Integrative analysis of DNA methylation and gene expression in papillary renal cell carcinoma

The Importance of HHLA2 in Solid Tumors-A Review of the Literature

Cancer immunotherapy is a rapidly developing field of medicine that aims to use the host's immune mechanisms to inhibit and eliminate cancer cells. Antibodies targeting CTLA-4, PD-1, and its ligand PD-L1 are used in various cancer therapies. However, the most thoroughly researched pathway targeting PD-1/PD-L1 has many limitations, and multiple malignancies resist its effects. Human endogenous retrovirus-H Long repeat-associating 2 (HHLA2, known as B7H5/B7H7/B7y) is the youngest known molecule from the B7 family. HHLA2/TMIGD2/KIRD3DL3 is one of the critical pathways in modulating the immune response. Recent studies have demonstrated that HHLA2 has a double effect in modulating the immune system. The connection of HHLA2 with TMIGD2 induces T cell growth and cytokine production via an AKT-dependent signaling cascade. On the other hand, the binding of HHLA2 and KIR3DL3 leads to the inhibition of T cells and mediates tumor resistance against NK cells. This review aimed to summarize novel information about HHLA2, focusing on immunological mechanisms and clinical features of the HHLA2/KIR3DL3/TMIGD2 pathway in the context of potential strategies for malignancy treatment.

The fungicide pyraclostrobin affects gene expression by altering the DNA methylation pattern in Magnaporthe oryzae

Introduction

Rice blast disease caused by Magnaporthe oryzae has long been the main cause of rice (Oryza sativa L.) yield reduction worldwide. The quinone external inhibitor pyraclostrobin is widely used as a fungicide to effectively control the spread of pathogenic fungi, including M. oryzae. However, M. oryzae can develop resistance through multiple levels of mutation, such as target protein cytb mutation G143A/S, leading to a decrease in the effectiveness of the biocide after a period of application. Therefore, uncovering the possible mutational mechanisms from multiple perspectives will further provide feasible targets for drug development.

Methods

In this work, we determined the gene expression changes in M. oryzae in response to pyraclostrobin stress and their relationship with DNA methylation by transcriptome and methylome.

Results

The results showed that under pyraclostrobin treatment, endoplasmic reticulum (ER)-associated and ubiquitin-mediated proteolysis were enhanced, suggesting that more aberrant proteins may be generated that need to be cleared. DNA replication and repair processes were inhibited. Glutathione metabolism was enhanced, while lipid metabolism was impaired. The number of alternative splicing events increased. These changes may be related to the elevated methylation levels of cytosine and adenine in gene bodies. Both hypermethylation and hypomethylation of differentially methylated genes (DMGs) mainly occurred in exons and promoters. Some DMGs and differentially expressed genes (DEGs) were annotated to the same pathways by GO and KEGG, including protein processing in the ER, ubiquitin-mediated proteolysis, RNA transport and glutathione metabolism, suggesting that pyraclostrobin may affect gene expression by altering the methylation patterns of cytosine and adenine.

Discussion

Our results revealed that 5mC and 6mA in the gene body are associated with gene expression and contribute to adversity adaptation in M. oryzae. This enriched the understanding for potential mechanism of quinone inhibitor resistance, which will facilitate the development of feasible strategies for maintaining the high efficacy of this kind of fungicide.

wMKL: multi-omics data integration enables novel cancer subtype identification via weight-boosted multi-kernel learning

BACKGROUND

Cancer is a heterogeneous disease driven by complex molecular alterations. Cancer subtypes determined from multi-omics data can provide novel insight into personalised precision treatment. It is recognised that incorporating prior weight knowledge into multi-omics data integration can improve disease subtyping.

METHODS

We develop a weighted method, termed weight-boosted Multi-Kernel Learning (wMKL) which incorporates heterogeneous data types as well as flexible weight functions, to boost subtype identification. Given a series of weight functions, we propose an omnibus combination strategy to integrate different weight-related P-values to improve subtyping precision.

