Cox-LASSO Analysis Reveals a Ten-lncRNA Signature to Predict Outcomes in Patients with High-Grade Serous Ovarian Cancer

Epigenetic Regulation in Exposome-Induced Tumorigenesis: Emerging Roles of ncRNAs

Environmental factors, including pollutants and lifestyle, constitute a significant role in severe, chronic pathologies with an essential societal, economic burden. The measurement of all environmental exposures and assessing their correlation with effects on individual health is defined as the exposome, which interacts with our unique characteristics such as genetics, physiology, and epigenetics. Epigenetics investigates modifications in the expression of genes that do not depend on the underlying DNA sequence. Some studies have confirmed that environmental factors may promote disease in individuals or subsequent progeny through epigenetic alterations. Variations in the epigenetic machinery cause a spectrum of different disorders since these mechanisms are more sensitive to the environment than the genome, due to the inherent reversible nature of the epigenetic landscape. Several epigenetic mechanisms, including modifications in DNA (e.g., methylation), histones, and noncoding RNAs can change genome expression under the exogenous influence. Notably, the role of long noncoding RNAs in epigenetic processes has not been well explored in the context of exposome-induced tumorigenesis. In the present review, our scope is to provide relevant evidence indicating that epigenetic alterations mediate those detrimental effects caused by exposure to environmental toxicants, focusing mainly on a multi-step regulation by diverse noncoding RNAs subtypes.

Ovarian cancer: epigenetics, drug resistance, and progression

Ovarian cancer (OC) is one of the most common malignant tumors in women. OC is associated with the activation of oncogenes, the inactivation of tumor suppressor genes, and the activation of abnormal cell signaling pathways. Moreover, epigenetic processes have been found to play an important role in OC tumorigenesis. Epigenetic processes do not change DNA sequences but regulate gene expression through DNA methylation, histone modification, and non-coding RNA. This review comprehensively considers the importance of epigenetics in OC, with a focus on microRNA and long non-coding RNA. These types of RNA are promising molecular markers and therapeutic targets that may support precision medicine in OC. DNA methylation inhibitors and histone deacetylase inhibitors may be useful for such targeting, with a possible novel approach combining these two therapies. Currently, the clinical application of such epigenetic approaches is limited by multiple obstacles, including the heterogeneity of OC, insufficient sample sizes in reported studies, and non-optimized methods for detecting potential tumor markers. Nonetheless, the application of epigenetic approaches to OC patient diagnosis, treatment, and prognosis is a promising area for future clinical investigation.

Long non-coding RNAs: A view to kill ovarian cancer

An emerging role of long non-coding RNAs (lncRNAs) in tumor progression has been revealed in the last decade. Through interactions with nucleic acids and proteins, lncRNAs could act as enhancers, scaffolds or decoys for a number of oncoproteins and tumor suppressors. The aberrant lncRNA expression or mutations are often associated with changes in a variety of cellular processes, including proliferation, stress response and cell death. Here, we will focus on the tumor-associated lncRNAs in ovarian cancer according to their contribution to cancer hallmarks, such as intense proliferation, cell death resistance, altered energy metabolism, invasion and metastasis, and immune evasion. Moreover, the potential clinical implications of lncRNAs and their significance for the diagnosis, prognosis and therapy of ovarian cancer will be discussed.

An integrated knowledge translation approach to address avoidable rehospitalisations and unplanned admissions for older people in South Australia: implementation and evaluation program plan

Background

Repeated admission to hospital can be stressful for older people and their families and puts additional pressure on the health care system. While there is some evidence about strategies to better integrate care, improve older patients’ experiences at transitions of care, and reduce preventable hospital readmissions, implementing these strategies at scale is challenging. This program of research comprises multiple, complementary research activities with an overall goal of improving the care for older people after discharge from hospital. The program leverages existing large datasets and an established collaborative network of clinicians, consumers, academics, and aged care providers.

