Public Gene Expression Databases Research Articles

Integrative analyses identified gap junction beta‐2 as a prognostic biomarker and therapeutic target for breast cancer

AbstractBackgroundIncreasing evidence has shown that connexins are involved in the regulation of tumor development, immune escape, and drug resistance. This study investigated the gene expression patterns, prognostic values, and potential mechanisms of connexins in breast cancer.MethodsWe conducted a comprehensive analysis of connexins using public gene and protein expression databases and clinical samples from our institution. Connexin mRNA expressions in breast cancer and matched normal tissues were compared, and multiomics studies were performed.ResultsGap junction beta‐2 mRNA was overexpressed in breast cancers of different pathological types and molecular subtypes, and its high expression was associated with poor prognosis. The tumor membrane of the gap junction beta‐2 mutated group was positive, and the corresponding protein was expressed. Somatic mutation and copy number variation of gap junction beta‐2 are rare in breast cancer. The gap junction beta‐2 transcription level in the p110α subunit of the phosphoinositide 3‐kinase mutant subgroup was higher than that in the wild‐type subgroup. Gap junction beta‐2 was associated with the phosphoinositide 3‐kinase‐Akt signaling pathway, extracellular matrix–receptor interaction, focal adhesion, and proteoglycans in cancer. Furthermore, gap junction beta‐2 overexpression may be associated with phosphoinositide 3‐kinase and histone deacetylase inhibitor resistance, and its expression level correlated with infiltrating CD8+ T cells, macrophages, neutrophils, and dendritic cells.ConclusionsGap junction beta‐2 may be a promising therapeutic target for targeted therapy and immunotherapy and may be used to predict breast cancer prognosis.

Meta-Analysis of Heat-Stressed Transcriptomes Using the Public Gene Expression Database from Human and Mouse Samples.

Climate change has significantly increased the frequency of our exposure to heat, adversely affecting human health and industries. Heat stress is an environmental stress defined as the exposure of organisms and cells to abnormally high temperatures. To comprehensively explain the mechanisms underlying an organism's response to heat stress, it is essential to investigate and analyze genes that have been under-represented or less well-known in previous studies. In this study, we analyzed heat stress-responsive genes using a meta-analysis of numerous gene expression datasets from the public database. We obtained 322 human and 242 mouse pairs as the heat exposure and control data. The meta-analysis of these data identified 76 upregulated and 37 downregulated genes common to both humans and mice. We performed enrichment, protein-protein interaction network, and transcription factor target gene analyses for these genes. Furthermore, we conducted an integrated analysis of these genes using publicly available chromatin immunoprecipitation sequencing (ChIP-seq) data for HSF1, HSF2, and PPARGC1A (PGC-1α) as well as gene2pubmed data from the existing literature. The results identified previously overlooked genes, such as ABHD3, ZFAND2A, and USPL1, as commonly upregulated genes. Further functional analysis of these genes can contribute to coping with climate change and potentially lead to technological advancements.

Loss of the Novel Mitochondrial Membrane Protein FAM210B Is Associated with Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is an aggressive and challenging disease to treat. Due to the lack of effective early diagnosis and therapy for the illness, it is crucial to identify novel biomarkers that can predict tumor behavior in HCC. In such cases, family with sequence similarity 210 member B (FAM210B) is abundant in various human tissues, but its regulatory mechanisms and role in various tissues remain unclear. In this study, we analyzed the expression pattern of FAM210B in HCC using public gene expression databases and clinical tissue samples. Our results confirmed that FAM210B was dysregulated in both HCC cell lines and HCC paraffin section samples. FAM210B depletion significantly increased the capacity of cells to grow, migrate, and invade in vitro, while overexpression of FAM210B suppressed tumor growth in a xenograft tumor model. Furthermore, we identified FAM210B's involvement in MAPK signaling and p-AKT signaling pathways, both of which are known oncogenic signaling pathways. In summary, our study provides a rational basis for the further investigation of FAM210B as a valuable biological marker for diagnosing and predicting the prognosis of HCC patients.

