基于拓扑分析方法鉴定与胃癌相关的生物标志物

胃癌(gastric cancer, GC)是最常见的恶性肿瘤之一。由于GC发病隐匿的特性,其早期检测困难。因此,研究与GC早期诊断和预后相关的生物标志物至关重要。从GEO数据库下载了3组基因表达数据集GSE79973、 GSE19826和GSE13911,通过Limma包筛选差异表达基因(differentially expressed genes, DEGs),并使用DEGs、 STRING V11数据库和Cytoscape构建了DEGs的蛋白质-蛋白质相互作用(protein-protein interaction, PPI)网络,通过4种拓扑分析方法取交集筛选hub基因,并通过单变量Cox分析、多变量Cox分析、 Lasso回归分析、生存分析、通路分析以及文献法验证hub基因。从3个数据集中分别筛选了1 599个、 333个和662个DEGs。通过拓扑分析方法筛选了4个hub基因,即CDK1、AURKA、PTTG1和UBE2C。GO和KEGG富集分析结果表明4个hub基因参与了细胞外基质-受体相互作用、糖尿病并发症中的AGE-RAGE信号通路、小细胞肺癌和蛋白质消化吸收等通路。...     

肾透明细胞癌Caki-1细胞系差异表达基因的生物信息学分析

目的比较肾透明细胞癌Caki-1细胞系与正常肾上皮细胞系ASE-5063中的差异表达基因（DEGs）,寻找潜在的肾透明细胞癌特异性分子标志物。 方法利用GEO数据库自带的GEO2R在线分析工具分析基因芯片GSE78179,将筛选出的DEGs分别导入Metascape、STRING以及Cytoscape进行综合分析并筛选出核心基因。最后使用FunRich等软件对筛选出的核心基因进行GO和KEGG富集分析。 结果共筛选出562个DEGs,其中上调基因345个,下调基因217个。进一步使用MCODE筛选出36个关键基因,GO功能分析发现这些基因与细胞粘附分子活性、趋化因子活性、细胞通讯和信号转导等密切相关;KEGG通路富集结果则表明差异基因主要集中在趋化因子信号通路、TNF信号通路以及NF-κB信号通路等多种与肿瘤相关的通路上。 结论运用生物信息学方法筛选出肾透明细胞癌Caki-1细胞系中DEGs,其中数个核心基因广泛参与多种肿瘤的病理进程,但尚未在肾透明细胞癌有相关研究报道,提示其可能是治疗肾透明细胞癌的潜在靶点。     

KLF基因家族在乳腺癌中的表达及临床意义

为了研究KLF基因家族成员在乳腺癌发生中的分子机制及其与患者预后的关系,我们利用GEPIA对KLF家族进行Kaplan-Meier生存分析,筛选出与患者预后显著相关家族成员;在Oncomine数据库中分析前面筛选出的与乳腺癌预后有关的KLF家族成员;采用c Bio Portal筛选出与患者预后有关的KLF家族成员共表达基因;STRING构建蛋白互作网络,Cytoscape软件中的MCODE插件识别网络模块,再用STRING对网络模块进行聚类分析。Kaplan-Meier生存分析显示KLF家族成员中KLF13和KLF15与乳腺癌预后显著相关(p0.05);Oncomine分析显示只有KLF13在乳腺癌组织表达显著高于正常组织(p0.01);c Bio Portal中筛选出KLF13共表达基因274个;Cytoscape软件中的MCODE插件识别出网络模块3个,STRING进行GO分析发现,网络模块中基因主要参与线粒体内的翻译和呼吸链氧化磷酸化生成ATP等生物过程,KEGG分析发现网络模块中涉及基因主要线粒体蛋白质翻译场所核糖体及其氧化磷酸化等信号通路有关。以上表明KLF13可能通过线粒体信号通路来影响乳腺癌的发生,可作为一个候选的乳腺癌临床预后标志物和潜在的治疗靶点。     

