Generalized portrait of cancer metabolic pathways inferred from a list of genes overexpressed in cancer

Untargeted serum metabolomics reveals potential biomarkers and metabolic pathways associated with the progression of gastroesophageal cancer

BACKGROUND

Previous metabolic studies in upper digestive cancer have mostly been limited to cross-sectional study designs, which hinders the ability to effectively predict outcomes in the early stage of cancer. This study aims to identify key metabolites and metabolic pathways associated with the multistage progression of epithelial cancer and to explore their predictive value for gastroesophageal cancer (GEC) formation and for the early screening of esophageal squamous cell carcinoma (ESCC).

METHODS

A case-cohort study within the 7-year prospective Esophageal Cancer Screening Cohort of Shandong Province included 77 GEC cases and 77 sub-cohort individuals. Untargeted metabolic analysis was performed in serum samples. Metabolites, with FDR q value < 0.05 and variable importance in projection (VIP) > 1, were selected as differential metabolites to predict GEC formation using Random Forest (RF) models. Subsequently, we evaluated the predictive performance of these differential metabolites for the early screening of ESCC.

RESULTS

We found a distinct metabolic profile alteration in GEC cases compared to the sub-cohort, and identified eight differential metabolites. Pathway analyses showed dysregulation in D-glutamine and D-glutamate metabolism, nitrogen metabolism, primary bile acid biosynthesis, and steroid hormone biosynthesis in GEC patients. A panel of eight differential metabolites showed good predictive performance for GEC formation, with an area under the receiver operating characteristic curve (AUC) of 0.893 (95% CI = 0.816-0.951). Furthermore, four of the GEC pathological progression-related metabolites were validated in the early screening of ESCC, with an AUC of 0.761 (95% CI = 0.716-0.805).

CONCLUSIONS

These findings indicated a panel of metabolites might be an alternative approach to predict GEC formation, and therefore have the potential to mitigate the risk of cancer progression at the early stage of GEC.

Anticancer Profile of Rhodanines: Structure-Activity Relationship (SAR) and Molecular Targets-A Review

The rhodanine core is a well-known privileged heterocycle in medicinal chemistry. The rhodanines, as subtypes of thiazolidin-4-ones, show a broad spectrum of biological activity, including anticancer properties. This review aims to analyze the anticancer features of the rhodanines described over the last decade in the scientific literature. The structure–activity relationship of rhodanine derivatives, as well as some of the molecular targets, were discussed. The information contained in this review could be of benefit to the design of new, effective small molecules with anticancer potential among rhodanine derivatives or their related heterocycles.

Measurement of Conditional Relatedness Between Genes Using Fully Convolutional Neural Network

Measuring conditional relatedness, the degree of relation between a pair of genes in a certain condition, is a basic but difficult task in bioinformatics, as traditional co-expression analysis methods rely on co-expression similarities, well known with high false positive rate. Complement with prior-knowledge similarities is a feasible way to tackle the problem. However, classical combination machine learning algorithms fail in detection and application of the complex mapping relations between similarities and conditional relatedness, so a powerful predictive model will have enormous benefit for measuring this kind of complex mapping relations. To this need, we propose a novel deep learning model of convolutional neural network with a fully connected first layer, named fully convolutional neural network (FCNN), to measure conditional relatedness between genes using both co-expression and prior-knowledge similarities. The results on validation and test datasets show FCNN model yields an average 3.0% and 2.7% higher accuracy values for identifying gene–gene interactions collected from the COXPRESdb, KEGG, and TRRUST databases, and a benchmark dataset of Xiao-Yong et al. research, by grid-search 10-fold cross validation, respectively. In order to estimate the FCNN model, we conduct a further verification on the GeneFriends and DIP datasets, and the FCNN model obtains an average of 1.8% and 7.6% higher accuracy, respectively. Then the FCNN model is applied to construct cancer gene networks, and also calls more practical results than other compared models and methods. A website of the FCNN model and relevant datasets can be accessed from https://bmbl.bmi.osumc.edu/FCNN.

Proteome-transcriptome alignment of molecular portraits achieved by self-contained gene set analysis: Consensus colon cancer subtypes case study

Gene set analysis (GSA) has become the common methodology for analyzing transcriptomics data. However, self-contained GSA techniques are rarely, if ever, used for proteomics data analysis. Here we present a self-contained proteome level GSA of four consensus molecular subtypes (CMSs) previously established by transcriptome dissection of colon carcinoma specimens. Despite notable difference in structure of proteomics and transcriptomics data, many pathway-wide characteristic features of CMSs found at the mRNA level were reproduced at the protein level. In particular, CMS1 features show heavy involvement of immune system as well as the pathways related to mismatch repair, DNA replication and functioning of proteasome, while CMS4 tumors upregulate complement pathway and proteins participating in epithelial-to-mesenchymal transition (EMT). In addition, protein level GSA yielded a set of novel observations visible at the proteome, but not at the transcriptome level, including possible involvement of major histocompatibility complex II (MHC-II) antigens in the known immunogenicity of CMS1 and a connection between cholesterol trafficking and the regulation of Integrin-linked kinase (ILK) in CMS3. Overall, this study proves utility of self-contained GSA approaches as a critical tool for analyzing proteomics data in general and dissecting protein-level molecular portraits of human tumors in particular.

