小桶
膀胱癌
肿瘤科
分级(工程)
比例危险模型
生存分析
医学
内科学
基因表达谱
基因表达
数据库
基因
癌症研究
生物
癌症
基因本体论
遗传学
生态学
计算机科学
作者
Xierzhati Aizezi,Bahatiguli Silafu,Yuan Ma,Zheng Wang,Jifu Li,Jinxing Huang
摘要
ABSTRACT Background Bladder cancer is a prevalent malignant tumor of the urinary system, primarily affecting middle‐aged and elderly populations. Objective To delineate the pathogenic significance of IGFL2 dysregulation and assess its clinical utility as a theranostic biomarker. Methods Data were retrieved from the TCGA database, whereas complementary datasets were acquired via the Gene Expression Profiling Interactive Analysis (GEPIA), the Human Protein Atlas (THPA), and cBioPortal databases. IGFL2 emerged as a prominently dysregulated gene and was screened by differential expression analysis. Kaplan–Meier survival curves and Cox regression model were used to analyze its relationship with patient survival. The degree of immune cell infiltration and its correlation with IGFL2 were evaluated, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were performed. All statistical analyses were performed using R software, and p < 0.05 was set as significant. Results IGFL2 expression was significantly upregulated in bladder cancer ( p < 0.05), with a diagnostic AUC of 0.828 (95% CI: 0.761–0.896), and was correlated with pathological TNM staging, histological grading, and overall survival (OS) outcomes. Patients with high expression of IGFL2 were associated with poorer OS and disease‐specific survival (DSS) ( p < 0.05). High expression of IGFL2 was an independent prognostic risk factor (HR = 3.049, 95% CI: 1.592–5.840, p < 0.001). IGFL2 may be involved in signaling pathways such as PI3K–Akt and MAPK. IGFL2 expression was positively correlated with the infiltration levels of macrophages, Th1 cells, and NK cells ( p < 0.05). Conclusion IGFL2 is highly expressed in bladder cancer and may be associated with a poor prognosis. IGFL2 may become a potential biomarker and an important therapeutic target for bladder cancer patients.
科研通智能强力驱动
Strongly Powered by AbleSci AI