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American Journal of Translational Research logoLink to American Journal of Translational Research
. 2022 Sep 15;14(9):6445–6466.

The prognostic and immune infiltration role of ITGB superfamily members in non-small cell lung cancer

Juan Wu 1, Wenjun Wang 1, Zhouhua Li 1, Xiaoqun Ye 1
PMCID: PMC9556481  PMID: 36247270

Abstract

Purpose: We aimed to explore the prognostic value of integrin-β superfamily members (ITGBs) and their role in immune cell infiltration in non-small cell lung cancer (NSCLC). Materials and Methods: Study cases were acquired from The Cancer Genome Atlas database and The Human Protein Atlas. We then used R package and several online tools to analyze and visualize the roles of ITGBs in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Results: We found that ITGBs were differentially expressed in NSCLC. In LUAD, high expression of ITGB1 and ITGB4 was an independent risk factor for poor prognosis, and ITGB7 was an independent protective factor for overall survival; in LUSC, high expression of ITGB1, 3, 5, and 6 was associated with poor prognosis, and ITGB8 was an independent protective factor for disease-specific survival. Protein-protein interaction networks for the most associated co-expressed genes revealed the following target genes of ITGBs: PTPRC, ITGAM, and ITGB2 in LUAD and FN1, PTPRC, and ITGB2 in LUSC. Gene ontology analysis revealed that functions related to adhesion, junction, and binding were highly enriched in LUAD and LUSC. ITGBs were significantly associated with immune cell infiltration and the expression of immunomodulation-related genes in LUAD and LUSC. Conclusion: ITGBs were differentially expressed in NSCLC. ITGB1, 4, and 7 and ITGB1, 3, 5, 6, and 8 were found as prognostic markers in LUAD and LUSC, respectively. ITGBs were significantly associated with immune cell infiltration and the expression of immunomodulation-related genes.

Keywords: ITGBs, NSCLC, LUAD, LUSC, prognosis, immune infiltration

Introduction

Although great efforts have been made to study and control lung cancer, it remains the most common cause of cancer-related deaths [1]. Non-small cell lung cancer (NSCLC) accounts for 85% of lung cancer cases, with lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) as the most common subsets [2]; therefore, it is crucial to identify the genes involved in promoting the progression of LUAD and LUSC.

Integrins are composed of α and β subunits, and constitute a large family of cell surface receptors [3]. Through binding to the extracellular matrix (ECM), integrins participate in cell survival, proliferation, and migration [3,4]. The deregulation of integrin signaling enables tumor cells to proliferate, invade, and survive [5]. Moreover, integrins can promote the expansion and self-renewal of cancer stem cells [6,7], disrupt epithelial adhesion [8,9], foster development of the tumor microenvironment (TME) [10], and encourage resistance to immune-targeted therapies [11-13]. Therefore, integrins play a vital role in tumor neogenesis, progression, colonization, recurrence, and resistance to therapy. The integrin-β (ITGB) superfamily comprises eight members, ITGB1-8 [4]. High expression of ITGB1, 4, and 8 is related to the progression and poor prognosis of lung cancer [14-16]. However, the roles of other ITGB superfamily members (ITGBs) in the prognosis and immune infiltration of NSCLC remain poorly understood. Furthermore, whether the role of ITGBs in prognosis and immune infiltration differ between LUAD and LUSC remains unclear.

Here, we conducted a systematic bioinformatics analysis to identify the gene expression levels, prognostic value, interactions, and related infiltrated immune cells of ITGBs in LUAD and LUSC. We further clarify the pathogenesis and possible therapeutic targets of NSCLC.

Materials and methods

Data source

The case information of mRNA expression profiles and clinical features was acquired from The Cancer Genome Atlas (TCGA) and downloaded from the University of California Santa Cruz Xena (UCSC Xena; https://xena.ucsc.edu/) platform. Immunohistochemistry (IHC)-based protein expression patterns were acquired from The Human Protein Atlas (HPA; https://www.proteinatlas.org/). Genetic variation data were obtained from cBioPortal (http://www.cbioportal.org). Promoter methylation data were obtained from the University of Alabama at Birmingham Cancer data analysis Portal (UALCAN, http://ualcan.path.uab.edu/analysis-prot.html, TCGA dataset). Data regarding the relationship between ITGBs and immune cell infiltration as well as immunomodulation-related gene expression were obtained from the Tumor Immune Estimation Resource (TIMER, version 2, timer.cistrome.org).

