Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis

Constructing metabolism-protein interaction relationship to identify glioma prognosis using deep learning

Glioma is heterogeneous disease that requires classification into subtypes with similar clinical phenotypes, prognosis or treatment responses. Metabolic-protein interaction (MPI) can provide meaningful insights into cancer heterogeneity. Moreover, the potential of lipids and lactate for identifying prognostic subtypes of glioma remains relatively unexplored. Therefore, we proposed a method to construct an MPI relationship matrix (MPIRM) based on a triple-layer network (Tri-MPN) combined with mRNA expression, and processed the MPIRM by deep learning to identify glioma prognostic subtypes. These Subtypes with significant differences in prognosis were detected in glioma (p-value < 2e-16, 95% CI). These subtypes had a strong correlation in immune infiltration, mutational signatures and pathway signatures. This study demonstrated the effectiveness of node interaction from MPI networks in understanding the heterogeneity of glioma prognosis.

Computational Biology Helps Understand How Polyploid Giant Cancer Cells Drive Tumor Success

Precision and organization govern the cell cycle, ensuring normal proliferation. However, some cells may undergo abnormal cell divisions (neosis) or variations of mitotic cycles (endopolyploidy). Consequently, the formation of polyploid giant cancer cells (PGCCs), critical for tumor survival, resistance, and immortalization, can occur. Newly formed cells end up accessing numerous multicellular and unicellular programs that enable metastasis, drug resistance, tumor recurrence, and self-renewal or diverse clone formation. An integrative literature review was carried out, searching articles in several sites, including: PUBMED, NCBI-PMC, and Google Academic, published in English, indexed in referenced databases and without a publication time filter, but prioritizing articles from the last 3 years, to answer the following questions: (i) "What is the current knowledge about polyploidy in tumors?"; (ii) "What are the applications of computational studies for the understanding of cancer polyploidy?"; and (iii) "How do PGCCs contribute to tumorigenesis?"

Explainable domain transfer of distant supervised cancer subtyping model via imaging-based rules extraction

Image texture analysis has for decades represented a promising opportunity for cancer assessment and disease progression evaluation, evolving in a discipline, i.e., radiomics. However, the road to a complete translation into clinical practice is still hampered by intrinsic limitations. As purely supervised classification models fail in devising robust imaging-based biomarkers for prognosis, cancer subtyping approaches would benefit from the employment of distant supervision, for instance exploiting survival/recurrence information. In this work, we assessed, tested, and validated the domain-generality of our previously proposed Distant Supervised Cancer Subtyping model on Hodgkin Lymphoma. We evaluate the model performance on two independent datasets coming from two hospitals, comparing and analyzing the results. Although successful and consistent, the comparison confirmed the instability of radiomics due to an across-center lack of reproducibility, leading to explainable results in one center and poor interpretability in the other. We thus propose a Random Forest-based Explainable Transfer Model for testing the domain-invariance of imaging biomarkers extracted from retrospective cancer subtyping. In doing so, we tested the predictive ability of cancer subtyping in a validation and perspective setting, which led to successful results and supported the domain-generality of the proposed approach. On the other hand, the extraction of decision rules enables to draw of risk factors and robust biomarkers to inform clinical decisions. This work shows the potentialities of the Distant Supervised Cancer Subtyping model to be further evaluated in larger multi-center datasets, to reliably translate radiomics into medical practice. The code is available at this GitHub repository.

Cellular Transcriptomics of Carboplatin Resistance in a Metastatic Canine Osteosarcoma Cell Line

Osteosarcoma prognosis has remained unchanged for the past three decades. In both humans and canines, treatment is limited to excision, radiation, and chemotherapy. Chemoresistance is the primary cause of treatment failure, and the trajectory of tumor evolution while under selective pressure from treatment is thought to be the major contributing factor in both species. We sought to understand the nature of platinum-based chemotherapy resistance by investigating cells that were subjected to repeated treatment and recovery cycles with increased carboplatin concentrations. Three HMPOS-derived cell lines, two resistant and one naïve, underwent single-cell RNA sequencing to examine transcriptomic perturbation and identify pathways leading to resistance and phenotypic changes. We identified the mechanisms of acquired chemoresistance and inferred the induced cellular trajectory that evolved with repeated exposure. The gene expression patterns indicated that acquired chemoresistance was strongly associated with a process similar to epithelial–mesenchymal transition (EMT), a phenomenon associated with the acquisition of migratory and invasive properties associated with metastatic disease. We conclude that the observed trajectory of tumor adaptability is directly correlated with chemoresistance and the phase of the EMT-like phenotype is directly affected by the level of chemoresistance. We infer that the EMT-like phenotype is a critical component of tumor evolution under treatment pressure and is vital to understanding the mechanisms of chemoresistance and to improving osteosarcoma prognosis.

Applying multi-omics toward tumor microbiome research

Rather than a "short-term tenant," the tumor microbiome has been shown to play a vital role as a "permanent resident," affecting carcinogenesis, cancer development, metastasis, and cancer therapies. As the tumor microbiome has great potential to become a target for the early diagnosis and treatment of cancer, recent research on the relevance of the tumor microbiota has attracted a wide range of attention from various scientific fields, resulting in remarkable progress that benefits from the development of interdisciplinary technologies. However, there are still a great variety of challenges in this emerging area, such as the low biomass of intratumoral bacteria and unculturable character of some microbial species. Due to the complexity of tumor microbiome research (e.g., the heterogeneity of tumor microenvironment), new methods with high spatial and temporal resolution are urgently needed. Among these developing methods, multi-omics technologies (combinations of genomics, transcriptomics, proteomics, and metabolomics) are powerful approaches that can facilitate the understanding of the tumor microbiome on different levels of the central dogma. Therefore, multi-omics (especially single-cell omics) will make enormous impacts on the future studies of the interplay between microbes and tumor microenvironment. In this review, we have systematically summarized the advances in multi-omics and their existing and potential applications in tumor microbiome research, thus providing an omics toolbox for investigators to reference in the future.

Genome-wide transcriptomics and copy number profiling identify patient-specific CNV-lncRNA-mRNA regulatory triplets in colorectal cancer

Screening cancer genomes has provided an in-depth characterization of genetic variants such as copy number variations (CNVs) and gene expression changes of non-coding transcripts. Single-dimensional experiments are often designed to differentiate a patient cohort into various sets with the aim of identifying molecular changes among groups; however, this may be inadequate to decipher the causal relationship between molecular signatures in individual patients. To overcome this challenge with respect to personalized medicine, we implemented a patient-specific multi-dimensional integrative approach to uncover coherent signals from multiple independent platforms. In particular, we focused on the consistent gene dosage effects of CNVs for both mRNA and long non-coding RNA (lncRNA) expression in nine colorectal cancer patients. We identified 511 CNV-lncRNA-mRNA regulatory triplets associated with CNVs and aberrant expression of both mRNAs and lncRNAs. By filtering out inconsistent changes among CNVs, mRNAs, and lncRNAs, we further characterized 165 coherent motifs associated with 56 genes. In total, 108 motifs were linked with 31 copy number gains, 44 upregulated lncRNAs, and 45 upregulated mRNAs. Another 57 coherent downregulated motifs were also collected. We discuss how for many of these CNV-lncRNA-mRNA regulatory triplets, their clinical impact remains to be explored, including survival time, microsatellite instability, tumor stage, and primary tumor sites. By validating two example CNV-lncRNA-mRNA triplets with up- and down-regulation, we confirmed that individual variations in multiple dimensions are a robust tool to identify reliable molecular signals for personalized medicine. In summary, we utilized a patient-specific computational pipeline to explore the consistent CNV-driven motifs consisting of lncRNAs and mRNAs. We also identified LSM14B as a potential promoter in colorectal cancer progression, suggesting that it may serve as a target for colorectal cancer treatment.

