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Surgical resection remains a critical treatment option for many patients with primary and secondary hepatic neoplasms. Extended hepatectomy (eHx) may be required for some patients with large tumors, which may cause liver failure and death. Partial hepatectomy (pHx) and eHx mouse models were constructed, liver tissues were sampled at 18, 36, and 72 h posthepatectomy. Transcriptome and metabolome analyses were employed to explore the different potential mechanisms in regeneration and injury between pHx and eHx. The results showed that eHx was associated with more severe liver injury and lower survival rates than pHx. Transcriptomics data showed there were 1842, 2129, and 1277 differentially expressed genes (DEGs) in eHx and 962, 1305, and 732 DEGs in pHx at 18, 36, and 72 h posthepatectomy, respectively, compared with the those in the sham groups. Compared with pHx, the number of DEGs in the eHx group reached a maximum of 230 at 18 h after surgery and decreased sequentially to 87 and 43 at 36 and 72 h. Metabolomics analysis identified a total of 1399 metabolites, and 48 significant differentially produced metabolites (DPMs) were screened between eHx and pHx. Combined analysis of DEGs and DPMs indicated that cholesterol metabolism and insulin resistance may be two important pathways for liver regeneration and mouse survival postextended hepatectomy. Our results showed the global influence of pHx and eHx on the transcriptome and metabolome in mouse liver, and revealed cholesterol metabolism and insulin resistance pathways might be involved in regeneration post-pHx and -eHx.
With advances in technologies, huge amounts of multiple types of high-throughput genomics data are available. These data have tremendous potential to identify new and clinically valuable biomarkers to guide the diagnosis, assessment of prognosis, and treatment of complex diseases, such as cancer. Integrating, analyzing, and interpreting big and noisy genomics data to obtain biologically meaningful results, however, remains highly challenging. Mining genomics datasets by utilizing advanced computational methods can help to address these issues.
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