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    Topic review

    Molecular Biomarkers of Nasopharyngeal Carcinoma

    Subjects: Oncology
    View times: 8
    Submitted by: Shiau Chuen Cheah


    Nasopharyngeal carcinoma (NPC) is a cancer that arises from the squamous epithelial cells that cover the lateral wall of the nasopharynx. In contrast to head and neck cancers, NPC has a distinct epidemiology, pathology, clinical characteristics, and treatment response. NPC is an endemic form of malignancy in certain parts of the world.

    1. Introduction

    A well-known risk factor of NPC is the Epstein–Barr virus (EBV). Despite that, distinct ethnic and geographical dissemination of NPC indicates both genetic and environmental factors (diet and tobacco smoking) play an important role in its aetiology [1]. Complex interactions of multiple factors including viral infection, an individual’s genetic susceptibility, environmental factors, and dietary factors have driven the pathogenesis of this malignancy.

    Up to 80% of NPC patients are diagnosed at advanced stages (clinical stages III and IV) and 10% at distant metastasis, which is associated with unfavourable outcome and poor prognosis [2][3][4][5]. This is mainly due to the fact that it is asymptomatic in its early stages, its high metastatic rate, and its inaccessibility for examination, whereby examination of the local primary tumour in the small curved structure of the nasal cavity is difficult [6]. The common symptoms of NPC include epistaxis, nasal obstruction, hearing loss, otitis media, headache, diplopia, numbness, and neck lump [7][8].

    In recent decades, the advancement of diagnostic imaging and the use of concurrent radio and systemic therapy have improved overall prognosis and treatment outcomes [2]. The tumour-node-metastasis (TNM) staging system developed by the American Joint Committee on Cancer and the National Comprehensive Cancer Network (NCCN) is used in treatment decisions for NPC patients at different stages. Radiotherapy (RT) is used as a standard treatment for early stage NPC, while concurrent chemotherapy (CT) followed by adjuvant chemotherapy is the preferred treatment for stages III and IV NPC.

    Although overall survival (OS) has improved due to these advanced treatments, there are still many controversies regarding these treatment approaches. For example: (1) patients still encounter tumour recurrence or develop distant metastasis after undergoing RT, especially those in the advanced stages, resulting in death [1]; (2) most patients, especially those in the advanced stages of NPC, did not benefit from the abovementioned NPC treatments [9][10]; (3) a weak tolerance to the high toxic side effects of these therapeutics has led to a delay in treatment, and ultimately death (for example, nasopharynx haemorrhage, a dangerous and serious condition resulting from radiotherapy has led to 35.7% to 100% mortality [7][11]); (4) these treatments eventually allow for tumour progression and emergency due to radio- or chemo-resistance [12][13]; (5) the advanced stages of NPC are associated with poor prognosis and poor response towards the available treatments; and (6) the absence of a reliable prediction tool for NPC recurrence and metastasis. Treatment failure for advanced stages (distant metastasis) is the primary cause of mortality from NPC, accounting for 50,000 deaths annually [4]. Since the 10-year OS rate for stage I patients is as high as 98%, it seems that the mortality rate can be reduced if the NPC is diagnosed at an earlier stage [14]. Currently, the TNM staging system does not provide information on predicting or identifying the risk of NPC progression. This has highlighted the issues of NPC diagnosis and prognosis, as well as treatment. Hence, most studies now focus on uncovering the molecular biomarkers in NPC to improve the early diagnosis approaches and discover prognostic indicators. In the current review, we have reviewed the research status of biomarkers in NPC for early diagnosis and prognosis (metastasis and recurrence).

    2. Diagnostic and Prognostic Biomarker Discovery for NPC

    The use of biomarkers in cancer management has recently been increased with advancements in genomics, proteomics, and transcriptomics, as well as associated technologies. Studying the biomarkers involved in NPC progression and metastasis enables us to understand the disease, identify an individual’s susceptibility to the disease, and predict or monitor patients’ response toward a therapeutic treatment. Based on their role in disease management, biomarkers can be categorised into two groups: (1) prognostics, which allow for the assessment of the risk of clinical outcomes including recurrence, metastasis, and progression; and (2) diagnostic markers, which identify whether an individual has the specific disease or condition.

