Identification and bioinformatic characterization of a serum miRNA signature for early detection of laryngeal squamous cell carcinoma

Abstract

Background: The growing understanding of cancer biology and the establishment of new treatment modalities has not yielded the expected results in terms of survival for Laryngeal Squamous Cell Cancer (LSCC). Early diagnosis, as well as prompt identification of patients with high risk of relapse would ensure greater chance of therapeutic success. However, this goal remains a challenge due to the absence of specific biomarkers for this neoplasm.

Methods: Serum samples from 45 LSCC patients and 23 healthy donors were collected for miRNA expression profiling by TaqMan Array analysis. Additional 20 patients and 42 healthy volunteers were included for the validation set, reaching an equal number of clinical samples for each group. The potential diagnostic ability of the such identified three-miRNA signature was confirmed by ROC analysis. Moreover, each miRNA was analyzed for the possible correlation with HNSCC patients' survival and TNM status by online databases Kaplan-Meier (KM) plotter and OncomiR. In silico analysis of common candidate targets and their network relevance to predict shared biological functions was finally performed by PANTHER and GeneMANIA software.

Results: We characterized serum miRNA profile of LSCC patients identifying a novel molecular signature, including miR-223, miR-93 and miR-532, as circulating marker endowed with high selectivity and specificity. The oncogenic effect and the prognostic significance of each miRNA was investigated by bioinformatic analysis, denoting significant correlation with OS. To analyse the molecular basis underlying the pro-tumorigenic role of the signature, we focused on the simultaneously regulated gene targets-IL6ST, GTDC1, MAP1B, CPEB3, PRKACB, NFIB, PURB, ATP2B1, ZNF148, PSD3, TBC1D15, PURA, KLF12-found by prediction tools and deepened for their functional role by pathway enrichment analysis. The results showed the involvement of 7 different biological processes, among which inflammation, proliferation, migration, apoptosis and angiogenesis.

Conclusions: In conclusion, we have identified a possible miRNA signature for early LSCC diagnosis and we assumed that miR-93, miR-223 and miR-532 could orchestrate the regulation of multiple cancer-related processes. These findings encourage the possibility to deepen the molecular mechanisms underlying their oncogenic role, for the desirable development of novel therapeutic opportunities based on the use of short single-stranded oligonucleotides acting as non-coding RNA antagonists in cancer.

Keywords: Cancer; Gene targets; Laryngeal Squamous Cell Cancer; Overall survival; ROC curve; miR-223; miR-532; miR-93; miRNA; miRNA signature.

Fig. 1

A Serum miRNAs global expression pattern among N⁺ or N^─ LSCC samples compared to healthy donor groups. The hierarchical clustering heatmap summarizes the 81 detectable serum miRNA expression levels, normalized by miR-222 as endogenous control. B Serum miR-93, miR-223, and miR-532 expression levels in LSCC patients. The expression levels of serum miR-93, miR-223, and miR-532 in LSCC patients (n = 65) compared with healthy donors (n = 65), validated by qRT-PCR. Exogenous cel-miR-39 was used as normalizer. The p-value was calculated by t-test ****p < 0.001. C Serum miR-93, miR-223, and miR-532 expression levels for each of 65 LSCC patients. The heatmap shows the miRNA expression levels, normalized on the exogenous cel-miR-39, for each individual patient

Fig. 2

ROC analysis for serum miR-93, miR-223, and miR-532 and cumulative ROC curve. Receiver operating characteristic (ROC) curve analyses demonstrated the diagnostic ability of miR-93 (A), miR-223 (B) and miR-532(C) in LSCC. AUC, sensitivity and specificity was examined comparing deltaCT between LSCC (n = 65) and healthy cohort (n = 65). Cumulative ROC curve analysis (D) demonstrated the diagnostic ability of the simultaneous detection of miR-93 (A), miR-223 (B) and miR-532(C) in LSCC

