Analysis
Pan-cancer analysis of ITGB3 as a potential prognostic and immunological biomarker
Changshun Chen1 . Lei Wen1 . Ge Chen1 . Fei Yang2 . Zhong Chen1 . Jianhua Ji1 . Jinyi Gu3
Received: 28 November 2024 / Accepted: 2 April 2025
Published online: 12 April 2025
@ The Author(s) 2025 OPEN
Abstract
Integrin 33 (ITGB3) acts as a crucial regulator and target within the tumor immune microenvironment (TIME) and is highly expressed in the TIME of various tumors. The TIMER, TCGA, GTEx, and CCLE databases were utilized to comprehensively analyze the differential expression of ITGB3 in tumor tissues. Kaplan-Meier analysis, forest plots, and univariate and mul- tivariate Cox regression were used to assess the genetic alterations, clinicopathological characteristics, and prognostic value of ITGB3. Additionally, the R software package was used to evaluate the relationship between ITGB3 expression and immune cell infiltration, immunomodulatory genes, and immune checkpoints, and potential signaling pathways were examined through differential expression and enrichment analysis. We found that the high expression of ITGB3 is a significant risk factor for six types of cancer, including adrenocortical carcinoma (ACC), and is closely associated with a lower survival rate. Anti-tumor immune cells (CD8 +T cells, CD4+Th1 cells, and NKT cells) were significantly reduced. By contrast, pro-tumor immune cells (Tregs and CD4 +Th2 cells), immune checkpoints (CTLA4 and PD-CD1), and nega- tively regulated co-stimulators of T-cell activation (CTLA4, PD-CD1, and IL10) were significantly elevated in most types of cancer with high ITGB3 expression. Overall, our preliminary results indicate that ITGB3 plays an important role in immunosuppression in the tumor microenvironment. Elevated levels of ITGB3 inhibit tumor immunity, facilitate tumor immune escape, and affect patient prognosis, and it may be a prognostic biomarker.
Keywords ITGB3 . Prognosis . Tumor immune microenvironment . Tumor immunosuppression . Tumor immunotherapy
1 Background
Cancer is a significant public health problem and a major cause of morbidity and mortality worldwide, imposing a significant health and economic burden on society [1]. With the continuous development and improvement of various public databases, it is important to use databases such as the Cancer Genome Atlas (TCGA) to conduct pan-cancer research, discover new therapeutic targets, evaluate gene connectivity, conduct expression analysis, find related signaling pathways, and perform clinical prognosis [2].
Changshun Chen and Lei Wen have contributed equally to this work and shared the first authorship.
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s12672-025- 02300-0.
☒ Jianhua Ji, kmjjh117@163.com; ☒ Jinyi Gu, janegu@ynu.edu.cn | 1Department of Orthopedics and Trauma Surgery, Affiliated Hospital of Yunnan University, Kunming 650032, China. 2Department of Orthopedics, Nanchong Central Hospital, Nanchong 637000, China. 3Clinical Laboratory of Affiliated Hospital of Yunnan University, Kunming 650032, China.
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(2025) 16:522 | https://doi.org/10.1007/s12672-025-02300-0
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Tumor occurrence and development are complex processes, and the tumor immune microenvironment (TIME) creates a long-term survival environment for tumor cells. Whether a tumor advances partly depends on the cur- rent proportion and characteristics of T cells. CD8 +T cells are responsible for killing tumor cells and are the most important effector cells in the tumor immune response. CD4 + Th1 cells play an important auxiliary role in anti-tumor immunity by producing cytokines and helping to activate CD8 +T cells [3]. Several preclinical studies also indicate that NKT cells can exert anti-tumor effects [4]. In the TIME, CD4 +Th2 cells facilitate tumor invasion and metastasis and mediate tumor progression by inhibiting CD8 +T cell activation, as confirmed in models such as breast cancer and pancreatic cancer [5]. Tregs suppress anti-tumor immunity by secreting granzyme and perforin, destroying NKT cells and CD8 +T cells [6]. Previous studies have demonstrated that oncogenes can reconstitute the TIME and promote tumor progression by directly or indirectly influencing immune or stromal cells [7]. Additionally, cancer immunotherapy specifically, the use of immune checkpoint inhibitors (ICIs) such as inhibitors to programmed death-1 (PD-1), programmed death ligand-1 (PD-L1), and cytotoxic T lymphocyte-associated Antigen 4 (CTLA-4) has shown significant benefits in enhancing the survival of cancer patients [8]. Tumor cells can evade immune responses by exploiting checkpoint genes such as PD-1, PD-L1, and CTLA-4 [9]. Despite several breakthroughs in cancer treat- ment with immunotherapy, many patients do not achieve a satisfactory response to ICI treatments, possibly due to a lack of biomarkers to guide the identification of personalized immune targets [10]. Some studies have indicated that cancer patients respond well to immunotherapy and that its efficacy is related to the TIME [11, 12]. Therefore, a comprehensive understanding of immune infiltration and identifying new immunotherapy targets and biomarkers are crucial for selecting individualized immunotherapy strategies and improving the efficacy of immunotherapy.
As the major cell adhesion receptors of the extracellular matrix (ECM), integrins are widely expressed in most cell membranes [13]. Integrins are adhesion receptors that connect cells with extracellular matrix ligands and counter- receptors on other cells, which can lead to changes in tumor cell behavior and microenvironmental states by activat- ing various signal transduction pathways [14]. Integrin activation can also regulate ECM assembly and the polarity of migrating cells, mediating tumor metastasis and non-tumor cell infiltration [15]. Indeed, the influence of the microenvironment on cell behavior can be determined based on the integrin expression pattern on the cell surface 16. Thus, integrins can serve as a group of hubs connecting tumor cells and their surrounding microenvironment.
Integrin 3 (ITGB3), also known as CD61 or GP3A, is one of the most widely studied members of the integrin fam- ily. As a key regulator and target in the TIME, ITGB3 plays a variety of key roles in malignant tumor progression and tumor microenvironment reprogramming [17]. ITGB3 promotes the occurrence and development of pancreatic car- cinoma [18], prostate adenocarcinoma [19], breast carcinoma [20, 21] and leukemia [22, 23]. In addition, it promotes the invasion and migration of stomach carcinoma [24, 25], hepatocellular carcinoma [26], colorectal cancer [27], nasopharyngeal carcinoma [28], salivary adenoid cystic carcinoma[29], triple-negative breast cancer [30-32], and melanoma [33]. Integrin 03-p38 MAPK signaling can enhance the invasion and metastasis of breast cancer cells [34], and cancer-associated fibroblasts facilitate tumor invasion through integrin-03-dependent fibronectin assembly [35]. However, the activation of EGFL7-ITGB3-KLF2 signaling improves survival in multiple myeloma in preclinical models [36]. Recently, studies have reported a strong correlation between ITGB3 and tumor drug resistance [22, 37]. For example, Naik et al. indicated that the NRP1-ITGB3 axis mediates the chemoresistance response in breast cancer cells [38]. Noh et al. showed that inhibiting ITGB3 expression increases the anti-tumor activity of ALK inhibitors in NSCLC [39]. In addition, a 2023 study by Guo et al. showed that ITGB3 + exosomes are potential diagnostic, prognostic, and therapeutic biomarkers for managing colorectal cancer (CRC) [40]. Although numerous studies have identified the role of ITGB3 in cancer, no study has fully elucidated its expression and prognostic value in pan-cancer. In addition, the correlation and regulatory mechanism of ITGB3 in modulating tumor immunity and affecting the prognosis of cancer patients are still unclear.
In this study, the TIMER, TCGA, GTEx, and CCLE databases were utilized to comprehensively analyze the expression differences in ITGB3 between tumor tissues. Kaplan-Meier analysis, forest plots, and univariate and multivariate Cox regression were employed to evaluate the genetic changes, clinicopathological features, and prognostic value of ITGB3. Furthermore, the R software package was used to evaluate the relationship between ITGB3 expression, immune cell infiltration, immunomodulatory genes, and immune checkpoints, and potential regulatory signaling pathways were explored through differential expression and enrichment analysis. We found that in certain tumors, high ITGB3 levels promote T-cell depletion, reshape the TIME, and suppress tumor immunity, thereby promoting tumor initiation and progression. However, this effect is reversed in cutaneous melanoma. ITGB3 may act as a prognostic biomarker and is a promising target for tumor immunotherapy in specific cancers. We believe these findings could form the
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basis for future prospective functional experiments, offering clinical guidance and a rationale for upcoming studies of specific tumors.
2 Methods
2.1 Expression profile of ITGB3
First, we used the Human Protein Atlas (HPA) database (http://www.proteinatlas.org/) to explore ITGB3 expression in the human body as a whole. Subsequently, RNA sequencing data and the clinical follow-up information of patients with 33 different types of cancer (Table 1) were analyzed using datasets from the TIMER (https://cistrome.shinyapps. io/timer/), TCGA (https://portal.gdc.cancer.gov), GTEx (www.genome.gov/), and CCLE (https://depmap.org/portal/ data_page/?tab=allData) databases. The pan-cancer dataset in the UCSC database (https://xenabrowser.net/) was used to analyze the expression of ITGB3 in different TNM stages, Stage and Grade staging.
| Abbreviation | Detail |
|---|---|
| ACC | Adrenocortical carcinoma |
| BLCA | Bladder urothelial carcinoma |
| BRCA | Breast invasive carcinoma |
| CESC | Cervical squamous cell carcinoma |
| CHOL | Cholangiocarcinoma |
| COAD | Colon adenocarcinoma |
| DLBC | Lymphoid neoplasm diffuse large B cell Lymphoma |
| ESCA | Esophageal carcinoma |
| GBM | Glioblastoma |
| LGG | Brain lower-grade glioma |
| HNSC | Head and neck squamous cell carcinoma |
| KICH | Kidney chromophobe |
| KIRC | Kidney renal clear cell carcinoma |
| KIRP | Kidney renal papillary cell carcinoma |
| LAML | Acute myeloid leukemia |
| LIHC | Liver hepatocellular carcinoma |
| LUAD | Lung adenocarcinoma |
| LUSC | Lung squamous cell carcinoma |
| MESO | Mesothelioma |
| OV | Ovarian serous cystadenocarcinoma |
| PAAD | Pancreatic adenocarcinoma |
| PCPG | Pheochromocytoma and paraganglioma |
| PRAD | Prostate adenocarcinoma |
| READ | Rectum adenocarcinoma |
| SARC | Sarcoma |
| SKCM | Skin cutaneous melanoma |
| STAD | Stomach adenocarcinoma |
| TGCT | Testicular germ cell tumors |
| THCA | Thyroid carcinoma |
| THYM | Thymoma |
| UCEC | Uterine corpus endometrial carcinoma |
| UCS | Uterine carcinosarcoma |
| UVM | Uveal melanoma |
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2.2 The Prognostic value of ITGB3
We used Kaplan-Meier analysis, forest plots, and univariate and multifactorial Cox regression to investigate the associa- tion between ITGB3 expression and patient prognosis in 33 diverse cancer types. The survival indicators included overall survival (OS), progression-free survival (PFS), disease-free survival (DFS), and disease-specific survival (DSS). Hazard ratios (HRs) and their corresponding 95% confidence intervals were calculated using one-way survival analysis.