IgA 肾病患者与膜性肾病患者外周血单核细胞mRNA分析

为了寻找诊断、鉴别IgA肾病(IgAN)和膜性肾病(MN)的血液特异性标记物,利用公共数据库中的IgAN和MN患者的外周血单核细胞(PBMCs)的转录组表达谱数据集识别特异性生物标记物,为诊断和鉴别提供简便、可靠的依据补充。从公共基因表达数据库(GEO)下载IgAN患者组(n=15)和MN患者组(n=8)芯片数据集,筛选前250个差异表达基因(DEGs)。通过分析筛选关键基因和途径,进行基因本体(GO)富集分析、京都基因与基因组百科全书(KEGG)通路分析和蛋白质与蛋白质相互作用关系(PPI)分析等进一步了解DEGs。通过分析共发现75个显著DEGs,其中73个上调基因,2个下调基因。GO富集分析的生物学过程(BP)主要包括蛋白质转运、内溶酶体到溶酶体转运、趋化因子介导的信号通路作用等。显著富集差异表达基因KEGG通路分析包括Endocytosis和Hepatitis B的相关信号通路。PPI筛选出EPS15、STAT4、CCL2、SUN2、SEC24C、SEC31A、GOLGB1、F2R,RAB12和PTK2B等关键基因。成功筛选出核心差异表达基因,为IgAN和MN的诊断和鉴别提供简便、可靠的依据补充,甚至提供治疗的新靶点。     

慢性鼻-鼻窦炎伴鼻息肉基因表达谱的生物信息学分析

本研究从美国国立生物信息中心(NCBI)的子数据库基因表达数据库(GEO)中选择基因表达谱GSE36830数据集,采用GEO2R筛选正常钩突和鼻息肉组织之间的差异表达基因(DEGs),对关键通路和差异表达基因进行数据库挖掘和分析,经筛选后的差异表达基因采用戴维在线工具对其进行基因本体富集分析(GO)、京都基因和基因组百科全书(KEGG)分析,然后将DEGs导入String数据库进行蛋白质互作网络分析,绘制差异表达基因互作网络图,将其数据导入Cytoscape软件中,筛选网络中心节点和关键基因,分析关键子网络。共筛选出699个DEGs,其中475个基因为上调表达基因,224个基因为下调表达基因。在GO分析中,针对生物过程,上调的DEGs包括:炎症反应、免疫反应、细胞趋化性、炎症反应的正向调节和细胞的粘附等;下调的DEGs主要参与:唾液分泌、生物矿物组织发展、细胞氨基酸生物合成过程、视网膜内稳态及离子跨膜转运等。在KEGG分析中,上调的DEGs主要在参与造血细胞系、细胞因子-细胞因子受体相互作用、破骨细胞分化、趋化因子信号通路、癌症中的转录失调、哮喘、金黄色葡萄球菌感染等信号通路中富集,而下调的DEGs在唾液腺分泌及胆汁分泌信号通路中富集。差异表达基因互作网络图筛选出前10个关键基因:ITGAM、IL10、CD86、TLR8、ITGAX、CCL2、CCR7、SRC、EGF及ITGB2。本研究得到了一组鼻息肉差异表达基因的生物信息学分析结果,但仍需进一步用基础试验来验证。本文分析的结论为慢性鼻-鼻窦炎、鼻息肉的研究提供了新的研究方向,也为鼻息肉发病机制研究的思路提供了一定的建设性作用。     

肺腺癌诊断标志物筛选及免疫细胞浸润分析

用生物信息学方法筛选肺腺癌(Lung adenocarcinoma,LUAD)的诊断生物标志物,并分析肺腺癌中免疫细胞浸润情况。从GEO和TCGA数据库下载肺腺癌的表达数据集,利用R软件筛选肺腺癌与正常肺组织间的差异表达基因(DEGs),使用DAVID网站对DEGs进行GO及KEGG富集分析,使用STRING及Cytoscape等工具对DEGs构建蛋白相互作用网络并筛选hub基因;利用Kaplan-Meier法对DEGs进行生存分析,并对hub基因进行ROC分析筛选诊断生物标志物,利用GSEA预测有预后价值的基因参与的信号通路;并用Cibersort软件反卷积算法分析肺腺癌中免疫细胞浸润情况。共得到肺腺癌的234个DEGs,这些基因主要参与信号转导、物质代谢、免疫反应等相关信号通路;构建PPI网络筛选出的20个hub基因中8个存在预后价值(CCNA2、DLGAP5、HMMR、MMP1、MMP9、MMP13、SPP1、TOP2A),ROC分析中DLGAP5、SPP1值分别是0.703、0.706;DLGAP5、SPP1基因表达水平与肺腺癌组织浆细胞、未活化的CD4⁺记忆细胞、调节T细胞、巨噬细胞M0、M1、M2及中性粒细胞浸润密切相关(P＜0.05)。肺腺癌中DLGAP5、SPP1具有较高诊断价值且参与肺腺癌组织免疫细胞浸润;DLGAP5、SPP1基因可作为肺腺癌诊断的生物标志物,可为肺腺癌的靶向治疗研究提供新思路。     

