Joint effects of CD8A and ICOS in Long QT Syndrome (LQTS) and Beckwith-Wiedemann Syndrome (BWS).

BACKGROUND: Long QT Syndrome (LQTS) and Beckwith-Wiedemann Syndrome (BWS) are complex disorders with unclear origins, underscoring the need for in-depth molecular investigations into their mechanisms. The main aim of this study is to identify the shared key genes between LQTS and BWS, shedding light on potential common molecular pathways underlying these syndromes. METHODS: The LQTS and BWS datasets are available for download from the GEO database. Differential expression genes (DEGs) were identified. Weighted gene co-expression network analysis (WGCNA) was used to detect significant modules and central genes. Gene enrichment analysis was performed. CIBERSORT was used for immune cell infiltration analysis. The predictive protein interaction (PPI) network of core genes was constructed using STRING, and miRNAs regulating central genes were screened using TargetScan. RESULTS: Five hundred DEGs associated with Long QT Syndrome and Beckwith-Wiedemann Syndrome were identified. GSEA analysis revealed enrichment in pathways such as T cell receptor signaling, MAPK signaling, and adrenergic signaling in cardiac myocytes. Immune cell infiltration indicated higher levels of memory B cells and naive CD4 T cells. Four core genes (CD8A, ICOS, CTLA4, LCK) were identified, with CD8A and ICOS showing low expression in the syndromes and high expression in normal samples, suggesting potential inverse regulatory roles. CONCLUSION: The expression of CD8A and ICOS is low in long QT syndrome and Beckwith-Wiedemann syndrome, indicating their potential as key genes in the pathogenesis of these syndromes. The identification of shared key genes between LQTS and BWS provides insights into common molecular mechanisms underlying these disorders, potentially facilitating the development of targeted therapeutic strategies.

PLK1 as one novel target for the poor prognosis of bladder cancer: An observational study.

Bladder cancer (BC) is one of the most common male malignant tumors and the most common urological tumor. However, the molecular mechanism and role of PLK1 on bladder cancer were unclear. Therefore, the study aims to explore the potential part of the overall survival of bladder cancer through bioinformatics analysis. GSE121711 and GSE130598, from the Gene Expression Omnibus database. The GEO2R screened differently expressed genes, and DAVID and Metascape were used for functional annotation. The cytoHubba made hub genes identification and expression. A total of 50 BC participants were recruited. After surgery, 50 BC tumor samples from BC patients and 50 adjacent standard bladder tissue samples were obtained. The RT-qPCR assay was performed to verify the expression of hub genes. The Kaplan-Meier Plotter analyzed the effect of hub gene expression for overall survival of BC. The compulsory module of Molecular Complex Detection tool analysis was shown, which included CDK1, TTK, AURKB, MELK, PLK1, and BUB1. And the six hub genes were up-regulated in the BC compared with the normal tissues. The relative expression levels of CDK1, TTK, AURKB, MELK, PLK1, and BUB1 were significantly higher in BC samples compared with the regular kidney tissue groups. The result demonstrated that CDK1, TTK, AURKB, MELK, PLK1, and BUB1 might be considered biomarkers for BC. Overall survival analysis showed that BC patients with high expression level of PLK1 had poorer overall survival times than those with low expression level (P < .05). The expression levels of CDK1, TTK, AURKB, MELK, and BUB1 was not related to the overall survival of BC patients (P > .05). The PLK1 gene might provide new ideas and evidence for bladder cancer research.

Screening and Identification of Key Biomarkers of Gastric Cancer: Three Genes Jointly Predict Gastric Cancer.

