Introduction
============

The most common malignant neoplasm of the head and neck regions is laryngeal cancer which presents a significant international health problem. This type of cancer has high rate of mortality because of the poor diagnosis in early stage of the disease. Despite favorable treatment in early-stage laryngeal cancers, survival rates for advanced-stage disease are less than 50%. Surgery and chemotherapy are two suitable treatment options that are used for laryngeal cancer. However, their combination is also is used. Recently the number of patients treated with radiotherapy and chemotherapy is increased ([@B1]). However, survival is decreased ([@B2]). Laryngeal cancer has been considered as a multifactorial disease associated with the interaction between environmental factors and genetic background ([@B3]). Environmental factors of laryngeal cancer are introduced as a lower consumption of vegetables and fruits, and higher consumption of milk, eggs, meat, tea, alcohol, and smoking ([@B4]). Recently, various studies have established the changes in molecular level which are associated with the development of laryngeal cancer. For example, several studies have investigated associations between CYP1A polymorphisms and laryngeal cancer risk ([@B5]). Alcohol consumption or smoking beside the uridine diphosphate glucuronosyl transferase enzyme (UGTs) rs4148323 act synergistically to increase the risk of laryngeal cancer ([@B6]). It has also reported the relationship between this type of cancer and nucleotide excision repair pathway genes such as ERCCs and XPA ([@B7]). The proteomics studies on laryngeal cancer show that the changed expression proteins regulate cellular proliferation, differentiation, and apoptosis that may directly related to the pathogenesis of cancer ([@B8]). Another one reported that some significantly changed expression proteins were the products of oncogenes and others were related to signal transduction and immune defense ([@B9]). Deeb A and colleagues showed that related DNA repair pathways are curtail in larynx cancer patients ([@B10]). For better understanding of molecular mechanisms of laryngeal cancer pathogenesis, protein-protein interaction (PPI) network analysis can provide an informative concept and detail schema ([@B11]-[@B20]). Therefore, we used a systems biology approach (based on the available proteomics literature data) as a rational strategy to reveal novel specific markers and probably therapeutic targets for laryngeal cancer.

Experimental
============

*Data collection*

In this study, the inclusion criteria were the studies on the human species using cell line and laryngeal squamous tissue samples involved in the comparison between the tumor and normal tissues. Exclusion criteria were the studies on non-human tissue and studies on samples of biological fluids, including plasma, serum, saliva, and urine. Studies only involved in comparison between the tumor tissue and tumor metastasis one. There was no limitation in methods in proteomic studies. We manually evaluated the publications in line with the above conditions; a total of 275 significantly changed expression proteinsextracted of which 136 proteins belong to up regulated protein group and 139 proteins were as down regulated proteins (See [Tables 1](#T1){ref-type="table"} and [2](#T2){ref-type="table"}).

