A Comprehensive Analysis of Metabolomics and Transcriptomics in Cervical Cancer

An atlas of transcriptomic changes in human immune cells driven by 364 endogenous and gut-microbiota-derived metabolites

Metabolites, particularly those derived from gut microbiota, play crucial roles in modulating immune responses, but the impact of most metabolites on immune cells remains unexplored. To systematically investigate the effect of metabolites on immune cells, we treated peripheral blood mononuclear cells (PBMCs) with 364 endogenous and gut microbiota metabolites and analyzed their impact on the PBMC transcriptome using RNA sequencing (RNA-seq). Clustering analysis revealed three distinct metabolite groups (Cluster 0, 1, 2), each exerting unique immunomodulatory effects. Cluster 1 metabolites, enhanced inflammatory pathways (e.g., cytokine signaling, neutrophil migration) and suppressed ferroptosis, potentially prolonging immune cell activity. In contrast, Cluster 0 metabolites promoted antigen presentation and extracellular matrix repair, while Cluster 2 metabolites upregulated autophagy-related pathways (e.g., GTPase signaling, ubiquitin-protein regulation), suggesting anti-inflammatory and tissue-homeostatic functions. Immune deconvolution highlighted Cluster 1-driven monocyte-to-M0 macrophage differentiation and elevated activated dendritic/mast cells, aligning with pro-inflammatory outcomes. Metabolites in Clusters 0/2 were enriched in the TCA cycle and alanine/aspartate metabolism, whereas Cluster 1 metabolites correlated with beta-alanine and branched-chain amino acid pathways. Gut microbiota analysis identified 23 species overrepresented in Cluster 1, linking dysbiosis to inflammatory metabolite profiles. Together, this high-throughput atlas elucidates how bloodborne metabolites shape PBMC function, offering insights into metabolic-immune crosstalk and potential therapeutic targets for inflammatory and autoimmune disorders.

Thieno[2,3-b]pyridines as a Novel Strategy Against Cervical Cancer: Mechanistic Insights and Therapeutic Potential

Cervical cancer is the fourth leading cause of cancer mortality in women worldwide, with limited therapeutic options for advanced or recurrent cases. In this study, the effects of a recent thieno[2,3-b]pyridine derivative, (E)-3-amino-5-(3-bromophenyl)acryloyl)-N-(3-chloro-2-methylphenyl)-6-methylthieno[2,3-b]pyridine-2-carboxamide (compound 1), on two cervical cancer cell lines, HeLa and SiHa, are investigated. Cytotoxicity was assessed by MTT assay, apoptosis rates were measured by flow cytometry, and metabolic profiling was performed by GC-MS. The study also examined the expression of eight glycosphingolipids (GSLs) in cancer stem cells (CSCs) and non-CSCs to assess glycophenotypic changes. Compound 1 showed significant cytotoxicity in both cell lines, with apoptosis identified as the primary mechanism of cell death. A significant reduction in the CSC population was observed, particularly in the SiHa cell line. Compound 1 treatment altered GSL expression and decreased GM2 levels in both CSCs and non-CSCs in the SiHa cell line and Gg3Cer levels in the HeLa cell line. Metabolic profiling identified 23 and 21 metabolites in the HeLa and SiHa cell lines, respectively, with significant differences in metabolite expression after treatment. These results underscore the potential of compound 1 as a promising therapeutic candidate for cervical cancer and warrant further investigation in preclinical and clinical settings.

Dilated cardiomyopathy signature metabolic marker screening: Machine learning and multi-omics analysis

Objects

Our aim was to identify changes in the metabolome in dilated cardiomyopathy (DCM) as well as to construct a metabolic diagnostic model for DCM.

Methods

We utilized non-targeted metabolomics with a cross-sectional cohort of age- and sex-matched DCM patients and controls. Metabolomics data were analyzed using orthogonal partial least squares-discriminant analysis (OPLS-DA) and pathway analysis. It was validated in combination with transcriptome sequencing data from public databases. Machine learning models were used for the diagnosis of DCM.

Results

Using multiple analytical techniques, 130 metabolite alterations were identified in DCM compared to healthy controls. Perturbations in glycerophospholipid metabolism (GPL) were identified and validated as a characteristic metabolic pathway in DCM. Through the least absolute shrinkage and selection operator (LASSO), we identified the 7 most important GPL metabolites, including LysoPA (16:0/0:0), LysoPA (18:1(9Z)/0:0), PC (20:3(8Z,11Z,14Z)/20:1(11Z)), PC (20:0/14:0), LysoPC (16:0), PS(15:0/18:0), and PE(16:0/20:4 (5Z,8Z,11Z,14Z)). The machine learning models based on the seven metabolites all had good accuracy in distinguishing DCM [All area under the curve (AUC) > 0.900], and the artificial neural network (ANN) model performed the most consistently (AUC: 0.919 ± 0.075).

Conclusions

This study demonstrates that GPL metabolism may play a contributing role in the pathophysiological mechanisms of DCM. The 7-GPL metabolite model may help for early diagnosis of DCM.

Spatial transcriptomics reveals unique metabolic profile and key oncogenic regulators of cervical squamous cell carcinoma

BACKGROUND

As a prevalent and deadly malignant tumor, the treatment outcomes for late-stage patients with cervical squamous cell carcinoma (CSCC) are often suboptimal. Previous studies have shown that tumor progression is closely related with tumor metabolism and microenvironment reshaping, with disruptions in energy metabolism playing a critical role in this process. To delve deeper into the understanding of CSCC development, our research focused on analyzing the tumor microenvironment and metabolic characteristics across different regions of tumor tissue.

METHODS

Utilizing spatial transcriptomics (ST) sequencing technology, we conducted a study on FFPE (formalin-fixed paraffin-embedded) tumor samples from CSCC patients. Coupled with single-cell RNA sequencing (scRNA-seq) data after deconvolution, we described spatial distribution maps of tumor leading edge and core regions in detail. Tumor tissues were classified into hypermetabolic and hypometabolic regions to analyze the metabolism profiles and tumor differentiation degree across different spatial areas. We also employed The Cancer Genome Atlas (TCGA) database to examine the analysis results of ST data.

RESULTS

Our findings indicated a more complex tumor microenvironment in hypermetabolic regions. Cell-cell communication analysis showed that various cells in tumor microenvironment were influenced by the signalling molecule APP released by cancer cells and higher expression of APP was observed in hypermetabolic regions. Furthermore, our results revealed the correlation between APP and the transcription factor TRPS1. Both APP and TRPS1 demonstrated significant effects on cancer cell proliferation, migration, and invasion, potentially contributing to tumor progression.

CONCLUSIONS

Utilizing ST, scRNA-seq, and TCGA database, we examined the spatial metabolic profiles of CSCC tissues, including metabolism distribution, metabolic variations, and the relationship between metabolism and tumor differentiation degree. Additionally, potential cancer-promoting factors were proposed, offering a valuable foundation for the development of more effective treatment strategies for CSCC.

The Chinese herbal prescription Kang-Gong-Yan alleviates cervicitis by modulating metabolites and gut microbiota

CONTEXT

Cervicitis is a common gynecological inflammatory disease. The Chinese herbal prescription Kang-Gong-Yan (KGY) is clinically effective against cervicitis; however, the chemical constituents and therapeutic mechanism of KGY remain elusive.

OBJECTIVE

To analyze the chemical constituents of KGY and explore the potential mechanism of KGY in treating cervicitis.

MATERIALS AND METHODS

UHPLC-Q-Exactive Plus Orbitrap MS was used to identify the active compounds of KGY; Sprague-Dawley (SD) female rats were randomly divided into the control, model, and KGY groups. Phenol mucilage (25%) was slowly injected into the vagina and cervix of the rats to establish the cervicitis model. Then, rats in the KGY groups (low dose: 1 g/kg/d; medium dose: 5 g/kg/d; high dose: 10 g/kg/d) were continuously gavaged KGY for one week. HE staining was used to observe the cervical tissues of rats; ELISA was used to detect inflammatory factors in plasma; non-targeted metabolomics was used to analyze metabolites; 16S rRNA sequencing was used to analyze intestinal microorganisms.

RESULTS

KGY exerted anti-cervicitis effects and decreased the levels of IL-6, IL-1β, and TNF-α. The mechanism of KGY in treating cervicitis is mainly associated with betaine, amino acid, pyrimidine, and phospholipid metabolism by regulating fifteen metabolites. Moreover, KGY reversed cervicitis-induced gut dysbiosis by mediating five bacteria.

