Abstract
Simple SummaryAlthough the biological function of lncRNAs has not been fully elucidated, we know that the aberrant expression of lncRNAs can drive the cancer phenotype. Therefore, a growing area of research is focusing on lncRNAs as putative diagnostic biomarkers and therapeutic targets. The aim of the study was the appraisal of the diagnostic value of 14 differentially expressed lncRNA in the early stages of NSCLC. We established two classifiers. The first recognized cancerous from noncancerous tissues, the second successfully discriminated NSCLC subtypes (LUAD vs. LUSC). Our results indicate that the panel of 14 lncRNAs can be a promising tool to support a routine histopathological diagnosis of NSCLC.LncRNAs have arisen as new players in the world of non-coding RNA. Disrupted expression of these molecules can be tightly linked to the onset, promotion and progression of cancer. The present study estimated the usefulness of 14 lncRNAs (HAGLR, ADAMTS9-AS2, LINC00261, MCM3AP-AS1, TP53TG1, C14orf132, LINC00968, LINC00312, TP73-AS1, LOC344887, LINC00673, SOX2-OT, AFAP1-AS1, LOC730101) for early detection of non-small-cell lung cancer (NSCLC). The total RNA was isolated from paired fresh-frozen cancerous and noncancerous lung tissue from 92 NSCLC patients diagnosed with either adenocarcinoma (LUAD) or lung squamous cell carcinoma (LUSC). The expression level of lncRNAs was evaluated by a quantitative real-time PCR (qPCR). Based on Ct and delta Ct values, logistic regression and gradient boosting decision tree classifiers were built. The latter is a novel, advanced machine learning algorithm with great potential in medical science. The established predictive models showed that a set of 14 lncRNAs accurately discriminates cancerous from noncancerous lung tissues (AUC value of 0.98 ± 0.01) and NSCLC subtypes (AUC value of 0.84 ± 0.09), although the expression of a few molecules was statistically insignificant (SOX2-OT, AFAP1-AS1 and LOC730101 for tumor vs. normal tissue; and TP53TG1, C14orf132, LINC00968 and LOC730101 for LUAD vs. LUSC). However for subtypes discrimination, the simplified logistic regression model based on the four variables (delta Ct AFAP1-AS1, Ct SOX2-OT, Ct LINC00261, and delta Ct LINC00673) had even stronger diagnostic potential than the original one (AUC value of 0.88 ± 0.07). Our results demonstrate that the 14 lncRNA signature can be an auxiliary tool to endorse and complement the histological diagnosis of non-small-cell lung cancer.
Highlights
Comprehensive analysis of the human genome unraveled far more transcriptionally active regions than had been previously anticipated
To provide novel insight to precision diagnosis, we evaluated the expression of 14 long non-coding RNA (lncRNA) (HAGLR, ADAMTS9-AS2, LINC00261, MCM3AP-AS1, TP53TG1, C14orf132, LINC00968, LINC00312, TP73-AS1, LOC344887, LINC00673, SOX2-OT, AFAP1-AS1, and LOC730101)
Taking into account all cases of non-small-cell lung cancer (NSCLC), we found that 9 out of 14 lncRNAs (HAGLR, ADAMTS9-AS2, LINC00261, MCM3AP-AS1, TP53TG1, C14orf132, LINC00968, LINC00312, TP73-AS1) were downregulated in the tumor tissue compared to normal lung tissue; two lncRNAs (LOC344887and LINC00673) were upregulated and three lncRNAs (SOX2-OT, AFAP1-AS1, LOC730101) showed statistically insignificant differences (Table 3, Figure 1A)
Summary
Comprehensive analysis of the human genome unraveled far more transcriptionally active regions than had been previously anticipated. The vast majority of the RNAs (over 80%) could neither be described as protein-coding nor considered “transcriptional noise”. This predominant bulk of the transcriptome has been named non-coding RNA (ncRNA) and with its regulatory capacities, has emerged as one of the frontline molecular players in a variety of biological phenomena. Non-coding RNAs are a heterogeneous population, including short ncRNA, middle-size ncRNA and long ncRNA. Researchers’ attention has focused mainly on short non-coding RNA (miRNA) but long non-coding RNA (lncRNA) has gradually gained importance [1]. According to LNCipedia, 56,946 lncRNA genes encoding 127,802 transcripts have been annotated [2]. LncRNAs can interact with transcription factors and RNA-binding proteins or even create molecular scaffolds to recruit different effectors [3,4]
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