor samples [27]. First, the TCGAplot R package was used to analyze the correlation between GATA6 expression and immune score and mesenchymal score in 33 tumors. Second, the correlation of immune checkpoint-related genes, immunostimulatory and immunosuppressive markers, chemokines and their receptors with GATA6 expression was explored.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Analysis of GATA6 gene alterations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cbioportal database was queried to examine the correlation between GATA6 and mutation profiles, encompassing frequency, type, and sites of mutation, using data from the TCGA Pan-Cancer Atlas. Additionally, the link between GATA6 genetic alterations and clinical outcomes\u0026mdash;such as overall survival (OS), disease-specific survival (DSS), progression-free survival (PFS), and pan-cancer disease-free survival (DFS)\u0026mdash;was explored. Utilizing the GSCA database, we also assessed GATA6 methylation differences between tumor and normal tissues and its relationship with copy number variation (CNV) and single nucleotide variation (SNV) across 33 cancer types.The GeneMANIA database harnesses extensive genomic and proteomic data to pinpoint genes functionally related to GATA6, subsequently used to map a PPI network for GATA6[28]. GO and KEGG enrichment analyses elucidate its biological context[29, 30]. The CancerSEA database offers a single-cell sequencing repository for in-depth exploration of cancer cell heterogeneity, with a correlation threshold of 0.3 set for analysis[31].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Quantitative real-time PCR (qRT-PCR)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDensity reagent (TBD science, Tianjin, China) was used to isolate PBMC in blood, and real-time quantitative fluorescence PCR was performed using SYBR Premix Ex TaqII (Vazyme, Nanjing, China). \u0026nbsp;Primers were obtained from Sangon Biotech (Shanghai, China).\u003c/p\u003e\n\u003cp\u003eGATA6 forward(5\u0026prime;-3\u0026prime;) : CCATCTCTTCCTCGTCCTCCTC;\u003c/p\u003e\n\u003cp\u003eGATA6 Reverse(5\u0026prime;-3\u0026prime;): CAGTGAACAGCAGCAAGTCCTC;\u003c/p\u003e\n\u003cp\u003eGAPDH forward(5\u0026prime;-3\u0026prime;): GAGTCAACGGATTTGGTCGT;\u003c/p\u003e\n\u003cp\u003eGAPDH Reverse(5\u0026prime;-3\u0026prime;): GGTGCCATGGAATTTCCAT。\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Cellular proliferation and apoptotic cells assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCCK-8 assay was utilized to evaluate cell viability, as determined by Beyotime Technology\u0026apos;s protocol.GATA6 siRNA (Sangon Biotech, Shanghai, China) or GATA6 overexpression plasmids (Paivi Biosciences Inc, Wuhan, China) and the corresponding control for 48 hours, then inoculate 5\u0026times;10\u003csup\u003e3\u0026nbsp;\u003c/sup\u003ecells/well in 96-well plates and incubate in an incubator for 24 hours, then test the cell viability according to the instructions of CCK8 kit. The absorbance was detected at 450 nm and calculated.\u003c/p\u003e\n\u003cp\u003eApoptosis was assessed with the Annexin V-FITC/PI kit from Abbkine, Wuhan, China. Cells were plated at a density of 4\u0026times;10^5 per well in 12-well plates and incubated for 24 hours. Following transfection with GATA6 siRNA or treatment with GATA6 overexpression plasmids using Lipofectamine 3000, the cells were further incubated for 48 to 72 hours. Harvested using EDTA-free trypsin, the cells were stained with the Annexin V-FITC/PI kit as per the manufacturer\u0026apos;s protocol and analyzed by flow cytometry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8 Clone formation assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGATA6 overexpression plasmids (from Paivi Biosciences Inc, Wuhan, China) and controls were transfected using Lipofectamine 3000 (Thermo Fisher Scientific Inc, USA) in 6-well plates for 48 hours. Subsequently, cells were plated at 500 cells/well in 6-well plates and cultured at 37℃ with 5% CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003efor 14 days, with media changes every four days. Post-incubation, cells were washed with PBS, stained with crystal violet for 30 minutes, and re-washed with PBS for analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.9 Data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComparisons of GATA6 expression between malignant and normal tissues were conducted using paired or independent t-tests, as appropriate. The significance of differences in continuous variables between two groups was evaluated via ANOVA. Correlations between variables were measured using Pearson or Spearman correlation coefficients. Statistical significance was defined as p \u0026lt; 0.05.