ZHENHUA LI¹, HUILAI LV¹, FAN ZHANG¹, ZIMING ZHU², QIANG GUO³, MINGBO WANG¹, CHAO HUANG¹, LIJUAN CHEN⁴, WENPAN ZHANG⁴, YUN LI^5,*, ZIQIANG TIAN^1,*

BIOCELL, Vol.48, No.1, pp. 123-138, 2024, DOI:10.32604/biocell.2023.031568

Abstract Introduction: DNA polymerases are crucial for maintaining genome stability and influencing tumorigenesis. However, the clinical implications of DNA polymerases in tumorigenesis and their potential as anti-cancer therapy targets are not well understood. Methods: We conducted a systematic analysis using TCGA Pan-Cancer Atlas data and Gene Set Cancer Analysis results to examine the expression profiles of 15 DNA polymerases (POLYs) and their clinical correlations. We also evaluated the prognostic value of POLYs by analyzing their expression levels in relation to overall survival time (OS) using Kaplan-Meier survival curves. Additionally, we investigated the correlations between POLY expression and immune cells, DNA damage… More >

Bo Ling, Zuliang Huang, Suoyi Huang, Li Qian, Genliang Li, Qianli Tang

Oncology Research, Vol.28, No.6, pp. 561-578, 2020, DOI:10.3727/096504020X15907428281601

Abstract There is growing evidence on the clinical significance of tumor microenvironment (TME) cells in predicting prognosis and therapeutic effects. However, cell interactions in tumor microenvironments have not been thoroughly studied or systematically analyzed so far. In this study, 22 immune cell components in the lung adenocarcinoma (LUAD) TME were analyzed using gene expression profile from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The TME-based molecular subtypes of LUAD were defined to evaluate further the relationship between molecular subtypes, prognosis, and clinical characteristics. A TME risk score model was constructed by using the differentially expressed genes (DEGs) of… More >

Beifen Dai^1,#, Zhihao Xu^2,#, Hongjue Li³, Bo Wang³, Jinsong Cai¹, Xiaomo Liu^4,*

Oncologie, Vol.24, No.1, pp. 113-130, 2022, DOI:10.32604/oncologie.2022.020259

Abstract Large-scale genomic studies are important ways to comprehensively decode the human genomics, and provide valuable insights to human disease causalities and phenotype developments. Genomic studies are in need of high throughput bioinformatics analyses to harness and integrate such big data. It is in this overarching context that artificial intelligence (AI) offers enormous potentials to advance genomic studies. However, racial bias is always an important issue in the data. It is usually due to the accumulation process of the dataset that inevitability involved diverse subjects with different races. How can race bias affect the outcomes of AI methods? In this work,… More >