Evaluation of Two Simultaneous Metabolomic and Proteomic Extraction Protocols Assessed by Ultra-High-Performance Liquid Chromatography Tandem Mass Spectrometry

Untargeted multi-omics analysis of plasma is an emerging tool for the identification of novel biomarkers for evaluating disease prognosis, and for developing a better understanding of molecular mechanisms underlying human disease. The successful application of metabolomic and proteomic approaches relies on reproducibly quantifying a wide range of metabolites and proteins. Herein, we report the results of untargeted metabolomic and proteomic analyses from blood plasma samples following analyte extraction by two frequently-used solvent systems: chloroform/methanol and methanol-only. Whole blood samples were collected from participants (n = 6) at University Hospital Sharjah (UHS) hospital, then plasma was separated and extracted by two methods: (i) methanol precipitation and (ii) 4:3 methanol:chloroform extraction. The coverage and reproducibility of the two methods were assessed by ultra-high-performance liquid chromatography-electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC-ESI-QTOF-MS). The study revealed that metabolite extraction by methanol-only showed greater reproducibility for both metabolomic and proteomic quantifications than did methanol/chloroform, while yielding similar peptide coverage. However, coverage of extracted metabolites was higher with the methanol/chloroform precipitation.

A classification method of gastric cancer subtype based on residual graph convolution network

Background: Clinical diagnosis and treatment of tumors are greatly complicated by their heterogeneity, and the subtype classification of cancer frequently plays a significant role in the subsequent treatment of tumors. Presently, the majority of studies rely far too heavily on gene expression data, omitting the enormous power of multi-omics fusion data and the potential for patient similarities. Method: In this study, we created a gastric cancer subtype classification model called RRGCN based on residual graph convolutional network (GCN) using multi-omics fusion data and patient similarity network. Given the multi-omics data's high dimensionality, we built an artificial neural network Autoencoder (AE) to reduce the dimensionality of the data and extract hidden layer features. The model is then built using the feature data. In addition, we computed the correlation between patients using the Pearson correlation coefficient, and this relationship between patients forms the edge of the graph structure. Four graph convolutional network layers and two residual networks with skip connections make up RRGCN, which reduces the amount of information lost during transmission between layers and prevents model degradation. Results: The results show that RRGCN significantly outperforms other classification methods with an accuracy as high as 0.87 when compared to four other traditional machine learning methods and deep learning models. Conclusion: In terms of subtype classification, RRGCN excels in all areas and has the potential to offer fresh perspectives on disease mechanisms and disease progression. It has the potential to be used for a broader range of disorders and to aid in clinical diagnosis.

Identification of potential microRNAs regulating metabolic plasticity and cellular phenotypes in glioblastoma

MicroRNAs (miRNAs) play important role in regulating cellular metabolism, and are currently being explored in cancer. As metabolic reprogramming in cancer is a major mediator of phenotypic plasticity, understanding miRNA-regulated metabolism will provide opportunities to identify miRNA targets that can regulate oncogenic phenotypes by taking control of cellular metabolism. In the present work, we studied the effect of differentially expressed miRNAs on metabolism, and associated oncogenic phenotypes in glioblastoma (GBM) using patient-derived data. Networks of differentially expressed miRNAs and metabolic genes were created and analyzed to identify important miRNAs that regulate major metabolism in GBM. Graph network-based approaches like network diffusion, backbone extraction, and different centrality measures were used to analyze these networks for identification of potential miRNA targets. Important metabolic processes and cellular phenotypes were annotated to trace the functional responses associated with these miRNA-regulated metabolic genes and associated phenotype networks. miRNA-regulated metabolic gene subnetworks of cellular phenotypes were extracted, and important miRNAs regulating these phenotypes were identified. The most important outcome of the study is the target miRNA combinations predicted for five different oncogenic phenotypes that can be tested experimentally for miRNA-based therapeutic design in GBM. Strategies implemented in the study can be used to generate testable hypotheses in other cancer types as well, and design context-specific miRNA-based therapy for individual patient. Their usability can be further extended to other gene regulatory networks in cancer and other genetic diseases.

Metabolomics-A Tool to Find Metabolism of Endocrine Cancer

Clinical endocrinology entails an understanding of the mechanisms involved in the regulation of tumors that occur in the endocrine system. The exact cause of endocrine cancers remains an enigma, especially when discriminating malignant lesions from benign ones and early diagnosis. In the past few years, the concepts of personalized medicine and metabolomics have gained great popularity in cancer research. In this systematic review, we discussed the clinical metabolomics studies in the diagnosis of endocrine cancers within the last 12 years. Cancer metabolomic studies were largely conducted using nuclear magnetic resonance (NMR) and mass spectrometry (MS) combined with separation techniques such as gas chromatography (GC) and liquid chromatography (LC). Our findings revealed that the majority of the metabolomics studies were conducted on tissue, serum/plasma, and urine samples. Studies most frequently emphasized thyroid cancer, adrenal cancer, and pituitary cancer. Altogether, analytical hyphenated techniques and chemometrics are promising tools in unveiling biomarkers in endocrine cancer and its metabolism disorders.

Lung cancer subtype diagnosis using weakly-paired multi-omics data

MOTIVATION

Cancer subtype diagnosis is crucial for its precise treatment and different subtypes need different therapies. Although the diagnosis can be greatly improved by fusing multiomics data, most fusion solutions depend on paired omics data, which are actually weakly paired, with different omics views missing for different samples. Incomplete multiview learning-based solutions can alleviate this issue but are still far from satisfactory because they: (i) mainly focus on shared information while ignore the important individuality of multiomics data and (ii) cannot pick out interpretable features for precise diagnosis.

RESULTS

We introduce an interpretable and flexible solution (LungDWM) for Lung cancer subtype Diagnosis using Weakly paired Multiomics data. LungDWM first builds an attention-based encoder for each omics to pick out important diagnostic features and extract shared and complementary information across omics. Next, it proposes an individual loss to jointly extract the specific information of each omics and performs generative adversarial learning to impute missing omics of samples using extracted features. After that, it fuses the extracted and imputed features to diagnose cancer subtypes. Experiments on benchmark datasets show that LungDWM achieves a better performance than recent competitive methods, and has a high authenticity and good interpretability.

AVAILABILITY AND IMPLEMENTATION

The code is available at http://www.sdu-idea.cn/codes.php?name=LungDWM.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Multi-omics consensus portfolio to refine the classification of lung adenocarcinoma with prognostic stratification, tumor microenvironment, and unique sensitivity to first-line therapies

BACKGROUND

Molecular classification of lung adenocarcinoma (LUAD) based on transcriptomic features has been widely studied. The complementarity of data obtained from multilayer molecular biology could help the LUAD classification via combining multi-omics information.

METHODS

We successfully divided samples from the The Cancer Genome Atlas (TCGA) (n=437) into four subtypes (CS1, CS2, CS3 and CS4) by 10 comprehensive multi-omics clustering methods in the "movics" R package. Meanwhile, external validation sets from different sequencing technologies proved the robustness of the grouping model. The relationship between subtypes, prognosis, molecular features, tumor microenvironment and response to first-line therapy was further analyzed. Next we used univariate Cox regression analysis and Lasso regression analysis to explore the application of biomarkers in clinical prognosis and constructed a prognostic model.

RESULTS

CS1 showed the worst overall survival (OS) among all four clusters, possibly related to its poor immune infiltration, higher tumor mutation and worse chromosomal stability. Patients in different subtypes differed significantly in cancer stem cell characteristics, activation of cancer-related pathways, sensitivity to chemotherapy and immunotherapy. The prognostic model showed good predictive performance. The 1-, 2- and 3-year areas under the curve of risk score were 0.779, 0.742 and 0.678, respectively. Seven genes (DKK1, TSPAN7, ID1, DLGAP5, HHIPL2, CD40 and SEMA3C) used to build the model may be potential therapeutic targets for LUAD.

