Evidence map›Paper›PMID 32103875›Full record

ArticleWorld journal of gastroenterology2020

Expression of miR-1304 in patients with esophageal carcinoma and risk factors for recurrence.

Yun-Gang Luo, Li-Wei Duan, Xuan Ji, Wen-Yuan Jia, Yun Liu, Mao-Lei Sun, Guo-Min Liu

Open access · hybridAbstract read
In one paragraph

Article in World journal of gastroenterology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.5field-weighted citation impact, top 45% 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

6 citing papers in PubMed, 8 citations in OpenAlex.

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  6. A novel lncRNA-mRNA-miRNA signature predicts recurrence and disease-free survival in cervical cancer.Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologica · 2021
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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 1 institution in 1 country.

Yun-Gang LuoJilin Provincial Medicine Anti-Tumor Engineering Center, the Second Hospital of Jilin University, Changchun 130041, Jilin Province, China.
Li-Wei DuanDepartment of Gastroenterology, the Second Hospital of Jilin University, Changchun 130041, Jilin Province, China.
Xuan JiJilin Provincial Medicine Anti-Tumor Engineering Center, the Second Hospital of Jilin University, Changchun 130041, Jilin Province, China.
Wen-Yuan JiaJilin Provincial Medicine Anti-Tumor Engineering Center, the Second Hospital of Jilin University, Changchun 130041, Jilin Province, China.
Yun LiuJilin Provincial Medicine Anti-Tumor Engineering Center, the Second Hospital of Jilin University, Changchun 130041, Jilin Province, China.
Mao-Lei SunJilin Provincial Medicine Anti-Tumor Engineering Center, the Second Hospital of Jilin University, Changchun 130041, Jilin Province, China.
Guo-Min LiuJilin Provincial Medicine Anti-Tumor Engineering Center, the Second Hospital of Jilin University, Changchun 130041, Jilin Province, China.
Second Affiliated Hospital of Jilin University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEsophageal carcinoma is a malignant gastrointestinal tumor with a very poor prognosis. MicroRNA (miR)-1304 is a newly discovered non-coding RNA, which shows differential expression in other cancers, and its clinical value in esophageal carcinoma remains unclear.

aimTo explore the expression of miR-1304 in patients with esophageal carcinoma and its clinical value.

methodsThe expression of miR-1304 in patients with esophageal carcinoma was analyzed based on the data on miR in esophageal carcinoma downloaded from The Cancer Genome Atlas database. Quantitative real-time polymerase chain reaction was adopted to determine the expression of miR-1304 in the tissues and serum of patients. The clinical diagnostic value of miR-1304 and independent factors for recurrence and prognosis of esophageal carcinoma were then analyzed. The potential target genes of miR-1304 were predicted, and then analyzed based on gene ontology, Kyoto Encyclopedia of Genes, and Genomes, and protein-protein interaction.

resultsThe expression of miR-1304 in the tissues and serum of patients with esophageal carcinoma increased, and was also increased according to the database. Patients with high expression of miR-1304 suffered increased rates of tumor ≥ 3 cm, low differentiation and stage II + III. miR-1304 had a diagnostic value in identifying esophageal carcinoma, tumor size, differentiation and TNM stage. Tumor size, differentiation, TNM stage, and miR-1304 were independent risk factors for recurrence of esophageal carcinoma, and they had certain predictive and diagnostic value for the recurrence of esophageal carcinoma. Seventy-eight patients showed a 3-year survival rate of 38.46%, and patients with high expression of miR-1304 had a relatively lower survival rate. Multivariate analysis revealed that tumor size, differentiation, recurrence and miR-1304 were independent factors for the prognosis of patients. MiRTarBase, miRDB, and Targetscan predicted 20 target genes in total. Gene ontology enrichment analysis found 18 functions with

conclusionmiR-1304 can be used as a potential indicator for the diagnosis and recurrence of esophageal carcinoma and for survival of patients with this disease.

Indexed as

Biomarkers, TumorCase-Control StudiesEsophageal NeoplasmsEsophageal Squamous Cell CarcinomaFemaleGene OntologyHumansKaplan-Meier EstimateMaleMicroRNAsMiddle AgedNeoplasm Recurrence, LocalPredictive Value of TestsPrognosisProportional Hazards ModelsReal-Time Polymerase Chain ReactionBiomarkers, TumorMicroRNAsMIRN1304 microRNA, humanBioinformatics analysisDiagnosisEsophageal carcinomamiR-1304PrognosisRecurrenceThe Cancer Genome Atlas

Identifiers

PMID32103875
PMCPMC7029348
OpenAlexW3006242053

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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.