CMBD: a manually curated cancer metabolic biomarker knowledge database

Systems biology approach delineates critical pathways associated with papillary thyroid cancer: a multi-omics data analysis

BACKGROUND

Papillary thyroid cancer (PTC) is the most prevalent follicular cell-derived subtype of thyroid cancer. A systems biology approach to PTC can elucidate the mechanism by which molecular components work and interact with one another to decipher a panoramic view of the pathophysiology.

METHODOLOGY

PTC associated genes and transcriptomic data were retrieved from DisGeNET and Gene Expression Omnibus database respectively. Published proteomic and metabolomic datasets in PTC from EMBL-EBI were used. Gene Ontology and pathway analyses were performed with SNPs, differentially expressed genes (DEGs), proteins, and metabolites linked to PTC. The effect of a nucleotide substitution on a protein's function was investigated. Additionally, significant transcription factors (TFs) and kinases were identified. An integrated strategy was used to analyse the multi-omics data to determine the key deregulated pathways in PTC carcinogenesis.

RESULTS

Pathways linked to carbohydrate, protein, and lipid metabolism, along with the immune response, signaling, apoptosis, gene expression, epithelial-mesenchymal transition (EMT), and disease onset, were identified as significant for the clinical and functional aspects of PTC. Glyoxylate and dicarboxylate metabolism and citrate cycle were the most common pathways among the PTC omics datasets. Commonality analysis deciphered five TFs and fifty-seven kinases crucial for PTC genesis and progression. Core deregulated pathways, TFs, and kinases modulate critical biological processes like proliferation, angiogenesis, immune infiltration, invasion, autophagy, EMT, and metastasis in PTC.

CONCLUSION

Identified dysregulated pathways, TFs and kinases are critical in PTC and may help in systems level understanding and device specific experiments, biomarkers, and drug targets for better management of PTC.

AEGAN-Pathifier: a data augmentation method to improve cancer classification for imbalanced gene expression data

BACKGROUND

Cancer classification has consistently been a challenging problem, with the main difficulties being high-dimensional data and the collection of patient samples. Concretely, obtaining patient samples is a costly and resource-intensive process, and imbalances often exist between samples. Moreover, expression data is characterized by high dimensionality, small samples and high noise, which could easily lead to struggles such as dimensionality catastrophe and overfitting. Thus, we incorporate prior knowledge from the pathway and combine AutoEncoder and Generative Adversarial Network (GAN) to solve these difficulties.

RESULTS

In this study, we propose an effective and efficient deep learning method, named AEGAN, which combines the capabilities of AutoEncoder and GAN to generate synthetic samples of the minority class in imbalanced gene expression data. The proposed data balancing technique has been demonstrated to be useful for cancer classification and improving the performance of classifier models. Additionally, we integrate prior knowledge from the pathway and employ the pathifier algorithm to calculate pathway scores for each sample. This data augmentation approach, referred to as AEGAN-Pathifier, not only preserves the biological functionality of the data but also possesses dimensional reduction capabilities. Through validation with various classifiers, the experimental results show an improvement in classifier performance.

CONCLUSION

AEGAN-Pathifier shows improved performance on the imbalanced datasets GSE25066, GSE20194, BRCA and Liver24. Results from various classifiers indicate that AEGAN-Pathifier has good generalization capability.

Differences in the molecular organisation of tumours along the colon are linked to interactions within the tumour ecosystem

Tumours exhibit significant heterogeneity in their molecular profiles across patients, largely influenced by the tissue of origin, where certain driver gene mutations are predominantly associated with specific cancer types. Here, we unveil an additional layer of complexity: some cancer types display anatomic location-specific mutation profiles akin to tissue-specificity. To better understand this phenomenon, we concentrate on colon cancer. While prior studies have noted changes of the frequency of molecular alterations along the colon, the underlying reasons and whether those changes occur rather gradual or are distinct between the left and right colon, remain unclear. Developing and leveraging stringent statistical models on molecular data from 522 colorectal tumours from The Cancer Genome Atlas, we reveal disparities in molecular properties between the left and right colon affecting many genes. Interestingly, alterations in genes responsive to environmental cues and properties of the tumour ecosystem, including metabolites which we quantify in a cohort of 27 colorectal cancer patients, exhibit continuous trends along the colon. Employing network methodologies, we uncover close interactions between metabolites and genes, including drivers of colon cancer, showing continuous abundance or alteration profiles. This underscores how anatomic biases in the composition and interactions within the tumour ecosystem help explaining gradients of carcinogenesis along the colon.

