Comparison Log
2025-09-14 09:28:51.294914
mwtab Python Library Version: 1.2.5
Source:      https://www.metabolomicsworkbench.org/rest/study/analysis_id/AN006807/mwtab/...
Study ID:    ST004105
Analysis ID: AN006807
Status:      Inconsistent

Sections "STUDY" contain missmatched items: {('STUDY_SUMMARY', "Colorectal cancer (CRC), a common cancer of the large intestine, is influenced by metabolic reprogramming due to hypoxia. Novel biomarkers may be identified through metabolomics. While many CRC studies have reported metabolomic profiling, the metabolic profile of CRC in the context of oxygen content has yet to be elucidated. Comprehending the metabolic alterations in cancer cells transitioning from normoxia (NMX) to hypoxia (HPX) and anoxia (ANX) is essential for the formulation of drugs that target particular metabolic pathways. Our study aimed to find metabolic changes in the HCT-116 CRC cell line under ANX, HPX, and NMX conditions, as well as to investigate novel biomarkers for CRC utilizing liquid chromatography-mass spectrometry (LC-MS/MS) based metabolomics approaches. Our findings showed significant changes in 77 metabolites in HCT-116 CRC cells across ANX, HPX, and NMX conditions, with 34 metabolites significantly disrupted in HPX compared to NMX, and 64 metabolites significantly changed in HPX compared to ANX. Significant differences included glutathione, gamma-glutamylcysteine, glycerophosphocholine, adenosine monophosphate, 5'-methylthioadenosine, guanosine 5'-diphosphate, threonic acid, and L-acetylcarnitine. Comprehending the metabolic changes in HPX, ANX, and NMX may uncover new pathways that could be targeted for potential treatments."), ('STUDY_SUMMARY', "Colorectal cancer (CRC), a common cancer of the large intestine, is influenced by metabolic reprogramming due to hypoxia. Novel biomarkers may be identified through metabolomics. While many CRC studies have reported metabolomic profiling, the metabolic profile of CRC in the context of oxygen content has yet to be elucidated. Comprehending the metabolic alterations in cancer cells transitioning from normoxia (NMX) to hypoxia (HPX) and anoxia (ANX) is essential for the formulation of drugs that target particular metabolic pathways. Our study aimed to find metabolic changes in the HCT-116 CRC cell line under ANX, HPX, and NMX conditions, as well as to investigate novel biomarkers for CRC utilizing liquid chromatography-mass spectrometry (LC-MS/MS) based metabolomics approaches. Our findings showed significant changes in 77 metabolites in HCT-116 CRC cells across ANX, HPX, and NMX conditions, with 34 metabolites significantly disrupted in HPX compared to NMX, and 64 metabolites significantly changed in HPX compared to ANX. Significant differences included glutathione, gamma-glutamylcysteine, glycerophosphocholine, adenosine monophosphate, 5''-methylthioadenosine, guanosine 5''-diphosphate, threonic acid, and L-acetylcarnitine. Comprehending the metabolic changes in HPX, ANX, and NMX may uncover new pathways that could be targeted for potential treatments.")}
Sections "PROJECT" contain missmatched items: {('PROJECT_SUMMARY', "Colorectal cancer (CRC), a common cancer of the large intestine, is influenced by metabolic reprogramming due to hypoxia. Novel biomarkers may be identified through metabolomics. While many CRC studies have reported metabolomic profiling, the metabolic profile of CRC in the context of oxygen content has yet to be elucidated. Comprehending the metabolic alterations in cancer cells transitioning from normoxia (NMX) to hypoxia (HPX) and anoxia (ANX) is essential for the formulation of drugs that target particular metabolic pathways. Our study aimed to find metabolic changes in the HCT-116 CRC cell line under ANX, HPX, and NMX conditions, as well as to investigate novel biomarkers for CRC utilizing liquid chromatography-mass spectrometry (LC-MS/MS) based metabolomics approaches. Our findings showed significant changes in 77 metabolites in HCT-116 CRC cells across ANX, HPX, and NMX conditions, with 34 metabolites significantly disrupted in HPX compared to NMX, and 64 metabolites significantly changed in HPX compared to ANX. Significant differences included glutathione, gamma-glutamylcysteine, glycerophosphocholine, adenosine