Alteration of plasma metabolites associated with chemoradiosensitivity in esophageal squamous cell carcinoma via untargeted metabolomics approach

A systematic review of circulating predictive and prognostic biomarkers to aid the personalised use of radiotherapy in the radical treatment of patients with oesophageal cancer

BACKGROUND

The availability of circulating biomarkers that are predictive of treatment response or prognostic of overall outcome could enable the personalised and adaptive use of radiotherapy (RT) in patients with oesophageal adenocarcinoma (OAC) and squamous cell carcinoma (OSCC).

METHODS

A systematic review was carried out following Preferred Reporting Items for Systematic Reviews guidance. Medline, EMBASE, PubMed, Cochrane Library, CINAHL, Scopus and the Web of Science databases were searched for studies published between January 2005-February 2023 relating to circulating biomarkers evaluated in the context of neoadjuvant or definitive RT delivered for OAC/OSCC. Study quality was assessed using predefined criteria.

RESULTS

A total of 3012 studies were screened and 57 subsequently included, across which 61 biomarkers were reported. A majority (43/57,75.4%) of studies were of Asian origin and retrospective (40/57, 70.2%), with most (52/57, 91.2%) biomarkers reported in the context of patients with OSCC. There was marked inter-study heterogeneity in patient populations, treatment characteristics, biomarker measurement and the cut points used to define biomarker positivity. Nevertheless, there is evidence for the prognostic and predictive value of circulating tumour DNA and numerous miRNAs in OAC and OSCC, as well as for the prognostic and predictive value of circulating levels of CYFRA21.1 in OSCC.

CONCLUSIONS

There is consistent evidence for the potential predictive and prognostic value of a small number of biomarkers in OSCC and OAC, though these data are insufficient for translation to current clinical practice. Well-designed prospective studies are now required to validate their role in stratified and personalised RT treatment approaches.

Pathological response to neoadjuvant chemoradiotherapy for oesophageal squamous cell carcinoma in Eastern versus Western countries: meta-analysis

OBJECTIVE

Locally advanced oesophageal squamous cell carcinoma can be treated with neoadjuvant chemoradiotherapy or chemotherapy followed by oesophagectomy. Discrepancies in pathological response rates have been reported between studies from Eastern versus Western countries. The aim of this study was to compare the pathological response to neoadjuvant chemoradiotherapy in Eastern versus Western countries.

METHODS

Databases were searched until November 2022 for studies reporting pCR rates after neoadjuvant chemoradiotherapy for oesophageal squamous cell carcinoma. Multi-level meta-analyses were performed to pool pCR rates separately for cohorts from studies performed in centres in the Sinosphere (East) or in Europe and the Anglosphere (West).

RESULTS

For neoadjuvant chemoradiotherapy, 51 Eastern cohorts (5636 patients) and 20 Western cohorts (3039 patients) were included. Studies from Eastern countries included more men, younger patients, more proximal tumours, and more cT4 and cN+ disease. Patients in the West were more often treated with high-dose radiotherapy, whereas patients in the East were more often treated with a platinum + fluoropyrimidine regimen. The pooled pCR rate after neoadjuvant chemoradiotherapy was 31.7% (95% c.i. 29.5% to 34.1%) in Eastern cohorts versus 40.4% (95% c.i. 35.0% to 45.9%) in Western cohorts (fixed-effect P = 0.003). For cohorts with similar cTNM stages, pooled pCR rates for the East and the West were 32.5% and 41.9% respectively (fixed-effect P = 0.003).

CONCLUSION

The pathological response to neoadjuvant chemoradiotherapy is less favourable in patients treated in Eastern countries compared with Western countries. Despite efforts to investigate accounting factors, the discrepancy in pCR rate cannot be entirely explained by differences in patient, tumour, or treatment characteristics.

Plasma metabolomic differences in early-onset compared to average-onset colorectal cancer

