Introduction

Oral squamous cell carcinoma (OSCC) is the most common type of oral cancer, accounting for approximately 350,000–400,000 cases per year. OSCC is twice as common in male than female due to risk factors, such as tobacco, alcohol and HPV. According to statistics, OSCC is the 6th and 8th particularly for incidence and mortality in both men, respectively1. Due to the high occurrence of secondary carcinoma and tumor heterogeneity, OSCC is often diagnosed in an advanced state with a poor prognosis2. Even though most cases of OSCC could be managed with complete surgical resection alone or a combination of ionizing radiation or chemo-radiation therapy, a certain proportion of advanced OSCCs remain unresponsive to treatment or exhibit loco-regional recurrence, resulting in a mortality rate of 50%3,4.

In the diagnosis and prevention of OSCC, emphasis is placed on identifying potential malignant lesions of the oral mucosa and local diseases that promote chronic inflammation, mainly relying on objective clinical examinations and surgical biopsy5,6. Although surgical biopsy is the gold standard for the diagnosis of OSCC, it is somewhat invasive and can sometimes be harmful to patients7. Moreover, conventional biopsy is temporally and spatially limited and often provides a brief snapshot of a single region of a heterogeneous tumor8. Therefore, it is crucial to find promising non-invasive biomarkers for monitoring or patient surveillance and further illuminate the pathogenesis of OSCC regarding tumor behavior at the molecular level. Blood samples are relatively easy to collect in a minimally invasive manner, and increasingly many recent studies have suggested that circulating microRNAs (miRNAs) are promising as potential biomarkers for disease diagnosis and monitoring9,10.

miRNAs are small, non-coding RNAs of 18–25 nucleotides in length that have been linked to essentially all known pathological and physiological processes, including cancer. Recent studies have reported that miRNAs can not only be utilized for diagnosis and prognosis, but also play integral and convoluted roles in the regulatory network of cancer. miRNA have been reported as diagnostic biomarkers for many cancers, including head and neck cancer11,12. However, the approach of using tissue-derived miRNA in surveillance or prognosis is commonly invasive in nature which may impede the screening. Furthermore, previous studies have demonstrated that miRNA can be quite stable in serum due to its protection from endogenous RNase activity and that it is readily detected by various assays13,14, presents the possibility to exploiting circulating miRNAs as biomarkers for early-stage cancer. Therefore, serum miRNA panel signatures have recently been identified as promising candidate biomarkers for liquid biopsy. However, studies have rarely examined circulating miRNA expression in patients with OSCC, leading to little noticeable and reliable signatures.

The aim of this study was to explore and validate the possibility that circulating miRNAs could overcome the limitations of tissue biopsy and act as potential biomarkers in liquid biopsy for the early diagnosis and dynamic monitoring of disease progression in OSCC patients.

Materials and methods

Patient and sample collection

Serum samples from 27 patients with OSCC and 21 age- and sex-matched healthy individuals were obtained at the Chungnam National University Hospital (CNUH) (Daejeon, Republic of Korea), between January 2017 and December 2019. The clinical information of patients with OSCC were summarized in Table S1. Tumor tissues and adjacent non-tumor tissues were collected from 7 patients with OSCC. All patients with OSCC were enrolled at the initial diagnosis, and the pathological diagnoses were subsequently confirmed. The study participant provided an informed consent form before participating. The Institutional Review Board of CNUH approved this study (CNUH 2019-07-041). All methods were performed in accordance with the Institutional Review Board of CNUH guideline and regulation.