BACKGROUND: Gastric cancer (GC) is one of the most common cancers all over the world, causing high mortality. Gastric cancer screening is one of the effective strategies used to reduce mortality. We expect that good biomarkers can be discovered to diagnose and treat gastric cancer as early as possible. METHODS: We download four gene expression profiling datasets of gastric cancer (GSE118916, GSE54129, GSE103236, GSE112369), which were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between gastric cancer and adjacent normal tissues were detected to explore biomarkers that may play an important role in gastric cancer. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of overlap genes were conducted by the Metascape online database; the protein-protein interaction (PPI) network was constructed by the STRING online database, and we screened the hub genes of the PPI network using the Cytoscape software. The survival curve analysis was conducted by km-plotter and the stage plots of hub genes were created by the GEPIA online database. PCR, WB, and immunohistochemistry were used to verify the expression of hub genes. A neural network model was established to quantify the predictors of gastric cancer. RESULTS: The relative expression level of cadherin-3 (CDH3), lymphoid enhancer-binding factor 1 (LEF1), and matrix metallopeptidase 7 (MMP7) were significantly higher in gastric samples, compared with the normal groups (p<0.05). Receiver operator characteristic (ROC) curves were constructed to determine the effect of the three genes' expression on gastric cancer, and the AUC was used to determine the degree of confidence: CDH3 (AUC = 0.800, P<0.05, 95% CI =0.857-0.895), LEF1 (AUC=0.620, P<0.05, 95%CI=0.632-0.714), and MMP7 (AUC=0.914, P<0.05, 95%CI=0.714-0.947). The high-risk warning indicator of gastric cancer contained 8

Bioinformatics analysis and verification of gene targets for renal clear cell carcinoma.

BACKGROUND: It is estimated that there are 338,000 new renal-cell carcinoma releases every year in the world. Renal cell carcinoma (RCC) is a heterogeneous tumor, of which more than 70% is clear cell renal cell carcinoma (ccRCC). It is estimated that about 30% of new renal-cell carcinoma patients have metastases at the time of diagnosis. However, the pathogenesis of renal clear cell carcinoma has not been elucidated. Therefore, it is necessary to further study the pathogenesis of ccRCC. METHODS: Two expression profiling datasets (GSE68417, GSE71963) were downloaded from the GEO database. Differentially expressed genes (DEGs) between ccRCC and normal tissue samples were identified by GEO2R. Functional enrichment analysis was made by the DAVID tool. Protein-protein interaction (PPI) network was constructed. The hub genes were excavated. The clustering analysis of expression level of hub genes was performed by UCSC (University of California Santa Cruz) Xena database. The hub gene on overall survival rate (OS) in patients with ccRCC was performed by Kaplan-Meier Plotter. Finally, we used the ccRCC renal tissue samples to verify the hub genes. RESULTS: 1182 common DEGs between the two datasets were identified. The results of GO and KEGG analysis revealed that variations in were predominantly enriched in intracellular signaling cascade, oxidation reduction, intrinsic to membrane, integral to membrane, nucleoside binding, purine nucleoside binding, pathways in cancer, focal adhesion, cell adhesion molecules. 10 hub genes ITGAX, CD86, LY86, TLR2, TYROBP, FCGR2A, FCGR2B, PTPRC, ITGB2, ITGAM were identified. FCGR2B and TYROBP were negatively correlated with the overall survival rate in patients with ccRCC (P < 0.05). RT-qPCR analysis showed that the relative expression levels of CD86, FCGR2A, FCGR2B, TYROBP, LY86, and TLR2 were significantly higher in ccRCC samples, compared with the adjacent renal tissue groups. CONCLUSIONS: In summary, bioinformatics technology could be a useful tool to predict the progression of ccRCC. In addition, there are DEGs between ccRCC tumor tissue and normal renal tissue, and these DEGs might be considered as biomarkers for ccRCC.

Bioinformatics analysis of gene and microRNA targets for fibromyalgia.

OBJECTIVES: Fibromyalgia (FM) is the most common chronic pain disease in middle-aged women. Patients may also complain of migraine, irritable bowel syndrome and depression, which seriously affect their work and life, causing huge economic losses to society. However, the pathogenesis of FM is still controversial and the effect of the current treatment is far from satisfactory. METHODS: Differentially expressed genes (DEGs) and miRNAs (DEMs) were found between FM and normal blood samples. The pathway and process enrichment analysis of the genes were performed. Protein-protein interaction network were constructed. Hub genes were found and analysed in The Comparative Toxicogenomics Database. RESULTS: A total of 102 genes were up-regulated and 46 down-regulated, 206 miRNAs down-regulated, and 15 up-regulated in FM. CD38, GATM, HDC, FOS were found as canditate genes. These genes were significantly associated with musculoskeletal disease, mental disorder, immune system disease. There was partial overlap between metformin therapy-related genes and FM-related genes. CONCLUSIONS: We found DEGs and DEMs in FM patients through bioinformatics analysis, which may be involved in the occurrence and development of FM and serve as potential targets for diagnosis and treatment.

