Acute Myeloid Leukemia with Translocation t(8;16) Demonstrates Specific Cytomorphological, Cytogenetic, and Gene Expression Characteristics and Can Clearly Be Discriminated from Other AML with Balanced Translocations.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 2897-2897
Author(s):  
Torsten Haferlach ◽  
Helmut Loeffler ◽  
Alexander Kohlmann ◽  
Martin Dugas ◽  
Wolfgang Hiddemann ◽  
...  
Keyword(s):  
Gene Expression ◽  
Who Classification ◽  
De Novo ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Support Vector ◽  
Gene Expression Patterns ◽  
Down Regulation ◽  
Specific Expression ◽  
Balanced Translocations

Abstract Balanced chromosomal rearrangements leading to fusion genes on the molecular level define distinct biological subsets in AML. The four balanced rearrangements (t(15;17), t(8;21), inv(16), and 11q23/MLL) show a close correlation to cytomorphology and gene expression patterns. We here focused on seven AML with t(8;16)(p11;p13). This translocation is rare (7/3515 cases in own cohort). It is more frequently found in therapy-related AML than in de novo AML (3/258 t-AML, and 4/3287 de novo, p=0.0003). Cytomorphologically, AML with t(8;16) is characterized by striking features: In all 7 cases the positivity for myeloperoxidase on bone marrow smears was >70% and intriguingly, in parallel >80% of blast cells stained strongly positive for non-specific esterase (NSE) in all cases. Thus, these cases can not be classified according to FAB categories. These data suggest that AML-t(8;16) arise from a very early stem cell with both myeloid and monoblastic potential. Furthermore, we detected erythrophagocytosis in 6/7 cases that was described as specific feature in AML with t(8;16). Four pts. had chromosomal aberrations in addition to t(8;16), 3 of these were t-AML all showing aberrations of 7q. Survival was poor with 0, 1, 1, 2, 20 and 18+ (after alloBMT) mo., one lost to follow-up, respectively. We then analyzed gene expression patterns in 4 cases (Affymetrix U133A+B). First we compared t(8;16) AML with 46 AML FAB M1, 41 M4, 9 M5a, and 16 M5b, all with normal karyotype. Hierachical clustering and principal component analyses (PCA) revealed that t(8;16) AML were intercalating with FAB M4 and M5b and did not cluster near to M1. Thus, monocytic characteristics influence the gene expression pattern stronger than myeloid. Next we compared the t(8;16) AML with the 4 other balanced subtypes according to the WHO classification (t(15;17): 43; t(8;21): 40; inv(16): 49; 11q23/MLL-rearrangements: 50). Using support vector machines the overall accuracy for correct subgroup assignment was 97.3% (10-fold CV), and 96.8% (2/3 training and 1/3 test set, 100 runs). In PCA and hierarchical cluster analysis the t(8;16) were grouped in the vicinity of the 11q23 cases. However, in a pairwise comparison these two subgroups could be discriminated with an accuracy of 94.4% (10-fold CV). Genes with a specific expression in AML-t(8;16) were further investigated in pathway analyses (Ingenuity). 15 of the top 100 genes associated with AML-t(8;16) were involved in the CMYC-pathway with up regulation of BCOR, COXB5, CDK10, FLI1, HNRPA2B1, NSEP1, PDIP38, RAD50, SUPT5H, TLR2 and USP33, and down regulation of ERG, GATA2, NCOR2 and RPS20. CEBP beta, known to play a role in myelomonocytic differentiation, was also up-regulated in t(8;16)-AML. Ten additional genes out of the 100 top differentially expressed genes were also involved in this pathway with up-regulation of DDB2, HIST1H3D, NSAP1, PTPNS1, RAN, USP4, TRIM8, ZNF278 and down regulation of KIT and MBD2. In conclusion, AML with t(8;16) is a specific subtype of AML with unique characteristics in morphology and gene expression patterns. It is more frequently found in t-AML, outcome is inferior in comparison to other AML with balanced translocations. Due to its unique features, it is a candidate for inclusion into the WHO classification as a specific entity.