Next-generation sequencing and analysis

Serum samples from 4 OSCC patients and 6 age- and sex-matched healthy controls were selected from CNUH cohort for next-generation sequencing10. The clinical information of patients with OSCC for NGS were presented in Table S2. Whole-transcriptome next-generation sequencing was performed by Macrogen Inc. (Seoul, Republic of Korea). Briefly, extracted RNA samples were used to prepare small RNA libraries using SMARTer smRNA-Seq Kit protocol and sequenced using a HiSeq 2500 sequencer (Illumina, San Diego, CA, USA), following the HiSeq 2500 System User Guide Document #15035786 v02 HCS 2.2.70 protocol. After sequencing, the raw sequence reads were filtered based on quality determined by the phred quality score at each cycle (Table S3). Both the trimmed reads and non-adapter reads as processed reads were used, to do analyzing long target (≧ 50 bp).The processed reads were gathered forming a unique cluster. In order to eliminate the effect of large amounts of ribosomal RNA (rRNA) from this study, the read was aligned to the rRNA sequence. rRNA removed reads were sequentially aligned to reference genome (UCSC Homo sapiens reference genome (GRCh37/hg19)), miRBase v21 and non-coding RNA database, RNAcentral 10.0 to classify known miRNAs and other type of RNA such as tRNA, snRNA, snoRNA etc. Novel miRNA prediction was performed by miRDeep2. The read counts for each miRNA were extracted from mapped miRNAs, differentially expressed miRNAs (DEmiRNAs) were determined through comparing across conditions each miRNA using statistical methods. Detailed work flow of sequencing and analysis were additionally described in the supplementary material. Figure S1 represents the small RNA composition of each sample.

Bioinformatics

Differentially expressed miRNAs (DEmiRNAs) between the evaluated groups were estimated using DESeq2 and edgeR15. The screening criteria were a fold change > 3 and p < 0.05. All genomic data of OSCC from The Cancer Genome Atlas (TCGA) were obtained from a specific portal (https://tcga-data.nci.nih.gov) and cancer browser (https://genome-cancer.ucsc.edu). To select miRNA differentially expressed between patients with OSCC and normal controls, false discovery rate-adjusted p values (< 0.05) were used to correct, using the Benjamin-Hochberg method. A volcano map, heatmap, and cluster analysis were conducted using an online analysis tool (https://www.chiplot.online/), a free online platform for data analysis and visualization. The target genes of miRNAs were predicted with the TargetScan 8.0 database (www.targetscan.org). Functional annotation was performed using the Database for Annotation, Visualization and Integrated Discovery (https://david.ncifcrf.gov/), a web-accessible tool for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. A network analysis of miRNA-mRNA interactions was carried out using Cytoscape (version 3.7.1), an open bioinformatics software.

RNA extraction and quantitative real time polymerase chain reaction (qRT-PCR)

Circulating miRNA was isolated from 200 μL of serum for RNA purification using miRNeasy serum/plasma kits (Qiagen, Hilden, Germany) according to the manufacturer’s protocol. Total RNA was extracted from tissue samples using the TRIzol reagent (Invitrogen, Waltham, MA, USA). The SYBR Green qRT-PCR assay was used for miRNA quantification. Total miRNA was used as the template for cDNA synthesis with miScript II RT Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. For miRNA analysis, qRT-PCR was performed using the miScript SYBR Green PCR kit (Qiagen, Hilden, Germany) with the manufacturer-provided miScript assays, using the universal primer and miRNA-specific forward primers with the 7500 system. The miRNA-specific primers were obtained from the miScript primer assays, the miRCURY LNA miRNA PCR Assay (Qiagen, Hilden, Germany), and Bioneer (Daejeon, Korea). All primer sequences used for qRT-PCR are listed in Table S4. miR-16 and miR-423-5p were used as references for serum miRNA analysis, and U6 small nuclear (RNU6) was used as the reference for the tissue expression of miRNA. At the end of the PCR cycles, melting curve analyses were performed. Each sample was run in triplicate for analysis. The 2 − ΔΔCT method was used to analyze the expression levels of miRNA.

Statistical analysis

All statistical analyses were performed using SPSS for Windows version 26 (IBM Corp., Armonk, NY, USA) and GraphPad Prism 8 (GraphPad Software, La Jolla, CA, USA). All experiments in the CNUH OSCC patient cohort were repeated three times. The data on the expression differences of miRNA between patients with OSCC and healthy controls, and between the serum samples and tissue samples from the same patients were analyzed using Mann–Whitney U test or independent t-test. The data on the expression differences of miRNA between the same patients before and after surgery were analyzed by paired t-test. Data are expressed as means ± SD. *p < 0.05, **p < 0.01, ***p < 0.001. Receiver operating characteristic (ROC) curves were used to analyze the diagnostic value of DEmiRNAs. A logistic regression model was constructed to determine the predicted probability of the combination of the 4 miRNAs. Pearson correlation coefficients were used to compare the miRNA levels in serum and tissue. The independent t-test was used to identify possible associations between miRNA concentrations and clinicopathological features of OSCC patients. The levels of miRNA in each group were presented as mean ± standard deviation (SD). All p values were two-sided, and a p value < 0.05 was considered statistically significant.