ITGBs expression level in pan-cancer, LUAD, and LUSC

We downloaded RNA-seq data (normalized as transcripts per million reads, TPM) of ITGBs from pan-cancer, LUAD, and LUSC datasets on the UCSC Xena platform, and then analyzed and visualized the data using the “ggplot2” package in R. Unpaired samples t-test was used to compare the expression level of ITGBs between the normal and tumor groups; statistical significance was set at P < 0.05.

Validate the protein expression of ITGBs in LUAD and LUSC

To verify the expression of ITGBs at the histological level, IHC-based protein expression patterns in normal human lung, LUAD, and LUSC tissues were acquired from the HPA.

ITGBs and pathological stages

Gene expression profiling interactive analysis (GEPIA) is a web server that integrates TCGA and genotype-tissue expression (GTEx) (http://gepia.cancer-pku.cn/) data. We used GEPIA to assess the correlation between ITGBs and pathological stages; statistical significance was set at P < 0.05.

Survival and prognostic analysis

Clinical datasets from TCGA were used to analyze the survival outcomes of patients with LUAD and LUSC. With 50% as the cutoff value, samples were divided into low and high groups. Overall survival (OS) and disease-specific survival (DSS) were used to evaluate survival outcomes. We performed Kaplan-Meier analysis with Cox regression using the “survminer” and “survival” packages in R. Univariate analyses were conducted using the “survival” package in R. Significant variables in univariate Cox regression analysis (P < 0.1) were subjected to a multivariate Cox regression model; statistical significance was set at P < 0.05.

Genetic variation

We collected data of 586 LUAD and 511 LUSC samples from TCGA and analyzed and visualized their genetic variation as well as the impact of genetic variation on OS using cBioPortal (http://www.cbioportal.org).

Correlation analyses

For correlation analysis between every pair of ITGBs, expression data were tested using Pearson’s correlation coefficient. The R package “ggplot2” was used to analyze and visualize the results; statistical significance was set at P < 0.05.

Co-expression heatmap and construction of protein-protein interaction (PPI) network

After being downloaded from TCGA, the co-expressed genes were ranked according to their co-expression correlation values with ITGBs. The top 20 genes that were significantly correlated with ITGBs were extracted to plot heatmaps in R using the “ggplot2” package. To explore the extent of interactions between the proteins expressed by these genes, the PPI network was constructed using STRING (https://cn.string-db.org, main parameters: network type: full STRING network, meaning of network edges: evidence, active interaction source: Textmining, Experiments, Databases, Co-expression, Neighborhood, Gene Fusion and Co-occurrence, minimum required interaction score: Medium confidence [0.400], max number of interactors to show: 1st shell [none/query proteins only]), and Cytoscape.

Functional annotation of ITGBs and the associated genes

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on ITGBs and the top five most relevant genes in R using the “ggplot2” and “clusterProfiler” packages, Fisher’s exact P-value was corrected using the Benjamini-Hochberg (BH) method; statistical significance was set at P adj < 0.05 and q value < 0.2.

Relationship of ITGBs with immune cell infiltration and expression of immunomodulation-related genes

We evaluated the relationship of ITGBs with immune cell infiltration and expression of immunomodulation-related genes using TIMER2.