MDS and AML Are Closely Related Diseases: Gene Expression Patterns Reveal Clear Similarities with Respect to Karyotypes and Are Less Related to the Bone Marrow Blast Percentages.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 471-471
Author(s):  
Torsten Haferlach ◽  
Wolfgang Kern ◽  
Alexander Kohlmann ◽  
Martin Dugas ◽  
Sylvia Merk ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiling ◽  
Genetic Background ◽  
Who Classification ◽  
Expression Patterns ◽  
Support Vector ◽  
Gene Expression Patterns ◽  
Vector Machines ◽  
Blast Count ◽  
The Common

Abstract MDS and AML are discriminated by percentages of blasts in the bone marrow (BM) according to the FAB as well as to the WHO classification. However, thresholds are arbitrary and demonstrate only a limited reproducibility in interlaboratory testings. Thus, other parameters have been assessed to discriminate these entities with respect to diagnosis and prognosis. In particular, in the majority of cases common karyotype aberrations have been observed between MDS and AML which have a higher prognostic impact than blast percentages. We applied gene expression profiling (U133A+B, Affymetrix) in 70 MDS and 238 AML cases. In accordance with the WHO classification we excluded cases with balanced translocations (i.e. t(8;21), t(15;17), inv(16), or 11q23) which are classified as AML irrespective of BM blast percentage. First we aimed at identifying genes of which the expression correlated to blast count (Spearman correlation). Out of the top 50 genes this analysis revealed only the FLT3 gene which showed a higher expression in cases with high blast count, while 12 genes with a higher expression in cases with lower blast counts were identified (ANXA3, ARG1, CAMP, CD24, CEACAM1, CEACAM6, CEACAM8, CRISP3, KIAA0922, LCN2, MMP9, STOM). Most of the latter genes are expressed in mature granulocytes and are involved in differentiation and apoptosis. In a second step we performed class prediction using support vector machines (SVM) to separate MDS and AML according to blast percentages as defined in the WHO classification (<5%: RA and 5q- syndrome; 5–9%: RAEB-1; 10–19%: RAEB-2; >19% AML). Using 10-fold cross validation and support vector machines the overall prediction accuracy was only 80%. In detail, 230/238 AML cases were correctly assigned to the AML group while 8 cases were classified as MDS RAEB-2. However, none of the RA, 5q- syndrome and RAEB-1 cases were correctly assigned to their groups, respectively, but were either classified as AML or RAEB-2. Furthermore, only 16 of 38 RAEB-2 cases were correctly predicted, while the 20 remaining cases were assigned to the AML group. Thus, no clear gene expression patterns were identified which correlated with AML and MDS subtypes according to WHO classification. Taking the common genetic background observed in MDS and AML into account, both entities were categorized in a third step according to cytogenetics and classified based on their gene expression profiles. In order to assess the impact of the common genetic background, the largest cytogenetically defined subgroups were compared to each other, i.e. AML and MDS with normal karyotype and with complex aberrant karyotype. Intriguingly, while correct classification of AML or MDS was found in 91%, classification into the correct cytogenetic groups was achieved in 95%. Consequently, all cases were devided into the two groups, complex aberrant karyotype (n=60) and other or no aberrations (n=248) irrespective of AML or MDS. A classification into these groups also yielded an accuracy of 93%. Our data suggests that gene expression profiling reveales the biology of MDS or AML to highly correlate with cytogenetics and less with the percentages of BM blasts. These results strengthen the need for a revision of the current MDS and AML classification centering now genetic abnormalities, which may also be used for clinical decisions.

AML with Translocation T(8;16) Shows Unique Cytomorphological, Cytogenetic, Molecular, and Prognostic Features and Therefore Qualifies as An Own Entity According to WHO Criteria.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 1197-1197
Author(s):  
Alexander Kohlmann ◽  
Martin Dugas ◽  
Hans-Ulrich Klein ◽  
Christian Ruckert ◽  
Wolfgang Kern ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
De Novo ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Gene Expression Patterns ◽  
Specific Gene ◽  
Differentiation Potential ◽  
Who Criteria ◽  
Prognostic Features

