KCNH3 Predicts Poor Prognosis and Promotes Progression in Ovarian Cancer

Exploring the tumor genomic landscape of aggressive prostate cancer by whole-genome sequencing of tissue or liquid biopsies

Treatment resistance remains a major issue in aggressive prostate cancer (PC), and novel genomic biomarkers may guide better treatment selection. Circulating tumor DNA (ctDNA) can provide minimally invasive information about tumor genomes, but the genomic landscape of aggressive PC based on whole-genome sequencing (WGS) of ctDNA remains incompletely characterized. Thus, we here performed WGS of tumor tissue (n = 31) or plasma ctDNA (n = 10) from a total of 41 aggressive PC patients, including 11 hormone-naïve, 15 hormone-sensitive, and 15 castration-resistant patients. Across all variant types, we found progressively more altered tumor genomic profiles in later stages of aggressive PC. The potential driver genes most frequently affected by single-nucleotide variants or insertions/deletions included the known PC-related genes TP53, CDK12, and PTEN and the novel genes COL13A1, KCNH3, and SENP3. Etiologically, aggressive PC was associated with age-related and DNA repair-related mutational signatures. Copy number variants most frequently affected 14q11.2 and 8p21.2, where no well-recognized PC-related genes are located, and also frequently affected regions near the known PC-related genes MYC, AR, TP53, PTEN, and BRCA1. Structural variants most frequently involved not only the known PC-related genes TMPRSS2 and ERG but also the less extensively studied gene in this context, PTPRD. Finally, clinically actionable variants were detected throughout all stages of aggressive PC and in both plasma and tissue samples, emphasizing the potential clinical applicability of WGS of minimally invasive plasma samples. Overall, our study highlights the feasibility of using liquid biopsies for comprehensive genomic characterization as an alternative to tissue biopsies in advanced/aggressive PC.

An immune related signature inhibits the occurrence and development of serous ovarian cancer by affecting the abundance of dendritic cells

Serous ovarian cancer is one of the major causes of cancer related death among women worldwide. The advanced diagnosis worsens the prognosis of patients with serous ovarian cancer. The immune system has an important impact on the progression of ovarian cancer. Herein, we aimed to establish an immune related prognostic signature to assist in the early diagnosis, treatment, and prognostic evaluation of patients with serous ovarian cancer. Multiple public data sets and immune related genes were obtained from various online public databases, and immune related prognostic signatures were developed through differential expression analysis, univariate Cox proportional hazard regression analysis, and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model. The nomogram model, Kaplan-Meier survival curve analysis, receiver operating characteristic (ROC) curve analysis, and decision curve analysis showed that this signature had a good prediction potential. In conclusion, an immune related signature with good prediction efficiency was established through systematic bioinformatics analysis, which may play a tumor inhibition role by affecting the abundance of activated dendritic cells.

LncRNA SLC25A21-AS1 increases the chemosensitivity and inhibits the progression of ovarian cancer by upregulating the expression of KCNK4

Owing to high mortality rate, ovarian cancer seriously threatens women's health. Extensive abdominal metastasis and chemoresistance are the leading causes of ovarian cancer deaths. Through lncRNA sequencing, our previous study identified lncRNA SLC25A21-AS1, which was significantly downregulated in chemoresistant ovarian cancer cells. In this study, we aimed to evaluate the role and mechanism of SLC25A21-AS1 in ovarian cancer. The expression of SLC25A21-AS1 was analyzed by qRT-PCR and online database GEPIA. The biological functions of SLC25A21-AS1 and KCNK4 were analyzed by CCK-8, transwell, and flow cytometry. The specific mechanism was analyzed by RNA-sequencing, RNA binding protein immunoprecipitation, rescue experiments, and bioinformatic analysis. SLC25A21-AS1 was decreased in ovarian cancer tissues and cell lines. Overexpression of SLC25A21-AS1 enhanced the sensitivity of ovarian cancer cells to paclitaxel and cisplatin, and inhibited cell proliferation, invasion, and migration, while SLC25A21-AS1-silencing showed the opposite effect. Potassium channel subfamily K member 4 (KCNK4) was significantly up-regulated upon enforced expression of SLC25A21-AS1. Overexpression of KCNK4 inhibited cell proliferation, invasion, migration ability, and enhanced the sensitivity of ovarian cancer cells to paclitaxel and cisplatin. Meanwhile, KNCK4-overexpression rescued the promotive effect of SLC25A21-AS1-silencing on cell proliferation, invasion and migration. In addition, SLC25A21-AS1 could interact with the transcription factor Enhancer of Zeste Homolog 2 (EZH2), while EZH2 knockdown increased the expression of KCNK4 in some of the ovarian cancer cell lines. SLC25A21-AS1 enhanced the chemosensitivity and inhibited the proliferation, migration, and invasion ability of ovarian cancer cells at least partially by blocking EZH2-mediated silencing of KCNK4.

Potassium channels, tumorigenesis and targeted drugs

Potassium channels play an important role in human physiological function. Recently, various molecular mechanisms have implicated abnormal functioning of potassium channels in the proliferation, migration, invasion, apoptosis, and cancer stem cell phenotype formation. Potassium channels also mediate the association of tumor cells with the tumor microenvironment. Meanwhile, potassium channels are important targets for cancer chemotherapy. A variety of drugs exert anti-cancer effects by modulating potassium channels in tumor cells. Therefore, there is a need to understand how potassium channels participate in tumor development and progression, which could reveal new, novel targets for cancer diagnosis and treatment. This review summarizes the roles of voltage-gated potassium channels, calcium-activated potassium channels, inwardly rectifying potassium channels, and two-pore domain potassium channels in tumorigenesis and the underlying mechanism of potassium channel-targeted drugs. Therefore, the study lays the foundation for rational and effective drug design and individualized clinical therapeutics.

Glycosylation-Related Genes Predict the Prognosis and Immune Fraction of Ovarian Cancer Patients Based on Weighted Gene Coexpression Network Analysis (WGCNA) and Machine Learning

Background

Ovarian cancer (OC) is a malignancy exhibiting high mortality in female tumors. Glycosylation is a posttranslational modification of proteins but research has failed to demonstrate a systematic link between glycosylation-related signatures and tumor environment of OC.

Purpose

This study is aimed at developing a novel model with glycosylation-related messenger RNAs (GRmRNAs) to predict the prognosis and immune function in OC patients.

Methods

The transcriptional profiles and clinical phenotypes of OC patients were collected from the Gene Expression Omnibus and The Cancer Genome Atlas databases. A weighted gene coexpression network analysis and machine learning were performed to find the optimal survival-related GRmRNAs. Least absolute shrinkage and selection operator regression (LASSO) and Cox regression were carried out to calculate the coefficients of each GRmRNA and compute the risk score of each patient as well as develop a prognostic model. A nomogram model was constructed, and several algorithms were used to investigate the relationship between risk subtypes and immune-infiltrating levels.

Results

A total of four signatures (ALG8, DCTN4, DCTN6, and UBB) were determined to calculate the risk scores, classifying patients into the high-and low-risk groups. High-risk patients exhibited significantly poorer survival outcomes, and the established nomogram model had a promising prediction for OC patients' prognosis. Tumor purity and tumor mutation burden were negatively correlated with risk scores. In addition, risk scores held statistical associations with pathway signatures such as Wnt, Hippo, and reactive oxygen species, and nonsynonymous mutation counts.

Conclusion

The currently established risk scores based on GRmRNAs can accurately predict the prognosis, the immune microenvironment, and the immunotherapeutic efficacy of OC patients.