Ferroptosis-Related lncRNA to Predict the Clinical Outcomes and Molecular Characteristics of Kidney Renal Papillary Cell Carcinoma.

KIRP clinical outcome ferroptosis lncRNA risk model

Journal

Current issues in molecular biology
ISSN: 1467-3045
Titre abrégé: Curr Issues Mol Biol
Pays: Switzerland
ID NLM: 100931761

Informations de publication

Date de publication:
29 Feb 2024
Historique:
received: 14 11 2023
revised: 21 12 2023
accepted: 27 02 2024
medline: 27 3 2024
pubmed: 27 3 2024
entrez: 27 3 2024
Statut: epublish

Résumé

Kidney renal papillary cell carcinoma (KIRP) is a highly heterogeneous type of kidney cancer, resulting in limited effective prognostic targets for KIRP patients. Long non-coding RNAs (lncRNAs) have emerged as crucial regulators in the regulation of ferroptosis and iron metabolism, making them potential targets for the treatment and prognosis of KIRP. In this study, we constructed a ferroptosis-related lncRNA risk score model (FRM) based on the TCGA-KIRP dataset, which represents a novel subtype of KIRP not previously reported. The model demonstrated promising diagnostic accuracy and holds potential for clinical translation. We observed significant differences in metabolic activities, immune microenvironment, mutation landscape, ferroptosis sensitivity, and drug sensitivity between different risk groups. The high-risk groups exhibit significantly higher fractions of cancer-associated fibroblasts (CAFs), hematopoietic stem cells (HSC), and pericytes. Drugs (IC50) analysis provided a range of medication options based on different FRM typing. Additionally, we employed single-cell transcriptomics to further analyze the impact of immune invasion on the occurrence and development of KIRP. Overall, we have developed an accurate prognostic model based on the expression patterns of ferroptosis-related lncRNAs for KIRP. This model has the potential to contribute to the evaluation of patient prognosis, molecular characteristics, and treatment modalities, and can be further translated into clinical applications.

Identifiants

pubmed: 38534739
pii: cimb46030123
doi: 10.3390/cimb46030123
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1886-1903

Subventions

Organisme : The major scientific and technological research project of Tianjin
ID : No. TSBICIP-KJGG-010
Organisme : The central government guides local science and technology development projects
ID : No. 2019ZYYD 032
Organisme : The Open Funding Project of the State Key Laboratory of Biocatalysis and Enzyme Engineering
ID : No. SKLBEE2021018
Organisme : Wuhan Polytechnic University
ID : WHPU2023Y20

Auteurs

Yubo Gong (Y)

School of Life Science and Technology, Wuhan Polytechnic University, Wuhan 430023, China.

Chenchen Zhang (C)

School of Life Science and Technology, Wuhan Polytechnic University, Wuhan 430023, China.

Hao Li (H)

School of Life Science and Technology, Wuhan Polytechnic University, Wuhan 430023, China.

Xiaojie Yu (X)

School of Life Science and Technology, Wuhan Polytechnic University, Wuhan 430023, China.

Yuejia Li (Y)

School of Life Science and Technology, Wuhan Polytechnic University, Wuhan 430023, China.

Zhiguo Liu (Z)

School of Life Science and Technology, Wuhan Polytechnic University, Wuhan 430023, China.

Ruyi He (R)

School of Life Science and Technology, Wuhan Polytechnic University, Wuhan 430023, China.
State Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Sciences, Hubei University, Wuhan 430062, China.

Classifications MeSH