Expression of ITGA3 in pan-cancer tissues and its relationship to prognosis and immune infiltration

Abstract

Cancer constitutes a significant global health challenge, contributing to escalating socioeconomic burdens and necessitating the urgent identification of novel biomarkers and therapeutic targets. This study seeks to investigate the expression levels of integrin α3 (ITGA3) across various cancer types and its association with immune cell infiltration, thereby elucidating its potential role in cancer progression at the pan-cancer level. By employing expression profile data from The Cancer Genome Atlas (TCGA) and supplementary data from the Human Protein Atlas (HPA), we performed differential expression analysis, single-cell RNA expression analysis, Western blotting, immunohistochemical analysis, and subcellular immunofluorescence staining analysis to evaluate the expression levels and patterns of ITGA3 in diverse cancer types. Utilizing mutation analysis from cBioPortal, functional enrichment, and protein–protein interaction analysis from the STRING database, alongside six advanced immune cell abundance estimation algorithms—TIMER, xCell, MCP-counter, CIBERSORT, EPIC, and quanTIseq—we identified that ITGA3 exhibits differential expression across various cancer types. This expression is correlated with numerous clinical features, immune cell infiltration, immunotherapy response, and mutation burden, ultimately influencing patient prognosis. These findings underscore the potential regulatory role of ITGA3 within the tumor microenvironment and offer novel insights into its potential as a biomarker and therapeutic target in immunotherapy at the pan-cancer level. Future studies should verify the specific mechanisms of ITGA3 in cancer progression and the immune microenvironment.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-025-03944-8.

Introduction

Malignant tumors pose a considerable challenge within the domain of global public health, acting as a major contributor to human mortality and economic loss. They significantly reduce patients' quality of life and place a substantial burden on social healthcare systems [1]. Contemporary clinical approaches to cancer treatment predominantly include surgical interventions, radiotherapy, chemotherapy, molecular targeted therapy, and biological immunotherapy. Nevertheless, these strategies encounter several limitations, such as tumor heterogeneity, treatment resistance, and adverse reactions [2, 3]. Therefore, advancing our comprehension of the pathogenesis of malignant tumors and devising novel therapeutic strategies is of utmost importance.

Immune infiltration within tumors, particularly the presence and functional activity of immune cells in the tumor microenvironment, is pivotal in cancer progression and therapeutic response [4]. Numerous studies have highlighted the importance of specific immune cell populations that infiltrate tumors. For example, the presence of CD8 + T cells, which are integral to anti-tumor immunity, signifies an active immune response against the tumor [5]. Tumors with elevated levels of tumor-infiltrating lymphocytes (TILs) frequently exhibit improved responses to therapies aimed at activating or enhancing immune responses, such as checkpoint inhibitors targeting PD-1 or PD-L1 [6, 7]. Additionally, the spatial distribution of immune cells provides valuable insights into tumor behavior. For instance, the proximity of immune cells to tumor cells may be associated with improved survival outcomes [8, 9]. The relationship between immune cell infiltration and clinical outcomes is often complex and multifactorial. Factors such as tumor mutational burden (TMB), microsatellite instability (MSI), and hypoxia within the tumor microenvironment can significantly influence immune activity and treatment response [6, 7, 10]. TMB, for example, is associated with neoantigen load and subsequent T-cell engagement; however, it does not consistently predict responses across different tumor types, resulting in variability in the efficacy of immunotherapy [6, 11, 12].

Integrin α3 (ITGA3), an essential member of the cell surface transmembrane protein family, is integral to biological processes such as cell adhesion, migration, and signal transduction. Its potential involvement in tumorigenesis and development is increasingly attracting scholarly attention [13]. Numerous studies have demonstrated that the regulation of ITGA3 expression is intricately linked to the progression and metastasis of various malignant tumors, with its expression levels potentially serving as critical indicators for evaluating tumor growth status and patient prognosis [14, 15]. Moreover, the expression characteristics of ITGA3 and its interactions with immune cells have opened new research avenues for elucidating the molecular mechanisms of the tumor microenvironment, while also providing innovative insights for the identification of tumor biomarkers [16]. Although preliminary investigations have examined the expression and function of ITGA3 in tumors, comprehensive and in-depth analyses of its differential expression profiles at the pan-cancer level and its regulatory mechanisms within the immune microenvironment remain insufficient [17].