Identification and Verification of Biomarker in Clear Cell Renal Cell Carcinoma via Bioinformatics and Neural Network Model.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer, which represents the 9th most frequently diagnosed cancer. However, the molecular mechanism of occurrence and development of ccRCC is indistinct. Therefore, the research aims to identify the hub biomarkers of ccRCC using numerous bioinformatics tools and functional experiments. METHODS: The public data was downloaded from the Gene Expression Omnibus (GEO) database, and the differently expressed genes (DEGs) between ccRCC and normal renal tissues were identified with GEO2R. Protein-protein interaction (PPI) network of the DEGs was constructed, and hub genes were screened with cytoHubba. Then, ten ccRCC tumor samples and ten normal kidney tissues were obtained to verify the expression of hub genes with the RT-qPCR. Finally, the neural network model was constructed to verify the relationship among the genes. RESULTS: A total of 251 DEGs and ten hub genes were identified. AURKB, CCNA2, TPX2, and NCAPG were highly expressed in ccRCC compared with renal tissue. With the increasing expression of AURKB, CCNA2, TPX2, and NCAPG, the pathological stage of ccRCC increased gradually (P < 0.05). Patients with high expression of AURKB, CCNA2, TPX2, and NCAPG have a poor overall survival. After the verification of RT-qPCR, the expression of hub genes was same as the public data. And there were strong correlations between the AURKB, CCNA2, TPX2, and NCAPG with the verification of the neural network model. CONCLUSION: After the identification and verification, AURKB, CCNA2, TPX2, and NCAPG might be related to the occurrence and malignant progression of ccRCC.

Progress in the correlation between PTPN12 gene expression and human tumors.

BACKGROUND: The global morbidity of cancer is rising rapidly. Despite advances in molecular biology, immunology, and cytotoxic and immune-anticancer therapies, cancer remains a major cause of death worldwide. Protein tyrosine phosphatase non-receptor type 12 (PTPN12) is a new member of the cytoplasmic protein tyrosine phosphatase family, isolated from a cDNA library of adult colon tissue. Thus far, no studies have reviewed the correlation between PTPN12 gene expression and human tumors. METHODS: This article summarizes the latest domestic and international research developments on how the expression of PTPN12 relates to human tumors. The extensive search in Web of Science and PubMed with the keywords including PTPN12, tumor, renal cell carcinoma, proto-oncogenes, tumor suppressor genes was undertaken. RESULTS: More and more studies have shown that a tumor is essentially a genetic disease, arising from a broken antagonistic function between proto-oncogenes and tumor suppressor genes. When their antagonistic effect is out of balance, it may cause uncontrolled growth of cells and lead to the occurrence of tumors. PTPN12 is a tumor suppressor gene, so inhibiting its activity will lead directly or indirectly to the occurrence of tumors. CONCLUSION: The etiology, prevention, and treatment of tumors have become the focus of research around the world. PTPN12 is a tumor suppressor gene. In the future, PTPN12 might serve as a novel molecular marker to benefit patients, and even the development of tumor suppressor gene activation agents can form a practical research direction.

Insights regarding novel biomarkers and the pathogenesis of primary colorectal carcinoma based on bioinformatic analysis.

BACKGROUND: Biomarkers are important in the study of tumor processes for early detection and precise treatment. The biomarkers that have been previously detected are not useful for clinical application for primary colorectal carcinoma (PCRC). The aim of this study was to explore clinically valuable biomarkers of PCRC based on integrated bioinformatic analysis. MATERIAL AND METHODS: Gene expression data were acquired from the GSE41258 dataset, and the differentially expressed genes were determined between PCRC and normal colorectal samples. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were implemented via Gene Set Enrichment Analysis. A protein-protein interaction (PPI) network was constructed. The significant modules and hub genes were screened and identified in the PPI network. RESULTS: A total of 202 DEGs were identified, including 58 upregulated and 144 downregulated genes in PCRC samples compared to those in normal colorectal samples. Enrichment analysis demonstrated that the gene sets enriched in PCRC were significantly related to bicarbonate transport, regulation of sodium ion transport, potassium ion homeostasis, regulation of telomere maintenance, and other processes. A total of 10 hub genes was identified by cytoHubba: PYY, CXCL3, CXCL11, CXCL8, CXCL12, CCL20, MMP3, P2RY14, NPY1R, and CXCL1. CONCLUSION: The hub genes, such as NPY1R, P2RY14, and CXCL12, and the electrolyte disequilibrium resulting from the differential expression of genes, especially bicarbonate imbalance, may provide novel insights and evidence for the future diagnosis and targeted therapy of PCRC.

