The clinical significance and potential molecular mechanism of integrin subunit beta 4 in laryngeal squamous cell carcinoma

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The clinical significance and potential molecular mechanism of integrin subunit beta 4 in laryngeal squamous cell carcinoma Feng Zhonga,1, Hui-Ping Lub,1, Gang Chenb, Yi-Wu Dangb, Guo-Sheng Lib, Xiao-Yi Chenb, Yong-Ying Qinb, Yu-Xuan Yaob, Xiao-Guohui Zhangb, Yao Liangb, Ming-Xuan Lib, Miao Moc, Kai-Lang Zhangc, Hua Dingc, Zhi-Guang Huangb,*, Zhu-Xin Weic,* a

Department of Pathology, Hengxian People’s Hospital, 141 Jiaoyu Road, Hengxian County of Nanning 530300, Guangxi Zhuang Autonomous Region, People’s Republic of China b Department of Pathology, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning 530021, Guangxi Zhuang Autonomous Region, People’s Republic of China c Department of Radiotherapy, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning 530021, Guangxi Zhuang Autonomous Region, People’s Republic of China

A R T I C LE I N FO

A B S T R A C T

Keywords: Integrin subunit beta 4 Larynx Squamous cell carcinoma Immunohistochemistry Microarray RNA-sequencing

The relationship between integrin beta 4 (ITGB4) expression and laryngeal squamous cell carcinoma (LSCC) remains unclarified. The object of the present study was to explore the clinical significance and potential molecular mechanism of ITGB4 in LSCC. The protein level of ITGB4 was significantly higher in 46 LSCC patients than in 26 non-LSCC tissues detected by in-house immunohistochemistry. Consistently, ITGB4 mRNA level was also greatly upregulated based on microarray and RNA-seq data (standard mean difference, SMD = 1.62, 95 % CI: 1.23–2.00). And the area under curves (AUC) of summary receiver operator characteristic (SROC) was 0.87 (95 % CI: 0.84–0.90) based on 172 cases of LSCC and 59 cases of non-cancerous controls. Ninety genes were intersected by the ITGB4 related genes and LSCC differential expressed genes (DEGs) from all available microarray and RNA-seq datasets. Based on Gene Ontology (GO) analysis, the top terms of biological process (BP), cellular component (CC) and molecular function (MF) for the 90 ITGB4 related DEGs were extracellular matrix organization, basement membrane and extracellular matrix structural constituent, respectively. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed that ITGB4 related DEGs mainly participated in the pathways of ECM-receptor interaction, Focal adhesion and Small cell lung cancer. Moreover, the Protein-Protein Interaction (PPI) network indicated that ITGA3, ITGA5, ITGB4, MET, LAMA3, and COL4A1 might be the core genes of LSCC development related to ITGB4. In conclusion, high ITGB4 expression may lead to the occurrence and development of LSCC via various signaling pathways.

1. Introduction Head and neck squamous cell carcinoma (HNSCC) ranks third among the most prevalent cancers in developing countries, accounting for 6 % of all cancers [1,2]. As a subtype of HNSCC, laryngeal cancer is the second highest incidence of head and neck cancer in the world, and it was estimated that, in 2015, the number of annual morbidity and deaths in China was 26,400 and 14,500, respectively [3]. The main pathological type of laryngeal cancer is highly aggressive laryngeal squamous cell carcinoma (LSCC) [4,5]. It was estimated that there would be 17,870 newly diagnosed LSCC cases in the United States in

2019, accounting for 33.7 % of HNSCC cases [6]. The prevention and early diagnosis of laryngeal cancer are the most effective means to improve cure rates and quality of life. In general, laryngeal cancer is often detected earlier than tumors located in other subparts of the head and neck. However, its symptoms may be nonspecific and may cause delays in diagnosis. Although there are some surgical and nonsurgical treatments, such as radiotherapy, surgery, and chemotherapy, the overall mortality from laryngeal cancer is still not improved due to the final tumor metastasis and treatment resistance [7,8]. In recent decades, patients with LSCC have benefited from early diagnosis and multimodal interventions, including routine surgery, chemotherapy,

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Corresponding authors. E-mail addresses: [email protected] (Z.-G. Huang), [email protected] (Z.-X. Wei). 1 Contributed equally as co-first authors. https://doi.org/10.1016/j.prp.2019.152785 Received 7 October 2019; Received in revised form 21 November 2019; Accepted 10 December 2019 0344-0338/ © 2019 Elsevier GmbH. All rights reserved.

Please cite this article as: Feng Zhong, et al., Pathology - Research and Practice, https://doi.org/10.1016/j.prp.2019.152785

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in the GEO database was as follows: “laryngeal cancer” or “glottic cancer” or “glottic carcinoma” and “squamous cell carcinoma” or “SCC,” and “mRNA” or “gene.” As of August 3, 2019, we retrieved four mRNA microarrays for laryngeal cancer from the GEO database. The inclusion criteria were as follows: (1) the species was Homo sapiens; (2) the tissue samples were extracted from the larynx; (3) the cancerous tissue was diagnosed as SCC; (4) the expression profile data of ITGB4 were provided; and (5) each microarray contained laryngeal cancer tissue samples and normal comparative samples. The exclusion criteria were duplicated or incomplete data.