Results

ITGB expression levels in pan-cancer data

We evaluated the pan-cancer mRNA expression of ITGBs from TCGA and GTEx (Figure 1). The analysis indicated that ITGB1 expression was upregulated in 16 tumors and downregulated in six tumors. ITGB2 expression was upregulated in 22 tumors and downregulated in four tumors. ITGB3 expression was upregulated in eight tumors and downregulated in 18 tumors. ITGB4 expression was upregulated in 23 tumors and downregulated in six tumors. ITGB5 expression was upregulated in 17 tumors and downregulated in nine tumors. ITGB6 expression was upregulated in 20 tumors and downregulated in seven tumors. ITGB7 expression was upregulated in 18 tumors and downregulated in four tumors. ITGB8 expression was upregulated in 19 tumors and downregulated in nine tumors (P < 0.05).

Figure 1.

Figure 1

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Expression level of ITGBs in pan-cancer data. ns, no statistical significance; *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Gene expression and validation of ITGBs in LUAD and LUSC

Owing to the scarcity of RNA-seq data from matched paracancerous tissues in TCGA-LUAD and TCGA-LUSC data, we acquired RNA-seq data from 288 normal lungs from the GTEx project. Finally, 515 LUAD cases matched with 347 controls (59 paracancerous tissues, 288 normal lung tissues), and 498 LUSC cases matched with 338 controls (50 paracancerous tissues and 288 normal lung tissues) were included in the analysis (Figure 2). In LUAD, high expression of ITGB4, 6, 7, and 8 and low expression of ITGB2, 3, and 5 were observed (P < 0.05). In LUSC, high expression of ITGB4, 5, and 8 and low expression of ITGB1, 2, 3, 6, and 7 were observed (P < 0.05).

Figure 2.

Figure 2

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Expression level of ITGBs in LUAD and LUSC. ns, no statistical significance; *, P < 0.05; **, P < 0.01; ***, P < 0.001.

To verify the expression of ITGBs at the histological level, IHC-based protein expression patterns in normal human lung (N), LUAD, and LUSC tissues were obtained from the HPA (Figure 3). These results validated the expression of ITGBs in LUAD and LUSC (except for ITGB7). IHC-based protein expression revealed that, ITGB4, 6, and 8 were upregulated, whereas ITGB2, 3, and 5 were downregulated in LUAD; ITGB4, 5, and 8 were up-regulated, whereas ITGB1, 2, 3, and 6 were downregulated in LUSC.

Figure 3.

Figure 3

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Validation of ITGB protein expression in LUAD and LUSC. N, normal human lung tissue; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma.

ITGB expression in different pathological stages

To identify whether ITGBs are differentially expressed among pathological stages, we analyzed the correlations between the expression of ITGBs and the pathological stages in LUAD and LUSC using GEPIA (Figure 4). The results revealed that ITGB4, 6, and 8 showed significantly differential expression among various pathological stages of LUSC (P < 0.05, Figure 4B). No significant differences were observed in the expression of other ITGBs in LUSC and in all the ITGBs in LUAD (P > 0.05, Figure 4A, 4B).

Figure 4.

Figure 4

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The expression level of ITGBs in different pathological stages. A. The expression level of ITGBs in different pathological stages of LUAD. B. The expression level of ITGBs in different pathological stages of LUSC.

The prognostic value of ITGBs in LUAD and LUSC

To thoroughly investigate the impact of ITGBs on the survival and prognosis of LUAD and LUSC, we chose OS and DSS as the prognostic indicators. In LUAD (Figure 5A), high expression of ITGB1 and 4 was related to decreased OS, whereas ITGB7 was related to increased OS (P < 0.05), and ITGB4 was related to decreased DSS (P < 0.05). In LUSC (Figure 5B), high expression of ITGB1, 3, 5, and 6 was associated with decreased OS (P < 0.05). ITGB1 was related to decreased DSS, whereas ITGB8 was related to increased DSS (P < 0.05). The expression of other ITGBs had no significant effect on OS and DSS in either LUAD or LUSC (P > 0.05).

Figure 5.

Figure 5

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The prognostic value of ITGBs. A. The prognostic value of ITGBs in LUAD. B. The prognostic value of ITGBs in LUSC.