2.3 Association of ITGB3 expression with infiltration of immune cells
The correlation between ITGB3 expression and the infiltration levels of 6 key immune cell types across 33 cancers was evaluated using the immune cell infiltration xCell algorithm combined with the Tumor Immune Estimation Resource (TIMER) database (https://cistrome.shinyapps.io/timer/). The immune cell types assessed included B cells, macrophages, dendritic cells (DCs), neutrophils, CD4+T cells, and CD8 +T cells. Gene expression levels were reported in log2-trans- formed transcripts per million (TPM) values.
2.4 Correlation between ITGB3 expression and immunomodulatory genes
After combining TIMER and xCell, the ‘Immunedeconv’ software package was used to generate a Spearman correlation analysis heatmap to describe the relationship between immune-checkpoint-related genes and ITGB3 gene expression in various cancers. In addition, gene co-expression analysis was performed to investigate the interaction between ITGB3 expression and immune-related genes in pan-cancer. The uniformly normalized pan-cancer dataset TCGA TARGET GTEx (PANCAN, N= 19,131, G=60,499) was obtained from the UCSC database (https://xenabrowser.net/). Subsequently, the ITGB3 gene and expression data for 60 marker genes representing two types of immune pathways (Inhibitory (24) and Stimulatory (36) were extracted from each sample. Normal samples were filtered, and log2 transformation was applied to normalize the expression values. Pearson correlation coefficients were computed between ITGB3 and the marker genes of the five immune pathways (chemokines, chemokine receptors, MHCs, immune inhibitors, and immunostimulators). Statistical analysis was carried out using R v4.0.3.
2.5 Evaluation of ITGB3 expression and immune checkpoints
Based on ITGB3 expression in their tumors, the patients were divided into a high-expression group and a low-expression group. The effect of ITGB3 on the expression of common immune checkpoints (CD274, CTLA4, HAVCR2, LAG3, PDCD1, PDCD1LG2, TREAM, SIGLEC15, ITPRIPL1, and IGSF8) was evaluated to measure the potential association between ITGB3 expression and immunotherapy response.
2.6 Correlation between ITGB3 expression and stromal component
We downloaded a standardized pan-cancer dataset from the UCSC database (https://xenabrowser.net/): TCGA TARGET GTEx (PANCAN, N= 19,131, G=60,499). The ENSG00000259207(ITGB3) gene expression data were then extracted from each sample, The sample sources were as follows: Primary Blood-Derived Cancer Peripheral Blood (TCGA-LAML), Primary Tumor, Metastatic and Primary Blood of TCGA-SKCM-Sample-Derived Cancer Bone Marrow, Primary Solid Tumor, and Recurrent Blood-Derived Cancer Bone Marrow. Each expression value was then log2(x+0.001) transformed, from which a gene expression profile for each tumor was extracted and mapped onto GeneSymbol. Then, using the R software package ESTIMATE (version 1.0.13, https://bioinformatics.mdanderson.org/public-software/estimate/, DOI:10.1038/ncomms3612), we calculated the stromal, immune, and ESTIMATE scores for patients in each tumor based on gene expression.
2.7 Analysis of ITGB3 expression and genomic heterogeneity
Tumor mutational burden (TMB) and microsatellite instability (MSI) scores were extracted from the TCGA dataset. Spear- man correlation analysis was used to explore the relationship between ITGB3 expression, TMB, and MSI. In addition, the genetic change frequency of ITGB3 in various cancers was assessed using the cbiopportal database (https://www.cbiop
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ortal.org/). Then, all level 4 TCGA samples from the Simple Nucleotide Variation dataset were downloaded from the GDC database (https://portal.gdc.cancer.gov/) and processed with the MuTect2 software (DOI:10.1038/nature08822). Sub- sequently, the mutation data of the samples were integrated, and the domain information for the protein was obtained with R v4.0.3.
2.8 Analysis of a protein-protein interaction network and gene set enrichment
A protein-protein interaction (PPI) network was established for 100 genes related to ITGB3, retrieved from the STRING database (https://cn.string-db.org/); the minimum interaction threshold was 0.4. In addition, differential expression analysis results for ITGB3 in different tumors were used for gene set enrichment analysis (GSEA) using cluster the Profiler R software package.
2.9 ITGB3 expression and DNA methylation
The DNA methylation levels of ITGB3 in different cancers and their corresponding paracancerous tissues were analyzed using the UALCAN database (http://ualcan.path.uab.edu/analysis.html). Statistical significance was determined using Student’s t-test, and a p-value <0.05 was considered statistically significant.
2.9.1 Statistical analysis
All bioinformatics statistical analyses were performed with R, version 4.3.3. Each experiment was repeated at least three times and expressed as the mean ± standard deviation (SD). Statistical analysis was performed using GraphPad Prism 8.0 (GraphPad Software Inc., San Diego, CA, USA). Significant differences between the two groups were analyzed using Student’s t-test; ANOVA was used for multiple group comparisons; and Dunnett’s test was used as a post hoc test. Sur- vival analysis was carried out using univariate and multivariable Cox regression analysis to calculate hazard ratios (HRs) and associated p-values. Kaplan-Meier analysis was utilized to classify patient survival time based on ITGB3 expression levels. A significance threshold of p <0.05 was applied to all statistical analyses. The Pearson chi-square test was used to compare the clinicopathological features. A p-value of less than 0.05 was considered statistically significant.
3 Result
3.1 Expression of ITGB3
Analysis of TIMER data for 33 common human cancers showed that ITGB3 expression was significantly increased in GBM and PCPG than in adjacent normal tissues. By contrast, its expression was reduced in BLCA, BRCA, CESE, COAD, KICH, KIRC, KIRP, LUSC, PRAD, SKCM, and UCEC (Fig. 1A). The TCGA database results showed that ITGB3 expression was increased in CHOL, GBM, HNCS, PCPG, and STAD compared with normal tissues. However, expression was significantly reduced in BLCA, BRCA, CESE, COAD, KICH, KIRC, LUSC, PRAD, READ, and UCEC (Fig. 1B). In addition, ITGB3 differential expression was found in 25 tumors in the TCGA and GTEx data, and the results were basically consistent with those of the TIMER and TCGA database analysis. The results showed that ITGB3 was highly expressed in CHOL, GBM, HNSC, LGG, LIHC, OV, PAAD, PCPG, SKCM, STAD, and THCA. However, its expression was low in BLCA, BRCA, CESE, COAD, DLBC, ESCA, KICH, KIRC, KIRP, LUAD, LUSC, PRAD, READ, TGCT, UCEC, and UCS (Fig. 1C). In addition, the Cancer Cell Line Encyclopedia (CCLE) database results for ITGB3 expression in various cancer cell lines showed higher expression in several tumors, including LGG, GBM, UCEC, THCA, KIRC, and SKCM (Fig. 1D). In addition, the Human Protein Atlas (HPA) database results showed that ITGB3 expression was concentrated in bone marrow, lymph, endocrine, digestive tract, and liver tissues as a whole (Fig. 1E). Interestingly, limited immunohistochemistry (IHC) results showed that ITGB3 expression was significantly lower in BRCA, COAD, LUSC, and PRAD than in normal tissues (Fig. 1G). In addition, ITGB3 was mainly expressed by the immunofluorescence (IF) of U251MG cells in the karyoplasm (Fig. 1F).
At different TNM stages, Stage and Grade staging, the expression of ITGB3 in BRCA, STES, KIPAN, STAD, KIRC, THCA, BLCA, and KICH tumors was significantly correlated with the T stage (Supplementary Fig. 1A). In KIRP and KIPAN, ITGB3 expression was significantly correlated with the N stage (Supplementary Fig. 1B). However, there was no significant correlation between ITGB3 expression and the M stage in most tumors (Supplementary Fig. 1C). In addition, ITGB3
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Fig. 1 Expression of ITGB3. A Analysis of TIMER data showed that ITGB3 expression was significantly upregulated in 14 cancers compared with normal tissues. Tumor tissues are shown in red boxes, and normal tissues are in blue boxes. B, C Differential expression of ITGB3 in paired cancer and adjacent normal tissues in TCGA and GTEx datasets. D The expression levels of ITGB3 mRNA in different tumor cell lines were analyzed based on the CCLE database. E The HPA database shows the global expression profile of ITGB3. F The expression of ITGB3 in U251MG cells was analyzed via immunofluorescence. G Limited IHC analysis of ITGB3 expression in normal versus tumor tissues. * p <0.05; ** p<0.01; *** p < 0.001
expression was significantly correlated with Stage staging in BRCA, KIRC, BLCA, and KICH (Supplementary Fig. 1D), and with Grade grading in GBM, LGG, ESCA, STES, STAD, HNSC, LIHC, PAAD, and OV (Supplementary Fig. 1E).
3.2 Prognostic value of ITGB3 in tumors
Univariate Cox regression analysis of 33 cancer types showed a significant correlation between ITGB3 expression and OS in 11 cancer types, including ACC, CESC, HNSC, KICH, LAML, LGG, MESO, PAAD, SARC, SKCM, and UVM (Fig. 2A). Further analysis using Kaplan-Meier survival curves showed that ITGB3 expression was positively correlated with OS in SKCM patients. By contrast, ITGB3 expression was negatively correlated with OS in ACC, CESC, ESCA, LAML, LGG, and STAD patients (Fig. 2B). In addition, ITGB3 expression in BLCA, CESC, HNSC, LGG, LIHC, LUSC, and UVM showed a significant correlation with PFS (Supplementary Fig. 2A). Cox regression analysis of DFS showed that high ITGB3 expression was a risk factor for CHOL and PAAD (Supplementary Fig. 2B). However, Cox regression analysis of DSS showed that high ITGB3 expression was a risk factor in ACC, CESC, KICH, LGG, LUSC, SKCM, UCEC, and UVM (Sup- plementary Fig. 2C).
