Pan-cancer analysis of Sp1 with a focus on immunological roles in gastric cancer

Pan-cancer analysis of Sp1 with a focus on immunological roles in gastric 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 Pan-cancer analysis of Sp1 with a focus on immunological roles in gastric cancer Yang Zhou, Zhenzhen Luo, Jinfeng Guo, Lixia Wu, Xiaoli Zhou, Junjie Huang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4623533/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Oct, 2024 Read the published version in Cancer Cell International → Version 1 posted 11 You are reading this latest preprint version Abstract Background Sp1, a transcription factor, plays a pivotal role in tumorigenesis across diverse cancers. However, its comprehensive pan-cancer analyses and immunological roles in gastric cancer (GC) remain inadequately elucidated. Methods Through a comprehensive analysis utilizing bioinformatics tools and datasets from TCGA, GEO, and THPA, we examined the multifaceted role of Sp1. Expression profiles were assessed across cell lines, tissues, and tumors, alongside exploration of genetic alterations, DNA methylation, and protein phosphorylation. Its associations with immune infiltration, tumor mutational burden, and immune checkpoint signaling were investigated. Additionally, single-cell transcriptome data showed its expression in different immune cells in GC. Validation of correlations between Sp1 and immune microenvironment in GC was performed using immunohistochemistry and multiple immunofluorescence in an immunotherapy-treated patient cohort. The prognostic value of Sp1 in GC receiving immunotherapy was evaluated with Cox regression model. Results Elevated Sp1 levels were observed in various cancers compared to normal tissues, with notable prominence in gastric cancer. High Sp1 expression correlated with advanced stage, poor prognosis, elevated tumor mutational burden (TMB), and microsatellite instability (MSI) status, particularly in GC. Sp1 levels also correlated with CD8 + T cell and M1 phenotype of tumor-associated macrophages infiltration. Furthermore, GC patients with higher Sp1 levels exhibited improved response to immunotherapy. Moreover, Sp1 emerged as a prognostic and predictive biomarker for GC patients undergoing immunotherapy. Conclusions Our pan-cancer analysis sheds light on Sp1's multifaceted role in tumorigenesis and underscores its potential as a prognostic and predictive biomarker for GC patients undergoing immunotherapy. Sp1 pan-cancer analysis prognosis immunotherapy response Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Gastric cancer (GC) is the fifth most common malignant tumor worldwide and the fourth leading cause of cancer-related deaths 1 , 2 . Despite its high incidence, most patients are diagnosed at an advanced stage due to the lack of clear clinical manifestations, which results in limited treatment options and poor prognosis 3 . At present, surgery and systemic chemotherapy remain the primary treatments for GC. For advanced GC patients, the median overall survival (OS) after chemotherapy is only 12 months 4 . Given the high incidence and poor short-term survival rate of GC, there is a pressing need to explore alternative treatment methods. Among them, anti-PD-1/anti-PD-L1 therapies have shown impressive efficacy and have significantly prolonged survival, especially in untreated patients with MSI-H or dMMR GC 5 . However, the incidence of MSI-H status in GC ranges from 8–25%, limiting its utility as a predictive biomarker for advanced GC immunotherapy 6 . Additionally, multiple clinical studies have evaluated PD-L1 expression levels, especially Combined Positive Score (CPS), as predictive biomarkers for immune checkpoint inhibitors (ICIs) response. However, reliable threshold values have yet to be established. Even with commonly used threshold of 1, 5, and 10, patients who benefit from immunotherapy are not consistently identified 7 – 9 . Therefore, there is an urgent need to identify reliable predictive biomarkers for immunotherapy to enable precise treatment stratification for GC patients. Transcription factor specificity protein 1 (Sp1) is a member of the SP/KLF family 10 , 11 . Previous studies have shown that Sp1 is overexpressed in various types of cancer and its high expression is associated with poor prognosis, including ovarian cancer 11 , glioblastoma 12 , lung cancer 13 , and breast cancer 14 . As a housekeeping gene, Sp1 can activate or inhibit the transformation of normal cells into cancer cells, thereby influencing cancer progression 15 , 16 . For example, Liu et al. demonstrated that Sp1 plays a crucial role in promoting proliferation, migration, and chemotherapy resistance in epithelial ovarian cancer 17 . In addition, studies have shown that Sp1 promotes cancer cell proliferation and inhibits apoptosis 17 . Our previous studies have also indicated that Sp1 promotes the development of GC and is a poor prognostic factor for GC 18 . However, most current research focuses on the role of Sp1 within tumor cells, with limited reports on its overall role, including its effects on the tumor microenvironment (TME). Recent studies have indicated that Sp1 is a key mediator involved in the epigenetic programming and reprogramming of HPV hosts. Inhibition of Sp1 has been shown to enhance anti PD-1 immunotherapy by reshaping the TME in cancer, suggesting that plicamycin inhibition may be a promising treatment option for HPV related cancers 19 . Despite these findings, there are no relevant reports on the immunological role of Sp1 in GC. Therefore, this study aims to evaluate the role of Sp1 in the immune microenvironment of GC and its impact on the efficacy of ICIs. In this study, we first applied online data analysis to demonstrate that Sp1 is highly expressed in various tumors and is associated with poor prognosis, consistent with previous research findings. Pathway analysis further revealed that Sp1 is involved in immune cell regulatory pathways. Single-cell data analysis showed that Sp1 is also expressed in immune cells and is particularly highly expressed in macrophages, neutrophils, and CD8 + immune cells in tumors compared to normal tissues. Importantly, gastric cancer patients with high Sp1 expression exhibit better responses to ICIs and OS benefits. Further investigation of its potential mechanisms revealed a significant positive correlation between Sp1 expression and tumor mutational burden (TMB), as well as with CD8 + T cells and M1 macrophage infiltration. This suggests that Sp1, as a pro-oncogene, promotes the reprogramming of multiple oncogenes in tumor cells, leading to the formation of numerous neoantigens and enhancing immune cell infiltration. Additionally, high Sp1 expression promotes PD-L1 expression, which contributes to immune escape but also leads to better responses to ICIs. Overall, Sp1 can serve as an effective biomarker for predicting the therapeutic efficacy of ICIs in GC. Methods Gene mapping and protein structure analysis Based on the UCSC genome browser on human Dec. 2013 (GRCh38/hg38) assembly ( http://genome.ucsc.edu/ ), the genome location information of Sp1 was obtained. We also applied the "HomoloGene" function of the NCBI (National Center for Biotechnology Information) to conduct conserved functional domain analysis of Sp1 in different species. Additionally, we obtained the phylogenetic tree of Sp1 in different species using the constraint-based multiple alignment on-line tool of the NCBI ( https://www.ncbi.nlm.nih.gov/tools/cobalt/ ). Gene expression analysis We first logged into the online HPA (Human protein atlas) database and obtained the expression data of the Sp1 in different normal tissues, cancerous tissues, and blood cells. “Low specificity” was defined by “NX (Normalized expression) ≥ 1 in at least one tissue/region/cell type but not elevated in any tissue/region/cell type”. We used TIMER2 (tumor immune estimation resource, version 2) website ( http://timer.cistrome.org/ ) to investigate the expression difference of Sp1 between cancerous and adjacent normal tissues in different tumors of the TCGA project. We also used “Box Plots” module of the GEPIA2 (Gene Expression Profiling Interactive Analysis, version 2) website ( http://gepia2.cancer-pku.cn/#analysis ) to acquire box plots of the expression difference of Sp1 between tumor tissues and the corresponding normal tissues of the GTEx (Genotype-Tissue Expression) database. In addition, the violin plots of Sp1 expression in different TNM stages of all TCGA tumors with the online tool HEPIA2. Furthermore, We explored the expression level of the total protein or phosphoprotein of Sp1 between cancerous and adjacent normal tissues via the UALCAN portal ( http://ualcan.path.uab.edu/analysis-prot.html ). The available CPTAC (Clinical proteomic tumor analysis consortium) datasets in the UALCAN portal include six tumors, namely, breast cancer (BRCA), ovarian cancer, colon cancer, renal cell carcinoma (RCC), Uterine corpus endometrial carcinoma (UCEC), and Lung adenocarcinoma (LUAD). Patients and specimens From January 2018 to December 2022, 26 patients undergoing gastrectomy for gastric cancer and 27 advanced GC patients receiving ICIs and chemotherapy in Changzhou No.2 People hospital. Cancerous and adjacent normal tissue was collected during surgery or puncturation, and histopathologically confirmed and staged according to the Union for International Cancer Control. Patients’ written informed consents and approval from the Ethics Committees of Changzhou No.2 People’s Hospital were obtained for the use of these clinical materials. Immunohistochemisty (IHC) Tissue sections were incubated in an oven at 55°C for 20 min followed by three 5-min washes with xylene for dewaxing then rehydrated by 5-min washes in 100%, 95%, and 80% ethanol and distilled water. Samples were heated at 95°C for 30 min in 10 mmol/L sodium citrate (pH 6.0) for antigen retrieval. Endogenous peroxidase activity was blocked by incubation in 3% H2O2 for 30 min. After 30 min blocking with the universal blocking serum (Dako Diagnostics, Carpinteria, CA), the sections were incubated with anti-Sp1 antibody at 4°C overnight and washed 3 times with PBS at room temperature. Then a secondary antibody was added for 30 min incubation (Dako Diagnostics). The samples were washed 3 times with PBS and developed using DAB followed by counterstaining with hematoxylin. Dehydration was performed following a standard procedure and the slides were sealed with cover slips. Images were scanned with a digital pathology slide scanner (KF-BIO, CHINA). Sp1 immunostaining signals were evaluated by two researchers, with the clinical information blinded to them, and scored. Brown cytoplasmic staining for Sp1 was considered positive. The percentage of Sp1-positive cells was scored with the following four categories: 1 ( 75%). The staining intensity of positive cells was scored as 0 (absent), 1 (weak infiltration), 2 (moderate infiltration), and 3 (strong infiltration). The final score was the sum of the intensity and the percentage. Survival analysis The “survival map” module of GEPIA2 was used to conduct the survival analysis of Sp1 across all TCGA tumors. Cutoff-high (50%) and cutoff-low (50%) values were used as the expression thresholds for splitting the high-expression and low-expression groups of OS (Overall survival) and DFS (Disease-free survival). Genetic alteration analysis We investigate the genetic alteration characteristics of Sp1 with the cbioportal website ( https://www.cbioportal.org/ ). The results of the alteration frequency, mutation type and Copy number alteration (CNA) were obtained in the “Cancer Types Summary” module. We also used the “Comparison” module to obtain the data of OS, progression-free survival (PFS), and DFS differences in the TCGA cancer cases with or without Sp1 genetic alteration. Analysis of tumor behavior states, immune infiltrates, and immune biomarkers The online tool Sangerbox ( http://sangerbox.com/index.html ) was used to investigate the correlations between TMB, MSI and Sp1 in all types of cancers in TCGA. The correlations between the Sp1 expression and a variety of genes involved in immune checkpoint signaling, such as CTLA4 were also evaluated with Sangerbox. Spearman’s correlation was performed and the P- value and partial correlation (cor) value were obtained. We used the TIMER2 online tool to explore the correlations between Sp1 expression and several types of immune cells, which includes B cells, CD4 + T cells, CD8 + T cells, dendritic cells, macrophages, and neutrophils in all types of tumors. The TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, XCELL, MCPCOUNTER and EPIC algorithms were applied for immune infiltration estimations, especially for CD8 + T cells. The P - values and correlation values were obtained via the purity-adjusted Spearman’s rank correlation test. The data were visualized as a heatmap and a scatter plot. DNA methylation analysis We also used the SangerBox tool to investigate the correlations between the Sp1 expression and four classical DNA methyltransferase including DNMT1, DNMT2, DNMT3A, and DNMT3B in all types of cancer. The MEXPRESS web ( https://mexpress.ugent.be/ ) was used to analyze the DNA methylation level of Sp1 of multiple probes in different cancers of TCGA database. The beta value of methylation, the Benjamini-Hochberg-adjusted P-value and Pearson correlation coefficient value of each sample were obtained. The promoter region probes were highlighted. Phosphorylation analysis We used iPTMnet database ( http://proteininformationresource.org/iPTMnet ) to analyze the predicted phosphorylation features of the S7, T42, S59, S101, T278, T453, S641, T668, S698, and S702 locus of Sp1. We also investigate the differences in phosphorylation levels of Sp1 between normal tissues and primary tumors, including BRCA, ovarian cancer, colon cancer, RCC, and UCEC, using the CPTAC analysis. Multiplex Immunofluorescence (mIF) Multiplex staining of was performed using TSA 6-color kit (H-D110061,yuanxibio). Primary antibodies panel included anti-CD8 (#BX50036, Biolynx), anti-CD68 (#BX50031-C3, Biolynx), anti-HLA-DR (#ab92511, Abcam), anti-PanCK (#GM351507, Gene Tech). Primary antibodies were sequentially applied, followed by horseradish peroxidase-conjugated secondary antibody incubation(Cat# DS9800, Lecia Biosystems), and tyramide signal amplification. The slides were washed with TBST buffer and heat-treated by microwave after each TSA operation. Nuclei were stained with DAPI (D1306, ThermoFisher) after all the antigens above being labeled, then washed in distilled water, and manually coverslipped. The stained slides were scanned to obtain multispectral images using the Pannoramic MIDI imaging system (3D HISTECH). Images was analyzed using Indica Halo software. Enrichment analysis of Sp1-related genes The STRING online tool ( https://string-db.org/ ) was applied to investigate the top 50 experimentally determined Sp1-binding proteins. The main parameters were set as follows: minimum required interaction score [“Low confidence (0.150)”], meaning of network edges (“evidence”), max number of interactors to show (“no more than 50 interactors” in 1st shell) and active interaction sources (“experiments”). The GEPIA2 was used to determine the top 100 Sp1-correlated genes based on the TCGA datasets. Furthermore, we used the “Gene_Corr” module