Abstract
Objectives
Integrin subunit beta-like 1 (ITGBL1), a member of the epidermal growth factor (EGF)-like protein family, encodes a beta integrin-related protein that is mainly associated with the development of specific tumours and immune-related signalling pathways. This work aimed to explore the possibility that ITGBL1 functions as a novel target gene for immunotherapy and could be a cancer prognostic molecule.
Methods
The mRNA data for ITGBL1 were obtained from the public databases The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) and Gene Expression Omnibus (GEO). Using GEPIA, the differential expression of ITGBL1 in different tumour stages was identified. Cancer prognostic correlations were explored using Kaplan–Meier survival analysis and forest plots. A combination of Gene Set Enrichment Analysis (GSEA), TIMER2.0 and the R package was applied to analyse the ITGBL1-enriched related pathways. The NCI-60 drug database was examined using CellMinerTM. Cytological experiments were conducted to confirm ITGBL1’s impact on cancer cells.
Results
Our research has shown that ITGBL1 is differentially expressed in 26 cancers, and high ITGBL1 expression predicts a poorer survival prognosis in some specific cancers. Additionally, we found that ITGBL1 is enriched in immune-related pathways, which are closely linked to immunomodulatory molecules, immune-infiltrating cells, and immunomodulatory factors. The results of tumor mutational burden (TMB) and microsatellite instability (MSI) also indicate that the expression of ITGBL1 is beneficial for improving tumor immunotherapy efficacy. Furthermore, a number of antitumor agents associated with ITGBL1 expression have been identified. Finally, knockdown of ITGBL1 restricts the ability of gastric and colorectal cancer cells to proliferate and migrate.
Conclusions
Our study demonstrates that ITGBL1 can be utilized to accurately prognosticate cancer and has opened up new avenues for the investigation of tumor immune mechanisms and the development of more efficacious immunotherapies.
Introduction
Integrin subunit beta-like 1 (ITGBL1), belonging to the EGF-like protein family, is a protein found in the extracellular matrix that mediates the interaction of cellular and extracellular mechanisms and is primarily involved in mutual cell adhesion and interactions [1, 2]. The extracellular matrix protein, as an important component of the cellular microenvironment, mainly regulates cellular functions and participates in the formation of the tumour microenvironment [3, 4]. Therefore, aberrant expression of ITGBL1 can lead to dysregulation of the extracellular matrix, thereby affecting cell transformation and metastasis, promoting inflammation and tumor vascularization, which has a significant impact on tumorigenesis and development [5]. High expression of ITGBL1 is closely associated with the epithelial-mesenchymal transition (EMT) phenotype, creating conditions for tumour cell metastasis [6]. In primary tumours, ITGBL1 induces the conversion of fibroblasts into cancer-associated fibroblasts (CAFs) to secrete proinflammatory factors via the NF-κB signalling pathway, which causes metastatic tumour growth [7]. Particularly in prostate cancer, this pathway has been demonstrated to be activated by ITGBL1 to promote the metastatic capability of prostate cancer cells. Through the TGF/β1/Smad pathway, ITGBL1 may also contribute to the development of pancreatic cancer [7, 8]. Abnormal expression of ITGBL1 can affect tumour progression. Therefore, we would like to confirm whether ITGBL1 can be an important biomarker for predicting tumour prognosis through a multi-cancer study.
Immunotherapy has become an important cancer treatment modality [9]. The main approach to cancer immunotherapy is to target cytokines that affect immune cell function [10]. In this regard, immunotherapy for a variety of malignancies has greatly benefited from the blocking of immune checkpoints such as cytotoxic T lymphocyte-associated protein 4 (CTLA-4) and programmed cell death protein-1 (PD-1) [11]. However, drug resistance is a common occurrence among many patients, which considerably reduces the effectiveness of immunotherapy [11]. Therefore, the search for new immunomodulatory genes could potentially increase the longevity of cancer patients receiving immunotherapy and provide more personalised and precise treatments to help patients prolong their survival. ITGBL1 has been demonstrated to acts as an immunomodulator, modulating the intrinsic tumour immune system in melanoma, reducing NK cell activity and inhibiting immune-mediated destruction of melanoma cells [12]. However, it remains unknown whether ITGBL1 could be involved in regulating immune infiltrating cells and immunomodulatory factors in multiple cancers. Hence, an in-depth investigation of the role that ITGBL1 may play in tumour immunotherapy is crucial to enhancing the effectiveness of cancer patients’ treatment and reducing drug resistance.
