{"paper_id":"4138546a-b273-476e-a8c0-8e5a081c9bb2","body_text":"Pan-cancer analysis: Predictive role of TAP1 in cancer prognosis and responses of immunotherapy | 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: Predictive role of TAP1 in cancer prognosis and responses of immunotherapy Zewei Tu, Kuangxun Li, Yuyang Huang, Shigang Lv, Jingying Li, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1544440/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Feb, 2023 Read the published version in BMC Cancer → Version 1 posted You are reading this latest preprint version Abstract Background : Transporter associated with antigen processing 1 (TAP1) is a transporter that processes and presents the major histocompatibility complex class I (MHC-I) restricted antigens, including tumor-associated antigens. TAP1 is aberrantly expressed in multiple cancer types, and also involves in tumor immunity. Therefore, the predictive role of TAP1 in cancer development and treatment is expected. Methods : Transcriptomic profiles were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) database. Genetic alteration, protein distribution and interaction information were downloaded from cbioPortal, Human Protein Atlas (HPA) and Compartmentalized Protein-Protein Interaction (ComPPI), respectively. Single-cell analysis was conducted on Tumor Immune Single-cell Hub (TISCH) website. Gene set enrichment analysis (GSEA) was employed to investigate the functioning mechanism of TAP1 by R package “clusterProfiler”. Immune cell infiltration of Pan-cancer was explored by Tumor Immune Estimation Resource (TIMER) 2.0 webtool and visualized by R programming language. Correlation between TAP1 expression and immunotherapy biomarkers was explored using Spearman’s correlation test. Association with immunotherapy responses of TAP1 was investigated using the information of the cancer cohorts with patients received immune checkpoint inhibitors (ICIs). Results : TAP1 expression was elevated in most pan-cancer types and exhibited distinct prognostic value in diverse cancer types. Within tumor tissues, immune cells expressed more TAP1 than malignant cells. TAP1 expression was significantly correlated with immune-related pathways, infiltration of T lymphocytes and immunotherapeutic biomarkers. Cohort validation revealed a significant correlation with immune therapeutic effects and verified the prognostic role of TAP1 in immunotherapy. Western blot assay indicated that TAP1 is upregulated in GBMs compared with adjacent normal brain tissues (NBTs). Conclusion : TAP1 was a robust tumor biomarker, and a novel predictor of clinical prognosis and immunotherapeutic responses in distinct cancer types. transporter associated with antigen processing 1 (TAP1) Pan-cancer Prognostic biomarker Cancer immunotherapy Immune Check-point Inhibitor (ICI). Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Transporter associated with antigen processing 1 (TAP1) is a member of ATP-binding cassette (ABC) superfamily, which forms heterodimeric complex with its homology TAP2 for intracellular translocation of antigenic peptide across endoplasmic reticulum (ER) membrane [ 1 , 2 ]. In the ER, TAP assists the loading of cytosolic peptides onto adjacent major histocompatibility complex class I (MHC-I) molecules, which then transport the peptide to cell surface for recognition by CD8 + cytotoxic T lymphocytes (CTL). MHC-I presentation occurs in every nucleated cell (i.e., not mature erythrocyte), and the presented antigens are generally derived from endogenous molecules [ 3 ]. Moreover, it is demonstrated that MHC-I also presents exogenous antigens derived from pathogens or dead cells in dendritic cells (DC) [ 4 ]. Because of the precise regulation, viral-infected cells and malignantly transformed cells that express abnormal protein are under strict immune surveillance and eliminated in time. Consequently, the pivotal function of TAP1 is prone to be hijacked by malignancy for immune evasion. TAP complex executes its role via an elaborate mechanism. The antigenic proteins, either endogenously expressed or internalized by antigen presenting cells, are marked by ubiquitin and get degraded into small fragments of 8–10 amino acid peptides [ 5 ]. CD8 + CTL recognize the antigenic peptides presented by MHC-I and initiate an immune attack. In the case when assembly or translocation of MHC-I complex goes wrong, CTL-mediated immune surveillance will be suppressed [ 2 ]. Therefore, malignant cells evolve strategies to escape from immune system by targeting and interfering the normal process of MHC-I antigen presentation, especially the “peptide pump” --TAP [ 6 ]. Within the TAP complex, TAP1 is reported to stabilize the assembly of TAP2 [ 7 ]. Thus, we