Identification of serum biomarkers of chemoradiosensitivity in esophageal cancer via the targeted metabolomics approach

Integration of clinical phenoms and metabolomics facilitates precision medicine for lung cancer

Lung cancer is a common malignancy that is frequently associated with systemic metabolic disorders. Early detection is pivotal to survival improvement. Although blood biomarkers have been used in its early diagnosis, missed diagnosis and misdiagnosis still exist due to the heterogeneity of lung cancer. Integration of multiple biomarkers or trans-omics results can improve the accuracy and reliability for lung cancer diagnosis. As metabolic reprogramming is a hallmark of lung cancer, metabolites, specifically lipids might be useful for lung cancer detection, yet systematic characterizations of metabolites in lung cancer are still incipient. The present study profiled the polar metabolome and lipidome in the plasma of lung cancer patients to construct an inclusive metabolomic atlas of lung cancer. A comprehensive analysis of lung cancer was also conducted combining metabolomics with clinical phenotypes. Furthermore, the differences in plasma lipid metabolites were compared and analyzed among different lung cancer subtypes. Alcohols, amides, and peptide metabolites were significantly increased in lung cancer, while carboxylic acids, hydrocarbons, and fatty acids were remarkably decreased. Lipid profiling revealed a significant increase in plasma levels of CER, PE, SM, and TAG in individuals with lung cancer as compared to those in healthy controls. Correlation analysis confirmed the association between a panel of metabolites and TAGs. Clinical trans-omics studies elucidated the complex correlations between lipidomic data and clinical phenotypes. The present study emphasized the clinical importance of lipidomics in lung cancer, which involves the correlation between metabolites and the expressions of other omics, ultimately influencing clinical phenotypes. This novel trans-omics network approach would facilitate the development of precision therapy for lung cancer.

Nomogram for predicting pathologic complete response to neoadjuvant chemoradiotherapy in patients with esophageal squamous cell carcinoma

PURPOSE

A pathologic complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) is seen in up to 40% of the patients with esophageal squamous cell carcinoma (ESCC). No nomogram has been constructed for the prediction of pCR for patients whose primary chemotherapy was a taxane-based regimen. The aim is to identify characteristics associated with a pCR through analyzing multiple pre- and post-nCRT variables and to develop a nomogram for the prediction of pCR for these patients by integrating clinicopathological characteristics and hematological biomarkers.

MATERIALS AND METHODS

We analyzed 293 patients with ESCC who underwent nCRT followed by esophagectomy. Clinicopathological factors, hematological parameters before nCRT, and hematotoxicity during nCRT were collected. Univariate and multivariate logistic regression analyses were performed to identify predictive factors for pCR. A nomogram model was built and evaluated for both discrimination and calibration.

RESULTS

After surgery, 37.88% of the study patients achieved pCR. Six variables were included in the nomogram: sex, cN stage, chemotherapy regimen, duration of nCRT, pre-nCRT neutrophil-to-lymphocyte ratio (NLR), and pre-nCRT platelet-to-lymphocyte ratio (PLR). The nomogram indicated good accuracy and consistency in predicting pCR, with a C-index of 0.743 (95% confidence interval: 0.686, 0.800) and a p value of 0.600 (>0.05) in the Hosmer-Lemeshow goodness-of-fit test.

CONCLUSIONS

Female, earlier cN stage, duration of nCRT (< 62 days), chemotherapy regimen of taxane plus platinum, pre-nCRT NLR (≥2.199), and pre-nCRT PLR (≥99.302) were significantly associated with a higher pCR in ESCC patients whose primary chemotherapy was a taxane-based regimen for nCRT. A nomogram was developed and internally validated, showing good accuracy and consistency.

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.

