Prognostic significance of ITGA2 expression in cervical cancer

Prognostic significance of ITGA2 expression in cervical cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognostic significance of ITGA2 expression in cervical cancer Jingyi Han, Yuchao Diao, Yunting Zhou, Na Zang, Chang Wang, Youjun Luo, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6181852/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Integrin alpha 2 (ITGA2) exhibits elevated expression in multiple cancer types. Nevertheless, its expression in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) and its correlation with patient prognosis remains unclear. The aim of the present study was to examine the clinical relevance of ITGA2 expression in CESC. The expression of ITGA2 in CESC was investigated using The Cancer Genome Atlas and Gene Expression Profiling Interactive Analysis 2 databases. By comparing the ITGA2 median expression, all CESC samples were split into the two following groups: The ITGA2 high-expression and the ITGA2 low-expression groups. Subsequently, in order to determine the functional distinctions between the two groups, the following databases were used: Gene set enrichment analysis, Kyoto Encyclopedia of Genes and Genomes and Gene Ontology. The expression levels of ITGA2 were examined in cervical cancer cells using real-time reverse transcription-polymerase chain reaction and western blot analyses. Immunohistochemical staining was conducted to assess the expression levels of the ITGA2 protein in CESC and to examine the association of ITGA2 expression with the clinicopathological features and disease prognosis. According to the results obtained, patients with cervical cancer exhibited higher levels of ITGA2 expression. The overall survival and progression-free survival of patients with ITGA2-positive expression were considerably lower than those of patients with ITGA2-negative expression. The ITGA2 high-expression group demonstrated increased immune infiltration and elevated expression of immune checkpoint inhibitor targets. In conclusion, the data indicated that ITGA2 could be a novel tumor biomarker, which can be utilized for evaluating the prognosis and immunotherapy of patients with cervical cancer. cervical cancer ITGA2 biomarker prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Cervical cancer is among the most common gynecological malignancies ( 1 ). Cervical cancer is considered to be primarily caused by the human papillomavirus infection ( 2 ). Although screening has reduced the incidence of cervical cancer ( 3 , 4 ), its overall five-year survival rate remains relatively low ( 5 , 6 ). Integrin alpha 2 (ITGA2), also known as cluster of differentiation (CD) 49b, is a cell surface receptor protein. It is a member of the integrin family ( 7 , 8 ). ITGA2-mediated adhesion to collagen provides the necessary traction for cells to migrate via the extracellular matrix (ECM) during wound healing ( 9 ). When ITGA2 binds to its ligands, such as collagen, it can trigger a series of intracellular signaling cascades( 10 ). In cancer, ITGA2 can play a significant role. Its overexpression may enhance tumor cell adhesion to the ECM, which can promote tumor cell invasion and metastasis ( 11 ). In addition, ITGA2 can also influence the tumor microenvironment by modulating immune cell infiltration ( 12 ). Current research indicates that high expression of ITGA2 in malignancies like gastric cancer and breast cancer is typically linked to a poor prognosis ( 13 , 14 ). The findings of the present study indicated that patients with cervical cancer exhibited higher levels of ITGA2 expression and investigated the association of ITGA2 expression with clinicopathological features and disease prognosis. Materials and methods Data sources and preprocessing . Transcriptome RNA-sequencing (seq) data for 309 endocervical adenocarcinoma (CESC) cases (3 normal samples and 306 tumor samples) along with the relevant clinical information were obtained from the The Cancer Genome Atlas (TCGA) database at level 3 using R (version 4.3.2) and the TCGA biolinks package ( 15 ). For subsequent studies, level 3 HTSeq-TPM was transformed into log2 (TPM + 1), while using HTSeq-counts for differential analysis. The data from TCGA are all from the latest version(32.0), with the most recent retrieval performed in September 2024. Gene Expression Profiling Interactive Analysis (GEPIA2) . The website GEPIA2 can be used to analyze the RNA-seq expression data from both normal and tumor samples derived from the GTEx and TCGA databases. GEPIA2 was utilized to ascertain the expression levels of the ITGA2 gene across various cancer types. Differential expressed genes (DEGs) . The expression profiling data (HTSeq-Counts) were analyzed to identify DEGs between the CESC and normal samples with the R package DESeq2. The criteria for threshold were an adjusted P 1. All CESC samples were divided into two groups based on the following median expression levels of ITGA2: The ITGA2 high-expression group and the ITGA2 low-expression group. Differentiation analysis of gene expression was carried out using package limma and DEGs were produced by comparing the ITGA2 low-expression group with the ITGA2 high-expression group ( 16 ). DEGs with a false discovery rate (FDR) 1 following log2 transformation (high-expression group/low-expression group) were deemed significant. Enrichment analysis . The tools clusterProfiler, enrichplot, and ggplot2 were used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses utilizing DEGs between the two groups. Gene Set Enrichment Analysis (GSEA) was conducted utilizing the R package clusterProfiler to identify the key functional differences between the two groups ( 17 ). Only terms with both P values and FDR q < 0.05 were deemed significantly enriched. Immune infiltration analysis . ESTIMATE is a technique that employs gene-expression profiles to deduce the relative abundance of stromal and immune cells in tumor specimens ( 18 ). The ESTIMATE algorithm can generate the three following scores: Stromal score, Immune score and ESTIMATE score. The percentage of each of the 22 immune cell types in each patient with CESC was calculated using CIBERSORT ( 19 ). Patients and tissue samples . All samples were obtained from paraffin block specimens that were surgically resected or biopsied and preserved in the Department of Pathology at the Affiliated Hospital of Qingdao University from May 2015 to May 2018. Finally, 40 patients with cervical squamous cell carcinoma (SCC), 20 patients with cervical adenocarcinoma (AC), 20 patients with high-grade squamous intraepithelial lesion (HSIL) and 20 patients with normal cervix were selected according to the screening criteria. The criteria for selecting patients were as follows: i) None of the patients had undergone chemotherapy prior to surgery. ii) No complications or secondary malignant tumors were present. iii) The patients had complete follow-up results and were followed up until August 2024. The present research study received approval from the Ethics Committee of the Affiliated Hospital of Qingdao University (approval no. QYFY WZLL 28790). Immunohistochemical (IHC) staining . The paraffin-embedded tissue samples were sectioned into 4 µm slices, mounted onto slides, and placed in a 60˚C oven for 30–60 min to ensure tight adhesion of the tissue sections to the slides. The slices were covered with the primary antibodies at a concentration of 1:500. The primary antibody against ITGA2 was rabbit anti-ITGA2 (cat. no. ab181548; Abcam). The slides should subsequently be kept at 4˚C overnight. While the samples were at room temperature, the goat anti-rabbit secondary antibody was added (diluted at a ratio of 1:500; cat. no. 511203; ZENBIO). Finally, the sections were stained with DAB chromogen, counterstained with hematoxylin, air-dried, mounted with neutral balsam and visualized under a microscope. The positive count and staining intensity of each slice were transformed into equivalent values to facilitate semi-quantitative tissue staining. The intensity of cell staining was scored as follows: 0 points for no coloration, 1 point for light yellow, 2 points for brownish-yellow and 3 points for brown colors. The score for the percentage of positive cells in the total number of cells was calculated as follows: 0 points for 75%. The sample was considered positive when the product of the two values was < 4. It is considered positive when the product of the two values is no less than 4. The IHC staining results were independently scored by two experienced pathologists. Cells and culture conditions . The human cervical cancer cell lines C33A, SiHa, CaSki and HeLa and the media were purchased from Procell Life Science & Technology Co., Ltd. Normal cervical cells H8 were purchased from the ATCC and cultured in a DMEM media supplemented with 10% FBS and 1% penicillin-streptomycin. The human cervical cancer cell lines SiHa, C33A and HeLa were cultured in a MEM media supplemented with 10% FBS, 1% penicillin-streptomycin. CaSki cells were cultured in a RPMI 1640 media supplemented with 10% FBS and 1% penicillin-streptomycin. The absence of mycoplasma infection was confirmed in the cells using a Mycoplasma Stain Assay Kit (cat. no. C0296; Beyotime Institute of Biotechnology). Reverse transcription-quantitative PCR (RT-qPCR) analysis . Total RNA was extracted from the cultured cell lines utilizing TRIzol® (Takara Bio USA, Inc.). Subsequently, the PrimeScript RT Reagent Kit (cat. no. RR037A, Takara Bio USA, Inc.) was used to synthesize cDNA according to the manufacturer’s protocol. Each component was added on ice, including RNase-free water (cat. no. 9012, Takara Bio USA, Inc.); the mixture was added into an RNase-free PCR tube to prepare the reaction. RT-qPCR was performed using TB Green Premix Ex Taq II SYBR (cat. no. RR820A, Takara Bio USA, Inc.) following the manufacturer’s protocol. The two-step PCR amplification protocol consists of an initial denaturation at 95°C for 30 seconds, followed by 40 cycles of denaturation at 95°C for 5 seconds and annealing/extension at 60°C for 30–34 seconds. The qPCR results were analyzed using the 2-ΔΔCq method. The qPCR results were analyzed using the 2 − ΔΔCq method. The subsequent primers were utilized: ITGA2-F, 5′- CACAAAGACACAGGTGGGGT-3′; ITGA2-R, 5′-TGGGATGTCTGGGATGTTGC-3′; GAPDH-F, 5′-GGAGCGAGATCCCTCCAAAAT − 3′; GAPDH-R, 