DISCUSSION AND CONCLUSIONS

The Chinese herbal prescription KGY may alleviate cervicitis by modulating metabolites and gut microbiota disorders. These findings provide a scientific basis for the clinical application of KGY and a new strategy for treating cervicitis in Chinese medicine.

Cervical mucus can be used for metabolite screening in cervical cancer

Approximately 660,000 women are diagnosed with cervical cancer annually. Current screening options such as cytology or human papillomavirus testing have limitations, creating a need to identify more effective ancillary biomarkers for triage. Here, we evaluated whether metabolomic analysis of cervical mucus metabolism could be used to identify biomarkers of cervical intraepithelial neoplasia (CIN) and cervical cancer. The case-control group consisted of 181 CIN, 69 squamous cell carcinoma (SCC) patients, and 48 healthy controls in the primary cohort. We undertook metabolomic analyses using ultra-HPLC-tandem mass spectrometry. Univariate and multivariate analyses were carried out to profile metabolite characteristics, and receiver operating characteristic (ROC) analysis identified biomarker candidates. Five metabolites conferred the highest discriminatory power for SCC: oxidized glutathione (GSSG) (area under the ROC curve, 0.924; 95% confidence interval, 0.877-0.971), malic acid (0.914, 0.859-0.968), kynurenine (0.884, 0.823-0.945), GSSG/glutathione (GSH) (0.936, 0.892-0.979), and kynurenine/tryptophan (0.909, 0.856-0.961). Malic acid was the best marker for detection of CIN2 or worse (0.858, 0.793-0.922) and was a clinically useful metabolite. We confirmed the reproducibility of the results by validation cohort. Additionally, metabolomic analyses revealed eight pathways strongly associated with cervical neoplasia. Of these, only the tricarboxylic acid cycle was strongly associated with all CINs and cancer, indicating active energy production. Aberrant arginine metabolism by decreasing arginine and increasing citrulline might reduce tumor immunity. Changes in cysteine-methionine and GSH pathways might drive the initiation and progression of cervical cancer. These results suggest that metabolic analysis can identify ancillary biomarkers and could improve our understanding of the pathophysiological mechanisms underlying cervical neoplasia.

GC-MS-based untargeted plasma metabolomics identifies a 2-biomarker panel for possible diagnosis of precancerous cervical intraepithelial neoplasia stages from cervical cancer

Cervical cancer (CC) remains a major health concern globally, much of the brunt of which is experienced by the low- and middle-income countries where screening in terms of cytology and DNA genotyping for the high-risk oncogenic subtypes of the human papilloma virus (hr-HPV) is either inadequate or performed rather late. In this study, we aimed to determine biomarkers or panels of biomarkers that are capable of diagnosing the precancerous cervical intraepithelial neoplasia (CIN) stages from healthy and CC patients via untargeted gas chromatography-mass spectrometry-based metabolomics. Various cross-comparisons were conducted from which differential metabolites were identified. The underlying metabolic pathways based on the differential metabolites identified from the various cross-comparisons mainly related to amino acids biosynthesis and metabolism and steroid hormone biosynthesis. From all cross-comparisons, two common metabolites namely, 2-methyl-1-propylamine (also known as isobutylamine) and estrone were found to possess excellent to good diagnostic abilities, especially in distinguishing the early stages of CIN (CIN I, CIN II) from healthy women and CC patients. These findings have clinical significance in the sense that, once validated the 2-biomarker panel could be adopted in clinical practice for early diagnosis of CIN and invasive carcinoma. This would therefore inform the choice of treatment to be initiated by the clinician.

Emerging biomarkers for non-invasive diagnosis and treatment of cancer: a systematic review

Cancer remains a global health challenge, necessitating continuous advancements in diagnostic and treatment strategies. This review focuses on the utility of non-invasive biomarkers in cancer diagnosis and treatment, their role in early detection, disease monitoring, and personalized therapeutic interventions. Through a systematic review of the literature, we identified 45 relevant studies that highlight the potential of these biomarkers across various cancer types, such as breast, prostate, lung, and colorectal cancers. The non-invasive biomarkers discussed include liquid biopsies, epigenetic markers, non-coding RNAs, exosomal cargo, and metabolites. Notably, liquid biopsies, particularly those based on circulating tumour DNA (ctDNA), have emerged as the most promising method for early, non-invasive cancer detection due to their ability to provide comprehensive genetic and epigenetic information from easily accessible blood samples. This review demonstrates how non-invasive biomarkers can facilitate early cancer detection, accurate subtyping, and tailored treatment strategies, thereby improving patient outcomes. It underscores the transformative potential of non-invasive biomarkers in oncology, highlighting their application for enhancing early detection, survival rates, and treatment precision in cancer care.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023474749 PROSPERO, identifier CRD42023474749.

An analysis of the vaginal microbiota and cervicovaginal metabolomics in cervical lesions and cervical carcinoma

Background

To explore the role of vaginal microbiota and metabolomics in the progression of cervical dysplasia.

Methods

The patient group consists of female patients with low-grade, high-grade cervical dysplasia, and cervical cancer. Normal cervix samples from health volunteers were used as controls. The metabolic fingerprints of cervicovaginal lavage were analyzed using liquid chromatography-mass spectrometry, while the vaginal microbiota was examined through 16S rRNA sequencing. Bioinformatic analysis was adopted to investigate the interplay between hosts and microbes. The vaginal metabolic and microbiota profiles of 90 female patients with cervical dysplasia and 10 controls were analyzed to discover the biological characteristics underlying the progression of cervical cancer.

Results

We found that Valyl-Glutamate, N, N'-Diacetylbenzidine, and Oxidized glutathione, which were involved in oxidative stress response, were discriminators to distinguish the normal cervix, invasive cervical carcinomas, and CIN3 from others. Cervical carcinoma was characterized by a large variety of vaginal microbes (dominated by non-Lactobacillus communities) compared to the control. These microbes affected amino acid and nucleotide metabolism, producing metabolites with cervical carcinoma and genital inflammation compared to the control group.

Conclusions

This study revealed that cervicovaginal metabolic profiles were determined by cervical cancer, vaginal microbiota, and their interplays. ROS metabolism can be used to discriminate normal cervix, CIN3, and invasive cervical carcinoma.

High-throughput metabolomics identifies new biomarkers for cervical cancer

BACKGROUND

Cervical cancer (CC) is a danger to women's health, especially in many developing countries. Metabolomics can make the connection between genotypes and phenotypes. It provides a wide spectrum profile of biological processes under pathological or physiological conditions.

METHOD

In this study, we conducted plasma metabolomics of healthy volunteers and CC patients and integratively analyzed them with public CC tissue transcriptomics from Gene Expression Omnibus (GEO).

RESULT

Here, we screened out a panel of 5 metabolites to precisely distinguish CC patients from healthy volunteers. Furthermore, we utilized multi-omics approaches to explore patients with stage I-IIA1 and IIA2-IV4 CC and comprehensively analyzed the dysregulation of genes and metabolites in CC progression. We identified that plasma levels of trimethylamine N-oxide (TMAO) were associated with tumor size and regarded as a risk factor for CC. Moreover, we demonstrated that TMAO could promote HeLa cell proliferation in vitro. In this study, we delineated metabolic profiling in healthy volunteers and CC patients and revealed that TMAO was a potential biomarker to discriminate between I-IIA1 and IIA2-IV patients to indicate CC deterioration.

CONCLUSION

Our study identified a diagnostic model consisting of five metabolites in plasma that can effectively distinguish CC from healthy volunteers. Furthermore, we proposed that TMAO was associated with CC progression and might serve as a potential non-invasive biomarker to predict CC substage.

IMPACT

These findings provided evidence of the important role of metabolic molecules in the progression of cervical cancer disease, as well as their ability as potential biomarkers.

