A novel LncRNA-based prognostic score reveals TP53-dependent subtype of lung adenocarcinoma with poor survival

Abstract

The prognostic signatures play an essential role in the era of personalised therapy for cancer patients including lung adenocarcinoma (LUAD). Long noncoding RNA (LncRNA), a relatively novel class of RNA, has shown to play a crucial role in all the areas of cancer biology. Here, we developed and validated a robust LncRNA-based prognostic signature for LUAD patients using three different cohorts. In the discovery cohort, four LncRNAs were identified with 10% false discovery rate and a hazard ratio of $>$ 10 using univariate Cox regression analysis. A risk score, generated from the four LncRNAs’ expression, was found to be a significant predictor of survival in the discovery and validation cohort (p = 9.97 $\times 10^{−8}$ and 1.41$\times 10^{-3}$, respectively). Further optimisation of four LncRNAs signature in the validation cohort, generated a three LncRNAs prognostic score (LPS), which was found to be an independent predictor of survival in both the cohorts ( p = 1.00 $\times 10 ^{−6}$ and 7.27 $\times 10^{−4}$, respectively). The LPS also significantly divided survival in clinically important subsets, including Stage I ( p = 9.00 $\times 10^{−4}$ and 4.40 $\times 10^{−2}$, respectively), KRAS wild-type (WT), KRAS mutant ( p = 4.00 $\times 10^{−3}$ and 4.30 $\times 10^{−2}$, respectively) and EGFR WT ( p = 2.00 $\times 10^{−4}$). In multivariate analysis LPS outperformed, eight previous prognosticators. Further, individual members of LPS showed a significant correlation with survival in microarray data sets. Mutation analysis showed that high-LPS patients have a higher mutation rate and inactivation of the TP53 pathway. In summary, we identified and validated a novel LncRNA signature LPS for LUAD.

Seema Khadirnaikar

Research Scholar

My research interests include application of supervised and unupervised machine learning techniques to precision medicine.