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Head and neck squamous cell carcinoma (HNSCC), as the most common type (>90%) of head and neck cancer, includes various epithelial malignancies that arise in the nasal cavity, oral cavity, pharynx, and larynx. In 2020, approximately 878 000 new cases and 444 000 deaths linked to HNSCC occurred worldwide (Sung et al., 2021). Due to the associated frequent recurrence and metastasis, HNSCC patients have poor prognosis with a five-year survival rate of 40%-50% (Jou and Hess, 2017). Therefore, novel prognostic biomarkers need to be developed to identify high-risk HNSCC patients and improve their disease outcomes.
Subject(s)
Humans , Biomarkers, Tumor/genetics , Head and Neck Neoplasms/genetics , Kaplan-Meier Estimate , RNA , Squamous Cell Carcinoma of Head and Neck , Survival Analysis , Survival RateABSTRACT
@#AIM: To explore the differentially expressed genes and crucial genes between epithelioid and mixed uveal melanoma(UM)based on bioinformatics analysis.<p>METHODS: Microarray datasets GSE22138 was extracted from gene expression omnibus database(GEO). The differentially expressed genes(DEGs)were screened out between epithelioid and mixed UM, and functional enrichment analysis were performed with DAVID database. STRING and cytoscape was applied to explore the protein-protein interaction(PPI)network and hub genes. Subsequently, cBioPortal was applied to explore the network of the hub genes, and GEPIA was adopted to study the survival analysis of hub genes.<p>RESULTS: Overall, 241 DEGs including 125 upregulated and 116 down regulated genes were identified. The DEGs mainly enriched in cell adhesion, response to drug and Positive regulation of endothelial cell proliferation. A total of 10 hub genes were identified. Survival analysis revealed the hub genes was associated with the prognosis of UM.<p>CONCLUSION: DEGs and hub genes identified by Bioinformatics analysis in the present study would be beneficial to understand mechanism and biological characteristics between epithelioid and mixed UM.
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The appropriate plot effectively conveys the author's conclusions to readers. Journal of Korean Medical Science will provide a series of special articles to show you how to make consistent and excellent plots easier. In the first of this series of special articles, I will cover Kaplan-Meier curve (or Kaplan-Meier plot) and the ease tools. This plot, generated as a result of the Survival Analysis, provides a visualization of the ‘Kaplan-Meier Survival Probability Estimate’ for each group.
Subject(s)
Survival AnalysisABSTRACT
Survival analysis mainly deals with the time to event, including death, onset of disease, and bankruptcy. The common characteristic of survival analysis is that it contains “censored” data, in which the time to event cannot be completely observed, but instead represents the lower bound of the time to event. Only the occurrence of either time to event or censoring time is observed. Many traditional statistical methods have been effectively used for analyzing survival data with censored observations. However, with the development of high-throughput technologies for producing “omics” data, more advanced statistical methods, such as regularization, should be required to construct the predictive survival model with high-dimensional genomic data. Furthermore, machine learning approaches have been adapted for survival analysis, to fit nonlinear and complex interaction effects between predictors, and achieve more accurate prediction of individual survival probability. Presently, since most clinicians and medical researchers can easily assess statistical programs for analyzing survival data, a review article is helpful for understanding statistical methods used in survival analysis. We review traditional survival methods and regularization methods, with various penalty functions, for the analysis of high-dimensional genomics, and describe machine learning techniques that have been adapted to survival analysis.
Subject(s)
Bankruptcy , Genomics , Machine Learning , Methods , Survival AnalysisABSTRACT
BACKGROUND: Although transplantation is the best treatment for many people with end-stage renal disease, the gap between the number of organs and the number of potential recipients continues to widen. In addition to living-related individuals, the primary source of donor kidney, the severe organ shortage has led to consideration of genetically unrelated but emotionally related persons as donor candidates. The aim of this study was to compare the results of spousal kidney transplantation with those of living-related kidney transplantation and to analyze the characteristics of spousal kidney transplantation. METHODS: Clinical data were retrospectively analyzed from 21 patients with spousal kidney transplantation and 205 patients with living-related kidney transplantation. Cumulative renal allograft survival was compared between the two groups using Kaplan-Meier curve and log-rank test. Subgroup analysis was done within the patients with spousal kidney transplantation. RESULTS: The patients were significantly older in spousal group (43.7+/-7.8 years) than in living-related group (36.2+/-10.8 years). Donor age was also significantly higher in spousal group (43.0+/-8.4 years) than in living-related group (39.8+/-13.9 years). The number of HLA mismatch was significantly larger in spousal group (3.79+/-1.03) than in living-related group (2.60+/-1.21). The episodes of acute rejection occurring within a year after the transplantation were more frequent in spousal group (5/21) than in living-related group (13/205). Kaplan-Meier curves for cumulative survival of renal allograft revealed no difference between spousal group and living-related group. Renal allograft survival rates in spousal group were 85.2% at 1 year, 75.2% at 5 years, and 67.7% at 10 years after the transplantation. In living-related group, renal allograft survival rates were 96.6% at 1 year, 85.9% at 5 years, and 69.9% at 10 years after the transplantation. Within the patients with spousal kidney transplantation, cumulative renal allograft survival was superior in cases with absent acute rejection, husband-to-wife transplantation, and the number of HLA mismatch less than 5. CONCLUSION: Spousal kidney transplantation shares comparable results with living-related kidney transplantation despite older age, poorer HLA matching and a higher rate of acute rejection. Spousal donor transplants could be a real alternative especially when the donors are husband and the number of HLA mismatch is less than 5.