DNA replication and sister chromatid cohesion 1 promotes breast carcinoma progression by modulating the Wnt/β-catenin signaling and p53 protein

A comprehensive multi-omics study reveals potential prognostic and diagnostic biomarkers for colorectal cancer

BACKGROUND AND OBJECTIVE

Colorectal cancer (CRC) is a complex disease with diverse genetic alterations and causes 10 % of cancer-related deaths worldwide. Understanding its molecular mechanisms is essential for identifying potential biomarkers and therapeutic targets for its effective management.

METHODS

We integrated copy number alterations (CNA) and mutation data via their differentially expressed genes termed as candidate genes (CGs) computed using bioinformatics approaches. Then, using the CGs, we perform Weighted correlation network analysis (WGCNA) and utilise several hazard models such as Univariate Cox, Least Absolute Shrinkage and Selection Operator (LASSO) Cox and multivariate Cox to identify the key genes involved in CRC progression. We used different machine-learning models to demonstrate the discriminative power of selected hub genes among normal and CRC (early and late-stage) samples.

RESULTS

The integration of CNA with mRNA expression identified over 3000 CGs, including CRC-specific driver genes like MYC and APC. In addition, pathway analysis revealed that the CGs are mainly enriched in endocytosis, cell cycle, wnt signalling and mTOR signalling pathways. Hazard models identified four key genes, CASP2, HCN4, LRRC69 and SRD5A1, that were significantly associated with CRC progression and predicted the 1-year, 3-years, and 5-years survival times. WGCNA identified seven hub genes: DSCC1, ETV4, KIAA1549, NOP56, RRS1, TEAD4 and ANKRD13B, which exhibited strong predictive performance in distinguishing normal from CRC (early and late-stage) samples.

CONCLUSIONS

Integrating regulatory information with gene expression improved early versus late-stage prediction. The identified potential prognostic and diagnostic biomarkers in this study may guide us in developing effective therapeutic strategies for CRC management.

DSCC1 Identified as Promising Tumor Biomarker and Potential Therapeutic Target Through Comprehensive Multi-omics Analysis and Experimental Validation

As a component of the alternative replication factor C (RFC) complex, DSCC1 plays a significant role in cancer progression due to its aberrant expression. However, the potential function of DSCC1 in a pan-cancer context remains unclear. In this study, we conducted a comprehensive analysis of DSCC1's role in tumors by integrating multi-omics bioinformatics tools. First, we utilized various databases to compare the expression of DSCC1 between tumor and normal tissues, revealing a strong association between its dysregulated expression and clinical diagnosis, prognosis, and staging. Additionally, we investigated different mutation types of DSCC1 and their contributions to cancer progression, finding that DSCC1 expression is regulated by epigenetics and RNA modifications. Furthermore, we explored the correlation between DSCC1 and immune-infiltrating cells, as well as immunotherapeutic biomarkers, suggesting that its expression influences the tumor immune microenvironment. By employing single-cell and spatial transcriptome data through platforms such as SingleCellBase, CancerSEA, and CROST, we further uncovered the heterogeneity of DSCC1 across various cancer types. Finally, we validated the significant upregulation of DSCC1 mRNA in multiple tumor cell lines using q-RTPCR, and demonstrated through CCK8 assays that silencing DSCC1 expression effectively suppressed cell proliferation. Our findings establish a foundational understanding of DSCC1's potential as a biomarker for cancer diagnosis, prognosis, and immunotherapy.

Prognostic Significance of DSCC1, a Biomarker Associated with Aggressive Features of Breast Cancer

Background and Objectives: Invasive breast cancer (BC) was traditionally investigated visually, and no technique could identify the key molecular drivers of patient survival. However, essential molecular drivers of invasive BC have now been discovered using innovative genomic, transcriptomic, and proteomic methodologies. Nevertheless, few evaluations of the prognostic factors of BC in Saudi Arabia have been performed. Evaluating the biomarkers associated with the development of early-stage BC could help determine the risk of metastasis and guide treatment decisions. In a previous study, using large BC cohorts and artificial neural network techniques, DNA replication and sister chromatid cohesion 1 (DSCC1) was found to be one of the principal genes in invasive BC samples. To date, no studies have addressed the prognostic significance of DSCC1 in invasive BC and its association with aggressive tumor behavior. This research aimed to address this gap. Materials and Methods: The association of clinicopathological features and patient outcomes with DSCC1 expression at the mRNA level was assessed using the Molecular Taxonomy Breast Cancer International Consortium (METABRIC; n = 1980) and The Cancer Genome Atlas (TCGA; n = 854) cohorts. DSCC1 was also evaluated at the protein level using immunohistochemistry on samples from invasive BC patients (n = 100) presenting to King Abdul Aziz Specialist Hospital in Saudi Arabia. The association of clinicopathological parameters (including patient age, tumor grade, tumor size, and patient outcome) with protein level was also evaluated. Results: In both METABRIC and TCGA cohorts, high expression of DSCC1 was significantly associated with high histological grade, large tumor size, lymphovascular invasion positivity, and hormone receptor negativity (all p < 0.001). A high DSCC1 mRNA level was associated with poor outcomes (p < 0.001 for METABRIC, p = 0.23 for TCGA). At the protein level, high DSCC1 expression was associated with high histological grade (p = 0.001), lymph node presence (p = 0.008), hormone receptor negativity (p = 0.005), high Ki67 expression (p = 0.036), and shorter survival (p = 0.008). Conclusions: This study confirmed the prognostic significance of DSCC1 in invasive BC patients. DSCC1 could be a therapeutic target in BC cases with poor outcomes.