radiotherapy, and biotherapy. However, the five-year survival rate has not improved, but has declined, and the molecular mechanism research results of LSCC are unsatisfactory [9]. Studies have shown that tobacco, excessive alcohol consumption, and betel nut intake may be major risk factors for the development of LSCC [10]. However, the molecular mechanism of the occurrence and progress of LSCC remains unclear. Therefore, it is important to study the key molecules involved in the development of LSCC and find effective biomarkers to develop more effective prevention and treatment strategies for patients with LSCC. Integrin subunit beta 4 (ITGB4) is the gene responsible for encoding a layer of adhesion protein receptor integrator beta 4, which is involved in the adhesion of extracellular matrix and cells. This subunit may play a key role in immersive cancer biology by transmitting signals that regulate gene expression and cell growth [11]. There was evidence that the overexpression of ITGB4 was associated with aggressive phenotypes and a poor prognosis of multiple malignancies [12]. For example, studies have shown that the overexpression of ITGB4 promotes the development of breast cancer [11] and gastric cancer [12]. In addition, it is involved in the invasion and metastasis of breast cancer [13] and prostate cancer [14]. ITGB4 was originally thought to be a specific antigen for tumors, but further studies have shown that it can be expressed in various squamous cells—for example, in the skin, lung, head, and neck—and cervical squamous cells (SCCs). High expression of ITGB4 leads to the patient’s condition developing in a bad direction and the survival rate is low [15]. However, studies on the clinical significance of ITGB4 in LSCC have not been constantly reported yet. Therefore, by studying the expression of ITGB4 in LSCC, we explored the association of ITGB4 and LSCC and the potential mechanism of ITGB4 in the development of LSCC, from which a new biological target for LSCC prevention and treatment may be explored. Today, high-throughput technology has been broadly applied in the biomedical area, including chips technology, such as gene chips, protein array, tissue array, and sequencing. Through bioinformatics analysis, it is possible to explore in depth the gene expression and molecular pathways in cancer, from where the mechanism of the role of related genes in cancer may be explored. In this study, we comprehensively analyzed the public mRNA expression data from Gene Expression Omnibus (GEO), Sequence Read Archive (SRA), ArrayExpress, Oncomine, The Cancer Genome Atlas (TCGA), the protein expression data in the in-house tissue microarrays, and literature of various types. All of these statistics assisted the researchers in investigating the expression of ITGB4 in LSCC and elucidating the correlations between clinical features and survival status. Moreover, we inspected the prospective molecular machinery of ITGB4 in the onset and development of LSCC, offering a novel insight into research on the onset and progress, diagnosis, treatment, and prevention.

2.3. RNA-seqs on patients with laryngeal cancer From the TCGA database, we achieved the mRNA expression and clinicopathological parameters in LSCC and selected data on ITGB4, resulting in 111 LSCC tissues and 12 normal laryngeal tissues. For comparison, using the data in the TCGA database, the expression of ITGB4 in LSCC, and clinical case parameters, we used the receiver operating characteristic (ROC) curve and Kaplan–Meier curves to evaluate the diagnostic and prognostic significance of ITGB4 in LSCC.

2.4. Interpretation of immunohistochemistry results ITGB4 was expressed in the cytoplasm, and the total score were recorded according to product of the score based on staining intensity and the score based on percentage of positive cells. The scoring criteria are shown in Table 1, which had been elaborated in previous studies with the total score of immunohistochemical (IHC) staining = score based on staining intensity * score based on percentage of positive cells [17].

2.5. ITGB4-related genes and differentially expressed genes in LSCC We performed a combined analysis on the four microarrays and one RNA sequence collected from GEO and TCGA database, respectively, after removing the batch effect to screen for differentially expressed genes (DEGs). Subsequently, we combined the microarrays obtained by a joint analysis with the Pearson correlation coefficient statistical method to select the gene associated with ITGB4. (The correlation coefficient was 0.3.) Meanwhile, the limma and edgeR R packages were used to screen the DEGs of chips and RNA sequencing, respectively. Finally, the DEGs were used to draw a volcano plot in R, which was intersected with ITGB4-related genes from the chips and RNA-seq data to compose the ITGB4 related DEGs via the Perl screenplay for subsequent functional annotation, pathway enrichment analysis, and protein–protein interaction (PPI) network.

2. Materials and methods 2.1. Immunohistochemistry A total of 46 LSCC tissues and 26 non-LSCC controls were included in the study, which were provided by the tissue microarray from Pantomics, Inc. (Richmond, CA; HNT961, HNT962, and HNT1021). The study was authorized by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University in Nanning, China, and both the clinicians and the patients agreed to use the organization’s research. All clinicopathological features were obtained to analyze the relationship with the expression of ITGB4 [16].

Table 1 Total score of ITGB4 expression level in immunohistochemical staining. Score based on staining intensity

0 1 2 3

2.2. Collection of the microarray data using GEO, SRA, ArrayExpress, and Oncomine The high-throughput microarray and RNA-sequencing (RNA-seq) data for the study came from the National Biotechnology Information Center, GEO, SRA, ArrayExpress, and Oncomine databases. Our search

(no staining) (light staining) (medium staining) (strong staining)

Total score(score based on staining intensity * score based on percentage of positive cells) 0 (< 5 %)

1 (5 %–25 %)

2 (26 %–50 %)

3 (51 %–75 %)

4 (76 %–100 %)

0 0 0 0

0 1 2 3

0 2 4 6

0 3 6 9

0 4 8 12

Notes: 0 (< 5 %), 1 (5 %–25 %), 2 (26 %–50 %), 3 (51 %–75 %) and 4 (76 %–100 %) were the scores based on percentage of positive cells. For example, the 12 in the table is the product of 3 (strong staining) and 4 (76 %–100 %). 2