Abstract Balanced chromosomal rearrangements define distinct biological subsets in acute myeloid leukemia (AML). It is recognized that recurrent balanced aberrations, such as t(15;17), t(8;21), inv(16), and 11q23/MLL translocations, show a close correlation to cytomorphology and also harbor specific gene expression signatures. We here present a cohort of 13 AML cases with t(8;16)(p11;p13). This translocation is rare with only 13 cases (6 males, 7 females) diagnosed from our overall cohort of 6124 cases of AML over recent years, and is more frequently found in therapy-related AML than in de novo AML (7/438 t-AML, and 6/5686 de novo, p=0.00001). Prognosis was poor with median overall survival of 4.7 months. Five patients deceased within the first month after diagnosis. AML with t(8;16) is characterized by striking cytomorphologic features: In all 13 cases the positivity for myeloperoxidase (MPO) on bone marrow smears was >30% (median: 85%) and intriguingly, in parallel also >40% (median: 88%) of blast cells stained strongly positive for non-specific esterase (NSE) in the same cell, suggesting that AML with t(8;16) arise from a very early stem cell with both myeloid and monoblastic differentiation potential. Therefore, AML with t(8;16) cases can not be classified according to standard FAB categories. Morphologically we also detected erythrophagocytosis in 7/13 cases, a specific feature in AML with t(8;16) that was previously described. With respect to cytogenetics, 6/13 patients had t(8;16)(p11;p13) as sole abnormality. 7/13 patients demonstrated additional non-recurrent abnormalities, 4 cases with single additional aberrations, and 3 cases with two or more additional aberrations. Molecular analyses detected the MYST3- CREBBP fusion transcript in all cases tested (12/12). We then compared gene expression patterns in 7 cases of AML with t(8;16) to: (i) AML FAB subtypes M1 and M4/5 with strong MPO or NSE with normal karyotype and to (ii) distinct AML subtypes with balanced chromosomal aberrations according to WHO classification. In a first series using Affymetrix HG-U133A+B microarrays 4 cases of AML with t(8;16) were compared to FAB M1 (n=46), M4 (n=41), M5a (n=9), and M5b (n=16). Hierarchical clustering and principal component analyses revealed that AML with t(8;16) were intercalating rather with FAB subtypes M4 and M5b and did not cluster near to FAB M1, although strong positivity for MPO was seen in all t(8;16) cases. Thus, monocytic characteristics influence the gene expression pattern stronger than myeloid features. When further compared to AML WHO subtypes t(15;17) (n=43), t(8;21) (n=43), inv(16) (n=49), and 11q23/MLL (n=50), AML with t(8;16) samples were repeatedly grouped in the vicinity of the 11q23/MLL cases. This can be explained by a similar expression of genes such as EAF2, HOXA9, HOXA10, PRKCD, or HNMT. Yet, in a subsequent pairwise comparison AML with t(8;16) could also be clearly discriminated from 11q23/MLL with differentially expressed genes including CAPRIN1, RAN, SMARCD2, LRRC41, or H2BFS, higher expressed in AML with t(8;16) and SOCS2, PRAME, RUNX3, or TPT1, lower expressed in AML with t(8;16), respectively. Moreover, the respective FAB-type or WHO-type signatures were validated on a separate cohort of patients (n=3 AML with t(8;16); n=107 other AML subtypes as above), all prospectively analyzed with the successor HG-U133 Plus 2.0 microarray. Again, in direct comparison to FAB-type or WHO-type cases, dominant and unique gene expression patterns were seen for AML with t(8;16), confirming the molecular distinctiveness of this rare AML entity. Using a classification algorithm we were able to correctly predict all AML with t(8;16) cases by their gene expression pattern. This accuracy was observed not only for both FAB-type and WHO-type signatures, but also correctly classified the cases across the different patient cohorts and microarray designs. In conclusion, AML with t(8;16) is a specific subtype of AML with very poor prognosis that often presents as treatment-related AML and with particular characteristics not only in morphology and clinical profile, but also on a molecular level. Due to these unique features, it qualifies as a specific recurrent entity according to WHO criteria.

scholarly journals Unique Gene Expression Patterns in Human T-cell Lines Generated from Multiple Sclerosis Patients by Stimulation with a Synthetic MOG Peptide