###### 

The list of up-regulated genes in tissue of human laryngeal cancer

  **NO.**   **Gene name**   **NO.**   **Gene name**   **NO.**   **Gene name**   **NO.**   **Gene name**
  --------- --------------- --------- --------------- --------- --------------- --------- ---------------
  1         ACAA1           35        EEF1D           69        HSPD1           103       PSMD2
  2         ACTR2           36        EEF1G           70        IDH1            104       RAB2A
  3         AKR1C2          37        EEF2            71        IMPDH2          105       RAP1B
  4         ALB             38        EIF2S1          72        ISOC2           106       RPL14
  5         ALDH3A1         39        EIF3F           73        KPNB1           107       RPL6
  6         ANXA11          40        EIF3H           74        LAP3            108       RPS15A
  7         ARHGAP1         41        EIF3I           75        LCP1            109       S100A16
  8         ARHGDIA         42        EIF4A1          76        LDHB            110       S100A8
  9         ARHGDIB         43        EIF5A           77        LGALS7          111       S100A9
  10        ARL1            44        ENO1            78        LTA4H           112       SERPINB3
  11        ARPC4           45        EPPK1           79        MAPRE1          113       SF3A3
  12        ATIC            46        EPS8L1          80        METAP1          114       SFPQ
  13        ATP6V1A         47        ERO1L           81        MPO             115       SND1
  14        BLVRB           48        FABP5           82        MYL6            116       STAT1
  15        C1QBP           49        FBP1            83        NAP1L1          117       TACSTD2
  16        CA2             50        FLOT1           84        NCL             118       TAGLN2
  17        CAND1           51        FN1             85        NDRG1           119       TALDO1
  18        CAP1            52        FSCN1           86        NDUFA8          120       TAPBP
  19        CAPN2           53        FTL             87        NP              121       TF
  20        CAPNS1          54        FUS             88        PABPC1          122       TFRC
  21        CCT6A           55        G3BP2           89        PDIA4           123       TKT
  22        CCT7            56        G6PD            90        PDXK            124       TLN1
  23        CDC37           57        GAPDH           91        PFN1            125       TPI1
  24        CES1            58        GCN1L1          92        PGAM1           126       TPT1
  25        CFL1            59        GFAP            93        PGK1            127       TRAP1
  26        CLIC1           60        GNAI2           94        PGM1            128       TXNDC5
  27        CMPK1           61        GSTP1           95        PLEC1           129       TYMP
  28        COL12A1         62        HADHA           96        PLS3            130       USP14
  29        CPSF6           63        HIST1H1B        97        PPA1            131       VASP
  30        CTSB            64        HMGA1           98        PPP2R1A         132       VCL
  31        CTSC            65        HNRNPA1         99        PRKRA           133       WARS
  32        CYCS            66        HNRNPD          100       PRTN3           134       WDR1
  33        DHX9            67        HNRPDL          101       PSMD11          135       XRCC5
  34        ECH1            68        HSP90B1         102       PSMD13          136       YWHAZ

###### 

The list of down-regulated genes in tissue of human laryngeal cancer

  **NO.**   **Gene name**   **NO.**   **Gene name**   **NO.**   **Gene name**   **NO.**   **Gene name**   **NO.**   **Gene name**
  --------- --------------- --------- --------------- --------- --------------- --------- --------------- --------- ---------------
  1         A1BG            29        CORO1A          57        HIST1H1C        85        MYH11           113       RPS11
  2         A2M             30        CORO1C          58        HNRNPL          86        MYH7            114       RPS15
  3         ABHD14B         31        CRYAB           59        HP              87        MYL2            115       RPS9
  4         ACADVL          32        CSTB            60        HSDL2           88        MYLPF           116       RRBP1
  5         ACAT1           33        CTNND1          61        HSP90           89        NDRG2           117       SDHA
  6         ACTG1           34        CYB5R3          62        HSPB1           90        NDUFA10         118       SERPINA1
  7         AGR2            35        DCN             63        HSPG2           91        NDUFA12         119       SFN
  8         AK3             36        DDOST           64        IARS2           92        NDUFS2          120       SLC4A1
  9         ALDH2           37        DLD             65        IGHA1           93        OGDH            121       SOD1
  10        ANXA2           38        DYNLL1          66        IGHG1           94        OGN             122       SOD3
  11        APOA1           39        ECHS1           67        IGKC            95        ORM1            123       SP140
  12        APOA2           40        EIF3A           68        IMMT            96        ORM2            124       SPTAN1
  13        ASPN            41        EPHX1           69        ITIH2           97        PA2G4           125       SPTBN1
  14        ATP5B           42        ERP29           70        JUP             98        PCYOX1          126       SSR4
  15        ATP5D           43        EVPL            71        KRT19           99        PHB             127       TGFBI
  16        ATP5F1          44        F13A1           72        LAMC1           100       PHB2            128       TMED10
  17        ATP5O           45        FAU             73        LGALS3          101       PRDX3           129       TNNT3
  18        BGN             46        FGB             74        LGALS3BP        102       PRELP           130       TPM1
  19        C1QC            47        FGG             75        LMAN1           103       PSMB1           131       TRIM29
  20        C3              48        FKBP4           76        LMAN2           104       PSME2           132       TROVE2
  21        CALM1           49        GGT5            77        LMNA            105       PYCR1           133       U2AF1
  22        CALML3          50        GLUD1           78        LMNB1           106       PYGB            134       UNC84B
  23        CANX            51        GOT2            79        LRP1            107       RAN             135       UQCRB
  24        CFH             52        GPD2            80        LTF             108       RPL10           136       UQCRC1
  25        CFL1            53        GRP94           81        LUM             109       RPL19           137       UQCRC2
  26        CKM             54        GSN             82        LYZ             110       RPL23A          138       VDAC1
  27        CKMT1A          55        GSTP1           83        MARCKS          111       RPL9            139       VDAC2
  28        COL15A1         56        H2AFY           84        MTPN            112       RPN1                      