5′- GGCTGTTGTCATACTTCTCATGG − 3′. Western blot analysis . Lysis buffer with protease inhibitors was used to lyse the cells. Following centrifugation at 12,000 × g for 30 min at 4˚C, the protein concentration was measured using a BCA protein kit (cat. no. P0012; Beyotime Institute of Biotechnology). The protein samples were separated using 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis and subsequently placed onto polyvinylidene difluoride (PVDF) membranes (cat. no. FFP39; Beyotime Institute of Biotechnology). Following blocking, the membranes were incubated with 5% non-fat milk at room temperature and the primary antibodies were added and left overnight at 4˚C. The primary antibodies used were the following: Rabbit anti-ITGA2 (1:5,000; cat. no. ab181548; Abcam). Following rinsing of the membrane with PBS, the PVDF membrane was incubated with a goat anti-rabbit secondary antibody (1:5,000; cat. no. 511203; ZENBIO) solution on a shaker at room temperature for 60 min. Following washing of the membrane with TBST, the bands were detected using the BioSpectrum Imaging System (UVP). Statistical analysis . R (4.3.2) and SPSS (26.0) software (IBM Corp.) were used for all statistical analyses. The χ 2 test or the continuity correction method was used to examine the association between ITGA2 expression and the clinicopathological parameters. The Wilcoxon rank-sum test was used to analyze the box plots. The correlation analysis was conducted using Spearman's coefficient. The R language was used to conduct the analysis and visualize the scatter plots of the correlation between clinicopathological characteristics and ITGA2 expression as well as between clinicopathological characteristics and survival curves. The figure panels were assembled using Adobe Illustrator (Creative Cloud 2024 version). Results Expression of ITGA2 in cancer types . The expression of ITGA2 was upregulated in numerous tumors, including cervical cancer, gastric cancer, pancreatic cancer, and esophageal squamous cell carcinoma, according to the results of the GEPIA2 analyses (Fig. 1 A). Volcano plot and heat map analyses illustrate the DEGs in CESC (Fig. 1 B and C). Among the DEGs in CESC, 2669 upregulated genes were represented by red dots, while 1688 downregulated genes were indicated by blue dots (Fig. 1 B). ITGA2 was also expressed at a considerably higher level in CESC(n = 306) than that noted in normal cervical tissue(n = 3), according to an analysis of data downloaded from the TCGA database (Fig. 1 D). Expression of ITGA2 proteins in CESC . The representative staining images of the ITGA2 proteins in each type of tissue are shown in Fig. 2 A-D. The positive expression rates of ITGA2 in SCC tissues, AC tissues and HSIL tissues were increased compared with those noted in normal cervical tissues (all P < 0.05; Fig. 2 E). The ITGA2 protein's positive expression in SCC varied significantly based on the FIGO stage (P < 0.05; Table I). Analysis of the relationship between IHC scores and clinicopathological characteristics revealed significant associations with FIGO stage and depth of myometrial invasion (Fig. 2 F-G). Association of ITGA2 expression with clinicopathological features and the prognosis of SCC . Patients with ITGA2-positive expression (n = 48) exhibited a significantly shorter median overall survival (OS) than those with negative ITGA2 expression (n = 12; 40 vs. 58 months, P < 0.05; Fig. 2 H), according to an analysis of the primary clinical data. Furthermore, the median progression-free survival (PFS) of patients with ITGA2-positive expression (n = 48) was notably lower compared with that of patients with ITGA2 negative expression (n = 12, 35 vs. 58 months, P < 0.05; Fig. 2 I). GO term and KEGG pathway enrichment analyses of DEGs . In the present study, all CESC samples (n = 306) were categorized into two groups according to the median expression of ITGA2: The high-expression group and the low-expression group. Enrichment analyses of the DEGs were conducted using the R program clusterProfiler. The changes in the cellular component (CC) of DEGs were mainly enriched in the apical part of the cell, apical plasma membrane and basal part of the cell (Fig. 3 A). According to the results of the GO analysis, the digestion, axoneme and pattern specification processes of the cell-cell adhesion were the primary areas where changes in the biological process (BP) of DEGs were enriched. The changes in the molecular function (MF) were significantly enriched in the serine-type endopeptidase inhibitor activity and in metal ion transmembrane transporter activity. The upregulated DEGs were primarily enriched in the calcium signaling pathway and neuroactive ligand-receptor interaction, according to KEGG pathway analysis (Fig. 3 B). The correlative signaling pathways of the two groups analyzed in GSEA . GSEA was implemented in the two groups, respectively. The genes of the ITGA2 high-expression group were predominantly enriched in the TGF-β signaling pathway (Fig. 3 C). The genes that were specifically upregulated in pancreatic beta cells belong to the ITGA2 low-expression group (Fig. 3 D). In addition, the gene sets enriched in the two groups within the C2 collection were defined by MSigDB. Gene set enrichment analysis revealed distinct pathway activation patterns between ITGA2 expression groups: the high-expression cohort demonstrated significant enrichment in cancer invasiveness-related pathways and TNF signaling targets (Fig. 3 E), whereas the low-expression group showed enrichment in tumor suppression pathways associated with nasopharyngeal carcinoma and gastric cancer progression (Fig. 3 F). Association between immune cell infiltration and ITGA2 expression . To gain an improved understanding of the variations in immunological function, ESTIMATE analysis was conducted. The ITGA2 high-expression group exhibited higher stromal scores in the ESTIMATE analysis (Fig. 4 A). To further ascertain the correlation between ITGA2 expression and the immune environment, the CIBERSORT algorithm was employed to assess the fraction of tumor-infiltrating cells (TICs) in cervical cancer tissues (Fig. 4 B). Six TIC types were associated with ITGA2 expression, according to the difference and correlation analyses (Fig. 4 C-E, Fig. S1 ). Four types of TICs indicated a positive correlation with ITGA2 expression, including activated mast cells, resting dendritic cells and macrophages M0 and M1; two types of TICs, namely CD8 + T cells and naive B cells, exhibited a negative correlation with ITGA2 expression. These findings provided further evidence that ITGA2 levels influenced the immune activity of the tumor microenvironment (TME). Assessment of immunotherapy sensitivity . Prospective immunomodulatory targets of therapy were used to determine the responsiveness of patients with CESC to immunotherapy. The expression levels of these immunomodulatory targets were subsequently compared between the two groups, revealing that the ITGA2 high-expression group exhibited significantly elevated expression of the majority of the targets (Fig. 5 A-C). The findings indicated that the ITGA2 high-expression group may exhibit a superior response to immunotherapy compared with the ITGA2 low-expression group. Verification of expression levels of ITGA2 in cervical cancer cells . The expression levels of ITGA2 were assessed in cervical cancer cells and normal cervical epithelial cells using RT-qPCR and western blotting analyses. The results indicated that cervical cancer cells exhibited higher expression levels of ITGA2 than those of normal cervical epithelial cells (Fig. 6 ). The expression of ITGA2 in CESC was further validated at the cellular level. Discussion Cervical cancer is one of the most prevalent and lethal malignancies affecting women. It is typically one of the primary or secondary leading causes of cancer cases and fatalities in low- and middle-income nations ( 1 ). Despite considerable advancements in the screening and treatment of cervical cancer, patient prognosis remains suboptimal ( 6 ). The pathogenesis of cervical cancer remains incompletely elucidated and limited effective therapeutic targets are currently available for the treatment of this disease. Recent reports indicate that ITGA2 is expressed in multiple tumor tissues and facilitates tumor progression, including gastric cancer ( 13 ), pancreatic cancer ( 20 , 21 ), breast cancer ( 14 ), liver cancer ( 22 ) and esophageal squamous cell carcinoma ( 23 ). However, the actual expression and clinical significance of ITGA2 in cancer, notably cervical cancer, have not been studied in depth. Compared with healthy cervical tissues, ITGA2 expression is noticeably higher in cervical cancer, according to the current analysis of the TCGA data. The GEPIA2 pan-cancer analysis further revealed that ITGA2 expression is significantly upregulated in various other malignancies, including gastric cancer, pancreatic cancer, and esophageal squamous cell carcinoma, aligning with the findings from prior studies as previously discussed. The results revealed that, in contrast to other cancers, ITGA2 expression is significantly downregulated in prostate cancer. These findings suggest that ITGA2 could function as a tumor suppressor in prostate cancer progression. Our findings align with those of Cruz S P et al., who reported that genomic deletion of ITGA1/ITGA2 leads to decreased expression and is significantly linked to the advancement of metastatic prostate cancer, indicating that ITGA1/ITGA2 status could serve as a potential prognostic marker for prostate cancer risk assessment ( 24 ). IHC staining of the cervical cancer tissue confirmed the aforementioned results and indicated that ITGA2 was predominantly localized in the cell membrane and additionally expressed in the cytoplasm. By examining the association between ITGA2 expression and various clinicopathological parameters of SCC, it was found that the positive expression rate was mainly related to the FIGO stage. It was also discovered that the OS and PFS of patients exhibiting positive ITGA2 expression were reduced. Ultimately, it was confirmed that the expression levels of ITGA2 were increased in cervical cancer cells via RT-qPCR and western blotting. GO analysis revealed that the differentially expressed genes (DEGs) are enriched in the apical part of the cell, apical plasma membrane, basal part of the cell, as well as in biological