Cancer stem cell biomarkers for head and neck squamous cell carcinoma: A bioinformatic analysis

  • Authors:
    • Meng Xiao
    • Liu Liu
    • Shijian Zhang
    • Xi Yang
    • Yan'an Wang
  • View Affiliations

  • Published online on: October 8, 2018     https://doi.org/10.3892/or.2018.6771
  • Pages: 3843-3851
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Abstract

Several putative biomarkers have been reported to identify cancer stem cells (CSCs) in head and neck squamous cell carcinoma (HNSCC). Herein, we aimed to demonstrate the validity and the underlying relationship for these biomarkers in HNSCC. Bioinformatic analyses for the reported CSC biomarkers of HNSCC were performed based on the TCGA primary HNSCC cohort using the UCSC Xena browser. Targeted strategies for the validated biomarkers were searched and summarized. A total of 27 reported CSC biomarkers for HNSCC were identified and comprehensively evaluated. In regards to the expression pattern of CD44 in HNSCC, the expression patterns for the remaining 26 biomarkers presented 3 different tendencies. We managed to include all the 27 CSC biomarkers for HNSCC into 3 groups. Moreover, the biomarkers in each group indicated distinct clinicopathological features and a different overall survival status for HNSCC patients. The above information suggested the existence of CSC subpopulations in HNSCC. Accordingly, we demonstrated that precisely targeted strategies based on the CSC subgrouping clusters might effectively supplement conventional therapies, and benefit HNSCC patients. Further relevant studies are still necessary to improve treatment strategies for HNSCC based on the CSC area.

Introduction

Head and neck squamous cell carcinoma (HNSCC) refers to a group of biologically similar cancers arising from the mucous squamous epithelia in the head and neck area. HNSCC is an aggressive cancer with poor overall survival (14). In spite of the recent advancements in treatment modalities for HNSCC, the long-term survival rates have not significantly improved over the past decade (5). Currently, emerging evidence suggests that cancer stem cells (CSCs) are responsible for local recurrence, metastatic spread, and treatment resistance in HNSCC (6).

To date, research on CSCs has become profound, and CSCs have been functionally defined as a subset of tumor cells that exhibit the ability of self-renewal and multipotency in cancerous malignancy (7). CSCs only account for a minor proportion of the total cancerous burden but can play paramount roles in determining the outcomes of cancers (8). Thus, identification of CSCs provides novel therapeutic promise for improving cancer treatment (9,10). Previous studies conducted in several types of cancer have reported that these CSCs exhibit increased expression of certain biomarkers resulting in the acquisition of stem-like properties (11,12). Confirmation of these CSCs requires the identification of such molecular biomarkers (9).

Discovering effective biomarkers is critical to a better understanding of the biological features of CSCs. To date, several putative protein molecules have been proposed to identify the CSCs in HNSCC, including CD44, CD133, Nanog, Oct4, Sox2 and ALDH1 (1215). However, validity of these CSC biomarkers has been questioned recently, and the clinical significance of these molecules in HNSCC remains to be ascertained, especially based on large cohort data. The Cancer Genome Atlas (TCGA) project holds great promise for a comprehensive understanding of human cancer with powerful and detailed data (16,17). The UCSC Cancer Genomics Browser presents the TCGA data in a coherent, integrated system with genomic, clinical annotation data in multiple views (18). In this study, we managed to collect the reported CSC biomarkers of HNSCC and analyze these biomarkers via bioinformatics based on the TCGA primary HNSCC cohort. We systematically demonstrated the expression patterns, clinical significance, and potential targeted strategies for these molecules in HNSCC.