focus on TAP1 as it might dominate the function of TAP proteins. Over these years, studies on the field of TAP1 emerge continuously, providing novel findings in several cancers [ 8 ]. Down-regulation or defects of TAP1 expression were observed in primary cancer or autologously metastatic lesions of different stages, such as bladder cancer [ 9 ], small cell lung cancer (SCLC)[ 10 ], glioma [ 11 ], prostatic cancer [ 12 ], head and neck squamous cell carcinoma (HNSC) [ 13 ], breast cancer [ 14 ] and colorectal cancer [ 15 ]. However, there are few studies proposing TAP1 associated therapeutic regimen against tumor. As a novel therapy, immune therapy delays or even completely blocks the development and progression of tumor, being honored as the gimmers of hope for many cancer patients. Nevertheless, expected responses towards immunotherapy are not observed in every patient. Besides, random application of immunotherapy brings disadvantages to patients who bear tolerance or toxic reaction to the drugs. Therefore, exploring a reliable biomarker that predicts the effect of immunotherapy for individual patient is of great urgence. As for TAP1 molecule, it regulates normal immune responses, and is also reported an abnormal expression in various cancer types. We proposed the TAP1 as a potential biomarker to predict the immunotherapeutic efficacy. In this study, we performed a comprehensive pan-cancer analysis and built up a landscape of TAP1 across various cancer types. Here, we reported the basic information of TAP1 in pan-cancer cohorts and explored the relationship between TAP1 expression and prognosis, enriched gene sets, immune cell infiltration, expression of immune regulators and immunotherapeutic effects in pan-cancer scale. Based on the information, TAP1 is proposed as a novel biomarker to predict the prognosis and effect of immunotherapy in diverse cancers. Hopefully, our study will lead a future direction for the research of TAP1. Methods And Materials Clinical samples and ethical statement The clinical samples of glioblastoma (GBM) were obtained from inpatients of the Second Affiliated Hospital of Nanchang University between 2021 and 2022. Tumor core and the para-tumorous normal tissue were excised and stored in -80˚C until use. This study was approved by Medical Ethics Committee of The Second Affiliated Hospital of Nanchang University. Sample acquisition and utilization for study were admitted by each patient and performed based on the approved guidelines. Data sources and processing The combinative mRNA expression profiles of TAP1 in tumor and corresponding normal tissues were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. The available data was downloaded from UCSC Xena database ( https://xenabrowser.net/datapages/ ) [16] , and the data format was transformed into TPM format (transcripts per kilobase million). On cBioPortal website ( https://www.cbioportal.org/ ), query of TPA1 in “TCGA pan-cancer atlas” was submitted. Gene alteration data (mutation, structural variant, amplification, deep deletion and multiple alterations) matched with 32 cancer types was obtained from “Cancer Type Summary” item. In subcellular level, immunofluorescence images of TPA1 were obtained from The Human Protein Atlas (HPA, https://www.proteinatlas.org/ ) database and present the subcellular distribution of TAP1 protein. Inside the Compartmentalized Protein-Protein Interaction Database (ComPPI, https://comppi.linkgroup.hu/ ), information of protein-protein Interaction (PPI) was explored. Names of protein were mapped using the “Retrieve/ID mapping” tool in the Uniprot website ( https://www.uniprot.org/ ), and visualized by R package “ggplot2” in R programming environment. The abbreviations of cancer types were summarized in the Supplementary Table 1 . Western blot Protein was extracted from the GBM cores and adjacent normal tissue in the collected GBM samples. The prepared samples were allocated to perform the western blot using the method and reagents we used in previous study [ 17 ]. The information of antibodies we used is as below: rabbit TAP1 polyclonal antibody (Proteintech number: 11114-1-AP), WB dilution concentration 1:1000). Single-cell analysis of TAP1 The single-cell analysis of TAP1 was conducted on Tumor Immune Single-cell Hub (TISCH, http://tisch.comp-genomics.org/ ) website [18] . Gene “TAP1” was input and cell-type annotation “major-lineage” was searched in “all cancers”. In present study, 33 cell types were investigated in 78 cancer lineages for TAP1 expression evaluation. Prognosis analysis in Cox regression analysis and Kaplan-Meier methods Based on the matched TAP1 expression and prognostic information from TCGA database, the role of TAP1 in predicting the