Peripheral Blood Serum NMR Metabolomics Is a Powerful Tool to Discriminate Benign and Malignant Ovarian Tumors

Ovarian cancer is the major cause of death from gynecological cancer and the third most common gynecological malignancy worldwide. Despite a slight improvement in the overall survival of ovarian carcinoma patients in recent decades, the cure rate has not improved. This is mainly due to late diagnosis and resistance to therapy. It is therefore urgent to develop effective methods for early detection and prognosis. We hypothesized that, besides being able to distinguish serum samples of patients with ovarian cancer from those of patients with benign ovarian tumors, 1H-NMR metabolomics analysis might be able to predict the malignant potential of tumors. For this, serum 1H-NMR metabolomics analyses were performed, including patients with malignant, benign and borderline ovarian tumors. The serum metabolic profiles were analyzed by multivariate statistical analysis, including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) methods. A metabolic profile associated with ovarian malignant tumors was defined, in which lactate, 3-hydroxybutyrate and acetone were increased and acetate, histidine, valine and methanol were decreased. Our data support the use of 1H-NMR metabolomics analysis as a screening method for ovarian cancer detection and might be useful for predicting the malignant potential of borderline tumors.

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.

Untargeted serum metabolomics reveals potential biomarkers and metabolic pathways associated with esophageal cancer

Metabolomics has been reported as an efficient tool to screen biomarkers that are related to esophageal cancer. However, the metabolic biomarkers identifying malignant degrees and therapeutic efficacy are still largely unknown in the disease. Here, GC-MS-based metabolomics was used to understand metabolic alteration in 137 serum specimens from patients with esophageal cancer, which is approximately two- to fivefold as many plasma specimens as the previous reports. The elevated amino acid metabolism is in sharp contrast to the reduced carbohydrate as a characteristic feature of esophageal cancer. Comparative metabolomics showed that most metabolic differences were determined between the early stage (0–II) and the late stage (III and IV) among the 0–IV stages of esophageal cancer and between patients who received treatment and those who did not receive treatment. Glycine, serine, and threonine metabolism and glycine were identified as the potentially overlapped metabolic pathway and metabolite, respectively, in both disease progress and treatment effect. Glycine, fructose, ornithine, and threonine can be a potential array for the evaluation of disease prognosis and therapy in esophageal cancer. These results highlight the means of identifying previously unknown biomarkers related to esophageal cancer by a metabolomics approach.

Metabolic Profiling of Bladder Cancer Patients' Serum Reveals Their Sensitivity to Neoadjuvant Chemotherapy

Numerous patients with muscle-invasive bladder cancer develop low responsiveness to cisplatin. Our purpose was to explore differential metabolites derived from serum in bladder cancer patients treated with neoadjuvant chemotherapy (NAC). Data of patients diagnosed with cT2-4aNxM0 was collected. Blood samples were retained prospectively before the first chemotherapy for untargeted metabolomics by ¹H-NMR and UPLC-MS. To identify characterized metabolites, multivariate statistical analyses were applied, and the intersection of the differential metabolites discovered by the two approaches was used to identify viable biomarkers. A total of 18 patients (6 NAC-sensitive patients and 12 NAC-resistant patients) were enrolled. There were 29 metabolites detected by ¹H-NMR and 147 metabolites identified by UPLC-MS. Multivariate statistics demonstrated that in the sensitive group, glutamine and taurine were considerably increased compared to their levels in the resistant group, while glutamate and hypoxanthine were remarkably decreased. Pathway analysis and enrichment analysis showed significant alterations in amino acid pathways, suggesting that response to chemotherapy may be related to amino acid metabolism. In addition, hallmark analysis showed that DNA repair played a regulatory role. Overall, serum metabolic profiles of NAC sensitivity are significantly different in bladder cancer patients. Glycine, hypoxanthine, taurine and glutamine may be the potential biomarkers for clinical treatment. Amino acid metabolism has potential value in enhancing drug efficacy.

Efficacy of Sirna-Loaded Nanoparticles in the Treatment of k-ras Mutant Lung Cancer in vitro

AIM

To design and develop K-RAS silencing small interfering RNA (siRNA)-loaded poly (D, L-lactic-co-glycolic acid) nanoparticles and evaluate their efficacy in the treatment of K-RAS mutant lung cancer.