    Therefore, biomarkers can improve the early diagnosis and prognosis approaches by assisting in identifying patients who are susceptible to developing NPC or who are at a high risk or distant metastasis or recurrence. Biomarkers are the key to preventing NPC progression, recurrence, and metastasis, as well as to developing effective therapeutic treatments. With the aid of high throughput ‘omics’ technologies, knowledge on the aetiology, tumorigenesis, and progression of NPC has progressed much faster, thus allowing researchers to identify potential molecular biomarkers. Several types of potential NPC molecular biomarker, including DNA (genomic), mRNA (transcriptomic), protein (proteomic), and metabolite (metabolomics) biomarkers, have been identified ( Table 1 ).

    Table 1. Potential biomarkers for early diagnosis of NPC.
    Biomolecules Full Name Role Aberration Sources
    Genomic biomarkers
    COX-2 Cyclooxygenase-2 Cell proliferation, apoptosis Polymorphism in rs5275 [15]
    MCP-1 Monocyte chemoattractant protein-1 Monocytes or macrophages migration and infiltration Polymorphism in rs1024611 [16]
    GRP78 Glucose-regulated protein Apoptosis Polymorphism in rs3216733 [17]
    DC-SIGN Dendritic cells specific intercellular adhesion molecule 3-grabbing nonintegrin Induced immune cells Polymorphism in rs7252229, rs735240, rs4804803 or rs2287886 [18][19]
    HLA-A2-B46 (Chinese) Human leukocyte antigen-A2-B46 Immune response Polymorphism in chromosome 6p21 [20][21]
    HLA-A2-B-17 (Chinese) Human leukocyte antigen-A2-B-17 Immune response
    HLA-B5 (Caucasians) Human leukocyte antigen-B5 Immune response
    IL-13 Interleukin-13   Polymorphism in rs20541 (TT genotype) [22]
    Chromosome 3p and 9p N/A N/A Chromosomal loss [23]
    Chromosome 12 N/A N/A Gain number [24]
    RASSF1 Ras association (RalGDS/AF-6) domain family member 1A Tumour suppression, cell growth, proliferation copy number variant in in 3p21 [25]
    CDKN2A, CDKN2B Cyclin-dependent kinase inhibitor 2A, 2B Tumour suppression, cell cycle Allelic deletion in 9p21.3 [26]
    EGFR Epidermal growth factor receptor Cell proliferation, cell cycles, apoptosis Upregulation [27][28]
    BamH1-W Bacillus amyloliquefaciens 1 WZhet Viral replicative cycle Upregulation [29][30]
    A73 N/A Cell proliferation and angiogenesis Polymorphism in A157154C [14][31]
    RPMS1 N/A Cell proliferation and angiogenesis Polymorphism in G155391A
    BALF2 N/A Viral infection and replication EBV variants with 162476_C or 163364_T [32]
    miRNA biomarkers
    miR17-92 MicroRNA17-92 Targeting PTEN and apoptosis protein Upregulation [33]
    miR-155 MicroRNA-155 Leucosis Upregulation [34]
    miR-378 MicroRNA-378 Affect tumour suppression, cell cycle Upregulation [35][36]
    miR-141 MicroRNA-141
    miR144-3p MicroRNA-144-3p Targeting PTEN/Akt, cell cycle, apoptosis Upregulation [37]
    miR-17-5p MicroRNA-17-5p
    miR-20a-5p MicroRNA-20a-5p
    miR-20b-5p MicroRNA-20b-5p
    miR-205-5p MicroRNA-205-5p
    miR-16 MicroRNA-16 Cell proliferation, invasion Upregulation [34]
