Preoperative plasma fatty acid metabolites inform risk of prostate cancer progression and may be used for personalized patient stratification

Metabolomics in atrial fibrillation - A review and meta-analysis of blood, tissue and animal models

BACKGROUND

Atrial fibrillation (AF) is a highly prevalent cardiac arrhythmia associated with severe cardiovascular complications. AF presents a growing global challenge, however, current treatment strategies for AF do not address the underlying pathophysiology. To advance diagnosis and treatment of AF, a deeper understanding of AF root causes is needed. Metabolomics is a fast approach to identify, quantify and analyze metabolites in a given sample, such as human serum or atrial tissue. In the past two decades, metabolomics have enabled research on metabolite biomarkers to predict AF, metabolic features of AF, and testing metabolic mechanisms of AF in animal models. Due to the field's rapid evolution, the methods of AF metabolomics studies have not always been optimal. Metabolomics research has lacked standardization and requires expertise to face methodological challenges.

PURPOSE OF THE REVIEW

We summarize and meta-analyze metabolomics research on AF in human plasma and serum, atrial tissue, and animal models. We present the current progress on metabolic biomarkers candidates, metabolic features of clinical AF, and the translation of metabolomics findings from animal to human. We additionally discuss strengths and weaknesses of the metabolomics method and highlight opportunities for future AF metabolomics research.

Targeted Metabolic Profiling in Determining the Metabolic Heterogeneity in Human Biopsies of Different Grades of Glioma

Gliomas are intricate tumors with numerous metabolic and genetic abnormalities contributing to their aggressive phenotypes and poor prognoses. The study aims at identifying the key molecular metabolic as well as gene expressional variations that could be used to differentiate between different grades of glioma to obtain deeper insights the about metabolic status of glioma that may serve as good candidates of diagnosis in future. In the present study, the metabolomic profiling along with clinical and expressional analyses of glioma biopsies (n = 52; patients comprising both of benign and malignant lesions) was analyzed. The biopsies were subjected to gene/protein expressional analysis using RT-PCR and western blotting and also were subjected to metabolite analyses. The results of the gene/protein expressional analysis exhibited elevated levels of carnitine palmitoyltransferase, monoglyceride lipase, human phosphofructokinase, and isocitrate dehydrogenase in higher grades of glioma when compared to those of control. Our study suggested that the metabolites and gene/protein expressional levels were found to be discriminative among the grades of glioma. The study is deemed as a provider of deeper insights that are essential for differential therapeutic approaches that specifically target the dysregulated metabolome to the benefit of patients.

Plasma metabolomics of immune-related adverse events for patients with lung cancer treated with PD-1/PD-L1 inhibitors

BACKGROUND

Metabolomics has the characteristics of terminal effects and reflects the physiological state of biological diseases more directly. Several current biomarkers of multiple omics were revealed to be associated with immune-related adverse events (irAEs) occurrence. However, there is a lack of reliable metabolic biomarkers to predict irAEs. This study aims to explore the potential metabolic biomarkers to predict risk of irAEs and to investigate the association of plasma metabolites level with survival in patients with lung cancer receiving PD-1/PD-L1 inhibitor treatment.

METHODS

The study collected 170 plasmas of 85 patients with lung cancer who received immune checkpoint inhibitors (ICIs) treatment. 58 plasma samples of 29 patients with irAEs were collected before ICIs treatment and at the onset of irAEs. 112 plasma samples of 56 patients who did not develop irAEs were collected before ICIs treatment and plasma matched by treatment cycles to onset of irAEs patients. Untargeted metabolomics analysis was used to identify the differential metabolites before initiating ICIs treatment and during the process that development of irAEs. Kaplan-Meier curves analysis was used to detect the associations of plasma metabolites level with survival of patients with lung cancer.

