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Integrative multi-omics deciphers the potential mechanism and microbial biomarkers for lymph node metastasis in
Min Seob Kwak1, Jae Myung Cha2, Chang Woo Kim3
1Department of Internal Medicine, Kyung Hee University Hospital at Gangdong, Kyung Hee University College of Medicine, 892 Dongnam-ro, Gandong-gu, Seoul, 05278, Republic of Korea. kwac63@khu.ac.kr.
Abstract:
Understanding and accurate diagnosis of lymph node metastasis (LNM) for patients with colorectal cancer (CRC) is essential to determine treatment and follow-up strategies. Therefore, in this study, we aimed to elucidate the biological process and identify the potential biomarker for LNM in CRC.A total of 30 patients who received a histologically confirmed diagnosis of CRC with Stage I to III and a curative surgery between November 2020 and July 2021 at Kyung Hee university hospital at Gangdong were included. We performed multi-omics approach integrating the data on somatic mutation, transcriptomic expression, DNA methylation, and microbiome with tumor and adjacent matched normal tissues of each patient. In total, 12 significant DEGs between the patients with and without LNM were identified, consisting of significantly upregulated S100A8 gene, a proinflammatory gene. The GSEA revealed that gene sets involving "MULTI CANCER INVASIVENESS" in terms related to epithelial-mesenchymal transition was significantly upregulated in the patients with LNM. Integrated functional analysis of DNA methylation with transcriptome profile shows that significantly hypomethylated promoters of the genes are enriched for LNM. The phylum Proteobacteria, unassigned (p_PU) presented significantly higher proportions in cancer tissues from the adjacent normal tissues. Notably, when compared to the patients without LNM, the gut microbiota of those with LNM appears to exhibit a significantly lower abundance of the p_PU, indicating its potential as promising biomarker for LNM in CRC. We explained the mechanism of tumor spreading using multi-omics analysis and identified the relevant metagenomic biomarker to predict the LNM in CRC by the recognition of host-microbial interaction, thereby can make the cancer surveillance of the patients more individualized and convincing.
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