Tumor Tissue-Specific Biomarkers of Colorectal Cancer by Anatomic Location and Stage

Untargeted metabolomics using UHPLC-Q-Orbitrap HRMS for identifying cytotoxic compounds on MCF-7 breast cancer cells from Annona muricata Linn leaf extracts as potential anticancer agents

INTRODUCTION

The leaves of Annona muricata L., known as "soursop" or "sirsak" in Indonesia, are used traditionally for cancer treatment. However, the bioactive components remain largely unidentified.

OBJECTIVE

This study used untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics to identify potential cytotoxic compounds in A. muricata leaf extracts on MCF-7 breast cancer cells in vitro.

METHODS

A. muricata leaves were macerated with water, 99% ethanol, and aqueous mixtures containing 30%, 50%, and 80% ethanol. Cytotoxic activity of the extracts against MCF-7 breast cancer cells was determined using the MTT assay. Ultra-high-performance liquid chromatography-Q-Orbitrap high-resolution mass spectroscopy (UHPLC-Q-Orbitrap-HRMS) was used to characterize the metabolite composition of each extract. The correlations between metabolite profile and cytotoxic activities were evaluated using orthogonal partial least square discriminant analysis (OPLS-DA). The binding of these bioactive compounds to the tumorigenic alpha-estrogen receptor (3ERT) was then evaluated by in silico docking simulations.

RESULTS

Ninety-nine percent ethanol extracts demonstrated the greatest potency for reducing MCF-7 cell viability (IC50 = 22 μg/ml). We detected 35 metabolites in ethanol extracts, including alkaloids, flavonoids, and acetogenins. OPLS-DA predicted that annoreticuin, squadiolin C, and xylopine, and six unknown acetogenin metabolites, might reduce MCF-7 cell viability. In silico analysis predicted that annoreticuin, squadiolin C, and xylopine bind to 3ERT with an affinity comparable to doxorubicin.

CONCLUSION

Untargeted metabolomics and in silico modeling identified cytotoxic compounds on MCF-7 cells and binding affinity to 3ERT in A. muricata leaf extracts. The findings need to be further verified to prove the screening results.

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.

Perfluorooctanesulfonic Acid and Perfluorooctanoic Acid Promote Migration of Three-Dimensional Colorectal Cancer Spheroids

Perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) are persistent environmental contaminants that are of increasing public concern worldwide. However, their relationship with colorectal cancer (CRC) is poorly understood. This study aims to comprehensively investigate the effect of PFOS and PFOA on the development and progression of CRC in vitro using a series of biological techniques and metabolic profiling. Herein, the migration of three-dimensional (3D) spheroids of two CRC cell lines, SW48 KRAS wide-type (WT) and SW48 KRAS G12A, were observed after exposure to PFOS and PFOA at 2 μM and 10 μM for 7 days. The time and dose-dependent migration phenotype induced by 10 μM PFOS and PFOA was further confirmed by wound healing and trans-well migration assays. To investigate the mechanism of action, derivatization-mass spectrometry-based metabolic profiles were examined from 3D spheroids of SW48 cell lines exposed to PFOS and PFOA (2 μM and 10 μM). Our findings revealed this exposure altered epithelial-mesenchymal transition related metabolic pathways, including fatty acid β-oxidation and synthesis of proteins, nucleotides, and lipids. Furthermore, this phenotype was confirmed by the downregulation of E-cadherin and upregulation of N-cadherin and vimentin. These findings show novel insight into the relationship between PFOS, PFOA, and CRC.

