Gene expression profile association with poor prognosis in epithelial ovarian cancer patients

Human papillomavirus infection of the fallopian tube as a potential risk factor for epithelial ovarian cancer

Human papillomaviruses (HPVs) and herpesviruses are detected in patients with epithelial ovarian cancer (EOC). We sought to analyze the prevalence of HPV's 16 and 18, cytomegalovirus (CMV), and Epstein-Barr virus (EBV) DNA in peripheral blood, ovarian, and fallopian tube (FT) tissue samples collected from 97 EOC patients, including 71 cases of high-grade serous ovarian carcinoma (HGSOC), and from 60 women with other tumors or non-neoplastic gynecological diseases. DNA isolates were analyzed by PCR methods, including droplet digital PCR. The results demonstrate that (1) HPV16 DNA has been detected in one-third of the FT and tumor samples from EOCs; (2) the prevalence and quantity of HPV16 DNA were significantly higher in FT samples from HGSOCs, non-HGSOCs, and ovarian metastases than in those from non-neoplastic diseases; (3) CMV and EBV have been detected in approximately one-seventh of EOC samples. The results suggest that HPV16 might be a potential risk factor for EOC development.

Iron affects the sphere-forming ability of ovarian cancer cells in non-adherent culture conditions

Introduction: Detachment from the extracellular matrix (ECM) is the first step of the metastatic cascade. It is a regulated process involving interaction between tumor cells and tumor microenvironment (TME). Iron is a key micronutrient within the TME. Here, we explored the role of iron in the ability of ovarian cancer cells to successfully detach from the ECM. Methods: HEY and PEO1 ovarian cancer cells were grown in 3D conditions. To mimic an iron rich TME, culture media were supplemented with 100 μM Fe3+. Cell mortality was evaluated by cytofluorimetric assay. The invasive potential of tumor spheroids was performed in Matrigel and documented with images and time-lapses. Iron metabolism was assessed by analyzing the expression of CD71 and FtH1, and by quantifying the intracellular labile iron pool (LIP) through Calcein-AM cytofluorimetric assay. Ferroptosis was assessed by quantifying mitochondrial reactive oxygen species (ROS) and lipid peroxidation through MitoSOX and BODIPY-C11 cytofluorimetric assays, respectively. Ferroptosis markers GPX4 and VDAC2 were measured by Western blot. FtH1 knockdown was performed by using siRNA. Results: To generate spheroids, HEY and PEO1 cells prevent LIP accumulation by upregulating FtH1. 3D HEY moderately increases FtH1, and LIP is only slightly reduced. 3D PEO1upregulate FtH1 and LIP results significantly diminished. HEY tumor spheroids prevent iron import downregulating CD71, while PEO1 cells strongly enhance it. Intracellular ROS drop down during the 2D to 3D transition in both cell lines, but more significantly in PEO1 cells. Upon iron supplementation, PEO1 cells continue to enhance CD71 and FtH1 without accumulating the LIP and ROS and do not undergo ferroptosis. HEY, instead, accumulate LIP, undergo ferroptosis and attenuate their sphere-forming ability and invasiveness. FtH1 knockdown significantly reduces the generation of PEO1 tumor spheroids, although without sensitizing them to ferroptosis. Discussion: Iron metabolism reprogramming is a key event in the tumor spheroid generation of ovarian cancer cells. An iron-rich environment impairs the sphere-forming ability and causes cell death only in ferroptosis sensitive cells. A better understanding of ferroptosis sensitivity could be useful to develop effective treatments to kill ECM-detached ovarian cancer cells.