To broaden the scope of ITGBL1 directions in cancer research, we used the TCGA and GEO database to shed light on how ITGBL1 is expressed in different types of cancer and validated it with tumour samples. Then, we analysed the correlation between ITGBL1 and pathological stages using GEPIA2. We also investigated whether ITGBL1 could influence overall survival (OS), disease-specific survival (DSS) and progression-free interval (PFI) to have an impact on the prognosis of various cancers. Based on the GSEA results, we found the enrichment of ITGBL1 in immune-related pathways. The association between ITGBL1 and immune cells that infiltrate tumours was further examined using the TIMER2.0 database and TSIDB and quantified by EPIC, xCell, TIMER, CIBERSORT, etc. Apart from this, we investigated immunomodulators closely related to ITGBL1 and determined the correlation between ITGBL1 and TMB and MSI. We identified several ITGBL1-sensitive and ITGBL1-tolerant targeting agents through the CellMiner™ database. Finally, the impact of ITGBL1 on the ability of tumour cells to migrate and proliferate was confirmed by cytological experiments. Collectively, our findings demonstrate that ITGBL1 is an effective indicator for estimating the prognosis of tumours and may have a potential role in research on tumour immune mechanisms.
Methods
Date source and analysis
After being uniformly processed by the Toil process, the RNAseq data in TPM format was downloaded from UCSC XENA (https://xenabrowser.net/datapages/) and the mRNA data for all 33 cancers was acquired from the TCGA (https://www.cancer.gov/ccg/research/genome-sequencing/tcga) and GTEx (https://www.gtexportal.org/home/) databases [13]. The RNAseq data in TPM (transcripts per million reads) format were log2-transformed and analysed by the Mann–Whitney U test to contrast ITGBL1 expression in paraneoplastic and tumour tissues. Differential expression of ITGBL1 with a p value analysis less than 0.05 was identified as differentially expressed. The 33 varieties of cancer included adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), acute myeloid leukemia (LAML), brain lower grade glioma (LGG), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), mesothelioma (MESO), ovarian serous cystadenocarcinoma (OV), pancreatic adenocarcinoma (PAAD), pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), sarcoma (SARC), skin cutaneous melanoma (SKCM), stomach adenocarcinoma (STAD), testicular germ cell tumors (TGCT), thyroid carcinoma (THCA), thymoma (THYM), uterine corpus endometrial carcinoma (UCEC), uterine carcinosarcoma (UCS), and uveal melanoma (UVM).
Colorectal and gastric cancer datasets were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/). The dataset GSE9348 for colorectal cancer contains 12 normal tissues and 70 tumour tissues. The dataset GSE44706 for colorectal cancer contains 148 normal tissues and 98 tumour tissues. The dataset GSE19826 for gastric cancer contains 15 normal tissues and 12 tumour tissues. The dataset GSE54129 for gastric cancer contains 21 normal tissues and 111 tumour tissues.
Clinical sample sources
Six sets of tissues, one for each colorectal cancer and one for paracancerous tissue, were taken from patients receiving treatment for colorectal cancer at the Second Affiliated Hospital of Suzhou University’s Department of General Surgery. These samples were preserved using liquid nitrogen. Every hospitalised patient gave their informed consent for this study. Clinical excision sample use was authorised by the Ethics Committee of the Second Affiliated Hospital of Soochow University (approval number: JD-LK2023001-IR01). Clinical samples were collected and used in strict accordance with the guidelines.
Clinicopathological stage analysis
The link between ITGBL1 across cancer types and clinicopathological stage was examined using GEPIA (http://gepia.cancer-pku.cn/). Using the pathological stage as the variable, differential expression was computed using one-way analysis of variance (ANOVA), the method of differential gene expression analysis. Expression data were first converted to log2(TPM+1) for differential analysis.
Cancer prognosis analysis
An analysis of survival performed on RNAseq data obtained from the TCGA database in HTSeq-FPKM format was statistically examined by log-rank and included OS, DSS, and PFI. Data on survival were visualised using the survminer package (version 0.4.9, https://cran.r-project.org/web/packages/survminer/) and statistical analysis was performed using the survival package (version 3.2-10, https://cran.r-project.org/web/packages/survival/). These packages come from the R project (version 4.1.3, https://www.r-project.org/). The prognostic supplemental data were from a Cell article [14].
Gene set enrichment analysis
The Molecular Signature Database (MSigDB, https://www.gseamsigdb.org/gsea/index.jsp) website provided a “GMT” file with 50 hallmark gene sets. This file was used to compute the normalised enrichment score (NES) and false discovery rate (FDR) for each biological process for every type of cancer. The R package “clusterProfiler” (https://bioconductor.org/) was used to carry out the gene set enrichment analysis [15], and the outcomes are summarised in the bubble plots described in the R package “ggplot2” (https://cran.r-project.org/web/packages/ggplot2/). These packages come from the R software (https://www.r-project.org/).
Immunological correlation analysis
The TIMER2.0 database (http://timer.cistrome.org/) was utilised to examine the relationship between ITGBL1 and 19 distinct categories of immune cells that infiltrate, such as B cells, CAFs, CD4+ T cells, CD8+ T cells, progenitor cells, dendritic cells, endothelial cells (Endos), eosinophils (Eos), γδ T cells, stem cells, macrophages, mast cells, monocytes, neutrophils, NK cells, NK T cells, T-cell follicular helper (TfH) cells and regulatory T cells (Tregs). TIMER2.0 has the ability to use algorithms such as EPIC, xCell, TIMER, CIBERSORT and others to assess the significance of ITGBL1 and other immune cells that infiltrate cancerous tissue.
