Predicting the pathological response to neoadjuvant chemoradiation using untargeted metabolomics in locally advanced rectal cancer

Plasma Metabolome Signatures to Predict Responsiveness to Neoadjuvant Chemotherapy in Breast Cancer

BACKGROUND

Neoadjuvant chemotherapy (NACT) has arisen as a treatment option for breast cancer (BC). However, the response to NACT is still unpredictable and dependent on cancer subtype. Metabolomics is a tool for predicting biomarkers and chemotherapy response. We used plasma to verify metabolomic alterations in BC before NACT, relating to clinical data.

METHODS

Liquid chromatography coupled to mass spectrometry (LC-MS) was performed on pre-NACT plasma from patients with BC (n = 75). After data filtering, an SVM model for classification was built and validated with 75%/25% of the data, respectively.

RESULTS

The model composed of 19 identified metabolites effectively predicted NACT response for training/validation sets with high sensitivity (95.4%/93.3%), specificity (91.6%/100.0%), and accuracy (94.6%/94.7%). In both sets, the panel correctly classified 95% of resistant and 94% of sensitive females. Most compounds identified by the model were lipids and amino acids and revealed pathway alterations related to chemoresistance.

CONCLUSION

We developed a model for predicting patient response to NACT. These metabolite panels allow clinical gain by building precision medicine strategies based on tumor stratification.

LC-MS metabolomics analysis of serum metabolites during neoadjuvant chemoradiotherapy in locally advanced rectal cancer

BACKGROUND

This study aimed to investigate the serum metabolite profiles during neoadjuvant chemoradiotherapy (NCRT) in locally advanced rectal cancer (LARC) using liquid chromatography-mass spectrometry (LC-MS) metabolomics analysis.

METHODS

60 serum samples were collected from 20 patients with LARC before, during, and after radiotherapy. LC-MS metabolomics analysis was performed to identify the metabolite variations. Functional annotation was applied to discover altered metabolic pathways. The key metabolites were screened and their ability to predict sensitivity to radiotherapy was calculated using random forests and ROC curves.

RESULTS

The results showed that NCRT led to significant changes in the serum metabolite profiles. The serum metabolic profiles showed an apparent separation between different time points and different sensitivity groups. Moreover, the functional annotation showed that the differential metabolites were associated with a series of important metabolic pathways. Pre-radiotherapy (3Z,6Z)-3,6-Nonadiena and pro-radiotherapy 1-Hydroxyibuprofen showed good predictive performance in discriminating the sensitive and non-sensitive group to NCRT, with an AUC of 0.812 and 0.75, respectively. Importantly, the combination of different metabolites significantly increased the predictive ability.

CONCLUSION

This study demonstrated the potential of LC-MS metabolomics for revealing the serum metabolite profiles during NCRT in LARC. The identified metabolites may serve as potential biomarkers and therapeutic targets for the management of this disease. Furthermore, the understanding of the affected metabolic pathways may help design more personalized therapeutic strategies for LARC patients.

Metabolic Signatures: Pioneering the Frontier of Rectal Cancer Diagnosis and Response to Neoadjuvant Treatment with Biomarkers-A Systematic Review

Colorectal cancer (CRC) is one of the most aggressive, heterogenous, and fatal types of human cancer for which screening, and more effective therapeutic drugs are urgently needed. Early-stage detection and treatment greatly improve the 5-year survival rate. In the era of targeted therapies for all types of cancer, a complete metabolomic profile is mandatory before neoadjuvant therapy to assign the correct drugs and check the response to the treatment given. The aim of this study is to discover specific metabolic biomarkers or a sequence of metabolomic indicators that possess precise diagnostic capabilities in predicting the efficacy of neoadjuvant therapy. After searching the keywords, a total of 108 articles were identified during a timeframe of 10 years (2013-2023). Within this set, one article was excluded due to the use of non-English language. Six scientific papers were qualified for this investigation after eliminating all duplicates, publications not referring to the subject matter, open access restriction papers, and those not applicable to humans. Biomolecular analysis found a correlation between metabolomic analysis of colorectal cancer samples and poor progression-free survival rates. Biomarkers are instrumental in predicting a patient's response to specific treatments, guiding the selection of targeted therapies, and indicating resistance to certain drugs.

Proteomic and metabolomic signatures of rectal tumor discriminate patients with different responses to preoperative radiotherapy

Background

Neoadjuvant radiotherapy (neo-RT) is widely used in locally advanced rectal cancer (LARC) as a component of radical treatment. Despite the advantages of neo-RT, which typically improves outcomes in LARC patients, the lack of reliable biomarkers that predict response and monitor the efficacy of therapy, can result in the application of unnecessary aggressive therapy affecting patients' quality of life. Hence, the search for molecular biomarkers for assessing the radio responsiveness of this cancer represents a relevant issue.

Methods

Here, we combined proteomic and metabolomic approaches to identify molecular signatures, which could discriminate LARC tumors with good and poor responses to neo-RT.

Results

The integration of data on differentially accumulated proteins and metabolites made it possible to identify disrupted metabolic pathways and signaling processes connected with response to irradiation, including ketone bodies synthesis and degradation, purine metabolism, energy metabolism, degradation of fatty acid, amino acid metabolism, and focal adhesion. Moreover, we proposed multi-component panels of proteins and metabolites which could serve as a solid base to develop biomarkers for monitoring and predicting the efficacy of preoperative RT in rectal cancer patients.

Conclusion

We proved that an integrated multi-omic approach presents a valid look at the analysis of the global response to cancer treatment from the perspective of metabolomic reprogramming.

Amino Acid Profiles in the Biological Fluids and Tumor Tissue of CRC Patients

Amino acids are the building blocks of proteins and essential players in pathways such as the citric acid and urea cycle, purine and pyrimidine biosynthesis, and redox cell signaling. Therefore, it is unsurprising that these molecules have a significant role in cancer metabolism and its metabolic plasticity. As one of the most prevalent malign diseases, colorectal cancer needs biomarkers for its early detection, prognostic, and prediction of response to therapy. However, the available biomarkers for this disease must be more powerful and present several drawbacks, such as high costs and complex laboratory procedures. Metabolomics has gathered substantial attention in the past two decades as a screening platform to study new metabolites, partly due to the development of techniques, such as mass spectrometry or liquid chromatography, which have become standard practice in diagnostic procedures for other diseases. Extensive metabolomic studies have been performed in colorectal cancer (CRC) patients in the past years, and several exciting results concerning amino acid metabolism have been found. This review aims to gather and present findings concerning alterations in the amino acid plasma pool of colorectal cancer patients.

Microbiome and metabolome dynamics during radiotherapy for prostate cancer

BACKGROUND

Prostate cancer patients treated with radiotherapy are susceptible to acute gastrointestinal (GI) toxicity due to substantial overlap of the intestines with the radiation volume. Due to their intimate relationship with GI toxicity, faecal microbiome and metabolome dynamics during radiotherapy were investigated.

MATERIAL & METHODS

This prospective study included 50 prostate cancer patients treated with prostate (bed) only radiotherapy (PBRT) (n = 28) or whole pelvis radiotherapy (WPRT) (n = 22) (NCT04638049). Longitudinal sampling was performed prior to radiotherapy, after 10 fractions, near the end of radiotherapy and at follow-up. Patient symptoms were dichotomized into a single toxicity score. Microbiome and metabolome fingerprints were analyzed by 16S rRNA gene sequencing and ultra-high-performance liquid chromatography hybrid high-resolution mass spectrometry, respectively.