Fig. 3

Heat map of the hierarchical clustering of the patient subgroups by the levels of miR-93, miR-223 and miR-532 for LSCC. The hierarchical clustering heatmap shows no significant correlation between miRNAs and the clinical information

Fig. 4

Survival analysis in HNSCC patients. Kaplan–Meier curves displaying the estimated survival probability for 2 different groups of HNSCC patients. A Patients either with low or high miR-93 expression levels, B patients either with low or high miR-223 expression levels, C patients either with low or high miR-532 expression levels. Hazard ratio (HR) and 95% confidence were calculated automatically by website tool. The values of each group are shown as the mean ± SD. p-value < 0.05 was regarded as statistically significant by using Log-rank test

Fig. 5

Survival analysis in miR-93 high- and low-expressing HNSCC groups. Kaplan–Meier curves displaying the estimated survival probability for 2 different groups of HNSCC patients in different physiological or clinical conditions, such as (A) tumor stage, B gender and (C) tumor grade. Hazard ratio (HR) and 95% confidence were calculated automatically by website tool. The values of each group are shown as the mean ± SD. p-value < 0.05 was regarded as statistically significant by using Log-rank test

Fig. 6

Survival analysis in miR-223 high- and low-expressing HNSCC groups. Kaplan–Meier curves displaying the estimated survival probability for 2 different groups of HNSCC patients in different physiological or clinical conditions, such as (A) tumor stage, B gender and (C) tumor grade. Hazard ratio (HR) and 95% confidence were calculated automatically by website tool. The values of each group are shown as the mean ± SD. p-value < 0.05 was regarded as statistically significant by using Log-rank test

Fig. 7

Survival analysis in miR-532 high- and low-expressing HNSCC groups. Kaplan–Meier curves displaying the estimated survival probability for 2 different groups of HNSCC patients in different physiological or clinical conditions, such as (A) tumor stage, B gender and (C) tumor grade. Hazard ratio (HR) and 95% confidence were calculated automatically by website tool. The values of each group are shown as the mean ± SD. p-value < 0.05 was regarded as statistically significant by using Log-rank test

Fig. 8

Venn Diagram of the molecular targets cross-regulated by miR-93, miR-223 and miR-532. The Venn plot highlights 13 common targets between miR-93, miR-223 and miR-532, 15 between miR-93 and miR-223, 3 for miR-93 and miR-532 and 4 for miR-223 and miR-532, respectively. The 13 genes shared by all three miRNAs are zoomed

Fig. 9

In silico analysis of common candidate targets performed by PANTHER software (v16.0). A Molecular common target classification. The bar graph depicts the stratification of the 13 co-regulated gene targets in protein classes. 5 genes are classified as gene-specific trascriptional regulators, while 2 belong to the protein-binding activity modulators. Each of the remaining 6 genes represents a single category. B Involvement of the common molecular targets in biological processes. The bar graph depicts the pathways involving the 13 gene targets shared by miR-93, miR-223 and miR-532. The “biological regulation”, “cellular process” and “metabolic process”are the top 3 groups of biological processes shared by the 13 co-regulated genes. C Main molecular functions of the cross-regulated gene targets. The bar graph depicts the molecular functions of the 13 gene targets shared by miR-93, miR-223 and miR-532. 10 genes share the binding molecular function and 5 genes share the transcription regulator activity

Fig. 10

Protein interactions among gene targets. Network image showed 33 nodes (genes): 13 gene targets shared by miR-93, miR-223 and miR-532 and 20 related genes, and underlined possible co-expressions, co-localizations, physical interactions, shared protein domains and functions among them

Fig. 11

Genome-wide overview of the pathway analysis. Reactome pathways are arranged in a hierarchy. The center of each of the circular “bursts” is the root of one toplevel pathway. Each step away from the center represents the next level lower in the pathway hierarchy. The color code denotes over-representation of that pathway; light grey signifies pathways which are not significantly over-represented