METHODS

The nanoparticles prepared by the double emulsion solvent evaporation method were characterized by TEM, FTIR and XPS analyzes and evaluated in vitro by XTT, PCR, ELISA, and Western-Blot. Metabolomic analyzes were performed to evaluate the changes in metabolic profiles of the cells after nanoparticles treatment.

RESULTS

The nanoparticles were obtained with a particle size less than 250 nm, a polydispersity index around 0.1, a surface charge of (-12) - (+14) mV, and 80% of the siRNA encapsulation. The nanoparticles didn't affect cell viability of the cells after 72 hours. In cancer cells, KRAS expression was decreased by up to 50%, protein levels were decreased by more than 90%.

CONCLUSION

The formulated siRNA delivery nanoparticles can be promising treatment in lung cancer.

Application of metabolomics in clinical and laboratory gastrointestinal oncology

Metabolites are versatile bioactive molecules. They are not only the substrates and/or the products of enzymatic reactions but also act as the regulators in the systemic metabolism. Metabolomics is a high-throughput analytical strategy to qualify or quantify as many metabolites as possible in the metabolomes. It is an indispensable part of systems biology. The leading techniques in this field are mainly based on mass spectrometry and nuclear magnetic resonance spectroscopy. The metabolomic analysis has gained wide use in bioscience fields. In the tumor research arena, metabolomics can be employed to identify biomarkers for prediction, diagnosis, and prognosis. Chemotherapeutic effect evaluation and personalized medicine decision-making can also benefit from metabolomic analysis of patient biofluid or biopsy samples. Many cell-level studies can help in disease exploration. In this review, the basic features and principles of varied metabolomic analysis are introduced. The value of metabolomics in clinical and laboratory gastrointestinal cancer studies is discussed, especially for mass spectrometry applications. Besides, combined use of metabolomics and other tools to solve problems in cancer practice is briefly illustrated. In summary, metabolomics paves a new way to explore cancerous diseases in the light of small molecules.

Integrating transcriptome and metabolome variability to reveal pathogenesis of esophageal squamous cell carcinoma

BACKGROUND

Esophageal Squamous Cell Carcinoma (ESCC) is an aggressive malignancy, leading to more than 250,000 deaths in China every year. However, the pathogenesis of ESCC remains unclear, which hinders the diagnosis and treatment of the disease in clinic.

METHOD

To elucidate underlying mechanism and identify potential biomarkers, an integrative strategy of combining transcriptome and metabolome has been implemented to find potential causal genes and metabolites for ESCC.

RESULTS

At the transcriptional level, dysregulated genes in ESCC patients were identified and pathway enrichment analysis discovered tyrosine metabolic pathway as a promising target. Subsequently, up- and down-stream metabolites of tyrosine pathway were explored through targeted metabolome approach. Five metabolites, i.e. phenylalanine, 4-hydroxyphenyllactic acid, 3,4-dihydroxyphenylalanine, 3,4-dihydroxyphenylacetic acid and tyrosine were identified as diagnosis biomarkers for ESCC and metastatic ESCC patients. A biological model incorporating both transcriptional and metabolic dysregulation was also established to illustrate the potential mechanism of tumorigenesis and metastasis for ESCC.

CONCLUSION

Integrative transcriptomics and metabolomics analysis suggested that tyrosine pathway was essential for the tumorigenesis and metastasis of ESCC primarily through altering immune response and regulating tumor microenvironment. This research sheds light on the pathogenesis of ESCC and discovers potential biomarkers for the diagnosis of the disease.

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

BACKGROUND

To investigate the differences in plasma metabolomic characteristics between pathological complete response (pCR) and non-pCR patients and identify biomarker candidates for predicting the response to neoadjuvant chemoradiotherapy (nCRT) in esophageal squamous cell carcinoma (ESCC).

METHODS

A total of 46 ESCC patients were included in this study. Gas chromatography time-of- flight mass spectrometry (GC-TOF/MS) technology was applied to detect the plasma samples collected before nCRT via untargeted metabolomics analysis.