RESULTS

wMKL models each data type with multiple kernel choices, thus alleviating the sensitivity and robustness issue due to selecting kernel parameters. Furthermore, wMKL integrates different data types by learning weights of different kernels derived from each data type, recognising the heterogeneous contribution of different data types to the final subtyping performance. The proposed wMKL outperforms existing weighted and non-weighted methods. The utility and advantage of wMKL are illustrated through extensive simulations and applications to two TCGA datasets. Novel subtypes are identified followed by extensive downstream bioinformatics analysis to understand the molecular mechanisms differentiating different subtypes.

CONCLUSIONS

The proposed wMKL method provides a novel strategy for disease subtyping. The wMKL is freely available at https://github.com/biostatcao/wMKL .

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.

Claudins in genitourinary tract neoplasms: mechanisms, prognosis, and therapeutic prospects

Genitourinary (GU) cancers are among the most prevalent neoplasms in the world, with bladder cancers constituting 3% of global cancer diagnoses. However, several pathogenetic mechanisms remain controversial and unclear. Claudins, for example, have been shown to play a significant role in several cancers of the human body. Their role in GU cancers has not been extensively studied. Aberrant expression of claudins -1, -2, -3, -4, -7, and -11 has been expressed in urothelial cell carcinomas. In prostate cancers, altered levels of claudins -1, -2, -3, -4, and -5 have been reported. Furthermore, the levels of claudins -1, -2, -3, -4, -6, -7, -8, and -10 have been studied in renal cell carcinomas. Specifically, claudins -7 and -8 have proven especially useful in differentiating between chromophobe renal cell carcinomas and oncocytomas. Several of these claudins also correlate with clinicopathologic parameters and prognosis in GU cancers. Although mechanisms underpinning aberrant expression of claudins in GU cancers are unclear, epigenetic changes, tumor necrosis factor-ɑ, and the p63 protein have been implicated. Claudins also provide therapeutic value through tailored immunotherapy via molecular subtyping and providing therapeutic targets, which have shown positive outcomes in preclinical studies. In this review, we aim to summarize the literature describing aberrant expression of claudins in urothelial, prostatic, and renal cell carcinomas. Then, we describe the mechanisms underlying these changes and the therapeutic value of claudins. Understanding the scope of claudins in GU cancers paves the way for several diagnostic, prognostic, and therapeutic innovations.

Artificial Intelligence in Renal Cell Carcinoma Histopathology: Current Applications and Future Perspectives

Renal cell carcinoma (RCC) is characterized by its diverse histopathological features, which pose possible challenges to accurate diagnosis and prognosis. A comprehensive literature review was conducted to explore recent advancements in the field of artificial intelligence (AI) in RCC pathology. The aim of this paper is to assess whether these advancements hold promise in improving the precision, efficiency, and objectivity of histopathological analysis for RCC, while also reducing costs and interobserver variability and potentially alleviating the labor and time burden experienced by pathologists. The reviewed AI-powered approaches demonstrate effective identification and classification abilities regarding several histopathological features associated with RCC, facilitating accurate diagnosis, grading, and prognosis prediction and enabling precise and reliable assessments. Nevertheless, implementing AI in renal cell carcinoma generates challenges concerning standardization, generalizability, benchmarking performance, and integration of data into clinical workflows. Developing methodologies that enable pathologists to interpret AI decisions accurately is imperative. Moreover, establishing more robust and standardized validation workflows is crucial to instill confidence in AI-powered systems' outcomes. These efforts are vital for advancing current state-of-the-art practices and enhancing patient care in the future.

Clinical Characteristics of Molecularly Defined Renal Cell Carcinomas

Kidney tumors comprise a broad spectrum of different histopathological entities, with more than 0.4 million newly diagnosed cases each year, mostly in middle-aged and older men. Based on the description of the 2022 World Health Organization (WHO) classification of renal cell carcinoma (RCC), some new categories of tumor types have been added according to their specific molecular typing. However, studies on these types of RCC are still superficial, many types of these RCC currently lack accurate diagnostic standards in the clinic, and treatment protocols are largely consistent with the treatment guidelines for clear cell RCC (ccRCC), which might result in worse treatment outcomes for patients with these types of molecularly defined RCC. In this article, we conduct a narrative review of the literature published in the last 15 years on molecularly defined RCC. The purpose of this review is to summarize the clinical features and the current status of research on the detection and treatment of molecularly defined RCC.