processes related to cell-cell adhesion. Studies have shown that ITGA2, expressed on the cell membrane, can promote cell-cell adhesion through its interaction with type I collagen, thereby facilitating tumor invasion and migration ( 25 ). KEGG analysis revealed that the differentially expressed genes (DEGs) were significantly enriched in the calcium signaling pathway, which aligns with previous studies demonstrating that integrins stimulate calcium signaling and that intracellular calcium plays a critical role in regulating integrin-mediated adhesion ( 26 ). By using GSEA analysis, it was found that the genes of the ITGA2 high-expression group were predominantly enriched in the TGF-β signaling pathway. TGF-β promotes tumor invasion and metastasis, especially in advanced tumors, and modulates immune cell activity to enhance tumor growth and survival ( 27 ). These results suggest that ITGA2 may promote tumor growth and that it is associated with the TME ( 28 ). Furthermore, studies have demonstrated that ITGA2 expression can suppress the activation of the TGF-β signaling pathway in pancreatic cancer. Inhibition of ITGA2 effectively enhances the anti-cancer effects of TGF-β, suggesting its potential as a therapeutic target for pancreatic cancer treatment ( 20 ). Significant enrichment of cancer invasiveness-related pathways and TNF signaling targets was observed in the high ITGA2 expression group, underscoring the pivotal role of ITGA2 in driving tumor cell migration and invasion. Furthermore, evidence suggests that TNF modulates immune adhesion of T lymphocytes by activating integrins ( 29 ). Enrichment of tumor suppression pathways related to nasopharyngeal carcinoma and gastric cancer progression was observed in the low ITGA2 expression group. These results imply that reduced ITGA2 expression may contribute to a tumor-suppressive microenvironment, which could limit the invasiveness and metastatic capacity of cervical cancer. Immune infiltration in the TME affects tumor development and prognosis ( 30 ). Therefore, the immune characteristics between the ITGA2 high-expression and the ITGA2 low-expression groups were compared. By using difference and correlation analyses, four types of TICs were identified that were positively correlated with ITGA2 expression, including activated mast cells, resting dendritic cells and macrophages M0 and M1. Dendritic cells are known to be essential antigen-presenting cells that activate T cells to improve antitumor immunity ( 31 , 32 ). M1 macrophages are pro-inflammatory cells that trigger an immune response, compromise tissue integrity and inhibit tumor progression by promoting strong T and natural killer cell anti-tumor responses ( 33 ). Our study reveals that CD8 + T cells had a negative correlation with ITGA2 expression. Studies have revealed that ITGA2 facilitates immune evasion in non-small-cell lung cancer by upregulating PD-L1 expression in exosomes, thereby suppressing CD8 + T-cell activity ( 34 ). Consequently, the ITGA2 high-expression group may exhibit enhanced immunological competence and a higher probability of benefiting from immunotherapy. Unlike traditional treatments such as surgery, chemotherapy and radiotherapy, immunotherapy works by modulating or manipulating the patient’s immune system ( 35 ). Programmed cell death 1 (PD1), programmed cell death 1 ligand 1 (PD-L1) and cytotoxic T-lymphocyte-antigen 4 (CTLA-4) are common targets of immune checkpoint inhibitors ( 36 ). The current therapeutic drugs designed to target immune checkpoints such as PD-1 and PD-L1 have been applied to the treatment of cervical cancer ( 37 ). Furthermore, a higher number of immune checkpoints have been identified recently, including CD47, T-cell immunoglobulin mucin 3 and lymphocyte activating gene 3 ( 38 ). Preclinical or clinical research is being conducted on inhibitors that target these proteins ( 39 – 41 ). It was found that certain of these targets were markedly elevated in the ITGA2 high-expression group, suggesting that the expression level of ITGA2 may affect the immune activity of TME. Studies have demonstrated that ITGA2 is crucial for driving cancer cell progression and regulating PD-L1 by activating the STAT3 pathway ( 42 ). The results imply that ITGA2 is pivotal in regulating immune responses in cancer ( 42 ). Jin L et al. also found that high expression of ITGA2 affects MET status and the expression of PD-L1, CD4, and CD8 in the immune microenvironment of pancreatic cancer patients, leading to poor prognosis ( 12 ). Therefore, targeting ITGA2 represents a promising strategy to improve the effectiveness of checkpoint immunotherapy in cancer treatment. The present study contains certain limitations. For example, it did not evaluate the specific role and potential mechanism of ITGA2 in cervical cancer cells and further verification of the correlation between ITGA2 and the TME is necessary. In the patient data collection process, the patients used were from the same hospital and the number of cases was not large enough, which may have caused admission bias and information bias. In conclusion, ITGA2 can be considered as a novel tumor biomarker, which can be utilized for evaluating the prognosis and immunotherapy of patients with cervical cancer. Abbreviations TCGA: The cancer genome atlas ITGA2: Integrin alpha 2 CESC: cervical squamous cell carcinoma and endocervical adenocarcinoma ICIs: immune checkpoint inhibitors GEPIA2: Gene Expression Profiling Interactive Analysis DEGs: Differential expressed genes GO: Gene Ontology KEGG: Kyoto Encyclopedia of Genes and Genomes SCC: cervical squamous cell carcinoma AC: adenocarcinoma HSIL: high-grade squamous intraepithelial lesion CC: cellular component BP: biological process MF: molecular function RT-qPCR: Reverse transcription quantitative polymerase chain reaction TME: tumor microenvironment PD1: Programmed cell death 1 PD-L1: programmed cell death 1 ligand 1 CTLA-4: cytotoxic T-lymphocyte-antigen 4 Declarations Acknowledgements This work was supported by the Affiliated Hospital of Qingdao University Youth Research Foundation (QDFYQN2023201) and Clinical Medicine + X Scientific Research Project of the Affiliated Hospital of Qingdao University (QDFY+X202101050). Funding This work was supported by the Affiliated Hospital of Qingdao University Youth Research Foundation (QDFYQN2023201) and Clinical Medicine + X Scientific Research Project of the Affiliated Hospital of Qingdao University (QDFY+X202101050). Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Author information Authors and Affiliations Department of Obstetrics and Gynecology, The Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, Shandong 266000, P.R. China Jingyi Han, Yunting Zhou & Na Zang Department of Anesthesiology, The Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, Shandong 266000, P.R. China Fang Yuan, Yuchao Diao, Chang Wang & Youjun Luo Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Jingyi Han, Yuchao Diao and Fang Yuan. Youjun Luo and Yunting Zhou performed the experiments and analysed the data. Na Zang and Chang Wang supervised the study. The first draft of the manuscript was written by Jingyi Han and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Corresponding authors Correspondence to Fang Yuan. Ethics declarations Conflict of interests The authors declare no competing interests. Ethical approval The studies involving human participants were reviewed and approved by the Ethics Committee of the Affiliated Hospital of Qingdao University (approval no. QYFY WZLL 28790). Consent to participate Informed consent was obtained from all individual participants included in the study. Consent to publish Not applicable. Data availability The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. 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Ferrall L, Lin KY, Roden RBS, Hung C-F and Wu T-C: Cervical Cancer Immunotherapy: Facts and Hopes. Clin Cancer Res 27: 4953–4973, 2021. Burmeister CA, Khan SF, Schäfer G, Mbatani N, Adams T, Moodley J and Prince S: Cervical cancer therapies: Current challenges and future perspectives. Tumour Virus Res 13: 200238, 2022. Li C, Cang W, Gu Y, Chen L and Xiang Y: The anti-PD-1 era of cervical cancer: achievement, opportunity, and challenge. Front Immunol 14: 1195476, 2023. Anderson AC, Joller N and Kuchroo VK: Lag-3, Tim-3, and TIGIT co-inhibitory receptors with specialized functions in immune regulation. Immunity 44: 989, 2016. Zhao L, Cheng S, Fan L, Zhang B and Xu S: TIM-3: An update on immunotherapy. Int Immunopharmacol 99: 107933, 2021. Aggarwal V, Workman CJ and Vignali DAA: LAG-3 as the third checkpoint inhibitor. Nat Immunol 24: 1415–1422, 2023. Logtenberg ME, Scheeren FA and Schumacher TN: The CD47-SIRPα immune checkpoint. Immunity 52: 742, 2020. Ren D, Zhao J, Sun Y, et al. : Overexpressed ITGA2 promotes malignant tumor aggression by up-regulating PD-L1 expression through the activation of the STAT3 signaling pathway. J Exp Clin Cancer Res : CR 38: 485, 2019. Table 1 Table I. Expression of ITGA2 in patients with SCC and different clinicopathologic features. Parameter N ITGA2+, n (%) P‑value Age (year) ≤50 13 9 (69.23) 0.276 >50 27 24 (88.89) Histological grade G1‑2 15 10 (66.67) 0.107 G3 25 23 (92.00) FIGO stage Ⅰ‑Ⅱ 14 8 (57.14) 0.008 Ⅲ‑Ⅳ 26 25 (96.15) Myometrial invasion ﹤1/2 14 9 (64.28) 0.074 ﹥1/2 26 24 (92.31) Lymphovascular invasion Yes 19 16 (84,21) ＞0.999 No 21 17 (80.95) Additional Declarations No competing interests reported. Supplementary Files tableS1.docx FigureS1.tif Figure S1 TICs with non-significant correlation with the expression of ITGA2 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6181852","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":426695707,"identity":"7783b5fa-05a4-4acf-8b6f-66302a3e2882","order_by":0,"name":"Jingyi Han","email":"","orcid":"","institution":"The Affiliated Hospital of Qingdao University, Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Jingyi","middleName":"","lastName":"Han","suffix":""},{"id":426695708,"identity":"552ac5c7-41b4-4b6a-9999-42b3abc57db2","order_by":1,"name":"Yuchao Diao","email":"","orcid":"","institution":"The Affiliated Hospital of Qingdao University, Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Yuchao","middleName":"","lastName":"Diao","suffix":""},{"id":426695709,"identity":"2d34b020-cd03-4022-87ae-4c529a951750","order_by":2,"name":"Yunting