Assessment of SGO1 and SGO1-AS1 contribution in breast cancer

Protooncogenic Role of ARHGAP11A and ARHGAP11B in Invasive Ductal Carcinoma: Two Promising Breast Cancer Biomarkers

Invasive duct carcinoma (IDC) is one of the most common types of breast cancer (BC) in women worldwide, with a high risk of malignancy, metastasis, recurrence, and death. So far, molecular patterns among IDC cases have not been fully defined. However, extensive evidence has shown that dysregulated Rho family small GTPases (Rho GTPases) including Rho GTPase activating proteins (RhoGAPs) have important roles in the invasive features of IDCs. In the current study, we analyzed the expression levels of two RhoGAP genes, ARHGAP11A and ARHGAP11B, in The Cancer Genome Atlas (TCGA) breast cancer (BRCA) and also our 51 IDC tumors compared to their matched normal tissues using quantitative polymerase chain reaction (qPCR). Our TCGA data analysis revealed higher expression of ARHGAP11A and ARHGAP11B in various cancers comprising BCs. Also, we found correlations between these genes and other genes in TCGA-BRCA. Moreover, our methylation analysis showed that their promotor methylation had a negative correlation with their overexpression. QPCR revealed their significant upregulation in our tumor samples. Furthermore, we found that the expression level of ARHGAP11A was considerably lower in women who were breastfeeding. Moreover, it had overexpression in cases who had regular menstrual cycles and early age (younger than 14) at menarche. However, ARHGAP11B had a higher expression in HER2-positive tumors versus HER2-positive and ER-positive tumors. Our study found possible protooncogenic roles for these genes and their involvement in IDC pathogenesis and malignancy. Therefore, they can be considered novel prognostic and diagnostic biomarkers for IDC.

SGOL2 is a novel prognostic marker and fosters disease progression via a MAD2-mediated pathway in hepatocellular carcinoma

Background

Shugoshin-like protein 2 (SGOL2) is a centromeric protein that ensures the correct and orderly process of mitosis by protecting and maintaining centripetal adhesions during meiosis and mitosis. Here, we examined the potential role of SGOL2 in cancers, especially in hepatocellular carcinoma (HCC).

Methods

One hundred ninety-nine normal adjacent tissues and 202 HCC samples were collected in this study. Human HCC cells (SK-HEP-1 and HEP-3B) were employed in the present study. Immunohistochemistry, immunofluorescence, western blot, Co-Immunoprecipitation technique, and bioinformatic analysis were utilized to assess the role of SGOL2 in HCC development process.

Results

Overexpression of SGOL2 predicted an unfavorable prognosis in HCC by The Cancer Genome Atlas database (TCGA), which were further validated in our two independent cohorts. Next, 47 differentially expressed genes positively related to both SGOL2 and MAD2 were identified to be associated with the cell cycle. Subsequently, we demonstrated that SGOL2 downregulation suppressed the malignant activities of HCC in vitro and in vivo. Further investigation showed that SGOL2 promoted tumor proliferation by regulating MAD2-induced cell-cycle dysregulation, which could be reversed by the MAD2 inhibitor M2I-1. Consistently, MAD2 upregulation reversed the knockdown effects of SGOL2-shRNA in HCC. Moreover, we demonstrated that SGOL2 regulated MAD2 expression level by forming a SGOL2-MAD2 complex, which led to cell cycle dysreuglation of HCC cells.

Conclusion

SGOL2 acts as an oncogene in HCC cells by regulating MAD2 and then dysregulating the cell cycle, providing a potential therapeutic target in HCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40364-022-00422-z.

Molecular Classification of Breast Cancer Utilizing Long Non-Coding RNA (lncRNA) Transcriptomes Identifies Novel Diagnostic lncRNA Panel for Triple-Negative Breast Cancer

Simple Summary

Breast cancer is the most commonly diagnosed cancer in women today and accounts for thousands of cancer-related deaths each year. While some breast cancer subtypes can be easily diagnosed and targeted for therapy, triple-negative breast cancer, which lacks receptor expression, is the most challenging to diagnose and treat. In this study, we use multiple RNA sequencing data to look specifically at long non-coding RNA (lncRNA) expression portraits at the transcript level and to identify lncRNA-based biomarkers associated with each breast cancer subtype. Receiver operating characteristic (ROC) analysis was used to validate their diagnostic potential, which was validated in two independent cohorts. Several lncRNA transcripts were found to be enriched in TNBC across all validation cohorts. Binary regression analysis identified a four lncRNA transcript signature with the highest diagnostic power for TNBC as potential novel biomarkers for diagnostic and therapeutic intervention. Interestingly, several of the identified lncRNAs were shown to have prognostic potential in TNBC.

