Identification of differentially expressed genes in pituitary adenomas by integrating analysis of microarray data

Strategy for Identifying a Robust Metabolomic Signature Reveals the Altered Lipid Metabolism in Pituitary Adenoma

Despite the well-established connection between systematic metabolic abnormalities and the pathophysiology of pituitary adenoma (PA), current metabolomic studies have reported an extremely limited number of metabolites associated with PA. Moreover, there was very little consistency in the identified metabolite signatures, resulting in a lack of robust metabolic biomarkers for the diagnosis and treatment of PA. Herein, we performed a global untargeted plasma metabolomic profiling on PA and identified a highly robust metabolomic signature based on a strategy. Specifically, this strategy is unique in (1) integrating repeated random sampling and a consensus evaluation-based feature selection algorithm and (2) evaluating the consistency of metabolomic signatures among different sample groups. This strategy demonstrated superior robustness and stronger discriminative ability compared with that of other feature selection methods including Student's t-test, partial least-squares-discriminant analysis, support vector machine recursive feature elimination, and random forest recursive feature elimination. More importantly, a highly robust metabolomic signature comprising 45 PA-specific differential metabolites was identified. Moreover, metabolite set enrichment analysis of these potential metabolic biomarkers revealed altered lipid metabolism in PA. In conclusion, our findings contribute to a better understanding of the metabolic changes in PA and may have implications for the development of diagnostic and therapeutic approaches targeting lipid metabolism in PA. We believe that the proposed strategy serves as a valuable tool for screening robust, discriminating metabolic features in the field of metabolomics.

Integrated bioinformatics approaches and expression assays identified new markers in pituitary adenomas

Pituitary adenomas (PA) include about one third of primary central nervous tumors in adolescent and young adult. Despite extensive research, the underlying mechanism of PA tumorigenesis is still unknown. In the present study, through bioinformatics analysis of a PA-related dataset downloaded from GEO database, we attempted to identify pair(s) of lncRNA/target mRNA whose expression changes may be involved in the tumorigenesis of PAs. For this end, we evaluated expression of a set of bioinformatically obtained genes in 46 PA tissues against adjacent non-tumor pituitary tissues. The bioinformatics step led to selection of four genes for validation through expression assays. Expression levels of HIF1A and MAPK1 were increased in NFPA tissues (P < 0.0001 and =0.0042, respectively). Expression level of BANCR was significantly decreased in tumor tissues (P < 0.0001). However, expression of STAT3 was not meaningfully different between the two tissue types (P = 0.56). Since there was no significant correlation between MAPK1 and BANCR expressions in either tumor or adjacent normal tissues, the regulatory effect of BANCR on MAPK1 was not confirmed. In conclusion, this study offers information about deregulation of bioinformatically identified genes in PA tumors and indicates that further studies in this field is needed to understand the involved molecular mechanisms.

Relationship between osteoporosis and Cushing syndrome based on bioinformatics

BACKGROUND

Many clinical studies have reported a relatively high incidence of osteoporosis and fragility fractures in patients with Cushing syndrome (CS). However, few papers have investigated osteoporosis and CS in terms of pathogenesis, so this study explores the association between the 2 and predicts upstream micro-ribonucleic acids (miRNAs) through bioinformatics, which provides potential targets for simultaneous pharmacological interventions in both diseases and also provides a basis for pathological screening.

METHODS

We used Genecards, Online Mendelian Inheritance in Man and Therapeutic Target Database databases to screen the targets of osteoporosis and Cushing syndrome; import target genes to Database for Annotation, Visualization and Integrated Discovery for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis; the intersecting genes were uploaded to Search Tool for the Retrieval of Genes and Genomes database to construct protein-protein interaction network; Cytoscape software was used to screen core genes, and Molecular Complex Detection module was used to analyze cluster modules; finally, the NetworkAnalyst data platform was used to predict the miRNAs that interact with core genes.

RESULTS

The core genes of osteoporosis and Cushing syndrome were insulin, tumor necrosis factor, signal transducer and activator of transcription 3 (STAT3), interleukin-6, insulin-like growth factor 1, etc. A total of 340 upstream miRNAs including hsa-let-7a-5p, hsa-mir-30a-5p and hsa-mir-125b-5p were predicted. The biological processes involved include regulating the transcription of ribonucleic acid polymerase II promoter and participating in the transduction of cytokine signaling pathways, which focus on the binding of nerve system ligand, JAK-STAT signaling pathway, Rap1 signaling pathway, PI3K-Akt signaling pathway, etc.

