A recurrence model for laryngeal cancer based on SVM and gene function clustering

Su, Jili; Zhang, Yanqiu; Su, Haodong; Zhang, Chuanhai; Li, Wei

doi:10.1080/00016489.2016.1247984

Cited by 17 publications

(8 citation statements)

References 18 publications

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“…Previous findings have shown that in aortic tissue, destruction of MYH11 can lead to vascular smooth muscle cell loss, disorganization and hyperplasia, which is one of the mechanisms leading to thoracic aortic aneurysms and dissections (6,7). In previous years, studies have indicated that mutations of the MYH11 gene are also associated with various types of cancer, including colorectal (8,9), bladder (10), laryngeal (11) and head and neck cancer (12). This may be due to the role of MYH11 role in cell migration, interaction with cell adhesion proteins and tumor suppression (13,14).…”

Section: Introductionmentioning

confidence: 99%

Clinical and prognostic significance of MYH11 in lung cancer

et al. 2020

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Myosin heavy chain 11 (MYH11), encoded by the MYH11 gene, is a protein that participates in muscle contraction through the hydrolysis of adenosine triphosphate. Although previous studies have demonstrated that MYH11 gene expression levels are downregulated in several types of cancer, its expression levels have rarely been investigated in lung cancer. The present study aimed to explore the clinical significance and prognostic value of MYH11 expression levels in lung cancer and to further study the underlying molecular mechanisms of the function of this gene. The Oncomine database showed that the MYH11 expression levels were decreased in lung cancer compared with those noted in the normal lung tissue (P<0.05). Kaplan-Meier plotter results revealed that the decreased MYH11 expression levels were correlated with poor prognosis in lung cancer patients. Among the lung cancer cases with gene alteration of MYH11, mutation was the most common of all alteration types. Coexpedia and Metascape analyses revealed that the target genes were primarily enriched in 'muscle contraction', 'contractile fiber part', 'actin cytoskeleton' and the 'adherens junction'. These results indicated that MYH11 is a potential novel drug target and prognostic indicator of lung cancer.

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Section: Introductionmentioning

confidence: 99%

Clinical and prognostic significance of MYH11 in lung cancer

et al. 2020

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“…In recent years, more and more researches have been conducted to verify the accuracy of tumor biomarker prediction based on SVM classifiers [12,24,25]. In this study, we identified five important biological functions through RFE which included GO: 0007242~intracellular signaling cascade, GO:0006140~regulation of nucleotide metabolic process, hsa04080: Neuroactive ligand-receptor interaction, hsa04120: Ubiquitin mediated proteolysis, hsa04142: Lysosome.…”

Section: Discussionmentioning

confidence: 99%

Identification of feature risk pathways of smoking-induced lung cancer based on SVM

Chen¹,

Lin

2020

PLoS ONE

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The present study aims to explore the role of smoking factors in the risk of lung cancer and screen the feature risk pathways of smoking-induced lung cancer. Methods The expression profiles of the patient data from GEO database were standardized, and differentially expressed genes (DEGs) were analyzed by limma algorithm. Samples and genes were analyzed by Unsupervised hierarchical clustering method, while GO and KEGG enrichment analyses were performed on DEGs. The data of the protein-protein interaction (PPI) network were downloaded from the BioGrid and HPRD databases, and the DEGs were mapped into the PPI network to identify the interaction relationship. The enriched significant pathways were used to calculate the anomaly score and RFE method was used to optimize the feature sets. The model was trained using the support vector machine (SVM) and the predicted results were plotted into ROC curves. The AUC value was calculated to evaluate the predictive performance of the SVM model. Results A total of 1923 DEGs were obtained, of which 826 were down-regulated and 1097 were upregulated. Unsupervised hierarchical clustering analysis showed that the diagnosis accuracy of lung cancer smokers was 74%, and that of non-lung cancer smokers was 75%. Five optimal feature pathway sets were obtained by screening, the clinical diagnostic ability of which was detected by SVM model with the accuracy improved to 84%. The diagnostic accuracy was 90% after combining clinical information. Conclusion We verified that five signaling pathways combined with clinical information could be used as a feature risk pathway for identifying lung cancer smokers and non-lung cancer smokers and increased the diagnostic accuracy.

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“…Several previous studies have revealed that MYH11 is a biomarker of BLCA (15,17), which is consistent with our conclusion. Additionally, the expression level of MYH11 has been reported to be dysregulated in colorectal cancer (33,34), lung cancer (35), laryngeal cancer (36) and acute myeloid leukemia (37). Based on these researches above mentioned, we speculate that myosin-dependent contractile activity in nonmuscle cells may be participated in cell adhesion and migration to promote tumor invasion.…”

Section: Discussionmentioning

confidence: 84%

Identification of Hub Genes Associated With Progression and Prognosis of Bladder Cancer by Integrated Bioinformatics Analysis.

Liu

Sheng

et al. 2021

Preprint

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Background: Bladder cancer (BLCA) is the most popular malignant carcinomas in genitourinary system which has a high incidence and is prone to relapse. However, the molecular mechanism of BLCA remains to be unclear. Moreover, there is still a shortage of effective biomarkers that can predict progression and prognosis of BLCA. The objective of current study is to screen significant genes as biomarkers to forecast the progression and prognosis of BLCA patients.Methods: Gene expression profile downloaded from TCGA database and GEO database was used. Differential gene expression analysis and WGCNA were conducted to identify differential co-expression genes. In addition, GO enrichment analysis and KEGG pathway analysis were used to explore the functions of these genes. Moreover, PPI network, OS and DFS were used to identify survival-related hub genes. Finally, the expression levels of these genes were validated by qRT-PCR and HPA database.Results: About 124 differential co-expression genes were identified. And these genes were mainly enriched in muscle system process and muscle contraction (BP), contractile fiber, myofibril, sarcomere, focal adhesion and cell-substrate junction (CC) and actin binding (MF) in GO enrichment analysis, while enriched in vascular smooth muscle contraction, focal adhesion, cardiac muscle contraction, hypertrophic cardiomyopathy, dilated cardiomyopathy and regulation of actin cytoskeleton in KEGG analysis. Furthermore, five survival-related hub genes (MYH11, ACTA2, CALD1, TPM1, MYLK) were identified via overall OS and DFS. In addition, the expression levels of the five survival-related genes were upregulated with the procession of BLCA, such as grade, stage and TNM stage. Finally, all survival-related hub genes were found to be down-regulated in BLCA via qRT-PCR and HPA database.Conclusions: Our current study verified five new key genes in BLCA, which could help us better understand the pathogenesis of BLCA. And these five hub genes may be involved in the development and progression of BLCA and served as potential biomarkers.

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A recurrence model for laryngeal cancer based on SVM and gene function clustering

Cited by 17 publications

References 18 publications

Clinical and prognostic significance of MYH11 in lung cancer

Clinical and prognostic significance of MYH11 in lung cancer

Identification of feature risk pathways of smoking-induced lung cancer based on SVM

Identification of Hub Genes Associated With Progression and Prognosis of Bladder Cancer by Integrated Bioinformatics Analysis.

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