High-Intensity Focused Ultrasound Combined with Ti₃C₂–TiO₂ to Enhance Electrochemiluminescence of Luminol for the Sensitive Detection of Polynucleotide Kinase

Luminol is a classic electrochemiluminescence (ECL) luminophore. The luminol–O2 ECL system suffers from a problem, that is, the conversion rate of dissolved O2 into reactive oxygen species (ROS) is low. In this work, we used high-intensity focused ultrasound (HIFU) pretreatment combined with Ti3C2–TiO2 to construct a highly sensitive luminol–O2 ECL system for the specific detection of polynucleotide kinase (PNK) first. On the one hand, HIFU generated ROS in situ as a coreactant via the cavitation effect to boost the luminol emission. On the other hand, Ti3C2–TiO2 was prepared in situ via Ti3C2 as a reducing agent, and it can aggregate and catalyze ROS generated in situ by HIFU. Moreover, the Ti on the Ti3C2–TiO2 surface could bind to phosphate groups through chelation, thereby realizing highly specific detection of PNK. The sensor has a linear relationship range of 1.0 × 10–5 to 10.0 U mL–1, and the limit of detection is 1.48 × 10–7 U mL–1, which is superior to most existing methods. The sensor performance in HeLa cell lysate was measured with a satisfactory result. The designed ECL biosensor has potential applications in biological analysis and clinical diagnosis

sj-tif-2-tct-10.1177_15330338231218218 - Supplemental material for miRNA-130a-3p/CPEB4 Axis Modulates Glioblastoma Growth and Progression

Supplemental material, sj-tif-2-tct-10.1177_15330338231218218 for miRNA-130a-3p/CPEB4 Axis Modulates Glioblastoma Growth and Progression by Hongchao Liu, Zhihao Wei, Kangke Shi, Yu Zhang and Jiaqiong Li in Technology in Cancer Research & Treatment</p

Data_Sheet_5_TGF-β based risk model to predict the prognosis and immune features in glioblastoma.PDF

BackgroundTransforming growth factor-β (TGF-β) is a multifunctional cytokine with an important role in tissue development and tumorigenesis. TGF-β can inhibit the function of many immune cells, prevent T cells from penetrating into the tumor center, so that the tumor cells escape from immune surveillance and lead to low sensitivity to immunotherapy. However, its potential roles in predicting clinical prognosis and tumor microenvironment (TME) immune features need to be deeply investigated in glioblastoma (GBM).MethodsThe TCGA-GBM dataset was obtained from the Cancer Genome Atlas, and the validation dataset was downloaded from Gene Expression Omnibus. Firstly, differentially expressed TGF-β genes (DEGs) were screened between GBM and normal samples. Then, univariate and multivariate Cox analyses were used to identify prognostic genes and develop the TGF-β risk model. Subsequently, the roles of TGF-β risk score in predicting clinical prognosis and immune characteristics were investigated.ResultsThe TGF-β risk score signature with an independent prognostic value was successfully developed. The TGF-β risk score was positively correlated with the infiltration levels of tumor-infiltrating immune cells, and the activities of anticancer immunity steps. In addition, the TGF-β risk score was positively related to the expression of immune checkpoints. Besides, the high score indicated higher sensitivity to immune checkpoint inhibitors.ConclusionsWe first developed and validated a TGF-β risk signature that could predict the clinical prognosis and TME immune features for GBM. In addition, the TGF-β signature could guide a more personalized therapeutic approach for GBM.</p

Data_Sheet_2_TGF-β based risk model to predict the prognosis and immune features in glioblastoma.PDF

