Evidence map›Paper›PMID 35689211›Full record

ArticleCancer cell international2022

KCNN4 may weaken anti-tumor immune response via raising Tregs and diminishing resting mast cells in clear cell renal cell carcinoma.

Yankang Cui, Tianyi Shen, Feng Xu, Jing Zhang, Yuhao Wang, Jiajin Wu, Hengtao Bu, Dian Fu, Bo Fang, Huichen Lv and 6 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer cell international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
3.5field-weighted citation impact, top 6% 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

18 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.

  1. miRNAs Related to Immune Checkpoint Inhibitor Response: A Systematic Review.International journal of molecular sciences · 2024
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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

16 authors at 5 institutions in 1 country.

Yankang Cui *Department of Urology, Clinical School of Medical College, Jinling Hospital, Nanjing University, Nanjing, China.
Tianyi Shen *Department of Urology, Clinical School of Medical College, Jinling Hospital, Nanjing University, Nanjing, China.
Feng Xu *Department of Urology, Clinical School of Medical College, Jinling Hospital, Nanjing University, Nanjing, China.
Jing ZhangSchool of Chemistry and Chemical Engineering, Jiangsu Province Hi-Tech Key Laboratory for Biomedical Research, Southeast University, Nanjing, China.
Yuhao WangDepartment of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Jiajin WuDepartment of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Hengtao BuDepartment of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Dian FuDepartment of Urology, Clinical School of Medical College, Jinling Hospital, Nanjing University, Nanjing, China.
Bo FangDepartment of Urology, Clinical School of Medical College, Jinling Hospital, Nanjing University, Nanjing, China.
Huichen LvDepartment of Urology, Clinical School of Medical College, Jinling Hospital, Nanjing University, Nanjing, China.
Suchun WangDepartment of Urology, Clinical School of Medical College, Jinling Hospital, Nanjing University, Nanjing, China.
Changjie ShiDepartment of Urology, Clinical School of Medical College, Jinling Hospital, Nanjing University, Nanjing, China.
Bianjiang LiuDepartment of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China. bjliu@njmu.edu.cn.
Haowei HeDepartment of Urology, Clinical School of Medical College, Jinling Hospital, Nanjing University, Nanjing, China. hehaowei1982@hotmail.com.
Hao TangDepartment of Urology, Clinical School of Medical College, Jinling Hospital, Nanjing University, Nanjing, China. th_doctor@163.com.
Jingping GeDepartment of Urology, Clinical School of Medical College, Jinling Hospital, Nanjing University, Nanjing, China. gjp_doctor@hotmail.com.
Nanjing General Hospital of Nanjing Military Command · CNNanjing University · CNJiangsu Province Hospital · CNNanjing Medical University · CNSoutheast University · CN

Funding

333 High-level Talents Training Project in Jiangsu Province, Professional from Six-Pronged Top-Talent Program LGY2018053National Nature Science Foundation of China 81972402Postgraduate Research & Practice Innovation Program of Jiangsu Province and International Exchange and Cooperation Program for Postgraduates of Nanjing Medical University KYCX19_1159Qing Lan Project of Jiangsu University JX2161015100The Fifth Batch of Outstanding Young and Middle-aged Teachers Support Program of Nanjing Medical University; A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions JX10231802The Six Talent Peak Project of High-level Talents in Jiangsu Province WSW-017
6 · The paper itself

Abstract

backgroundStudies over the past decade have shown that competitive endogenous RNA (ceRNA) plays an essential role in the tumorigenesis and progression of clear cell renal cell carcinoma (ccRCC). Meanwhile, immune checkpoint blocker is gradually moving towards the first-line treatment of ccRCC. Hence, it's urgent to develop a new prediction model for the efficiency of immunotherapy. At present, there is no study to reveal the effect of ceRNA network on the efficiency of immunotherapy for ccRCC.

methodsTo systematically analyze the effect of ceRNA hub genes in ccRCCon immune response, we constructed prognosis models based on ceRNAs and immune cells, respectively. We constructed ceRNA network using hypergeometric distribution test and correlation analysis with R script based on The Cancer Genome Atlas (TCGA) database. We then applied the Cibersort algorithm to simulate the infiltration overview of immune cells in kidney renal clear carcinoma (KIRC) samples. Prognosis-related immune cells were screened and a predictive model of these cells was constructed. Prognosis-related immune cells and ceRNA hub genes were performed with co-expression analysis. Finally, qRT-PCR and immunofluorescence assays were performed to validate the results.

resultsThe construction of ceRNA related prognosis model contained 8 hub genes, including RELT, MYO9B, KCNN4, SIX1, OTOGL, MALAT1, hsa-miR-130b-3p, and hsa-miR-21-5p. The area under the receiver operating characteristic curve (AUC) was 0.77 at 5 years. For the construction of immune cells prognosis model, 3 immune cells (T cells regulatory, Macrophages, Mast cells resting) were adopted, and the AUC was 0.65 at 5 years. We then merged the two models by correlation analysis and co-expression analysis. Finally, we found that KCNN4 positively correlates with T cells regulatory (Tregs) and negatively correlates with mast cells resting significantly. Furthermore, higher expression of KCNN4 may lead to a higher potential for immune evasion and lower efficiency for immune checkpoint inhibitors (ICIs).

conclusionsGenerally, this is the first study to assess the prognostic value of immune related ceRNA hub genes in ccRCC, and KCNN4 was finally demonstrated to be a key regulatory factor with strong correlation with Tregs and mast cells resting.

Indexed as

ceRNAImmune cellsKCNN4PrognosisRenal cancer

Identifiers

PMID35689211
PMCPMC9185981
OpenAlexW4281677615

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Registered trials

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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.