1H nuclear magnetic resonance-based extracellular metabolomic analysis of multidrug resistant Tca8113 oral squamous carcinoma cells

Transcriptome profiling and metabolic pathway analysis towards reliable biomarker discovery in early-stage lung cancer

Earlier diagnosis of lung cancer is crucial for reducing mortality and morbidity in high-risk patients. Liquid biopsy is a critical technique for detecting the cancer earlier and tracking the treatment outcomes. However, noninvasive biomarkers are desperately needed due to the lack of therapeutic sensitivity and early-stage diagnosis. Therefore, we have utilized transcriptomic profiling of early-stage lung cancer patients to discover promising biomarkers and their associated metabolic functions. Initially, PCA highlights the diversity level of gene expression in three stages of lung cancer samples. We have identified two major clusters consisting of highly variant genes among the three stages. Further, a total of 7742, 6611, and 643 genes were identified as DGE for stages I-III respectively. Topological analysis of the protein-protein interaction network resulted in seven candidate biomarkers such as JUN, LYN, PTK2, UBC, HSP90AA1, TP53, and UBB cumulatively for the three stages of lung cancers. Gene enrichment and KEGG pathway analyses aid in the comprehension of pathway mechanisms and regulation of identified hub genes in lung cancer. Importantly, the medial survival rates up to ~ 70 months were identified for hub genes during the Kaplan-Meier survival analysis. Moreover, the hub genes displayed the significance of risk factors during gene expression analysis using TIMER2.0 analysis. Therefore, we have reason that these biomarkers may serve as a prospective targeting candidate with higher treatment efficacy in early-stage lung cancer patients.

NMR Metabolomics and Chemometrics of Commercial Varieties of Phaseolus vulgaris L. Seeds from Italy and In Vitro Antioxidant and Antifungal Activity

The metabolite fingerprinting of four Italian commercial bean seed cultivars, i.e., Phaseolus Cannellino (PCANN), Controne (PCON), Vellutina (PVEL), and Occhio Nero (PON), were investigated by Nuclear Magnetic Resonance (NMR) spectroscopy and multivariate data analysis. The hydroalcoholic and organic extract analysis disclosed more than 32 metabolites from various classes, i.e., carbohydrates, amino acids, organic acids, nucleosides, alkaloids, and fatty acids. PVEL, PCON, and PCANN varieties displayed similar chemical profiles, albeit with somewhat different quantitative results. The PON metabolite composition was slightly different from the others; it lacked GABA and pipecolic acid, featured a higher percentage of malic acid than the other samples, and showed quantitative variations of several metabolites. The lipophilic extracts from all four cultivars demonstrated the presence of omega-3 and omega-6 unsaturated fatty acids. After the determination of the total phenolic, flavonoids, and condensed tannins content, in vitro antioxidant activity was then assessed using the DPPH scavenging activity, the ABTS scavenging assay, and ferric-reducing antioxidant power (FRAP). Compared to non-dark seeds (PCON, PCANN), brown seeds (PVEL, PON) featured a higher antioxidant capacity. Lastly, only PON extract showed in vitro antifungal activity against the sclerotia growth of S. rolfsii, by inhibiting halo growth by 75%.

Construction of the XGBoost model for early lung cancer prediction based on metabolic indices

BACKGROUND

Lung cancer is a malignant tumour, and early diagnosis has been shown to improve the survival rate of lung cancer patients. In this study, we assessed the use of plasma metabolites as biomarkers for lung cancer diagnosis. In this work, we used a novel interdisciplinary mechanism, applied for the first time to lung cancer, to detect biomarkers for early lung cancer diagnosis by combining metabolomics and machine learning approaches.

