Screening of prognostic biomarkers for endometrial carcinoma based on a ceRNA network

Mechanism of regulation of KIF23 on endometrial cancer cell growth and apoptosis

OBJECTIVE

The global incidence of endometrial cancer, a malignant tumor in females, is on the rise. It is one of the most common gynecological cancers. Early-stage endometrial cancers can often be treated successfully with uterine extirpation. However, those diagnosed at a later stage have a poor prognosis and encounter treatment challenges. Therefore, additional research is necessary to develop primary prevention strategies for high-risk women and improve survival rates among patients with endometrial cancer. Hence, gene therapy targeting KIF23 shows promise as an advanced strategy for the treatment of endometrial cancer.

METHODS

Immunohistochemistry, Western blotting, and PCR were used to examine the expression of KIF23 and its associated pathway factors in endometrial cancer tissue (specifically Ishikawa and SNGM cells, respectively). We investigated the functional roles of KIF23 using CCK-8, colony-forming proliferation assays, Transwell migration assays, and xenotransplantation in mice.

RESULTS

Immunohistochemistry analysis showed variations in the expression levels of KIF23 between endometrial cancer tissue and normal endometrium tissue. KIF23 downregulated BAX and caspase-3 protein expression while upregulating BCL-2 protein expression. Additionally, knocking out KIF23 inhibits endometrial cancer cell proliferation and migration while promoting cell death. Mechanistically, our study provides evidence that KIF23 promotes endometrial cancer cell proliferation by activating the ERK and AKT/PI3K pathways, while simultaneously inhibiting programmed cell death in endometrial cancer.

CONCLUSION

Our study provides evidence to support the inhibition of endometrial cancer by KIF23 knockdown. This offers valuable insights for future research on potential therapeutic strategies for this type of cancer.

CD24 May Serve as an Immunotherapy Target in Triple-Negative Breast Cancer by Regulating the Expression of PD-L1

Purpose

CD24 mediates a "don't eat me" signal to escape the immune environment. However, the correlation between CD24 and PD-L1 is unclear. This study aimed to assess if CD24 can serve as a target for immunotherapy of triple-negative breast cancer (TNBC).

Methods

Data on CD24 expression in breast cancer were acquired using the Oncomine and UALCAN tools. The role of CD24 expression on the prognosis of patients with TNBC was assessed using Kaplan-Meier analyses. Subsequently, STRING and TISIDB databases were used to construct protein-protein interaction networks and to explore immune-related molecules regulated by CD24. Immunofluorescence and immunohistochemistry assays were conducted to validate CD24 and PD-L1 expression and tumor infiltration lymphocyte (TIL) level. Survival analysis was also performed to explore the effect of CD24 and PD-L1 expression and TIL level in patients with TNBC. ShRNA was also used to explore the regulation role of CD24 on PD-L1 expression.

Results

CD24 expression was significantly higher in breast cancer than in normal tissues, with high expression being significantly associated with a worse prognosis. CD24 was found to be significantly regulated by chemokines, immunoinhibitors, immunostimulators and TILs. Furthermore, CD24 expression showed a significant positive correlation with PD-L1 expression and a negative correlation with TIL level. In association with PD-L1, CD24 was found to positively regulate lymphocyte costimulation, T cell costimulation, and leukocyte activation. Furthermore, CD24 and PD-L1 co-expression contributed to worse survival outcomes. In addition, CD24 expression was found to attenuate the positive effects of high-level TILs on the prognosis of patients with TNBC. CD24 can also regulate the expression of PD-L1 in TNBC cells.

Conclusion

CD24 may attenuate the positive effects of high TIL levels on survival and may facilitate the immune escape of TNBC by regulating PD-L1 expression. Thus, it is a potential target for immunotherapy in TNBC.

The interplay of sex steroid hormones and microRNAs in endometrial cancer: current understanding and future directions

Introduction

Endometrial cancer is a hormone-dependent malignancy, and sex steroid hormones play a crucial role in its pathogenesis. Recent studies have demonstrated that microRNAs (miRNAs) can regulate the expression of sex steroid hormone receptors and modulate hormone signaling pathways. Our aim is to provide an overview of the current understanding of the role of miRNAs in endometrial cancer regulated by sex steroid hormone pathways.