Candida albicans Infection Disrupts the Metabolism of Vaginal Epithelial Cells and Inhibits Cellular Glycolysis

Vulvovaginal candidiasis (VVC) is a common gynecologic disorder caused by fungal infections of the vaginal mucosa, with the most common pathogen being Candida albicans (C. albicans). Exploring metabolite changes in the disease process facilitates further discovery of targets for disease treatment. However, studies on the metabolic changes caused by C. albicans are still lacking. In this study, we used C. albicans-infected vaginal epithelial cells to construct an in vitro model of VVC, analyzed the metabolites by UHPLC-Q-Exactive MS, and screened the potential metabolites based on metabolomics. The results showed that C. albicans infection resulted in significant up-regulation of D-arabitol, palmitic acid, adenosine, etc.; significant down-regulation of lactic acid, nicotinamide (NAM), nicotinate (NA), etc.; and disruption of amino acid metabolism, and that these significantly altered metabolites might be potential therapeutic targets of VVC. Further experiments showed that C. albicans infection led to a decrease in glycolytic enzymes in damaged cells, inhibiting glycolysis and leading to significant alterations in glycolytic metabolites. The present study explored the potential metabolites of VVC induced by C. albicans infection based on metabolomics and verified the inhibitory effect of C. albicans on vaginal epithelial cell glycolysis, which is valuable for the diagnosis and treatment of VVC.

The Correlation Between Essential Amino Acid Tryptophan, Lysine, Phenylalanine and Chemotherapy of Breast Cancer

To investigate the differences in serum tryptophan, lysine, and phenylalanine levels in breast cancer patients, the correlation between the three amino acids with the chemotherapy regimen, and their significance in the clinical diagnosis and treatment of breast cancer.Clinical data were collected from the Department of Breast Surgery at Yunnan Cancer Hospital, encompassing 216 cases from July to December 2020, including 91 healthy individuals, 38 with benign tumors, and 87 with cancer. Amino acid levels were measured using liquid chromatography-tandem mass spectrometry. Statistical analyses, such as the Kruskal-Wallis H-test and Wilcoxon test, were conducted to compare the levels of these amino acids across the healthy group, benign tumor group, and breast cancer group. The χ2 test and Fisher's exact probability method were employed to assess the relationship between amino acid levels and breast cancer stage, grade, and chemotherapy regimen.The results indicated that there were significant differences in serum lysine (H = 36.13, P < .001) and phenylalanine (H = 34.03, P < .001) levels among the three groups. However, tryptophan levels did not show statistically significant variances. Specifically, lysine and phenylalanine levels were significantly different when comparing the healthy group with the breast cancer group and the benign tumor group with the breast cancer group. These differences were not significant when comparing the healthy group with the benign tumor group. Furthermore, there were no statistically significant distinctions observed in lysine (F = 0.836, P > .05) and phenylalanine (F = 1.466, P > .05) levels across different conventional chemotherapy regimens among the breast cancer cases studied.Serum lysine and phenylalanine levels might serve as potential biomarkers for breast cancer, and the choice of chemotherapy regimen is unlikely to impact significant changes in these amino acid levels.

Research progress of metabolomics in cervical cancer

INTRODUCTION

Cervical cancer threatens women's health seriously. In recent years, the incidence of cervical cancer is on the rise, and the age of onset tends to be younger. Prevention, early diagnosis and specific treatment have become the main means to change the prognosis of cervical cancer patients. Metabolomics research can directly reflect the changes of biochemical processes and microenvironment in the body, which can provide a comprehensive understanding of the changes of metabolites in the process of disease occurrence and development, and provide new ways for the prevention and diagnosis of diseases.

OBJECTIVES

The aim of this study is to review the metabolic changes in cervical cancer and the application of metabolomics in the diagnosis and treatment.

METHODS

PubMed, Web of Science, Embase and Scopus electronic databases were systematically searched for relevant studies published up to 2022.

RESULTS

With the emergence of metabolomics, metabolic regulation and cancer research are further becoming a focus of attention. By directly reflecting the changes in the microenvironment of the body, metabolomics research can provide a comprehensive understanding of the patterns of metabolites in the occurrence and development of diseases, thus providing new ideas for disease prevention and diagnosis.

CONCLUSION

With the continuous, in-depth research on metabolomics research technology, it will bring more benefits in the screening, diagnosis and treatment of cervical cancer with its advantages of holistic and dynamic nature.

Endocrine-disrupting compounds and metabolomic reprogramming in breast cancer

Endocrine-disrupting chemicals pose a growing threat to human health through their increasing presence in the environment and their potential interactions with the mammalian endocrine systems. Due to their structural similarity to hormones like estrogen, these chemicals can interfere with endocrine signaling, leading to many deleterious effects. Exposure to estrogenic endocrine-disrupting compounds (EDC) is a suggested risk factor for the development of breast cancer, one of the most frequently diagnosed cancers in women. However, the mechanisms through which EDCs contribute to breast cancer development remain elusive. To rapidly proliferate, cancer cells undertake distinct metabolic programs to utilize existing nutrients in the tumor microenvironment and synthesize macromolecules de novo. EDCs are known to dysregulate cell signaling pathways related to cellular metabolism, which may be an important mechanism through which they exert their cancer-promoting effects. These altered pathways can be studied via metabolomic analysis, a new advancement in -omics technologies that can interrogate molecular pathways that favor cancer development and progression. This review will summarize recent discoveries regarding EDCs and the metabolic reprogramming that they may induce to facilitate the development of breast cancer.

Metabolomic profiling identifies hair as a robust biological sample for identifying women with cervical cancer

Metabolomics serves as a useful tool for identifying biomarkers of disease and uncovering pathogenic mechanisms. However, most metabolomic studies use biological fluids such as blood and urine as biospecimens, which could be dramatically influenced by daily activities and dietary variation, resulting in measurement fluctuations. In contrast, hair may serve as a robust source of stable longitudinal metabolite information. Here, we conducted a pilot study to investigate the possibility of using hair as a biospecimen for the metabolomic analysis of cervical cancer. Hair, plasma, urine, and cervical tissue samples from cervical cancer and benign tumor patients were collected. Biospecimens were then tested using a gas chromatography-mass spectrometry-based metabolomic platform. The expressions of enzymatic genes related to metabolic changes were validated using qPCR. Statistical analyses were calculated via the R-console platform. Metabolite profiles in both hair and cervical tissue samples were significantly different between cancer and control groups, while no difference was observed in plasma and urine samples. Further analysis showed that most of the altered metabolites in hair were upregulated, and they had a negative correlation with those in the cervical tissue. Eight common metabolites showed an area under the Receiver Operating Characteristic curve greater than 0.95. These metabolites primarily participated in amino acid metabolism, cofactor synthesis, ferroptosis, and glycolysis. The gene expressions (IDH1, OGDH, GLUD1, ENO1, GSS, and GPX4) associated with the shortlisted metabolic pathways were also upregulated. Our study is the first to reveal metabolomic changes of hair in cervical cancer patients and demonstrates the potential for the hair metabolome to be used for biomarker identification in cervical cancer.

Prediction of Postoperative Pathologic Risk Factors in Cervical Cancer Patients Treated with Radical Hysterectomy by Machine Learning

Pretherapeutic serological parameters play a predictive role in pathologic risk factors (PRF), which correlate with treatment and prognosis in cervical cancer (CC). However, the method of pre-operative prediction to PRF is limited and the clinical availability of machine learning methods remains unknown in CC. Overall, 1260 early-stage CC patients treated with radical hysterectomy (RH) were randomly split into training and test cohorts. Six machine learning classifiers, including Gradient Boosting Machine, Support Vector Machine with Gaussian kernel, Random Forest, Conditional Random Forest, Naive Bayes, and Elastic Net, were used to derive diagnostic information from nine clinical factors and 75 parameters readily available from pretreatment peripheral blood tests. The best results were obtained by RF in deep stromal infiltration prediction with an accuracy of 70.8% and AUC of 0.767. The highest accuracy and AUC for predicting lymphatic metastasis with Cforest were 64.3% and 0.620, respectively. The highest accuracy of prediction for lymphavascular space invasion with EN was 59.7% and the AUC was 0.628. Blood markers, including D-dimer and uric acid, were associated with PRF. Machine learning methods can provide critical diagnostic prediction on PRF in CC before surgical intervention. The use of predictive algorithms may facilitate individualized treatment options through diagnostic stratification.

