Dataset Information

Identification of Biomarkers Associated with Liver Metastasis Progression from Colorectal Cancer Using Exosomal RNA Profiling.

ABSTRACT: This study aimed to identify novel biomarkers for metastatic colorectal cancer progression using exosomal RNA expression profiling. The exosomal RNA expression profiles of 54 patients with mCRC were investigated. Exosomal RNA profiling was performed at the time of relapse immediately before metastasectomy and cancer recurrence or progression after metastasectomy. The up- and down-regulated RNA expression profiles were screened and analyzed using H-cluster, principle component analysis and gene ontology. The tissue expression profile of the liver metastases was compared with the GSE 41258 set using GSEA tools. We identified two distinctive biological process gene sets (IFNA and PCDB families) related to metastatic progression. The interferon-α response gene set was enriched, especially when the tumor volume was ≥1 cm³. CXCL10, CXCL11 and SAMD 9 mRNA were highly expressed in the plasma exosome samples of patients with mCRC to the liver. Furthermore, high expression of CXCL10 but not CXCL11 or SAMD9 was associated with a poor prognosis and shorter progression-free survival. Conclusions: Cancer-derived exosomal CXCL10 may be a novel biomarker for liver metastasis of mCRC and a potential target for the prevention and treatment of mCRC with liver metastasis.

SUBMITTER: Lee S

PROVIDER: S-EPMC9562015 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Identification of Biomarkers Associated with Liver Metastasis Progression from Colorectal Cancer Using Exosomal RNA Profiling.

Lee Soohyeon S Park Young Soo YS Kim Jwa Hoon JH Lim Ah Reum AR Hyun Myung Han MH Kim Boyeon B Lee Jong Won JW Lee Saet Byeol SB Kim Yeul Hong YH

Cancers 20220928 19

This study aimed to identify novel biomarkers for metastatic colorectal cancer progression using exosomal RNA expression profiling. The exosomal RNA expression profiles of 54 patients with mCRC were investigated. Exosomal RNA profiling was performed at the time of relapse immediately before metastasectomy and cancer recurrence or progression after metastasectomy. The up- and down-regulated RNA expression profiles were screened and analyzed using H-cluster, principle component analysis and gene o ...[more]

PMID: 36230645

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Project description:BackgroundLiver metastases can occur even in CRC patients who underwent curative surgery. While evidence suggested that adjuvant chemotherapy can help to reduce the occurrence of liver metastases for certain patients, it is not a recommended routine as the side effects outweigh the potential benefits, especially in Stage II CRC patients. This study aims to construct a model for predicting liver metastasis risk using differential methylation signals in primary CRC tumors, which can facilitate the decision for adjuvant chemotherapy.MethodsFifty-nine stage I/II and IV CRC patients were enrolled. Primary tumor, adjacent normal tissue, and metastatic tumor tissues were subject to targeted bisulfite sequencing for DNA methylation. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was used to identify potential DMRs for predicting liver metastasis of CRC.ResultsWe identified a total of 241,573 DMRs by comparing the DNA methylation profile of primary tumors of stage II patients who developed metastasis to those who were metastasis-free during the follow up period. 213 DMRs were associated with poor prognosis, among which 182 DMRS were found to be hypermethylated in the primary tumor of patients with metastases. Furthermore, by using the LASSO regression model, we identified 23 DMRs that contributed to a high probability of liver metastasis of CRC. The leave-one-out cross validation (LOOCV) was used to evaluate model predictive performance at an AUC of 0.701. In particular, 7 out of those 23 DMRs were found to be in the promoter region of genes that were previously reported prognostic biomarkers in diverse tumor types, including TNNI2, PAX8, GUF1, KLF4, EVI2B, CEP112, and long non-coding RNA AC011298. In addition, the model was also able to distinguish metastases of different sites (liver or lung) at an AUC of 0.933.ConclusionWe have identified DNA methylation biomarkers associated with the risk of cancer liver metastasis in early-stage CRC patients. A risk prediction model based on those epigenetic markers was proposed for outcome assessment.

Dataset Information

Identification of Biomarkers Associated with Liver Metastasis Progression from Colorectal Cancer Using Exosomal RNA Profiling.

Publications

Identification of Biomarkers Associated with Liver Metastasis Progression from Colorectal Cancer Using Exosomal RNA Profiling.

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