RESULTS

A total of 24 differential metabolites were identified to predict the occurrence of irAEs. Baseline acylcarnitines and steroids levels are significantly higher in patients with irAEs, and the model of eight acylcarnitine and six steroid metabolites baseline level predicts irAEs occurrence with area under the curve of 0.91. Patients with lower concentration of baseline decenoylcarnitine(AcCa(10:1) 2, decenoylcarnitine(AcCa(10:1) 3 and hexanoylcarnitine(AcCa(6:0) in plasma would have better overall survival (OS). Moreover, 52 differential metabolites were identified related to irAEs during ICIs treatment, dehydroepiandrosterone sulfate, corticoserone, cortisol, thyroxine and sphinganine 1-phaosphate were significantly decreased in irAEs group while oxoglutaric acid and taurocholic acid were significantly increased in irAEs group.

CONCLUSIONS

High levels of acylcarnitines and steroid hormone metabolites might be risk factor to development of irAEs, and levels of decenoylcarnitine (AcCa(10:1) 2, decenoylcarnitine (AcCa(10:1) 3 and hexanoylcarnitine (AcCa(6:0) could be used to predict OS for patients with lung cancer received ICIs treatment.

Lipid metabolism, amino acid metabolism, and prostate cancer: a crucial metabolic journey

Prostate cancer (PCa) is one of the most common malignancies in males worldwide, and its development and progression involve the regulation of multiple metabolic pathways. Alterations in lipid metabolism affect the proliferation and metastatic capabilities of PCa cells. Cancer cells increase lipid synthesis and regulate fatty acid oxidation to meet their growth and energy demands. Similarly, changes occur in amino acid metabolism in PCa. Cancer cells exhibit an increased demand for specific amino acids, and they regulate amino acid transport and metabolic pathways to fulfill their proliferation and survival requirements. These changes are closely associated with disease progression and treatment response in PCa cells. Therefore, a comprehensive investigation of the metabolic characteristics of PCa is expected to offer novel insights and approaches for the early diagnosis and treatment of this disease.

Urinary fatty acid biomarkers for prostate cancer detection

The lack of accuracy in the current prostate specific antigen (PSA) test for prostate cancer (PCa) screening causes around 60-75% of unnecessary prostate biopsies. Therefore, alternative diagnostic methods that have better accuracy and can prevent over-diagnosis of PCa are needed. Researchers have examined various potential biomarkers for PCa, and of those fatty acids (FAs) markers have received special attention due to their role in cancer metabolomics. It has been noted that PCa metabolism prefers FAs over glucose substrates for continued rapid proliferation. Hence, we proposed using a urinary FAs based model as a non-invasive alternative for PCa detection. Urine samples collected from 334 biopsy-designated PCa positive and 232 biopsy-designated PCa negative subjects were analyzed for FAs and lipid related compounds by stir bar sorptive extraction coupled with gas chromatography/mass spectrometry (SBSE-GC/MS). The dataset was split into the training (70%) and testing (30%) sets to develop and validate logit models and repeated for 100 runs of random data partitioning. Over the 100 runs, we confirmed the stability of the models and obtained optimal tuning parameters for developing the final FA based model. A PSA model using the values of the patients' PSA test results was constructed with the same cohort for the purpose of comparing the performances of the FA model against PSA test. The FA final model selected 20 FAs and rendered an AUC of 0.71 (95% CI = 0.67-0.75, sensitivity = 0.48, and specificity = 0.83). In comparison, the PSA model performed with an AUC of 0.51 (95% CI = 0.46-0.66, sensitivity = 0.44, and specificity = 0.71). The study supports the potential use of urinary FAs as a stable and non-invasive alternative test for PCa diagnosis.

Palmitic acid-activated GPRs/KLF7/CCL2 pathway is involved in the crosstalk between bone marrow adipocytes and prostate cancer

BACKGROUND

Obesity-induced abnormal bone marrow microenvironment is one of the important risk element for bone metastasis in prostate cancer (PCa). The present study aimed to determine whether obesity-induced elevation in palmitic acid (PA), which is the most abundant of the free fatty acids (FFAs), increased CCL2 via the GPRs/KLF7 pathway in bone marrow adipocytes (BMA) to facilitate PCa growth and metastasis.

METHODS

We constructed a bone-tumor bearing mouse model with obesity through high-fat diet, and observed the tumor formation ability of PCa cells. In vitro, observe the effect of PA on the expression level of CCL2 in BMA through GPRs/KLF7 signaling pathway. After co-culture of BMA and PCa cells, CCK8 assay and transwell experiment were used to detect the changes in biological behavior of PCa cells stimulated by BMA.