Mutational signatures and mutable motifs in cancer genomes

Cancer is a genetic disorder, meaning that a plethora of different mutations, whether somatic or germ line, underlie the etiology of the 'Emperor of Maladies'. Point mutations, chromosomal rearrangements and copy number changes, whether they have occurred spontaneously in predisposed individuals or have been induced by intrinsic or extrinsic (environmental) mutagens, lead to the activation of oncogenes and inactivation of tumor suppressor genes, thereby promoting malignancy. This scenario has now been recognized and experimentally confirmed in a wide range of different contexts. Over the past decade, a surge in available sequencing technologies has allowed the sequencing of whole genomes from liquid malignancies and solid tumors belonging to different types and stages of cancer, giving birth to the new field of cancer genomics. One of the most striking discoveries has been that cancer genomes are highly enriched with mutations of specific kinds. It has been suggested that these mutations can be classified into 'families' based on their mutational signatures. A mutational signature may be regarded as a type of base substitution (e.g. C:G to T:A) within a particular context of neighboring nucleotide sequence (the bases upstream and/or downstream of the mutation). These mutational signatures, supplemented by mutable motifs (a wider mutational context), promise to help us to understand the nature of the mutational processes that operate during tumor evolution because they represent the footprints of interactions between DNA, mutagens and the enzymes of the repair/replication/modification pathways.

Volatilomic insight of head and neck cancer via the effects observed on saliva metabolites

Head and neck cancer (HNC) is a heterogeneous malignant disease with distinct global distribution. Metabolic adaptations of HNC are significantly gaining clinical interests nowadays. Here, we investigated effects of HNC on differential expression of volatile metabolites in human saliva. We applied headspace solid phase microextraction coupled with gas chromatography-mass spectrometry analysis of saliva samples collected from 59 human subjects (HNC − 32, Control − 27). We identified and quantified 48 volatile organic metabolites (VOMs) and observed profound effects of HNC on these metabolites. These effects were VOM specific and significantly differed in the biologically comparable healthy controls. HNC induced changes in salivary VOM composition were well attributed to in vivo metabolic effects. A panel of 15 VOMs with variable importance in projection (VIP) score >1, false discovery rate (FDR) corrected p-value < 0.05 and log₂ fold change (log₂ FC) value of ≥0.58/≤−0.58 were regarded as discriminatory metabolites of pathophysiological importance. Afterwards, receiver operator characteristic curve (ROC) projected certain VOMs viz., 1,4-dichlorobenzene, 1,2-decanediol, 2,5-bis1,1-dimethylethylphenol and E-3-decen-2-ol with profound metabolic effects of HNC and highest class segregation potential. Moreover, metabolic pathways analysis portrayed several dysregulated pathways in HNC, which enhanced our basic understanding on salivary VOM changes. Our observations could redefine several known/already investigated systemic phenomenons (e.g. biochemical pathways). These findings will inspire further research in this direction and may open unconventional avenues for non-invasive monitoring of HNC and its therapy in the future.

ENO1 Overexpression in Pancreatic Cancer Patients and Its Clinical and Diagnostic Significance

We investigated in this study the expression of ENO1 in tissues and plasma of PDAC patients to evaluate its clinicopathological and diagnostic significance. ENO1 protein expression was detected in tissue microarray of human PDAC and adjacent noncancer tissues. Electrochemiluminescence immunoassay and amplified luminescent proximity homogeneous assay (AlphaLISA) were performed to measure CA19-9 and ENO1 concentration in plasma from PDAC patients and healthy controls. We demonstrated that ENO1 overexpression is positively correlated with clinical stage, lymph node metastasis, and poor prognosis of PDAC; ENO1 may function as a hopeful candidate diagnostic marker in combination with CA19-9 in PDAC diagnosis.

The embryonic stress response to in vitro culture: insight from genomic analysis

Recent genomic studies have shed light on the impact of in vitro culture (IVC) on embryonic homeostasis and the differential gene expression profiles associated with lower developmental competence. Consistently, the embryonic stress responses to IVC conditions correlate with transcriptomic changes in pathways related to energetic metabolism, extracellular matrix remodelling and inflammatory signalling. These changes appear to result from a developmental adaptation that enhances a Warburg-like effect known to occur naturally during blastulation. First discovered in cancer cells, the Warburg effect (increased glycolysis under aerobic conditions) is thought to result from mitochondrial dysfunction. In the case of IVC embryos, culture conditions may interfere with mitochondrial maturation and oxidative phosphorylation, forcing cells to rely on glycolysis in order to maintain energetic homeostasis. While beneficial in the short term, such adaptations may lead to epigenetic changes with potential long-term effects on implantation, foetal growth and post-natal health. We conclude that lessening the detrimental effects of IVC on mitochondrial activity would lead to significantly improved embryo quality.

ccmGDB: a database for cancer cell metabolism genes

Accumulating evidence has demonstrated that rewiring of metabolism in cells is an important hallmark of cancer. The percentage of patients killed by metabolic disorder has been estimated to be 30% of the advanced-stage cancer patients. Thus, a systematic annotation of cancer cell metabolism genes is imperative. Here, we present ccmGDB (Cancer Cell Metabolism Gene DataBase), a comprehensive annotation database for cell metabolism genes in cancer, available at http://bioinfo.mc.vanderbilt.edu/ccmGDB. We assembled, curated, and integrated genetic, genomic, transcriptomic, proteomic, biological network and functional information for over 2000 cell metabolism genes in more than 30 cancer types. In total, we integrated over 260 000 somatic alterations including non-synonymous mutations, copy number variants and structural variants. We also integrated RNA-Seq data in various primary tumors, gene expression microarray data in over 1000 cancer cell lines and protein expression data. Furthermore, we constructed cancer or tissue type-specific, gene co-expression based protein interaction networks and drug-target interaction networks. Using these systematic annotations, the ccmGDB portal site provides 6 categories: gene summary, phenotypic information, somatic mutations, gene and protein expression, gene co-expression network and drug pharmacological information with a user-friendly interface for browsing and searching. ccmGDB is developed and maintained as a useful resource for the cancer research community.