monophosphate, 5'-methylthioadenosine, guanosine 5'-diphosphate, threonic acid, and L-acetylcarnitine. Comprehending the metabolic changes in HPX, ANX, and NMX may uncover new pathways that could be targeted for potential treatments."), ('PROJECT_SUMMARY', "Colorectal cancer (CRC), a common cancer of the large intestine, is influenced by metabolic reprogramming due to hypoxia. Novel biomarkers may be identified through metabolomics. While many CRC studies have reported metabolomic profiling, the metabolic profile of CRC in the context of oxygen content has yet to be elucidated. Comprehending the metabolic alterations in cancer cells transitioning from normoxia (NMX) to hypoxia (HPX) and anoxia (ANX) is essential for the formulation of drugs that target particular metabolic pathways. Our study aimed to find metabolic changes in the HCT-116 CRC cell line under ANX, HPX, and NMX conditions, as well as to investigate novel biomarkers for CRC utilizing liquid chromatography-mass spectrometry (LC-MS/MS) based metabolomics approaches. Our findings showed significant changes in 77 metabolites in HCT-116 CRC cells across ANX, HPX, and NMX conditions, with 34 metabolites significantly disrupted in HPX compared to NMX, and 64 metabolites significantly changed in HPX compared to ANX. Significant differences included glutathione, gamma-glutamylcysteine, glycerophosphocholine, adenosine monophosphate, 5''-methylthioadenosine, guanosine 5''-diphosphate, threonic acid, and L-acetylcarnitine. Comprehending the metabolic changes in HPX, ANX, and NMX may uncover new pathways that could be targeted for potential treatments.")}
Sections "MS" contain missmatched items: {('MS_COMMENTS', "The MS analysis was performed using a TimsTOF (Bruker, Darmstadt, Germany) with Apollo II electrospray ionization (ESI) source. The drying gas was set to flow at 10 L/min and the drying temperature to 220°C and the nebulizer pressure to 2.2 bar. The capillary voltage was 4500 V and the end plate offset 500V. For metabolomics the scan range was 20-1300 m/z. The collision energy was set to 20 eV, the cycle time to 0.5 seconds with a relative minimum intensity threshold of 400 counts per thousand and target intensity of 20,000. Sodium formate was injected as an external calibrant in the first 0.3 minutes of each LC-MS/MS run. MetaboScape 4.0 software was used for metabolite processing and statistical analysis (Bruker Daltonics). The following parameters for molecular feature identification and bucketing were set in the T-ReX 2D/3D workflow: For peak detection, a minimum intensity threshold of 1,000 counts is required, as well as a minimum peak duration of 7 spectra, with feature quantification determine using peak area. The file masses were recalibrated based on the external calibrant injected between 0-0.3 min. Data Processing and Metabolite Identification: The acquired data were analyzed utilizing MetaboScape 4.0 software (Bruker Daltonics, Billerica, MA, USA) using the following parameters: an intensity threshold of 1000, a peak length spanning seven spectra, and a peak area for quantification. Mass spectra calibration was performed within the 0-0.3-minute interval. The auto-MS/MS scan adhered to the average method, with a retention duration span of 0.3 to 25 min and a mass range of 20 to 1300 m/z. Metabolites were identified by aligning the MS/MS spectra and retention time (RT) with human metabolome database (HMBD) 4.0, and precisely designed to meet the distinct requirements of the metabolomic community. The peak intensity of every metabolite was utilized to create the quantitative data matrix. To ensure comprehensive metabolic coverage, metabolites identified in both organic and aqueous phases were combined for data analysis. The metabolite datasets were transferred as CSV files and subsequently imported into MetaboAnalyst software (version 6.0; https://www.metaboanalyst.ca), McGill University, Montreal, Canada) [26]. For sample classification, the partial least squares discriminant analysis (PLS-DA) method in MetaboAnalyst was utilized to choose the most distinguishing characteristics within the studied conditions. This process aimed to reduce the rate of false positives. The corrections for multiple hypothesis testing