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Table 2 The relationships between Integrin subunit beta 4 (ITGB4) expression and the clinicopathologic parameters of the immunohistochemistry of the 46 cases of laryngeal squamous cell cancer (LSCC)samples and 26 cases of Non-LSCC samples. Clinicopathologic parameters

Number

ITGB4 Expression (Mean ± SD)

T

P

LSCC Non-LSCC

46 26

10.29 ± 2.0 3.23 ± 1.24

18.79

< 0.001

Male Female

48 1

10.33 ± 1.99 8±0

1.16

0.252

≥60 < 60

21 28

10.28 ± 2.02 10.28 ± 2.01

0

1

III-IV I-II

16 33

12.00 ± 0.00 9.45 ± 1.95

7.48

< 0.001

III-IV I-II

34 15

10.35 ± 1.99 10.13 ± 2.06

0.35

0.73

T3-T4 T1-T2

20 29

10.8 ± 1.88 9.931 ± 2.03

1.54

0.132

N1 N0

24 22

10.16 ± 2.03 10.18 ± 2.03

0.03

0.98

Tissues

Sex

Age

Tumor grading

Stage

T

Fig. 2. Flow chart of study selection for integrin subunit beta 4 (ITGB4) microarrays based on GEO datasets. GEO, Gene Expression Omnibus; LSCC, laryngeal squamous cell carcinoma.

N

[18–20]. Finally, the CytoHubba plug-in of Cytoscape v3.6.1 was used to screen hub genes.

Notes: T, T value of student's t test; P, P value of student's t-test.

2.6. Functional annotation, pathway enrichment analysis, and PPI network construction

2.7. Statistical analysis Using four data extracted from the GEO database and one from the TCGA database, we used the Student t-test in SPSS v22.0 (IBM Corp., Armonk, NY, USA) to observe differences in the expression of ITGB4 between LSCC and normal-tissue control samples. GraphPad Prism v8.0 was used to plot the survival curve (log-rank) on the basis of RNA-seq data and calculate the hazard ratio, which was also used to plot scatterplots and ROC curves. The scatterplots were used to compare the differential expression of ITGB4 in LSCC and normal-tissue control

Bioinformatics tools were used to further explore the underlying mechanisms of ITGB4 in LSCC. We used the clusterProfiler R package to determine the mechanism of action of ITGB4 in LSCC on the basis of the ITGB4 related DEGs. At the same time, the ITGB4 related DEGs based on gene chip and RNA-seq data, were input into the STRING database, and the database was used to perform the PPI network analysis of the potential molecular mechanisms of LSCC from a systemic perspective

Fig. 1. The expression levels of integrin subunit beta 4 (ITGB4) in laryngeal squamous cell cancer (LSCC) tissues and normal larynx tissues. A: light staining of Integrin subunit beta 4 (ITGB4) in normal larynx tissues (×100, ×200). B: strong staining of ITGB4 in advanced differentiated laryngeal squamous cell cancer (LSCC) tissues (×100, ×200). C: strong staining of ITGB4 in intermediate differentiated LSCC tissues (×100, ×200). D: strong staining of ITGB4 in primary LSCC tissues (×100, ×200).

3

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Fig. 3. Screening process for the ITGB4 related DEGs. Integrin subunit beta 4 (ITGB4)-related genes were derived from Pearson correlation coefficients, which was more than or equal to 0.3. GEO, Gene Expression Omnibus; SRA, Sequence Read Archive; NHI, the logo of The Cancer Genome Atlas Program; LSCC, laryngeal squamous cell carcinoma; ITGB4, integrin subunit beta 4; DEGs: differential expression genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; PPI, Protein-Protein Interaction.

3.3. Clinical significance of ITGB4 in LSCC using chips and RNA-seq data.

samples, and the ROC curves were used to assess the diagnostic value of ITGB4 for LSCC. The continuous variable analysis was performed by evaluating the overall standard mean difference (SMD) and 95 % CI, using the Stata v12.0 via the fixed-effect model. The heterogeneity was analyzed with chi-squared and I2 tests. The results were considered statistically significant if the observed SMD with 95 % CI did not exceed zero. In addition, we built Begg’s and Egger’s funnel plots to detect the publication bias. The summary ROC curve (SROC) was drawn to obtain the specificity, sensitivity, and diagnostic value of ITGB4 for the diagnosis of LSCC [17,18]. Furthermore, we calculated the positive and negative likelihood ratios to estimate its diagnostic value.

The relationships between the expression of ITGB4 and clinicopathological parameters using RNA-seq data are presented in Table 4. There was no significant relationship between the expression level of ITGB4 and sex, age, stage, TNM staging, or smoking history of patients with LSCC, but significant differences in the expression level of ITGB4 were still found between LSCC tissues and non-LSCC tissues (P < 0.0001). Meanwhile, we used the area under the curve (AUC) of the ROC curve to assess the diagnostic value of ITGB4 for LSCC and normal tissues. The results are shown in Fig. 4, with GSE29330 (AUC = 0.8923, P = 0.0120), GSE51985 (AUC = 0.8100, P = 0.0191), GSE59102 (AUC = 0.9178, P < 0.0001), GSE84957 (AUC = 0.8148, P = 0.0243), and TCGA (AUC = 0.9176, P < 0.0001). Among the survival curve on the basis of RNA-seq data, no significant correlation (loglank P = 0.6351; Fig. 6) was found between the expression level of ITGB4 and the prognostic outcome of LSCC. Meanwhile, the SROC curve showed that the AUC was 0.87 with 95 % CI = 0.84–0.90, whereas the corresponding sensitivity and specificity were 0.82 (95 % CI = 0.73–0.88) and 1.00 (95 % CI = 0.10–1.00), respectively (Fig. 7 and 8A). Finally, the diagnosis likelihood ratio positive (LRP+) and negative (LRP−) were 194.72 (0.10*3.9e–0.5) and 0.18 (0.12–0.27), respectively (Fig. 8B).

