Highly efficient prevention of radiation dermatitis using a PEGylated superoxide dismutase dissolving microneedle patch.

PEGylated superoxide dismutase (PEG-SOD) is commonly used as a cytoprotective agent in radiotherapy. However, its effectiveness in preventing radiation dermatitis is limited owing to its poor skin permeability. To address this issue, a PEG-SOD-loaded dissolving microneedle (PSMN) patch was developed to effectively prevent radiation dermatitis. Initially, PSMN patches were fabricated using a template mold method with polyvinylpyrrolidone K90 as the matrix material. PSMNs exhibited a conical shape with adequate mechanical strength to penetrate the stratum corneum. More than 90 % of PEG-SOD was released from the PSMN patches within 30 min. Notably, the PSMN patches showed a significantly higher drug skin permeation than the PEG-SOD solutions, with a 500-fold increase. In silico simulations and experiments on skin pharmacokinetics confirmed that PSMN patches enhanced drug permeation and skin absorption, in contrast to PEG-SOD solutions. More importantly, PSMN patches efficiently mitigated ionizing radiation-induced skin damage, accelerated the healing process of radiation-affected skin tissues, and exhibited highly effective radioprotective activity for DNA in the skin tissue. Therefore, PSMN patches are promising topical remedy for the prevention of radiation dermatitis.

α-Hederin promotes ferroptosis and reverses cisplatin chemoresistance in non-small cell lung cancer.

BACKGROUND: Cisplatin is a core chemotherapy regimen in non-small cell lung cancer (NSCLC). However, chemoresistance to cisplatin leads to a poor prognosis in NSCLC. α-Hederin is a natural compound extracted from Nigella sativa. The study aims to explore the effects of α-Hederin on cisplatin resistance in NSCLC. METHODS: NSCLC cisplatin-resistant cell lines A549/DPP and PC-9 were cultured to evaluate the efficacy of α-Hederin in the treatment of NSCLC in vitro and in vivo. Metabolomics and RNA-seq analysis were used to determine the potential mechanisms of action of α-Hederin. RESULTS: The results showed that α-Hederin inhibited cisplatin-resistant NSCLC cells proliferation and metastasis. Mice xenograft, orthotopic, and metastatic A549/DPP cell models also showed the anti-tumor effects of α-Hederin. The metabolomics and RNA-seq analysis results showed that α-Hederin activated DDIT3/ATF3 pathway and ferroptosis via silencing SLC7A11 and GPX4. Furthermore, α-Hederin enhanced the nuclear expression of EGR1. Bioinformatics and luciferase experiments confirmed that EGR1 binds to the miR-96-5p promoter region, inhibiting transcription. In addition, miR-96-5p directly suppressed the levels of DDIT3. CONCLUSION: This study revealed that α-Hederin activated EGR1 nuclear translocation and directly repressed miR-96-5p. It also promoted DDIT3/ATF3-mediated ferroptosis and reversed cisplatin resistance in NSCLC.

Single-cell transcriptome analysis reveals T population heterogeneity and functions in tumor microenvironment of colorectal cancer metastases.

Cell mediated immune escape, a microenvironment factor, induces tumorigenesis and metastasis. The purpose of this study was to display the characteristics of T cell populations in immune microenvironments for colorectal cancer (CRC) metastasis. Unsupervised cluster analysis was conducted to identify functionally distinct T cell clusters from 3,003 cells in peripheral blood and 4,656 cells in tissues. Subsequently, a total of 8 and 4 distinct T cell population clusters were identified from tumor tissue and peripheral blood, respectively. High levels of CD8+TEX, CD4+TRM, TH1-like T cells, CD8+TEM, tumor-Treg from tissues, and CD4+TN from peripheral blood are essential components of immune microenvironment for the prediction of CRC metastasis. Moreover, exhausted T cells are characterized by higher expression of multiple inhibitory receptors, including PDCD1 and LAG3. Some genes such as PFKFB3, GNLY, circDCUN1D4, TXNIP and NR4A2 in T cells of cluster were statistically different between CRC metastasis and non-metastasis. The ligand-receptor interactions identified between different cluster cells and metastases-related DEGs identified from each cluster revealed that the communications of cells, alterations of functions, and numbers of T subsets may contribute to the metastasis of CRC. The mutation frequency of KiAA1551, ATP8B4 and LNPEP in T cells from tissues and SOR1 from peripheral blood were higher in metastatic CRC than that in non-metastatic CRC. In conclusion, the discovery of differential genes in T cells may provide potential targets for immunotherapy of CRC metastasis and relevant insights into the clinical prediction and prognosis of CRC metastasis.

