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Transcriptomic analysis identifies a tumor subtype mRNA classifier for invasive non-functioning pituitary neuroendocrine tumor diagnostics.

ABSTRACT: Rationale: The invasive behavior of non-functioning pituitary neuroendocrine tumors (NF-PitNEts) presents obstacles for complete surgical resection and is indicative of poor prognosis. Therefore, developing reliable diagnostic tools for identifying invasive PitNEts would be helpful in guiding surgical decisions and, in particular, the follow-up treatment. Methods: We analyzed differential gene expression profiles between 39 non-invasive and 22 invasive NF-PitNEts by high-throughput sequencing, gene co-expression, and functional annotation. Twenty-one transcripts were further validated by Taqman-qPCR in another 143 NF-PitNEt samples. The histological expression and serum-exosomal mRNA of three candidate genes were examined by tissue microarray and droplet digital PCR. Results: Non-invasive and invasive NF-PitNEts were clustered into distinct groups with a few outliers because of their gonadotroph, corticotroph, or null cell lineages. The gene signature with strong invasive potential was enriched in 'Pathways in cancers' and 'MAPK pathway', with significantly higher in situ INSM1 and HSPA2 protein expression in invasive NF-PitNEts. Further integration of the 20 qPCR-validated differentially expressed genes and pituitary cell lineages provided a gene-subtype panel that performed 80.00-90.24% diagnostic accuracy for the invasiveness of NF-PitNEts. Conclusion: Our approach defined new characteristics in the core molecular network for patients at risk for invasive NF-PitNEt, representing a significant clinical advance in invasive PitNEt diagnostics.

SUBMITTER: Bao X

PROVIDER: S-EPMC7681103 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Transcriptomic analysis identifies a tumor subtype mRNA classifier for invasive non-functioning pituitary neuroendocrine tumor diagnostics.

Bao Xinjie X Wang Gengchao G Yu Shan S Sun Jian J He Liu L Zhao Hualu H Ma Yanni Y Wang Fang F Wang Xiaoshuang X Wang Renzhi R Yu Jia J

Theranostics 20210101 1

<b>Rationale:</b> The invasive behavior of non-functioning pituitary neuroendocrine tumors (NF-PitNEts) presents obstacles for complete surgical resection and is indicative of poor prognosis. Therefore, developing reliable diagnostic tools for identifying invasive PitNEts would be helpful in guiding surgical decisions and, in particular, the follow-up treatment. <b>Methods:</b> We analyzed differential gene expression profiles between 39 non-invasive and 22 invasive NF-PitNEts by high-throughput ...[more]

PMID: 33391466

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Project description:Context : Non Functioning Pituitary Adenomas (NFPAs), although typically benign, may be locally invasive. Few datas are currently available related to the molecular process of invasion in sporadic pituitary adenomas. Markers of invasiveness, important for helping the clinician in the therapeutic strategy particularly in the decision for adjuvant radiotherapy, are currently lacking. Objective: Evaluate if invasive NFPAs display a specific expression profile compared with non invasive tumors. Methods: To address this issue, we selected 40 NFPAs (38 of the gonadotroph type) and classified them as invasive (n=22) or non invasive (n=18) on the basis of MRI and surgical findings. Then we performed pangenomic analysis based on Agilent Human Whole genome Gene Expression oligonucleotide microarray (44k) Expression in order to identify genes differentially expressed between invasive and non invasive NFPA. Experiements are made in dual color with tumor samples labelled in cyanine 5 and a pool of all tumors labelled in cyanine 3.The expressions of some genes identified by microarray screening were confirmed by real-time quantitative RT-PCR. The selection of genes was made on the basis of Ingenuity networks and degree of upregulation between invasive and non invasive tumors. Moreover, some genes of interest already described in the literature were added to the analysis. Results: Prediction class analysis showed that 346 genes discriminated between invasive and non invasive NFPAs (p<0.001); 233 were up- and 113 were down-regulated between the two groups. We then tested On the basis of Ingenuity networks and degree of upregulation between invasive and non invasive tumors, a set of eight genes, including MYO5A, IGFBP5, FLT3, NFE2L1, PTTG, MMP9, NCAM and NR1H3, was tested in quantitative PCR analysis and. Results confirmed those obtained in microarrays results.analysis. At the protein level, Myosin 5A (MYO5A) demonstrated stronger immunostaining in invasive NFPAs compared to non invasive NFPAs. Conclusions: We propose a molecular signature of eight genes of grossly invasive NFPAs as compared with non invasive tumors: The product of one of these genes, MYO5A, may be a useful marker of invasive process in tumoral specimens. The role of these genes in the invasiveness process and as prognostic markers of pituitary adenomas needs to be confirmedinvestigated. Method: Microarray analyses the transcriptome of 22 invasive Non Functional Pituitary Adenomas (NFPAs) and 18 non invasive NFPAs in dual color (each sample labelled in cyanine 5 and a pool of all tumors labelled in cyanine 3).