Multi-Omics Analysis of Transcriptomic and Metabolomics Profiles Reveal the Molecular Regulatory Network of Marbling in Early Castrated Holstein Steers

The intramuscular fat (IMF), or so-called marbling, is known as potential determinant of the high quality beef in China, Korea, and Japan. Of the methods that affect IMF content in cattle, castration is markedly regarded as an effective and economical way to improve the deposition of IMF but with little attention to its multi-omics in early-castrated cattle. The aim of this study was to investigate the liver transcriptome and metabolome of early-castrated Holstein cattle and conduct a comprehensive analysis of two omics associated with the IMF deposition using transcriptomics and untargeted metabolomics under different treatments: non−castrated and slaughtered at 16 months of age (GL16), castrated at birth and slaughtered at 16 months of age (YL16), and castrated at birth and slaughtered at 26 months of age (YL26). The untargeted metabolome was analyzed using ultrahigh-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. The transcriptome of the hepatic genes was analyzed to identify marbling-related genes. Using untargeted metabolomics, the main altered metabolic pathways in the liver of cattle, including those for lipid and amino acid metabolism, were detected in the YL16 group relative to the GL16 and YL26 groups. Significant increases in the presence of betaine, alanine, and glycerol 3-phosphate were observed in the YL16 group (p < 0.05), which might have contributed to the improved beef-marbling production. Compared to the GL16 and YL26 groups, significant increases in the presence of glutathione, acetylcarnitine, and riboflavin but decreases in diethanolamine and 2-hydroxyglutarate were identified in YL16 group (p < 0.05), which might have been beneficial to the beef’s enhanced functional quality. The gene expressions of GLI1 and NUF2 were downregulated and that of CYP3A4 was upregulated in the YL16 group; these results were strongly correlated with the alanine, betaine, and leucine, respectively, in the liver of the cattle. In conclusion, implementation of early castration modified the hepatic metabolites and the related biological pathways by regulating the relevant gene expressions, which could represent a better rearing method for production of high marbled and healthier beef products.

Emerging metabolomic tools to study cancer metastasis

Metastasis is responsible for 90% of deaths in patients with cancer. Understanding the role of metabolism during metastasis has been limited by the development of robust and sensitive technologies that capture metabolic processes in metastasizing cancer cells. We discuss the current technologies available to study (i) metabolism in primary and metastatic cancer cells and (ii) metabolic interactions between cancer cells and the tumor microenvironment (TME) at different stages of the metastatic cascade. We identify advantages and disadvantages of each method and discuss how these tools and technologies will further improve our understanding of metastasis. Studies investigating the complex metabolic rewiring of different cells using state-of-the-art metabolomic technologies have the potential to reveal novel biological processes and therapeutic interventions for human cancers.

Plasma-Based Metabolomics Profiling of High-Risk Human Papillomavirus and their Emerging Roles in the Progression of Cervical Cancer

High-risk human papillomavirus (HR-HPV) is the main etiological factor for cervical cancer. Accumulating evidence has suggested the active role of metabolites in the initiation and progression of cancers. This study explored the plasma metabolic profiles of HPV-16 positive (HPV16 (+)), HPV-18 positive (HPV18 (+)), and HPV negative (CTL) individuals using a nontargeted metabolomics approach. C8 ceramide-1-Phosphate (d18 : 1/8 : 0) was found to inhibit cervical cancer cell proliferation and migration in vitro, evidenced by CCK8 experiments, a cell migration test, RT-qPCR, and western blotting. The underlying mechanism demonstrated that C8 inhibited proliferation and migration in cervical cancer cells via the MAPK/JNK1 signaling pathway. These findings may contribute to the clinical treatment of HR-HPV-induced cervical cancer by intervening in its initiation and progression.

Microbiome-metabolome analysis reveals cervical lesion alterations

Cervical cancer (CC) continues to be one of the most common cancers among females worldwide. It takes a few years or even decades for CC to arise in a minority of women with cervical precancers. An increasing corpus of studies today indicates that local microecology and carcinogenesis are intimately related. To investigate the changes in cericovaginal microecology with the development of cervical cancer, we performed 16S rDNA sequencing and metabolomic analysis in cericovaginal fluid from 10 LSIL patients, 10 HSIL patients, 10 CC patients and 10 healthy controls to reveal the differential flora and metabolites during cervical carcinogenesis. Carcinogenesis is associated with alterations in microbiome diversity, individual taxa, and functions with notable changes in Lactobacillus, Prevotella and Aquabacterium, as well as in cervicovaginal metabolites that correlate with cervicovaginal microbial patterns. Increased bacterial diversity and a decline in the relative abundance of Lactobacillus, the dominant species in the cericovaginal flora, are observed when cervical lesions advance. According to KEGG pathway enrichment analysis, lipids and organic acids change as cervical cancer progresses, and the phenylalanine, tyrosine, and tryptophan biosynthesis pathway is essential for the development of cervical cancer. Our results reveal that microbic and metabolomic profiling is capable of distinguishing CC from precancer and highlights potential biomarkers for the early detection of cervical dysplasia. These differential microorganisms and metabolites are expected to become a potential tool to assist in the diagnosis of cervical cancer.

Exploration of biomarkers for the diagnosis, treatment and prognosis of cervical cancer: a review

As the fourth most diagnosed cancer, cervical cancer (CC) is one of the major causes of cancer-related mortality affecting females globally, particularly when diagnosed at advanced stage. Discoveries of CC biomarkers pave the road to precision medicine for better patient outcomes. High throughput omics technologies, characterized by big data production further accelerate the process. To date, various CC biomarkers have been discovered through the advancement in technologies. Despite, very few have successfully translated into clinical practice due to the paucity of validation through large scale clinical studies. While vast amounts of data are generated by the omics technologies, challenges arise in identifying the clinically relevant data for translational research as analyses of single-level omics approaches rarely provide causal relations. Integrative multi-omics approaches across different levels of cellular function enable better comprehension of the fundamental biology of CC by highlighting the interrelationships of the involved biomolecules and their function, aiding in identification of novel integrated biomarker profile for precision medicine. Establishment of a worldwide Early Detection Research Network (EDRN) system helps accelerating the pace of biomarker translation. To fill the research gap, we review the recent research progress on CC biomarker development from the application of high throughput omics technologies with sections covering genomics, transcriptomics, proteomics, and metabolomics.

Study on the mechanism of anti-hepatic fibrosis of Glycyrrhiza Uralensis-Salvia miltiorrhiza prescription based on serum and urine metabolomics and network pharmacology

Hepatic fibrosis (HF) is a kind of chronic epidemic liver disease. Glycyrrhiza Uralensis and Salvia Miltiorrhiza (GUSM), traditional Chinese medicine, has the obvious clinical treatment of liver fibrosis. This study aimed to investigate the mechanisms of GUSM against HF by an integrated strategy combining untargeted metabolomics with network pharmacology. The results showed that GUSM prescription can improve the morphology and structure of liver tissue, inhibit the proliferation of collagen fibers and reducing the inflammatory response of the liver and so on. Endogenous metabolites and HF-related potential biomarkers in serum and urine were detected by ultra-high-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF/MS). The metabolic pathways were enriched by MetaboAnalyst. GUSM prescription showed an antifibrotic effect on rats by regulating metabolic pathways, mainly pentose and glucuronate interconversions and arachidonic acid metabolism. Network pharmacology was then applied to find 42 overlapping targets of GUSM-HF. Quercetin was found to be the main active component and STAT3 was the main active target in GUSM prescription. Molecular docking showed high affinities between quercetin and STAT3. Therefore, GUSM has protective effects on HF by regulating the metabolism and different signaling pathways. The work also shows that the metabolomic and network pharmacology methods are promising tools to gain insight into the efficacy and mechanism research of traditional Chinese medicines.

Eleutheroside E functions as anti-cervical cancer drug by inhibiting the phosphatidylinositol 3-kinase pathway and reprogramming the metabolic responses

OBJECTIVES

Cervical cancer (CC) is the common female malignant tumour with non-negligible morbidity and mortality. Eleutheroside E (EE) has anti-oxidative stress, anti-inflammatory and anti-proliferation effects in diverse disease models. However, its anti-tumour role remains unclear.

METHODS

The cell viability, apoptosis rate and protein expressions were detected by CCK-8, flow cytometry and western blot assays, respectively. The metabolic profile was performed by GC/MS analysis. Furthermore, the effect of EE on CC was verified in nude mice.

KEY FINDINGS

EE notably decreased the viability and increased the cell apoptosis, which could be reversed with 740Y-P treatment. EE treatment changed the metabolic categories of SiHa cells. The fatty acids signalling pathway was the most outstanding differential pathway. Myo-inositol prominently enhanced the level of phosphorylated Akt in a dose-dependent way. Moreover, EE declined the tumour volume and weight and the proliferation, but promoted the apoptosis in vivo. EE reduced the relative expression of phosphorylated PI3K and Akt. However, all these in-vivo results were observably antagonized with myo-inositol treatment.