CONCLUSION

Osteoporosis and Cushing syndrome are closely related in terms of targets and molecular mechanisms. In this study, bioinformatics methods were used to identify their targets and mechanisms, providing potential targets for drug simultaneous regulation of the 2 diseases, and providing a new direction for exploring the relationship between diseases.

O-GlcNAcylation Is Essential for Rapid Pomc Expression and Cell Proliferation in Corticotropic Tumor Cells

Pituitary adenomas have a staggering 16.7% lifetime prevalence and can be devastating in many patients because of profound endocrine and neurologic dysfunction. To date, no clear genomic or epigenomic markers correlate with their onset or severity. Herein, we investigate the impact of the O-GlcNAc posttranslational modification in their etiology. Found in more than 7000 human proteins to date, O-GlcNAcylation dynamically regulates proteins in critical signaling pathways, and its deregulation is involved in cancer progression and endocrine diseases such as diabetes. In this study, we demonstrated that O-GlcNAc enzymes were upregulated, particularly in aggressive adrenocorticotropin (ACTH)-secreting tumors, suggesting a role for O-GlcNAcylation in pituitary adenoma etiology. In addition to the demonstration that O-GlcNAcylation was essential for their proliferation, we showed that the endocrine function of pituitary adenoma is also dependent on O-GlcNAcylation. In corticotropic tumors, hypersecretion of the proopiomelanocortin (POMC)-derived hormone ACTH leads to Cushing disease, materialized by severe endocrine disruption and increased mortality. We demonstrated that Pomc messenger RNA is stabilized in an O-GlcNAc-dependent manner in response to corticotrophin-releasing hormone (CRH). By affecting Pomc mRNA splicing and stability, O-GlcNAcylation contributes to this new mechanism of fast hormonal response in corticotropes. Thus, this study stresses the essential role of O-GlcNAcylation in ACTH-secreting adenomas' pathophysiology, including cellular proliferation and hypersecretion.

Integrated bioinformatics analysis and screening of hub genes in papillary thyroid carcinoma

BACKGROUND

With the increasing incidence of papillary thyroid carcinoma (PTC), PTC continues to garner attention worldwide; however its pathogenesis remains to be elucidated. The purpose of this study was to explore key biomarkers and potential new therapeutic targets for, PTC.

METHODS

GEO2R and Venn online software were used for screening of differentially expressed genes. Hub genes were screened via STRING and Cytoscape, followed by Gene Ontology and KEGG enrichment analysis. Finally, survival analysis and expression validation were performed using the UALCAN online software and immunohistochemistry.

RESULTS

We identified 334 consistently differentially expressed genes (DEGs) comprising 136 upregulated and 198 downregulated genes. Gene Ontology enrichment analysis results suggested that the DEGs were mainly enriched in cancer-related pathways and functions. PPI network visualization was performed and 17 upregulated and 13 downregulated DEGs were selected. Finally, the expression verification and overall survival analysis conducted using the Gene Expression Profiling Interactive Analysis Tool (GEPIA) and UALCAN showed that LPAR5, TFPI, and ENTPD1 were associated with the development of PTC and the prognosis of PTC patients, and the expression of LPAR5, TFPI and ENTPD1 was verified using a tissue chip.

CONCLUSIONS

In summary, the hub genes and pathways identified in the present study not only provide information for the development of new biomarkers for PTC but will also be useful for elucidation of the pathogenesis of PTC.

Identification of Key Candidate Genes and Chemical Perturbagens in Diabetic Kidney Disease Using Integrated Bioinformatics Analysis