Abstract

Breast cancer remains the world’s most prevalent cancer, responsible for around 685,000 deaths globally despite international research efforts and advances in clinical management. While estrogen receptor positive (ER+), progesterone receptor positive (PR+), and human epidermal growth factor receptor positive (HER2+) subtypes are easily classified and can be targeted, there remains no direct diagnostic test for triple-negative breast cancer (TNBC), except for the lack of receptors expression. The identification of long non-coding RNAs (lncRNAs) and the roles they play in cancer progression has recently proven to be beneficial. In the current study, we utilize RNA sequencing data to identify lncRNA-based biomarkers associated with TNBC, ER+ subtypes, and normal breast tissue. The Marker Finder algorithm identified the lncRNA transcript panel most associated with each molecular subtype and the receiver operating characteristic (ROC) analysis was used to validate the diagnostic potential (area under the curve (AUC) of ≥8.0 and p value < 0.0001). Focusing on TNBC, findings from the discovery cohort were validated in an additional two cohorts, identifying 13 common lncRNA transcripts enriched in TNBC. Binary regression analysis identified a four lncRNA transcript signature (ENST00000425820.1, ENST00000448208.5, ENST00000521666.1, and ENST00000650510.1) with the highest diagnostic power for TNBC. The ENST00000671612.1 lncRNA transcript correlated with worse refractory free survival (RFS). Our data provides a step towards finding a novel diagnostic lncRNA-based panel for TNBC with potential therapeutic implications.

Unveiling functional heterogeneity in breast cancer multicellular tumor spheroids through single-cell RNA-seq

Heterogeneity is an intrinsic characteristic of cancer. Even in isogenic tumors, cell populations exhibit differential cellular programs that overall supply malignancy and decrease treatment efficiency. In this study, we investigated the functional relationship among cell subtypes and how this interdependency can promote tumor development in a cancer cell line. To do so, we performed single-cell RNA-seq of MCF7 Multicellular Tumor Spheroids as a tumor model. Analysis of single-cell transcriptomes at two-time points of the spheroid growth, allowed us to dissect their functional relationship. As a result, three major robust cellular clusters, with a non-redundant complementary composition, were found. Meanwhile, one cluster promotes proliferation, others mainly activate mechanisms to invade other tissues and serve as a reservoir population conserved over time. Our results provide evidence to see cancer as a systemic unit that has cell populations with task stratification with the ultimate goal of preserving the hallmarks in tumors.

Comprehensive analysis of DNA methylation and gene expression profiles in cholangiocarcinoma

BACKGROUND

The incidence of cholangiocarcinoma (CCA) has risen in recent years, and it has become a significant health burden worldwide. However, the mechanisms underlying tumorigenesis and progression of this disease remain largely unknown. An increasing number of studies have demonstrated crucial biological functions of epigenetic modifications, especially DNA methylation, in CCA. The present study aimed to identify and analyze methylation-regulated differentially expressed genes (MeDEGs) involved in CCA tumorigenesis and progression by bioinformatics analysis.

METHODS

The gene expression profiling dataset (GSE119336) and gene methylation profiling dataset (GSE38860) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially methylated genes (DMGs) were identified using the limma packages of R and GEO2R, respectively. The MeDEGs were obtained by overlapping the DEGs and DMGs. Functional enrichment analyses of these genes were then carried out. Protein-protein interaction (PPI) networks were constructed using STRING and visualized in Cytoscape to determine hub genes. Finally, the results were verified based on The Cancer Genome Atlas (TCGA) database.

RESULTS

We identified 98 hypermethylated, downregulated genes and 93 hypomethylated, upregulated genes after overlapping the DEGs and DMGs. These genes were mainly enriched in the biological processes of the cell cycle, nuclear division, xenobiotic metabolism, drug catabolism, and negative regulation of proteolysis. The top nine hub genes of the PPI network were F2, AHSG, RRM2, AURKB, CCNA2, TOP2A, BIRC5, PLK1, and ASPM. Moreover, the expression and methylation status of the hub genes were significantly altered in TCGA.

CONCLUSIONS

Our study identified novel methylation-regulated differentially expressed genes (MeDEGs) and explored their related pathways and functions in CCA, which may provide novel insights into a further understanding of methylation-mediated regulatory mechanisms in CCA.