Taiji Hamada
YOU?
Author Swipe
View article: 10104-GEN-3 Quantitative Methylation Analysis of the Glioblastoma MGMT Promoter by BSAS and Its Clinical Significance
10104-GEN-3 Quantitative Methylation Analysis of the Glioblastoma MGMT Promoter by BSAS and Its Clinical Significance Open
Objective In glioblastoma (GBM), methylation of the O6-methylguanine-DNA methyltransferase (MGMT) gene promoter is a critical predictive biomarker for temozolomide (TMZ) therapy. Conventional qualitative assessment by methylation-specific …
View article: MUC1 promoter methylation pattern diversity and its association with TET3 expression and prognosis in cholangiocarcinoma
MUC1 promoter methylation pattern diversity and its association with TET3 expression and prognosis in cholangiocarcinoma Open
Cholangiocarcinoma (CC) is a highly lethal malignancy that urgently requires reliable prognostic biomarkers. Although MUC1 expression and promoter methylation have been implicated in CC, the clinical significance of promoter methylation pa…
View article: Electroporation Induces Unexpected Alterations in Gene Expression: A Tip for Selection of Optimal Transfection Method
Electroporation Induces Unexpected Alterations in Gene Expression: A Tip for Selection of Optimal Transfection Method Open
Electroporation is an efficient method for nucleotide and protein transfer, and is used for clustered regularly interspaced short palindromic repeat (CRISPR)-associated protein 9 (Cas9)-mediated genome editing. In this study, we investigat…
View article: Exercise Suppresses Head and Neck Squamous Cell Carcinoma Growth via Oncostatin M
Exercise Suppresses Head and Neck Squamous Cell Carcinoma Growth via Oncostatin M Open
Major advances have been made in cancer treatment, but the prognosis for elderly cancer patients with sarcopenia and frailty remains poor. Myokines, which are thought to exert preventive effects against sarcopenia, have been reported to be…
View article: MALAT1 functions as a transcriptional promoter of MALAT1::GLI1 fusion for truncated GLI1 protein expression in cancer
MALAT1 functions as a transcriptional promoter of MALAT1::GLI1 fusion for truncated GLI1 protein expression in cancer Open
Background The long non-coding RNA metastasis-associated lung adenocarcinoma transcript 1 ( MALAT1 ) is a cancer biomarker. Furthermore, fusion of the MALAT1 gene with glioma-associated oncogene 1 ( GLI1 ) is a diagnostic marker of plexifo…
View article: Data from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning
Data from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning Open
Purpose:Pancreatic cancer remains a disease of high mortality despite advanced diagnostic techniques. Mucins (MUC) play crucial roles in carcinogenesis and tumor invasion in pancreatic cancers. MUC1 and MUC4 expression are related to the a…
View article: Supplementary table 1 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning
Supplementary table 1 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning Open
Comparison of SVM predict ability among these kernels in LOOCV
View article: Supplementary figure 3 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning
Supplementary figure 3 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning Open
Cox proportional hazard regression analysis on a comparison between Cluster 1 and other clusters as selected by cluster analysis of the methylation status of mucin genes MUC1, MUC2, and MUC4 in non-neoplastic regions (A) and neoplastic reg…
View article: Supplementary table 3 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning
Supplementary table 3 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning Open
Univariate and multivariate analyses for overall survival (Cox proportional hazard model).
View article: Supplementary table 3 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning
Supplementary table 3 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning Open
Univariate and multivariate analyses for overall survival (Cox proportional hazard model).
View article: Supplementary table 4 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning
Supplementary table 4 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning Open
Comparison of feature between training and test cohort.
View article: Supplementary figure 1 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning
Supplementary figure 1 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning Open
Cox proportional hazard regression analysis on a comparison between Cluster 1 and Cluster 2 selected by cluster analysis of mRNA expression levels of MUC1, MUC2, and MUC4 in non-neoplastic regions (A) and neoplastic regions (B). Red solid …
View article: Supplementary figure 3 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning
Supplementary figure 3 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning Open
Cox proportional hazard regression analysis on a comparison between Cluster 1 and other clusters as selected by cluster analysis of the methylation status of mucin genes MUC1, MUC2, and MUC4 in non-neoplastic regions (A) and neoplastic reg…
View article: Supplementary table 1 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning
Supplementary table 1 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning Open
Comparison of SVM predict ability among these kernels in LOOCV
View article: Supplementary figure 4 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning
Supplementary figure 4 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning Open
Prognosis prediction by machine learning classifier in the k-fold CV test. Cox proportional hazard regression analysis for the comparison between the positive and negative groups selected by each classifier. Red solid line: predicted high-…
View article: Supplementary table 2 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning
Supplementary table 2 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning Open
LOOCV test and clinicopathological data
View article: Supplementary table 4 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning
Supplementary table 4 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning Open
Comparison of feature between training and test cohort.