of TIMER2 to supply the heatmap data of the selected genes, which contains the correlation and P -value in the Spearman’s rank correlation test. The log2 TPM was applied for the dot plot. The P -value and the correlation coefficient (R) were indicated. Venny 2.1.0 ( https://bioinfogp.cnb.csic.es/tools/venny/index.html ) was used to conduct an intersection analysis to compare the Sp1-binding and interacted genes. Then, these two sets of genes were combined and submitted to DAVID for additional functional annotation, such as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). In this work, we mainly focused on three aspects of GO analysis: biological processes (BP), cellular components (CC), and molecular functions (MF). In addition, we used KEGG analysis to investigate the pathways in which the Sp1-binding and interacted genes were involved. Gene Set Enrichment Analysis (GSEA) GSEA was used to explore the up-downregulations among different pathways associated with Sp1 in STAD. The functional gene set was set to c2.cp.kegg.v7.4.symbols.gmt, the analysis parameters were "No collapse", the number of permutations was set to "1000", the permutation type was set to "Phenotype", and the above files were analyzed by GSEA software (version 3.0). In this study, GESA was used to explore Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways significantly associated with high and low Sp1 expression, and mapped the top five pathways. P-value < 0.05 and FDR < 0.25 were considered statistically significant. The scRNA-seq data analysis The GC scRNA-seq data (GSE163558) were obtained from the GEO database ( https://www.ncbi.nlm.nih.gov/geo/ ), which included 10 fresh human tissue samples of six patients, including primary tumors, adjacent non-tumoral samples, and six metastases from various organs or tissues (liver, peritoneum, ovary, lymph node). Data filtering and preprocessing were conducted using the R package “Seurat”. The initial screening criteria included: genes expressed in at least three cells; each cell expresses at least 250 genes; the percentage feature set function was used to calculate the percentage of mitochondria and rRNA, ensuring that each cell expresses more than 200 genes and less than 5000 genes; mitochondrial genes comprising less than 15% of the total genome. Following data filtering, samples were merged for further analysis. To address batch effects and integrate different single-cell transcriptome samples, the FindIntegrationAnchors and IntegrateData functions in the Seurat package were employed, identifying 4000 highly variable genes with the FindVariableFeatures function.Then, principal component analysis (PCA) was performed using the RunPCA function. Cell clustering was carried out with the FindNeighbors and FindClusters functions (resolution = 0.1, dim = 50). Dimensionality reduction was achieved using the UMAP method. Marker genes for each cluster were identified using the FindAllMarkers function (logFC = 0.75, min.pct = 0.25, p-adj < 0.05). Results Gene expression analysis We tried to investigate the oncogenic role of Sp1 (Supplementary Fig. 1A). The structure of Sp1 demonstrates relative conservation across various species, characterized by the Zinc-finger double domain (Supplementary Fig. 1B). To elucidate the evolutionary relationships of Sp1 across diverse species, we presented a phylogenetic tree in Supplementary Fig. 1C. We conducted a comprehensive analysis of Sp1 expression patterns across various cell lines, normal tissues, and blood cells. As depicted in Supplementary Fig. 2A, Sp1 shows the highest expression in early spermatids, closely followed by urothelial cells. Utilizing integrated data from the Human Protein Atlas (HPA), Genotype-Tissue Expression (GTEx), and Function Annotation of the Mammalian Genome 5 (FANTOM5) datasets, we observed that the expression of Sp1 is most prominent in the esophagus among all normal tissues, as illustrated in Supplementary Fig. 2B. Additionally, Sp1 demonstrated its highest expression in neutrophils compared to other types of blood cells (Supplementary Fig. 2C). We further utilized TIMER2 to examine the expression levels of Sp1 in different types of cancers within the TCGA database. Notably, the expression levels of Sp1 in BRCA, cholangiocarcinoma (CHOL), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), kidney chromophobe (KICH), kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), stomach adenocarcinoma (STAD), thyroid carcinoma (THCA), and UCEC exhibited significant differences compared to the corresponding normal tissues (P < 0.001, Fig. 1 A). Additionally, we observed significant differences in the expression levels of Sp1 between cancerous tissues and adjacent normal tissues across various cancer types, including CHOL, GBM, brain lower grade glioma (LGG), pancreatic adenocarcinoma (PAAD), STAD, and uterine carcinosarcoma (UCS), when normal tissues from the GTEx dataset were included as controls (P < 0.01, Fig. 1 B). The analysis of the CPTAC dataset revealed elevated protein levels of Sp1 in BRCA, colon cancer, and LUAD compared to normal tissues (P < 0.001, Fig. 1 C). Conversely, the total protein expression of Sp1 was found to be higher in normal tissues than in primary clear cell renal cell carcinoma (P = 0.009) and UCEC (P = 0.007). In addition, no significant difference in Sp1 protein levels was observed between ovarian cancer and normal tissues (P = 0.055). Using the online tool GEPIA2, we identified a notable correlation between the expression levels of Sp1 and the pathological stages of certain cancers, specifically KIRC and LIHC (P < 0.05, Fig. 1 D). However, such correlation was not evident in other cancer types. Moreover, we observed a potential association between the expression of Sp1 and the pathological stages of LUSC (P = 0.0829) and SKCM (P = 0.054). Through the examination of cancerous and adjacent normal tissues from 26 patients enrolled in our hospital, we validated a significant increase in the protein levels of Sp1 in GC compared to adjacent normal tissues (P = 0.006, Fig. 1 E). Survival analysis We investigated the correlation between Sp1 expression and survival outcomes using the online tool GEPIA2. As illustrated in Supplementary Fig. 3A, the median OS of patients with low Sp1 expression was significantly longer compared to those of patients with high Sp1 expression in LGG, LIHC, PAAD, and THCA (P < 0.05). In contrast, patients with low Sp1 expression in KIRC exhibited a shorter median OS than those with high Sp1 expression (P < 0.001). Moreover, a high Sp1 expression in patients with LGG, ACC, BLCA, and LIHC was correlated with poor disease-free survival (DFS) (Supplementary Fig. 3B). Conversely, a low Sp1 expression in patients with KIRC was correlated with poor DFS (P < 0.001). However, no significant correlations were observed between OS, DFS, and Sp1 expression in other types of cancers. Genetic alteration analysis We investigate the genetic alteration of Sp1 in different types of cancers using the TCGA database (Supplementary Fig. 4). The highest alteration frequency of Sp1, at 10.53%, was observed in patients with uterine carcinosarcoma, predominantly characterized by amplification (Supplementary Fig. 4A). All patients with skin cutaneous melanoma (SCM) and liver hepatocellular carcinoma with genetic alteration (> 1% frequency) exhibited mutations in Sp1. In addition, the “structural variant” type of Sp1 was the predominant type in uterine carcinosarcoma, accounting for a genetic alteration frequency of 3.51%. The sites and types of Sp1 genetic alterations, along with post-translational modifications, are demonstrated in Supplementary Fig. 4B. We found the missense mutation and truncating mutation were the primary genetic alteration types. Given that uterine carcinosarcoma displayed the highest alteration frequency of Sp1, we further investigated the correlations between the alteration status of Sp1 and clinical survival prognosis. Intriguingly, we found significant correlations between the alteration status of Sp1 and PFS (P = 0.0294). However, no significant correlations were observed between the alteration status of Sp1 and OS, DFS, and disease-specific survival (Supplementary Fig. 4C, P > 0.05). DNA methylation analysis We used the SangerBox approach to investigate the correlation between the Sp1 expression and four classical DNA methyltransferase including DNMT1, DNMT2, DNMT3A, and DNMT3B (Supplementary Fig. 5). As shown in Supplementary Fig. 5, Sp1 expression exhibited a significant correlation with the expression levels of DNMT1, DNMT2, DNMT3A, and DNMT3B expression in THCA, UVM, DLBC, LGG, and LIHC (all R > 0.3, P < 0.001). We further investigate the association between Sp1 DNA methylation and the pathogenesis of various types of cancers using the TCGA database. In the patients with GC, we found a significant negative correlation between Sp1 DNA methylation and gene expression at multiple probes, including those in non-promoter regions and promoter regions, such as cg14794577 (R=-0.339, P < 0.001, Supplementary Fig. 6). Protein phosphorylation analysis Supplementary Fig. 7A annotates several important Sp1 phosphorylation sites. We used iPTMnet database to analyze the phosphorylation sites of Sp1 and identified S7, T42, S59, S101, T278, T453, S641, T668, S698, and S702 as loci supported by the highest confidence data. Furthermore, we investigated differences in Sp1 phosphorylation levels between normal tissues and primary tumors, specifically in BRCA, ovarian cancer, colon cancer, clear cell renal cell carcinoma, and UCEC, using CPTAC analysis. Notably, the T42 locus within Sp1 exhibited significantly higher phosphorylation levels in the primary tumor of colon cancer compared to normal tissues (P < 0.001). Conversely, higher phosphorylation levels of the T42 locus were observed in normal tissues compared to the primary tumors of BRCA (P = 0.045), ovarian cancer (P = 0.003), and UCEC (P = 0.019). However, the T42 locus showed no significant difference in phosphorylation levels between normal tissues and primary clear cell renal cell carcinoma (P = 0.134) (Supplementary Fig. 7B). Enrichment analysis of Sp1-related genes To investigate the molecular mechanisms of the Sp1 in tumorigenesis, we tried to identify Sp1-binding proteins and genes correlated with Sp1 expression for pathway enrichment analyses. Utilizing the STRING tool, we identified a total of 50 Sp1-binding proteins, with their interaction network illustrated in Fig. 2 A. The GEPIA2 tool was used to analyze TCGA tumor expression data, resulting in the identification of the top 100 genes correlated with Sp1 expression. Among these genes, Sp1 expression showed significantly positive correlations with ASXL2 (R = 0.78), ATF7 (R = 0.75), BAZ2A (R = 0.75), MAP3K2 (R = 0.75) and PKN2 (R = 0.75) genes (all P < 0.001). The corresponding heatmap data also depicted a positive correlation between Sp1 and these five genes across various cancer types (Fig. 2 B). An intersection analysis of the two groups revealed two common members, namely, CRREBP and EP300 (Fig. 2 C). We integrated the two datasets to conduct KEGG and GO enrichment analyses. The KEGG data, depicted in Fig. 2 D, suggested that pathways such as “viral carcinogenesis” and “pathways in cancer” might contribute to Sp1's impact on tumor pathogenesis. Furthermore, the GO enrichment analysis data revealed that a majority of these genes are associated with pathways or cellular processes related to transcription, including DNA binding, protein binding, transcription factor binding, chromatin binding, histone deacetylase binding, and others (Supplementary Fig. 8). Using the TCGA pan-cancer dataset, we conducted Gene Set Enrichment Analysis (GSEA) which showed that the T cell receptor signaling pathway, chronic myeloid leukemia, and small cell lung cancer (SCLC) were predominantly enriched in the Sp1 high-expressed group. Conversely, glycerolipid metabolism and olfactory transduction were primarily enriched in the Sp1 low-expressed group (Fig. 2 E). Pathway analysis has shown that Sp1 is involved in immune cell regulatory pathways, and substantial evidence indicates that the host immune system plays a crucial role in both inhibiting and promoting tumor growth and metastasis. Understanding Sp1's impact on the immune microenvironment is therefore essential for developing more effective cancer treatments. Using the TIMER database, we investigated the potential correlations between Sp1 expression and several types of immune cells, including B cells, CD4+ T cells, CD8+ T cells, dendritic cells, macrophages, and neutrophils. The analysis demonstrated that the most significant correlations between Sp1 expression and these immune cells in patients with COAD, HNSC, and KIRC (P < 0.005 for all) (Supplementary Fig. 9). Previous studies demonstrated that Sp1 plays a crucial role in regulating the expression and function of various immune cells, including CD8 + T cells, macrophages, and etc. To assess the correlations between CD8 + T cells, M1 phenotype of TAMs, M2 phenotype of TAMs, and Sp1 expression, we used the EPIC, MCPCOUNTER, and TIMER algorithms (Supplementary Fig. 10A). As illustrated in Supplementary Fig. 8B-E, there were significantly positive correlations between Sp1 expression and CD8 + T cells in patients with STAD, BRCA-Basal, SKCM, and PRAD using all three algorithms (P < 0.05 for all). We also found a positive correlations between Sp1 expression and M1 phenotype of TAMs in patients with STAD with all three algorithms. Expression level of Sp1 in single cell transcriptome data Through single-cell RNA sequencing (scRNA-seq) data analysis of gastric cancer from GSE163558, a total of 42,968 cells were included. To investigate the expression of Sp1 in different immune cells, we have further subdivided the composition of immune cells into eight subclusters (Fig. 3 A). The data revealed that Sp1 expression was mainly observed in the clusters of macrophages and T cells, confirming a strong correlation between Sp1 and immune cells in STAD. Specifically, Sp1 was expressed in both M1 and M2 phenotypes of TAMs (Fig. 3 B). Furthermore, compared to normal tissue, the expression levels of Sp1 were higher in macrophage cells and monocyte cells within tumor tissue, while lower in T cells, but with no significant difference in B cells, Neutrophil cells, NK cells and pDC cells (Fig. 3 C). Value of Sp1 expression for predicting immunotherapy efficacy We further validate the correlations between the CD8+ T cells, M1 phenotype of TAMs, M2 phenotype of TAMs, and Sp1 expression in advanced GC patients treated with ICIs and chemotherapy. Figure 6 depicted the multiple immunofluorescence (mIHC) images of CD8+ T cells, M1 phenotype of TAM (CD68 + HLADR+), and M2 phenotype of TAM (CD68 + HLADR-) (Fig. 4 A). As shown in Fig. 4 B, Sp1 expression was positively correlated with CD8 + T cells (r = 0.409, P = 0.047) and M1 phenotype of TAM (r = 0.432, P = 0.035) in GC. However, no significant correlation was found between Sp1 expression and M2 phenotype of TAM (P = 0.350). The immunochemistry images depicting different expression levels (low and high) of Sp1 in GC were presented in Fig. 4 C. We also found that the patients achieving partial response (PR) exhibited higher levels of Sp1 in cancerous tissues compared to those achieving stable disease (SD) or progressive disease (PD) (Fig. 