Globally, nearly 40 percent of all diabetic patients develop serious diabetic kidney disease (DKD). The identification of the potential early-stage biomarkers and elucidation of their underlying molecular mechanisms in DKD are required. In this study, we performed integrated bioinformatics analysis on the expression profiles GSE111154, GSE30528 and GSE30529 associated with early diabetic nephropathy (EDN), glomerular DKD (GDKD) and tubular DKD (TDKD), respectively. A total of 1,241, 318 and 280 differentially expressed genes (DEGs) were identified for GSE30258, GSE30529, and GSE111154 respectively. Subsequently, 280 upregulated and 27 downregulated DEGs shared between the three GSE datasets were identified. Further analysis of the gene expression levels conducted on the hub genes revealed SPARC (Secreted Protein Acidic And Cysteine Rich), POSTN (periostin), LUM (Lumican), KNG1 (Kininogen 1), FN1 (Fibronectin 1), VCAN (Versican) and PTPRO (Protein Tyrosine Phosphatase Receptor Type O) having potential roles in DKD progression. FN1, LUM and VCAN were identified as upregulated genes for GDKD whereas the downregulation of PTPRO was associated with all three diseases. Both POSTN and SPARC were identified as the overexpressed putative biomarkers whereas KNG1 was found as downregulated in TDKD. Additionally, we also identified two drugs, namely pidorubicine, a topoisomerase inhibitor (LINCS ID- BRD-K04548931) and Polo-like kinase inhibitor (LINCS ID- BRD-K41652870) having the validated role in reversing the differential gene expression patterns observed in the three GSE datasets used. Collectively, this study aids in the understanding of the molecular drivers, critical genes and pathways that underlie DKD initiation and progression.

Identification of Transcriptional Metabolic Dysregulation in Subtypes of Pituitary Adenoma by Integrated Bioinformatics Analysis

BACKGROUND

Pituitary adenoma (PA) is a prevalent intracranial tumor. Metabolites differ between pituitary tumor and healthy tissues or among different tumor subtypes. However, the transcriptional changes in metabolic enzymes, which are usually seemed as targets for metabolic therapy, remain unidentified.

METHODS

Using microarray data for 160 samples from the Gene Expression Omnibus database, across the four most common tumor subtypes, we present the integrated identification of differentially expressed genes (DEGs) between tumors and controls.

RESULTS

Subtype-specific DEGs revealed 1081 prolactin tumor-specific DEGs, 437 nonfunctioning tumor-specific DEGs, and 217 common DEGs among the four subtypes. Functional enrichment showed that a lot of biological functions related to metabolism had changed. Twenty-one prolactin and twenty-three nonfunctioning tumor-specific metabolic-related DEGs are mainly involved in fatty acid and nucleotide metabolism, redox reaction, and gluconeogenesis. Eighteen metabolic-related DEGs enriched in the metabolism of xenobiotics by the cytochrome P450 pathway, sulfur metabolism, retinoid metabolism, and glucose homeostasis were abnormal in all subtypes of PA.

CONCLUSION

Based on a comprehensive bioinformatics analysis of the available PA-related transcriptomics data, we identified specific DEGs related to metabolism, and some of them might be new attractive therapeutic targets. Especially, PDK4 and PCK1 might be new attractive biomarkers and therapeutic targets.

Regulating the CCNB1 gene can affect cell proliferation and apoptosis in pituitary adenomas and activate epithelial-to-mesenchymal transition

The aim of the present study was to investigate the role and potential regulatory mechanisms of cyclin B1 (CCNB1) in the proliferation, apoptosis and epithelial-to-mesenchymal transition (EMT) in pituitary adenomas. A total of 24 specimens were included in the present study. The expression levels of CCNB1 protein in two normal pituitary and 22 pituitary adenoma tissues were determined by western blotting. CCNB1 was knocked-down by lentiviral-mediated infection of short hairpin RNA (shRNA) in GH3 and MMQ cell lines. The proliferation, cell cycle and apoptosis of GH3 and MMQ cell lines were detected using a Cell Counting Kit-8 and flow cytometer. Reverse transcription-quantitative PCR was utilized to detect the expression level of CCNB1 gene and EMT markers. In the present study, resveratrol (RES) was used as an inhibitor of CCNB1. The protein expression level of CCNB1 in pituitary adenomas was higher than that in normal pituitary tissue, as assessed by western blot analysis. In addition, the expression level of CCNB1 in invasive pituitary adenomas was higher when comparing invasive pituitary adenomas and non-invasive pituitary adenomas. Knockdown of CCNB1 resulted in significant decreases in cell viability and proliferation, arrested cell cycle at the G₂/M phase and increased apoptosis. In addition, knockdown of CCNB1 significantly decreased the expression levels of the mesothelial cell marker N-cadherin (P<0.001), but significantly increased the expression levels of the epithelial cell markers E-cadherin (P<0.01) and p120-catenin (P<0.001). Further analyses identified that RES inhibited the expression level of CCNB1, and RES treatment exhibited a similar effect as CCNB1 shRNA infection. The present study suggested that suppressing the expression level of CCNB1 could regulate the proliferation and apoptosis of pituitary tumor cells and alter the expression level of various EMT markers. In addition, RES treatment could be used as an inhibitor of CCNB1. The present study also identified the molecular mechanisms underlying CCNB1 role in EMT.