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.

Screening of potential hub genes and key pathways associated with breast cancer by bioinformatics tools

Breast cancer (BC) remains the leading cause of cancer-related death in women worldwide. The development of new targeted therapies that may improve patient survival remains an area of growing interest. This study aimed to identify new biomarkers involved in BC progression that could be used as potential targeted therapies. DEGs were selected from three gene expression profiles, GSE55715, GSE124646, and GSE87049, using the GEO2R tool and Venn diagram software. Gene Ontology and KEGG pathways were then performed using DAVID software. Next, the PPI network was constructed using STRING and visualized using Cytoscape software, and hub genes were extracted using the cytoHubba plug-in. Survival analysis was performed using the Kaplan-Meier Plotter, while the expression of hub genes in BC was verified using the GEPIA2 tool. Finally, transcription the factors of hub genes were determined using the NetworkAnalyst database, and the TIMER tool was employed to explore the infiltration levels of tumor immune cells with related genes. A total of 146 DEGs were identified in the three datasets, including 60 upregulated genes that were enriched in the cell cycle, and 86 downregulated genes that were mainly enriched in the TNF signaling pathway and pathways in cancer. Ten genes were identified: BUB1, CDK1, HMMR, MAD2L1, CEP55, AURKA, CCNB2, TPX2, MELK, and KIF20A. The overexpression of hub genes, except CDK1, was associated with poor survival in BC and was regulated by several transcription factors involved in DNA binding activity and transcription regulation. The infiltration levels of immune cells were positively correlated with hub genes, particularly macrophages and CD4+ T cells. This study identified new reliable molecular biomarkers that can serve as potential therapeutic targets for BC 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.

High DSCC1 Level Predicts Poor Prognosis of Lung Adenocarcinoma

Purpose

To evaluate the role of DSCC1 in LUAD.

Patients and Methods

Based on TCGA and GTEx, the Wilcoxon rank-sum test was used to compare the expression differences of DSCC1 between the normal samples of GTEx combined TCGA and the unpaired tumor samples of TCGA, and to compare DSCC1 expression values between tumor tissues and paired normal LUAD tissues in the TCGA cohort. Kruskal–Wallis rank-sum test, Wilcoxon rank-sum test, and logistics regression were used to compare the relationship between the expression of DSCC1 and the clinicopathological parameters. The biological function of DSCC1 was annotated by GSEA and ssGSEA, while Kaplan–Meier and Cox regression analysis were used to evaluate the prognostic value of DSCC1. Furthermore, the time-dependent ROC curve was used to analyze the diagnostic efficacy of DSCC1 in LUAD.

Results

We downloaded the RNA-Seq data of 513 LUAD cases. The expression of DSCC1 was significantly correlated with T stage (OR = 1.04(1.02–1.07), P = 0.002), pathological stage (OR=1.03 (1.01–1.05), P = 0.008) and TP53 status (OR=1.10 (1.07–1.14), P < 0.001). The high expression of DSCC1 was significantly correlated with DSS (HR=1.56 (1.07–2.26), P = 0.021) and OS (HR=1.53 (1.14–2.05), P = 0.004). Moreover, ROC curve analysis (AUC=0.845, CI (0.820-0.870)) indicated DSCC1 as a potential diagnostic molecule for LUAD. In the group with high DSCC1 expression phenotype, down-regulation of EGFR signal, reduction of IL-6 deprivation, cell cycle, and p53 signal pathway were significantly abundant. Spearman correlation analysis showed that the expression of DSCC1 was positively correlated with the infiltration of Th2 cells, T Helper cells.

Conclusion

Our results suggest that DSCC1 may be an important biomarker for the treatment of LUAD.