4 D). Furthermore, Kaplan-meier analysis showed that patients with high expression of Sp1 had better OS than those with low expression of Sp1 (17.3 vs. 7.8 months, P = 0.004, Fig. 4 E). In the univariate analysis, factors including ECOG PS at ICI initiation, CD8 + T cells, M1 phenotype of TAM, M2 phenotype of TAM, and Sp1 expression were identified as potential prognostic factors in patients with STAD treated by ICIs (Table 1 ). Notably, ECOG PS at ICI initiation (P = 0.034), CD8 + T cells (P = 0.033), M2 phenotype of TAM (P = 0.015), and Sp1 (P = 0.002) showed independent prognostic value in the multivariate Cox regression model (Table 1 ). Table 1 Univariate and multivariate analysis of prognostic factors in patients with GC treated by ICIs Univariate analysis Multivariate analysis Characteristics HR 95%CI P-value HR 95%CI P-value Age > 65 1.118 0.392–3.190 0.835 1% 0.348 0.118–1.022 0.055 < 1% Reference Antibiotic use Yes 3.346 0.908–12.327 0.069 No Reference Corticosteroids use Yes 1.672 0.590–4.739 0.334 No Reference CD8 + T cells (%) 0.552 0.374–0.817 0.003 0.523 0.289–0.948 0.033 M1 phenotype of TAMs 0.832 0.710–0.975 0.023 1.026 0.789–1.334 0.850 M2 phenotype of TAMs 1.112 1.005–1.231 0.041 1.200 1.036–1.389 0.015 CEA (ng/ml) 1.005 1.000-1.010 0.051 Sp1 High expression 0.180 0.049–0.664 0.010 0.069 0.013–0.385 0.002 Low expression Reference Reference The immunological role of Sp1 In addition, we investigated the potential correlation between Sp1 expression and TMB as well as MSI across all types of cancers in the TCGA database (Fig. 5 ). Figure 5 A showed a positive correlation between Sp1 expression and TMB in patients with GC (P = 0.0012), COAD (P = 0.006), LGG (P = 0.013), PAAD (P = 0.036), and THYM (P = 0.0039). Conversely, we found a negative correlation between Sp1 expression and TMB in patients with BRCA (P = 2.9e-11), and THCA (P = 1e-06). Furthermore, Sp1 expression was positively correlated with MSI of COAD (P = 1.5e-07), LUSC (P = 6.1e-05), READ (P = 0.0013), and UCEC (P = 0.001). Conversely, a negative correlation was observed between Sp1 expression and MSI in patients with BRCA (P = 2.8e-06), DLBC (P = 4.8e-07), HNSC (P = 4.9e-09), LGG (P = 0.033), PRAD (P = 0.00017), SKCM (P = 7.4e-07), and THCA (P = 0.0029) (Fig. 5 B). The ICIs play a pivotal role in the treatment of cancers. We also investigated the correlations between Sp1 expression and a variety of genes involved in immune checkpoint signaling, such as CTLA4 (Fig. 5 C). Sp1 expression demonstrated significant correlations with CD200, NRP1, CD200R1, CD276, CD160, and TNFSF15 in most types of cancers (P < 0.01). Additionally, we found that Sp1 expression was significantly correlated with most of these genes in patients with GC. Discussion Despite an extensive literature search, we did not find any comprehensive pan-cancer analysis studies of Sp1 that utilized multiple databases. Our study is the first to provide an extensive analysis of the genetic characteristics and predictive value of Sp1 across a spectrum of cancers, with a particular focus on its role in the immunotherapy of GC. We utilized genomics, single-cell omics, and data from patient in our hospital. Initially, we identified conserved sequences of Sp1 across various species, suggesting that despite continuous evolutionary processes, Sp1 remains a crucial factor necessary for fundamental cellular functions, stability, or proliferation. This conservation highlights the importance of Sp1 in maintaining essential biological processes that are invariant across different organisms 20 , 21 . Our analysis revealed that the transcription levels of Sp1 were significantly elevated in the majority of the cancer types compared to normal tissues, particularly in the digestive system, including CHOL, ESCA, LIHC, PAAD, and STAD. These findings align with previous research, including our own studies in PAAD and LIHC 20 , 22 – 27 . Interestingly, we observed lower Sp1 expression in certain cancers, particularly those associated with hormones, such as BRCA, PRAD, THCA and UCEC. While Sp1 has been reported to regulate several hormone receptors, which may influence treatment outcomes with endocrine therapies, the specific mechanisms remain unclear and warrant further investigation 28 – 32 . At the protein level, most alterations aligned with the mRNA trends, as verified by our GC cohort through IHC. However, the expression pattern of Sp1 in BRCA contradicted the mRNA findings, which may be attributable to post-transcriptional regulation, the limited sample size of CPTAC data (only 18 controls), or association with undifferentiated subtypes. Additionally, we observed significant associations between Sp1 expression and clinical stages in several cancer types. Collectively, these findings suggest that Sp1 may play diverse roles in different stages of tumorigenesis and progression across various anatomical sites. Kaplan-Meier analysis corroborated that elevated Sp1 expression corresponded to poorer OS in LGG, LIHC, PAAD, and THCA, and was associated with adverse DFS in LGG and ACC. Conversely, in KIRC, higher Sp1 expression correlated with better OS and DFS. These prognostic indicator values were supported by both clinical and experimental verification 24–26,32−36 . It was worth noting that in THCA, the relationship between Sp1 mRNA expression and OS appeared inconsistent compared to other cancers. Given that Sp1 has been implicated in modulating both tumor suppressor genes and oncogenes in THCA 34 , 37 and displays distinctive expression patterns 38 – 40 , we hypothesize that its mechanisms are complex and require more in-depth research. Gene mutations significantly influence the occurrence, progression, and therapeutic response of tumors. Our findings indicate that Sp1 mutations are most prevalent in UCS and may serve as a protective biomarker for patients with this type of cancer. TMB and MSI reflect the frequency of mutations within the tumor genome and are indicative of the efficacy of ICIs across various tumors. Generally, tumors characterized by high TMB levels and MSI status exhibit a favorable response to immunotherapy 41 – 43 . Our results showed that upregulated Sp1 was strongly associated with TMB and MSI across multiple cancer types. Specifically, Sp1 level was positively correlated with both TMB and MSI in STAD and COAD. DNA methylation is a chemical modification process that involves the transfer of active methyl groups to specific bases in the DNA chain, catalyzed by DNA methylation transferases (DNMTs) 44 – 47 . Typically, cancer cells exhibit a global loss of genetic modifications alongside abnormal methylation at enhancer and promoter regions. These alterations in methylation distribution lead to the inhibition of tumor suppressor gene expression and an increase in proto-oncogene expression, thereby further promoting tumorigenesis. In our study, we utilized the SangerBox approach to investigate the association between Sp1 expression and four classical DNA methyltransferases (DNMT1, DNMT2, DNMT3A, and DNMT3B) across different tumors. Our findings revealed significant correlations between Sp1 expression and DNMT1, DNMT2, DNMT3A, and DNMT3B expression levels in THCA, UVM, DLBC, LGG, and LIHC compared with normal tissues. Interestingly, in patients with GC, we observed a significant negative association between Sp1 DNA methylation status both at non-promoter regions and multiple probes within promoter regions with gene expression levels. These results suggest that Sp1 may promote tumorigenesis through its involvement in DNA methylation processes. KEGG and GO enrichment indicated that the top gene sets most closely associated with Sp1 are significantly related to cancer-associated pathways, particularly those involved in viral carcinogenesis. It is well known that the onset of many cancers can be caused by viral infections, such as HPV leading to cervical cancer, HBV leading to liver cancer, and EBV leading to nasopharyngeal carcinoma, lymphoma, and GC 48 – 52 . Combined with other research findings, we are confident that Sp1 plays a key role in cancers caused by viral infections warranting further attention 19 , 53 – 55 . Through the screening and integration of Sp1-interacting proteins and the most relevant genes, we identified two intersecting genes, CREBBP and EP300. CREBBP and EP300 are well-known homologous lysine acetyltransferases frequently mutated in hematological malignancies and have become promising drug targets 56 , 57 . However, there is a lack of studies focusing on the mechanism between Sp1 and CREBBP/EP300 in different cancers. Through GSEA analysis focused on GC, we selected the TOP five pathways. We observed that differential genes in the high Sp1 expression subgroup upregulated the T cell receptor signaling pathway. It is well known that malignant tumor cells establish a complex TME conducive to their growth and proliferation. The TME encompasses not only tumor cells but also the surrounding stromal cells, immune cells, inflammatory cells, secretory factors, and microvessels. Among these components, immune cells such as CD8+ T cells and macrophages play a crucial role in supervising and eliminating tumor cells while regulating their growth and dissemination 58 . "Cold" tumors, which lack T-cell infiltration, exhibit poor responsiveness to immunotherapy compared to "hot" tumors, characterized by abundant T-cell infiltration and favorable responses to immunotherapy 59 , 60 . Subsequent analyses of immune infiltration uncovered significant correlations between Sp1 expression and various immune components in certain types of cancer (Fig. 12). Our previous studies, along with those of other researchers, have shown that a higher proportion of CD8+ T cells infiltration is associated with better immune therapeutic outcomes 61 – 64 . We assessed the correlation between Sp1 expression and the proportion of CD8+ T cells using different algorithms, finding a positive correlation in BRCA-Basal, PRAD, SKCM, and STAD based on all three algorithms. This result was confirmed in our own STAD cohort. Further scRNA data supported that immune components were the main altered cluster between tumor and normal tissues. Meanwhile, Sp1 expression was higher in macrophages and monocytes but lower in T cells within tumors. High expression of Sp1 in tumor cells has been associated with macrophage infiltration, correlating with poorer prognosis in colorectal cancer 65 . Macrophage Sp1 expression deficiency promoted the transition from M2 to M1 phenotype, inducing apoptosis in lung cancer. Conversely, HDAC2 can deacetylate Sp1, thereby facilitating the transition of macrophages from M1 to M2 phenotype, which promotes lung cancer growth 66 . HDAC2 is also significantly overexpressed in gastric cancer and is associated with poor prognosis, but predicts a better outcome of immunotherapy by enhancing CD8 + T cell infiltration and cytotoxicity in a "hot" tumor status 64 , 67 , 68 . This suggests that Sp1 may play a similar crucial role in shaping TME by reprogramming macrophage phenotypic transitions and activating CD8 + T cell in GC. Although we observed a positive correlation between Sp1 expression and M1-phenotype of TAMs, which suggests a tumor-suppressive effect, recent insights highlight the complexity of macrophage phenotypic transitions. It is essential to recognize a broader spectrum of macrophage classifications beyond the M1 and M2 extremes 69 – 72 . Sp1 has been noted to maintain the naive state of CD8+ T cells 73 . In GC, we observed a reduction of Sp1 in T cells within tumor tissues, potentially indicating a transformation process towards mature CD8+ T cells. Alternatively, this reduction could reflect a tumor extracellular matrix response mechanism aimed at preventing CD8+ T cells from approaching the parenchymal region 74 . ICIs play a crucial role in cancer treatment, with immune checkpoint genes serving as important therapeutic targets 75 , 76 . In GC, researches have demonstrated that Sp1 can bind to the PD-L1 promoter region, contributing to PD-L1 overexpression and thus promoting cancer development 77 . Our study identified multiple immune checkpoints, including CD200R1, CD276, CD160, TNFSF15, and NRP1, that exhibit a positive correlation with Sp1 expression across various tumors. This suggests that Sp1 may represent a novel target for tumor immunotherapy. In GC, the majority of immune checkpoints, such as LAG3, NRP1, TIGIT, and CTLA4, showed a significant positive correlation with Sp1 expression. These checkpoints are known to contribute to the exhaustion of CD8 + T-cells, thereby facilitating immune evasion by the tumor. These findings underscore the potential of targeting Sp1 in enhancing the efficacy of immunotherapy in GC. Additionally, our immunohistochemistry analysis of tumor tissue from patients with advanced GC indicated higher levels of Sp1 in cases with partial response (PR) compared to those with stable disease (SD) or progressive disease (PD). Kaplan-Meier survival analysis further revealed that patients with high Sp1 expression had improved OS compared to those with low Sp1 expression. Univariate analysis identified ECOG PS and Sp1 expression as potential prognostic factors for GC patients treated with ICIs. Multifactor Cox regression models confirmed that both ECOG PS and Sp1 expression at the onset of ICI treatment independently predicted patient prognosis. Based on our analysis, we postulate that higher Sp1 expression in tumor tissue promotes the maturation and infiltration of CD8+ T cells while concurrently enhancing the expression of immunosuppressive molecules. This dual action results in an increased number of CD8+ T cells, but their functionality remains in a state of exhaustion. Exhausted CD8+ T cell (CD8+ Tex) is a key mechanism in tumor immune evasion. Recent studies have categorized CD8+ Tex into ICI permissive and ICI-refractory subsets, highlighting potential mechanisms underlying resistance to immunotherapy 78 , 79 . Therefore, patients who respond positively to ICIs may have a higher proportion of reversibly exhausted CD8+ Tex or a predominance of total CD8 + cell infiltration. Of course, there are certain limitations to this study. Firstly, the sample size is limited, which may introduce some bias. Additionally, while we observed this phenomenon in human specimens, we did not conduct molecular-level validation on the specific mechanisms by which Sp1 promotes gastric cancer progression and enhances immunotherapy efficacy. We plan to conduct more in-depth mechanistic studies which will focus on Sp1's interactions within the TME, particularly its interactions with CD8 + Tex, to obtain more robust evidence. Conclusions In summary, this study explored a novel role for Sp1 in tumors, particularly in GC. Patients with high Sp1 expression demonstrated better responses to ICIs and overall survival benefits. Further analysis revealed a significant positive correlation between Sp1 expression and TMB, as well as the infiltration of CD8 + T cells and M1 phenotypes of TAMs. This suggests that Sp1, as a proto-oncogene, may promote the reprogramming of multiple oncogenes in tumor cells, leading to the formation of numerous new antigens and enhanced immune cell infiltration. Additionally, high Sp1 expression also promotes PD-L1 expression, contributing to immune escape but paradoxically leading to better immunotherapy efficacy. Therefore, Sp1 could serve as an effective biomarker for predicting the treatment efficacy of ICIs in GC. Abbreviations Sp1, Transcription factor specificity protein 1 GC, Gastric cancer TCGA, The Cancer Genome Atlas GEO, Gene Expression Omnibus THPA, The Human Protein Atlas TMB, Tumor Mutational Burden MSI, Microsatellite Instability OS, Overall Survival CPS, Combined Positive Score ICIs, Immune Checkpoint Inhibitors TME, Tumor microenvironment BRCA, Breast cancer RCC, Renal cell carcinoma UCEC, Uterine corpus endometrial carcinoma LUAD, Lung adenocarcinoma IHC, Immunohistochemisty CNA, Copy number alteration PFS, Progression-free Survival mIF, Multiplex Immunofluorescence GO, Gene Ontology KEGG, Kyoto Encyclopedia of Genes and Genomes BP, Biological processes CC, Cellular components MF, Molecular functions GSEA, Gene Set Enrichment Analysis HPA, Human Protein Atlas GTEx, Genotype-Tissue Expression FANTOM5, Function Annotation of the Mammalian Genome 5 CHOL, Cholangiocarcinoma ESCA, Esophageal carcinoma GBM, Glioblastoma multiforme KICH, Kidney chromophobe KIRP, Kidney renal papillary cell carcinoma LIHC, Liver hepatocellular carcinoma STAD, Stomach adenocarcinoma THCA, Thyroid carcinoma LGG, Brain lower grade glioma PAAD, Pancreatic adenocarcinoma UCS, Uterine carcinosarcoma DFS, Disease-free Survival SCM, Skin cutaneous melanoma GSEA, Gene Set Enrichment Analysis SCLC, Small cell lung cancer scRNA-seq, Single-cell RNA sequencing PR, Partial response SD, Stable disease PD, Progressive disease DNMTs, DNA methylation transferases CD8+ Tex, Exhausted CD8+ T cell Declarations Ethics approval and consent to participate Patients’ written informed consents and approval from the Ethics Committees of Changzhou No.2 People’s Hospital were obtained for the use of these clinical materials. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This project was supported by the National Natural Science Foundation of China (81902955), the Medical Scientific Research Foundation of Guangdong Province (A2024172), the Youth Foundation of Health Committee of Shanghai Jing’an District (2021QN03), Shenzhen Key Medical Discipline Construction Fund (SZXK013), Changzhou Medical Talents Project for Domestic and Foreign Training (JW2023001), and Qing Miao Talent Project of Changzhou Health Committee (CZQM2022010). Authors' contributions Yang Zhou, Jianhua Chang, Libao Gong, and Junjie Hang designed the study. Yang Zhou, Zhenzhen Luo, Lixia Wu, Xiaoli Zhou, and Junjie Hang performed the experiments. 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Nature immunology 20, 326-336, doi:10.1038/s41590-019-0312-6 (2019). Additional Declarations No competing interests reported. Supplementary Files Supplementarymateria.docx Cite Share Download PDF Status: Published Journal Publication published 14 Oct, 2024 Read the published version in Cancer Cell International → Version 1 posted Editorial decision: Revision requested 25 Aug, 2024 Reviews received at journal 25 Aug, 2024 Reviews received at journal 06 Aug, 2024 Reviewers agreed at journal 04 Aug, 2024 Reviews received at journal 04 Aug, 2024 Reviewers agreed at journal 04 Aug, 2024 Reviewers agreed at journal 02 Aug, 2024 Reviewers invited by journal 02 Aug, 2024 Editor assigned by journal 09 Jul, 2024 Submission checks completed at journal 09 Jul, 2024 First submitted to journal 22 Jun, 2024 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. 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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-4623533","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":328478052,"identity":"01ed8f2e-d3e6-47cb-b82f-be87e459619b","order_by":0,"name":"Yang Zhou","email":"","orcid":"","institution":"The Affiliated Changzhou Second People’s Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Zhou","suffix":""},{"id":328478055,"identity":"cace380d-0334-43bf-aba2-3936a49a50bc","order_by":1,"name":"Zhenzhen Luo","email":"","orcid":"","institution":"National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital \u0026 Shenzhen Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Zhenzhen","middleName":"","lastName":"Luo","suffix":""},{"id":328478058,"identity":"1987c51f-c51d-47fb-a953-8e7592b63a8d","order_by":2,"name":"Jinfeng Guo","email":"","orcid":"","institution":"National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital \u0026 Shenzhen Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Jinfeng","middleName":"","lastName":"Guo","suffix":""},{"id":328478060,"identity":"8d404d6c-7b0d-45da-af5a-a15f2776fcb8","order_by":3,"name":"Lixia Wu","email":"","orcid":"","institution":"Shanghai JingAn District ZhaBei Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lixia","middleName":"","lastName":"Wu","suffix":""},{"id":328478061,"identity":"ad1a404d-1a15-42d5-9d71-fe1c6a2d4845","order_by":4,"name":"Xiaoli Zhou","email":"","orcid":"","institution":"The Affiliated Changzhou Second People’s Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoli","middleName":"","lastName":"Zhou","suffix":""},{"id":328478065,"identity":"c92c9e08-9191-417c-99ed-1489d116859e","order_by":5,"name":"Junjie Huang","email":"","orcid":"","institution":"The Chinese University of Hong Kong, Chinese University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Junjie","middleName":"","lastName":"Huang","suffix":""},{"id":328478066,"identity":"f4fd51b1-803e-49a9-a621-f3fa23e5d420","order_by":6,"name":"Daijia Huang","email":"","orcid":"","institution":"National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital \u0026 Shenzhen Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Daijia","middleName":"","lastName":"Huang","suffix":""},{"id":328478067,"identity":"9bd5b5cf-c4df-4093-a01d-0e7134cfd4dc","order_by":7,"name":"Xiao Li","email":"","orcid":"","institution":"National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital \u0026 Shenzhen Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Li","suffix":""},{"id":328478069,"identity":"60d07d18-7738-4a51-8f5b-c353604be34f","order_by":8,"name":"Qiuhua Duan","email":"","orcid":"","institution":"The Affiliated Changzhou Second People’s Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qiuhua","middleName":"","lastName":"Duan","suffix":""},{"id":328478071,"identity":"214c61ce-74a5-43fe-afb6-242a216e1a5f","order_by":9,"name":"Jianhua Chang","email":"","orcid":"","institution":"National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital \u0026 Shenzhen Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Jianhua","middleName":"","lastName":"Chang","suffix":""},{"id":328478073,"identity":"a2e2b3ae-4562-40d5-a155-334c13ee8bc0","order_by":10,"name":"Libao Gong","email":"","orcid":"","institution":"Fifth Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Libao","middleName":"","lastName":"Gong","suffix":""},{"id":328478074,"identity":"f9fcc7d7-a211-4009-9411-5a718823ca1e","order_by":11,"name":"Junjie Hang","email":"data:image/png;base64,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","orcid":"","institution":"National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital \u0026 Shenzhen Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College","correspondingAuthor":true,"prefix":"","firstName":"Junjie","middleName":"","lastName":"Hang","suffix":""}],"badges":[],"createdAt":"2024-06-23 02:23:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4623533/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4623533/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12935-024-03521-z","type":"published","date":"2024-10-14T15:58:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62156491,"identity":"3e5893dc-d968-4ad3-94d7-6b1108655f00","added_by":"auto","created_at":"2024-08-09 21:08:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2107047,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of Sp1 in different types of tumors and pathological stages. The expression of Sp1 in different types of tumors (A). For the type of CHOL, GBM, LGG, PAAD, STAD, and UCS in the TCGA project, the corresponding normal tissues were included as controls (B). The protein level of Sp1 between normal tissue and breast cancer, ovarian cancer, colon cancer, clear cell RCC, UCEC and LUAD (C). Based on the TCGA datasets, the levels of the Sp1 were analyzed according to pathological stages (stage I, stage II, stage III, and stage IV) of KIRC, LIHC, LUSC, and SKCM. Log2 (TPM+1) was applied for log-scale. * P\u0026lt;0.05; ** P\u0026lt;0.01; *** P\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4623533/v1/ba2b2f8b63658812bf13c80b.png"},{"id":62156492,"identity":"7a739ea7-ae95-472d-a9ec-41b8075fa0a0","added_by":"auto","created_at":"2024-08-09 21:08:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3308561,"visible":true,"origin":"","legend":"\u003cp\u003eSp1-related gene enrichment analysis. We identified 50 Sp1-binding proteins with their interaction network using the STRING tool (A). The corresponding heatmap depicted the top five genes correlated with Sp1 expression using the GEPIA2 tool (B). The Venn plot revealed an intersection analysis of the two gene groups (C). KEGG pathway analysis was performed based on the Sp1-binding and interacted genes (D). Using the TCGA pan-cancer dataset, we conducted Gene Set Enrichment Analysis (GSEA) according to Sp1 expression (E).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4623533/v1/e9b3d961b6551444b5129431.png"},{"id":62156495,"identity":"3485a753-c9ac-4c3c-b046-bdf9470ea133","added_by":"auto","created_at":"2024-08-09 21:08:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":615666,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression level of Sp1 in single cell transcriptome data. \u0026nbsp;The composition of immune cells in GSE163558 dataset were divided into eight subclusters (A). Sp1 was expressed in both M1 and M2 phenotypes of TAMs (B). The expression levels of Sp1 in different immune cell subclusters were compared between GC and adjacent normal tissue (C).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4623533/v1/c7b5a9aee084238fdf45fd30.png"},{"id":62156496,"identity":"9467ef41-1175-4c72-8059-29aa78eb3da0","added_by":"auto","created_at":"2024-08-09 21:08:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":632253,"visible":true,"origin":"","legend":"\u003cp\u003eSp1 is related to the efficacy of immunotherapy. The correlation between the CD8\u003csup\u003e+\u003c/sup\u003e T cells, M1 phenotype of TAMs, M2 phenotype of TAMs, and Sp1 expression. A representative image using multiple quantitative fluorescence staining. Blue, DAPI; White, CD8; green, CD68; red, HLADR; Scale bars =400μm (A). Correlations between M1, M2 phenotype of TAMs and Sp1 expression in gastric cancer (B). The representative immunochemistry images depicting different expression levels of Sp1 in gastric cancer (C). The Sp1 expression levels in gastric cancer patients with different immunotherapy efficacy were shown (D). The correlation between Sp1 expression and overall survival (E).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4623533/v1/da01851435a60d58d7193115.png"},{"id":62156494,"identity":"221e4c71-ba57-4784-a637-906dfaf5441b","added_by":"auto","created_at":"2024-08-09 21:08:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3695866,"visible":true,"origin":"","legend":"\u003cp\u003eThe immunological role of Sp1. The correlation between tumor mutational burden (A), microsatellite instability (B) and Sp1 expression across all types of cancers in the TCGA database. The correlations between Sp1 expression and various genes involved in immune checkpoint signaling (C).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4623533/v1/802d223d59216e4257fbf8d0.png"},{"id":67149119,"identity":"b9aa2bcd-b221-4365-b93b-5e915515fa68","added_by":"auto","created_at":"2024-10-21 16:12:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10780209,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4623533/v1/733bdb4e-52e5-48ad-8841-6c41ee0a1f85.pdf"},{"id":62157324,"identity":"8e5c85bb-c963-481f-9efe-f89169f83ede","added_by":"auto","created_at":"2024-08-09 21:16:40","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":1591246,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymateria.docx","url":"https://assets-eu.researchsquare.com/files/rs-4623533/v1/9a0d9254e39015708cbbcdd3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pan-cancer analysis of Sp1 with a focus on immunological roles in gastric cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGastric cancer (GC) is the fifth most common malignant tumor worldwide and the fourth leading cause of cancer-related deaths\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Despite its high incidence, most patients are diagnosed at an advanced stage due to the lack of clear clinical manifestations, which results in limited treatment options and poor prognosis\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. At present, surgery and systemic chemotherapy remain the primary treatments for GC. For advanced GC patients, the median overall survival (OS) after chemotherapy is only 12 months\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Given the high incidence and poor short-term survival rate of GC, there is a pressing need to explore alternative treatment methods. Among them, anti-PD-1/anti-PD-L1 therapies have shown impressive efficacy and have significantly prolonged survival, especially in untreated patients with MSI-H or dMMR GC\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. However, the incidence of MSI-H status in GC ranges from 8\u0026ndash;25%, limiting its utility as a predictive biomarker for advanced GC immunotherapy\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Additionally, multiple clinical studies have evaluated PD-L1 expression levels, especially Combined Positive Score (CPS), as predictive biomarkers for immune checkpoint inhibitors (ICIs) response. However, reliable threshold values have yet to be established. Even with commonly used threshold of 1, 5, and 10, patients who benefit from immunotherapy are not consistently identified\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Therefore, there is an urgent need to identify reliable predictive biomarkers for immunotherapy to enable precise treatment stratification for GC patients.\u003c/p\u003e \u003cp\u003eTranscription factor specificity protein 1 (Sp1) is a member of the SP/KLF family\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Previous studies have shown that Sp1 is overexpressed in various types of cancer and its high expression is associated with poor prognosis, including ovarian cancer\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, glioblastoma\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, lung cancer\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, and breast cancer\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. As a housekeeping gene, Sp1 can activate or inhibit the transformation of normal cells into cancer cells, thereby influencing cancer progression\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. For example, Liu et al. demonstrated that Sp1 plays a crucial role in promoting proliferation, migration, and chemotherapy resistance in epithelial ovarian cancer\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In addition, studies have shown that Sp1 promotes cancer cell proliferation and inhibits apoptosis\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Our previous studies have also indicated that Sp1 promotes the development of GC and is a poor prognostic factor for GC \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. However, most current research focuses on the role of Sp1 within tumor cells, with limited reports on its overall role, including its effects on the tumor microenvironment (TME). Recent studies have indicated that Sp1 is a key mediator involved in the epigenetic programming and reprogramming of HPV hosts. Inhibition of Sp1 has been shown to enhance anti PD-1 immunotherapy by reshaping the TME in cancer, suggesting that plicamycin inhibition may be a promising treatment option for HPV related cancers\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Despite these findings, there are no relevant reports on the immunological role of Sp1 in GC. Therefore, this study aims to evaluate the role of Sp1 in the immune microenvironment of GC and its impact on the efficacy of ICIs.