Global metabolomics profiling of colorectal cancer in Malaysian patients

Hashim, Nurul Azmir Amir; Ab‐Rahim, Sharaniza; Ngah, Wan Zurinah Wan; Nathan, Sheila; Mutalib, Nurul Syakima Ab; Sagap, Ismail; Jamal, A. Rahman A.; Mazlan, Musalmah

doi:10.34172/bi.2021.05

Cited by 22 publications

(19 citation statements)

References 59 publications

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“…The metabolome data were obtained from the serum of 50 healthy controls and 50 CRC patients collected at UKM Medical Centre. Data analysis was conducted as described by Amir Hashim et al [ 9 ].…”

Section: Methodsmentioning

confidence: 99%

“…Additional information on the individual molecules was obtained from external databases; for example, variants were linked to a dbSNP identifier (ID) [ 10 ] and COSMIC v70 ID [ 11 ]; genes were compared against GeneCards [ 12 ], Protein Databank (PDB) [ 13 ], RefSeq ncRNA [ 14 ] and UniProt [ 15 ]; metabolites were identified using an ID browser from Mass Profiler Professional software for the Metlin database [ 9 ]. Twenty-three CRC driver genes were identified by Abdullah and Muhammad [ 16 ].…”

Section: Methodsmentioning

confidence: 99%

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TCGA-My: A Systematic Repository for Systems Biology of Malaysian Colorectal Cancer

Azuwar

Muhammad

Afiqah‐Aleng

et al. 2022

Life

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Colorectal cancer (CRC) ranks second among the most commonly occurring cancers in Malaysia, and unfortunately, its pathobiology remains unknown. CRC pathobiology can be understood in detail with the implementation of omics technology that is able to generate vast amounts of molecular data. The generation of omics data has introduced a new challenge for data organization. Therefore, a knowledge-based repository, namely TCGA-My, was developed to systematically store and organize CRC omics data for Malaysian patients. TCGA-My stores the genome and metabolome of Malaysian CRC patients. The genome and metabolome datasets were organized using a Python module, pandas. The variants and metabolites were first annotated with their biological information using gene ontologies (GOs) vocabulary. The TCGA-My relational database was then built using HeidiSQL PorTable 9.4.0.512, and Laravel was used to design the web interface. Currently, TCGA-My stores 1,517,841 variants, 23,695 genes, and 167,451 metabolites from the samples of 50 CRC patients. Data entries can be accessed via search and browse menus. TCGA-My aims to offer effective and systematic omics data management, allowing it to become the main resource for Malaysian CRC research, particularly in the context of biomarker identification for precision medicine.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

TCGA-My: A Systematic Repository for Systems Biology of Malaysian Colorectal Cancer

Azuwar

Muhammad

Afiqah‐Aleng

et al. 2022

Life

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“…The study of blood-related metabolites as potential non-invasive biomarkers of CRC has increased in the recent years [92]. The comparison between healthy individuals and CRC patients revealed a decrease in serum glucose levels, as well as lower concentration of novel circulating long-chain hydroxy fatty acids, especially GTA-446 [93,94].…”

Section: Metabolomicsmentioning

confidence: 99%

Use of Omics Technologies for the Detection of Colorectal Cancer Biomarkers

Alorda-Clara

Torrens‐Mas

Morla-Barcelo

et al. 2022

Cancers

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Colorectal cancer (CRC) is one of the most frequently diagnosed cancers with high mortality rates, especially when detected at later stages. Early detection of CRC can substantially raise the 5-year survival rate of patients, and different efforts are being put into developing enhanced CRC screening programs. Currently, the faecal immunochemical test with a follow-up colonoscopy is being implemented for CRC screening. However, there is still a medical need to describe biomarkers that help with CRC detection and monitor CRC patients. The use of omics techniques holds promise to detect new biomarkers for CRC. In this review, we discuss the use of omics in different types of samples, including breath, urine, stool, blood, bowel lavage fluid, or tumour tissue, and highlight some of the biomarkers that have been recently described with omics data. Finally, we also review the use of extracellular vesicles as an improved and promising instrument for biomarker detection.

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“…Metabolomics aimed to identify biomarkers and the affected metabolic pathwaysby revealing the differences in the identified differential metabolites in CRC versus healthy controls (Amir et al, 2021).…”

Section: Metabolomicsmentioning

confidence: 99%

Multi-omics analysis of human gut microbiota in colorectal cancer patients

Abo-Hammam¹,

Khattab²,

Salah

et al. 2022

Records of Pharmaceutical and Biomedical Sciences

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Colorectal cancer (CRC) is a critical health problem. It is the second cause of death globally and the third world's most frequently diagnosed. Multiple evidences and suggestion imply the relationship of gut microbiota and colorectal cancer carcinogenesis. The different omics techniques like, metagenomic, metaproteomic and metabolomic approaches have led to important advances in the study of the intestinal microbiome, the host as well as the intestinal environment. Different bacterial species, proteins and metabolites have a crucial role in colorectal cancer screening, detection and recurrence. Integration of these three omics analysis in drawing attention to reveal taxonomic and functional structure of human gut microbiota in addition several protein and metabolites detection had helped in construction of microbial communities and their diversity in colorectal cancer patients and healthy controls besides that these bacterial species, metabolites and proteins could be used as a critical biomarkers candidate in colorectal cancer detection, prognosis and recurrence.

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Global metabolomics profiling of colorectal cancer in Malaysian patients

Cited by 22 publications

References 59 publications

TCGA-My: A Systematic Repository for Systems Biology of Malaysian Colorectal Cancer

TCGA-My: A Systematic Repository for Systems Biology of Malaysian Colorectal Cancer

Use of Omics Technologies for the Detection of Colorectal Cancer Biomarkers

Multi-omics analysis of human gut microbiota in colorectal cancer patients

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