RESULTS

In total, 478 lung cancer patients and 370 subjects with benign lung nodules were enrolled from a hospital in Dalian, Liaoning Province. We selected 47 serum amino acid and carnitine indicators from targeted metabolomics studies using LC‒MS/MS and age and sex demographic indicators of the subjects. After screening by a stepwise regression algorithm, 16 metrics were included. The XGBoost model in the machine learning algorithm showed superior predictive power (AUC = 0.81, accuracy = 75.29%, sensitivity = 74%), with the metabolic biomarkers ornithine and palmitoylcarnitine being potential biomarkers to screen for lung cancer. The machine learning model XGBoost is proposed as an tool for early lung cancer prediction. This study provides strong support for the feasibility of blood-based screening for metabolites and provide a safer, faster and more accurate tool for early diagnosis of lung cancer.

CONCLUSIONS

This study proposes an interdisciplinary approach combining metabolomics with a machine learning model (XGBoost) to predict early the occurrence of lung cancer. The metabolic biomarkers ornithine and palmitoylcarnitine showed significant power for early lung cancer diagnosis.

Comparative NMR metabolomics of the responses of A2780 human ovarian cancer cells to clinically established Pt-based drugs

Pt-based drugs play a very important role in current cancer treatments; yet, their cellular and mechanistic aspects are not fully understood. NMR metabolomics provides a powerful tool to investigate the...

Bean cultivars (Phaseolus vulgaris L.) under the spotlight of NMR metabolomics

The seeds of Phaseolus vulgaris are a rich source of protein consumed around the world and are considered as the most important source of proteins and antioxidants in the Mexican diet. This work reports on the 1H NMR metabolomics profiling of the cultivars Peruano (FPe), Pinto (FPi), Flor de mayo (FM), Negro (FN) and Flor de junio (FJ). Total phenolics, total flavonoids and total protein contents were determined to complement the nutritional facts in seeds and leaves. According to our results, the metabolomics fingerprint of beans seeds and leaves were very similar, showing the presence of 52 metabolites, 46 in seeds and 48 in leaves, including 8 sugars, 17 amino acids, 15 organic acids, 5 nucleosides and 7 miscellaneous compounds. In seeds, free amino acids were detected in higher concentrations than in the leaves, whereas organic acids were more abundant in leaves than in seeds. With multivariate and cluster analysis it was possible to rank the cultivars according to their nutritional properties according to NMR profiling, then a machine learning algorithm was used to reveal the most important differential metabolites which are the key for correct classification. The results coincide in highlighting the FN seeds and FPe leaves for the best nutritional facts. Finally, in terms of cultivars, FN and FM present the best nutritional properties, with high protein and flavonoids content, as well as, a high concentration of amino acids and nucleosides.

The Application of Chemometrics for Efficiency Enhancement and Toxicity Reduction in Cancer Treatment with Combined Therapy

Chemometrics is an important emerging discipline with unique charm formed by the intersection of mathematics, statistics, chemistry and computer science. The application of chemometrics in the field of pharmacy has injected fresh blood into the scientific research and clinical practice of medicine and has provided a sufficient scientific basis for drug analysis and content determination to solve the problem of cancer treatment with combined therapy in different ranges. This paper introduces the basic principles, advantages and disadvantages of several commonly used pattern recognition and multidimensional correction methods of chemometrics, reviews the application of chemometrics for efficiency enhancement and toxicity reduction in cancer treatment with combined therapy and summarizes its development and prospects in the future.

Response of Osteosarcoma Cell Metabolism to Platinum and Palladium Chelates as Potential New Drugs

This paper reports the first metabolomics study of the impact of new chelates Pt₂Spm and Pd₂Spm (Spm = Spermine) on human osteosarcoma cellular metabolism, compared to the conventional platinum drugs cisplatin and oxaliplatin, in order to investigate the effects of different metal centers and ligands. Nuclear Magnetic Resonance metabolomics was used to identify meaningful metabolite variations in polar cell extracts collected during exposure to each of the four chelates. Cisplatin and oxaliplatin induced similar metabolic fingerprints of changing metabolite levels (affecting many amino acids, organic acids, nucleotides, choline compounds and other compounds), thus suggesting similar mechanisms of action. For these platinum drugs, a consistent uptake of amino acids is noted, along with an increase in nucleotides and derivatives, namely involved in glycosylation pathways. The Spm chelates elicit a markedly distinct metabolic signature, where inverse features are observed particularly for amino acids and nucleotides. Furthermore, Pd₂Spm prompts a weaker response from osteosarcoma cells as compared to its platinum analogue, which is interesting as the palladium chelate exhibits higher cytotoxicity. Putative suggestions are discussed as to the affected cellular pathways and the origins of the distinct responses. This work demonstrates the value of untargeted metabolomics in measuring the response of cancer cells to either conventional or potential new drugs, seeking further understanding (or possible markers) of drug performance at the molecular level.