A novel model based on immune-related genes for differentiating biliary atresia from other cholestatic diseases

Based on a public gene expression database, this study established the immune-related genetic model that distinguished BA from other cholestasis diseases (DC) for the first time. We explored the molecular mechanism of BA based on the gene model. The BA microarray dataset GSE46960, containing BA, other cause of intrahepatic cholestasis than biliary atresia and normal liver gene expression data, was downloaded from the Gene Expression Omnibus (GEO) database. We performed a comprehensive bioinformatics analysis to establish and validate an immune-related gene model and subsequently identified hub genes as biomarkers associated with the molecular mechanisms of BA. To assess the model's performance for separating BA from other cholestasis diseases, we used receiver operating characteristic (ROC) curves and the area under the curve (AUC) of the ROC. Independent datasets GSE69948 and GSE122340 were used for the validation process. The model was built using eight immune-related genes, including EDN1, HAMP, SAA1, SPP1, ANKRD1, MMP7, TACSTD2, and UCA1. In the GSE46960 and validation group, it presented excellent results, and the prediction accuracy of BA in comparison to other cholestasis diseases was good. Functional enrichment analysis revealed significant immunological differences between BA and other cholestatic diseases. Finally, we found that the TNFα-NF-κB pathway is associated with EDN1 gene expression and may explain fibrosis progression, which may become a new therapeutic target. In summary, we have successfully constructed an immune-related gene model that can distinguish BA from other cholestatic diseases, while identifying the hub gene. Our exploration of immune genes provides new clues for the early diagnosis, molecular mechanism, and clinical treatment of biliary atresia.

Integrated Transcriptomics and Reverse Pharmacophore Mapping-based Network Pharmacology to Explore the Mechanisms of Natural Compounds against Doxorubicin-induced Cardiotoxicity.

Doxorubicin-Induced Cardiotoxicity (DIC) has greatly limited the clinical benefits of this frontline drug in oncotherapy. Drug combination with Natural Compounds (NCs) that possess potency against DIC is considered as a promising intervention strategy. However, the Mechanisms of Action (MoAs) underlying such drug interactions remain poorly understood. The aim of this study was to systematically pursuit of the molecular mechanisms of NCs against DIC. First, the gene expression signatures of DIC were characterized from transcriptomics datasets with doxorubicin-treated and untreated cardiomyocytes using differentially expressed gene identification, functional enrichment analysis, and protein-protein interaction network analysis. Secondly, reverse pharmacophore mapping-based network pharmacology was employed to illustrate the MoAs of 82 publicly reported NCs with anti-DIC potency. Cluster analysis based on their enriched pathways was performed to gain systematic insights into the anti-DIC mechanisms of the NCs. Finally, the typical compounds were validated using Gene Set Enrichment Analysis (GSEA) of the relevant gene expression profiles from a public gene expression database. Based on their anti-DIC MoAs, the 82 NCs could be divided into four groups, which corresponded to ten MoA clusters. GSEA and literature evidence on these compounds were provided to validate the MoAs identified through this bioinformatics analysis. The results suggested that NCs exerted potency against DIC through both common and different MoAs. This strategy integrating different types of bioinformatics approaches is expected to create new insights for elucidating the MoAs of NCs against DIC.

Abstract 1778: NR2F2 mediates cell growth and endocrine resistance in NF1 loss, ER+ breast cancer

Abstract Genomic loss of in Neurofibromin 1 (NF1) is a recurrent mechanism driving metastatic progression and resistance to endocrine therapy in ER-positive breast cancer, via activation of the MAPK pathway, induction of cyclin D1 expression and modulation of estrogen receptor signaling. To identify specific mediators of tumor growth in NF1 mutant cancers, we performed a genetic knockout (KO) screen in isogenic WT and NF1 KO breast cancer cells. After employing stringent cutoff criteria, we found 28 genes that are specifically essential for NF1 KO cells but not WT cells. Among these genes, NR2F2, encoding an orphan nuclear receptor, was one of the top candidates. We validated that its loss significantly impaired the growth of multiple models of NF1 null, ER+ breast cancer. Loss of NF1 leads to the hyperactivation of Ras which sequentially activates multiple effector pathways, including the MAPK pathway and the PI3K-AKT pathway. We found that MAPK activation upon NF1 loss led to elevated NR2F2 expression levels. A negative correlation between NF1 and NR2F2 expression was further confirmed in public gene expression databases. These data suggested that induction of NR2F2 expression might be one mechanism that mediates endocrine resistance caused by NF1 loss. Consistently, we found that exogenous expression of NR2F2 could confer endocrine resistance while its knockout hypersensitized ER+ breast cancer cells to endocrine therapies and significantly attenuated NF1 loss induced resistance. RNA-seq analysis revealed that estrogen response gene sets were among the most enriched signatures upon NF1 KO, NR2F2 over-expression (OE) or NR2F2 KO. NF1 KO or NR2F2 OE decreased the expression of early estrogen response genes and impaired their E2-induced expression, most of which could be rescued by NR2F2 KO, suggesting a regulatory effect of NR2F2 on ER transcriptional activity. To further define these mechanisms, ongoing integrative analyses of NR2F2 qPlex-RIME, ER and NR2F2 ChIP-Sequencing and ATAC-Sequencing will be presented. Taken together, our studies identify NR2F2 as a MAPK driven transcript whose induction mediates endocrine resistance, and may serve as a novel therapeutic target to overcome anti-estrogen resistant breast cancer growth. Citation Format: Yanyan Cai, Peihua Zhao, Fan Wu, Hong Shao, Pedram Razavi, Guotai Xu, Maurizio Scaltriti, Sarat Chandarlapaty. NR2F2 mediates cell growth and endocrine resistance in NF1 loss, ER+ breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1778.

