ArticleFrontiers in genetics2023
Correlation evaluation between cancer microenvironment related genes and prognosis based on intelligent medical internet of things.
Article in Frontiers in genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- The Contribution of Cholesterol and Squalene Synthase in Cancer: Molecular Mechanisms, Lipid Rafts and Therapeutic Approaches.Medicinal research reviews · 2026Review
- In Vitro Effects of Cabazitaxel and Menadione on Cell Growth, Metabolism, and Transcriptomic Profile of Human Prostate Cancer Cell Lines.Prostate cancer · 2026Article
- Multi-Omics Analysis Reveals m7G Methylation-Related Genes May Be Involved in TGF-β Signaling-Mediated Anti-PD-L1 Response in Bladder Cancer.ImmunoTargets and therapy · 2026Article
- Meta single-cell atlas and xQTL post-GWAS analysis revealed the pathogenic features of thyroid cancer for target therapy: A multi-omics study.Cancer gene therapy · 2026Article
- An 11-gene glycosyltransferases-related model for the prognosis of patients with bladder urothelial carcinoma: development and validation based on TCGA and GEO datasets.Translational andrology and urology · 2024Article
- Article
- A signature of five 7-methylguanosine-related genes is a prognostic marker for lung squamous cell carcinoma.Journal of thoracic disease · 2023Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The study of tumor microenvironment plays an important role in the treatment of cancer patients. In this paper, intelligent medical Internet of Things technology was used to analyze cancer tumor microenvironment-related genes. Through experiments designed and analyzed cancer-related genes, this study concluded that in cervical cancer, patients with high expression of P16 gene had a shorter life cycle and a survival rate of 35%. In addition, through investigation and interview, it was found that patients with positive expression of P16 and Twist genes had a higher recurrence rate than patients with negative expression of both genes; high expression of FDFT1, AKR1C1, and ALOX12 in colon cancer is associated with short survival; high expressions of HMGCR and CARS1 is associated with longer survival; overexpression of NDUFA12, FD6, VEZT, GDF3, PDE5A, GALNTL6, OPMR1, and AOAH in thyroid cancer is associated with shortened survival; high expressions of NR2C1, FN1, IPCEF1, and ELMO1 is associated with prolonged survival. Among the genes associated with the prognosis of liver cancer, the genes associated with shorter survival period are AGO2, DCPS, IFIT5, LARP1, NCBP2, NUDT10, and NUDT16; the genes associated with longevity are EIF4E3, EIF4G3, METTL1, NCBP1, NSUN2, NUDT11, NUDT4, and WDR4. Depending on the prognostic role of genes in different cancers, they can influence patients to achieve the effect of reducing patients' symptoms. In the process of disease analysis of cancer patients, this paper uses bioinformation technology and Internet of things technology to promote the development of medical intelligence.
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