Low Expression of ADCY4 Predicts Worse Survival of Lung Squamous Cell Carcinoma Based on Integrated Analysis and Immunohistochemical Verification.

PURPOSE: The molecular mechanism underlying the carcinogenesis and development of lung squamous cell carcinoma (LUSC) has not been sufficiently elucidated. This analysis was performed to find pivotal genes and explore their prognostic roles in LUSC. METHODS: A microarray dataset from GEO (GSE19188) and a TCGA-LUSC dataset were used to identify differentially co-expressed genes through Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis. We conducted functional enrichment analyses of differentially co-expressed genes and established a protein-protein interaction (PPI) network. Then, we identified the top 10 hub genes using the Maximal Clique Centrality (MCC) algorithm. We performed overall survival (OS) analysis of these hub genes among LUSC cases. GSEA analyses of survival-related hub genes were conducted. Ultimately, the GEO and The Human Protein Atlas (THPA) databases and immunohistochemistry (IHC) results from the real world were used to verify our findings. RESULTS: A list of 576 differentially co-expressed genes were selected. Functional enrichment analysis indicated that regulation of vasculature development, cell-cell junctions, actin binding and PPAR signaling pathways were mainly enriched. The top 10 hub genes were selected according to the ranking of MCC scores, and 5 genes were closely correlated with OS of LUSC. Additionally, GSEA analysis showed that spliceosome and cell adhesion molecules were associated with the expression of GNG11 and ADCY4, respectively. The GSE30219 and THPA databases and IHC results from the real world indicated that although GNG11 was not detected, ADCY4 was obviously downregulated in LUSC tissues at the mRNA and protein levels. CONCLUSIONS: This analysis showed that survival-related hub genes are highly correlated to the tumorigenesis and development of LUSC. Additionally, ADCY4 is a candidate therapeutic and prognostic biomarker of LUSC.

Identification of a Novel Four-Gene Signature Correlated With the Prognosis of Patients With Hepatocellular Carcinoma: A Comprehensive Analysis.

PURPOSE: Hepatocellular carcinoma (HCC) is a common solid-tumor malignancy with high heterogeneity, and accurate prognostic prediction in HCC remains difficult. This analysis was performed to find a novel prognostic multigene signature. METHODS: The TCGA-LIHC dataset was analyzed for differentially coexpressed genes through weighted gene coexpression network analysis (WGCNA) and differential gene expression analysis. A protein-protein interaction (PPI) network and univariate Cox regression analysis of overall survival (OS) were utilized to identify their prognostic value. Next, we used least absolute shrinkage and selection operator (LASSO) Cox regression to establish a prognostic module. Subsequently, the ICGC-LIRI-JP dataset was applied for further validation. Based on this module, HCC cases were stratified into high-risk and low-risk groups, and differentially expressed genes (DEGs) were identified. Functional enrichment analyses of these DEGs were conducted. Finally, single-sample gene set enrichment analysis (ssGSEA) was performed to explore the correlation between the prognostic signature and immune status. RESULTS: A total of 393 differentially coexpressed genes were obtained. Forty differentially coexpressed hub genes were identified using the CytoHubba plugin, and 38 of them were closely correlated with OS. Afterward, we established the four-gene prognostic signature with an acceptable accuracy (area under the curve [AUC] of 1-year survival: 0.739). The ICGC-LIRI-JP dataset also supported the acceptable accuracy (AUC of 1-year survival:0.752). Compared with low-risk cohort, HCC cases in the high-risk cohort had shorter OS, higher tumor grades, and higher T stages. The risk scores of this signature still act as independent predictors of OS (P<0.001). Functional enrichment analyses suggest that it was mainly organelle fission and nuclear division that were enriched. Finally, ssGSEA revealed that this signature is strongly associated with the immune status of HCC patients. CONCLUSIONS: The proposed prognostic signature of four differentially coexpressed hub genes has satisfactory prognostic ability, providing important insight into the prediction of HCC prognosis.