\u003c/p\u003e \u003cp\u003eIn this study, we first applied online data analysis to demonstrate that Sp1 is highly expressed in various tumors and is associated with poor prognosis, consistent with previous research findings. Pathway analysis further revealed that Sp1 is involved in immune cell regulatory pathways. Single-cell data analysis showed that Sp1 is also expressed in immune cells and is particularly highly expressed in macrophages, neutrophils, and CD8\u0026thinsp;+\u0026thinsp;immune cells in tumors compared to normal tissues. Importantly, gastric cancer patients with high Sp1 expression exhibit better responses to ICIs and OS benefits. Further investigation of its potential mechanisms revealed a significant positive correlation between Sp1 expression and tumor mutational burden (TMB), as well as with CD8\u0026thinsp;+\u0026thinsp;T cells and M1 macrophage infiltration. This suggests that Sp1, as a pro-oncogene, promotes the reprogramming of multiple oncogenes in tumor cells, leading to the formation of numerous neoantigens and enhancing immune cell infiltration. Additionally, high Sp1 expression promotes PD-L1 expression, which contributes to immune escape but also leads to better responses to ICIs. Overall, Sp1 can serve as an effective biomarker for predicting the therapeutic efficacy of ICIs in GC.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eGene mapping and protein structure analysis\u003c/p\u003e\n\u003cp\u003eBased on the UCSC genome browser on human Dec. 2013 (GRCh38/hg38) assembly (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://genome.ucsc.edu/\u003c/span\u003e\u003c/span\u003e), the genome location information of Sp1 was obtained. We also applied the \u0026quot;HomoloGene\u0026quot; function of the NCBI (National Center for Biotechnology Information) to conduct conserved functional domain analysis of Sp1 in different species. Additionally, we obtained the phylogenetic tree of Sp1 in different species using the constraint-based multiple alignment on-line tool of the NCBI (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/tools/cobalt/\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eGene expression analysis\u003c/p\u003e\n\u003cp\u003eWe first logged into the online HPA (Human protein atlas) database and obtained the expression data of the Sp1 in different normal tissues, cancerous tissues, and blood cells. \u0026ldquo;Low specificity\u0026rdquo; was defined by \u0026ldquo;NX (Normalized expression)\u0026thinsp;\u0026ge;\u0026thinsp;1 in at least one tissue/region/cell type but not elevated in any tissue/region/cell type\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003eWe used TIMER2 (tumor immune estimation resource, version 2) website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.cistrome.org/\u003c/span\u003e\u003c/span\u003e) to investigate the expression difference of Sp1 between cancerous and adjacent normal tissues in different tumors of the TCGA project. We also used \u0026ldquo;Box Plots\u0026rdquo; module of the GEPIA2 (Gene Expression Profiling Interactive Analysis, version 2) website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia2.cancer-pku.cn/#analysis\u003c/span\u003e\u003c/span\u003e) to acquire box plots of the expression difference of Sp1 between tumor tissues and the corresponding normal tissues of the GTEx (Genotype-Tissue Expression) database. In addition, the violin plots of Sp1 expression in different TNM stages of all TCGA tumors with the online tool HEPIA2. Furthermore,\u003c/p\u003e\n\u003cp\u003eWe explored the expression level of the total protein or phosphoprotein of Sp1 between cancerous and adjacent normal tissues via the UALCAN portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ualcan.path.uab.edu/analysis-prot.html\u003c/span\u003e\u003c/span\u003e). The available CPTAC (Clinical proteomic tumor analysis consortium) datasets in the UALCAN portal include six tumors, namely, breast cancer (BRCA), ovarian cancer, colon cancer, renal cell carcinoma (RCC), Uterine corpus endometrial carcinoma (UCEC), and Lung adenocarcinoma (LUAD).\u003c/p\u003e\n\u003cp\u003ePatients and specimens\u003c/p\u003e\n\u003cp\u003eFrom January 2018 to December 2022, 26 patients undergoing gastrectomy for gastric cancer and 27 advanced GC patients receiving ICIs and chemotherapy in Changzhou No.2 People hospital. Cancerous and adjacent normal tissue was collected during surgery or puncturation, and histopathologically confirmed and staged according to the Union for International Cancer Control. Patients\u0026rsquo; written informed consents and approval from the Ethics Committees of Changzhou No.2 People\u0026rsquo;s Hospital were obtained for the use of these clinical materials.\u003c/p\u003e\n\u003cp\u003eImmunohistochemisty (IHC)\u003c/p\u003e\n\u003cp\u003eTissue sections were incubated in an oven at 55\u0026deg;C for 20 min followed by three 5-min washes with xylene for dewaxing then rehydrated by 5-min washes in 100%, 95%, and 80% ethanol and distilled water. Samples were heated at 95\u0026deg;C for 30 min in 10 mmol/L sodium citrate (pH 6.0) for antigen retrieval. Endogenous peroxidase activity was blocked by incubation in 3% H2O2 for 30 min. After 30 min blocking with the universal blocking serum (Dako Diagnostics, Carpinteria, CA), the sections were incubated with anti-Sp1 antibody at 4\u0026deg;C overnight and washed 3 times with PBS at room temperature. Then a secondary antibody was added for 30 min incubation (Dako Diagnostics). The samples were washed 3 times with PBS and developed using DAB followed by counterstaining with hematoxylin. Dehydration was performed following a standard procedure and the slides were sealed with cover slips. Images were scanned with a digital pathology slide scanner (KF-BIO, CHINA).\u003c/p\u003e\n\u003cp\u003eSp1 immunostaining signals were evaluated by two researchers, with the clinical information blinded to them, and scored. Brown cytoplasmic staining for Sp1 was considered positive. The percentage of Sp1-positive cells was scored with the following four categories: 1 (\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;25%), 2 (25\u0026ndash;50%), 3 (50\u0026ndash;75%), and 4 (\u003cem\u003e\u0026gt;\u003c/em\u003e\u0026thinsp;75%). The staining intensity of positive cells was scored as 0 (absent), 1 (weak infiltration), 2 (moderate infiltration), and 3 (strong infiltration). The final score was the sum of the intensity and the percentage.\u003c/p\u003e\n\u003cp\u003eSurvival analysis\u003c/p\u003e\n\u003cp\u003eThe \u0026ldquo;survival map\u0026rdquo; module of GEPIA2 was used to conduct the survival analysis of Sp1 across all TCGA tumors. Cutoff-high (50%) and cutoff-low (50%) values were used as the expression thresholds for splitting the high-expression and low-expression groups of OS (Overall survival) and DFS (Disease-free survival).\u003c/p\u003e\n\u003cp\u003eGenetic alteration analysis\u003c/p\u003e\n\u003cp\u003eWe investigate the genetic alteration characteristics of Sp1 with the cbioportal website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbioportal.org/\u003c/span\u003e\u003c/span\u003e). The results of the alteration frequency, mutation type and Copy number alteration (CNA) were obtained in the \u0026ldquo;Cancer Types Summary\u0026rdquo; module. We also used the \u0026ldquo;Comparison\u0026rdquo; module to obtain the data of OS, progression-free survival (PFS), and DFS differences in the TCGA cancer cases with or without Sp1 genetic alteration.\u003c/p\u003e\n\u003cp\u003eAnalysis of tumor behavior states, immune infiltrates, and immune biomarkers\u003c/p\u003e\n\u003cp\u003eThe online tool Sangerbox (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://sangerbox.com/index.html\u003c/span\u003e\u003c/span\u003e) was used to investigate the correlations between TMB, MSI and Sp1 in all types of cancers in TCGA. The correlations between the Sp1 expression and a variety of genes involved in immune checkpoint signaling, such as CTLA4 were also evaluated with Sangerbox. Spearman\u0026rsquo;s correlation was performed and the \u003cem\u003eP-\u003c/em\u003evalue and partial correlation (cor) value were obtained.\u003c/p\u003e\n\u003cp\u003eWe used the TIMER2 online tool to explore the correlations between Sp1 expression and several types of immune cells, which includes B cells, CD4\u003csup\u003e+\u003c/sup\u003e T cells, CD8\u003csup\u003e+\u003c/sup\u003e T cells, dendritic cells, macrophages, and neutrophils in all types of tumors. The TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, XCELL, MCPCOUNTER and EPIC algorithms were applied for immune infiltration estimations, especially for CD8\u003csup\u003e+\u003c/sup\u003e T cells. The P\u003cem\u003e-\u003c/em\u003evalues and correlation values were obtained via the purity-adjusted Spearman\u0026rsquo;s rank correlation test. The data were visualized as a heatmap and a scatter plot.\u003c/p\u003e\n\u003cp\u003eDNA methylation analysis\u003c/p\u003e\n\u003cp\u003eWe also used the SangerBox tool to investigate the correlations between the Sp1 expression and four classical DNA methyltransferase including DNMT1, DNMT2, DNMT3A, and DNMT3B in all types of cancer. The MEXPRESS web (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mexpress.ugent.be/\u003c/span\u003e\u003c/span\u003e) was used to analyze the DNA methylation level of Sp1 of multiple probes in different cancers of TCGA database. The beta value of methylation, the Benjamini-Hochberg-adjusted P-value and Pearson correlation coefficient value of each sample were obtained. The promoter region probes were highlighted.\u003c/p\u003e\n\u003cp\u003ePhosphorylation analysis\u003c/p\u003e\n\u003cp\u003eWe used iPTMnet database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://proteininformationresource.org/iPTMnet\u003c/span\u003e\u003c/span\u003e) to analyze the predicted phosphorylation features of the S7, T42, S59, S101, T278, T453, S641, T668, S698, and S702 locus of Sp1. We also investigate the differences in phosphorylation levels of Sp1 between normal tissues and primary tumors, including BRCA, ovarian cancer, colon cancer, RCC, and UCEC, using the CPTAC analysis.\u003c/p\u003e\n\u003cp\u003eMultiplex Immunofluorescence (mIF)\u003c/p\u003e\n\u003cp\u003eMultiplex staining of was performed using TSA 6-color kit (H-D110061,yuanxibio). Primary antibodies panel included anti-CD8 (#BX50036, Biolynx), anti-CD68 (#BX50031-C3, Biolynx), anti-HLA-DR (#ab92511, Abcam), anti-PanCK (#GM351507, Gene Tech). Primary antibodies were sequentially applied, followed by horseradish peroxidase-conjugated secondary antibody incubation(Cat# DS9800, Lecia Biosystems), and tyramide signal amplification. The slides were washed with TBST buffer and heat-treated by microwave after each TSA operation. Nuclei were stained with DAPI (D1306, ThermoFisher) after all the antigens above being labeled, then washed in distilled water, and manually coverslipped. The stained slides were scanned to obtain multispectral images using the Pannoramic MIDI imaging system (3D HISTECH). Images was analyzed using Indica Halo software.\u003c/p\u003e\n\u003cp\u003eEnrichment analysis of Sp1-related genes\u003c/p\u003e\n\u003cp\u003eThe STRING online tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003c/span\u003e) was applied to investigate the top 50 experimentally determined Sp1-binding proteins. The main parameters were set as follows: minimum required interaction score [\u0026ldquo;Low confidence (0.150)\u0026rdquo;], meaning of network edges (\u0026ldquo;evidence\u0026rdquo;), max number of interactors to show (\u0026ldquo;no more than 50 interactors\u0026rdquo; in 1st shell) and active interaction sources (\u0026ldquo;experiments\u0026rdquo;). The GEPIA2 was used to determine the top 100 Sp1-correlated genes based on the TCGA datasets. Furthermore, we used the \u0026ldquo;Gene_Corr\u0026rdquo; module of TIMER2 to supply the heatmap data of the selected genes, which contains the correlation and \u003cem\u003eP\u003c/em\u003e-value in the Spearman\u0026rsquo;s rank correlation test. The log2 TPM was applied for the dot plot. The \u003cem\u003eP\u003c/em\u003e-value and the correlation coefficient (R) were indicated. Venny 2.1.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinfogp.cnb.csic.es/tools/venny/index.html\u003c/span\u003e\u003c/span\u003e) was used to conduct an intersection analysis to compare the Sp1-binding and interacted genes. Then, these two sets of genes were combined and submitted to DAVID for additional functional annotation, such as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). In this work, we mainly focused on three aspects of GO analysis: biological processes (BP), cellular components (CC), and molecular functions (MF). In addition, we used KEGG analysis to investigate the pathways in which the Sp1-binding and interacted genes were involved.\u003c/p\u003e\n\u003cp\u003eGene Set Enrichment Analysis (GSEA)\u003c/p\u003e\n\u003cp\u003eGSEA was used to explore the up-downregulations among different pathways associated with Sp1 in STAD. The functional gene set was set to c2.cp.kegg.v7.4.symbols.gmt, the analysis parameters were \u0026quot;No collapse\u0026quot;, the number of permutations was set to \u0026quot;1000\u0026quot;, the permutation type was set to \u0026quot;Phenotype\u0026quot;, and the above files were analyzed by GSEA software (version 3.0). In this study, GESA was used to explore Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways significantly associated with high and low Sp1 expression, and mapped the top five pathways. P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.25 were considered statistically significant.