CONCLUSIONS

Four LUAD subtypes with different molecular characteristics and clinical implications were identified successfully through bioinformatic analysis. Our results may contribute to precision medicine and inform the development of rational clinical strategies for targeted and immune therapies.

Whole-plant microbiome profiling reveals a novel geminivirus associated with soybean stay-green disease

Microbiota colonize every accessible plant tissue and play fundamental roles in plant growth and health. Soybean stay-green syndrome (SGS), a condition that causes delayed leaf senescence (stay-green), flat pods and abnormal seeds of soybean, has become the most serious disease of soybean in China. However, the direct cause of SGS is highly debated, and little is known about how SGS affect soybean microbiome dynamics, particularly the seed microbiome. We studied the bacterial, fungal, and viral communities associated with different soybean tissues with and without SGS using a multi-omics approach, and investigated the possible pathogenic agents associated with SGS and how SGS affects the assembly and functions of plant-associated microbiomes. We obtained a comprehensive view of the composition, function, loads, diversity, and dynamics of soybean microbiomes in the rhizosphere, root, stem, leaf, pod, and seed compartments, and discovered that soybean SGS was associated with dramatically increased microbial loads and dysbiosis of the bacterial microbiota in seeds. Furthermore, we identified a novel geminivirus that was strongly associated with soybean SGS, regardless of plant cultivar, sampling location, or harvest year. This whole-plant microbiome profiling of soybean provides the first demonstration of geminivirus infection associated with microbiota dysbiosis, which might represent a general microbiological symptom of plant diseases.

Multi-omics analysis: Paving the path toward achieving precision medicine in cancer treatment and immuno-oncology

The acceleration of large-scale sequencing and the progress in high-throughput computational analyses, defined as omics, was a hallmark for the comprehension of the biological processes in human health and diseases. In cancerology, the omics approach, initiated by genomics and transcriptomics studies, has revealed an incredible complexity with unsuspected molecular diversity within a same tumor type as well as spatial and temporal heterogeneity of tumors. The integration of multiple biological layers of omics studies brought oncology to a new paradigm, from tumor site classification to pan-cancer molecular classification, offering new therapeutic opportunities for precision medicine. In this review, we will provide a comprehensive overview of the latest innovations for multi-omics integration in oncology and summarize the largest multi-omics dataset available for adult and pediatric cancers. We will present multi-omics techniques for characterizing cancer biology and show how multi-omics data can be combined with clinical data for the identification of prognostic and treatment-specific biomarkers, opening the way to personalized therapy. To conclude, we will detail the newest strategies for dissecting the tumor immune environment and host–tumor interaction. We will explore the advances in immunomics and microbiomics for biomarker identification to guide therapeutic decision in immuno-oncology.

A critical review of datasets and computational suites for improving cancer theranostics and biomarker discovery

Cancer has been constantly evolving and so is the research pertaining to cancer diagnosis and therapeutic regimens. Early detection and specific therapeutics are the key features of modern cancer therapy. These requirements can only be fulfilled with the integration of diverse high-throughput technologies. Integration of advanced omics methodology involving genomics, epigenomics, proteomics, and transcriptomics provide a clear understanding of multi-faceted cancer. In the past few years, tremendous high-throughput data have been generated from cancer genomics and epigenomic analyses, which on further methodological analyses can yield better biological insights. The major epigenetic alterations reported in cancer are DNA methylation levels, histone post-translational modifications, and epi-miRNA regulating the oncogenes and tumor suppressor genes. While the genomic analyses like gene expression profiling, cancer gene prediction, and genome annotation divulge the genetic alterations in oncogenes or tumor suppressor genes. Also, systems biology approach using biological networks is being extensively used to identify novel cancer biomarkers. Therefore, integration of these multi-dimensional approaches will help to identify potential diagnostic and therapeutic biomarkers. Here, we reviewed the critical databases and tools dedicated to various epigenomic and genomic alterations in cancer. The review further focuses on the multi-omics resources available for further validating the identified cancer biomarkers. We also highlighted the tools for cancer biomarker discovery using a systems biology approach utilizing genomic and epigenomic data. Biomarkers predicted using such integrative approaches are shown to be more clinically relevant.

Advances in the application of proteomics in lung cancer

Although the incidence and mortality of lung cancer have decreased significantly in the past decade, it is still one of the leading causes of death, which greatly impairs people's life and health. Proteomics is an emerging technology that involves the application of techniques for identifying and quantifying the overall proteins in cells, tissues and organisms, and can be combined with genomics, transcriptomics to form a multi-omics research model. By comparing the content of proteins between normal and tumor tissues, proteomics can be applied to different clinical aspects like diagnosis, treatment, and prognosis, especially the exploration of disease biomarkers and therapeutic targets. The applications of proteomics have promoted the research on lung cancer. To figure out potential applications of proteomics associated with lung cancer, we summarized the role of proteomics in studies about tumorigenesis, diagnosis, prognosis, treatment and resistance of lung cancer in this review, which will provide guidance for more rational application of proteomics and potential therapeutic strategies of lung cancer.

The Metabolomic Profile in Amyotrophic Lateral Sclerosis Changes According to the Progression of the Disease: An Exploratory Study

Amyotrophic lateral sclerosis (ALS) is a multifactorial neurodegenerative pathology of the upper or lower motor neuron. Evaluation of ALS progression is based on clinical outcomes considering the impairment of body sites. ALS has been extensively investigated in the pathogenetic mechanisms and the clinical profile; however, no molecular biomarkers are used as diagnostic criteria to establish the ALS pathological staging. Using the source-reconstructed magnetoencephalography (MEG) approach, we demonstrated that global brain hyperconnectivity is associated with early and advanced clinical ALS stages. Using nuclear magnetic resonance (¹H-NMR) and high resolution mass spectrometry (HRMS) spectroscopy, here we studied the metabolomic profile of ALS patients' sera characterized by different stages of disease progression-namely early and advanced. Multivariate statistical analysis of the data integrated with the network analysis indicates that metabolites related to energy deficit, abnormal concentrations of neurotoxic metabolites and metabolites related to neurotransmitter production are pathognomonic of ALS in the advanced stage. Furthermore, analysis of the lipidomic profile indicates that advanced ALS patients report significant alteration of phosphocholine (PCs), lysophosphatidylcholine (LPCs), and sphingomyelin (SMs) metabolism, consistent with the exigency of lipid remodeling to repair advanced neuronal degeneration and inflammation.

Identification of phenolics from miracle berry (Synsepalum dulcificum) leaf extract and its antiangiogenesis and anticancer activities

Miracle berry is well-known for its ability to convert sour foods to sweet. In this study, the secondary metabolites of miracle berry leaves (MBL) were identified by UPLC-DAD-MS, and its antiangiogenesis and anticancer activities were evaluated by using a zebrafish model and the MCF-7 xenograft mouse model, respectively. The result showed that 18 phenolic compounds were identified in MBL extract, and dominated by the derivatives of quercetin and myricetin. The MBL extract showed low toxicity and high antiangiogenesis activity, it significantly inhibited the subintestinal vein vessels development in zebrafish at very low concentration. Furthermore, the MBL extract could promote the apoptosis of tumor cells and significantly inhibit the growth of MCF-7 xenograft tumor. In addition, the analysis of metabolites revealed that the MBL extract inhibited tumor growth by activating the metabolic pathways of unsaturated fatty acids and purines. Overall, this study suggests that MBL extract can be used as a natural anticancer adjuvant in the fields of functional foods.

