High expression of GMNN predicts malignant progression and poor prognosis in ACC

Zhao, Xinzhao; Zhang, Xuezhou; Shao, Shixiu; Yang, Qingbo; Shen, Chengquan; Yang, Xuecheng; Jiao, Wei; Liu, Jing; Wang, Yonghua

doi:10.1186/s40001-022-00950-2

Cited by 4 publications

(1 citation statement)

References 21 publications

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“…The GMNN gene regulates the cell cycle. By inhibiting DNA replication licensing and histone H4 acetylation, GMNN promotes cell proliferation 39 . It is thought that CALHM6 regulates infection-related immunity 40 .…”

Section: Discussionmentioning

confidence: 99%

Development and verification of a combined diagnostic model for primary Sjögren's syndrome by integrated bioinformatics analysis and machine learning

Yang

Wang

et al. 2023

Sci Rep

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Primary Sjögren’s syndrome (pSS) is a chronic, systemic autoimmune disease mostly affecting the exocrine glands. This debilitating condition is complex and specific treatments remain unavailable. There is a need for the development of novel diagnostic models for early screening. Four gene profiling datasets were downloaded from the Gene Expression Omnibus database. The ‘limma’ software package was used to identify differentially expressed genes (DEGs). A random forest-supervised classification algorithm was used to screen disease-specific genes, and three machine learning algorithms, including artificial neural networks (ANN), random forest (RF), and support vector machines (SVM), were used to build a pSS diagnostic model. The performance of the model was measured using its area under the receiver operating characteristic curve. Immune cell infiltration was investigated using the CIBERSORT algorithm. A total of 96 DEGs were identified. By utilizing a RF classifier, a set of 14 signature genes that are pivotal in transcription regulation and disease progression in pSS were identified. Through the utilization of training and testing datasets, diagnostic models for pSS were successfully designed using ANN, RF, and SVM, resulting in AUCs of 0.972, 1.00, and 0.9742, respectively. The validation set yielded AUCs of 0.766, 0.8321, and 0.8223. It was the RF model that produced the best prediction performance out of the three models tested. As a result, an early predictive model for pSS was successfully developed with high diagnostic performance, providing a valuable resource for the screening and early diagnosis of pSS.

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Section: Discussionmentioning

confidence: 99%

Development and verification of a combined diagnostic model for primary Sjögren's syndrome by integrated bioinformatics analysis and machine learning

Yang

Wang

et al. 2023

Sci Rep

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Methylation and transcriptome analyses construct a prognostic model and reveal the suppressor role of VMO1 in lung adenocarcinoma

Wang,

Yin,

Zhang

et al. 2024

Cellular Signalling

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Evaluation of BUBR1, MCM2, and GMNN as oral cancer biomarkers

Abrahim,

Cavalcante,

Pardini

et al. 2024

European Journal of Cancer Prevention

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Oral cancer is a public health problem worldwide. Late diagnosis results in a low survival rate. However, this tumor can arise from oral precancerous lesions and identification of biomarkers in precursor lesions has the potential for early diagnosis, improving patient survival. In this context, proteins involved in the cell cycle control are potentially promising. This study aimed to evaluate the importance of immunohistochemical expression of BUBR1, MCM2, and GMNN as biomarkers of oral carcinogenesis considering different oral sites. Sixty-six samples of oral epithelial dysplasia (from 33 males and 33 females) and 63 samples of oral squamous cell carcinoma (from 44 males and 19 females) were subjected to immunohistochemistry to detect some human proteins. Ki67 expression was included as a marker of cell proliferation. Marker expression was quantified by manually counting at least 1000 cells, and the labeling index was used in all statistical analyses. GMNN, MCM2, BUBR1 (nuclear and cytoplasmic labeling), and Ki67 expression levels were higher in carcinomas than in dysplasia (P < 0.05). Cytoplasmic BUBR1 was a good marker of malignancy (AUC = 0.8525, P < 0.05), but Ki67 was not (AUC = 0.5943, P = 0.0713). GMNN, MCM2, BUBR1, and Ki67 had higher expression in carcinoma than in dysplasia, regardless of the site of the lesion. Cytoplasmic BUBR1 has the potential to be used as a marker of tumor progression.

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High expression of GMNN predicts malignant progression and poor prognosis in ACC

Cited by 4 publications

References 21 publications

Development and verification of a combined diagnostic model for primary Sjögren's syndrome by integrated bioinformatics analysis and machine learning

Development and verification of a combined diagnostic model for primary Sjögren's syndrome by integrated bioinformatics analysis and machine learning

Methylation and transcriptome analyses construct a prognostic model and reveal the suppressor role of VMO1 in lung adenocarcinoma

Evaluation of BUBR1, MCM2, and GMNN as oral cancer biomarkers

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