DNA Replication and Sister Chromatid Cohesion 1 (DSCC1) of the Replication Factor Complex CTF18-RFC is Critical for Colon Cancer Cell Growth

Complementary biomarkers of computed tomography for diagnostic grading of gastric cancer: DSCC1 and GINS1

OBJECTIVE

Computed tomography (CT) is an important tool for grading gastric cancer. Gastric cancer typically originates from epithelial cells of gastric mucosa. However, complementary markers for gastric cancer, relationship between DSCC1, GINS1 and gastric cancer remain unclear.

METHODS

Gastric cancer data were obtained from gene expression omnibus (GEO). Differentially expressed genes (DEGs) were identified, weighted gene co-expression network analysis (WGCNA) was conducted. Protein-protein interaction (PPI) network was constructed and analyzed. Functional enrichment analysis, gene set enrichment analysis (GSEA), gene expression heatmaps, immune infiltration analysis were performed. The most relevant diseases related to core genes were identified using Comparative Toxicogenomics Database (CTD). TargetScan was used to screen miRNAs. Validation was carried out using Western blotting (WB) and reverse transcription-polymerase chain reaction (RT-PCR).

RESULTS

1243 DEGs were identified. Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) analyses revealed significant enrichment in cell cycle regulation, macrophage migration control, basement membrane, extracellular regions, growth factor binding, protein complex binding, P53 signaling pathway, protein digestion and absorption, metabolic pathways. Immune infiltration analysis indicated that high expression of activated Mast cells and Neutrophils, with a strong positive correlation between them, may influence progression of gastric cancer. CTD analysis revealed associations between DSCC1, GINS1 and gastric tumors, gastrointestinal diseases, tumors, gastritis, inflammation, necrosis. WB and RT-PCR results demonstrated high expression of DSCC1 and GINS1 in gastric cancer.

CONCLUSION

The expressions of DSCC1 and GINS1 are up-regulated in gastric cancer, which can be used as supplementary markers for CT diagnostic grading of gastric cancer.

Roles of DSCC1 and GINS1 in gastric cancer

Gastric carcinoma is a common malignant tumor originating from gastric mucosal epithelium. However, role of DS-cell cycle-dependent protein 1 (DSCC1) and GINS1 in gastric carcinoma remains unclear. The gastric carcinoma datasets GSE79973 and GSE118916 were downloaded from gene expression omnibus. Multiple datasets were merged and batched. Differentially expressed genes (DEGs) were screened and weighted gene co-expression network analysis was performed. Functional enrichment analysis, gene set enrichment analysis and immune infiltration analysis were performed. Construction and analysis of protein-protein interaction Network. Survival analysis and comparative toxicogenomics database were performed. A heat map of gene expression was drawn. Target Scan screen miRNAs regulating DEGs. Two thousand forty-four DEGs were identified. According to gene ontology analysis, in biological process, they were mainly enriched in cell migration, transforming growth factor β receptor signaling pathway, angiogenesis, and steroid metabolism process. In cellular component, they were mainly enriched in extracellular vesicles, basement membrane, endoplasmic reticulum lumen, and extracellular space. In molecular function, they focused on extracellular matrix structural components, protein binding, platelet-derived growth factor binding, and catalytic activity. In Kyoto encyclopedia of genes and genomes, they were mainly enriched in protein digestion and absorption, metabolic pathways, fatty acid degradation, Glycerophospholipid metabolism, ether lipid metabolism. Gene set enrichment analysis showed that DEGs were mainly enriched in transforming growth factor β receptor signaling pathway, steroid metabolism process, basement membrane, endoplasmic reticulum lumen, structural components of extracellular matrix, platelet-derived growth factor binding, Glycerophospholipid metabolism, ether lipid metabolism. The results of immune infiltration analysis showed that expression of T cell CD4 memory resting was lower in the samples of gastric cancer. The core genes (TRIP13, CHEK1, DSCC1, GINS1) are protective factors, their expression shows a downward trend with increase of risk score. Comparative toxicogenomics database analysis showed that TRIP13, CHEK1, DSCC1, GINS1 were related to gastric tumors, gastric diseases, tumors, inflammation, and necrosis. DSCC1 and GINS1 are highly expressed in gastric cancer. Higher expression levels of DSCC1 and GINS1, worse the prognosis.