Zhou","email":"","orcid":"","institution":"The Affiliated Hospital of Qingdao University, Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Yunting","middleName":"","lastName":"Zhou","suffix":""},{"id":426695712,"identity":"8a19695b-7926-4f95-a1e8-4c8605c1dc26","order_by":3,"name":"Na Zang","email":"","orcid":"","institution":"The Affiliated Hospital of Qingdao University, Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Zang","suffix":""},{"id":426695714,"identity":"dae28dfd-7c68-4d54-948f-26c29544f5e0","order_by":4,"name":"Chang Wang","email":"","orcid":"","institution":"The Affiliated Hospital of Qingdao University, Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Chang","middleName":"","lastName":"Wang","suffix":""},{"id":426695715,"identity":"f3f64d84-e4c3-4da6-b9bb-f54a0bfd3ab9","order_by":5,"name":"Youjun Luo","email":"","orcid":"","institution":"Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Youjun","middleName":"","lastName":"Luo","suffix":""},{"id":426695716,"identity":"b4249884-0c64-4ea1-94a5-cba36453d60f","order_by":6,"name":"Fang Yuan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYLCCCiBmY29gkADSjA1EaTkDxHw8B0jVIieRQKQWgxvJzx4cqLlj1yb5eONtHgYb2Q0HmJ89wK8lzdzgwLFnyW3SacXWPAxpxhsOsJkb4NeSYCb9ge1wMpt0jpk0D8PhxA0HeNgk8GtJ/yZx4B9Qi+QZkJb/xGjJMZM42HbYjk2CB6TlAGEtkmfelEkc7DucwMaTVmw5xyDZeOZhNjO8WviOp2+TOPDtsL18++GNN95U2Mn2HW9+hleLwgEIndgAdCQYMTDjUw8E8g0Q2p4Bon4UjIJRMApGASYAANB/S9WnDVYIAAAAAElFTkSuQmCC","orcid":"","institution":"The Affiliated Hospital of Qingdao University, Qingdao University","correspondingAuthor":true,"prefix":"","firstName":"Fang","middleName":"","lastName":"Yuan","suffix":""}],"badges":[],"createdAt":"2025-03-08 04:23:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6181852/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6181852/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78522241,"identity":"ea76028d-cfef-452f-bb91-8717ad9145f8","added_by":"auto","created_at":"2025-03-14 12:22:57","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4618710,"visible":true,"origin":"","legend":"\u003cp\u003eHigh expression of ITGA2 in a variety of tumors. (A) Differential expression of the ITGA2 genes in the GEPIA2. (B) Volcano plot of DEGs in CESC. 2669 upregulated genes were represented by red dots, while 1688 downregulated genes were indicated by blue dots. (C) Heatmap of DEGs in CESC. The heatmap displays gene expression profiles, with rows representing genes and columns representing samples. Expression levels are color-coded, ranging from blue (low expression) to red (high expression). (D) Comparison of ITGA2 expression between CESC (n=306) and normal cervix tissues (n=3). *P \u0026lt; 0.05\u003c/p\u003e","description":"","filename":"Fig.1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6181852/v1/f6f9b0c1bb5819ce3206d28f.jpg"},{"id":78522244,"identity":"1a14ffb5-775e-4c57-99f4-946a72ff4eff","added_by":"auto","created_at":"2025-03-14 12:22:57","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3601086,"visible":true,"origin":"","legend":"\u003cp\u003eStaining of ITGA2 protein in different tissue. The expression of ITGA2 in SCC tissue (A), AC tissue (B), HSIL tissue (C) and normal cervix tissue (D). (E) The IHC results of the 40 patients with SCC, 20 patients with AC, 20 patients with HSIL and 20 patients with normal cervix indicated that 33 patients (82.5%) with SCC, 15 patients (75%) with AC, 13 patients with HSIL (65%) and 2 patients (10%) with normal cervix were positive for the expression of the ITGA2 protein. IHC scores were significantly associated with FIGO stage (F) and myometrial invasion depth (G). (H) and PFS (I) according to ITGA2 expression.\u003c/p\u003e","description":"","filename":"Fig.2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6181852/v1/bd17420fcccc20a202699880.jpg"},{"id":78522236,"identity":"942d022e-7434-4661-a24a-4a21aa79467b","added_by":"auto","created_at":"2025-03-14 12:22:57","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2041872,"visible":true,"origin":"","legend":"\u003cp\u003eGO, KEGG and GSEA pathway enrichment analyses were conducted for DEGs in 306 CESC patients. (A) The GO analysis of DEGs. (B) KEGG bar plot. (C) The enriched gene sets in HALLMARK collection by the high ITGA2 expression sample. Each line representing one particular gene set with unique color. Only gene sets with NOM p \u0026lt; 0.05 and FDR q \u0026lt; 0.06 were considered significant. (D) The enriched gene sets in HALLMARK by samples with low ITGA2 expression. (E) Enriched gene sets in C2 collection by samples of high ITGA2 expression. Only several leading gene sets are shown in plot. (F) Enriched gene sets in C2 by the low ITGA2 expression.\u003c/p\u003e","description":"","filename":"Fig.3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6181852/v1/74663e6c414155a11e256306.jpg"},{"id":78522247,"identity":"72d93b83-289f-4890-8c44-e29f82669d47","added_by":"auto","created_at":"2025-03-14 12:22:57","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2495437,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of immune characteristics between two groups. (A) Comparison of stromal score, immune score and ESTIMATE score. (B) TIC profile in tumor samples. (C) The ratio differentiation of 22 kinds of immune cells between CESC tumor samples with low or high ITGA2 expression relative to the median of ITGA2 expression level. (D) Venn plot displayed six kinds of TICs correlated with ITGA2 expression codetermined by difference and correlation tests displayed in box and scatter plots, respectively. (E) Scatter plot showed the correlation of 6 kinds of TICs proportion with the ITGA2 expression (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). The blue line in each plot was fitted linear model indicating the proportion tropism of the immune cell along with ITGA2 expression. *P \u0026lt; 0.05, **P \u0026lt; 0.01, and ***P \u0026lt; 0.001 were regarded as indicators of statistical significance.\u003c/p\u003e","description":"","filename":"Fig.4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6181852/v1/33c1601f6a2df60c76b52e54.jpg"},{"id":78522243,"identity":"65b5ed59-22c5-4cee-831a-01eeeaee053b","added_by":"auto","created_at":"2025-03-14 12:22:57","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":884103,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of immunomodulatory drugs’ targets between two groups. *P \u0026lt; 0.05, **P \u0026lt; 0.01, and ***P \u0026lt; 0.001 were regarded as indicators of statistical significance.\u003c/p\u003e","description":"","filename":"Fig.5.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6181852/v1/cddc1c0ee9307a1a6cbbf842.jpg"},{"id":78523052,"identity":"4a2bfe06-7d37-462d-87eb-c70dba209c0e","added_by":"auto","created_at":"2025-03-14 12:30:57","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":371106,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression of ITGA2 in cervical cancer cells and normal cervical epithelial cells. ITGA2 expression levels in cervical cancer cells and normal cervical epithelial cells at transcriptional (A) and translational (B) level. *P \u0026lt; 0.05, **P \u0026lt; 0.01, and ***P \u0026lt; 0.001 were regarded as indicators of statistical significance.\u003c/p\u003e","description":"","filename":"Fig.6.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6181852/v1/80075d9ae145e8d511c65011.jpg"},{"id":78590590,"identity":"b424202a-4f3a-43cd-9b79-27a70a15039e","added_by":"auto","created_at":"2025-03-15 22:01:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":14579098,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6181852/v1/2daac2e1-2135-42ee-8019-74ae02672ca3.pdf"},{"id":78522235,"identity":"c732258a-979d-427d-b628-4a560bab7ebc","added_by":"auto","created_at":"2025-03-14 12:22:57","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20000,"visible":true,"origin":"","legend":"","description":"","filename":"tableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6181852/v1/1e7041a1c9a19d89b448ce92.docx"},{"id":78522249,"identity":"e7adbeb0-52b6-4d6c-bdc2-e61c945467e4","added_by":"auto","created_at":"2025-03-14 12:22:57","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":4060008,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1 TICs with non-significant correlation with the expression of ITGA2\u003c/p\u003e","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-6181852/v1/4eb465e434cd152eabe5d4cc.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic significance of ITGA2 expression in cervical cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCervical cancer is among the most common gynecological malignancies (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Cervical cancer is considered to be primarily caused by the human papillomavirus infection (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Although screening has reduced the incidence of cervical cancer (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), its overall five-year survival rate remains relatively low (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Integrin alpha 2 (ITGA2), also known as cluster of differentiation (CD) 49b, is a cell surface receptor protein. It is a member of the integrin family (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). ITGA2-mediated adhesion to collagen provides the necessary traction for cells to migrate via the extracellular matrix (ECM) during wound healing (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). When ITGA2 binds to its ligands, such as collagen, it can trigger a series of intracellular signaling cascades(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In cancer, ITGA2 can play a significant role. Its overexpression may enhance tumor cell adhesion to the ECM, which can promote tumor cell invasion and metastasis (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). In addition, ITGA2 can also influence the tumor microenvironment by modulating immune cell infiltration (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Current research indicates that high expression of ITGA2 in malignancies like gastric cancer and breast cancer is typically linked to a poor prognosis (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe findings of the present study indicated that patients with cervical cancer exhibited higher levels of ITGA2 expression and investigated the association of ITGA2 expression with clinicopathological features and disease prognosis.