Materials and methods

Searching for the reported CSC biomarkers for HNSCC

Studies were scanned by searching the electronic database PubMed with the terms ‘cancer stem-like cells’, ‘cancer stem cells’, ‘tumor stem-like cells’, ‘tumor stem cells’, ‘CSCs’, and ‘head and neck squamous cell carcinoma’, ‘HNSCC’. In order to be included for further summary, the following criteria were met: i) an original research paper in a peer-reviewed journal; ii) studies in humans; iii) studies with validated evidence to demonstrate the reported biomarkers tightly concerned with the CSC characteristics of HNSCC. Conference abstracts, reviews, comments, case reports, and letters to the editor were excluded. Subsequently, all potentially eligible studies were retrieved and the following information was extracted: i) name of the reported CSC biomarker; ii) the reported clinical significance for each CSC biomarker in HNSCC areas. In addition, the encoding genes for the reported CSC biomarkers were annotated. We demonstrated the cellular location and biological roles for each reported CSC-related molecule based on The Human Protein Atlas. Search results for the CSC biomarkers of HNSCC are listed in Table I.

Table I.

Search results for the reported CSC biomarkers of HNSCC.

Table I.

Search results for the reported CSC biomarkers of HNSCC.

CSC biomarkerEncoded geneCellular locationBiological rolesClinical significance
CD44CD44Plasma membraneSignal transducerLymph node metastasis, recurrence
CD24CD24   Cellular vesiclesSignal transducerTumorigenicity, angiogenesis
CD98SLC7A5, SLC3A2Nucleus, plasma membrane, cytosolSignal transducer; Amino acid transportTumorigenicity, recurrence
EpCAMEPCAMPlasma membraneSignal transducer Chemoresistance
c-MetMETPlasma membrane, cytosolSignal transducerChemoresistance, metastasis
CD133PROM1Plasma membrane, cytoplasmSignal transducerMetastasis, tumorigenicity, chemoresistance
CD166ALCAMPlasma membrane, cytoplasmSignal transducerRecurrence
Notch1NOTCH1NucleoplasmSignal transducerTumorigenicity, chemoresistance
CD10MMEPlasma membraneZinc-dependent metalloendoproteaseTumorigenicity, chemoresistance
MT1-MMPMMP14CytoplasmZinc-dependent metalloendoproteaseRecurrence, chemoresistance, metastasis
ALDH1ALDH1A1CytosolDetoxifying enzymeRecurrence, radiochemoresistance
SOX2SOX2NucleoplasmTranscription factorLymph node metastasis, recurrence, chemoresistance
Oct4POU5F1Nucleoplasm, cytosolTranscription factorLymph node metastasis, chemoresistance
NanogNANOGNucleoplasmTranscription factorChemoresistance, recurrence, lymph node metastasis
KLF4KLF4NucleoplasmTranscription factorLymph node metastasis, distant metastasis
BrachyuryTNucleoplasmTranscription factorLymph node metastasis, distant metastasis
Bmi-1BMI1Nucleus, nuclear bodies, cytosolTranscriptional repressorsChemoresistance, metastasis
Topoisomerase I, IIα, IIIαTOP1, TOP2A, TOP3ANucleusTopoisomeraseLymph node metastasis
TAZTAZPlasma membraneTranscriptional regulationTumor growth, lymph node metastasis
EHMT2EHMT2NucleoplasmEuchromatic methyltransferaseLymph node metastasis
JMJD6JMJD6NucleoplasmArginine demethylase, lysine hydroxylaseRecurrence, chemoresistance
ABCG2ABCG2Plasma membrane, nucleusABC transporter proteinLymph node metastasis, recurrence, chemoresistance
ABCG5ABCG5NucleusABC transporter protein Chemoresistance
SLC2A13SLC2A13Nuclear membrane H+-myo-inositol transporterTumorigenicity
GRP78HSPA5CytosolEndoplasmic reticulum chaperoneRecurrence, radioresistance, tumorigenicity

[i] CSC, cancer stem cell; HNSCC, head and neck squamous cell carcinoma.