Metabolomics analyses of cancer tissue from patients with colorectal cancer

The alteration of metabolism is essential for the initiation and progression of numerous types of cancer, including colorectal cancer (CRC). Metabolomics has been used to study CRC. At present, the reprogramming of the metabolism in CRC remains to be fully elucidated. In the present study, comprehensive untargeted metabolomics analysis was performed on the paired CRC tissues and adjacent normal tissues from patients with CRC (n=35) using ultra‑high‑performance liquid chromatography‑mass spectrometry. Subsequently, bioinformatic analysis was performed on the differentially expressed metabolites. The changes in these differential metabolites were compared among groups of patients based on sex, anatomical tumor location, grade of tumor differentiation and stage of disease. A total of 927 metabolites were detected in the tissue samples, and 24 metabolites in the CRC tissue were significantly different compared with the adjacent normal tissue. The present study revealed that the levels of three amino acid metabolites were increased in the CRC tissue, specifically, N‑α‑acetyl‑ε‑(2‑propenal)‑Lys, cyclo(Glu‑Glu) and cyclo(Phe‑Glu). The metabolites with decreased levels in the CRC tissue included quinaldic acid (also referred to as quinoline‑2‑carboxilic acid), 17α‑ and 17β‑estradiol, which are associated with tumor suppression activities, as well as other metabolites such as, anhydro‑β‑glucose, Asp‑Arg, lysophosphatidylcholine, lysophosphatidylethanolamine (lysoPE), lysophosphatidylinositol, carnitine, 5'‑deoxy‑5'‑(methylthio) adenosine, 2'‑deoxyinosine‑5'‑monophosphate and thiamine monophosphate. There was no difference in the levels of the differential metabolites between male and female patients. The differentiation of CRC also showed no impact on the levels of the differential metabolites. The levels of lysoPE were increased in the right side of the colon compared with the left side of the colon and rectum. Analysis of the different tumor stages indicated that 2‑aminobenzenesulfonic acid, P‑sulfanilic acid and quinoline‑4‑carboxylic acid were decreased in stage I CRC tissue compared with stage II, III and IV CRC tissue. The levels of N‑α‑acetyl‑ε‑(2‑propenal)‑Lys, methylcysteine and 5'‑deoxy‑5'‑(methylthio) adenosine varied at different stages of tumorigenesis. These differential metabolites were implicated in multiple metabolism pathways, including carbohydrate, amino acid, lipid, nucleotide and hormone. In conclusion, the present study demonstrated that CRC tumors had altered metabolites compared with normal tissue. The data from the metabolic profile of CRC tissues in the present study provided supportive evidence to understand tumorigenesis.

Untargeted plasma metabolomics and risk of colorectal cancer-an analysis nested within a large-scale prospective cohort

BACKGROUND

Colorectal cancer (CRC) is a leading cause of cancer-related death worldwide, but if discovered at an early stage, the survival rate is high. The aim of this study was to identify novel markers predictive of future CRC risk using untargeted metabolomics.

METHODS

This study included prospectively collected plasma samples from 902 CRC cases and 902 matched cancer-free control participants from the population-based Northern Sweden Health and Disease Study (NSHDS), which were obtained up to 26 years prior to CRC diagnosis. Using reverse-phase liquid chromatography-mass spectrometry (LC-MS), data comprising 5015 metabolic features were obtained. Conditional logistic regression was applied to identify potentially important metabolic features associated with CRC risk. In addition, we investigated if previously reported metabolite biomarkers of CRC risk could be validated in this study population.

RESULTS

In the univariable analysis, seven metabolic features were associated with CRC risk (using a false discovery rate cutoff of 0.25). Two of these could be annotated, one as pyroglutamic acid (odds ratio per one standard deviation increase = 0.79, 95% confidence interval, 0.70-0.89) and another as hydroxytigecycline (odds ratio per one standard deviation increase = 0.77, 95% confidence interval, 0.67-0.89). Associations with CRC risk were also found for six previously reported metabolic biomarkers of prevalent and/or incident CRC: sebacic acid (inverse association) and L-tryptophan, 3-hydroxybutyric acid, 9,12,13-TriHOME, valine, and 13-OxoODE (positive associations).

CONCLUSIONS

These findings suggest that although the circulating metabolome may provide new etiological insights into the underlying causes of CRC development, its potential application for the identification of individuals at higher risk of developing CRC is limited.

Multi-omic analysis reveals metabolic pathways that characterize right-sided colon cancer liver metastasis

There are well demonstrated differences in tumor cell metabolism between right sided (RCC) and left sided (LCC) colon cancer, which could underlie the robust differences observed in their clinical behavior, particularly in metastatic disease. As such, we utilized liquid chromatography-mass spectrometry to perform an untargeted metabolomics analysis comparing frozen liver metastasis (LM) biobank samples derived from patients with RCC (N = 32) and LCC (N = 58) to further elucidate the unique biology of each. We also performed an untargeted RNA-seq and subsequent network analysis on samples derived from an overlapping subset of patients (RCC: N = 10; LCC: N = 18). Our biobank redemonstrates the inferior survival of patients with RCC-derived LM (P = 0.04), a well-established finding. Our metabolomic results demonstrate increased reactive oxygen species associated metabolites and bile acids in RCC. Conversely, carnitines, indicators of fatty acid oxidation, are relatively increased in LCC. The transcriptomic analysis implicates increased MEK-ERK, PI3K-AKT and Transcription Growth Factor Beta signaling in RCC LM. Our multi-omic analysis reveals several key differences in cellular physiology which taken together may be relevant to clinical differences in tumor behavior between RCC and LCC liver metastasis.