RESULTS

The individual α-diversity did not significantly change over time. Microbiota composition (β-diversity) changed significantly over treatment (PERMANOVA p-value = 0.03), but there was no significant difference in stability when comparing PBRT versus WPRT. Levels of various metabolites were significantly altered during radiotherapy. Baseline α-diversity was not associated with any toxicity outcome. Based on the metabolic fingerprint, no natural clustering according to toxicity profile could be achieved.

CONCLUSIONS

Radiation dose and treatment volume demonstrated limited effects on microbiome and metabolome fingerprints. In addition, no distinctive signature for toxicity status could be established. There is an ongoing need for toxicity risk stratification tools for diagnostic and therapeutic purposes, but the current evidence implies that the translation of metabolic and microbial biomarkers into routine clinical practice remains challenging.

Development and multicenter validation of a multiparametric imaging model to predict treatment response in rectal cancer

OBJECTIVES

To develop and validate a multiparametric model to predict neoadjuvant treatment response in rectal cancer at baseline using a heterogeneous multicenter MRI dataset.

METHODS

Baseline staging MRIs (T2W (T2-weighted)-MRI, diffusion-weighted imaging (DWI) / apparent diffusion coefficient (ADC)) of 509 patients (9 centres) treated with neoadjuvant chemoradiotherapy (CRT) were collected. Response was defined as (1) complete versus incomplete response, or (2) good (Mandard tumor regression grade (TRG) 1-2) versus poor response (TRG3-5). Prediction models were developed using combinations of the following variable groups: (1) Non-imaging: age/sex/tumor-location/tumor-morphology/CRT-surgery interval (2) Basic staging: cT-stage/cN-stage/mesorectal fascia involvement, derived from (2a) original staging reports, or (2b) expert re-evaluation (3) Advanced staging: variables from 2b combined with cTN-substaging/invasion depth/extramural vascular invasion/tumor length (4) Quantitative imaging: tumour volume + first-order histogram features (from T2W-MRI and DWI/ADC) Models were developed with data from 6 centers (n = 412) using logistic regression with the Least Absolute Shrinkage and Selector Operator (LASSO) feature selection, internally validated using repeated (n = 100) random hold-out validation, and externally validated using data from 3 centers (n = 97).

RESULTS

After external validation, the best model (including non-imaging and advanced staging variables) achieved an area under the curve of 0.60 (95%CI=0.48-0.72) to predict complete response and 0.65 (95%CI=0.53-0.76) to predict a good response. Quantitative variables did not improve model performance. Basic staging variables consistently achieved lower performance compared to advanced staging variables.

CONCLUSIONS

Overall model performance was moderate. Best results were obtained using advanced staging variables, highlighting the importance of good-quality staging according to current guidelines. Quantitative imaging features had no added value (in this heterogeneous dataset).

CLINICAL RELEVANCE STATEMENT

Predicting tumour response at baseline could aid in tailoring neoadjuvant therapies for rectal cancer. This study shows that image-based prediction models are promising, though are negatively affected by variations in staging quality and MRI acquisition, urging the need for harmonization.

KEY POINTS

This multicenter study combining clinical information and features derived from MRI rendered disappointing performance to predict response to neoadjuvant treatment in rectal cancer. Best results were obtained with the combination of clinical baseline information and state-of-the-art image-based staging variables, highlighting the importance of good quality staging according to current guidelines and staging templates. No added value was found for quantitative imaging features in this multicenter retrospective study. This is likely related to acquisition variations, which is a major problem for feature reproducibility and thus model generalizability.

Persistent Biofluid Small-Molecule Alterations Induced by Trypanosoma cruzi Infection Are Not Restored by Parasite Elimination

Chagas disease (CD), caused by Trypanosoma cruzi (T. cruzi) protozoa, is a complicated parasitic illness with inadequate medical measures for diagnosing infection and monitoring treatment success. To address this gap, we analyzed changes in the metabolome of T. cruzi-infected mice via liquid chromatography tandem mass spectrometry of clinically accessible biofluids: saliva, urine, and plasma. Urine was the most indicative of infection status across mouse and parasite genotypes. Metabolites perturbed by infection in urine include kynurenate, acylcarnitines, and threonylcarbamoyladenosine. Based on these results, we sought to implement urine as a tool for the assessment of CD treatment success. Strikingly, it was found that mice with parasite clearance following benznidazole antiparasitic treatment had an overall urine metabolome comparable to that of mice that failed to clear parasites. These results provide a complementary hypothesis to explain clinical trial data in which benznidazole treatment did not improve patient outcomes in late-stage disease, even in patients with successful parasite clearance. Overall, this study provides insights into new small-molecule-based CD diagnostic methods and a new approach to assess functional responses to treatment.

Validated Pretreatment Prediction Models for Response to Neoadjuvant Therapy in Patients with Rectal Cancer: A Systematic Review and Critical Appraisal

Pretreatment response prediction is crucial to select those patients with rectal cancer who will benefit from organ preservation strategies following (intensified) neoadjuvant therapy and to avoid unnecessary toxicity in those who will not. The combination of individual predictors in multivariable prediction models might improve predictive accuracy. The aim of this systematic review was to summarize and critically appraise validated pretreatment prediction models (other than radiomics-based models or image-based deep learning models) for response to neoadjuvant therapy in patients with rectal cancer and provide evidence-based recommendations for future research. MEDLINE via Ovid, Embase.com, and Scopus were searched for eligible studies published up to November 2022. A total of 5006 studies were screened and 16 were included for data extraction and risk of bias assessment using Prediction model Risk Of Bias Assessment Tool (PROBAST). All selected models were unique and grouped into five predictor categories: clinical, combined, genetics, metabolites, and pathology. Studies generally included patients with intermediate or advanced tumor stages who were treated with neoadjuvant chemoradiotherapy. Evaluated outcomes were pathological complete response and pathological tumor response. All studies were considered to have a high risk of bias and none of the models were externally validated in an independent study. Discriminative performances, estimated with the area under the curve (AUC), ranged per predictor category from 0.60 to 0.70 (clinical), 0.78 to 0.81 (combined), 0.66 to 0.91 (genetics), 0.54 to 0.80 (metabolites), and 0.71 to 0.91 (pathology). Model calibration outcomes were reported in five studies. Two collagen feature-based models showed the best predictive performance (AUCs 0.83-0.91 and good calibration). In conclusion, some pretreatment models for response prediction in rectal cancer show encouraging predictive potential but, given the high risk of bias in these studies, their value should be evaluated in future, well-designed studies.

Blood metabolomics reveals the therapeutic effect of Pueraria polysaccharide on calf diarrhea

BACKGROUND

Neonatal calf diarrhea (NCD) is typically treated with antibiotics, while long-term application of antibiotics induces drug resistance and antibiotic residues, ultimately decreasing feed efficiency. Pueraria polysaccharide (PPL) is a versatile antimicrobial, immunomodulatory, and antioxidative compound. This study aimed to compare the therapeutic efficacy of different doses of PPL (0.2, 0.4, 0.8 g/kg body weight (BW)) and explore the effect of plasma metabolites in diarrheal calves by the best dose of PPL.