Bioinformatic analysis for the reported CSC biomarkers of HNSCC based on the TCGA primary HNSCC cohort

Bioinformatic analyses were performed based on the TCGA primary HNSCC cohort using the UCSC Xena Browser. Totally, 604 cases were searched, and only cases of primary HNSCC were filtered and included for further analysis for the gene expression patterns of each reported CSC biomarker. Expression heat-maps and Kaplan-Meier curves stratified by the defined gene were generated and clustered online, and detailed data were downloaded for subsequent statistical analysis. To illustrate the clinicopathological features of the reported CSC biomarkers, we downloaded and analyzed the detailed data for the expression level, pathological nodal extracapsular spread, lymphovascular invasion, neoplasm histologic grade, tumor size, nodal status, and pathologic stage.

Searching for targeted treatment based on the reported CSC biomarkers in cancer areas

Studies were scanned by searching electronic database PubMed for the targeted treatment based on the reported CSC biomarkers in the pan-cancer areas. Articles were reviewed to figure out and summary the targeted strategies in cancer areas based on the reported CSC biomarkers of HNSCC.

Statistical analysis

Statistical analyses were conducted with SPSS 20.0 software (IBM Corp., Armonk, NY, USA). Based on the detailed data for each biomarker, all cases involved were divided equally into two groups, a high-expression group and low-expression group. To illustrate the underlying relationship among the reported CSC biomarkers, crosstab analyses were performed and Chi-squared tests were used to assess the statistical significance for correlations between the gene expression level of CD44 and the gene expression levels of other biomarkers. In addition, Chi-squared tests were used to assess the statistical significance for correlations between the gene expression level of each biomarker and each clinicopathological variable. Xena Browser compares the different Kaplan-Meier curves using the log-rank test. p<0.05 was considered to indicate a statistically significant difference. Venn diagrams were generated for clustering analyses.

Results

Detailed information for the reported CSC biomarkers of HNSCC

A total of 27 molecules, encoded by 28 genes, were demonstrated and reported to be tightly linked with the CSC properties of HNSCC. Detailed information for each reported biomarker has been summarized. As shown in Fig. 1A, cellular locations for these molecules are designated. Four molecules are located at the plasma membrane (CD44, EpCAM, CD10 and TAZ), 4 molecules in the cytoplasm (CD24, MT1-MMP, ALDH1 and GRP78), and 12 molecules in the nucleus (topoisomerase I/IIα/IIIα, Notch1, Brachyury, ABCG5, Sox2, SLC2A13, Nanog, KLF4, JMJD6 and EHMT2). In addition, there are 3 molecules distributed at both the plasma membrane and cytoplasm (c-Met, CD133, and CD166), 2 molecules at both the cytoplasm and nucleus (Oct4 and Bmi-1), and 1 molecule at both the plasma membrane and nucleus (ABCG2). Additionally, CD98 is widely scattered among the plasma membrane, cytoplasma, and nucleus.

Furthermore, we also characterized the biological roles for these molecules into 6 categories (Fig. 1B): replication regulation (topoisomerase I, IIα and IIIα), transcription regulation (TAZ, Oct4, Bmi-1, KLF4, Sox2, Nanog, EHMT2 and Brachyury), signal transducer (CD24, EpCAM, c-Met, CD133, CD44, CD166, CD98 and Notch1), transporter protein (SLC2A13, ABCG2 and ABCG5), enzyme (MT1-MMP, ALDH1, CD10 and JMJD6) and chaperone protein (GRP78). Accordingly, we observed some heterogeneity existing among these biomarkers more or less, indicating that there might be variable mechanisms in regulating the CSC behaviors of HNSCC for these molecules.

Underlying relationship among the reported CSC biomarkers based on the TCGA primary HNSCC cohort

Although these biomarkers have been reported to regulate the stem-like ability of HNSCC cells, evidence for the underlying relationship among these molecules has not been demonstrated previously. Herein, we aimed to choose the TCGA primary HNSCC cohort to comprehensively evaluate the underlying relationship and validate the clinical significance for each biomarker. To date, CD44 has reported to be the most frequently used biomarker for identifying the CSCs in HNSCC (19,20). In this study, we chose CD44 as a reference biomarker for the reported CSC biomarkers in HNSCC to analyze the clinical significance and underlying relationship among them. In total, there are 604 cases of HNSCC in the TCGA cohort, and only 528 cases of primary HNSCC were filtered and included for further analysis. By using the UCSC Xena browser, we generated a series of heatmaps referencing to the expression pattern of CD44 among the 520 cases (Fig. 2).