Regression of ovarian cancer xenografts by depleting or inhibiting RLIP

Ovarian cancer (OC) is the most prevalent and deadly cancer of the female reproductive system. Women will continue to be impacted by OC-related morbidity and mortality. Despite the fact that chemotherapy with cisplatin is the main component as the first-line anticancer treatment for OC, chemoresistance and unfavorable side effects are important obstacles to effective treatment. Targets for effective cancer therapy are required for cancer cells but not for non-malignant cells because they are expressed differently in cancer cells compared to normal cells. Targets for cancer therapy should preferably be components that already exist in biochemical and signalling frameworks and that significantly contribute to the development of cancer or regulate the response to therapy. RLIP is an important mercapturic acid pathway transporter that is crucial for survival and therapy resistance in cancers, therefore, we examined the role of RLIP in regulating essential signalling proteins involved in relaying the inputs from upstream survival pathways and mechanisms contributing to chemo-radiotherapy resistance in OC. The findings of our research offer insight into a novel anticancer effect of RLIP depletion/inhibition on OC and might open up new therapeutic avenues for OC therapy.

A new immune checkpoint-associated nine-gene signature for prognostic prediction of glioblastoma

Glioblastoma (GBM) is a highly malignant neurological tumor that has a poor prognosis. While pyroptosis affects cancer cell proliferation, invasion and migration, function of pyroptosis-related genes (PRGs) in GBM as well as the prognostic significance of PRGs remain obscure. By analyzing the mechanisms involved in the association between pyroptosis and GBM, our study hopes to provide new insights into the treatment of GBM. Here, 32 out of 52 PRGs were identified as the differentially expressed genes between GBM tumor versus normal tissues. And all GBM cases were assigned to 2 groups according to the expression of the differentially expressed genes using comprehensive bioinformatics analysis. The least absolute shrinkage and selection operator analysis led to the construction of a 9-gene signature, and the cancer genome atlas cohort of GBM patients were categorized into high risk and low risk subgroups. A significant increase in the survival possibility was found in low risk patients in comparison with the high risk ones. Consistently, low risk patients of a gene expression omnibus cohort displayed a markedly longer overall survival than the high risk counterparts. The risk score calculated using the gene signature was found to be an independent predictor of survival of GBM cases. Besides, we observed significant differences in the expression levels of immune checkpoints between the high risk versus low risk GBM cases, providing instructive suggestions for immunotherapy of GBM. Overall, the present study developed a new multigene signature for prognostic prediction of GBM.

Performance Analysis of Ovarian Cancer Detection and Classification for Microarray Gene Data

The most common gynecologic cancer, behind cervical and uterine, is ovarian cancer. Ovarian cancer is a severe concern for women. Abnormal cells form and spread throughout the body. Ovarian cancer microarray data can diagnose and prognosis. Typically, ovarian cancer microarray data contains tens of thousands of genes. In order to reduce computational complexity, selecting the most critical genes or attributes in the entire dataset is necessary. Because microarray datasets have limited samples and many characteristics, classifier detection lags. So, dimensionality reduction measures are essential to protect disease classification genes. In this research, initially the ANOVA method is used for gene selection and then two clustering-based and three transform-based feature extraction methods, namely, Fuzzy C Means, Softmax Discriminant Algorithm (SDA), Hilbert Transform, Fast Fourier Transform (FFT), and Discrete Cosine Transform (DCT), respectively, are used to select relevant genes further. Six classifiers further classify the features as normal and abnormal. The NLR classifier gives the highest accuracy for SDA features at 92%, and KNN gives the lowest accuracy of 55% for SDA, Hilbert, and DCT features. With correlation distance feature selection, the NLR classifier attains the lowest accuracy of 53%, and the highest accuracy of 88% is obtained by the GMM classifier.

Identification of New Molecular Biomarkers in Ovarian Cancer Using the Gene Expression Profile