RESULTS

The BMA distribution in the bone marrow cavity of BALB/c nude mice fed with the high-fat diet (HFD) was evidently higher than that in the mice fed with the normal diet (ND). Moreover, HFD-induced obesity promoted KLF7/CCL2 expression in BMA and PCa cell growth in the bone marrow cavity of the mice. In the vitro experiment, a conditioned medium with increased CCL2 obtained from the BMA cultured with PA (CM-BMA-PA) was used for culturing the PCa cell lines, which evidently enhanced the proliferation, invasion, and migration ability. KLF7 significantly increased the CCL2 expression and secretion levels in BMA by targeting the promoter region of the CCL2 gene. In addition, GPR40/120 engaged in the PA-induced high KLF7/CCL2 levels in BMA to facilitate the malignant progression of PC-3 cells.

CONCLUSIONS

PA-activated GPRs/KLF7/CCL2 pathway in BMA facilitates prostate cancer growth and metastasis.

A free fatty acid receptor agonist inducing autophagy in HT-29 cells by downregulating the AKT/mTOR signaling pathway

AIMS

GW9508, a free fatty acid receptor agonist acts in a G-coupled protein receptor 40 (GPR40)-dependent pathway. Here, we investigated the induction of stress oxidative and autophagy by GW9508 in the human colorectal cancer cell line (HT-29) and the crosstalk between autophagy and apoptotic in HT-29 cells.

METHODS

HT-29 was treated with GW9508 at a concentrations range of 50-500 μM in fibrin gel. Cell viability was investigated using an MTT assay. Induction of autophagy and apoptosis was assessed through Western blotting for associated proteins, acridine orange staining, MDC staining, qRT-PCR, and electron microscopy. Also, we estimated the molecular interactions between GW9805 and some markers through molecular docking.

RESULTS

GW9508 inhibited HT-29 cell proliferation, induced apoptosis, and resulted in autophagy. The induced autophagy in cells was confirmed by the observation of autophagosomes, the presence of autophagy markers, including beclin-1, LC3, AMPK, and lack expression of mTOR and AKT. Moreover, GW9508 treatment significantly increased the expression of catalase and superoxide dismutase in cells.

DISCUSSION

Our results indicated that GW9508 could induce autophagy by inhibiting the Akt/mTOR in HT-29. Hence, GW9508 is suggested as a novel anticancer reagent.

Extensive metabolic consequences of human glycosyltransferase gene knockouts in prostate cancer

BACKGROUND

Naturally occurring germline gene deletions (KO) represent a unique setting to interrogate gene functions. Complete deletions and differential expression of the human glycosyltransferase UGT2B17 and UGT2B28 genes are linked to prostate cancer (PCa) risk and progression, leukaemia, autoimmune and other diseases.

METHODS

The systemic metabolic consequences of UGT deficiencies were examined using untargeted and targeted mass spectrometry-based metabolomics profiling of carefully matched, treatment-naive PCa cases.

RESULTS

Each UGT KO differentially affected over 5% of the 1545 measured metabolites, with divergent metabolic perturbations influencing the same pathways. Several of the perturbed metabolites are known to promote PCa growth, invasion and metastasis, including steroids, ceramides and kynurenine. In UGT2B17 KO, reduced levels of inactive steroid-glucuronides were compensated by sulfated derivatives that constitute circulating steroid reservoirs. UGT2B28 KO presented remarkably lower levels of oxylipins paralleled by reduced inflammatory mediators, but higher ceramides unveiled as substrates of the enzyme in PCa cells.

CONCLUSION

The distinctive and broad metabolic rewiring caused by UGT KO reinforces the need to examine their unique and divergent functions in PCa biology.

Serum metabolites as early detection markers of non-muscle invasive bladder cancer in Chinese patients

Background

Biomarkers of different stages and grades of bladder cancer (BC) are important in clinical work. The objective of our study was to investigate new biomarkers of early-stage BC with liquid chromatography-high resolution mass spectrometry (LC-HRMS) using serum samples.

