Table_5_Exploration of a Novel Prognostic Nomogram and Diagnostic Biomarkers Based on the Activity Variations of Hallmark Gene Sets in Hepatocellular Carcinoma.xlsx

BackgroundThe initiation and progression of tumors were due to variations of gene sets rather than individual genes. This study aimed to identify novel biomarkers based on gene set variation analysis (GSVA) in hepatocellular carcinoma.MethodsThe activities of 50 hallmark pathways were scored in three microarray datasets with paired samples with GSVA, and differential analysis was performed with the limma R package. Unsupervised clustering was conducted to determine subtypes with the ConsensusClusterPlus R package in the TCGA-LIHC (n = 329) and LIRI-JP (n = 232) cohorts. Differentially expressed genes among subtypes were identified as initial variables. Then, we used TCGA-LIHC as the training set and LIRI-JP as the validation set. A six-gene model calculating the risk scores of patients was integrated with the least absolute shrinkage and selection operator (LASSO) and stepwise regression analyses. Kaplan–Meier (KM) and receiver operating characteristic (ROC) curves were performed to assess predictive performances. Multivariate Cox regression analyses were implemented to select independent prognostic factors, and a prognostic nomogram was integrated. Moreover, the diagnostic values of six genes were explored with the ROC curves and immunohistochemistry.ResultsPatients could be separated into two subtypes with different prognoses in both cohorts based on the identified differential hallmark pathways. Six prognostic genes (ASF1A, CENPA, LDHA, PSMB2, SRPRB, UCK2) were included in the risk score signature, which was demonstrated to be an independent prognostic factor. A nomogram including 540 patients was further integrated and well-calibrated. ROC analyses in the five cohorts and immunohistochemistry experiments in solid tissues indicated that CENPA and UCK2 exhibited high and robust diagnostic values.ConclusionsOur study explored a promising prognostic nomogram and diagnostic biomarkers in hepatocellular carcinoma.</p

Table_2_Exploration of a Novel Prognostic Nomogram and Diagnostic Biomarkers Based on the Activity Variations of Hallmark Gene Sets in Hepatocellular Carcinoma.xlsx

BackgroundThe initiation and progression of tumors were due to variations of gene sets rather than individual genes. This study aimed to identify novel biomarkers based on gene set variation analysis (GSVA) in hepatocellular carcinoma.MethodsThe activities of 50 hallmark pathways were scored in three microarray datasets with paired samples with GSVA, and differential analysis was performed with the limma R package. Unsupervised clustering was conducted to determine subtypes with the ConsensusClusterPlus R package in the TCGA-LIHC (n = 329) and LIRI-JP (n = 232) cohorts. Differentially expressed genes among subtypes were identified as initial variables. Then, we used TCGA-LIHC as the training set and LIRI-JP as the validation set. A six-gene model calculating the risk scores of patients was integrated with the least absolute shrinkage and selection operator (LASSO) and stepwise regression analyses. Kaplan–Meier (KM) and receiver operating characteristic (ROC) curves were performed to assess predictive performances. Multivariate Cox regression analyses were implemented to select independent prognostic factors, and a prognostic nomogram was integrated. Moreover, the diagnostic values of six genes were explored with the ROC curves and immunohistochemistry.ResultsPatients could be separated into two subtypes with different prognoses in both cohorts based on the identified differential hallmark pathways. Six prognostic genes (ASF1A, CENPA, LDHA, PSMB2, SRPRB, UCK2) were included in the risk score signature, which was demonstrated to be an independent prognostic factor. A nomogram including 540 patients was further integrated and well-calibrated. ROC analyses in the five cohorts and immunohistochemistry experiments in solid tissues indicated that CENPA and UCK2 exhibited high and robust diagnostic values.ConclusionsOur study explored a promising prognostic nomogram and diagnostic biomarkers in hepatocellular carcinoma.</p

Table_7_Exploration of a Novel Prognostic Nomogram and Diagnostic Biomarkers Based on the Activity Variations of Hallmark Gene Sets in Hepatocellular Carcinoma.xlsx