This study seeks to systematically elucidate the expression characteristics of ITGA3 at the pan-cancer level and its correlation with immune cell infiltration by integrating multi-omics data. The objective is to identify novel diagnostic markers and therapeutic targets for tumors. To achieve this, the research utilizes a range of bioinformatics analysis methods, including gene expression analysis, single-cell RNA sequencing, and immunohistochemical analysis, to conduct a comprehensive evaluation of ITGA3's role in pan-cancer. By employing these advanced technological approaches, the study not only thoroughly characterizes the expression patterns of ITGA3 across different cancer types but also investigates the complex network of interactions among various cell types within the tumor microenvironment at the single-cell level. Notably, leveraging single-cell RNA sequencing data, the study accurately identifies the cell-specific expression characteristics of ITGA3 within the tumor microenvironment and further examines the potential associations between its expression levels and immune responses. This study offers novel insights into the intricate regulatory mechanisms governing the tumor microenvironment and clarifies the pivotal role of ITGA3 in cancer development and progression at the molecular level at the pan-cancer level. Employing a multi-dimensional and multi-level analytical approach, the research not only broadens the understanding of ITGA3's functional characteristics across various cancer types but also establishes a critical theoretical foundation and data support for the identification of cancer biomarkers and their clinical applications. The findings are expected to advance the field of cancer biology and provide valuable perspectives for the development of precision medicine strategies.

Materials and methods

Data collection

Gene expression data, clinical prognostic information, and somatic mutation data for 33 distinct tumor types were sourced from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). In addition, gene expression data from various normal tissues and diverse tumor cell lines were collected through genotype and gene expression level association analysis from the Genotype-Tissue Expression (GTEx) database (https://commonfund.nih.gov/GTEx) and the Cancer Cell Line Encyclopedia (CCLE) database (https://sites.broadinstitute.org/ccle), respectively. Moreover, the Human Protein Atlas (HPA) database was employed to acquire immunohistochemistry data from human tissues, alongside single-cell data and subcellular localization immunofluorescence data. Data visualization was conducted using XianTaoZi (https://www.xiantaozi.com/) and Assistant for Clinical Bioinformatics (https://www.aclbi.com/static/index.html#/single_cell_analysis). The comprehensive flowchart of the study is illustrated in Fig. 1 below.

Fig. 1.

The comprehensive flowchart of the study

Gene expression analysis

We procured STAR-counts data along with corresponding clinical information for 33 tumor types from the TCGA database (accessible at https://portal.gdc.cancer.gov). The data were subsequently extracted in TPM format and subjected to log2(TPM + 1) normalization. Only samples containing both RNA sequencing data and clinical information were retained for further analysis. The GTEx data employed in this study were obtained from version V8, with comprehensive details available on the GTEx official website (https://gtexportal.org/home/datasets). Statistical analyses were executed using R software version 4.0.3, and a P-value of less than 0.05 was deemed statistically significant.

Prognostic analysis

We procured STAR-counts data along with the associated clinical information for 33 tumor samples from the TCGA database (https://portal.gdc.cancer.gov). The data were subsequently converted into Transcripts Per Million (TPM) format and subjected to log2(TPM + 1) normalization. Samples possessing both RNA sequencing data and clinical information were retained for further analysis. For the generation of Kaplan–Meier survival curves, P-values and hazard ratios (HR), accompanied by 95% confidence intervals (CI), were calculated using the log-rank test and univariate Cox proportional hazards regression. Statistical analyses were executed utilizing R software, version 4.0.3. A P-value of less than 0.05 was deemed indicative of statistical significance.

Single cell analysis

We performed a single-cell analysis utilizing the supplementary file obtained from the GEO database (https://www.ncbi.nlm.nih.gov/geo/) through the single-cell analysis module of the Assistant for Clinical Bioinformatics (https://www.aclbi.com/static/index.html#/single_cell_analysis). Data objects were generated using the Seurat package, and low-quality cells were excluded during standard data preprocessing protocols. We assessed the number of genes, the number of cells, and the percentage of mitochondrial content, applying filtering criteria that excluded genes detected in fewer than three cells and cells with fewer than 200 detected genes. The corrected and normalized data were subsequently subjected to standard analyses, including Principal Component Analysis (PCA). Cell clustering was conducted using the FindClusters function within the Seurat R package.