基于生物信息学分析的宫颈鳞状细胞癌预后相关基因的筛选

为了分析宫颈鳞状细胞癌(cervical squamous cell carcinoma, CESC)与正常组织中的差异表达基因(differentially expressed genes, DEGs),鉴定与CESC预后相关的关键基因,从GEO和TCGA数据库下载CESC的基因表达谱数据,利用R软件筛选CESC组织与正常组织中的DEGs,并对这些DEGs开展功能和通路富集分析;然后构建蛋白质-蛋白质相互作用(protein-protein interaction, PPI)网络,筛选出关键(hub)基因;最后对hub基因进行LASSO COX回归及总体生存率(overall survival, OS)分析。研究共筛选出167个DEGs,这些基因主要涉及染色体分离、DNA复制等生物过程,介导染色质结合、G蛋白偶联受体结合等分子功能,富集于染色体区域、纺锤体和MCM复合体。GSEA分析结果显示,富集的通路主要涉及DNA复制和细胞周期信号通路。此外,从PPI网络中筛选出20个hub基因, LASSO COX回归结果显示MAD2L1、ZWINT、RRM2、TTK、CDC6、PBK、TOP2A、KIF11、KIF20A、NCAPG、NUSAP1、CCNB1及CDK1与CESC患者的预后相关; Kaplan-Meier曲线显示, ZWINT、DTL、CCNB1、CDC6、TOP2A、CDK1、PBK、RFC4及NUSAP1的m RNA表达水平与CESC患者生存预后相关。本研究结果表明, ZWINT、CDC6、PBK、TOP2A、NUSAP1、CCNB1和CDK1为CESC的预后关键基因,为阐明CESC的分子机制提供了理论依据。     

胰腺癌中CDK1的表达与预后的生物信息学分析

为探讨胰腺癌的发病机制并为胰腺癌的防治提供生物信息学依据,用GEO2R在线工具分析GSE16515中胰腺癌患者肿瘤组织和相应正常组织的差异表达基因(differentially expressed genes, DEGs),通过DAVID数据库对DEGs进行GO分析和KEGG通路富集分析,然后通过STRING数据库构建蛋白质相互作用(protein-protein interaction, PPI)网络,用Cytoscape软件进行关键基因(hub基因)筛选和功能模块分析,并在GEPIA数据库对hub基因进行验证,用CCLE数据库检测靶基因在胰腺癌组织及细胞系中的表达水平。分析结果显示胰腺癌中筛选出的376个DEGs主要涉及细胞周期、p53信号通路、蛋白质消化吸收、ECM-受体相互作用、PI3K-Akt信号通路、血小板激活信号通路。GEPIA数据库验证结果显示10个hub基因均在胰腺癌组织中高表达,其中8个hub基因与胰腺癌患者的不良预后有关。CCLE数据库检测结果显示周期蛋白依赖性激酶1 (cyclin-dependent kinase 1, CDK1)在胰腺癌组织和细胞中均有较高的表达水平。本研究结果表明CDK1可能与胰腺癌的发生发展最为相关,为进一步探究胰腺癌的发病机制提供了生物信息学依据。     