Univariate and multivariate COX regression analyses were conducted to explore whether ITGBs are independent risk factors for the prognosis of LUAD and LUSC. We found that, for LUAD (Table 1), ITGB1 and 4 were independent risk factors for decreased OS, whereas ITGB7 was an independent protective factor for OS (P < 0.05) and ITGB4 was an independent risk factor for decreased DSS (P < 0.05). For LUSC (Table 2), ITGB8 was an independent protective factor for DSS (P < 0.05), and none of the ITGBs were an independent risk factor for OS (P > 0.05).

Table 1.

Univariate and Multivariate analysis of OS and DSS in LUAD

Characteristics Univariate analysis Multivariate analysis
OS DSS OS DSS
HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value
Gender Female Reference Reference
Male 1.070 (0.803-1.426) 0.642 0.989 (0.687-1.424) 0.954
Age ≤65 Reference Reference
>65 1.223 (0.916-1.635) 0.172 1.013 (0.701-1.464) 0.944
Smoker No Reference Reference
Yes 0.894 (0.592-1.348) 0.591 1.040 (0.602-1.796) 0.889
Pathological stage I&II Reference Reference
III&IV 2.664 (1.960-3.621) < 0.001 2.436 (1.645-3.605) < 0.001 2.466 (1.809-3.363) < 0.001 2.217 (1.490-3.298) < 0.001
ITGB1 Low Reference Reference
High 1.514 (1.132-2.025) 0.005 1.435 (0.996-2.069) 0.053 1.438 (1.058-1.956) 0.020 1.258 (0.865-1.827) 0.229
ITGB2 Low Reference Reference
High 0.886 (0.665-1.182) 0.412 0.792 (0.549-1.143) 0.213
ITGB3 Low Reference Reference
High 1.089 (0.818-1.451) 0.557 1.088 (0.757-1.563) 0.650
ITGB4 Low Reference Reference
High 1.686 (1.262-2.251) < 0.001 1.788 (1.238-2.584) 0.002 1.445 (1.047-1.995) 0.025 1.571 (1.075-2.296) 0.020
ITGB5 Low Reference Reference
High 1.311 (0.984-1.747) 0.064 1.190 (0.828-1.711) 0.346 1.024 (0.739-1.419) 0.886
ITGB6 Low Reference Reference
High 0.999 (0.750-1.332) 0.997 0.863 (0.599-1.242) 0.427
ITGB7 Low Reference Reference
High 0.734 (0.548-0.982) 0.037 0.737 (0.510-1.066) 0.105 0.699 (0.519-0.943) 0.019
ITGB8 Low Reference Reference
High 0.965 (0.724-1.285) 0.807 1.074 (0.747-1.545) 0.700
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OS: Overall survival, DSS: Disease Specific survival, HR: Hazard Ratio, CI: Confidence interval.

Table 2.

Univariate and Multivariate analysis of OS and DSS in LUSC

Characteristics Univariate analysis Multivariate analysis
OS DSS OS DSS
HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value
Gender Female Reference Reference
Male 1.211 (0.879-1.669) 0.241 1.386 (0.833-2.307) 0.209
Age ≤65 Reference Reference
> 65 1.279 (0.960-1.704) 0.093 1.028 (0.668-1.582) 0.899 1.237 (0.921-1.661) 0.158
Smoker No Reference Reference
Yes 0.585 (0.259-1.325) 0.199 0.393 (0.123-1.251) 0.114
Pathological stage I&II Reference Reference
III&IV 1.570 (1.139-2.163) 0.006 2.600 (1.648-4.102) < 0.001 1.601 (1.159-2.212) 0.004 2.626 (1.662-4.150) < 0.001
ITGB1 Low Reference Reference
High 1.413 (1.076-1.854) 0.013 1.638 (1.068-2.510) 0.024 1.123 (0.814-1.549) 0.481 1.545 (0.931-2.564) 0.092
ITGB2 Low Reference Reference
High 1.051 (0.802-1.378) 0.719 0.998 (0.655-1.521) 0.992
ITGB3 Low Reference Reference
High 1.319 (1.005-1.731) 0.046 1.487 (0.972-2.273) 0.067 1.140 (0.840-1.546) 0.401 1.227 (0.754-1.996) 0.409
ITGB4 Low Reference Reference
High 1.105 (0.843-1.450) 0.470 1.282 (0.840-1.958) 0.250
ITGB5 Low Reference Reference
High 1.358 (1.034-1.785) 0.028 1.294 (0.847-1.975) 0.234 1.242 (0.921-1.674) 0.155
ITGB6 Low Reference Reference
High 1.437 (1.095-1.886) 0.009 1.501 (0.981-2.294) 0.061 1.221 (0.902-1.653) 0.196 1.205 (0.741-1.960) 0.451
ITGB7 Low Reference Reference
High 0.968 (0.737-1.270) 0.814 0.851 (0.557-1.300) 0.454
ITGB8 Low Reference Reference
High 0.993 (0.757-1.301) 0.957 0.641 (0.417-0.984) 0.042 0.594 (0.384-0.918) 0.019
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OS: Overall survival, DSS: Disease Specific survival, HR: Hazard Ratio, CI: Confidence interval.