Insight into Nephrotoxicity and Processing Mechanism of Arisaema erubescens (Wall.) Schott by Metabolomics and Network Analysis

Background

Arisaematis Rhizome (AR) has been used as a damp-drying, phlegm-resolving, wind-expelling, pain-alleviating, and swelling-relieving drug for thousands of years. However, the toxicity limits its clinical applications. Therefore, AR is usually processed (Paozhi in Chinese) prior to clinical use. In this study, the integration of ultra-high performance liquid chromatography-quadrupole/ time-of-flight mass spectrometry-based metabolomics and network analysis was adopted to investigate the metabolic shifts induced by AR and explore the processing mechanism.

Materials and Methods

Extracts of crude and processed AR products (1g/kg) were intragastrically administered to rats once daily for four consecutive weeks. The renal function was evaluated by blood urea nitrogen, creatinine, interleukin-1 beta (IL-1β) and tumor necrosis factor-alpha (TNF-α), malondialdehyde (MDA), super oxide dismutase (SOD), the ratio of glutathione/glutathione disulfide (GSH/GSSH), glutathione peroxidase (GSH-Px) and histopathological examination. Furthermore, the chemical composition of AR was clarified by ultra-high performance liquid chromatography-quadrupole/ time-of-flight mass spectrometry, after which the integration of metabolomics and network analysis was adopted to investigate the metabolic shifts induced by AR and explore the processing mechanism.

Results

Crude AR caused renal damage by stimulating inflammation and oxidative stress, as confirmed by the increased production of IL-1β, TNF-α and MDA, and decreased levels of SOD, GSH/GSSH and GSH-Px. Processing with ginger juice, alumen and bile juice alleviated the damage to kidney. Metabolomics results showed that a total of 35 potential biomarkers enriched in amino acid metabolism, glycerophospholipid metabolism, fatty acid-related pathways, etc. were deduced to be responsible for the nephrotoxicity of AR and the toxicity-reducing effect of processing.

Conclusion

This work provided theoretical and data support for the in-depth study of the processing mechanism, showing that processing reduces AR nephrotoxicity through multiple metabolic pathways.

Serum metabolite signatures in normal individuals and patients with colorectal adenoma or colorectal cancer using UPLC-MS/MS method

Colorectal cancer (CRC) is one of the main causes of cancer-related deaths worldwide. Sporadic CRC develops from normal mucosa via adenoma to adenocarcinoma, which provides a long screening window for clinical detection. However, early diagnosis of sporadic colorectal adenoma (CRA) and CRC using serum metabolic screening remains unclear. The purpose of this study was to identify some promising signatures for distinguishing the different pathological metabolites of colorectal mucosal malignant transformation. A total of 238 endogenous metabolites were elected. We found that CRA and CRC patients had 72 and 73 different metabolites compared with healthy controls, respectively. There were 20 different metabolites between CRA and CRC patients. The potential metabolites of tumor growth (including patients with CRA and CRC) were found, such as A-d-glucose, D-mannose, N-acetyl-D-glucosamine, L-cystine, Sarcosine, TXB 2, 12-Hete, and chenodeoxycholic acid. Compared with CRA, 3,4,5-trimethoxybenzoic acid was significantly higher in CRC patients. There results prompt us to use the potential serum signatures to screen CRC as the novel strategy. Serum metabolite screening is useful for early detection of mucosal intestinal malignancy. We will further investigate the roles of these promising biomarkers during intestinal tumorigenesis in future. SIGNIFICANCE: CRC is one of the main causes of cancer-related deaths worldwide. Sporadic CRC develops from normal mucosa via adenomas to adenocarcinoma, which provides a long screening window for about 5-10 years. We adopt the metabolic analysis of extensive targeted metabolic technology. The main purpose of the metabolic group analysis is to detect and screen the different metabolites, thereby performing related functional prediction and analysis of the differential metabolites. In our study, 30 samples are selected, divided into 3 groups for metabolic analysis, and 238 metabolites are elected. In 238 metabolites, we find that CRA patients have 72 different metabolites compared with health control. Compared with health control, CRC have 73 different metabolites. Compared with CRA and CRC patients, there are 20 different metabolites. The annotation results of the significantly different metabolites are classified according to the KEGG pathway type. The potential metabolites of tumor growth stage (including patients with CRA and CRC) are found, such as A-d-glucose, D-mannose, N-acetyl-D-glucosamine, L-cystine, sarcosine, TXB 2, 12-Hete and chenodeoxycholic acid. Compared with CRA patients, CRC patients had significantly higher 3,4,5-trimethoxybenzoic acid level. It is prompted to use serum different metabolites to screen CRC to provide new possibilities.