Methods

A total of 215 cases were included in our study, including 109 healthy adults as the control group and 106 non-muscle invasive bladder cancer (NMIBC) patients as the NMIBC group. Serum samples were collected from BC patients in the early stage, called NMIBC, and healthy people before surgery. We used LC-HRMS to distinguish the NMIBC group from the control group and the low-grade NMIBC group from the control group.

Results

An apparent difference between the NMIBC group and the control group was visualized by unsupervised principal component analysis (PCA). Metabolite panels for 16-hydroxy-10-oxohexadecanoic acid, PGF2a ethanolamide, sulfoglycolithocholate, and threoninyl-alanine were used to distinguish the two groups. The area under the curve (AUC) of the panels was 0.985, and the sensitivity and specificity were 98.63% and 98.59%, respectively. To distinguish the low-grade NMIBC group from the control group, serum metabolic profiling differences between the low-grade NMIBC group and control group samples were also analyzed. Metabolite panels of L-octanoylcarnitine, PGF2a ethanolamide, and threoninyl-alanine showed good discrimination performance. The AUC of the panels was 0.999, and the sensitivity and specificity were 97.8% and 100%, respectively.

Conclusion

Metabolomics analysis of serum samples can distinguish the NMIBC group from the control group, particularly the early-stage low-grade NMIBC group.

Acylcarnitines: Nomenclature, Biomarkers, Therapeutic Potential, Drug Targets, and Clinical Trials

Acylcarnitines are fatty acid metabolites that play important roles in many cellular energy metabolism pathways. They have historically been used as important diagnostic markers for inborn errors of fatty acid oxidation and are being intensively studied as markers of energy metabolism, deficits in mitochondrial and peroxisomal β -oxidation activity, insulin resistance, and physical activity. Acylcarnitines are increasingly being identified as important indicators in metabolic studies of many diseases, including metabolic disorders, cardiovascular diseases, diabetes, depression, neurologic disorders, and certain cancers. The US Food and Drug Administration-approved drug L-carnitine, along with short-chain acylcarnitines (acetylcarnitine and propionylcarnitine), is now widely used as a dietary supplement. In light of their growing importance, we have undertaken an extensive review of acylcarnitines and provided a detailed description of their identity, nomenclature, classification, biochemistry, pathophysiology, supplementary use, potential drug targets, and clinical trials. We also summarize these updates in the Human Metabolome Database, which now includes information on the structures, chemical formulae, chemical/spectral properties, descriptions, and pathways for 1240 acylcarnitines. This work lays a solid foundation for identifying, characterizing, and understanding acylcarnitines in human biosamples. We also discuss the emerging opportunities for using acylcarnitines as biomarkers and as dietary interventions or supplements for many wide-ranging indications. The opportunity to identify new drug targets involved in controlling acylcarnitine levels is also discussed. SIGNIFICANCE STATEMENT: This review provides a comprehensive overview of acylcarnitines, including their nomenclature, structure and biochemistry, and use as disease biomarkers and pharmaceutical agents. We present updated information contained in the Human Metabolome Database website as well as substantial mapping of the known biochemical pathways associated with acylcarnitines, thereby providing a strong foundation for further clarification of their physiological roles.