Identification of Liver Immune Microenvironment-Related Hub Genes in Liver of Biliary Atresia.

Background: Biliary atresia (BA) is one of the most common and fatal abnormalities of newborns. Increasing evidences indicated that immunology was the critical part of the etiology. This research used a public gene expression database to explore the immune microenvironment of BA liver.Methods: The gene expression profiles GSE46960, GSE159720, and GSE15235, containing BA and normal liver gene expression data, were obtained from the Expression Omnibus Gene. We applied CIBERSORTx to quantify 22 subsets of immune cells in BA liver. The differentially expressed genes (DEGs) and immune cells were used to further explore their relationship with liver fibrosis and the inflammation status of BA.Results: The expression of immune-related genes CXCL6, CXCL8, CXCL10, CCL20, IL32, TGFB2, SPP1, and SLIT2 was significantly different between BA and normal liver, among which CXCL8 was the hub gene. Six of 22 immune cell proportions were significantly different between BA and normal liver. Specifically, M0 macrophages and resting memory CD4+ T cells were upregulated in BA liver compared with normal liver. Meanwhile, monocytes, resting natural killer (NK) cells, plasma cells, and regulatory T (Treg) cells were downregulated. A further correlation analysis revealed that SLIT2 and CXCL6 owned high positive correlation coefficients with fibrosis grade, while the proportion of resting NK cells was negatively correlated. Proportions of resting CD4+ memory T cells were strongly related to the inflammation grade of BA liver.Conclusion: Biliary atresia is a disease strongly correlated with immune response. Our results might provide a clue for further exploration of BA etiology, which may promote a potential prediction model based on immune infiltration features.

Targeting Melanoma-Initiating Cells by Caffeine: In Silico and In Vitro Approaches.

The beneficial effects of coffee on human diseases are well documented, but the molecular mechanisms of its bioactive compounds on cancer are not completely elucidated. This is likely due to the large heterogeneity of coffee preparations and different coffee-based beverages, but also to the choice of experimental models where proliferation, differentiation and immune responses are differently affected. The aim of the present study was to investigate the effects of one of the most interesting bioactive compounds in coffee, i.e., caffeine, using a cellular model of melanoma at a defined differentiation level. A preliminary in silico analysis carried out on public gene-expression databases identified genes potentially involved in caffeine’s effects and suggested some specific molecular targets, including tyrosinase. Proliferation was investigated in vitro on human melanoma initiating cells (MICs) and cytokine expression was measured in conditioned media. Tyrosinase was revealed as a key player in caffeine’s mechanisms of action, suggesting a crucial role in immunomodulation through the reduction in IL-1β, IP-10, MIP-1α, MIP-1β and RANTES secretion onto MICs conditioned media. The potent antiproliferative effects of caffeine on MICs are likely to occur by promoting melanin production and reducing inflammatory signals’ secretion. These data suggest tyrosinase as a key player mediating the effects of caffeine on melanoma.

Angiotensin-Converting Enzyme 2 Gene Expression in Breast Tissue.