Ovarian cancer is a common cause of death among women worldwide. The current diagnostic and prognostic procedures available for the treatment of ovarian cancer are either not specific or are very expensive. Gene expression profiling has proved to be a very effective tool in the exploration of new molecular markers in patients with ovarian cancer, although the link between such markers and patient survival and clinical outcomes is still elusive. We are looking for genes that may function in the development and progression of ovarian cancer. The aim of our study was to evaluate the expression of selected suppressor genes (ATM, BRCA1, BRCA2), proto-oncogenes (KRAS, c-JUN, c-FOS), pro-apoptotic genes (NOXA, PUMA), genes related to chromatin remodeling (MEN1), and genes related to carcinogenesis (NOD2, CHEK2, EGFR). Tissue samples from 30 normal ovaries and 60 ovarian carcinoma tumors were provided for analysis of the gene and protein expression. Gene expression analysis was performed using the real-time PCR method. The protein concentrations from tissue homogenates were determined using the ELISA technique according to the manufacturers’ protocols. An increase in the expression level of mRNA and protein in women with ovarian cancer was observed for KRAS, c-FOS, PUMA, and EGFR. No significant changes in the transcriptional levels we observed for BRCA1, BRCA2, NOD2, or CHEK2. In conclusion, we suggest that KRAS, NOXA, PUMA, c-FOS, and c-JUN may be associated with poor prognosis in ovarian cancer.

An Ensemble Deep Learning Model with a Gene Attention Mechanism for Estimating the Prognosis of Low-Grade Glioma

While estimating the prognosis of low-grade glioma (LGG) is a crucial problem, it has not been extensively studied to introduce recent improvements in deep learning to address the problem. The attention mechanism is one of the significant advances; however, it is still unclear how attention mechanisms are used in gene expression data to estimate prognosis because they were designed for convolutional layers and word embeddings. This paper proposes an attention mechanism called gene attention for gene expression data. Additionally, a deep learning model for prognosis estimation of LGG is proposed using gene attention. The proposed Gene Attention Ensemble NETwork (GAENET) outperformed other conventional methods, including survival support vector machine and random survival forest. When evaluated by C-Index, the GAENET exhibited an improvement of 7.2% compared to the second-best model. In addition, taking advantage of the gene attention mechanism, HILS1 was discovered as the most significant prognostic gene in terms of deep learning training. While HILS1 is known as a pseudogene, HILS1 is a biomarker estimating the prognosis of LGG and has demonstrated a possibility of regulating the expression of other prognostic genes.

The Therapeutic Potential of Common Herbal and Nano-Based Herbal Formulations against Ovarian Cancer: New Insight into the Current Evidence

Ovarian cancer (OCa) is characterized as one of the common reasons for cancer-associated death in women globally. This gynecological disorder is chiefly named the "silent killer" due to lacking an association between disease manifestations in the early stages and OCa. Because of the disease recurrence and resistance to common therapies, discovering an effective therapeutic way against the disease is a challenge. According to documents, some popular herbal formulations, such as curcumin, quercetin, and resveratrol, can serve as an anti-cancer agent through different mechanisms. However, these herbal products may be accompanied by some pharmacological limitations, such as poor bioavailability, instability, and weak water solubility. On the contrary, using nano-based material, e.g., nanoparticles (NPs), micelles, liposomes, can significantly solve these limitations. Therefore, in the present study, we will summarize the anti-cancer aspects of these herbal and-nano-based herbal formulations with a focus on their mechanisms against OCa.

Identification of an epithelial-mesenchymal transition-related lncRNA prognostic signature for patients with glioblastoma

Glioblastoma (GBM) is a strikingly heterogeneous and lethal brain tumor with very poor prognosis. LncRNAs play critical roles in the tumorigenesis of GBM through regulation of various cancer-related genes and signaling pathways. Here, we focused on the essential role of EMT and identified 78 upregulated EMT-related genes in GBM through differential expression analysis and Gene set enrichment analysis (GSEA). A total of 301 EMT-related lncRNAs were confirmed in GBM through Spearman correlation analysis and a prognostic signature consisting of seven EMT-related lncRNAs (AC012615.1, H19, LINC00609, LINC00634, POM121L9P, SNHG11, and USP32P3) was established by univariate and multivariate Cox regression analyses. Significantly, Kaplan-Meier analysis and receiver-operating-characteristic (ROC) curve validated the accuracy and efficiency of the signature to be satisfactory. Quantitative real-time (qRT)-PCR assay demonstrated the expression alterations of the seven lncRNAs between normal glial and glioma cell lines. Functional enrichment analysis revealed multiple EMT and metastasis-related pathways were associated with the EMT-related lncRNA prognostic signature. In addition, we observed the degree of immune cell infiltration and immune responses were significantly increased in high-risk subgroup compared with low-risk subgroup. In conclusion, we established an effective and robust EMT-related lncRNA signature which was expected to predict the prognosis and immunotherapy response for GBM patients.

