Role of different non-coding RNAs as ovarian cancer biomarkers

Unravelling driver genes as potential therapeutic targets in ovarian cancer via integrated bioinformatics approach

Target-driven cancer therapy is a notable advancement in precision oncology that has been accompanied by substantial medical accomplishments. Ovarian cancer is a highly frequent neoplasm in women and exhibits significant genomic and clinical heterogeneity. In a previous publication, we presented an extensive bioinformatics study aimed at identifying specific biomarkers associated with ovarian cancer. The findings of the network analysis indicate the presence of a cluster of nine dysregulated hub genes that exhibited significance in the underlying biological processes and contributed to the initiation of ovarian cancer. Here in this research article, we are proceeding our previous research by taking all hub genes into consideration for further analysis. GEPIA2 was used to identify patterns in the expression of critical genes. The KM plotter analysis indicated that the out of all genes 5 genes are statistically significant. The cBioPortal platform was further used to investigate the frequency of genetic mutations across the board and how they affected the survival of the patients. Maximum mutation was reported by ELAVL2. In order to discover viable therapeutic candidates after competitive inhibition of ELAVL2 with small molecular drug complex, high throughput screening and docking studies were used. Five compounds were identified. Overall, our results suggest that the ELAV-like protein 2-ZINC03830554 complex was relatively stable during the molecular dynamic simulation. The five compounds that have been found can also be further examined as potential therapeutic possibilities. The combined findings suggest that ELAVL2, together with their genetic changes, can be investigated in therapeutic interventions for precision oncology, leveraging early diagnostics and target-driven therapy.

Current strategies for early epithelial ovarian cancer detection using miRNA as a potential tool

Ovarian cancer is one of the most aggressive and significant malignant tumor forms in the female reproductive system. It is the leading cause of death among gynecological cancers owing to its metastasis. Since its preliminary disease symptoms are lacking, it is imperative to develop early diagnostic biomarkers to aid in treatment optimization and personalization. In this vein, microRNAs, which are short sequence non-coding molecules, displayed great potential as highly specific and sensitive biomarker. miRNAs have been extensively advocated and proven to serve an instrumental part in the clinical management of cancer, especially ovarian cancer, by promoting the cancer cell progression, invasion, delayed apoptosis, epithelial-mesenchymal transition, metastasis of cancer cells, chemosensitivity and resistance and disease therapy. Here, we cover our present comprehension of the most up-to-date microRNA-based approaches to detect ovarian cancer, as well as current diagnostic and treatment strategies, the role of microRNAs as oncogenes or tumor suppressor genes, and their significance in ovarian cancer progression, prognosis, and therapy.

Prediction of prognosis and treatment response in ovarian cancer patients from histopathology images using graph deep learning: a multicenter retrospective study

BACKGROUND

Ovarian cancer (OV) is a prevalent and deadly disease with high mortality rates. The development of accurate prognostic tools and personalized therapeutic strategies is crucial for improving patient outcomes.

METHODS

A graph-based deep learning model, the Ovarian Cancer Digital Pathology Index (OCDPI), was introduced to predict prognosis and response to adjuvant therapy using hematoxylin and eosin (H&E)-stained whole-slide images (WSIs). The OCDPI was developed using formalin-fixed, paraffin-embedded (FFPE) WSIs from the TCGA-OV cohort, and was externally validated in two independent cohorts from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) and Harbin Medical University Cancer Hospital (HMUCH).

RESULTS

The OCDPI showed prognostic ability for overall survival prediction in the PLCO (HR, 1.916; 95% CI, 1.380-2.660; log-rank test, P < 0.001) and HMUCH (HR, 2.796; 95% CI, 1.404-5.568; log-rank test, P = 0.0022) cohorts. Patients with low OCDPI experienced better survival benefits and lower recurrence rates following adjuvant therapy compared to those with high OCDPI. Multivariable analyses, adjusting for clinicopathological factors, consistently identified OCDPI as an independent prognostic factor across all cohorts (all P < 0.05). Furthermore, OCDPI performed well in patients with low-grade tumors or fresh-frozen slides, and could differentiate between HRD-deficient or HRD-intact patients with and without sensitivity to adjuvant therapy.

CONCLUSION

The results from this multicenter cohort study indicate that the OCDPI may serve as a valuable and labor-saving tool to improve prognostic and predictive clinical decision-making in patients with OV.