    miR-21 MicroRNA-21 Targets PDCD4, PTEN, SPRY, ERCK, and Bcl-2 family proteins
    miR-24 MicroRNA-24 Epithelial-to-mesenchymal transition Upregulation
    miR-146a   Inflammation Upregulation [38]
    miR-34 MicroRNA-34 Tumour suppression Downregulation [33]
    miR-143 MicroRNA-143 Tumour suppression
    miR-145 MicroRNA-145 Tumour suppression
    let-7b-5p MicroRNA let-7b-5p Cell proliferation Downregulation [37]
    miR-140-3p MicroRNA-140-3p Cell proliferation
    Platelet miR-34c-3p MicroRNA-34c-3p Tumour suppression Upregulation [22]
    Platelet miR-18a-5p MicroRNA-18a-5p Tumour suppression
    MALAT1 metastasis associated with lung adenocarcinoma transcript 1 Invasion Upregulation [39]
    AFAP1-AS1 actin filament-associated protein 1-antisense RNA1 Invasion
    AL359062 N/A N/A
    EBER Epstein–Barr encoding region Cell proliferation, apoptosis, innate immunity Four base deletion SNPs [40]
    miR-BART7-3p BamH1 A rightward transcript 7-3p Cell proliferation targeting NF-κB signalling, angiogenesis targeting AMPK/mTOR/HIF1 signalling Upregulation [2][41][42]
    miR-BART13-3p BamH1 A rightward transcript 13-3p Cell proliferation targeting NF-κB signalling, angiogenesis targeting AMPK/mTOR/HIF1 signalling
    Protein biomarkers
    PAI-1 Plasminogen activator inhibitor 1 Angiogenesis, signalling activities Upregulation [43]
    Fibronectin N/A Cell adhesion
    Mac-2 BP Mac-2-binding protein Cell adhesion
    CTSD Cathepsin D Apoptosis Upregulation [44]
    POSTN Periostin Cell adhesion Upregulation [45]
    CK18 Cytokeratin 18 Transcription Upregulation [46]
    KRT8 Keratin-8 Tumour necrosis factor-mediated signaling pathway, cell differentiation Upregulation [44]
    STMN1 Stathmin-1 Signal transduction
    LCP1 L-plastin Cell differentiation Upregulation [47]
    LGALS1 Galectin-1 Apoptosis Upregulation [48]
    S100A9 S100 calcium-binding protein A9 Cell proliferation, innate immunity, apoptosis Upregulation [47]
    CCL5 C-C motif chemokine 5 Cell adhesion, migration, apoptosis Upregulation [49]
    CLIC1 Chloride intracellular channel 1 Cell cycle, signal transduction Upregulation [50]
    LMP1 Latent membrane protein Signalling activities Upregulation [51]
    P-Thr-sv-5 N/A Gene expression (sub-variant of EBNA1) subvariant of EBNA1 [52]
    EBNA1/IgA EBV nuclear antigens immunoglobulin A Antibody against EBV antigen Increased level [53][54]
    VCA/IgA Viral capsid antigen immunoglobulin A Antibody against EBV antigen
    BALF2/Ab BALF2 antibodies Antibody against EBV antigen Increased level [32]
    Metabolite biomarkers
    kynurenine N/A Metabolism Upregulation [55]
    N-acetylglucosaminylamine N/A Metabolism
    N-acetylglucosamine hydroxyphenylpyruvate N/A Metabolism
    Pyroglutamate N/A Metabolism Upregulation [56]
    Glucose N/A Metabolism
    Glutamate N/A Metabolism
    Glycerol 1-hexadecanoate N/A Metabolism Upregulation [57]
    b-hydroxybutyrate N/A Metabolism
    Arachidonic acid N/A Metabolism
    Stearic acid N/A Metabolism
    Linoleic acid N/A Metabolism
    Proline N/A Metabolism

    N/A. Not available.