Identification of biomarkers for the diagnosis and treatment of primary colorectal cancer based on microarray technology.

BACKGROUND: Primary colorectal cancer (PCRC) is one of the most common malignant tumors in clinic, and is characterized by high heterogeneity occurring between tumors and intracellularly. Therefore, this study aimed to explore potential gene targets for the diagnosis and treatment of PCRC via bioinformatic technology. METHODS: Gene Expression Omnibus (GEO) was used to download the data used in this study. Differently expressed genes (DEGs) were identified with GEO2R, and the gene set enrichment analysis (GSEA) was implemented for enrichment analysis. Then, the researchers constructed a protein-protein interaction (PPI) network, a significant module, and a hub genes network. RESULTS: The GSE81558 dataset was downloaded, and a total of 97 DEGs were found. There were 23 up-regulated DEGs and 74 down-regulated DEGs in the PCRC samples, compared with the control group. The PPI network included a total of 42 nodes and 63 edges. One module network consisted of 11 nodes and 25 edges. Another module network consisted of 4 nodes and 6 edges. The hub genes network was created by cytoHubba using GCG, GUCA2B, CLCA4, ZG16, TMIGD1, GUCA2A, CHGA, PYY, SST, and MS4A12. CONCLUSIONS: Ten hub genes were found from the genomic samples of patients with PCRC and normal controls by bioinformatics analysis. The hub genes might provide novel ideas and evidence for the diagnosis and targeted therapy of PCRC.

Identification and Functional Enrichment Analysis of Potential Diagnostic and Therapeutic Targets in Adamantinomatous Craniopharyngioma.

Adamantinomatous craniopharyngioma (ACP) is a congenital epithelial tumor in the sellar region with benign histological manifestation but invasive. Currently, surgery is the main treatment for it, but its recurrence rate is high. Therefore, it is of great importance to explore the mechanism of occurrence and development of ACP and to identify new molecules. One gene expression profile, GSE94349, was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified by the limma package. Gene set enrichment analysis was used to make enrichment analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Then, we performed the construction and analysis of the protein-protein interaction (PPI) network and significant module. The analysis of the GSE94349 dataset identified 109 DEGs, consisting of 80 upregulated genes and 29 downregulated genes in ACP samples compared with normal brain tissues. Functional and pathway enrichment analysis of DEGs provided a comprehensive overview of some major pathophysiological mechanisms in ACP: RNA polymerase II promoter, glutamate receptor binding, and so on. A total of 10 hub genes of DEGs were obtained from the PPI network, which provided potential therapeutic targets for the ACP. In summary, there were DEGs between ACP tissues and normal brain tissues, which may be involved in the mechanisms of occurrence and development of ACP, especially via the regulation of RNA polymerase II promoter and glutamate receptor binding. Key genes in DEGs could serve as new research targets for the diagnosis and treatment of ACP.

Screening and Identification of Key Biomarkers in Pancreatic Cancer: Evidence from Bioinformatic Analysis.

Pancreatic cancer (PC) whose mortality is comparable to morbidity is a highly fatal disease. Early approaches of diagnosis and treatment for PC are quite limited, so it is of great urgency to figure out the exact tumorigenesis and development mechanism of PC. To identify the related molecular markers of pancreatic oncogenesis, we downloaded three microarray datasets (GSE63111, GSE101448, and GSE107610) from Gene Expression Omnibus (GEO) database. The common differentially expressed genes (DEGs) among them were identified, and the corresponding function enrichment analyses were accomplished. The protein-protein interaction network was conducted by Search Tool for the Retrieval of Interacting Genes (STRING), and the corresponding module analysis was accomplished by Cytoscape. There were 55 DEGs found in total. The molecular function and biological processes (BP) of these DEGs mainly include cytokinesis, mitotic nuclear division, cell division, cell proliferation, microtubule-based movement, and mineral absorption. Among the 55 DEGs, 14 hub genes were further confirmed and it was concluded that they mainly function in mitotic cytokinesis, microtubule-based movement, mitotic chromosome condensation, and mitotic spindle assembly from the BP analysis. The survival analysis showed that all the 14 hub genes, especially nucleolar and spindle associated protein 1 and abnormal spindle microtubule assembly, may involve in the tumorigenesis and development of PC. And they might be used as new biomarkers for auxiliary diagnosis and potential targets for immunotherapy of PC.