ITGBL1 and immune subtypes as well as molecular subtypes in pancancer were found to be related using the TISIDB database (http://cis.hku.hk/TISIDB/). The main immune and molecular subtypes are the following: C1 (wound healing), C2 (IFN-gamma dominant), C3 (inflammatory), C4 (lymphocyte depleted), C5 (immunologically quiet), and C6 (TGF-β dominant).
Drug sensitivity of ITGBL1
Data processed for drug sensitivity was uploaded from the CellMiner™ database (https://discover.nci.nih.gov/cellminer/home.do) [16]. For 60 cancer cell lines, drug sensitivity data is available in the CellMiner™ database. The R packages “impute” (https://bioconductor.org/), “limma” (https://bioconductor.org/), “ggplot2” (https://cran.r-project.org/web/packages/ggplot2/), and “ggpubr” (https://cran.r-project.org/web/packages/ggpubr/index.html) were employed for analysis and visualisation of all the data.
Cell culture
Human gastric cancer cell line MGC803 and the human colorectal cancer cell line HCT-8 were used in the study. The HCT8 cells were purchased from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). The MGC803 cells were purchased from Beyotime Biotechnology (Shanghai, China). All cells were tested for mycoplasma. RPIM (cat No.: SH30809.01, HyClone, USA) medium containing 10 % foetal bovine serum (FBS, #A5669701, Gibco, Thermo Fisher Scientific, USA) and 1 % penicillin streptomycin (#15140122, Gibco, Thermo Fisher Scientific, USA) was employed to cultivate HCT-8 and MGC803 cells at 37 °C and 5 % CO2.
Western blot
We lysed the cells and tissues with radio-immunoprecipitation assay (RIPA) buffer (Cat# R0020, Solarbio, Beijing, China). 5× SDS-PAGE Protein loading buffer (Cat: 20315ES20, YEASEN, Shanghai, China) was used to prepare the proteins. The primary antibodies included ITGBL1 antibody (Absin, Shanghai, China), horseradish peroxidase (HRP)-labelled goat anti-rabbit IgG (H+L) (Beyotime, Shanghai, China) and horseradish peroxidase (HRP)-labelled goat anti-mouse IgG (H+L) (Beyotime, Shanghai, China). We used polyvinylidene difluoride (PVDF) film (IPVH00010, Merck Millipore, USA) for transfer and 5 % skimmed milk powder for closure and then applied the primary antibody at 4 °C for the entire night and the secondary antibody for 1 h the following day at room temperature. An ultrasensitive electrochemiluminescence (ECL) chemiluminescence kit (Cat. No: P10100, NCM Biotech, Suzhou, China) was used to detect protein expression levels. Amersham ImageQuant 800 (cytiva, USA) was used for the detection and analysis of proteins in membranes. ImageJ (v1.54a) was used for quantitative analysis.
CCK8 proliferation assay
Cell Counting Kit-8 (Cat. No: C6005, NCM Biotech, Suzhou, China) was employed in order to identify the proliferation of cancer cells. We spread cancer cells from the negative control and experimental groups in 96-well plates at 2000 cells per well and set up five replicate wells. At 0 h, 24 h, 48 h and 72 h, 10 μL of CCK8 solution was added to each well and incubated at 37 °C for 1 h. The optical density (OD) values at 450 nm were measured using a microplate spectrophotometert (EPOCH-SN, BioTek Epoch, USA) under light-avoiding conditions. GraphPad Prism software (v9.0.0) was used to assess the proliferative capacity of the different groups.
Migration assay
We first spread the negative control and experimental group cells in six-well plates, and after the cells reached 90 % confluence, a straight line was gently scratched in the centre from top to bottom with the tip of the pipette. Finally, the plate was washed three times with phosphate buffer saline (1× PBS, pH 7.4, #C10010500BT, gibco, USA), an appropriate amount of culture medium was added to each well, and microscopic photographs were captured at 0 and 48 h to evaluate the capacity to heal scratches. A fluorescence microscope (IX73, Olympus Corporation, Tokyo, Japan) was used for image acquisition. GraphPad Prism software (v9.0.0) was used to analyse and compare migration capabilities.
Statistical analysis
An unpaired t-test was used to compare the two groups, and the data were presented as the mean±standard deviation. The Kaplan–Meier method was employed to assess the relationship between prognosis and ITGBL1 expression levels in patients. p <0.05 was the threshold for statistical significance in this case. The data analysis and statistics were performed using GraphPad Prism software (v9.0.0).