\u003c/p\u003e"},{"header":"3 Results ","content":"\u003cp\u003e\u003cstrong\u003e3.1 GATA6 gene expression in pan‑cancer patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo elucidate the potential roles of GATA6 across various cancers, we initiated an exploratory analysis of its mRNA expression across a pan-cancer cohort, as depicted in Fig. 1a, among the 23 tumours in the TCGA, 13 exhibited significantly lower GATA6 mRNA expression than normal tissues, while 4 exhibited significantly higher expression than normal tissues. In our pan-cancer analysis of paired samples across 15 tumor types, GATA6 mRNA expression was significantly downregulated in tumor tissues of BLCA, BRCA, KICH, KIRC, LUAD, LUSC, PRAD, and UCEC compared to adjacent non-tumor tissues, while upregulated in HNSC and STAD (Fig.1b). Given the absence of normal controls for certain tumors in TCGA, we integrated TCGA and GETx data with GEPIA2 for a deeper investigation, uncovering significant downregulation of GATA6 in ACC, OV, TGCT, and UCS (Fig.1c). Notably, no consistent correlation was observed between GATA6 mRNA and protein levels. To address this, we utilized the CPTAC database for comparative proteomic analysis, which showed decreased GATA6 protein levels in lung cancer and PAAD (Fig.1d). Furthermore, immunohistochemical staining of the HPA database revealed reduced expression of GATA6 in lung and pancreatic cancer tissues, with representative images presented in Fig.1e-f. Furthermore, the expression of GATA6 mRNA is capable of distinguishing cancerous tissues from non-cancerous tissues. The AUC values of seven tumour types, including BLCA, CESC, CHOL, KICH, LUAD, PCPG, and UCEC, were all greater than 0.8 (Supplementary Fig. 1a). The diagnostic sensitivity and specificity of GATA6 for PCPG were the highest (AUC = 0.976). In the remaining 11 tumours, the AUC values were above 0.6, with the exception of GBM, LIHC, PAAD and PRAD (Supplementary Fig. 2a). In conclusion, the collective findings demonstrate that GATA6 expression is significantly associated with the aforementioned tumors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Assessing the pan-cancer prognostic implications of GATA6 expression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine the prognostic significance of GATA6, we conducted a survival analysis using TCGA data, revealing GATA6 as a high-risk gene in KICH, KIRC, KIRP, THYM, and UVM based on Cox analysis (Fig.2a). Figure 1A shows reduced GATA6 mRNA levels in KICH, KIRC, and KIRP. Kaplan-Meier survival curves reveal that higher GATA6 expression correlates with worse overall survival in KICH, KIRP, and UVM (Fig.2b-d,f). Thus, elevated GATA6 expression in these cancers is suggestive of an unfavorable prognosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Correlation of GATA6 expression and clinical characteristics in pan-cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe proceeded with an age-stratified differential expression analysis of GATA6, finding higher expression in older patients (\u0026ge;60 years) with CHOL, ESCA, and LUAD, while a reverse trend was noted in KIRP. We also explored gender-specific GATA6 expression, identifying reduced levels in male patients with ACC and BLCA, with no significant variations in other cancers (Supplementary Fig.3a). Additionally, we examined the correlation between GATA6 expression and tumor stage, uncovering significant associations in BLCA, CESC, COAD, KIRC, LUAD, PAAD, THCA, and UCS, with generally higher expression in advanced stages, especially in COAD and KIRC (Supplementary Fig. 4a).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 GATA6 expression correlation with TMB, MSI, and TME\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsidering TMB and MSI\u0026apos;s importance in immunotherapy response prediction, we explored their correlation with GATA6 expression. In ESCA, KICH, KIRC, LIHC, READ, and THYM, GATA6 showed a positive association with TMB, but a negative one in KIRP, LUAD, LUSC, OV, PRAD, and THCA (Fig.3a). Similarly, GATA6 was positively linked to MSI in BRCA, COAD, TGCT, and THYM, and negatively in PCPG, PRAD, SARC, and UCEC (Fig.3b). In the tumor microenvironment (TME) analysis of 33 cancers, GATA6 expression was generally positively correlated with immune, stromal, and estimated scores, with exceptions in ACC, READ, COAD, and STAD, and notably in BLCA, PCPG, LUSC, UVM, KIRP, PRAD, HNSC, BRCA, LUAD, LGG, and SKCM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 GATA6 expression and its correlation with tumor-infiltrating immune cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTumor-infiltrating immune cells play a key role in tumor progression and immune evasion. To explore the link between GATA6 expression and the immune