Immunohistochemistry and immunofluorescence

Access the Human Protein Atlas (HPA) database and utilize the search bar on the homepage to query the gene ITGA3. Subsequently, navigate to the 'TISSUE' section to download the relevant immunohistochemical charts for normal tissues. Proceed to the ‘CANCER’ module to obtain the corresponding immunohistochemical charts for tumor tissues. Then, access the 'SUBCELL' module to acquire the relevant immunofluorescence charts that illustrate subcellular co-localization in tumor tissues. Finally, select the 'SINGLE CELL' module to download the pertinent single-cell data charts for tumors. Furthermore, considering that the research direction of this study is pan-cancer, tissue samples from cancer and normal control tissues were obtained from multiple human sites, including the brain, renal, lung, breast, stomach, bladder, esophagus, and liver. The ethics review of the Third Affiliated Hospital of Zhengzhou University (No. 2024–174) has been passed. Informed consent was obtained from all individual participants included in the study. Immunohistochemistry (IHC) was performed on these cancer and normal control tissues to evaluate the relative expression levels of ITGA3 protein in these tissues. Key immunohistochemical steps, including sample preparation, deparaffinization and hydration, antigen retrieval, blocking and blocking, antibody incubation, color development and counterstaining, dehydration, mounting, and observation, were performed according to standard methods.

Western blotting

Considering that the research direction of this study is pan-cancer, two cancer cell lines and one normal control cell line were obtained for each of nine human cancers, such as endometrial cancer (RL95-2 and HEC-1B) and normal control (Heec), cervical cancer (Caski and Hela) and normal control (HUCEC), ovarian cancer (SKOV3 and A2780) and normal control (IOSE80), glioma (U251 and T98) and normal control (HA), prostate cancer (DU145 and PC-3) and normal control (RWPE-1), renal cancer (Caki-1 and A498) and normal control (HK2), breast cancer (MB-231 and MB-468) and normal control (MCF-10A), osteosarcoma (U2OS and 143B) and normal control (hFob1.19), colorectal cancer (RKO and SW480) and normal control (NCM460). The culture medium components, culture conditions, sources and other relevant information of these cell lines were recorded in the "Supplementary material-Cell line information" file. Western blotting was performed on these cancer cell lines and normal control cell lines to evaluate the relative expression levels of ITGA3 protein in these cell lines. Key steps in the Western blotting experiment, including protein sample preparation, SDS-PAGE electrophoresis, transfer, blocking, antibody incubation, membrane washing, and development, were performed according to standard methods. The raw data related to Western blotting (the most complete possible blot image) was displayed in the “Supplementary material—Raw data of Western blotting” file.

Gene enrichment analysis

Utilizing the protein–protein interaction retrieval platform (https://string-db.org), we identified binding proteins associated with ITGA3 and performed an enrichment analysis of the protein–protein interaction network. Genes involved in the relevant pathways were collected and analyzed using the GSVA package in R software, with the parameter method set to 'ssgsea' for single-sample gene set enrichment analysis (ssGSEA). Subsequently, we examined the correlation between gene expression and pathway scores through Spearman correlation analysis, conducting statistical analysis with R software version 4.0.3. Results were deemed statistically significant when the P-value was less than 0.05.

Immune correlation analysis

We obtained the STAR-counts data pertaining to tumors, along with the associated clinical information, from the TCGA database (https://portal.gdc.cancer.gov). For a rigorous assessment of immune correlations, we utilized the R package immunedeconv, which incorporates six advanced algorithms—TIMER, xCell, MCP-counter, CIBERSORT, EPIC, and quanTIseq—to estimate immune cell concentrations. The transcripts related to immune checkpoints include SIGLEC15, IDO1, CD274, HAVCR2, PDCD1, CTLA4, LAG3, and PDCD1LG2. We will extract the expression values of these eight genes to analyze the expression patterns associated with immune checkpoints. Four cancer immunotherapy cohorts (GSE176307, GSE136114, GSE35640, and GSE91061) were retrieved from the GEO database and stratified according to immunotherapy response (PD: progressive disease; CR: complete response; SD: stable disease; PR: partial response; NR: non-response; R: response). The expression levels of ITGA3 in the different immunotherapy response groups of these four immunotherapy cohorts were analyzed to evaluate the potential relationship between ITGA3 and immunotherapy response.Statistical analyses will be conducted using R software version 4.0.3, with statistical significance defined as a P-value of less than 0.05.

Analysis of gene mutation-related

The cBioPortal website (https://www.cbioportal.org) functions as an advanced platform for the analysis and visualization of cancer multi-omics data. In this investigation, we employed the platform to perform a comprehensive mutation analysis of the ITGA3 gene, focusing on mutation frequency, mutation types, copy number variations, and the association between mutations and survival prognosis. Tumor mutational burden (TMB) was sourced from the study “The Immune Landscape of Cancer” by Vesteinn Thorsson et al., published in 2018, while microsatellite instability (MSI) data was obtained from the study "Landscape of Microsatellite Instability Across 39 Cancer Types" by Russell Bonneville et al., published in 2017. Statistical analyses were conducted using R software version 4.0.3, with statistical significance determined at a P-value threshold of less than 0.05.