Modular and mechanistic changes across stages of colorectal cancer

BACKGROUND

While mechanisms contributing to the progression and metastasis of colorectal cancer (CRC) are well studied, cancer stage-specific mechanisms have been less comprehensively explored. This is the focus of this manuscript.

METHODS

Using previously published data for CRC (Gene Expression Omnibus ID GSE21510), we identified differentially expressed genes (DEGs) across four stages of the disease. We then generated unweighted and weighted correlation networks for each of the stages. Communities within these networks were detected using the Louvain algorithm and topologically and functionally compared across stages using the normalized mutual information (NMI) metric and pathway enrichment analysis, respectively. We also used Short Time-series Expression Miner (STEM) algorithm to detect potential biomarkers having a role in CRC.

RESULTS

Sixteen Thousand Sixty Two DEGs were identified between various stages (p-value ≤ 0.05). Comparing communities of different stages revealed that neighboring stages were more similar to each other than non-neighboring stages, at both topological and functional levels. A functional analysis of 24 cancer-related pathways indicated that several signaling pathways were enriched across all stages. However, the stage-unique networks were distinctly enriched only for a subset of these 24 pathways (e.g., MAPK signaling pathway in stages I-III and Notch signaling pathway in stages III and IV). We identified potential biomarkers, including HOXB8 and WNT2 with increasing, and MTUS1 and SFRP2 with decreasing trends from stages I to IV. Extracting subnetworks of 10 cancer-relevant genes and their interacting first neighbors (162 genes in total) revealed that the connectivity patterns for these genes were different across stages. For example, BRAF and CDK4, members of the Ser/Thr kinase, up-regulated in cancer, displayed changing connectivity patterns from stages I to IV.

CONCLUSIONS

Here, we report molecular and modular networks for various stages of CRC, providing a pseudo-temporal view of the mechanistic changes associated with the disease. Our analysis highlighted similarities at both functional and topological levels, across stages. We further identified stage-specific mechanisms and biomarkers potentially contributing to the progression of CRC.

Metabolic profile of the Warburg effect as a tool for molecular prognosis and diagnosis of cancer

INTRODUCTION

Adaptations of eukaryotic cells to environmental changes are important for their survival. However, under some circumstances, microenvironmental changes promote that eukaryotic cells utilize a metabolic signature resembling a unicellular organism named the Warburg effect. Most cancer cells share the Warburg effect displaying lactic fermentation and high glucose uptake. The Warburg effect also induces a metabolic rewiring stimulating glutamine consumption and lipid synthesis, also considered cancer hallmarks. Amino acid metabolism alteration due to the Warburg effect increases plasma levels of proline and branched-chain amino acids in several cancer types. Proline and lipids are probably used as electron transfer molecules in carcinogenic cells. In addition, branched-chain amino acids fuel the Krebs cycle, protein synthesis, and signaling in cancer cells.

AREAS COVERED

This review covers how metabolomics studies describe changes in some metabolites and proteins associated with the Warburg effect and related metabolic pathways.

EXPERT OPINION

In this review, we analyze the metabolic signature of the Warburg effect and related phenotypes and propose some Warburg effect-related metabolites and proteins (lactate, glucose uptake, glucose transporters, glutamine, branched-chain amino acids, proline, and some lipogenic enzymes) as promising cancer biomarkers.

Untargeted Metabolomics Identify a Panel of Urinary Biomarkers for the Diagnosis of Urothelial Carcinoma of the Bladder, as Compared to Urolithiasis with or without Urinary Tract Infection in Dogs

