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.

An Update on Silent Corticotroph Adenomas: Diagnosis, Mechanisms, Clinical Features, and Management

“…We next analyzed three invasion-related molecular markers (INSM1, HSPA2, and CDK6) in three subtypes of NFPAs (SCAs, SGAs, and null-cell adenomas) and found that INSM1 and HSPA2 expressions were higher in SCAs than other subtypes and that CDK6 showed a tendency of positive correlation with invasive SCAs. These results further supported the strong invasiveness of SCAs [ 18 ].…”

“…We also validated the transcription factors at the RNA level in a subset of 61 NFPAs (25 of them were SCAs) after they were reclassified according to transcription factor IHC. We demonstrated good concordance between the molecular and IHC identification of the NFPAs subtypes [ 18 ]. According to these results, the molecular characterization of T-PIT complements the IHC results, and whenever IHC study of T-PIT is not available, RT-qPCR of T-PIT could help pathologists, neurosurgeons, and endocrinologists for better identification of SCAs.…”

“…Some studies on SCAs have been conducted following the 2017 WHO classification guidelines [ 9 , 15 , 16 , 17 , 18 , 19 ]. All have seen an increase of the proportion of SCAs among the NFPAs with the detection of T-PIT-positive ACTH-negative SCAs as T-PIT immunostaining reduces the number of false-negatives and false-positives.…”

“…By high-throughput sequencing, weighted gene co-expression network analysis, and functional annotation, we observed gene signatures correlated with the invasiveness of SCAs [ 18 ]. Twenty differentially expressed genes were identified enriched in the “Pathway in cancers” and “MAPK pathway” and contributed to alterations of related factors.…”

Comparative Proteomic Study Shows the Expression of Hint-1 in Pituitary Adenomas

“…In PA investigation, several experimental strategies (histochemistry and immunohistochemistry techniques) have been performed, which allow for the identification and classification of PAs according to stain affinity (basophils, chromophobes, and acidophils), type of hormonal content (prolactin, growth hormone, LH, FSH, TSH, ACTH), PCR technique (endpoint PCR, RT-PCR, ddPCR), microarrays, RNA-Seq, bidimensional polyacrylamide gel electrophoresis (2-DE), mass spectrometry, and laser-capture microdissection (LCM) [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. Furthermore, DNA sequencing has been applied to find genetic alterations and epigenetic changes [ 27 ], but only partial tumor genesis information has been found, since the transcriptome and the proteome are dynamic.…”