Genetic variation of ITGBs in LUAD and LUSC

The genetic variation of ITGBs was explored using the cBioPortal database (Figure 6). The results showed that 23.11% (116/502) of the LUAD cases and 16.67% (86/516) of the LUSC cases harbored genetic variations. Amplification is the most frequent alteration in both LUAD and LUSC (Figure 6A). We then investigated the variation in ITGBs in LUAD and LUSC and found that ITGB8 (8%) and ITGB5 (11%) were the most frequently altered in LUAD and LUSC, respectively (Figure 6B, 6C). However, no significant effect of genetic variation was observed on OS in LUAD and LUSC (Figure 6D, 6E) (P > 0.05).

Figure 6.

Figure 6

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Genetic variation of ITGBs. A. Summary of genetic variation of ITGBs in LUAD and LUSC. B. Genetic variation of ITGBs in LUAD. C. Genetic variation of ITGBs in LUSC. D. The effect of genetic variation on OS in LUAD. E. The effect of genetic variation on OS in LUSC.

Promoter methylation level of ITGBs in LUAD and LUSC

Gene expression can be regulated at different levels [17], such as post-translational modifications [18] and chemical modifications of nucleobases [19]. Methylation is one of the ways in which nucleobases are chemically modified; genes can be silenced and reactivated by the methylation and demethylation of cytosines in the promoter region [19]. To explore changes in the methylation level of promoters of ITGBs in NSCLC, we obtained the methylation data of ITGBs in LUAD and LUSC from TCGA database using UALCAN (Figure 7). In LUAD (Figure 7A), the promoters of ITGB2, 4, 5, 6, and 8 showed decreased methylation levels, whereas ITGB3 and 7 showed increased methylation levels; in LUSC (Figure 7B), the methylation levels of the promoters of ITGB2, 4, 6, and 7 were decreased, while those of ITGB1 and 8 were increased (P < 0.05).

Figure 7.

Figure 7

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The promoter methylation level of ITGBs. A. The promoter methylation level of ITGBs in LUAD. B. The promoter methylation level of ITGBs in LUSC. NS, no statistical significance; *, p < 0.05; **, p < 0.01; ***, p < 0.001

These above results suggest that different gene variants and promoter methylation levels of ITGBs may contribute to their differential expression in LUAD and LUSC.

Correlation between ITGBs in LUAD and LUSC

Differential expression and prognostic effects of ITGBs in LUAD and LUSC suggest that ITGBs in these two NSCLC subtypes share different correlations. As shown in Figure 8A, ITGBs were positively correlated with each other in LUAD; in LUSC (Figure 8B), most ITGBs were positively correlated, and a few ITGBs were negatively correlated with each other. We further analyzed these results and found that in LUSC, the expression of most positively correlated ITGBs was upregulated or downregulated (except for ITGB5 and ITGB1), while the negatively correlated ITGBs showed the opposite expression trend. For example, ITGB4 and 8 were positively correlated, the expression of both ITGB4 and 8 was upregulated, while ITGB2 and 8 were negatively correlated, the expression of ITGB2 was downregulated, and ITGB8 was upregulated. However, this phenomenon was not observed in LUAD; for example, ITGB2 and 7 showed a significant positive correlation with a correlation coefficient of 0.65, while upregulation of ITGB2 and downregulation of ITGB7 were observed. Based on the above information, we speculate that in LUAD, the ITGBs mostly showed an indirect relation, whereas in LUSC, the ITGBs mostly showed a direct relation.

