Evidence map›Paper›PMID 28415685›Full record

ArticleOncotarget2017

Identification of molecular targets for esophageal carcinoma diagnosis using miRNA-seq and RNA-seq data from The Cancer Genome Atlas: a study of 187 cases.

Jiang-Hui Zeng, Dan-Dan Xiong, Yu-Yan Pang, Yu Zhang, Rui-Xue Tang, Dian-Zhong Luo, Gang Chen

Open access · diamondAbstract read
In one paragraph

Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 4 pooled it
2.7field-weighted citation impact, top 9% of its field
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

27 citing papers in PubMed, 4 syntheses or guidelines pooled it, 37 citations in OpenAlex.

  1. Pooled it
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  3. Expression of microRNA-99a-3p in Prostate Cancer Based on Bioinformatics Data and Meta-Analysis of a Literature Review of 965 Cases.Medical science monitor : international medical journal of experimental and clinical research · 2018
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  8. Long Noncoding RNAs in the Prediction of Survival of Patients with Digestive Cancers.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2023
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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

7 authors at 2 institutions in 1 country.

Jiang-Hui ZengDepartment of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, China.
Dan-Dan XiongDepartment of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, China.
Yu-Yan PangDepartment of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, China.
Yu ZhangDepartment of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, China.
Rui-Xue TangDepartment of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, China.
Dian-Zhong LuoDepartment of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, China.
Gang ChenDepartment of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, China.
Guangxi Medical University · CNFirst Affiliated Hospital of GuangXi Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Esophageal carcinoma (ESCA) is one of the most common malignancies worldwide, and its pathogenesis is complex. In this study, we identified differentially expressed miRNAs (DEMs) and genes (DEGs) of ESCA from The Cancer Genome Atlas (TCGA) database. The diagnostic values of DEMs were determined by receiver operating characteristic (ROC) analyses and validated based on data from Gene Expression Omnibus (GEO). The top five DEMs with the best diagnostic values were selected, and their potential targets were predicted by various in silico methods. These target genes were then identified among the DEGs from TCGA. Furthermore, the overlapping genes were subjected to protein-protein interaction (PPI) analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The miRNA-transcription factor (TF) regulatory relations were determined using CircuitsDB and TransmiR. Finally, the regulatory networks of miRNA-TF and miRNA-gene were constructed and analyzed. A total of 136 DEMs and 3541 DEGs were identified in ESCA. The top five DEMs with the highest area under the receiver operating characteristic curve (AUC) values were miRNA-93 (0.953), miRNA-21 (0.928), miRNA-4746 (0.915), miRNA-196a-1 (0.906) and miRNA-196a-2 (0.906). The combined AUC of these five DEMs was 0.985. The KEGG analysis with 349 overlapping genes showed that the calcium signaling pathway and the neuroactive ligand-receptor interaction were the most relevant pathways. The regulatory networks of miRNA-TF and miRNA-gene, including 38 miRNA-TF and 560 miRNA-gene pairs, were successfully established. Our findings may provide new insights into the molecular mechanisms of ESCA pathogenesis. Future research will aim to explore the role of novel miRNAs in the pathogenesis and improve the early diagnosis of ESCA.

Indexed as

Gene Expression Regulation, NeoplasticRNA InterferenceComputational BiologyDatabases, GeneticEsophageal NeoplasmsGene Regulatory NetworksHigh-Throughput Nucleotide SequencingHumansMicroRNAsMolecular Sequence AnnotationProtein Interaction MappingProtein Interaction MapsReproducibility of ResultsRNA, MessengerROC CurveSequence Analysis, RNAMicroRNAsRNA, MessengerTranscription FactorsDEGsDEMsdiagnosisesophageal carcinomaTCGA

Identifiers

PMID28415685
PMCPMC5482608
OpenAlexW2591948158

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.