2005 ◽  
Vol 12 (3) ◽  
pp. 203-209 ◽  
Author(s):  
Mathilda Mandel ◽  
Michael Gurevich ◽  
Gad Lavie ◽  
Irun R. Cohen ◽  
Anat Achiron
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
T Cell ◽  
Cell Lines ◽  
Healthy Subjects ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Gene Expression Patterns ◽  
T Cell Lines ◽  
Myelin Antigens

Multiple sclerosis (MS) is an autoimmune disease where T-cells activated against myelin antigens are involved in myelin destruction. Yet, healthy subjects also harbor T-cells responsive to myelin antigens, suggesting that MS patient-derived autoimmune T-cells might bear functional differences from T-cells derived from healthy individuals. We addressed this issue by analyzing gene expression patterns of myelin oligodendrocytic glycoprotein (MOG) responsive T-cell lines generated from MS patients and healthy subjects. We identified 150 transcripts that were differentially expressed between MS patients and healthy controls. The most informative 43 genes exhibited >1.5-fold change in expression level. Eighteen genes were up-regulated including BCL2, lifeguard, IGFBP3 and VEGF. Twenty five genes were down-regulated, including apoptotic activators like TNF and heat shock protein genes. This gene expression pattern was unique to MOG specific T-cell lines and was not expressed in T-cell lines reactive to tetanus toxin (TTX). Our results indicate that activation in MS that promotes T-cell survival and expansion, has its own state and that the unique gene expression pattern that characterize autoreactive T-cells in MS represent a constellation of factors in which the chronicity, timing and accumulation of damage make the difference between health and disease.

Biological Image Analysis via Matrix Approximation

2011 ◽  
pp. 166-170
Author(s):  
Jieping Ye ◽  
Ravi Janardan ◽  
Sudhir Kumar
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Controlled Vocabulary ◽  
Gene Expression Patterns ◽  
Image Content ◽  
Gene Expressions ◽  
Microarray Gene Expression ◽  
Individual Gene

Understanding the roles of genes and their interactions is one of the central challenges in genome research. One popular approach is based on the analysis of microarray gene expression data (Golub et al., 1999; White, et al., 1999; Oshlack et al., 2007). By their very nature, these data often do not capture spatial patterns of individual gene expressions, which is accomplished by direct visualization of the presence or absence of gene products (mRNA or protein) (e.g., Tomancak et al., 2002; Christiansen et al., 2006). For instance, the gene expression pattern images of a Drosophila melanogaster embryo capture the spatial and temporal distribution of gene expression patterns at a given developmental stage (Bownes, 1975; Tsai et al., 1998; Myasnikova et al., 2002; Harmon et al., 2007). The identification of genes showing spatial overlaps in their expression patterns is fundamentally important to formulating and testing gene interaction hypotheses (Kumar et al., 2002; Tomancak et al., 2002; Gurunathan et al., 2004; Peng & Myers, 2004; Pan et al., 2006). Recent high-throughput experiments of Drosophila have produced over fifty thousand images (http://www. fruitfly.org/cgi-bin/ex/insitu.pl). It is thus desirable to design efficient computational approaches that can automatically retrieve images with overlapping expression patterns. There are two primary ways of accomplishing this task. In one approach, gene expression patterns are described using a controlled vocabulary, and images containing overlapping patterns are found based on the similarity of textual annotations. In the second approach, the most similar expression patterns are identified by a direct comparison of image content, emulating the visual inspection carried out by biologists [(Kumar et al., 2002); see also www.flyexpress.net]. The direct comparison of image content is expected to be complementary to, and more powerful than, the controlled vocabulary approach, because it is unlikely that all attributes of an expression pattern can be completely captured via textual descriptions. Hence, to facilitate the efficient and widespread use of such datasets, there is a significant need for sophisticated, high-performance, informatics-based solutions for the analysis of large collections of biological images.