Deleterious effects of environmental exposures may contribute to the rising incidence of early-onset colorectal cancer (eoCRC). We assessed the metabolomic differences between patients with eoCRC, average-onset CRC (aoCRC), and non-CRC controls, to understand pathogenic mechanisms. Patients with stage I-IV CRC and non-CRC controls were categorized based on age ≤ 50 years (eoCRC or young non-CRC controls) or  ≥ 60 years (aoCRC or older non-CRC controls). Differential metabolite abundance and metabolic pathway analyses were performed on plasma samples. Multivariate Cox proportional hazards modeling was used for survival analyses. All P values were adjusted for multiple testing (false discovery rate, FDR P < 0.15 considered significant). The study population comprised 170 patients with CRC (66 eoCRC and 104 aoCRC) and 49 non-CRC controls (34 young and 15 older). Citrate was differentially abundant in aoCRC vs. eoCRC in adjusted analysis (Odds Ratio = 21.8, FDR P = 0.04). Metabolic pathways altered in patients with aoCRC versus eoCRC included arginine biosynthesis, FDR P = 0.02; glyoxylate and dicarboxylate metabolism, FDR P = 0.005; citrate cycle, FDR P = 0.04; alanine, aspartate, and glutamate metabolism, FDR P = 0.01; glycine, serine, and threonine metabolism, FDR P = 0.14; and amino-acid t-RNA biosynthesis, FDR P = 0.01. 4-hydroxyhippuric acid was significantly associated with overall survival in all patients with CRC (Hazards ratio, HR = 0.4, 95% CI 0.3-0.7, FDR P = 0.05). We identified several unique metabolic alterations, particularly the significant differential abundance of citrate in aoCRC versus eoCRC. Arginine biosynthesis was the most enriched by the differentially altered metabolites. The findings hold promise in developing strategies for early detection and novel therapies.

Metabolomic biomarkers in liquid biopsy: accurate cancer diagnosis and prognosis monitoring

Liquid biopsy, a novel detection method, has recently become an active research area in clinical cancer owing to its unique advantages. Studies on circulating free DNA, circulating tumor cells, and exosomes obtained by liquid biopsy have shown great advances and they have entered clinical practice as new cancer biomarkers. The metabolism of the body is dynamic as cancer originates and progresses. Metabolic abnormalities caused by cancer can be detected in the blood, sputum, urine, and other biological fluids via systemic or local circulation. A considerable number of recent studies have focused on the roles of metabolic molecules in cancer. The purpose of this review is to provide an overview of metabolic markers from various biological fluids in the latest clinical studies, which may contribute to cancer screening and diagnosis, differentiation of cancer typing, grading and staging, and prediction of therapeutic response and prognosis.

Multi-omics approaches for biomarker discovery in predicting the response of esophageal cancer to neoadjuvant therapy: A multidimensional perspective

Neoadjuvant chemoradiotherapy (NCRT) followed by surgery has been established as the standard treatment strategy for operable locally advanced esophageal cancer (EC). However, achieving pathologic complete response (pCR) or near pCR to NCRT is significantly associated with a considerable improvement in survival outcomes, while pCR patients may help organ preservation for patients by active surveillance to avoid planned surgery. Thus, there is an urgent need for improved biomarkers to predict EC chemoradiation response in research and clinical settings. Advances in multiple high-throughput technologies such as next-generation sequencing have facilitated the discovery of novel predictive biomarkers, specifically based on multi-omics data, including genomic/transcriptomic sequencings and proteomic/metabolomic mass spectra. The application of multi-omics data has shown the benefits in improving the understanding of underlying mechanisms of NCRT sensitivity/resistance in EC. Particularly, the prominent development of artificial intelligence (AI) has introduced a new direction in cancer research. The integration of multi-omics data has significantly advanced our knowledge of the disease and enabled the identification of valuable biomarkers for predicting treatment response from diverse dimension levels, especially with rapid advances in biotechnological and AI methodologies. Herein, we summarize the current status of research on the use of multi-omics technologies in predicting NCRT response for EC patients. Current limitations, challenges, and future perspectives of these multi-omics platforms will be addressed to assist in experimental designs and clinical use for further integrated analysis.

Molecular docking, synthesis, anticancer activity, and metabolomics study of boronic acid ester-containing fingolimod derivatives

In recent years, drugs that contain boronic acid groups, such as ixazomib (Ninlaro™) and bortezomib (Velcade™), have been used in the treatment of bone marrow cancer. The activity of compounds has been found to increase with the addition of boron atoms to the structure. In addition to these compounds, studies have found that fingolimod (FTY720) is more effective against breast cancer than cisplatin. Therefore, in this study, the first examples of boron-containing derivatives of fingolimod were designed and synthesized; in addition, their structures were confirmed by spectroscopic techniques. The synthesized boron-containing drug candidates were found to significantly inhibit cell proliferation and induce apoptosis-mediated cell death in HT-29 (colorectal cells), SaOs-2 (osteosarcoma cells), and U87-MG (glioblastoma cells). Moreover, we revealed that the anticancer effects of boron-containing fingolimod compounds were found to be significantly enhanced over boron-free control groups and, strikingly, over the widely used anticancer drug 5-fluorouracil. The metabolomic analysis confirmed that administration of the boron-containing drug candidates induces significant changes in the metabolite profiles in HT-29, SaOs-2, and U87-MG cells. Altogether, our results showed that boron-containing fingolimod compounds can be further examined to reveal their potential as anticancer drug candidates.

