Identification of 15 lncRNAs Signature for Predicting Survival Benefit of Advanced Melanoma Patients Treated with Anti-PD-1 Monotherapy.
Antibodies, Monoclonal, Humanized
/ therapeutic use
Antineoplastic Agents, Immunological
/ therapeutic use
Biomarkers, Tumor
/ genetics
Female
Follow-Up Studies
Humans
Male
Melanoma
/ drug therapy
Middle Aged
Prognosis
Programmed Cell Death 1 Receptor
/ antagonists & inhibitors
RNA, Long Noncoding
/ genetics
Retrospective Studies
Survival Rate
LASSO
PD-1
WGCNA
advanced melanoma
immune checkpoint inhibitor
lncRNA
predictor
survival benefit
Journal
Cells
ISSN: 2073-4409
Titre abrégé: Cells
Pays: Switzerland
ID NLM: 101600052
Informations de publication
Date de publication:
22 04 2021
22 04 2021
Historique:
received:
01
03
2021
revised:
14
04
2021
accepted:
17
04
2021
entrez:
30
4
2021
pubmed:
1
5
2021
medline:
9
11
2021
Statut:
epublish
Résumé
The blockade of programmed cell death protein 1 (PD-1) as monotherapy has been widely used in melanoma, but to identify melanoma patients with survival benefit from anti-PD-1 monotherapy is still a big challenge. There is an urgent need for prognostic signatures improving the prediction of immunotherapy responses of these patients. We analyzed transcriptomic data of pre-treatment tumor biopsies and clinical profiles in advanced melanoma patients receiving only anti-PD-1 monotherapy (nivolumab or pembrolizumab) from the PRJNA356761 and PRJEB23709 data sets as the training and validation cohort, respectively. Weighted gene co-expression network analysis was used to identify the key module, then least absolute shrinkage and selection operator was conducted to determine prognostic-related long noncoding RNAs (lncRNAs). Subsequently, the differentially expressed genes between different clusters were identified, and their function and pathway annotation were performed. In this investigation, 92 melanoma patients with complete survival information (51 from training cohort and 41 from validation cohort) were included in our analyses. We initiallyidentified the key module (skyblue) by weighted gene co-expression network analysis, and then identified a 15 predictive lncRNAs (AC010904.2, LINC01126, AC012360.1, AC024933.1, AL442128.2, AC022211.4, AC022211.2, AC127496.5, NARF-AS1, AP000919.3, AP005329.2, AC023983.1, AC023983.2, AC139100.1, and AC012615.4) signature in melanoma patients treated with anti-PD-1 monotherapy by least absolute shrinkage and selection operator in the training cohort. These results were then validated in the validation cohort. Finally, enrichment analysis showed that the functions of differentially expressed genes between two consensus clusters were mainly related to the immune process and treatment. In summary, the 15 lncRNAs signature is a novel effective predictor for prognosis in advanced melanoma patients treated with anti-PD-1 monotherapy.
Identifiants
pubmed: 33922038
pii: cells10050977
doi: 10.3390/cells10050977
pmc: PMC8143567
pii:
doi:
Substances chimiques
Antibodies, Monoclonal, Humanized
0
Antineoplastic Agents, Immunological
0
Biomarkers, Tumor
0
PDCD1 protein, human
0
Programmed Cell Death 1 Receptor
0
RNA, Long Noncoding
0
pembrolizumab
DPT0O3T46P
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Research Programs of Science and Technology Commission Foundation of Zunyi City
ID : HZ2019-11, HZ2019-07
Organisme : National Natural Science Foundation of China
ID : 81660512
Organisme : Research Programs of Health Commission Foundation of Guizhou Province
ID : gzwjkj2019-1-073, gzwjkj2019-1-172
Organisme : European Commission
ID : MSCA-ITN-ETN; HYPERBOOST; 955625
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