Protein and gene expression patterns of endo-β-mannanase following germination of rice

2008 ◽  
Vol 18 (3) ◽  
pp. 139-149 ◽  
Author(s):  
Yanfang Ren ◽  
J. Derek Bewley ◽  
Xiaofeng Wang
Keyword(s):  
Gene Expression ◽  
Blot Analysis ◽  
De Novo ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Aleurone Layer ◽  
Starchy Endosperm ◽  
Rice Grains ◽  
Inactive Form ◽  
Mannanase Activity

AbstractThe rice (Oryza sativa L.) cv. Taichung 65, a japonica subspecies, was used to characterize the isoform, protein and gene expression patterns of endo-β-mannanase during and after seed germination. Activity assays and isoform analyses of whole grains or seed parts (scutellum, aleurone layer and starchy endosperm) revealed that seeds began to express endo-β-mannanase activity at 48 h from the start of imbibition at 25°C, after the completion of germination of most seeds. Three isoforms of endo-β-mannanase (pI 8.86, pI 8.92 and pI 8.98) were detected in the aleurone layer and starchy endosperm, but only two (pI 8.86 and pI 8.92) were present in the scutellum. The endo-β-mannanase in the starchy endosperm was mainly from the aleurone layer. Western blot analysis, using a tomato anti-endo-β-mannanase antibody, indicated that an endo-β-mannanase protein was present in an inactive form in dry grains. The amount of this protein decreased in the scutellum, but increased in the aleurone layer during and after germination. Thus, the increase in endo-β-mannanase activity in rice grains may be due to the activation of extant proteins and/or the de novo synthesis of the enzyme. Northern blot analysis showed that four putative rice endo-β-mannanase genes (OsMAN1, OsMAN2, OsMAN6 and OsMANP) were expressed in germinating and germinated rice grains. However, OsMANP was not expressed in the scutellum. The amount of OsMAN6 mRNA decreased after the completion of germination and paralleled the decline in endo-β-mannanase protein. In the aleurone layer, the increase of OsMAN2, OsMAN6 and OsMANP mRNA was prior to the increase of endo-β-mannanase protein.

scholarly journals BEST: A Novel Computational Approach for Comparing Gene Expression Patterns From Early Stages of Drosophila melanogaster Development

Genetics ◽  
2002 ◽  
Vol 162 (4) ◽  
pp. 2037-2047
Author(s):  
Sudhir Kumar ◽  
Karthik Jayaraman ◽  
Sethuraman Panchanathan ◽  
Rajalakshmi Gurunathan ◽  
Ana Marti-Subirana ◽  
...  
Keyword(s):  
Gene Expression ◽  
Developmental Biology ◽  
Drosophila Melanogaster ◽  
Expression Pattern ◽  
Early Stage ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Computational Approach ◽  
Gene Expression Patterns ◽  
Early Stages

Abstract Embryonic gene expression patterns are an indispensable part of modern developmental biology. Currently, investigators must visually inspect numerous images containing embryonic expression patterns to identify spatially similar patterns for inferring potential genetic interactions. The lack of a computational approach to identify pattern similarities is an impediment to advancement in developmental biology research because of the rapidly increasing amount of available embryonic gene expression data. Therefore, we have developed computational approaches to automate the comparison of gene expression patterns contained in images of early stage Drosophila melanogaster embryos (prior to the beginning of germ-band elongation); similarities and differences in gene expression patterns in these early stages have extensive developmental effects. Here we describe a basic expression search tool (BEST) to retrieve best matching expression patterns for a given query expression pattern and a computational device for gene interaction inference using gene expression pattern images and information on the associated genotypes and probes. Analysis of a prototype collection of Drosophila gene expression pattern images is presented to demonstrate the utility of these methods in identifying biologically meaningful matches and inferring gene interactions by direct image content analysis. In particular, the use of BEST searches for gene expression patterns is akin to that of BLAST searches for finding similar sequences. These computational developmental biology methodologies are likely to make the great wealth of embryonic gene expression pattern data easily accessible and to accelerate the discovery of developmental networks.

scholarly journals GEsture: an online hand-drawing tool for gene expression pattern search

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4927 ◽  
Author(s):  
Chunyan Wang ◽  
Yiqing Xu ◽  
Xuelin Wang ◽  
Li Zhang ◽  
Suyun Wei ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Expression Pattern ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Pattern Search ◽  
Clustering Methods ◽  
Specific Expression ◽  
Time Series Gene Expression ◽  
Specific Expression Pattern