Results
Analysis of ITGBL1 expression in normal and tumour tissues
By means of RNAseq data analysis from the GTEx and TCGA databases, we ascertained the variation of ITGBL1 expression in 33 different types of cancer. According to the results, ITGBL1 was expressed differently in 27 cancers, and a statistically significant difference was evident with a p-value <0.05. Among them, the expression of ITGBL1 was greater in tumour tissues compared to normal tissues in 13 cancers (Figure 1A). To increase the reliability of the data, we also performed ITGBL1 validation through the GEO database. We examined the expression of ITGBL1 in normal and tumour tissues using the colorectal cancer datasets GSE9348 and GSE44706, and the results showed that the expression of ITGBL1 in tumour tissues was elevated (Figure 1B). Comparably, ITGBL1 expression in the gastric cancer datasets GSE19826 and GSE54129 showed similar final results (Figure 1C).

Expression of ITGBL1 in pancancerous tissues. (A) Evaluation of transcript level expression of ITGBL1 in tumour and normal tissues. (B, C) Variations in ITGBL1 expression between tumour and healthy tissues in the GEO dataset. (D) ITGBL1 expression in colorectal cancer samples and surrounding tissues was confirmed by western blot analysis. *p<0.05, **p<0.01, ***p<0.001. ns, not statistically significant.
Furthermore, we confirmed ITGBL1’s protein expression in six pairs of colorectal and paracancerous tissues obtained from clinical samples, and the findings demonstrated that colorectal cancer tissues had elevated levels of ITGBL1 (Figure 1D). Fascinatingly, this outcome is in line with the GEO database analysis, opening the door for a thorough investigation into ITGBL1 as a potential high-risk factor in cancer.
Clinicopathological stages
The influence of ITGBL1 on tumour pathological stages was examined using the GEPIA database. We screened and analysed tumours with statistically valid results and found that in ACC, ESCA, HNSC, BLCA, KIRP, STAD, THCA, and OV, ITGBL1 was substantially linked to the formation of tumours (Figure 2). In ACC, ESCA, HNSC, BLCA, KIRP, and THCA, the expression of ITGBL1 tended to increase with tumour progression, indicating a positive correlation between ITGBL1 and the progression of these tumours. However, in stage IV, ESCA and OV, ITGBL1 was expressed at low levels (Figure 2B and H). Figure 2F reveals an opposite trend of ITGBL1 in STAD progression, indicating a negative correlation between ITGBL1 and STAD staging (Figure 2F). In summary, ITGBL1 may have important diagnostic value in the progressive stage of tumours.

Relationships between ITGBL1 expression and major pathological stages. Clinicopathological stages included stage I, stage II, stage III, and stage IV of ACC (p=0.003), ESCA (p<0.05), HSNC (p<0.05), BLCA (p<0.001), KIRP (p<0.001), STAD (p=0.001), THCA (p<0.001) and OV (p<0.05).
Analysis of the prognostic significance of ITGBL1 in pancancer
To investigate more thoroughly the necessity of ITGBL1 in the progression of tumours, we used univariate Cox regression analysis and three types of survival analysis, including OS, DSS, and PFI, to investigate the prognostic significance of ITGBL1. The Cox risk proportional model revealed that ITGBL1 had a connection with the OS of ACC (p=0.036), BLCA (p=0.005), CESC (p=0.02), COAD (p=0.007), DLBC (p=0.041), GBM (p<0.001), KIRP (p=0.001), LAML (p=0.025), LGG (p<0.001), MESO (p=0.048), OV (p=0.03) and STAD (p=0.018) (Figure 3A). Except in DLBC and LAML, where ITGBL1 is a low-risk factor, ITGBL1 is a high-risk factor for every other type of cancer. Kaplan–Meier survival analysis was also consistent, with high expression of ITGBL1 in ACC, BLCA, CESC, COAD, GBM, KIRP, LGG, MESO, OV, and STAD predicting a worse outcome. In contrast, patients with high ITGBL1 expression in LAML and DLBC had a longer OS (Figure 3B–M). In addition, high expression of ITGBL1 in patients with ACC, BLCA, CESC, COAD, GBM, KIRP, LGG, MESO, and OV was also observed in DSS, predicting shorter survival (Figure S1). Similar findings were found by the PFI analysis, which indicated that high ITGBL1 expression and poor prognosis were associated with these cancers (Figure S2). A combination of the three survival analyses suggests that ITGBL1 is likely to be a good marker of survival prognosis.

Examination of the connection between ITGBL1 expression and overall survival (OS). (A) Forest plot for the association between OS and ITGBL1 in 33 different disease categories. (B–M) The Kaplan–Meier survival analysis examines the relationship between OS and ITGBL1 expression.
GSEA and immunological correlation
To further investigate the possible functions of ITGBL1 in tumour mechanisms, we searched for ITGBL1-associated cancer hallmarks by analysing differentially expressed genes (DEGs) between high- and low-expressing ITGBL1 subgroups in 33 cancers and performing GSEA. According to the GSEA results, ITGBL1 was extensively enriched in immune-related pathways across cancers, including the inflammatory response, IL-6 JAK-STAT3 signalling, IL-2 JAK-STAT3 signalling, KRAS signalling pathway and allograft rejection pathway (Figure 4A). Janus kinase (JAK) signal transducer and activator of transcription (STAT) pathways perform a crucial function in immune system modulation, especially cytokine receptors, which regulate the polarization of T helper cells [17, 18]. KRAS mutations are associated with tumour proinflammatory responses and KRAS-induced immune regulation, leading to immune escape in the tumour microenvironment [19]. Allograft rejection, which T cells are involved in suppressing, is inextricably linked to the immune system [20]. These results indicated that ITGBL1 was likely to be involved in the tumour microenvironment and related to tumour infiltrating immune cells, paving the way for further studies on immune function.

