Machine Learning-Based Integrated Multiomics Characterization of Colorectal Cancer Reveals Distinctive Metabolic Signatures.

The metabolic signature identification of colorectal cancer is critical for its early diagnosis and therapeutic approaches that will significantly block cancer progression and improve patient survival. Here, we combined an untargeted metabolic analysis strategy based on internal extractive electrospray ionization mass spectrometry and the machine learning approach to analyze metabolites in 173 pairs of cancer samples and matched normal tissue samples to build robust metabolic signature models for diagnostic purposes. Screening and independent validation of metabolic signatures from colorectal cancers via machine learning methods (Logistic Regression_L1 for feature selection and eXtreme Gradient Boosting for classification) was performed to generate a panel of seven signatures with good diagnostic performance (the accuracy of 87.74%, sensitivity of 85.82%, and specificity of 89.66%). Moreover, seven signatures were evaluated according to their ability to distinguish between cancer and normal tissues, with the metabolic molecule PC (30:0) showing good diagnostic performance. In addition, genes associated with PC (30:0) were identified by multiomics analysis (combining metabolic data with transcriptomic data analysis) and our results showed that PC (30:0) could promote the proliferation of colorectal cancer cell SW480, revealing the correlation between genetic changes and metabolic dysregulation in cancer. Overall, our results reveal potential determinants affecting metabolite dysregulation, paving the way for a mechanistic understanding of altered tissue metabolites in colorectal cancer and design interventions for manipulating the levels of circulating metabolites.

Metabolic-Dysregulation-Based iEESI-MS Reveals Potential Biomarkers Associated with Early-Stage and Progressive Colorectal Cancer.

The application of rapid and accurate diagnostic methods can improve colorectal cancer (CRC) survival rates dramatically. Here, we used a non-targeted metabolic analysis strategy based on internal extractive electrospray ionization mass spectrometry (iEESI-MS) to detect metabolite ions associated with the progression of CRC from 172 tissues (45 stage I/II CRC, 41 stage III/IV CRC, and 86 well-matched normal tissues). A support vector machine (SVM) model based on 10 differential metabolite ions for differentiating early-stage CRC from normal tissues was built with a good prediction accuracy of 92.6%. The biomarker panel consisting of lysophosphatidylcholine (LPC) (18:0) has good diagnostic potential in differentiating early-stage CRC from advanced-stage CRC. We showed that the down-regulation of LPC (18:0) in tumor tissues is associated with CRC progression and related to the regulation of the epidermal growth factor receptor. Pathway analysis showed that metabolic pathways in CRC are related to glycerophospholipid metabolism and purine metabolism. In conclusion, we built an SVM model with good performance to distinguish between early-stage CRC and normal groups based on iEESI-MS and found that LPC (18:0) is associated with the progression of CRC.

Selenium Vacancy Engineering Using Bi2Se3 Nanodots for Boosting Highly Efficient Photonic Hyperthermia.

Despite bismuth-based energy conversion nanomaterials having attracted extensive attention for nanomedicine, the nanomaterials suffer from major shortcomings including low tumor accumulation, long internal retention time, and undesirable photothermal conversion efficiency (PCE). To combat these challenges, bovine serum albumin and folic acid co-modified Bi2Se3 nanomedicine with rich selenium vacancies (abbreviated as VSe-BS) was fabricated for the second near-infrared (NIR-II) light-triggered photonic hyperthermia. More importantly, selenium vacancies on the crystal planes (0 1 5) and (0 1 11) of VSe-BS with similar formation energies could be distinctively observed via aberration-corrected scanning transmission electron microscopy images. The defect engineering endows VSe-BS with enhanced conductivity, making VSe-BS possess outstanding PCE (54.1%) in the NIR-II biowindow and desirable photoacoustic imaging performance. Tumor ablation studies indicate that VSe-BS possesses satisfactory therapeutic outcomes triggered by NIR-II light. These findings give rise to inspiration for further broadening the biological applications of defect engineering bismuth-based nanomaterials.

A new pH/NIR responsive theranostic agent for magnetic resonance imaging guided synergistic therapy.

New nano-reagents with diagnostic imaging and therapeutic functions are very important for precision medicine against cancer. In this work, a new nanotheraontic agent for magnetic resonance imaging (MRI) guided combined photothermal therapy (PTT) and chemotherapy was constructed based on polydopamine (PDA) functionalized copper ferrite nanospheres (PDA@CFNs). The high relaxivity makes it possible for PDA@CFNs to become a promising MRI contrast agent, providing necessary and exhaustive information for tumor diagnosis. In addition, because both CFNs and PDA have strong near-infrared (NIR) absorption, PDA@CFNs exhibit excellent photothermal performance. Highly effective tumor ablation is achieved in a mouse model through PTT and pH/NIR triggered on-demand chemotherapy. These findings reveal that constructing smart pH/NIR responsive multifunctional theranostic agents is a feasible strategy for precision cancer therapy.