    3. NPC Diagnostic Biomarkers

    Consistent findings have revealed that NPC diagnostic accuracy could be enhanced by using a panel of miRNA biomarkers. Liu et al. (2013) reported the sensitivity and specificity of an NPC diagnostic method using five plasma mi-RNAs (miR-16, miR-21, miR-24, miR-155, and miR-378) were 87.7% and 82.0%, respectively [34]. Another study compiling 12-miRNA signatures for early diagnosis of NPC demonstrated an accuracy of up to 100% [58]. These 12-miRNA were found to play an important role in NPC development by modulating its target genes to inhibit NF-κB kinase regulator apoptosis and regulate platelet-derived growth factor receptor α. Collectively, these findings have provided an encouraging message on the use of miRNA as a biomarker for the early diagnosis of NPC.

    Recently, tumour-educated platelets that have accurate diagnostic efficiency in various other types of cancer look like a promising avenue for NPC diagnostic marker discovery. Two platelet miRNAs, namely miR-34c-3p and miR-18a-5p, which have been detected in NPC patients and healthy controls, were found to have high diagnostic ability with a sensitivity of 92.59% and specificity of 86.11% [22]. However, further functional and validation studies were not carried out. Nevertheless, it still seems to be promising as the platelets can alter the transcriptome and molecular signal by affecting its pre-mRNA splicing upon instructions given by the tumour [59]. Additionally, in contrast to other samples, its RNA expression is not affected by the genomic DNA, thus the RNA expression truly corresponds to the pathological condition of the cancer.

    Proteins are found to be involved in regulating many physiological processes, including immune response, metabolism, and cellular signalling pathways, while tumour cells can utilise the protein by-product to make their favourite proteins, thus affecting anabolism and catabolism, eventually leading to an alteration of protein expression patterns. Therefore, these tumour synthesised oncogenic proteins can be used to reflect the real time state of diseases and used for NPC biomarker research.

    Most of these studies have used high throughput mass spectrometry technology, data processing, system integration, cluster index analysis, and integration with information modelling to look for metabolites that reflect clinical disease phenotypes [60]. Numerous metabolites, including kynurenine, N-acetylglucosaminylamine, N-acetylglucosamine hydroxyphenylpyruvate, pyroglutamate, glucose, and glutamate, have been evaluated as potential biomarkers for early NPC diagnosis [55][56]. Further studies conducted in larger NPC cohorts also validated that a panel of seven metabolites including glycerol 1-hexadecanoate, b-hydroxybutyrate, linoleic acid, arachidonic acid, stearic acid, glucose, and proline provided strong NPC diagnosis from disease free controls, with a sensitivity of 88.0% and a specificity of 92.0% [57].

    4. NPC Prognosis Biomarkers

    Up to 40% of NPC patients have disease recurrence or distant metastasis even after they receive a series of CT or RT [61]. This indicates that tumour cells are able to recover from damaged cells and survive by having resistance to current therapies (CT or RT). Therefore, prediction of NPC recurrence or metastasis risk after treatment is crucial since it is the major cause of mortality in NPC patients. Particularly, molecular components that are metastasis susceptible or capable of affecting the radio- or chemo-sensitivity can be used as a prognosis biomarker ( Table 2 ).