RESULTS

PPL could effectively improve the daily weight gain, fecal score, and dehydration score, and the dosage of 0.4 g/kg BW could reach curative efficacy against calf diarrhea (with effective rates 100.00%). Metabolomic analysis suggested that diarrhea mainly affect the levels of taurocholate, DL-lactate, LysoPCs, and intestinal flora-related metabolites, trimethylamine N-oxide; however, PPL improved liver function and intestinal barrier integrity by modulating the levels of DL-lactate, LysoPC (18:0/0:0) and bilirubin, which eventually attenuated neonatal calf diarrhea. It also suggested that the therapeutic effect of PPL is related to those differential metabolites in diarrheal calves.

CONCLUSIONS

The results showed that 0.4 g/kg BW PPL could restore the clinical score of diarrhea calves by improving the blood indexes, biochemical indexes, and blood metabolites. And it is a potential medicine for the treatment of calf diarrhea.

FOSL2 promotes intertumoral infiltration of T cells and increases pathological complete response rates in locally advanced rectal cancer patients

The outcome of neoadjuvant chemoradiotherapy (nCRT) remains highly unpredictable for individuals with locally advanced rectal cancer (LARC). We set out to characterize effective biomarkers that promote a pathological complete response (pCR). We quantified the abundances of 6483 high-confidence proteins in pre-nCRT biopsies of 58 LARC patients from two hospitals with pressure cycling technology (PCT)-assisted pulse data-independent acquisition (PulseDIA) mass spectrometry. Compared with non-pCR patients, pCR patients achieved long-term disease-free survival (DFS) and had higher tumor immune infiltration, especially CD8+ T cell infiltration, before nCRT. FOSL2 was selected as the candidate biomarker for predicting pCR and was found to be significantly upregulated in pCR patients, which was verified in another 54 pre-nCRT biopsies of LARC patients by immunohistochemistry. FOSL2 expression was able to predict pCR by multiple reaction monitoring (MRM) with high efficiency (Area under curve (AUC) = 0.939, specificity = 1.000, sensitivity = 0.850), and high FOSL2 expression was associated with long-term DFS (p = 0.044). When treated with simulated nCRT, FOSL2 sufficiency resulted in more significant inhibition of cell proliferation, and more significant promotion of cell cycle arrest and cell apoptosis. Moreover, CXCL10 secretion with abnormal cytosolic dsDNA accumulation was found in FOSL2-wildtype (FOSL2-WT) tumor cells over nCRT, which might elevate CD8+ T-cell infiltration and CD8+ T-cell-mediated cytotoxicity to promote nCRT-induced antitumor immunity. Our study revealed proteomic profiles in LARC patients before nCRT and highlighted immune activation in the tumors of patients who achieved pCR. We identified FOSL2 as a promising biomarker to predict pCR and promote long-term DFS by contributing to CD8+ T-cell infiltration.

Energy Metabolism Is Altered in Radioresistant Rectal Cancer

Resistance to neoadjuvant chemoradiation therapy is a significant clinical challenge in the management of rectal cancer. There is an unmet need to identify the underlying mechanisms of treatment resistance to enable the development of biomarkers predictive of response and novel treatment strategies to improve therapeutic response. In this study, an in vitro model of inherently radioresistant rectal cancer was identified and characterized to identify mechanisms underlying radioresistance in rectal cancer. Transcriptomic and functional analysis demonstrated significant alterations in multiple molecular pathways, including the cell cycle, DNA repair efficiency and upregulation of oxidative phosphorylation-related genes in radioresistant SW837 rectal cancer cells. Real-time metabolic profiling demonstrated decreased reliance on glycolysis and enhanced mitochondrial spare respiratory capacity in radioresistant SW837 cells when compared to radiosensitive HCT116 cells. Metabolomic profiling of pre-treatment serum samples from rectal cancer patients (n = 52) identified 16 metabolites significantly associated with subsequent pathological response to neoadjuvant chemoradiation therapy. Thirteen of these metabolites were also significantly associated with overall survival. This study demonstrates, for the first time, a role for metabolic reprograming in the radioresistance of rectal cancer in vitro and highlights a potential role for altered metabolites as novel circulating predictive markers of treatment response in rectal cancer patients.

Alterations in gut microbiota and urine metabolomics in infants with yin-deficiency constitution aged 0–2 years

Background

Based on the constitution theroy, infants are classified into balanced constitution (BC) and unbalanced constitution. Yin-deficiency constitution (YINDC) is a common type of unbalanced constitutions in Chinese infants. An infant's gut microbiota directly affects the child's health and has long-term effects on the maturation of the immune and endocrine systems throughout life. However, the gut microbiota of infants with YINDC remains unknown. Herein, we aimed to evaluate the intestinal flora profiles and urinary metabolites in infant with YINDC, find biomarkers to identify YINDC, and promote our understanding of infant constitution classification.

Methods

Constitutional Medicine Questionnaires were used to assess the infants' constitution types. 47 infants with 21 cases of YINDC and 26 cases of BC were included, and a cross-sectional sampling of stool and urine was conducted. Fecal microbiota was characterized using 16S rRNA sequencing, and urinary metabolomics was profiled using UPLC-Q-TOF/MS method. YINDC markers with high accuracy were identified using receiver operating characteristic (ROC) analysis.

Results

The diversity and composition of intestinal flora and urinary metabolites differed significantly between the YINDC and BC groups. A total of 13 obviously different genera and 55 altered metabolites were identified. Stool microbiome shifts were associated with urine metabolite changes. A combined marker comprising two genera may have a high potential to identify YINDC with an AUC of 0.845.

Conclusions

Infants with YINDC had a unique gut microbiota and metabolomic profile resulting in a constitutional microclassification. The altered gut microbiome in YINDC may account for the higher risk of cardiovascular diseases. Metabolomic analysis of urine showed that metabolic pathways, including histidine metabolism, proximal tubule bicarbonate reclamation, arginine biosynthesis, and steroid hormone biosynthesis, were altered in infants with YINDC. Additionally, the combined bacterial biomarker had the ability to identify YINDC. Identifying YINDC in infancy and intervening at an early stage is crucial for preventing cardiovascular diseases.

Damp-heat constitution influences gut microbiota and urine metabolism of Chinese infants

Background

As an increasingly popular complementary and alternative approach for early detection and treatment of disease, traditional Chinese medicine constitution (TCMC) divides human beings into those with balanced constitution (BC) and unbalanced constitution, where damp-heat constitution (DHC) is one of the most unbalanced constitutions. Many studies have been carried out on the microscopic mechanism of constitution classification; however, most of these studies were conducted in adults and rarely in infants. Many diseases are closely related to intestinal microbiota, and metabolites produced by the interaction between microbiota and the body may impact constitution classification. Herein, we investigated the overall constitution distribution in Chinese infants, and analyzed the profiles of gut microbiota and urine metabolites of DHC to further promote the understanding of infants constitution classification.

Methods

General information was collected and TCMC was evaluated by Constitutional Medicine Questionnaires. 1315 questionnaires were received in a cross-sectional study to investigate the constitution composition in Chinese infants. A total of 56 infants, including 30 DHC and 26 BC, were randomly selected to analyze gut microbiota by 16S rRNA sequencing and urine metabolites by UPLC-Q-TOF/MS method.

Results

BC was the most common constitution in Chinese infants, DHC was the second common constitution. The gut microbiota and urine metabolites in the DHC group showed different composition compared to the BC group. Four differential genera and twenty differential metabolites were identified. In addition, the combined marker composed of four metabolites may have the high potential to discriminate DHC from BC with an AUC of 0.765.