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e \u003cem\u003eData sources and preprocessing\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eTranscriptome RNA-sequencing (seq) data for 309 endocervical adenocarcinoma (CESC) cases (3 normal samples and 306 tumor samples) along with the relevant clinical information were obtained from the The Cancer Genome Atlas (TCGA) database at level 3 using R (version 4.3.2) and the TCGA biolinks package (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). For subsequent studies, level 3 HTSeq-TPM was transformed into log2 (TPM\u0026thinsp;+\u0026thinsp;1), while using HTSeq-counts for differential analysis. The data from TCGA are all from the latest version(32.0), with the most recent retrieval performed in September 2024.\u003c/p\u003e \u003cp\u003e \u003cem\u003eGene Expression Profiling Interactive Analysis (GEPIA2)\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe website GEPIA2 can be used to analyze the RNA-seq expression data from both normal and tumor samples derived from the GTEx and TCGA databases. GEPIA2 was utilized to ascertain the expression levels of the ITGA2 gene across various cancer types.\u003c/p\u003e \u003cp\u003e \u003cem\u003eDifferential expressed genes (DEGs)\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe expression profiling data (HTSeq-Counts) were analyzed to identify DEGs between the CESC and normal samples with the R package DESeq2. The criteria for threshold were an adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and a |log2FoldChange|\u0026gt;1.\u003c/p\u003e \u003cp\u003eAll CESC samples were divided into two groups based on the following median expression levels of ITGA2: The ITGA2 high-expression group and the ITGA2 low-expression group. Differentiation analysis of gene expression was carried out using package limma and DEGs were produced by comparing the ITGA2 low-expression group with the ITGA2 high-expression group (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). DEGs with a false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and a fold change\u0026thinsp;\u0026gt;\u0026thinsp;1 following log2 transformation (high-expression group/low-expression group) were deemed significant.\u003c/p\u003e \u003cp\u003e \u003cem\u003eEnrichment analysis\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe tools clusterProfiler, enrichplot, and ggplot2 were used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses utilizing DEGs between the two groups. Gene Set Enrichment Analysis (GSEA) was conducted utilizing the R package clusterProfiler to identify the key functional differences between the two groups (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Only terms with both P values and FDR q\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were deemed significantly enriched.\u003c/p\u003e \u003cp\u003e \u003cem\u003eImmune infiltration analysis\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eESTIMATE is a technique that employs gene-expression profiles to deduce the relative abundance of stromal and immune cells in tumor specimens (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The ESTIMATE algorithm can generate the three following scores: Stromal score, Immune score and ESTIMATE score. The percentage of each of the 22 immune cell types in each patient with CESC was calculated using CIBERSORT (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003ePatients and tissue samples\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eAll samples were obtained from paraffin block specimens that were surgically resected or biopsied and preserved in the Department of Pathology at the Affiliated Hospital of Qingdao University from May 2015 to May 2018. Finally, 40 patients with cervical squamous cell carcinoma (SCC), 20 patients with cervical adenocarcinoma (AC), 20 patients with high-grade squamous intraepithelial lesion (HSIL) and 20 patients with normal cervix were selected according to the screening criteria. The criteria for selecting patients were as follows: i) None of the patients had undergone chemotherapy prior to surgery. ii) No complications or secondary malignant tumors were present. iii) The patients had complete follow-up results and were followed up until August 2024. The present research study received approval from the Ethics Committee of the Affiliated Hospital of Qingdao University (approval no. QYFY WZLL 28790).\u003c/p\u003e \u003cp\u003e \u003cem\u003eImmunohistochemical (IHC) staining\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe paraffin-embedded tissue samples were sectioned into 4 \u0026micro;m slices, mounted onto slides, and placed in a 60˚C oven for 30\u0026ndash;60 min to ensure tight adhesion of the tissue sections to the slides. The slices were covered with the primary antibodies at a concentration of 1:500. The primary antibody against ITGA2 was rabbit anti-ITGA2 (cat. no. ab181548; Abcam). The slides should subsequently be kept at 4˚C overnight. While the samples were at room temperature, the goat anti-rabbit secondary antibody was added (diluted at a ratio of 1:500; cat. no. 511203; ZENBIO). Finally, the sections were stained with DAB chromogen, counterstained with hematoxylin, air-dried, mounted with neutral balsam and visualized under a microscope.\u003c/p\u003e \u003cp\u003eThe positive count and staining intensity of each slice were transformed into equivalent values to facilitate semi-quantitative tissue staining. The intensity of cell staining was scored as follows: 0 points for no coloration, 1 point for light yellow, 2 points for brownish-yellow and 3 points for brown colors. The score for the percentage of positive cells in the total number of cells was calculated as follows: 0 points for \u0026lt;\u0026thinsp;5%, 1 point for 5\u0026ndash;25%, 2 points for 26\u0026ndash;50%, 3 points for 51\u0026ndash;75% and 4 points for \u0026gt;\u0026thinsp;75%. The sample was considered positive when the product of the two values was \u0026lt;\u0026thinsp;4. It is considered positive when the product of the two values is no less than 4. The IHC staining results were independently scored by two experienced pathologists.\u003c/p\u003e \u003cp\u003e \u003cem\u003eCells and culture conditions\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe human cervical cancer cell lines C33A, SiHa, CaSki and HeLa and the media were purchased from Procell Life Science \u0026amp; Technology Co., Ltd. Normal cervical cells H8 were purchased from the ATCC and cultured in a DMEM media supplemented with 10% FBS and 1% penicillin-streptomycin. The human cervical cancer cell lines SiHa, C33A and HeLa were cultured in a MEM media supplemented with 10% FBS, 1% penicillin-streptomycin. CaSki cells were cultured in a RPMI 1640 media supplemented with 10% FBS and 1% penicillin-streptomycin. The absence of mycoplasma infection was confirmed in the cells using a Mycoplasma Stain Assay Kit (cat. no. C0296; Beyotime Institute of Biotechnology).\u003c/p\u003e \u003cp\u003e \u003cem\u003eReverse transcription-quantitative PCR (RT-qPCR) analysis\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eTotal RNA was extracted from the cultured cell lines utilizing TRIzol\u0026reg; (Takara Bio USA, Inc.). Subsequently, the PrimeScript RT Reagent Kit (cat. no. RR037A, Takara Bio USA, Inc.) was used to synthesize cDNA according to the manufacturer\u0026rsquo;s protocol. Each component was added on ice, including RNase-free water (cat. no. 9012, Takara Bio USA, Inc.); the mixture was added into an RNase-free PCR tube to prepare the reaction. RT-qPCR was performed using TB Green Premix Ex Taq II SYBR (cat. no. RR820A, Takara Bio USA, Inc.) following the manufacturer\u0026rsquo;s protocol. The two-step PCR amplification protocol consists of an initial denaturation at 95\u0026deg;C for 30 seconds, followed by 40 cycles of denaturation at 95\u0026deg;C for 5 seconds and annealing/extension at 60\u0026deg;C for 30\u0026ndash;34 seconds. The qPCR results were analyzed using the 2-ΔΔCq method. The qPCR results were analyzed using the 2\u0026thinsp;\u0026minus;\u0026thinsp;ΔΔCq method. The subsequent primers were utilized: ITGA2-F, 5\u0026prime;- CACAAAGACACAGGTGGGGT-3\u0026prime;; ITGA2-R, 5\u0026prime;-TGGGATGTCTGGGATGTTGC-3\u0026prime;; GAPDH-F, 5\u0026prime;-GGAGCGAGATCCCTCCAAAAT \u0026minus;\u0026thinsp;3\u0026prime;; GAPDH-R, 5\u0026prime;- GGCTGTTGTCATACTTCTCATGG \u0026minus;\u0026thinsp;3\u0026prime;.\u003c/p\u003e \u003cp\u003e \u003cem\u003eWestern blot analysis\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eLysis buffer with protease inhibitors was used to lyse the cells. Following centrifugation at 12,000 \u0026times; g for 30 min at 4˚C, the protein concentration was measured using a BCA protein kit (cat. no. P0012; Beyotime Institute of Biotechnology). The protein samples were separated using 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis and subsequently placed onto polyvinylidene difluoride (PVDF) membranes (cat. no. FFP39; Beyotime Institute of Biotechnology). Following blocking, the membranes were incubated with 5% non-fat milk at room temperature and the primary antibodies were added and left overnight at 4˚C. The primary antibodies used were the following: Rabbit anti-ITGA2 (1:5,000; cat. no. ab181548; Abcam). Following rinsing of the membrane with PBS, the PVDF membrane was incubated with a goat anti-rabbit secondary antibody (1:5,000; cat. no. 511203; ZENBIO) solution on a shaker at room temperature for 60 min. Following washing of the membrane with TBST, the bands were detected using the BioSpectrum Imaging System (UVP).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e.\u003c/h2\u003e \u003cp\u003eR (4.3.2) and SPSS (26.0) software (IBM Corp.) were used for all statistical analyses. The χ\u003csup\u003e2\u003c/sup\u003e test or the continuity correction method was used to examine the association between ITGA2 expression and the clinicopathological parameters. The Wilcoxon rank-sum test was used to analyze the box plots. The correlation analysis was conducted using Spearman's coefficient. The R language was used to conduct the analysis and visualize the scatter plots of the correlation between clinicopathological characteristics and ITGA2 expression as well as between clinicopathological characteristics and survival curves. The figure panels were assembled using Adobe Illustrator (Creative Cloud 2024 version).