A diagnostic miRNA panel to detect recurrence of ovarian cancer through artificial intelligence approaches

The role of microRNA-9 in ovarian and cervical cancers: An updated overview

Ovarian and cervical cancers are the two most frequent kind of gynaecological cancers (GCs). In spite of advances in prevention, screening and treatment, cervical cancer still leads to an increased morbidity and mortality worldwide. Ovarian cancer is often detected at a late stage, which significantly reduces the effectiveness of available treatments. Therefore, novel methods are desperately needed to improve the clinical care of GC patients. MicroRNAs, also known as short noncoding RNAs (miRNAs/miRs), are a diverse group of RNAs with a length of 22 nucleotides. These typically cause translational repression and mRNA degradation by interacting with target mRNAs' 3' untranslated region (3'-UTR), together with other regions and gene promoters. Under certain conditions, they are also able to activate translation or regulate transcription. It has been demonstrated that miRNAs are crucial to several biological processes leading to tumorigenesis, including GCs. Recent research has shown that miR-9 affects carcinogenesis. In this review, we will provide an overview of current research on the potential utility of miR-9 in the diagnosis, prognosis, and therapy of ovarian and cervical malignancies.

Advancing miRNA cancer research through artificial intelligence: from biomarker discovery to therapeutic targeting

MicroRNAs (miRNAs), a class of small non-coding RNAs, play a vital role in regulating gene expression at the post-transcriptional level. Their discovery has profoundly impacted therapeutic strategies, particularly in cancer treatment, where RNA therapeutics, including miRNA-based targeted therapies, have gained prominence. Advances in RNA sequencing technologies have facilitated a comprehensive exploration of miRNAs-from fundamental research to their diagnostic and prognostic potential in various diseases, notably cancers. However, the manual handling and interpretation of vast RNA datasets pose significant challenges. The advent of artificial intelligence (AI) has revolutionized biological research by efficiently extracting insights from complex data. Machine learning algorithms, particularly deep learning techniques are effective for identifying critical miRNAs across different cancers and developing prognostic models. Moreover, the integration of AI has led to the creation of comprehensive miRNA databases for identifying mRNA and gene targets, thus facilitating deeper understanding and application in cancer research. This review comprehensively examines current developments in the application of machine learning techniques in miRNA research across diverse cancers. We discuss their roles in identifying biomarkers, elucidating miRNA targets, establishing disease associations, predicting prognostic outcomes, and exploring broader AI applications in cancer research. This review aims to guide researchers in leveraging AI techniques effectively within the miRNA field, thereby accelerating advancements in cancer diagnostics and therapeutics.

Evolving landscape of detection and targeting miRNA/epigenetics for therapeutic strategies in ovarian cancer

Ovarian cancer (OC) accounts for the highest mortality rates among all gynecologic malignancies. The high mortality of OC is often associated with delayed detection, prolonged latency, enhanced metastatic potential, acquired drug resistance, and frequent recurrence. This review comprehensively explores key aspects of OC, including cancer diagnosis, mechanisms of disease resistance, and the pivotal role of epigenetic regulation, particularly by microRNAs (miRs) in cancer progression. We highlight the intricate regulatory mechanisms governing miR expression within the context of OC and the current status of epigenetic advancement in the therapeutic development and clinical trial progression. Through network analysis we elucidate the regulatory interactions between dysregulated miRs in OC and their targets which are involved in different signaling pathways. By exploring these interconnected facets and critical analysis, we endeavor to provide a nuanced understanding of the molecular dynamics underlying OC, its detection and shedding light on potential avenues for miRs and epigenetics-based therapeutic intervention and management strategies.

Preoperative CECT-Based Multitask Model Predicts Peritoneal Recurrence and Disease-Free Survival in Advanced Ovarian Cancer: A Multicenter Study

RATIONALE AND OBJECTIVES

Peritoneal recurrence is the predominant pattern of recurrence in advanced ovarian cancer (AOC) and portends a dismal prognosis. Accurate prediction of peritoneal recurrence and disease-free survival (DFS) is crucial to identify patients who might benefit from intensive treatment. We aimed to develop a predictive model for peritoneal recurrence and prognosis in AOC.

METHODS

In this retrospective multi-institution study of 515 patients, an end-to-end multi-task convolutional neural network (MCNN) comprising a segmentation convolutional neural network (CNN) and a classification CNN was developed and tested using preoperative CT images, and MCNN-score was generated to indicate the peritoneal recurrence and DFS status in patients with AOC. We evaluated the accuracy of the model for automatic segmentation and predict prognosis.

RESULTS

The MCNN achieved promising segmentation performances with a mean Dice coefficient of 84.3% (range: 78.8%-87.0%). The MCNN was able to predict peritoneal recurrence in the training (AUC 0.87; 95% CI 0.82-0.90), internal test (0.88; 0.85-0.92), and external test set (0.82; 0.78-0.86). Similarly, MCNN demonstrated consistently high accuracy in predicting recurrence, with an AUC of 0.85; 95% CI 0.82-0.88, 0.83; 95% CI 0.80-0.86, and 0.85; 95% CI 0.83-0.88. For patients with a high MCNN-score of recurrence, it was associated with poorer DFS with P < 0.0001 and hazard ratios of 0.1964 (95% CI: 0.1439-0.2680), 0.3249 (95% CI: 0.1896-0.5565), and 0.3458 (95% CI: 0.2582-0.4632).

CONCLUSION

The MCNN approach demonstrated high performance in predicting peritoneal recurrence and DFS in patients with AOC.

