The Necroptotic Process-Related Signature Predicts Immune Infiltration and Drug Sensitivity in Kidney Renal Papillary Cell Carcinoma.
Necroptosis
drug sensitivity
gene signature
immune infiltration
kidney renal papillary cell carcinoma.
prognosis
Journal
Current cancer drug targets
ISSN: 1873-5576
Titre abrégé: Curr Cancer Drug Targets
Pays: Netherlands
ID NLM: 101094211
Informations de publication
Date de publication:
08 Apr 2024
08 Apr 2024
Historique:
received:
10
11
2023
revised:
03
03
2024
accepted:
12
03
2024
medline:
15
4
2024
pubmed:
15
4
2024
entrez:
15
4
2024
Statut:
aheadofprint
Résumé
It remains controversial whether the current subtypes of kidney renal papillary cell carcinoma (KIRP) can be used to predict the prognosis independently. This observational study aimed to identify a risk signature based on necroptotic pro-cess-related genes (NPRGs) in KIRP. In the training cohort, LASSO regression was applied to construct the risk signature from 158 NPRGs, followed by the analysis of Overall Survival (OS) using the Kaplan-Meier method. The signature accuracy was evaluated by the Receiver Operating Characteristic (ROC) curve, which was further validated by the test cohort. Wilcoxon test was used to compare the expressions of immune-related genes, neoantigen genes, and immune infiltration between differ-ent risk groups, while the correlation test was performed between NPRGs expressions and drug sensitivity. Gene set enrichment analysis was used to investigate the NPRGs' signature's biologi-cal functions. We finally screened out 4-NPRGs (BIRC3, CAMK2B, PYGM, and TRADD) for con-structing the risk signature with the area under the ROC curve (AUC) reaching about 0.8. The risk score could be used as an independent OS predictor. Consistent with the enriched signaling, the NPRGs signature was found to be closely associated with neoantigen, immune cell infiltration, and immune-related functions. Based on NPRGs expressions, we also predicted multiple drugs potentially sensitive or resistant to treatment. The novel 4-NPRGs risk signature can predict the prognosis, immune infiltration, and therapeutic sensitivity of KIRP.
Sections du résumé
BACKGROUND
BACKGROUND
It remains controversial whether the current subtypes of kidney renal papillary cell carcinoma (KIRP) can be used to predict the prognosis independently.
OBJECTIVE
OBJECTIVE
This observational study aimed to identify a risk signature based on necroptotic pro-cess-related genes (NPRGs) in KIRP.
METHODS
METHODS
In the training cohort, LASSO regression was applied to construct the risk signature from 158 NPRGs, followed by the analysis of Overall Survival (OS) using the Kaplan-Meier method. The signature accuracy was evaluated by the Receiver Operating Characteristic (ROC) curve, which was further validated by the test cohort. Wilcoxon test was used to compare the expressions of immune-related genes, neoantigen genes, and immune infiltration between differ-ent risk groups, while the correlation test was performed between NPRGs expressions and drug sensitivity. Gene set enrichment analysis was used to investigate the NPRGs' signature's biologi-cal functions.
RESULTS
RESULTS
We finally screened out 4-NPRGs (BIRC3, CAMK2B, PYGM, and TRADD) for con-structing the risk signature with the area under the ROC curve (AUC) reaching about 0.8. The risk score could be used as an independent OS predictor. Consistent with the enriched signaling, the NPRGs signature was found to be closely associated with neoantigen, immune cell infiltration, and immune-related functions. Based on NPRGs expressions, we also predicted multiple drugs potentially sensitive or resistant to treatment.
CONCLUSION
CONCLUSIONS
The novel 4-NPRGs risk signature can predict the prognosis, immune infiltration, and therapeutic sensitivity of KIRP.
Identifiants
pubmed: 38616744
pii: CCDT-EPUB-139660
doi: 10.2174/0115680096286503240321040556
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.