    Table 2. Potential prognosis and predictive biomarkers for NPC therapeutic resistance or metastasis and recurrence after treatment.
    Biomolecules Name Role Aberration Sources
    β-catenin 1 Beta-catenin1 Activate multiple downstream growth signalling components such as cyclin D1 and c-Myc Polymorphism in rs1880481 or rs3864004 [62]
    GSK-3β glycogen synthase kinase-3β Cell growth, metabolism, gene transcription, protein translation, cytoskeletal organisation Polymorphism in rs3755557
    APC adenomatous polyposis coli Cell adhesion Polymorphism in rs454886
    XRCC1 X-ray repair cross-complementing 1 DNA repair Polymorphism in rs25489 or Codon399 [63][64][65][66]
    CT Calcitonin receptor Calcium homeostasis Polymorphism in rs2528521
    VCP Valosin-containing protein Proteolysis Polymorphism in rs2074549
    IL-13 Interleukin-13 Chinese population with IL-13 rs20541 Polymorphisms in rs20541 [22]
    ERCC1 Excision repair 1 endonuclease non-catalytic subunit DNA repair Polymorphism with C118T genotype [67]
    EBV-DNA Epstein–Barr virus-DNA EBV genome Upregulation [28]
    YBX3 Y-Box Binding Protein 3 Apoptosis, Gene expression Upregulation [68]
    CBR3 Carbonyl reductase 3 Xenobiotic metabolic process
    LRIG1 Leucine-rich repeats and immunoglobulin-like domains 1 Negative regulator of tyrosine kinases signalling
    CXCL10 Chemokine C-X-C motif ligand 10 Chemokine receptors recruit tumour infiltrating T-lymphocytes, tumour microenvironment
    DCTN1 Dynactin-1 G2/M transition of mitotic cell cycle Downregulation
    GRM4 Glutamate metabotropic receptor 4 Tumour suppression
    HDLBP High density lipoprotein binding protein Cholesterol metabolic process
    ANXA1 Annexin Cell cycle, apoptosis
    POLR2M RNA polymerase II subunit M Negative regulator of transcriptional
    CLASP1 Cytoplasmic linker associated protein 1 Dynamic microtubules stabilization
    FNDC3B Fibronectin type III domain-containing protein 3B Positive regulator of adipogenesis
    WSB2 WD repeat and SOCS box-containing protein 2 Protein ubiquitination, post-translation modification
    WNK1 lysine deficient protein kinase 1 T-cell receptor signalling pathway
    miR-203 MicroRNA-203 Targeting IL-8/Akt signalling Downregulation [69]
    miR-324-3p MicroRNA-324-3p Tumour suppression Downregulation [70][71]
    miR-93-3p MicroRNA-93-3p Targeting Wnt/β-catenin signalling
    miR-4501 MicroRNA-4501 Cellular process
    miR-371a-5p MicroRNA-371a-5p Cellular pathway, apoptosis Upregulation
    miR-34c-5p MicroRNA-34c-5p Cell proliferation, apoptosis, targeting JAK2/STAT3 signalling pathway
    miR-1323 MicroRNA-1323 DNA repair
    miR-9 MicroRNA-9 MHC class I and interferon-regulated gene expression Downregulation [72]
    miR-92a MicroRNA-92a Invasion, migration Upregulation [73]
    miR-574-5p MicroRNA-574-5p Mesenchymal transition Downregulation [3]
    miR-296-3p Micro-296-3p Cytoplasmic Translocation of c-Myc Downregulation [74][75]
    RNA_0000285   homeodomain interacting protein kinase 3 (HIPK3) Upregulation [76]
    EGFR Epidermal growth factor receptor Cell proliferation, cell cycles, apoptosis Upregulation [77]
    GSTP1 Glutathione S-transferase P1 Cell adhesion, apoptosis, negative regulator of NF-kB signaling Methylation [78]
    IGF-1R Insulin-like growth factor-1 receptor Cell proliferation, cell cycles and apoptosis Upregulation [77]
    Jab1 C-Jun activation domain-binding protein-1 Cell proliferation, targeting negative regulator proteins and tumour suppressors (p27 and p53) Upregulation [79]
    EMT Epithelial-to-mesenchymal transition Carcinogenesis and metastatic progression Upregulation [80]
    β-catenin N/A Activate multiple downstream growth signalling components such as cyclin D1 and c-Myc Upregulation [81]
    E-cadherin N/A Cell adhesion, tumour suppression Downregulation
    GnT-V N-acetylglucosaminyltransferase-V Protein glycosylation, cell proliferation Upregulation [82]
    Bcl2 B-cell lymphoma 2 Apoptosis Upregulation [83][84]
    SPARC Secreted protein acidic and Cysteine rich Extracellular matrix synthesis, cell shape Upregulation [85]
    ERPIND1 Serpin family D member 1S Invasion
    C4B Complement C4B Component of the classical activation pathway
    PPIB Ppeptidylprolyl lsomerase B Cyclosporine A-mediated immunosuppression
    FAM173A Family with sequence similarity 173 member A Adenine nucleotide translocase
    Maspin Mammary serine protease inhibitor Tumour suppression Upregulation [86][87]
    GRP78 Glucose-regulated protein Apoptosis
    Mn-SOD Manganese superoxide dismutase Apoptosis
    14-3-3σ 14-3-3 protein sigma Cell cycle arrest, DNA damage response, signal transduction Downregulation
    ANXA1,3 Annexin A1, A3 Cell cycle, apoptosis Downregulation [88][89][90]
    Nm23 H1 Non-metastatic clone 23, isoform H1 TGF-β signaling Upregulation
    KRT1 Keratin 1 Angiogenesis Upregulation [91]
    SAA Serum amyloid A MAPK activities, innate immune response Downregulation [92]
    HSP27 Heat shock protein 27 Apoptosis, cell differentiation Upregulation [93]