CONCLUSIONS

EE plays an anti-tumour role in CC via inhibiting the PI3K pathway and reprogramming the metabolic responses.

Shotgun Lipidomics for Differential Diagnosis of HPV-Associated Cervix Transformation

A dramatic increase in cervical diseases associated with human papillomaviruses (HPV) in women of reproductive age has been observed over the past decades. An accurate differential diagnosis of the severity of cervical intraepithelial neoplasia and the choice of the optimal treatment requires the search for effective biomarkers with high diagnostic and prognostic value. The objective of this study was to introduce a method for rapid shotgun lipidomics to differentiate stages of HPV-associated cervix epithelium transformation. Tissue samples from 110 HPV-positive women with cervicitis (n = 30), low-grade squamous intraepithelial lesions (LSIL) (n = 30), high-grade squamous intraepithelial lesions (HSIL) (n = 30), and cervical cancers (n = 20) were obtained. The cervical epithelial tissue lipidome at different stages of cervix neoplastic transformation was studied by a shotgun label-free approach. It is based on electrospray ionization mass spectrometry (ESI-MS) data of a tissue extract. Lipidomic data were processed by the orthogonal projections to latent structures discriminant analysis (OPLS-DA) to build statistical models, differentiating stages of cervix transformation. Significant differences in the lipid profile between the lesion and surrounding tissues were revealed in chronic cervicitis, LSIL, HSIL, and cervical cancer. The lipids specific for HPV-induced cervical transformation mainly belong to glycerophospholipids: phosphatidylcholines, and phosphatidylethanolamines. The developed diagnostic OPLS-DA models were based on 23 marker lipids. More than 90% of these marker lipids positively correlated with the degree of cervix transformation. The algorithm was developed for the management of patients with HPV-associated diseases of the cervix, based on the panel of 23 lipids as a result. ESI-MS analysis of a lipid extract by direct injection through a loop, takes about 25 min (including preparation of the lipid extract), which is significantly less than the time required for the HPV test (several hours for hybrid capture and about an hour for PCR). This makes lipid mass spectrometric analysis a promising method for express diagnostics of HPV-associated neoplastic diseases of the cervix.

Artificial intelligence in cancer target identification and drug discovery

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

Small extracellular vesicles (exosomes) and their cargo in pancreatic cancer: Key roles in the hallmarks of cancer

Pancreatic cancer (PC) is a devastating disease, offering poor mortality rates for patients. The current challenge being faced is the inability to diagnose patients in a timely manner, where potentially curative resection provides the best chance of survival. Recently, small/nanosized extracellular vesicles (sEVs), including exosomes, have gained significant preclinical and clinical attention due to their emerging roles in cancer progression and diagnosis. Extracellular vesicles (EVs) possess endogenous properties that offer stability and facilitate crossing of biological barriers for delivery of molecular cargo to cells, acting as a form of intercellular communication to regulate function and phenotype of recipient cells. This review provides an overview of the role of EVs, their subtypes and their oncogenic cargo (as characterised by targeted studies as well as agnostic '-omics' analyses) in the pathobiology of pancreatic cancer. The discussion covers the progress of 'omics technology' that has enabled elucidation of the molecular mechanisms that mediate the role of EVs and their cargo in pancreatic cancer progression.

CKMT1A is a novel potential prognostic biomarker in patients with endometrial cancer

PURPOSE

The International Federation of Gynecology and Obstetrics (FIGO) stage remains the standard staging system for the assessment of endometrial cancer (EC) prognosis. Thus, we aim to identify the significant genes or biomarkers associated with the stage of endometrial cancer, which may also help reveal the mechanism of EC progression and assess the prognosis of patients with EC.

MATERIALS AND METHODS

We compared the mRNA expression levels of EC patients with stages I and II as well as stages III and IV in the Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) of EC patients at different stages were selected by volcano plot and Venn analysis. Gene Ontology (GO) and Pathways were applied to analyze the identified genes. Protein protein interaction (PPI) network was employed to identify the correlation. The survival analyses based on TCGA database were conducted for further screening. The Human Protein Atlas, quantitative PCR and immunohistochemistry were utilized to confirm the differences in expression of DEGs in endometrial cancer samples at different FIGO stages.

RESULTS

CKMT1A was identified as a candidate gene. Through survival analyses, we found that CKMT1A may be a poor prognostic factor in the overall survival of endometrial cancer patients. GO and Pathways revealed that CKMT1A is closely associated with the metabolic process. More importantly, Human Protein Atlas and quantitative PCR confirmed the differences in expression of CKMT1A in endometrial cancer samples at different FIGO stages.

CONCLUSION

In summary, this study shows that CKMT1A is a newly identified essential tumor progression regulator of endometrial cancer, which may give rise to novel therapeutic strategies in the management of endometrial cancer patients to prolong its prognosis and prevent tumor progression.

Metabolomics Strategy Assisted by Transcriptomics Analysis to Identify Potential Biomarkers Associated with Tuberculosis

Purpose

To investigate the dysregulated pathways and identify reliable diagnostic biomarkers for tuberculosis using integrated analysis of metabolomics and transcriptomics.

Methods

Three groups of samples, untargeted metabolomics analysis of healthy controls (HC), latent tuberculosis infection patients (LTBI), and active tuberculosis patients (TB), were analyzed using gas chromatography time-of-flight mass spectrometry (GC-TOF MS) and ultra-high performance liquid chromatography-quantitative mass spectrometry (UHPLC-QE-MS). Both univariate and multivariate and statistical analyses were used to select differential metabolites (DMs) among group comparison, and LASSO regression analysis was employed to discover potential diagnostic biomarkers. Metabolite set enrichment analysis was performed to identify the altered metabolic pathways specifically in patients with TB. Meanwhile, a transcriptomic dataset GSEG4992 was downloaded from the GEO database to explore the differentially expressed genes (DEGs) between TB and HC identified in significantly enriched pathways. Finally, an integrative analysis of DMs and DEGs was performed to investigate the possible molecular mechanisms of TB.

Results

Thirty-three specific metabolites were significantly different between TB and HC, of which 7 (5-hydroxyindoleacetic acid, isoleucyl-isoleucine, heptadecanoic acid, indole acetaldehyde, 5-ethyl-2,4-dimethyloxazole, and 2-hydroxycaproic acid, unknown 71) were chosen as combinational potential biomarkers for TB. The area under the curve (AUC) value of these biomarkers was 0.97 (95% CI: 0.92–1.00). Metabolites set enrichment analysis (MSEA) displayed that there were 3 significantly enriched pathways among all. The genes in 3 significantly enriched pathways were further analyzed, of which 9(ALDH3B1, BCAT1, BCAT2, GLYAT, GOT1, IL4I1, MIF, SDS, SDSL) were expressed differentially. The area under the curve (AUC) values of these DEGs enriched in pathways mostly were greater than 0.8. As a result, a connected network of metabolites and genes in the pathways were established, which provides insights into the credibility of selected metabolites.

Conclusion

The newly identified metabolic biomarkers display a high potential to be developed into a promising tool for TB screening, diagnosis, and therapeutic effect monitoring.

Identification of Cross-Pathway Connections via Protein-Protein Interactions Linked to Altered States of Metabolic Enzymes in Cervical Cancer

Metabolic reprogramming is one of the emerging hallmarks of cancer cells. Various factors, such as signaling proteins (S), miRNA, and transcription factors (TFs), may play important roles in altering the metabolic status in cancer cells by interacting with metabolic enzymes either directly or via protein-protein interactions (PPIs). Therefore, it is important to understand the coordination among these cellular pathways, which may provide better insight into the molecular mechanism behind metabolic adaptations in cancer cells. In this study, we have designed a cervical cancer-specific supra-interaction network where signaling pathway proteins, TFs, and microRNAs (miRs) are connected to metabolic enzymes via PPIs to investigate novel molecular targets and connections/links/paths regulating the metabolic enzymes. Using publicly available omics data and PPIs, we have developed a Hidden Markov Model (HMM)-based mathematical model yielding 94, 236, and 27 probable links/paths connecting signaling pathway proteins, TFs, and miRNAs to metabolic enzymes, respectively, out of which 83 paths connect to six common metabolic enzymes (RRM2, NDUFA11, ENO2, EZH2, AKR1C2, and TYMS). Signaling proteins (e.g., PPARD, BAD, GNB5, CHECK1, PAK2, PLK1, BRCA1, MAML3, and SPP1), TFs (e.g., KAT2B, ING1, MED1, ZEB1, AR, NCOA2, EGR1, TWIST1, E2F1, ID4, RBL1, ESR1, and HSF2), and miR (e.g., mir-147a, mir-593-5p, mir-138-5p, mir-16-5p, and mir-15b-5p) were found to regulate two key metabolic enzymes, EZH2 and AKR1C2, with altered metabolites (L-lysine and tetrahydrodeoxycorticosterone, THDOC) status in cervical cancer. We believe, the biology-based approach of our system will pave the way for future studies, which could be aimed toward identifying novel signaling, transcriptional, and post-transcriptional regulators of metabolic alterations in cervical cancer.