By data mining, we explored the expression patterns among these reported CSC biomarkers of HNSCC. We found that by referring to the expression pattern of CD44, the expression patterns of the remaining 26 biomarkers were clustered into three subgroups (Fig. 2). In the subgroup with a significantly positive correlation to the expression pattern of CD44 (Group A), the following biomarkers were included: CD98, c-Met, MT1-MMP, GRP78 and topoisomerase I. In the subgroup with a significantly negative correlation to the expression pattern of CD44 (Group B), the following molecules were included: CD133, JMJD6, topoisomerase IIα, Notch1, Nanog, Oct4, EpCAM, ALDH1, Sox2, TAZ and EHMT2. Moreover, the expression pattern of the following molecules were observed without significant correlation to that of CD44 (Group C): CD10, Bmi-1, ABCG5, ABCG2, SLC2A13, CD166, KLF4, CD24 and topoisomerase IIIα. Among these, the detailed data downloaded for Brachyury (encoded by T) was not enough for further studies. Thus, the incomplete heat-map for T was added at the end of Fig. 2 and no further analysis was performed for this biomarker. The above data indicated that great heterogeneity existed among the expression pattern of the reported CSC biomarkers in HNSCC, suggesting that subgrouping clusters might exist for all the CSCs in HNSCC.

Clinicopathological features and overall survival evaluation for the reported CSC biomarkers based on the TCGA primary HNSCC cohort

Based on the reported studies, the included 27 molecules have been identified to regulate cancer-stem like behaviors of HNSCC. The above data showed powerful evidence to indicate that these molecules might exert their roles with variable mechanisms. However, the validity and clinical significance for these biomarkers need to be further ascertained. Herein, we managed to validate the clinical significance for each biomarker based on the TCGA primary HNSCC cohort (Table II, Fig. 3).

Table II.

Clinicopathological evaluation for the reported CSC biomarkers based on the TCGA primary HNSCC cohort.

Table II.

Clinicopathological evaluation for the reported CSC biomarkers based on the TCGA primary HNSCC cohort.

Pathological nodal extracapsular spreadLymphovascular invasionNeoplasm histologic gradeTumor sizeNodal statusPathologic stage
Yes/NoYes/NoG1+G2/G3+G4T1+T2/T3+T4 N0/N1+I+II/III+IV
CD440.9320.2750.0740.8480.4030.011b
Subgroup with a significantly positive correlation to the expression pattern of CD44 (Group A)
  SLC3A20.004a0.7780.001b0.004a0.4510.154
  SLC7A50.6740.6130.001b0.7330.045b0.857
  MET0.2430.9080.9720.5250.8330.180
  MMP140.7140.3920.9930.4550.2980.986
  HSPA50.022a0.7540.1770.3240.2670.785
  TOP10.9940.007b0.014b0.1640.2350.244
Subgroup with a significantly negative correlation to the expression pattern of CD44 (Group B)
  TOP2A0.0970.3170.000a0.8390.0680.915
  NOTCH10.7320.0870.0680.2160.2640.160
  NANOG0.3270.032a0.043a0.7750.9340.812
  PROM10.9010.1210.3930.8070.8180.523
  JMJD60.016a0.0530.5490.005*0.017a0.000a
  EPCAM0.3750.000a0.0770.3380.1180.025a
  ALDH1A10.3690.1790.0851.0000.1980.429
  SOX21.0000.1800.1570.9250.6920.214
  POU5F10.6080.1790.2240.2570.6930.653
  TAZ0.3040.0930.3620.5720.6930.142
  EHMT20.1260.0920.011a0.1320.003a1.000
Subgroup without a significant correlation to the expression patter of CD44 (Group C)
  MME0.5220.1450.015b0.2200.4890.736
  BMI10.1230.1170.012a0.4510.6930.572
  ABCG20.5230.1800.1290.9250.3741.000
  ABCG50.007a0.007a0.4200.3000.0930.072
  SLC2A130.6080.8231.0000.6370.7670.822
  CD240.2480.014b0.001b0.1320.0940.115
  KLF40.5230.3130.000b0.6370.4890.574
  ALCAM0.4410.3150.020a0.7070.5530.142
  TOP3A0.029*0.0920.3620.8510.0930.258

a Positive correlation with significance

b negative correlation with significance. CSC, cancer stem cell; HNSCC, head and neck squamous cell carcinoma.