Methods

The program of research will take place in South Australia focusing on people aged 65 and over. Three inter-linked research activities will be the following: (1) analyse existing registry data to profile individuals at high risk of emergency department encounters and hospital admissions; (2) evaluate the cost-effectiveness of existing ‘out-of-hospital’ programs provided within the state; and (3) implement a state-wide quality improvement collaborative to tackle key interventions likely to improve older people’s care at points of transitions. The research is underpinned by an integrated approach to knowledge translation, actively engaging a broad range of stakeholders to optimise the relevance and sustainability of the changes that are introduced.

Discussion

This project highlights the uniqueness and potential value of bringing together key stakeholders and using a multi-faceted approach (risk profiling; evaluation framework; implementation and evaluation) for improving health services. The program aims to develop a practical and scalable solution to a challenging health service problem for frail older people and service providers.

Systematic Profiling of Alternative Splicing Events in Ovarian Cancer

Alternative splicing (AS) is significantly related to the development of tumor and the clinical outcome of patients. In this study, our aim was to systematically analyze the survival-related AS signal in ovarian serous cystadenocarcinoma (OV) and estimate its prognostic validity in 48,049 AS events out of 21,854 genes. We studied 1,429 AS events out of 1,125 genes, which were significantly related to the overall survival (OS) in patients with OV. We established alternative splicing features on the basis of seven AS events and constructed a new comprehensive prognostic model. Kaplan-Meier curve analysis showed that seven AS characteristics and comprehensive prognostic models could strongly stratify patients with ovarian cancer and make them distinctive prognosis. ROC analysis from 0.781 to 0.888 showed that these models were highly efficient in distinguishing patient survival. We also verified the prognostic characteristics of these models in a testing cohort. In addition, uni-variate and multivariate Cox analysis showed that these models were superior independent risk factors for OS in patients with OV. Interestingly, AS events and splicing factor (SFs) networks revealed an important link between these prognostic alternative splicing genes and splicing factors. We also found that the comprehensive prognosis model signature had higher prediction ability than the mRNA signature. In summary, our study provided a possible prognostic prediction model for patients with OV and revealed the splicing network between AS and SFs, which could be used as a potential predictor and therapeutic target for patients with OV.

Identification of LncRNA Prognostic Markers for Ovarian Cancer by Integration of Co-expression and CeRNA Network

Background

Ovarian cancer (OC), one of the most prevalent gynecological malignancies, is characterized by late detection and dismal prognosis. Recent studies show that long non-coding RNAs (lncRNAs) in competitive endogenous RNA (ceRNA) networks influence immune infiltration and cancer prognosis. However, the function of lncRNA in OC immune infiltration and prognosis remains unclear.

Methods

Transcriptomes of 378 OC samples and clinical data were retrieved from the TCGA repository. Modules related to immune cells were identified using weighted gene co-expression network analysis (WGCNA). Functional enrichment analysis and survival analysis were then performed for the identification of immune-related lncRNAs in the brown module using Cox regression model. Finally, a ceRNA network was constructed by using the lncRNAs and mRNAs from the brown module.

Results

We found lncRNAs and mRNAs in the brown module to be significantly associated with immune cells in OC and identified 4 lncRNAs as potential OC prognostic markers. We further established that lncRNAs in the ceRNA network influence OC immune infiltration and prognosis by regulating miRNA, ultimately modulating mRNA levels.

Conclusion

We have identified 4 lncRNAs as independent immune prognostic factors for OC. Furthermore, our findings offer novel insight into lncRNAs as OC immune and prognostic biomarkers.

Novel biomarkers and prediction model for the pathological complete response to neoadjuvant treatment of triple-negative breast cancer

