Comprehensive Analysis of the Expression of Cell Adhesion Molecules Genes in Hepatocellular Carcinoma and their Prognosis, and Biological Significance

Collagen-related cell adhesion molecules (CAMs) are a major component of the extracellular matrix (ECM) and often accumulate in the liver during chronic liver disease or hepatocellular carcinoma (HCC). In this study we identified several promising collagens related to CAMs that may be of clinical us...

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Veröffentlicht in:Frontiers in bioscience (Landmark. Print) 2024-02, Vol.29 (2), p.76-76
Hauptverfasser: Wang, Jia, Hu, Yuting, Zhao, Kunpeng, Fan, Jian, Zhu, Jian, Li, Qingya, Chen, Qilong, Lu, Yiyu
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Sprache:eng
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Zusammenfassung:Collagen-related cell adhesion molecules (CAMs) are a major component of the extracellular matrix (ECM) and often accumulate in the liver during chronic liver disease or hepatocellular carcinoma (HCC). In this study we identified several promising collagens related to CAMs that may be of clinical use for the diagnosis and prognosis of HCC. We obtained multi-omics data including RNA sequencing (RNA-Seq) data, microarray data, proteomic data from the TCGA, GEO databases, GTEx, and NODE. Bioinformatics analyses were then performed to investigate correlations between the expression patterns of significant genes and HCC. Tumor tissue and para-cancerous tissue samples from HCC patients were also used to validate the results using RT-PCR. A literature research and LASSO-COX analysis identified three significant collagen-related CAM genes: , , and . Immunohistochemistry images in the Human Protein Atlas Project database showed that SERPINH1 and ITGB1 proteins were moderately or highly expressed in HCC tumor tissues compared to para-cancerous tissue, whereas DCN expression was lower in HCC tumor tissue. These results were validated by RT-PCR. Low- and high-risk groups of HCC patients were distinguished by the logistic panel in the TCGA database. These showed significantly different prognosis, clinicopathological features, and immune cell infiltration. Logistic regression was used to construct predictive models based on the individual expression levels of , , and . These showed highly accurate diagnostic ability (AUC = 0.987). The current findings suggest that the collagen-related CAMs , , and may be potential therapeutic targets in HCC. Logistic panels of , and could serve as non-invasive and effective diagnostic biomarkers for HCC. Identifier: NCT03189992. Registered on June 4, 2017. Retrospectively registered (https://clinicaltrials.gov/).
ISSN:2768-6701
2768-6698
DOI:10.31083/j.fbl2902076