Identification of pivotal genes associated with the prognosis of gastric carcinoma through integrated analysis.

PURPOSE: Detecting and diagnosing gastric cancer (GC) during its early period remains greatly difficult. Our analysis was performed to detect core genes correlated with GC and explore their prognostic values. METHODS: Microarray datasets from the Gene Expression Omnibus (GEO) (GSE54129) and The Cancer Genome Atlas (TCGA)-stomach adenocarcinoma (STAD) datasets were applied for common differentially co-expressed genes using differential gene expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA). Functional enrichment analysis and protein-protein interaction (PPI) network analysis of differentially co-expressed genes were performed. We identified hub genes via the CytoHubba plugin. Prognostic values of hub genes were explored. Afterward, Gene Set Enrichment Analysis (GSEA) was used to analyze survival-related hub genes. Finally, the tumor-infiltrating immune cell (TIC) abundance profiles were estimated. RESULTS: Sixty common differentially co-expressed genes were found. Functional enrichment analysis implied that cell-cell junction organization and cell adhesion molecules were primarily enriched. Hub genes were identified using the degree, edge percolated component (EPC), maximal clique centrality (MCC), and maximum neighborhood component (MNC) algorithms, and serpin family E member 1 (SERPINE1) was highly associated with the prognosis of GC patients. Moreover, GSEA demonstrated that extracellular matrix (ECM) receptor interactions and pathways in cancers were correlated with SERPINE1 expression. CIBERSORT analysis of the proportion of TICs suggested that CD8+ T cell and T-cell regulation were negatively associated with SERPINE1 expression, showing that SERPINE1 may inhibit the immune-dominant status of the tumor microenvironment (TME) in GC. CONCLUSIONS: Our analysis shows that SERPINE1 is closely correlated with the tumorigenesis and progression of GC. Furthermore, SERPINE1 acts as a candidate therapeutic target and prognostic biomarker of GC.

Intestinal microorganisms involved in colorectal cancer complicated with dyslipidosis.

BACKGROUND: Abnormal lipid metabolism is considered to be one of main promoters of colorectal cancer (CRC), and intestinal microorganisms may be involved in CRC in patients with abnormal lipid metabolism. OBJECTIVE: To investigate lipid metabolism in CRC patients and explore the role of intestinal microorganisms in CRC complicated with abnormal lipid metabolism. METHODS: Overall, 150 CRC patients in Huzhou Central Hospital from January 2016 to September 2017 were recruited in the present study. Basic patient information and clinical serological indicators were investigated and analyzed. Twenty-one stool samples were collected from patients after receiving informed consent. Next-generation sequencing technology was used to sequence bacterial 16S ribosomal RNA. Bioinformatics analysis was used to profile the microbial composition and screen distinctive bacteria in patients with CRC complicated with abnormal lipid metabolism. RESULTS: Apo B and FFA levels were higher in patients with stage I disease than in patients with other stages. HDL, LDL, Apo B and FFA levels were higher in female patients than in male patients. FFA level was higher in rectal cancer patients than in colon cancer patients. These differences were statistically significant (p < 0.05). The proportion of Escherichia/Shigella was increased in CRC patients with hyperlipoidaemia and hypercholesteremia; the abundance of Streptococcus was increased in CRC patients with hyperlipoidaemia; the abundance of Clostridium XIVa was reduced in CRC patients with hyperlipoidaemia and hypercholesteremia; and the abundance of Ruminococcaceae was reduced in CRC patients with hypercholesteremia. Bilophila and Butyricicoccus were closely related to CRC patients without hyperlipoidaemia or hypercholesteremia, and Selenomonas, Clostridium, Bacteroidetes Slackia, Burkholderiales and Veillonellaceae were closely related to CRC patients with hyperlipoidaemia. Some pathways, including secretion system, chaperones and folding catalysts, amino sugar and nucleotide sugar metabolism, arginine and proline metabolism, glycine, serine and threonine metabolism, histidine metabolism, pores and ion channels, nitrogen metabolism and sporulation, may be involved in lipid metabolism abnormality in CRC patients. CONCLUSIONS: Many CRC patients have abnormal lipid metabolism, and the intestinal microbiota is altered in these CRC patients.