BackgroundThe initiation and progression of tumors were due to variations of gene sets rather than individual genes. This study aimed to identify novel biomarkers based on gene set variation analysis (GSVA) in hepatocellular carcinoma.MethodsThe activities of 50 hallmark pathways were scored in three microarray datasets with paired samples with GSVA, and differential analysis was performed with the limma R package. Unsupervised clustering was conducted to determine subtypes with the ConsensusClusterPlus R package in the TCGA-LIHC (n = 329) and LIRI-JP (n = 232) cohorts. Differentially expressed genes among subtypes were identified as initial variables. Then, we used TCGA-LIHC as the training set and LIRI-JP as the validation set. A six-gene model calculating the risk scores of patients was integrated with the least absolute shrinkage and selection operator (LASSO) and stepwise regression analyses. Kaplan–Meier (KM) and receiver operating characteristic (ROC) curves were performed to assess predictive performances. Multivariate Cox regression analyses were implemented to select independent prognostic factors, and a prognostic nomogram was integrated. Moreover, the diagnostic values of six genes were explored with the ROC curves and immunohistochemistry.ResultsPatients could be separated into two subtypes with different prognoses in both cohorts based on the identified differential hallmark pathways. Six prognostic genes (ASF1A, CENPA, LDHA, PSMB2, SRPRB, UCK2) were included in the risk score signature, which was demonstrated to be an independent prognostic factor. A nomogram including 540 patients was further integrated and well-calibrated. ROC analyses in the five cohorts and immunohistochemistry experiments in solid tissues indicated that CENPA and UCK2 exhibited high and robust diagnostic values.ConclusionsOur study explored a promising prognostic nomogram and diagnostic biomarkers in hepatocellular carcinoma.</p

Table_6_Exploration of a Novel Prognostic Nomogram and Diagnostic Biomarkers Based on the Activity Variations of Hallmark Gene Sets in Hepatocellular Carcinoma.xlsx

BackgroundThe initiation and progression of tumors were due to variations of gene sets rather than individual genes. This study aimed to identify novel biomarkers based on gene set variation analysis (GSVA) in hepatocellular carcinoma.MethodsThe activities of 50 hallmark pathways were scored in three microarray datasets with paired samples with GSVA, and differential analysis was performed with the limma R package. Unsupervised clustering was conducted to determine subtypes with the ConsensusClusterPlus R package in the TCGA-LIHC (n = 329) and LIRI-JP (n = 232) cohorts. Differentially expressed genes among subtypes were identified as initial variables. Then, we used TCGA-LIHC as the training set and LIRI-JP as the validation set. A six-gene model calculating the risk scores of patients was integrated with the least absolute shrinkage and selection operator (LASSO) and stepwise regression analyses. Kaplan–Meier (KM) and receiver operating characteristic (ROC) curves were performed to assess predictive performances. Multivariate Cox regression analyses were implemented to select independent prognostic factors, and a prognostic nomogram was integrated. Moreover, the diagnostic values of six genes were explored with the ROC curves and immunohistochemistry.ResultsPatients could be separated into two subtypes with different prognoses in both cohorts based on the identified differential hallmark pathways. Six prognostic genes (ASF1A, CENPA, LDHA, PSMB2, SRPRB, UCK2) were included in the risk score signature, which was demonstrated to be an independent prognostic factor. A nomogram including 540 patients was further integrated and well-calibrated. ROC analyses in the five cohorts and immunohistochemistry experiments in solid tissues indicated that CENPA and UCK2 exhibited high and robust diagnostic values.ConclusionsOur study explored a promising prognostic nomogram and diagnostic biomarkers in hepatocellular carcinoma.</p

Table_3_Exploration of a Novel Prognostic Nomogram and Diagnostic Biomarkers Based on the Activity Variations of Hallmark Gene Sets in Hepatocellular Carcinoma.xlsx