Curcumin reverses doxorubicin resistance in colon cancer cells at the metabolic level

Doxorubicin (Dox) is commonly used for the treatment of malignant tumors, including colon cancer. However, the development of P-glycoprotein (P-gp)-mediated multidrug resistance (MDR) in tumor chemotherapy has seriously reduced the therapeutic efficacy of Dox. Natural product curcumin (Cur) was demonstrated to have a variety of pharmacological effects, such as anti-tumor, anti-oxidation and anti-aging activities. Here, we examined the MDR reversal capability of Cur in drug sensitive-(SW620) and resistant-(SW620/Ad300) colon cancer cells, and elucidated the underlying molecular mechanisms at the metabolic level. It was found that Cur reversed P-gp-mediated resistance in SW620/Ad300 cells by enhancing the Dox-induced cytotoxicity and apoptosis. Further mechanistic studies indicated that Cur inhibited the ATP-dependent transport activity of P-gp, thereby increasing the intra-celluar accumulation of Dox in drug-resistant cells. Metabolomics analysis based on UPLC-MS/MS showed that the MDR phenomenon in SW620/Ad300 cells was closely correlated with the upregulation of spermine and spermidine synthesis and D-glutamine metabolism. Cur significantly inhibited the biosynthesis of spermine and spermidine by decreasing the expression of ornithine decarboxylase (ODC) and suppressed D-glutamine metabolism, which in turn decreased the anti-oxidative stress ability and P-gp transport activity of SW620/Ad300 cells, eventually reversed MDR. These findings indicated the MDR reversal activity and the related mechanism of action of Cur, suggesting that Cur could be a promising MDR reversal agent for cancer treatment.

Early lung cancer diagnostic biomarker discovery by machine learning methods

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Early diagnosis could improve lung cancer survival rate.

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The availability of blood-based screening could increase lung cancer patient uptake.

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An interdisciplinary mechanism combines metabolomics and machine learning methods.

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Metabolic biomarkers could be potential screening biomarkers for early detection of lung cancer.

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Naïve Bayes is recommended as an exploitable tool for early lung tumor prediction.

Early diagnosis has been proved to improve survival rate of lung cancer patients. The availability of blood-based screening could increase early lung cancer patient uptake. Our present study attempted to discover Chinese patients’ plasma metabolites as diagnostic biomarkers for lung cancer. In this work, we use a pioneering interdisciplinary mechanism, which is firstly applied to lung cancer, to detect early lung cancer diagnostic biomarkers by combining metabolomics and machine learning methods. We collected total 110 lung cancer patients and 43 healthy individuals in our study. Levels of 61 plasma metabolites were from targeted metabolomic study using LC-MS/MS. A specific combination of six metabolic biomarkers note-worthily enabling the discrimination between stage I lung cancer patients and healthy individuals (AUC = 0.989, Sensitivity = 98.1%, Specificity = 100.0%). And the top 5 relative importance metabolic biomarkers developed by FCBF algorithm also could be potential screening biomarkers for early detection of lung cancer. Naïve Bayes is recommended as an exploitable tool for early lung tumor prediction. This research will provide strong support for the feasibility of blood-based screening, and bring a more accurate, quick and integrated application tool for early lung cancer diagnostic. The proposed interdisciplinary method could be adapted to other cancer beyond lung cancer.

Metabolic landscape of oral squamous cell carcinoma

BACKGROUND

Head and neck cancers are the seventh most common type of cancer worldwide, with almost half of the cases affecting the oral cavity. Oral squamous cell carcinoma (OSCC) is the most common form of oral cancer, showing poor prognosis and high mortality. OSCC molecular pathogenesis is complex, resulting from a wide range of events that involve the interplay between genetic mutations and altered levels of transcripts, proteins, and metabolites. Metabolomics is a recently developed sub-area of omics sciences focused on the comprehensive analysis of small molecules involved in several biological pathways by high throughput technologies.