BackgroundTransforming growth factor-β (TGF-β) is a multifunctional cytokine with an important role in tissue development and tumorigenesis. TGF-β can inhibit the function of many immune cells, prevent T cells from penetrating into the tumor center, so that the tumor cells escape from immune surveillance and lead to low sensitivity to immunotherapy. However, its potential roles in predicting clinical prognosis and tumor microenvironment (TME) immune features need to be deeply investigated in glioblastoma (GBM).MethodsThe TCGA-GBM dataset was obtained from the Cancer Genome Atlas, and the validation dataset was downloaded from Gene Expression Omnibus. Firstly, differentially expressed TGF-β genes (DEGs) were screened between GBM and normal samples. Then, univariate and multivariate Cox analyses were used to identify prognostic genes and develop the TGF-β risk model. Subsequently, the roles of TGF-β risk score in predicting clinical prognosis and immune characteristics were investigated.ResultsThe TGF-β risk score signature with an independent prognostic value was successfully developed. The TGF-β risk score was positively correlated with the infiltration levels of tumor-infiltrating immune cells, and the activities of anticancer immunity steps. In addition, the TGF-β risk score was positively related to the expression of immune checkpoints. Besides, the high score indicated higher sensitivity to immune checkpoint inhibitors.ConclusionsWe first developed and validated a TGF-β risk signature that could predict the clinical prognosis and TME immune features for GBM. In addition, the TGF-β signature could guide a more personalized therapeutic approach for GBM.</p

Table_1_TGF-β based risk model to predict the prognosis and immune features in glioblastoma.pdf

BackgroundTransforming growth factor-β (TGF-β) is a multifunctional cytokine with an important role in tissue development and tumorigenesis. TGF-β can inhibit the function of many immune cells, prevent T cells from penetrating into the tumor center, so that the tumor cells escape from immune surveillance and lead to low sensitivity to immunotherapy. However, its potential roles in predicting clinical prognosis and tumor microenvironment (TME) immune features need to be deeply investigated in glioblastoma (GBM).MethodsThe TCGA-GBM dataset was obtained from the Cancer Genome Atlas, and the validation dataset was downloaded from Gene Expression Omnibus. Firstly, differentially expressed TGF-β genes (DEGs) were screened between GBM and normal samples. Then, univariate and multivariate Cox analyses were used to identify prognostic genes and develop the TGF-β risk model. Subsequently, the roles of TGF-β risk score in predicting clinical prognosis and immune characteristics were investigated.ResultsThe TGF-β risk score signature with an independent prognostic value was successfully developed. The TGF-β risk score was positively correlated with the infiltration levels of tumor-infiltrating immune cells, and the activities of anticancer immunity steps. In addition, the TGF-β risk score was positively related to the expression of immune checkpoints. Besides, the high score indicated higher sensitivity to immune checkpoint inhibitors.ConclusionsWe first developed and validated a TGF-β risk signature that could predict the clinical prognosis and TME immune features for GBM. In addition, the TGF-β signature could guide a more personalized therapeutic approach for GBM.</p

sj-docx-4-tct-10.1177_15330338231218218 - Supplemental material for miRNA-130a-3p/CPEB4 Axis Modulates Glioblastoma Growth and Progression

Supplemental material, sj-docx-4-tct-10.1177_15330338231218218 for miRNA-130a-3p/CPEB4 Axis Modulates Glioblastoma Growth and Progression by Hongchao Liu, Zhihao Wei, Kangke Shi, Yu Zhang and Jiaqiong Li in Technology in Cancer Research & Treatment</p

Data_Sheet_7_TGF-β based risk model to predict the prognosis and immune features in glioblastoma.PDF

BackgroundTransforming growth factor-β (TGF-β) is a multifunctional cytokine with an important role in tissue development and tumorigenesis. TGF-β can inhibit the function of many immune cells, prevent T cells from penetrating into the tumor center, so that the tumor cells escape from immune surveillance and lead to low sensitivity to immunotherapy. However, its potential roles in predicting clinical prognosis and tumor microenvironment (TME) immune features need to be deeply investigated in glioblastoma (GBM).MethodsThe TCGA-GBM dataset was obtained from the Cancer Genome Atlas, and the validation dataset was downloaded from Gene Expression Omnibus. Firstly, differentially expressed TGF-β genes (DEGs) were screened between GBM and normal samples. Then, univariate and multivariate Cox analyses were used to identify prognostic genes and develop the TGF-β risk model. Subsequently, the roles of TGF-β risk score in predicting clinical prognosis and immune characteristics were investigated.ResultsThe TGF-β risk score signature with an independent prognostic value was successfully developed. The TGF-β risk score was positively correlated with the infiltration levels of tumor-infiltrating immune cells, and the activities of anticancer immunity steps. In addition, the TGF-β risk score was positively related to the expression of immune checkpoints. Besides, the high score indicated higher sensitivity to immune checkpoint inhibitors.ConclusionsWe first developed and validated a TGF-β risk signature that could predict the clinical prognosis and TME immune features for GBM. In addition, the TGF-β signature could guide a more personalized therapeutic approach for GBM.</p