###### 

Presentation of the hub proteins in the up-regulated and down-regulated protein--protein interaction networks of laryngeal cancer (top 20 in each PPI network). The hub nodes that play as bottleneck node are asterisked (for more details see Table 4 and discussion

                   **ID**       **Degree**   **ID**    **Degree**   **ID**    **Degree**   **ID**   **Degree**
  ---------------- ------------ ------------ --------- ------------ --------- ------------ -------- ------------
  Up regulated     YWHAZ^\*^    1634         CAND1\*   827          PSMD2\*   636          ALB\*    524
  FN1\*            1538         PABPC1       725       FUS\*        631       NCL          508      
  PPP2R1A\*        1208         MAPRE1\*     716       KPNB1\*      618       STAT1\*      503      
  CDC37\*          1158         HNRNPD\*     703       DHX9         554       ACTR2\*      492      
  HNRNPA1\*        1054         XRCC5\*      661       EEF1G        538       CCT7         471      
  Down regulated   HSP90AA1\*   2019         ACTG1\*   681          RPL23A    449          LMNA\*   407
  CALM3\*          1276         P31947       569       CANX\*       427       Q13813       390      
  HSPB1\*          1038         RPL9P9       484       P20618       424       PHB2         364      
  RPL10\*          992          RAN\*        479       EIF3A        412       HNRNPL\*     351      
  DYNLL1\*         792          RPS9         450       IGHG1\*      411       U2AF1        348      

###### 

The list of top 20 up-regulated and down-regulated genes ranked based on BC from largest to smallest values

                   **ID**     **BC**    **ID**    **BC**    **ID**    **BC**     **ID**    **BC**
  ---------------- ---------- --------- --------- --------- --------- ---------- --------- ---------
  Up regulated     PDXK       1.0       HNRNPA1   0.07400   FUS       0.04727    PSMD2     0.03599
  KHC              1.0        CDC37     0.06998   ENO1      0.04595   ALB        0.03178   
  YWHAZ            0.13462    GNAI2     0.06835   HNRNPD    0.03861   HSPD1      0.03167   
  FN1              0.13420    PPP2R1A   0.06310   ACTR2     0.03749   XRCC5      0.03007   
  CAND1            0.07829    MAPRE1    0.04832   KPNB1     0.03667   STAT1      0.02821   
  Down regulated   HSP90AA1   0.20507   DYNLL1    0.06243   LGALS3    0.04051    APOA1     0.02707
  CALM3            0.13699    C3        0.06131   A2M       0.03737   IGHG1      0.02688   
  HSPB1            0.07676    CANX      0.05931   RAN       0.03283   SOD1       0.02663   
  ACTG1            0.07472    SFN       0.04720   FN1       0.03122   HNRNPL     0.02442   
  RPL10            0.06626    LMNA      0.04078   PSMB1     0.02739   LGALS3BP   0.02210   

###### 

The modules of up regulated and down regulated PPI networks of human tissue of laryngeal cancer. The asterisked proteins are hub-bottleneck nodes

                   **Category**       **MCODE score, nodes and edges**   **Seed**                       **Hub**
  ---------------- ------------------ ---------------------------------- ------------------------------ ----------------------------------------
  Up regulated     Up-1               7.6, 65 and 358                    NPM1                           HNRNPD\*, DHX9, FUS\*, NCL and YWHAZ\*
  Up-2             5.8, 65 and 320    HSPA9                              KPNB1\*, XRCC5\* and CAND1\*   
  Up-3             4.0, 52 and 219    NS                                 PPP2R1A\*                      
  Up-4             3.8, 49 and 115    \-\-\--                            HNRNPA1\*                      
  Up-5             3.3, 13 and 44     \-\-\--                            ACTR2                          
  Down regulated   Down-1             5.87, 65 and 219                   UQCRC1                         \-\-\--
  Down-2           4.06 , 30 and 80   \-\-\--                            RPL9P9 ,DYNLL1\*               
  Down-3           4.0 , 15 and 43    \-\-\--                            \-\-\--                        
  Down-4           4.0 , 10 and 30    \-\-\--                            CALM3\*                        
  Down-5           4.0 , 18 and 80    \-\-\--                            ACTG1\* , HSP90AA1\*           
  Down-6           3.25, 17 and 42    \-\-\--                            PHB2, U2AF1                    