Network diffusion-based approach for survival prediction and identification of biomarkers using multi-omics data of papillary renal cell carcinoma

Identification of cancer subtypes based on molecular knowledge is crucial for improving the patient diagnosis, prognosis, and treatment. In this work, we integrated copy number variations (CNVs) and transcriptomic data of Kidney Papillary Renal Cell Carcinoma (KIRP) using a network diffusion strategy to stratify cancers into clinically and biologically relevant subtypes. We constructed GeneNet, a KIRP specific gene expression network from RNA-seq data. The copy number variation data was projected onto GeneNet and propagated on the network for clustering. We identified robust subtypes that are biologically informative and significantly associated with patient survival, tumor stage and clinical subtypes of KIRP. We performed a Singular Value Decomposition (SVD) analysis of KIRP subtypes, which revealed the genes/silent players related to poor survival. A differential gene expression analysis between subtypes showed that genes related to immune, extracellular matrix organization, and genomic instability are upregulated in the poor survival group. Overall, the network-based approach revealed the molecular subtypes of KIRP and captured the relationship between gene expression and CNVs. This framework can be further expanded to integrate other omics data.

An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS)

Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation.

Artificial intelligence-based multi-omics analysis fuels cancer precision medicine

With biotechnological advancements, innovative omics technologies are constantly emerging that have enabled researchers to access multi-layer information from the genome, epigenome, transcriptome, proteome, metabolome, and more. A wealth of omics technologies, including bulk and single-cell omics approaches, have empowered to characterize different molecular layers at unprecedented scale and resolution, providing a holistic view of tumor behavior. Multi-omics analysis allows systematic interrogation of various molecular information at each biological layer while posing tricky challenges regarding how to extract valuable insights from the exponentially increasing amount of multi-omics data. Therefore, efficient algorithms are needed to reduce the dimensionality of the data while simultaneously dissecting the mysteries behind the complex biological processes of cancer. Artificial intelligence has demonstrated the ability to analyze complementary multi-modal data streams within the oncology realm. The coincident development of multi-omics technologies and artificial intelligence algorithms has fuelled the development of cancer precision medicine. Here, we present state-of-the-art omics technologies and outline a roadmap of multi-omics integration analysis using an artificial intelligence strategy. The advances made using artificial intelligence-based multi-omics approaches are described, especially concerning early cancer screening, diagnosis, response assessment, and prognosis prediction. Finally, we discuss the challenges faced in multi-omics analysis, along with tentative future trends in this field. With the increasing application of artificial intelligence in multi-omics analysis, we anticipate a shifting paradigm in precision medicine becoming driven by artificial intelligence-based multi-omics technologies.

Applications of Digital and Computational Pathology and Artificial Intelligence in Genitourinary Pathology Diagnostics

As machine learning (ML) solutions for genitourinary pathology image analysis are fostered by a progressively digitized laboratory landscape, these integrable modalities usher in a revolution in histopathological diagnosis. As technology advances, limitations stymying clinical artificial intelligence (AI) will not be extinguished without thorough validation and interrogation of ML tools by pathologists and regulatory bodies alike. ML solutions deployed in clinical settings for applications in prostate pathology yield promising results. Recent breakthroughs in clinical artificial intelligence for genitourinary pathology demonstrate unprecedented generalizability, heralding prospects for a future in which AI-driven assistive solutions may be seen as laboratory faculty, rather than novelty.

Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence

Diagnostic devices, methodological approaches, and traditional constructs of clinical pathology practice, cultivated throughout centuries, have transformed radically in the wake of explosive technological growth and other, e.g., environmental, catalysts of change. Ushered into the fray of modern laboratory medicine are digital imaging devices and machine-learning (ML) software fashioned to mitigate challenges, e.g., practitioner shortage while preparing clinicians for emerging interconnectivity of environments and diagnostic information in the era of big data. As computer vision shapes new constructs for the modern world and intertwines with clinical medicine, cultivating clarity of our new terrain through examining the trajectory and current scope of computational pathology and its pertinence to clinical practice is vital. Through review of numerous studies, we find developmental efforts for ML migrating from research to standardized clinical frameworks while overcoming obstacles that have formerly curtailed adoption of these tools, e.g., generalizability, data availability, and user-friendly accessibility. Groundbreaking validatory efforts have facilitated the clinical deployment of ML tools demonstrating the capacity to effectively aid in distinguishing tumor subtype and grade, classify early vs. advanced cancer stages, and assist in quality control and primary diagnosis applications. Case studies have demonstrated the benefits of streamlined, digitized workflows for practitioners alleviated by decreased burdens.

Artificial intelligence for renal cancer: From imaging to histology and beyond

Artificial intelligence (AI) has made considerable progress within the last decade and is the subject of contemporary literature. This trend is driven by improved computational abilities and increasing amounts of complex data that allow for new approaches in analysis and interpretation. Renal cell carcinoma (RCC) has a rising incidence since most tumors are now detected at an earlier stage due to improved imaging. This creates considerable challenges as approximately 10%–17% of kidney tumors are designated as benign in histopathological evaluation; however, certain co-morbid populations (the obese and elderly) have an increased peri-interventional risk. AI offers an alternative solution by helping to optimize precision and guidance for diagnostic and therapeutic decisions. The narrative review introduced basic principles and provide a comprehensive overview of current AI techniques for RCC. Currently, AI applications can be found in any aspect of RCC management including diagnostics, perioperative care, pathology, and follow-up. Most commonly applied models include neural networks, random forest, support vector machines, and regression. However, for implementation in daily practice, health care providers need to develop a basic understanding and establish interdisciplinary collaborations in order to standardize datasets, define meaningful endpoints, and unify interpretation.

Artificial Intelligence in Kidney Cancer

Artificial intelligence is rapidly expanding into nearly all facets of life, particularly within the field of medicine. The diagnosis, characterization, management, and treatment of kidney cancer is ripe with areas for improvement that may be met with the promises of artificial intelligence. Here, we explore the impact of current research work in artificial intelligence for clinicians caring for patients with renal cancer, with a focus on the perspectives of radiologists, pathologists, and urologists. Promising preliminary results indicate that artificial intelligence may assist in the diagnosis and risk stratification of newly discovered renal masses and help guide the clinical treatment of patients with kidney cancer. However, much of the work in this field is still in its early stages, limited in its broader applicability, and hampered by small datasets, the varied appearance and presentation of kidney cancers, and the intrinsic limitations of the rigidly structured tasks artificial intelligence algorithms are trained to complete. Nonetheless, the continued exploration of artificial intelligence holds promise toward improving the clinical care of patients with kidney cancer.

Human endogenous retrovirus-H long terminal repeat-associating 2: The next immune checkpoint for antitumour therapy

Human endogenous retrovirus-H long terminal repeat-associating 2 (HHLA2) is a newly emerging immune checkpoint that belongs to B7 family. HHLA2 has a co-stimulatory receptor transmembrane and immunoglobulin domain containing 2 (TMIGD2) and a newly discovered co-inhibitory receptor killer cell Ig-like receptor, three Ig domains, and long cytoplasmic tail (KIR3DL3), which endows it with both immunostimulant and immunosuppression functions in cancer development. In this review, we summarize the HHLA2 expression profile in human cancers, its association with cancer prognosis and clinical features, and its dual roles in regulating cancer immune response through up-to-date literatures. Furthermore, we highlight that precision cancer immunotherapy through manipulating HHLA2-KIR3DL3/TMIGD2 interaction is a promising antitumour strategy.

Methylation and expression levels of microRNA-23b/-24-1/-27b, microRNA-30c-1/-30e, microRNA-301a and let-7g are dysregulated in clear cell renal cell carcinoma

BACKGROUND

Renal cell carcinoma is the most common form of kidney cancer in adults. DNA methylation of regulatory sequences at the genomic level and interaction between microRNAs and the messenger RNAs of target genes at the posttranscriptional level contribute to the dynamic regulation of gene activity. Aberrations in these mechanisms can result in impaired functioning of cell signaling pathways, such as that observed in malignant tumors. We hypothesized that microRNA genes methylation may be associated with renal cancer in patients.