3. Results 3.1. Clinical value of ITGB4 in LSCC by IHC The correlations between the expression of ITGB4 and the clinicopathological parameters of the immunohistochemistry of the 46 cases of patients with LSCC and 26 cases of non-LSCC samples are provided in Table 2. We analyzed the correlations between the expression of ITGB4 and clinicopathological features, such as sex, age, and tumor stage. However, it was found only tumor grading showed significant difference (P < 0.05). We carried out IHC staining on normal tissues and poorly, moderately, and highly differentiated LSCC samples. The results showed that the staining of ITGB4 on normal tissues was light, whereas the staining of poorly, moderately, and highly differentiated LSCC samples was strong (Fig. 1).

3.4. Functional annotation, pathway enrichment analysis, and PPI network analysis of ITGB4 related DEGs We downloaded the chips and RNA-seq data from the GEO and TCGA databases, extracted the DEGs using chips and RNA-seq, respectively, and drew volcano plots with R package to visualize the DEGs. The orange plots were upregulated genes with log2 (fold change) ≥1, whereas the green plots represented the downregulated genes with log2 (fold change) ≤−1 (Figs. 9 and 10). GO and KEGG analyses were performed with the 90 genes intersected (Table 5). Using GO analysis, the top terms of biological process (BP), cellular component (CC), and molecular function (MF) were extracellular matrix organization, basement membrane, and extracellular matrix structural constituent, respectively (Fig. 11). The KEGG pathway enrichment analysis showed that the genes intersected with ITGB4-related genes and the DEGs mainly participated in the ECM–receptor interaction and focal adhesion pathways (Fig. 12). Both of the top five results of GO and KEGG analyses were listed in Table 6. The PPI network on the basis of the top three KEGG pathways (Fig. 13) indicated that it was possible for ITGA3, ITGA5, ITGA6, ITGB4, and LAMA3 to become the core genes of the development of LSCC, which need further research and experiments to verify.

3.2. Analysis of the expression of ITGB4 between LSCC and normal tissues using chips and RNA-seq data In total, four chip studies came from gene chips (GSE29330, GSE51985, GSE59102, and GSE84957). The flow process can be seen in Fig. 2, and the ITGB4 related DEG screening process can be found in Fig. 3. Using the filtered chips and RNA-seq data, scatterplots were generated. All the results indicate that ITGB4 was highly expressed in LSCC compared to noncancerous laryngeal tissues, which was statistically significant (P < 0.05; Fig. 4A–C, G, and H). This can also be confirmed by the Student t-test (all showing P < 0.05; Table 3). Meanwhile, compared to noncancerous laryngeal tissues, the forest plot showed that ITGB4 was also highly expressed in LSCC tissues (SMD = 1.62, 95 % CI = 1.23–2.00; Fig. 5A) and no significant heterogeneity was found between studies (P = 0.814, I2 = 0.0 %; Fig. 5A), so it made sense for us to choose the fixed-effect model. Begg’s and Egger’s funnel plots indicated that the significant publication bias was not found with the P-value of both test methods being 0.473 (Fig. 5B and C). 4

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Fig. 4. The integrin subunit beta 4 (ITGB4) expression and receiver operating characteristic (ROC) curve in laryngeal squamous cell cancer (LSCC) in 4 microarrays from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets. A: the ITGB4 expression from GSE29330. B: the ITGB4 expression from GSE51985. C: the ROC curve of ITGB4 expression from GSE59102. D: the ROC curve of ITGB4 from GSE29330. E: the ROC curve of ITGB4 from GSE51985. F: the ROC curve of ITGB4 from GSE59102. G: the ITGB4 expression from GSE84957. H: the ITGB4 expression from TCGA. I: the ROC curve of ITGB4 from GSE84957. J: the ROC curve of ITGB4 from TCGA.

4. Discussion

LSCC has not yet been clarified [22], and therefore, further research is needed. A large number of existing studies have shown that a variety of cancer tissues overexpress ITGB4; the gene-coded integrator beta 4 subbase was a laminin receptor, which was involved in extracellular matrix; and cell adhesion and cell growth signal regulation might play a key role in immersive cancer biology. In this study, we initially researched the clinical significance of ITGB4 in LSCC and the mechanism

Laryngeal cancer, a serious threat to human health, has seen a 5year survival rate in the past 40 years [21]. LSCC accounts for more than 95 % of laryngeal cancer [21] and is limited by traditional examination methods and a lack of accurate diagnostic indicators. The current research has shown that the etiology and mechanism of 5

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Table 3 The basic features of the microarrays and RNA-seq of Integrin subunit beta 4 (ITGB4) expression profiling included in this study. Data

GSE29330 GSE51985 GSE59102 GSE84957 TCGA

Platform

GPL570 GPL10558 GPL6480 GPL17843 –

Number of samples

ITGB4 Expression (Mean ± SD)

Tumor

Normal

Tumor

Normal

13 10 29 9 111

5 10 13 9 12

6.95 ± 1.03 8.54 ± 0.72 13.76 ± 0.67 6.87 ± 1.29 0.92 ± 0.80

5.37 ± 0.47 7.31 ± 1.02 12.53 ± 0.60 6.65 ± 0.73 5.61 ± 0.65

T

P

3.25 3.11 5.74 2.48 5.45

0.005 0.006 < 0.0001 0.025 < 0.0001

Notes: T, T value of student's t test; P, P value of student's t-test.