生物信息学分析筛选结直肠癌靶基因及评估预后价值

为寻找与结直肠癌发展和预后相关的潜在关键基因及信号通路。从美国国立信息中心NCBI的GEO数据库获得结直肠癌基因表达数据集GSE106582,通过PCA对样本进行分组,利用GEO2R进行综合分析,筛选结直肠癌与癌旁对照组的差异表达基因;通过DAVID在线工具对差异表达基因进行GO本体分析和KEGG通路富集分析,初步分析差异表达基因的生物学作用;基于STRING数据库对差异表达基因进行蛋白质相互作用网络分析,利用Cytoscape软件进行可视化并筛选关键基因;用生存分析和ROC曲线诊断对关键基因进行鉴定并通过数据集GSE21510进行验证。共鉴定出199个差异表达基因,其中53个为上调基因,146个为下调基因;上调的差异表达基因主要富集在与胶原蛋白分解代谢过程、细胞外基质分解、细胞外基质受体相互作用和PI3K/AKT信号通路等生物学过程;下调的差异表达基因主要富集在碳酸氢盐运输、一碳代谢过程、矿物质吸收、药物代谢-细胞色素P450和氮代谢通路等生物学过程;MCODE分析、生存分析和ROC诊断共发现3个基因分别为BGN、COL1A2和TIMP1可能与结直肠癌的发生发展有关,它们在肿瘤组织中的异常高表达与患者较差的生存期呈正相关,GSE21510的验证结果与GSE106582的分析结果相同。本研究采用生物信息学方法对CRC基因芯片数据进行挖掘,从基因水平探讨CRC潜在的发病机制、肿瘤标志物的及患者预后分子的筛选,以及可能的药物治疗靶点提供了一定的参考价值和理论基础。     

Identification of differentially expressed genes and biological characteristics of colorectal cancer by integrated bioinformatics analysis

Colorectal cancer (CRC) ranks as one of the most common malignant tumors worldwide. Its mortality rate has remained high in recent years. Therefore, the aim of this study was to identify significant differentially expressed genes (DEGs) involved in its pathogenesis, which may be used as novel biomarkers or potential therapeutic targets for CRC. The gene expression profiles of GSE21510, GSE32323, GSE89076, and GSE113513 were downloaded from the Gene Expression Omnibus (GEO) database. After screening DEGs in each GEO data set, we further used the robust rank aggregation method to identify 494 significant DEGs including 212 upregulated and 282 downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by DAVID and the KOBAS online database, respectively. These DEGs were shown to be significantly enriched in different cancer-related functions and pathways. Then, the STRING database was used to construct the protein–protein interaction network. The module analysis was performed by the MCODE plug-in of Cytoscape based on the whole network. We finally filtered out seven hub genes by the cytoHubba plug-in, including PPBP, CCL28, CXCL12, INSL5, CXCL3, CXCL10, and CXCL11. The expression validation and survival analysis of these hub genes were analyzed based on The Cancer Genome Atlas database. In conclusion, the robust DEGs associated with the carcinogenesis of CRC were screened through the GEO database, and integrated bioinformatics analysis was conducted. Our study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for CRC.     

Identification of Key Genes and Pathways Involved in the Heterogeneity of Intrinsic Growth Ability Between Neurons After Spinal Cord Injury in Adult Zebrafish

In the adult central nervous system (CNS), axon regeneration is a major hurdle for functional recovery after trauma. The intrinsic growth potential of an injured axon varies widely between neurons. The underlying molecular mechanisms of such heterogeneity are largely unclear. In the present study, the adult zebrafish dataset GSE56842 were downloaded. Differentially expressed genes (DEGs) were sorted and deeply analyzed by bioinformatics methods. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed with the DAVID. A DEGs-associated protein–protein interaction network was constructed from the STRING database and visualized with Cytoscape software. In total, 621 DEGs were identified. GO analysis showed that the biological processes of DEGs focused mainly on the Notch signaling pathway, cell differentiation and positive regulation of neuron differentiation. The molecular functions mainly included calcium-transporting ATPase activity and calcium ion binding and structural constituents of the cytoskeleton. The cellular components included the plasma membrane, spectrin, and cytoplasmic and membrane-bound vesicles. KEGG pathway analysis showed that these DEGs were mainly involved in the metabolic pathway and Notch signaling pathway, and subnetworks revealed that genes within modules were involved in the metabolic pathway, Wnt signaling pathway, and calcium signaling pathway. This study identified DEG candidate genes and pathways involved in the heterogeneity of the intrinsic growth ability between neurons after spinal cord injury in adult zebrafish, which could facilitate our understanding of the molecular mechanisms underlying axon regeneration, and these candidate genes and pathways could be therapeutic targets for the treatment of CNS injury.