METHODS AND RESULTS

We examined methylation levels of 22 microRNA genes in tumor and normal kidney tissue of 30 patients with TNM Stage III clear cell renal cell carcinoma using a pathway-specific real-time polymerase chain reaction array (EpiTect Methyl II PCR Arrays, Qiagen). MicroRNA expression analysis by quantitative polymerase chain reaction was also performed. Significant differences in methylation levels were found in two genes and in two clusters of microRNA genes. MicroRNA-23b/-24-1/-27b, microRNA -30c-1/-30e and let-7 g was hypermetylated in clear cell renal cell carcinoma tissue, microRNA -301a was hypomethylated in tumor compared with the adjacent normal tissues. Expression of microRNA-301a, microRNA-23b in the clear cell renal cell carcinoma tissues was significantly overexpressed when compared with the adjacent normal tissues and let-7 g was significantly downregulated in tumor.

CONCLUSIONS

Our results may indicate the contribution of microRNA-301a, microRNA-23b and let-7 g in the pathogenesis of renal cancer, but further studies are needed to determine the functional significance of the detected changes.

Kidney cancer management 3.0: can artificial intelligence make us better?

PURPOSE OF REVIEW

Artificial intelligence holds tremendous potential for disrupting clinical medicine. Here we review the current role of artificial intelligence in the kidney cancer space.

RECENT FINDINGS

Machine learning and deep learning algorithms have been developed using information extracted from radiomic, histopathologic, and genomic datasets of patients with renal masses.

SUMMARY

Although artificial intelligence applications in medicine are still in their infancy, they already hold immediate promise to improve accuracy of renal mass characterization, grade, and prognostication. As algorithms become more robust and generalizable, artificial intelligence is poised to significantly disrupt kidney cancer care.

Epigenetic Regulatory Enzymes: mutation Prevalence and Coexistence in Cancers

Epigenetic regulation is an important layer of transcriptional control with the particularity to affect the broad spectrum of genome. Over the years, largely due to the substantial number of recurrent mutations, there have been hundreds of novel driver genes characterized in various cancers. Additionally, the relative contribution of two dysregulated epigenomic entities (DNA methylation and histone modifications) that gradually drive the cancer phenotype remains in the research focus. However, a complex scenario arises when the disease phenotype does not harbor any relevant mutation or an abnormal transcription level. Although the cancer landscape involves the contribution of multiple genetic and non-genetic factors, herein, we discuss specifically the mutation spectrum of epigenetically-related enzymes in cancer. In addition, we address the coexistence of these two epigenetic entities in malignant human diseases, especially cancer. We suggest that the study of epigenetically-related somatic mutations in the early cellular differentiation stage of embryonic development might help to understand their later-staged footprints in the cancer genome. Furthermore, understanding the co-occurrence and/or inverse association of different disease types and redefining the general definition of "healthy" controls could provide insights into the genome reorganization.

The Role of Artificial Intelligence in the Diagnosis and Prognosis of Renal Cell Tumors

The increasing availability of molecular data provided by next-generation sequencing (NGS) techniques is allowing improvement in the possibilities of diagnosis and prognosis in renal cancer. Reliable and accurate predictors based on selected gene panels are urgently needed for better stratification of renal cell carcinoma (RCC) patients in order to define a personalized treatment plan. Artificial intelligence (AI) algorithms are currently in development for this purpose. Here, we reviewed studies that developed predictors based on AI algorithms for diagnosis and prognosis in renal cancer and we compared them with non-AI-based predictors. Comparing study results, it emerges that the AI prediction performance is good and slightly better than non-AI-based ones. However, there have been only minor improvements in AI predictors in terms of accuracy and the area under the receiver operating curve (AUC) over the last decade and the number of genes used had little influence on these indices. Furthermore, we highlight that different studies having the same goal obtain similar performance despite the fact they use different discriminating genes. This is surprising because genes related to the diagnosis or prognosis are expected to be tumor-specific and independent of selection methods and algorithms. The performance of these predictors will be better with the improvement in the learning methods, as the number of cases increases and by using different types of input data (e.g., non-coding RNAs, proteomic and metabolic). This will allow for more precise identification, classification and staging of cancerous lesions which will be less affected by interpathologist variability.