As summarized in Table II, the molecules represented significantly positive correlation to the clinicopathological features were filtered and analyzed based on the TCGA primary HNSCC cohort. For the evaluation of nodal extracapsular spread, the molecules CD98 and GRP78 (Group A), JMJD6 (Group B), ABCG5 and topoisomerase IIIα (Group C) were identified as significant. For the evaluation of lymphovascular invasion, the molecules Nanog and EpCAM (Group B), and ABCG5 (Group C) were identified as significant. For the evaluation of histologic grade, the molecules topoisomerase IIα, Nanog and EHMT2 (Group B), and BMI-1 and CD166 (Group C) were identified as significant. For the evaluation of tumor size, the molecules CD98 (Group A) and JMJD6 (Group B) were identified as significant. For the evaluation of nodal status, the molecules JMJD6 and EHMT2 (Group B) were identified as significant. For the evaluation of pathologic stage, the molecules JMJD6 and EpCAM (Group B) were identified as significant. A series of Kaplan-Meier curves were generated to evaluate the prognostic significance for each reported CSC biomarker (Fig. 3). Accordingly, the higher expression of CD98, topoisomerase I, and c-Met (Group A), and CD10 (Group C) indicate significantly poorer overall survival (OS) for patients with HNSCC. Unexpectedly, we did not observe any significant correlations between the expression of biomarkers in Group B and the overall survival (OS) of the HNSCC patients.

By analyzing the clinical significance for each underlying subgroup, we proposed that biomarkers in Group A were mainly tightly related to clinical outcomes for HNSCC patients, and biomarkers in Group B were mainly tightly concerned with the malignant progression in HNSCC. The above data strongly indicate that the validated biomarkers might regulate CSC properties and affect the clinicopathological features in HNSCC through different mechanisms, which warrant further attention to demonstrate the underlying heterogeneity.

Targeted treatment strategies for these CSC biomarkers in cancer areas

Currently, there are no targeted therapies for the CSCs in HNSCC. Despite the fact that HNSCC is a highly prevalent and deadly cancer, the survival rate for HNSCC patients has not shown any improvements for years. CSCs are responsible for relapse, chemoresistance and poor OS, and offer an attractive therapeutic target. Herein, we searched and summarized the reported targeted therapies for these molecules in pan-cancer areas (Table III). To date, various agents have been tested, including compounds, antibodies, and others. Moreover, some of these agents have been well developed and used in other types of cancer clinically. No wonder, targeted therapies based on the validated CSC biomarkers would benefit more patients with HNSCC. Accordingly, we demonstrate that targeted therapies against c-Met, topoisomerase I, and GRP78 may improve the survival rate for HNSCC patients, and targeted therapies against topoisomerase IIα, EpCAM, and EHMT2 might greatly suppress the malignant progression of HNSCC.

Table III.

Targeted therapies for the reported CSC markers of HNSCC in cancer areas.

Table III.

Targeted therapies for the reported CSC markers of HNSCC in cancer areas.