     

Differences in potential key genes and pathways between primary and radiation-associated angiosarcoma of the breast

BackgroundAngiosarcoma of the breast is a high-grade malignant soft tissue tumor, it can be divided into primary and radiation-associated angiosarcoma(secondary). However, the differences between primary and secondary angiosarcomas in terms of pathogenesis, clinical behavior, early diagnosis biomarkers, genetic abnormalities, and therapeutic targets remain to be fully elucidated. At the same time, due to its rarity, most of current information relating to angiosarcoma is provided by case reports. Therefore, exploring the mechanisms of primary and secondary breast angiosarcoma have important value for the discovery of new biomarkers and research into potential therapeutic targets.MethodsThe differentially expressed genes (DEGs) between 36 cases of primary angiosarcoma and 54 cases of secondary angiosarcoma were screened. Then, the DEGs were used to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Then, a protein-protein interaction (PPI) network was constructed using the STRING database.ResultsA total of 18 DEGs were identified, of which 13 were upregulated and 5 were downregulated in secondary breast angiosarcoma. The GO enrichment analysis showed that the DEGs were most enriched in metabolism, energy pathways, and protein metabolism in biological processes. The enriched signaling pathways of DEGs were the transforming growth factor-β (TGF-β), Wnt, Hippo and PI3K-Akt signaling pathways. Then, the PPI network was conducted and hub genes were identified and they were involved in thyroid hormone, Hippo and other signaling pathways.ConclusionThis study lay the foundation for the discovery of effective and reliable molecular biomarkers and essential therapeutic targets for these malignancies.     

Integrative analyses of genes associated with idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF), characterized by irreversible scarring and progressive destruction of the lung tissue, is one of the most common types of idiopathic interstitial pneumonia worldwide. However, there are no reliable candidates for curative therapies. Hence, elucidation of the mechanisms of IPF genesis and exploration of potential biomarkers and prognostic indicators are essential for accurate diagnosis and treatment of IPF. Recently, efficient microarray and bioinformatics analyses have promoted an understanding of the molecular mechanisms of disease occurrence and development, which is necessary to explore genetic alternations and identify potential diagnostic biomarkers. However, high false-positive rates results have been observed based on single microarray datasets. In the current study, we performed a comprehensive analysis of the differential expression, biological functions, and interactions of IPF-related genes. Three publicly available microarray datasets including 54 IPF samples and 34 normal samples were integrated by performing gene set enrichment analysis and analyzing differentially expressed genes (DEGs). Our results identified 350 DEGs genetically associated with IPF. Gene ontology analyses revealed that the changes in the modules were mostly enriched in the positive regulation of smooth muscle cell proliferation, positive regulation of inflammatory responses, and the extracellular space. Kyoto encyclopedia of genes and genomes enrichment analysis of DEGs revealed that IPF involves the TNF signaling pathway, NOD-like receptor signaling pathway, and PPAR signaling pathway. To identify key genes related to IPF in the protein-protein interaction network, 20 hub genes were screened out with highest scores. Our results provided a framework for developing new pathological molecular networks related to specific diseases in silico.     

Identification of core genes and outcomes in hepatocellular carcinoma by bioinformatics analysis

Hepatocellular carcinoma (HCC) is the most common malignant liver disease in the world. However, the mechanistic relationships among various genes and signaling pathways are still largely unclear. In this study, we aimed to elucidate potential core candidate genes and pathways in HCC. The expression profiles GSE14520, GSE25097, GSE29721, and GSE62232, which cover 606 tumor and 550 nontumour samples, were downloaded from the Gene Expression Omnibus (GEO) database. Furthermore, HCC RNA-seq datasets were also downloaded from the Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were filtered using R software, and we performed gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using the online databases DAVID 6.8 and KOBAS 3.0. Furthermore, the protein-protein interaction (PPI) network complex of these DEGs was constructed by Cytoscape software, the molecular complex detection (MCODE) plug-in and the online database STRING. First, a total of 173 DEGs (41 upregulated and 132 downregulated) were identified that were aberrantly expressed in both the GEO and TCGA datasets. Second, GO analysis revealed that most of the DEGs were significantly enriched in extracellular exosomes, cytosol, extracellular region, and extracellular space. Signaling pathway analysis indicated that the DEGs had common pathways in metabolism-related pathways, cell cycle, and biological oxidations. Third, 146 nodes were identified from the DEG PPI network complex, and two important modules with a high degree were detected using the MCODE plug-in. In addition, 10 core genes were identified, TOP2A, NDC80, FOXM1, HMMR, KNTC1, PTTG1, FEN1, RFC4, SMC4, and PRC1. Finally, Kaplan-Meier analysis of overall survival and correlation analysis were applied to these genes. The abovementioned findings indicate that the identified core genes and pathways in this bioinformatics analysis could significantly enrich our understanding of the development and recurrence of HCC; furthermore, these candidate genes and pathways could be therapeutic targets for HCC treatment.     

