The m6A-Related Long Noncoding RNA Signature Predicts Prognosis and Indicates Tumor Immune Infiltration in Ovarian Cancer

Background: OV is the most lethal gynecological malignancy. M6A and lncRNAs have a great impact on OV development and patient immunotherapy response. In this paper, we decided to establish a reliable signature of mRLs. Method: The lncRNAs associated with m6A in OV were analyzed and obtained by co-ex...

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Bibliographic Details
Published in:Cancers 2022-08, Vol.14 (16), p.4056
Main Authors: Geng, Rui, Chen, Tian, Zhong, Zihang, Ni, Senmiao, Bai, Jianling, Liu, Jinhui
Format: Article
Language:English
Subjects:
Colorectal cancer
Gene expression
Genetic aspects
Health aspects
Immunomodulation
Immunotherapy
Infiltration
Malignancy
Medical prognosis
Metastases
Metastasis
N6-methyladenosine
Non-coding RNA
Ovarian cancer
Pathogenesis
Patients
Prognosis
Regression analysis
RNA
Therapeutic applications
Therapeutic targets
Tumor microenvironment
Tumorigenesis
Tumors
Variance analysis
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Summary:Background: OV is the most lethal gynecological malignancy. M6A and lncRNAs have a great impact on OV development and patient immunotherapy response. In this paper, we decided to establish a reliable signature of mRLs. Method: The lncRNAs associated with m6A in OV were analyzed and obtained by co-expression analysis of the TCGA-OV database. Univariate, LASSO and multivariate Cox regression analyses were employed to establish the model of mRLs. K-M analysis, PCA, GSEA and nomogram based on the TCGA-OV and GEO database were conducted to prove the predictive value and independence of the model. The underlying relationship between the model and TME and cancer stemness properties were further investigated through immune feature comparison, consensus clustering analysis and pan-cancer analysis. Results: A prognostic signature comprising four mRLs, WAC-AS1, LINC00997, DNM3OS and FOXN3-AS1, was constructed and verified for OV according to the TCGA and GEO database. The expressions of the four mRLs were confirmed by qRT-PCR in clinical samples. Applying this signature, one can identify patients more effectively. The samples were divided into two clusters, and the clusters had different overall survival rates, clinical features and tumor microenvironments. Finally, pan-cancer analysis further demonstrated that the four mRLs were significantly related to immune infiltration, TME and cancer stemness properties in various cancer types. Conclusions: This study provided an accurate prognostic signature for patients with OV and elucidated the potential mechanism of the mRLs in immune modulation and treatment response, giving new insights into identifying new therapeutic targets.
ISSN:2072-6694
2072-6694
DOI:10.3390/cancers14164056