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Shi, R.* ; Bao, X.* ; Unger, K. ; Sun, J.* ; Lu, S.* ; Manapov, F.* ; Wang, X.* ; Belka, C.* ; Li, M.*

Identification and validation of hypoxia-derived gene signatures to predict clinical outcomes and therapeutic responses in stage I lung adenocarcinoma patients.

Theranostics 11, 5061-5076 (2021)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Rationale: The current tumour-node-metastasis (TNM) staging system is insufficient for precise treatment decision-making and accurate survival prediction for patients with stage I lung adenocarcinoma (LUAD). Therefore, more reliable biomarkers are urgently needed to identify the high-risk subset in stage I patients to guide adjuvant therapy. Methods: This study retrospectively analysed the transcriptome profiles and clinical parameters of 1,400 stage I LUAD patients from 14 public datasets, including 13 microarray datasets from different platforms and 1 RNA-Seq dataset from The Cancer Genome Atlas (TCGA). A series of bioinformatic and machine learning approaches were combined to establish hypoxia-derived signatures to predict overall survival (OS) and immune checkpoint blockade (ICB) therapy response in stage I patients. In addition, enriched pathways, genomic and copy number alterations were analysed in different risk subgroups and compared to each other. Results: Among various hallmarks of cancer, hypoxia was identified as a dominant risk factor for overall survival in stage I LUAD patients. The hypoxia-related prognostic risk score (HPRS) exhibited more powerful capacity of survival prediction compared to traditional clinicopathological features, and the hypoxia-related immunotherapeutic response score (HIRS) outperformed conventional biomarkers for ICB therapy. An integrated decision tree and nomogram were generated to optimize risk stratification and quantify risk assessment. Conclusions: In summary, the proposed hypoxia-derived signatures are promising biomarkers to predict clinical outcomes and therapeutic responses in stage I LUAD patients.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Clinical Outcomes ; Genomic Alterations ; Hypoxia ; Machine Learning ; Stage I Lung Adenocarcinoma
e-ISSN 1838-7640
Zeitschrift Theranostics
Quellenangaben Band: 11, Heft: 10, Seiten: 5061-5076 Artikelnummer: , Supplement: ,
Verlag Ivyspring
Verlagsort Po Box 4546, Lake Haven, Nsw 2263, Australia
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Translational Metabolic Oncology (TMO)
Förderungen China Scholarship Council (CSC)