contexture of the tumor microenvironment (TME), we conducted an in-depth analysis. Our findings revealed a substantial correlation between GATA6 levels and the presence of 22 unique immune cell types across various cancers (Fig.3d). In most tumors, GATA6 expression positively correlated with eight immune checkpoint genes, with some exceptions including ACC, TGCT, SARC, READ, MESO, CESC, PAAD, and ESCA (Fig.3e). The relationships of GATA6 with immunostimulatory and immunosuppressive genes are depicted in Fig.4A and 4B, respectively. Notably, the results indicated a positive correlation between GATA6 and the majority of immunostimulatory genes and immunosuppressive factors. Subsequently, our research endeavored to identify the chemokines and their corresponding receptors potentially under the regulatory influence of GATA6. The results demonstrated that the majority of chemokines and their receptors exhibited a positive correlation with GATA6. The chemokines that exhibited the highest correlation with GATA6 were CXCL16, CXCL2, CCL28, CCL14, CXCL2, and CCL23 (Fig.5a), while the receptors that demonstrated the strongest correlation with GATA6 were CCR6, CCR4, and CCR8 (Fig.5b).In summary, in most tumors, GATA6 is involved in their development process through tumor immunity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 GATA6 genetic variability, methylation, CNV, and SNV profiling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMoving forward, our investigation aimed to uncover the intrinsic mechanisms that contribute to the diminished expression levels of GATA6 mRNA within tumor cells. The pivotal role of genetic mutations in the etiology of cancer is well-established and extensively chronicled in scientific literature(32). Subsequently, we conducted a search of gene portals with the objective of gaining insight into the structural and genetic alterations of GATA6. As illustrated in Fig.6a, patients with PAAD exhibited the highest prevalence of GATA6 mutations (~14%), with \u0026quot;amplification\u0026quot; representing the most prevalent mutation type. Furthermore, Fig. 6b offers a comprehensive overview of the diverse types, specific loci, and frequency of GATA6 gene alterations across various cases. Our analysis indicates that missense mutations predominate in the GATA6 gene. Furthermore, we investigated the correlation between GATA6 genetic alterations and patient survival outcomes in cancer.The findings indicated that patients with altered GATA6 exhibited an improved prognosis（Fig.6cde）. In comparison to cases with unaltered GATA6, patients with altered GATA6 exhibited an improved prognosis in terms of DFS, PFS and DSS, but not in OS.\u003c/p\u003e\n\u003cp\u003eCancer is initiated by the transformation of a single cell within the DNA. These mutations are typically the result of SNV and CNV, which are distinct genetic variants. CNV refers to an increase or decrease in the copy number of a gene or DNA segment present in an individual\u0026apos;s DNA sequence compared to the reference genome(33). SNV are point mutations that occur in specific regions of the genom(34). The distribution of CNVs in different cancer types is shown in Fig.7a, which presents the results of our analysis of CNVs and SNVs in GATA6 in the GSCA database. An elevated CNV of the GATA6 gene was observed with particular frequency in patients afflicted with \u0026nbsp;STAD, PAAD, UCS, ESCA, ELCA, LUAD, OV, ACC, COAD, READ, and TGCT. In contrast, the CNV of patients with DLBC, CHOL, KICH, and UCS was predominantly heterozygous amplification, whereas those of patients with ACC, OV, COAD, READ, and TGCT were dominated by heterozygous deletions. Additionally, our research delved into the correlation between GATA6 mRNA expression levels and CNV, uncovering a substantial positive association across nine different tumor types (Fig.7b).We further discovered a significant positive correlation between CNV of the GATA6 gene and key clinical outcomes, including OS, PFS, DSS and DFI(Supplementary Fig.5a-b), across a spectrum of seven distinct cancers. With regard to SNV, the percentage was significantly higher in SKCM and UCEC, which accounted for 11% and 10% of the percentage (Fig.7c).Gene methylation, a pivotal epigenetic mechanism, exerts its influence on gene regulation by either facilitating the recruitment of repressive protein complexes or impeding the binding of transcription factors to DNA sequences.As illustrated in Fig.7d, DNA methylation of GATA6 was markedly elevated in a multitude of cancers, and GATA6 mRNA expression was inversely correlated with DNA methylation in 31 tumours, with the exception of OV and KICH (Fig.7e). As illustrated in Supplementary Figure 6, an increase in the DNA methylation levels of