RESULTS

Five differentially expressed metabolites (out of 109) was found in plasma between pCR and non-pCR groups. Compared with non-pCR group, isocitric acid (p = 0.0129), linoleic acid (p = 0.0137), citric acid (p = 0.0473) were upregulated, while L-histidine (p = 0.0155), 3'4 dihydroxyhydrocinnamic acid (p = 0.0339) were downregulated in the pCR plasma samples. Pathway analyses unveiled that citrate cycle (TCA cycle), glyoxylate and dicarboxylate metabolic pathway were associated with ESCC chemoradiosensitivity.

CONCLUSION

The present study provided supporting evidence that GC-TOF/MS based metabolomics approach allowed identification of metabolite differences between pCR and non-pCR patients in plasma levels, and the systemic metabolic status of patients may reflect the response of ESCC patient to neoadjuvant chemoradiotherapy.

Nicotinamide N-methyltransferase decreases 5-fluorouracil sensitivity in human esophageal squamous cell carcinoma through metabolic reprogramming and promoting the Warburg effect

Esophageal squamous cell carcinoma (ESCC) is a common malignant tumor with poor prognosis. And different individuals respond to the same drug differently. Increasing evidence has confirmed that metabolism reprogramming was involved in the drug sensitivity of tumor cells. However, the potential molecular mechanism of 5-fluorouracil (5-FU) sensitivity remains to be elucidated in ESCC cells. In this study, we found that the 5-FU sensitivity of TE1 cells was lower than that of EC1 and Eca109 cells. Gas chromatography-mass spectrometry analysis results showed that nicotinate and nicotinamide metabolism and tricarboxylic acid cycle were significantly different in these three cell lines. Nicotinamide N-methyltransferase (NNMT), a key enzyme of nicotinate and nicotinamide metabolism, was significantly higher expressed in TE1 cells than that in EC1 and Eca109 cells. Therefore, the function of NNMT on 5-FU sensitivity was analyzed in vitro and in vivo. NNMT downregulation significantly increased 5-FU sensitivity in TE1 cells. Meanwhile, the glucose consumption and lactate production were decreased, and the expression of glycolysis-related enzymes hexokinase 2, lactate dehydrogenase A, and phosphoglycerate mutase 1 were downregulated in NNMT knockdown TE1 cells. Besides, overexpression of NNMT in EC1 and Eca109 cells caused the opposite effects. Moreover, when glycolysis was inhibited by 2-deoxyglucose, the roles of NNMT on 5-FU sensitivity was weakened. In vivo experiments showed that NNMT knockdown significantly increased the sensitivity of xenografts to 5-FU and suppressed the Warburg effect. Overall, these results demonstrated that NNMT decreases 5-FU sensitivity in human ESCC cells through promoting the Warburg effect, suggesting that NNMT may contribute to predict the treatment effects of the clinical chemotherapy in ESCC.

Serum level of octanoic acid predicts the efficacy of chemotherapy for colorectal cancer

The survival times of patients with advanced colorectal cancer (CRC) have increased due to the introduction of chemotherapy involving irinotecan and cetuximab. However, further studies are required on the effective pretreatment methods for identifying patients with CRC who would respond to particular treatments. The aim of the present study was to identify biomarkers for predicting the efficacy of chemotherapy for CRC. A total of 123 serum samples were collected from 31 patients with CRC just prior to each of the first four rounds of chemotherapy. Serum metabolome analysis was performed using a multiplatform metabolomics system, and univariate Cox regression hazards analysis of the time to disease progression was conducted. Octanoic acid and 1,5-anhydro-D-glucitol were identified as biomarker candidates. In addition, the serum level of octanoic acid was indicated to be significantly associated with the time to disease progression (hazard ratio, 3.3; 95% confidence interval, 1.099–11.840; P=0.033). The serum levels of fatty acids, in particular polyunsaturated fatty acids, tended to be downregulated in the partial response group. The findings of the present study suggest that the serum level of octanoic acid may serve as a useful predictor for the prognosis of CRC.