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Single-cell transcriptome analysis of small cell neuroendocrine tumor of the endometrium reveals ISL1 as a potential biomarker for diagnosis and treatment

ABSTRACT: In this study, we performed single-cell RNA sequencing on a specimen from a patient with endometrial neuroendocrine carcinoma pathologically diagnosed. By analyzing the data, we found that the expression of Islet-1 was significantly up-regulated in tumor cells. Furthermore, we verified it in neuroendocrine tumor cell lines, and obtained cells with high expression of Islet-1 and cells with low expression of Islet-1 by flow sorting. Cell function experiments were performed in vitro to verify that the cell proliferation and cell migration abilities of the high expression Islet-1 group were significantly higher than those of the low expression group. In summary, Islet-1 protein in neuroendocrine tumors could act as a promising biomarker for the tumorigenesis and progression of neuroendocrine tumors.

ORGANISM(S): Homo sapiens

PROVIDER: GSE233447 | GEO | 2023/12/27

REPOSITORIES: GEO

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Project description:Two prognostically significant subtypes of high-grade lung neuroendocrine tumors independent of small-cell and large-cell neuroendocrine carcinomas identified by gene expression profiles. BACKGROUND: Classification of high-grade neuroendocrine tumors (HGNT) of the lung currently recognises large-cell neuroendocrine carcinoma (LCNEC) and small-cell lung carcinoma (SCLC) as distinct groups. However, a similarity in histology for these two carcinomas and uncertain clinical course have led to suggestions that a single HGNT classification would be more appropriate. Gene expression profiling, which can reproduce histopathological classification, and often defines new subclasses with prognostic significance, can be used to resolve HGNT classification. METHODS: We used cDNA microarrays with 40386 elements to analyze the gene expression profiles of 38 surgically resected samples of lung neuroendocrine tumors and 11 SCLC cell lines. Samples of large-cell carcinoma, adenocarcinoma, and normal lung were also included to give a total of 105 samples analyzed. The data were subjected to filtering to yield informative genes before unsupervised hierarchical clustering that identified relatedness of tumor samples. FINDINGS: Distinct groups for carcinoids, large-cell carcinoma, adenocarcinoma, and normal lung were readily identified. However, we were unable to distinguish LCNEC from SCLC by gene expression profiling. Three independent rounds of unsupervised hierarchical clustering consistently divided SCLC samples into two main groups with LCNEC samples largely integrated with these groups. Furthermore, patients in one of the groups identified by clustering had a significantly better clinical outcome than the other (83% vs 12% survived for 5 years; p=0.0094. None of the highly proliferative SCLC cell lines subsequently analyzed clustered with this good-prognosis group. INTERPRETATION: Our findings show that HGNT of the lung can be classified into two groups independent of SCLC and LCNEC. To this end, we have identified many genes, some of which encode well-characterized markers of cancer that distinguish the HGNT groups. These results have implications for the diagnosis, classification, and treatment of lung neuroendocrine tumors, and provide important insights into their underlying biology. Keywords: other

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Single-cell transcriptome analysis of small cell neuroendocrine tumor of the endometrium reveals ISL1 as a potential biomarker for diagnosis and treatment

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