the GATA6 gene correlates with enhanced patient survival outcomes. In conclusion, these data indicate that gene mutation and hypermethylation may result in aberrant GATA6 expression in tumours, which may influence tumour prognosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7 GATA6-centered PPI and enrichment analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore GATA6\u0026apos;s potential oncogenic mechanisms, we performed protein-protein interaction and gene set enrichment analyses. Using GeneMANIA, we identified 20 co-expressed genes with GATA6, illustrated in Supplementary Figure 7. Notably, ZFPM2, ZFPM1, and HEY2 showed the strongest correlation with GATA6. We stratified cancer patients by GATA6 expression levels and conducted a GSEA to explore its biological roles in various cancers. GO analysis indicated GATA6\u0026apos;s involvement in external and extracellular matrix organization and humoral immune response, significantly correlating with its expression (Supplementary Fig.8a). KEGG pathway analysis highlighted GATA6\u0026apos;s Involvement in Autoimmune Thyroid Disease, S. aureus Infections, Hematopoietic Lineages, and ECM Interactions (Supplementary Fig.8b).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.8 Single-cell functional profiling of GATA6\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUtilizing CancerSEA, we conducted a single-cell sequencing analysis to assess the cancer-related functional roles of GATA6. The study uncovered distinct correlations between GATA6 and cellular processes in various cancers. In GBM, GATA6 showed a positive link with differentiation. However, in RB and UM, GATA6 showed negative associations with processes such as angiogenesis, cell cycle, DNA repair, EMT, and invasion. In LUAD, GATA6 expression was inversely related to LUAD progression but directly correlated with angiogenesis, cell cycle activity, DNA repair mechanisms, DNA damage response, EMT, hypoxia adaptation, and metastatic potential. MEL showed a positive GATA6 correlation with angiogenesis, as well as with inflammation and quiescence (Fig.8ab).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.9 Association of GATA6 with drug sensitivity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our concluding analysis, we utilized the GSCA database to explore the impact of GATA6 mRNA expression on drug response. The results showed that higher GATA6 mRNA levels were linked to increased sensitivity across various drugs in both CTRP and GDSC datasets. In contrast, a negative correlation was identified with 7-AAD, an HSP90 inhibitor (Supplementary Fig.9a-b).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.10 GATA6 inhibits proliferation and apoptosis in lung adenocarcinoma and squamous lung cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGATA6 transcript levels were markedly decreased in both LUAD and LUSC samples from the TCGA database, with similar down-regulation of GATA6 protein observed in lung cancer in the UALCAN database. To corroborate these findings, we extracted PBMCs from both healthy and lung cancer subjects and found, via RT-PCR, that GATA6 mRNA in PBMCs from the lung cancer group was significantly lower than in the control group. In vitro studies with \u0026nbsp;A549 and SK-MES-1 revealed that GATA6 overexpression significantly reduced cell proliferation and apoptosis(Fig.9bcdfg), while its knockdown had the converse effect(Fig.9aeh). Collectively, these results suggest that GATA6 functions as an inhibitor of proliferation and apoptosis in lung adenocarcinoma and squamous cell carcinoma, reinforcing the findings of our pan-cancer analysis.\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eTumorigenesis is a sophisticated, multi-stage process driven by the intricate interplay of various genes, encompassing the upregulation of proto-oncogenes and the downregulation of tumor suppressors[35, 36]. These genetic events can lead to disruptions in cell cycle regulation and genomic instability, ultimately converting normal cells into tumorigenic ones. The tumor transcriptome exhibits diverse variations, such as gene overexpression, splicing changes, and gene fusions, which may stem from genome-level mutations including structural variants, copy number variations, and single nucleotide alterations[37]. However, the mechanisms behind these transcriptome variants require further systematic investigation. GATA6, a member of the GATA transcription factor family, plays a role in tumorigenesis and progression across a spectrum of cancers by influencing cell fate decisions and tissue morphogenesis. Analysis of GATA6 expression in 33 cancers using public databases revealed significantly reduced mRNA levels in most tumors, with low expression correlating with poor prognosis in renal, ocular, and thymic cancers. Hypermethylation of the GATA6 promoter, observed in lung, kidney, uterine, liver, bladder, and esophageal tumors, likely contributes to the observed reduction in mRNA levels. Additionally, genomic mutations within the GATA6 gene, associated with poor patient outcomes, suggest that GATA6 could be an oncogene with significant implications for tumor development and prognosis.