Evidence map›Paper›PMID 33750388›Full record

ArticleBMC medical genomics2021

MicroRNA related prognosis biomarkers from high throughput sequencing data of kidney renal clear cell carcinoma.

Minjiang Huang, Ti Zhang, Zhi-Yong Yao, Chaoqung Xing, Qingyi Wu, Yuan-Wu Liu, Xiao-Liang Xing

Abstract read
In one paragraph

Article in BMC medical genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

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  8. Gastric tubular adenocarcinoma with diffuse neutrophils infiltrating: characteristics and probable treatment strategy.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2024
    Article
  9. Biomarkers in renal cell carcinoma and their targeted therapies: a review.Exploration of targeted anti-tumor therapy · 2023
    Review
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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.

Minjiang Huang *Hunan University of Medicine, Huaihua, 418000, Hunan, People's Republic of China.
Ti Zhang *Hunan University of Medicine, Huaihua, 418000, Hunan, People's Republic of China.
Zhi-Yong YaoHunan University of Medicine, Huaihua, 418000, Hunan, People's Republic of China.
Chaoqung XingThe First Affiliated Hospital of Hunan University of Medicine, Huaihua, 418000, Hunan, People's Republic of China.
Qingyi WuHunan University of Medicine, Huaihua, 418000, Hunan, People's Republic of China.
Yuan-Wu LiuBeijing Advanced Innovation Center for Food Nutrition and Human Health, China Agricultural University, 100193, Beijing, People's Republic of China.
Xiao-Liang XingHunan University of Medicine, Huaihua, 418000, Hunan, People's Republic of China. xiaoliangxinghnm@126.com.ORCID 0000-0003-3075-3102

Funding

Hunan Provincial Education Department 20B417Hunan Provincial Education Department 20C1328Hunan Provincial Science & Technology Department 2020SK51202Hunan University of Medicine Foundation 2020122004
6 · The paper itself

Abstract

backgroundKidney renal clear cell carcinoma (KIRC) is the most common type of kidney cell carcinoma which has the worst overall survival rate. Almost 30% of patients with localized cancers eventually develop to metastases despite of early surgical treatment carried out. MicroRNAs (miRNAs) play a critical role in human cancer initiation, progression, and prognosis. The aim of our study was to identify potential prognosis biomarkers to predict overall survival of KIRC.

methodsAll data were downloaded from an open access database The Cancer Genome Atlas. DESeq2 package in R was used to screening the differential expression miRNAs (DEMs) and genes (DEGs). RegParallel and Survival packages in R was used to analysis their relationships with the KIRC patients. David version 6.8 and STRING version 11 were used to take the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis.

resultsWe found 2 DEGs (TIMP3 and HMGCS1) and 3 DEMs (hsa-miR-21-5p, hsa-miR-223-3p, and hsa-miR-365a-3p) could be prognosis biomarkers for the prediction of KIRC patients. The constructed prognostic model based on those 2 DEGs could effectively predict the survival status of KIRC. And the constructed prognostic model based on those 3 DEMs could effectively predict the survival status of KIRC in 3-year and 5-year.

conclusionThe current study provided novel insights into the miRNA related mRNA network in KIRC and those 2 DEGs biomarkers and 3 DEMs biomarkers may be independent prognostic signatures in predicting the survival of KIRC patients.

Indexed as

Biomarkers, TumorCarcinoma, Renal CellHigh-Throughput Nucleotide SequencingKidney NeoplasmsMicroRNAsGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMalePrognosisBiomarkers, TumorMicroRNAsMIRN21 microRNA, humanMIRN365 microRNA, humanBiomarkersDEGsDEMsKIRCOverall survivals

Identifiers

PMID33750388
PMCPMC7941961

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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.