Construction and validation of a prognostic risk assessment model for lung adenocarcinoma based on miR-34 family target genes
- VernacularTitle:基于miR-34家族靶基因的肺腺癌预后风险评估模型构建与验证
- Author:
Lingyu GU
1
;
Ang GELEMA
2
;
Dan YANG
2
;
Huifeng WANG
3
;
Lixin WANG
1
;
Hui DONG
4
Author Information
- Publication Type:Journal Article
- Keywords: miR-34 family target genes; lung adenocarcinoma; overall survival; nomogram; prognosis; risk model
- From: Acta Universitatis Medicinalis Anhui 2026;61(1):118-126
- CountryChina
- Language:Chinese
- Abstract: ObjectiveTo establish a tumor prognostic risk assessment model related to target genes of the miR-34 family. MethodsTarget genes of the miR-34 family were screened， and the scores of miR-34 target genes were assessed in 16 tumor types. Univariate Cox regression analysis was used to identify the tumor type with the strongest correlation between miR-34 target gene scores and overall survival （OS）. Gene Ontology （GO） and Kyoto Encyclopedia of Genes and Genomes （KEGG） analyses were performed to elucidate the functional roles and signaling pathways of miR-34 target genes. A prognostic risk model based on the miR-34 target genes was constructed using univariate Cox and LASSO regression analyses. Quantitative real-time PCR （qPCR） and dual-luciferase reporter assays were conducted to validate whether the target genes bind to miR-34 and measure their RNA expression levels in the relevant tumors. Additionally， the risk score was integrated with other clinical indicators to develop a nomogram prediction model for patient survival. ResultsA total of 65 target genes of the miR-34 family were screened. The cancer type exhibiting stronger correlation between the target gene scores and OS was lung adenocarcinoma （P = 0.003， HR= 5.150）. Furthermore， miR-34 target genes were predominantly enriched in oxidative stress pathways and various tumor-related processes. Three genes， LDHA， GALNT7， and SATB2， were identified as core components of the prognostic analysis model for lung adenocarcinoma. Additionally， the constructed nomogram model demonstrated robust predictive performance. ConclusionThe risk model and prognosis model of lung adenocarcinoma constructed based on the key target genes of miR-34 have good predictive performance.