The emerging roles of long non-coding RNA (lncRNA) H19 in gynecologic cancers

Long non-coding RNA (lncRNA) H19 has gained significant recognition as a pivotal contributor to the initiation and advancement of gynecologic cancers, encompassing ovarian, endometrial, cervical, and breast cancers. H19 exhibits a complex array of mechanisms, demonstrating dualistic effects on tumorigenesis as it can function as both an oncogene and a tumor suppressor, contingent upon the specific context and type of cancer being investigated. In ovarian cancer, H19 promotes tumor growth, metastasis, and chemoresistance through modulation of key signaling pathways and interaction with microRNAs. Conversely, in endometrial cancer, H19 acts as a tumor suppressor by inhibiting proliferation, inducing apoptosis, and regulating epithelial-mesenchymal transition. Additionally, H19 has been implicated in cervical and breast cancers, where it influences cell proliferation, invasion, and immune evasion. Moreover, H19 has potential as a diagnostic and prognostic biomarker for gynecologic cancers, with its expression levels correlating with clinical parameters and patient outcomes. Understanding the functional roles of H19 in gynecologic cancers is crucial for the development of targeted therapeutic strategies and personalized treatment approaches. Further investigation into the intricate molecular mechanisms underlying H19's involvement in gynecologic malignancies is warranted to fully unravel its therapeutic potential and clinical implications. This review aims to elucidate the functional roles of H19 in various gynecologic malignancies.

Identification of long noncoding RNAs downregulated specifically in ovarian high-grade serous carcinoma

Purpose

To investigate whether long noncoding RNAs (lncRNAs) are involved in the development or malignant behavior of ovarian high-grade serous carcinoma (HGSC), we attempted to identify lncRNAs specific to HGSC.

Methods

Total RNAs were isolated from HGSC, normal ovarian, and fallopian tube tissue samples and were subjected to a PCR array that can analyze 84 cancer-associated lncRNAs. The lncRNAs that were upregulated and downregulated in HGSC in comparison to multiple samples of normal ovary and fallopian tube were validated by real-time RT-PCR. To infer the function, ovarian cancer cell lines that overexpress the identified lncRNAs were established, and the activation of cell proliferation, migration, and invasion was analyzed.

Results

Eleven lncRNAs (ACTA2-AS1, ADAMTS9-AS2, CBR3-AS1, HAND2-AS1, IPW, LINC00312, LINC00887, MEG3, NBR2, TSIX, and XIST) were downregulated in HGSC samples. We established the cell lines that overexpress ADAMTS9-AS2, CBR3-AS1, or NBR2. In cell lines overexpressing ADAMTS9-AS2, cell proliferation was suppressed, but migration and invasion were promoted. In cell lines overexpressing CBR3-AS1 or NBR2, cell migration tended to be promoted, although cell proliferation and invasion were unchanged.

Conclusion

We identified eleven lncRNAs that were specifically downregulated in HGSC. Of these, CBR3-AS1, NBR2, and ADAMTS9-AS2 had unique functions in the malignant behaviors of HGSC.

Prognostic Value of hsa_circ_0007615 in Epithelial Ovarian Cancer and its Regulatory Effect on Tumor Progression

This study aimed to interrogate the functional and clinical significance of hsa_circ_0007615 in epithelial ovarian cancer (EOC). GSE192410 was screened for upregulated circRNAs in ovarian cancer. The expression levels of hsa_circ_0007615 were evaluated in a patient cohort comprising 113 EOC tissues and matched normal tissues. Subsequently, the prognostic value was confirmed by the relevance of hsa_circ_0007615 with clinical parameters, Kaplan-Meier analysis and Cox proportional risk model. Cell functional analyses were performed in EOC cell lines using a cell proliferation kit, transwell and cell death kit. Our data revealed that hsa_circ_0007615 was significantly upregulated in EOC tissues and cell lines, compared with normal ones. Multivariate survival analysis revealed that hsa_circ_0007615 emerged as an independent risk factor for overall survival and recurrence of EOC patients. Knockdown of hsa_circ_0007615 in EOC cells led to the blocking of cell proliferation, migration and invasion, but an increase of cell death presenting as ferroptosis. Tumor suppressive effects of hsa_circ_0007615 knockdown can be abolished by miR-874-3p inhibition. TUBB3 was a targeting gene of miR-874-3p. Hsa_circ_0007615 has the functional and clinical significance of EOC. Mechanistically, hsa_circ_0007615 may contribute to EOC by sponging miR-874-3p and moderating TUBB3.

Bioinformatics in Early Cancer Detection

Bioinformatics is a pretty recent branch of biology that encompasses the use of algebraic, analytic, and computing approaches to the processing and interpretation of biological information. A wide term, "bioinformatics" refers to the use of digital technology to study biological processes using high-dimensional data collected from many resources. The design and testing of the software tools required to evaluate the information are the core of bioinformatics research, which is conducted in great portions in silico and typically involves the synthesis of new learning from available data. Early diagnosis of cancer results in improved prognosis, but at the same time, it is difficult to conform to diagnosis at a very early stage. The use of DNA microarrays and proteomics studies for large-scale gene expression research has advanced technology, thus elevating the significance of bioinformatics tools. In today's research, wet experimentation and the application of bioinformatics analytics go side by side. Molecular profiling of tumor biopsies is becoming more and more crucial to both cancer research and the treatment of cancer.