Advances of Epigenetic Biomarkers and Epigenome Editing for Early Diagnosis in Breast Cancer

Epigenetic modifications are known to regulate cell phenotype during cancer progression, including breast cancer. Unlike genetic alterations, changes in the epigenome are reversible, thus potentially reversed by epi-drugs. Breast cancer, the most common cause of cancer death worldwide in women, encompasses multiple histopathological and molecular subtypes. Several lines of evidence demonstrated distortion of the epigenetic landscape in breast cancer. Interestingly, mammary cells isolated from breast cancer patients and cultured ex vivo maintained the tumorigenic phenotype and exhibited aberrant epigenetic modifications. Recent studies indicated that the therapeutic efficiency for breast cancer regimens has increased over time, resulting in reduced mortality. Future medical treatment for breast cancer patients, however, will likely depend upon a better understanding of epigenetic modifications. The present review aims to outline different epigenetic mechanisms including DNA methylation, histone modifications, and ncRNAs with their impact on breast cancer, as well as to discuss studies highlighting the central role of epigenetic mechanisms in breast cancer pathogenesis. We propose new research areas that may facilitate locus-specific epigenome editing as breast cancer therapeutics.

Integration of transcriptomics, proteomics, and metabolomics data to reveal HER2-associated metabolic heterogeneity in gastric cancer with response to immunotherapy and neoadjuvant chemotherapy

Background

Currently available prognostic tools and focused therapeutic methods result in unsatisfactory treatment of gastric cancer (GC). A deeper understanding of human epidermal growth factor receptor 2 (HER2)-coexpressed metabolic pathways may offer novel insights into tumour-intrinsic precision medicine.

Methods

The integrated multi-omics strategies (including transcriptomics, proteomics and metabolomics) were applied to develop a novel metabolic classifier for gastric cancer. We integrated TCGA-STAD cohort (375 GC samples and 56753 genes) and TCPA-STAD cohort (392 GC samples and 218 proteins), and rated them as transcriptomics and proteomics data, resepectively. 224 matched blood samples of GC patients and healthy individuals were collected to carry out untargeted metabolomics analysis.

Results

In this study, pan-cancer analysis highlighted the crucial role of ERBB2 in the immune microenvironment and metabolic remodelling. In addition, the metabolic landscape of GC indicated that alanine, aspartate and glutamate (AAG) metabolism was significantly associated with the prevalence and progression of GC. Weighted metabolite correlation network analysis revealed that glycolysis/gluconeogenesis (GG) and AAG metabolism served as HER2-coexpressed metabolic pathways. Consensus clustering was used to stratify patients with GC into four subtypes with different metabolic characteristics (i.e. quiescent, GG, AAG and mixed subtypes). The GG subtype was characterised by a lower level of ERBB2 expression, a higher proportion of the inflammatory phenotype and the worst prognosis. However, contradictory features were found in the mixed subtype with the best prognosis. The GG and mixed subtypes were found to be highly sensitive to chemotherapy, whereas the quiescent and AAG subtypes were more likely to benefit from immunotherapy.

Conclusions

Transcriptomic and proteomic analyses highlighted the close association of HER-2 level with the immune status and metabolic features of patients with GC. Metabolomics analysis highlighted the co-expressed relationship between alanine, aspartate and glutamate and glycolysis/gluconeogenesis metabolisms and HER2 level in GC. The novel integrated multi-omics strategy used in this study may facilitate the development of a more tailored approach to GC therapy.

Multimodal Dimension Reduction and Subtype Classification of Head and Neck Squamous Cell Tumors

Traditional analysis of genomic data from bulk sequencing experiments seek to group and compare sample cohorts into biologically meaningful groups. To accomplish this task, large scale databases of patient-derived samples, like that of TCGA, have been established, giving the ability to interrogate multiple data modalities per tumor. We have developed a computational strategy employing multimodal integration paired with spectral clustering and modern dimension reduction techniques such as PHATE to provide a more robust method for cancer sub-type classification. Using this integrated approach, we have examined 514 Head and Neck Squamous Carcinoma (HNSC) tumor samples from TCGA across gene-expression, DNA-methylation, and microbiome data modalities. We show that these approaches, primarily developed for single-cell sequencing can be efficiently applied to bulk tumor sequencing data. Our multimodal analysis captures the dynamic heterogeneity, identifies new and refines subtypes of HNSC, and orders tumor samples along well-defined cellular trajectories. Collectively, these results showcase the inherent molecular complexity of tumors and offer insights into carcinogenesis and importance of targeted therapy. Computational techniques as highlighted in our study provide an organic and powerful approach to identify granular patterns in large and noisy datasets that may otherwise be overlooked.

Capturing the latent space of an Autoencoder for multi-omics integration and cancer subtyping

BACKGROUND AND OBJECTIVE

The motivation behind cancer subtyping is to identify subgroups of cancer patients with distinguishable phenotypes of clinical importance. It can assist in advancement of subtype-targeted based treatments. Subtype identification is a complicated task, therefore requires multi-omics data integration to identify the precise patients' subgroup. Over the years, several computational attempts have been made to identify the cancer subtypes accurately using integrative multi-omics analysis. Some studies have used Autoencoders (AE) to capture multi-omics feature integration in lower dimensions for identifying subtypes in specific types of cancer. However, capturing the highly informative latent space by learning the deep architectures of AE to attain a satisfactory generalized performance is required. Therefore, in this study, a novel AE-assisted cancer subtyping framework is presented that utilizes the compressed latent space of a Sparse AE neural network for multi-omics clustering.

METHODS

The proposed framework first performs a supervised feature selection based on the survival status of the patients. The selected features from each of the omic data are passed to the AE. The information embedded in the latent space of the trained AE neural networks are then used for cancer subtyping using Spectral clustering. The AE architecture designed in this study exhaustively searches the best compression for multi-omics data by varying the number of neurons in the hidden layers and penalizing activations within the layers.

RESULTS AND CONCLUSION

The proposed framework is applied to five different multi-omics cancer datasets taken from The Cancer Genome Atlas. It is observed that for getting a robust information bottleneck, a compression of 10-20% of the input features along with an L1 regularization penalty of 0.01 or 0.001 performs well for most of the cancer datasets. Clustering performed on this latent representation generates clusters with better silhouette scores and significantly varying survival patterns. For further biological assessment, differential expression analysis is performed between the identified subtypes of Glioblastoma multiforme (GBM), followed by enrichment analysis of the differentially expressed biomarkers. Several pathways and disease ontology terms coherent to GBM are found to be significantly associated. Varying responses of the identified GBM subtypes towards the drug Temozolomide is also tested to demonstrate its clinical importance. Hence, the study shows that AE-assisted multi-omics integration can be used for the prediction of clinically significant cancer subtypes.

Molecular Modelling Hurdle in the Next-Generation Sequencing Era

There are challenges in the genetic diagnosis of rare diseases, and pursuing an optimal strategy to identify the cause of the disease is one of the main objectives of any clinical genomics unit. A range of techniques are currently used to characterize the genomic variability within the human genome to detect causative variants of specific disorders. With the introduction of next-generation sequencing (NGS) in the clinical setting, geneticists can study single-nucleotide variants (SNVs) throughout the entire exome/genome. In turn, the number of variants to be evaluated per patient has increased significantly, and more information has to be processed and analyzed to determine a proper diagnosis. Roughly 50% of patients with a Mendelian genetic disorder are diagnosed using NGS, but a fair number of patients still suffer a diagnostic odyssey. Due to the inherent diversity of the human population, as more exomes or genomes are sequenced, variants of uncertain significance (VUSs) will increase exponentially. Thus, assigning relevance to a VUS (non-synonymous as well as synonymous) in an undiagnosed patient becomes crucial to assess the proper diagnosis. Multiple algorithms have been used to predict how a specific mutation might affect the protein's function, but they are far from accurate enough to be conclusive. In this work, we highlight the difficulties of genomic variability determined by NGS that have arisen in diagnosing rare genetic diseases, and how molecular modelling has to be a key component to elucidate the relevance of a specific mutation in the protein's loss of function or malfunction. We suggest that the creation of a multi-omics data model should improve the classification of pathogenicity for a significant amount of the detected genomic variability. Moreover, we argue how it should be incorporated systematically in the process of variant evaluation to be useful in the clinical setting and the diagnostic pipeline.

