Diagnostic performance of clusterin in hepatocellular carcinoma: A meta-analysis

Therapeutic Potential of Clusterin Inhibition in Human Cancer

Clusterin (CLU) protein is involved in various pathophysiological processes including carcinogenesis and tumor progression. In recent years, the role of the secretory isoform has been demonstrated in tumor cells, where it inhibits apoptosis and favors the acquisition of resistance to conventional treatments used to treat cancer. To determine the possible therapeutic potential of inhibiting this protein, numerous studies have been carried out in this field. In this article, we present the existing knowledge to date on the inhibition of this protein in different types of cancer and analyze the importance it could have in the development of new therapies targeted against this disease.

The Ins and Outs of Clusterin: Its Role in Cancer, Eye Diseases and Wound Healing

Clusterin (CLU) is a glycoprotein originally discovered in 1983 in ram testis fluid. Rapidly observed in other tissues, it was initially given various names based on its function in different tissues. In 1992, it was finally named CLU by consensus. Nearly omnipresent in human tissues, CLU is strongly expressed at fluid-tissue interfaces, including in the eye and in particular the cornea. Recent research has identified different forms of CLU, with the most prominent being a 75-80 kDa heterodimeric protein that is secreted. Another truncated version of CLU (55 kDa) is localized to the nucleus and exerts pro-apoptotic activities. CLU has been reported to be involved in various physiological processes such as sperm maturation, lipid transportation, complement inhibition and chaperone activity. CLU was also reported to exert important functions in tissue remodeling, cell-cell adhesion, cell-substratum interaction, cytoprotection, apoptotic cell death, cell proliferation and migration. Hence, this protein is sparking interest in tissue wound healing. Moreover, CLU gene expression is finely regulated by cytokines, growth factors and stress-inducing agents, leading to abnormally elevated levels of CLU in many states of cellular disturbance, including cancer and neurodegenerative conditions. In the eye, CLU expression has been reported as being severely increased in several pathologies, such as age-related macular degeneration and Fuch's corneal dystrophy, while it is depleted in others, such as pathologic keratinization. Nevertheless, the precise role of CLU in the development of ocular pathologies has yet to be deciphered. The question of whether CLU expression is influenced by these disorders or contributes to them remains open. In this article, we review the actual knowledge about CLU at both the protein and gene expression level in wound healing, and explore the possibility that CLU is a key factor in cancer and eye diseases. Understanding the expression and regulation of CLU could lead to the development of novel therapeutics for promoting wound healing.

Construction of a Colorectal Cancer Prognostic Risk Model and Screening of Prognostic Risk Genes Using Machine-Learning Algorithms

This study is aimed at constructing a prognostic risk model for colorectal cancer (CRC) using machine-learning algorithms to provide accurate staging and screening of credible prognostic risk genes. We extracted CRC data from GSE126092 and GSE156355 of the Gene Expression Omnibus (GEO) database and datasets from TCGA to analyze the differentially expressed genes (DEGs) using bioinformatics analysis. Among the 330 shared DEGs related to CRC prognosis, we divided the analysis period into different phases and applied univariate COX regression, LASSO, and multivariate COX regression analysis. GO analysis and KEGG analysis revealed that the functions of these DEGs were primarily focused on cell cycle, DNA replication, cell mitosis, and other related functions, and this confirmed our results from a biological perspective. Finally, a prognostic risk model for CRC based on the CHGA, CLU, PLK1, AXIN2, NR3C2, IL17RB, GCG, and AJUBA genes was constructed, and the risk score enabled us to predict the prognosis for CRC. To obtain a comprehensive and accurate model, we used both internal and external evaluations, and the model was able to correctly differentiate patients with CRC into a high-risk group with poor prognosis and a low-risk group with good prognosis. The AUC values of the 3-, 5-, and 10-year survival ROC curves were 0.715, 0.721, and 0.777, respectively, according to the internal evaluation, and the AUC values were 0.606, 0.698, and 0.608, respectively, for the external evaluation using GSE39582 from the GEO database. We determined that CLU, PLK1, and IL17RB could be considered to be independent prognostic factors for CRC with significantly different expression (P < 0.05). Using machine-learning methods, a prognostic risk model comprised of eight genes was constructed. Not only does this model provide improved treatment guidance, but it also provides a novel perspective for analyzing survival conditions at a deeper biological level.