Utilization of miRNAs as Biomarkers for the Diagnosis, Prognosis, and Metastasis in Gynecological Malignancies

Gynecological cancer is a term referring to malignancies that typically involve ovarian, cervical, uterine, vaginal, and vulvar cancer. Combined, these cancers represent major causes of morbidity and mortality in women with a heavy socioeconomic impact. MiRNAs are small non-coding RNAs that are intensively studied in the field of cancer and changes in them have been linked to a variety of processes involved in cancer that range from tumorigenesis to prognosis and metastatic potential. This review aims to summarize the existing literature that has linked miRNAs with each of the female malignancies as potential biomarkers in diagnosis (circulating miRNAs), in tumor histology and prognosis (as tissue biomarkers), and for local (lymph node) and distant metastatic disease.

An overview of challenges associated with exosomal miRNA isolation toward liquid biopsy-based ovarian cancer detection

As one of the deadliest gynaecological cancers, ovarian cancer has been on the list. With lesser-known symptoms and lack of an accurate detection method, it is still difficult to catch it early. In terms of both the diagnosis and outlook for cancer, liquid biopsy has come a long way with significant advancements. Exosomes, extracellular components commonly shed by cancerous cells, are nucleic acid-rich particles floating in almost all body fluids and hold enormous promise, leading to minimallyinvasive molecular diagnostics. They have been shown as potential biomarkers in liquid biopsy, being implicated in tumour growth and metastasis. In order to address the drawbacks of ovarian cancer tumor heterogeneity, a liquid biopsy-based approach is being investigated by detecting cell-free nucleic acids, particularly non-coding RNAs, having the advantage of being less invasive and more prominent in nature. microRNAs are known to actively contribute to cancer development and their existence inside exosomes has also been made quite apparent which can be leveraged to diagnose and treat the disease. Extraction of miRNAs and exosomes is an arduous execution, and while other approaches have been investigated, none have produced results that are as encouraging due to limits in time commitment, yield, and, most significantly, damage to the exosomal structure resulting discrepancies in miRNA-based expression profiling for disease diagnosis. We have briefly outlined and reviewed the difficulties with exosome isolation techniques and the need for their standardization. The several widely used procedures and their drawbacks in terms of the exosomal purity they may produce have also been outlined.

Decoding the Possible Molecular Mechanisms in Pediatric Wilms Tumor and Rhabdoid Tumor of the Kidney through Machine Learning Approaches

Objective: Wilms tumor (WT) and Rhabdoid tumor (RT) are pediatric renal tumors and their differentiation is based on histopathological and molecular analysis. The present study aimed to introduce the panels of mRNAs and microRNAs involved in the pathogenesis of these cancers using deep learning algorithms. Methods: Filter, graph, and association rule mining algorithms were applied to the mRNAs/microRNAs data. Results: Candidate miRNAs and mRNAs with high accuracy (AUC: 97%/93% and 94%/97%, respectively) could differentiate the WT and RT classes in training and test data. Let-7a-2 and C19orf24 were identified in the WT, while miR-199b and RP1-3E10.2 were detected in the RT by analysis of Association Rule Mining. Conclusion: The application of the machine learning methods could identify mRNA/miRNA patterns to discriminate WT from RT. The identified miRNAs/mRNAs panels could offer novel insights into the underlying molecular mechanisms that are responsible for the initiation and development of these cancers. They may provide further insight into the pathogenesis, prognosis, diagnosis, and molecular-targeted therapy in pediatric renal tumors.

Unraveling the link between PTBP1 and severe asthma through machine learning and association rule mining method

Severe asthma is a chronic inflammatory airway disease with great therapeutic challenges. Understanding the genetic and molecular mechanisms of severe asthma may help identify therapeutic strategies for this complex condition. RNA expression data were analyzed using a combination of artificial intelligence methods to identify novel genes related to severe asthma. Through the ANOVA feature selection approach, 100 candidate genes were selected among 54,715 mRNAs in blood samples of patients with severe asthmatic and healthy groups. A deep learning model was used to validate the significance of the candidate genes. The accuracy, F1-score, AUC-ROC, and precision of the 100 genes were 83%, 0.86, 0.89, and 0.9, respectively. To discover hidden associations among selected genes, association rule mining was applied. The top 20 genes including the PTBP1, RAB11FIP3, APH1A, and MYD88 were recognized as the most frequent items among severe asthma association rules. The PTBP1 was found to be the most frequent gene associated with severe asthma among those 20 genes. PTBP1 was the gene most frequently associated with severe asthma among candidate genes. Identification of master genes involved in the initiation and development of asthma can offer novel targets for its diagnosis, prognosis, and targeted-signaling therapy.

Key therapeutic targets implicated at the early stage of hepatocellular carcinoma identified through machine-learning approaches

Hepatocellular carcinoma (HCC) is the most frequent type of primary liver cancer. Early-stage detection plays an essential role in making treatment decisions and identifying dominant molecular mechanisms. We utilized machine learning algorithms to find significant mRNAs and microRNAs (miRNAs) at the early and late stages of HCC. First, pre-processing approaches, including organization, nested cross-validation, cleaning, and normalization were applied. Next, the t-test/ANOVA methods and binary particle swarm optimization were used as a filter and wrapper method in the feature selection step, respectively. Then, classifiers, based on machine learning and deep learning algorithms were utilized to evaluate the discrimination power of selected features (mRNAs and miRNAs) in the classification step. Finally, the association rule mining algorithm was applied to selected features for identifying key mRNAs and miRNAs that can help decode dominant molecular mechanisms in HCC stages. The applied methods could identify key genes associated with the early (e.g., Vitronectin, thrombin-activatable fibrinolysis inhibitor, lactate dehydrogenase D (LDHD), miR-590) and late-stage (e.g., SPRY domain containing 4, regucalcin, miR-3199-1, miR-194-2, miR-4999) of HCC. This research could establish a clear picture of putative candidate genes, which could be the main actors at the early and late stages of HCC.