BackgroundThe initiation and progression of tumors were due to variations of gene sets rather than individual genes. This study aimed to identify novel biomarkers based on gene set variation analysis (GSVA) in hepatocellular carcinoma.MethodsThe activities of 50 hallmark pathways were scored in three microarray datasets with paired samples with GSVA, and differential analysis was performed with the limma R package. Unsupervised clustering was conducted to determine subtypes with the ConsensusClusterPlus R package in the TCGA-LIHC (n = 329) and LIRI-JP (n = 232) cohorts. Differentially expressed genes among subtypes were identified as initial variables. Then, we used TCGA-LIHC as the training set and LIRI-JP as the validation set. A six-gene model calculating the risk scores of patients was integrated with the least absolute shrinkage and selection operator (LASSO) and stepwise regression analyses. Kaplan–Meier (KM) and receiver operating characteristic (ROC) curves were performed to assess predictive performances. Multivariate Cox regression analyses were implemented to select independent prognostic factors, and a prognostic nomogram was integrated. Moreover, the diagnostic values of six genes were explored with the ROC curves and immunohistochemistry.ResultsPatients could be separated into two subtypes with different prognoses in both cohorts based on the identified differential hallmark pathways. Six prognostic genes (ASF1A, CENPA, LDHA, PSMB2, SRPRB, UCK2) were included in the risk score signature, which was demonstrated to be an independent prognostic factor. A nomogram including 540 patients was further integrated and well-calibrated. ROC analyses in the five cohorts and immunohistochemistry experiments in solid tissues indicated that CENPA and UCK2 exhibited high and robust diagnostic values.ConclusionsOur study explored a promising prognostic nomogram and diagnostic biomarkers in hepatocellular carcinoma.</p

sj-docx-1-ine-10.1177_15910199221125094 - Supplemental material for Direct carotid artery puncture for acute ischemic stroke: Local experience and systematic review

Supplemental material, sj-docx-1-ine-10.1177_15910199221125094 for Direct carotid artery puncture for acute ischemic stroke: Local experience and systematic review by Chun Yin, Yasuo Ding and Hao Chang in Interventional Neuroradiology</p

Table_1_Exploration of a Novel Prognostic Nomogram and Diagnostic Biomarkers Based on the Activity Variations of Hallmark Gene Sets in Hepatocellular Carcinoma.xlsx

BackgroundThe initiation and progression of tumors were due to variations of gene sets rather than individual genes. This study aimed to identify novel biomarkers based on gene set variation analysis (GSVA) in hepatocellular carcinoma.MethodsThe activities of 50 hallmark pathways were scored in three microarray datasets with paired samples with GSVA, and differential analysis was performed with the limma R package. Unsupervised clustering was conducted to determine subtypes with the ConsensusClusterPlus R package in the TCGA-LIHC (n = 329) and LIRI-JP (n = 232) cohorts. Differentially expressed genes among subtypes were identified as initial variables. Then, we used TCGA-LIHC as the training set and LIRI-JP as the validation set. A six-gene model calculating the risk scores of patients was integrated with the least absolute shrinkage and selection operator (LASSO) and stepwise regression analyses. Kaplan–Meier (KM) and receiver operating characteristic (ROC) curves were performed to assess predictive performances. Multivariate Cox regression analyses were implemented to select independent prognostic factors, and a prognostic nomogram was integrated. Moreover, the diagnostic values of six genes were explored with the ROC curves and immunohistochemistry.ResultsPatients could be separated into two subtypes with different prognoses in both cohorts based on the identified differential hallmark pathways. Six prognostic genes (ASF1A, CENPA, LDHA, PSMB2, SRPRB, UCK2) were included in the risk score signature, which was demonstrated to be an independent prognostic factor. A nomogram including 540 patients was further integrated and well-calibrated. ROC analyses in the five cohorts and immunohistochemistry experiments in solid tissues indicated that CENPA and UCK2 exhibited high and robust diagnostic values.ConclusionsOur study explored a promising prognostic nomogram and diagnostic biomarkers in hepatocellular carcinoma.</p

Image_1_Exploration of a Novel Prognostic Nomogram and Diagnostic Biomarkers Based on the Activity Variations of Hallmark Gene Sets in Hepatocellular Carcinoma.tif