Screening and authentication of molecular markers in malignant glioblastoma based on gene expression profiles.

Glioblastoma (GBM) is a malignant tumor of the central nervous system with high mortality rates. Gene expression profiling may determine the chemosensitivity of GBMs. However, the molecular mechanisms underlying GBM remain to be determined. To screen the novel key genes in its occurrence and development, two glioma databases, GSE122498 and GSE104291, were analyzed in the present study. Bioinformatics analyses were performed using the Database for Annotation, Visualization and Integrated Discovery, the Search Tool for the Retrieval of Interacting Genes, Cytoscape, cBioPortal, and Gene Expression Profiling Interactive Analysis softwares. Patients with recurrent GBM showed worse overall survival rate. Overall, 341 differentially expressed genes (DEGs) were authenticated based on two microarray datasets, which were primarily enriched in 'cell division', 'mitotic nuclear division', 'DNA replication', 'nucleoplasm', 'cytosol, nucleus', 'protein binding', 'ATP binding', 'protein C-terminus binding', 'the cell cycle', 'DNA replication', 'oocyte meiosis' and 'valine'. The protein-protein interaction network was composed of 1,799 edges and 237 nodes. Its significant module had 10 hub genes, and CDK1, BUB1B, NDC80, NCAPG, BUB1, CCNB1, TOP2A, DLGAP5, ASPM and MELK were significantly associated with carcinogenesis and the development of GBM. The present study indicated that the DEGs and hub genes, identified based on bioinformatics analyses, had significant diagnostic value for patients with GBM.

Screening and identification of biomarkers for systemic sclerosis via microarray technology.

Systemic sclerosis (SSc) is a complex autoimmune disease. The pathogenesis of SSc is currently unclear, although like other rheumatic diseases its pathogenesis is complicated. However, the ongoing development of bioinformatics technology has enabled new approaches to research this disease using microarray technology to screen and identify differentially expressed genes (DEGs) in the skin of patients with SSc compared with individuals with healthy skin. Publicly available data were downloaded from the Gene Expression Omnibus (GEO) database and intra­group data repeatability tests were conducted using Pearson's correlation test and principal component analysis. DEGs were identified using an online tool, GEO2R. Functional annotation of DEGs was performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Finally, the construction and analysis of the protein­protein interaction (PPI) network and identification and analysis of hub genes was carried out. A total of 106 DEGs were detected by the screening of SSc and healthy skin samples. A total of 10 genes [interleukin­6, bone morphogenetic protein 4, calumenin (CALU), clusterin, cysteine rich angiogenic inducer 61, serine protease 23, secretogranin II, suppressor of cytokine signaling 3, Toll­like receptor 4 (TLR4), tenascin C] were identified as hub genes with degrees ≥10, and which could sensitively and specifically predict SSc based on receiver operator characteristic curve analysis. GO and KEGG analysis showed that variations in hub genes were mainly enriched in positive regulation of nitric oxide biosynthetic processes, negative regulation of apoptotic processes, extracellular regions, extracellular spaces, cytokine activity, chemo­attractant activity, and the phosphoinositide 3 kinase­protein kinase B signaling pathway. In summary, bioinformatics techniques proved useful for the screening and identification of biomarkers of disease. A total of 106 DEGs and 10 hub genes were linked to SSc, in particular the TLR4 and CALU genes.

Pivotal micro factors associated with endothelial cells.

