High TKTL1 expression as a sign of poor prognosis in colorectal cancer with synchronous rather than metachronous liver metastases

221 Background: 5-year survival of patients with resectable colorectal liver metastases is 25-40%. Mechanisms of disease progression are heterogenous and do not follow a clearly defined pathway from genotype to phenotype. In stage I-III colorectal cancer (CRC), patients with high tumor stroma exhibit poor prognosis, while those with high immune cell infiltrate do well following resection. We hypothesise that stromally-dense phenotypes lead to T cell exclusion, myeloid cell accumulation and aggressive metastatic progression. Here, we examine relationships between histological tumor phenotype, cellular infiltrate and outcomes in metastatic CRC. Methods: A unique cohort of synchronously resected primary CRC and matched liver metastases (n = 46) were assessed for immune cell infiltration (CD3, CD4, CD8, CD68, CD66b), inflammatory signalling (CXCR2, PDL-1, MMP9) and hypoxia (CAIX) using immunohistochemistry. Tumors were phenotypically subtyped using immune infiltrate (Klintrup-Makinen Grade (KM)), stromal invasion (tumor-stroma percentage (TSP)) and proliferation (Ki67). Results: Phenotypic subtype of primary tumors was predictive of metastatic subtype (rho = 0.522, p = 0.003). Immune phenotypes were associated with good prognosis and stromal phenotypes with poor prognosis (p = 0.004). Infiltration of macrophages and granulocytes associated with poor outcomes (p = 0.018) and increased CXCR2 expression (p = 0.03) at both sites. Increased CXCR2+ cells and macrophages at both sites associated with stromal phenotype (p = 0.02), tumour budding (p = 0.002), low KM grade (p = 0.05) and poor prognosis (p = 0.002). Macrophage and MMP9 levels increased in metastases compared to primary tumour, but no changes were seen in lymphocyte infiltration, CXCR2 and CD66b. Conclusions: Density of immune cell infiltrate, in the primary and metastatic niche, conferred good prognosis. In contrast, stromal, myeloid rich tumors convey poor prognosis. This clinically relevant and histologically efficient process permits segregation of disease and supports further study of relationships in the tumour microenvironment of CRC in the context of chemotherapy to better target therapeutics to individual patients.

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Novel Therapeutic Targets in the Cancer Associated Stroma Define Poor Prognosis Colorectal Cancer and Colorectal Liver Metastases

HPB ◽

10.1016/j.hpb.2020.11.050 ◽

2021 ◽

Vol 23 ◽

pp. S21

Author(s):

K.M. Brown ◽

A. Xue ◽

A.J. Gill ◽

T.J. Hugh

Keyword(s):

Colorectal Cancer ◽

Liver Metastases ◽

Poor Prognosis ◽

Colorectal Liver Metastases ◽

Therapeutic Targets ◽

Novel Therapeutic

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Identification of Significant Genes with poor prognosis in Liver Metastasis of Colorectal Cancer via Bioinformatical Analysis

10.21203/rs.3.rs-78881/v1 ◽

2020 ◽

Author(s):

Junsheng Chen ◽

Hongzhou Liu

Keyword(s):

Colorectal Cancer ◽

Cytochrome P450 ◽

Liver Metastases ◽

Poor Prognosis ◽

Prion Diseases ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Chemical Carcinogenesis ◽

Venn Diagram ◽

Underlying Mechanisms

Abstract Background: Colorectal cancer (CRC) is a common malignant tumor in the world wild, and more than 50% patients have liver metastases. Purpose: The purpose of this study is to identify significant genes with poor outcome and the underlying mechanisms of CRC liver metastases. Methods: Gene expression profiles of GSE50760, GSE41568 and GSE14297 are available on website of GEO database. Differentially expressed genes (DEGs) between CRC liver metastases and primary tissues were picked out by GEO2R tool and Venn diagram software. The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to analyze Kyoto Encyclopedia of Gene and Genome (KEGG) pathway and gene ontology (GO). Then protein-protein interaction (PPI) of these DEGs was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING). Results: There were total of 147 consistently expressed genes in the three datasets, including 123 up-regulated genes and 24 down-regulated genes enriched in complement and coagulation cascades, drug metabolism-cytochrome P450, metabolism of xenobiotics by cytochrome P450, prion diseases, chemical carcinogenesis, staphylococcus aureus infection and linoleic acid metabolism. Of PPI network analyzed by Molecular Complex Detection (MCODE) plug-in, all 39 genes were selected. Moreover, for the analysis of CRC survival among those genes, Kaplan–Meier analysis was implemented and 4 (SERPING1 ITIH2 CDH2 APOE) of 39 genes had a significantly worse prognosis. Conclusion: we have identified four significant DEGs with poor prognosis in CRC liver metastases on the basis of integrated bioinformatical methods, which could be potential therapeutic targets for CRC patients with liver metastases.

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