\u003c/p\u003e\n\u003cp\u003eThe scRNA-seq data analysis\u003c/p\u003e\n\u003cp\u003eThe GC scRNA-seq data (GSE163558) were obtained from the GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003c/span\u003e), which included 10 fresh human tissue samples of six patients, including primary tumors, adjacent non-tumoral samples, and six metastases from various organs or tissues (liver, peritoneum, ovary, lymph node). Data filtering and preprocessing were conducted using the R package \u0026ldquo;Seurat\u0026rdquo;. The initial screening criteria included: genes expressed in at least three cells; each cell expresses at least 250 genes; the percentage feature set function was used to calculate the percentage of mitochondria and rRNA, ensuring that each cell expresses more than 200 genes and less than 5000 genes; mitochondrial genes comprising less than 15% of the total genome. Following data filtering, samples were merged for further analysis. To address batch effects and integrate different single-cell transcriptome samples, the FindIntegrationAnchors and IntegrateData functions in the Seurat package were employed, identifying 4000 highly variable genes with the FindVariableFeatures function.Then, principal component analysis (PCA) was performed using the RunPCA function. Cell clustering was carried out with the FindNeighbors and FindClusters functions (resolution\u0026thinsp;=\u0026thinsp;0.1, dim\u0026thinsp;=\u0026thinsp;50). Dimensionality reduction was achieved using the UMAP method. Marker genes for each cluster were identified using the FindAllMarkers function (logFC\u0026thinsp;=\u0026thinsp;0.75, min.pct\u0026thinsp;=\u0026thinsp;0.25, p-adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGene expression analysis\u003c/h2\u003e \u003cp\u003eWe tried to investigate the oncogenic role of Sp1 (Supplementary Fig.\u0026nbsp;1A). The structure of Sp1 demonstrates relative conservation across various species, characterized by the Zinc-finger double domain (Supplementary Fig.\u0026nbsp;1B). To elucidate the evolutionary relationships of Sp1 across diverse species, we presented a phylogenetic tree in Supplementary Fig.\u0026nbsp;1C.\u003c/p\u003e \u003cp\u003eWe conducted a comprehensive analysis of Sp1 expression patterns across various cell lines, normal tissues, and blood cells. As depicted in Supplementary Fig.\u0026nbsp;2A, Sp1 shows the highest expression in early spermatids, closely followed by urothelial cells. Utilizing integrated data from the Human Protein Atlas (HPA), Genotype-Tissue Expression (GTEx), and Function Annotation of the Mammalian Genome 5 (FANTOM5) datasets, we observed that the expression of Sp1 is most prominent in the esophagus among all normal tissues, as illustrated in Supplementary Fig.\u0026nbsp;2B. Additionally, Sp1 demonstrated its highest expression in neutrophils compared to other types of blood cells (Supplementary Fig.\u0026nbsp;2C).\u003c/p\u003e \u003cp\u003eWe further utilized TIMER2 to examine the expression levels of Sp1 in different types of cancers within the TCGA database. Notably, the expression levels of Sp1 in BRCA, cholangiocarcinoma (CHOL), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), kidney chromophobe (KICH), kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), stomach adenocarcinoma (STAD), thyroid carcinoma (THCA), and UCEC exhibited significant differences compared to the corresponding normal tissues (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eAdditionally, we observed significant differences in the expression levels of Sp1 between cancerous tissues and adjacent normal tissues across various cancer types, including CHOL, GBM, brain lower grade glioma (LGG), pancreatic adenocarcinoma (PAAD), STAD, and uterine carcinosarcoma (UCS), when normal tissues from the GTEx dataset were included as controls (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eThe analysis of the CPTAC dataset revealed elevated protein levels of Sp1 in BRCA, colon cancer, and LUAD compared to normal tissues (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Conversely, the total protein expression of Sp1 was found to be higher in normal tissues than in primary clear cell renal cell carcinoma (P\u0026thinsp;=\u0026thinsp;0.009) and UCEC (P\u0026thinsp;=\u0026thinsp;0.007). In addition, no significant difference in Sp1 protein levels was observed between ovarian cancer and normal tissues (P\u0026thinsp;=\u0026thinsp;0.055).\u003c/p\u003e \u003cp\u003eUsing the online tool GEPIA2, we identified a notable correlation between the expression levels of Sp1 and the pathological stages of certain cancers, specifically KIRC and LIHC (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). However, such correlation was not evident in other cancer types. Moreover, we observed a potential association between the expression of Sp1 and the pathological stages of LUSC (P\u0026thinsp;=\u0026thinsp;0.0829) and SKCM (P\u0026thinsp;=\u0026thinsp;0.054).\u003c/p\u003e \u003cp\u003eThrough the examination of cancerous and adjacent normal tissues from 26 patients enrolled in our hospital, we validated a significant increase in the protein levels of Sp1 in GC compared to adjacent normal tissues (P\u0026thinsp;=\u0026thinsp;0.006, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSurvival analysis\u003c/h2\u003e \u003cp\u003eWe investigated the correlation between Sp1 expression and survival outcomes using the online tool GEPIA2. As illustrated in Supplementary Fig.\u0026nbsp;3A, the median OS of patients with low Sp1 expression was significantly longer compared to those of patients with high Sp1 expression in LGG, LIHC, PAAD, and THCA (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, patients with low Sp1 expression in KIRC exhibited a shorter median OS than those with high Sp1 expression (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Moreover, a high Sp1 expression in patients with LGG, ACC, BLCA, and LIHC was correlated with poor disease-free survival (DFS) (Supplementary Fig.\u0026nbsp;3B). Conversely, a low Sp1 expression in patients with KIRC was correlated with poor DFS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, no significant correlations were observed between OS, DFS, and Sp1 expression in other types of cancers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eGenetic alteration analysis\u003c/h2\u003e \u003cp\u003eWe investigate the genetic alteration of Sp1 in different types of cancers using the TCGA database (Supplementary Fig.\u0026nbsp;4). The highest alteration frequency of Sp1, at 10.53%, was observed in patients with uterine carcinosarcoma, predominantly characterized by amplification (Supplementary Fig.\u0026nbsp;4A). All patients with skin cutaneous melanoma (SCM) and liver hepatocellular carcinoma with genetic alteration (\u0026gt;\u0026thinsp;1% frequency) exhibited mutations in Sp1. In addition, the \u0026ldquo;structural variant\u0026rdquo; type of Sp1 was the predominant type in uterine carcinosarcoma, accounting for a genetic alteration frequency of 3.51%. The sites and types of Sp1 genetic alterations, along with post-translational modifications, are demonstrated in Supplementary Fig.\u0026nbsp;4B. We found the missense mutation and truncating mutation were the primary genetic alteration types. Given that uterine carcinosarcoma displayed the highest alteration frequency of Sp1, we further investigated the correlations between the alteration status of Sp1 and clinical survival prognosis. Intriguingly, we found significant correlations between the alteration status of Sp1 and PFS (P\u0026thinsp;=\u0026thinsp;0.0294). However, no significant correlations were observed between the alteration status of Sp1 and OS, DFS, and disease-specific survival (Supplementary Fig.\u0026nbsp;4C, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eDNA methylation analysis\u003c/p\u003e \u003cp\u003eWe used the SangerBox approach to investigate the correlation between the Sp1 expression and four classical DNA methyltransferase including DNMT1, DNMT2, DNMT3A, and DNMT3B (Supplementary Fig.\u0026nbsp;5). As shown in Supplementary Fig.\u0026nbsp;5, Sp1 expression exhibited a significant correlation with the expression levels of DNMT1, DNMT2, DNMT3A, and DNMT3B expression in THCA, UVM, DLBC, LGG, and LIHC (all R\u0026thinsp;\u0026gt;\u0026thinsp;0.3, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eWe further investigate the association between Sp1 DNA methylation and the pathogenesis of various types of cancers using the TCGA database. In the patients with GC, we found a significant negative correlation between Sp1 DNA methylation and gene expression at multiple probes, including those in non-promoter regions and promoter regions, such as cg14794577 (R=-0.339, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Supplementary Fig.\u0026nbsp;6).\u003c/p\u003e \u003cp\u003eProtein phosphorylation analysis\u003c/p\u003e \u003cp\u003eSupplementary Fig.\u0026nbsp;7A annotates several important Sp1 phosphorylation sites. We used iPTMnet database to analyze the phosphorylation sites of Sp1 and identified S7, T42, S59, S101, T278, T453, S641, T668, S698, and S702 as loci supported by the highest confidence data. Furthermore, we investigated differences in Sp1 phosphorylation levels between normal tissues and primary tumors, specifically in BRCA, ovarian cancer, colon cancer, clear cell renal cell carcinoma, and UCEC, using CPTAC analysis. Notably, the T42 locus within Sp1 exhibited significantly higher phosphorylation levels in the primary tumor of colon cancer compared to normal tissues (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, higher phosphorylation levels of the T42 locus were observed in normal tissues compared to the primary tumors of BRCA (P\u0026thinsp;=\u0026thinsp;0.045), ovarian cancer (P\u0026thinsp;=\u0026thinsp;0.003), and UCEC (P\u0026thinsp;=\u0026thinsp;0.019). However, the T42 locus showed no significant difference in phosphorylation levels between normal tissues and primary clear cell renal cell carcinoma (P\u0026thinsp;=\u0026thinsp;0.134) (Supplementary Fig.\u0026nbsp;7B).\u003c/p\u003e \u003cp\u003eEnrichment analysis of Sp1-related genes\u003c/p\u003e \u003cp\u003eTo investigate the molecular mechanisms of the Sp1 in tumorigenesis, we tried to identify Sp1-binding proteins and genes correlated with Sp1 expression for pathway enrichment analyses. Utilizing the STRING tool, we identified a total of 50 Sp1-binding proteins, with their interaction network illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA. The GEPIA2 tool was used to analyze TCGA tumor expression data, resulting in the identification of the top 100 genes correlated with Sp1 expression. Among these genes, Sp1 expression showed significantly positive correlations with ASXL2 (R\u0026thinsp;=\u0026thinsp;0.78), ATF7 (R\u0026thinsp;=\u0026thinsp;0.75), BAZ2A (R\u0026thinsp;=\u0026thinsp;0.75), MAP3K2 (R\u0026thinsp;=\u0026thinsp;0.75) and PKN2 (R\u0026thinsp;=\u0026thinsp;0.75) genes (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The corresponding heatmap data also depicted a positive correlation between Sp1 and these five genes across various cancer types (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). An intersection analysis of the two groups revealed two common members, namely, CRREBP and EP300 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eWe integrated the two datasets to conduct KEGG and GO enrichment analyses. The KEGG data, depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, suggested that pathways such as \u0026ldquo;viral carcinogenesis\u0026rdquo; and \u0026ldquo;pathways in cancer\u0026rdquo; might contribute to Sp1's impact on tumor pathogenesis. Furthermore, the GO enrichment analysis data revealed that a majority of these genes are associated with pathways or cellular processes related to transcription, including DNA binding, protein binding, transcription factor binding, chromatin binding, histone deacetylase binding, and others (Supplementary Fig.\u0026nbsp;8).\u003c/p\u003e \u003cp\u003eUsing the TCGA pan-cancer dataset, we conducted Gene Set Enrichment Analysis (GSEA) which showed that the T cell receptor signaling pathway, chronic myeloid leukemia, and small cell lung cancer (SCLC) were predominantly enriched in the Sp1 high-expressed group. Conversely, glycerolipid metabolism and olfactory transduction were primarily enriched in the Sp1 low-expressed group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePathway analysis has shown that Sp1 is involved in immune cell regulatory pathways, and substantial evidence indicates that the host immune system plays a crucial role in both inhibiting and promoting tumor growth and metastasis. Understanding Sp1's impact on the immune microenvironment is therefore essential for developing more effective cancer treatments. Using the TIMER database, we investigated the potential correlations between Sp1 expression and several types of immune cells, including B cells, CD4+ T cells, CD8+ T cells, dendritic cells, macrophages, and neutrophils. The analysis demonstrated that the most significant correlations between Sp1 expression and these immune cells in patients with COAD, HNSC, and KIRC (P\u0026thinsp;\u0026lt;\u0026thinsp;0.005 for all) (Supplementary Fig.\u0026nbsp;9).\u003c/p\u003e \u003cp\u003ePrevious studies demonstrated that Sp1 plays a crucial role in regulating the expression and function of various immune cells, including CD8\u003csup\u003e+\u003c/sup\u003e T cells, macrophages, and etc. To assess the correlations between CD8\u003csup\u003e+\u003c/sup\u003e T cells, M1 phenotype of TAMs, M2 phenotype of TAMs, and Sp1 expression, we used the EPIC, MCPCOUNTER, and TIMER algorithms (Supplementary Fig.\u0026nbsp;10A). As illustrated in Supplementary Fig.\u0026nbsp;8B-E, there were significantly positive correlations between Sp1 expression and CD8\u003csup\u003e+\u003c/sup\u003e T cells in patients with STAD, BRCA-Basal, SKCM, and PRAD using all three algorithms (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all). We also found a positive correlations between Sp1 expression and M1 phenotype of TAMs in patients with STAD with all three algorithms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eExpression level of Sp1 in single cell transcriptome data\u003c/h2\u003e \u003cp\u003eThrough single-cell RNA sequencing (scRNA-seq) data analysis of gastric cancer from GSE163558, a total of 42,968 cells were included. To investigate the expression of Sp1 in different immune cells, we have further subdivided the composition of immune cells into eight subclusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The data revealed that Sp1 expression was mainly observed in the clusters of macrophages and T cells, confirming a strong correlation between Sp1 and immune cells in STAD. Specifically, Sp1 was expressed in both M1 and M2 phenotypes of TAMs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Furthermore, compared to normal tissue, the expression levels of Sp1 were higher in macrophage cells and monocyte cells within tumor tissue, while lower in T cells, but with no significant difference in B cells, Neutrophil cells, NK cells and pDC cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eValue of Sp1 expression for predicting immunotherapy efficacy\u003c/p\u003e \u003cp\u003eWe further validate the correlations between the CD8+ T cells, M1 phenotype of TAMs, M2 phenotype of TAMs, and Sp1 expression in advanced GC patients treated with ICIs and chemotherapy. Figure\u0026nbsp;6 depicted the multiple immunofluorescence (mIHC) images of CD8+ T cells, M1 phenotype of TAM (CD68\u0026thinsp;+\u0026thinsp;HLADR+), and M2 phenotype of TAM (CD68\u0026thinsp;+\u0026thinsp;HLADR-) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, Sp1 expression was positively correlated with CD8\u003csup\u003e+\u003c/sup\u003e T cells (r\u0026thinsp;=\u0026thinsp;0.409, P\u0026thinsp;=\u0026thinsp;0.047) and M1 phenotype of TAM (r\u0026thinsp;=\u0026thinsp;0.432, P\u0026thinsp;=\u0026thinsp;0.035) in GC. However, no significant correlation was found between Sp1 expression and M2 phenotype of TAM (P\u0026thinsp;=\u0026thinsp;0.350).