3.3 Evaluation of ITGB3 as an independent prognostic factor in pan-cancer
To assess whether ITGB3 acts as an independent prognostic factor for pan-cancer, univariate and multivariate Cox regres- sion analyses were performed for age, T stage, N stage, M stage, and TNM stage. Univariate Cox regression analysis showed that ITGB3 expression (p<0.01), T stage (p<0.0001), M stage (p<0.0001), and TNM stage (p<0.0001) were significantly correlated with the overall survival (OS) of ACC patients (Fig. 3A). Multivariate Cox regression analysis showed a signifi- cant correlation between ITGB3 expression (p<0.05), T stage (p <0.05), and overall survival (OS) in ACC patients (Fig. 3B). Moreover, in CESC, univariate Cox regression analysis showed that ITGB3 expression (p<0.05), T stage (p<0.0001), N stage (p <0.01), M stage (p<0.05), and TNM stage (p<0.001) were significantly correlated with OS (Supplementary Fig. 3A). Multivariate Cox regression analysis showed that the T stage (p <0.05) was significantly associated with OS (Sup- plementary Fig. 3B). In UVM, univariate Cox regression analysis showed that ITGB3 expression (p <0.0001), age (p <0.05), M stage (p <0.01), and TNM stage (p <0.05) were significantly correlated with OS (Supplementary Fig. 4A). Multivariate Cox regression analysis showed that ITGB3 expression (p<0.001) and M stage (p<0.05) were significantly associated with OS (Supplementary Fig. 4B). In addition, a nomogram model was constructed based on the multivariate analysis, and the results showed that the prognosis C-indexes of ACC, CESC, and UVM were 0.819, 0.928, and 0.78, respectively. The C-indexes and calibration curves confirmed the accuracy of predicting the 1-, 3-, and 5-year OS of cancer patients (Fig. 3C, D; Supplementary Figs. 3C, D and 4C, D).
3.4 Correlation between ITGB3 expression and immune cell infiltration
To investigate the relationship between ITGB3 expression and immune cell infiltration in the TIME, the correlation between ITGB3 expression in cancer tissues and the infiltration levels of six key immune cell types (B cells, macrophages, dendritic cells (DCs), neutrophils, CD4 +T cells, and CD8 +T cells) in the TIME was analyzed by combining the TIMER and xCell databases. The results showed that ITGB3 expression was significantly negatively correlated with CD4 + Th1 cell infiltration in 23 tumors, NKT cell infiltration in 10 tumors, and CD8 +T cell infiltration in 6 tumors. By contrast, ITGB3 expression was significantly positively correlated with Treg infiltration in nine tumors and CD4 +Th2 infiltration in six tumors (Fig. 4A). Overall, ITGB3 expression was significantly positively correlated with immunosuppressive cells such as Tregs and CD4 +Th2 cells in most tumors, supporting tumor cell escape. However, it was negatively correlated with tumor killer cells such as NKT cells, CD8 +T cells, and CD4 +Th1 cells and suppressed tumor immunity (Fig. 4A, B).
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A
ITGB3 Expression Level (log2 TPM)
B
TCGA
0 O
6
ch S
in
*
*
-
10.0
*
-
-
ACC.Tumor (n=79)
BLCA. Tumor (n=408)-
BLCA.Normal (n=19)-
ITGB3 leg2(TPM+1)
7.5
ITGB3 log?(TPM+1)
7.5
BRCA. Tumor (n=1093)
0
BRCA.Normal (n=112)-
BRCA-Basal. Tumor (n=190)
25
BRCA-Her2.Tumor (n=82)
BRCA-LumA. Tumor (n=564)
00
BRCA-LumB. Tumor (n=217)
0.0
CESC.Tumor (n=304)
ACC(T-79,N=0)
BLCA(T=406,N=19)
BRCA(T=1101;N=113)
CESC(T=306;N=3)
CHOL(T-35:N=9)
COAD(T=455,N=41)
DLBC(T-48;N-0)
ESCA(T=163,N=11)
GBM(T=IS);N=5)
HNSC(T-504,N=44)
KICH(T-65;N=25)
KIRCIT-532,N=72)
KIRP(T=290;N=32)
LAML(T=150;N-0)
LOG(T-513:N=0)
LIHC(T-371;N=50)
CESC.Normal (n=3)
CHOL.Tumor (n=36)
..
CHOL.Normal (n=9)
COAD.Tumor (n=457)
Tumor
COAD.Normal (n=41)-
Normal
&
-
**
**
-
**
-
-
DLBC.Tumor (n=48)-
ESCA. Tumor (n=184)-
TTGB3 log2(TPM+1)
ESCA.Normal (n=11)-
ITGB3 log2(TPM+1)
GBM. Tumor (n=153)
GBM.Normal (n=5)
V
HNSC. Tumor (n=520)
HNSC.Normal (n=44)
A
HNSC-HPV+.Tumor (n=97)
0%
HNSC-HPV -. Tumor (n=421)
0
KICH.Tumor (n=66)
KICH.Normal (n=25)
LUAD(T-516;N=59)
LUSCIT-501,N=49)
MESO(T-87,N=0)
OV(T-376:N=0)
PAAD(T=179,N=4)
PCPG(T=18);N=3)
PRAD(T-498;N=52)
READ(T-165;N=10)
SARC(T=260;N=2)
SKCM(T-471;N=1)
STAD(T=375;N=32)
TGCT(T=134,N=0)
THCA(T-512;N=59)
THYM(T=120:N=2)
UCEC(T=$45;N=35)
UCS(T-ST,NG)
UVM(T-SON-0)
KIRC. Tumor (n=533)
KIRC,Normal (n=72)-
KIRP.Tumor (n=290)
KIRP.Normal (n=32)-
:
LAML. Tumor (n=173)-
C
TCGA+GTEx
LGG. Tumor (n=516)-
-
10.0
*
..
1
**
LIHC. Tumor (n=371)-
LIHC.Normal (n=50)-
ITGB3_log2(TPM+1)
7.5
ITGB3 log2(TPM+1)
15
LUAD. Tumor (n=515)-
LUAD.Normal (n=59)
5.0
LUSC. Tumor (n=501)
LUSC.Normal (n=51)
‘s
MESO.Tumor (n=87)
OV.Tumor (n=303)
0.0
PAAD. Tumor (n=178)
0.0
PAAD.Normal (n=4)
@
ACC(T=79,N=258)
BLCA(T=406;N=40)
BRCA(T=1101:N=572)
CESC(T=306;N=22)
CHOLET-35:N=9)
COADX(T-455:2820)
DLBC(T=48;N=929)
ESCA(T-163;N=1456)
GBM(T-153;N=2647)
HNSC(T-564;N=44)
KICH(T-65:N=114)
KIRC(T=$32;N=161)
KIRP(T-290,N=121)
LAML(T=150;N-0)
LOG(T-513:N=2642)
LIHC(T-371;N=276)
PCPG.Tumor (n=179)-
PCPG.Normal (n=3)-
t
*
PRAD.Tumor (n=497)
PRAD.Normal (n=52)-
…
READ. Tumor (n=166)
Tumor
READ.Normal (n=10)
Normal
8
…
*
…
…
-
-
-
…
-
…
…
SARC. Tumor (n=259)
SKCM. Tumor (n=103)
SKCM.Metastasis (n=368)
:
ITGB3 log2(TPM+1)
ITGB3 log2(TPM+1)
STAD. Tumor (n=415)
STAD.Normal (n=35)
TGCT.Tumor (n=150)
THCA. Tumor (n=501)
1
THCA.Normal (n=59)
0
0
THYM. Tumor (n=120)
UCEC. Tumor (n=545)-
LUAD(T-516;N=637)
LUSC(T-501;N=627)
MESO(T-87;N=0)
OV(T-376;N=180)
PAAD(T=179;N=332)
PCPG(T-181;N=3)
PRAD(T=498;N=297)
READ(T=165;N=789)
SARCIT-260,N=2)
SKCM(T=471;N=1810)
STAD(T=375;N=391)
TGCT(T=134;N=361)
THCA(T=512:N=712)
THYM(T=120,N=2)
UCEC(T-545;N=177)
UCS(T-57:N=142)
UVM(T-80;N-0)
UCEC.Normal (n=35)-
UCS.Tumor (n=57)
UVM.Tumor (n=80)
D
E
F
5
7.5-
ITGB3 expression
6.0
…
A
F.
:
25-
. ..
.
1
-
0.0
MM
ESCA
Ewings_sarcoma
BRCA
COAD_READ
PRAD
STAD
LAML
DLBC
ALL
LUSC
NSC
HNSC
PAAD
BLCA
SCLC
LUAD
LCML
MESO
NB
CESC
LIHC
MB
SARC
oy
CLL
LGG
GBM
UCEC
THCA
KIRC
SKCM
G
COAD
PRAD
ITGB3 Expression
ITGB3 Expression
6.