sj-tif-5-tct-10.1177_15330338231218218 - Supplemental material for miRNA-130a-3p/CPEB4 Axis Modulates Glioblastoma Growth and Progression

Supplemental material, sj-tif-5-tct-10.1177_15330338231218218 for miRNA-130a-3p/CPEB4 Axis Modulates Glioblastoma Growth and Progression by Hongchao Liu, Zhihao Wei, Kangke Shi, Yu Zhang and Jiaqiong Li in Technology in Cancer Research & Treatment</p

Table_2_TGF-β based risk model to predict the prognosis and immune features in glioblastoma.pdf

BackgroundTransforming growth factor-β (TGF-β) is a multifunctional cytokine with an important role in tissue development and tumorigenesis. TGF-β can inhibit the function of many immune cells, prevent T cells from penetrating into the tumor center, so that the tumor cells escape from immune surveillance and lead to low sensitivity to immunotherapy. However, its potential roles in predicting clinical prognosis and tumor microenvironment (TME) immune features need to be deeply investigated in glioblastoma (GBM).MethodsThe TCGA-GBM dataset was obtained from the Cancer Genome Atlas, and the validation dataset was downloaded from Gene Expression Omnibus. Firstly, differentially expressed TGF-β genes (DEGs) were screened between GBM and normal samples. Then, univariate and multivariate Cox analyses were used to identify prognostic genes and develop the TGF-β risk model. Subsequently, the roles of TGF-β risk score in predicting clinical prognosis and immune characteristics were investigated.ResultsThe TGF-β risk score signature with an independent prognostic value was successfully developed. The TGF-β risk score was positively correlated with the infiltration levels of tumor-infiltrating immune cells, and the activities of anticancer immunity steps. In addition, the TGF-β risk score was positively related to the expression of immune checkpoints. Besides, the high score indicated higher sensitivity to immune checkpoint inhibitors.ConclusionsWe first developed and validated a TGF-β risk signature that could predict the clinical prognosis and TME immune features for GBM. In addition, the TGF-β signature could guide a more personalized therapeutic approach for GBM.</p

Data_Sheet_4_TGF-β based risk model to predict the prognosis and immune features in glioblastoma.PDF

BackgroundTransforming growth factor-β (TGF-β) is a multifunctional cytokine with an important role in tissue development and tumorigenesis. TGF-β can inhibit the function of many immune cells, prevent T cells from penetrating into the tumor center, so that the tumor cells escape from immune surveillance and lead to low sensitivity to immunotherapy. However, its potential roles in predicting clinical prognosis and tumor microenvironment (TME) immune features need to be deeply investigated in glioblastoma (GBM).MethodsThe TCGA-GBM dataset was obtained from the Cancer Genome Atlas, and the validation dataset was downloaded from Gene Expression Omnibus. Firstly, differentially expressed TGF-β genes (DEGs) were screened between GBM and normal samples. Then, univariate and multivariate Cox analyses were used to identify prognostic genes and develop the TGF-β risk model. Subsequently, the roles of TGF-β risk score in predicting clinical prognosis and immune characteristics were investigated.ResultsThe TGF-β risk score signature with an independent prognostic value was successfully developed. The TGF-β risk score was positively correlated with the infiltration levels of tumor-infiltrating immune cells, and the activities of anticancer immunity steps. In addition, the TGF-β risk score was positively related to the expression of immune checkpoints. Besides, the high score indicated higher sensitivity to immune checkpoint inhibitors.ConclusionsWe first developed and validated a TGF-β risk signature that could predict the clinical prognosis and TME immune features for GBM. In addition, the TGF-β signature could guide a more personalized therapeutic approach for GBM.</p