Figure 8.

Figure 8

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Correlation between ITGBs. A. Correlation between ITGBs in LUAD. B. Correlation between ITGBs in LUSC.

Co-expressed genes and PPI network construction

To investigate the function of ITGBs in LUAD and LUSC, we first identified the co-expressed genes of ITGBs and displayed the top 20 in a heatmap plot (Figure 9A, 9B). Next, PPI networks of the top 20 correlated genes and ITGBs were constructed using STRING and Cytoscape. Our results showed the most closely interacting genes to be PTPRC, ITGAM, and ITGB2 in LUAD and FN1, PTPRC, and ITGB2 in LUSC, which may also act as target molecules of ITGBs in LUAD and LUSC, respectively (Figure 9C, 9D).

Figure 9.

Figure 9

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Top 20 co-expressed genes of ITGBs. A. Top 20 co-expressed genes of ITGBs in LUAD. B. Top 20 co-expressed genes of ITGBs in LUSC. C. PPI network of ITGBs and related top 20 co-expressed genes in LUAD. D. PPI network of ITGBs and related top 20 co-expressed genes in LUSC.

GO and KEGG enrichment analysis

We performed GO and KEGG functional enrichment analyses on the top five correlated genes and ITGBs (in total 88 genes were included) and presented the top five results. As shown in Figure 10A, GO analysis revealed that the above genes were highly enriched for functions related to adhesion, junction, and binding in both LUAD and LUSC. KEGG pathway enrichment analysis revealed that regulation of the actin cytoskeleton, ECM-receptor interaction, hypertrophic cardiomyopathy, and arrhythmogenic right ventricular cardiomyopathy-related pathways were enriched in both LUAD and LUSC. The network plot shows that almost all ITGBs directly affected the clustering functions acquired by GO analysis in LUAD and LUSC. Target molecules other than ITGB2 obtained by PPI, namely PTPRC and ITGAM in LUAD, and FN1 and PTPRC in LUSC, indirectly affected the functions obtained in GO analysis (Figure 10B, 10C).

Figure 10.

Figure 10

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GO and KEGG enrichment analysis. A. GO [in biological process (BP), cellular component (CC), molecular function (MF)] and KEGG enrichment analysis in LUAD and LUSC. B. Crosstalk between enriched functions (BP+CC) and genes in LUAD and LUSC. C. Crosstalk between enriched functions (MF+KEGG) and gene s in LUAD and LUSC.

Relationship between ITGBs and immune cell infiltration in LUAD and LUSC

To further clarify the role of ITGBs in LUAD and LUSC, we assessed the relationship between ITGBs and tumor-infiltrating immune cells using TIMER2 (Figure 11, purity adjustment).

Figure 11.

Figure 11

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Relationship between ITGBs and immune cell infiltration. A. Relationship between ITGBs and immune cell infiltration in LUAD and LUSC. B. Representative images of the relationship between ITGBs and immune cell infiltration in LUAD and LUSC.

In LUAD (Figure 11A, representative images are shown in Figure 11B), nearly all ITGBs were positively associated with the infiltration of CD8+ T cells, CD4+ T cells, dendritic cells (DCs), macrophages, and neutrophils (P < 0.05). Except ITGB7, which was positively correlated with B cell infiltration, ITGB1, 3, 4, and 5 were all negatively correlated with B cell infiltration (P < 0.05).

In LUSC (Figure 11A, representative images are shown in Figure 11B), ITGB1, 2, 3, 5, 6, and 7 were almost all positively correlated with CD8+ T cells, CD4+ T cells, DCs, macrophages, and neutrophils (except ITGB5, which was negatively correlated with neutrophils infiltration) (P < 0.05). ITGB4 and 8 were not associated with almost all infiltrating immune cells (except ITGB4, which was negatively correlated with B cell infiltration) (P > 0.05).