###### 

GO functional enrichment analysis of up- regulated and down-regulated PPI network modules. Top three terms of each module are tabulated

                   **Category**   **Term**                                                                                              **Description**
  ---------------- -------------- ----------------------------------------------------------------------------------------------------- --------------------------------------
  Up regulated     Up-1           GO:0006396                                                                                            RNA processing
                   GO:0000380     Alternative mRNA splicing                                                                             
                   GO:0071826     Ribonucleoprotein complex subunit organization                                                        
  Up-2             GO:0000082     G1/S transition of mitotic cell cycle                                                                 
                   GO:0042769     DNA damage response                                                                                   
                   GO:1901992     Positive regulation of mitotic cell cycle phase transition                                            
  Up-3             GO:0031398     Positive regulation of ubiquitination                                                                 
                   GO:0046364     Monosaccharide biosynthetic process                                                                   
                   GO:0006098     Pentose-phosphate shut                                                                                
  Up-4             GO:0008380     RNA splicing                                                                                          
                   GO:0022613     Ribonucleoprotein complex biogenesis                                                                  
                   GO:0031123     RNA 3 -end processing                                                                                 
  Down regulated   Down-1         GO:0022904                                                                                            Respiratory electron transport chain
                   GO:0046034     ATP metabolic process                                                                                 
                   GO:1902600     Hydrogen transmembrane transport                                                                      
  Down-2           GO:1900739     Regulation of protein insertion into mitochondrial membrane involved in apoptotic signaling pathway   
                   GO:0031110     Regulation of microtubule (de) polymerization                                                         
                   GO:0016259     Selenocystein metabolic process                                                                       
  Down-3           GO:0010257     NADH dehydrogenase complex assembly                                                                   
                   GO:0006099     Tricarboxylic acid cycle                                                                              

![Protein-protein interaction network for up-regulated differentially expressed proteins in tissue of human laryngeal cancer include of 7312 nodes and 33757 edges](ijpr-17-415-g001){#F1}

![Up: Centrality analysis of protein-protein interaction network for down-regulated differentially expressed proteins in tissue of human laryngeal cancer consist of 6707 nodes and 27422 edges. Down: The dense and central part of upper network is shown in more details](ijpr-17-415-g002){#F2}

![Modules of the protein-protein interaction network for up-regulated differentially expressed proteins (MCODE score \> 3 and node \> 6). The yellow cycles indicate seed proteins and the pink cycles reagent proteins in modules. There are no seed in Up-4 and Up-5 modules](ijpr-17-415-g003){#F3}

![Modules of the protein-protein interaction network for down-regulated differentially expressed proteins (MCODE score \> 3 and node \> 6). The yellow cycles indicate seed proteins and the pink cycles reagent proteins in modules. Only Down -1 module has seed and the other ones have no seed](ijpr-17-415-g004){#F4}

*PPI network analysis*

PPI network analyzed by Cytoscape Version 3.4 and Betweenness centrality (BC) and node degree the two major centrality parameters were analyzed by using a Cytoscape plug-in called 'Network Analyzer' ([@B21]). Degree indicates the number of connectivity belongs to a node and nodes having high degree were introduced as hub proteins. BC value the other centrality index reflects the shortest paths that pass through a node ([@B22]).

*Screening of network modules and functional analysis*

The modules of the two constructed networks (including up and down regulated networks) were provided by MCODE analysis and parameters including Node Score Cutoff: 0.2, K-Core: 2, Degree Cutoff: 2 and, Max depth = 100 were used as the cut-off criteria for network module screening. MCODE score \> 3 and node \> 6 were considered for functional enrichment analysis of the modules. Kappa statistic ≥ 0.4 and Bonferroni step down method for probability value correction were used for annotation analysis of the selected modules.

Results
=======

After the submission of up-regulated and down-regulated proteins into Cytoscape, a total of 7312 and 6707 nodes related to the up-regulated and down-regulated proteins are included in the networks, respectively. In the final networks ([Figures 1](#F1){ref-type="fig"} and [2](#F2){ref-type="fig"}), the node›s degree was organized based on size; the nodes with high degree have bigger size and the blue to brown color represented low to high BC values for each node. \\ The nodes with high degree were considered as key proteins. Then, the top 20 proteins with highest connectivity were identified as the hub proteins for each of the networks and similarly, the top 20 proteins based on betweenness centrality value were selected as bottleneck proteins (See [Tables 3](#T3){ref-type="table"} and [4](#T4){ref-type="table"}).

*Module analysis*

A total of 11 modules including 5 up-regulated and 6 down-regulated network modules were obtained using default criteria. It was selected modules with MCODE score \> 3 and node \> 6. Five up-regulated modules (Up, 1-5) ([Figure 3](#F3){ref-type="fig"}), and six down-regulated modules (Down, 1-6) ([Figure 4](#F4){ref-type="fig"}) were selected for enrichment analysis.

There were some key proteins (hubs) in total of 5 up-regulated modules and 3 up-regulated network modules among them have 3 seed proteins (see [Table 5](#T5){ref-type="table"}). While, in down-regulated network modules, only Down-1 module has seed. The hubs in this network are distributed as tabulated data in [Table 5](#T5){ref-type="table"}.

*Functional enrichment analysis for modules*

Four up-regulated modules (Up, 1-4) and three down-regulated modules (Down, 1-3) were enriched based on functional annotation. The top three GO terms for each module are shown in [Table 6](#T6){ref-type="table"}.

