Multi-omics analyses of human colorectal cancer revealed three mitochondrial genes potentially associated with poor outcomes of patients.

2021 
The identification of novel functional biomarkers is essential for recognizing high-risk patients, predicting recurrence, and searching for appropriate treatment. However, no prognostic biomarker has been applied for colorectal cancer (CRC) in the clinic. Integrated with transcriptomic data from public databases, multi-omics examinations were conducted to search prognostic biomarkers for CRC. Moreover, the potential biological functions and regulatory mechanism of these predictive genes were also explored. In this study, we revealed that three mitochondrial genes were associated with the poor prognosis of CRC. Integrated analyses of transcriptome and proteome of CRC patients disclosed numerous down-regulated mitochondrial genes at both mRNA and protein levels, suggesting a vital role of mitochondria in carcinogenesis. Combined with the bioinformatics studies of transcriptomic datasets of 538 CRC patients, three mitochondrial prognostic genes were eventually selected out, including HIGD1A, SUCLG2, and SLC25A24. The expression of HIGD1A exhibited a significant reduction in two subtypes of adenoma and six subtypes of CRC, while the down-regulation of SUCLG2 and SLC25A24 showed more advantages in rectal mucinous adenocarcinoma. Moreover, we unveiled that these three genes had common expressions and might collaboratively participate in the synthesis of ribosomes. Our original multi-omics datasets, including DNA methylation, structural variants, chromatin accessibility, and phosphoproteome, further depicted the altered modifications on their potential transcriptional factors. In summary, HIGD1A, SUCLG2, and SLC25A24 might serve as predictive biomarkers for CRC. The biological activities they involved in and their upstream regulators we uncovered would provide a functional context for the further-in-depth mechanism study.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    40
    References
    0
    Citations
    NaN
    KQI
    []