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cem\u003eExpression of ITGA2 in cancer types\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe expression of ITGA2 was upregulated in numerous tumors, including cervical cancer, gastric cancer, pancreatic cancer, and esophageal squamous cell carcinoma, according to the results of the GEPIA2 analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Volcano plot and heat map analyses illustrate the DEGs in CESC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and C). Among the DEGs in CESC, 2669 upregulated genes were represented by red dots, while 1688 downregulated genes were indicated by blue dots (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). ITGA2 was also expressed at a considerably higher level in CESC(n\u0026thinsp;=\u0026thinsp;306) than that noted in normal cervical tissue(n\u0026thinsp;=\u0026thinsp;3), according to an analysis of data downloaded from the TCGA database (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eExpression of ITGA2 proteins in CESC\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe representative staining images of the ITGA2 proteins in each type of tissue are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-D. The positive expression rates of ITGA2 in SCC tissues, AC tissues and HSIL tissues were increased compared with those noted in normal cervical tissues (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). The ITGA2 protein's positive expression in SCC varied significantly based on the FIGO stage (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Table I). Analysis of the relationship between IHC scores and clinicopathological characteristics revealed significant associations with FIGO stage and depth of myometrial invasion (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF-G).\u003c/p\u003e \u003cp\u003e \u003cem\u003eAssociation of ITGA2 expression with clinicopathological features and the prognosis of SCC\u003c/em\u003e.\u003c/p\u003e \u003cp\u003ePatients with ITGA2-positive expression (n\u0026thinsp;=\u0026thinsp;48) exhibited a significantly shorter median overall survival (OS) than those with negative ITGA2 expression (n\u0026thinsp;=\u0026thinsp;12; 40 vs. 58 months, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH), according to an analysis of the primary clinical data. Furthermore, the median progression-free survival (PFS) of patients with ITGA2-positive expression (n\u0026thinsp;=\u0026thinsp;48) was notably lower compared with that of patients with ITGA2 negative expression (n\u0026thinsp;=\u0026thinsp;12, 35 vs. 58 months, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI).\u003c/p\u003e \u003cp\u003e \u003cem\u003eGO term and KEGG pathway enrichment analyses of DEGs\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eIn the present study, all CESC samples (n\u0026thinsp;=\u0026thinsp;306) were categorized into two groups according to the median expression of ITGA2: The high-expression group and the low-expression group. Enrichment analyses of the DEGs were conducted using the R program clusterProfiler. The changes in the cellular component (CC) of DEGs were mainly enriched in the apical part of the cell, apical plasma membrane and basal part of the cell (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). According to the results of the GO analysis, the digestion, axoneme and pattern specification processes of the cell-cell adhesion were the primary areas where changes in the biological process (BP) of DEGs were enriched. The changes in the molecular function (MF) were significantly enriched in the serine-type endopeptidase inhibitor activity and in metal ion transmembrane transporter activity. The upregulated DEGs were primarily enriched in the calcium signaling pathway and neuroactive ligand-receptor interaction, according to KEGG pathway analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eThe correlative signaling pathways of the two groups analyzed in GSEA\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eGSEA was implemented in the two groups, respectively. The genes of the ITGA2 high-expression group were predominantly enriched in the TGF-β signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The genes that were specifically upregulated in pancreatic beta cells belong to the ITGA2 low-expression group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). In addition, the gene sets enriched in the two groups within the C2 collection were defined by MSigDB. Gene set enrichment analysis revealed distinct pathway activation patterns between ITGA2 expression groups: the high-expression cohort demonstrated significant enrichment in cancer invasiveness-related pathways and TNF signaling targets (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE), whereas the low-expression group showed enrichment in tumor suppression pathways associated with nasopharyngeal carcinoma and gastric cancer progression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003cem\u003eAssociation between immune cell infiltration and ITGA2 expression\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eTo gain an improved understanding of the variations in immunological function, ESTIMATE analysis was conducted. The ITGA2 high-expression group exhibited higher stromal scores in the ESTIMATE analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). To further ascertain the correlation between ITGA2 expression and the immune environment, the CIBERSORT algorithm was employed to assess the fraction of tumor-infiltrating cells (TICs) in cervical cancer tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Six TIC types were associated with ITGA2 expression, according to the difference and correlation analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-E, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Four types of TICs indicated a positive correlation with ITGA2 expression, including activated mast cells, resting dendritic cells and macrophages M0 and M1; two types of TICs, namely CD8\u003csup\u003e+\u003c/sup\u003e T cells and naive B cells, exhibited a negative correlation with ITGA2 expression. These findings provided further evidence that ITGA2 levels influenced the immune activity of the tumor microenvironment (TME).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eAssessment of immunotherapy sensitivity\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eProspective immunomodulatory targets of therapy were used to determine the responsiveness of patients with CESC to immunotherapy. The expression levels of these immunomodulatory targets were subsequently compared between the two groups, revealing that the ITGA2 high-expression group exhibited significantly elevated expression of the majority of the targets (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-C). The findings indicated that the ITGA2 high-expression group may exhibit a superior response to immunotherapy compared with the ITGA2 low-expression group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eVerification of expression levels of ITGA2 in cervical cancer cells\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe expression levels of ITGA2 were assessed in cervical cancer cells and normal cervical epithelial cells using RT-qPCR and western blotting analyses. The results indicated that cervical cancer cells exhibited higher expression levels of ITGA2 than those of normal cervical epithelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The expression of ITGA2 in CESC was further validated at the cellular level.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCervical cancer is one of the most prevalent and lethal malignancies affecting women. It is typically one of the primary or secondary leading causes of cancer cases and fatalities in low- and middle-income nations (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Despite considerable advancements in the screening and treatment of cervical cancer, patient prognosis remains suboptimal (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The pathogenesis of cervical cancer remains incompletely elucidated and limited effective therapeutic targets are currently available for the treatment of this disease. Recent reports indicate that ITGA2 is expressed in multiple tumor tissues and facilitates tumor progression, including gastric cancer (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), pancreatic cancer (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), breast cancer (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), liver cancer (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) and esophageal squamous cell carcinoma (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). However, the actual expression and clinical significance of ITGA2 in cancer, notably cervical cancer, have not been studied in depth.\u003c/p\u003e \u003cp\u003eCompared with healthy cervical tissues, ITGA2 expression is noticeably higher in cervical cancer, according to the current analysis of the TCGA data. The GEPIA2 pan-cancer analysis further revealed that ITGA2 expression is significantly upregulated in various other malignancies, including gastric cancer, pancreatic cancer, and esophageal squamous cell carcinoma, aligning with the findings from prior studies as previously discussed. The results revealed that, in contrast to other cancers, ITGA2 expression is significantly downregulated in prostate cancer. These findings suggest that ITGA2 could function as a tumor suppressor in prostate cancer progression. Our findings align with those of Cruz S P et al., who reported that genomic deletion of ITGA1/ITGA2 leads to decreased expression and is significantly linked to the advancement of metastatic prostate cancer, indicating that ITGA1/ITGA2 status could serve as a potential prognostic marker for prostate cancer risk assessment (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). IHC staining of the cervical cancer tissue confirmed the aforementioned results and indicated that ITGA2 was predominantly localized in the cell membrane and additionally expressed in the cytoplasm. By examining the association between ITGA2 expression and various clinicopathological parameters of SCC, it was found that the positive expression rate was mainly related to the FIGO stage. It was also discovered that the OS and PFS of patients exhibiting positive ITGA2 expression were reduced. Ultimately, it was confirmed that the expression levels of ITGA2 were increased in cervical cancer cells via RT-qPCR and western blotting.