Fig. 5. The figure composes of a forest plot as well as a Begg’s funnel plot and an Egger’s funnel plot for publication bias test. A: the forest plot of studies evaluating standard mean difference (SMD) of integrin subunit beta 4(ITGB4) expression between laryngeal squamous cell cancer (LSCC) tissues and normal laryngeal tissues based on Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. B: Begg’s funnel plot for publication bias test after calculating SMD based on chips and RNA-seq. C: Egger’s funnel plot for publication bias test after calculating SMD based on chips and RNA-seq.

and then the molecular pathway analysis of the resulting intersection gene was conducted to deeply explore the potential molecular mechanism of ITGB4 in the development process of LSCC. Therefore, in this study, using the in-house chip data, the chip data of the public database, and the RNA-seq data through the analysis and processing of bioinformatics, we thoroughly explored the expression of different genes and molecular pathways in LSCC so as to study the expression significance of ITGB4 in LSCC and the mechanism of potential molecular action. From here, a new way to promote the early diagnosis, treatment, and prevention of LSCC can be provided. ITGB4 was responsible for encoding the integrator subunit beta 4 in layer adhesion–integrator α6β4 to participate in the adhesion of

of potential molecular action. At the onset of this study, we immunized the collected LSCC samples and normal-tissue control samples to explore the expression of ITGB4 in LSCC and normal lung tissues. Meanwhile, we performed a comprehensive search of ArrayExpress, GEO, SRA, Oncomine, literature of various types, and TCGA. The data on LSCC were obtained from these sources, including 218 LSCC samples and 75 normal-tissue control samples. Using these data, we researched the expression level of ITGB4 in LSCC and the relationship between clinical parameters and prognosis. In addition, we screened out the DEGs in LSCC and the ITGB4-related genes, and the genes were crossed to get the intersection genes. In other words, the intersection gene we chose was not only the DEG in LSCC tissues but also related to ITGB4, 6

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Table 4 The relationships between Integrin subunit beta 4 (ITGB4) expression and the clinicopathologic parameters in RNA-seq data. Clinicopathologic parameters

Number

ITGB4 Expression (Mean ± SD)

T

P

Tumor Normal

111 12

6.92 ± 0.8 5.61 ± 0.65

5.45

< 0.001

Male Female

91 20

6.99 ± 0.79 6.61 ± 0.78

1.9

0.057

≥60 years < 60 years

73 38

6.85 ± 0.72 7.06 ± 0.94

1.30

0.198

III-IV I-II

93 14

6.91 ± 0.82 7.12 ± 0.58

0.93

0.353

T3-T4 TX, T1-T2

87 23

6.92 ± 0.83 6.88 ± 0.7

0.24

0.815

N1 NX, N0

50 60

6.79 ± 0.85 7.02 ± 0.75

1.50

0.137

M1 MX, M0

0.235 107

108 ± 0.82 6.91 ± 0.81

0.28

0.783

≥2 years < 2 years

47 61

7.03 ± 0.79 6.88 ± 0.73

1.03

0.303

Tissues

Sex

Age

Stage

T

Fig. 7. The summary receiver operator characteristic (SROC) analysis of ITGB4 for discriminating LSCC tissues based on Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. AUC: the area under the curve.

N

we researched the clinical significance of the expression of ITGB4 in LSCC. We studied its expression in 46 LSCC tissues and 26 control tissues using IHC and found that it was more expressive in LSCC tissues than in normal tissues. Moreover, during the course of the study, we combined the chip and RNA-seq data from the public database to study the differences of the expression of ITGB4 in LSCC tissues and normal tissues. The results showed that LSCC tissues had higher expression of ITGB4 than did normal tissues, similar to many previous studies showing high-lysing ITGB4 of cancer tissues. In addition, we studied the relationship between the expression level of ITGB4 and the clinical pathological parameters such as sex, age, TNM staging, and smoking history of patients with LSCC. However, we used these clinical indicators in addition to tumor grading, none of which were clinical indicators of poor disease outcome. The SROC curve analysis of ITGB4 data from gene chips (GSE29330, GSE51985, GSE59102, and GSE84957) and RNA-seq demonstrated that expression levels could provide high diagnostic value for LSCC with an AUC of 0.87 (95 % CI = 0.84–0.90). Moreover, as to whether or not ITGB4 can be used as a prognostic indicator for cancer, previous studies have shown that ITGB4 was associated with a decrease in survival rates of patients with cancer, such as the Sage breast cancer [28] and non-small-cell lung cancer [29], suggesting that ITGB4 may be the indicator of a poor prognosis for these patients with cancer [30]. However, using RNA-seq data, our results have shown that there was no statistical difference between the expression level of ITGB4 and the survival rate of LSCC. In fact, according to the current research, some scholars have tried to clarify the role of ITGB4 in the development of a variety of tumors, particularly malignant tumors. On the basis of various studies, ITGB4 is likely to play an important role in the progression of cancer by interacting with TP53, FAK, EGFR, and other molecules, activating downstream pathways such as PI3 K and AKT, and maintaining the proliferation signal, invasion, metastasis, and escape from apoptosis [30]. For example, DNA damage in cancer cells is often considered to be closely related to the activation of wild-type p53, whereas studies have shown that ITGB4 reduces the activation of wild-type p53 because of DNA damage, reducing the sensitivity of colorectal cancer cells to chemotherapy [31]. At the same time, another study on colon cancer showed that the combination of the integratin beta and focal adhesion kinase (FAK) activates the FAK, which is considered an important factor in the progression of multiple cancers, including colon cancer [32]. Studies related to ovarian cancer have also shown that ITGB4 mediated and promoted cancer cell attacks by activating the FAK molecular [33]. Similarly, in the study of liver cancer, ITGB4 promoted metastasis in liver cancer by activating the FAK–AKT pathway through the role of EGFR [34]. In research on SCC, most of the genes associated with ITGB4, including laminins, collagens, and CD151, were concentrated on