Metabolic reprogramming in cervical cancer and metabolomics perspectives

Cumulative studies have shown that metabolic reprogramming is a hallmark of malignant tumors. The emergence of technological advances, such as omics studies, has strongly contributed to the knowledge of cancer metabolism. Cervical cancer is among the most common cancers in women worldwide. Because cervical cancer is a virus-associated cancer and can exist in a precancerous state for years, investigations targeting the metabolic phenotypes of cervical cancer will enhance our understanding of the interference of viruses on host cells and the progression of cervical carcinogenesis. The purpose of this review was to illustrate metabolic perturbations in cervical cancer, the role that human papillomavirus (HPV) plays in remodeling cervical cell metabolism and recent approaches toward application of metabolomics in cervical disease research. Cervical cancer displays typical cancer metabolic profiles, including glycolytic switching, high lactate levels, lipid accumulation and abnormal kynurenine/tryptophan levels. HPV, at least in part, contributes to these alterations. Furthermore, emerging metabolomics data provide global information on the metabolic traits of cervical diseases and may aid in the discovery of biomarkers for diagnosis and therapy.

Protein tyrosine phosphatase, receptor type B is a potential biomarker and facilitates cervical cancer metastasis via epithelial-mesenchymal transition

Cervical cancer (CC) is one of the most common malignant tumors. This study analyzed the impact of protein tyrosine phosphatase, receptor type B (PTPRB) on malignant behavior of CC and explored its possible molecular mechanism. RT-PCR, western blot and Immunohistochemistry were applied to examine the expression of PTPRB in CC specimens and cells. Aberrant PTPRB expression in CC and survival outcomes were constructed using The Cancer Genome Atlas (TCGA) database and tissue microarray cervical squamous cell carcinoma cohort. Cultured human CC cells were assayed for viability, apoptosis, migration, and invasion in vitro and in vivo. Kyoto Encyclopedia of Genes and Genomes (KEGG) assays and gene set enrichment analysis (GSEA) assays were used to delve into PTPRB-related pathways using TCGA datasets. The levels of proteins associated with the epithelial-mesenchymal transition (EMT) pathway and modulated by PTPRB were examined through Western blot. We found that the levels of PTPRB in CC tissues and cells were distinctly up-regulated. PTPRB was also an unfavorable prognostic factor for CC patients. Functionally, PTPRB knockdown exhibits tumor-suppressive function via reducing cell proliferation and metastasis and inducing cell apoptosis. KEGG assays and GSEA assays suggested PTPRB overexpression was associated with several tumor-related pathways. The results of Western blot assays suggested that N-cadherin was decreased in the PTPRB-knockdown CC cells, while E-cadherin was increased. Overall, PTPRB is highly expressed in CC and can effectively enhance the proliferation, metastasis and EMT process of tumor cells. PTPRB is expected to be a therapeutic target for CC.

Free Amino Acid Alterations in Patients with Gynecological and Breast Cancer: A Review

Gynecological and breast cancers still remain a significant health problem worldwide. Diagnostic methods are not sensitive and specific enough to detect the disease at an early stage. During carcinogenesis and tumor progression, the cellular need for DNA and protein synthesis increases leading to changes in the levels of amino acids. An important role of amino acids in many biological pathways, including biosynthesis of proteins, nucleic acids, enzymes, etc., which serve as an energy source and maintain redox balance, has been highlighted in many research articles. The aim of this review is a detailed analysis of the literature on metabolomic studies of gynecology and breast cancers with particular emphasis on alterations in free amino acid profiles. The work includes a brief overview of the metabolomic methodology and types of biological samples used in the studies. Special attention was paid to the possible role of selected amino acids in the carcinogenesis, especially proline and amino acids related to its metabolism. There is a clear need for further research and multiple external validation studies to establish the role of amino acid profiling in diagnosing gynecological and breast cancers.

Cross-Species Comparison of Metabolomics to Decipher the Metabolic Diversity in Ten Fruits

Fruits provide humans with multiple kinds of nutrients and protect humans against worldwide nutritional deficiency. Therefore, it is essential to understand the nutrient composition of various fruits in depth. In this study, we performed LC-MS-based non-targeted metabolomic analyses with ten kinds of fruit, including passion fruit, mango, starfruit, mangosteen, guava, mandarin orange, grape, apple, blueberry, and strawberry. In total, we detected over 2500 compounds and identified more than 300 nutrients. Although the ten fruits shared 909 common-detected compounds, each species accumulated a variety of species-specific metabolites. Additionally, metabolic profiling analyses revealed a constant variation in each metabolite’s content across the ten fruits. Moreover, we constructed a neighbor-joining tree using metabolomic data, which resembles the single-copy protein-based phylogenetic tree. This indicates that metabolome data could reflect the genetic relationship between different species. In conclusion, our work enriches knowledge on the metabolomics of fruits, and provides metabolic evidence for the genetic relationships among these fruits.

From infection to cancer: how DNA tumour viruses alter host cell central carbon and lipid metabolism

Infections cause 13% of all cancers globally, and DNA tumour viruses account for almost 60% of these cancers. All viruses are obligate intracellular parasites and hijack host cell functions to replicate and complete their life cycles to produce progeny virions. While many aspects of viral manipulation of host cells have been studied, how DNA tumour viruses manipulate host cell metabolism and whether metabolic alterations in the virus life cycle contribute to carcinogenesis are not well understood. In this review, we compare the differences in central carbon and fatty acid metabolism in host cells following infection, oncogenic transformation, and virus-driven cancer of DNA tumour viruses including: Epstein-Barr virus, hepatitis B virus, human papillomavirus, Kaposi's sarcoma-associated herpesvirus and Merkel cell polyomavirus.

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

While cost-effective high-throughput technologies provide an increasing amount of data, the analyses of single layers of data seldom provide causal relations. Multi-omics data integration strategies across different cellular function levels, including genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes offer unparalleled opportunities to understand the underlying biology of complex diseases, such as cancer. We review some of the most frequently used data integration methods and outline research areas where multi-omics significantly benefit our understanding of the process and outcome of the malignant transformation. We discuss algorithmic frameworks developed to reveal cancer subtypes, disease mechanisms, and methods for identifying driver genomic alterations and consider the significance of multi-omics in tumor classifications, diagnostics, and prognostications. We provide a comprehensive summary of each omics strategy's most recent advances within the clinical context and discuss the main challenges facing their clinical implementations. Despite its unparalleled advantages, multi-omics data integration is slow to enter everyday clinics. One major obstacle is the uneven maturity of different omics approaches and the growing gap between generating large volumes of data compared to data processing capacity. Progressive initiatives to enforce the standardization of sample processing and analytical pipelines, multidisciplinary training of experts for data analysis and interpretation are vital to facilitate the translatability of theoretical findings.

UPLC-MS/MS-based Lipidomic Profiles Revealed Aberrant Lipids Associated with Invasiveness of Silent Corticotroph Adenoma

CONTEXT

The accumulation of aberrant lipids and abnormal lipid metabolism in silent corticotroph adenomas (SCAs) could contribute to changes in clinical phenotypes, especially sphenoid sinus invasion.

OBJECTIVE

To systematically investigate lipidomic and transcriptomic alterations associated with invasiveness and their potential molecular mechanisms in SCAs and to provide candidate biomarkers for predicting invasiveness and novel treatment options for invasive SCAs by targeting lipids.