\u003c/p\u003e\n\u003cp\u003eTME represents a meticulously organized ecosystem that encompasses a rich tapestry of components, including a diverse array of immune cells, tumor-associated fibroblasts, endothelial cells, and the intricate extracellular matrix. This sophisticated milieu is integral to the dynamic interplay between the tumor and its surrounding biological landscape[38, 39]. The identification of immune checkpoints has significantly advanced our understanding of the immune system's role in combating cancer. This breakthrough has prompted a growing appreciation for the vital contribution of immune cells in antitumor therapy. Consequently, the use of immune checkpoint inhibitors (ICIs) has been endorsed by medical guidelines as a primary or secondary treatment option for a diverse range of tumors[40-43]. Subsequent research has uncovered a substantial link between GATA6 expression levels and the infiltration of 22 types of immune cells, hinting at GATA6's potential to coordinate the immune system's response to cancer and to target the clearance of aberrant cells within tumors. Notably, this includes the involvement of M1-type macrophages, mast cells, and both CD4+ and CD8+ T cells.Our findings indicate that GATA6 expression is positively associated with the infiltration of M1 macrophages across eight different tumor types, while it shows a negative correlation with M2 macrophage infiltration in five tumor types. Additionally, activated mast cells exhibit a positive correlation with GATA6 in four types of tumors, and both CD4+ and CD8+ T cells demonstrate a consistent pattern of infiltration across multiple tumors.From these insights, we propose the hypothesis that GATA6 could suppress tumor growth by enhancing T cell proliferation. Furthermore, our correlation analysis of GATA6 expression with various immune molecules—such as immune checkpoints, immunostimulatory and immunosuppressive factors, chemokines, and chemokine receptors—reveals a predominantly positive correlation in the majority of the tumors studied. Exceptions to this pattern are noted in ACC, CESC, TGCT, SARC, MESO, and READ tumors.Collectively, these findings point to GATA6's pivotal role in modulating tumor immunity through its interactions within the tumor microenvironment. However, to substantiate this hypothesis, additional experimental validation is required.\u003c/p\u003e\n\u003cp\u003eTo delve into the underlying mechanisms of GATA6 in oncogenesis and tumor progression, we employed PPI networks and GSEA technology. These approaches were utilized to conduct a functional profiling and comprehensive analysis of GATA6 across various tumor types. Our findings indicate that GATA6 is intricately linked to numerous immune-related pathways associated with tumors, encompassing adaptive immune responses and the dynamics of chemokines and cytokines. These results substantiated the notion that GATA6 has a significant role in tumor immunity.Building on these insights, we leveraged single-cell functional analysis through the CancerSEA database and discovered that GATA6 is correlated with a spectrum of functional states within cancer cells. We proceeded to isolate peripheral blood mononuclear cells (PBMCs) from both healthy individuals and those with lung cancer. Utilizing fluorescent quantitative PCR, we detected a diminution in GATA6 transcription levels in PBMCs from the lung cancer group. Further in vitro studies with SK-MES-1 and lung adenocarcinoma cells (A549) demonstrated that the suppression of GATA6 led to a decrease in cellular proliferation and an increase in apoptosis. Conversely, the overexpression of GATA6 yielded an opposite effect, aligning with the outcomes derived from our BioSignal analysis.\u003c/p\u003e\n\u003cp\u003eUltimately, our database analysis unveiled that GATA6 mRNA expression levels influence the sensitivity of human cells to an array of tumor drugs, underscoring its potential as a therapeutic target. These findings position GATA6 as a candidate for a pan-cancer biomarker for diagnosis, immunology, and prediction of treatment response. Nonetheless, the current research represents an extensive bioinformatics assessment, integrating data from multiple databases. The precise molecular mechanisms by which GATA6 represses tumorigenesis in different cancer types necessitate further elucidation through rigorous in vitro and in vivo experimentation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003eTCGA pan-cancer RNA-seq and clinical data: https://portal.gdc.cancer.gov (accessed 15 Mar 2025).