DSCC1 interacts with HSP90AB1 and promotes the progression of lung adenocarcinoma via regulating ER stress

Lung cancer is a leading cause of cancer-related deaths, and the most common type is lung adenocarcinoma (LUAD). LUAD is frequently diagnosed in people who never smoked, patients are always diagnosed at advanced inoperable stages, and the prognosis is ultimately poor. Thus, there is an urgent need for the development of novel targeted therapeutics to suppress LUAD progression. In this study, we demonstrated that the expression of DNA replication and sister chromatid cohesion 1 (DSCC1) was higher in LUAD samples than normal tissues, and the overexpression of DSCC1 or its coexpressed genes were highly correlated with poor outcomes of LUAD patients, highlighting DSCC1 might be involved in LUAD progression. Furthermore, the expression of DSCC1 was positively correlated with multiple genetic mutations which drive cancer development, including TP53, TTN, CSMD, and etc. More importantly, DSCC1 could promote the cell proliferation, stemness, EMT, and metastatic potential of LUAD cells. In addition, DSCC1 interacted with HSP90AB1 and promoted the progression of LUAD via regulating ER stress. Meanwhile, DSCC1 expression negatively correlated with immune cell infiltration in lung cancer, and DSCC1 positively regulated the expression of PD-L1 in LUAD cells. Collectively, this study revealed that DSCC1 is a novel therapeutic target to treat LUAD and a biomarker for predicting the efficiency of PD-1/PD-L1 blockade treatment.

Identification of target and pathway of aspirin combined with Lipitor treatment in prostate cancer through integrated bioinformatics analysis

PURPOSE

Our previous studies have confirmed that aspirin combined with Lipitor inhibited the development of prostate cancer (PCa), but the mechanisms need to be comprehensively expounded. The study aims to screen out the hub genes of combination therapy and to explore their association with the pathogenesis and prognosis of PCa.

METHODS

Gene expressions were quantified by RNA sequencing (RNA-seq). Altered biological function, pathways of differentially expressed genes (DEGs), protein-protein interaction network, the filtering of hub genes, gene co-expression and the pathogenesis and prognosis were revealed by bioinformatics analysis. The correlation between hub gene expression and patient survival was validated by Kaplan-Meier. The effects of silent DNA replication and sister chromatid cohesion 1 (siDSCC1) combined with Lipitor and aspirin on DSCC1 expression, viability, invasion and migration of PCa cells were detected by qRT-PCR, Wound healing and transwell assays.

RESULTS

157 overlapped DEGs involved in FoxO, PI3K-Akt and p53 signaling pathways were identified. Ten hub genes (NEIL3, CDC7, DSCC1, CDC25C, PRIM1, MCM10, FBXO5, DTL, SERPINE1, EXO1) were verified to be correlated with the pathology and prognosis of PCa. DSCC1 silencing not only inhibited the viability, migration and invasion of PCa cells, but also strengthened the suppressing effects of Lipitor and aspirin alone or in combination on PCa cells.

CONCLUSION

The enrichment pathways and targets of Lipitor combined with aspirin in PCa are discovered, and DSCC1 silencing can potentiate the effect of Lipitor combined with aspirin in the treatment of PCa.

In silico recognition of a prognostic signature in basal-like breast cancer patients

Background

Triple-negative breast cancers (TNBCs) display poor prognosis, have a high risk of tumour recurrence, and exhibit high resistance to drug treatments. Based on their gene expression profiles, the majority of TNBCs are classified as basal-like breast cancers. Currently, there are not available widely-accepted prognostic markers to predict outcomes in basal-like subtype, so the selection of new prognostic indicators for this BC phenotype represents an unmet clinical challenge.

Results

Here, we attempted to address this challenging issue by exploiting a bioinformatics pipeline able to integrate transcriptomic, genomic, epigenomic, and clinical data freely accessible from public repositories. This pipeline starts from the application of the well-established network-based SWIM methodology on the transcriptomic data to unveil important (switch) genes in relation with a complex disease of interest. Then, survival and linear regression analyses are performed to associate the gene expression profiles of the switch genes with both the patients’ clinical outcome and the disease aggressiveness. This allows us to identify a prognostic gene signature that in turn is fed to the last step of the pipeline consisting of an analysis at DNA level, to investigate whether variations in the expression of identified prognostic switch genes could be related to genetic (copy number variations) or epigenetic (DNA methylation differences) alterations in their gene loci, or to the activities of transcription factors binding to their promoter regions. Finally, changes in the protein expression levels corresponding to the so far identified prognostic switch genes are evaluated by immunohistochemical staining results taking advantage of the Human Protein Atlas.

Conclusion

The application of the proposed pipeline on the dataset of The Cancer Genome Atlas (TCGA)-Breast Invasive Carcinoma (BRCA) patients affected by basal-like subtype led to an in silico recognition of a basal-like specific gene signature composed of 11 potential prognostic biomarkers to be further investigated.