Gene expression profiling data provide useful information for the investigation of biological function and process. However, identifying a specific expression pattern from extensive time series gene expression data is not an easy task. Clustering, a popular method, is often used to classify similar expression genes, however, genes with a ‘desirable’ or ‘user-defined’ pattern cannot be efficiently detected by clustering methods. To address these limitations, we developed an online tool called GEsture. Users can draw, or graph a curve using a mouse instead of inputting abstract parameters of clustering methods. GEsture explores genes showing similar, opposite and time-delay expression patterns with a gene expression curve as input from time series datasets. We presented three examples that illustrate the capacity of GEsture in gene hunting while following users’ requirements. GEsture also provides visualization tools (such as expression pattern figure, heat map and correlation network) to display the searching results. The result outputs may provide useful information for researchers to understand the targets, function and biological processes of the involved genes.

scholarly journals Transcriptome Sequencing of Trichoderma Koningiopsis Strain Tk1 to Identify and Analyze Differential Gene Expression Patterns

2020 ◽  
Author(s):  
Mei Luo ◽  
Zhangyong Dong ◽  
Yongxin Shu ◽  
Mobing Chen
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Differential Gene Expression ◽  
De Novo ◽  
Transcriptome Assembly ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Antimicrobial Effect ◽  
Carbohydrate Active Enzymes ◽  
Differential Gene

Abstract Background: Trichoderma koningiopsis strain Tk1 shows good biocontrol potential. However, its biocontrol function may differ under different conditions. The objective of this study is to elucidate the biological and transcriptome differences of T. koningiopsis Tk1 under different media. Results: In this study, the mycelium weight and sporulation of T. koningiopsis Tk1 was found to differ in various media. Further, the Tk1 strain inhibited the growth of the pathogen Fusarium oxysporum in the three media tested. Fries3, PD, and PS were collected for RNA sequencing of Tk1 mycelia to identify the genes that are differentially expressed genes (DEGs) between Tk1 grown on different media. De novo transcriptome assembly resulted in identification of 14,208 unigenes. The differential gene expression pattern was more similar between the Fries3 and PS samples, whereas PD samples showed a different expression pattern. The DEGs were enriched in some metabolic and biosynthetic pathways. Additional analysis of the DEGs identified a set of carbohydrate-active enzymes that are upregulated or downregulated under different conditions.Conclusions: These results indicate that the Tk1 strain cultured in Fires3 and PS mediums can produce specific metabolic and carbohydrate-active enzymes to enhance their antimicrobial effect, providing a foundation for the subsequent mining of specific genes.

scholarly journals In Silico Analysis of the Gene Expression Patterns between Aldosterone-Producing Adenoma and Nonfunctional Adrenocortical Adenoma

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yongfa Dai ◽  
Jing Li ◽  
Hong Wen ◽  
Jie Liu ◽  
Jianling Li
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Primary Aldosteronism ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Gene Expression Patterns ◽  
Pathophysiological Mechanism ◽  
Adrenocortical Adenoma ◽  
Hub Genes ◽  
Aldosterone Producing Adenoma

Primary aldosteronism is the most common form of secondary hypertension, and aldosteronoma makes up a significant proportion of primary aldosteronism cases. Aldosteronoma is also called aldosterone-producing adenoma (APA). Although there have been many studies about APA, the pathogenesis of this disease is not yet fully understood. In this study, we aimed to find out the difference of gene expression patterns between APA and nonfunctional adrenocortical adenoma (NFAA) using a weighted gene coexpression network (WGCNA) and differentially expressed gene (DEG) analysis; only the genes that meet the corresponding standards of both methods were defined as real hub genes and then used for further analysis. Twenty-nine real hub genes were found out, most of which were enriched in the phospholipid metabolic process. WISP2, S100A10, SSTR5-AS1, SLC29A1, APOC1, and SLITRK4 are six real hub genes with the same gene expression pattern between the combined and validation datasets, three of which indirectly or directly participate in lipid metabolism including WISP2, S100A10, and APOC1. According to the gene expression pattern of DEGs, we speculated five candidate drugs with potential therapeutic value for APA, one of which is cycloheximide, an inhibitor for phospholipid biosynthesis. All the evidence suggests that phospholipid metabolism may be an important pathophysiological mechanism for APA. Our study provides a new perspective regarding the pathophysiological mechanism of APA and offers some small molecules that may possibly be effective drugs against APA.

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