A miR-375/YAP axis regulates neuroendocrine differentiation and tumorigenesis in lung carcinoid cells.

Lung carcinoids are variably aggressive and mechanistically understudied neuroendocrine neoplasms (NENs). Here, we identified and elucidated the function of a miR-375/yes-associated protein (YAP) axis in lung carcinoid (H727) cells. miR-375 and YAP are respectively high and low expressed in wild-type H727 cells. Following lentiviral CRISPR/Cas9-mediated miR-375 depletion, we identified distinct transcriptomic changes including dramatic YAP upregulation. We also observed a significant decrease in neuroendocrine differentiation and substantial reductions in cell proliferation, transformation, and tumor growth in cell culture and xenograft mouse disease models. Similarly, YAP overexpression resulted in distinct and partially overlapping transcriptomic changes, phenocopying the effects of miR-375 depletion in the same models as above. Transient YAP knockdown in miR-375-depleted cells reversed the effects of miR-375 on neuroendocrine differentiation and cell proliferation. Pathways analysis and confirmatory real-time PCR studies of shared dysregulated target genes indicate that this axis controls neuroendocrine related functions such as neural differentiation, exocytosis, and secretion. Taken together, we provide compelling evidence that a miR-375/YAP axis is a critical mediator of neuroendocrine differentiation and tumorigenesis in lung carcinoid cells.

Classifying Lung Neuroendocrine Neoplasms through MicroRNA Sequence Data Mining.

Lung neuroendocrine neoplasms (NENs) can be challenging to classify due to subtle histologic differences between pathological types. MicroRNAs (miRNAs) are small RNA molecules that are valuable markers in many neoplastic diseases. To evaluate miRNAs as classificatory markers for lung NENs, we generated comprehensive miRNA expression profiles from 14 typical carcinoid (TC), 15 atypical carcinoid (AC), 11 small cell lung carcinoma (SCLC), and 15 large cell neuroendocrine carcinoma (LCNEC) samples, through barcoded small RNA sequencing. Following sequence annotation and data preprocessing, we randomly assigned these profiles to discovery and validation sets. Through high expression analyses, we found that miR-21 and -375 are abundant in all lung NENs, and that miR-21/miR-375 expression ratios are significantly lower in carcinoids (TC and AC) than in neuroendocrine carcinomas (NECs; SCLC and LCNEC). Subsequently, we ranked and selected miRNAs for use in miRNA-based classification, to discriminate carcinoids from NECs. Using miR-18a and -155 expression, our classifier discriminated these groups in discovery and validation sets, with 93% and 100% accuracy. We also identified miR-17, -103, and -127, and miR-301a, -106b, and -25, as candidate markers for discriminating TC from AC, and SCLC from LCNEC, respectively. However, these promising findings require external validation due to sample size.

Characterizing and classifying neuroendocrine neoplasms through microRNA sequencing and data mining.

Neuroendocrine neoplasms (NENs) are clinically diverse and incompletely characterized cancers that are challenging to classify. MicroRNAs (miRNAs) are small regulatory RNAs that can be used to classify cancers. Recently, a morphology-based classification framework for evaluating NENs from different anatomical sites was proposed by experts, with the requirement of improved molecular data integration. Here, we compiled 378 miRNA expression profiles to examine NEN classification through comprehensive miRNA profiling and data mining. Following data preprocessing, our final study cohort included 221 NEN and 114 non-NEN samples, representing 15 NEN pathological types and 5 site-matched non-NEN control groups. Unsupervised hierarchical clustering of miRNA expression profiles clearly separated NENs from non-NENs. Comparative analyses showed that miR-375 and miR-7 expression is substantially higher in NEN cases than non-NEN controls. Correlation analyses showed that NENs from diverse anatomical sites have convergent miRNA expression programs, likely reflecting morphological and functional similarities. Using machine learning approaches, we identified 17 miRNAs to discriminate 15 NEN pathological types and subsequently constructed a multilayer classifier, correctly identifying 217 (98%) of 221 samples and overturning one histological diagnosis. Through our research, we have identified common and type-specific miRNA tissue markers and constructed an accurate miRNA-based classifier, advancing our understanding of NEN diversity.