\u003c/p\u003e \u003cp\u003eThe immunochemistry images depicting different expression levels (low and high) of Sp1 in GC were presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC. We also found that the patients achieving partial response (PR) exhibited higher levels of Sp1 in cancerous tissues compared to those achieving stable disease (SD) or progressive disease (PD) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Furthermore, Kaplan-meier analysis showed that patients with high expression of Sp1 had better OS than those with low expression of Sp1 (17.3 vs. 7.8 months, P\u0026thinsp;=\u0026thinsp;0.004, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). In the univariate analysis, factors including ECOG PS at ICI initiation, CD8\u0026thinsp;+\u0026thinsp;T cells, M1 phenotype of TAM, M2 phenotype of TAM, and Sp1 expression were identified as potential prognostic factors in patients with STAD treated by ICIs (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Notably, ECOG PS at ICI initiation (P\u0026thinsp;=\u0026thinsp;0.034), CD8\u0026thinsp;+\u0026thinsp;T cells (P\u0026thinsp;=\u0026thinsp;0.033), M2 phenotype of TAM (P\u0026thinsp;=\u0026thinsp;0.015), and Sp1 (P\u0026thinsp;=\u0026thinsp;0.002) showed independent prognostic value in the multivariate Cox regression model (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate analysis of prognostic factors in patients with GC treated by ICIs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026gt;\u003c/span\u003e\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.392\u0026ndash;3.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.677\u0026ndash;6.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECOG PS at ICI initiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.098\u0026ndash;9.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.169\u0026ndash;58.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD-L1 expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026gt;\u003c/span\u003e\u0026thinsp;1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.118\u0026ndash;1.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntibiotic use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.908\u0026ndash;12.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorticosteroids use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.590\u0026ndash;4.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD8\u0026thinsp;+\u0026thinsp;T cells (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.374\u0026ndash;0.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.289\u0026ndash;0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1 phenotype of TAMs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.710\u0026ndash;0.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.789\u0026ndash;1.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM2 phenotype of TAMs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.005\u0026ndash;1.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.036\u0026ndash;1.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000-1.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSp1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.049\u0026ndash;0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.013\u0026ndash;0.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe immunological role of Sp1\u003c/p\u003e \u003cp\u003eIn addition, we investigated the potential correlation between Sp1 expression and TMB as well as MSI across all types of cancers in the TCGA database (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA showed a positive correlation between Sp1 expression and TMB in patients with GC (P\u0026thinsp;=\u0026thinsp;0.0012), COAD (P\u0026thinsp;=\u0026thinsp;0.006), LGG (P\u0026thinsp;=\u0026thinsp;0.013), PAAD (P\u0026thinsp;=\u0026thinsp;0.036), and THYM (P\u0026thinsp;=\u0026thinsp;0.0039). Conversely, we found a negative correlation between Sp1 expression and TMB in patients with BRCA (P\u0026thinsp;=\u0026thinsp;2.9e-11), and THCA (P\u0026thinsp;=\u0026thinsp;1e-06). Furthermore, Sp1 expression was positively correlated with MSI of COAD (P\u0026thinsp;=\u0026thinsp;1.5e-07), LUSC (P\u0026thinsp;=\u0026thinsp;6.1e-05), READ (P\u0026thinsp;=\u0026thinsp;0.0013), and UCEC (P\u0026thinsp;=\u0026thinsp;0.001). Conversely, a negative correlation was observed between Sp1 expression and MSI in patients with BRCA (P\u0026thinsp;=\u0026thinsp;2.8e-06), DLBC (P\u0026thinsp;=\u0026thinsp;4.8e-07), HNSC (P\u0026thinsp;=\u0026thinsp;4.9e-09), LGG (P\u0026thinsp;=\u0026thinsp;0.033), PRAD (P\u0026thinsp;=\u0026thinsp;0.00017), SKCM (P\u0026thinsp;=\u0026thinsp;7.4e-07), and THCA (P\u0026thinsp;=\u0026thinsp;0.0029) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eThe ICIs play a pivotal role in the treatment of cancers. We also investigated the correlations between Sp1 expression and a variety of genes involved in immune checkpoint signaling, such as CTLA4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Sp1 expression demonstrated significant correlations with CD200, NRP1, CD200R1, CD276, CD160, and TNFSF15 in most types of cancers (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Additionally, we found that Sp1 expression was significantly correlated with most of these genes in patients with GC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDespite an extensive literature search, we did not find any comprehensive pan-cancer analysis studies of Sp1 that utilized multiple databases. Our study is the first to provide an extensive analysis of the genetic characteristics and predictive value of Sp1 across a spectrum of cancers, with a particular focus on its role in the immunotherapy of GC. We utilized genomics, single-cell omics, and data from patient in our hospital. Initially, we identified conserved sequences of Sp1 across various species, suggesting that despite continuous evolutionary processes, Sp1 remains a crucial factor necessary for fundamental cellular functions, stability, or proliferation. This conservation highlights the importance of Sp1 in maintaining essential biological processes that are invariant across different organisms\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur analysis revealed that the transcription levels of Sp1 were significantly elevated in the majority of the cancer types compared to normal tissues, particularly in the digestive system, including CHOL, ESCA, LIHC, PAAD, and STAD. These findings align with previous research, including our own studies in PAAD and LIHC \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan additionalcitationids=\"CR23 CR24 CR25 CR26\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Interestingly, we observed lower Sp1 expression in certain cancers, particularly those associated with hormones, such as BRCA, PRAD, THCA and UCEC. While Sp1 has been reported to regulate several hormone receptors, which may influence treatment outcomes with endocrine therapies, the specific mechanisms remain unclear and warrant further investigation\u003csup\u003e\u003cspan additionalcitationids=\"CR29 CR30 CR31\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAt the protein level, most alterations aligned with the mRNA trends, as verified by our GC cohort through IHC. However, the expression pattern of Sp1 in BRCA contradicted the mRNA findings, which may be attributable to post-transcriptional regulation, the limited sample size of CPTAC data (only 18 controls), or association with undifferentiated subtypes. Additionally, we observed significant associations between Sp1 expression and clinical stages in several cancer types. Collectively, these findings suggest that Sp1 may play diverse roles in different stages of tumorigenesis and progression across various anatomical sites.\u003c/p\u003e \u003cp\u003eKaplan-Meier analysis corroborated that elevated Sp1 expression corresponded to poorer OS in LGG, LIHC, PAAD, and THCA, and was associated with adverse DFS in LGG and ACC. Conversely, in KIRC, higher Sp1 expression correlated with better OS and DFS. These prognostic indicator values were supported by both clinical and experimental verification\u003csup\u003e24\u0026ndash;26,32\u0026minus;36\u003c/sup\u003e. It was worth noting that in THCA, the relationship between Sp1 mRNA expression and OS appeared inconsistent compared to other cancers. Given that Sp1 has been implicated in modulating both tumor suppressor genes and oncogenes in THCA\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003eand displays distinctive expression patterns\u003csup\u003e\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, we hypothesize that its mechanisms are complex and require more in-depth research.\u003c/p\u003e \u003cp\u003eGene mutations significantly influence the occurrence, progression, and therapeutic response of tumors. Our findings indicate that Sp1 mutations are most prevalent in UCS and may serve as a protective biomarker for patients with this type of cancer. TMB and MSI reflect the frequency of mutations within the tumor genome and are indicative of the efficacy of ICIs across various tumors. Generally, tumors characterized by high TMB levels and MSI status exhibit a favorable response to immunotherapy\u003csup\u003e\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Our results showed that upregulated Sp1 was strongly associated with TMB and MSI across multiple cancer types. Specifically, Sp1 level was positively correlated with both TMB and MSI in STAD and COAD.\u003c/p\u003e \u003cp\u003eDNA methylation is a chemical modification process that involves the transfer of active methyl groups to specific bases in the DNA chain, catalyzed by DNA methylation transferases (DNMTs) \u003csup\u003e\u003cspan additionalcitationids=\"CR45 CR46\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Typically, cancer cells exhibit a global loss of genetic modifications alongside abnormal methylation at enhancer and promoter regions. These alterations in methylation distribution lead to the inhibition of tumor suppressor gene expression and an increase in proto-oncogene expression, thereby further promoting tumorigenesis. In our study, we utilized the SangerBox approach to investigate the association between Sp1 expression and four classical DNA methyltransferases (DNMT1, DNMT2, DNMT3A, and DNMT3B) across different tumors. Our findings revealed significant correlations between Sp1 expression and DNMT1, DNMT2, DNMT3A, and DNMT3B expression levels in THCA, UVM, DLBC, LGG, and LIHC compared with normal tissues. Interestingly, in patients with GC, we observed a significant negative association between Sp1 DNA methylation status both at non-promoter regions and multiple probes within promoter regions with gene expression levels. These results suggest that Sp1 may promote tumorigenesis through its involvement in DNA methylation processes.\u003c/p\u003e \u003cp\u003eKEGG and GO enrichment indicated that the top gene sets most closely associated with Sp1 are significantly related to cancer-associated pathways, particularly those involved in viral carcinogenesis. It is well known that the onset of many cancers can be caused by viral infections, such as HPV leading to cervical cancer, HBV leading to liver cancer, and EBV leading to nasopharyngeal carcinoma, lymphoma, and GC \u003csup\u003e\u003cspan additionalcitationids=\"CR49 CR50 CR51\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Combined with other research findings, we are confident that Sp1 plays a key role in cancers caused by viral infections warranting further attention\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Through the screening and integration of Sp1-interacting proteins and the most relevant genes, we identified two intersecting genes, CREBBP and EP300. CREBBP and EP300 are well-known homologous lysine acetyltransferases frequently mutated in hematological malignancies and have become promising drug targets\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. However, there is a lack of studies focusing on the mechanism between Sp1 and CREBBP/EP300 in different cancers.\u003c/p\u003e \u003cp\u003eThrough GSEA analysis focused on GC, we selected the TOP five pathways. We observed that differential genes in the high Sp1 expression subgroup upregulated the T cell receptor signaling pathway. It is well known that malignant tumor cells establish a complex TME conducive to their growth and proliferation. The TME encompasses not only tumor cells but also the surrounding stromal cells, immune cells, inflammatory cells, secretory factors, and microvessels. Among these components, immune cells such as CD8+ T cells and macrophages play a crucial role in supervising and eliminating tumor cells while regulating their growth and dissemination\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. \"Cold\" tumors, which lack T-cell infiltration, exhibit poor responsiveness to immunotherapy compared to \"hot\" tumors, characterized by abundant T-cell infiltration and favorable responses to immunotherapy\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Subsequent analyses of immune infiltration uncovered significant correlations between Sp1 expression and various immune components in certain types of cancer (Fig.\u0026nbsp;12). Our previous studies, along with those of other researchers, have shown that a higher proportion of CD8+ T cells infiltration is associated with better immune therapeutic outcomes\u003csup\u003e\u003cspan additionalcitationids=\"CR62 CR63\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. We assessed the correlation between Sp1 expression and the proportion of CD8+ T cells using different algorithms, finding a positive correlation in BRCA-Basal, PRAD, SKCM, and STAD based on all three algorithms. This result was confirmed in our own STAD cohort. Further scRNA data supported that immune components were the main altered cluster between tumor and normal tissues. Meanwhile, Sp1 expression was higher in macrophages and monocytes but lower in T cells within tumors.