Integrated Molecular Characterization of Fumarate Hydratase-deficient Renal Cell Carcinoma

PURPOSE

Fumarate hydratase-deficient renal cell carcinoma (FH-deficient RCC) is a rare but lethal subtype of RCC. Little is known about the genomic profile of FH-deficient RCC, and the therapeutic options for advanced disease are limited. To this end, we performed a comprehensive genomics study to characterize the genomic and epigenomic features of FH-deficient RCC.

EXPERIMENTAL DESIGN

Integrated genomic, epigenomic, and molecular analyses were performed on 25 untreated primary FH-deficient RCCs. Complete clinicopathologic and follow-up data of these patients were recorded.

RESULTS

We identified that FH-deficient RCC manifested low somatic mutation burden (median 0.58 mutations per megabase), but with frequent somatic copy-number alterations. The majority of FH-deficient RCCs were characterized by a CpG sites island methylator phenotype, displaying concerted hypermethylation at numerous CpG sites in genes of transcription factors, tumor suppressors, and tumor hallmark pathways. However, a few cases (20%) with low metastatic potential showed relatively low DNA methylation levels, indicating the heterogeneity of methylation pattern in FH-deficient RCC. Moreover, FH-deficient RCC is potentially highly immunogenic, characterized by increased tumor T-cell infiltration but high expression of immune checkpoint molecules in tumors. Clinical data further demonstrated that patients receiving immune checkpoint blockade-based treatment achieved improved progression-free survival over those treated with antiangiogenic monotherapy (median, 13.3 vs. 5.1 months; P = 0.03).

CONCLUSIONS

These results reveal the genomic features and provide new insight into potential therapeutic strategies for FH-deficient RCC.

Comprehensive Analysis of DNA Methylation and Prediction of Response to NeoadjuvantTherapy in Locally Advanced Rectal Cancer

The treatment for locally advanced rectal carcinomas (LARC) is based on neoadjuvant chemoradiotherapy (nCRT) and surgery, which results in pathological complete response (pCR) in up to 30% of patients. Since epigenetic changes may influence response to therapy, we aimed to identify DNA methylation markers predictive of pCR in LARC patients treated with nCRT. We used high-throughput DNA methylation analysis of 32 treatment-naïve LARC biopsies and five normal rectal tissues to explore the predictive value of differentially methylated (DM) CpGs. External validation was carried out with The Cancer Genome Atlas-Rectal Adenocarcinoma (TCGA-READ 99 cases). A classifier based on three-CpGs DM (linked to OBSL1, GPR1, and INSIG1 genes) was able to discriminate pCR from incomplete responders with high sensitivity and specificity. The methylation levels of the selected CpGs confirmed the predictive value of our classifier in 77 LARCs evaluated by bisulfite pyrosequencing. Evaluation of external datasets (TCGA-READ, GSE81006, GSE75546, and GSE39958) reproduced our results. As the three CpGs were mapped near to regulatory elements, we performed an integrative analysis in regions associated with predicted cis-regulatory elements. A positive and inverse correlation between DNA methylation and gene expression was found in two CpGs. We propose a novel predictive tool based on three CpGs potentially useful for pretreatment screening of LARC patients and guide the selection of treatment modality.

Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer

Cancer is the manifestation of abnormalities of different physiological processes involving genes, DNAs, RNAs, proteins, and other biomolecules whose profiles are reflected in different omics data types. As these bio-entities are very much correlated, integrative analysis of different types of omics data, multi-omics data, is required to understanding the disease from the tumorigenesis to the disease progression. Artificial intelligence (AI), specifically machine learning algorithms, has the ability to make decisive interpretation of "big"-sized complex data and, hence, appears as the most effective tool for the analysis and understanding of multi-omics data for patient-specific observations. In this review, we have discussed about the recent outcomes of employing AI in multi-omics data analysis of different types of cancer. Based on the research trends and significance in patient treatment, we have primarily focused on the AI-based analysis for determining cancer subtypes, disease prognosis, and therapeutic targets. We have also discussed about AI analysis of some non-canonical types of omics data as they have the capability of playing the determiner role in cancer patient care. Additionally, we have briefly discussed about the data repositories because of their pivotal role in multi-omics data storing, processing, and analysis.

Bioinformatics profiling integrating a four immune-related long non-coding RNAs signature as a prognostic model for papillary renal cell carcinoma

Background: Papillary renal cell carcinoma (pRCC) was the 2^nd most common subtype, accounting for approximately 15% incidence of renal cell carcinoma (RCC). Immune related long non-coding RNAs (IR-lncRs) plentiful in immune cells and immune microenvironment (IME) are potential in evaluating prognosis and assessing the effects of immunotherapy. A completed and meaningful IR-lncRs analysis based on abundant pRCC gene samples from The Cancer Genome Atlas (TCGA) will provide insight in this field.

Results: 17 IR-lncRs were selected by Pearson correlation analysis of immune score and the lncRNA expression level, and 5 sIRlncRs were significantly correlated with the OS of pRCC patients. 4 sIRlncRs (AP001267.3, AC026471.3, SNHG16 and ADAMTS9-AS1) with the most remarkable prognostic values were identified to establish the IRRS model and the OS of the low-risk group was longer than that in the high-risk group. The IRRS was certified as an independent prognosis factor and correlated with the OS. The high-risk group and low-risk group showed significantly different distributions and immune status through PCA and GSEA. In addition, we further found the expression levels of SNHG16 was remarkably enhanced in female patients with more advanced T-stages, but ADAMTS9-AS1 showed the opposite results.

Conclusion: The IRRS model based on the identified 4 sIRlncRs showed the significant values on forecasting prognoses of pRCC patients, with the longer OS in the low-risk group.

Methods: We integrated the expression profiles of LncRNA and overall survival (OS) in the 322 pRCC patients based on the TCGA dataset. The immune scores calculated on account of the expression level of immune-related genes were used to verify the most relevant IR-lncRs. Survival-related IR-lncRs (sIRlncRs) were estimated by COX regression analysis in pRCC patients. The high-risk group and low-risk group were identified by the median immune-related risk score (IRRS) model established by the screened sIRlncRs. Functional annotation was displayed by gene set enrichment analysis (GSEA) and principal component analysis (PCA), and the immune composition and purity of the tumor were evaluated through microenvironment cell count records. The expression levels of sIRlncRs of pRCC samples were verified by real-time quantitative PCR.

Identification and Comprehensive Validation of a DNA Methylation-Driven Gene-Based Prognostic Model for Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most prevalent renal malignancy in adults with generally poor prognosis. This study aimed to establish a DNA methylation-driven gene-based prognostic model for ccRCC. We collected DNA methylation and gene expression profiles of over 1500 ccRCC samples from The Cancer Genome Atlas (TCGA) dataset, four Gene Expression Omnibus (GEO) datasets, the Genotype-Tissue Expression (GTEx) dataset, and cancer cell lines from Cancer Cell Line Encyclopedia database and performed comprehensive bioinformatics analysis. As a result, a total of 31 differentially expressed methylation-driven genes (DEMDGs) were identified. After univariate Cox regression, least absolute shrinkage and selection operator, and multivariate Cox regression analyses, four (NFE2L3, HHLA2, IFI16, and ZNF582) were finally selected to construct a risk score prognostic model. The high-risk group demonstrated significantly poor prognosis than the low-risk group did in TCGA training (hazard ratio [HR] = 3.533, p < 0.001), TCGA internal, and GEO external validation datasets. Furthermore, the nomogram, including the prognostic model and clinical factors, showed promising prognostic value (HR = 5.756, p < 0.001, and area under the curve at 1 year = 0.856). In addition, the model was found to be significantly associated with drug sensitivity of eight targeted agents. These findings provided a novel and reliable four DEMDG-based prognostic model for ccRCC.