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BMC Cancer. 2019 Jan 8;19(1):34. doi: 10.1186/s12885-018-5206-8.

Development of a miRNA-seq based prognostic signature in lung adenocarcinoma.

Author information

1
Bioinformatics and Biostatistics Cores, Department of Complementary and Integrative Medicine, University of Hawaii John A. Burns School of Medicine, Honolulu, HI, 96813, USA. cksiri@hawaii.edu.
2
Bioinformatics and Biostatistics Cores, Department of Complementary and Integrative Medicine, University of Hawaii John A. Burns School of Medicine, Honolulu, HI, 96813, USA.
3
Bioinformatics and Biostatistics Cores, Department of Complementary and Integrative Medicine, University of Hawaii John A. Burns School of Medicine, Honolulu, HI, 96813, USA. dengy@hawaii.edu.

Abstract

BACKGROUND:

We utilized miRNAs expression and clinical data to develop a prognostic signature for patients with lung adenocarcinoma, with respect to their overall survival, to identify high-risk subjects based on their miRNA genomic profile.

METHODS:

MiRNA expressions based on miRNA sequencing and clinical data of lung adenocarcinoma patients (n = 479) from the Cancer Genome Atlas were randomly partitioned into non-overlapping Model (n = 320) and Test (n = 159) sets, respectively, for model estimation and validation.

RESULTS:

Among the ten miRNAs identified using the univariate Cox analysis, six from miR-8, miR-181, miR-326, miR-375, miR-99a, and miR-10, families showed improvement of the overall survival chance, while two miRNAs from miR-582 and miR-584 families showed a worsening of survival chances. The final prognostic signature was developed with five miRNAs-miR-375, miR-582-3p, miR-326, miR-181c-5p, and miR-99a-5p-utilizing a stepwise variable selection procedure. Using the KEGG pathway analysis, we found potential evidence supporting their significance in multiple cancer pathways, including non-small cell lung cancer. We defined two risk groups with a score calculated using the Cox regression coefficients. The five-year survival rates for the low-risk group was approximately 48.76% (95% CI = (36.15, 63.93)); however, it was as low as 7.50% (95% CI = (2.34, 24.01)) for the high-risk group. Furthermore, we demonstrated the effect of the genomic profile using the miRNA signature, quantifying survival rates for hypothetical subjects in different pathological stages of cancer.

CONCLUSIONS:

The proposed prognostic signature can be used as a reliable tool for identifying high-risk subjects regarding survival based on their miRNA genomic profile.

KEYWORDS:

Lung adenocarcinoma; MiRNA; Prognostic signature; Survival

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