RAF1 promotes lymphatic metastasis of hypopharyngeal carcinoma via regulating LAGE1: an experimental research

BACKGROUND

Lymphatic metastasis was an independent prognostic risk factor for hypopharyngeal carcinoma and was the main cause of treatment failure. The purpose of this study was to screen the differential genes and investigate the mechanism of lymphatic metastasis in hypopharyngeal carcinoma.

METHODS

Transcriptome sequencing was performed on primary tumors of patients, and differential genes were screened by bioinformatics analysis. The expression of differential genes was verified by qRT-PCR, western-blotting and immunohistochemical, and prognostic value was analyzed by Kaplan-Meier and log-rank test and Cox's test. Next, FADU and SCC15 cell lines were used to demonstrate the function of differential genes both in vitro by EdU, Flow cytometry, Wound Healing and Transwell assays and in vivo by a foot-pad xenograft mice model. Proteomic sequencing was performed to screen relevant targets. In addition, in vitro and in vivo experiments were conducted to verify the mechanism of lymphatic metastasis.

RESULTS

Results of transcriptome sequencing showed that RAF1 was a significantly differential gene in lymphatic metastasis and was an independent prognostic risk factor. In vitro experiments suggested that decreased expression of RAF1 could inhibit proliferation, migration and invasion of tumor cells and promote apoptosis. In vivo experiments indicated that RAF1 could promote tumor growth and lymphatic metastasis. Proteomic sequencing and subsequent experiments suggested that LAGE1 could promote development of tumor and lymphatic metastasis, and was regulated by RAF1.

CONCLUSIONS

It suggests that RAF1 can promote lymphatic metastasis of hypopharyngeal carcinoma by regulating LAGE1, and provide a basis for the exploring of novel therapeutic target and ultimately provide new guidance for the establishment of intelligent diagnosis and precise treatment of hypopharyngeal carcinoma.

Correlation-guided Network Integration (CoNI), an R package for integrating numerical omics data that allows multiform graph representations to study molecular interaction networks

Summary

Today's immense growth in complex biological data demands effective and flexible tools for integration, analysis and extraction of valuable insights. Here, we present CoNI, a practical R package for the unsupervised integration of numerical omics datasets. Our tool is based on partial correlations to identify putative confounding variables for a set of paired dependent variables. CoNI combines two omics datasets in an integrated, complex hypergraph-like network, represented as a weighted undirected graph, a bipartite graph, or a hypergraph structure. These network representations form a basis for multiple further analyses, such as identifying priority candidates of biological importance or comparing network structures dependent on different conditions.

Availability and implementation

The R package CoNI is available on the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/CoNI/) and GitLab (https://gitlab.com/computational-discovery-research/coni). It is distributed under the GNU General Public License (version 3).

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Application of informatics in cancer research and clinical practice: Opportunities and challenges

Cancer informatics has significantly progressed in the big data era. We summarize the application of informatics approaches to the cancer domain from both the informatics perspective (e.g., data management and data science) and the clinical perspective (e.g., cancer screening, risk assessment, diagnosis, treatment, and prognosis). We discuss various informatics methods and tools that are widely applied in cancer research and practices, such as cancer databases, data standards, terminologies, high-throughput omics data mining, machine-learning algorithms, artificial intelligence imaging, and intelligent radiation. We also address the informatics challenges within the cancer field that pursue better treatment decisions and patient outcomes, and focus on how informatics can provide opportunities for cancer research and practices. Finally, we conclude that the interdisciplinary nature of cancer informatics and collaborations are major drivers for future research and applications in clinical practices. It is hoped that this review is instrumental for cancer researchers and clinicians with its informatics-specific insights.

Structurally related (-)-epicatechin metabolites and gut microbiota derived metabolites exert genomic modifications via VEGF signaling pathways in brain microvascular endothelial cells under lipotoxic conditions: Integrated multi-omic study

Dysfunction of blood-brain barrier formed by endothelial cells of cerebral blood vessels, plays a key role in development of neurodegenerative disorders. Epicatechin exerts vasculo-protective effects through genomic modifications, however molecular mechanisms of action, particularly on brain endothelial cells, are largely unknow. This study aimed to use a multi-omic approach (transcriptomics of mRNA, miRNAs and lncRNAs, and proteomics), to provide novel in-depth insights into molecular mechanisms of how metabolites affect brain endothelial cells under lipid-stressed (as a model of BBB dysfunction) at physiological concentrations. We showed that metabolites can simultaneously modulate expression of protein-coding, non-coding genes and proteins. Integrative analysis revealed interactions between different types of RNAs and form functional groups of genes involved in regulation of processing like VEGF-related functions, cell signaling, cell adhesion and permeability. Molecular modeling of genomics data predicted that metabolites decrease endothelial cell permeability, increased by lipotoxic stress. Correlation analysis between genomic modifications observed and genomic signature of patients with vascular dementia and Alzheimer's diseases showed opposite gene expression changes. Taken together, this study describes for the first time a multi-omic mechanism of action by which (-)-epicatechin metabolites could preserve brain vascular endothelial cell integrity and reduce the risk of neurodegenerative diseases. SIGNIFICANCE: Dysfunction of the blood-brain barrier (BBB), characterized by dysfunction of endothelial cells of cerebral blood vessels, result in an increase in permeability and neuroinflammation which constitute a key factor in the development neurodegenerative disorders. Even though it is suggested that polyphenols can prevent or delay the development of these disorders, their impact on brain endothelial cells and underlying mechanisms of actions are unknow. This study aimed to use a multi-omic approach including analysis of expression of mRNA, microRNA, long non-coding RNAs, and proteins to provide novel global in-depth insights into molecular mechanisms of how (-)-epicatechin metabolites affect brain microvascular endothelial cells under lipid-stressed (as a model of BBB dysfunction) at physiological relevant conditions. The results provide basis of knowledge on the capacity of polyphenols to prevent brain endothelial dysfunction and consequently neurodegenerative disorders.

Identification of Tumor Mutation Burden, Microsatellite Instability, and Somatic Copy Number Alteration Derived Nine Gene Signatures to Predict Clinical Outcomes in STAD

Genomic features, including tumor mutation burden (TMB), microsatellite instability (MSI), and somatic copy number alteration (SCNA), had been demonstrated to be involved with the tumor microenvironment (TME) and outcome of gastric cancer (GC). We obtained profiles of TMB, MSI, and SCNA by processing 405 GC data from The Cancer Genome Atlas (TCGA) and then conducted a comprehensive analysis though “iClusterPlus.” A total of two subgroups were generated, with distinguished prognosis, somatic mutation burden, copy number changes, and immune landscape. We revealed that Cluster1 was marked by a better prognosis, accompanied by higher TMB, MSIsensor score, TMEscore, and lower SCNA burden. Based on these clusters, we screened 196 differentially expressed genes (DEGs), which were subsequently projected into univariate Cox survival analysis. We constructed a 9-gene immune risk score (IRS) model using LASSO-penalized logistic regression. Moreover, the prognostic prediction of IRS was verified by receiver operating characteristic (ROC) curve analysis and nomogram plot. Another independent Gene Expression Omnibus (GEO) contained specimens from 109 GC patients was designed as an external validation. Our works suggested that the 9‐gene‐signature prediction model, which was derived from TMB, MSI, and SCNA, was a promising predictive tool for clinical outcomes in GC patients. This novel methodology may help clinicians uncover the underlying mechanisms and guide future treatment strategies.