Serum Metabolomic Profiling Reveals Biomarkers for Early Detection and Prognosis of Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most common aggressive malignancies worldwide, particularly in northern China. The absence of specific early symptoms and biomarkers leads to late-stage diagnosis, while early diagnosis and risk stratification are crucial for improving overall prognosis. We performed UPLC-MS/MS on 450 ESCC patients and 588 controls consisting of a discovery group and two validation groups to identify biomarkers for early detection and prognosis. Bioinformatics and clinical statistical methods were used for profiling metabolites and evaluating potential biomarkers. A total of 105 differential metabolites were identified as reliable biomarker candidates for ESCC with the same tendency in three cohorts, mainly including amino acids and fatty acyls. A predictive model of 15 metabolites [all-trans-13,14-dihydroretinol, (±)-myristylcarnitine, (2S,3S)-3-methylphenylalanine, 3-(pyrazol-1-yl)-L-alanine, carnitine C10:1, carnitine C10:1 isomer1, carnitine C14-OH, carnitine C16:2-OH, carnitine C9:1, formononetin, hyodeoxycholic acid, indole-3-carboxylic acid, PysoPE 20:3, PysoPE 20:3(2n isomer1), and resolvin E1] was developed by logistic regression after LASSO and random forest analysis. This model held high predictive accuracies on distinguishing ESCC from controls in the discovery and validation groups (accuracies > 89%). In addition, the levels of four downregulated metabolites [hyodeoxycholic acid, (2S,3S)-3-methylphenylalanine, carnitine C9:1, and indole-3-carboxylic acid] were significantly higher in early cancer than advanced cancer. Furthermore, three independent prognostic markers were identified by multivariate Cox regression analyses with and without clinical indicators: a high level of MG(20:4)isomer and low levels of 9,12-octadecadienoic acid and L-isoleucine correlated with an unfavorable prognosis; the risk score based on these three metabolites was able to stratify patients into low or high risk. Moreover, pathway analysis indicated that retinol metabolism and linoleic acid metabolism were prominent perturbed pathways in ESCC. In conclusion, metabolic profiling revealed that perturbed amino acids and lipid metabolism were crucial metabolic signatures of ESCC. Both panels of diagnostic and prognostic markers showed excellent predictive performances. Targeting retinol and linoleic acid metabolism pathways may be new promising mechanism-based therapeutic approaches. Thus, this study would provide novel insights for the early detection and risk stratification for the clinical management of ESCC and potentially improve the outcomes of ESCC.

Identification of HCC Subtypes With Different Prognosis and Metabolic Patterns Based on Mitophagy

Background: Mitophagy is correlated with tumor initiation and development of malignancy. However, HCC heterogeneity with reference to mitophagy has yet not been systematically explored.

Materials and Methods: Mitophagy-related, glycolysis-related, and cholesterol biosynthesis-related gene sets were obtained from the Reactome database. Mitophagy-related and metabolism-related subtypes were identified using the ConsensusClusterPlus algorithm. Univariate Cox regression was analysis was performed to identify prognosis-related mitophagy regulators. Principal component analysis (PCA) was used to create composite measures of the prognosis-related mitophagy regulators (mitophagyscore). Individuals with a mitophagyscore higher or lower than the median value were classified in high- or low-risk groups. Kaplan-Meier survival and ROC curve analyses were utilized to evaluate the prognostic value of the mitophagyscore. The nomogram and calibration curves were plotted using the“rms” R package. The package “limma” was used for differential gene expression analysis. Differentially expressed genes (DEGs) between high- and low-risk groups were used as queries in the CMap database. R package “pRRophetic” and Genomics of Drug Sensitivity in Cancer (GDSC) database were used to determine the sensitivity of 21 previously reported anti-HCC drugs.

Results: Three distinct HCC subtypes with different mitophagic accumulation (low, high, and intermediate mitophagy subtypes) were identified. High mitophagy subtype had the worst outcome and highest glycolysis level. The lowest degree of hypoxia and highest cholesterol biosynthesis was observed in the low mitophagy subtype; oncogenic dedifferentiation level in the intermediate mitophagy subtype was the lowest. Mitophagyscore could serve as a novel prognostic indicator for HCC.High-risk patients had a poorer prognosis (log-rank test, p < 0.001). The area under the ROC curve for mitophagyscore in 1-year survival was 0.77 in the TCGA cohort and 0.75 in the ICGC cohort. Nine candidate small molecules which were potential drugs for HCC treatment were identified from the CMap database. A decline in the sensitivity towards 21 anti-HCC drugs was observed in low-risk patients by GDSC database. We also identified a novel key gene, SPP1, which was highly associated with different mitophagic subtypes.

Conclusion: Based on bioinformatic analyses, we systematically examined the HCC heterogeneity with reference to mitophagy and observed three distinct HCC subtypes having different prognoses and metabolic patterns.