CSC moleculeTargeted compoundTargeted antibodyOthers
CD44Hyaluronic acid-based drug deliveryRG7356
Subgroup with significantly positive correlation to the expression pattern of CD44 (Group A)
  c-MetCabozantinib, crizotinib, tepotinib, tivantinib, other small-molecule inhibitorsBsAbs, mAb
  Topoisomerase ICamptothecin, DXd, organic non-camptothecin compounds, topotecan, LMP-400, NSC724998, Iirinotecan, betulinic acid, SN-38 Metal complexes, OSI-211
  GRP78Medicarpin, isoliquiritigenin, HA15 Fusion protein, GMBP1, KP1339/IT-139
  MT1-MMP Peptide-inhibitor
Subgroup with significantly negative correlation to the expression pattern of CD44 (Group B)
  Topoisomerase IIαPixantrone, glycyrrhetinic acid, halogenated triterpenoid, 2α-bromo-dihydrobetulonic acid, CS1 D11
  Notch1PF-03084014Brontictuzumab
  EpCAMEpCAM aptamer-mediated deliverymAbs, catumaxomabImmunotoxin, adoptive T-cell therapy, cytolytic fusion protein
  ALDH1 Diethylaminobenzaldehyde
  Oct4Metformin
  CD133CD133 aptamer-mediated delivery Immunotoxin
  EHMT2UNC0638, BIX-01294
Subgroup without significant correlation to the expression patter of CD44 (Group C)
  CD24Anti-CD24 based drug deliverymAb
  CD166, CD10 mAbs
  Bmi-1PTC-209, PTC-028, PTC596
  ABCG2Anti-ABCG2 based drug delivery, Ko143, PZ-39, MBL-II-141, YHO-13351, glafenineKo143

[i] CSC, cancer stem cell; HNSCC, head and neck squamous cell carcinoma.

Discussion

Emerging studies suggest that cancer stem cells are responsible for tumor initiation, cancer progression, metastasis and treatment resistance in HNSCC (4,13). Several molecules have been identified to isolate and characterize CSCs in HNSCC, and almost all the CSC biomarkers established to date with a special emphasis on their impact on malignant progression and their potentially clinical significance in HNSCC (12,14,21). However, none of these biomarkers or their combinations have been well acknowledged or systematically validated. Consequently, there are no approved targeted strategies in regards to CSCs for the treatment of HNSCC patients (22). Thus, there is a critical need for comprehensive evidence to evaluate the reported CSC biomarkers for HNSCC.

To date, a total of 27 molecules have been reported as potential CSC biomarkers for HNSCC. Nevertheless, the validity and underlying relationship among these molecules have not been demonstrated. Furthermore, several studies have reported the limitations and pitfalls underlying the isolation of CSCs with a single biomarker (23). Thus, we must analyze and discover the underlying heterogeneity among all the reported CSC biomarkers (12,13,2440). Primarily, we cannot deny the potential heterogeneity from the inconsistent experimental conditions, the power of experimental evidence, and the limited sample size for each study reporting the CSC biomarkers. What's more, it is of paramount importance to identify a reliable strategy to realize the essential heterogeneity derived from the CSCs of HNSCC. Recently, different CSC phenotypes have been implicated in breast cancer (41,42). In HNSCC, it has been reported that Oct4, Sox2 and CD133 are not consistently expressed in isolated CSCs (43). Herein, we proposed that the essential heterogeneity may result from the possible CSC subpopulations existing in HNSCC. Besides, understanding the underlying relationship among these CSC-related molecules is vitally important for demonstrating the biological roles for CSCs in HNSCC.

Recently, large-scale bioinformatic analyses based on the TCGA cohort have shown great priority for cancer research (17,18), which could greatly avoid the potential heterogeneity from the experimental results and limited clinical sample sizes. In this study, we conducted a comprehensive analysis for the expression files of the reported CSC biomarkers in a large number of primary HNSCC patients from TCGA. By data mining, we managed to discover the relationship among these molecules and validate the significance for each molecule in clinicopathological features and OS for HNSCC. Consequently, the reported CSC biomarkers were clustered into 3 groups according to their expression pattern, indicating that there might be subgrouping clusters existing for all CSCs in HNSCC. Accordingly, we might propose 2 molecular signatures for the possible CSC clusters existing in HNSCC, 3 validated biomarkers in group A (CD98, GRP78 and topoisomerase I), and 5 validated biomarkers in group B (JMJD6, Nanog, EpCAM, topoisomerase IIα and EHMT2).