Statistical analysis methods

Statistical analyses were performed utilizing R software (version 4.2.1). The Wilcoxon rank-sum test was applied to evaluate expression differences of SPARC across various cancer types, whereas the Log-rank test was employed to examine differences in survival prognosis. Furthermore, correlation analysis was conducted using the Spearman test. Differences between two groups were assessed via the t-test, and comparisons among multiple groups were made using one-way ANOVA.

Results

Differential expression of ITGA3 in pan cancer

As illustrated in Fig. 2A, B, there is a marked differential expression of ITGA3 between tumor and normal tissues. Notably, elevated levels of ITGA3 are detected in the tumor tissues of Bladder Urothelial Carcinoma (BLCA), Cholangiocarcinoma (CHOL), Esophageal Carcinoma (ESCA), Glioblastoma Multiforme (GBM), Head and Neck Squamous Cell Carcinoma (HNSC), Kidney Renal Clear Cell Carcinoma (KIRC), Kidney Renal Papillary Cell Carcinoma (KIRP), Liver Hepatocellular Carcinoma (LIHC), Pancreatic Adenocarcinoma (PAAD), Stomach Adenocarcinoma (STAD), and Thyroid Carcinoma (THCA). Conversely, in breast invasive carcinoma (BRCA), colon adenocarcinoma (COAD), kidney chromophobe (KICH), lung squamous cell carcinoma (LUSC), and prostate adenocarcinoma (PRAD), ITGA3 expression is notably reduced in tumor tissues. Figure 2C further demonstrates that ITGA3 is predominantly enriched in epithelial-like cells, with subsequent enrichment in neural and muscle cells. Furthermore, considering that the research direction of this study is pan-cancer, the relative protein expression levels of ITGA3 were verified using clinical tissue samples and corresponding cell lines from various cancers. The specific results were shown in Supplementary material-Figure S1. Figure S1A showed that, based on Western blotting, among the nine human cancers, the expression levels of ITGA3 in the latter two cancer cell lines were higher than that in the former corresponding normal control cell line. Figure S1B showed that, based on immunohistochemistry, ITGA3 expression levels were higher in cancer tissues compared to the corresponding normal control tissues for eight human tissue sites.

Fig. 2.

The differential expression of the ITGA3 gene at the pan-cancer level. A The violin plot shows ITGA3 gene expression in tumor versus normal tissues. B The box plot details ITGA3 expression in tumor and paired normal tissues, with the x-axis for sample groups and y-axis for gene expression. Colors indicate groups, and asterisks show significance: *P < 0.05, **P < 0.01, and ***P < 0.001. C The heatmap depicts ITGA3 distribution across tissues and cell components

To elucidate the distribution of ITGA3 across various cellular components, we performed a single-cell component analysis utilizing four ovarian cancer single-cell datasets (OV_EMTAB8107, OV_GSE147082, OV_GSE130000, OV_GSE151214). Our findings indicate that ITGA3 is predominantly localized within malignant and epithelial components (refer to Fig. 3A). Additionally, immunofluorescence staining of ITGA3 in three tumor cell lines (A431, U-251, and U2OS) sourced from the HPA database demonstrated its primary localization on the cell membrane (refer to Fig. 3B). To further verify the differential expression of ITGA3, we explored its expression pattern in different tumor types using immunohistochemistry experiments. As depicted in Fig. 3C, significant differential expression was observed in thyroid, lung, colorectal, breast, cervical, and gastric cancers.

Fig. 3.

The differences in single-cell, protein expression, and the subcellular localization of ITGA3. A The tSNE plot shows ITGA3 gene expression in four ovarian cancer single-cell datasets. B The immunofluorescence images display ITGA3 (green) in three tumor cell lines (A431, U-251, U2OS), with nuclei in blue. C The bar graph and immunohistochemical images reveal ITGA3 protein levels in 20 tumor types, highlighting significant differences in 10 compared to normal tissues