Binding to angiotensin-converting enzyme 2 (ACE2) receptor is a critical step for severe acute respiratory syndrome coronavirus 2 (SARSCoV- 2) to mediate its entry into target cells. ACE2 is expressed in many human tissues, including the lungs. However, no research has demonstrated that SARS-CoV-2 can infect human breast tissue. This study aimed to investigate ACE2 gene expression in human breast tissue using a public database. A search of a public gene expression database was performed to investigate ACE2 gene expression in in human breast tissue. The gene expression profile demonstrated that ACE2 gene expression was higher in human breast tissue than human lung tissue. Our knowledge about coronavirus disease-2019 (COVID-19) is expanding rapidly. Clinicians are eager for vetted information regarding all aspects of this new illness, and this study demonstrates that the level of ACE2 expression in human breast tissue is higher than that in the lung tissue, a major target tissue affected by SARS-CoV-2 infection. This finding strongly suggests that SARS-CoV-2 infection causes breast pathology.

Meta-Analysis of Oxidative Transcriptomes in Insects.

Data accumulation in public databases has resulted in extensive use of meta-analysis, a statistical analysis that combines the results of multiple studies. Oxidative stress occurs when there is an imbalance between free radical activity and antioxidant activity, which can be studied in insects by transcriptome analysis. This study aimed to apply a meta-analysis approach to evaluate insect oxidative transcriptomes using publicly available data. We collected oxidative stress response-related RNA sequencing (RNA-seq) data for a wide variety of insect species, mainly from public gene expression databases, by manual curation. Only RNA-seq data of Drosophila melanogaster were found and were systematically analyzed using a newly developed RNA-seq analysis workflow for species without a reference genome sequence. The results were evaluated by two metric methods to construct a reference dataset for oxidative stress response studies. Many genes were found to be downregulated under oxidative stress and related to organ system process (GO:0003008) and adherens junction organization (GO:0034332) by gene enrichment analysis. A cross-species analysis was also performed. RNA-seq data of Caenorhabditis elegans were curated, since no RNA-seq data of insect species are currently available in public databases. This method, including the workflow developed, represents a powerful tool for deciphering conserved networks in oxidative stress response.

Gene co-expression network analysis reveals immune cell infiltration as a favorable prognostic marker in non-uterine leiomyosarcoma

The present study aimed to improve the understanding of non-uterine leiomyosarcoma (NULMS) prognostic genes through system biology approaches. This cancer is heterogeneous and rare. Moreover, gene interaction networks have not been reported in NULMS yet. The datasets were obtained from the public gene expression databases. Seven co-expression modules were identified from 5000 most connected genes; using weighted gene co-expression network analysis. Using Cox regression, the modules showed favorable (HR = 0.6, 95% CI = 0.4–0.89, P = 0.0125), (HR = 0.65, 95% CI = 0.44–0.98, P = 0.04) and poor (HR = 1.55, 95% CI = 1.06–2.27, P = 0.025) prognosis to the overall survival (OS) (time = 3740 days). The first one was significant in multivariate HR estimates (HR = 0.4, 95% CI = 0.28–0.69, P = 0.0004). Enriched genes through the Database for Annotation, Visualization, and Integrated Discovery (DAVID) revealed significant immune-related pathways; suggesting immune cell infiltration as a favorable prognostic factor. The most significant protective genes were ICAM3, NCR3, KLRB1, and IL18RAP, which were in one of the significant modules. Moreover, genes related to angiogenesis, cell–cell adhesion, protein glycosylation, and protein transport such as PYCR1, SRM, and MDFI negatively affected the OS and were found in the other related module. In conclusion, our analysis suggests that NULMS might be a good candidate for immunotherapy. Moreover, the genes found in this study might be potential candidates for targeted therapy.

Gene Expression of Angiotensin-Converting Enzyme 2 Receptor in Skin and the Implications for COVID-19.

Binding to the angiotensin-converting enzyme 2 (ACE2) receptor is a critical step for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to enter target cells. This enzyme is expressed in many human tissues including the lungs, but no research has demonstrated that SARS-CoV-2 can infect human skin or subcutaneous fat tissue, despite the increasing number of reported skin manifestations. The aim of this study was to investigate ACE2 gene expression in skin using a public database. A search of transcriptomic data sets from a public gene expression database to investigate ACE2 gene expression in human tissues. Human skin keratinocytes and basal cells express more ACE2 than lung epithelial cells. In contrast, both fibroblasts and melanocytes from human skin express less ACE2 than human lung epithelial cells. The high expression of ACE2 in keratinocytes and basal cells of human skin indicates that they may be directly susceptible to SARS-CoV-2 infection via the ACE2 receptor, especially in conditions of skin barrier dysfunction, and are therefore a potential target for the coronavirus.