DRPPM-EASY: A Web-Based Framework for Integrative Analysis of Multi-Omics Cancer Datasets

High-throughput transcriptomic and proteomic analyses are now routinely applied to study cancer biology. However, complex omics integration remains challenging and often time-consuming. Here, we developed DRPPM-EASY, an R Shiny framework for integrative multi-omics analysis. We applied our application to analyze RNA-seq data generated from a USP7 knockdown in T-cell acute lymphoblastic leukemia (T-ALL) cell line, which identified upregulated expression of a TAL1-associated proliferative signature in T-cell acute lymphoblastic leukemia cell lines. Next, we performed proteomic profiling of the USP7 knockdown samples. Through DRPPM-EASY-Integration, we performed a concurrent analysis of the transcriptome and proteome and identified consistent disruption of the protein degradation machinery and spliceosome in samples with USP7 silencing. To further illustrate the utility of the R Shiny framework, we developed DRPPM-EASY-CCLE, a Shiny extension preloaded with the Cancer Cell Line Encyclopedia (CCLE) data. The DRPPM-EASY-CCLE app facilitates the sample querying and phenotype assignment by incorporating meta information, such as genetic mutation, metastasis status, sex, and collection site. As proof of concept, we verified the expression of TP53 associated DNA damage signature in TP53 mutated ovary cancer cells. Altogether, our open-source application provides an easy-to-use framework for omics exploration and discovery.

Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer

Cancer is defined as a large group of diseases that is associated with abnormal cell growth, uncontrollable cell division, and may tend to impinge on other tissues of the body by different mechanisms through metastasis. What makes cancer so important is that the cancer incidence rate is growing worldwide which can have major health, economic, and even social impacts on both patients and the governments. Thereby, the early cancer prognosis, diagnosis, and treatment can play a crucial role at the front line of combating cancer. The onset and progression of cancer can occur under the influence of complicated mechanisms and some alterations in the level of genome, proteome, transcriptome, metabolome etc. Consequently, the advent of omics science and its broad research branches (such as genomics, proteomics, transcriptomics, metabolomics, and so forth) as revolutionary biological approaches have opened new doors to the comprehensive perception of the cancer landscape. Due to the complexities of the formation and development of cancer, the study of mechanisms underlying cancer has gone beyond just one field of the omics arena. Therefore, making a connection between the resultant data from different branches of omics science and examining them in a multi-omics field can pave the way for facilitating the discovery of novel prognostic, diagnostic, and therapeutic approaches. As the volume and complexity of data from the omics studies in cancer are increasing dramatically, the use of leading-edge technologies such as machine learning can have a promising role in the assessments of cancer research resultant data. Machine learning is categorized as a subset of artificial intelligence which aims to data parsing, classification, and data pattern identification by applying statistical methods and algorithms. This acquired knowledge subsequently allows computers to learn and improve accurate predictions through experiences from data processing. In this context, the application of machine learning, as a novel computational technology offers new opportunities for achieving in-depth knowledge of cancer by analysis of resultant data from multi-omics studies. Therefore, it can be concluded that the use of artificial intelligence technologies such as machine learning can have revolutionary roles in the fight against cancer.

Functional metabolome profiling may improve individual outcomes in colorectal cancer management implementing concepts of predictive, preventive, and personalized medical approach

Objectives

Colorectal cancer (CRC) is one of the most common solid tumors worldwide, but its diagnosis and treatment are limited. The objectives of our study were to compare the metabolic differences between CRC patients and healthy controls (HC), and to identify potential biomarkers in the serum that can be used for early diagnosis and as effective therapeutic targets. The aim was to provide a new direction for CRC predictive, preventive, and personalized medicine (PPPM).

Methods

In this study, CRC patients (n = 30) and HC (n = 30) were recruited. Serum metabolites were assayed using an ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) technology. Subsequently, CRC cell lines (HCT116 and HCT8) were treated with metabolites to verify their function. Key targets were identified by molecular docking, thermal shift assay, and protein overexpression/inhibition experiments. The inhibitory effect of celastrol on tumor growth was also assessed, which included IC50 analysis, nude mice xenografting, molecular docking, protein overexpression/inhibition experiments, and network pharmacology technology.

Results

In the CRC group, 15 serum metabolites were significantly different in comparison with the HC group. The level of glycodeoxycholic acid (GDCA) was positively correlated with CRC and showed high sensitivity and specificity for the clinical diagnostic reference (AUC = 0.825). In vitro findings showed that GDCA promoted the proliferation and migration of CRC cell lines (HCT116 and HCT8), and Poly(ADP-ribose) polymerase-1 (PARP-1) was identified as one of the key targets of GDCA. The IC50 of celastrol in HCT116 cells was 121.1 nM, and the anticancer effect of celastrol was supported by in vivo experiments. Based on the potential of GDCA in PPPM, PARP-1 was found to be significantly correlated with the anticancer functions of celastrol.

Conclusion

These findings suggest that GDCA is an abnormally produced metabolite of CRC, which may provide an innovative molecular biomarker for the predictive identification and targeted prevention of CRC. In addition, PARP-1 was found to be an important target of GDCA that promotes CRC; therefore, celastrol may be a potential targeted therapy for CRC via its effects on PARP-1. Taken together, the pathophysiology and progress of tumor molecules mediated by changes in metabolite content provide a new perspective for predictive, preventive, and personalized medical of clinical cancer patients based on the target of metabolites in vivo.Clinical trials registration number: ChiCTR2000039410.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-021-00269-8.

Metabolic Phenotyping in Prostate Cancer Using Multi-Omics Approaches

Prostate cancer (PCa), one of the most frequently diagnosed cancers among men worldwide, is characterized by a diverse biological heterogeneity. It is well known that PCa cells rewire their cellular metabolism to meet the higher demands required for survival, proliferation, and invasion. In this context, a deeper understanding of metabolic reprogramming, an emerging hallmark of cancer, could provide novel opportunities for cancer diagnosis, prognosis, and treatment. In this setting, multi-omics data integration approaches, including genomics, epigenomics, transcriptomics, proteomics, lipidomics, and metabolomics, could offer unprecedented opportunities for uncovering the molecular changes underlying metabolic rewiring in complex diseases, such as PCa. Recent studies, focused on the integrated analysis of multi-omics data derived from PCa patients, have in fact revealed new insights into specific metabolic reprogramming events and vulnerabilities that have the potential to better guide therapy and improve outcomes for patients. This review aims to provide an up-to-date summary of multi-omics studies focused on the characterization of the metabolomic phenotype of PCa, as well as an in-depth analysis of the correlation between changes identified in the multi-omics studies and the metabolic profile of PCa tumors.

Recent Multiomics Approaches in Endometrial Cancer

Endometrial cancer is the most common gynecological cancers in developed countries. Many of the mechanisms involved in its initiation and progression remain unclear. Analysis providing comprehensive data on the genome, transcriptome, proteome, and epigenome could help in selecting molecular markers and targets in endometrial cancer. Multiomics approaches can reveal disturbances in multiple biological systems, giving a broader picture of the problem. However, they provide a large amount of data that require processing and further integration prior to analysis. There are several repositories of multiomics datasets, including endometrial cancer data, as well as portals allowing multiomics data analysis and visualization, including Oncomine, UALCAN, LinkedOmics, and miRDB. Multiomics approaches have also been applied in endometrial cancer research in order to identify novel molecular markers and therapeutic targets. This review describes in detail the latest findings on multiomics approaches in endometrial cancer.

Multi-omics data integration approaches for precision oncology

Next-generation sequencing (NGS) has been pivotal to enhance the molecular characterization of human malignancies, allowing multiple omics data types to be available for cancer researchers and practitioners. In this context,...

