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Record W4307846934 · doi:10.1186/s13048-022-01053-6

Expression of long noncoding RNAs in the ovarian granulosa cells of women with diminished ovarian reserve using high-throughput sequencing

2022· article· en· W4307846934 on OpenAlexaff
Li Dong, Xin Xin, Hsun‐Ming Chang, Peter C. K. Leung, Yu Chen, Fang Lian, Haicui Wu

Bibliographic record

VenueJournal of Ovarian Research · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsBC Children's HospitalUniversity of British Columbia
FundersNatural Science Foundation of Shandong ProvinceKey Technology Research and Development Program of ShandongNational Natural Science Foundation of ChinaShandong University
KeywordsBiologyDownregulation and upregulationLong non-coding RNAMessenger RNAGene expressionGeneReverse transcription polymerase chain reactionAndrologyGeneticsMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: Infertility is a global reproductive-health problem, and diminished ovarian reserve (DOR) is one of the common causes of female infertility. Long noncoding RNAs (lncRNAs) are crucial regulators of numerous physiological and pathological processes in humans. However, whether lncRNAs are involved in the development of DOR remains to be elucidated. METHODS: Ovarian granulosa cells (OGCs) extracted from infertile women with DOR and from women with normal ovarian reserve (NOR) were subjected to high-throughput sequencing. Comprehensive bioinformatics analysis was conducted to identify the differential expression of messenger RNAs (mRNAs) and lncRNAs. Sequencing results were validated by the selection of lncRNAs and mRNAs using real-time reverse transcription-quantitative polymerase chain reaction (RT-qPCR). RESULTS: Compared with the NOR group, a total of 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 mRNAs had differential expression between the two groups. Of these, 169 were upregulated and 288 were downregulated. Bioinformatics analysis revealed that the differentially expressed genes of mRNA and lncRNAs were considerably enriched in "cell adhesion and apoptosis", "steroid biosynthesis", and "immune system". A co-expression network comprising lncRNAs and their predicted target genes revealed the possible involvement of the "thyroid hormone signaling pathway" and "protein binding, digestion and absorption" in DOR pathogenesis. The expression of SLC16A10 was positively regulated by multiple lncRNAs. After RT-qPCR validation of seven differentially expressed lncRNAs and mRNAs, respectively, the expression of lncRNA NEAT1, GNG12, ZEB2-AS1, and mRNA FN1, HAS3, RGS4, SUOX were in accordance with RNA-sequencing. CONCLUSIONS: We presented the first data showing that the expression profiles of lncRNA and mRNA in OGCs between NOR and DOR patients using RNA sequencing. The lncRNAs and mRNAs that we identified may serve as novel diagnostic biomarkers for patients with DOR.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.051
GPT teacher head0.335
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations25
Published2022
Admission routes1
Has abstractyes

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