Project description:BackgroundTumor surrounding the internal carotid artery or invading to the cavernous sinus is an important characteristic of invasive pituitary adenoma, and a pivotal factor of tumor residue and regrowth. Without specific changes in serum hormone related to the adenohypophyseal cell of origin, clinically non-functioning pituitary adenoma is more likely to be diagnosed at invasive stages compared with functioning pituitary adenoma. The underlying mechanism of tumor invasion remains unknown. In this study, we aimed to identify key genes in tumor invasion by integrating analyses of DNA methylation and gene expression profiles.MethodGenome-wide DNA methylation and mRNA microarray analysis were performed for tumor samples from 68 patients at the Beijing Tiantan Hospital. Differentially expressed genes and methylated probes were identified based on an invasive vs non-invasive grouping. Differentially methylated probes in the promoter region of targeted genes were assessed. Pearson correlation analysis was used to identify genes with a strong association between DNA methylation status and expression levels. Pyrosequencing and RT-PCR were used to validate the methylation status and expression levels of candidate genes, respectively.ResultsA total of 8842 differentially methylated probes, located on 4582 genes, and 661 differentially expressed genes were identified. Both promoter methylation and expression alterations were observed for 115 genes with 58 genes showing a negative correlation between DNA methylation status and expression level. Nineteen genes that exhibited notably negative correlations between DNA methylation and gene expression levels, are involved in various gene ontologies and pathways, or played an important role in different diseases, were regarded as candidate genes. We found an increased methylation with a decreased expression of PHYHD1, LTBR, C22orf42, PRR5, ANKDD1A, RAB13, CAMKV, KIFC3, WNT4 and STAT6, and a decreased methylation with an increased expression of MYBPHL. The methylation status and expression levels of these genes were validated by pyrosequencing and RT-PCR.ConclusionsThe DNA methylation and expression levels of PHYHD1, LTBR, MYBPHL, C22orf42, PRR5, ANKDD1A, RAB13, CAMKV, KIFC3, WNT4 and STAT6 are associated with tumor invasion, and these genes may become the potential genes for targeted therapy.

Project description:The human microbiota is associated with various disease states and holds a great promise for non-invasive diagnostics. However, microbiota data is challenging for traditional diagnostic approaches: It is high-dimensional, sparse and comprises of high inter-personal variation. State of the art machine learning tools are therefore needed to achieve this goal. While these tools have the ability to learn from complex data and interpret patterns therein that cannot be identified by humans, they often operate as black boxes, offering no insight into their decision-making process. In most cases, it is difficult to represent the learning of a classifier in a comprehensible way, which makes them prone to be mistrusted, or even misused, in a clinical environment. In this study, we aim to elucidate microbiota-based classifier decisions in a biologically meaningful context to allow their interpretation.We applied a method for explanation of classifier decisions on two microbiota datasets of increasing complexity: gut versus skin microbiota samples, and inflammatory bowel disease versus healthy gut microbiota samples. The algorithm simulates bacterial species as being unknown to a pre-trained classifier, and measures its effect on the outcome. Consequently, each patient is assigned a unique quantitative estimation of which species in their microbiota defined the classification of their sample. The algorithm was able to explain the classifier decisions well, demonstrated by our validation method, and the explanations were biologically consistent with recent microbiota findings.Application of a method for explaining individual classifier decisions for complex microbiota analysis proved feasible and opens perspectives on personalized therapy. Providing an explanation to support a microbiota-based diagnosis could guide decisions of clinical microbiologists, and has the potential to increase their confidence in the outcome of such decision support systems. This may facilitate the development of new diagnostic applications.

Dataset Information

Transcriptomic analysis identifies a tumor subtype mRNA classifier for invasive non-functioning pituitary neuroendocrine tumor diagnostics.

Publications

Transcriptomic analysis identifies a tumor subtype mRNA classifier for invasive non-functioning pituitary neuroendocrine tumor diagnostics.

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