Progesterone Receptor Membrane Component (PGRMC)1 and PGRMC2 and Their Roles in Ovarian and Endometrial Cancer

Cancers of the female reproductive tract are both lethal and highly prevalent. For example, the five-year survival rate of women diagnosed with ovarian cancer is still less than 50%, and endometrial cancer is the fourth most common cancer in women with > 65,000 new cases in the United States in 2020. Among the many genes already established as key participants in ovarian and endometrial oncogenesis, progesterone receptor membrane component (PGRMC)1 and PGRMC2 have gained recent attention given that there is now solid correlative information supporting a role for at least PGRMC1 in enhancing tumor growth and chemoresistance. The expression of PGRMC1 is significantly increased in both ovarian and endometrial cancers, similar to that reported in other cancer types. Xenograft studies using human ovarian and endometrial cancer cell lines in immunocompromised mice demonstrate that reduced expression of PGRMC1 results in tumors that grow substantially slower. While the molecular underpinnings of PGRMCs' mechanisms of action are not clearly established, it is known that PGRMCs regulate survival pathways that attenuate stress-induced cell death. The objective of this review is to provide an overview of what is known about the roles that PGRMC1 and PGRMC2 play in ovarian and endometrial cancers, particularly as related to the mechanisms through which they regulate mitosis, apoptosis, chemoresistance, and cell migration.

Increased Plasma Levels of lncRNAs LINC01268, GAS5 and MALAT1 Correlate with Negative Prognostic Factors in Myelofibrosis

Simple Summary

Myelofibrosis (MF) displays the worst prognosis among Philadelphia-negative chronic myeloproliferative neoplasms. There is no curative therapy for MF, except for bone marrow transplantation, which however has a consistent percentage of failure. There is thus an urgent need of novel biomarkers to complement current stratification models and to enable better management of patients. To address this issue, we herein measured the plasma levels of several long noncoding RNAs (lncRNAs). Circulating lncRNAs has been already largely described as potential non-invasive biomarkers in cancers. In our study we unveiled that LINC01268, MALAT1 (both p < 0.0001) and GAS5 (p = 0.0003) plasma levels are significantly higher in MF patients if compared with healthy donors, and their increased plasma levels correlate with several detrimental features in MF. Among them, LINC01268 is an independent variable for both OS (p = 0.0297) and LFS (p = 0.0479), thus representing a putative new biomarker suitable for integrate contemporary prognostic models.

Abstract

Long non-coding RNAs (lncRNAs) have been recently described as key mediators in the development of hematological malignancies. In the last years, circulating lncRNAs have been proposed as a new class of non-invasive biomarkers for cancer diagnosis and prognosis and to predict treatment response. The present study is aimed to investigate the potential of circulating lncRNAs as non-invasive prognostic biomarkers in myelofibrosis (MF), the most severe among Philadelphia-negative myeloproliferative neoplasms. We detected increased levels of seven circulating lncRNAs in plasma samples of MF patients (n = 143), compared to healthy controls (n = 65). Among these, high levels of LINC01268, MALAT1 or GAS5 correlate with detrimental clinical variables, such as high count of leukocytes and CD34+ cells, severe grade of bone marrow fibrosis and presence of splenomegaly. Strikingly, high plasma levels of LINC01268 (p = 0.0018), GAS5 (p = 0.0008) or MALAT1 (p = 0.0348) are also associated with a poor overall-survival while high levels of LINC01268 correlate with a shorter leukemia-free-survival. Finally, multivariate analysis demonstrated that the plasma level of LINC01268 is an independent prognostic variable, suggesting that, if confirmed in future in an independent patients’ cohort, it could be used for further studies to design an updated classification model for MF patients.