BackgroundThe initiation and progression of tumors were due to variations of gene sets rather than individual genes. This study aimed to identify novel biomarkers based on gene set variation analysis (GSVA) in hepatocellular carcinoma.MethodsThe activities of 50 hallmark pathways were scored in three microarray datasets with paired samples with GSVA, and differential analysis was performed with the limma R package. Unsupervised clustering was conducted to determine subtypes with the ConsensusClusterPlus R package in the TCGA-LIHC (n = 329) and LIRI-JP (n = 232) cohorts. Differentially expressed genes among subtypes were identified as initial variables. Then, we used TCGA-LIHC as the training set and LIRI-JP as the validation set. A six-gene model calculating the risk scores of patients was integrated with the least absolute shrinkage and selection operator (LASSO) and stepwise regression analyses. Kaplan–Meier (KM) and receiver operating characteristic (ROC) curves were performed to assess predictive performances. Multivariate Cox regression analyses were implemented to select independent prognostic factors, and a prognostic nomogram was integrated. Moreover, the diagnostic values of six genes were explored with the ROC curves and immunohistochemistry.ResultsPatients could be separated into two subtypes with different prognoses in both cohorts based on the identified differential hallmark pathways. Six prognostic genes (ASF1A, CENPA, LDHA, PSMB2, SRPRB, UCK2) were included in the risk score signature, which was demonstrated to be an independent prognostic factor. A nomogram including 540 patients was further integrated and well-calibrated. ROC analyses in the five cohorts and immunohistochemistry experiments in solid tissues indicated that CENPA and UCK2 exhibited high and robust diagnostic values.ConclusionsOur study explored a promising prognostic nomogram and diagnostic biomarkers in hepatocellular carcinoma.</p

Table_4_Exploration of a Novel Prognostic Nomogram and Diagnostic Biomarkers Based on the Activity Variations of Hallmark Gene Sets in Hepatocellular Carcinoma.xlsx

BackgroundThe initiation and progression of tumors were due to variations of gene sets rather than individual genes. This study aimed to identify novel biomarkers based on gene set variation analysis (GSVA) in hepatocellular carcinoma.MethodsThe activities of 50 hallmark pathways were scored in three microarray datasets with paired samples with GSVA, and differential analysis was performed with the limma R package. Unsupervised clustering was conducted to determine subtypes with the ConsensusClusterPlus R package in the TCGA-LIHC (n = 329) and LIRI-JP (n = 232) cohorts. Differentially expressed genes among subtypes were identified as initial variables. Then, we used TCGA-LIHC as the training set and LIRI-JP as the validation set. A six-gene model calculating the risk scores of patients was integrated with the least absolute shrinkage and selection operator (LASSO) and stepwise regression analyses. Kaplan–Meier (KM) and receiver operating characteristic (ROC) curves were performed to assess predictive performances. Multivariate Cox regression analyses were implemented to select independent prognostic factors, and a prognostic nomogram was integrated. Moreover, the diagnostic values of six genes were explored with the ROC curves and immunohistochemistry.ResultsPatients could be separated into two subtypes with different prognoses in both cohorts based on the identified differential hallmark pathways. Six prognostic genes (ASF1A, CENPA, LDHA, PSMB2, SRPRB, UCK2) were included in the risk score signature, which was demonstrated to be an independent prognostic factor. A nomogram including 540 patients was further integrated and well-calibrated. ROC analyses in the five cohorts and immunohistochemistry experiments in solid tissues indicated that CENPA and UCK2 exhibited high and robust diagnostic values.ConclusionsOur study explored a promising prognostic nomogram and diagnostic biomarkers in hepatocellular carcinoma.</p

Surface Oxidation and Wetting Synergistic Effect of Liquid Metals

Various functions of liquid metals are closely related to their surface performances, among which oxidation and wetting are the two most important surface processes. The two processes of liquid metals are inseparable in most practical applications; however, the coupling of oxidation and wetting of liquid metals has received little attention. Here, we demonstrate the synergistic effect of oxidation and wetting of liquid metals through establishing a liquid system containing the copper ion acid solution. By modulating the concentrations of copper ions and hydrogen ions, three different modes of the liquid metal surface are presented, where the oxidation process and the wetting process are in a competitive relationship. Whichever of the two processes is dominant can determine the stability of copper particles produced on the surface of liquid metals, that is, affect whether the “phagocytosis” process can occur. It is revealed that the magnitude of current density on the surface of liquid metals, caused by galvanic corrosion behavior between liquid metals and copper particles, is the key factor influencing the dominance of different surface processes of liquid metals. Utilizing the synergistic effect, we prepare a liquid metal film with adjustable reflectivity, in which surface states can be changed repeatedly between the bright state and the darken state by simple solution immersion. The liquid metal film with different surface states can show obvious difference in optical performance, which has application potential in color camouflage. Understanding the surface synergistic effect will facilitate further exploration of the abundant exotic liquid metal interface phenomena