Cancer pharmacoinformatics: Databases and analytical tools

Cancer is a subject of extensive investigation, and the utilization of omics technology has resulted in the generation of substantial volumes of big data in cancer research. Numerous databases are being developed to manage and organize this data effectively. These databases encompass various domains such as genomics, transcriptomics, proteomics, metabolomics, immunology, and drug discovery. The application of computational tools into various core components of pharmaceutical sciences constitutes "Pharmacoinformatics", an emerging paradigm in rational drug discovery. The three major features of pharmacoinformatics include (i) Structure modelling of putative drugs and targets, (ii) Compilation of databases and analysis using statistical approaches, and (iii) Employing artificial intelligence/machine learning algorithms for the discovery of novel therapeutic molecules. The development, updating, and analysis of databases using statistical approaches play a pivotal role in pharmacoinformatics. Multiple software tools are associated with oncoinformatics research. This review catalogs the databases and computational tools related to cancer drug discovery and highlights their potential implications in the pharmacoinformatics of cancer.

Salivary metabolomics in early detection of oral squamous cell carcinoma - a meta-analysis

INTRODUCTION

Oral squamous cell carcinoma (OSCC) represents the most prevalent form of oral cancer. Potentially malignant disorders of oral mucosa exhibit an elevated propensity for malignant progression. A substantial proportion of cases are discerned during advanced stages, significantly impacting overall survival. This investigation aims to ascertain salivary metabolites with potential utility in the early detection of OSCC.

METHODS

A search encompassing PubMed, EMBASE, Scopus, Ovid, Science Direct, and Web of Science databases was conducted to identify eligible articles. The search strategy employed precise terms. The quality assessment of the included studies was executed using the QUADAS 2 ROB tool. This was registered with PROSPERO CRD42021278217.

RESULTS

Upon removing duplicate articles and publications that didn't satisfy the inclusion criteria, seven articles were included in the current study. The Random Effects Maximum Likelihood (REML) model adopted for quantitative synthesis identified Nacetyl glucosamine as the sole metabolite in two studies included in this metaanalysis. The pathways significantly influenced by these identified metabolites were delineated.

CONCLUSION

This study highlights Nacetyl glucosamine as a distinctive metabolite with the potential to serve as an early diagnostic marker for OSCC. Nevertheless, further research is warranted to validate its clinical utility.

TheMarker: a comprehensive database of therapeutic biomarkers

Distinct from the traditional diagnostic/prognostic biomarker (adopted as the indicator of disease state/process), the therapeutic biomarker (ThMAR) has emerged to be very crucial in the clinical development and clinical practice of all therapies. There are five types of ThMAR that have been found to play indispensable roles in various stages of drug discovery, such as: Pharmacodynamic Biomarker essential for guaranteeing the pharmacological effects of a therapy, Safety Biomarker critical for assessing the extent or likelihood of therapy-induced toxicity, Monitoring Biomarker indispensable for guiding clinical management by serially measuring patients' status, Predictive Biomarker crucial for maximizing the clinical outcome of a therapy for specific individuals, and Surrogate Endpoint fundamental for accelerating the approval of a therapy. However, these data of ThMARs has not been comprehensively described by any of the existing databases. Herein, a database, named 'TheMarker', was therefore constructed to (a) systematically offer all five types of ThMAR used at different stages of drug development, (b) comprehensively describe ThMAR information for the largest number of drugs among available databases, (c) extensively cover the widest disease classes by not just focusing on anticancer therapies. These data in TheMarker are expected to have great implication and significant impact on drug discovery and clinical practice, and it is freely accessible without any login requirement at: https://idrblab.org/themarker.