Urothelial carcinoma (UC), the most common urologic cancer in dogs, is often diagnosed late because the clinical signs are shared by other non-malignant lower urinary tract disorders (LUTD). The urine-based BRAFV595E test for UC is highly effective only in certain breeds; hence additional non-invasive biomarkers of UC are needed. Here, urine from dogs with UC (n = 27), urolithiasis (n = 8), or urolithiasis with urinary tract infection (UTI) (n = 8) were subjected to untargeted metabolomics analyses, using GC-TOF-MS for primary metabolites, QTOF-MS for complex lipids, and HILIC-QTOF MS for secondary and charged metabolites. After adjusting for age and sex, we identified 1123 known metabolites that were differentially expressed between UC and LUTD. Twenty-seven metabolites were significant (1.5 ≤ log2FC ≤ −1.5, adjusted p-value < 0.05); however, 10 of these could be attributed to treatment-related changes. Of the remaining 17, 6 (hippuric acid, N-Acetylphenylalanine, sarcosine, octanoylcarnitine, N-alpha-methylhistamine, glycerol-3-galactoside) discriminated between UC and LUTD (area under the ROC curve > 0.85). Of the 6 metabolites, only hippuric acid and N-alpha-methylhistamine were discriminatory in both male (n = 20) and female (n = 23) dogs, while sarcosine was an effective discriminator in several breeds, but only in females. Further investigation of these metabolites is warranted for potential use as non-invasive diagnostic biomarkers of dogs with UC that present with LUTD-related clinical signs.

Mass spectrometry imaging of L-[ring-13C6]-labeled phenylalanine and tyrosine kinetics in non-small cell lung carcinoma

BACKGROUND

Metabolic reprogramming is a common phenomenon in tumorigenesis and tumor progression. Amino acids are important mediators in cancer metabolism, and their kinetics in tumor tissue are far from being understood completely. Mass spectrometry imaging is capable to spatiotemporally trace important endogenous metabolites in biological tissue specimens. In this research, we studied L-[ring-¹³C₆]-labeled phenylalanine and tyrosine kinetics in a human non-small cell lung carcinoma (NSCLC) xenografted mouse model using matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry imaging (MALDI-FTICR-MSI).

METHODS

We investigated the L-[ring-¹³C₆]-Phenylalanine (¹³C₆-Phe) and L-[ring-¹³C₆]-Tyrosine (¹³C₆-Tyr) kinetics at 10 min (n = 4), 30 min (n = 3), and 60 min (n = 4) after tracer injection and sham-treated group (n = 3) at 10 min in mouse-xenograft lung tumor tissues by MALDI-FTICR-MSI.

RESULTS

The dynamic changes in the spatial distributions of 19 out of 20 standard amino acids are observed in the tumor tissue. The highest abundance of ¹³C₆-Phe was detected in tumor tissue at 10 min after tracer injection and decreased progressively over time. The overall enrichment of ¹³C₆-Tyr showed a delayed temporal trend compared to ¹³C₆-Phe in tumor caused by the Phe-to-Tyr conversion process. Specifically, ¹³C₆-Phe and ¹³C₆-Tyr showed higher abundances in viable tumor regions compared to non-viable regions.

CONCLUSIONS

We demonstrated the spatiotemporal intra-tumoral distribution of the essential aromatic amino acid ¹³C₆-Phe and its de-novo synthesized metabolite ¹³C₆-Tyr by MALDI-FTICR-MSI. Our results explore for the first time local phenylalanine metabolism in the context of cancer tissue morphology. This opens a new way to understand amino acid metabolism within the tumor and its microenvironment.

Tissue metabolic profiling reveals major metabolic alteration in colorectal cancer

Metabolic reprogramming is a hallmark of cancer, which is still far from being fully understood in colorectal cancer. In order to characterize the metabolic changes in colorectal cancer, we performed metabolomics analysis of paired colon tissues from colorectal cancer patients by using a liquid chromatography-mass spectrometry (LC-MS)-based method. Bioinformation analysis was used to define important metabolites and metabolic pathways, as well as the prognosis significance and expression levels of the key molecules. The results indicated that the metabolite phenotype in cancerous colon tissues was obviously different from their normal counterpart, and we identified a series of important metabolic changes in colorectal cancer, including decreased trends of glucose, citrate, serotonin, 5-hydroxytryptophol and 5-hydroxyindoleacetate, as well as increased trends of glutamate, glutathione, creatine, proline, lactate, fructose 1,6-bisphosphate, succinate, tryptophan, kynurenine and long chain acyl-carnitines. These metabolites are mainly implicated in energy metabolism, amino acid metabolism, glutathione metabolism and fatty acid metabolism. In addition, we found that the expression levels of several key molecules in these pathways were closely correlated with the prognosis of colorectal cancer patients. This study characterizes the metabolic profile in colorectal cancer tissues and provides more insightful understanding of the metabolic reprogramming of colorectal cancer.