Preliminary Findings on the Salivary Metabolome of Hookah and Cigarette Smokers

The aim of the study was to evaluate the salivary metabolomic profile of patients who habitually smoke hookah and cigarettes. The groups consisted of 33 regular and exclusive hookah smokers, 26 regular and exclusive cigarette smokers, and 30 nonsmokers. Unstimulated whole saliva was collected for the measurement of salivary metabolites by gas chromatography coupled with tandem mass spectrometry (GC-MS/MS). The MetaboAnalyst software was used for statistical analysis and evaluation of biomarkers. 11 smoking salivary biomarkers were identified using the area under receiving-operator curver criterion and threshold of 0.9. Xylitol and octadecanol were higher in cigarette smokers compared to controls; arabitol and maltose were higher in controls compared to cigarette smokers; octadecanol and tyramine were higher in hookah smokers compared to controls; phenylalanine was higher in controls compared to hookah smokers; and fructose, isocitric acid, glucuronic acid, tryptamine, maltose, tyramine, and 3-hydroxyisolvaleric acid were higher in hookah smokers compared to cigarettes smokers. Conclusions: The evaluation of the salivary metabolome of hookah smokers, showing separation between the groups, especially between the control versus hookah groups and cigarette versus hookah groups, and it seems to demonstrate that the use of hookah tobacco is more damaging to health.

Microbiome and metabolic features of tissues and feces reveal diagnostic biomarkers for colorectal cancer

Microbiome and their metabolites are increasingly being recognized for their role in colorectal cancer (CRC) carcinogenesis. Towards revealing new CRC biomarkers, we compared 16S rRNA gene sequencing and liquid chromatography-mass spectrometry (LC-MS) metabolite analyses in 10 CRC (TCRC) and normal paired tissues (THC) along with 10 matched fecal samples (FCRC) and 10 healthy controls (FHC). The highest microbial phyla abundance from THC and TCRC were Firmicutes, while the dominant phyla from FHC and FCRC were Bacteroidetes, with 72 different microbial genera identified among four groups. No changes in Chao1 indices were detected between tissues or between fecal samples whereas non-metric multidimensional scaling (NMDS) analysis showed distinctive clusters among fecal samples but not tissues. LEfSe analyses indicated Caulobacterales and Brevundimonas were higher in THC than in TCRC, while Burkholderialese, Sutterellaceaed, Tannerellaceaea, and Bacteroidaceae were higher in FHC than in FCRC. Microbial association networks indicated some genera had substantially different correlations. Tissue and fecal analyses indicated lipids and lipid-like molecules were the most abundant metabolites detected in fecal samples. Moreover, partial least squares discriminant analysis (PLS-DA) based on metabolic profiles showed distinct clusters for CRC and normal samples with a total of 102 differential metabolites between THC and TCRC groups and 700 metabolites different between FHC and FCRC groups. However, only Myristic acid was detected amongst all four groups. Highly significant positive correlations were recorded between genus-level microbiome and metabolomics data in tissue and feces. And several metabolites were associated with paired microbes, suggesting a strong microbiota-metabolome coupling, indicating also that part of the CRC metabolomic signature was attributable to microbes. Suggesting utility as potential biomarkers, most such microbiome and metabolites showed directionally consistent changes in CRC patients. Nevertheless, further studies are needed to increase sample sizes towards verifying these findings.

Changes of serum metabolites levels during neoadjuvant chemoradiation and prediction of the pathological response in locally advanced rectal cancer

INTRODUCTION

Previous studies have explored prediction value of serum metabolites in neoadjuvant chemoradiation therapy (NCRT) response for rectal cancer. To date, limited literature is available for serum metabolome changes dynamically through NCRT.

OBJECTIVES

This study aimed to explore temporal change pattern of serum metabolites during NCRT, and potential metabolic biomarkers to predict the pathological response to NCRT in locally advanced rectal cancer (LARC) patients.

METHODS

Based on dynamic UHPLC-QTOF-MS untargeted metabolomics design, this study included 106 LARC patients treated with NCRT. Biological samples of the enrolled patients were collected in five consecutive time-points. Untargeted metabolomics was used to profile serum metabolic signatures from LARC patients. Then, we used fuzzy C-means clustering (FCM) to explore temporal change patterns in metabolites cluster and identify monotonously changing metabolites during NCRT. Repeated measure analysis of variance (RM-ANOVA) and multilevel partial least-squares discriminant analysis (ML-PLS-DA) were performed to select metabolic biomarkers. Finally, a panel of dynamic differential metabolites was used to build logistic regression prediction models.