\u003c/p\u003e \u003cp\u003eHigh expression of Sp1 in tumor cells has been associated with macrophage infiltration, correlating with poorer prognosis in colorectal cancer\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Macrophage Sp1 expression deficiency promoted the transition from M2 to M1 phenotype, inducing apoptosis in lung cancer. Conversely, HDAC2 can deacetylate Sp1, thereby facilitating the transition of macrophages from M1 to M2 phenotype, which promotes lung cancer growth\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. HDAC2 is also significantly overexpressed in gastric cancer and is associated with poor prognosis, but predicts a better outcome of immunotherapy by enhancing CD8\u0026thinsp;+\u0026thinsp;T cell infiltration and cytotoxicity in a \"hot\" tumor status \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. This suggests that Sp1 may play a similar crucial role in shaping TME by reprogramming macrophage phenotypic transitions and activating CD8\u003csup\u003e+\u003c/sup\u003e T cell in GC. Although we observed a positive correlation between Sp1 expression and M1-phenotype of TAMs, which suggests a tumor-suppressive effect, recent insights highlight the complexity of macrophage phenotypic transitions. It is essential to recognize a broader spectrum of macrophage classifications beyond the M1 and M2 extremes\u003csup\u003e\u003cspan additionalcitationids=\"CR70 CR71\" citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. Sp1 has been noted to maintain the naive state of CD8+ T cells\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. In GC, we observed a reduction of Sp1 in T cells within tumor tissues, potentially indicating a transformation process towards mature CD8+ T cells. Alternatively, this reduction could reflect a tumor extracellular matrix response mechanism aimed at preventing CD8+ T cells from approaching the parenchymal region\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eICIs play a crucial role in cancer treatment, with immune checkpoint genes serving as important therapeutic targets\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. In GC, researches have demonstrated that Sp1 can bind to the PD-L1 promoter region, contributing to PD-L1 overexpression and thus promoting cancer development\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. Our study identified multiple immune checkpoints, including CD200R1, CD276, CD160, TNFSF15, and NRP1, that exhibit a positive correlation with Sp1 expression across various tumors. This suggests that Sp1 may represent a novel target for tumor immunotherapy. In GC, the majority of immune checkpoints, such as LAG3, NRP1, TIGIT, and CTLA4, showed a significant positive correlation with Sp1 expression. These checkpoints are known to contribute to the exhaustion of CD8\u0026thinsp;+\u0026thinsp;T-cells, thereby facilitating immune evasion by the tumor. These findings underscore the potential of targeting Sp1 in enhancing the efficacy of immunotherapy in GC.\u003c/p\u003e \u003cp\u003eAdditionally, our immunohistochemistry analysis of tumor tissue from patients with advanced GC indicated higher levels of Sp1 in cases with partial response (PR) compared to those with stable disease (SD) or progressive disease (PD). Kaplan-Meier survival analysis further revealed that patients with high Sp1 expression had improved OS compared to those with low Sp1 expression. Univariate analysis identified ECOG PS and Sp1 expression as potential prognostic factors for GC patients treated with ICIs. Multifactor Cox regression models confirmed that both ECOG PS and Sp1 expression at the onset of ICI treatment independently predicted patient prognosis.\u003c/p\u003e \u003cp\u003eBased on our analysis, we postulate that higher Sp1 expression in tumor tissue promotes the maturation and infiltration of CD8+ T cells while concurrently enhancing the expression of immunosuppressive molecules. This dual action results in an increased number of CD8+ T cells, but their functionality remains in a state of exhaustion. Exhausted CD8+ T cell (CD8+ Tex) is a key mechanism in tumor immune evasion. Recent studies have categorized CD8+ Tex into ICI permissive and ICI-refractory subsets, highlighting potential mechanisms underlying resistance to immunotherapy\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e,\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e. Therefore, patients who respond positively to ICIs may have a higher proportion of reversibly exhausted CD8+ Tex or a predominance of total CD8\u0026thinsp;+\u0026thinsp;cell infiltration. Of course, there are certain limitations to this study. Firstly, the sample size is limited, which may introduce some bias. Additionally, while we observed this phenomenon in human specimens, we did not conduct molecular-level validation on the specific mechanisms by which Sp1 promotes gastric cancer progression and enhances immunotherapy efficacy. We plan to conduct more in-depth mechanistic studies which will focus on Sp1's interactions within the TME, particularly its interactions with CD8\u0026thinsp;+\u0026thinsp;Tex, to obtain more robust evidence.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, this study explored a novel role for Sp1 in tumors, particularly in GC. Patients with high Sp1 expression demonstrated better responses to ICIs and overall survival benefits. Further analysis revealed a significant positive correlation between Sp1 expression and TMB, as well as the infiltration of CD8\u0026thinsp;+\u0026thinsp;T cells and M1 phenotypes of TAMs. This suggests that Sp1, as a proto-oncogene, may promote the reprogramming of multiple oncogenes in tumor cells, leading to the formation of numerous new antigens and enhanced immune cell infiltration. Additionally, high Sp1 expression also promotes PD-L1 expression, contributing to immune escape but paradoxically leading to better immunotherapy efficacy. Therefore, Sp1 could serve as an effective biomarker for predicting the treatment efficacy of ICIs in GC.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eSp1, Transcription factor\u0026nbsp;specificity\u0026nbsp;\u0026nbsp;protein 1\u003c/p\u003e\n\u003cp\u003eGC, \u0026nbsp;Gastric cancer\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTCGA, The Cancer Genome Atlas\u003c/p\u003e\n\u003cp\u003eGEO, Gene Expression Omnibus\u003c/p\u003e\n\u003cp\u003eTHPA, The Human Protein Atlas\u003c/p\u003e\n\u003cp\u003eTMB, Tumor\u0026nbsp;Mutational\u0026nbsp;Burden\u003c/p\u003e\n\u003cp\u003eMSI, \u0026nbsp; Microsatellite\u0026nbsp;Instability\u003c/p\u003e\n\u003cp\u003eOS, Overall Survival\u003c/p\u003e\n\u003cp\u003eCPS,\u0026nbsp;Combined Positive Score\u003c/p\u003e\n\u003cp\u003eICIs,\u0026nbsp;Immune\u0026nbsp;Checkpoint\u0026nbsp;Inhibitors\u003c/p\u003e\n\u003cp\u003eTME,\u0026nbsp;Tumor microenvironment\u003c/p\u003e\n\u003cp\u003eBRCA, Breast cancer\u003c/p\u003e\n\u003cp\u003eRCC, Renal cell carcinoma\u003c/p\u003e\n\u003cp\u003eUCEC, Uterine corpus endometrial carcinoma\u003c/p\u003e\n\u003cp\u003eLUAD, Lung adenocarcinoma\u003c/p\u003e\n\u003cp\u003eIHC,\u0026nbsp;Immunohistochemisty\u003c/p\u003e\n\u003cp\u003eCNA, Copy number alteration\u003c/p\u003e\n\u003cp\u003ePFS, Progression-free Survival\u0026nbsp;\u003c/p\u003e\n\u003cp\u003emIF,\u0026nbsp;Multiplex\u0026nbsp;Immunofluorescence\u003c/p\u003e\n\u003cp\u003eGO,\u0026nbsp;Gene Ontology\u003c/p\u003e\n\u003cp\u003eKEGG,\u0026nbsp;Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003eBP,\u0026nbsp;Biological processes\u003c/p\u003e\n\u003cp\u003eCC,\u0026nbsp;Cellular components\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMF,\u0026nbsp;Molecular functions\u003c/p\u003e\n\u003cp\u003eGSEA, Gene Set Enrichment Analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHPA,\u0026nbsp;Human Protein Atlas\u003c/p\u003e\n\u003cp\u003eGTEx,\u0026nbsp;Genotype-Tissue Expression\u003c/p\u003e\n\u003cp\u003eFANTOM5,\u0026nbsp;Function Annotation of the Mammalian Genome 5\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCHOL,\u0026nbsp;Cholangiocarcinoma\u003c/p\u003e\n\u003cp\u003eESCA,\u0026nbsp;Esophageal carcinoma\u003c/p\u003e\n\u003cp\u003eGBM, Glioblastoma multiforme\u003c/p\u003e\n\u003cp\u003eKICH,\u0026nbsp;Kidney chromophobe\u003c/p\u003e\n\u003cp\u003eKIRP,\u0026nbsp;Kidney renal papillary cell carcinoma\u003c/p\u003e\n\u003cp\u003eLIHC,\u0026nbsp;\u0026nbsp;Liver hepatocellular carcinoma\u003c/p\u003e\n\u003cp\u003eSTAD,\u0026nbsp;Stomach adenocarcinoma\u003c/p\u003e\n\u003cp\u003eTHCA,\u0026nbsp;Thyroid carcinoma\u003c/p\u003e\n\u003cp\u003eLGG,\u0026nbsp;Brain lower grade glioma\u003c/p\u003e\n\u003cp\u003ePAAD,\u0026nbsp;Pancreatic adenocarcinoma\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUCS,\u0026nbsp;Uterine carcinosarcoma\u003c/p\u003e\n\u003cp\u003eDFS, Disease-free Survival\u003c/p\u003e\n\u003cp\u003eSCM, Skin cutaneous melanoma\u003c/p\u003e\n\u003cp\u003eGSEA,\u0026nbsp;Gene Set Enrichment Analysis\u003c/p\u003e\n\u003cp\u003eSCLC,\u0026nbsp;Small cell lung cancer\u003c/p\u003e\n\u003cp\u003escRNA-seq,\u0026nbsp;Single-cell RNA sequencing\u003c/p\u003e\n\u003cp\u003ePR, Partial response\u003c/p\u003e\n\u003cp\u003eSD, Stable disease\u003c/p\u003e\n\u003cp\u003ePD, Progressive disease\u003c/p\u003e\n\u003cp\u003eDNMTs, DNA methylation transferases\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCD8+ Tex, Exhausted CD8+ T cell\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003ePatients\u0026rsquo; written informed consents and approval from the Ethics Committees of\u0026nbsp;Changzhou No.2 People\u0026rsquo;s Hospital\u0026nbsp;were obtained for the use of these clinical materials.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was supported by the National Natural Science Foundation of\u0026nbsp;China\u0026nbsp;(81902955),\u0026nbsp;the Medical Scientific Research Foundation of Guangdong Province (A2024172), the Youth Foundation of Health Committee of Shanghai Jing\u0026rsquo;an District (2021QN03),\u0026nbsp;Shenzhen Key Medical Discipline Construction Fund (SZXK013), Changzhou Medical Talents Project for Domestic and Foreign Training (JW2023001), and\u0026nbsp;Qing Miao Talent Project of Changzhou Health Committee (CZQM2022010).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYang Zhou,\u0026nbsp;Jianhua Chang,\u0026nbsp;Libao Gong,\u0026nbsp;and\u0026nbsp;Junjie Hang\u0026nbsp;designed the study.\u0026nbsp;Yang Zhou, Zhenzhen Luo, Lixia Wu, Xiaoli Zhou, and Junjie Hang\u0026nbsp;performed the experiments.\u0026nbsp;Jinfeng Guo, Lixia Wu,\u0026nbsp;Junjie Huang,\u0026nbsp;Qiuhua Duan, and\u0026nbsp;Junjie Hang\u0026nbsp;performed bioinformatic analysis.\u0026nbsp;Jinfeng Guo, Lixia Wu, Daijia Huang, Li Xiao, and Junjie Hang\u0026nbsp;prepared the Figures.\u0026nbsp;Yang Zhou, Qiuhua Duan,\u0026nbsp;Libao Gong, and Junjie Hang\u0026nbsp;collected and analyzed the data.\u0026nbsp;Yang Zhou, Zhenzhen Luo, Lixia Wu, Jianhua Chang, Libao Gong, and Junjie Hang\u0026nbsp;wrote the manuscript. All authors\u0026nbsp;revised\u0026nbsp;and approved the\u0026nbsp;final manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel, R. L., Giaquinto, A. N. \u0026amp; Jemal, A. Cancer statistics, 2024. CA: a cancer journal for clinicians 74, 12-49, doi:10.3322/caac.21820 (2024).\u003c/li\u003e\n\u003cli\u003eSung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: a cancer journal for clinicians 71, 209-249, doi:10.3322/caac.21660 (2021).\u003c/li\u003e\n\u003cli\u003eQiu, H., Cao, S. \u0026amp; Xu, R. Cancer incidence, mortality, and burden in China: a time-trend analysis and comparison with the United States and United Kingdom based on the global epidemiological data released in 2020. 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Nature immunology 20, 326-336, doi:10.1038/s41590-019-0312-6 (2019).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cancer-cell-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ccin","sideBox":"Learn more about [Cancer Cell International](http://cancerci.biomedcentral.com/)","snPcode":"12935","submissionUrl":"https://submission.nature.com/new-submission/12935/3","title":"Cancer Cell International","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Sp1, pan-cancer analysis, prognosis, immunotherapy response","lastPublishedDoi":"10.21203/rs.3.rs-4623533/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4623533/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSp1, a transcription factor, plays a pivotal role in tumorigenesis across diverse cancers. However, its comprehensive pan-cancer analyses and immunological roles in gastric cancer (GC) remain inadequately elucidated.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThrough a comprehensive analysis utilizing bioinformatics tools and datasets from TCGA, GEO, and THPA, we examined the multifaceted role of Sp1. Expression profiles were assessed across cell lines, tissues, and tumors, alongside exploration of genetic alterations, DNA methylation, and protein phosphorylation. Its associations with immune infiltration, tumor mutational burden, and immune checkpoint signaling were investigated. Additionally, single-cell transcriptome data showed its expression in different immune cells in GC. Validation of correlations between Sp1 and immune microenvironment in GC was performed using immunohistochemistry and multiple immunofluorescence in an immunotherapy-treated patient cohort. The prognostic value of Sp1 in GC receiving immunotherapy was evaluated with Cox regression model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eElevated Sp1 levels were observed in various cancers compared to normal tissues, with notable prominence in gastric cancer. High Sp1 expression correlated with advanced stage, poor prognosis, elevated tumor mutational burden (TMB), and microsatellite instability (MSI) status, particularly in GC. Sp1 levels also correlated with CD8\u0026thinsp;+\u0026thinsp;T cell and M1 phenotype of tumor-associated macrophages infiltration. Furthermore, GC patients with higher Sp1 levels exhibited improved response to immunotherapy. Moreover, Sp1 emerged as a prognostic and predictive biomarker for GC patients undergoing immunotherapy.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur pan-cancer analysis sheds light on Sp1's multifaceted role in tumorigenesis and underscores its potential as a prognostic and predictive biomarker for GC patients undergoing immunotherapy.\u003c/p\u003e","manuscriptTitle":"Pan-cancer analysis of Sp1 with a focus on immunological roles in gastric cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-09 21:08:35","doi":"10.21203/rs.3.rs-4623533/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-25T23:07:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-25T16:14:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-07T02:15:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10226873714141121411672658870068787000","date":"2024-08-04T13:48:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-04T12:35:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"97521647525742191168862100648516971882","date":"2024-08-04T06:03:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109347429157571226026768650509341621348","date":"2024-08-02T13:17:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-02T13:01:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-10T03:40:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-10T03:40:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Cell International","date":"2024-06-23T02:19:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-cell-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ccin","sideBox":"Learn more about [Cancer Cell International](http://cancerci.biomedcentral.com/)","snPcode":"12935","submissionUrl":"https://submission.nature.com/new-submission/12935/3","title":"Cancer Cell International","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"98511cdb-498a-4478-a72e-eda60f32ffd8","owner":[],"postedDate":"August 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-10-21T16:05:30+00:00","versionOfRecord":{"articleIdentity":"rs-4623533","link":"https://doi.org/10.1186/s12935-024-03521-z","journal":{"identity":"cancer-cell-international","isVorOnly":false,"title":"Cancer Cell International"},"publishedOn":"2024-10-14 15:58:11","publishedOnDateReadable":"October 14th, 2024"},"versionCreatedAt":"2024-08-09 21:08:35","video":"","vorDoi":"10.1186/s12935-024-03521-z","vorDoiUrl":"https://doi.org/10.1186/s12935-024-03521-z","workflowStages":[]},"version":"v1","identity":"rs-4623533","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4623533","identity":"rs-4623533","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}