Conclusions

The study revealed the systematic differences in the gut microbiota and urine metabolites between infants with DHC and BC. Moreover, the differential microbiota and metabolites may offer objective evidences for constitution classification.

Serum metabolic traits reveal therapeutic toxicities and responses of neoadjuvant chemoradiotherapy in patients with rectal cancer

Neoadjuvant chemoradiotherapy (nCRT) has become the standard treatment for patients with locally advanced rectal cancer (LARC). Therapeutic efficacy of nCRT is significantly affected by treatment-induced diarrhea and hematologic toxicities. Metabolic alternations in cancer therapy are key determinants to therapeutic toxicities and responses, but exploration in large-scale clinical studies remains limited. Here, we analyze 743 serum samples from 165 LARC patients recruited in a phase III clinical study using untargeted metabolomics and identify responsive metabolic traits over the course of nCRT. Pre-therapeutic serum metabolites successfully predict the chances of diarrhea and hematologic toxicities during nCRT. Particularly, levels of acyl carnitines are linked to sex disparity in nCRT-induced diarrhea. Finally, we show that differences in phenylalanine metabolism and essential amino acid metabolism may underlie distinct therapeutic responses of nCRT. This study illustrates the metabolic dynamics over the course of nCRT and provides potential to guide personalized nCRT treatment using responsive metabolic traits.

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.

Predicting neoadjuvant chemoradiotherapy response with functional imaging and liquid biomarkers in locally advanced rectal cancer

INTRODUCTION

Non-invasive predictive quantitative biomarkers are required to guide treatment individualization in patients with locally advanced rectal cancer (LARC) in order to maximise therapeutic outcomes and minimise treatment toxicity. Magnetic resonance imaging (MRI), positron emission tomography (PET) and blood biomarkers have the potential to predict chemoradiotherapy (CRT) response in LARC.

AREAS COVERED

This review examines the value of functional imaging (MRI and PET) and liquid biomarkers (circulating tumor cells (CTCs) and circulating tumor nucleic acid (ctNA)) in the prediction of CRT response in LARC. Selected imaging and liquid biomarker studies are presented and the current status of the most promising imaging (apparent diffusion co-efficient (ADC), K^trans, SUV_max, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) and liquid biomarkers (circulating tumor cells (CTCs), circulating tumor nucleic acid (ctNA)) is discussed. The potential applications of imaging and liquid biomarkers for treatment stratification and a pathway to clinical translation are presented.

EXPERT OPINION

Functional imaging and liquid biomarkers provide novel ways of predicting CRT response. The clinical and technical validation of the most promising imaging and liquid biopsy biomarkers in multi-centre studies with harmonised acquisition techniques is required. This will enable clinical trials to investigate treatment escalation or de-escalation pathways in rectal cancer.

Fermentation of NaHCO3-treated corn germ meal by Bacillus velezensis CL-4 promotes lignocellulose degradation and nutrient utilization

Sodium bicarbonate pretreatment and solid-state fermentation (SSF) were used to maximize the nutritional value of corn germ meal (CGM) by inoculating it with Bacillus velezensis CL-4 (isolated from chicken cecal contents and capable of degrading lignocellulose). Based on genome sequencing, B. velezensis CL-4 has a 4,063,558 bp ring chromosome and 46.27% GC content. Furthermore, genes associated with degradation of lignocellulose degradation were detected. Pretreatment of CGM (PCGM) with sodium bicarbonate (optimized to 0.06 g/mL) neutralized low pH. Fermented and pretreated CGM (FPCGM) contained more crude protein (CP), soluble protein of trichloroacetic acid (TCA-SP), and total amino acids (aa) than CGM and PCGM. Degradation rates of cellulose and hemicellulose were reduced by 21.33 and 71.35%, respectively, after 48 h fermentation. Based on electron microscopy, FPCGM destroys the surface structure and adds small debris of the CGM substrate, due to lignocellulose breakdown. Furthermore, 2-oxoadipic acid and dimethyl sulfone were the most important metabolites during pretreatment. Concentrations of adenosine, cytidine, guanosine, S-methyl-5'-thioadenosine, and adenine decreased significantly after 48 h fermentation, whereas concentrations of probiotics, enzymes, and fatty acids (including palmitic, 16-hydroxypalmitic, and linoleic acids) were significantly improved after fermentation. In conclusion, the novel pretreatment of CGM provided a proof of concept for using B. velezensis CL-4 to degrade lignocellulose components, improve nutritional characteristics of CGM, and expand CGM lignocellulosic biological feed production. KEY POINTS: • Sodium bicarbonate (baking soda) can be used as an economical and green additive to pretreat corn germ meal; • Fermentation with B. velezensis degrades the cellulose and hemicellulose component of corn germ meal and improves its feed quality; • As a novel qualified presumption of safety (QPS) strain, B. velezensis should have broad potential applications in food and feed industries.

Deep learning-based pseudo-mass spectrometry imaging analysis for precision medicine

Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics provides systematic profiling of metabolic. Yet, its applications in precision medicine (disease diagnosis) have been limited by several challenges, including metabolite identification, information loss and low reproducibility. Here, we present the deep-learning-based Pseudo-Mass Spectrometry Imaging (deepPseudoMSI) project (https://www.deeppseudomsi.org/), which converts LC-MS raw data to pseudo-MS images and then processes them by deep learning for precision medicine, such as disease diagnosis. Extensive tests based on real data demonstrated the superiority of deepPseudoMSI over traditional approaches and the capacity of our method to achieve an accurate individualized diagnosis. Our framework lays the foundation for future metabolic-based precision medicine.

Molecular Composition of Serum Exosomes Could Discriminate Rectal Cancer Patients with Different Responses to Neoadjuvant Radiotherapy

Identification of biomarkers that could be used for the prediction of the response to neoadjuvant radiotherapy (neo-RT) in locally advanced rectal cancer remains a challenge addressed by different experimental approaches. Exosomes and other classes of extracellular vesicles circulating in patients' blood represent a novel type of liquid biopsy and a source of cancer biomarkers. Here, we used a combined proteomic and metabolomic approach based on mass spectrometry techniques for studying the molecular components of exosomes isolated from the serum of rectal cancer patients with different responses to neo-RT. This allowed revealing several proteins and metabolites associated with common pathways relevant for the response of rectal cancer patients to neo-RT, including immune system response, complement activation cascade, platelet functions, metabolism of lipids, metabolism of glucose, and cancer-related signaling pathways. Moreover, the composition of serum-derived exosomes and a whole serum was analyzed in parallel to compare the biomarker potential of both specimens. Among proteins that the most properly discriminated good and poor responders were GPLD1 (AUC = 0.85, accuracy of 74%) identified in plasma as well as C8G (AUC = 0.91, accuracy 81%), SERPINF2 (AUC = 0.91, accuracy 79%) and CFHR3 (AUC = 0.90, accuracy 81%) identified in exosomes. We found that the proteome component of serum-derived exosomes has the highest capacity to discriminate samples of patients with different responses to neo-RT when compared to the whole plasma proteome and metabolome. We concluded that the molecular components of exosomes are associated with the response of rectal cancer patients to neo-RT and could be used for the prediction of such response.