\u003c/p\u003e \u003cp\u003eGO analysis revealed that the differentially expressed genes (DEGs) are enriched in the apical part of the cell, apical plasma membrane, basal part of the cell, as well as in biological processes related to cell-cell adhesion. Studies have shown that ITGA2, expressed on the cell membrane, can promote cell-cell adhesion through its interaction with type I collagen, thereby facilitating tumor invasion and migration (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). KEGG analysis revealed that the differentially expressed genes (DEGs) were significantly enriched in the calcium signaling pathway, which aligns with previous studies demonstrating that integrins stimulate calcium signaling and that intracellular calcium plays a critical role in regulating integrin-mediated adhesion (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). By using GSEA analysis, it was found that the genes of the ITGA2 high-expression group were predominantly enriched in the TGF-β signaling pathway. TGF-β promotes tumor invasion and metastasis, especially in advanced tumors, and modulates immune cell activity to enhance tumor growth and survival (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). These results suggest that ITGA2 may promote tumor growth and that it is associated with the TME (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Furthermore, studies have demonstrated that ITGA2 expression can suppress the activation of the TGF-β signaling pathway in pancreatic cancer. Inhibition of ITGA2 effectively enhances the anti-cancer effects of TGF-β, suggesting its potential as a therapeutic target for pancreatic cancer treatment (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Significant enrichment of cancer invasiveness-related pathways and TNF signaling targets was observed in the high ITGA2 expression group, underscoring the pivotal role of ITGA2 in driving tumor cell migration and invasion. Furthermore, evidence suggests that TNF modulates immune adhesion of T lymphocytes by activating integrins (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Enrichment of tumor suppression pathways related to nasopharyngeal carcinoma and gastric cancer progression was observed in the low ITGA2 expression group. These results imply that reduced ITGA2 expression may contribute to a tumor-suppressive microenvironment, which could limit the invasiveness and metastatic capacity of cervical cancer.\u003c/p\u003e \u003cp\u003eImmune infiltration in the TME affects tumor development and prognosis (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Therefore, the immune characteristics between the ITGA2 high-expression and the ITGA2 low-expression groups were compared. By using difference and correlation analyses, four types of TICs were identified that were positively correlated with ITGA2 expression, including activated mast cells, resting dendritic cells and macrophages M0 and M1. Dendritic cells are known to be essential antigen-presenting cells that activate T cells to improve antitumor immunity (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). M1 macrophages are pro-inflammatory cells that trigger an immune response, compromise tissue integrity and inhibit tumor progression by promoting strong T and natural killer cell anti-tumor responses (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Our study reveals that CD8\u0026thinsp;+\u0026thinsp;T cells had a negative correlation with ITGA2 expression. Studies have revealed that ITGA2 facilitates immune evasion in non-small-cell lung cancer by upregulating PD-L1 expression in exosomes, thereby suppressing CD8\u0026thinsp;+\u0026thinsp;T-cell activity (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Consequently, the ITGA2 high-expression group may exhibit enhanced immunological competence and a higher probability of benefiting from immunotherapy.\u003c/p\u003e \u003cp\u003eUnlike traditional treatments such as surgery, chemotherapy and radiotherapy, immunotherapy works by modulating or manipulating the patient\u0026rsquo;s immune system (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Programmed cell death 1 (PD1), programmed cell death 1 ligand 1 (PD-L1) and cytotoxic T-lymphocyte-antigen 4 (CTLA-4) are common targets of immune checkpoint inhibitors (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). The current therapeutic drugs designed to target immune checkpoints such as PD-1 and PD-L1 have been applied to the treatment of cervical cancer (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Furthermore, a higher number of immune checkpoints have been identified recently, including CD47, T-cell immunoglobulin mucin 3 and lymphocyte activating gene 3 (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Preclinical or clinical research is being conducted on inhibitors that target these proteins (\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). It was found that certain of these targets were markedly elevated in the ITGA2 high-expression group, suggesting that the expression level of ITGA2 may affect the immune activity of TME. Studies have demonstrated that ITGA2 is crucial for driving cancer cell progression and regulating PD-L1 by activating the STAT3 pathway (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). The results imply that ITGA2 is pivotal in regulating immune responses in cancer (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Jin L et al. also found that high expression of ITGA2 affects MET status and the expression of PD-L1, CD4, and CD8 in the immune microenvironment of pancreatic cancer patients, leading to poor prognosis (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Therefore, targeting ITGA2 represents a promising strategy to improve the effectiveness of checkpoint immunotherapy in cancer treatment.\u003c/p\u003e \u003cp\u003eThe present study contains certain limitations. For example, it did not evaluate the specific role and potential mechanism of ITGA2 in cervical cancer cells and further verification of the correlation between ITGA2 and the TME is necessary. In the patient data collection process, the patients used were from the same hospital and the number of cases was not large enough, which may have caused admission bias and information bias.\u003c/p\u003e \u003cp\u003eIn conclusion, ITGA2 can be considered as a novel tumor biomarker, which can be utilized for evaluating the prognosis and immunotherapy of patients with cervical cancer.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTCGA: The cancer genome atlas\u003c/p\u003e\n\u003cp\u003eITGA2: Integrin alpha 2\u003c/p\u003e\n\u003cp\u003eCESC: cervical squamous cell carcinoma and endocervical adenocarcinoma\u003c/p\u003e\n\u003cp\u003eICIs: immune checkpoint inhibitors\u003c/p\u003e\n\u003cp\u003eGEPIA2: Gene Expression Profiling Interactive Analysis\u003c/p\u003e\n\u003cp\u003eDEGs: Differential expressed genes\u003c/p\u003e\n\u003cp\u003eGO: Gene Ontology\u003c/p\u003e\n\u003cp\u003eKEGG: Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003eSCC: cervical squamous cell carcinoma\u003c/p\u003e\n\u003cp\u003eAC: adenocarcinoma\u003c/p\u003e\n\u003cp\u003eHSIL: high-grade squamous intraepithelial lesion\u003c/p\u003e\n\u003cp\u003eCC: cellular component\u003c/p\u003e\n\u003cp\u003eBP: biological process\u003c/p\u003e\n\u003cp\u003eMF: molecular function\u003c/p\u003e\n\u003cp\u003eRT-qPCR: Reverse transcription quantitative polymerase chain reaction\u003c/p\u003e\n\u003cp\u003eTME: tumor microenvironment\u003c/p\u003e\n\u003cp\u003ePD1: Programmed cell death 1\u003c/p\u003e\n\u003cp\u003ePD-L1: programmed cell death 1 ligand 1\u003c/p\u003e\n\u003cp\u003eCTLA-4: cytotoxic T-lymphocyte-antigen 4\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Affiliated Hospital of Qingdao University Youth Research Foundation (QDFYQN2023201) and Clinical Medicine + X Scientific Research Project of the Affiliated Hospital of Qingdao University (QDFY+X202101050).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Affiliated Hospital of Qingdao University Youth Research Foundation (QDFYQN2023201) and Clinical Medicine + X Scientific Research Project of the Affiliated Hospital of Qingdao University (QDFY+X202101050).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Obstetrics and Gynecology, The Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, Shandong 266000, P.R. China\u003c/p\u003e\n\u003cp\u003eJingyi Han, Yunting Zhou \u0026amp; Na Zang\u003c/p\u003e\n\u003cp\u003eDepartment of Anesthesiology, The Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, Shandong 266000, P.R. China\u003c/p\u003e\n\u003cp\u003eFang Yuan, Yuchao Diao, Chang Wang \u0026amp; Youjun Luo\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Jingyi Han, Yuchao Diao and Fang Yuan. Youjun Luo and Yunting Zhou performed the experiments and analysed the data. Na Zang and Chang Wang supervised the study. The first draft of the manuscript was written by Jingyi Han and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Fang Yuan.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving human participants were reviewed and approved by the Ethics Committee of the Affiliated Hospital of Qingdao University (approval no. QYFY WZLL 28790).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFrick C, Rumgay H, Vignat J, Ginsburg O, Nolte E, Bray F and Soerjomataram I: Quantitative estimates of preventable and treatable deaths from 36 cancers worldwide: a population-based study. 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CA Cancer J Clin 71: 209\u0026ndash;249, 2021.\u003c/li\u003e\n\u003cli\u003eArbyn M, Weiderpass E, Bruni L, de Sanjos\u0026eacute; S, Saraiya M, Ferlay J and Bray F: Estimates of incidence and mortality of cervical cancer in 2018: a worldwide analysis. Lancet, Glob Health 8: e191\u0026ndash;e203, 2019.\u003c/li\u003e\n\u003cli\u003eCampbell ID and Humphries MJ: Integrin structure, activation, and interactions. Cold Spring Harb Perspect Biol 3: a004994, 2011.\u003c/li\u003e\n\u003cli\u003eTakada Y, Ye X and Simon S: The integrins. Genome Biol 8: 215, 2007.\u003c/li\u003e\n\u003cli\u003eChastney MR, Kaivola J, Lepp\u0026auml;nen V-M and Ivaska J: The role and regulation of integrins in cell migration and invasion. Nat Rev Mol Cell Biol 26: 147\u0026ndash;167, 2025.