Methods

A thorough literature search was carried out in the PubMed database. The articles published from 2018 to the present were included. Keywords related to miRNAs, endometrial cancer, and sex steroid hormones were used in the search.

Results

Dysregulation of miRNAs has been linked to abnormal sex steroid hormone signaling and the development of endometrial cancer. Various miRNAs have been identified as modulators of estrogen and progesterone receptor expression, and the miRNA expression profile has been shown to be a predictor of response to hormone therapy. Additionally, specific miRNAs have been implicated in the regulation of genes involved in hormone-related signaling pathways, such as the PI3K/Akt/mTOR and MAPK/ERK pathways.

Conclusion

The regulation of sex steroid hormones by miRNAs is a promising area of research in endometrial cancer. Future studies should focus on elucidating the functional roles of specific miRNAs in sex steroid hormone signaling and identifying novel miRNA targets for hormone therapy in endometrial cancer management.

Establishment of a prognostic-related microRNAs risk model for glioma by bioinformatics analysis

Background

To explore the specific prognosis related microRNAs (miRNAs) of glioma.

Methods

The miRNA-Seq data and clinical information of glioma patients were downloaded from the TCGA (510 cases) and GEO (GSE112009, 25 cases) database. LASSO & COX regression was used to develop a miRNA-based model for predicting patient survival in the training set (n=255), to carry out glioma prognostic related miRNAs screening, and to construct a linear risk model based on the expression profiles of seven miRNAs. COX regression analysis was used to determine whether the miRNAs risk model was an independent prognostic factor.

Results

Seven survival-related miRNAs (miR-140-5p, miR-145-5p, miR-148a-3p, miR-183-5p, miR-222-3p, miR-223-3p, and miR-374a-5p) were identified in the training set. This showed that the overall survival time of the high-risk group was significantly lower than that of the low-risk group in the training set, prediction set, and validation set (P<0.05). Further analysis revealed that age and Karnofsky score both affected the risk of glioma. By crossing seven potential target genes of microRNAs, 620 effective target genes were obtained and GO analysis showed that these were related to the positive regulation of cell migration, neuron migration, and the response of transforming growth factor, and KEGG analysis showed they were related to the TGF-beta signaling pathway, MAPK signaling, and AGE-RAGE signaling pathway in diabetic complications.

Conclusions

Seven miRNAs which regulate target genes to participate in related signaling pathways and lead to a poor prognosis were identified as biomarkers of glioma.

Identification of the 11-lncRNA signatures associated with the prognosis of endometrial carcinoma

Endometrial carcinoma (EC) is the fourth most common cancer in women. Some long non-coding RNAs (lncRNAs) are regarded as potential prognostic biomarkers or targets for treatment of many types of cancers. We aim to screen prognostic-related lncRNAs and build a possible lncRNA signature which can effectively predict the survival of patients with EC. We obtained lncRNA expression profiling from the TCGA database. The patients were classified into training set and verification set. By performing Univariate Cox regression model, Robust likelihood-based survival analysis, and Cox proportional hazards model, we developed a risk score with the Cox co-efficient of individual lncRNAs in the training set. The optimum cut-off point was selected by ROC analysis. Patients were effectively divided into high-risk group and low-risk group according to the risk score. The OS of the low-risk patients was significantly prolonged compared with that of the high-risk group. At last, we validated this 11-lncRNA signature in the verification set and the complete set. We identified an 11-lncRNA expression signature with high stability and feasibility, which can predict the survival of patients with EC. These findings provide new potential biomarkers to improve the accuracy of prognosis prediction of EC.