M

Smoking History

Notes: T, T value of student's t test; P, P value of student's t-test.

Fig. 6. The survival curve (Log-rank) of integrin subunit beta 4(ITGB4) in laryngeal squamous cell cancer (LSCC). The orange curve represents a high expression sample that is greater than the median ITGB4 expression level in cancer tissue samples, and the green curve represents a low expression sample that is less than the median ITGB4 expression level. HR: Hazard Ratio; n: the number of LSCC samples.

extracellular matrix and cells and to transmit signals that regulate gene expression and cell growth, which might play a key role in the biology of invasive cancer. Numerous studies have shown that ITGB4 in laryngeal cancer [23], breast cancer [24], liver cancer [25], and stomach cancer [25] and other cancer cells in ITGB4 have a high expression phenomenon, and overexpressed ITGB4 is likely related to the low survival rate of patients with certain types of tumors [26]. Meanwhile, high expression of ITGB4 can also be seen in cancer cells, such as pancreatic cancer [14] and bladder cancer [27]. In addition, studies of SCC also showed that ITGB4 expressed very high in lung SCC compared to normal tissues [26]. Thus, several studies have shown that ITGB4 has abnormally high expression in a variety of cancer cells, suggesting that it may be closely related to the occurrence and progression of cancer. However, research on the pathological significance of ITGB4 in LSCC has not been conducted ever. Therefore, for the first time in this study, 7

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Fig. 8. The figure composed of sensitivity and specificity values, diagnosis likelihood ratio positive and the diagnosis likelihood ratio negative of the included studies. A：sensitivity and specificity values of the included studies. B: diagnosis likelihood ratio positive (LRP+) and the diagnosis likelihood ratio negative (LRP-). LRP: likelihood ratio positive. TCGA: The Cancer Genome Atlas.

the mechanism of its role in the development of a variety of tumors. However, its comprehensive status and clinical implication in LSCC have not yet been reported. Therefore, to further study the potential molecular mechanisms of ITGB4 in the development of LSCC, we first screened the DEGs of chip data (GSE29330, GSE51985, GSE59102, and GSE84957) and RNA-seq data. The ITGB4-related genes (the Spearman correlation coefficient equaling to 0.3) in the chip and RNA-seq data were then screened, and the genes were then crossed to obtain the intersection of genes, which was followed by GO, KEGG, and PPI analyses. Using GO analysis, extracellular matrix organization, basement

PI3 K pathways, which indicated ITGB4 might be involved in the occurrence and development of cancer because of participation in the PI3 K–AKT pathway [29,35]. Studies related to glioblastoma have also shown that ITGB4 promotes the interaction between cell proliferation PMID by activating the AKT–mTOR signaling pathway [36]. In addition to the above molecules, it can also regulate the SPARC protein, which mainly participates in extracellular and cell motility, thereby promoting the invasion of cancer cells, and the process of the SPARC protein regulated by ITGB4 might be closely related to miR-29a [37]. In other words, it appeared that ITGB4 has a large number of research studies on 8

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Fig. 9. Volcano plots of the differential expression genes (DEGs) in Gene Expression Omnibus (GEO) database. A: the volcano plots of the DEGs in GSE29330; B: the volcano plots of the DEGs in GSE51985; C: the volcano plots of the DEGs in GSE59102; D: the volcano plots of the DEGs in GSE84957. Based on the absolute value of the log2 (fold change) is greater than 1 (P. adjust < 0.05), the boundary line was set and the orange table represents the upregulated gene while the green dot represents the down-regulated gene. Names of the top ten genes that were most significant in expressing up and down had been marked.

pathway (hsa04510), and small-cell lung cancer pathway (hsa05222), suggesting that ITGB4 may affect these pathways to play a role in promoting the development of cancer. Moreover, the PPI network analysis of these three pathways has shown that ITGA3, ITGA6, and LAMA3 may be the core genes in these pathways, indicating that these genes may be necessary for the development of LSCC, and affected the occurrence and progress of LSCC, which needs further research and confirmation. Although our results revealed to some extent the clinical significance of ITGB4 in LSCC and its potential molecular mechanism, there were some limitations. For example, this study lacked the corresponding in vivo studies. At the same time, the further inclusion of additional sample data could be considered to illustrate the problem. In addition, in immunohistochemistry, fewer samples of women were included in the study. Therefore, the results of this study may have some offsets, and further research is needed to verify the relationship between the clinical significance of ITGB4 and the pathological parameters of LSCC, as well as the potential molecular mechanism of action. In summary, we studied the expression level of ITGB via the utilization of LSCC chip data, RNA-seq data, and immunohistochemistry. Our results confirmed the abnormally high expression of ITGB4 in LSCC and its potential clinical significance, but no significant correlation between the expression level of ITGB4 and prognosis in patients with LSCC was found. In order to explore the potential molecular mechanism of ITGB4 in LSCC, we studied the DEGs of LSCC and ITGB4-related genes, including GO annotation and KEGG pathway enrichment analysis. Extracellular matrix organization, basement membrane, and extracellular matrix structural constituent were the top terms of biological process, cellular component, and molecular function, respectively, using GO analysis, whereas the KEGG pathway enrichment analysis showed that the ITGB4-related genes and DEGs mainly participated in the ECM–receptor interaction, focal adhesion, and small-cell lung