Objective: To develop and validate a prediction model for the pathological complete response (pCR) to neoadjuvant chemotherapy (NCT) of triple-negative breast cancer (TNBC). Methods: We systematically searched Gene Expression Omnibus, ArrayExpress, and PubMed for the gene expression profiles of operable TNBC accessible to NCT. Molecular heterogeneity was detected with hierarchical clustering method, and the biological profiles of differentially expressed genes were investigated by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analyses, and Gene Set Enrichment Analysis (GSEA). Next, machine-learning algorithms including random-forest analysis and least absolute shrinkage and selection operator (LASSO) analysis were synchronously performed and, then, the intersected proportion of significant genes was undergone binary logistic regression to fulfill variables selection. The predictive response score (pRS) system was built as the product of the gene expression and coefficient obtained from the logistic analysis. Last, the cohorts were randomly divided in a 7:3 ratio into training cohort and validation cohort for the introduction of a robust model, and a nomogram was constructed with the independent predictors for pCR rate. Results: A total of 217 individuals from four cohort datasets (GSE32646, GSE25065, GSE25055, GSE21974) with complete clinicopathological information were included. Based on the microarray data, a six-gene panel (ATP4B, FBXO22, FCN2, RRP8, SMERK2, TET3) was identified. A robust nomogram, adopting pRS and clinical tumor size stage, was established and the performance was successively validated by calibration curves and receiver operating characteristic curves with the area under curve 0.704 and 0.756, respectively. Results of GSEA revealed that the biological processes including apoptosis, hypoxia, mTORC1 signaling and myogenesis, and oncogenic features of EGFR and RAF were in proactivity to attribute to an inferior response. Conclusions: This study provided a robust prediction model for pCR rate and revealed potential mechanisms of distinct response to NCT in TNBC, which were promising and warranted to further validate in the perspective.

Extracellular Matrix-Related Six-lncRNA Signature as a Novel Prognostic Biomarker for Bladder Cancer

Introduction

Bladder cancer (BC) is the fourth-commones cancer and the sixth-leading cause of cancer-related death among men. However, a lack of reliable biomarkers remains a problem forprognosis and treatment of BC. lncRNAs have been shown to play important roles in various cancers, and have emerged as promising biomarkers for cancer prognosis and treatment.

Methods

In this study, using univariate and multivariate Cox regression analysis, we examined the differential expression profiles of 1,651 lncRNAs in the TCGA BLCA cohort and created a prognostic gene signature composed of six lncRNAs (for SNHG12, MAFG-DT, ASMTL-AS1, LINC02321, LINC01322, and LINC00922), designed the SMALLL signature.

Results

The SMALLL signature displayed significant prognostic power for overall survival for BC patients in multiple cohorts. Gene Ontology analysis showed that genes coexpressed with the SMALLL signature were associated with the extracellular matrix network, and immune cell–infiltration analysis showed that activated naïve B cells, regulatory T cells, M0 macrophages, eosinophils, resting memory CD4 T cells and resting NK cells were significantly different in high- and low-risk groups. We also confirmed differential expression of the lncRNAs of the SMALLL signature in BC tissue and paracancer normal tissue by qRT-PCR analysis. Cell-invasion and -migration experiments showed that MAFG-AS1, ASMTL-AS1, LINC02321, and LINC00922 significantly affected cell invasion and migration.

Conclusion

Our study revealed that the lncRNA signature is an important predictive factor of prognosis and provides a promising biomarker for BC.

Comprehensive pathway-related genes signature for prognosis and recurrence of ovarian cancer

Background

Ovarian cancer (OC) is a highly malignant disease with a poor prognosis and high recurrence rate. At present, there is no accurate strategy to predict the prognosis and recurrence of OC. The aim of this study was to identify gene-based signatures to predict OC prognosis and recurrence.

Methods

mRNA expression profiles and corresponding clinical information regarding OC were collected from The Cancer Genome Atlas (TCGA) database. Gene set enrichment analysis (GSEA) and LASSO analysis were performed, and Kaplan–Meier curves, time-dependent ROC curves, and nomograms were constructed using R software and GraphPad Prism7.

Results

We first identified several key signalling pathways that affected ovarian tumorigenesis by GSEA. We then established a nine-gene-based signature for overall survival (OS) and a five-gene-based-signature for relapse-free survival (RFS) using LASSO Cox regression analysis of the TCGA dataset and validated the prognostic value of these signatures in independent GEO datasets. We also confirmed that these signatures were independent risk factors for OS and RFS by multivariate Cox analysis. Time-dependent ROC analysis showed that the AUC values for OS and RFS were 0.640, 0.663, 0.758, and 0.891, and 0.638, 0.722, 0.813, and 0.972 at 1, 3, 5, and 10 years, respectively. The results of the nomogram analysis demonstrated that combining two signatures with the TNM staging system and tumour status yielded better predictive ability.

