Comprehensive Analysis of the Mechanism of Anoikis in Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC), ranking as the second-leading cause of global mortality among malignancies, poses a substantial burden on public health worldwide. Anoikis, a type of programmed cell death, serves as a barrier against the dissemination of cancer cells to distant organs, thereby constraining the progression of cancer. Nevertheless, the mechanism of genes related to anoikis in HCC is yet to be elucidated. Methods: This paper's data (TCGA-HCC) were retrieved from the database of the Cancer Genome Atlas (TCGA). Differential gene expression with prognostic implications for anoikis was identified by performing both the univariate Cox and differential expression analyses. Through unsupervised cluster analysis, we clustered the samples according to these DEGs. By employing the least absolute shrinkage and selection operator Cox regression analysis (CRA), a clinical predictive gene signature was generated from the DEGs. The Cell-Type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to determine the proportions of immune cell types. The external validation data (GSE76427) were procured from Gene Expression Omnibus (GEO) to verify the performance of the clinical prognosis gene signature. Western blotting and immunohistochemistry (IHC) analysis confirmed the expression of risk genes. Results: In total, 23 prognostic DEGs were identified. Based on these 23 DEGs, the samples were categorized into four distinct subgroups (clusters 1, 2, 3, and 4). In addition, a clinical predictive gene signature was constructed utilizing ETV4, PBK, and SLC2A1. The gene signature efficiently distinguished individuals into two risk groups, specifically low and high, demonstrating markedly higher survival rates in the former group. Significant correlations were observed between the expression of these risk genes and a variety of immune cells. Moreover, the outcomes from the validation cohort analysis aligned consistently with those obtained from the training cohort analysis. The results of Western blotting and IHC showed that ETV4, PBK, and SLC2A1 were upregulated in HCC samples. Conclusion: The outcomes of this paper underscore the effectiveness of the clinical prognostic gene signature, established utilizing anoikis-related genes, in accurately stratifying patients. This signature holds promise in advancing the development of personalized therapy for HCC.

LncRNA Malat1 regulates iPSC-derived ß-cell differentiation by targeting the miR-15b-5p/Ihh axis.

BACKGROUND: Differentiation of induced pluripotent stem cells (iPSCs)-derived ß-like cells is a novel strategy for treatment of type 1 diabetes. Elucidation of the regulatory mechanisms of long noncoding RNAs (lncRNAs) in ß-like cells derived from iPSCs is important for understanding the development of the pancreas and pancreatic ß-cells and may improve the quality of ß-like cells for stem cell therapy. METHODS: ß-like cells were derived from iPSCs in a three-step protocol. RNA sequencing and bioinformatics analysis were carried out to screen the differentially expressed lncRNAs and identify the putative target genes separately. LncRNA Malat1 was chosen for further research. Series of loss and gain of functions experiments were performed to study the biological function of LncRNA Malat1. Quantitative real-time PCR (qRT-PCR), Western blot (WB) analysis and immunofluorescence (IF) staining were carried out to separately detect the functions of pancreatic ß-cells at the mRNA and protein levels. Cytoplasmic and nuclear RNA fractionation and fluorescence in situ hybridization (FISH) were used to determine the subcellar location of lncRNA Malat1 in ß-like cells. Enzyme-linked immunosorbent assays (ELISAs) were performed to examine the differentiation and insulin secretion of ß-like cells after stimulation with different glucose concentrations. Structural interactions between lncRNA Malat1 and miR-15b-5p and between miR-15b-5p/Ihh were detected by dual luciferase reporter assays (LRAs). RESULTS: We found that the expression of lncRNA Malat1 declined during differentiation, and overexpression (OE) of lncRNA Malat1 notably impaired the differentiation and maturation of ß-like cells derived from iPSCs in vitro and in vivo. Most importantly, lncRNA Malat1 could function as a competing endogenous RNA (ceRNA) of miR-15b-5p to regulate the expression of Ihh according to bioinformatics prediction, mechanistic analysis and downstream experiments. CONCLUSION: This study established an unreported regulatory network of lncRNA Malat1 and the miR-15b-5p/Ihh axis during the differentiation of iPSCs into ß-like cells. In addition to acting as an oncogene promoting tumorigenesis, lncRNA Malat1 may be an effective and novel target for treatment of diabetes in the future.

Comprehensive analysis and experimental verification of the mechanism of anoikis related genes in pancreatic cancer.

Background: Pancreatic cancer (PC), characterized by its aggressive nature and low patient survival rate, remains a challenging malignancy. Anoikis, a process inhibiting the spread of metastatic cancer cells, is closely linked to cancer progression and metastasis through anoikis-related genes. Nonetheless, the precise mechanism of action of these genes in PC remains unclear. Methods: Study data were acquired from the Cancer Genome Atlas (TCGA) database, with validation data accessed at the Gene Expression Omnibus (GEO) database. Differential expression analysis and univariate Cox analysis were performed to determine prognostically relevant differentially expressed genes (DEGs) associated with anoikis. Unsupervised cluster analysis was then employed to categorize cancer samples. Subsequently, a least absolute shrinkage and selection operator (LASSO) Cox regression analysis was conducted on the identified DEGs to establish a clinical prognostic gene signature. Using risk scores derived from this signature, patients with cancer were stratified into high-risk and low-risk groups, with further assessment conducted via survival analysis, immune infiltration analysis, and mutation analysis. External validation data were employed to confirm the findings, and Western blot and immunohistochemistry were utilized to validate risk genes for the clinical prognostic gene signature. Results: A total of 20 prognostic-related DEGs associated with anoikis were obtained. The TCGA dataset revealed two distinct subgroups: cluster 1 and cluster 2. Utilizing the 20 DEGs, a clinical prognostic gene signature comprising two risk genes (CDKN3 and LAMA3) was constructed. Patients with pancreatic adenocarcinoma (PAAD) were classified into high-risk and low-risk groups per their risk scores, with the latter exhibiting a superior survival rate. Statistically significant variation was noted across immune infiltration and mutation levels between the two groups. Validation cohort results were consistent with the initial findings. Additionally, experimental verification confirmed the high expression of CDKN3 and LAMA3 in tumor samples. Conclusion: Our study addresses the gap in understanding the involvement of genes linked to anoikis in PAAD. The clinical prognostic gene signature developed herein accurately stratifies patients with PAAD, contributing to the advancement of precision medicine for these patients.