Evidence map›Paper›PMID 40506486›Full record

ArticleZhongguo fei ai za zhi = Chinese journal of lung cancer2025

[Predictive Value of miRNAs Markers for Advanced Lung Squamous Cell Carcinoma].

Anna Wang, Jingjing Cong, Yingjia Wang, Xin'ge Li, Junjian Pi, Kaijing Liu, Hongjie Zhang, Xiaoyan Yan, Hongmei Li

Abstract readEnglish Abstract
In one paragraph

Article in Zhongguo fei ai za zhi = Chinese journal of lung cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Anna WangDepartment of Oncology, The Affiliated Hospital of Qingdao University, Qingdao 266000, China.
Jingjing CongDepartment of Oncology, The Affiliated Hospital of Qingdao University, Qingdao 266000, China.
Yingjia WangCollege of Basic Medical Sciences, Shandong First Medical University, Jinan 250117, China.
Xin'ge LiCollege of Medicine, Hainan Vocational University of Science 
and Technology, Haikou 570000, China.
Junjian PiQingdao Medical College, Qingdao University, Qingdao 266071, China.
Kaijing LiuDepartment of Oncology, The Affiliated Hospital of Qingdao University, Qingdao 266000, China.
Hongjie ZhangDepartment of Oncology, The Affiliated Hospital of Qingdao University, Qingdao 266000, China.
Xiaoyan YanDepartment of Oncology, The Affiliated Hospital of Qingdao University, Qingdao 266000, China.
Hongmei LiDepartment of Oncology, The Affiliated Hospital of Qingdao University, Qingdao 266000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLung cancer is one of the leading causes of cancer-related mortality worldwide, with above 80% of cases be non-small cell lung cancer (NSCLC), among which lung squamous cell carcinoma (LUSC) occupies a significant proportion. Although comprehensive cancer therapies have considerably improved the overall survival of patients, patients with advanced LUSC have a poorer prognosis. Therefore, there is a need for a biomarker to predict the progress of advanced LUSC in order to improve prognosis through early diagnosis. Previous studies have shown that miRNAs are differentially expressed in lung cancer tissues and play roles as potential oncogenes or tumor suppressors. The aim of this study is to identify differentially expressed miRNAs between early-stage and advanced-stage LUSC, and to establish a set of miRNAs that can predict the progress of advanced LUSC.

methodsClinical data and miRNA-related data of LUSC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Bioinformatic methods were applied to analyze the data. Receiver operating characteristic (ROC) curves were plotted, and various online tools were used to predict target genes, with subsequent analysis of the potential biological mechanisms of these genes.

resultsA total of 58 differentially expressed miRNAs were identified between the experiment group and the control group. Seven miRNAs were selected for potential construction of a miRNA biomarker through LASSO regression, and based on the area under the curve (AUC) values of each miRNA, four of these miRNAs (miR-377-3p, miR-4779, miR-6803-5p, miR-3960) were ultimately chosen as biomarkers for predicting advanced LUSC. The AUC under the ROC curve for the combined four miRNAs was 0.865. Enrichment analysis showed that these target genes were involved in several pathways, including cancer-related pathways, mitogen-activated protein kinase (MAPK) signaling pathway, serine/threonine kinase, and tyrosine kinase signaling pathways.

conclusionsThe combined use of miR-377-3p, miR-4779, miR-6803-5p and miR-3960 provides a good predictive ability for the progress of advanced LUSC patients, with an AUC of 0.865.

Indexed as

Biomarkers, TumorCarcinoma, Squamous CellLung NeoplasmsMicroRNAsFemaleGene Expression Regulation, NeoplasticHumansMaleMiddle AgedPrognosisROC CurveBiomarkers, TumorMicroRNAsAdvanced lung squamous cell carcinomaBiomarkersEnrichment analysisLung neoplasmsmiRNAs

Identifiers

PMID40506486
PMCPMC12171578

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.