COL10A1 promotes tumorigenesis by modulating CD276 in pancreatic adenocarcinoma.

BACKGROUND: Pancreatic adenocarcinoma (PAAD) is a lethal malignant tumour. Further study is needed to determine the molecular mechanism and identify novel biomarkers of PAAD. METHODS: Gene expression data from the GSE62165 microarray were analysed with the online software Morpheus to identify differentially expressed genes (DEGs). The STRING database was used to generate a protein‒protein interaction (PPI) network for these DEGs. Hub genes were identified with Cytoscape. COL10A1 expression in PAAD was analysed via the GEPIA database. COL10A1 expression in pancreatic cancer cell lines was measured by using qRT‒PCR. The LinkedOmics database was utilized to perform survival analysis of pancreatic adenocarcinoma patients grouped based on COL10A1 expression level. CCK-8, wound healing, and Transwell assays were used to study the role of COL10A1 in pancreatic cancer cell viability, migration, and invasion. Differentially expressed genes that were related to COL10A1 in PAAD were analysed via the LinkedOmics portal. After COL10A1 was knocked down, CD276 expression was assessed by western blotting. RESULTS: COL10A1 was identified as one of the hub genes in PAAD by bioinformatics analysis of the GSE62165 microarray with Morpheus, the STRING database and Cytoscape. GEPIA revealed elevated expression of COL10A1 in PAAD samples vs. normal samples. COL10A1 expression was also increased in pancreatic cancer cells vs. control cells. Survival analysis of PAAD patients via LinkedOmics revealed that high expression of COL10A1 was associated with a poorer prognosis. Knockdown of COL10A1 inhibited the proliferation, migration, and invasion of cells in functional assays. Furthermore, mechanistic studies indicated that CD276 was a target of COL10A1 and that knockdown of COL10A1 decreased CD276 expression. Overexpression of CD276 in cells reversed COL10A1 knockdown-induced repression of proliferation and migration. CONCLUSIONS: Our research suggests that COL10A1 promotes pancreatic adenocarcinoma tumorigenesis by regulating CD276. This study provides new insight into biomarkers and possible targets for pancreatic cancer treatment.

Up-frameshift Protein 1 Promotes Tumor Progression by Regulating Apoptosis and Epithelial-Mesenchymal Transition of Colorectal Cancer.

Background: Recently, accumulating evidence confirmed that up-frameshift protein 1 (UPF1) was aberrantly expressed in various cancers. However, the molecular mechanism mediated by UPF1 underlying colorectal carcinogenesis remains unclear. Method: Immunohistochemistry (IHC) and quantitative real-time polymerase chain reaction analysis were used to determine the expression level of UPF1 in colorectal cancer (CRC) tissues. CCK-8, EdU, transwell assay, and flow cytometry were performed to investigate the biological significance of UPF1. Epithelial-mesenchymal transition (EMT) and apoptosis associated markers were detected by western blotting. Results: We found that UPF1 expression was upregulated in CRC tissues and cell lines. Clinical analysis revealed that high UPF1 expression was positively correlated with advanced stage, lymph node metastasis and shorter survival. Knockdown of UPF1 suppressed cell proliferation and cell cycle progression. Functionally, UPF1 promotes tumor metastasis by inducing epithelial to mesenchymal transition. Further investigations revealed that knockdown of UPF1 promoted apoptosis through triggering DNA damage. Conclusions: Taken together, this research revealed that UPF1 plays an oncogenic role in CRC via regulating EMT and apoptosis and may be a potential therapeutic target for CRC.

Identification and Validation of EMT-Related lncRNA Prognostic Signature for Colorectal Cancer.

Background: This study aimed to explore the biological functions and prognostic role of Epithelial-mesenchymal transition (Epithelial-mesenchymal transition)-related lncRNAs in colorectal cancer (CRC). Methods: The Cancer Genome Atlas database was applied to retrieve gene expression data and clinical information. An EMT-related lncRNA risk signature was constructed relying on univariate Cox regression, Least Absolute Shrinkage and Selector Operation (LASSO) and multivariate Cox regression analysis of the EMT-related lncRNA expression data and clinical information. Then, an individualized prognostic prediction model based on the nomogram was developed and the predictive accuracy and discriminative ability of the nomogram were determined by the receiver operating characteristic curve and calibration curve. Finally, a series of analyses, such as functional analysis and unsupervised cluster analysis, were conducted to explore the influence of independent lncRNAs on CRC. Results: A total of 581 patients were enrolled and an eleven-EMT-related lncRNA risk signature was identified relying on the comprehensive analysis of the EMT-related lncRNA expression data and clinical information in the training cohort. Then, risk scores were calculated to divide patients into high and low-risk groups, and the Kaplan-Meier curve analysis showed that low-risk patients tended to have better overall survival (OS). Multivariate Cox regression analysis indicated that the EMT-related lncRNA signature was significantly associated with prognosis. The results were subsequently confirmed in the validation dataset. Then, we constructed and validated a predictive nomogram for overall survival based on the clinical factors and risk signature. Functional characterization confirmed this signature could predict immune-related phenotype and was associated with immune cell infiltration (i.e., macrophages M0, M1, Tregs, CD4 memory resting cells, and neutrophils), tumor mutation burden (TMB). Conclusions: Our study highlighted the value of the 11-EMT-lncRNA signature as a predictor of prognosis and immunotherapeutic response in CRC.