\u003c/li\u003e\n\u003cli\u003eHynes RO: Integrins: bidirectional, allosteric signaling machines. 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Genes Dis 8: 493\u0026ndash;508, 2021.\u003c/li\u003e\n\u003cli\u003eColaprico A, Silva TC, Olsen C, \u003cem\u003eet al.\u003c/em\u003e: TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res 44: e71, 2016.\u003c/li\u003e\n\u003cli\u003eLove MI, Huber W and Anders S: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 15: 550, 2014.\u003c/li\u003e\n\u003cli\u003eYu G, Wang L-G, Han Y and He Q-Y: clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16: 284\u0026ndash;287, 2012.\u003c/li\u003e\n\u003cli\u003eYoshihara K, Shahmoradgoli M, Mart\u0026iacute;nez E, \u003cem\u003eet al.\u003c/em\u003e: Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun 4: 2612, 2013.\u003c/li\u003e\n\u003cli\u003eChen B, Khodadoust MS, Liu CL, Newman AM and Alizadeh AA: Profiling Tumor Infiltrating Immune Cells with CIBERSORT. Methods Mol Biol 1711: 243\u0026ndash;259, 2018.\u003c/li\u003e\n\u003cli\u003eCai H, Guo F, Wen S, Jin X, Wu H and Ren D: Overexpressed integrin alpha 2 inhibits the activation of the transforming growth factor \u0026beta; pathway in pancreatic cancer via the TFCP2-SMAD2 axis. J Exp Clin Cancer Res 41: 73, 2022.\u003c/li\u003e\n\u003cli\u003eMeng J, Cai H, Sun Y, Wen S, Wu H and Ren D: ITGA2 induces STING expression in pancreatic cancer by inducing DNMT1 degradation. Cell Oncol (Dordr Neth) 45: 1421\u0026ndash;1434, 2022.\u003c/li\u003e\n\u003cli\u003eZhang L, Huang Y, Ling J, Zhuo W, Yu Z, Luo Y and Zhu Y: Is Integrin Subunit Alpha 2 Expression a Prognostic Factor for Liver Carcinoma? A Validation Experiment Based on Bioinformatics Analysis. Pathol Oncol Res 25: 1545\u0026ndash;1552, 2019.\u003c/li\u003e\n\u003cli\u003eHuang W, Zhu J, Shi H, Wu Q and Zhang C: ITGA2 overexpression promotes esophageal squamous cell carcinoma aggression via FAK/AKT signaling pathway. OncoTargets Ther 14: 3583\u0026ndash;3596, 2021.\u003c/li\u003e\n\u003cli\u003eCruz SP, Zhang Q, Devarajan R, \u003cem\u003eet al.\u003c/em\u003e: Dampened regulatory circuitry of TEAD1/ITGA1/ITGA2 promotes TGF\u0026beta;1 signaling to orchestrate prostate cancer progression. Adv Sci (Weinh Baden-Wurtt Ger) 11: e2305547, 2024.\u003c/li\u003e\n\u003cli\u003eVihinen P, Riikonen T, Laine A and Heino J: Integrin alpha 2 beta 1 in tumorigenic human osteosarcoma cell lines regulates cell adhesion, migration, and invasion by interaction with type I collagen. Cell Growth Differ: Mol Biol J Am Assoc Cancer Res 7: 439\u0026ndash;447, 1996.\u003c/li\u003e\n\u003cli\u003eSjaastad MD and Nelson WJ: Integrin-mediated calcium signaling and regulation of cell adhesion by intracellular calcium. BioEssays: News Rev Mol Cell Dev Biol 19: 47\u0026ndash;55, 1997.\u003c/li\u003e\n\u003cli\u003eDeng Z, Fan T, Xiao C, Tian H, Zheng Y, Li C and He J: TGF-\u0026beta; signaling in health, disease, and therapeutics. Signal Transduct Target Ther 9: 61, 2024.\u003c/li\u003e\n\u003cli\u003eLain\u0026eacute; A, Labiad O, Hernandez-Vargas H, \u003cem\u003eet al.\u003c/em\u003e: Regulatory T cells promote cancer immune-escape through integrin \u0026alpha;v\u0026beta;8-mediated TGF-\u0026beta; activation. Nat Commun 12: 6228, 2021.\u003c/li\u003e\n\u003cli\u003eLi Q, Huth S, Adam D and Selhuber-Unkel C: Reinforcement of integrin-mediated T-lymphocyte adhesion by TNF-induced inside-out signaling. Sci Rep 6: 30452, 2016.\u003c/li\u003e\n\u003cli\u003eSchreiber RD, Old LJ and Smyth MJ: Cancer immunoediting: integrating immunity\u0026rsquo;s roles in cancer suppression and promotion. Sci (N Y NY) 331: 1565\u0026ndash;1570, 2011.\u003c/li\u003e\n\u003cli\u003eHato L, Vizcay A, Eguren I, \u003cem\u003eet al.\u003c/em\u003e: Dendritic Cells in Cancer Immunology and Immunotherapy. Cancers (Basel) 16: 981, 2024.\u003c/li\u003e\n\u003cli\u003ePalucka K and Banchereau J: Cancer immunotherapy via dendritic cells. Nat Rev Cancer 12: 265\u0026ndash;277, 2012.\u003c/li\u003e\n\u003cli\u003eLi M, Yang Y, Xiong L, Jiang P, Wang J and Li C: Metabolism, metabolites, and macrophages in cancer. J Hematol Oncol 16: 80, 2023.\u003c/li\u003e\n\u003cli\u003eJing H, Meng M, Ye M, \u003cem\u003eet al.\u003c/em\u003e: Integrin \u0026alpha;2 promotes immune escape in non-small-cell lung cancer by enhancing PD-L1 expression in exosomes to inhibit CD8 + T-cell activity. J Investig Med: Off Publ Am Fed Clin Res 72: 57\u0026ndash;66, 2024.\u003c/li\u003e\n\u003cli\u003eFerrall L, Lin KY, Roden RBS, Hung C-F and Wu T-C: Cervical Cancer Immunotherapy: Facts and Hopes. Clin Cancer Res 27: 4953\u0026ndash;4973, 2021.\u003c/li\u003e\n\u003cli\u003eBurmeister CA, Khan SF, Sch\u0026auml;fer G, Mbatani N, Adams T, Moodley J and Prince S: Cervical cancer therapies: Current challenges and future perspectives. Tumour Virus Res 13: 200238, 2022.\u003c/li\u003e\n\u003cli\u003eLi C, Cang W, Gu Y, Chen L and Xiang Y: The anti-PD-1 era of cervical cancer: achievement, opportunity, and challenge. Front Immunol 14: 1195476, 2023.\u003c/li\u003e\n\u003cli\u003eAnderson AC, Joller N and Kuchroo VK: Lag-3, Tim-3, and TIGIT co-inhibitory receptors with specialized functions in immune regulation. Immunity 44: 989, 2016.\u003c/li\u003e\n\u003cli\u003eZhao L, Cheng S, Fan L, Zhang B and Xu S: TIM-3: An update on immunotherapy. Int Immunopharmacol 99: 107933, 2021.\u003c/li\u003e\n\u003cli\u003eAggarwal V, Workman CJ and Vignali DAA: LAG-3 as the third checkpoint inhibitor. Nat Immunol 24: 1415\u0026ndash;1422, 2023.\u003c/li\u003e\n\u003cli\u003eLogtenberg ME, Scheeren FA and Schumacher TN: The CD47-SIRP\u0026alpha; immune checkpoint. Immunity 52: 742, 2020.\u003c/li\u003e\n\u003cli\u003eRen D, Zhao J, Sun Y, \u003cem\u003eet al.\u003c/em\u003e: Overexpressed ITGA2 promotes malignant tumor aggression by up-regulating PD-L1 expression through the activation of the STAT3 signaling pathway. J Exp Clin Cancer Res : CR 38: 485, 2019.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable I. Expression of ITGA2 in patients with SCC and different clinicopathologic features.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"548\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u0026nbsp; ITGA2+, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eP‑value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026le;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e9 (69.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026gt;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e24 (88.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eHistological grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eG1‑2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e10 (66.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e23 (92.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eFIGO stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eⅠ‑Ⅱ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e8 (57.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eⅢ‑Ⅳ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e25 (96.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eMyometrial invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e﹤1/2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e9 (64.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e﹥1/2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e24 (92.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eLymphovascular invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e16 (84,21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e＞0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e17 (80.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"cervical cancer, ITGA2, biomarker, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-6181852/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6181852/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntegrin alpha 2 (ITGA2) exhibits elevated expression in multiple cancer types. Nevertheless, its expression in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) and its correlation with patient prognosis remains unclear. The aim of the present study was to examine the clinical relevance of ITGA2 expression in CESC. The expression of ITGA2 in CESC was investigated using The Cancer Genome Atlas and Gene Expression Profiling Interactive Analysis 2 databases. By comparing the ITGA2 median expression, all CESC samples were split into the two following groups: The ITGA2 high-expression and the ITGA2 low-expression groups. Subsequently, in order to determine the functional distinctions between the two groups, the following databases were used: Gene set enrichment analysis, Kyoto Encyclopedia of Genes and Genomes and Gene Ontology. The expression levels of ITGA2 were examined in cervical cancer cells using real-time reverse transcription-polymerase chain reaction and western blot analyses. Immunohistochemical staining was conducted to assess the expression levels of the ITGA2 protein in CESC and to examine the association of ITGA2 expression with the clinicopathological features and disease prognosis. According to the results obtained, patients with cervical cancer exhibited higher levels of ITGA2 expression. The overall survival and progression-free survival of patients with ITGA2-positive expression were considerably lower than those of patients with ITGA2-negative expression. The ITGA2 high-expression group demonstrated increased immune infiltration and elevated expression of immune checkpoint inhibitor targets. In conclusion, the data indicated that ITGA2 could be a novel tumor biomarker, which can be utilized for evaluating the prognosis and immunotherapy of patients with cervical cancer.\u003c/p\u003e","manuscriptTitle":"Prognostic significance of ITGA2 expression in cervical cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-14 12:22:52","doi":"10.21203/rs.3.rs-6181852/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4a60d0bc-a243-4ce0-bbbb-86b0f71f1ab8","owner":[],"postedDate":"March 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-15T21:53:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-14 12:22:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6181852","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6181852","identity":"rs-6181852","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}