OBJECTIVE: Recent studies have shown the important influence of various micro factors on the general biological activity and function of endothelial cells (ECs). Vascular endothelial growth factor (VEGF) and angiogenin (ANG) are classic micro factors that promote proliferation, differentiation, and migration of ECs. The underlying pathophysiological mechanisms and related pathways of these micro factors remain the focus of current research. DATA SOURCES: An extensive search was undertaken in the PubMed database by using keywords including "micro factors" and "endothelial cell." This search covered relevant research articles published between January 1, 2007 and December 31, 2018. STUDY SELECTION: Original articles, reviews, and other articles were searched and reviewed for content on micro factors of ECs. RESULTS: VEGF and ANG have critical functions in the occurrence, development, and status of the physiological pathology of ECs. Other EC-associated micro factors include interleukin 10, tumor protein P53, nuclear factor kappa B subunit, interleukin 6, and tumor necrosis factor. The results of Gene Ontology analysis revealed that variations were mainly enriched in positive regulation of transcription by the RNA polymerase II promoter, cellular response to lipopolysaccharides, negative regulation of apoptotic processes, external side of the plasma membrane, cytoplasm, extracellular regions, cytokine activity, growth factor activity, and identical protein binding. The results of the Kyoto Encyclopedia of Genes and Genomes analysis revealed that micro factors were predominantly enriched in inflammatory diseases. CONCLUSIONS: In summary, the main mediators, factors, or genes associated with ECs include VEGF and ANG. The effect of micro factors on ECs is complex and multifaceted. This review summarizes the correlation between ECs and several micro factors.

Identification of biomarkers in macrophages of atherosclerosis by microarray analysis.

BACKGROUND: Atherosclerotic cardiovascular disease (ASCVD) refers to a series of diseases caused by atherosclerosis (AS). It is one of the most important causes of death worldwide. According to the inflammatory response theory, macrophages play a critical role in AS. However, the potential targets associated with macrophages in the development of AS are still obscure. This study aimed to use bioinformatics tools for screening and identifying molecular targets in AS macrophages. METHODS: Two expression profiling datasets (GSE7074 and GSE9874) were obtained from the Gene Expression Omnibus dataset, and differentially expressed genes (DEGs) between non-AS macrophages and AS macrophages were identified. Functional annotation of the DEGs was performed by analyzing the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. STRING and Cytoscape were employed for constructing a protein-protein interaction network and analyzing hub genes. RESULTS: A total of 98 DEGs were distinguished between non-AS macrophages and AS macrophages. The functional variations in DEGs were mainly enriched in response to hypoxia, respiratory gaseous exchange, protein binding, and intracellular, ciliary tip, early endosome membrane, and Lys63-specific deubiquitinase activities. Three genes were identified as hub genes, including KDELR3, CD55, and DYNC2H1. CONCLUSION: Hub genes and DEGs identified by using microarray techniques can be used as diagnostic and therapeutic biomarkers for AS.

Case Report: Severe back pain, epigastric distress and refractory nausea; an unusual presentation of mediastinal bronchogenic cyst.

Background: Bronchogenic cysts are congenital malformations from abnormal budding of embryonic foregut and tracheobronchial tree. We present a case of bronchogenic cyst with severe back pain, epigastric distress and refractory nausea and vomiting.   Case Presentation: A 44-year-old Hispanic female presented with a 3-week history of recurrent sharp interscapular pain radiating to epigastrium with refractory nausea and vomiting. She underwent cholecystectomy 2-years ago. Computed tomography (CT) abdomen at that time showed a subcarinal mass measuring 5.4 X 5.0 cm. Subsequent endoscopic ultrasound diagnosed it as a bronchogenic cyst. Endobronchial ultrasound (EBUS) guided aspiration resulted in incomplete drainage and she was discharged after partial improvement. Current physical examination showed tachycardia and tachypnea with labs showing leukocytosis, elevated inflammatory markers, and hypokalemic metabolic alkalosis. CT chest showed an increased size of the bronchogenic cyst (9.64 X 7.7 cm) suggestive of possible partial cyst rupture or infected cyst. X-ray esophagram ruled out esophageal compression or contrast extravasation. Patient's symptoms were refractory to conservative management. The patient ultimately underwent right thoracotomy with cyst excision that resulted in complete resolution of symptoms. Conclusion: Bronchogenic cysts are the most common primary cysts of mediastinum with the prevalence of 6%. The most common symptoms are chest pain, dyspnea, cough, and stridor. Diagnosis is made by chest X-Ray and CT chest. Magnetic resonance imaging chest and EBUS are more sensitive and specific. Symptomatic cysts should be resected unless surgical risks are high. Asymptomatic cysts in younger patients should be removed due to low surgical risk and potential late complications. Watchful waiting has been recommended for asymptomatic adults or high-risk patients. This case presents mediastinal bronchogenic cyst as a cause of back, nausea and refractory vomiting. Immediate surgical excision in such cases should be attempted, which will lead to resolution of symptoms and avoidance of complications.