Prognostic value of ITGA3

To elucidate the prognostic significance of ITGA3 across various tumor types, we performed a COX survival analysis. This analysis demonstrated that the differential expression of ITGA3 significantly influences patient survival outcomes in glioblastoma (GBM; P = 2.15e−03), head and neck squamous cell carcinoma (HNSC; P = 4.29e−02), lower-grade glioma (LGG; P = 3.58e−12), hepatocellular carcinoma (LIHC; P = 2.91e−02), lung squamous cell carcinoma (LUSC; P = 4.68e−03), and pancreatic adenocarcinoma (PAAD; P = 1.37e−04) (Fig. 4A). Complementary Kaplan–Meier survival analysis further indicated that ITGA3 expression differentially affects patient prognosis and survival in lower-grade glioma (LGG; P = 4.43e−03), lung squamous cell carcinoma (LUSC; P = 1.23e−03), and pancreatic adenocarcinoma (PAAD; P = 3.15e−03) (Fig. 4A). Moreover, overall survival was found to be significantly associated with ITGA3 expression in lower-grade glioma (LGG; P < 0.05), lung squamous cell carcinoma (LUSC; P < 0.05), and pancreatic adenocarcinoma (PAAD; P < 0.05) (Fig. 4B). The expression of ITGA3 was found to significantly influence disease-specific survival in several cancer types, including breast cancer (BRCA; P < 0.05), head and neck squamous cell carcinoma (HNSC; P < 0.05), lower grade glioma (LGG; P < 0.05), lung squamous cell carcinoma (LUSC; P < 0.05), pancreatic adenocarcinoma (PAAD; P < 0.05), and uterine corpus endometrial carcinoma (UCEC; P < 0.05), as illustrated in Fig. 4B. Moreover, ITGA3 expression also affected progression-free survival in cervical cancer (CESC; P < 0.05), glioblastoma (GBM; P < 0.05), low-grade glioma (LGG; P < 0.05), lung squamous cell carcinoma (LUSC; P < 0.05), pancreatic cancer (PAAD; P < 0.05), rectal adenocarcinoma (READ; P < 0.05), and gastric cancer (STAD; P < 0.05), as shown in Fig. 4B. Additionally, Kaplan–Meier survival analysis revealed a significant impact of ITGA3 expression on disease survival in lower-grade glioma (LGG; P = 0.002), lung squamous cell carcinoma (LUSC; P = 0.001), and pancreatic cancer (PAAD; P = 0.003), as depicted in Fig. 4C.

Fig. 4.

The prognostic analysis of ITGA3. A The forest plots show Cox and Kaplan–Meier analysis results for ITGA3 at the pan-cancer level. B The heatmap presents ITGA3 expression in these tumors, categorized by Overall Survival, Disease Specific Survival, and Progress Free Interval, with asterisks marking significant P-values (*P < 0.05). C The line graph depicts Kaplan–Meier overall survival analysis in six tumors, with red for high ITGA3 expression and blue for low

Clinical correlation analysis of ITGA3

As previously observed, the differential expression of ITGA3 varies across different tumors. We further investigated the correlation between ITGA3 and clinical stages as well as histological grades across multiple tumor types. As shown in Fig. 5A, the correlation analysis of clinical stages revealed a significant association between the differential expression of ITGA3 and the clinical stages of thyroid cancer (THCA), clear cell renal cell carcinoma (KIRC), and uveal melanoma (UVM) (P < 0.05). Similarly, Fig. 5B illustrates that the correlation analysis of histological grades demonstrated a significant correlation between the differential expression of ITGA3 and the histological grades of esophageal cancer (ESCA) and ovarian cancer (OV) (P < 0.05).

Fig. 5.

The clinical correlation analysis of ITGA3. A The box plot demonstrates the association between ITGA3 expression levels and clinical stage across ten distinct tumor types. B The violin plot demonstrates the correlation between ITGA3 expression and histological grade within the same set of tumor types. Statistical significance is indicated by asterisks, where *P < 0.05, **P < 0.01, and ***P < 0.001

Tumor mutation burden analysis and immune correlation analysis of ITGA3

To thoroughly elucidate the biological function of ITGA3 in tumorigenesis and cancer progression, we conducted an analysis of its gene mutation status across a diverse array of cancer types, incorporating assessments of tumor mutation burden (TMB) and microsatellite instability (MSI). As depicted in Fig. 6A, ITGA3 showed a significant positive correlation with TMB in testicular cancer (TCGT), kidney renal clear cell carcinoma (KIRC), colon adenocarcinoma (COAD), lung squamous cell carcinoma (LUSC), and pancreatic adenocarcinoma (PAAD). In contrast, ITGA3 exhibited a notable negative correlation with TMB in uterine carcinosarcoma (UCS), kidney chromophobe (KICH), rectal adenocarcinoma (READ), mesothelioma (MESO), and prostate adenocarcinoma (PRAD). Furthermore, Fig. 6B illustrates that ITGA3 is significantly positively correlated with microsatellite instability (MSI) in thymic carcinoma (THYM), skin cutaneous melanoma (SKCM), pancreatic adenocarcinoma (PAAD), and acute myeloid leukemia (LAML). In contrast, within prostate adenocarcinoma (PRAD), breast invasive carcinoma (BRCA), diffuse large B-cell lymphoma (DLBC), and adrenocortical carcinoma (ACC), ITGA3 demonstrates a significant negative correlation with microsatellite instability (MSI). Figure 6C classifies the mutation types into five categories: base mutations, structural variations, amplifications, deep deletions, and multiple mutations. Notably, ITGA3 presents the highest mutation frequency in breast invasive carcinoma, where amplification emerges as the predominant mutation type, occurring at a frequency of 6.57%. Similarly, in endometrial carcinoma, amplification is identified as the primary mutation type, with a frequency exceeding 4%. Moreover, base mutations are the most prevalent mutation type in endometrial carcinoma, colorectal cancer, skin cancer, and gastric cancer, with base mutations constituting over 4% in both endometrial carcinoma and skin cancer.