Natural products derived from medicinal plants and microbes might act as a game-changer in breast cancer: a comprehensive review of preclinical and clinical studies

Breast cancer (BC) is the most prevalent neoplasm among women. Genetic and environmental factors lead to BC development and on this basis, several preventive - screening and therapeutic interventions have been developed. Hormones, both in the form of endogenous hormonal signaling or hormonal contraceptives, play an important role in BC pathogenesis and progression. On top of these, breast microbiota includes both species with an immunomodulatory activity enhancing the host's response against cancer cells and species producing proinflammatory cytokines associated with BC development. Identification of novel multitargeted therapeutic agents with poly-pharmacological potential is a dire need to combat advanced and metastatic BC. A growing body of research has emphasized the potential of natural compounds derived from medicinal plants and microbial species as complementary BC treatment regimens, including dietary supplements and probiotics. In particular, extracts from plants such as Artemisia monosperma Delile, Origanum dayi Post, Urtica membranacea Poir. ex Savigny, Krameria lappacea (Dombey) Burdet & B.B. Simpson and metabolites extracted from microbes such as Deinococcus radiodurans and Streptomycetes strains as well as probiotics like Bacillus coagulans and Lactobacillus brevis MK05 have exhibited antitumor effects in the form of antiproliferative and cytotoxic activity, increase in tumors' chemosensitivity, antioxidant activity and modulation of BC - associated molecular pathways. Further, bioactive compounds like 3,3'-diindolylmethane, epigallocatechin gallate, genistein, rutin, resveratrol, lycopene, sulforaphane, silibinin, rosmarinic acid, and shikonin are of special interest for the researchers and clinicians because these natural agents have multimodal action and act via multiple ways in managing the BC and most of these agents are regularly available in our food and fruit diets. Evidence from clinical trials suggests that such products had major potential in enhancing the effectiveness of conventional antitumor agents and decreasing their side effects. We here provide a comprehensive review of the therapeutic effects and mechanistic underpinnings of medicinal plants and microbial metabolites in BC management. The future perspectives on the translation of these findings to the personalized treatment of BC are provided and discussed.

Biological and clinical significance of radiomics features obtained from magnetic resonance imaging preceding pre-carbon ion radiotherapy in prostate cancer based on radiometabolomics

Introduction

We aimed to investigate the feasibility of metabolomics to explain the underlying biological implications of radiomics features obtained from magnetic resonance imaging (MRI) preceding carbon ion radiotherapy (CIRT) in patients with prostate cancer and to further explore the clinical significance of radiomics features on the prognosis of patients, based on their biochemical recurrence (BCR) status.

Methods

Metabolomic results obtained using high-performance liquid chromatography coupled with tandem mass spectrometry of urine samples, combined with pre-RT radiomic features extracted from MRI images, were evaluated to investigate their biological significance. Receiver operating characteristic (ROC) curve analysis was subsequently conducted to examine the correlation between these biological implications and clinical BCR status. Statistical and metabolic pathway analyses were performed using MetaboAnalyst and R software.

Results

Correlation analysis revealed that methionine alteration extent was significantly related to four radiomic features (Contrast, Difference Variance, Small Dependence High Gray Level Emphasis, and Mean Absolute Deviation), which were significantly correlated with BCR status. The area under the curve (AUC) for BCR prediction of these four radiomic features ranged from 0.704 to 0.769, suggesting that the higher the value of these four radiomic features, the greater the decrease in methionine levels after CIRT and the lower the probability of BCR. Pre-CIRT MRI radiomic features were associated with CIRT-suppressed metabolites.

Discussion

These radiomic features can be used to predict the alteration in the amplitude of methionine after CIRT and the BCR status, which may contribute to the optimization of the CIRT strategy and deepen the understanding of PCa.

FLUXestimator: a webserver for predicting metabolic flux and variations using transcriptomics data

Quantitative assessment of single cell fluxome is critical for understanding the metabolic heterogeneity in diseases. Unfortunately, laboratory-based single cell fluxomics is currently impractical, and the current computational tools for flux estimation are not designed for single cell-level prediction. Given the well-established link between transcriptomic and metabolomic profiles, leveraging single cell transcriptomics data to predict single cell fluxome is not only feasible but also an urgent task. In this study, we present FLUXestimator, an online platform for predicting metabolic fluxome and variations using single cell or general transcriptomics data of large sample-size. The FLUXestimator webserver implements a recently developed unsupervised approach called single cell flux estimation analysis (scFEA), which uses a new neural network architecture to estimate reaction rates from transcriptomics data. To the best of our knowledge, FLUXestimator is the first web-based tool dedicated to predicting cell-/sample-wise metabolic flux and metabolite variations using transcriptomics data of human, mouse and 15 other common experimental organisms. The FLUXestimator webserver is available at http://scFLUX.org/, and stand-alone tools for local use are available at https://github.com/changwn/scFEA. Our tool provides a new avenue for studying metabolic heterogeneity in diseases and has the potential to facilitate the development of new therapeutic strategies.