Identification of Potential Biomarkers with Diagnostic Value in Pituitary Adenomas Using Prediction Analysis for Microarrays Method

Pituitary adenomas are the most common intrasellar tumors. Patients should be identified at an early stage so that effective treatment can be implemented. The study aims at detecting the potential biomarkers with diagnostic value of pituitary adenomas. Using a total of seven gene expression profiles (GEPs) of the datasets from the Gene Expression Omnibus (GEO) database, we first screened 1980 significant differentially expressed genes (DEGs). Then, we employed the prediction analysis for microarray (PAM) algorithm to identify 340 significant DEGs able to differ pituitary tumor from normal samples, which include 208 upregulated DEGs and 132 downregulated DEGs. DAVID database was used to carry out the enrichment analysis on Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) pathways. We found that upregulated candidates were enriched in protein folding and metabolic pathways. Downregulated DEGs saw a significant enrichment in insulin receptor signaling pathway and hedgehog signaling pathway. Based on the protein-protein interaction (PPI) network as well as module analysis, we determined ten hub genes including PHLPP, ENO2, ACTR1A, EHHADH, EHMT2, FOXO1, DLD, CCT2, CSNK1D, and CETN2 that could be potential biomarkers with diagnostic value in pituitary adenomas. In conclusion, the study contributes to reliable and potential molecular biomarkers with diagnostic value. Moreover, these potential biomarkers may be used for prognosis and new therapeutic targets for the pituitary adenomas.

Identification and analysis of genes associated with papillary thyroid carcinoma by bioinformatics methods

The molecular mechanism of the occurrence and development of papillary thyroid carcinoma (PTC) has been widely explored, but has not been completely elucidated. The present study aimed to identify and analyze genes associated with PTC by bioinformatics methods. Two independent datasets were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between PTC tissues and matched non-cancerous tissues were identified using GEO2R tool. The common DEGs in the two datasets were screened out by VennDiagram package, and analyzed by the following tools: KOBAS, Database for Annotation, Visualization, and Integrated Discovery (DAVID), Search tool for the retrieval of interacting genes/proteins (STRING), UALCAN and Gene expression profiling interactive analysis (GEPIA). A total of 513 common DEGs, including 259 common up-regulated and 254 common down-regulated genes in PTC, were screened out. These common up-regulated and down-regulated DEGs were most significantly enriched in cytokine–cytokine receptor interaction and metabolic pathways, respectively. Protein–protein interactions (PPI) network analysis showed that the up-regulated genes: FN1, SDC4, NMU, LPAR5 and the down-regulated genes: BCL2 and CXCL12 were key genes. Survival analysis indicated that the high expression of FN1 and NMU genes significantly decreased disease-free survival of patients with thyroid carcinoma. In conclusion, the genes and pathways identified in the current study will not only contribute to elucidating the pathogenesis of PTC, but also provide prognostic markers and therapeutic targets for PTC.

Integrative analysis of gene expression profiles reveals distinct molecular characteristics in oral tongue squamous cell carcinoma

Oral tongue squamous cell carcinoma (OTSCC) is the most common type of oral cancer. Despite advances in knowledge regarding the genome-scale gene expression pattern of oral cancer, the molecular portrait of OTSCC biology has remained unclear over the last few decades. Furthermore, studies concerning OTSCC gene-expression profiles are limited or inconsistent owing to tissue heterogeneity in single-cohort studies. Consequently, the present study integrated the profile datasets of three cohorts in order to screen for differentially expressed genes (DEGs), and subsequently identified the potential candidate genes and pathways in OTSCC through gene enrichment analysis and protein-protein interaction (PPI) network construction. Using the selected Gene Expression Omnibus datasets GSE13601, GSE31056 and GSE78060, 206 DEGs (125 upregulated and 81 downregulated) were identified in OTSCC, principally associated with extracellular matrix (ECM) organization and the phosphoinositide 3-kinase/protein kinase B signaling pathway. Furthermore, 146/206 DEGs were filtered into the PPI network and 20 hub genes were sorted. Further results indicated that the two most significant modules filtered from the PPI network were associated with ECM organization and human papillomavirus infection, which are important factors affecting OTSCC pathology. Overall, a set of OTSCC-associated DEGs has been identified, including certain key candidate genes that may be of vital importance for diagnosis, therapy and prevention of this disease.

