Identification of Four Novel Prognostic Biomarkers and Construction of Two Nomograms in Adrenocortical Carcinoma: A Multi-Omics Data Study via Bioinformatics and Machine Learning Methods

被引:5
|
作者
Yi, Xiaochun [1 ]
Wan, Yueming [1 ]
Cao, Weiwei [1 ]
Peng, Keliang [1 ]
Li, Xin [1 ]
Liao, Wangchun [1 ]
机构
[1] Hunan Normal Univ, Yueyang Peoples Hosp, Dept Urol, Yueyang, Peoples R China
关键词
adrenocortical carcinoma; WGCNA; hub genes; nomogram; prognosis; immune microenvironment; copy number variations; GENE-EXPRESSION; DNA-REPLICATION; SURVIVAL; TUMORS; SIGNATURES; CANCER;
D O I
10.3389/fmolb.2022.878073
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Background: Adrenocortical carcinoma (ACC) is an orphan tumor which has poor prognoses. Therefore, it is of urgent need for us to find candidate prognostic biomarkers and provide clinicians with an accurate method for survival prediction of ACC via bioinformatics and machine learning methods.Methods: Eight different methods including differentially expressed gene (DEG) analysis, weighted correlation network analysis (WGCNA), protein-protein interaction (PPI) network construction, survival analysis, expression level comparison, receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA) were used to identify potential prognostic biomarkers for ACC via seven independent datasets. Linear discriminant analysis (LDA), K-nearest neighbor (KNN), support vector machine (SVM), and time-dependent ROC were performed to further identify meaningful prognostic biomarkers (MPBs). Cox regression analyses were performed to screen factors for nomogram construction.Results: We identified nine hub genes correlated to prognosis of patients with ACC. Furthermore, four MPBs (ASPM, BIRC5, CCNB2, and CDK1) with high accuracy of survival prediction were screened out, which were enriched in the cell cycle. We also found that mutations and copy number variants of these MPBs were associated with overall survival (OS) of ACC patients. Moreover, MPB expressions were associated with immune infiltration level. Two nomograms [OS-nomogram and disease-free survival (DFS)-nomogram] were established, which could provide clinicians with an accurate, quick, and visualized method for survival prediction.Conclusion: Four novel MPBs were identified and two nomograms were constructed, which might constitute a breakthrough in treatment and prognosis prediction of patients with ACC.
引用
收藏
页数:17
相关论文
共 5 条
  • [1] Identification of multi-omics biomarkers and construction of the novel prognostic model for hepatocellular carcinoma
    Liu, Xiao
    Xiao, Chiying
    Yue, Kunyan
    Chen, Min
    Zhou, Hang
    Yan, Xiaokai
    SCIENTIFIC REPORTS, 2022, 12 (01)
  • [2] Machine learning combining multi-omics data and network algorithms identifies adrenocortical carcinoma prognostic biomarkers
    Martin-Hernandez, Roberto
    Espeso-Gil, Sergio
    Domingo, Clara
    Latorre, Pablo
    Hervas, Sergi
    Hernandez Mora, Jose Ramon
    Kotelnikova, Ekaterina
    FRONTIERS IN MOLECULAR BIOSCIENCES, 2023, 10
  • [3] Construction of a prognostic model and identification of key genes in liver hepatocellular carcinoma based on multi-omics data
    Tang, Kun
    Liu, Mingjiang
    Zhang, Cuisheng
    SCIENTIFIC REPORTS, 2025, 15 (01):
  • [4] Machine learning algorithms and biomarkers identification for pancreatic cancer diagnosis using multi-omics data integration
    Rouzbahani, Arian Karimi
    Khalili-Tanha, Ghazaleh
    Rajabloo, Yasamin
    Khojasteh-Leylakoohi, Fatemeh
    Garjan, Hassan Shokri
    Nazari, Elham
    Avan, Amir
    PATHOLOGY RESEARCH AND PRACTICE, 2024, 263
  • [5] Machine learning based identification of hub genes in renal clear cell carcinoma using multi-omics data
    Zhang, Lichao
    Liu, Mingjun
    Zhang, Zhenjiu
    Chen, Dong
    Chen, Gang
    Liu, Mingyang
    METHODS, 2022, 207 : 110 - 117