Disease Markers最新文献

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[This retracts the article DOI: 10.1155/2022/1758113.].

Background: Liver hepatocellular carcinoma (LIHC) is the most frequently seen type of primary liver cancer. Cuproptosis is a novel form of cell death highly associated with mitochondrial metabolism. However, the clinical impact and pertinent mechanism of cuproptosis genes in LIHC remain largely unknown.

Methods: From public databases, we systematically assessed common genes from LIHC differentially expressed genes (DEGs) and cuproptosis-related genes using bioinformatics analysis. These common genes were then analyzed by enrichment analysis, mutation analysis, risk score model, and others to find candidate hub genes related to LIHC and cuproptosis. Next, hub genes were determined by expression, clinical factors, immunoassay, and prognostic nomogram.

Results: Based on 129 cuproptosis-related genes and 3492 LIHC DEGs, we totally identified 21 downregulated and 18 upregulated common genes, and they were enriched in pathways, such as zinc ion homeostasis and oxidative phosphorylation. In the mutation analysis, missense mutation was the most common type in LIHC patients, and the common gene F5 had the highest mutation frequency. After LASSO-Cox regression analysis and prognostic analysis, CDK1, ABCB6, LCAT, and COA6 were identified as prognostic signature genes. Among them, ABCB6 and LCAT were lowly expressed in tumors, and CDK1 and COA6 were highly expressed in tumors. In addition, ABCB6 and LCAT were negatively correlated with 6 kinds of immune cells, while CDK1 and COA6 were positively correlated with them. CDK1 and COA6 were identified as hub genes related to LIHC by Cox regression analysis and prognostic nomogram.

Conclusion: CDK1 and COA6 are two oncogenes in LIHC, which are involved in the molecular mechanism of cuproptosis and LIHC. Besides, CDK1 and COA6 can positively regulate the expressions of immune cells in LIHC. In clinical practice, they can be used as immunotherapeutic targets and prognostic predictors in LIHC, which sheds new light on the scientific fields of cuproptosis and LIHC.

Objective: This study is to investigate the difference in HIV-1 RNA pol gene expression in AIDS patients before and after antiviral treatment and its effect on the expression level of CD4⁺/CD8⁺ T cells in peripheral blood.

Methods: The participants included 200 AIDS patients who had undergone antiviral medication, and the quantity of HIV-1 RNA pol gene was determined using nested polymerase chain reaction (nPCR). The levels of CD3+, CD4+, and CD8+ T lymphocytes in peripheral blood were measured by flow cytometry before and after therapy. The receiver operating characteristics (ROC) curve was used to assess the impact of HIV-1 RNA pol gene expression and the CD4+/CD8+ ratio on the prognosis of AIDS patients.

Results: After three months of therapy, the levels of HIV-1 RNA and viral load in the patients showed a drastic decline, while the levels of CD4+/CD8+ were markedly elevated (P < 0.05). Logistic analysis revealed that patients' viral loads were positively correlated with HIV-1 RNA and negatively correlated with CD4+/CD8+ (P < 0.05). The alanine aminotransferase (ALT), white blood cell (WBC) count, Serum creatinine (Cr), total cholesterol (TC), triglyceride (TG), and platelet (PLT) levels significantly increased following a 24-month therapy, while no significant changes were observed in the level of aspartate aminotransferase (AST), red blood cell (RBC), and neutrophil (NEU) (%). (P > 0.05).

Conclusion: Antiviral drugs significantly inhibit the HIV-1 RNA POL gene expression and viral load in AIDS patients but upregulate the expression level of CD4⁺/CD8⁺ T cells in peripheral blood.

Background: Asthma is one of the most common respiratory diseases and one of the largest burdens of health care resources across the world. This study is aimed at using bioinformatics methods to find effective clinical indicators for asthma and conducting experimental validation.

Methods: We downloaded GSE64913 data and performed differentially expressed gene (DEG) screening. Weighted gene coexpression network analysis (WGCNA) on DEGs was applied to identify key module most associated with asthma for protein-protein interaction (PPI) analysis. According to the degree value, ten genes were obtained and subjected to expression analysis and receiver operating characteristic (ROC) analysis. Next, key genes were screened for expression analysis and immunological analysis. Finally, cell counting kit-8 (CCK-8) and qRT-PCR were also conducted to observe the influence of hub gene on cell proliferation and inflammatory cytokines.

Results: From the GSE64913 dataset, 711 upregulated and 684 downregulated DEGs were found. In WGCNA, the top 10 genes in the key module were examined by expression analysis in asthma, and CYCS was determined as an asthma-related oncogene with a good predictive ability for the prognosis of asthmatic patients. CYCS is significantly associated with immune cells, such as HHLA2, IDO1, TGFBR1, and CCL18 and promoted the proliferation of asthmatic cells in vitro.

Conclusion: CYCS plays an oncogenic role in the pathophysiology of asthma, indicating that this gene may become a novel diagnostic biomarker and promising target of asthma treatment.