4
1
P
Expression difference
COAD
Expression difference
PRAD
LUSC
8
BRCA
8
ITGB3 Expression
6
ITGB3 Expression
6
4
4
2
2
0
Tumor
Normal
0
Tumor
Normal
Expression difference
LUSC
Expression difference
BRCA
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| A Cancer | Pvalue | beta | wald | se | Hazard Ratio(95% CI) | |||
|---|---|---|---|---|---|---|---|---|
| ACC | 4.93€-03 | 1.17e-01 | 2.81e+00 | 4.15e-02 | 1.12(1.04,1.22) | |||
| BLCA | 5.56e-02 | 1.28e-01 | 1.91e+00 | 6.7e-02 | 1.14(0.997,1.3) | |||
| BRCA | 1.04e-01 | 1.37e-01 | 1.63e+00 | 8.46e-02 | 1.15(0.972,1.35) | |||
| CESC | 3.19e-02 | 1.96e-01 | 2.15e+00 | 9.14e-02 | 1.22(1.02,1.46) | |||
| CHOL | 1.4c-01 | 7.27e-02 | 1.47e+00 | 4.93-02 | 1.07(0.976,1.19) | |||
| COAD | 5.04c-01 | -2e-02 | -6.7e-01 | 2.99€-02 | 0.98(0.924,1.04) | |||
| DLBC | le+00 | -1.43e-04 | De+00 | 4.39e-01 | 1(0.423,2.36) | |||
| ESCA | 9.24e-01 | -2.01e-03 | -le-01 | 2.1e-02 | 0.998(0.958,1.04) | |||
| GBM | 1.02e-01 | 3.01e-02 | 1.63e+00 | 1.84e-02 | 1.03(0.994,1.07) | |||
| HNSC | 2.26c-02 | 2.79c-02 | 2.28c+00 | 1.22e-02 | 1.03(1,1.05) | |||
| KICH | 9.03c-03 | 6.28e-01 | 2.6le+00 | 2.41e-01 | 1.88(1.17,3) | |||
| KIRC | 4.11e-01 | -6.87e-02 | -8.2e-01 | 8.35e-02 | 0.934(0.793,1.1) | |||
| KIRP | 1.59e-01 | 1.16e-01 | 1.41e+00 | 8.26e-02 | 1.12(0.955,1.32) | |||
| LAML | 1.5e-02 | 1.42e-01 | 2.43e+00 | 5.82e-02 | 1.15(1.03,1.29) | |||
| LGG | 6.73c-13 | 6.24e-01 | 7.18c+00 | 8.69e-02 | 1.87(1.57,2.21) | |||
| LIHC | 7.12e-01 | 1.4le-02 | 3.7e-01 | 3.8e-02 | 1.01(0.941,1.09) | |||
| LUAD | 8.14e-01 | -1.32e-02 | -2.4e-01 | 5.63€-02 | 0.987(0.884,1.1) | |||
| LUSC | 3.27e-01 | 1.67e-02 | 9.8e-01 | 1.7le-02 | 1.02(0.983,1.05) | |||
| MESO | 1.42e-02 | 4.7e-02 | 2.45€+00 | 1.92e-02 | 1.05(1.01,1.09) | |||
| OV | 6.42e-01 | -2.66e-02 | -4.6e-01 | 5.71e-02 | 0.974(0.871,1.09) | |||
| PAAD | 3.77e-02 | 4.3le-02 | 2.08c+00 | 2.07e-02 | 1.04(1,1.09) | |||
| PCPG | 8.09€-01 | -2.3c-02 | -2.4c-01 | 9.5le-02 | 0.977(0.811,1.18) | |||
| PRAD | 3.88c-01 | -1.94e-01 | -8.6c-01 | 2.24e-01 | 0.824(0.531,1.28) | |||
| READ | 9.52e-01 | 7.38e-03 | 6e-02 | 1.22e-01 | 1.01(0.794,1.28) | |||
| SARC | 4.95e-02 | -1.22e-01 | -1.96e+00 | 6.23e-02 | 0.885(0.783,1) | |||
| SKCM | 6.54e-03 | -8.16e-02 | -2.72e+00 | 3e-02 | 0.922(0.869,0.977) | |||
| STAD | 3.05c-01 | 1.4le-02 | 1.03c+00 | 1.37e-02 | 1.01(0.987,1.04) | |||
| TGCT | 2.64c-01 | 7.16c-02 | 1.12e+00 | 6.4le-02 | 1.07(0.947,1.22) | |||
| THCA | 6.83e-01 | -9.8c-02 | -4.1e-01 | 2.4e-01 | 0.907(0.566,1.45) | |||
| THYM | 8.87e-01 | -5.94e-02 | -1.4e-01 | 4.2e-01 | 0.942(0.414,2.15) | |||
| UCEC | 7.17e-02 | 1.27e-01 | 1.8e+00 | 7.04e-02 | 1.14(0.989,1.3) | |||
| UCS | 3.72e-01 | 1.24c-01 | 8.9e-01 | 1.39e-01 | 1.13(0.862,1.49) | |||
| UVM | 1.05€-05 | 1.04c+00 | 4.4le+00 | 2.35e-01 | 2.82(1.78,4.46) | |||
B
1.0
ITGB3
1.0
ITGB3
1.00
ITGB3
Low
Low
Low
0.9
High
High
High
Survival probability
0.8
ACC
Survival probability
0.8
CESC
Survival probability
0.75
ESCA
0.7
0.50
0.6
0.6
0.25
0.5
Overall Survival-+ HR = 2.38 (1.10 -
Overall Survival HR = 1.79 (1.12 - 2.88)
Overall Survival HR = 1.42 (0.87 - 2.32)
5.17)
P = 0.028
0.4
+ +
P = 0.016
0.00
P = 0.159
0
1000
2000
3000
4000
0
2000
4000
6000
0
500
1000
1500
2000
2500
Time (days)
Time (days)
Time (days)
1.00
ITGB3
1.00
ITGB3
1.0
H
ITGB3
Low
Low
Low
High
High
High
Survival probability
0.75
Survival probability
0.75
Survival probability
0.8
LAML
LGG
STAR
0.50
0.6
0.50
0.4
0.25
Overall Survival, HR = 1.60 (1.04
0.25
Overall Survival HR = 2.89 (1.99 - 4.21)
Overall Survival
HR = 5.50 (1.87 - 16,18)
P = 0.031
P < 0.001
0.2
P = 0.002
0
1000
2000
0
2000
4000
6000
0
500
1000
1500
2000
2500
Time (days)
Time (days)
Time (days)
1.00
ITGB3
Low
High
Survival probability
0.75
SKCM
0.50
0.25
0.5 1 1.5 2 2.5 3 3.5 4 4.5 Hazard Ratio
Overall Survival HR = 0.74 (0.57 - 0.98)
0.00
P = 0.034
0
3000
6000
9000
Time (days)
3.5 Association of ITGB3 expression with immune-related genes and immune checkpoint
To further confirm the critical role of ITGB3 in regulating tumor immunity, we combined TIMER and xCell with the Immunedeconv package to generate a Spearman correlation analysis heatmap to describe the relationship between immune checkpoint-related genes and ITGB3 gene expression in various cancers. The results showed that ITGB3 expression was significantly and positively correlated with various chemokines, chemokine receptors, MHCs, immune inhibitors, and immunostimulators in most types of cancer (Fig. 5A). In addition, we focused on the relationship between the expression of 10 important immune checkpoints in different tumors (CD274, CTLA4, HAVCR2, LAG3, PDCD1, PDCD1LG2, TIGIT, SIGLEC15, ITPRIPL1, and IGSF8) and ITGB3. The results showed that the ITGB3 high-expression group had significantly higher expressions of most immune checkpoint genes in BLCA, BRCA, CESE, COAD, KICH, KIRP, LUSC, PCPG, PRAD, SKCM, and UCEC than the ITGB3 low-expression group (Fig. 5B). Notably, negative regulatory costimulatory molecules on the surface of T cells, such as CTLA4 [21] and PDCD1 (PD-1) [22], are associated with tumor immunosuppression.
3.6 Correlation between ITGB3 expression and stromal component
Using the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm, we calculated ESTIMATE scores (Fig. 6), stromal signatures (Supplementary Fig. 5A), and immune signa- tures (Supplementary Fig. 5B). We also used cancer sample transcription profiles to infer tumor cell and infiltrating immune and stromal cell content. Analysis of the relationship between ITGB3 expression and tumor purity showed that ITGB3 expression was proportional to tumor purity in the vast majority of 27 tumors, but no correlation was observed between ITGB3 expression and tumor purity in CESC and KIRP.
Discover
ACC
| Uni_cox | Pvalue | Hazard Ratio(95% CI) | |
|---|---|---|---|
| ITGB3 | 0.00316 | 1.71518(1.19874,2.45413) | H |
| Age | 0.37934 | 1.01097(0.98667,1.03587) | |
| pT_stage | <0.0001 | 3.37759(2.11006,5.40653) | |
| pN_stage | 0.15195 | 2.03844(0.76943,5.4004) | |
| pM_stage | le-05 | 6.15025(2.70974,13.95908) | |
| pTNM_stage | le-05 | 2.62824(1.71405,4.03) |
| Mult_cox | p.value | Hazard Ratio(95% CI) | |
|---|---|---|---|
| ITGB3 | 0.02251 | 1.57794(1.06641,2.33485) | H |
| Age | 0.75830 | 1.00456(0.97589,1.03406) | |
| pT_stage | 0.01047 | 3.73603(1.36197,10.24835) | |
| pN_stage | 0.18871 | 2.31916(0.66156,8.13001) | |
| pM_stage | 0.84058 | 1.19183(0.21558,6.58899) | |
| pTNM_stage | 0.71932 | 0.7516(0.15835,3.56749) |
A
B
1
123456789 11 13 Hazard Ratio
0.15835 3 4 5 6 7 8 9 10
Hazard Ratio
C
D
1.0
II
0
10
20
30
40
50
60
70
80
90
100
Points
1-year
C-index : 0.819 (0.769-0.87) p < 0.001
3-year
5-year
ITGB3
0.8
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
pT_stage
T2
T4
TI
T3
Observed(%)
0.6
Total Points
0
20
40
60
80
100
120
140
160
180
0.4
×
Linear Predictor
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
3
3.5
1-year survival Pro
0.2
×
0.95
0.9
0.8
0.7
0.6
0.5
0.4
3-year survival Pro
0.0
0.95
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
5-year survival Pro
0.0
0.2
0.4
0.6
0.8
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
Nomogram-prediced(%)
n=77 d=27 p=4, 25.6666666666667 subjects per group
Gray: ideal
- resampling optimism added, B=200 Based on observed-predicted
3.7 Analysis of ITGB3 expression and genomic heterogeneity
Spearman’s correlation analysis was used to analyze the correlation between ITBG3 expression, TMB, and MSI. ITGB3 expression was strongly positively correlated with TMB in a variety of cancers, including ACC, GBM, LGG, and OV, whereas it was negatively correlated with TMB in STAD, STES, LUAD, THCA, HNSC, and PRAD (Fig. 7A, B). In addition, the association between ITGB3 expression and MSI in different cancer types was examined. ITGB3 expression was positively correlated with MSI in ACC, CESC, SKCM, READ, MESO, KICH, LGG, THYM, TGCT, PCPG, and UVM. However, negative correlations were observed in ACC, AML, BRCA, BLCA, COAD, CHOL, DLBC, GBM, HNSC, KIPAN, STAD, PAAD, PRAD, UCS, THCA, LUAD, and OV (Fig. 7C, D). In addition, we investigated the frequency of ITGB3 alterations in dif- ferent cancer types according to the cBioPortal database. The frequency of ITGB3 variants is highest in melanoma and endometrial cancer, with mutation and amplification as the predominant variant types (Fig. 7E). The mutation lollipop plot further revealed that most tumors could harbor missense mutations at multiple sites (Fig. 7F).
