DataSheet1_Identification and Validation of a Novel Tumor Microenvironment-Related Prognostic Signature of Patients With Hepatocellular Carcinoma.ZIP

Background: In recent years, immunotherapy has changed the therapeutic landscape of hepatocellular carcinoma (HCC). Since the efficacy of immunotherapy is closely related to the tumor microenvironment (TME), in this study, we constructed a prognostic model based on TME to predict the prognosis and immunotherapy effect of HCC patients.Methods: Transcriptome and follow-up data of 374 HCC patients were acquired from the TCGA Cancer Genome Atlas (TCGA) database. The immune/stromal/estimate scores (TME scores) and tumor purity were calculated using the ESTIMATE algorithm and the module most associated with TME scores were screened by the weighted gene co-expression network analysis (WGCNA). A TME score-related prognostic model was constructed and patients were divided into a high-risk group and a low-risk group. Kaplan-Meier survival curves and receiver operator characteristic curve (ROC) were used to evaluate the performance of the TME risk prognostic model and validated with the external database International Cancer Genome Consortium (ICGC) cohort. Combined with clinicopathologic factors, a prognostic nomogram was established. The nomogram’s ability to predict prognosis was assessed by ROC, calibration curve, and the decision curve analysis (DCA). Gene Set Enrichment Analyses (GSEA) were conducted to explore the underlying biological functions and pathways of this risk signature. Moreover, the possible correlation of risk signature with TME immune cell infiltration, immune checkpoint inhibitor (ICI) treatment response, single-nucleotide polymorphisms (SNPs), and drug sensitivity were assessed. Finally, real-time PCR was used to verify the gene expression levels in normal liver cells and cancer cells.Results: KM survival analysis results indicated that high immune/stromal/estimate score groups were closely associated with a better prognosis, while the tumor purity showed a reverse trend (p Conclusion: It provided a theoretical basis for predicting the prognosis and personalized treatment of patients with HCC.</p

Additional file 3 of Development of a novel immune-related lncRNA prognostic signature for patients with hepatocellular carcinoma

Additional file 3: Supplementary Figure 1. (A) The best-fit OS-related lncRNAs were chosen by Lasso regression analysis. (B) The Lasso regression was performed with the optimal value of λ. (C-D) Distribution of risk scores, survival status. Supplementary Figure 2. (A-D) PCA among all genes, immune genes, immune LncRNA, and risk immune LncRNA

Data_Sheet_1_EYA2 Correlates With Clinico-Pathological Features of Breast Cancer, Promotes Tumor Proliferation, and Predicts Poor Survival.PDF

Eyes absent homolog 2 (EYA2), a transcriptional activator, is pivotal for organ development, but aberrant regulation of EYA2 has been reported in multiple human tumors. However, the role of EYA2 in breast cancer is still lack of full understanding. To explore the biological significance of EYA2 in breast cancer, we conducted data analysis on public breast cancer datasets, and performed immunohistochemistry (IHC) analysis, colony-forming unit assays, EdU assay, western blotting, and immunofluorescence (IF). Meta-analysis showed that EYA2 mRNA expression was correlated with tumor grade, the status of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). IHC analysis displayed that EYA2 protein abundance was inversely associated with the status of ER and PR, and enriched in triple-negative breast cancer in comparison with luminal-type tumors. Additionally, correlation analysis reflected that EYA2 mRNA was negatively correlated with luminal markers, and positively associated with markers of basal cells, epithelial-mesenchymal transition and cancer stem cells. Clone-forming assay and EdU experiment showed that EYA2 overexpression enhanced proliferation of breast cancer cells. Results from western blotting and IF displayed that overexpression of EYA2 up-regulated the protein abundance of proliferation markers. Importantly, survival analysis indicated that higher EYA2 mRNA level predicted worse overall survival, relapse-free survival and metastasis-free survival among whole enrolled breast cancer patients. Collectively, EYA2 was closely correlated with clinico-pathological characteristics, and served as a proliferation stimulator for breast cancer cells and an unfavorable prognostic element for breast cancer patients, suggesting that EYA2 is involved in the progression of breast carcinoma.</p

Image1_Identification and Validation of a Novel Tumor Microenvironment-Related Prognostic Signature of Patients With Hepatocellular Carcinoma.TIF

Background: In recent years, immunotherapy has changed the therapeutic landscape of hepatocellular carcinoma (HCC). Since the efficacy of immunotherapy is closely related to the tumor microenvironment (TME), in this study, we constructed a prognostic model based on TME to predict the prognosis and immunotherapy effect of HCC patients.Methods: Transcriptome and follow-up data of 374 HCC patients were acquired from the TCGA Cancer Genome Atlas (TCGA) database. The immune/stromal/estimate scores (TME scores) and tumor purity were calculated using the ESTIMATE algorithm and the module most associated with TME scores were screened by the weighted gene co-expression network analysis (WGCNA). A TME score-related prognostic model was constructed and patients were divided into a high-risk group and a low-risk group. Kaplan-Meier survival curves and receiver operator characteristic curve (ROC) were used to evaluate the performance of the TME risk prognostic model and validated with the external database International Cancer Genome Consortium (ICGC) cohort. Combined with clinicopathologic factors, a prognostic nomogram was established. The nomogram’s ability to predict prognosis was assessed by ROC, calibration curve, and the decision curve analysis (DCA). Gene Set Enrichment Analyses (GSEA) were conducted to explore the underlying biological functions and pathways of this risk signature. Moreover, the possible correlation of risk signature with TME immune cell infiltration, immune checkpoint inhibitor (ICI) treatment response, single-nucleotide polymorphisms (SNPs), and drug sensitivity were assessed. Finally, real-time PCR was used to verify the gene expression levels in normal liver cells and cancer cells.Results: KM survival analysis results indicated that high immune/stromal/estimate score groups were closely associated with a better prognosis, while the tumor purity showed a reverse trend (p Conclusion: It provided a theoretical basis for predicting the prognosis and personalized treatment of patients with HCC.</p