Related functional pathways and immunophenotyping of ITGBL1. (A) The hallmark gene set enrichment analysis and immune subtypes of ITGBL1 across cancers. The FDR value is indicated by the size of the circles, and the NES score is denoted by the colour. (B) Correlation of ITGBL1 expression with pancancer immune subtypes investigated by TISIDB in ACC, BLCA, CESC, COAD, LGG, KIRP, MESO and OV. C1 (wound healing); C2 (IFN-gamma dominant); C3 (inflammatory); C4 (lymphocyte depleted); C5 (immunologically quiet); C6 (TGF-β dominant). (C) ITGBL1 expression correlates with TISIDB-investigated molecular subtypes in COAD, KIRP, LGG, and OV.
In addition, ITGBL1 was significantly characterized in EMT in all 33 cancers, which was related to the function of ITGBL1 itself (Figure 4A). EMT enables tumour cells to acquire mesenchymal cell properties, increasing the invasiveness and drug resistance of cancer cells while suppressing antitumour immune responses [21]. ITGBL1 also participated in the ultraviolet light (UV) response, myogenesis pathway, etc.
Based on the results of GSEA across cancers, ITGBL1 is probably involved in the immune regulation of tumours, so it is necessary to investigate the immune correlation between ITGBL1 and tumours. We first used TISIDB to analyse whether ITGBL1 correlates with immune subtypes and molecular subtypes. The results indicated a relationship between ITGBL1 and immune subtypes in ACC (p=1.81e-02), BLCA (p=3.56e-05), CESC (p=1.94e-02), COAD (p=8.27e-03), LGG (p=9.71e-03), KIRP (p=9.55e-04), MESO (p=7.18e-02) and OV (p=1.57e-05). Among the eight kinds of cancers, the C4 type showed low expression of ITGBL1 (Figure 4B). The expression of ITGBL1 was statistically significant in the molecular subtypes COAD (p=4.16e-02), KIRP (p=1.95e-04), LGG (p=1.7e-21), and OV (p=7.29e-24) (Figure 4C). In the HM-indel subtype of COAD, ITGBL1 expression was low, whereas in the CIN subtype, it was highly expressed. ITGBL1 is expressed at low levels in the C1 isoform of KIRP and highly expressed in the C2c-CIMP isoform. There was relatively low expression of ITGBL1 in the G-CIMP-LOW isoform of LGG and relatively high expression of ITGBL1 in the PA-like isoform. ITGBL1 is upregulated in the mesenchymal isoform of OV, while it is downregulated in the proliferative isoform.
Analysis of infiltrating immune cells in pan carcinoma
Together, various immune cell subsets impact the tumour microenvironment and further impact the effectiveness of tumour immunotherapy [22], so we made use of the TIMER2.0 database to investigate whether the expression level of ITGBL1 could be corroborated with infiltrating immune cells in a range of cancers. The results indicated that the relevance of ITGBL1 and infiltrating immune cells was remarkable in most cancer species, especially B cells, CAFs, dendritic cells, Endos, stem cells, macrophages, monocytes, neutrophils, NK cells, and Tregs (Figure 5). We screened seven cancers and found that the expression of ITGBL1 had a favourable association with B cells and CD8+ T cells in COAD, ESCA, HNSC, LGG, OV, STAD, and THCA (Figure S3). However, the expression trend of ITGBL1 in LGG was opposite to that in CD4+ T cells, neutrophils, macrophages and myeloid dendritic cells (Figure S3d). In the ESCA, there was a negative correlation between neutrophil expression and ITGBL1, while the rest of the infiltrating immune cells showed a positive tendency (Figure S3b).

The relevance of B cells and infiltrating immune cells in pancytosis. Infiltrating immune cells include B cells, CAFs, CD4+ T cells, CD8+ T cells, progenitor cells, dendritic cells, Endos, EOSs, γδ T cells, stem cells, macrophages, mast cells, monocytes, neutrophils, NK cells, NKT cells, TFH cells and regulatory T cells. Negative correlations are shown in blue, and positive correlations are shown in red.
Immunotherapy related to ITGBL1
The advent of immunotherapy has revolutionised the way that tumours are discovered and given advanced cancer patients new options for treatment [23]. In immunotherapy, immunostimulatory substances prime the body’s defences to identify and combat cancerous cells, while immunosuppressive factors cause cancer cells to undergo immune escape. According to the heatmap, positive correlations were observed between ITGBL1 and the majority of immunostimulatory and inhibitory factors (Figure 6A and B). Vital immune system mediators are chemokines and chemokine receptors, and the heatmap demonstrates that ITGBL1 is positively correlated with both in the majority of cancers (Figure 6C and D) [24]. As an illustration, CXCL12 and its receptor CXCR4, which are extremely related to ITGBL1, can promote tumour growth, angiogenesis and metastasis and are an excellent immunotherapeutic strategy [25].