Results

Serum miRNA profiling to identify differential expression between healthy individuals and OSCC patients

To identify potential circulating miRNA biomarkers of OSCC, we measured serum expression levels in 4 patients with OSCC and 6 healthy controls by NGS (small RNA-sequencing). The overview of the research workflow is illustrated in Fig. S2. In the initial screening, we identified 272 DEmiRNAs between patients with OSCC and healthy controls based on the exactTest using DESeq2 and edgeR. We screened out 42 DEmiRNAs, including 26 up- and 16 down-regulated DEmiRNAs, based on 2 criteria: (1) compared to the healthy group, the DEmiRNAs in the OSCC group had at least a threefold change in expression; and (2) the p values had statistical significance (p ≤ 0.05) with adjustment by the Benjamin-Hochberg procedure for multiple testing correction (Fig. 1A). The volcano plot directly presents the miRNA expression levels, and the most significantly up-regulated miRNA was shown to be miR-92a-3p, which had a log2 fold change of 2.46 (Fig. 1B). Using each sample’s normalized value, principal component analysis showed a circulating miRNA expression signature that segregated the serum samples of OSCC from those of healthy controls (Fig. 1C). We also identified the similarity between samples of the same group through Pearson correlation coefficients for the normalized values (Fig. 1D). Studies of microRNA in serum specific to cancer patients is based on the premise that it is expressed through the process of being released into the bloodstream from cancer tissue. The mechanism is not yet clear, but it is known to be a product of tumor cell death and dissolution or release from tumor-derived microsomes or exosomes16,17. Therefore, to discover specific candidate miRNAs, we combined our small RNA-sequencing results and data from TCGA, a large-scale tissue-derived database. Finally, 9 miRNAs were identified as candidates due to their differential expression in both the serum and tissue of OSCC patients. A Venn diagram (Fig. 1E) shows the screening pattern, and Fig. 1F and Table S5 present the 9 candidates, including 5 up- and 4 down-regulated DEmiRNAs. To further investigate the functions and pathways by which the dysregulation of the DEmiRNAs influences OSCC development, we predicted the target genes of the 9 DEmiRNAs and performed GO and KEGG pathway enrichment analysis. The target genes of the 9 DEmiRNAs participated in cancer progression-related processes, such as the PI3K-Akt signaling pathway and signaling pathways regulating choline metabolism (Fig. S3A-B). For biological processes, cellular components, and molecular function, the target genes of DEmiRNAs were significantly concentrated in cellular nitrogen compound metabolic process, organelle, ion binding, and biosynthetic process (Fig. S3C-D). Next, we identified downstream targets associated with DEmiRNA that could play a regulatory role in OSCC progression. The miRNA target predictions were performed using the TargetScan databases, and then we used Cytoscape software to visualize and analyze the predicted data for interactions in miRNA-mRNA regulatory networks (Fig. S3E-F). It was confirmed that one miRNA regulated the signal transduction pathway in association with several mRNAs and was also interconnected with other miRNAs.

Figure 1
figure 1

miRNA profiling identifies differential expression in OSCC. (A) Heatmap of miRNAs that were differentially expressed between oral squamous cell carcinoma (OSCC) patients and normal controls (p < 0.05). Four pre-treatment OSCC serum samples and 6 normal serum samples are shown in the heatmap. (B) A volcano plot shows that many miRNAs were significantly different between normal and OSCC patients. Yellow: up-regulation with a fold change of more than 3; blue: down-regulation with a fold change of more than −3 (p > 0.05). (C) Principal component analysis (PCA). The fold change in expression between matched normal and tumor samples was used to perform PCA. (D) Heatmap of correlations based on the OSCC and normal serum samples. The correlogram shows correlation coefficients for all pairs of variables with coefficients colored based on their sign. (E) Venn diagram of differentially expressed miRNAs (DEmiRNAs) obtained from The Cancer Genome Atlas (TCGA) database and RNA sequencing results (fold change ≥ 3 or ≤ −3, p < 0.05). The Venn diagram shows that there are 9 overlap** DEmiRNAs. (F) Heatmap of one-way hierarchical clustering revealed 9 DEmiRNAs. The heatmap, volcano plot and PCA plot were conducted using an online tool ChiPlot (https://www.chiplot.online/).