3.8 Analysis of protein-protein interaction network and gene set enrichment
To explore the ways ITGB3 regulates tumorigenesis, we performed GO and KEGG analyses of 300 ITGB3-related genes in several cancers (Fig. 8A, B). GO analysis revealed that ITGB3 negatively regulates protein kinase and serine/threo- nine kinase activity; is involved in thyroid hormone metabolism; and is associated with enzyme inhibitor activity and serine-type endopeptidase inhibitors (Fig. 8A). The KEGG analysis revealed that ITGB3 expression was associated with thyroid hormone synthesis, small-cell lung cancer, and glycosphingolipid biosynthesis in the ganglion (Fig. 8B).
Discover
A
stroma score
**
microenvironment score
**
**
**
immune score
-
..
..
..
…
..
T cell regulatory (Tregs)
**
T cell gamma delta
**
**
**
T cell NK
…
…
…
…
…
T cell CD8+ naive
**
**
**
**
**
**
**
T cell CD8+ effector memory
**
T cell CD8+ central memory
**
…
…
…
T cell CD8+
T cell CD4+ naive
T cell CD4+ memory
..
..
..
T cell CD4+ effector memory
**
**
T cell CD4+ central memory
**
**
**
**
**
T cell CD4+ Th2
…
…
…
..
…
* p<0.05
T cell CD4+ Thì
…
.**
** p < 0.01
T cell CD4+ (non-regulatory)
**
Plasmacytoid dendritic cell
**
*** p<0.001
XCELL
Neutrophil
**
**
Correlation
NK cell
**
**
**
*
**
*
0.50
Myeloid dendritic cell activated
…
**
0.25
Myeloid dendritic cell
**
**
**
*
0.00
Monocyte
**
**
**
**
**
**
-0.25
Mast cell
**
**
**
-0.50
Macrophage M2
**
**
**
.
Macrophage MI
**
*
Macrophage
**
**
**
Hematopoietic stem cell
…
*
**
Granulocyte-monocyte progenitor
…
**
**
Eosinophil
**
**
Endothelial cell
*
Common myeloid progenitor
..
Common lymphoid progenitor
**
**
**
**
**
**
**
Class-switched memory B cell
**
**
B cell plasma
…
B cell naive
B cell memory
B cell
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TOCT
THCA
THYM
UCEC
UCS UVM
B
T cell CD8+
*
**
*
*p< 0.05
**
**
** p < 0.01
T cell CD4+
**
**
**
*
*** p<0.001
TIMER
Neutrophil
**
*
**
Correlation
Myeloid dendritic cell
**
**
*
**
**
**
**
0.6
0.4
Macrophage
*
*
*
**
*
*
0.2
0.0
B cell
**
*
*
**
**
*
-0.2
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LGG
LINC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TOCT
THCA
THYM
UCEC
UCS
UVM
To better understand the biological role of ITGB3 in tumors, we constructed a protein-protein interaction (PPI) net- work for 100 ITGB3-related genes using the STRING website (Fig. 8C). Subsequently, to further elucidate the function of ITGB3, a gene set enrichment analysis (GSEA) based on a differential expression analysis of ITGB3 was used to elucidate its biological function in tumors, especially the relationship between ITGB3 expression and prognosis and the tumor immune microenvironment. We focused our analysis on SKCM (Fig. 8D), a tumor with low ITGB3 expression and associ- ated poor survival; LGG (Fig. 8E); and STAD (Fig. 8F), a tumor with high ITGB3 expression and associated poor survival. In addition, the results showed that ITGB3 was mainly related to the calcium signaling pathway, the PI3K-AKT signaling pathway, and the TGF-ß signaling pathway (Fig. 8D, F).
3.9 ITGB3 and methylation
We combined the UALCAN database with the TCGA database to study the DNA methylation of ITGB3. The UALCAN data- base results showed that ITGB3 methylation levels were significantly decreased in TGCT and significantly increased in BLCA, BRCA, CESC, CHOL, COAD, HNSC, KIRC, KIRP, LIHC, LUAD, PRAD, READ, and UCEC (Fig. 9A). There was no difference from normal tissue in ESCA, GBM, LUSC, PAAD, PCPG, SNRC, STAD, THCA, or THYM (Fig. 9B).
4 Discussion
Cancer poses a significant threat to human health due to its high incidence and mortality rate. The most common cancer treatments include surgical resection, radiation therapy, and adjuvant chemotherapy. However, their efficacy remains limited. Early detection and effective treatment are crucial prerequisites for enhancing the prognosis of cancer patients
Discover
A
COAD
correlation coefficient
-1.0-0.50.0 0.5 1.0
pValue
0.0
0.5
1.0
Type:
Inhibitory
Stimulaotry
*
*
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*
*
*
*
*
*
*
*
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Type
BTN3A2
BTN3A1
CD40
TNF
IL1B
IL1A
CX3CL1
ITGB2
CD28
TNFRSF9
CD80
TNFSF4
IL2RA
ICAM1
SELP
HMGB1
TLR4
ENTPD1
IL2
IFNA1
IFNA2
TNFRSF14
COSLG
CD70
TNFSF9
TNFRSF4
CD27
TNFRSF18
CCL5
GZMA
IFNG
CXCL9
CXCL10
COS
CD40LG
PRF1
IL10
HAVCR2
CD274
VEGFA
TGFB1
CD276
C10orf54
EDNRB
KIR2DL3
KIR2DL1
ARG1
L13
VTCN1
IL12A
VEGFB
L4
LAG3
PDCD1
BTLA
TIGIT
CTLA4
SLAMF7
IDO1
ADORA2A
Group
High
Low
B
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[41]. Finding new tumor biomarkers and understanding their molecular mechanisms in tumorigenesis and development can better provide effective diagnostic methods and treatment strategies for clinical practice.
In this study, we employed a variety of bioinformatics methods to explore the potential tumor-promoting or -inhibit- ing effects of ITGB3 by investigating the significant correlation between ITGB3 expression and cancer patient prognosis, DNA methylation, TMB, MSI, immune cell infiltration levels, and immune checkpoint genes, based on databases such as TCGA, GTEx, UALCAN, and cBioPortal. We comprehensively analyzed the expression of ITGB3 in the pan-cancer dataset, as well as the relationship between ITGB3 expression levels and the TIME, along with potential regulatory mechanisms.
Discover
4,000
TCGA-GBM(N=
52)
r=0.20
4,000
TCGA-LGG(N=504)
4,000
TCGA-UCEC(N=178)
4,000
TCGA-BRCA(N=
1077)
4,000
TCGA-CESC(N=29
ESTIMATEScore
2,000
ESTIMATEScore
2,000
r=0.52
r=0.09
p=0.01
p=7.1e-36
ESTIMATEScore
2,000
p=0.26
ESTIMATEScore
2,000
r=0.24
ESTIMATEScore
2,000
r =- 1.1e-3
p=5.5e
p=0.99
0
0
0
0
0
-2,000
-2,000
-2,000
-2,000
-2,000
-4,000
-4,000
-4,000
-4,000
-4,000
-6,000
-6,000
-6,000
-6,000
-6,000
-8,000
-8,000
-8,000
-8,000
-8,000
-5
0
5
-5
0
5
-5
0
5
-5
0
5
-5
0
5
ITGB3 Expression
ITGB3 Expression
ITGB3 Expression
ITGB3 Expression
ITGB3 Expression
4,000
TCGA-LUAD(N+500)
ESTIMATEScore
r=0.37
4,000
TCGA-ESCA(N=18
181)
TCGA-STES(N=$69)
TCGA-SARC(N=258)
TCGA-KIRP(N=28:
2,000
ESTIMATEScore
2,000
r=0.47
4,000
r=0.44
4,000
r=0.07
4,000
p=2.8e-
p=1.7e-11
ESTIMATEScore
2,000
ESTIMATEScore
ESTIMATEScore
r =- 0.10
0
0
0
p=1.4e-
2,000
p=0.26
2,000
p=0.09
0
0
-2,000
-2,000
-2,000
-2,000
-2,000
-4,000
-4,000
-4,000
-4,000
-4,000
-6,000
-6,000
-6,000
-6,000
-6,000
-8,000
-8,000
-8,000
-8,000
-8,000
-5
0
5
-5
0
5
-5
0
5
-5
0
5
-5
0
5
ITGB3 Expression
ITGB3 Expression
ITGB3 Expression
ITGB3 Expression
ITGB3 Expression
4,000
TCGA-KIPAN(N
878)
TCGA-COAD(N=
282)
4,000
TCGA-PRAD(N=495)
4,000
TCGA-STAD(N=388)
4,000
TCGA-HNSC(N=$1’
ESTIMATEScore
4,000
2,000
r=0.09
p=5.3e-
ESTIMATEScore
2,000
r=0.65
ESTIMATEScore
2,000
r=0.33
ESTIMATEScore
2,000
r=0.45
ESTIMATEScore
2,000
=0.35
p=8.8e-
0
0
p=1.9e-35
0
p=4.6e-14
p=6.9e-21
0
0
-2,000
-2,000