Correlation of ITGBL1 and immune regulation. (A) The heatmap depicts the correlation between ITGBL1 and immunostimulatory factors. (B) The heatmap indicates the correlation between ITGBL1 and immunosuppressive factors. (C) The heatmap depicts the correlation between ITGBL1 and chemokines. (D) The heatmap represents the correlation between ITGBL1 and chemokine receptors. (E) Correlation of ITGBL1 expression and TMB. (F) Link between MSI and the expression of ITGBL1. *p<0.05, **p<0.01, ***p<0.001.
TMB and MSI are promising biomarkers for prediction in tumour immunotherapy [26]. According to Figure 6E, the expression of ITGBL1 was remarkably associated with TMB in 11 out of 33 cancers, including PRAD, THYM, LAML, LIHC, STAD, KIRP, BRCA, KIRC, LUSC, SKCM, and LUAD (Figure 6E). ITGBL1 was positively correlated with the TMB of PRAD, THYM and LAML but negatively correlated with the other eight cancers. Therefore, high expression of ITGBL1 predicts a higher number of tumour mutations in PRAD, THYM and LAML, which is more beneficial for improving the efficacy of tumour immunotherapy. High expression of ITGBL1 indicated a high incidence of gene mismatches in PRAD and SARC, whereas low expression of ITGBL1 indicated a high incidence of gene mismatches in STAD, LUSC, HNSC, and BRCA (Figure 6F). Overall, ITGBL1 can play an effective guiding role in tumour immunotherapy.
The drug sensitivity of ITGBL1
CellMiner™ is an online tool designed to mine publicly accessible datasets of human cancer cell lines related to genetics, pharmacology, and genomics, and we used this database to explore the relationship between ITGBL1 and drug sensitivity [27]. The analysis results showed that ITGBL1 was positively correlated with SGX-523, JNJ-3887618, AZD-1390, JNJ-38877605, dimethylfasudil, PF-04217903, olaparib, P-529, IDN-C227, and staurosporine. Conversely, ITGBL1 increased the sensitivity to APR-246, HYPOTHEMYCIN, bosutinib, BP-1-102, BGB-283, and LY-2606368 (Figure 7A–P). The results suggest that ITGBL1 can influence the drug sensitivity of these inhibitors, which will also provide more research directions for ITGBL1 in tumour mechanisms and improving prognosis.

Analysis of drug sensitivity towards ITGBL1 according to the CellMiner™ database.
Elevated ITGBL1 induces cancer cell multiplication and migration
The database results indicated that ITGBL1 was highly expressed in colorectal and gastric cancers, which also predicted a poor prognosis, so we decided to validate the functional experiments in MGC830 and HCT8 cells. We transfected cancer cells with three different ITGBL1 siRNAs and validated them using Western blotting. The results showed that the knockdown effect of si-ITGBL1-1 was most significant in HCT8 cells, and the knockdown effect of si-ITGBL1-2 in MGC803 cells was the best (Figure 8A and B). Therefore, we decided to perform relevant experiments after the knockdown of HCT8 and MGC803 with these two siRNAs. The CCK8 assay can reflect the proliferation ability of cancer cells, which was used to demonstrate that the knockdown of ITGBL1 reduced the capacity of colorectal cancer cells and gastric cancer to proliferate over time (Figure 8C and D). Using scratch assays, we compared the migratory capacities of the cells in the negative control and knockdown groups to examine the impact of ITGBL1 on tumour cell migration. The results of our analysis showed that downregulating ITGBL1 can decrease the migratory capacity of cancer cells (Figure 8E and F). Consequently, we hypothesised that ITGBL1 is a tumour-causing oncogene that may contribute to the progression of cancer; therefore, targeting it with medication might enhance patient survival.

Functional implications of ITGBL1 on proliferation and migration of cancer cells. (A, B) Western blot analysis of the transfection efficiency of three siRNAs after transfection of HCT8 and MGC803 cells. (C, D) Effect of ITGBL1 knockdown on the proliferation of colorectal cancer cells and gastric carcinoma cells. (E, F) Impact of ITGBL1 suppression on gastric and colorectal cancer cells’ ability to migrate. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.
Discussion
ITGBL1 was first discovered in the overlapping sequences of cDNA libraries from embryonic lung and osteoblasts and has structural similarity to integrin β [1]. Its EGF-like repetitive sequence is highly homologous to the repetitive amino acid sequence in integrin β, and its signal peptide can drive protein translocation and secretion [28]. Numerous growth factors, receptors, and adhesion molecules contain structures resembling those of EGF that may affect properties such as cell adhesion, motility and invasiveness and may also display growth factor activity [29, 30]. These characteristics are important signs of tumours [31]. Therefore, we wanted to investigate whether ITGBL1 has an effect on tumour cells.