Identification and bioinformatics analysis of overlapping differentially expressed genes in depression, papillary thyroid cancer and uterine fibroids

It is hypothesized that there may be common characteristics between the genetic regulatory networks of different diseases. To identify these potential similarities, analysis of overlapping differentially expressed genes (DEGs) in several diseases, which are believed to be associated in traditional Chinese medicine (TCM) was performed in the present study. The gene expression profiles associated with depression, papillary thyroid carcinoma (PTC) and uterine fibroids (UF) were preliminarily analyzed using Gene Expression Omnibus 2R tools. Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis and protein-protein interaction network analysis of the overlapping DEGs in depression, PTC and UF was performed. The results indicated that multiple genes, including activating transcription factor 3 and WSC domain containing 2 and the phosphoinositide 3 kinase/protein kinase b signaling pathway and its downstream effectors may be common factors associated with depression, PTC and/or UF. The neuroendocrine functions of the hypothalamic-pituitary-ovarian axis and hypothalamic-pituitary-thyroid axis were also identified as being mutually associated with depression, PTC and/or UF. However, due to the limitations of DNA microassays, it is recommended that future studies take epigenetics into consideration. Further transcriptomic, methylomic and metabolomic analyses of depression, PTC and UF are also required to identify and elucidate the key associated biomarkers. In conclusion, the results of the current study shed light on the potential genetic interconnections between depression, PTC and UF, which may be beneficial for understanding their underlying coregulatory mechanisms and contributing to the development of homeotherapy based on bioinformatics prediction.

Elucidating mechanistic insights into drug action for atopic dermatitis: a systems biology approach

BACKGROUND

Topical Betamethasone (BM) and Pimecrolimus (PC) are widely used drugs in the treatment of atopic dermatitis (AD). Though the biomolecules and biological pathways affected by the drugs are known, the causal inter-relationships among these pathways in the context of skin is not available. We aim to derive this insight by using transcriptomic data of AD skin samples treated with BM and PC using systems biology approach.

METHODS

Transcriptomic datasets of 10 AD patients treated with Betamethasone and Pimecrolimus were obtained from GEO datasets. We used a novel computational platform, eSkIN ( www.persistent.com/eskin ), to perform pathway enrichment analysis for the given datasets. eSkIN consists of 35 skin specific pathways, thus allowing skin-centric analysis of transcriptomic data. Fisher's exact test was used to compute the significance of the pathway enrichment. The enriched pathways were further analyzed to gain mechanistic insights into the action of these drugs.

RESULTS

Our analysis highlighted the molecular details of the mechanism of action of the drugs and corroborated the known facts about these drugs i.e. BM is more effective in triggering anti-inflammatory response but also causes more adverse effect on skin barrier than PC. In particular, eSkIN helped enunciate the biological pathways activated by these drugs to trigger anti-inflammatory response and its effect on skin barrier. BM suppresses pathways like TNF and TLRs, thus inhibiting NF-κB while PC targets inflammatory genes like IL13 and IL6 via known calcineurin-NFAT pathway. Furthermore, we show that the reduced skin barrier function by BM is due to the suppression of activators like AP1 transcription factors, CEBPs.

CONCLUSION

We thus demonstrate the detailed mechanistic insight into drug action of AD using a novel computational approach.

Corrigendum to "Identification of Differentially Expressed Genes in Pituitary Adenomas by Integrating Analysis of Microarray Data"

[This corrects the article DOI: 10.1155/2015/164087.].

Identification of common key genes in breast, lung and prostate cancer and exploration of their heterogeneous expression

Cancer is one of the leading causes of mortality worldwide, and in particular, breast cancer in women, prostate cancer in men, and lung cancer in both women and men. The present study aimed to identify a common set of genes which may serve as indicators of important molecular and cellular processes in breast, prostate and lung cancer. Six microarray gene expression profile datasets [GSE45827, GSE48984, GSE19804, GSE10072, GSE55945 and GSE26910 (two datasets for each cancer)] and one RNA-Seq expression dataset (GSE62944 including all three cancer types), were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified in each individual cancer type using the LIMMA statistical package in R, and then a comparison of the resulting gene lists was performed to identify common DEGs across cancer types. This analysis was performed for microarray and RNA-Seq datasets individually, revealing a set of 62 and 1,290 differentially expressed genes respectively, which may be associated with the three cancers. Out of these genes, 44 were common to both analyses, and hence termed key genes. Gene Ontology functional annotation, Kyoto Encyclopedia of Genes and Genomes pathway mapping and literature citations were used to confirm the role of the key genes in cancer. Finally, the heterogeneity of expression of the key genes was explored using the I² statistic (meta package in R). The results demonstrated non-heterogeneous expression of 6 out of the 44 key genes, whereas the remaining genes exhibited significant heterogeneity in expression across microarray samples. In conclusion, the identified DEGs may play important roles in the pathogenesis of breast, prostate and lung cancer and may be used as biomarkers for the development of novel diagnostic and therapeutic strategies.

