Ask about this productRelated genes to: ZNF486 Blocking Peptide
- Gene:
- ZNF486 NIH gene
- Name:
- zinc finger protein 486
- Previous symbol:
- KRBO2
- Synonyms:
- MGC2396
- Chromosome:
- 19p12
- Locus Type:
- gene with protein product
- Date approved:
- 2003-11-20
- Date modifiied:
- 2015-08-27
Related products to: ZNF486 Blocking Peptide
Related articles to: ZNF486 Blocking Peptide
- Traditional paradigms of pharmaceutical innovation have long relied on the "one drug, one disease" premise. However, a network mindset in unpacking disease mechanisms can be fruitful to move toward a "one drug, polydisease" paradigm of drug discovery and development. A case in point is obstructive sleep apnea (OSA) and lung cancer, which are two prevalent respiratory disorders that share common risk factors and may potentially exhibit overlapping molecular mechanisms. The putative mechanistic linkages between OSA and lung cancer remain underexplored; however, this study offers new evidence on overlapping genetic signatures between OSA and lung cancer with an in-silico approach. Bioinformatics analysis of the publicly available datasets (GSE135917 and GSE268175) identified 123 upregulated and 13 downregulated genes in OSA and 3175 upregulated and 2272 downregulated genes in lung cancer. A total of four genes (, , , and ) were significantly upregulated with both disorders, highlighting potentially shared genetic and molecular mechanisms. Pathway and cell enrichment analysis indicated that mucin type O-glycan biosynthesis pathway and endothelial cells are strongly associated with these shared genes, lending support for their potential roles in both diseases. Moreover, hsa-miR-34a-5p, hsa-let-7g-5p, and hsa-miR-19a-3p were found to be associated with these common genes. Validation using the GEPIA2 tool confirmed the consistent expression patterns of these four genes in lung cancer. Machine learning analysis highlighted as the most significant biomarker candidate for distinguishing OSA and lung cancer from controls. In summary, this study supports the overarching concept that human diseases can have shared mechanistic pathways in the specific example of OSA and lung cancer. While these findings call for further research and validation, they invite rethinking the current pharmaceutical innovation paradigms to move beyond the "one drug, one disease" concept. - Source: PubMed
Publication date: 2025/04/08
Dasgupta Sanjukta - Multiple myeloma (MM) is a highly heterogeneous hematological malignancy that is currently incurable. Individualized therapeutic approaches based on accurate risk assessment are essential for improving the prognosis of MM patients. Nevertheless, current prognostic models for MM exhibit certain limitations and prognosis heterogeneity still an unresolved issue. Recent studies have highlighted the pivotal involvement of mitochondrial autophagy in the development and drug sensitivity of MM. This study seeks to conduct an integrative analysis of the prognostic significance and immune microenvironment of mitophagy-related signature in MM, with the aim of constructing a novel predictive risk model. GSE4581 and GSE47552 datasets were acquired from the Gene Expression Omnibus database. MM-differentially expressed genes (DEGs) were identified by limma between MM samples and normal samples in GSE47552. Mitophagy key module genes were obtained by weighted gene co-expression network analysis in the Cancer Genome Atlas (TCGA)-MM dataset. Mitophagy DEGs were identified by the overlap genes between MM-DEGs and mitophagy key module genes. Prognostic genes were selected through univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis, and a risk model was subsequently constructed based on these prognostic genes. Subsequently, the MM samples were stratified into high- and low-risk groups based on their median risk scores. The validity of the risk model was further evaluated using the GSE4581 dataset. Moreover, a nomogram was developed using the independent prognostic factors identified from the risk score and various clinical indicators. Additionally, analyses were conducted on immune infiltration, immune scores, immune checkpoint, and chemotherapy drug sensitivity. The 17 mitophagy DEGs were obtained by intersection of 803 MM-DEGs and 1084 mitophagy key module genes. Five prognostic genes (CDC6, PRIM1, SNRPB, TOP2A, and ZNF486) were selected via LASSO and univariate cox regression