Additional file 4 of Development of a novel immune-related lncRNA prognostic signature for patients with hepatocellular carcinoma

Additional file 4: Supplementary Table 3. All the primer sequences

Additional file 1 of Development of a novel immune-related lncRNA prognostic signature for patients with hepatocellular carcinoma

Additional file 1: Table 1. Clinical characteristics of hepatocellular carcinoma in train and validation cohort

Additional file 2 of Development of a novel immune-related lncRNA prognostic signature for patients with hepatocellular carcinoma

Additional file 2: Supplementary Table 2. 76 prognostic associated lncRNAs

Image2_Identification and Validation of a Novel Tumor Microenvironment-Related Prognostic Signature of Patients With Hepatocellular Carcinoma.TIF

Background: In recent years, immunotherapy has changed the therapeutic landscape of hepatocellular carcinoma (HCC). Since the efficacy of immunotherapy is closely related to the tumor microenvironment (TME), in this study, we constructed a prognostic model based on TME to predict the prognosis and immunotherapy effect of HCC patients.Methods: Transcriptome and follow-up data of 374 HCC patients were acquired from the TCGA Cancer Genome Atlas (TCGA) database. The immune/stromal/estimate scores (TME scores) and tumor purity were calculated using the ESTIMATE algorithm and the module most associated with TME scores were screened by the weighted gene co-expression network analysis (WGCNA). A TME score-related prognostic model was constructed and patients were divided into a high-risk group and a low-risk group. Kaplan-Meier survival curves and receiver operator characteristic curve (ROC) were used to evaluate the performance of the TME risk prognostic model and validated with the external database International Cancer Genome Consortium (ICGC) cohort. Combined with clinicopathologic factors, a prognostic nomogram was established. The nomogram’s ability to predict prognosis was assessed by ROC, calibration curve, and the decision curve analysis (DCA). Gene Set Enrichment Analyses (GSEA) were conducted to explore the underlying biological functions and pathways of this risk signature. Moreover, the possible correlation of risk signature with TME immune cell infiltration, immune checkpoint inhibitor (ICI) treatment response, single-nucleotide polymorphisms (SNPs), and drug sensitivity were assessed. Finally, real-time PCR was used to verify the gene expression levels in normal liver cells and cancer cells.Results: KM survival analysis results indicated that high immune/stromal/estimate score groups were closely associated with a better prognosis, while the tumor purity showed a reverse trend (p Conclusion: It provided a theoretical basis for predicting the prognosis and personalized treatment of patients with HCC.</p

DataSheet1_Tantalum-carbon-integrated nanozymes as a nano-radiosensitizer for radiotherapy enhancement.docx

Radiotherapy (RT) plays a pivotal role in the comprehensive treatment of multiple malignant tumors, exerting its anti-tumor effects through direct induction of double-strand breaks (DSBs) or indirect induction of reactive oxygen species (ROS) production. However, RT resistance remains a therapeutic obstacle that leads to cancer recurrence and treatment failure. In this study, we synthesised a tantalum-carbon-integrated nanozyme with excellent catalase-like (CAT-like) activity and radiosensitivity by immobilising an ultrasmall tantalum nanozyme into a metal-organic framework (MOF)-derived carbon nanozyme through in situ reduction. The integrated tantalum nanozyme significantly increased the CAT activity of the carbon nanozyme, which promoted the production of more oxygen and increased the ROS levels. By improving hypoxia and increasing the level of ROS, more DNA DSBs occur at the cellular level, which, in turn, improves the sensitivity of RT. Moreover, tantalum–carbon-integrated nanozymes combined with RT have demonstrated notable anti-tumor activity in vivo. Therefore, exploiting the enzymatic activity and the effect of ROS amplification of this nanozyme has the potential to overcome resistance to RT, which may offer new horizons for nanozyme-based remedies for biomedical applications.</p

Presentation_1.PDF

<p>Persistent activation of mitogen-activated protein kinase (MAPK) is believed to be involved in psoriasis pathogenesis. MAPK phosphatase-1 (MKP-1) is an important negative regulator of MAPK activity, but the cellular and molecular mechanisms of MKP-1 in psoriasis development are largely unknown. In this study, we found that the expression of MKP-1 was decreased in the imiquimod (IMQ)-induced psoriasiform mouse skin. MKP-1-deficient (MKP-1^−/−) mice were highly susceptible to IMQ-induced skin inflammation, which was associated with increased production of inflammatory cytokines and chemokines. MKP-1 acted on both hematopoietic and non-hematopoietic cells to regulate psoriasis pathogenesis. MKP-1 deficiency in macrophages led to enhanced p38 activation and higher expression of interleukin (IL)-1β, CXCL2, and S100a8 upon R848 stimulation. Moreover, MKP-1 deficiency in the non-hematopoietic compartments led to an enhanced IL-22 receptor signaling and higher expression of CXCL1 and CXCL2 upon IMQ treatment. Collectively, our data suggest a critical role for MKP-1 in the regulation of skin inflammation.</p