Relationship of ITGBs with the expression of immunomodulation-related genes in LUAD and LUSC

To explore the mechanism by which ITGBs affect immune cell infiltration, we downloaded data from TIMER2 on the relationship between ITGBs and the expression of immune activation-related (Figure 12A), immunosuppression-related (Figure 12B), chemokine (Figure 12C), and chemokine receptor genes (Figure 12D) in LUAD and LUSC (purity adjustment). The results showed that in LUAD, ITGBs showed a generally positive correlation with the expression of these immunomodulation-related genes; in LUSC, ITGB1, 2, 3, 5, 6, 7 showed a generally positive correlation with the expression of these immunomodulation-related genes, whereas ITGB4 and 8 showed a generally negative correlation with these genes.

Figure 12.

Figure 12

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Relationship of ITGBs with the expression of immunomodulation-related genes. A. The relationship of ITGBs and the expression of immune activation-related genes in LUAD and LUSC. B. The relationship of ITGBs and the expression of immunosuppression-related genes in LUAD and LUSC. C. The relationship of ITGBs and the expression of chemokine genes in LUAD and LUSC. D. The relationship of ITGBs and the expression of chemokine receptor genes in LUAD and LUSC.

Based on the above information, we speculated that ITGBs may affect immune cell infiltration in LUAD and LUSC through the expression of immunomodulation-related genes.

Discussion

In the present study, for the first time, we used multiple public database platforms to conduct an in-depth exploration of the role of ITGBs in NSCLC with respect to mRNA and protein expression, clinical outcome, and tumor-infiltrating immune cells. Our results indicate that ITGBs are differentially expressed in LUAD and LUSC, possibly because of different mutation degrees/types and promoter methylation levels of ITGBs in LUAD and LUSC. Prognostic analysis revealed that in LUAD, ITGB1, 4, and 7 could be prognostic markers, while in LUSC, ITGB1, 3, 5, 6, and 8 could be prognostic markers. Analysis of the most associated co-expressed genes and their PPI network revealed that the most closely interacting genes in LUAD were PTPRC, ITGAM, and ITGB2 whereas those in LUSC were FN1, PTPRC, and ITGB2, which may be the target molecules of ITGBs in these cancers. GO analysis showed that the most related genes were highly enriched for functions related to adhesion, junction, and binding in both LUAD and LUSC, whereas the target molecules of PTPRC and ITGAM in LUAD, and those of FN1 and PTPRC in LUSC, indirectly affected the functions obtained by GO analysis. Immune cell infiltration analysis indicated that ITGBs were significantly related to immune cell infiltration in NSCLC, which may be affected by the expression of immunomodulation-related genes.

ITGB1 associates with at least ten α-subunits, forming the largest integrin subfamily [20]. Overexpression of ITGB1 has been observed in several solid tumors [21,22]. Consistent with our results, Deng et al. found that upregulation of ITGB1 indicated poor prognosis in patients with LUAD [23]. In addition, ITGB1 is also involved in the initiation, metastasis, stemness, and radioresistance of lung cancer [24-27]. Thus, ITGB1 may be a potential therapeutic target for lung cancer.

Similar to ITGB1, ITGB3 downregulation could restrain the migration and invasion of NSCLC [28,29], and inhibition of ITGB3 could promote the antitumor activity of ALK inhibitors in NSCLC [30]. Another study revealed that ITGB3 is overexpressed in both drug resistance and mesenchymal status, indicating its potential as a target to overcome chemoresistance in lung cancer [31].

Through the intracytoplasmic region, ITGB4 can activate intracellular signaling and maintain epithelial cell integrity [32]. Huang et al. performed bioinformatics analysis and found that ITGB4 is a pan-cancer oncogene across 33 different human tumors [33]. ITGB4 overexpression is associated with venous invasion and decreased OS in NSCLC [34]. Furthermore, ITGB4 is also closely related to other diseases of the respiratory system, such as airway inflammation and hyperresponsiveness, acute lung injury, and spontaneous pulmonary inflammation [35-37].

