Dataset Information

Transcriptomic analyses of high-grade neuroendocrine carcinoma of gynecologic origin

ABSTRACT: High-grade neuroendocrine carcinomas (NECs) of the cervix are rare, aggressive cancers accounting for about 1-1.5% of all cervical cancer. The 5-year survival is up to 36% for early-stage disease; however, the advanced-stage disease has <10% survival, with relapse rates exceeding 90%. These cancers are likely to have a vascular invasion and nodal or visceral metastasis. Unfortunately, NEC of the cervix affects young women with a median age of 37. High-grade NECs of other gynecologic origins are even rarer, share similar aggressive behavior and poor outcomes. We performed stranded paired-end RNA-seq of 13 samples, including 12 samples with matched whole exome sequencing (WES) data. Sites of origin for tumors in our cohort were the cervix (69%), ovary (19%), and endometrium (12%). The median number of prior lines of therapy was 1. Median PFS and OS were 1 and 12 months, respectively, indicating their highly lethal nature. Comparing transcriptomic profiles with TCGA cervical and ovarian cancers, chromatin assembly and nucleosome organization were the top GO functions for significantly over-expressed genes in our cohort. Remarkably, under-expressed genes in our cohort were enriched for protein modification and catabolic processes, and neutrophil-mediated immunity. Compared to small cell lung cancer (SCLC), our cohort showed highly distinct transcriptomic patterns, and represented the YAP1 high molecular subtype.

ORGANISM(S): Homo sapiens

PROVIDER: GSE157601 | GEO | 2021/09/01

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Background. MicroRNAs (miRNAs) are short (~22 nt) non-coding regulatory RNAs that control gene expression at the translational level. Deregulation of miRNA expression has been discovered in a wide variety of tumours and it is now clear that they contribute to cancer development and progression. This prompted the development of miRNA-chips for cancer diagnosis or prognosis, opening a new door to understand carcinogenesis. Cervical cancer is one of the most common cancers in women worldwide. Therefore, there is a strong need for a non-invasive, fast and efficient method to diagnose the disease. We investigated miRNA expression profiles in cervical cancer using a microarray platform developed in house containing probes for mature miRNAs. Results. We have evaluated miRNA expression profiles of a heterogeneous set of cervical tissues from 25 different patients. This set included 19 normal cervical tissues, 4 squamous cell carcinoma, 5 high-grade squamous intraepithelial lesion (HSIL) and 9 low-grade squamous intraepithelial lesion (LSIL) samples. We observed high variability in miRNA expression especially among normal cervical samples, which prevented us from obtaining a unique miRNA expression signature for this tumour type. However, miRNAs deregulation in malignant and pre-malignant cervical tissues was detected after tackling the high variability observed. We were also able to identify putative targets of relevant candidate miRNAs. Conclusions. Our results show that miRNA deregulation may play an important role in the malignant transformation of cervical squamous cells. In addition, deregulated miRNAs highlight new candidate targets allowing a better understanding of the molecular mechanism of this tumour type. In this study we used a common reference design experiment where the common reference used was a commercial RNA from normal cervix (Ambion) and the test samples were 4 pre-treatment squamous cell cervical carcinoma, 7 high-grade Squamous Intraepithelial Lesion (CINII, n=2 and CIN III, n=5) sample, 9 low-grade Squamous Intraepithelial Lesion (CIN I) samples, 19 normal cervix samples and 4 pools of normal cervix samples.

Dataset Information

Transcriptomic analyses of high-grade neuroendocrine carcinoma of gynecologic origin

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets