An overview of advances in multi-omics analysis in prostate cancer

From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine

Prostate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer's initiation and progression. However, single-omic studies usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.

Elucidating Cancer Subtypes by Using the Relationship between DNA Methylation and Gene Expression

Advancements in the field of next generation sequencing (NGS) have generated vast amounts of data for the same set of subjects. The challenge that arises is how to combine and reconcile results from different omics studies, such as epigenome and transcriptome, to improve the classification of disease subtypes. In this study, we introduce sCClust (sparse canonical correlation analysis with clustering), a technique to combine high-dimensional omics data using sparse canonical correlation analysis (sCCA), such that the correlation between datasets is maximized. This stage is followed by clustering the integrated data in a lower-dimensional space. We apply sCClust to gene expression and DNA methylation data for three cancer genomics datasets from the Cancer Genome Atlas (TCGA) to distinguish between underlying subtypes. We evaluate the identified subtypes using Kaplan-Meier plots and hazard ratio analysis on the three types of cancer-GBM (glioblastoma multiform), lung cancer and colon cancer. Comparison with subtypes identified by both single- and multi-omics studies implies improved clinical association. We also perform pathway over-representation analysis in order to identify up-regulated and down-regulated genes as tentative drug targets. The main goal of the paper is twofold: the integration of epigenomic and transcriptomic datasets followed by elucidating subtypes in the latent space. The significance of this study lies in the enhanced categorization of cancer data, which is crucial to precision medicine.

Histomolecular characterisation of hepatitis B virus induced liver cancer

Hepatitis B virus (HBV)-associated liver cancer is the third most prevalent cancer-related cause of death worldwide. Different studies have been done on the histomolecular analysis of HBV induced-liver cancer including epigenetics which are dynamic molecular mechanisms to control gene expression without altering the host deoxyribonucleic acid, genomics characterise the integration of the viral genome with host genome, proteomics characterise how gene modifies and results overexpression of proteins, glycoproteomics discover different glyco-biomarker candidates and show glycosylation in malignant hepatocytes, metabolomics characterise how HBV impairs a variety of metabolic functions during hepatocyte immortalisation, exosomes characterise immortalised liver cells in terms of their differentiation and proliferation, and autophagy plays a role in the development of hepatocarcinogenesis linked to HBV infection.

Integration of transcriptomics and metabolomics analysis for unveiling the toxicological profile in the liver of mice exposed to uranium in drinking water

Uranium is a contaminate in the underground water in many regions of the world, which poses health risks to the local populations through drinking water. Although the health hazards of natural uranium have been concerned for decades, the controversies about its detrimental effects continue at present since it is still unclear how uranium interacts with molecular regulatory networks to generate toxicity. Here, we integrate transcriptomic and metabolomic methods to unveil the molecular mechanism of lipid metabolism disorder induced by uranium. Following exposure to uranium in drinking water for twenty-eight days, aberrant lipid metabolism and lipogenesis were found in the liver, accompanied with aggravated lipid peroxidation and an increase in dead cells. Multi-omics analysis reveals that uranium can promote the biosynthesis of unsaturated fatty acids through dysregulating the metabolism of arachidonic acid (AA), linoleic acid, and glycerophospholipid. Most notably, the disordered metabolism of polyunsaturated fatty acids (PUFAs) like AA may contribute to lipid peroxidation induced by uranium, which in turn triggers ferroptosis in hepatocytes. Our findings highlight disorder of lipid metabolism as an essential toxicological mechanism of uranium in the liver, offering insight into the health risks of uranium in drinking water.

Advancements in MRI-Based Radiomics and Artificial Intelligence for Prostate Cancer: A Comprehensive Review and Future Prospects

The use of multiparametric magnetic resonance imaging (mpMRI) has become a common technique used in guiding biopsy and developing treatment plans for prostate lesions. While this technique is effective, non-invasive methods such as radiomics have gained popularity for extracting imaging features to develop predictive models for clinical tasks. The aim is to minimize invasive processes for improved management of prostate cancer (PCa). This study reviews recent research progress in MRI-based radiomics for PCa, including the radiomics pipeline and potential factors affecting personalized diagnosis. The integration of artificial intelligence (AI) with medical imaging is also discussed, in line with the development trend of radiogenomics and multi-omics. The survey highlights the need for more data from multiple institutions to avoid bias and generalize the predictive model. The AI-based radiomics model is considered a promising clinical tool with good prospects for application.

Advances and potential of omics studies for understanding the development of food allergy

The prevalence of food allergy continues to rise globally, carrying with it substantial safety, economic, and emotional burdens. Although preventative strategies do exist, the heterogeneity of allergy trajectories and clinical phenotypes has made it difficult to identify patients who would benefit from these strategies. Therefore, further studies investigating the molecular mechanisms that differentiate these trajectories are needed. Large-scale omics studies have identified key insights into the molecular mechanisms for many different diseases, however the application of these technologies to uncover the drivers of food allergy development is in its infancy. Here we review the use of omics approaches in food allergy and highlight key gaps in knowledge for applying these technologies for the characterization of food allergy development.

Comparative Proteomic and Transcriptomic Analysis of the Impact of Androgen Stimulation and Darolutamide Inhibition

Several inhibitors of androgen receptor (AR) function are approved for prostate cancer treatment, and their impact on gene transcription has been described. However, the ensuing effects at the protein level are far less well understood. We focused on the AR signaling inhibitor darolutamide and confirmed its strong AR binding and antagonistic activity using the high throughput cellular thermal shift assay (CETSA HT). Then, we generated comprehensive, quantitative proteomic data from the androgen-sensitive prostate cancer cell line VCaP and compared them to transcriptomic data. Following treatment with the synthetic androgen R1881 and darolutamide, global mass spectrometry-based proteomics and label-free quantification were performed. We found a generally good agreement between proteomic and transcriptomic data upon androgen stimulation and darolutamide inhibition. Similar effects were found both for the detected expressed genes and their protein products as well as for the corresponding biological programs. However, in a few instances there was a discrepancy in the magnitude of changes induced on gene expression levels compared to the corresponding protein levels, indicating post-transcriptional regulation of protein abundance. Chromatin immunoprecipitation DNA sequencing (ChIP-seq) and Hi-C chromatin immunoprecipitation (HiChIP) revealed the presence of androgen-activated AR-binding regions and long-distance AR-mediated loops at these genes.

LncRNA RHPN1-AS1 inhibition induces autophagy and apoptosis in prostate cancer cells via the miR-7-5p/EGFR/PI3K/AKT/mTOR signaling pathway

LncRNA RHPN1-AS1 (RHPN1-AS1) has been confirmed to promote tumor progression in multiple cancers and is upregulated in prostate cancer (PCa), but whether it has an effect on PCa progression remains unclear. In this study, we found that PCa patients with high RHPN1-AS1 expression had a shorter survival time, and RHPN1-AS1 was significantly upregulated in PCa tissues and cells. Based on informatics analysis we predicted that miR-7-5p binds to 3'UTR of RHPN1-AS1 and epidermal growth factor receptor (EGFR) and verified it by luciferase reporter gene assay. Subsequently, we transfected PCa cells with RHPN1-AS1 overexpression vector (RHPN1-AS1), knockdown plasmids (sh-RHPN1-AS1) and/or miR-7-5p mimics or inhibitor and/or overexpression vector (EGFR) or small interfering RNA of EGFR (si-EGFR) or its control, and found that overexpression of RHPN1-AS1 inhibited miR-7-5p expression and promoted EGFR expression, silencing RHPN1-AS1 inhibited proliferation and invasion, and induced G2/M arrest, apoptosis and autophagy in PCa cells. 3MA (an inhibitor of autophagy)-mediated autophagy inhibition attenuated RHPN1-AS1 inhibition-induced apoptosis. Overexpression miR-7-5p or silencing EGFR promoted LC3-I to LC3-II conversion, enhanced autophagy activity, induced cleaved-caspase-3 expression and apoptosis in PCa cells. Furthermore, overexpression of RHPN1-AS1 promoted phosphorylation of phosphatidylinositol 3-kinase (PI3K), AKT and mTOR, inhibited LC3-I to LC3-II conversion and reduced apoptosis in PCa cells, while GSK2126458 (an inhibitor of PI3K) reversed the effect of RHPN1-AS1 on PCa cells. In summary, RHPN1-AS1 acted as a ceRNA of miR-7-5p to upregulate EGFR expression, silencing RHPN1-AS1 suppressed PCa tumor progression by inducing autophagy and apoptosis in PCa cells through the miR-7-5p/EGFR/PI3K/AKT/mTOR pathway.

Food allergy, mechanisms, diagnosis and treatment: Innovation through a multi-targeted approach

The incidence of food allergy (FA) has continued to rise over the last several decades, posing significant burdens on health and quality of life. Significant strides into the advancement of FA diagnosis, prevention, and treatment have been made in recent years. In an effort to lower reliance on resource-intensive food challenges, the field has continued work toward the development of highly sensitive and specific assays capable of high-throughput analysis to assist in the diagnosis FA. In looking toward early infancy as a critical period in the development of allergy or acquisition of tolerance, evidence has increasingly suggested that early intervention via the early introduction of food allergens and maintenance of skin barrier function may decrease the risk of FA. As such, large-scale investigations are underway evaluating infant feeding and the impact of emollient and steroid use in infants with dry skin for the prevention of allergy. On the other end of the spectrum, the past few years have been witness to an explosive increase in clinical trials of novel and innovative therapeutic strategies aimed at the treatment of FA in those whom the disease has already manifested. A milestone in the field, 2020 marked the approval of the first drug, oral peanut allergen, for the indication of peanut allergy. With a foundation of promising data supporting the safety and efficacy of single- and multi-allergen oral immunotherapy, current efforts have turned toward the use of probiotics, biologic agents, and modified allergens to optimize and improve upon existing paradigms. Through these advancements, the field hopes to gain footing in the ongoing battle against FA.

Overcoming Obstacles in Liquid Biopsy Developments for Prostate Cancer

Prostate cancer is one of the most common malignancies in men. Over time, it can metastasize and become lethal once it exhausts hormonal therapies and transitions into castration-resistant prostate cancer (CRPC). Several therapies have been recently approved for advanced prostate cancer, but identifying biomarkers for current treatments and searching for more effective treatments are urgently needed. Liquid biopsy is a powerful tool for isolating genetic material, proteins, and whole tumor cells from the blood. In recent decades, this technology has rapidly advanced, allowing for better insights into the pathogenesis and treatment response in different stages of prostate cancer. In this review, we summarize important clinical studies involving liquid biopsies in prostate cancer with a focus on advanced disease, notably regarding circulating tumor DNA, circulating tumor cells, and exosomes. We highlight the progress and the challenges that still exist for these technologies. Finally, we discuss promising avenues that will further expand the importance of liquid biopsy in the care for prostate cancer patients.

From Omics to Multi-Omics Approaches for In-Depth Analysis of the Molecular Mechanisms of Prostate Cancer

Cancer arises following alterations at different cellular levels, including genetic and epigenetic modifications, transcription and translation dysregulation, as well as metabolic variations. High-throughput omics technologies that allow one to identify and quantify processes involved in these changes are now available and have been instrumental in generating a wealth of steadily increasing data from patient tumors, liquid biopsies, and from tumor models. Extensive investigation and integration of these data have led to new biological insights into the origin and development of multiple cancer types and helped to unravel the molecular networks underlying this complex pathology. The comprehensive and quantitative analysis of a molecule class in a biological sample is named omics and large-scale omics studies addressing different prostate cancer stages have been performed in recent years. Prostate tumors represent the second leading cancer type and a prevalent cause of cancer death in men worldwide. It is a very heterogenous disease so that evaluating inter- and intra-tumor differences will be essential for a precise insight into disease development and plasticity, but also for the development of personalized therapies. There is ample evidence for the key role of the androgen receptor, a steroid hormone-activated transcription factor, in driving early and late stages of the disease, and this led to the development and approval of drugs addressing diverse targets along this pathway. Early genomic and transcriptomic studies have allowed one to determine the genes involved in prostate cancer and regulated by androgen signaling or other tumor-relevant signaling pathways. More recently, they have been supplemented by epigenomic, cistromic, proteomic and metabolomic analyses, thus, increasing our knowledge on the intricate mechanisms involved, the various levels of regulation and their interplay. The comprehensive investigation of these omics approaches and their integration into multi-omics analyses have led to a much deeper understanding of the molecular pathways involved in prostate cancer progression, and in response and resistance to therapies. This brings the hope that novel vulnerabilities will be identified, that existing therapies will be more beneficial by targeting the patient population likely to respond best, and that bespoke treatments with increased efficacy will be available soon.

The Integration of Metabolomics with Other Omics: Insights into Understanding Prostate Cancer

Our understanding of prostate cancer (PCa) has shifted from solely caused by a few genetic aberrations to a combination of complex biochemical dysregulations with the prostate metabolome at its core. The role of metabolomics in analyzing the pathophysiology of PCa is indispensable. However, to fully elucidate real-time complex dysregulation in prostate cells, an integrated approach based on metabolomics and other omics is warranted. Individually, genomics, transcriptomics, and proteomics are robust, but they are not enough to achieve a holistic view of PCa tumorigenesis. This review is the first of its kind to focus solely on the integration of metabolomics with multi-omic platforms in PCa research, including a detailed emphasis on the metabolomic profile of PCa. The authors intend to provide researchers in the field with a comprehensive knowledge base in PCa metabolomics and offer perspectives on overcoming limitations of the tool to guide future point-of-care applications.

The promising role of new molecular biomarkers in prostate cancer: from coding and non-coding genes to artificial intelligence approaches

Background

Risk stratification or progression in prostate cancer is performed with the support of clinical-pathological data such as the sum of the Gleason score and serum levels PSA. For several decades, methods aimed at the early detection of prostate cancer have included the determination of PSA serum levels. The aim of this systematic review is to provide an overview about recent advances in the discovery of new molecular biomarkers through transcriptomics, genomics and artificial intelligence that are expected to improve clinical management of the prostate cancer patient.

Methods

An exhaustive search was conducted by Pubmed, Google Scholar and Connected Papers using keywords relating to the genetics, genomics and artificial intelligence in prostate cancer, it includes “biomarkers”, “non-coding RNAs”, “lncRNAs”, “microRNAs”, “repetitive sequence”, “prognosis”, “prediction”, “whole-genome sequencing”, “RNA-Seq”, “transcriptome”, “machine learning”, and “deep learning”.

Results

New advances, including the search for changes in novel biomarkers such as mRNAs, microRNAs, lncRNAs, and repetitive sequences, are expected to contribute to an earlier and accurate diagnosis for each patient in the context of precision medicine, thus improving the prognosis and quality of life of patients. We analyze several aspects that are relevant for prostate cancer including its new molecular markers associated with diagnosis, prognosis, and prediction to therapy and how bioinformatic approaches such as machine learning and deep learning can contribute to clinic. Furthermore, we also include current techniques that will allow an earlier diagnosis, such as Spatial Transcriptomics, Exome Sequencing, and Whole-Genome Sequencing.

Conclusion

Transcriptomic and genomic analysis have contributed to generate knowledge in the field of prostate carcinogenesis, new information about coding and non-coding genes as biomarkers has emerged. Synergies created by the implementation of artificial intelligence to analyze and understand sequencing data have allowed the development of clinical strategies that facilitate decision-making and improve personalized management in prostate cancer.

Unravelling Prostate Cancer Heterogeneity Using Spatial Approaches to Lipidomics and Transcriptomics

Simple Summary

Prostate cancer is a heterogenous disease in terms of disease aggressiveness and therapy response, leading to dilemmas in treatment decisions. This heterogeneity reflects the multifocal nature of prostate cancer and its diversity in cellular and molecular composition, necessitating spatial molecular approaches. Here in view of the emerging importance of rewired lipid metabolism as a source of biomarkers and therapeutic targets for prostate cancer, we highlight recent advancements in technologies that enable the spatial mapping of lipids and related metabolic pathways associated with prostate cancer development and progression. We also evaluate their potential for future implementation in treatment decision-making in the clinical management of prostate cancer.

Abstract

Due to advances in the detection and management of prostate cancer over the past 20 years, most cases of localised disease are now potentially curable by surgery or radiotherapy, or amenable to active surveillance without treatment. However, this has given rise to a new dilemma for disease management; the inability to distinguish indolent from lethal, aggressive forms of prostate cancer, leading to substantial overtreatment of some patients and delayed intervention for others. Driving this uncertainty is the critical deficit of novel targets for systemic therapy and of validated biomarkers that can inform treatment decision-making and to select and monitor therapy. In part, this lack of progress reflects the inherent challenge of undertaking target and biomarker discovery in clinical prostate tumours, which are cellularly heterogeneous and multifocal, necessitating the use of spatial analytical approaches. In this review, the principles of mass spectrometry-based lipid imaging and complementary gene-based spatial omics technologies, their application to prostate cancer and recent advancements in these technologies are considered. We put in perspective studies that describe spatially-resolved lipid maps and metabolic genes that are associated with prostate tumours compared to benign tissue and increased risk of disease progression, with the aim of evaluating the future implementation of spatial lipidomics and complementary transcriptomics for prognostication, target identification and treatment decision-making for prostate cancer.

Advances in Multi-Omics Applications in HBV-Associated Hepatocellular Carcinoma

Hepatitis B virus (HBV) specifically infects liver cells, leading to progressive liver cirrhosis and significantly increasing the risk of hepatocellular carcinoma (HCC). The maturity of sequencing technology, improvement in bioinformatics data analysis and progress of omics technologies had improved research efficiency. The occurrence and progression of HCC are affected by multisystem and multilevel pathological changes. With the application of single-omics technologies, including genomics, transcriptomics, metabolomics and proteomics in tissue and body fluid samples, and even the novel development of multi-omics analysis on a single-cell platform, HBV-associated HCC changes can be better analyzed. The review summarizes the application of single omics and combined analysis of multi-omics data in HBV-associated HCC and proposes the importance of multi-omics analysis in the type of HCC, which provide the possibility for the precise diagnosis and therapy of HBV-associated HCC.

Parallel, High-Quality Proteomic and Targeted Metabolomic Quantification Using Laser Capture Microdissected Tissues

Quantitative proteomics/metabolomics investigation of laser-capture-microdissection (LCM) cell populations from clinical cohorts affords precise insights into disease/therapeutic mechanisms, nonetheless high-quality quantification remains a prominent challenge. Here, we devised an LC/MS-based approach allowing parallel, robust global-proteomics and targeted-metabolomics quantification from the same LCM samples, using biopsies from prostate cancer (PCa) patients as the model system. The strategy features: (i) an optimized molecular weight cutoff (MWCO) filter-based separation of proteins and small-molecule fractions with high and consistent recoveries; (ii) microscale derivatization and charge-based enrichment for ultrasensitive quantification of key androgens (LOQ = 5 fg/1k cells) with excellent accuracy/precision; (iii) reproducible/precise proteomics quantification with low-missing-data using a detergent-cocktail-based sample preparation and an IonStar pipeline for reproducible and precise protein quantification with excellent data quality. Key parameters enabling robust/reproducible quantification have been meticulously evaluated and optimized, and the results underscored the importance of surveying quantitative performances against key parameters to facilitate fit-for-purpose method development. As a proof-of-concept, high-quality quantification of the proteome and androgens in LCM samples of PCa patient-matched cancerous and benign epithelial/stromal cells was achieved (N = 16), which suggested distinct androgen distribution patterns across cell types and regions, as well as the dysregulated pathways involved in tumor-stroma crosstalk in PCa pathology. This strategy markedly leverages the scope of quantitative-omics investigations using LCM samples, and combining with IonStar, can be readily adapted to larger-cohort clinical analysis. Moreover, the capacity of parallel proteomics/metabolomics quantification permits precise corroboration of regulatory processes on both protein and small-molecule levels, with decreased batch effect and enhanced utilization of samples.

Establishment of Novel DNA Methylation-Based Prostate Cancer Subtypes and a Risk-Predicting Eight-Gene Signature

Prostate cancer (PCa) is the most common malignant tumor affecting males worldwide. The substantial heterogeneity in PCa presents a major challenge with respect to molecular analyses, patient stratification, and treatment. Least absolute shrinkage and selection operator was used to select eight risk-CpG sites. Using an unsupervised clustering analysis, called consensus clustering, we found that patients with PCa could be divided into two subtypes (Methylation_H and Methylation_L) based on the DNA methylation status at these CpG sites. Differences in the epigenome, genome, transcriptome, disease status, immune cell composition, and function between the identified subtypes were explored using The Cancer Genome Atlas database. This analysis clearly revealed the risk characteristics of the Methylation_H subtype. Using a weighted correlation network analysis to select risk-related genes and least absolute shrinkage and selection operator, we constructed a prediction signature for prognosis based on the subtype classification. We further validated its effectiveness using four public datasets. The two novel PCa subtypes and risk predictive signature developed in this study may be effective indicators of prognosis.