The oncogenic effects of ITGB5 have previously been observed in prostate, colorectal, and hepatocellular carcinoma [38-40]. Currently, research on the role of ITGB5 in lung cancer is limited. Our study indicates that ITGB5 is a risk factor for OS in patients with LUSC. Hu et al. reported that ITGB5 is involved in regulating lung cancer cell motility [41]. Moreover, several studies have reported the impact of ITGB5 on the lungs [42,43].

SMYD3, together with ITGB6 and TGFβ1-Smad3, can facilitate the adhesion and invasion of ovarian cancer cells [44]. ITGB6 is a pro-tumorigenic gene that has also been identified in gastric and pancreatic cancers [45,46]. Few studies have evaluated the effects of ITGB6 on lung cancer. Most studies on the relationship between ITGB6 and lung disease have focused on pulmonary fibrosis and emphysema. Both ELK1 and TGF-β can aggravate pulmonary fibrosis by increasing the expression of ITGB6 [47,48]. Congenital deletion of ITGB6 can cause severe emphysema [49]. Overall, these studies demonstrate that ITGB6 plays a complex role in lung diseases.

However, no study has investigated the influence of ITGB7 on lung cancer. Our results indicate that ITGB7 is a protective factor against OS in LUAD, and the same protective effect has been observed in colorectal cancer [50]. Zhang et al. demonstrated that ITGB7 limits colorectal cancer progression by maintaining antitumor immunity [50].

In our study, ITGB8 was found to act as a protective factor for DSS in LUSC; however, upregulation of ITGB8 has been previously reported to indicate poor prognosis in lung cancer [51-53]. The different prognostic indicators might be responsible for the discrepancy between our results and those of previous studies, as our prognostic indicator was DSS, whereas that used in the previous studies was OS.

The TME is an integral part of cancer that significantly affects treatment response and clinical outcomes. As part of the TME, immune cells have an important impact on tumor progression and prognosis [54]. Our results demonstrated the most closely interacting genes to be PTPRC, ITGAM, and ITGB2 in LUAD and FN1, PTPRC, and ITGB2 in LUSC. PTPRC, also known as CD45, is important for regulating B- and T-cell antigen receptor-mediated activation [55]. Wei et al. showed that PTPRC may be involved in regulating the TME immune status, affecting the function of immune cells in LUAD [56]. The ITGB2 subfamily is often referred to as leukocyte integrins [57]. Altered ITGB2 expression causes adhesion defects in circulation and weakens the ability of the immune system to combat foreign antigens [58]. ITGAM combines with ITGB2 to form a leucocyte-specific integrin, which exerts an important influence on the adhesion and migration of leukocytes [57]. A predictive marker has been previously reported for ITGB2 immunotherapy in gliomas [59]. FN1 is widely expressed in multiple cells and is involved in cell adhesion and migration [60]. FN1 is associated with the function of infiltrating macrophages and T cells in the TME of lung cancer [61,62]. Based on the above information and our findings, we speculate that ITGBs may influence tumor cells and infiltrating immune cells by affecting their adhesion, junction, and binding, thereby affecting the prognosis of NSCLC.

However, our study has some limitations. First, our research was based on data obtained from TCGA, without further validation of the results using cellular, animal, and human specimens. Further, we failed to systematically explore the pathophysiological mechanisms underlying our findings. Thus, further studies are required to understand the mechanisms underlying our findings.

Conclusion

ITGBs were differentially expressed in NSCLC. ITGB1, 4, and 7 and ITGB1, 3, 5, 6, and 8 were found as prognostic markers in LUAD and LUSC, respectively. ITGBs were significantly associated with immune cell infiltration and the expression of immunomodulation-related genes.

Acknowledgements

This work was supported by the Natural Science Foundation of Jiangxi Province (Grants No. 20202ACBL206019).

Disclosure of conflict of interest

None.

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