-2,000
-2,000
-2,000
-4,000
-4,000
-4,000
-4,000
-4,000
-6,000
-6,000
-6,000
-6,000
-6,000
-8,000
-8,000
-8,000
-8,000
-8,000
-5
0
5
-5
0
5
-5
0
5
-5
0
5
-5
0
5
ITGB3 Expression
ITGB3 Expression
ITGB3 Expression
ITGB3 Expression
ITGB3 Expression
4,000
TCGA-KIRC(N=$28)
4,000
TCGA-LUSC(N=491)
4,000
TCGA-SKCM(N=452)
4,000
TCGA-BLCA(N=405)
r=0.18
r=0.48
r=0.15
r=0.54
4,000
TARGET-NB(N=
5
ESTIMATEScore
2,000
p=2.2e-5
ESTIMATEScore
2,000
p=1.1e-29
ESTIMATEScore
2,000
p=1.5e-3
ESTIMATEScore
2,000
p=7.5e-32
ESTIMATEScore
2,000
r=0.38
p=1.6e-6
0
0
0
0
0
-2,000
-2,000
-2,000
-2,000
-2,000
-4,000
-4,000
-4,000
-4,000
-4,000
-6,000
-6,000
-6,000
-6,000
-6,000
-8,000
-8,000
-8,000
-8,000
-8,000
-5
0
5
-5
0
5
-5
0
5
-5
0
5
-5
0
5
ITGB3 Expression
ITGB3 Expression
ITGB3 Expression
ITGB3 Expression
ITGB3 Expression
4,000
TCGA-MESO(N=85)
ESTIMATEScore
4,000
TCGA-READ(N=91)
TCGA-PAAD(N=
7
ESTIMATEScore
2,000
r=0.23
2,000
r=0.69
4,000
TCGA-OV(N=417)
4,000
TCGA-UVM(N=79)
ESTIMATEScore
r=0.29
ESTIMATEScore
r=0.37
ESTIMATEScore
4,000
r=0.46
p=0.03
p=5.0e-14
2,000
2,000
p=9.4e-4
2,000
p=1.7e-10
0
0
0
p=3.0e-9
0
0
-2,000
-2,000
-2,000
-2,000
-2,000
-4,000
-4,000
-4,000
-4,000
-4,000
-6,000
-6,000
-6,000
-6,000
-6,000
-8,000
-8,000
-8,000
-8,000
-8,000
-5
0
5
-5
0
5
-5
0
5
-5
0
5
-5
0
5
ITGB3 Expression
ITGB3 Expression
ITGB3 Expression
ITGB3 Expression
ITGB3 Expression
4,000
TCGA-LAML(N=
214)
r=0.27
4,000
TCGA-PCPG(N=177)
4,000
TCGA-ACC(N=77)
4,000
TCGA-DLBC(N=
46)
ESTIMATEScore
2,000
p=6.9e-5
ESTIMATEScore
2,000
r=0.39
r=0.25
2,000
+ 0.42
p=6.6e-8
ESTIMATEScore
2,000
p=0.03
ESTIMATEScore
p=3.9e-3
0
0
0
0
-2,000
-2,000
-2,000
-2,000
-4,000
-4,000
-4,000
-4,000
-6,000
-6,000
-6,000
-6,000
-8,000
-8,000
-8,000
-8,000
-5
0
5
-5
0
5
-5
0
5
-5
0
5
ITGB3 Expression
ITGB3 Expression
ITGB3 Expression
ITGB3 Expression
Discover
A
B
STAD(N=409)
SampleSiz
STES(N=589
·
4.0-
TGCT(N=143)
LUAD(N=509)
200
STAD(N=409)
CHOL(N=36)
UCEC(N=175
400
THCA(N=489)
3.5
SKCM(N=102)
600
HNSC(N=498
KIRP(N=279
800
PRAD(N=492
3.0
STES(N-589)
BRCA(N=981
BLCA(N=407
LAML(N=126)
CESC(N=286
pValue
KIRC(N=334)
0.0
2.5
LUSC(N=486)
0.2
-log10(pValue)
LIHC(N=357
LUAD(N=509)
ACC(N=77)
ESCA(N=180
MESO(N=82
0.4
2.0
GBMLGG(N=650)
KIPAN(N=679)
UVM(N=79 READ(N=90 DLBC(N=37
0.6
0.8
1.5
THCA(N=489)
OV(N=303)
PCPG(N=177
DADREAD(N=372)
-1.0
KICH(N=66)
HNSC(N=498)
PAAD(N=171
PRAD(N=492)
LGG(N=501)
COAD(N=282
1.0
TGCT(N=143)
GBM(N=149)
LGG(N=501)
BRCA(N-981)
SARC(N=234)
KIRP(N=279)
SARC(N=234)
THYM(N=118)
GBMLGG(N=650)
0.5
UCEC(N=175)
ESCA(N=180)
THYM(N=118)
UCS(N=57)
OV(N=303
GBM(N=149
BLCA(N=407)
SKCM(N=102)
LUSC(N=486)
COAD(N-282)
PAAD(N=171)
UCS(N=57
CESC(N=286)
CHOL(N-36)
KIRC(N=334)
COADREAD(N=372)
ACC(N=77)
KIPAN(N=679)
MESO(N-82)
0.0
LIHC(N=357)
LAML(N=126) PCPG(N-177) KICH(N=66)
DLBC(N-37) READ(N-90) UVM(N-79)
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.30
Correlation coefficient(pearson)
Correlation coefficient(pearson)
C
D
GBMLGG(N=657)
SampleSiz
CHOL(N=36)
·
HNSC(N=500)
STAD(N=412
200
13
GBMLGG(N=657)
PRAD(N=495
DLBC(N=47
400
STES(N=592
12
UCS(N=57
600
KIPAN(N=688)
800
11
LUSC(N=490
BRCA(N=1039
THCA(N=493
E1,00
10
LUAD(N=511
BLCA(N=407
pValue
9
LAML(N=129
PAAD(N=176
0.0
OV(N=303)
0.2
-log10(pValue)
8
ESCA(N=180
KIRP(N=285
DADREAD(N=374)
0.4
7
COAD(N=285
LIHC(N=367
0.6
6
KIRC(N=337)
HNSC(N=500)
UCEC(N=180
0.8
SARC(N=252
5
PRAD(N=495)
LGG(N=506)
1.0
STAD(N=412)
PCPG(N=177
4
GBM(N=151)
THYM(N=118)
STES(N=592)
CESC(N=302 TGCT(N=148)
3
2
KIPAN(N=688)
BRCA(N=1039)
ACC(N=77
SKCM(N=102
LUSC(N=490)
LUAD(N=511)
READ(N=89
THCA(N=493)
KICH(N-66)
UV
MESO(N=83)
1
CHOL(N=36)
DLBC(N
CESC(N=302)
READ(N=89)
KICH(N=66)
BLCA(N=40 OV(N=303)
PCPG(ACC(N=77)
MESO(N=83)
UVM(N=79)
ESCA(N=180)
SKCM(N-102)
0
UCS(N=57) LAMIIN 120 COAD(N-285) LGG(N-
AAD(N-17( KIRP(N=285)
GBM THYM(N-1| |GCT(N=148)
-0.2
-0.1
0.0
0.1
0.2
0.3
COADREAD(N-374) LIHC(N=367)
KIRC(N=3 SARC(N=252) UCEC(N-180)
-0.25
-0.20
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
E
Correlation coefficient(pearson)
Correlation coefficient(pearson)
F
Mutation
Structural Variant
Amplification
Deep Deletion
Multiple Alterations
¥=500,0.2%)-
Nonsense_Mutation
¥-286,1.4%)
Missense_Mutation
6%
¥-508,1.8%)-
Frame_Shift_Del
Splice_Site
Alteration Frequency
¥=282,2.8%)-
¥=980,0.5%)-
¥=180,0.6%)-
4%
V=589,1.4%)-
¥=234,0.4%)-
V=279,0.7%)-
2%
V=409,1.7%)-
N=175,4.0%)-
V=498,1.0%)-
v=485.2.3%)-
N=118,0.8%)
Structural variant data
¥-356,1.1%)-
Mutation data
¥=487,0.4%)
CNA data
(N=90,2.2%)
v=168,0.6%)
Melanoma
Endometrial Cancer
Pleural Mesothelioma
Bladder Cancer
Colorectal Cancer
Non-Small Cell Lung Cancer
Pancreatic Cancer
Mature B-Cell Neoplasms Esophagogastric Cancer
Hepatobiliary Cancer
Breast Cancer
Head and Neck Cancer
Thymic Epithelial Tumor
Prostate Cancer
Pheochromocytoma
Cervical Cancer
Ocular Melanoma
Ovarian Epithelial Tumor
Sarcoma
Non-Seminomatous Germ Cell Tumor
Renal Non-Clear Cell Carcinoma
Thyroid Cancer
Glioblastoma
Leukemia
Renal Clear Cell Carcinoma
Glioma
Adrenocortical Carcinoma
Cholangiocarcinoma
Miscellaneous Neuroepithelial Tumor
Seminoma
¥=143,0.7%)
V=102,1.0%)
V=407,2.2%)
(N=37,2.7%)
788aa
Integrin_beta
Integrin_B_tail
Integrin_b_cy
The results indicated that dysregulation in ITGB3 expression in various human cancers is consistent with the findings of other clinical and preclinical data.
Overall, ITGB3 was predominantly expressed in bone marrow, lymph, endocrine, digestive tract, and liver tissues (Fig. 1E). Using the TCGA database, ITGB3 expression in different types of cancer and their corresponding adjacent
A
B
C
DAPKALNS DOCKSLEKHO2-13$13G/ČNIH3GHE ”
negative regulation of protein
kinase activity
TCTN3
¢
.:
ITIH6
negative regulation of protein
MGAT4C
AFF1
10
TRAK2
serine/threonine kinase
PTPRM
OXL3
GAS7
LURAP1
activity
8
LO
az
thyroid hormone metabolic process
Thyroid hormone synthesis
AFAP1L2
-
FRMD3
CRYBG3
2
«
HIRA
LOXL4
2
FSCN2
DCSTAMP
CPVL
thyroid hormone generation
P adj
CE
3
PTCHD4
0.09
P adj
A
SNX22
ENTPD1
&
0.08
0.06
MSN
SNX1
collagen-containing extracellular
5
SHROOM4
14
APLP2
MUC15
0.07
-
0.04
PDESA
8
NPC2
-
matrix
0.06
1
C
PROS
basal part of cell
0.05
0.02
EFCAB13
PRKA
A
ITGA9
SERPINE2
KEGG
*
4
+
AD
*
CTSB
ITIHS
8
Counts
Small cell lung cancer
Counts
SLC27A6
basal plasma membrane
·
RXRG
SLC5A3
5
CPQ
3
3
Z
PDE1A
4
C12orf49
RAPGEF
4
NB
·
endoplasmic reticulum-Golgi
ES
LAMA4
O
NRP2
5
LAMC1
intermediate compartment
6
5
MES
6
A
PAX8
MRP$6
6
PMT
7
O 6
LPCAT2
LRPAPI
HH
P
ge?
6
7
14
enzyme inhibitor activity
8
NOD1
SPRY4
X4
TSHR
KCNJ16
RASA1
”
P
14
X
BBS12
peptidase regulator activity
Glycosphingolipid biosynthesis
LTBP3
RP
류
- ganglio series
PLPP3
SPRED1
KCNJ15
CREB3L2
serine-type endopeptidase inhibitor
-
$100A13
¥
DUSP6
9
FBXO7
activity
A
9
RBMS3
0
MA
cyclic nucleotide binding
**
INPPSK
TMEM243
S
LC17CBH19ACVREECRG-TOER ARM
Co
4
M
D
0.04 0.06 0.08 0.10 GeneRatio
0.03 0.04 0.05 0.06
2
-
.