View article: Supplementary figure 1 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning
Supplementary figure 1 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning Open
Cox proportional hazard regression analysis on a comparison between Cluster 1 and Cluster 2 selected by cluster analysis of mRNA expression levels of MUC1, MUC2, and MUC4 in non-neoplastic regions (A) and neoplastic regions (B). Red solid …
View article: Supplementary table 2 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning
Supplementary table 2 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning Open
LOOCV test and clinicopathological data
View article: Supplementary figure 4 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning
Supplementary figure 4 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning Open
Prognosis prediction by machine learning classifier in the k-fold CV test. Cox proportional hazard regression analysis for the comparison between the positive and negative groups selected by each classifier. Red solid line: predicted high-…
View article: Supplementary figure 2 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning
Supplementary figure 2 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning Open
Difference in the expression level of mucins between Cluster 1 and other clusters selected by cluster analysis of the methylation status of mucin genes MUC1, MUC2, and MUC4. Expression levels show relative quantification (log10).
View article: Data from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning
Data from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning Open
Purpose:Pancreatic cancer remains a disease of high mortality despite advanced diagnostic techniques. Mucins (MUC) play crucial roles in carcinogenesis and tumor invasion in pancreatic cancers. MUC1 and MUC4 expression are related to the a…
View article: Supplementary figure 2 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning
Supplementary figure 2 from Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning Open
Difference in the expression level of mucins between Cluster 1 and other clusters selected by cluster analysis of the methylation status of mucin genes MUC1, MUC2, and MUC4. Expression levels show relative quantification (log10).
View article: Favorable prognostic impact of <i>phosphatase and tensin homolog</i> alterations in wild-type isocitrate dehydrogenase and <i>telomerase reverse transcriptase</i> promoter glioblastoma
Favorable prognostic impact of <i>phosphatase and tensin homolog</i> alterations in wild-type isocitrate dehydrogenase and <i>telomerase reverse transcriptase</i> promoter glioblastoma Open
Background Telomerase reverse transcriptase promoter (TERTp) mutations are a biological marker of glioblastoma; however, the prognostic significance of TERTp mutational status is controversial. We evaluated this impact by retrospectively a…
View article: Alterations in <i>EGFR</i> and <i>PDGFRA</i> are associated with the localization of contrast-enhancing lesions in glioblastoma
Alterations in <i>EGFR</i> and <i>PDGFRA</i> are associated with the localization of contrast-enhancing lesions in glioblastoma Open
Background Glioblastoma (GBM) is a malignant brain tumor, with radiological and genetic heterogeneity. We examined the association between radiological characteristics and driver gene alterations. Methods We analyzed the driver genes of 12…
View article: Genome Editing Using Cas9 Ribonucleoprotein Is Effective for Introducing PDGFRA Variant in Cultured Human Glioblastoma Cell Lines
Genome Editing Using Cas9 Ribonucleoprotein Is Effective for Introducing PDGFRA Variant in Cultured Human Glioblastoma Cell Lines Open
Many variants of uncertain significance (VUS) have been detected in clinical cancer cases using next-generation sequencing-based cancer gene panel analysis. One strategy for the elucidation of VUS is the functional analysis of cultured can…
View article: Distribution and favorable prognostic implication of genomic <scp><i>EGFR</i></scp> alterations in <scp><i>IDH</i></scp>‐wildtype glioblastoma
Distribution and favorable prognostic implication of genomic <span><i>EGFR</i></span> alterations in <span><i>IDH</i></span>‐wildtype glioblastoma Open
Background We aimed to evaluate the mutation profile, transcriptional variants, and prognostic impact of the epidermal growth factor receptor ( EGFR ) gene in isocitrate dehydrogenase ( IDH )‐wildtype glioblastomas (GBMs). Methods We seque…