Identification of KIF23 as a prognostic signature for ovarian cancer based on large-scale sampling and clinical validation

The purpose of this study is to explore the expression and clinical significance of KIF23 in ovarian cancer (OV) and identify potential targets for clinical treatment. Oncomine, GEO, and TCGA databases were used to analysis the expression of KIF23 in OV. The prognostic value of KIF23 gene was analyzed by the Kaplan-Meier plotter database. The molecular mechanism of KIF23 activity was analyzed from the perspective of immunology, gene mutation, copy number variation (CNV). Finally, immunohistochemistry was conducted to validate the expression of KIF23, univariable and multivariate cox analysis were used to determine its relationship with clinical characteristics and OV prognosis. It showed that highly expressed KIF23 is an adverse independent prognostic biomarker for OV patients. Genomics analysis showed that KIF23 expression was associated with mutations such as FLG2 and TTN, and was significantly enriched in DNA replication and the cell cycle tumor-related signaling pathways. Immunology analysis showed that KIF23 is closely related to the immune infiltration. KIF23 can not only performed as a prognosis signature in OV but also as a target of immune molecular therapeutics.

Identification of circRNA-lncRNA-miRNA-mRNA Competitive Endogenous RNA Network as Novel Prognostic Markers for Acute Myeloid Leukemia

Background: Acute myeloid leukemia (AML) is one of the most common malignant and aggressive hematologic tumors, and its pathogenesis is associated with abnormal post-transcriptional regulation. Unbalanced competitive endogenous RNA (ceRNA) promotes tumorigenesis and progression, and greatly contributes to tumor risk classification and prognosis. However, the comprehensive analysis of the circular RNA (circRNA)-long non-coding RNA (lncRNA)-miRNA-mRNA ceRNA network in the prognosis of AML is still rarely reported. Method: We obtained transcriptome data of AML and normal samples from The Cancer Genome Atlas (TCGA), Genotype-tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases, and identified differentially expressed (DE) mRNAs, lncRNAs, and circRNAs. Then, the targeting relationships among lncRNA-miRNA, circRNA-miRNA, and miRNA-mRNA were predicted, and the survival related hub mRNAs were further screened by univariate and multivariate Cox proportional hazard regression. Finally, the AML prognostic circRNA-lncRNA-miRNA-mRNA ceRNA regulatory network was established. Results: We identified prognostic 6 hub mRNAs (TM6SF1, ZMAT1, MANSC1, PYCARD, SLC38A1, and LRRC4) through Cox regression model, and divided the AML samples into high and low risk groups according to the risk score obtained by multivariate Cox regression. Survival analysis verified that the survival rate of the high-risk group was significantly reduced (p < 0.0001). The prognostic ceRNA network of 6 circRNAs, 32 lncRNAs, 8 miRNAs, and 6 mRNAs was established according to the targeting relationship between 6 hub mRNAs and other RNAs. Conclusion: In this study, ceRNA network jointly participated by circRNAs and lncRNAs was established for the first time. It comprehensively elucidated the post-transcriptional regulatory mechanism of AML, and identified novel AML prognostic biomarkers, which has important guiding significance for the clinical diagnosis, treatment, and further scientific research of AML.

Comprehensive Analysis of Prognostic Alternative Splicing Signatures in Endometrial Cancer

Background

Alternative splicing (AS) is one of the critical post-transcriptional regulatory mechanisms of various cancers and also plays a crucial role in the development of cancers, including endometrial cancer (EC).

Methods

The splicing data and gene expression profiles of EC were obtained from The Cancer Genome Atlas. The corresponding clinical data were extracted from TCGA-CDR. With univariate Cox regression analysis, least absolute shrinkage and selection operator model, and multivariate Cox regression analysis, the survival-related AS events were selected. Functional enrichment analysis was also performed to investigate the functions of these AS events. Splicing factors and AS regulation network were constructed to understand the correlation among these AS events.

Result

A total of 1826 AS events were identified as survival-related events. Functional enrichment analysis showed that these AS events were associated with several immune system-related processes. Then, the prognostic signatures were developed based on these survival-related events and acted as an independent prognostic factor for EC. Splicing factors and AS regulation network were also constructed to understand the regulatory mechanisms of AS events in EC.

Conclusion

This study systematically analyzed the role of AS events in EC and developed the prognostic model for EC.