Association of high level gene expression of ACE2 in adipose tissue with mortality of COVID-19 infection in obese patients

IntroductionObese patients have an increased risk of COVID-19 critical illness leading to ICU admission or death compared to normal weight individuals. SARS-CoV-2 binding to angiotensin-converting enzyme 2 (ACE2) receptor is a critical step mediate virus entry into target cells. Articles have alluded that the level of ACE2 gene expression in adipose tissue is higher than lung tissue, but a PubMed search found no results in articles to demonstrate this. The aim of this study was to investigate ACE2 gene expression in adipose tissue and lung tissue using a public database. Material and methodsA search of a public gene expression database to investigate ACE2 gene expression in human tissues. ResultsACE2 gene expression was present in both visceral and subcutaneous adipose tissues. The gene expression profile demonstrated that ACE2 gene expression was higher in human visceral and subcutaneous adipose tissues than human lung tissue. ConclusionThis study demonstrates that ACE2 gene expression is higher in visceral and subcutaneous adipose tissue than that in lung tissue, a major target tissue affected by SARS-CoV-2 infection. This suggests a mechanism by which excess adiposity may drive greater infection severity in patients with COVID-19.

Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data.

Precision medicine in oncology aims at obtaining data from heterogeneous sources to have a precise estimation of a given patient's state and prognosis. With the purpose of advancing to personalized medicine framework, accurate diagnoses allow prescription of more effective treatments adapted to the specificities of each individual case. In the last years, next-generation sequencing has impelled cancer research by providing physicians with an overwhelming amount of gene-expression data from RNA-seq high-throughput platforms. In this scenario, data mining and machine learning techniques have widely contribute to gene-expression data analysis by supplying computational models to supporting decision-making on real-world data. Nevertheless, existing public gene-expression databases are characterized by the unfavorable imbalance between the huge number of genes (in the order of tenths of thousands) and the small number of samples (in the order of a few hundreds) available. Despite diverse feature selection and extraction strategies have been traditionally applied to surpass derived over-fitting issues, the efficacy of standard machine learning pipelines is far from being satisfactory for the prediction of relevant clinical outcomes like follow-up end-points or patient's survival. Using the public Pan-Cancer dataset, in this study we pre-train convolutional neural network architectures for survival prediction on a subset composed of thousands of gene-expression samples from thirty-one tumor types. The resulting architectures are subsequently fine-tuned to predict lung cancer progression-free interval. The application of convolutional networks to gene-expression data has many limitations, derived from the unstructured nature of these data. In this work we propose a methodology to rearrange RNA-seq data by transforming RNA-seq samples into gene-expression images, from which convolutional networks can extract high-level features. As an additional objective, we investigate whether leveraging the information extracted from other tumor-type samples contributes to the extraction of high-level features that improve lung cancer progression prediction, compared to other machine learning approaches.

All of gene expression (AOE): An integrated index for public gene expression databases.

Gene expression data have been archived as microarray and RNA-seq datasets in two public databases, Gene Expression Omnibus (GEO) and ArrayExpress (AE). In 2018, the DNA DataBank of Japan started a similar repository called the Genomic Expression Archive (GEA). These databases are useful resources for the functional interpretation of genes, but have been separately maintained and may lack RNA-seq data, while the original sequence data are available in the Sequence Read Archive (SRA). We constructed an index for those gene expression data repositories, called All Of gene Expression (AOE), to integrate publicly available gene expression data. The web interface of AOE can graphically query data in addition to the application programming interface. By collecting gene expression data from RNA-seq in the SRA, AOE also includes data not included in GEO and AE. AOE is accessible as a search tool from the GEA website and is freely available at https://aoe.dbcls.jp/.

Meta-Analysis of Hypoxic Transcriptomes from Public Databases.

Hypoxia is the insufficiency of oxygen in the cell, and hypoxia-inducible factors (HIFs) are central regulators of oxygen homeostasis. In order to obtain functional insights into the hypoxic response in a data-driven way, we attempted a meta-analysis of the RNA-seq data from the hypoxic transcriptomes archived in public databases. In view of methodological variability of archived data in the databases, we first manually curated RNA-seq data from appropriate pairs of transcriptomes before and after hypoxic stress. These included 128 human and 52 murine transcriptome pairs. We classified the results of experiments for each gene into three categories: upregulated, downregulated, and unchanged. Hypoxic transcriptomes were then compared between humans and mice to identify common hypoxia-responsive genes. In addition, meta-analyzed hypoxic transcriptome data were integrated with public ChIP-seq data on the known human HIFs, HIF-1 and HIF-2, to provide insights into hypoxia-responsive pathways involving direct transcription factor binding. This study provides a useful resource for hypoxia research. It also demonstrates the potential of a meta-analysis approach to public gene expression databases for selecting candidate genes from gene expression profiles generated under various experimental conditions.

