Comparative epigenomics by machine learning approach for neuroblastoma

Application and clinical translational value of a predictive model based on N7-methylguanosine-related long non-coding RNAs in cervical squamous cell carcinoma

Cervical squamous cell carcinoma (CSCC) is one of the most common gynecological malignancies affecting women globally. The present study aimed to develop a predictive model based on N7-methylguanosine-related long non-coding RNAs (lncRNAs) to evaluate risk stratification, analyze immune infiltration and guide the selection of sensitive drugs in CSCC. Pearson's correlation, univariate Cox and Least Absolute Shrinkage and Selection Operator regression analyses of transcriptome data from The Cancer Genome Atlas and the Genotype-Tissue Expression database were conducted to construct a prognostic risk prediction model for CSCC. The stability of the model was tested before evaluating its prognostic value in CSCC. Further analysis of enrichment, immune infiltration and drug resistance provided directions for clinical translation. The lncRNAs used to construct the model were validated using reverse transcription-quantitative PCR. The developed predictive model was stable and may hold notable clinical translational value for immunotherapy and drug selection in CSCC in the future.

Machine Learning Approaches for Neuroblastoma Risk Prediction and Stratification

Machine learning (ML) holds great promise in advancing risk prediction and stratification for neuroblastoma, a highly heterogeneous pediatric cancer. By utilizing large-scale biological and clinical data, ML models can detect complex patterns that traditional approaches often overlook, enabling more personalized treatments and better patient outcomes. Various ML techniques, such as support vector machines, random forests, and deep learning, have shown superior performance in predicting survival, relapse, and treatment responses in neuroblastoma patients compared to conventional methods. However, challenges like limited data size, model interpretability, data variability, and difficulties in clinical integration hinder broader adoption. Additionally, ethical concerns related to bias and privacy must be addressed. Future work should focus on improving data quality, enhancing model transparency, and conducting thorough clinical validation. With these advancements, ML has the potential to revolutionize neuroblastoma care by refining early diagnosis, risk assessment, and therapeutic decision-making.

Artificial Intelligence Applications in Oral Cancer and Oral Dysplasia

Oral squamous cell carcinoma (OSCC) is a highly unpredictable disease with devastating mortality rates that have not changed over the past decades, in the face of advancements in treatments and biomarkers, which have improved survival for other cancers. Delays in diagnosis are frequent, leading to more disfiguring treatments and poor outcomes for patients. The clinical challenge lies in identifying those patients at the highest risk of developing OSCC. Oral epithelial dysplasia (OED) is a precursor of OSCC with highly variable behavior across patients. There is no reliable clinical, pathological, histological, or molecular biomarker to determine individual risk in OED patients. Similarly, there are no robust biomarkers to predict treatment outcomes or mortality in OSCC patients. This review aims to highlight advancements in artificial intelligence (AI)-based methods to develop predictive biomarkers of OED transformation to OSCC or predictive biomarkers of OSCC mortality and treatment response. Biomarkers such as S100A7 demonstrate promising appraisal for the risk of malignant transformation of OED. Machine learning-enhanced multiplex immunohistochemistry workflows examine immune cell patterns and organization within the tumor immune microenvironment to generate outcome predictions in immunotherapy. Deep learning (DL) is an AI-based method using an extended neural network or related architecture with multiple "hidden" layers of simulated neurons to combine simple visual features into complex patterns. DL-based digital pathology is currently being developed to assess OED and OSCC outcomes. The integration of machine learning in epigenomics aims to examine the epigenetic modification of diseases and improve our ability to detect, classify, and predict outcomes associated with epigenetic marks. Collectively, these tools showcase promising advancements in discovery and technology, which may provide a potential solution to addressing the current limitations in predicting OED transformation and OSCC behavior, both of which are clinical challenges that must be addressed in order to improve OSCC survival.

Integrating Omics Data and AI for Cancer Diagnosis and Prognosis

Cancer is one of the leading causes of death, making timely diagnosis and prognosis very important. Utilization of AI (artificial intelligence) enables providers to organize and process patient data in a way that can lead to better overall outcomes. This review paper aims to look at the varying uses of AI for diagnosis and prognosis and clinical utility. PubMed and EBSCO databases were utilized for finding publications from 1 January 2020 to 22 December 2023. Articles were collected using key search terms such as "artificial intelligence" and "machine learning." Included in the collection were studies of the application of AI in determining cancer diagnosis and prognosis using multi-omics data, radiomics, pathomics, and clinical and laboratory data. The resulting 89 studies were categorized into eight sections based on the type of data utilized and then further subdivided into two subsections focusing on cancer diagnosis and prognosis, respectively. Eight studies integrated more than one form of omics, namely genomics, transcriptomics, epigenomics, and proteomics. Incorporating AI into cancer diagnosis and prognosis alongside omics and clinical data represents a significant advancement. Given the considerable potential of AI in this domain, ongoing prospective studies are essential to enhance algorithm interpretability and to ensure safe clinical integration.

Predicting Neuroblastoma Patient Risk Groups, Outcomes, and Treatment Response Using Machine Learning Methods: A Review

Neuroblastoma, a paediatric malignancy with high rates of cancer-related morbidity and mortality, is of significant interest to the field of paediatric cancers. High-risk NB tumours are usually metastatic and result in survival rates of less than 50%. Machine learning approaches have been applied to various neuroblastoma patient data to retrieve relevant clinical and biological information and develop predictive models. Given this background, this study will catalogue and summarise the literature that has used machine learning and statistical methods to analyse data such as multi-omics, histological sections, and medical images to make clinical predictions. Furthermore, the question will be turned on its head, and the use of machine learning to accurately stratify NB patients by risk groups and to predict outcomes, including survival and treatment response, will be summarised. Overall, this study aims to catalogue and summarise the important work conducted to date on the subject of expression-based predictor models and machine learning in neuroblastoma for risk stratification and patient outcomes including survival, and treatment response which may assist and direct future diagnostic and therapeutic efforts.