METHODS

Fifty-four SCAs (34 invasive/20 noninvasive) were subjected to lipidomic analysis based on ultraperformance liquid chromatography mass spectrometry, and 42 clinically nonfunctioning pituitary adenomas (23 invasive/19 noninvasive) were subjected to transcriptomic analysis. Differential analysis was performed to determine differential lipids and genes between invasive and noninvasive tumors. A functionally connected network was constructed with the molecular pathways as cores. Multiple machine learning methods were applied to identify the most critical lipids, which were further used to construct a lipidomic signature to predict invasive SCAs by multivariate logistic regression, and its performance was evaluated by receiver operating characteristic analysis.

RESULTS

Twenty-eight differential lipids were identified, and a functionally connected network was constructed with 2 lipids, 17 genes, and 4 molecular pathways. Connectivity Map (CMap) analysis further revealed 32 potential drugs targeting 4 genes and related pathways. The 4 most critical lipids were identified as risk factors contributing to the invasive phenotype. A lipidomic signature was constructed and showed excellent performance in discriminating invasive and noninvasive SCAs.

CONCLUSIONS

The lipidomic signature could serve as a promising predictor for the invasive SCA phenotype and provide potential therapeutic targets for SCAs.

Metabolomics for Diagnosis and Prognosis of Uterine Diseases? A Systematic Review

This systematic review analyses the contribution of metabolomics to the identification of diagnostic and prognostic biomarkers for uterine diseases. These diseases are diagnosed invasively, which entails delayed treatment and a worse clinical outcome. New options for diagnosis and prognosis are needed. PubMed, OVID, and Scopus were searched for research papers on metabolomics in physiological fluids and tissues from patients with uterine diseases. The search identified 484 records. Based on inclusion and exclusion criteria, 44 studies were included into the review. Relevant data were extracted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) checklist and quality was assessed using the QUADOMICS tool. The selected metabolomics studies analysed plasma, serum, urine, peritoneal, endometrial, and cervico-vaginal fluid, ectopic/eutopic endometrium, and cervical tissue. In endometriosis, diagnostic models discriminated patients from healthy and infertile controls. In cervical cancer, diagnostic algorithms discriminated patients from controls, patients with good/bad prognosis, and with/without response to chemotherapy. In endometrial cancer, several models stratified patients from controls and recurrent from non-recurrent patients. Metabolomics is valuable for constructing diagnostic models. However, the majority of studies were in the discovery phase and require additional research to select reliable biomarkers for validation and translation into clinical practice. This review identifies bottlenecks that currently prevent the translation of these findings into clinical practice.

Cancer Omics in Africa: Present and Prospects

During the last century, cancer biology has been arguably one of the most investigated research fields. To gain deeper insight into cancer mechanisms, scientists have been attempting to integrate multi omics data in cancer research. Cancer genomics, transcriptomics, metabolomics, proteomics, and metagenomics are the main multi omics strategies used currently in the diagnosis, prognosis, treatment, and biomarker discovery in cancer. In this review, we describe the use of different multi omics strategies in cancer research in the African continent and discuss the main challenges facing the implementation of these approaches in African countries such as the lack of training programs in bioinformatics in general and omics strategies in particular and suggest paths to address deficiencies. As a way forward, we advocate for the establishment of an "African Cancer Genomics Consortium" to promote intracontinental collaborative projects and enhance engagement in research activities that address indigenous aspects for cancer precision medicine.

Integrative Metabolomics to Identify Molecular Signatures of Responses to Vaccines and Infections

Approaches to the identification of metabolites have progressed from early biochemical pathway evaluation to modern high-dimensional metabolomics, a powerful tool to identify and characterize biomarkers of health and disease. In addition to its relevance to classic metabolic diseases, metabolomics has been key to the emergence of immunometabolism, an important area of study, as leukocytes generate and are impacted by key metabolites important to innate and adaptive immunity. Herein, we discuss the metabolomic signatures and pathways perturbed by the activation of the human immune system during infection and vaccination. For example, infection induces changes in lipid (e.g., free fatty acids, sphingolipids, and lysophosphatidylcholines) and amino acid pathways (e.g., tryptophan, serine, and threonine), while vaccination can trigger changes in carbohydrate and bile acid pathways. Amino acid, carbohydrate, lipid, and nucleotide metabolism is relevant to immunity and is perturbed by both infections and vaccinations. Metabolomics holds substantial promise to provide fresh insight into the molecular mechanisms underlying the host immune response. Its integration with other systems biology platforms will enhance studies of human health and disease.

Targeted metabolomics study of early pathological features in hippocampus of triple transgenic Alzheimer's disease male mice

Alzheimer's disease (AD) is a serious neurodegenerative disease in people of age 65 or above. The detailed etiology and pathogenesis of AD have not been elucidated yet. In this study, the hippocampi of 2- and 6-month-old triple transgenic Alzheimer's disease male mice and age-sex-matched wild-type (WT) mice were analyzed by using targeted metabolomics approach. Compared with WT mice, 24 and 60 metabolites were found with significant differences in 2- and 6-month-old AD mice. Among these, 14 metabolites were found common while 10 metabolites showed consistent variable trends in both groups. These differential metabolites are found associated with amino acid, lipid, vitamin, nucleotide-related base, neurotransmitter and energy metabolisms, and oxidative stress. The results suggest that these differential metabolites might play a critical role in AD pathophysiology, and may serve as potential biomarkers for AD. Moreover, the results highlight the involvement of abnormal purine, pyrimidine, arginine, and proline metabolism, along with glycerophospholipid metabolism in early pathology of AD. For the first time, several differential metabolites are found to be associated with AD in this study. Targeted metabolomics can be used for rapid and accurate quantitative analysis of specific target metabolites associated with AD.

Laser-assisted rapid evaporative ionisation mass spectrometry (LA-REIMS) as a metabolomics platform in cervical cancer screening

Background

The introduction of high-risk human papillomavirus (hrHPV) testing as part of primary cervical screening is anticipated to improve sensitivity, but also the number of women who will screen positive. Reflex cytology is the preferred triage test in most settings but has limitations including moderate diagnostic accuracy, lack of automation, inter-observer variability and the need for clinician-collected sample. Novel, objective and cost-effective approaches are needed.

Methods

In this study, we assessed the potential use of an automated metabolomic robotic platform, employing the principle of laser-assisted Rapid Evaporative Ionisation Mass Spectrometry (LA-REIMS) in cervical cancer screening.

Findings

In a population of 130 women, LA-REIMS achieved 94% sensitivity and 83% specificity (AUC: 91.6%) in distinguishing women testing positive (n = 65) or negative (n = 65) for hrHPV. We performed further analysis according to disease severity with LA-REIMS achieving sensitivity and specificity of 91% and 73% respectively (AUC: 86.7%) in discriminating normal from high-grade pre-invasive disease.

Interpretation

This automated high-throughput technology holds promise as a low-cost and rapid test for cervical cancer screening and triage. The use of platforms like LA-REIMS has the potential to further improve the accuracy and efficiency of the current national screening programme.

Funding

Work was funded by the MRC Imperial Confidence in Concept Scheme, Imperial College Healthcare Charity, British Society for Colposcopy and Cervical Pathology, National Research Development and Innovation Office of Hungary, Waters corporation and NIHR BRC.

Targeted Metabolomics Analyses Reveal Specific Metabolic Alterations in High-Grade Prostate Cancer Patients

Prostate cancer (PCa) is a hormone-dependent tumor characterized by an extremely heterogeneous prognosis. Despite recent advances in partially uncovering some of the biological processes involved in its progression, there is still an urgent need for identifying more accurate and specific prognostic procedures to differentiate between disease stages. In this context, targeted approaches, focused on mapping dysregulated metabolic pathways, could play a critical role in identifying the mechanisms driving tumorigenesis and metastasis. In this study, a targeted analysis of the nuclear magnetic resonance-based metabolomic profile of PCa patients with different tumor grades, guided by transcriptomics profiles associated with their stages, was performed. Serum and urine samples were collected from 73 PCa patients. Samples were classified according to their Gleason score (GS) into low-GS (GS < 7) and high-GS PCa (GS ≥ 7) groups. A total of 36 metabolic pathways were found to be dysregulated in the comparison between different PCa grades. Particularly, the levels of glucose, glycine and 1-methlynicotinamide, metabolites involved in energy metabolism and nucleotide synthesis were significantly altered between both groups of patients. These results underscore the potential of targeted metabolomic profiling to characterize relevant metabolic changes involved in the progression of this neoplastic process.