RESULTS

Metabolite profiles showed a clearly tendency of separation between different follow-up panels. We identified two clusters of 155 serum metabolites with monotonously changing patterns during NCRT (74 decreased metabolites and 81 increased metabolites). Using RM-ANOVA and ML-PLS-DA, 8 metabolites (L-Norleucine, Betaine, Hypoxanthine, Acetylcholine, 1-Hexadecanoyl-sn-glycero-3-phosphocholine, Glycerophosphocholine, Alpha-ketoisovaleric acid, N-Acetyl-L-alanine) were further identified as dynamic differential biomarkers for predicting NCRT sensitivity. The area under the ROC curve (AUC) of prediction model combined with the baseline measurement was 0.54 (95%CI = 0.43 ~ 0.65). By incorporating the variability indexes of 8 dynamic differential metabolites, the prediction model showed better discrimination performance than baseline measurement, with AUC = 0.67 (95%CI 0.57 ~ 0.77), 0.64 (0.53 ~ 0.75), 0.60 (0.50 ~ 0.71), and 0.56 (0.45 ~ 0.67) for the variability index of difference, linear slope, ratio, and standard deviation, respectively.

CONCLUSION

This study identified eight metabolites as dynamic differential biomarkers to discriminate NCRT-sensitive and resistant patients. The changes of metabolite level during NCRT show better performance in predicting NCRT sensitivity. These findings highlight the clinical significance of metabolites variabilities in metabolomics analysis.

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.

Biomarkers for the prediction of esophageal cancer neoadjuvant chemoradiotherapy response: A systemic review

Neoadjuvant chemoradiotherapy followed by surgery has been established as the standard treatment for locally advanced esophageal cancer. For patients with complete regression after neoadjuvant chemotherapy, active surveillance rather than planned surgery has been proposed as an organ preservation strategy. Reliable biomarkers to predict chemoradiation response is needed. We first summarized the previous reports of biomarkers with the potential to predict the treatment response of esophageal cancer neoadjuvant chemoradiotherapy. These traditional biomarkers are classified into three groups: genetic biomarkers, RNA biomarkers, and protein biomarkers. We then summarized some special types of biomarkers, including metabolites biomarkers, immune and tumor microenvironment biomarkers, and microbiome biomarkers.

Metabolomic analysis of exosomal-markers in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a worldwide malignancy with high mortality rates and poor prognosis due to the lack of effective biomarkers for early detection. Exosomes have been extensively explored as attractive biomarkers for cancer diagnosis and treatment. However, little is known about exosome metabolomics and their roles in ESCC. Here, we performed a targeted metabolomic analysis of plasma exosomes and identified 196 metabolites, mainly including lipid fatty acids, benzene, amino acids, organic acids, carbohydrates and fatty acyls. We systematically compared metabolome patterns of exosomes via machine learning from patients with recrudescence and patients without recrudescence and demonstrated a marker set consisting of 3'-UMP, palmitoleic acid, palmitaldehyde, and isobutyl decanoate for predicting ESCC recurrence with an AUC of 98%. These metabolome signatures of exosomes retained a high absolute fold change value at all ESCC stages and were very likely associated with cancer metabolism, which could be potentially applied as novel biomarkers for diagnosis and prognosis of ESCC.

Metabolomics Analysis of the Development of Sepsis and Potential Biomarkers of Sepsis-Induced Acute Kidney Injury

Sepsis-induced acute kidney injury (SI-AKI) is a serious condition in critically ill patients. Currently, the diagnosis is based on either elevated serum creatinine levels or oliguria, which partially contribute to delayed recognition of AKI. Metabolomics is a potential approach for identifying small molecule biomarkers of kidney diseases. Here, we studied serum metabolomics alterations in rats with sepsis to identify early biomarkers of sepsis and SI-AKI. A rat model of SI-AKI was established by intraperitoneal injection of lipopolysaccharide (LPS). Thirty Sprague-Dawley (SD) rats were randomly divided into the control (CT) group and groups treated for 2 hours (LPS2) and 6 hours (LPS6) with LPS (10 rats per group). Nontargeted metabolomics screening was performed on the serum samples from the control and SI-AKI groups. Combined multivariate and univariate analysis was used for pairwise comparison of all groups to identify significantly altered serum metabolite levels in early-stage AKI in rats with sepsis. Orthogonal partial least squares discriminant analysis (OPLS-DA) showed obvious separation between the CT and LPS2 groups, CT and LPS6 groups, and LPS2 and LPS6 groups. All comparisons of the groups identified a series of differential metabolites according to the threshold defined for potential biomarkers. Intersections and summaries of these differential metabolites were used for pathway enrichment analysis. The results suggested that sepsis can cause an increase in systemic aerobic and anaerobic metabolism, an impairment of the oxygen supply, and uptake and abnormal fatty acid metabolism. Changes in the levels of malic acid, methionine sulfoxide, and petroselinic acid were consistently measured during the progression of sepsis. The development of sepsis was accompanied by the development of AKI, and these metabolic disorders are directly or indirectly related to the development of SI-AKI.