Metabolomics based plasma biomarkers for diagnosis of oral squamous cell carcinoma and oral erosive lichen planus

Backgrounds: To identify diagnostic biomarkers for differentiating oral squamous cell carcinoma (OSCC) from oral erosive lichen planus (OELP) and investigate potential biomarkers associated with malignant transformation. Methods: In this study, 72 patients with OSCC, 75 patients with OELP subjects were recruited. Their plasma samples were analyzed by ultra-high-performance liquid chromatography quadrupole-Orbitrap high-resolution accurate mass spectrometry, (UHPLC/Q-Orbitrap HRMS). Principal component analysis, orthogonal partial least square discrimination analysis, t-test analysis and false discovery rate were used to identify different metabolites in patients with OSCC and OELP. The metabolic pathway analysis was performed by MetaboAnalyst. To further screen and identify the biomarkers of OSCC and establish a diagnostic panel, binary logistic regression analysis and receiver operating characteristic analysis were used. The data were then combined with blood samples from healthy individuals for mass spectrometry analysis to obtain biomarkers related to malignant transformation. Results: A total of 20 kinds of endogenous metabolites were identified from plasma samples of OSCC patients and OELP patients. Metabolic pathway analysis showed that the biomarkers associated with OSCC were closely related to cholic acid metabolism and amino acid metabolism. Finally, a diagnostic panel composed of decanoylcarnitine, cysteine and cholic acid was established. This diagnostic panel had good diagnostic efficiency with the AUC=0.998. Other metabolites including uridine, taurine, glutamate, citric acid and LysoPC(18:1) were identified to be general biomarkers for malignant transformation of OELP. Conclusion: Biomarkers based on plasma metabolomics are of great significance for the prediction of malignant transformation of OELP and early diagnosis of OSCC.

Profiling of Carnitine Shuttle System Intermediates in Gliomas Using Solid-Phase Microextraction (SPME)

Alterations in the carnitine shuttle system may be an indication of the presence of cancer. As such, in-depth analyses of this pathway in different malignant tumors could be important for the detection and treatment of this disease. The current study aims to assess the profiles of carnitine and acylcarnitines in gliomas with respect to their grade, the presence of isocitrate dehydrogenase (IDH) mutations, and 1p/19q co-deletion. Brain tumors obtained from 19 patients were sampled on-site using solid-phase microextraction (SPME) immediately following excision. Analytes were desorbed and then analyzed via liquid chromatography–high-resolution mass spectrometry. The results showed that SPME enabled the extraction of carnitine and 22 acylcarnitines. An analysis of the correlation factor revealed the presence of two separate clusters: short-chain and long-chain carnitine esters. Slightly higher carnitine and acylcarnitine concentrations were observed in the higher-malignancy tumor samples (high vs. low grade) and in those samples with worse projected clinical outcomes (without vs. with IDH mutation; without vs. with 1p/19q co-deletion). Thus, the proposed chemical biopsy approach offers a simple solution for on-site sampling that enables sample preservation, thus supporting comprehensive multi-method analyses.

Specific Gut Microbiome and Serum Metabolome Changes in Lung Cancer Patients

Background

Lung cancer (LC) is one of the most aggressive, prevalent and fatal malignancies. Gut microbes and their associated metabolites are thought to cause and modulate LC development, albeit influenced by the host genetic make-up and environment. Herein, we identified and classified gut microbiota and serum metabolites associated with LC.

Methods

Stool samples were collected from 41 LC patients and 40 healthy volunteers. The gut microbiota was analyzed using 16S rRNA gene sequencing. Serum samples were collected from the same LC patients (n=30) and healthy volunteers (n=30) and serum metabolites were analyzed using liquid chromatography-mass spectrometry (LC-MS). Microbiome and metabolome data were analyzed separately and integrated for combined analysis using various bioinformatics methods.

Results

Serum metabolomics uncovered 870 metabolites regulated in 76 metabolic pathways in both groups. Microbial diversity analyses identified 15967 operational taxonomic units (OTUs) in groups. Of these, the abundance of 232 OTUs was significantly different between HC and LC groups. Also, serum levels of glycerophospholipids (LysoPE 18:3, LysoPC 14:0, LysoPC 18:3), Imidazopyrimidines (Hypoxanthine), AcylGlcADG 66:18; AcylGlcADG (22:6/22:6/22:6) and Acylcarnitine 11:0 were substantially different between HC and LC groups. Combined analysis correlated LC-associated microbes with metabolites, such as Erysipelotrichaceae_UCG_003, Clostridium and Synergistes with glycerophospholipids.