Impact of Pelvic Radiation Therapy for Prostate Cancer on Global Metabolic Profiles and Microbiota-Driven Gastrointestinal Late Side Effects: A Longitudinal Observational Study

PURPOSE

Radiation therapy to the prostate and pelvic lymph nodes (PLNRT) is part of the curative treatment of high-risk prostate cancer. Yet, the broader influence of radiation therapy on patient physiology is poorly understood. We conducted comprehensive global metabolomic profiling of urine, plasma, and stools sampled from patients undergoing PLNRT for high-risk prostate cancer.

METHODS AND MATERIALS

Samples were taken from 32 patients at 6 timepoints: baseline, 2 to 3 and 4 to 5 weeks of PLNRT; and 3, 6, and 12 months after PLNRT. We characterized the global metabolome of urine and plasma using 1H nuclear magnetic resonance spectroscopy and ultraperformance liquid chromatography-mass spectrometry, and of stools with nuclear magnetic resonance. Linear mixed-effects modeling was used to investigate metabolic changes between timepoints for each biofluid and assay and determine metabolites of interest.

RESULTS

Metabolites in urine, plasma and stools changed significantly after PLNRT initiation. Metabolic profiles did not return to baseline up to 1 year post-PLNRT in any biofluid. Molecules associated with cardiovascular risk were increased in plasma. Pre-PLNRT fecal butyrate levels directly associated with increasing gastrointestinal side effects, as did a sharper fall in those levels during and up to 1 year postradiation therapy, mirroring our previous results with metataxonomics.

CONCLUSIONS

We showed for the first time that an overall metabolic effect is observed in patients undergoing PLNRT up to 1 year posttreatment. These metabolic changes may effect on long-term morbidity after treatment, which warrants further investigation.

LC-MS-based lipidomic analysis in distinguishing patients with nonalcoholic steatohepatitis from nonalcoholic fatty liver

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD) is one of the main liver diseases, and its pathologic profile includes nonalcoholic fatty liver (NAFL) and nonalcoholic steatohepatitis (NASH). However, there is no reliable non-invasive parameter in distinguishing NASH from NAFL in clinical practice. The present study was to find a non-invasive way to differentiate these two categories of NAFLD via lipidomic analysis.

METHODS

Lipidomic analysis was used to determine the changes of lipid moieties in blood from 20 NAFL and 10 NASH patients with liver biopsy. Liver histology was evaluated after hematoxylin and eosin staining and Masson's trichrome staining. The profile of lipid metabolites in correlation with steatosis, inflammation, hepatocellular necroptosis, fibrosis, and NAFLD activity score (NAS) was analyzed.

RESULTS

Compared with NAFL patients, NASH patients had higher degree of steatosis, ballooning degeneration, lobular inflammation. A total of 434 different lipid molecules were identified, which were mainly composed of various phospholipids and triacylglycerols. Many lipids, such as phosphatidylcholine (PC) (P-22:0/18:1), sphingomyelin (SM) (d14:0/18:0), SM (d14:0/24:0), SM (d14:0/22:0), phosphatidylethanolamine (PE) (18:0/22:5), PC (O-22:2/12:0), and PC (26:1/11:0) were elevated in the NASH group compared to those in the NAFL group. Specific analysis revealed an overall lipidomic profile shift from NAFL to NASH, and identified valuable lipid moieties, such as PCs [PC (14:0/18:2), PE (18:0/22:5) and PC (26:1/11:0)] or plasmalogens [PC (O-22:0/0:0), PC (O-18:0/0:0), PC (O-16:0/0:0)], which were significantly altered in NASH patients. In addition, PC (14:0/18:2), phosphatidic acid (18:2/24:4) were positively correlated with NAS; whereas PC (18:0/0:0) was correlated positively with fibrosis score.

CONCLUSIONS

The present study revealed overall lipidomic profile shift from NAFL to NASH, identified valuable lipid moieties which may be non-invasive biomarkers in the categorization of NAFLD. The correlations between lipid moieties and NAS and fibrosis scores indicate that these lipid biomarkers may be used to predict the severity of the disease.

A Pipeline for Predicting the Treatment Response of Neoadjuvant Chemoradiotherapy for Locally Advanced Rectal Cancer Using Single MRI Modality: Combining Deep Segmentation Network and Radiomics Analysis Based on "Suspicious Region"

Patients with locally advanced rectal cancer (LARC) who achieve a pathologic complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) typically have a good prognosis. An early and accurate prediction of the treatment response, i.e., whether a patient achieves pCR, could significantly help doctors make tailored plans for LARC patients. This study proposes a pipeline of pCR prediction using a combination of deep learning and radiomics analysis. Taking into consideration missing pre-nCRT magnetic resonance imaging (MRI), as well as aiming to improve the efficiency for clinical application, the pipeline only included a post-nCRT T2-weighted (T2-w) MRI. Unlike other studies that attempted to carefully find the region of interest (ROI) using a pre-nCRT MRI as a reference, we placed the ROI on a "suspicious region", which is a continuous area that has a high possibility to contain a tumor or fibrosis as assessed by radiologists. A deep segmentation network, termed the two-stage rectum-aware U-Net (tsraU-Net), is designed to segment the ROI to substitute for a time-consuming manual delineation. This is followed by a radiomics analysis model based on the ROI to extract the hidden information and predict the pCR status. The data from a total of 275 patients were collected from two hospitals and partitioned into four datasets: Seg-T (N = 88) for training the tsraUNet, Rad-T (N = 107) for building the radiomics model, In-V (N = 46) for internal validation, and Ex-V (N = 34) for external validation. The proposed method achieved an area under the curve (AUC) of 0.829 (95% confidence interval [CI]: 0.821, 0.837) on In-V and 0.815 (95% CI, 0.801, 0.830) on Ex-V. The performance of the method was considerable and stable in two validation sets, indicating that the well-designed pipeline has the potential to be used in real clinical procedures.

Short-chain fatty acids can improve lipid and glucose metabolism independently of the pig gut microbiota

BACKGROUND

Previous studies have shown that exogenous short-chain fatty acids (SCFAs) introduction attenuated the body fat deposition in conventional mice and pigs. However, limited studies have evaluated the effects of exogenously introduced SCFAs on the lipid and glucose metabolism independently of the gut microbiota. This study was to investigate the effects of exogenous introduction of SCFAs on the lipid and glucose metabolism in a germ-free (GF) pig model.

METHODS

Twelve hysterectomy-derived newborn pigs were reared in six sterile isolators. All pigs were hand-fed with sterile milk powder for 21 d, then the sterile feed was introduced to pigs for another 21 d. In the second 21-d period, six pigs were orally administrated with 25 mL/kg sterile saline per day and considered as the GF group, while the other six pigs were orally administrated with 25 mL/kg SCFAs mixture (acetic, propionic, and butyric acids, 45, 15, and 11 mmol/L, respectively) per day and regarded as FA group.

RESULTS

Orally administrated with SCFAs tended to increase the adiponectin concentration in serum, enhance the CPT-1 activity in longissimus dorsi, and upregulate the ANGPTL4 mRNA expression level in colon (P < 0.10). Meanwhile, the mRNA abundances of ACC, FAS, and SREBP-1C in liver and CD36 in longissimus dorsi of the FA group were decreased (P < 0.05) compared with those in the GF group. Besides, the mRNA expression of PGC-1α in liver and LPL in longissimus dorsi tended to (P < 0.10) upregulate and downregulate respectively in the FA group. Moreover, oral administration of SCFAs tended to increase the protein level of GPR43 (P < 0.10) and decrease the protein level of ACC (P < 0.10) in liver. Also, oral administration of SCFAs upregulated the p-AMPK/AMPK ratio and the mRNA expressions of GLUT-2 and GYS2 in liver (P < 0.05). In addition, the metabolic pathway associated with the biosynthesis of unsaturated fatty acids was most significantly promoted (P < 0.05) by oral administration of SCFAs.

