Evidence map›Paper›PMID 38007640›Full record

SynthesisCancer biomarkers : section A of Disease markers2023

The identification of N6-methyladenosine-related miRNAs predictive of hepatocellular carcinoma prognosis and immunotherapy efficacy.

Renrui Zou, Yaqian Liu, Sangsang Qiu, Ya Lu, Yan Chen, Hui Yu, Hangju Zhu, Wenbo Zhu, Longbiao Zhu, Jifeng Feng and 1 more

Open access · hybridAbstract readMeta-AnalysisValidation Study
In one paragraph

Synthesis in Cancer biomarkers : section A of Disease markers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.9field-weighted citation impact, top 24% 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

5 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. 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.

Renrui ZouJiangsu Cancer Centre, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Yaqian LiuNanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China.
Sangsang QiuThe Affiliated Wuxi People's Hospital of Nanjing Medical University, Jiangsu, China.
Ya LuJiangsu Cancer Centre, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Yan ChenJiangsu Cancer Centre, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Hui YuJiangsu Cancer Centre, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Hangju ZhuJiangsu Cancer Centre, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Wenbo ZhuJiangsu Cancer Centre, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Longbiao ZhuDepartment of The Sixth Dental Division, The Affiliated Stomatological Hospital of Nanjing Medical University, Jiangsu Province Key Laboratory of Oral Diseases, Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Jiangsu, China.
Jifeng FengJiangsu Cancer Centre, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Jing HanJiangsu Cancer Centre, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Jiangsu Cancer Hospital · CNNanjing Drum Tower Hospital · CNNanjing Medical University · CNWuxi People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHepatocellular carcinoma (HCC) has a high degree of malignancy and poor prognosis. N6-methyladenosine (m6A) modifications and microRNAs (miRNAs) play pivotal roles in tumorigenesis and development. However, the role of m6A-related miRNAs in HCC has not been clarified yet. This study aimed to identify the role of m6A-miRNAs in HCC prognosis through bioinformatics analysis.

methodsThe clinicopathological information and RNA sequencing data of 369 HCC tumor tissues and 49 tumor-adjacent tissues were downloaded from the TCGA database. A total of 23 m6A regulators were extracted to evaluated the m6A-related miRNAs using Pearson's correlation analysis. Then, we selected prognosis-related m6A-miRNAs using a univariate Cox regression model and used the consensus cluster analysis to explore the characteristics of the m6A-miRNAs. The coefficient of the least absolute shrinkage and selection operator (LASSO) Cox regression was applied to construct a prognostic risk score model. The receiver operated characteristic (ROC) analysis was applied to evaluate the prognostic value of the signature. The biological functions of targeted genes were predicted by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Then, to validate the potential predictive value for prognosis, the miRNA expression profiles from the GSE76903 and GSE6857 were used. Single sample Gene Set Enrichment Analysis (ssGSEA) and Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) were applied to assess the immune microenvironment of HCC. Additionally, a meta-analysis was used to verify the prognostic value of the m6A-microRNAs. RT-PCR was applied to validated the expression of miRNAs in HCC tissues. Cell viability, transwell assay and RNA m6A dot blot assays of HCC cells was applied to access the function of miR-17-5p.

resultsThe expression of 48 m6A-related miRNAs was identified and 17 prognostic m6A-miRNAs was discovered. The expression profile of those 17 miRNAs was divided into three clusters, and these clusters were associated with the tumor microenvironment (TME) and prognosis. The nine m6A-related miRNA signature was associated with the prognosis of HCC, the AUC of the ROC was 0.771(TCGA dataset), 0.788(GSE76903) and 0.646(GSE6857). The TME and the expression of immune checkpoint molecules were associated with the risk score. The meta-analysis also validated the prognostic value of the m6A-related miRNAs (miR182-5p (HR:1.58, 95%CI:1.04-2.40) and miR-17-5p (HR:1.58, 95%CI: 1.04-2.40)). The expression of miR-17-5p was upregulated in HCC tissues and miR-17-5p showed an oncogenic role in HCC cells.

conclusionThe clinical innovation is the use of m6A-miRNAs as biomarkers for predicting prognosis regarding immunotherapy response in HCC patients.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsMicroRNAsAdenosineHumansImmunotherapyPrognosisTumor MicroenvironmentAdenosineMicroRNAsHepatocellular carcinomaimmune checkpointm6A related miRNAsprognosistumor microenvironment

Identifiers

PMID38007640
PMCPMC12412880
OpenAlexW4388860591

What OpenQuestion holds

Textmetadata
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.