Conclusions

There is an intricate relationship between gut microbiome and levels of several metabolites such as glycerophospholipids and imidazopyrimidines. Microbial-associated metabolites are potential diagnostic biomarkers and therapeutic targets for LC.

Comparison of the metabolome in urine prior and eight weeks after radical prostatectomy uncovers pathologic and molecular features of prostate cancer

Prostate cancer (PCa) is associated with cellular metabolism alterations leading to changes of the metabolome. So far, studies investigating these alterations mainly focused on comparisons of metabolite profiles of PCa patients and healthy controls. In the present study we compared for the first time metabolite profiles in a significant number of paired urine samples collected before and eight weeks after radical prostatectomy (rPX) in 34 patients with PCa. Our comprehensive non-targeted liquid chromatographic-mass spectrometric metabolomics approach covered > 3000 metabolite ion masses. We annotated 23 metabolites showing significant changes eight weeks after rPX. While the levels of uridine and six acylcarnitines in urine were increased before surgery, lower levels were detected for 16 metabolites, like e.g. citrate, phenyl-lactic acid, choline, myo-inositol, emphasizing a relevant pathophysiological role of these biomarkers and the associated metabolic pathways. These results have important implications for potential use of metabolome analyses for detection of prostate cancer and related pathologic and molecular features.

Relationship between the n-3 index, serum metabolites and breast cancer risk

The present study aimed to investigate the relationship between the n-3 index, serum metabolites and breast cancer risk. A total of 104 newly diagnosed breast cancer patients and 70 healthy controls were recruited. The erythrocyte phospholipid fatty acid composition was determined by gas-liquid chromatography, and the n-3 index was calculated with the percentage of eicosapentaenoic acid plus docosahexaenoic acid in total fatty acids. Serum metabolomic profiles were analyzed by UHPLC-Q-Exactive Orbitrap/MS. The results showed that the erythrocyte phospholipid n-3 index was significantly lower in breast cancer patients than in healthy controls, and it was inversely associated with breast cancer risk (OR = 0.60; 95% CI: 0.36-0.84). Metabolomics analyses showed that serum 16α-hydroxy dehydroepiandrosterone (DHEA) 3-sulfate, lysophatidylethanolamines (LPE) 22:0/0:0 and hexanoylcarnitine were significantly higher, while thromboxane B₃, prostaglandin E₃ (PGE₃) and 18β-glycyrrhetinic acid were significantly lower in breast cancer patients than those in healthy controls. In addition, serum 16α-hydroxy DHEA 3-sulfate was inversely correlated with the n-3 index (r = -0.412, p = 0.036). In conclusion, our findings suggest that the lack of n-3 PUFAs might be a potential risk factor for breast cancer, and the serum metabolite 16α-hydroxy DHEA 3-sulfate may play an important role in linking n-3 PUFA deficiency and breast disease etiology.

Differential but Concerted Expression of HSD17B2, HSD17B3, SHBG and SRD5A1 Testosterone Tetrad Modulate Therapy Response and Susceptibility to Disease Relapse in Patients with Prostate Cancer

Simple Summary

Over the last two decades, our improved understanding of the pathobiology of androgen-addicted prostate cancer (PCa), and documented therapeutic advances/breakthroughs have not translated into any substantial or sustained curative benefit for patients treated with traditional ADT or novel immune checkpoint blockade therapeutics. This is invariably connected with the peculiar biology and intratumoral heterogeneity of PCa. Castration-resistant PCa, constituting ~30% of all PCa, remains a clinically enigmatic and therapeutically challenging disease sub-type, that is therapy-refractory and characterized by high risk for recurrence after initial response. Our findings highlight the role and exploitability of testosterone metabolic reprogramming in prostate TME for patient stratification and personalized/precision medicine based on the differential but concerted expression of molecular components of the proposed testosterone tetrad in patients with therapy-refractory, locally advanced, or recurrent PCa. The therapeutic exploitability and clinical feasibility of our proposed approach is suggested by our preclinical findings.