Conclusion

In conclusion, the two-gene-based signatures established in this study may serve as novel and independent prognostic indicators for OS and RFS.

Six-lncRNA Immune Prognostic Signature for Cervical Cancer

Background

This study searched for immune-related long noncoding RNAs (lncRNAs) to predict the prognosis of patients with cervical cancer.

Method

We obtained immunologically relevant lncRNA expression profiles and clinical follow-up data from cervical cancer patients from The Cancer Genome Atlas database and the Molecular Signatures Database. Cervical cancer patients were randomly divided into a training group, testing group and combined group. The immune prognostic signature was constructed by Least Absolute Shrinkage and Selection Operator Cox regression, prognosis was analyzed by Kaplan-Meier curves between different groups, and the accuracy of the prognostic model was assessed by receiver operating characteristic-area under the curve (ROC-AUC) analysis.

Results

A six-lncRNA immune prognostic signature (LIPS) was constructed to predict the prognosis of cervical cancer. The six lncRNAs are as follows: AC009065.8, LINC01871, MIR210HG, GEMIN7-AS1, GAS5-AS1, and DLEU1. A ROC-AUC analysis indicated that the model could predict the prognosis of cervical cancer patients in different subgroups. A Kaplan-Meier analysis showed that patients with high risk scores had a poor prognosis; these results were equally meaningful in the subgroup analyses. Risk scores differed depending on the clinical pathology and tumor grade and were independent risk factors for cervical cancer prognosis. Gene set enrichment analysis revealed an association between the LIPS and the immune response, Wnt signaling pathway, and TGF beta signaling pathway.

Conclusion

Our study shows that the six-LIPS can predict the prognosis of cervical cancer and contribute to decisions regarding the immunotherapeutic strategy.

The Interplay between Long Noncoding RNAs and Proteins of the Epigenetic Machinery in Ovarian Cancer

Comprehensive large-scale sequencing and bioinformatics analyses have uncovered a myriad of cancer-associated long noncoding RNAs (lncRNAs). Aberrant expression of lncRNAs is associated with epigenetic reprogramming during tumor development and progression, mainly due to their ability to interact with DNA, RNA, or proteins to regulate gene expression. LncRNAs participate in the control of gene expression patterns during development and cell differentiation and can be cell and cancer type specific. In this review, we described the potential of lncRNAs for clinical applications in ovarian cancer (OC). OC is a complex and heterogeneous disease characterized by relapse, chemoresistance, and high mortality rates. Despite advances in diagnosis and treatment, no significant improvements in long-term survival were observed in OC patients. A set of lncRNAs was associated with survival and response to therapy in this malignancy. We manually curated databases and used bioinformatics tools to identify lncRNAs implicated in the epigenetic regulation, along with examples of direct interactions between the lncRNAs and proteins of the epigenetic machinery in OC. The resources and mechanisms presented herein can improve the understanding of OC biology and provide the basis for further investigations regarding the selection of novel biomarkers and therapeutic targets.

Identification of lncRNA-associated ceRNA network in high-grade serous ovarian cancer metastasis

Aim: To uncover a novel lncRNA-miRNA-mRNA network associated with high-grade serous ovarian cancer metastasis. Material & methods: The candidate differentially expressed lncRNAs were obtained from RNA-sequencing data and determined by functional experiments. The downstream miRNAs and mRNAs were identified by bioinformatic prediction and subjected to functional enrichment analysis. Results: The expression levels of lncRNA ENTPD1-AS1/PRANCR/NR2F2-AS1 were reduced in omental metastatic tissues. Similar differential expression patterns of these lncRNAs were also found in lnCAR database and we verified their tumor suppressive roles by performing functional experiments. Furthermore, we predicted miRNAs and mRNAs via bioinformatic tools and validated their alteration in expression levels in presence of lncRNA interference. Conclusion: We proposed a potential ceRNA regulatory mechanism in high-grade serous ovarian cancer omental metastasis.