Fig. 10. The volcano plot of the differential expression genes (DEGs) in The Cancer Genome Atlas (TCGA) database. Based on the absolute value of the log2 (fold change) is greater than 1 (P. adjust < 0.05), the boundary line was set and the orange table represents the up-regulated gene while the green dot represents the down-regulated gene. Names of the top ten genes that were most significant in expressing up and down had been marked.

membrane, and extracellular matrix structural constituent were the top terms of biological process, cellular component, and molecular function, respectively, whereas the KEGG pathway enrichment analysis showed that ITGB4-related genes and DEGs mainly participated in the ECM–receptor interaction pathway (hsa04512), focal adhesion 9

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Table 5 A total of 90 ITGB4 related DEGs. Gene name ITGB4 EXT1 FAP ALDH9A1 PPARG SDK2 CDC25B TNFRSF12A TGFBI PMEPA1 EFNB1 BEX4 PTK7

RMND5B MYO1B FEZ1 CD276 LAMC2 KLF7 HOMER3 ITGA6 MET TCEA3 JAG2 ANGPT2 TK1

COL4A1 HLF DCBLD2 TM7SF2 MANSC1 IL24 RRAGD SPHK1 TNC RTKN SERPINE1 CYP27B1 COL4A5

SLC37A1 MSN COL5A1 SERPINH1 AGRN AFAP1L2 SYNGR1 TRIP13 STX1A PTHLH PTGFRN ARSJ LPIN1

GPT2 TPX2 DCBLD1 ANKRD35 FADS3 LAMA3 PLOD1 BSPRY CDH3 DUSP14 MYO5A BMP2 PLXNA1

MGST2 SNAI2 FBLIM1 RAB25 SLC3A2 PLEK2 PITX1 ITGA5 CAV1 FSCN1 FJX1 KIAA1644 CLDN7

C1QTNF6 SH2D5 LAMB3 DFNA5 MYO10 KIF14 AURKA PLOD2 TPBG ITGA3 ALDH3A1 FERMT1

Fig. 11. The columnar graphs of biological process (A), cellular component (B) and molecular function(C) of Gene Ontology (GO) annotation. The color represents the adjusted P value. The smaller the P. adjusted value, the higher the degree of genes enrichment and the more the color is biased toward red. On the contrary, it is biased towards blue. P. adjust: adjusted P value.

still needs to be further studied. Among the major malignant tumors of the larynx, LSCC accounts for 85 %–90 % of all laryngeal tumors [38]. Its etiology is still unclear [39]. Currently, its detection and prediction are mainly based on the histopathological classification and TNM staging system. However, because

cancer pathways. Besides, the PPI network results suggested that ITGA3, ITGA6, and LAMA3 may be the core genes of the DEGs of LSCC and ITGB4-related genes, although this requires further testing. Meanwhile, the role of ITGB4 in the in vitro and in vivo occurrence and development of LSCC, as well as the potential molecular mechanism, 10

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In our study, ITGB4 was highly expressed in the cancer tissues of the five sample sets, and it was used as a diagnostic molecular marker for LSCC with high sensitivity and specificity. It is suggested that ITGB4 might play a role as a cancer-promoting gene of LSCC. On the survival curve, there was no significant difference between the high expression and low expression groups of ITGB4. The prognosis of ITGB4 on LSCC was less significant, which was inconsistent with the results of other studies as a poor prognosis. According to the results of GO analysis, the enrichment result of MF is “cell adhesion molecule binding mediates cell adhesion,” that of CC is “extracellular matrix containing collagen,” and that of BP is “extracellular matrix and structural composition.” The KEGG pathway showed that ITGB4 is mainly involved (lysine degradation), indicating that the gene may play a role in lysine degradation on extracellular matrix tissues and is mainly involved in cell membrane receptor interaction and mediates molecular adhesion. In addition, the PPI network indicates that the core genes of LSCC are ITGA3, ITGA5, ITGB4, MET, LAMA3, and COL4A1. Among them, ITGA3 and ITGB5 express high in oral squamous cell carcinoma [40], and MET is also an upregulated gene in the early stage non-small-cell lung cancers [41]. LAMA3 has also been reported as a poor prognostic factor for HNSCC [42] and COL4A1 was found to be highly expressed in digestive system tumors, such as esophageal cancer [43], gastric cancer [44], and pancreatic cancer [45]. However, the clinical significance of these genes in LSCC needs to be analyzed using more gene chips and clinical data analysis. Our study still has some flaws because our clinical samples were insufficient. In addition, because of the limited number of chips we collected and the large amount of data coming from the public databases, we remain at the level of bioinformatics. The high expression of ITGB4 in the current chip has implications for the promotion and occurrence of LSCC, and larger samples may be needed to increase the credibility of the results. We carried out GO and KEGG analyses and the PPI network construction and analyzed the related molecular targets and pathways of ITGB4 in the occurrence of LSCC. However, some limitations can also be found in our study. We did not study the expression levels of ITGB4 in serum in LSCC patients. Moreover, the specific mechanism of the function of ITGB4 in LSCC still needs more in vitro and in vitro research experiments to verify. In conclusion, we demonstrate that ITGB4 may act as an LSCC oncogene by inducing the degradation of lysine in the extracellular matrix and mediating adhesion between cellular molecules. In addition, ITGB4