CONCLUSIONS

Exogenous introduction of SCFAs might attenuate the fat deposition and to some extent improve the glucose control in the pig model, which occurred independently of the gut microbiota.

Identification of potential metabolic biomarkers of rectal cancer and of the effect of neoadjuvant radiochemotherapy

We report a pilot study on the feasibility of determinations of circulating levels of paraoxonase-1 (PON1) and compounds related to energy metabolism as biomarkers for the evaluation of patients with rectal cancer (RC), and the effects produced by neoadjuvant radiochemotherapy (NRCT). We studied 32 patients treated with radiotherapy plus capecitabine concomitant chemotherapy and 48 control subjects. We identified pre-NRCT PON1 and α-ketoglutarate as the parameters that best discriminated between RC patients and the control group. Receiver operating characteristics analysis of the combination of the two parameters showed an area under the curve (AUC) of 0.918. Moreover, patients who presented a pathological complete response (pCR) to treatment had lower plasma pre-NRCT valine concentrations (AUC of 0.826). Patients who had a relapse had lower concentrations of succinate (AUC of 0.833). The results of the present study illustrate the usefulness of investigating alterations in oxidative stress and metabolism in RC. Due to the small number of patients studied, our results must be considered preliminary, but they suggest that the determination of circulating levels of PON1 and α-ketoglutarate might be a valuable tool for the early diagnosis of RC, while the determination of valine and succinate might effectively predict pCR and the appearance of relapse.

Plasma metabolome alteration in dairy cows with left displaced abomasum before and after surgical correction

Left displaced abomasum (LDA) leads to substantial changes in the metabolism of dairy cows. Surgical correction of LDA can rapidly improve the health of cows; however, changes in metabolism following surgery are rarely described. To investigate the changes of plasma metabolome in cows with LDA before and after surgical correction, blood samples were collected from 10 healthy postpartum cows and 10 cows with LDA on the day of diagnosis, then again from the LDA cows 14 d after surgery. Serum nonesterified fatty acid, β-hydroxybutyric acid, cortisol and histamine concentration, and antioxidant enzyme (superoxide dismutase and glutathione peroxidase) activities were evaluated, and the metabolic profile in plasma was analyzed using ultra-high-performance liquid chromatography time-of-flight mass spectrometry. The results demonstrated that cows with LDA experienced severe negative energy balance and oxidative stress, which can be improved by surgical correction. The metabolic profile was analyzed using multidimensional and univariate statistical analyses, and different metabolites were identified. In total, 102 metabolites differed between cows with LDA and healthy cows. After surgical correction, 65 metabolites changed in cows with LDA, compared with these cows during the LDA event. Following surgical correction, AA levels tended to increase, and lipid levels tended to decrease in cows with LDA. Pathway analysis indicated marked changes in linoleic acid metabolism, Arg biosynthesis, and Gly, Ser, and Thr metabolism in cows at the onset of LDA and following surgical correction. Surgical treatment reversed the changes in AA and lipid metabolism in cows with LDA.

Effects of Clostridium butyricum on growth performance, metabonomics and intestinal microbial differences of weaned piglets

Background

Weaning stress of piglets causes a huge economic loss to the pig industry. Balance and stability of the intestinal microenvironment is an effective way to reduce the occurance of stress during the weaning process. Clostridium butyricum, as a new microecological preparation, is resistant to high temperature, acid, bile salts and some antibiotics. The aim of present study is to investigate the effects of C. butyricum on the intestinal microbiota and their metabolites in weaned piglets.

Results

There was no statistical significance in the growth performance and the incidence of diarrhoea among the weaned piglets treated with C. butyricum during 0–21 days experimental period. Analysis of 16S rRNA gene sequencing results showed that the operational taxonomic units (OTUs), abundance-based coverage estimator (ACE) and Chao index of the CB group were found to be significantly increased compared with the NC group (P < 0.05). Bacteroidetes, Firmicutes and Tenericutes were the predominant bacterial phyla in the weaned piglets. A marked increase in the relative abundance of Megasphaera, Ruminococcaceae_NK4A214_group and Prevotellaceae_UCG-003, along with a decreased relative abundance of Ruminococcaceae_UCG-005 was observed in the CB group, when compared with the NC group (P < 0.05). With the addition of C. butyricum, a total of twenty-two significantly altered metabolites were obtained in the feces of piglets. The integrated pathway analysis by MetaboAnalyst indicated that arginine and proline metabolism; valine, leucine and isoleucine biosynthesis; and phenylalanine metabolism were the main three altered pathways, based on the topology. Furthermore, Spearman’s analysis revealed some altered gut microbiota genus such as Oscillospira, Ruminococcaceae_NK4A214_group, Megasphaera, Ruminococcaceae_UCG-005, Prevotella_2, Ruminococcaceae_UCG-002, Rikenellaceae_RC9_gut_group and Prevotellaceae_UCG-003 were associated with the alterations in the fecal metabolites (P < 0.05), indicating that C. butyricum presented a potential protective impact through gut microbiota. The intestinal metabolites changed by C. butyricum mainly involved the variation of citrulline, dicarboxylic acids, branched-chain amino acid and tryptophan metabolic pathways.

Conclusions

Overall, this study strengthens the idea that the dietary C. butyricum treatment can significantly alter the intestinal microbiota and metabolite profiles of the weaned piglets, and C. butyricum can offer potential benefits for the gut health.

Pre-Treatment T2-WI Based Radiomics Features for Prediction of Locally Advanced Rectal Cancer Non-Response to Neoadjuvant Chemoradiotherapy: A Preliminary Study

Locally advanced rectal cancer (LARC) response to neoadjuvant chemoradiotherapy (nCRT) is very heterogeneous and up to 30% of patients are considered non-responders, presenting no tumor regression after nCRT. This study aimed to determine the ability of pre-treatment T2-weighted based radiomics features to predict LARC non-responders. A total of 67 LARC patients who underwent a pre-treatment MRI followed by nCRT and total mesorectal excision were assigned into training (n = 44) and validation (n = 23) groups. In both datasets, the patients were categorized according to the Ryan tumor regression grade (TRG) system into non-responders (TRG = 3) and responders (TRG 1 and 2). We extracted 960 radiomic features/patient from pre-treatment T2-weighted images. After a three-step feature selection process, including LASSO regression analysis, we built a radiomics score with seven radiomics features. This score was significantly higher among non-responders in both training and validation sets (p < 0.001 and p = 0.03) and it showed good predictive performance for LARC non-response, achieving an area under the curve (AUC) = 0.94 (95% CI: 0.82–0.99) in the training set and AUC = 0.80 (95% CI: 0.58–0.94) in the validation group. The multivariate analysis identified the radiomics score as an independent predictor for the tumor non-response (OR = 6.52, 95% CI: 1.87–22.72). Our results indicate that MRI radiomics features could be considered as potential imaging biomarkers for early prediction of LARC non-response to neoadjuvant treatment.

