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

• Main Authors: Chathura Siriwardhana, Vedbar S. Khadka, John J. Chen, Youping Deng
• Format: Article
• Language: English
• Published: BMC 2019-01-01
• Series: BMC Cancer
• Subjects: Lung adenocarcinoma; MiRNA; Prognostic signature; Survival
• Online Access: http://link.springer.com/article/10.1186/s12885-018-5206-8

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.