Full size image

Validation of the candidate miRNAs in the CNUH OSCC patient cohort by qRT-PCR

To search for and validate potential miRNA signatures to distinguish OSCC patients from healthy controls, we planned to validate the 9 candidate DEmiRNAs in the CNUH OSCC patient cohort. We collected serum samples from 23 OSCC patients with different subsites (tongue, n = 18; buccal mucosa, n = 2; retromolar trigone, n = 2; and floor of mouth mucosa, n = 1) and 15 healthy controls from the cohort for validation by qRT-PCR. The clinical information of patients with OSCC were summarized in Table S1.

Previous studies have shown that RNU6 can generally be used as an endogenous control (EC) to normalize the expression of miRNA in tissue or cells, but it is unstably expressed in the plasma and serum24. Beyond OSCC, it has been reported that miR-92a-3p also plays a role as an oncogenic component in gastric cancer25, esophageal squamous cell cancer

Data availability

The TCGA data presented in this study are openly available in a specific portal (https://tcga-data.nci.nih.gov) and cancer browser (https://genome-cancer.ucsc.edu). Further information is available from the corresponding author upon request.

Abbreviations

AUC:

Area under the curve

BP:

Biological processes

CC:

Cellular components

CI:

Confidence interval

CNUH:

Chungnam National University Hospital

DEmiRNAs:

Differentially expressed miRNAs

EC:

Endogenous control

GO:

Gene ontology

HNSCC:

Head and neck cancer

KEGG:

Kyoto encyclopedia of genes and genomes

MF:

Molecular function

miRNA:

MicroRNA

NGS:

Next-generation sequencing

OSCC:

Oral squamous cell carcinoma

PCA:

Principal component analysis

ROC:

Receiver operating characteristics

RT-PCR:

Real-time polymerase chain reaction

TCGA:

The cancer genome atlas

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Funding

This research was supported by Chungnam National University Hospital Research Fund 2019 [to BS Koo], Chungnam National University Sejong Hospital Research Fund 2021 [to HR Won], and the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning [grant numbers 2019R1A2C1084125 to BS Koo, 2022R1C1C1008265 to HR Won and 2021R1C1C1014142 to JW Chang], and by the Korea Health Technology R&D Project through the Korea health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HR20C0025 and HR22C1734), and by Korea Medical Device Development Fund (Ministry of Science and ICT, Ministry of Trade, Industry and Energy, Ministry of Health & Welfare, Ministry of Food and Drug Safety; Project Number: 1711138229, KMDF_PR_20200901_0124). 

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Author notes

  1. These authors contributed equally: Ho-Ryun Won and Bon Seok Koo.

Authors and Affiliations

  1. Department of Medical Science, Chungnam National University College of Medicine, Daejeon, Republic of Korea

    Yudan Piao, Chan Oh, Yan Li **, Hae Jong Kim, Quoc Khanh Nguyen, Jae Won Chang, Ho-Ryun Won & Bon Seok Koo

  2. Department of Otolaryngology-Head and Neck Surgery, Chungnam National University College of Medicine, Daejeon, 35015, Republic of Korea

    Seung-Nam Jung, Mi Ae Lim, Jae Won Chang, Ho-Ryun Won & Bon Seok Koo

  3. Department of Otolaryngology-Head and Neck Surgery, Chungnam National University College of Medicine, Chungnam National University Sejong Hospital, 20, Bodeum 7-ro, Sejong, 30099, Republic of Korea

    Ho-Ryun Won

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  1. Yudan Piao
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  5. Yan Li **
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Contributions

The work reported in the paper has been performed by the authors, unless clearly specified in the text. Y.P., contributed to conception, design, data acquisition, and interpretation, drafted and critically revised the manuscript; S.-N.J., M.A.L., J.W.C. contributed to design and data acquisition, C.O., Y.L.J., H.J.K., N.Q.K. contributed to data analysis; H.-R.W., B.S.K. contributed to conception, design and critically revised the manuscript. All authors gave final approval and agree to be accountable for all aspects of the work.

Corresponding authors

Correspondence to Ho-Ryun Won or Bon Seok Koo.

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Piao, Y., Jung, SN., Lim, M.A. et al. A circulating microRNA panel as a novel dynamic monitor for oral squamous cell carcinoma. Sci Rep 13, 2000 (2023). https://doi.org/10.1038/s41598-023-28550-y

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