AIM OF REVIEW

This review summarizes and evaluates studies focused on the metabolomics analysis of OSCC and oral premalignant disorders to better interpret the complex process of oral carcinogenesis. Additionally, the metabolic biomarkers signatures identified so far are also included. Moreover, we discuss the limitations of these studies and make suggestions for future investigations.

KEY SCIENTIFIC CONCEPTS

Although many questions about the metabolic features of OSCC have already been answered in metabolomic studies, further validation and optimization are still required to translate these findings into clinical applications.

Metabolomics study of oral cancers

BACKGROUND

Oral cancer is one of the most frequently occurring cancers. Metabolic reprogramming is an important hallmark of cancer. Metabolomics characterizes all the small molecules in a biological sample, and a complete set of small molecules in such sample is referred as metabolome. Nuclear magnetic resonance spectroscopy and mass spectrometry are two widely used techniques in metabolomics studies. Increasing evidence demonstrates that metabolomics techniques can be used to explore the metabolic signatures in oral cancer. Elucidation of metabolic alterations in oral cancer is also important for the understanding of its pathological mechanisms.

AIM OF REVIEW

In this paper, we summarize the latest progress of metabolomics study in oral cancer and provide the suggestions for the future studies.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The metabolomics studies in saliva, serum, and tumor tissues revealed the existence of metabolic signatures in bio-fluids and tissues of oral cancer, and several tumor-specific metabolites identified in individual study could discriminate oral cancer from healthy controls or precancerous lesions, which are potential biomarkers for the screening or early diagnosis of oral cancer. Metabolomics study of oral cancers in the future should aim to establish a routine procedure with high sensitivity, profile intracellular metabolites to find out the metabolic characteristics of tumor cells, and investigate the mechanism behind metabolomic alterations and the metabolic response of cancer cells to chemotherapy.

Applications of Metabolomics in Cancer Studies

Since the start of metabolomics as a field of research, the number of studies related to cancer has grown to such an extent that cancer metabolomics now represents its own discipline. In this chapter, the applications of metabolomics in cancer studies are explored. Different approaches and analytical platforms can be employed for the analysis of samples depending on the goal of the study and the aspects of the cancer metabolome being investigated. Analyses have concerned a range of cancers including lung, colorectal, bladder, breast, gastric, oesophageal and thyroid, amongst others. Developments in these strategies and methodologies that have been applied are discussed, in addition to exemplifying the use of cancer metabolomics in the discovery of biomarkers and in the assessment of therapy (both pharmaceutical and nutraceutical). Finally, the application of cancer metabolomics in personalised medicine is presented.

Non-targeted and targeted metabolomics approaches to diagnosing lung cancer and predicting patient prognosis

Lung cancer is the most common cause of cancer death in China. We characterized metabolic alterations in lung cancer using two analytical platforms: a non-targeted metabolic profiling strategy based on proton nuclear magnetic resonance (1H-NMR) spectroscopy and a targeted metabolic profiling strategy based on rapid resolution liquid chromatography (RRLC). Changes in serum metabolite levels during oncogenesis were evaluated in 25 stage I lung cancer patients and matched healthy controls. We identified 25 metabolites that were differentially regulated between the lung cancer patients and matched controls. Of those, 16 were detected using the non-targeted approach and 9 were identified using the targeted approach. Both groups of metabolites could differentiate between lung cancer patients and healthy controls with 100% sensitivity and specificity. The principal metabolic alternations in lung cancer included changes in glycolysis, lipid metabolism, choline phospholipid metabolism, one-carbon metabolism, and amino acid metabolism. The targeted metabolomics approach was more sensitive, accurate, and specific than the non-targeted metabolomics approach. However, our data suggest that both metabolomics strategies could be used to detect early-stage lung cancer and predict patient prognosis.