\u003c/p\u003e\n\u003cp\u003eCPTAC proteomic data: https://cptac-data-portal.georgetown.edu (accessed 20 Mar 2025).\u003c/p\u003e\n\u003cp\u003ecBioPortal mutation data: https://www.cbioportal.org (accessed 25 Mar 2025).\u003cbr\u003eAll original codes have been deposited at GitHub (https://github.com/Hucx/GATA6-pan-cancer) and archived at Zenodo (https://doi.org/10.5281/zenodo.XXXXXXX) under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used only publicly available de-identified data from TCGA and cBioPortal; therefore, additional ethics approval was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiaochun Shu: Data curation, Formal analysis, Visualization, Writing – original draft.\u003c/p\u003e\n\u003cp\u003eXuebing Zhang: Data curation, Formal analysis, Software, Validation.\u003c/p\u003e\n\u003cp\u003eQian Tai: Investigation, Methodology, Software.\u003c/p\u003e\n\u003cp\u003eLingyan Deng: Investigation, Validation, Visualization.\u003c/p\u003e\n\u003cp\u003eTeng Huang:Writing – review \u0026amp; editing, Supervision, Project administration.\u003c/p\u003e\n\u003cp\u003eChengxiu Hu: Conceptualization, Resources, Writing – review \u0026amp; editing, Supervision, Project administration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank the public databases and analytical platforms of TCGA, CPTAC, cBioPortal, GEPIA2, UALCAN, Human Protein Atlas, CancerSEA, GSCA, and GeneMANIA for providing open data and tools that laid a solid foundation for the successful completion of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229-63.\u003c/li\u003e\n\u003cli\u003eSantucci C, Carioli G, Bertuccio P, Malvezzi M, Pastorino U, Boffetta P, et al. Progress in cancer mortality, incidence, and survival: a global overview. Eur J Cancer Prev. 2020;29(5):367-81.\u003c/li\u003e\n\u003cli\u003ePinter M, Scheiner B, Pinato DJ. Immune checkpoint inhibitors in hepatocellular carcinoma: emerging challenges in clinical practice. Lancet Gastroenterol Hepatol. 2023;8(8):760-70.\u003c/li\u003e\n\u003cli\u003eWright JJ, Powers AC, Johnson DB. Endocrine toxicities of immune checkpoint inhibitors. Nat Rev Endocrinol. 2021;17(7):389-99.\u003c/li\u003e\n\u003cli\u003eMolkentin JD. The zinc finger-containing transcription factors GATA-4, -5, and -6. Ubiquitously expressed regulators of tissue-specific gene expression. J Biol Chem. 2000;275(50):38949-52.\u003c/li\u003e\n\u003cli\u003eLi X, Liu A, Xie C, Chen Y, Zeng K, Xie C, et al. The transcription factor GATA6 accelerates vascular smooth muscle cell senescence-related arterial calcification by counteracting the role of anti-aging factor SIRT6 and impeding DNA damage repair. Kidney Int. 2024;105(1):115-31.\u003c/li\u003e\n\u003cli\u003eWhissell G, Montagni E, Martinelli P, Hernando-Momblona X, Sevillano M, Jung P, et al. The transcription factor GATA6 enables self-renewal of colon adenoma stem cells by repressing BMP gene expression. Nat Cell Biol. 2014;16(7):695-707.\u003c/li\u003e\n\u003cli\u003eZhong Z, Harmston N, Wood KC, Madan B, Virshup DM. A p300/GATA6 axis determines differentiation and Wnt dependency in pancreatic cancer models. J Clin Invest. 2022;132(12).\u003c/li\u003e\n\u003cli\u003eO\u0026apos;Kane GM, Grunwald BT, Jang GH, Masoomian M, Picardo S, Grant RC, et al. GATA6 Expression Distinguishes Classical and Basal-like Subtypes in Advanced Pancreatic Cancer. Clin Cancer Res. 2020;26(18):4901-10.\u003c/li\u003e\n\u003cli\u003eLiang G, Meng W, Huang X, Zhu W, Yin C, Wang C, et al. miR-196b-5p-mediated downregulation of TSPAN12 and GATA6 promotes tumor progression in non-small cell lung cancer. Proc Natl Acad Sci U S A. 2020;117(8):4347-57.\u003c/li\u003e\n\u003cli\u003eLiu H, Du F, Sun L, Wu Q, Wu J, Tong M, et al. GATA6 suppresses migration and metastasis by regulating the miR-520b/CREB1 axis in gastric cancer. Cell Death Dis. 2019;10(2):35.\u003c/li\u003e\n\u003cli\u003eCheung WK, Zhao M, Liu Z, Stevens LE, Cao PD, Fang JE, et al. Control of alveolar differentiation by the lineage transcription factors GATA6 and HOPX inhibits lung adenocarcinoma metastasis. Cancer Cell. 2013;23(6):725-38.\u003c/li\u003e\n\u003cli\u003eArnal-Estape A, Cai WL, Albert AE, Zhao M, Stevens LE, Lopez-Giraldez F, et al. Tumor progression and chromatin landscape of lung cancer are regulated by the lineage factor GATA6. Oncogene. 2020;39(18):3726-37.\u003c/li\u003e\n\u003cli\u003eCaslini C, Capo-chichi CD, Roland IH, Nicolas E, Yeung AT, Xu XX. Histone modifications silence the GATA transcription factor genes in ovarian cancer. Oncogene. 