Development of computational models using omics data for the identification of effective cancer metabolic biomarkers

Identification of novel biomarkers has been an active area of study for the effective diagnosis, prognosis and treatment of cancers. Among various types of cancer biomarkers, metabolic biomarkers, including enzymes, metabolites and metabolic genes, deserve attention as they can serve as a reliable source for diagnosis, prognosis and treatment of cancers. In particular, efforts to identify novel biomarkers have been greatly facilitated by a rapid increase in the volume of multiple omics data generated for a range of cancer cells. These omics data in turn serve as ingredients for developing computational models that can help derive deeper insights into the biology of cancer cells, and identify metabolic biomarkers. In this review, we provide an overview of omics data generated for cancer cells, and discuss recent studies on computational models that were developed using omics data in order to identify effective cancer metabolic biomarkers.

PRL-3 dephosphorylates p38 MAPK to promote cell survival under stress

Hypoxia within the tumor microenvironment, which leads to excessive ROS and genomic instability, is one of the hallmarks of cancer, contributing to self-renewal capability, metastasis, and radio-chemotherapy resistance. PRL-3 is an oncoprotein involved in various pro-survival signaling pathways, such as Ras/Erk, PI3K/Akt, Src/STAT, mTORC1 and JAK/STAT. However, there is little evidence connecting PRL-3-mediated apoptosis resistance to tumor microenvironmental stress. In this study, by profiling the PRL-3 expression of multiple tumor types retrieved from public databases (TCGA and NCBI GEO), we confirmed the oncogenic function of PRL-3 and found an intriguing connection between PRL-3 expression and tumor hypoxia signature genes. Moreover, by using CoCl2, a hypoxia mimetic and ROS inducer, we discovered that cells stably expressing PRL-3, but not catalytically-inactive mutant PRL-3 C104S, showed significant resistance to CoCl2 -induced apoptosis. This resistance to apoptosis was found to depend on p38 MAPK signaling and was further confirmed in other conditions of microenvironmental stress, including UV, H2O2 and hypoxia. Mechanistically, we proved that PRL-3 is a direct phosphatase of p38 MAPK under stressed conditions. Additionally, in mouse models of tumor metastasis, higher lung metastatic burden and lower p38 MAPK phosphorylation were found in mice seeded with GFP-PRL-3 expressing cells compared with those seeded with GFP-Ctrl cells. Taken together, our study identified a critical role of RPL-3 in tumorigenesis by negatively regulating p38 MAPK activity in order to facilitate tumor cell adaptation to a hypoxic stressed tumor microenvironment and suggests that PRL-3 could serve as a promising novel therapeutic target for cancer patients.

Molecular alterations that precede the establishment of the hallmarks of cancer: An approach on the prevention of hepatocarcinogenesis

Chronic liver injury promotes the molecular alterations that precede the establishment of cancer. Usually, several decades of chronic insults are needed to develop the most common primary liver tumor known as hepatocellular carcinoma. As other cancer types, liver cancer cells are governed by a common set of rules collectively called the hallmarks of cancer. Although those rules have provided a conceptual framework for understanding the complex pathophysiology of established tumors, therapeutic options are still ineffective in advanced stages. Thus, the molecular alterations that precede the establishment of cancer remain an attractive target for therapeutic interventions. Here, we first summarize the chemopreventive interventions targeting the early liver carcinogenesis stages. After an integrative analysis on the plethora of molecular alterations regulated by anticancer agents, we then underline and discuss that two critical processes namely oxidative stress and genetic alterations, play the role of 'dirty work laborer' in the initial cell damage and drive the transformation of preneoplastic into neoplastic cells, respectively; besides, the activation of cellular senescence works as a key mechanism in attempting to prevent the onset and establishment of liver cancer. Whereas the detrimental effects of the binomial made up of oxidative stress and genetic alterations are either eliminated or reduced, senescence activation is promoted by anticancer agents. We argue that collectively, oxidative stress, genetic alterations, and senescence are key events that influence the fate of initiated cells and the establishment of the hallmarks of cancer.

Computational approaches leveraging integrated connections of multi-omic data toward clinical applications

In line with the advances in high-throughput technologies, multiple omic datasets have accumulated to study biological systems and diseases coherently. No single omics data type is capable of fully representing cellular activity. The complexity of the biological processes arises from the interactions between omic entities such as genes, proteins, and metabolites. Therefore, multi-omic data integration is crucial but challenging. The impact of the molecular alterations in multi-omic data is not local in the neighborhood of the altered gene or protein; rather, the impact diffuses in the network and changes the functionality of multiple signaling pathways and regulation of the gene expression. Additionally, multi-omic data is high-dimensional and has background noise. Several integrative approaches have been developed to accurately interpret the multi-omic datasets, including machine learning, network-based methods, and their combination. In this review, we overview the most recent integrative approaches and tools with a focus on network-based methods. We then discuss these approaches according to their specific applications, from disease-network and biomarker identification to patient stratification, drug discovery, and repurposing.

Characterizing the breast cancer lipidome and its interaction with the tissue microbiota

Breast cancer is the most diagnosed cancer amongst women worldwide. We have previously shown that there is a breast microbiota which differs between women who have breast cancer and those who are disease-free. To better understand the local biochemical perturbations occurring with disease and the potential contribution of the breast microbiome, lipid profiling was performed on non-tumor breast tissue collected from 19 healthy women and 42 with breast cancer. Here we identified unique lipid signatures between the two groups with greater amounts of lysophosphatidylcholines and oxidized cholesteryl esters in the tissue from women with breast cancer and lower amounts of ceramides, diacylglycerols, phosphatidylcholines, and phosphatidylethanolamines. By integrating these lipid signatures with the breast bacterial profiles, we observed that Gammaproteobacteria and those from the class Bacillus, were negatively correlated with ceramides, lipids with antiproliferative properties. In the healthy tissues, diacylglyerols were positively associated with Acinetobacter, Lactococcus, Corynebacterium, Prevotella and Streptococcus. These bacterial groups were found to possess the genetic potential to synthesize these lipids. The cause-effect relationships of these observations and their contribution to disease patho-mechanisms warrants further investigation for a disease afflicting millions of women around the world.

The Role of Omics Approaches to Characterize Molecular Mechanisms of Rare Ovarian Cancers: Recent Advances and Future Perspectives

Rare ovarian cancers are ovarian cancers with an annual incidence of less than 6 cases per 100,000 women. They generally have a poor prognosis due to being delayed diagnosis and treatment. Exploration of molecular mechanisms in these cancers has been challenging due to their rarity and research efforts being fragmented across the world. Omics approaches can provide detailed molecular snapshots of the underlying mechanisms of these cancers. Omics approaches, including genomics, transcriptomics, proteomics, and metabolomics, can identify potential candidate biomarkers for diagnosis, prognosis, and screening of rare gynecological cancers and can aid in identifying therapeutic targets. The integration of multiple omics techniques using approaches such as proteogenomics can provide a detailed understanding of the molecular mechanisms of carcinogenesis and cancer progression. Further, omics approaches can provide clues towards developing immunotherapies, cancer recurrence, and drug resistance in tumors; and form a platform for personalized medicine. The current review focuses on the application of omics approaches and integrative biology to gain a better understanding of rare ovarian cancers.