Non-coding RNA's prevalence as biomarkers for prognostic, diagnostic, and clinical utility in breast cancer

Noncoding RNAs (ncRNAs), which make up a significant portion of the mammalian transcriptome and plays crucial regulatory roles in expression of genes and other biological processes, have recently been found. The most extensively researched of the sncRNAs, microRNAs (miRNAs), have been characterized in terms of their synthesis, roles, and significance in the tumor development. Its crucial function in the stem cell regulation, another class of sncRNAs known as aspirRNAs, has attracted attention in cancer research. The investigations have shown that long non-coding RNAs have a crucial role in controlling developmental stages, such as mammary gland development. Additionally, it has been discovered that lncRNA dysregulation precedes the development of several malignancies, including breast cancer. The functions of sncRNAs (including miRNAs and piRNAs) and lncRNAs in the onset and development of the breast cancer are described in this study. Additionally, future perspectives of various ncRNA-based diagnostic, prognostic, and therapeutic approaches also discussed.

Strategy for Pre-Clinical Development of Active Targeting MicroRNA Oligonucleotide Therapeutics for Unmet Medical Needs

We present here an innovative modular and outsourced model of drug research and development for microRNA oligonucleotide therapeutics (miRNA ONTs). This model is being implemented by a biotechnology company, namely AptamiR Therapeutics, in collaboration with Centers of Excellence in Academic Institutions. Our aim is to develop safe, effective and convenient active targeting miRNA ONT agents for the metabolic pandemic of obesity and metabolic-associated fatty liver disease (MAFLD), as well as deadly ovarian cancer.

Evaluation of expression of biomarkers of PLAGL1 (ZAC1), microRNA, and their non-coding RNAs in patients with endometriosis

Since the PLAGL1 (ZAC1) gene is expressed in the human endometrium. It may be involved in the etiology of endometrial disorders by its abnormal regulation and expression. This study aimed to investigate the Zac1 gene and related microRNA and LncRNA and its alterations in patients with endometriosis. Blood plasma, ectopic (EC) and eutopic (EU) endometrial samples were gathered from 30 patients with endometriosis and 30 healthy fertile women, and the Q-PCR technique was used to determine the expression level of Zac1 mRNA and microRNAs (miR-1271-5p, hsa-miR-490-3pin) and LncRNAs (TONSL-AS1 TONSL, KCNQ1OT1 KCNQ1). According to the results, the Zac1 gene and KCNQ1OT1 KCNQ1, TONSL-AS1 TONSL LncRNA expression were significantly decreased in the endometriosis group versus the control group (P < 0.05). MiR-1271-5p and hsa-miR-490-3pin microRNA expression were significantly raised in the endometriosis group as opposed to the control group (P < 0.05). In summary, this research for the first time revealed that identifying Zac1 expression provides us with new indicators for evaluating endometriosis.

Clinical Application of Small Extracellular Vesicles in Gynecologic Malignancy Treatments

Small extracellular vesicles (sEVs) are the key mediators of intercellular communication. They have the potential for clinical use as diagnostic or therapeutic biomarkers and have been explored as vectors for drug delivery. Identification of reliable and noninvasive biomarkers, such as sEVs, is important for early diagnosis and precise treatment of gynecologic diseases to improve patient prognosis. Previous reviews have summarized routine sEVs isolation and identification methods; however, novel and unconventional methods have not been comprehensively described. This review summarizes a convenient method of isolating sEVs from body fluids and liquid biopsy-related sEV markers for early, minimally invasive diagnosis of gynecologic diseases. In addition, the characteristics of sEVs as drug carriers and in precision treatment and drug resistance are introduced, providing a strong foundation for identifying novel and potential therapeutic targets for sEV therapy. We propose potential directions for further research on the applications of sEVs in the diagnosis and treatment of gynecologic diseases.