Profiling of serum metabolites in advanced colon cancer using liquid chromatography-mass spectrometry.

Lymph node metastasis remains a key factor that affects the prognosis of patients with colon cancer. The aim of the present study was to identify and evaluate serum metabolites as biomarkers for the detection of tumor lymph node metastasis and the prediction of patient survival. The present study analyzed the metabolites in the serum of patients with advanced colon cancer both with and without lymph node metastasis. Blood samples from 104 patients with stage T3 colon cancer were collected and analyzed using liquid chromatography-mass spectrometry. The metabolites were structurally confirmed with data from the Human Metabolome Database. The association between the serum metabolites and the clinicopathological characteristics and survival time of patients from the present study was analyzed. Overall, 227 different metabolites were identified in the serum of patients with stage T3 colon cancer with or without lymph node metastasis. Furthermore, 17 of these metabolites may potentially distinguish those patients with lymph node metastasis from those patients without. In addition, five factors, including abscisic acid, calcitroic acid and glucosylsphingosine presence in the serum, age and sex, were identified as independent predictors for lymph node metastasis (P<0.05). Furthermore, three factors, including abscisic acid, calcitroic acid and glucosylsphingosine presence in the serum were independent predictors for patient survival (P<0.05). In conclusion, the serum levels of abscisic acid, calcitroic-acid and glucosylsphingosine may be considered as potential biomarkers to predict the occurrence of lymph node metastasis and the survival time of patients with colon cancer.

Association of serum levels of deoxyribose 1-phosphate and S-lactoylglutathione with neoadjuvant chemotherapy sensitivity in patients with gastric cancer: A metabolomics study.

The present study screened serum samples from patients with advanced-stage gastric cancer and known sensitivities to neoadjuvant chemotherapy, in order to identify metabolites that may serve as potential biomarkers for chemotherapy sensitivity. A total of 47 patients with stage III (T4b) or IV gastric cancer, including 31 in the training group and 16 in a validation group, were classified based on their responses to conversion therapy consisting of oxaliplatin, tegafur and continuous hyperthermic peritoneal perfusion with cisplatin. Serum samples were analyzed by liquid chromatography-mass spectrometry to obtain a metabolite profile of each patient. Patients who were responsive and non-responsive to neoadjuvant chemotherapy exhibited significant differences in serum levels of deoxyribose 1-phosphate, S-lactoylglutathione, lysophosphatidylcholine (16:0) and O-arachidonoyl ethanolamine. Logistic regression analysis indicated that deoxyribose 1-phosphate and S-lactoylglutathione were independently associated with chemosensitivity. Serum levels of deoxyribose 1-phosphate and S-lactoylglutathione were independently associated with the sensitivity of gastric cancer to neoadjuvant chemotherapy, therefore, serving as potential predictors of patient response.

Serum metabolomic profiling of human gastric cancer and its relationship with the prognosis.

OBJECTIVE: This study was aimed to investigate serum metabolites in gastric cancer (GC) patients and their relationships with the prognosis of GC in order to find potential specific serum biomarkers for GC. METHODS: Blood samples of 125 GC patients of unifocal GC at initial stage and 38 healthy people recruited in our hospital from September 2008 to August 2009 were analyzed by using high performance liquid chromatography coupled with electrospray ionization/quadrupole-time-of-flight mass spectrometry (HPLCESI/Q-TOFMS). Multiple statistical methods like principal component analysis (PCA), hierarchical clustering analysis, partial least squares discriminant analysis (PLS-DA), multivariate COX regression analysis, variance analysis and K-M survival curve were applied to analyze the raw obtained mass data in order to analyze the independent prognostic factors of GC. The structures of these metabolites were confirmed by comparing the m/z ratio and ion mode of with the data published from HMDB (www.hmdb.ca) databases. RESULTS: By PLS-DA test, 16 serum metabolites in ESI+ mode of VIP>1 in both test group and validation group could definitely distinguish GC patients from healthy peoples (p<0.05). Multivariate COX regression analysis showed TNM staging, 2,4-hexadienoic acid, 4-methylphenyl dodecanoate and glycerol tributanoate were independent prognostic factors of GC (p<0.05). In the K-M survival analysis, the survival rate in high level group of the 3 selected serum metabolites together or alone was significant lower than in those in low level group (p<0.05). CONCLUSION: Low serum levels of 2,4-hexadienoic acid, 4-methylphenyl dodecanoate and glycerol tributanoate may be important independent prognostic factors of GC.