Fig. 12. The columnar graphs of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The color represents the adjusted P value. The smaller the P. adjusted value, the higher the degree of enrichment of the genes in the corresponding pathway and the more the color is biased toward red. On the contrary, it is biased towards blue. P. adjust: adjusted P value.

of limitations of these methods, certain LSCCs may not be detectable in some patients. This leads to a late diagnosis of LSCC and a lower 5-year survival rate. ITGB4 is usually expressed in high-grade malignancies, such as bladder cancer, cervical cancer, lung cancer, and breast cancer. In addition, because it maintains proliferative signals against malignant tumors, escapes apoptosis, promotes tissue infiltration and metastasis, and promotes angiogenesis, its overexpression has been considered to be a poor prognostic marker for SCC, such as breast cancer, pancreas cancer and HNSCC [30].

Table 6 The GO annotation and KEGG pathway enrichment analysis of the 90 ITGB4 related DEGs. ID Biological process GO:0030198 GO:0031581 GO:0021700 GO:0042476 GO:0032963 Cellular component GO:0005604 GO:0044420 GO:0062023 GO:0030175 GO:0071437 Molecular function GO:0005201 GO:0050839 GO:0005178 GO:0050840 GO:0000146 KEGG hsa04512 hsa04510 hsa05222 hsa04151 hsa05165

Description

Count

P adjust

Gene ID

extracellular matrix organization hemidesmosome assembly developmental maturation odontogenesis collagen metabolic process

20 5 10 7 6

1.05E-13 8.72E-07 0.000129 0.000409 0.00144

ITGB4/COL4A1/SERPINH1/PLOD1/COL4A5/FSCN1, etc. ITGB4/LAMC2/ITGA6/LAMB3/LAMA3 CDC25B/PPARG/AGRN/TRIP13/AURKA/MYO5A, etc. ITGB4/ITGA6/JAG2/SERPINE1/MYO5A/TNC/BMP2 SERPINH1/PLOD1/PPARG/FAP/COL5A1/PLOD2

basement membrane extracellular matrix component collagen-containing extracellular matrix filopodium invadopodium

10 7 12 7 4

1.63E-09 1.79E-07 1.48E-06 1.39E-05 2.41E-05

COL4A1/COL4A5/LAMC2/AGRN/ITGA6/LAMB3, etc. COL4A1/COL4A5/LAMC2/LAMB3/COL5A1/LAMA3/TNC COL4A1/SERPINH1/COL4A5/LAMC2/AGRN/ITGA6, etc. FSCN1/MYO1B/MSN/ITGA6/MYO10/MYO5A/ITGA3 FSCN1/MSN/FAP/ITGA3

extracellular matrix structural constituent cell adhesion molecule binding integrin binding extracellular matrix binding microfilament motor activity

8 12 6 4 3

0.000156 0.000297 0.002318 0.00759 0.00759

COL4A1/COL4A5/LAMC2/AGRN/COL5A1/LAMA3, etc. ITGB4/FSCN1/MYO1B/MSN/SLC3A2/ITGA6/ITGA5, etc. ITGB4/ITGA5/FAP/COL5A1/TGFBI/ITGA3 AGRN/ITGA6/TGFBI/ITGA3 MYO1B/MYO10/MYO5A

ECM-receptor interaction Focal adhesion Small cell lung cancer PI3K-Akt signaling pathway Human papillomavirus infection

11 12 7 12 10

6.60E-10 1.81E-07 5.13E-05 5.13E-05 0.000907

ITGB4/COL4A1/LAMC2/AGRN/ITGA6/ITGA5, etc. ITGB4/COL4A1/LAMC2/ITGA6/ITGA5/LAMB3, etc. COL4A1//LAMC2/ITGA6/LAMB3/LAMA3/ITGA3, etc. ITGB4/COL4A1/LAMC2/ITGA6/ITGA5/LAMB3, etc. ITGB4/COL4A1/LAMC2/ITGA6/ITGA5/LAMB3, etc.

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Fig. 13. The Protein-Protein Interaction (PPI) network of genes enriching in ECM-receptor interaction, Small cell lung cancer and Focal adhesion based on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. A node in a PPI network represents a protein, and an edge between two nodes represents a relationship between genes. For a gene, the more genes that interact with it, the deeper the node's color, which is more biased to red.

may promote the occurrence of LSCC by intermodulation with genes such as ITGA3, ITGA5, MET, LAMA3, and COL4A1. It suggests the potential value of ITGB4 as a molecular marker for LSCC and the significance of the diagnosis and treatment of LSCC.

Funding The study was supported by the funds of Guangxi Medical and Health Appropriate Technology Development, and Popularization and Application Project (S2017020) and the Promoting Project of Basic Capacity for Young and Middle-aged University Teachers in Guangxi, China (2018KY0123).

Declaration of Competing Interest None. 12

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