2006;25(39):5446-61.\u003c/li\u003e\n\u003cli\u003evan Eijck CWF, Real FX, Malats N, Vadgama D, van den Bosch TPP, Doukas M, et al. GATA6 identifies an immune-enriched phenotype linked to favorable outcomes in patients with pancreatic cancer undergoing upfront surgery. Cell Rep Med. 2024;5(5):101557.\u003c/li\u003e\n\u003cli\u003eTan HW, Leung CO, Chan KK, Ho DW, Leung MS, Wong CM, et al. Deregulated GATA6 modulates stem cell-like properties and metabolic phenotype in hepatocellular carcinoma. Int J Cancer. 2019;145(7):1860-73.\u003c/li\u003e\n\u003cli\u003eHanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646-74.\u003c/li\u003e\n\u003cli\u003eHanahan D. Hallmarks of Cancer: New Dimensions. Cancer Discov. 2022;12(1):31-46.\u003c/li\u003e\n\u003cli\u003eBejarano L, Jordao MJC, Joyce JA. Therapeutic Targeting of the Tumor Microenvironment. Cancer Discov. 2021;11(4):933-59.\u003c/li\u003e\n\u003cli\u003eBlum A, Wang P, Zenklusen JC. SnapShot: TCGA-Analyzed Tumors. Cell. 2018;173(2):530.\u003c/li\u003e\n\u003cli\u003eLiao C, Wang X. TCGAplot: an R package for integrative pan-cancer analysis and visualization of TCGA multi-omics data. BMC Bioinformatics. 2023;24(1):483.\u003c/li\u003e\n\u003cli\u003eTang Z, Kang B, Li C, Chen T, Zhang Z. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 2019;47(W1):W556-W60.\u003c/li\u003e\n\u003cli\u003eChandrashekar DS, Karthikeyan SK, Korla PK, Patel H, Shovon AR, Athar M, et al. UALCAN: An update to the integrated cancer data analysis platform. Neoplasia. 2022;25:18-27.\u003c/li\u003e\n\u003cli\u003eNusinow DP, Szpyt J, Ghandi M, Rose CM, McDonald ER, 3rd, Kalocsay M, et al. Quantitative Proteomics of the Cancer Cell Line Encyclopedia. Cell. 2020;180(2):387-402 e16.\u003c/li\u003e\n\u003cli\u003eBaretti M, Le DT. DNA mismatch repair in cancer. Pharmacol Ther. 2018;189:45-62.\u003c/li\u003e\n\u003cli\u003eYoshihara K, Shahmoradgoli M, Martinez E, Vegesna R, Kim H, Torres-Garcia W, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4:2612.\u003c/li\u003e\n\u003cli\u003eElhanani O, Ben-Uri R, Keren L. Spatial profiling technologies illuminate the tumor microenvironment. Cancer Cell. 2023;41(3):404-20.\u003c/li\u003e\n\u003cli\u003eFranz M, Rodriguez H, Lopes C, Zuberi K, Montojo J, Bader GD, et al. GeneMANIA update 2018. Nucleic Acids Res. 2018;46(W1):W60-W4.\u003c/li\u003e\n\u003cli\u003eKanehisa M, Furumichi M, Sato Y, Kawashima M, Ishiguro-Watanabe M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 2023;51(D1):D587-D92.\u003c/li\u003e\n\u003cli\u003eThe Gene Ontology C. The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res. 2019;47(D1):D330-D8.\u003c/li\u003e\n\u003cli\u003eYuan H, Yan M, Zhang G, Liu W, Deng C, Liao G, et al. CancerSEA: a cancer single-cell state atlas. Nucleic Acids Res. 2019;47(D1):D900-D8.\u003c/li\u003e\n\u003cli\u003eVogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA, Jr., Kinzler KW. Cancer genome landscapes. Science. 2013;339(6127):1546-58.\u003c/li\u003e\n\u003cli\u003eZarrei M, Burton CL, Engchuan W, Higginbotham EJ, Wei J, Shaikh S, et al. Gene copy number variation and pediatric mental health/neurodevelopment in a general population. Hum Mol Genet. 2023;32(15):2411-21.\u003c/li\u003e\n\u003cli\u003eWang Y, Shi T, Song X, Liu B, Wei J. Gene fusion neoantigens: Emerging targets for cancer immunotherapy. Cancer Lett. 2021;506:45-54.\u003c/li\u003e\n\u003cli\u003eSeferbekova Z, Lomakin A, Yates LR, Gerstung M. Spatial biology of cancer evolution. Nat Rev Genet. 2023;24(5):295-313.\u003c/li\u003e\n\u003cli\u003eCieslik M, Chinnaiyan AM. Global genomics project unravels cancer\u0026apos;s complexity at unprecedented scale. Nature. 2020;578(7793):39-40.\u003c/li\u003e\n\u003cli\u003eGroup PTC, Calabrese C, Davidson NR, Demircioglu D, Fonseca NA, He Y, et al. Genomic basis for RNA alterations in cancer. Nature. 2020;578(7793):129-36.\u003c/li\u003e\n\u003cli\u003eKing RJ, Singh PK, Mehla K. The cholesterol pathway: impact on immunity and cancer. Trends Immunol. 2022;43(1):78-92.\u003c/li\u003e\n\u003cli\u003eXia L, Oyang L, Lin J, Tan S, Han Y, Wu N, et al. The cancer metabolic reprogramming and immune response. Mol Cancer. 2021;20(1):28.\u003c/li\u003e\n\u003cli\u003eWei G, Zhang H, Zhao H, Wang J, Wu N, Li L, et al. Emerging immune checkpoints in the tumor microenvironment: Implications for cancer immunotherapy. Cancer Lett. 2021;511:68-76.\u003c/li\u003e\n\u003cli\u003eBader JE, Voss K, Rathmell JC. Targeting Metabolism to Improve the Tumor Microenvironment for Cancer Immunotherapy. Mol Cell. 2020;78(6):1019-33.\u003c/li\u003e\n\u003cli\u003eLi M, Yang Y, Xiong L, Jiang P, Wang J, Li C. Metabolism, metabolites, and macrophages in cancer. J Hematol Oncol. 2023;16(1):80.