E
GeneRatio
F
0.6
0.6
0.4
Enrichment Score
Enrichment Score
0.3
Enrichment Score
0.4
0.2
0.0
0.2
0.0
[KEGG] Calcium Signaling Pathway
[WikiPathways] Pi3kakt Signaling Pathway
-0.3
[KEGG] Calcium Signaling Pathway
[KEGG] Tgf Beta Signaling Pathway
[WikiPathways] Pi3kakt Signaling Pathway
[KEGG] Calcium Signaling Pathway
[KEGG] Tgf Beta Signaling Pathway
0.0
[WikiPathways] Pi3kakt-Signaling Pathway
[KEGG] Tgf Beta Signaling Pathway
Ranked list metric
7.5
Ranked list metric
Ranked list metric
5.0
SKCM
4
LGG
2.5
2.5
STAD
2
0.0
0.0
0
-2.5
-2
-2.5
0
10000
20000
30000
0
10000
20000
30000
0
10000
20000
30000
Rank in Ordered Dataset
Rank in Ordered Dataset
Rank in Ordered Dataset
normal tissues was investigated, comparing the differences between 33 cancers (Fig. 1A, B). Given that some of these tumors lacked corresponding normal tissue results as controls, we combined TCGA and normal genotype-tissue expres- sion (GTEx) data. By analyzing the TIMER, TCGA, GTEx, and CCLE databases, we discovered that ITGB3 expression was significantly elevated in GBM and PCPG. Conversely, expression was significantly reduced in BLCA, BRCA, CESE, COAD, KICH, KIRC, KIRP, LUSC, PRAD, and UCEC (Fig. 1A-D). The IHC results also indicated that ITGB3 expression was significantly lower in BRCA, COAD, LUSC, and PRAD than in normal tissues, which was aligned with the database prediction (Fig. 1G). These results suggest that ITGB3 may play distinct and important roles in certain tumors, which is consistent with some previous findings [18, 32, 42, 43].
In this study examining the relationship between ITGB3 expression, clinical stage, and pathological grade in different tumor patients, we found that ITGB3 expression levels were significantly correlated with the T stage and N stage; however, there was no significant correlation with the M stage. In addition, ITGB3 expression levels in KIPAN significantly varied at different T and N stages. In BRCA, BLCA, KICH, and KIRC, ITGB3 expression levels significantly varied in different T stages. In STES and STAD, ITGB3 expression levels significantly varied in different T stages and grades (Supplementary Fig. 1A-E). These results indicate that ITGB3 expression varies in different tumors and tumor stages, suggesting that ITGB3 may play an important role in tumor occurrence, development, and prognosis evaluation.
Given the above differences in ITGB3 expression in different tumor stages, we further investigated the specificity and significance of ITGB3 in predicting cancer patient prognosis. We investigated the correlation between ITGB3 expression levels and OS, PFS, DFS, and DSS in different cancer types (Fig. 2; Supplementary Fig. 2A-C). Univariate and multivariate Cox regression indicated that high ITGB3 expression was a significant independent risk factor in certain cancers (such as ACC, CESC, ESCA, LAML, LGG, and STAD) and was significantly associated with poor prognosis. However, it is interesting that highly expressed ITGB3 appears to be protective in SKCM. In addition, a nomogram
Discover
A
0.6-
TGCT
0.7
BLICA
-
0.9-
BRCA
0.8-
CESE
0.8-
CHOL
Promoter methylation level of ITGB3
Promoter methylation level of ITGB3
Promoter methylation level of ITGB3
Promoter methylation level of ITGB3
Promoter methylation level of ITGB3
0.6
O.B
0.5
0.7
07
4
0.5-
9.6
0.6
0.5
.3
0.4-
9.5
0.4
0.3
0.3
0,3
0.1
0.2
0.2
0.2
0.4
Seminoma (n=63)
Non-seminoma (n=69)
Normal (n=21)
Primary tumor
Normal ( == 97)
Primary tumor
Normal (n=3)
Primary tumor ( == 307)
Normal
Primary tumor ( == 36)
0.9-
COAD
HENSC
0.65-
KIRC
0,65-
KIRP
0.9-
LINC
Promoter methylation level of ITGB3
Promoter methylation level of ITGB3
Promoter methylation level of ITGB3
0.6
Promoter methylation level of ITGB3
Promoter methylation level of ITGB3
0.8-
0.7
0.6
0.7
.55
.56
0.5
0.7
0.6-
0.5
0,5
0.45
0.6
5
0,4
1.45
0,5-
04
0.35
0.4
03
3
0.3
3.35
4
0.2
0.2
0.25
0.3
0.3
Normal (m37)
Primary tumor (m313)
Nommal
Primary tumor
Normal (n=160)
Primary tumor (n=324)
Normal (n=45)
Primary tumor (n=275)
Normal (n=50)
Primary tumor (n=377)
0.7
LUAD
0.7
7-
0.8
Promoter methylation level of ITGB3
Promoter methylation level of ITGB3
PRAD
0.9
Promoter methylation level of ITGB3
UCEC
READ
Promoter methylation level of ITGB3
0.6
6
1.7
0,7
0.5
0.5
.6
0.6-
0,5
4
1.4
5
3
0.3
4
2
0.2
0.2
0.1
0,3
Normal (n=32)
Primary tumor (n=473)
Nomal (n=50)
Primary tumor ( == 502)
Normal (46)
Primary tumor (n=438)
Nommal ( == 7)
Primary tumor (n=98)
B
1 -
ESCA
0,7-
GBM
Promoter methylation level of ITGB3
0.7
LUSC
0.6
Promoter methylation level of ITGB3
PAAD
0.6
PCPG
Promoter methylation level of ITGB3
Promoter methylation level of ITGB3
ns
Promoter methylation level of ITGB3
ns
ns
ns
0,56
0.6-
.5
0.5
0,6
0.5
15
0.45
.4
.3
0.4
2
3
13
0.35
2
0
0.2
0.2
0.3
Normal (rm10)
0.1
Normal (n=16)
Primary tumor (n=185)
Normal
Primary tumor 0=140)
Normal (n=42)
Primary tumor (n=370)
Primary tumor (n=184)
Normal
Primary tumor (=179)
0.6
Promoter methylation level of ITGB3
SARC
ns
17
STAD
0.6-
Promoter methylation level of ITGB3
Promoter methylation level of ITGB3
TICA
0.6-
ns
ns
Promoter methylation level of ITGB3
THYM
UB
1.55
ns
0.4
0,5
1.4
1.45
4
0.2
0.4
2
3
0.35
0
Normal (nm4)
Primary tumor (n=261)
0
0.2
0.3
Normal
Primary tumor
Normal (n=56)
Primary tumor (n=507)
Normal (n=2)
Primary tumor (n=124)
including ITGB3 expression and clinicopathological features was constructed, showing that ITGB3 has good predic- tive power for the prognosis of ACC, CESC, and UVM patients (Fig. 3; Supplementary Fig. 3, 4). These observations and clinicopathological features suggest that ITGB3 is a newly discovered multi-cancer-related gene with a role in predicting cancer risk and prognosis.
The role of ITGB3 in the tumor immune microenvironment has not been fully studied. To further evaluate its role in modulating the TIME and the relevance of this regulatory function to its predictive capabilities regarding cancer risk and prognosis, we first examined the correlation between its expression level and the abundance of different immune cell infiltrations in different tumor types. This revealed the relationship between ITGB3 and tumor immune cell infiltration in the TIME. We also investigated the immune status of tumor patients by detecting the expression level of ITGB3. The results showed that ITGB3 expression was significantly positively correlated with CD4 + Th2 cells and Tregs and negatively correlated with NKT cells, CD8 +T cells, and CD4+Th1 cells in most tumors (Fig. 4). In conjunction with prognosis-related results, we found that anti-tumor immune cells (CD8 +T cells, CD4 +Th1 cells, and NKT cells) were significantly reduced, and tumor-promoting immune cells (CD4 +Th2 cells and Tregs) were significantly increased in six poor-prognosis tumors with high ITGB3 expression (ACC, CESC, ESCA, LAML, LGG, and STAD). However, the opposite result was observed in SKCM tumors, with high ITGB3 expression as a protective factor. CD8 +T cells, CD4 +Th1 cells, and NKT cells are considered the key cells in killing tumors. However, the immunosuppressive effects of Tregs and CD4 +Th2 cells contribute to the immune escape of tumor cells, indirectly accelerate the proliferation of tumor cells, and affect the prognosis of cancer patients [44]. These results suggest that ITGB3 is not only involved in remodeling the TIME by regulating tumor immune cells but may also affect the progression and prognosis of cancer patients by regulating the TIME.
Discover
To confirm the involvement of ITGB3 in regulating the TIME, we investigated the relationship between the expression level of ITGB3 and important immune-related factors (chemokines, chemokine receptors, MHCs, immune inhibitors, and immunostimulators) in the TIME. The results showed that ITGB3 expression levels were significantly positively correlated with multiple immune-related factors across most types of cancer. This confirms that ITGB3 plays a key role in regulating tumor immunity. Among these immune-related factors, negatively regulated co-stimulators of T-cell activation such as CTLA4, PD-CD1, IL10, and TGFB3 were significantly positively correlated with ITGB3 expression levels in most types of cancer (Fig. 5A). CTLA-4 can terminate activated T cell responses and mediate the suppressive function of Tregs [45]. IL-10 and TGFB1 are the most important immunosuppressive molecules in the TIME, inhibiting effector T cells and inducing Tregs infiltration [46]. As a negative costimulatory molecule on the surface of T cells, PD-CD1 is linked to T-cell activa- tion. High levels of PD-CD1 have been reported to promote apoptosis in antigen-specific and tumor-reactive T cells, suppressing anti-tumor immunity [47, 48]. ITGB3 activates immune cells under the continuous action of tumor antigens, over-activates T cells, and causes T cell immune exhaustion. These results indicate that ITGB3 impairs T cell function and may even induce T cell apoptosis, which, in turn, impacts the TIME. Therefore, ITGB3 can be used as a potential target to regulate the immune function of T cells and provide a potential drug target for cancer immunotherapy.