Identification of Driver Genes and miRNAs in Ovarian Cancer through an Integrated In-Silico Approach

Ovarian cancer is the eighth-most common cancer in women and has the highest rate of death among all gynecological malignancies in the Western world. Increasing evidence shows that miRNAs are connected to the progression of ovarian cancer. In the current study, we focus on the identification of miRNA and its associated genes that are responsible for the early prognosis of patients with ovarian cancer. The microarray dataset GSE119055 used in this study was retrieved via the publicly available GEO database by NCBI for the analysis of DEGs. The miRNA GSE119055 dataset includes six ovarian carcinoma samples along with three healthy/primary samples. In our study, DEM analysis of ovarian carcinoma and healthy subjects was performed using R Software to transform and normalize all transcriptomic data along with packages from Bioconductor. Results: We identified miRNA and its associated hub genes from the samples of ovarian cancer. We discovered the top five upregulated miRNAs (hsa-miR-130b-3p, hsa-miR-18a-5p, hsa-miR-182-5p, hsa-miR-187-3p, and hsa-miR-378a-3p) and the top five downregulated miRNAs (hsa-miR-501-3p, hsa-miR-4324, hsa-miR-500a-3p, hsa-miR-1271-5p, and hsa-miR-660-5p) from the network and their associated genes, which include seven common genes (SCN2A, BCL2, MAF, ZNF532, CADM1, ELAVL2, and ESRRG) that were considered hub genes for the downregulated network. Similarly, for upregulated miRNAs we found two hub genes (PRKACB and TAOK1).

ncRNA-mediated low expression of P2RY14 correlates with poor prognosis and tumor immune infiltration in ovarian carcinoma

Background

Ovarian cancer (OV) has been puzzling clinicians because of its poor prognosis. More and more evidence show that the G protein coupled receptor P2RY14 plays a key role in the initiation and progression of various types of human cancer. The purpose of our study is to explore the correlation between P2RY14 and the prognosis of ovarian cancer patients and the relevant mechanism.

Methods

First, the differentially expressed gene P2RY14 was screened from The Cancer Genome Atlas (TCGA) database. Explored possible P2RY14 related miRNAs and lncRNAs through multiple public databases, predicted and analyzed the expression level of candidate miRNAs and candidate lncRNAs that can bind to candidate miRNAs in OV through StarBase database. The TIMER database was used to comprehensively analyze the expression of tumor infiltrating immune cells, and to analyze the correlation between the expression level of P2RY14 and the level of immune cell infiltration in OV or the expression level of immune checkpoints.

Results

Patients with P2RY14 overexpression had better overall survival (OS) and progression-free interval (PFI). In the Targetscan database, 22 upstream miRNAs that may bind to P2RY14 were predicted. According to the regulatory network constructed by the Cytoscape software, correlation analysis and the role of miRNAs in the prognosis of OV, we first determined that the candidate miRNAs were miR-34c-5p. Then, we predicted the upstream lncRNAs of miR-34c-5p in the StarBase database, the expression level of these lncRNAs in OV in the Gene Expression Profiling Interactive Analysis (GEPIA) database, and the role in prognosis. We determined that LINC00665 is the most potential lncRNA upstream of ovarian cancer miRNA (hsa-miR-34c-5p)-P2RY14. Then, we analyzed the results in the Timer database, suggesting that P2RY14 expression was positively correlated with CD8⁺T Cell, CD4⁺T Cell, Macrophage, Neutral and Dendritic cells, and negatively correlated with B cells. Meanwhile, P2RY14 was positively correlated with CD274 and PDCD1.

Conclusions

P2RY14 can be used as a new predictive biomarker of ovarian cancer. Intervention of P2RY14 can affect the prognosis of ovarian cancer by affecting LINC00665-miR-34c-5p-P2RY14 axis. These findings provide a potential target for the development of anti-cancer strategies for ovarian cancer.

Comparative glycoproteomics study on the surface of SKOV3 versus IOSE80 cell lines

Objective: Site- and structure-specific quantitative N-glycoproteomics study of differential cell-surface N-glycosylation of ovarian cancer SKOV3 cells with the non-cancerous ovarian epithelial IOSE80 cells as the control.

Methods: C18-RPLC-MS/MS (HCD with stepped normalized collision energies) was used to analyze the 1: 1 mixture of labeled intact N-glycopeptides from SKOV3 and IOSE80 cells, and the site- and structure-specific intact N-glycopeptide search engine GPSeeker was used to conduct qualitative and quantitative search on the obtained raw datasets.

Results: With the control of the spectrum-level false discovery rate ≤1%, 13,822 glycopeptide spectral matches coming from 2,918 N-glycoproteins with comprehensive N-glycosite and N-glycan structure information were identified; 3,733 N-glycosites and 3,754 N-glycan sequence structures were confirmed by site-determining and structure-diagnostic fragment ions, respectively. With the control of no less than two observations among the three technical replicates, fold change ≥1.5, and p-value ≤ 0.05, 746 DEPGs in SKOV3 cells relative to IOSE80 cells were quantified, where 421 were upregulated and 325 downregulated.

Conclusion: Differential cell-surface N-glycosylation of ovarian cancer SKOV3 cells were quantitatively analyzed by isotopic labeling and site- and structure-specific N-glycoproteomics. This discovery study provides putative N-glycoprotein biomarker candidates for future validation study using multiple reaction monitoring and biochemical methods.