were employed utilizing the false discovery rate (FDR) approach. The identification of significantly changed metabolites in the ANX and HPX groups, as opposed to the NMX condition, was performed using a two-tailed independent Student''s t-test. This resulted in the formation of a volcano plot, graphically depicting the statistical significance and fold change (p<0.05, FC=2), emphasizing the dysregulation of cellular metabolites for each group. One-way analysis of variance (ANOVA) was utilized for an extensive comparison between ANX, HPX, and NMX conditions. The threshold for significance was set at p <0.05. Reference: [26] J. Xia, D.S. Wishart, Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst, Nature protocols 6(6) (2011) 743-760. doi: 10.1038/nprot.2011.319."), ('MS_COMMENTS', 'The MS analysis was performed using a TimsTOF (Bruker, Darmstadt, Germany) with Apollo II electrospray ionization (ESI) source. The drying gas was set to flow at 10 L/min and the drying temperature to 220°C and the nebulizer pressure to 2.2 bar. The capillary voltage was 4500 V and the end plate offset 500V. For metabolomics the scan range was 20-1300 m/z. The collision energy was set to 20 eV, the cycle time to 0.5 seconds with a relative minimum intensity threshold of 400 counts per thousand and target intensity of 20,000. Sodium formate was injected as an external calibrant in the first 0.3 minutes of each LC-MS/MS run. MetaboScape 4.0 software was used for metabolite processing and statistical analysis (Bruker Daltonics). The following parameters for molecular feature identification and "bucketing" were set in the T-ReX 2D/3D workflow: For peak detection, a minimum intensity threshold of 1,000 counts is required, as well as a minimum peak duration of 7 spectra, with feature quantification determine using peak area. The file masses were recalibrated based on the external calibrant injected between 0-0.3 min. Data Processing and Metabolite Identification: The acquired data were analyzed utilizing MetaboScape 4.0 software (Bruker Daltonics, Billerica, MA, USA) using the following parameters: an intensity threshold of 1000, a peak length spanning seven spectra, and a peak area for quantification. Mass spectra calibration was performed within the 0-0.3-minute interval. The auto-MS/MS scan adhered to the average method, with a retention duration span of 0.3 to 25 min and a mass range of 20 to 1300 m/z. Metabolites were identified by aligning the MS/MS spectra and retention time (RT) with human metabolome database (HMBD) 4.0, and precisely designed to meet the distinct requirements of the metabolomic community. The peak intensity of every metabolite was utilized to create the quantitative data matrix. To ensure comprehensive metabolic coverage, metabolites identified in both organic and aqueous phases were combined for data analysis. The metabolite datasets were transferred as CSV files and subsequently imported into MetaboAnalyst software (version 6.0; https://www.metaboanalyst.ca), McGill University, Montreal, Canada) [26]. For sample classification, the partial least squares discriminant analysis (PLS-DA) method in MetaboAnalyst was utilized to choose the most distinguishing characteristics within the studied conditions. This process aimed to reduce the rate of false positives. The corrections for multiple hypothesis testing were employed utilizing the false discovery rate (FDR) approach. The identification of significantly changed metabolites in the ANX and HPX groups, as opposed to the NMX condition, was performed using a two-tailed independent Student\'s t-test. This resulted in the formation of a volcano plot, graphically depicting the statistical significance and fold change (p<0.05, FC=2), emphasizing the dysregulation of cellular metabolites for each group. One-way analysis of variance (ANOVA) was utilized for an extensive comparison between ANX, HPX, and NMX conditions. The threshold for significance was set at p <0.05. Reference: [26] J. Xia, D.S. Wishart, Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst, Nature protocols 6(6) (2011) 743-760. doi: 10.1038/nprot.2011.319.')}
'Metabolites' section of 'MS_METABOLITE_DATA' block do not match.
'Data' section of 'MS_METABOLITE_DATA' block do not match.