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Long noncoding RNAs expression profiles in granulosa of woman with diminished ovarian reserve based on high-throughput sequencing

GSE193136 Homo sapiens (human) Non-coding RNA profiling 12 samples Submitted 2024/03/27 Platform GPL24676
At a glance

Non-coding RNA profiling · human · 12 samples

What it is
Non-coding RNA profilingLevels of RNAs that do not code for proteins, such as miRNA or lncRNA.
Organism
Homo sapiens (human)
Samples
12 samples: 6 × Ovarian granulosa cells-DOR vs 6 × Ovarian granulosa cells-NORGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Expression of long noncoding RNAs in the ovarian granulosa cells of women with diminished ovarian reserve using high-throughput sequencingJournal of ovarian research · 2022
Summary (written by the submitters)
Background: Infertility is one of the major health problems and the third most common disease, and diminished ovarian reserve (DOR) is a common cause of infertility. DOR is defined as decreased number or quality of oocytes in reproductive-age women, but the underlying molecular mechanisms remain unclear. Long noncoding RNAs (lncRNAs) has been recognized to play an important role in physiological and pathological processes of the human body, but it is unknown whether lncRNAs are involved in the development of DOR.
Read the full summary ↓
Methods: The ovarian granulosa cells from infertility women with DOR and normal ovarian reserve (NOR) were obtained and subjected to high-throughput sequencing. A comprehensive bioinformatics analysis was conducted to characterize the mRNAs and lncRNAs expression profiles between DOR and NOR women. Then, the sequencing results were validated by selected randomly lncRNAs using real-time quantitative PCR. Results: A total of 92,853 lncRNAs transcripts were identified between DOR and NOR GCs. Compared with NOR control, 244 lncRNAs were upregulated (53 known and 191 novel) and 222 lncRNAs were downregulated (36 known and 186 novel) in the DOR group. Similarly, 457 differently expressed mRNAs were found between the two groups, with 169 and 288 up- and downregulated respectively. Further functional analysis revealed that differentially expressed genes of mRNA and lncRNA were significantly enriched in various biological processes such as cell adhesion and apoptosis, steroid biosynthesis, and immune system. The results of correlated expression networks between lncRNAs and their predicted target genes uncovered the possible involvement of the thyroid hormone signaling pathway and protein binding, digestion and absorption in the pathogenesis of DOR and showed that SLC16A10 is positively regulated by multiple lncRNAs. In addition, it was verified that the expression patterns of three of the seven selected differentially expressed lncRNAs were consistent with RNA-Seq. Aberrant lncRNAs might be used to develop new diagnostic biomarkers or drugs to target DOR. Conclusion: This study is the first to elucidate the lncRNA and mRNA expression profiles between NOR and DOR patients in the ovarian granulosa cells using sequencing technology. The lncRNAs identified in this study can potentially be novel diagnostic biomarkers and therapeutic targets for DOR.
Published in
Expression of long noncoding RNAs in the ovarian granulosa cells of women with diminished ovarian reserve using high-throughput sequencing
Dong L, Xin X, Chang HM et al. · Journal of ovarian research 2022 · PMID 36309699 · doi:10.1186/s13048-022-01053-6
This dataset
Open GSE193136 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE193136_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

Also filed as BioProject PRJNA795091 and SRA study SRP353766. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 36309699) ↗
Study design
6 × Ovarian granulosa cells-DOR vs 6 × Ovarian granulosa cells-NOR

Supports a between-group comparison across 12 samples.

How we read this: 2 replicated groups read from 12 sample titles; they account for 12 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
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