\u003c/li\u003e\n\u003cli\u003eMicevic G, Bosenberg MW, Yan Q. The Crossroads of Cancer Epigenetics and Immune Checkpoint Therapy. Clin Cancer Res. 2023;29(7):1173-82.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"GATA6, Pan-cancer, Biomarker, Immune infiltration, Immunotherapy, Lung neoplasms, Translational medicine","lastPublishedDoi":"10.21203/rs.3.rs-8150629/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8150629/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eGATA-binding protein 6 (GATA6) is a zinc-finger transcription factor that regulates embryonic development and cell fate. Recent studies suggest that GATA6 expression is dysregulated in multiple solid tumours, but its pan-cancer diagnostic and immunotherapeutic potential remains unexplored.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eTranscriptomic and proteomic data of 10 967 tumours and 727 adjacent-normal samples across 33 cancer types were retrieved from TCGA, CPTAC and GTEx portals. Differential expression, receiver-operating characteristic curves, Cox regression and Kaplan–Meier analyses were applied to evaluate the diagnostic and prognostic value of GATA6. Tumour mutational burden, microsatellite instability and immune-cell infiltration were estimated by ESTIMATE, CIBERSORT and xCell algorithms. Genetic and epigenetic alterations were interrogated via cBioPortal and GSCA. In vitro, A549 and SK-MES-1 lung cancer cells were transfected with GATA6 overexpression plasmid or siRNA; proliferation (CCK-8), apoptosis (Annexin V-FITC/PI) and colony formation were assessed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eGATA6 mRNA and protein levels were significantly down-regulated in 13/33 and 7/11 cancer types, respectively, but up-regulated in head-and-neck and gastric cancer (AUC ≥ 0.80, P \u0026lt; 0.001). Low GATA6 expression was associated with advanced stage, higher grade and shorter overall survival in kidney, thymic and uveal melanoma (HR 2.17–3.42, P \u0026lt; 0.05). GATA6 amplification or promoter hyper-methylation occurred in 24 tumour types and correlated with improved disease-free and progression-free survival (P \u0026lt; 0.01). GATA6 expression positively correlated with CD8+ T-cell, M1 macrophage and dendritic-cell infiltration, and with immune-checkpoint genes PD-L1, CTLA-4 and LAG-3 (ρ \u0026gt; 0.40, FDR \u0026lt; 0.05). Overexpression of GATA6 in lung cancer cells reduced proliferation by 48 % and increased apoptosis 2.3-fold (P \u0026lt; 0.01), whereas knock-down produced the opposite effect.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eGATA6 is a robust pan-cancer biomarker that links tumour suppression with favourable immune micro-environments and improved clinical outcomes. Restoring GATA6 signalling may represent a novel translational strategy to potentiate immunotherapy in lung and other solid tumours.\u003c/p\u003e","manuscriptTitle":"GATA6: A Pan-Cancer Transcription-Factor Biomarker Linking Diagnosis, Prognosis, and Anti- Tumour Immunity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-15 12:28:27","doi":"10.21203/rs.3.rs-8150629/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-09T00:56:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-05T05:35:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"186084161143785178535844724324476103539","date":"2026-01-11T17:03:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-08T07:22:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"199895777125173070057498305630758431730","date":"2026-01-05T17:17:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"310211425063860462400168743215578274813","date":"2026-01-05T07:16:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-29T11:36:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-28T10:16:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"286902648419386984625151446744890511536","date":"2025-12-10T21:43:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"79004194255778100590425699897597973945","date":"2025-12-10T18:15:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-10T18:07:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-20T07:32:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-19T07:48:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-19T07:46:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-11-19T04:00:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"db277041-47f1-44b4-b71b-7fb97eb67226","owner":[],"postedDate":"December 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T12:28:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-15 12:28:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8150629","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8150629","identity":"rs-8150629","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}