Immune checkpoints are inhibitory regulatory molecules in the immune system that are essential for maintaining self-tolerance, preventing self-overimmunity, and reducing tissue damage by controlling the timing and intensity of the immune response [49]. We investigated the expression differences in immune checkpoint-related genes between the ITGB3 high- and low-expression groups. The results revealed that the expression of most immune checkpoint genes in the ITGB3 high-expression group was significantly higher than that in the low-expression group (Fig. 5B). In addition, this study found that the expression level of ITGB3 was closely associated with tumor mutational burden (TMB) and microsatellite instability (MSI) across most cancer types (Fig. 7A-D). TMB and MSI analyses form the foundation for cancer immunotherapy decisions and are two emerging biomarkers that have been used to routinely diagnose cancer treatment in recent years. These results suggest that the high expression of ITGB3 can also inhibit the function of immune cells by inducing the expression of immune checkpoint molecules, making the body unable to produce effective anti-tumor immune responses and, thus, facilitating immune escape for tumor cells. In addition, these results confirmed the critical role of ITGB3 in regulating tumor immunity and the TIME and targeting tumor immunotherapy.
Similar to previous studies, our GO analysis revealed that ITGB3 negatively regulates protein kinase and serine/threo- nine kinase activity, is involved in thyroid hormone metabolism, and is associated with enzyme inhibitor activity and serine-type endopeptidase inhibitors (Fig. 8A). KEGG analysis confirmed that thyroid hormone synthesis, small-cell lung cancer, and gangliosphingolipid biosynthesis were strongly associated with ITGB3 expression (Fig. 8B). GSEA analysis showed that ITGB3 was mainly associated with the calcium signaling pathway, the PI3K-AKT signaling pathway, and the TGF-ß signaling pathway (Fig. 8D-F). TGF-ß is the main upstream factor in integrin, which controls the malignant phenotypes of tumors-such as invasion, stemness, and immunosuppression-by regulating the expression of integrin ligands and stimulating the expression of integrin proteins [50]. Chronic TGF-ß exposure through sustained extracellular signal-regulated kinase (ERK) 1/2 activation upregulates ITGB3 expression, increasing levels of mesenchymal-like cancer cells [51]. After neutrophil infiltration [52], the chronic and sustained release of reactive oxygen species (ROS) (H2O2/ HOCI) can stimulate the upregulation of integrin 33 expression, and ITGB3 regulation can enhance the TGF-B/ H202/ HOCl signaling pathway, transforming non-metastatic tumors into metastatic phenotypes [53]. At the same time, ITGB3 is a core regulator of the ROS-mediated activation of the PI3K-AKT-mTOR pathway [27]. Furthermore, the results suggest that integrin B3 regulates MMP2 expression by activating FAK-PI3K-AKT signaling, promoting the possibility of tumor metastasis into HCC residual cancer [54]. MFGE8, another key ligand of 33 integrin, is involved in the ß3 integrin/FAK/ PI3K/AKT pathway [55]. ITGB3-AKT signaling pathway mediates the platelet-induced proliferation of hemangioendothe- lioma cells [56]. In addition to the TGF-ß and PI3K-AKT signaling pathways, HOXD3 is an upstream transcription factor associated with ß3 expression, which activates the 03 integrin-mediated WNT/ B-catenin signaling pathway, essential for maintaining cancer stemness [57]. In addition, ITGB3 can activate the P21 (RAC1)-activated kinase 4 (PAK4)-Yes-associated protein 1 (YAP) axis, which contributes to the enhanced expression of GLUT3, a driver of cancer stem cells and glycolytic capacity [58]. Taken together, the PI3K-AKT and TGF-ß signaling pathways play important roles in ITGB3’s regulation of the TIME and influence over cancer progression.
DNA methylation is a significant form of epigenetic DNA modification that regulates gene expression without alter- ing the DNA sequence [59]. DNA methylation often represses gene expression by altering the chromatin structure, DNA stability, and DNA conformation [60]. As research has progressed, the relationship between DNA methylation and cancer has been gradually discovered. Our study revealed that ITGB3 DNA methylation levels were significantly high in most cancers compared with normal tissues (Fig. 9), consistent with the high expression of ITGB3 in cancer. Hypermethylation
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in promoter regions typically results in silencing or activating tumor suppressor genes in cancer cells [59]. These results further demonstrate that high ITGB3 expression can facilitate tumor progression. However, the relationship between the DNA methylation of ITGB3, tumor immunosuppression, and TIME homeostasis requires further investigation.
This study provides evidence for the relationship between ITGB3 expression, tumor immune suppression, and TIME homeostasis. We found that high ITGB3 expression in ACC, CESC, ESCA, LAML, LGG, and STAD is significantly positively correlated with pro-tumor immune cells (Tregs and CD4 +Th2 cells) and immune checkpoints (CTLA4 and PD-CD1). It is significantly negatively correlated with anti-tumor immune cells (CD8 +T cells, CD4 +Th1 cells, and NKT cells) and pre- dicts poor prognosis for cancer patients. However, the opposite result was observed in SKCM tumors, with high ITGB3 expression acting as a protective factor. High ITGB3 expression can also inhibit the function of immune cells by inducing the expression of immune checkpoint molecules, preventing the body from generating an effective anti-tumor immune response, thus facilitating immune escape for tumor cells. In addition, GSEA suggested that ITGB3 was mainly related to the calcium, PI3K-AKT, and TGF-ß signaling pathways. Therefore, remodeling the TIME by regulating ITGB3 expression- specifically by recombining tumor-specific NKT cells, CD8 +T cells, Tregs, CD4 +Th1, and Th2 cells-may be a promising strategy for enhancing the efficacy of tumor immunotherapy.
Integrin beta 3 (ITGB3) is a new tumor prognostic biomarker and has attracted extensive attention from researchers in recent years. Its expression in various tumor types is closely related to tumor invasion, metastasis, and the prognosis of patients [18-33]. Firstly, ITGB3 expression changes significantly in different types of tumors (breast cancer, lung can- cer, colon cancer, etc.) [20, 21, 27], which provides it with a potential application value in early diagnosis and prognosis evaluation [20, 21, 27]. By contrast, traditional biomarkers such as CA-125, CEA, and PSA may demonstrate low specific- ity in some cases. In addition, ITGB3 participates in the tumor microenvironment and plays a key role in the interaction between tumor cells and stromal cells, which makes ITGB3 not only a marker of tumor development but also a potential therapeutic target [14-17]. With the deepening of pharmacological research on ITGB3, various tumor inhibitors target- ing it have been studied, some of which have entered clinical trials, including cilengitide, MK0429, and vitacine [17]. However, there is heterogeneity in ITGB3 expression levels in different tumor types and patients, which may affect its usefulness as a universal biomarker. In addition, the present study found that ITGB3 expression was reduced in various cancer tissues, such as CESC, compared with adjacent normal tissues. ITGB3 is widely distributed in normal tissues, which may reduce its specificity as a biomarker and therapeutic target [17]. Studies have shown that ITGB3 is highly expressed in non-tumor cells and associated with cross-talk between tumor and stromal cells, as well as immune cells. For example, Zhao et al. [61] reported that ITGB3 -/ “mice could improve bone loss induced by ovariectomy (OVX). Secondly, the specific mechanism of ITGB3 in the tumor microenvironment is not fully understood, and large-scale, multi-center clinical trials are needed to verify the reliability and effectiveness of its clinical application. A single biomarker is often not accurate enough, so ITGB3 usually needs to be used in combination with other markers, such as HER2 and Ki-67, to improve the accuracy of prognosis.
However, our study contains some limitations. For example, this study relied on bioinformatics analyses without experimental validation, and potential biases exist in public datasets (such as incomplete clinical information or vari- ability in data quality). Functional and further prospective studies are needed for a comprehensive analysis to draw strong conclusions.
5 Conclusion
Our preliminary results indicate that ITGB3 plays an important role in immunosuppression in the tumor microenviron- ment. ITGB3 may inhibit tumor immunity, facilitate tumor immune escape, and influence patient prognosis by promot- ing pro-tumor immune cells (Tregs and CD4+ Th2 cells) and immune checkpoints (CTLA4 and PD-CD1) and inhibiting anti-tumor immune cells (CD8 +T cells, CD4+ Th1 cells, and NKT cells). ITGB3 may be a prognostic biomarker in specific cancers and a drug target for specifically targeted tumor immunotherapy.
Author contributions C.C .: Writing-original draft, conceptualization, visualization, methodology, investigation. L.W .: Writing-original draft, visualization, methodology, investigation. G.C .: Formal analysis, investigation, methodology. F.Y .: Formal analysis, investigation, methodology. Z.C. J.J. and J.G .: Writing-review and editing, supervision, project administration, funding acquisition, conceptualization. All authors have read and agreed to the published version of the manuscript.
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Funding This work was supported by the Association Foundation Program of the Yunnan Provincial Science and Technology Department and Kunming Medical University (202401AY070001-176 and 202401AY070001-180); the Xing Dian Talent Support Plan (to Zhong Chen); the Yun- nan Province’14th Five-Year Plan’ Provincial Key Clinical Specialty Construction Project (Traumatology Surgery); and the Yunnan University Medical Research Foundation (YDYXJJ2024-0029, YDYXJJ2024-0040 and YDYXJJ2024-0017).
Data availability We downloaded STAR-counts data and corresponding clinical information for 33 tumors from the TIMER (https://cistrome. shinyapps.io/timer/), TCGA (https://portal.gdc.cancer.gov), GTEx(www.genome.gov/), and CCLE (https://depmap.org/portal/data_page/? tab=allData) databases. The GTEx data we used are from the V8 version (https://gtexportal.org/home/datasets). We downloaded STAR-counts data and corresponding clinical information for 33 tumors from the TCGA database (https://portal.gdc.cancer.gov). We downloaded the har- monized pan-cancer dataset from the UCSC (https://xenabrowser.net/) database-TCGA Pan-Cancer (PANCAN, N= 10,535, G=60,499) and TCGA TARGET GTEx (PANCAN, N= 19,131, G=60,499)-from which ENSG00000259207(ITGB3) was subsequently extracted. All level 4 TCGA samples from the Simple Nucleotide Variation dataset were downloaded from the GDC database (https://portal.gdc.cancer.gov/) using the MuTect2 software (DOI: 10.1038 / nature08822). All data generated or analyzed during this study are included in this published article [and its supplementary information files].
Declarations
Competing interests The authors declare no competing interests.
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