Evidence map›Paper›PMID 36373308›Full record

ArticleCancer biomarkers : section A of Disease markers2022

Analysis of aberrant miRNA-mRNA interaction networks in prostate cancer to conjecture its molecular mechanisms.

Shuang Peng, Cheng Liu, Xingchen Fan, Jingfeng Zhu, Shiyu Zhang, Xin Zhou, Tongshan Wang, Feng Gao, Wei Zhu

Abstract read
In one paragraph

Article in Cancer biomarkers : section A of Disease markers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Shuang PengDepartment of Oncology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Cheng LiuDepartment of Gastroenterology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Xingchen FanDepartment of Oncology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Jingfeng ZhuDepartment of Nephrology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Shiyu ZhangDepartment of Oncology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Xin ZhouDepartment of Oncology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Tongshan WangDepartment of Oncology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Feng GaoDepartment of Osteology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Wei ZhuDepartment of Oncology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMicroRNAs (miRNAs) capable of post-transcriptionally regulating mRNA expression are essential to tumor occurrence and progression.

objectiveThis study aims to find negatively regulatory miRNA-mRNA pairs in prostate adenocarcinoma (PRAD).

methodsCombining The Cancer Genome Atlas (TCGA) RNA-Seq data with Gene Expression Omnibus (GEO) mRNA/miRNA expression profiles, differently expressed miRNA/mRNA (DE-miRNAs/DE-mRNAs) were identified. MiRNA-mRNA pairs were screened by miRTarBase and TarBase, databases collecting experimentally confirmed miRNA-mRNA pairs, and verified in 30 paired prostate specimens by real-time reverse transcription polymerase chain reaction (RT-qPCR). The diagnostic values of miRNA-mRNA pairs were measured by receiver operation characteristic (ROC) curve and Decision Curve Analysis (DCA). DAVID-mirPath database and Connectivity Map were employed in GO/KEGG analysis and compounds research. Interactions between miRNA-mRNA pairs and phenotypic features were analyzed with correlation heatmap in hiplot.

resultsBased on TCGA RNA-Seq data, 22 miRNA and 14 mRNA GEO datasets, 67 (20 down and 47 up) miRNAs and 351 (139 up and 212 down) mRNAs were selected. After screening from 2 databases, 8 miRNA (up)-mRNA (down) and 7 miRNA (down)-mRNA (up) pairs were identified with Pearson's correlation in TCGA. By external validation, miR-221-3p (down)/GALNT3 (up) and miR-20a-5p (up)/FRMD6 (down) were chosen. The model combing 4 signatures possessed better diagnostic value. These two miRNA-mRNA pairs were significantly connected with immune cells fraction and tumor immune microenvironment.

conclusionsThe diagnostic model containing 2 negatively regulatory miRNA-mRNA pairs was established to distinguish PRADs from normal controls.

Indexed as

MicroRNAsProstatic NeoplasmsGene Expression ProfilingGene Expression Regulation, NeoplasticGene Regulatory NetworksHumansMaleRNA, MessengerTranscriptomeTumor MicroenvironmentMicroRNAsRNA, MessengermiRNAmiRNA-mRNA networksPCRprostate adenocarcinomaTCGA

Identifiers

PMID36373308
PMCPMC12364255

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.