Pancancer analysis can be applied to find cancer biomarkers to aid in early diagnosis and targeted therapy [32], so we utilised it to study the prognostic impact of ITGBL1 on different tumours. The experimental results demonstrated that ITGBL1 was differentially expressed in all 27 cancers and overexpressed in 13 cancers, suggesting that ITGBL1 may have a pro-cancer function in some tumours. To confirm whether ITGBL1 can predict the prognosis of cancer patients, we found a correlation between ITGBL1 and clinicopathological staging in ACC, ESCA, HNSC, BLCA, KIRP, STAD, THCA and OV using the GEPIA database. The results of survival analysis using OS, DSS and PFI revealed that elevated ITGBL1 was associated with lower survival, which was particularly significant in ACC, BLCA, CESC, COAD, GBM, KIRP, DLBC, LGG, MESO, OV and STAD. It has been demonstrated that ITGBL1 is upregulated in gastric and colorectal cancers and is associated with poor prognosis [7, 33], while we further explored the prognosis of more cancers corresponding to high ITGBL1 expression, suggesting that ITGBL1 has more potential in predicting tumour prognosis and is a gene worthy of further investigation in cancer mechanisms.
For an in-depth study of the ITGBL1-associated functions, we performed GSEA on ITGBL1, and the results showed that ITGBL1 is mainly involved in the inflammatory response and the IL-6 JAK-STAT3 signalling, IL-2 JAK-STAT3 signalling, KRAS signalling pathway and allograft rejection pathway, all of which mediate immune regulation; thus, ITGBL1 most likely has an impact on how immune cells respond to cancer. The nucleus receives signals from cell membrane receptors via the Janus kinase signal transduction and activator of transcription (JAK-STAT) pathway [34], which is a process that involves a variety of cytokines that are indispensable in the innate and adaptive immune regimes and essential for the myeloid and lymphoid lineages [35]. KARS mutations are considered a major factor in carcinogenesis and can induce a variety of inflammatory cytokines and chemokines and synergize with IL-6 to activate the STAT3 pathway to exert procancer effects [36]. Allograft rejection is also due to an adaptive immune response triggered by the recognition of donor antigens by T lymphocytes [37]. It has been documented that ITGBL1 can act as a novel immunomodulator and inhibit the cytotoxicity of natural killer cells in vitro and in vivo to promote melanoma progression, and articles have also explored the relevance of ITGBL1 in gastric cancer and infiltrating immune cells [12, 38]. Using TISIDB, we found immunological subtypes of ITGBL1 in ACC, BLCA, CESC, COAD, LGG, KIRP, MESO and OV, as well as molecular subtypes in COAD, KIRP, LGG and OV. Our study not only validated the immune relevance of ITGBL1 in melanoma and gastric cancer but also more broadly examined the immune role played by ITGBL1 in different cancers. As one of the main causes of death for women, cervical and ovarian cancer demand attention from the public [39, 40]. Therefore, ITGBL1 has been shown to be a high-risk factor for predicting survival in gynaecological tumours, and studies on immune correlation have indicated that ITGBL1 can influence the effectiveness of immunotherapy in gynaecological tumours. We further advocate for the use of ITGBL1 as a target for comprehensive pathogenesis studies and the investigation of targeted therapy options in order to increase the survival rate of females.
A key element in the development of tumours is the infiltration of immune cells. The most common immune cells in the tumour microenvironment are macrophages and T cells. Macrophages are primarily linked to a poor prognosis, as they are required for angiogenesis, invasion, and metastasis [41]. In solid tumours, T cells are involved in tumour progression and metastasis, B cells and mast cells are important contributors to immune-mediated tumour growth, and macrophages and dendritic cells have important functions in antitumour immune processes for antigen presentation and T-cell activation as well as cytokine production and immunosuppression of established tumours [42]. There are still few studies on the mechanism of ITGBL1 in tumour immune regulation, but our findings illustrated the connection between ITGBL1 and infiltrating immune cells across cancers, with significant correlations between ITGBL1 and B cells, CAFs, dendritic cells, Endos, stem cells, macrophages, monocytes, neutrophils, NK cells, and regulatory T cells. Therefore, researching the potential role of ITGBL1 in immune system regulation is a worthwhile endeavour when exploring the tumour mechanism.