UHRF1 Could Be a Prognostic Biomarker and Correlated with Immune Cell Infiltration in Hepatocellular Carcinoma.

OBJECTIVE: This study was performed to investigate the relationship among UHRF1 expression, its biological function and immune infiltration in human hepatocellular carcinoma (HCC). METHODS: Gene Expression Profiling Interactive Analysis (GEPIA), Oncomine, and The Cancer Genome Atlas (TCGA) databases were used to analyze UHRF1 expression between HCC and normal tissues. Subsequently, GEPIA, TCGA-Portal, Kaplan-Meier Plotter, Protein Atlas and SurvExpress databases were utilized for survival analysis. UHRF1 co-expression genes were identified via the cBioPortal and LinkedOmics databases. Further, gene ontology (GO) analysis as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed. Protein-protein interaction (PPI) networks was constructed by STRING database and Cytoscape 3.7.1. Single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT algorithm were employed to assess the correlation between UHRF1 and tumor immune infiltrates on TCGA database. TIMER 2.0 database was used to explore the correlation of UHRF1 expression and immune infiltration level in HCC. Additionally, RT-qPCR was used to analyze the expression of UHRF1 and the relative genes in HCC cell lines. RESULTS: Expression level of UHRF1 was upregulated in HCC tissues compared with paired normal tissues (P < 0.05 in GEPIA; P = 1.78E-6 in Oncomine; and P < 0.0001 in TCGA). Its high expression was significantly related with a shorter overall survival in five databases (P < 0.05). Function enrichment analysis demonstrated that functions of UHRF1 concentrated in cell division process and cell cycle (P < 0.05). High UHRF1 expression exhibited dysregulated immune infiltration (ie, neutrophils, eosinophils, dendritic cells resting, macrophages M2, macrophages M0) and poor survival of high UHRF1 expression was tight correlated with immune infiltration status. Moreover, TP53 mutation can lead to high expression of UHRF1 (P = 4.2E-10). CONCLUSION: UHRF1 might function as an oncogene via inducing dysregulated immune infiltration in HCC and was identified as a novel prognostic biomarker and potential therapeutic target for HCC.

UPF1: a potential biomarker in human cancers.

Recently, Up-frameshift protein 1 (UPF1) is reported to be downregulated in various cancers and its low expression is closely correlated with poor prognosis. UPF1 is well known as a master regulator of nonsense-mediated mRNA decay (NMD), which serves as a highly conserved mRNA surveillance process protecting cells from aberrant toxic transcripts. Due to dysfunction of UPF1, NMD fails to proceed, which contributes to tumor initiation and progression. This review shows a brief summary of the aberrant expression, functional roles and molecular mechanisms of UPF1 during tumorigenesis. Increasing evidence has indicated that UPF1 could serve as a potential biomarker for cancer diagnosis and treatment for future clinical applications in cancer.

AVL9 is Upregulated in and Could Be a Predictive Biomarker for Colorectal Cancer.

PURPOSE: This study aimed to explore the function and clinical significance of AVL9 in colorectal cancer (CRC). MATERIALS AND METHODS: The GEO, TCGA, and GEPIA databases were searched to evaluate the expression level of AVL9, while the SurvExpress online tool was used to explore its related clinical survival prognosis. The cBioPortal and LinkedOmics databases were used to identify AVL9 expression-related genes. Protein-protein interaction (PPI) networks were analyzed using Cytoscape 3.7.1 and DAVID6.8, which was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) signal pathway enrichment. The immunohistochemistry of AVL9 in CRC was detected using an online tool protein atlas. RNA isolation and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) assays were used to detect AVL9 expression in tissue and plasma samples. RESULTS: Our study confirmed that AVL9 was highly expressed in CRC lesions versus the adjacent normal tissues (P < 0.001). High AVL9 expression was negatively associated with survival outcomes (P < 0.05). GO analysis showed that AVL9 expression-related genes were enriched in single organismal cell-cell adhesion, post-transcriptional regulation of gene expression, and negative regulation of the vascular endothelial growth factor receptor signaling pathway (P < 0.05). On a KEGG pathway analysis, these genes were mainly involved in progesterone-mediated oocyte maturation, axon guidance, the insulin signaling pathway, and the ubiquitin-mediated proteolysis signaling pathways (P < 0.05). In the PPI analysis, the KBTBD2, KIAA1147, EPDR1, and RNF216 genes interacted with AVL9, and GEPIA predicted that their expression levels were all positively correlated with AVL9. Furthermore, a clinicopathological parameter analysis found that high AVL9 expression was positively correlated with differentiation and TNM stage. RT-qPCR analysis further showed that plasma AVL9 expression was upregulated in CRC patients versus healthy controls. CONCLUSION: AVL9 could serve as a potential biomarker and therapeutic target for CRC.