Characterization of metabolism-associated molecular patterns in prostate cancer

BACKGROUND

Metabolism is a hallmark of cancer and it involves in resistance to antitumor treatment. Therefore, the purposes of this study are to classify metabolism-related molecular pattern and to explore the molecular and tumor microenvironment characteristics for prognosis predicting in prostate cancer.

METHODS

The mRNA expression profiles and the corresponding clinical information for prostate cancer patients from TCGA, cBioPortal, and GEO databases. Samples were classified using unsupervised non-negative matrix factorization (NMF) clustering based on differentially expressed metabolism-related genes (MAGs). The characteristics of disease-free survival (DFS), clinicopathological characteristics, pathways, TME, immune cell infiltration, response to immunotherapy, and sensitivity to chemotherapy between subclusters were explored. A prognostic signature was constructed by LASSO cox regression analysis based on differentially expressed MAGs and followed by the development for prognostic prediction.

RESULTS

A total of 76 MAGs between prostate cancer samples and non-tumorous samples were found, then 489 patients were divided into two metabolism-related subclusters for prostate cancer. The significant differences in clinical characteristics (age, T/N stage, Gleason) and DFS between two subclusters. Cluster 1 was associated with cell cycle and metabolism-related pathways, and epithelial-mesenchymal transition (EMT), etc., involved in cluster 2. Moreover, lower ESTIMATE/immune/stromal scores, lower expression of HLAs and immune checkpoint-related genes, and lower half-maximal inhibitory concentration (IC50) values in cluster 1 compared with cluster 2. The 10 MAG signature was identified and constructed a risk model for DFS predicting. The patients with high-risk scores showed poorer DFS. The area under the curve (AUC) values for 1-, 3-, 5-year DFS were 0.744, 0.731, 0.735 in TCGA-PRAD dataset, and 0.668, 0.712, 0.809 in GSE70768 dataset, 0.763, 0.802, 0.772 in GSE70769 dataset. Besides, risk score and Gleason score were identified as independent factors for DFS predicting, and the AUC values of risk score and Gleason score were respectively 0.743 and 0.738. The nomogram showed a favorable performance in DFS predicting.

CONCLUSION

Our data identified two metabolism-related molecular subclusters for prostate cancer that were distinctly characterized in prostate cancer. Metabolism-related risk profiles were also constructed for prognostic prediction.

Multiomics integration reveals the effect of Orexin A on glioblastoma

Objectives: This study involved a multi-omics analysis of glioblastoma (GBM) samples to elaborate the potential mechanism of drug treatment. Methods: The GBM cells treated with or without orexin A were acquired from sequencing analysis. Differentially expressed genes/proteins/metabolites (DEGs/ DEPs/ DEMs) were screened. Next, combination analyses were conducted to investigate the common pathways and correlations between the two groups. Lastly, transcriptome-proteome-metabolome association analysis was carried out to determine the common pathways, and the genes in these pathways were analyzed through Kaplan-Meier (K-M) survival analysis in public databases. Cell and animal experiments were performed to investigate the anti-glioma activity of orexin A. Results: A total of 1,527 DEGs, 52 DEPs, and 153 DEMs were found. Moreover, the combination analyses revealed that 6, 4, and 1 common pathways were present in the transcriptome-proteome, proteome-metabolome, and transcriptome-metabolome, respectively. Certain correlations were observed between the two data sets. Finally, 11 common pathways were discovered in association analysis, and 138 common genes were screened out in these common pathways. Six genes showed significant differences in terms of survival in both TCGA and CGGA. In addition, orexin A inhibited the proliferation, migration, and invasion of glioma in vitro and in vivo. Conclusion: Eleven common KEGG pathways with six common genes were found among different omics participations, revealing the underlying mechanisms in different omics and providing theoretical basis and reference for multi-omics research on drug treatment.

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.