Mass spectrometry imaging of endogenous metabolites in response to doxorubicin in a novel 3D osteosarcoma cell culture model

Three-dimensional (3D) cell culture is a rapidly emerging field, which mimics some of the physiological conditions of human tissues. In cancer biology, it is considered a useful tool in predicting in vivo chemotherapy responses, compared with conventional two-dimensional (2D) cell culture. We have developed a novel 3D cell culture model of osteosarcoma composed of aggregated proliferative tumour spheroids, which shows regions of tumour heterogeneity formed by aggregated spheroids of polyclonal tumour cells. Aggregated spheroids show local necrotic and apoptotic regions and have sizes suitable for the study of spatial distribution of metabolites by mass spectrometry imaging (MSI). We have used this model to perform a proof-of-principle study showing a heterogeneous distribution of endogenous metabolites that colocalise with the necrotic core and apoptotic regions in this model. Cytotoxic chemotherapy (doxorubicin) responses were significantly attenuated in our 3D cell culture model compared with those of standard cell culture, as determined by resazurin assay, despite sufficient doxorubicin diffusion demonstrated by localisation throughout the 3D constructs. Finally, changes to the distribution of endogenous metabolites in response to doxorubicin were readily detected by MSI. Principal component analysis identified 50 metabolites which differed most in their abundance between treatment groups, and of these, 10 were identified by both in-software t test and mixed-effects analysis of variance (ANOVA). Subsequent independent MSIs of identified species were consistent with principle component analysis findings. This proof-of-principle study shows for the first time that chemotherapy-induced changes in metabolite abundance and distribution may be determined in 3D cell culture by MSI, highlighting this method as a potentially useful tool in the elucidation of chemotherapy responses as an alternative to in vivo testing.

LC-HRMS based approach to identify novel sphingolipid biomarkers in breast cancer patients

Perturbations in lipid metabolic pathways to meet the bioenergetic and biosynthetic requirements is a principal characteristic of cancer cells. Sphingolipids (SPLs) are the largest class of bioactive lipids associated to various aspects of tumorigenesis and have been extensively studied in cancer cell lines and experimental models. The clinical relevance of SPLs in human malignancies however is still poorly understood and needs further investigation. In the present study, we adopted a UHPLC-High resolution (orbitrap) Mass spectrometry (HRMS) approach to identify various sphingolipid species in breast cancer patients. A total of 49 SPLs falling into 6 subcategories have been identified. Further, integrating the multivariate analysis with metabolomics enabled us to identify an elevation in the levels of ceramide phosphates and sphingosine phosphates in tumor tissues as compared to adjacent normal tissues. The expression of genes involved in the synthesis of reported metabolites was also determined in local as well as TCGA cohort. A significant upregulation in the expression of CERK and SPHK1 was observed in tumor tissues in local and TCGA cohort. Sphingomyelin levels were found to be high in adjacent normal tissues. Consistent with the above findings, expression of SGMS1 in tumor tissues was downregulated in TCGA cohort only. Clinical correlations of the selected metabolites and their performance as biomarkers was also evaluated. Significant ROC and positive correlation with Ki67 index highlight the diagnostic potential and clinical relevance of ceramide phosphates in breast cancer.

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 Profiles of Bovine Mammary Epithelial Cells Stimulated by Lipopolysaccharide

Bovine mammary epithelial cells (bMECs) are the main cells of the dairy cow mammary gland. In addition to their role in milk production, they are effector cells of mammary immunity. However, there is little information about changes in metabolites of bMECs when stimulated by lipopolysaccharide (LPS). This study describes a metabolomics analysis of the LPS-stimulated bMECs to provide a basis for the identification of potential diagnostic screening biomarkers and possible treatments for bovine mammary gland inflammation. In the present study, bMECs were challenged with 500 ng/mL LPS and samples were taken at 0 h, 12 h and 24 h post stimulation. Metabolic changes were investigated using high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (HPLC-Q-TOF MS) with univariate and multivariate statistical analyses. Clustering and metabolic pathway changes were established by MetaboAnalyst. Sixty-three differential metabolites were identified, including glycerophosphocholine, glycerol-3-phosphate, L-carnitine, L-aspartate, glutathione, prostaglandin G2, α-linolenic acid and linoleic acid. They were mainly involved in eight pathways, including D-glutamine and D-glutamic acid metabolism; linoleic acid metabolism; α-linolenic metabolism; and phospholipid metabolism. The results suggest that bMECs are able to regulate pro-inflammatory, anti-inflammatory, antioxidation and energy-producing related metabolites through lipid, antioxidation and energy metabolism in response to inflammatory stimuli.

Metabolome-based prediction of yield heterosis contributes to the breeding of elite rice

Metabolite profiles obtained from parental seedlings can predict yield heterosis of rice hybrids with correctly dissected population structure across different growth conditions.

Improvement of the breeding efficiencies of heterotic crops adaptive to different conditions can mitigate the food shortage crisis due to overpopulation and climate change. To date, diverse molecular markers have been used to guide field phenotypic selection, whereas accurate predictions of complex heterotic traits are rarely reported. Here, we present a practical metabolome-based strategy for predicting yield heterosis in rice. The dissection of population structure based on untargeted metabolite profiles as the initial critical step in multivariate modeling performed better than the screening of predictive variables. Then the assessment of each predictive variable’s contribution to predictive models according to all latent factors was more precise than the conventional first one. Metabolites belonging to specific pathways were closely associated with yield heterosis, and the up-regulation of galactose metabolism promoted robust yield heterosis in hybrids under different growth conditions. Our study demonstrates that metabolome-based predictive models with correctly dissected population structure and screened predictive variables can facilitate accurate predictions of yield heterosis and have great potential for establishing molecular marker–based precision breeding programs.

Metabolomic Profiling Reveals the Difference on Reproductive Performance between High and Low Lactational Weight Loss Sows

Sows suffering excess weight loss during lactation may delay weaning to estrus interval (WEI) and have a detrimental effect on subsequent reproductive performance, however, the underlying mechanism is not completely clear. Therefore, the goal of this study was to investigate physiological profiles manifested in plasma originating from high (HWL) and low lactational weight loss (LWL) sows. The plasma biochemical parameters, hormones, antioxidant parameters, and milk compositions were assessed. Furthermore, plasma metabolites were analyzed using ultrahigh-performance liquid chromatography/time-of-flight mass spectrometry in positive and negative ion modes. Results showed that HWL sows had a lower feed intake and higher lactational weight loss and prolonged WEI, but had similar litter performance and milk composition compared to LWL sows. These changes were associated with lower plasma insulin-like growth factor 1 and higher fibroblast growth factor 21 levels in the HWL sows. Moreover, HWL led to a severe oxidative stress and metabolic damage, as accompanied by excessive protein breakdown and lipids mobilization at weaning. Metabolomic analysis revealed differences in 46 compounds between HWL and LWL sows, and the identified compounds were enriched in metabolic pathways related to amino acids metabolism, fatty acids oxidation metabolism, bile acids biosynthesis, and nucleoside metabolism. These results provide the evidence for physiological mechanism in sows with excessive lactational weight loss that delayed the WEI. Metabolomic data provides essential information and gives rise to potential targets for the development of nutritional intervention strategies.

