Your browser doesn't support javascript.
loading
Identifying Potential Diagnostic Genes for Diabetic Nephropathy Based on Hypoxia and Immune Status.
Li, Changyan; Su, Feng; Zhang, Le; Liu, Fang; Fan, Wenxing; Li, Zhen; Ma, JingYuan.
Afiliação
  • Li C; Department of Nephrology, the First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan Province, People's Republic of China.
  • Su F; Department of Nephrology, the First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan Province, People's Republic of China.
  • Zhang L; Institute for Integrative Genome Biology, University of California Riverside, Riverside, CA, USA.
  • Liu F; Department of Nephrology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, People's Republic of China.
  • Fan W; Department of Nephrology, the First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan Province, People's Republic of China.
  • Li Z; Organ Transplantation Center, the First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan Province, People's Republic of China.
  • Ma J; Department of Nephrology, the First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan Province, People's Republic of China.
J Inflamm Res ; 14: 6871-6891, 2021.
Article em En | MEDLINE | ID: mdl-34934337
ABSTRACT

BACKGROUND:

The prognosis of diabetic nephropathy is poor, and early diagnosis of diabetic nephropathy is challenging. Fortunately, searching for DN-specific markers based on machine algorithms can facilitate diagnosis.

METHODS:

xCell model and CIBERSORT algorithm were used to analyze the relationship between immune cells and DN, and WGCNA analysis was used to evaluate the regulatory relationship between hypoxia gene and DN-related immune cells. Lasso regression and ROC regression were used to detect the ability of core genes to diagnose DN, the PPI network of core genes with high diagnostic ability was constructed, and the interaction between core genes was discussed.

RESULTS:

There were 519 differentially expressed genes in renal tubules and 493 differentially expressed genes in glomeruli. Immune and hypoxia responses are involved in the regulation of renal glomerulus and renal tubules. We found that there are 16 hypoxia-related genes involved in the regulation of hypoxia response. Seventeen hypoxia-related genes in renal tubules are involved in regulating hypoxia response on the proteasome signal pathway. Lasso and ROC regression were used to screen anoxic core genes. Further, we found that TGFBR3, APOLD1, CPEB1, and KDR are important in diagnosing DN glomerulopathy, respectively, PSMB8, PSMB9, RHOA, VCAM1, and CDKN1B, which have high specificity for renal tubulopathy in DN.

CONCLUSION:

Hypoxia and immune reactions are involved in the progression of DN. T cells are the central immune response cells. TGFBR3, APOLD1, CPEB1, and KDR have higher diagnostic accuracy in the diagnosis of DN. PSMB8, PSMB9, RHOA, VCAM1, and CDKN1B have higher diagnostic accuracy in DN diagnosis.
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Screening_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Screening_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article