A self-organizing deep neuro-fuzzy system approach for classification of kidney cancer subtypes using miRNA genomics data

On integrative analysis of multi-level gene expression data in Kidney cancer subgrouping

Kidney cancer is one of the most dangerous cancer mainly targeting men. In 2020, around 430, 000 people were diagnosed with this disease worldwide. It can be divided into three prime subgroups such as kidney renal cell carcinoma (KIRC), kidney renal papilliary cell carcinoma (KIRP) and kidney chromophobe (KICH). Correct identification of these subgroups on time is crucial for the initiation and determination of proper treatment. On-time identification of this disease and its subgroup can help both the clinicians and patients to improve the situation. Hence, this study checks the possibility of using multi-omics data in the kidney cancer subgrouping, whether integrating multiple omics data will increase the subgrouping accuracy or not. Four different molecular data such as genomics, proteomics, epigenomics and miRNA from The Cancer Genome Atlas (TCGA) are used in this study. As the data is in a very high dimension world, this study starts with selecting the relevant features of the study using Pearson's correlation coefficient. Those selected features are used with three different classification algorithms such as k-nearest neighbor (KNN), supporting vector machines (SVMs) and random forest. Performances are compared to see whether the integration of multi-omics data can improve the accuracy of kidney cancer subgrouping. This study shows that integration of multi-omics data can improve the performance of the kidney cancer subgrouping. The highest performance (accuracy value of 0.98±0.03) is gained by top 400 features selected from integrated multi-omics data, with support vector machines.

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.

Integrated multi-omics with machine learning to uncover the intricacies of kidney disease

The development of omics technologies has driven a profound expansion in the scale of biological data and the increased complexity in internal dimensions, prompting the utilization of machine learning (ML) as a powerful toolkit for extracting knowledge and understanding underlying biological patterns. Kidney disease represents one of the major growing global health threats with intricate pathogenic mechanisms and a lack of precise molecular pathology-based therapeutic modalities. Accordingly, there is a need for advanced high-throughput approaches to capture implicit molecular features and complement current experiments and statistics. This review aims to delineate strategies for integrating multi-omics data with appropriate ML methods, highlighting key clinical translational scenarios, including predicting disease progression risks to improve medical decision-making, comprehensively understanding disease molecular mechanisms, and practical applications of image recognition in renal digital pathology. Examining the benefits and challenges of current integration efforts is expected to shed light on the complexity of kidney disease and advance clinical practice.

Artificial Intelligence in Pathomics and Genomics of Renal Cell Carcinoma

The integration of artificial intelligence (AI) with histopathology images and gene expression patterns has led to the emergence of the dynamic fields of pathomics and genomics. These fields have revolutionized renal cell carcinoma (RCC) diagnosis and subtyping and improved survival prediction models. Machine learning has identified unique gene patterns across RCC subtypes and grades, providing insights into RCC origins and potential treatments, as targeted therapies. The combination of pathomics and genomics using AI opens new avenues in RCC research, promising future breakthroughs and innovations that patients and physicians can anticipate.

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.

Multi-omics data fusion using adaptive GTO guided Non-negative matrix factorization for cancer subtype discovery

BACKGROUND AND OBJECTIVE

Cancer subtype discovery is essential for personalized clinical treatment. With the onset of progressive profile techniques for cancer, a large amount of heterogeneous and high-dimensional transcriptomic, proteomic and genomic datasets are easily accumulated. Integrative clustering of such multi-omics data is crucial to recognize their latent structure and to acknowledge the correlation within and across them. Although the integrative analysis of diversified multi-omics data is informative, it is challenging when multiplicity in data inflicts poor accordance w.r.t. clustering structure. The objective of this work is to develop an effective integrative analysis framework that encapsulates the heterogeneity of various biological mechanisms and predicts homogeneous subgroups of cancer patients.

METHOD

In this paper, improved sparse-joint non-negative matrix factorization (sparse-jNMF) has been devised for the problem of cancer-subtype discovery. The initial points of sparse-jNMF have improved using a novel meta-heuristic algorithm adaptive gorilla troops optimizer (Ada-GTO). Improving the initialization of sparse-jNMF enhances its convergence behavior and further strengthens the clustering performance. In addition, the consensus clustering approach has been adopted to construct a patient-patient similarity matrix for obtaining stable clusters of patient samples.

RESULT

The proposed framework has been applied to 4 different real-life multi-omics cancer datasets, namely colon adenocarcinoma, breast-invasive carcinoma, kidney-renal clear-cell carcinoma, and lung adenocarcinoma. The proposed method results in patient clusters with better silhouette scores and cluster purity than classical initialization and similar meta-heuristics for initial point estimation approaches. Survival probabilities estimated using Kaplan-Meier (KM) curve show statistically significant difference (p < 0.05) for the homogenous cancer patient clusters obtained using the proposed method as compared to iCluster. The presented approach further identified the somatic mutations for the classified subgroups, which is beneficial to provide targeted treatments.

CONCLUSION

This paper proposes Ada-GTO guided sparse-jNMF framework for cancer subtype discovery, considering the information from multiple omic features that provide comprehension. The proposed meta-guided framework outperforms all other state-of-the-art counterparts. It also has great potential for obtaining the homogeneous subgroups of other diseases.