Effect of Fermented Cottonseed Meal on the Lipid-Related Indices and Serum Metabolic Profiles in Broiler Chickens

This study aimed to investigate the changes of lipid-related gene and serum metabolites in broiler chickens fed with fermented cottonseed meal (FCSM) diet, through quantitative real-time PCR and metabolomics analysis. Totally, 180 1-day-old Cobb broilers were randomly assigned to two groups with six replicates of 15 birds in each. The two diets consisted of a control diet supplemented with 0% FCSM (CON group) and an experimental diet with 6% FCSM (fermented by Candida tropicalis) replacing the soybean meal (FCSM group). The results showed that both abdominal fat content and subcutaneous fat thickness significantly reduced (p < 0.05) in response to dietary FCSM supplementation at the age of 21 d. Serum concentrations of glucose, triglyceride, and low-density lipoprotein cholesterol decreased (p < 0.05) in FCSM fed broilers compared with CON fed broilers, while the levels of epinephrine and growth hormone in serum, liver and abdominal fat tissue were higher (p < 0.05) in FCSM than in CON fed broilers. The activity of hormone-sensitive esterase and lipoprotein lipase (LPL) in the liver and abdominal fat were higher (p < 0.05) in FCSM than CON group. Additionally, compared with the CON group (p < 0.05), the expression of peroxisome proliferator-activated receptor alpha and LPL genes were upregulated in the livers of FCSM group broilers. Gene expressions of hormone-sensitive lipase and LPL in the abdominal fat tissue were also upregulated (p < 0.05) with the broilers fed with FCSM diets. A total of 20 significantly different metabolites were obtained in the serum of different dietary FCSM supplemented fed broilers. The mainly altered pathways were clustered into organic acid metabolism, fatty acid metabolism, and amino acid metabolism. These results not only provide a better understanding of broilers' lipid metabolism with FCSM but also can be helpful in further improvement of the broilers' healthy production and utilization of FCSM.

Accurate outcome prediction after neo-adjuvant radio-chemotherapy for rectal cancer based on a TCP-based early regression index

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A TCP-based early regression index (ERI_TCP) was previously introduced.

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ERI_TCP was associated to improved survival after neo-adjuvant therapy for rectal cancer.

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Distant-metastasis-free survival was predicted by ERI_TCP and 5-FU dose.

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The resulting AUC (0.86) was significantly higher than models not including T ERI_TCP.

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ERI_TCP is a promising tool for therapy personalization.

Background and purpose

An early tumor regression index (ERI_TCP) was previously introduced and found to predict pathological response after neo-adjuvant radio-chemotherapy of rectal cancer. ERI_TCP was tested as a potential biomarker in predicting long-term disease-free survival.

Materials and methods

Data of 65 patients treated with an early regression-guided adaptive boosting technique (ART) were available. Overall, loco-regional relapse-free and distant metastasis-free survival (OS, LRFS, DMFS) were considered. Patients received 41.4 Gy in 18 fractions (2.3 Gy/fr), including ART concomitant boost on the residual GTV during the last 6 fractions (3 Gy/fr, D_mean: 45.6 Gy). Chemotherapy included oxaliplatin and 5-fluorouracil (5-FU). T2-weighted MRI taken before (MRI_pre) and at half therapy (MRI_half) were available and GTVs were contoured (V_pre, V_half). The parameter ERI_TCP = −ln[(1 − (V_half/V_pre))^Vpre] was calculated for all patients. Cox regression models were assessed considering several clinical and histological variables. Cox models not including/including ERI_TCP (CONV_model and REGR_model respectively) were assessed and their discriminative power compared.

Results

At a median follow-up of 47 months, OS, LRFS and DMFS were 94%, 95% and 78%. Due to too few events, multivariable analyses focused on DMFS: the resulting CONV_model included pathological complete remission or clinical complete remission followed by surgery refusal (HR: 0.15, p = 0.07) and 5-FU dose >90% (HR: 0.29, p = 0.03) as best predictors, with AUC = 0.75. REGR_model included ERI_TCP (HR: 1.019, p < 0.0001) and 5-FU dose >90% (HR: 0.18, p = 0.005); AUC was 0.86, significantly higher than CONV_model (p = 0.05). Stratifying patients according to the best cut-off value for ERI_TCP and to 5-FU dose (> vs <90%) resulted in 47-month DMFS equal to 100%/69%/0% for patients with two/one/zero positive factors respectively (p = 0.0002). ERI_TCP was also the only variable significantly associated to OS (p = 0.01) and LRFS (p = 0.03).

Conclusion

ERI_TCP predicts long-term DMFS after radio-chemotherapy for rectal cancer: an independent impact of the 5-FU dose was also found. This result represents a first step toward application of ERI_TCP in treatment personalization: additional confirmation on independent cohorts is warranted.

Long non-coding RNA IQCJ-SCHIP1 antisense RNA 1 is downregulated in colorectal cancer and inhibits cell proliferation

Background

IQCJ-SCHIP1 antisense RNA 1 (IQCJ-SCHIP1-AS1) was a functional novel long non-coding RNA (lncRNA) revealed by our previous expression profile. In this study, we aim to investigate its clinical relevance and biological significance in colorectal cancer (CRC).

Methods

We measured the expression levels of IQCJ-SCHIP1-AS1 in 86 paired CRC tissues using quantitative RT-PCR assay, and then analyzed its association with patient prognoses. Moreover, gain-of-function and loss-of-function studies were performed to examine the biological functions of IQCJ-SCHIP1-AS1. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment analysis (GSEA) were used to elucidate potential mechanisms of IQCJ-SCHIP1-AS1 in CRC.

Results

More than 2-fold decreased expression of IQCJ-SCHIP1-AS1 was found in half of CRC tissues (53.5%, 46/86). IQCJ-SCHIP1-AS1 down-regulation was correlated with poor differentiation (P=0.025), advanced depth of tumor (P=0.022), lymphatic invasion (P=0.010), advanced tumor stage (P=0.006), and poor prognosis (P=0.0027) in CRC patients. The Cox proportional hazards model demonstrated that IQCJ-SCHIP1-AS1 expression was an independent prognostic factor for CRC (HR =0.247, 95% CI: 0.081-0.752, P=0.014). Moreover, knockdown of IQCJ-SCHIP1-AS1 promoted CRC cell proliferation through increasing cell cycle progression and impairing cell apoptosis. Additionally, bioinformatics analysis showed that differential expression genes in IQCJ-SCHIP1-AS1-depleted CRC cells were enriched in the pathways of cell cycle, DNA replication, and p53.

Conclusions

Our results demonstrate that IQCJ-SCHIP1-AS1 has an indicative tumor suppressor role and appears to be a potential prognostic factor in CRC for the first time.

Plasma metabolite changes in dairy cows during parturition identified using untargeted metabolomics

The metabolic responses of cows undergo substantial changes during the transition from late pregnancy to early lactation. However, the molecular mechanisms associated with these changes in physiological metabolism have not been clearly elucidated. The objective of this study was to investigate metabolic changes in transition cows from the perspective of plasma metabolites. Plasma samples collected from 24 multiparous dairy cows on approximately d 21 prepartum and immediately postpartum were analyzed using ultra-high-performance liquid chromatography/time-of-flight mass spectrometry in positive and negative ion modes. In conjunction with multidimensional statistical methods (principal component analysis and orthogonal partial least squares discriminant analysis), differences in plasma metabolites were identified using the t-test and fold change analysis. Sixty-seven differential metabolites were identified consisting of AA, lipids, saccharides, and nucleotides. The levels of 32 plasma metabolites were significantly higher and those of 35 metabolites significantly lower after parturition than on d 21 prepartum. Pathway analysis indicated that the metabolites that increased from late pregnancy to early lactation were primarily involved in lipid metabolism and energy metabolism, whereas decreased metabolites were related to AA metabolism.