Fig. 6.

The Mutation Signature Analysis of ITGA3. A The lollipop plot demonstrates the correlation between ITGA3 expression and tumor mutational burden (TMB) at the pan-cancer level. B The bar distribution plot illustrates the association between ITGA3 expression and microsatellite instability (MSI) at the pan-cancer level. C The frequency distribution plot presents the tumor mutation signatures linked to ITGA3 expression at the pan-cancer level

This study examines the association between ITGA3 and immune checkpoints, as well as the infiltration of various immune cell types. Initially, as depicted in Fig. 7A, a significant correlation is observed between ITGA3 and several immune checkpoints, including CD274, CTLA4, HAVCR2, LAG3, PDCD1, and TIGIT, across multiple tumor types such as THYM, THCA, TGCT, PRAD, OV, LIHC, LGG, BRCA, and GBM. Subsequently, Fig. 7B illustrates that ITGA3 is also significantly correlated with the infiltration of diverse immune cells, including mast cells, NK cells, macrophages, CD8 T cells, neutrophils, and B cells, in various tumors such as PRAD, TGCT, THYM, OV, LAML, LGG, BRCA, and LUAD. Lastly, using data from various Human Protein Atlas (HPA) databases, Fig. 7C highlights the significant distribution of ITGA3 across different immune cells, including granulocytes, monocytes, T cells, B cells, dendritic cells, NK cells, and total precursor peripheral blood mononuclear cells (PBMCs). In addition, this study explored the potential relationship between ITGA3 and immunotherapy response based on a public immunotherapy cohort from the GEO database. As shown in Fig. 7D, for cancers such as colorectal cancer and melanoma, the relative expression level of ITGA3 was higher in groups with poor immunotherapy efficacy (such as the non-response group or the progressive disease group), suggesting that ITGA3 may be associated with poor immunotherapy response.

Fig. 7.

The immunological correlation analysis of ITGA3. A The heatmap presents the correlation analysis between ITGA3 expression and immune checkpoint markers across various cancer types. B The heatmap demonstrates the correlation between ITGA3 expression and immune cell infiltration across multiple cancer types. C The bar chart illustrates the distribution of ITGA3 expression in relation to different immune cell infiltrations within tumor samples. D ITGA3 expression levels in different immunotherapy efficacy groups in immunotherapy cohorts of various cancers based on the GEO database (GSE176307, bladder cancer; GSE136114, colorectal cancer; GSE35640, melanoma; and GSE91061, melanoma). Immunotherapy efficacy groups were (PD: progressive disease; CR: complete response; SD: stable disease; PR: partial response; NR: non-response; R: response)

Functional enrichment analysis of ITGA3

To elucidate the role of ITGA3 in tumorigenesis and development, along with its associated interacting proteins, we conducted a systematic analysis of the proteins that bind to ITGA3, identifying the ten proteins with the most significant interactions (refer to Fig. 8A). These proteins include FN1, ITGB1, CD151, ITGB5, ITGB4, ITGB3, PTK2, ITGB6, ITGA2, and CD9. Our correlation analysis demonstrated that FN1 and ITGB1 exhibit the strongest correlation with ITGA3 across various tumor types, with correlation coefficients reaching up to 0.999. Subsequently, we identified genes with high similarity to ITGA3, selecting the top 100 genes for further analysis. We then performed an intersection analysis with the interacting proteins of ITGA3, followed by an enrichment analysis of these 100 genes. The results of the KEGG pathway analysis suggest that ITGA3 is potentially involved in pathways related to apoptosis, P53 mutation, and the PI3K/AKT signaling pathway in tumorigenesis and tumor progression (refer to Fig. 8B).

