Hepatocellular Carcinoma Subtyping and Prognostic Model Construction Based on Chemokine-Related Genes.

BACKGROUND: Chemokines not only regulate immune cells but also play significant roles in development and treatment of tumors and patient prognoses. However, these effects have not been fully explained in hepatocellular carcinoma (HCC). MATERIALS AND METHODS: We conducted a clustering analysis of chemokine-related genes. We then examined the differences in survival rates and analyzed immune levels using single-sample Gene Set Enrichment Analysis (ssGSEA) for each subtype. Based on chemokine-related genes of different subtypes, we built a prognostic model in The Cancer Genome Atlas (TCGA) dataset using the survival package and glmnet package and validated it in the Gene Expression Omnibus (GEO) dataset. We used univariate and multivariate regression analyses to select independent prognostic factors and used R package rms to draw a nomogram reflecting patient survival rates at 1, 3, and 5 years. RESULTS: We identified two chemokine subtypes and, after screening, found that Cluster1 had higher survival rates than Cluster2. In addition, in terms of immune evaluation, stromal evaluation, ESTIMATE evaluation, immune abundance, immune function, and expressions of various immune checkpoints, immune levels of Cluster1 were significantly better than those of Cluster2. The immunophenoscore (IPS) of HCC patients in Cluster1 was significantly higher than that in Cluster2. Furthermore, we established a prognostic model consisting of 9 genes, which correlated with chemokines. Through testing, Riskscore was revealed as an independent prognostic factor, and the model could effectively predict HCC patients' prognoses in both TCGA and GEO datasets. CONCLUSION: This study resulted in the development of a novel prognostic model related to chemokine genes, providing new targets and theoretical support for HCC patients.

Identification of Biomarkers Associated with the Prognoses of Colorectal Cancer Patients.

INTRODUCTION: Colorectal cancer (CRC) is a common cancer. As metastasis and recurrence are main causes of CRC death, it is of great significance to find prognostic biomarkers. METHODS: Data related to CRC were collected from GEO database. The patients were grouped based on clinical information, and the differentially expressed genes (DEGs) were obtained by differential analysis. GO and KEGG pathway enrichment analyses were conducted based on DEGs. Cox combined with LASSO regression analysis was applied to screen out the key genes that used to build the prognostic model. Survival curve and receiver operating characteristic curve were employed to evaluate the validity and reliability of the model. Cox regression analysis was applied to determine the independence of risk score. GSEA and GSVA analyses were performed on patients with different risks according to the risk model scores, and the prognostic nomogram was plotted combined with clinical data. Also, qRT-PCR was applied to examine the expression status of the screened signatures in clinical cases. RESULTS: We obtained 302 DEGs by dividing CRC patients into early-stage and advanced-stage groups. The results of enrichment analyses demonstrated that the DEGs were mainly concentrated in tissues of extracellular matrix, epithelial cell proliferation, and cell adhesion-related pathways. Regression identified 9 hub genes notably correlated with prognosis, including CLK1, SLC2A3, LIPG, EPHB2, ATOH1, PLCB4, GZMB, CKMT2, and CXCL11. The validation of the risk model proved that the risk model was accurate and could independently determine the prognosis of patients. Finally, differences were found in pathway activity of extracellular matrix secretion, plaque secretion, Notch signaling pathway, and tight junctions in high-risk and low-risk patients. In addition to LIPG and CKMT2, other feature genes were notably overexpressed in CRC tumor tissues. CONCLUSION: The results proved that the expression levels of the 9 biomarkers could be used to predict the prognosis of CRC patients.

DNA methylation of RNA-binding protein for multiple splicing 2 functions as diagnosis biomarker in gastric cancer pathogenesis and its potential clinical significance.

Higher methylation levels of RNA-binding protein for multiple splicing 2 (RBPMS2) was reported to be related with unfavorable outcome in gastric cancer (GC). However, molecular function and diagnostic significance of DNA methylation of RBPMS2 remains indistinct. Here we aimed to whether DNA methylation of RBPMS2 acts as a diagnosis biomarker in GC pathogenesis and its potential clinical significance. Western blot and immunochemistry assays were carried out to explore the level of RBPMS2. GC malignancy behaviors were determined by cell counting kit-8, Transwell, flow cytometry analysis and terminal-deoxynucleoitidyl transferase mediated nick end labeling staining. The inflammatory cell infiltration in xenograft model was observed by hematoxylin and eosin staining. CpG Islands was predicted by MethPrimer and the DNA methylation of RBPMS2 was evaluated by methylation-specific polymerase chain reaction. The results showed that RBPMS2 was downregulated in GC specimens. Poor survival rates were associated with low RBPMS2 expression. Overexpression of RBPMS2 inhibited GC growth while facilitated apoptosis in GC cells. In addition, level of DNA methylation of RBPMS2 in GC tissues was increased and DNA methylation of RBPMS2 was strongly associated with tumor invasion, Borrmann classification and TNM stage. We also observed that DNA methylation inhibitors counteracted the role of RBPMS2 in restraining GC development and tumorigenesis. To sum, our data demonstrated that DNA methylation of RBPMS2 was responsible for its downregulation in GC and promoted tumor progression, indicating DNA methylation of RBPMS2 might serve as a valuable potential parameter in GC pathogenesis.

Silencing circular RNAPTPN12 promoted the growth of keloid fibroblasts by activating Wnt signaling pathway via targeting microRNA-21-5p.

Keloid is a skin disease marked by fibroplasia, and fibroblasts viability plays a considerable part in keloid. Our research was devoted to assessing the involvement and mechanism of circPTPN12 in keloid. The level of circPTPN12 and miR-21-5p was estimated by qRT-PCR in keloid tissues and cells. MTT analysis was devoted to evaluating the multiplication of keloid fibroblasts. Additionally, transwell assay was dedicated to verifying cell migration and invasion. Furthermore, keloid fibroblasts apoptosis level was assessed adopting flow cytometry, and the relevancy between miR-21-5p and circPTPN12, miR-21-5p, and SMAD7 was assessed by dual luciferase assay. Similarly, RIP and RNA pull-down assay verified the relevance between genes. Moreover, levels of SMAD7 and proteins concerned in Wnt signaling pathway were appraised by Western blot. The level of circPTPN12 declined in keloid. circPTPN12 knockout could enhance the multiplication, migration, invasion, and decline apoptosis of keloid fibroblasts. Indeed, miR-21-5p could be packed with circPTPN12 sponge, SMAD7 was downstream effect factor of miR-21-5p, and miR-21-5p inhibitors partially reversed the promoting effect of silencing circPTPN12 on keloid formation. Otherwise, the level of SMAD7 was adjusted by circPTPN12 and miR-21-5p. Silencing circPTPN12 targeted miR-21-5p and activated Wnt pathway to accelerate keloid fibroblasts growth. Taken together, silencing circPTPN12 promotes the growth of keloid fibroblasts by activating Wnt pathway targeting miR-21-5p. CircPTPN12 may play a considerable part in keloid formation, which supplies a reference for molecularly targeted therapy keloid.