Upregulation of cyclin B1 plays potential roles in the invasiveness of pituitary adenomas

Predicting aggressive or malignant behavior of pituitary adenomas (PAs) remains challenging. Aberrant expression of cyclin B1 (CCNB1) occurred in various tumors including PAs. Our study was aimed to explore its roles in the development of PAs aggressiveness. According to the integrated analysis, the expression of CCNB1 was evaluated. Following bioinformatics analysis was performed to uncover the pathways CCNB1 involved in and the upstream transcriptional regulation factors. The mRNA expression of CCNB1 was verified by qRT-PCR. Immunohistochemistry analysis was conducted to examine the expression of CCNB1 protein in three groups of PAs (non-invasive, invasive and aggressive-invasive). In this study, CCNB1 was up-regulated in PAs versus normal pituitary. Functional annotation revealed CCNB1 was mainly involved in p53 signaling pathway and cell cycle, which affected proliferation and contributed to tumorigenesis. The constructed transcriptional regulatory network contained 22 upstream transcriptional factors for CCNB1. Moreover, the network also suggested the interactions between CCNB1 and other genes implicated in proliferation (BUB1, CDC25C and TTK). Immunohistochemistry showed staining of CCNB1 was positive in PAs, and its expression was gradually enhanced with the increased invasiveness. In conclusion, up-regulation of CCNB1, together with other regulatory molecules in cell cycle, may play roles in the PAs pathology and could be an indicator for invasiveness of PAs.

Microarray technology reveals potentially novel genes and pathways involved in non-functioning pituitary adenomas

Microarray data of non-functioning pituitary adenomas (NFPAs) were analyzed to disclose novel genes and pathways involved in NFPA tumorigenesis. Raw microarray data were downloaded from Gene Expression Omnibus. Data pre-treatment and differential analysis were conducted using packages in R. Functional and pathway enrichment analyses were performed using package GOs-tats. A protein-protein interaction (PPI) network was constructed using server STRING and Cytoscape. Known genes involved in pituitary adenomas (PAs), were obtained from the Comparative Toxicogenomics Database. A total of 604 differentially expressed genes (DEGs) were identifed between NFPAs and controls, including 177 up- and 427 down-regulated genes. Jak-STAT and p53 signaling pathways were significantly enriched by DEGs. The PPI network of DEGs was constructed, containing 99 up- and 288 down-regulated known disease genes (e.g. EGFR and ESR1) as well as 16 up- and 17 down-regulated potential novel NFPAs-related genes (e.g. COL4A5, LHX3, MSN, and GHSR). Genes like COL4A5, LHX3, MSN, and GHSR and pathways such as p53 signaling and Jak-STAT signaling, might participate in NFPA development. Although further validations are required, these findings might provide guidance for future basic and therapy researches.

Exploring the intrinsic differences among breast tumor subtypes defined using immunohistochemistry markers based on the decision tree

Exploring the intrinsic differences among breast cancer subtypes is of crucial importance for precise diagnosis and therapeutic decision-making in diseases of high heterogeneity. The subtypes defined with several layers of information are related but not consistent, especially using immunohistochemistry markers and gene expression profiling. Here, we explored the intrinsic differences among the subtypes defined by the estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 based on the decision tree. We identified 30 mRNAs and 7 miRNAs differentially expressed along the tree's branches. The final signature panel contained 30 mRNAs, whose performance was validated using two public datasets based on 3 well-known classifiers. The network and pathway analysis were explored for feature genes, from which key molecules including FOXQ1 and SFRP1 were revealed to be densely connected with other molecules and participate in the validated metabolic pathways. Our study uncovered the differences among the four IHC-defined breast tumor subtypes at the mRNA and miRNA levels, presented a novel signature for breast tumor subtyping, and identified several key molecules potentially driving the heterogeneity of such tumors. The results help us further understand breast tumor heterogeneity, which could be availed in clinics.