Previous studies have reported that CSCs are responsible for cancer initiation and progression, and are especially resistant to conventional therapy (1,12,30). In this study, filtered biomarkers belonging to group A were observed without significant correlation to the malignant progression of HNSCC, but significantly indicating worse OS for HNSCC patients. On the contrary, filtered biomarkers belonging to group B were shown to be significantly correlated to the malignant characteristics of HNSCC, but without significant correlation to the OS rates of HNSCC patients. As we know, the clinical outcomes for HNSCC are determined by malignant phenotypes and treatment responses of cancer cells. Thus, we may conclude that some CSCs are responsible for malignant phenotypes, but poorer responses to treatment strategies, and some CSCs may be responsible for worse malignant phenotypes, but better responses to treatment. Further studies for the underlying heterogeneity among all the CSCs in HNSCC are critically necessary in the future.

Treatment decisions for HNSCC are complex, and according to the US guidelines, a multidisciplinary approach is recommended (2). However, the prognosis of HNSCC remains very poor. Besides, there are still no approved targeted strategies for CSCs in HNSCC. Targeted strategies based on the validated CSC biomarkers may effectively supplement conventional therapies, and benefit HNSCC patients. In this study, we proposed that targeted strategies against c-Met, topoisomerase I, and GRP78 show great possible to improve the prognosis of HNSCC patients, and targeted strategies against topoisomerase IIα, EpCAM and EHMT2 may potentially suppress the malignant progression of HNSCC.

In conclusion, we comprehensively evaluated the 27 reported CSC biomarkers for HNSCC based on the TCGA primary HNSCC cohort. Accordingly, we managed to illustrate the underlying subgroup clusters among all the CSCs in HNSCC. We proposed that precisely targeted strategies based on the CSC subgroup clusters may well supplement conventional therapies, and benefit HNSCC patients. There is no doubt that numerous studies have improved and greatly furthered our understanding of the CSCs of HNSCC. However, more laboratory research and well-designed retrospective or prospective large-scale studies are still necessary to validate the conclusions derived from our study for eventually clinical translation.

Acknowledgements

Not applicable.

Funding

The present study was supported by the National Natural Science Foundation of China (nos. 81371164 and 81602367), the Science and Technology Commission of Shanghai Municipality (no. 15411950300).

Availability of data and materials

The datasets used during the present study are available from the corresponding author upon reasonable request.

Authors' contributions

XY and YW conceived and designed the experiments. MX and LL performed the experiments. MX, LL and SZ summarized and analyzed the data. MX, LL and XY contributed to writing and revising the manuscript. All authors read and approved the manuscript and agree to be accountable for all aspects of the research in ensuring that the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Ethics approval and consent to participate

This study was approved by the Medical Ethics Committee of the Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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December-2018
Volume 40 Issue 6

Print ISSN: 1021-335X
Online ISSN:1791-2431

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Spandidos Publications style
Xiao M, Liu L, Zhang S, Yang X and Wang Y: Cancer stem cell biomarkers for head and neck squamous cell carcinoma: A bioinformatic analysis. Oncol Rep 40: 3843-3851, 2018.
APA
Xiao, M., Liu, L., Zhang, S., Yang, X., & Wang, Y. (2018). Cancer stem cell biomarkers for head and neck squamous cell carcinoma: A bioinformatic analysis. Oncology Reports, 40, 3843-3851. https://doi.org/10.3892/or.2018.6771
MLA
Xiao, M., Liu, L., Zhang, S., Yang, X., Wang, Y."Cancer stem cell biomarkers for head and neck squamous cell carcinoma: A bioinformatic analysis". Oncology Reports 40.6 (2018): 3843-3851.
Chicago
Xiao, M., Liu, L., Zhang, S., Yang, X., Wang, Y."Cancer stem cell biomarkers for head and neck squamous cell carcinoma: A bioinformatic analysis". Oncology Reports 40, no. 6 (2018): 3843-3851. https://doi.org/10.3892/or.2018.6771