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.ac.uk/pub/databases/metabolights/studies/public/MTBLS9021"],"metabolite_identification_protocol":["<p>Metabolite identification was achieved based on Tracefinder (Thermofisher, CA) searches using a home-built database. We applied the standard 80% rule to minimize the effect of missing values and retain only metabolites detectable in 80% or more patients in at least one group. QC samples were prepared by pooling equivalent aliquots of plasma from each of the samples and injected intermittently into the analytical process to monitor the stability of the method. Variables presenting relative standard deviations (RSD) greater than 30% in quality control (QC) samples were removed, replaced with one-half of the minimum value found in that data set.</p><p><br></p>"],"repository":["MetaboLights"],"study_status":["Public"],"ptm_modification":[""],"instrument_platform":["Liquid Chromatography MS - negative - reverse phase","Liquid Chromatography MS - positive - reverse phase"],"chromatography_protocol":["<p>We applied the standard 80% rule to minimize the effect of missing values and retain only metabolites detectable in 80% or more patients in at least one group. QC samples were prepared by pooling equivalent aliquots of plasma from each of the samples and injected intermittently into the analytical process to monitor the stability of the method. Variables presenting relative standard deviations (RSD) greater than 30% in quality control (QC) samples were removed, replaced with one-half of the minimum value found in that data set. </p>"],"publication":["Multi-omics of the gut microbial ecosystem in the immunotherapy resistance in microsatellite instability-high gastrointestinal cancer patients."],"submitter_name":["Chuangzhao Qiu"],"submitter_affiliation":["Xbiome"],"organism_part":["Serum","plasma"],"technology_type":["mass spectrometry assay"],"disease":[""],"extraction_protocol":["<p>In preparation for metabolomics analysis, plasma samples were thawed and centrifuged at 14,000 x g for 20 min in a cold room (4–8 °C), and supernatants were transferred to sterile 1.5 ml microfuge tubes.&nbsp;A volume of 400 μL methanol (pre-chilled to −80 °C) was added to each 100 μL supernatant. The final 80% (v/v) methanol solution was shaken and incubated at −80 °C for 2h. Following centrifugation (14,000 x g, 10 min, 4°C).</p>"],"organism":["Mus musculus","Homo sapiens"],"full_dataset_link":["https://www.ebi.ac.uk/metabolights/MTBLS9021"],"author":["Bohan Zhang. peking university cancer hospital. 1260454623@qq.com.","Qiu Chuangzhao. ncbi_submit@xbiome.com."],"data_transformation_protocol":["<p>Total area normalization and logarithmic transformation was performed to stabilize variance across the intensity range. Mean-normalized data from positive and negative ion modes were combined for downstream analysis. PCA, Permanova, and beta dispersion analyses were performed on normalized feature tables to identify potential confounding factors, and pathway enrichment analyses were achieved using the MetaboAnalystR package.</p><p><br></p>"],"study_factor":["Experimental group"],"submitter_email":["ncbi_submit@xbiome.com"],"sample_collection_protocol":["<p>Baseline blood samples were drawn from 70 patients, and plasma was prepared by centrifugation at 3,000 rpm for 10 min at 4 ℃ and stored at –80 ℃. </p>"],"omics_type":["Metabolomics"],"study_design":["Orbitrap Mass Spectrometry (Thermo Fisher)","Mus musculus","untargeted analysis","gastrointestinal cancer","Immunotherapy","High-Frequency Microsatellite Instability","gut microbiome measurement","Homo sapiens","Serum","experimental blank","Ultimate 3000 UHPLC (Dionex)","plasma"],"curator_keywords":["Orbitrap Mass Spectrometry (Thermo Fisher)","Mus musculus","untargeted analysis","gastrointestinal cancer","Immunotherapy","High-Frequency Microsatellite Instability","gut microbiome measurement","Homo sapiens","Serum","experimental blank","Ultimate 3000 UHPLC (Dionex)","plasma"],"mass_spectrometry_protocol":["<p>Ultimate 3000 UHPLC (Dionex) coupled with Orbitrap mass spectrometry (Thermo Fisher) was used to perform LC separation. In the negative mode, the BEH C18 column (2.1×100 mm, Waters) was applied for analysis at 0.25 mL/min. Mobile phase A was crafted by mixing 1L of HPLC-grade water containing 0.3953 g of Ammonium bicarbonate (pH ~8). Mobile phase B was 100% ACN. A gradient was established as follows: 0~3min, 1% B; 10~17 min, 99% B; 17.1~20.0 min, 1% B. In positive mode, samples were passed over a Waters BEH amide column (100 *2.1 mm, 1.7 μm) heated to 40°C using a gradient of 1% B to 99% B over 12.5 min (solvent A: 95% ACN + 5% H2O + 10mM NH4FA; solvent B: 50% IPA + 50% ACN +10mM NH4FA). Data with masses ranging from m/z 80-1200 to m/z 70-1050 were acquired at both the positive and negative ion modes with data dependent MSMS acquisition. The full scan and fragment spectra were collected at a resolution of 60,000 and 15,000, respectively. Detailed mass spectrometer parameters were as follows: spray voltage at 2.8 kV for negative and 3.2 kV for positive; capillary temperature set at 320°C; heater temperature set at 300°C; sheath gas flow rate set to 35; auxiliary gas flow rate set to 10. Metabolite identification was achieved based on Tracefinder (Thermofisher, CA) searches using a home-built database.</p><p><br></p>"],"metabolite_name":["(+)-a-Pinene"],"additional_accession":[]},"is_claimable":false,"name":"Multi-omics of the gut microbial ecosystem in the immunotherapy resistance in microsatellite instability-high gastrointestinal cancer patients","description":"Despite the encouraging efficacy of anti-PD-1/PD-L1 immunotherapy in microsatellite instability-high/deficient mismatch repair (MSI-H/dMMR) advanced gastrointestinal cancer, many patients exhibit primary or acquired resistance. Using multi-omics approaches, we interrogated gut microbiome, blood metabolome and cytokines/chemokines of MSI-H/dMMR gastrointestinal cancer patients (N = 77) at baseline and during the treatment. We identified a number of microbes (e.g. Porphyromonadaceae) and metabolites (e.g. arginine) highly associated with primary resistance to immunotherapy. An independent validation cohort (N = 40) and mouse model were used to further confirm our findings. A predictive machine learning model for primary resistance was also built and achieved an accuracy of 0.79 on the external validation set. Furthermore, several microbes were pinpointed which gradually changed during the process of acquired resistance. In summary, our study demonstrated the essential role of gut microbiome in drug resistance, and this could be utilized as a preventative diagnosis tool and therapeutic targets in the future.","dates":{"publication":"2026-07-16","submission":"2026-07-16"},"accession":"MTBLS9021","cross_references":{}}