analyses. The predictive performance of the risk model, which was constructed based on the five prognostic genes, demonstrated favorable results in both TCGA-MM and GSE4581 datasets as indicated by the receiver operating characteristic (ROC) curves. In addition, calibration curve, ROC curve, and decision curve analysis curve corroborated that the nomogram exhibited superior predictive accuracy for MM. Furthermore, immune analysis results indicated a significant difference in stromal scores of two risk groups categorized on median risk scores. And four immune checkpoints (CD274, CTLA4, LAG3, and PDCD1LG2) showed significant differences in different risk groups. The analysis of chemotherapy drug sensitivity revealed that etoposide and doxorubicin, which target TOP2A, exhibited superior treatment outcomes in the high-risk group. A novel prognostic model for MM was developed and validated, demonstrating significant potential in predicting patient outcomes and providing valuable guidance for personalized immunotherapy counseling. - Source: PubMed
Publication date: 2024/10/29
Min RuiHu ZeyuZhou Yulan - In this study, we aimed to explore cyclophosphamide (Cytoxan) response-associated genes and constructed a model to predict the prognosis of breast cancer (BRCA) patients. Samples obtained from TCGA and GEO databases were subjected to Weighted Gene Coexpression Network Analysis (WGCNA) and univariate Cox and LASSO Cox regression analysis to identify and validate the Cytoxan response-related prognostic signature. Moreover, multivariate Cox regression analysis was performed to analyze the independence of factors, and the nomogram model was constructed by including all the independent factors. WGCNA revealed that 159 genes are significantly correlated with Cytoxan response in BRCA samples, and the samples with a different prognosis could be effectively distinguished based on the expression of those 159 genes. Ten genes were further selected to be related to the prognosis of BRCA patients, including , , , , , , , , , and , based on the Risk Score model. Among them, expression was validated in cells and patient samples. Multivariate Cox regression analysis confirmed that the Risk Score is an independent factor. Furthermore, the nomogram model showed that the predicted survival probability is closely related to the actual survival probability. In conclusion, we identified 159 genes potentially correlated with the Cytoxan response of BRCA patients, which had prognostic value in BRCA. - Source: PubMed
Publication date: 2021/10/26
Du JiaweiDong YanruLi Yuhong - Prostate cancer (PCa) which was the second commonly diagnosed malignancy, contributed to the top fifth carcinoma death in men. Nevertheless, the main chemotherapeutic agent docetaxel came to failure due to chemoresistance. Recently, increasing evidence suggested the importance of tumour microenvironment (TME) in PCa. The present study aimed to explore the specific TME in PCa and find biomarkers related to both immune infiltration and docetaxel. The docetaxel-specific genes and differential expression genes comparing PCa with normal control samples were derived using DESeq2 and zinbwave with GSE140440, TCGA and GTEx datasets. Immune-infiltration-related genes were identified using CIBERSORT and co-expression network analysis. Key genes related to both docetaxel and immune infiltrating in PCa, including nine genes, namely ZNF486, IFI6, TMOD2, HSPA4L, ITPR1, LRRC37A7P, APOC1, APOBEC3G, and ITGA2, were determined by overlapping above three gene sets. ITGA2 was then defined as the hub gene for its significant prognostic implications. Further validations conducted on Oncomine, GEO, TISIDB, MSigDB, and The Human Protein Atlas confirmed the docetaxel-specific and immune infiltrating characteristics of ITGA2. To sum up, our findings could provide a better understanding of immune infiltrating and docetaxel-resistance in PCa, mostly, ITGA2 could serve as potential prognosis biomarkers and targets for the combination of docetaxel. - Source: PubMed
Publication date: 2021/05/21
Peng YunDong ShiqiangYang ZhikaiSong YuxuanDing JinHou DingkunWang LiliZhang ZheyuLi NanWang Haitao - To identify hub genes and pathways involved in castrate-resistant prostate cancer (CRPC). - Source: PubMed
Publication date: 2020/07/13
Wu Yu-PengKe Zhi-BinLin FeiWen Yao-AnChen ShengLi Xiao-DongChen Shao-HaoSun Xiong-LinHuang Jin-BeiZheng Qing-ShuiXue Xue-YiWei YongXu Ning