Metabolomic characterization of colorectal cancer cell lines highlighting stage-specific alterations during cancer progression

Introduction: Metabolomic studies on various colorectal cancer (CRC) cell lines have improved our understanding of the biochemical events underlying the disease. However, the metabolic profile dynamics associated with different stages of CRC progression is still lacking. Such information can provide further insights into the pathophysiology and progression of the disease that will prove useful in identifying specific targets for drug designing and therapeutics. Thus, our study aims to characterize the metabolite profiles in the established cell lines corresponding to different stages of CRC. Methods: Metabolite profiling of normal colon cell lines (CCD 841 CoN) and CRC cell lines corresponding to different stages, i.e., SW 1116 (stage A), HT 29 and SW 480 (stage B), HCT 15 and DLD-1 (stage C), and HCT 116 (stage D), was carried out using liquid chromatography-mass spectrometry (LC-MS). Mass Profiler Professional and Metaboanalyst 4.0 software were used for statistical and pathway analysis. METLIN database was used for the identification of metabolites. Results: We identified 72 differential metabolites compared between CRC cell lines of all the stages and normal colon cells. Principle component analysis and partial least squares discriminant analysis score plot were used to segregate normal and CRC cells, as well as CRC cells in different stages of the disease. Variable importance in projection score identified unique differential metabolites in CRC cells of the different stages. We identified 7 differential metabolites unique to stage A, 3 in stage B, 5 in stage C, and 5 in stage D. Conclusion: This study highlights the differential metabolite profiling in CRC cell lines corresponding to different stages. The identification of the differential metabolites in CRC cells at individual stages will lead to a better understanding of the pathophysiology of CRC development and progression and, hence, its application in treatment strategies.

Integration of Transcriptome and Metabolome Provides Unique Insights to Pathways Associated With Obese Breast Cancer Patients

Information regarding transcriptome and metabolome has significantly contributed to identifying potential therapeutic targets for the management of a variety of cancers. Obesity has profound effects on both cancer cell transcriptome and metabolome that can affect the outcome of cancer therapy. The information regarding the potential effects of obesity on breast cancer (BC) transcriptome, metabolome, and its integration to identify novel pathways related to disease progression are still elusive. We assessed the whole blood transcriptome and serum metabolome, as circulating metabolites, of obese BC patients compared them with non-obese BC patients. In these patients' samples, 186 significant differentially expressed genes (DEGs) were identified, comprising 156 upregulated and 30 downregulated. The expressions of these gene were confirmed by qRT-PCR. Furthermore, 96 deregulated metabolites were identified as untargeted metabolomics in the same group of patients. These detected DEGs and deregulated metabolites enriched in many cellular pathways. Further investigation, by integration analysis between transcriptomics and metabolomics data at the pathway levels, revealed seven unique enriched pathways in obese BC patients when compared with non-obese BC patients, which may provide resistance for BC cells to dodge the circulating immune cells in the blood. In conclusion, this study provides information on the unique pathways altered at transcriptome and metabolome levels in obese BC patients that could provide an important tool for researchers and contribute further to knowledge on the molecular interaction between obesity and BC. Further studies are needed to confirm this and to elucidate the exact underlying mechanism for the effects of obesity on the BC initiation or/and progression.

Integration of transcriptomics, proteomics and metabolomics identifies biomarkers for pulmonary injury by polyhexamethylene guanidine phosphate (PHMG-p), a humidifier disinfectant, in rats

Polyhexamethylene guanidine phosphate (PHMG-p) was used as a humidifier disinfectant in Korea. PHMG induced severe pulmonary fibrosis in Koreans. The objective of this study was to elucidate mechanism of pulmonary toxicity caused by PHMG-p in rats using multi-omics analysis. Wistar rats were intratracheally instilled with PHMG-p by single (1.5 mg/kg) administration or 4-week (0.1 mg/kg, 2 times/week) repeated administration. Histopathologic examination was performed with hematoxylin and eosin staining. Alveolar macrophage aggregation and granulomatous inflammation were observed in rats treated with single dose of PHMG-p. Pulmonary fibrosis, chronic inflammation, bronchiol-alveolar fibrosis, and metaplasia of squamous cell were observed in repeated dose group. Next generation sequencing (NGS) was performed for transcriptome profiling after mRNA isolation from bronchiol-alveoli. Bronchiol-alveoli proteomic profiling was performed using an Orbitrap Q-exactive mass spectrometer. Serum and urinary metabolites were determined using 1H-NMR. Among 418 differentially expressed genes (DEGs) and 67 differentially expressed proteins (DEPs), changes of 16 mRNA levels were significantly correlated with changes of their protein levels in both single and repeated dose groups. Remarkable biological processes represented by both DEGs and DEPs were defense response, inflammatory response, response to stress, and immune response. Arginase 1 (Arg1) and lipocalin 2 (Lcn2) were identified to be major regulators for PHMG-p-induced pulmonary toxicity based on merged analysis using DEGs and DEPs. In metabolomics study, 52 metabolites (VIP > 0.5) were determined in serum and urine of single and repeated-dose groups. Glutamate and choline were selected as major metabolites. They were found to be major factors affecting inflammatory response in association with DEGs and DEPs. Arg1 and Lcn2 were suggested to be major gene and protein related to pulmonary damage by PHMG-p while serum or urinary glutamate and choline were endogenous metabolites related to pulmonary damage by PHMG-p.

Mass spectrometry based proteomics and metabolomics in personalized oncology

Precision medicine (PM) means the customization of healthcare with decisions and practices adjusted to the individual patient. It includes personalized diagnostics, patients' sub-classification, individual treatment selection and the monitoring of its effectiveness. Currently, in oncology, PM is based on the molecular and cellular features of a tumor, its microenvironment and the patient's genetics and lifestyle. Surprisingly, the available targeted therapies were found effective only in a subset of patients. An in-depth understanding of tumor biology is crucial to improve their effectiveness and develop new therapeutic targets. Completion of genetic information with proteomics and metabolomics can give broader knowledge about tumor biology which consequently provides novel biomarkers and indicates new therapeutic targets. Recently, metabolomics and proteomics have extensively been applied in the field of oncology. In the context of PM, human studies, with the use of mass spectrometry (MS) which allows the detection of thousands of molecules in a large number of samples, are the most valuable. Such studies, focused on cancer biomarkers discovery or patients' stratification, are presented in this review. Moreover, the technical aspects of MS-based clinical proteomics and metabolomics are described.

Metabolomic Biomarkers in Gynecology: A Treasure Path or a False Path?

Omic-technologies (genomics, transcriptomics, proteomics and metabolomics) have become more important in current medical science. Among them, it is metabolomics that most accurately reflects the minor changes in body functioning, as it focuses on metabolome - the group of the metabolism products, both intermediate and end. Therefore, metabolomics is actively engaged in fundamental and clinical studies and search for potential biomarkers. The biomarker could be used in diagnostics, management and stratification of the patients, as well as in prognosing the outcomes. The good example is gynecology, since many gynecological diseases lack effective biomarkers. In the current review, we aimed to summarize the results of the studies, devoted to the search of potential metabolomic biomarkers for the most common gynecological diseases.

Blood Metabolites Associate with Prognosis in Endometrial Cancer

Endometrial cancer has a high prevalence among post-menopausal women in developed countries. We aimed to explore whether certain metabolic patterns could be related to the characteristics of aggressive disease and poorer survival among endometrial cancer patients in Western Norway. Patients with endometrial cancer with short survival (n = 20) were matched according to FIGO (International Federation of Gynecology and Obstetrics, 2009 criteria) stage, histology, and grade, with patients with long survival (n = 20). Plasma metabolites were measured on a multiplex system including 183 metabolites, which were subsequently determined using liquid chromatography-mass spectrometry. Partial least square discriminant analysis, together with hierarchical clustering, was used to identify patterns which distinguished short from long survival. A proposed signature of metabolites related to survival was suggested, and a multivariate receiver operating characteristic (ROC) analysis yielded an area under the curve (AUC) of 0.820–0.965 (p ≤ 0.001). Methionine sulfoxide seems to be particularly strongly associated with poor survival rates in these patients. In a subgroup with preoperative contrast-enhanced computed tomography data, selected metabolites correlated with the estimated abdominal fat distribution parameters. Metabolic signatures may predict prognosis and be promising supplements when evaluating phenotypes and exploring metabolic pathways related to the progression of endometrial cancer. In the future, this may serve as a useful tool in cancer management.