Evidence map›Paper›PMID 40447626›Full record

ArticleScientific reports2025

Transcriptome sequencing reveals the expression profiles of lncRNAs and mRNAs in goat skin tissues with different types of wool coats.

Peng Zhao, Hongyu Guo, Jieru Han, Zhiwu Wang, Yanrong Xue, Li Zhang

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Peng ZhaoCollege of Animal Science, Shanxi Agricultural University, Taiyuan, China.
Hongyu GuoCollege of Animal Science, Shanxi Agricultural University, Taiyuan, China.
Jieru HanCollege of Animal Science, Shanxi Agricultural University, Taiyuan, China.
Zhiwu WangCollege of Animal Science, Shanxi Agricultural University, Taiyuan, China.
Yanrong XueCollege of Animal Science, Shanxi Agricultural University, Taiyuan, China.
Li ZhangCollege of Animal Science, Shanxi Agricultural University, Taiyuan, China. zl206480@sxau.edu.cn.

Funding

Shanxi Agricultural University Team Cultivation Project dkykyzx202207the Basic Research Project of Shanxi Province 202203021222165the China Agriculture Research System CARS-39-24the Earmarked Fund for Modern Agro-industry Technology Research System 2023-14
6 · The paper itself

Abstract

The diverse coat types of cashmere goats significantly influence cashmere yield, yet the regulatory mechanisms mediated by non-coding RNAs (ncRNAs) remain poorly characterized. In this study, we collected skin tissues from two distinct coat types (CHLC_ESA and CHSC_ESA) of The Jinlan Cashmere Goat for transcriptome sequencing analysis. Our findings revealed 130 differentially expressed (DE) lncRNAs and 341 DE mRNAs. GO functional enrichment analysis uncovered some crucial terms such as keratin filament, intermediate filament, cytokine activity, and cytokine receptor binding, along with two pivotal DE lncRNAs: TCONS_00050130 and TCONS_00050142. Furthermore, KEGG pathway analysis identified several important pathways including the Estrogen signaling pathway, Amino sugar and nucleotide sugar metabolism, Adipocytokine signaling pathway, and cytokine-cytokine receptor interaction. Lastly, by constructing a ceRNA regulatory network among DE lncRNAs and DE mRNAs, we identified multiple ceRNA regulatory axes targeting genes such as EPPK1, IRF4, CCL20, and CXCR7. These mechanistic insights not only advance our understanding of ncRNA-mediated hair follicle biology but also provide actionable targets for precision breeding strategies to enhance cashmere quality and yield across caprine species.

Indexed as

GoatsRNA, Long NoncodingRNA, MessengerSkinTranscriptomeWoolAnimalsGene Expression ProfilingGene Regulatory NetworksRNA, Long NoncodingRNA, MessengerCashmere goatHair follicle developmentLncRNA and mRNARNA-seq

Identifiers

PMID40447626
PMCPMC12125334

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.