Fig. 8.

The functional enrichment analysis of ITGA3. A The interaction network of ITGA3 proteins is depicted, emphasizing the top 10 associated proteins. B The diagram of functional enrichment Spearman correlation analysis illustrates the relationship between pathway scores and ITGA3 gene expression. The x-axis denotes the distribution of ITGA3 gene expression, whereas the y-axis represents the distribution of pathway scores. The numerical values at the top indicate the P-value and correlation coefficient derived from the Spearman correlation analysis

Discussion

Cancer has become a predominant global health threat, contributing to a substantial mortality rate and imposing considerable economic burdens [18]. The intricacy of cancer is highlighted by its marked heterogeneity and drug resistance, which pose significant challenges to current treatment strategies, including surgery, radiotherapy, and chemotherapy [19]. Consequently, comprehensive research into the biological characteristics of cancer, as well as the development of novel diagnostic, prognostic, and therapeutic approaches, holds significant clinical importance [20–22].

This study examines integrin α3 (ITGA3), a cell adhesion protein that is intricately linked to the initiation, progression, and metastasis of various cancers [23]. Prior research has suggested that ITGA3 expression levels may function as a biomarker for multiple cancer types, and its role within the tumor microenvironment has garnered considerable interest [16, 24]. The objective of this study is to explore ITGA3 expression levels across different cancers, assess its prognostic significance, and analyze its association with immune cell infiltration. Utilizing a range of research methodologies, including gene expression analysis and validation (Fig. 2 and Supplementary File-Figure S1), single-cell analysis, immunofluorescence, and immunohistochemical analysis (Fig. 3), we offer a comprehensive evaluation of ITGA3 as a tumor biomarker and its expression variability. The findings reveal that ITGA3 displays significant differential expression across various cancer types and is predominantly localized on the cell membrane, thereby providing preliminary insights into the fundamental expression and distribution patterns of ITGA3. Subsequent prognostic analyses (Fig. 4) and correlations with clinical features (Fig. 5) demonstrated that ITGA3 significantly impacts survival rates and clinical characteristics across multiple tumor types, providing novel insights into the intricate role of ITGA3 in various cancers. Furthermore, preliminary identification of significant correlations between ITGA3 and tumor mutational signatures (Fig. 6), in addition to immune cell infiltration and immunotherapy response (Fig. 7), suggests potential biomarkers and therapeutic targets for early cancer diagnosis and immunotherapy, with substantial clinical implications.

This study examines the biological functions and expression characteristics of ITGA3 across various malignant tumors, with a particular emphasis on its regulatory mechanisms within the tumor microenvironment (TME). Gene expression profiling reveals a significant upregulation of ITGA3 in multiple malignant tumor tissues, a feature closely linked to tumor biological behavior and patient prognosis [14]. The research substantiates the potential of ITGA3 as a novel biomarker and offers innovative directions for early tumor diagnosis and treatment strategies [25]. Utilizing single-cell transcriptome analysis, we identified that ITGA3 is highly expressed in tumor-associated cell populations and epithelial-derived cells, indicating its potentially critical role in modulating the tumor microenvironment. Existing literature corroborates that ITGA3, through mediating cell-extracellular matrix (ECM) interactions, is instrumental in regulating the adhesive properties, migratory capacity, and proliferative activity of tumor cells [13]. Concurrently, ITGA3 is integral to the interaction network between tumor cells and immune cells, a discovery that enhances our comprehension of tumor heterogeneity. Studies demonstrate a significant correlation between ITGA3 expression levels and the infiltration degree of various immune cells, offering critical insights into its role in modulating the tumor immune microenvironment. ITGA3 is highly expressed in B lymphocytes and macrophages within the tumor microenvironment, and this upregulation may affect the clinical efficacy of tumor immunotherapy by influencing immune escape pathways. Numerous clinical datasets have demonstrated a significant correlation between the expression intensity of ITGA3 and the response rate of patients undergoing immunotherapy, underscoring its potential utility as a predictive biomarker for immunotherapy outcomes. Through comprehensive multi-cancer expression profiling analyses, it has been identified that ITGA3 exhibits considerable heterogeneity in expression levels across various malignancies. Particularly in gliomas and pancreatic ductal adenocarcinomas, variations in ITGA3 expression are closely linked to patient survival outcomes. These observations imply that ITGA3 may have distinct biological functions across different tumor types. Future research should aim to investigate the potential synergistic application of ITGA3 alongside other molecular markers to enhance the prognostic evaluation framework for cancer patients.