Detection of Vasodilators From Herbal Components by a Transcriptome-Based Functional Gene Module Reference Approach.

Vasodilatation is one of the key therapeutic strategies for the treatment of various cardiovascular diseases with high blood pressure. Therefore, development of drugs assisting blood vessel dilation is promising. It has been proven that many drugs display definite vasorelaxant effects. However, there are very few studies that systemically explore the effective vasodilators. In this work, we build a transcriptome-based functional gene module reference approach for systematic pursuit of agents with vasorelaxant effects. We firstly curate two functional gene modules that are specifically involved in positive and negative regulation of vascular diameter based on the known gene functional interaction knowledge. Secondly, a collection of gene expression profiles following herbal component treatment are collected from a public gene expression database. Then, the correlation of the gene modules is evaluated in each herbal component–induced gene expression profile by gene set enrichment analysis. The vasorelaxant effects of the candidate compounds can be predicted and ordered by the values of a defined index. Finally, the top 10 candidate compounds are experimentally tested for their vasorelaxant effects on vessel contraction induced by Phe in aortic rings. This strategy integrating different types of technologies is expected to help to create new opportunities for the development of novel vasodilators.

An integrated index of public gene expression databases and its application for meta-analysis of hypoxic transcriptomes

An integrated index of public gene expression databases and its application for meta-analysis of hypoxic transcriptomes

Network-based transcriptomic analysis reveals novel melatonin-sensitive genes in cardiovascular system

Heart disease is a major cause of mortality and disability worldwide. Melatonin is a neuroendocrine hormone and has been found to be protective in heart disease. However, the molecular basis underlying this cardioprotective effect is not fully understood. Here we aim to investigate melatonin-sensitive genes in cardiovascular system using public gene expression databases. An innovative genomic analysis method, the weighted gene co-expression network analysis (WGCNA) combined with differential gene expression analysis, was used in this study. The algorithm was implemented in R/Bioconductor. Using this method, we provide a comprehensive characterization of transcriptional profiles associated with melatonin treatment. We found that 357 differentially expressed genes (DEGs) were highly sensitive to melatonin in mouse myocardium. Enrichment analysis showed that these 357 genes were mostly related to GO:0051984 (positive regulation of chromosome segregation), GO:0016605 (PML body) and GO:0006281 (DNA repair). We further obtained 5 hub genes from the 357 DEGs, including Set, Dhx40, Scaf11, Cfh, and Nup43. We identified numerous melatonin-sensitive genes and further identified five hub genes. The five novel genes are possibly associated with the myocardial benefits of melatonin.

Down‐regulation of Fatty Acid Transporter‐encoding Gene SLC27A3 Modulates Immuno‐response and Correlates with Poor Prognosis in Lung Adenocarcinoma

Fatty acid transporter 3 (FATP3, also named as SLC27A3) plays a critical role in brain development and monocyte‐macrophage differentiation. It has been reported that SLC27A3 were upregulated in CD16+ Dendritic‐like cells and CD4+ T memory cells. However, the prognostic significance and functional consequence of SLC27A3 downregulation remains unclear in human cancers. The aim of this study is to determine the prognostic values of SLC27A3. First, we searched public gene expression database and analyzed SLC27A3 protein expression by immunohistochemical staining on cancer tissue microarray samples. We showed that reduced levels of SLC27A3 were widely detected in tumors compared to normal tissues and reflect a poor clinical outcome in non‐small cell lung cancer and glioma, especially in lung adenocarcinoma. Furthermore, we verified the functional role of immune‐regulation of SLC27A3 in NSCLC cell lines. Since SLC27A3 expressed in T memory cells, we raised the hypothesis that SLC27A3‐expressed cancer cells may establish microenvironment that fit for T cells. Therefore, T cells will recruit to kill cancer cells in the nearby. In vitro T‐cell proliferation assay showed that SLC27A3 modulated the inhibition of T‐cell proliferation exerted by lung cancer cells. In conclusion, downregulation of SLC27A3 expression is an independent prognostic factor in lung adenocarcinoma patients and may play critical roles in the inhibition of immuno‐response in lung cancer.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.