Immune landscape-based machine-learning-assisted subclassification, prognosis, and immunotherapy prediction for glioblastoma

Introduction

As a malignant brain tumor, glioblastoma (GBM) is characterized by intratumor heterogeneity, a worse prognosis, and highly invasive, lethal, and refractory natures. Immunotherapy has been becoming a promising strategy to treat diverse cancers. It has been known that there are highly heterogeneous immunosuppressive microenvironments among different GBM molecular subtypes that mainly include classical (CL), mesenchymal (MES), and proneural (PN), respectively. Therefore, an in-depth understanding of immune landscapes among them is essential for identifying novel immune markers of GBM.

Methods and results

In the present study, based on collecting the largest number of 109 immune signatures, we aim to achieve a precise diagnosis, prognosis, and immunotherapy prediction for GBM by performing a comprehensive immunogenomic analysis. Firstly, machine-learning (ML) methods were proposed to evaluate the diagnostic values of these immune signatures, and the optimal classifier was constructed for accurate recognition of three GBM subtypes with robust and promising performance. The prognostic values of these signatures were then confirmed, and a risk score was established to divide all GBM patients into high-, medium-, and low-risk groups with a high predictive accuracy for overall survival (OS). Therefore, complete differential analysis across GBM subtypes was performed in terms of the immune characteristics along with clinicopathological and molecular features, which indicates that MES shows much higher immune heterogeneity compared to CL and PN but has significantly better immunotherapy responses, although MES patients may have an immunosuppressive microenvironment and be more proinflammatory and invasive. Finally, the MES subtype is proved to be more sensitive to 17-AAG, docetaxel, and erlotinib using drug sensitivity analysis and three compounds of AS-703026, PD-0325901, and MEK1-2-inhibitor might be potential therapeutic agents.

Conclusion

Overall, the findings of this research could help enhance our understanding of the tumor immune microenvironment and provide new insights for improving the prognosis and immunotherapy of GBM patients.

Panels of mRNAs and miRNAs for decoding molecular mechanisms of Renal Cell Carcinoma (RCC) subtypes utilizing Artificial Intelligence approaches

Renal Cell Carcinoma (RCC) encompasses three histological subtypes, including clear cell RCC (KIRC), papillary RCC (KIRP), and chromophobe RCC (KICH) each of which has different clinical courses, genetic/epigenetic drivers, and therapeutic responses. This study aimed to identify the significant mRNAs and microRNA panels involved in the pathogenesis of RCC subtypes. The mRNA and microRNA transcripts profile were obtained from The Cancer Genome Atlas (TCGA), which were included 611 ccRCC patients, 321 pRCC patients, and 89 chRCC patients for mRNA data and 616 patients in the ccRCC subtype, 326 patients in the pRCC subtype, and 91 patients in the chRCC for miRNA data, respectively. To identify mRNAs and miRNAs, feature selection based on filter and graph algorithms was applied. Then, a deep model was used to classify the subtypes of the RCC. Finally, an association rule mining algorithm was used to disclose features with significant roles to trigger molecular mechanisms to cause RCC subtypes. Panels of 77 mRNAs and 73 miRNAs could discriminate the KIRC, KIRP, and KICH subtypes from each other with 92% (F1-score ≥ 0.9, AUC ≥ 0.89) and 95% accuracy (F1-score ≥ 0.93, AUC ≥ 0.95), respectively. The Association Rule Mining analysis could identify miR-28 (repeat count = 2642) and CSN7A (repeat count = 5794) along with the miR-125a (repeat count = 2591) and NMD3 (repeat count = 2306) with the highest repeat counts, in the KIRC and KIRP rules, respectively. This study found new panels of mRNAs and miRNAs to distinguish among RCC subtypes, which were able to provide new insights into the underlying responsible mechanisms for the initiation and progression of KIRC and KIRP. The proposed mRNA and miRNA panels have a high potential to be as biomarkers of RCC subtypes and should be examined in future clinical studies.

Topology preserving stratification of tissue neoplasticity using Deep Neural Maps and microRNA signatures

Background

Accurate cancer classification is essential for correct treatment selection and better prognostication. microRNAs (miRNAs) are small RNA molecules that negatively regulate gene expression, and their dyresgulation is a common disease mechanism in many cancers. Through a clearer understanding of miRNA dysregulation in cancer, improved mechanistic knowledge and better treatments can be sought.

Results

We present a topology-preserving deep learning framework to study miRNA dysregulation in cancer. Our study comprises miRNA expression profiles from 3685 cancer and non-cancer tissue samples and hierarchical annotations on organ and neoplasticity status. Using unsupervised learning, a two-dimensional topological map is trained to cluster similar tissue samples. Labelled samples are used after training to identify clustering accuracy in terms of tissue-of-origin and neoplasticity status. In addition, an approach using activation gradients is developed to determine the attention of the networks to miRNAs that drive the clustering. Using this deep learning framework, we classify the neoplasticity status of held-out test samples with an accuracy of 91.07%, the tissue-of-origin with 86.36%, and combined neoplasticity status and tissue-of-origin with an accuracy of 84.28%. The topological maps display the ability of miRNAs to recognize tissue types and neoplasticity status. Importantly, when our approach identifies samples that do not cluster well with their respective classes, activation gradients provide further insight in cancer subtypes or grades.

Conclusions

An unsupervised deep learning approach is developed for cancer classification and interpretation. This work provides an intuitive approach for understanding molecular properties of cancer and has significant potential for cancer classification and treatment selection.