Profiling of Carnitine Shuttle System Intermediates in Gliomas Using Solid-Phase Microextraction (SPME)

Alterations in the carnitine shuttle system may be an indication of the presence of cancer. As such, in-depth analyses of this pathway in different malignant tumors could be important for the detection and treatment of this disease. The current study aims to assess the profiles of carnitine and acylcarnitines in gliomas with respect to their grade, the presence of isocitrate dehydrogenase (IDH) mutations, and 1p/19q co-deletion. Brain tumors obtained from 19 patients were sampled on-site using solid-phase microextraction (SPME) immediately following excision. Analytes were desorbed and then analyzed via liquid chromatography–high-resolution mass spectrometry. The results showed that SPME enabled the extraction of carnitine and 22 acylcarnitines. An analysis of the correlation factor revealed the presence of two separate clusters: short-chain and long-chain carnitine esters. Slightly higher carnitine and acylcarnitine concentrations were observed in the higher-malignancy tumor samples (high vs. low grade) and in those samples with worse projected clinical outcomes (without vs. with IDH mutation; without vs. with 1p/19q co-deletion). Thus, the proposed chemical biopsy approach offers a simple solution for on-site sampling that enables sample preservation, thus supporting comprehensive multi-method analyses.

Microbiome Assisted Tumor Microenvironment: Emerging Target of Breast Cancer

The microbiome assisted tumor microenvironment (TME) supports the tumors by modulating multiple mechanisms. Recent studies reported that microbiome dysbiosis is the main culprit of immune suppressive phenotypes of TME. Further, it has been documented that immune suppressive stimulate metastatic phenotype in TME via modulating signaling pathways, cell differentiation, and innate immune response. This review aims at providing comprehensive developments in microbiome and breast TME interface. The combination of microbiome and breast cancer, breast TME and microbiome or microbial dysbiosis, microbiome and risk of breast cancer, microbiome and phytochemicals or anticancer drugs were as used keywords to retrieve literature from PubMed, Google scholar, Scopus, Web of Science from 2015 onwards. Based on the literature, we presented the impact of TME assisted microbiome dysbiosis and estrobolome in breast cancer risk, drug resistance, and antitumor immunity. We have discussed the influence of antibiotics on the breast microbiome. we also presented the possible dietary phytochemicals that target microbiome dysbiosis to restore the tumor suppression immune environment in breast TME. We presented the microbiome as a possible marker for breast cancer diagnosis. This study will help in the identification of microbiome as a novel target and diagnostic markers and phytochemicals and microbiome metabolites for breast cancer treatment.

Multi-Omic Approaches to Breast Cancer Metabolic Phenotyping: Applications in Diagnosis, Prognosis, and the Development of Novel Treatments

Breast cancer (BC) is characterized by high disease heterogeneity and represents the most frequently diagnosed cancer among women worldwide. Complex and subtype-specific gene expression alterations participate in disease development and progression, with BC cells known to rewire their cellular metabolism to survive, proliferate, and invade. Hence, as an emerging cancer hallmark, metabolic reprogramming holds great promise for cancer diagnosis, prognosis, and treatment. Multi-omics approaches (the combined analysis of various types of omics data) offer opportunities to advance our understanding of the molecular changes underlying metabolic rewiring in complex diseases such as BC. Recent studies focusing on the combined analysis of genomics, epigenomics, transcriptomics, proteomics, and/or metabolomics in different BC subtypes have provided novel insights into the specificities of metabolic rewiring and the vulnerabilities that may guide therapeutic development and improve patient outcomes. This review summarizes the findings of multi-omics studies focused on the characterization of the specific metabolic phenotypes of BC and discusses how they may improve clinical BC diagnosis, subtyping, and treatment.

Integrative Transcriptomic, Lipidomic, and Metabolomic Analysis Reveals Potential Biomarkers of Basal and Luminal Muscle Invasive Bladder Cancer Subtypes

Muscle invasive bladder cancer (MIBC) is a heterogeneous disease with a high recurrence rate and poor clinical outcomes. Molecular subtype provides a new framework for the study of MIBC heterogeneity. Clinically, MIBC can be classified as basal and luminal subtypes; they display different clinical and pathological characteristics, but the molecular mechanism is still unclear. Lipidomic and metabolomic molecules have recently been considered to play an important role in the genesis and development of tumors, especially as potential biomarkers. Their different expression profiles in basal and luminal subtypes provide clues for the molecular mechanism of basal and luminal subtypes and the discovery of new biomarkers. Herein, we stratified MIBC patients into basal and luminal subtypes using a MIBC classifier based on transcriptome expression profiles. We qualitatively and quantitatively analyzed the lipids and metabolites of basal and luminal MIBC subtypes and identified their differential lipid and metabolite profiles. Our results suggest that free fatty acids (FFAs) and sulfatides (SLs), which are closely associated with immune and stromal cell types, can contribute to the diagnosis of basal and luminal subtypes of MIBC. Moreover, we showed that glycerophosphocholine (GCP)/imidazoles and nucleosides/imidazoles ratios can accurately distinguish the basal and luminal tumors. Overall, by integrating transcriptomic, lipidomic, and metabolomic data, our study reveals specific biomarkers to differentially diagnose basal and luminal MIBC subtypes and may provide a basis for precision therapy of MIBC.

The Comprehensive "Omics" Approach from Metabolomics to Advanced Omics for Development of Immune Checkpoint Inhibitors: Potential Strategies for Next Generation of Cancer Immunotherapy

In the past decade, immunotherapies have been emerging as an effective way to treat cancer. Among several categories of immunotherapies, immune checkpoint inhibitors (ICIs) are the most well-known and widely used options for cancer treatment. Although several studies continue, this treatment option has yet to be developed into a precise application in the clinical setting. Recently, omics as a high-throughput technique for understanding the genome, transcriptome, proteome, and metabolome has revolutionized medical research and led to integrative interpretation to advance our understanding of biological systems. Advanced omics techniques, such as multi-omics, single-cell omics, and typical omics approaches, have been adopted to investigate various cancer immunotherapies. In this review, we highlight metabolomic studies regarding the development of ICIs involved in the discovery of targets or mechanisms of action and assessment of clinical outcomes, including drug response and resistance and propose biomarkers. Furthermore, we also discuss the genomics, proteomics, and advanced omics studies providing insights and comprehensive or novel approaches for ICI development. The overview of ICI studies suggests potential strategies for the development of other cancer immunotherapies using omics techniques in future studies.

Omics in urology: An overview on concepts, current status and future perspectives

Recent technological advances in molecular biology have led to great progress in the knowledge of structure and function of cells and their main constituents. In this setting, 'omics' is standing out in order to significantly improve the understanding of etiopathogenetic mechanisms of disease and contribute to the development of new biochemical diagnostics and therapeutic tools. 'Omics' indicates the scientific branches investigating every aspect of cell's biology, including structures, functions and dynamics pathways. The main 'omics' are genomics, epigenomics, proteomics, transcriptomics, metabolomics and radiomics. Their diffusion, success and proliferation, addressed to many research fields, has led to many important acquisitions, even in Urology. Aim of this narrative review is to define the state of art of 'omics' application in Urology, describing the most recent and relevant findings, in both oncological and non-oncological diseases, focusing the attention on urinary tract infectious, interstitial cystitis, urolithiasis, prostate cancer, bladder cancer and renal cell carcinoma. In Urology the majority of 'omics' applications regard the pathogenesis and diagnosis of the investigated diseases. In future, its role should be implemented in order to develop specific predictors and tailored treatments.

Integration strategies of multi-omics data for machine learning analysis

Increased availability of high-throughput technologies has generated an ever-growing number of omics data that seek to portray many different but complementary biological layers including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. New insight from these data have been obtained by machine learning algorithms that have produced diagnostic and classification biomarkers. Most biomarkers obtained to date however only include one omic measurement at a time and thus do not take full advantage of recent multi-omics experiments that now capture the entire complexity of biological systems.

Multi-omics data integration strategies are needed to combine the complementary knowledge brought by each omics layer. We have summarized the most recent data integration methods/ frameworks into five different integration strategies: early, mixed, intermediate, late and hierarchical. In this mini-review, we focus on challenges and existing multi-omics integration strategies by paying special attention to machine learning applications.