Currently, immunotherapy has become the most promising treatment option for tumours and can be used to fight tumours by modulating the immune response, such as PD-1/PD-L1, interferon gene stimulating factors, Toll-like receptors and chemokine receptors [23]. Immune checkpoints including co-stimulatory and inhibitory signals can maintain the stability of T cell activity, and tumour cells can then break this regulation to form an immune resistance. More notably, more than five hundred clinical trials have been conducted to date on nine types of antibodies against PD-1 inhibitors, which have resulted in effective remissions for several common cancers [43]. Thus, immune checkpoint inhibitors hold great promise for the treatment of solid tumours [44]. MSI and TMB are genomic features associated with tumour immunity and immunotherapeutic response. Therefore, we identified the immunostimulatory factors, immunosuppressive factors, chemokines and chemokine receptors most associated with ITGBL1 and found that TMB and MSI are correlated with ITGBL1 in certain species of cancer, which were absent in previous studies. In conclusion, ITGBL1 is significant for the research and application of immune checkpoint inhibitors and has a guiding role in tumour immunotherapy, which deserves deeper mechanistic investigation. Furthermore, we used the CellMiner™ database to identify some targeted inhibitors with positive and negative sensitivity to ITGBL1 expression, which will be important for subsequent clinical applications. But the exact mechanisms involved and the actual impact on tumours remain to be explored.
In addition to validation using public databases, we also selected gastric cancer cells and colorectal cancer cells to explore whether ITGBL1 could function as an oncogene. Most tumours undergo the process of tumour cells spreading from the primary focus to the surrounding tissues, which is the primary reason why cancer patients pass on [45]. The outcomes of the experiment demonstrated that by blocking ITGBL1 expression, the migration and proliferation of tumour cells could be attenuated. This suggests that ITGBL1 deserves to be treated as a target to investigate the mechanisms of tumour development and to provide a reference for tumour prognosis.
In all honesty, while our results demonstrate the relevance of ITGBL1 and immune cells, the precise molecular pathways of ITGBL1 in particular tumours and the possible immunotherapy mechanisms are still poorly understood. Moreover, a large portion of our analysis is predicated on publicly accessible databases, which may be consistently biassed. Rigid in vitro and in vivo experiments are still required to more strongly validate functional studies of this molecule.
Overall, we discovered that the expression of ITGBL1 varied considerably among malignancies. It is highly correlated with clinicopathological stage, and its high expression predicts poor prognosis of cancer, suggesting that ITGBL1 is likely to be used as a tumour prognostic molecule. Moreover, GSEA found that ITGBL1 is involved in the regulation of immune-related pathways and infiltrating immune cells. Besides, ITGBL1 is also closely associated with immune checkpoints, chemokines and their receptors, TMB and MSI. Cellular experiments also verified that inhibition of ITGBL1 function can slow down the growth and migration of cancer cells. Based on the findings of our investigation, ITGBL1 is probably going to be a new target for tumour immune-targeted therapy, opening up new avenues for future tumour immunotherapy research.
Conclusions
Using database and tumour sample analysis as a basis, our experimental results imply that ITGBL1 plays a crucial role in cancer development and could serve as an effective prognostic marker in specific types of cancer. Immunocorrelation analysis revealed a close association between ITGBL1 and immune cells within the tumour microenvironment, indicating its potential as a therapeutic target in immunotherapy. CellMiner™ database summarises anti-tumour drugs related to ITGBL1. Biological experiments disclosed that inhibition of ITGBL1 function could alleviate the development of tumour cells. These results open up exciting new directions for the investigation of ITGBL1 as an immunotherapeutic target and offer insightful information about the study of tumour immune mechanisms. However, further mechanistic research is still required to establish a foundation for clinical applications.
Funding source: Nuclear Technology Application Innovation Team, General Hospital of Nuclear Industry
Award Identifier / Grant number: XKTJ-HTD2021004
Funding source: Suzhou Health Care Commission Medical Talent Project
Award Identifier / Grant number: GSWS2020037
Funding source: the project of Suzhou Technology Bureau
Award Identifier / Grant number: SKY2021043, SKY2022156, SLJ2022011
Acknowledgments
We thank the AJE editorial team (https://www.aje.cn/) for the English-language editing of this manuscript.
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Research ethics: This study was approved by the institutional review board of the Second Affiliated Hospital of Soochow University Ethics Committee (Jiangsu Province, China) (approval number: JD-LK2023001-IR01).
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Informed consent: The study protocol was approved by the Ethics Committee and written informed consent was obtained from all participants.
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Author Contributions: The authors confirm contributions to the paper as follows:ZYW and ZHL conceived and designed the experiments; ZYW, ZHL and CJG wrote, reviewed and revised the manuscript; ZYW and ZHL developed the methodology; ZYW, CJG analysed and interpreted the data; YW, YNL and ZYZ supervised the study. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interests: The authors declare that they have no conflict of interest.
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Research funding: This work was supported by the project of Suzhou Technology Bureau (SKY2021043, SKY2022156, SLJ2022011), Suzhou Health Care Commission Medical Talent Project (GSWS2020037), Nuclear Technology Application Innovation Team, General Hospital of Nuclear Industry (XKTJ-HTD2021004).
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Data availability: The manuscript is original, has not been published before, and is not currently being considered for publication elsewhere. The authors have obtained permission to use any copyrighted material included in the manuscript.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/oncologie-2023-0455).
© 2023 the author(s), published by De Gruyter, Berlin/Boston
This work is licensed under the Creative Commons Attribution 4.0 International License.
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