小儿脓毒症休克相关基因的筛选及网络构建

为探究脓毒症休克与SIRS的差异表达基因及网络的构建,筛选潜在的核心基因,从GEO数据库下载相关基因表达谱GSE26378,数据分为脓毒症休克与SIRS各29个样本,通过在线软件GCBI对其进行标准化及差异基因筛选;对差异基因进行GO分析;基于KEGG进行功能通路分析以及基因信号网络分析;差异基因共表达网络分析。结果表明:两组中总共有1 456个基因被识别为差异基因(P0.05),与SIRS组相比,脓毒症休克组中有条859条下调基因,597条上调基因。GO功能富集分析显示差异基因主要参与了细胞周期、细胞免疫、细胞代谢。KEGG功能通路分析显示差异基因主要参与了MAPK信号通路、P53信号通路、wnt信号通路、细胞凋亡信号通路,细胞周期受体信号通路等。共表达分析发现基因CCNB1、NUSAP1、OIP5、SHCBP1、ZWINT、TOP2A、DLGAP5等位于网络中央部位,而基因信号网络分析发现基因PLCB1、PIK3CA、STAT3、CAMK2D、PRKCB、CREB1位于网络核心。基因芯片分析有助于发现脓毒症休克与SIRS患儿外周血单核细胞在转录组学上的改变,而生物信息学网络分析有助于发现潜在的靶点。     

Identification of key candidate genes and pathways in multiple myeloma by integrated bioinformatics analysis

Multiple myeloma (MM) is a common hematologic malignancy for which the underlying molecular mechanisms remain largely unclear. This study aimed to elucidate key candidate genes and pathways in MM by integrated bioinformatics analysis. Expression profiles GSE6477 and GSE47552 were obtained from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) with p < .05 and [logFC] > 1 were identified. Functional enrichment, protein–protein interaction network construction and survival analyses were then performed. First, 51 upregulated and 78 downregulated DEGs shared between the two GSE datasets were identified. Second, functional enrichment analysis showed that these DEGs are mainly involved in the B cell receptor signaling pathway, hematopoietic cell lineage, and NF-kappa B pathway. Moreover, interrelation analysis of immune system processes showed enrichment of the downregulated DEGs mainly in B cell differentiation, positive regulation of monocyte chemotaxis and positive regulation of T cell proliferation. Finally, the correlation between DEG expression and survival in MM was evaluated using the PrognoScan database. In conclusion, we identified key candidate genes that affect the outcomes of patients with MM, and these genes might serve as potential therapeutic targets.     

Genetic Basis of Bilateral Macronodular Hyperplasia

Objective: To review the genetic basis of bilateral macronodular hyperplasia (BMAH).Methods: Case presentation, review of literature, table, and bullet point conclusions.Results: BMAH, also known as adrenocorticotropic hormone (ACTH)-independent macronodular hyperplasia (AIMH), can cause Cushing syndrome or mild hypercortisolism. Recent studies have demonstrated that hyperplastic tissue reproduces ectopic ACTH, implying that BMAH is the more proper term, as the syndrome is not ACTH-independent. BMAH was thought to be sporadic, but recent data have shown that there is likely a genetic component in the majority of cases. Mutations in ARMC5, a putative suppressor gene, have been found in many familial cases of BMAH and are thought to be responsible for the disorder. As these nodules inefficiently produce cortisol, large nodules are required to produce a clinical syndrome. ARMC5 likely requires a second somatic mutation to become clinically apparent. Clinical manifestations are not generally noted until the fifth to sixth decades of life.Conclusion: BMAH is an underrecognized genetic condition that can lead to Cushing syndrome and should be screened for in patients and susceptible family members.Abbreviations: ACTH = adrenocorticotropic hormone AIMAH = ACTH-independent macronodular adrenal hyperplasia ARMC5 = armadillo-repeat containing 5 BMAH = bilateral macronodular adrenal hyperplasia CAH = congenital adrenal hyperplasia CT = computed tomography MEN1 = multiple endocrine neoplasia 1 UFC = urinary free cortisol