Evidence map›Paper›PMID 33247622›Full record

ArticleJournal of clinical laboratory analysis2021

miR-30b-5p up-regulation related to the dismal prognosis for patients with renal cell cancer.

Chunduo Zhang, Xiang Pan, Xiqi Peng, Kaihao Liu, Jingyao Wang, Liwen Zhao, Xuan Chen, Guocheng Huang, Hang Li, Jing Ye and 1 more

Open access · goldAbstract read
In one paragraph

Article in Journal of clinical laboratory analysis, 2021. 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
0.6field-weighted citation impact, top 37% 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.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
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

11 authors at 4 institutions in 1 country.

Chunduo ZhangGuangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Shenzhen, China.
Xiang PanDepartment of Urology, Affiliated Hospital of Yangzhou University, Yangzhou, China.
Xiqi PengGuangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Shenzhen, China.
Kaihao LiuGuangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Shenzhen, China.
Jingyao WangGuangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Shenzhen, China.ORCID https://orcid.org/0000-0003-1932-1931
Liwen ZhaoGuangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Shenzhen, China.
Xuan ChenGuangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Shenzhen, China.
Guocheng HuangGuangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Shenzhen, China.
Hang LiGuangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Shenzhen, China.
Jing YeGuangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Shenzhen, China.
Yongqing LaiGuangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Shenzhen, China.ORCID https://orcid.org/0000-0002-5353-9355
Peking University Shenzhen Hospital · CNShantou University · CNAnhui Medical University · CNNorthern Jiangsu People's Hospital · CN

Funding

Peking University Shenzhen Hospital JCYJ2017001Peking University Shenzhen Hospital JCYJ2017004Peking University Shenzhen Hospital JCYJ2017005Peking University Shenzhen Hospital JCYJ2017006Peking University Shenzhen Hospital JCYJ2017007Peking University Shenzhen Hospital JCYJ2017012Peking University Shenzhen Hospital LCYJ2017001Shenzhen Health Commission SZLY2018023the Science and Technology Innovation Committee of Shenzhen Municipality JCYJ20180507183102747
6 · The paper itself

Abstract

The diagnosis of renal cell carcinoma (RCC) is often made late since there is no early symptom, which thus results in dismal patient prognosis. As a result, new biomarkers are urgently needed and efforts should be made to identify their functions in predicting RCC prognosis. microRNAs (miRNAs) are a class of small noncoding RNAs that are about 20-22 nucleotides in length, and they have been demonstrated to function as prognostic markers in numerous tumors. This study aimed to assess the role of miR-30b-5p in predicting the prognosis of RCC postoperatively. In this study, RNA was extracted from 284 formalin-fixed and paraffin-embedded kidney cancer tissue samples. After cDNA synthesis, real-time quantitative PCR (RT-qPCR) was adopted for detecting the relative miR-30b-5p level. Then, the Kaplan-Meier method, Cox regression analysis, and the receiver operating characteristic curve analysis were applied in analyzing the miR-30b-5p effect on the prognosis for patients. Our findings indicated that, following adjustment for age, gender, tumor stage, and tumor size, patients with low miR-30b-5p expression had remarkably longer overall survival. Thus, the miR-30b-5p level might be related to RCC prognosis.

Indexed as

AgedBiomarkers, TumorCarcinoma, Renal CellFemaleGene Expression Regulation, NeoplasticHumansKaplan-Meier EstimateKidney NeoplasmsMaleMicroRNAsMiddle AgedPrognosisROC CurveUp-RegulationBiomarkers, TumorMicroRNAsMIRN30b microRNA, humanmiR-30b-5pmiRNAsprognostic biomarkersrenal cell carcinoma

Identifiers

PMID33247622
PMCPMC7891535
OpenAlexW3109033559

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.