Abstract

Background: Testosterone plays a critical role in prostate development and pathology. However, the impact of the molecular interplay between testosterone-associated genes on therapy response and susceptibility to disease relapse in PCa patients remains underexplored. Objective: This study investigated the role of dysregulated or aberrantly expressed testosterone-associated genes in the enhanced dissemination, phenoconversion, and therapy response of treatment-resistant advanced or recurrent PCa. Methods: Employing a combination of multi-omics big data analyses, in vitro, ex vivo, and in vivo assays, we assessed the probable roles of HSD17B2, HSD17B3, SHBG, and SRD5A1-mediated testosterone metabolism in the progression, therapy response, and prognosis of advanced or castration-resistant PCa (CRPC). Results: Our bioinformatics-aided gene expression profiling and immunohistochemical staining showed that the aberrant expression of the HSD17B2, HSD17B3, SHBG, and SRD5A1 testosterone metabolic tetrad characterize androgen-driven PCa and is associated with disease progression. Reanalysis of the TCGA PRAD cohort (n = 497) showed that patients with SRD5A1-dominant high expression of the tetrad exhibited worse mid-term to long-term (≥5 years) overall survival, with a profoundly shorter time to recurrence, compared to those with low expression. More so, we observed a strong association between enhanced HSD17B2/SRD5A1 signaling and metastasis to distant lymph nodes (M1a) and bones (M1b), while upregulated HSD17B3/SHBG signaling correlated more with negative metastasis (M0) status. Interestingly, increased SHBG/SRD5A1 ratio was associated with metastasis to distant organs (M1c), while elevated SRD5A1/SHBG ratio was associated with positive biochemical recurrence (BCR) status, and shorter time to BCR. Molecular enrichment and protein–protein connectivity network analyses showed that the androgenic tetrad regulates testosterone metabolism and cross-talks with modulators of drug response, effectors of cell cycle progression, proliferation or cell motility, and activators/mediators of cancer stemness. Moreover, of clinical relevance, SHBG ectopic expression (SHBG_OE) or SRD5A1 knockout (sgSRD5A1) induced the acquisition of spindle fibroblastoid morphology by the round/polygonal metastatic PC-3 and LNCaP cells, attenuated their migration and invasion capability, and significantly suppressed their ability to form primary or secondary tumorspheres, with concomitant downregulation of stemness KLF4, OCT3/4, and drug resistance ABCC1, ABCB1 proteins expression levels. We also showed that metronomic dutasteride synergistically enhanced the anticancer effect of low-dose docetaxel, in vitro, and in vivo. Conclusion: These data provide proof of concept that re-reprogramming of testosterone metabolism through “SRD5A1 withdrawal” or “SHBG induction” is a workable therapeutic strategy for shutting down androgen-driven oncogenic signals, reversing treatment resistance, and repressing the metastatic/recurrent phenotypes of patients with PCa.

Potential Lipid Signatures for Diagnosis and Prognosis of Sepsis and Systemic Inflammatory Response Syndrome

Systemic inflammatory response syndrome (SIRS) and sepsis are two conditions which are difficult to differentiate clinically and which are strongly impacted for prompt intervention. This study identified potential lipid signatures that are able to differentiate SIRS from sepsis and to predict prognosis. Forty-two patients, including 21 patients with sepsis and 21 patients with SIRS, were involved in the study. Liquid chromatography coupled to mass spectrometry and multivariate statistical methods were used to determine lipids present in patient plasma. The obtained lipid signatures revealed 355 features for the negative ion mode and 297 for the positive ion mode, which were relevant for differential diagnosis of sepsis and SIRS. These lipids were also tested as prognosis predictors. Lastly, L-octanoylcarnitine was found to be the most promising lipid signature for both the diagnosis and prognosis of critically ill patients, with accuracies of 75% for both purposes. In short, we presented the determination of lipid signatures as a potential tool for differential diagnosis of sepsis and SIRS and prognosis of these patients.