Discussion
==========

Protein-protein interaction (PPI) network analysis has a significant growth in cancer studies to facilitate introducing early stage biomarkers ([@B23]). In our study, the laryngeal cancer related proteins were analyzed via PPI network construction, hub gene identification, module analysis, and functional enrichment analysis of most significant modules. These stages were carried out for up-regulated proteins and down-regulated ones in human laryngeal cancer tissue, separately. As it is shown in [Tables 1](#T1){ref-type="table"} and [2](#T2){ref-type="table"}, there are 275 changed expression proteins (including up and down regulated proteins) related to the human tissue of laryngeal cancer. Data management and analysis is a difficult process due to huge numbers of the collected proteins. Since PPI network analysis is a powerful method in categorization and ranking of the candidate and related proteins for a certain disease, here the up and down regulated networks are constructed separately ([Figures 1](#F1){ref-type="fig"} and [2](#F2){ref-type="fig"}). Topological analysis of the networks lead to rank of the nodes based on networks properties ([@B18]). By using two centrality indices including degree and betweenness, totally 80 nodes are selected among 275 initial proteins as important proteins (see [Tables 3](#T3){ref-type="table"} and [4](#T4){ref-type="table"}). However, the number of 80 nodes can not be considered as a suitable biomarker panel related to laryngeal cancer and more screening is required. The hub-bottleneck nodes for the up and down regulated networks are shown in [Table 3](#T3){ref-type="table"}. As it is shown in this Table there are 15 and 11 hub-bottlenecks for up and down regulated networks respectively. Module is a part of a network including closed related proteins havig specific biological function ([@B20]). Determined modules of network can provide informative perspective about different roles of the nodes ([@B24]). As it is shown in [Figures 3](#F3){ref-type="fig"} and 4 and [Table 5](#T5){ref-type="table"} there are 5 and 6 modules for the up and down regulated networks respectively. Functional enrichment analysis for top score modules indicated that RNA processing and splicing, mitotic cell cycle regulation and sugar biosynthesis are affected by up-regulated modules while metabolic pathways and mitochondria are the main affected subjects by down regulated modules (see [Table 6](#T6){ref-type="table"}). The most significant pathways in four modules Up, 1-4 were RNA processing, G1/S transition mitotic cell cycle, protein ubiquitination and RNA splicing. It has been revealed overlapping between important pathways involved in the conversion of pre-mRNA to mature mRNA. In previous studies, it shows that polymorphisms of mRNA processing genes can be considered as risk factors for development of laryngeal cancer ([@B25]). The most significant pathways in down regulated modules (Down, 1-3) were respiratory electron transport chain, regulation of protein insertion in to mitochondrial membrane involved in apoptotic signaling pathway, and NADH dehydrogenase complex assembly. Proliferating cancer cells, such as laryngeal cancer, preferentially use anaerobic glycolysis rather than oxidative phosphorylation for energy production ([@B26]). In one system biology study, the glycolysis/gluconeogenesis pathway has been introduced as the most important pathway in laryngeal cancer ([@B27]). Then, the production of energy from mitochondrial respiratory may shift to glycolysis in laryngeal cancer. To prove this hypothesis and determine the energy supply sources of laryngeal cancer cells, more studies are needed. Regulation of protein insertion into mitochondrial membrane involved in apoptotic signaling pathway is the other important pathway in down regulated modules. One of the mechanisms impaired cancer cells is apoptosis. Apoptosis can be activated through several different signaling pathways, but a part of this mechanism is controlled in mitochondrial membrane through insertion apoptotic proteins ([@B28]). According to these results, in laryngeal cancer, apoptotic mechanism may disturb through the impairment of transporter proteins which transform apoptotic proteins into mitochondria. According the results of [Table 5](#T5){ref-type="table"}, the scattering of hubs in up-modules was more than down ones. Interestingly, the finding indicate that the seeds and hubs in up-modules have the similar functions with each other that are associated with regulation of cell cycle ([@B29], [@B30]). Among 26 hub-bottleneck nodes 12 proteins (8 up-regulated and 4 down-regulated proteins) are distributed in 8 modules (see [Table 5](#T5){ref-type="table"}). These proteins are tabulated in supplementary Table S1 and are ranked based on amounts of degree value. Here two suggestions are feasible: first investigation about expression changes of these 12 genes in the field and the second idea is selection of the top up and down regulated genes for more examinations. We choose cutoff 1200 for degree and therefore YWHAZ and PPP2R1A as the top two up-regulated genes and also HSP90AA1 and CALM3 as the top two down-regulated genes are introduced as human laryngeal cancer. YWHAZ gene with the highest degree and BC scores encodes 14-3-3 protein zeta/delta that has an essential role in tumor cell proliferation ([@B31]) through the regulation of multiple cellular processes, such as cell cycle control, anti-apoptosis, signal transduction, inflammation, and cell adhesion/motility ([@B32]). YWHAZ has been introduced as candidate proto-oncogene in head and neck squamous cell carcinoma whose reduced expression causes lower level of DNA synthesis rates ([@B33]). 14-3-3 proteins could be a key regulatory components in many processes that are crucial for development of cancers ([@B34]) such as laryngeal cancer ([@B8]). PPP2R1A gene encodes one subunit of protein phosphatase 2. This protein phosphatase is involved in control of cell growth and cell division processes. The role of this subunit in integrity of enzyme is highlighted. Therefore, it is expected that PPP2R1A plays a crucial regulatory role in cell proliferation in cancer cell line([@B35]). HSP90AA1 and CALM3 were found as two top ranked genes in the down-regulated PPI network. These proteins belong to family of proteins which involved in the regulation of specific target proteins in cell cycle control and programmed cell death ([@B36], [@B37]). On the other hand, CALMs in addition to cell cycle, related to centrosome cycle and deregulation of this protein can be the origin of chromosomal instability in cancer ([@B38]). Interestingly, all determined possible biomarkers are related to the cell cycle process.

Conclusion
==========

In this study, it has been represented a model of important proteins and pathways that provide a new level of information for laryngeal cancer that increases our knowledge about diagnostic and therapeutic aspects of this disease. Finally, a possible biomarker panel including YWHAZ and PPP2R1A as the two up-regulated genes and HSP90AA1 and CALM3 as the two down-regulated genes for human laryngeal cancer is introduced.

Supplementary Materials
=======================

###### 

Table S1