In recent years, the implementation of immune checkpoint blockade therapy has markedly enhanced the prognosis of individuals with malignant tumors, with certain patients experiencing long-term survival benefits and exhibiting potential for cure [26, 27]. This study conducts a systematic analysis of the correlation between ITGA3 gene expression levels and various immune checkpoint genes. The findings suggest that ITGA3 may modulate the tumor immune microenvironment through interactions with critical immune checkpoint genes, including PDCD1 and CTLA4. These results offer a prospective molecular target for the development of innovative anti-tumor immunotherapies. In the domain of biomarker research, TMB has been demonstrated to be an effective predictor of the efficacy of immune checkpoint inhibitors, serving as a reliable prognostic indicator across diverse cancer types [28]. Similarly, MSI is instrumental not only in evaluating a patient's sensitivity to immunotherapy but also in predicting the tumor's response to specific chemotherapeutic agents and its resistance profiles [29]. Building on these findings, it is anticipated that these research outcomes will yield valuable biomarkers and therapeutic targets, facilitating the early screening of malignant tumors and the development of precise treatment strategies.

In summary, this study offered unique advantages compared to some studies. Unlike previous studies, which often focus on a single oncogene in a specific malignancy, this study attempted to explore and validate the multifaceted value of ITGA3 at a pan-cancer level (across multiple cancers), including multifaceted expression levels, prognostic value, mutation analysis, correlation with immune infiltration and immunotherapy response, and potential pathways. This exploration of pan-cancer markers (such as P53) that play a key role in multiple cancers is highly significant. Of course, this study also inevitably has some limitations.The limitations of this study are primarily centered on the absence of experimental validation and the limited scope of the sample. Although the expression patterns of ITGA3 across various cancers were elucidated through the integration of multi-omics data, the insufficient diversity and scale of the samples may undermine the generalizability and accuracy of the research conclusions. Additionally, the analysis of neutral datasets may be affected by inter-batch variability, potentially compromising the stability of the research results and, to some extent, constraining the in-depth interpretation of the functional mechanisms of ITGA3 within the tumor microenvironment. Therefore, future research should prioritize expanding the sample size and conducting appropriate experimental validation to enhance the scientific and practical value of the findings. Furthermore, future studies should employ spatial transcriptomics or single-cell RNA sequencing-based cell–cell communication analysis to further investigate the specific mechanisms by which ITGA3 regulates the tumor immune microenvironment.

Conclusion

This study examines ITGA3 expression in various cancers, highlighting its abnormal patterns and links to patient prognosis, clinicopathological features, tumor mutational burden, and the immune microenvironment. ITGA3 expression varies significantly across cancer types, with upregulation associated with malignancy and poor prognosis. It may affect tumor invasiveness and metastasis by altering tumor cell interactions with the extracellular matrix and aiding immune evasion. These insights suggest ITGA3 as a potential biomarker and target for new cancer treatments, though further research is needed to confirm its molecular role and value in immunotherapy.

Author contributions

All authors contributed to the study conception and design. Data collection and analysis were performed by Zidi Zhang, Mengjun Zhang. The first draft of the manuscript was written by Zidi Zhang, Mengjun Zhang. Hongyang Liu, Meidi Liang and Lindong Zhang wrote some of the content and revised the paper. Lindong Zhang was approved the final version and contributed to the conception of the paper. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding

This work was supported by Henan Province Science and Technology Research Project (No.242102310464), PhD research startup foundation of the Third Affiliated Hospital of Zhengzhou University (BS20230104), Henan Province Science and Technology Research Project (LHGJ20230372), National Natural Science Foundation of China (No. 82073239).

Data availability

We downloaded STAR count data and corresponding clinical information for 33 tumor types from the TCGA (https://portal.gdc.cancer.gov), GTEx (www.genome.gov/), CCLE (https://depmap.org/portal/data_page/?tab=allData), and HPA (https://www.proteinatlas.org/) databases. The GTEx data utilized in this study is from version V8 (https://gtexportal.org/home/datasets). Single-cell analysis was performed using the analysis module of the Researcher's Home platform (https://www.aclbi.com/static/index.html#/single_cell_analysis). All data generated or analyzed during this study are included in this published article.

Declarations

Ethics approval and consent to participate

This study was performed in line with the principles of the Declaration of Helsinki. The ethics review of the Third Affiliated Hospital of Zhengzhou University (No. 2024–174) has been passed. Informed consent was obtained from all individual participants included in the study.

Consent for publication

The authors affirm that human research participants provided informed consent for publication of the data.

Competing interests

The authors declare no competing interests.