    N/A. Not available

    One study acknowledged the value of EBV-DNA for early NPC recurrence after treatment [94]. Most of the patients had EBV-DNA elevated prior to the disease recurrence [28]. The accuracy, sensitivity, and specificity of recurrence diagnostic using EBV-DNA were 87.0%, 82.3%, and 80.0%, respectively [28]. In another study, the circulating EBV-DNA concentration was found to be higher in recurrent NPC plasma compared to primary NPC plasma, thus implying that recurrence risk can be predicted by detecting the circulating EBV-DNA [95]. The National Comprehensive Cancer Network also recommends monitoring NPC patients with EBV-DNA [96]. This EBV-DNA biomarker was further strengthened by combination with a predictive tool, namely distant metastasis gene signature (DMGN), which constitutes 13 genes including DCTN1 , YBX3 , GRM4 , HDLBP, POLR2M , CLASP1 , CBR3 , FNDC3B , WSB2 , LRIG1 , ANXA1 , WNK1 , and CXCL10 to examine whether the patients can benefit from concurrent CT. The patients with the higher predicted metastasis risk would have less sensitivity to concurrent CT [68].

    Moreover, by looking at mRNA involved in NPC progression, the subtype of disease, prognosis, and therapeutic effect in NPC could be predicted [97][98][99]. For example, analysed miRNA expression profile of radioresistant and radiosensitive NPC cell lines by next generation deep sequencing have revealed that downregulation of miR-203, miR-324-3p, miR-93-3p, and miR-4501 and upregulation of miR-371a-5p, miR-34c-5p, and miR-1323 contribute to mediating radio-resistance in NPC [69][70][88]. Additionally, miR-574-5p, miR-9 and miR92a, which modulate the expression of MHC class I and interferon-regulated genes associated with NPC metastasis, could potentially be non-invasive blood-based biomarkers for metastasis prediction [72][73]. RNA sequencing of NPC patients’ peripheral blood mononuclear cells (PBMC) before and after RT has revealed 11 potential mRNA prognostic biomarkers for NPC for post-RT evaluation [100]. RNA_0000285 at homeodomain interacting protein kinase 3 (HIPK3) was observed in high level radio-resistance NPC patients and low radiosensitive NPC patients, thus showing its ability to predict NPC radiosensitivity [76].

    Furthermore, as mentioned previously, the residue of cigarette smoke promotes cancer progression. Cigarette smoke was found to be associated with poor prognosis of chemotherapy and radiotherapy. Nicotine in cigarette smoke promoted chemoresistance by affecting the ATP-biding cassette transporter G2 via downregulation of miR-296-3p and Akt-mediated pathways [74][75]. Furthermore, hypoxia induced through smoking can facilitate tumour angiogenesis, invasion, reoccurrence, and metastasis. Therefore, the downregulation of miR-296-3p in patients could be a potential prognosis or predictive biomarker for recurrence and metastasis.

    The entry is from 10.3390/cancers13143490


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