prognosis in pan-cancer was explored. Univariate Cox regression analysis and Kaplan-Meier analysis were performed to assess the effect of TAP1 expression on patients’ prognosis in diverse cancer types, the prognostic indicators covering overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI) and progression-free interval (PFI). TAP1 expression pattern as continuous variable was tested by univariate Cox regression, and the expression level as bivariate was tested by Kaplan-Meier method. Within the algorithm, “surv-cutpoint” function of “surminer” R package was utilized to determine the cut-off point with maximal rank statistics that divides the undefined expression data into high- or low-expression. Because of the abnormal distribution of survival data, non-parametric test was recruited and the log-ranked P value was computed in our K-M analysis. For Cox regression analysis, hazard ratio (HR) and 95% confidence interval (CI) were presented. Differential expression gene (DEG) screening from low and high TAP1 expression subgroups Cancer patients were ordered according to their TAP1 expression value. 30% of the patient population at the top and bottom of the array was defined as high and low expression subgroup. Using “limma” R package [19] , differential expression analysis was performed, with log2(fold change) and rectified P-value calculated. Genes showing P-value under 0.05 was considered as DEGs. The DEGs list derived from our analysis was displayed in Supplementary Table 2 . Gene set enrichment analysis (GSEA) To further explore the possible mechanisms or biological processes TAP1 may involve in, GSEA was performed. Hallmarks gene set (containing 50 gene sets) file was downloaded from MSigDB as “gmt” format. Then, “clusterProfiler” R package was performed to run GSEA based on the available data obtained from differential expression analysis, with false discovery rate (FDR) and normalized enrichment score (NES) computed for every hallmark in each cancer type [ 20 ]. The TAP1 enrichment data in multiple pathways matched with corresponding pan-cancer types was visualized using “ggplot2” R package. Tumor microenvironment analysis in pan-cancer Tumor mass is normally infiltrated by a variety of immune cells and other functional cells that affect the cancer progression and therapeutic effect. The infiltrating cells and molecules inside tumor matrix comprise a so-called tumor microenvironment (TME) [ 17 ]. To explore the quantity of immune infiltration cells inside tumor tissue, Tumor Immune Estimation Resource 2.0 (TIMER 2.0) was employed to evaluate immune cell infiltration by using the advantage of transcriptome data from pan-cancer cohort. Correlations between TAP1 expression and infiltrating cells of interest were investigated using Spearman’s rank correlation analysis. The candidate cells include: CD4 + T cell, cancer-associated fibroblast (CAF), lymphoid progenitor, myeloid progenitor, granulocyte-monocyte progenitor, endothelial, hematopoietic stem cell (HSC), T cell follicular helper (Tfh), T cell gamma delta (γδT), NK T cell, regulatory T cell (Treg), myeloid-derived suppressor cell (MDSC), B cell, neutrophil, monocyte, macrophage, dendritic cell, NK cell, mast cell, CD8 + T cell. The infiltration pattern was visualized by R package “ggplot2”. As biomarker to predict TME condition, the microsatellite instability (MSI) and tumor mutational burden (TMB) were evaluated [21, 22] . Correlations between TAP1 mRNA expression and MSI, TMB were investigated by Spearman’s correlation test. According to previous study, 47 immune checkpoints (ICP) were recruited and also estimated for their correlation with TAP1 expression [ 23 ]. Immune check-point inhibitor (ICI) Cohort Validation A comprehensive study that summarized the clinical effect of immune checkpoint blockade therapy was conducted. Clinical information including the prognosis of immunotherapy matched with TPA1 expression data were obtained from previous studies [ 24 – 27 ]. The high or low expression was defined by method applied in K-M survival analysis, and the prognosis of patients with different TAP1 levels was compared by log-rank test. To assess the response towards immune ICIs, chi-square test was utilized to compare the proportion of patients that respond to ICI therapy. Statistical analysis To test the difference of TAP1 expression between cancer and para-cancerous normal tissues, a Wilcoxon sum test was conducted to calculate the statistical significance. In the comparison of protein expression, a paired t-test was performed. To investigate the correlation between cancer prognosis and TAP1 expression, univariate Cox regression analysis and Kaplan-Meier method were recruited. In Cox regression test, Cox P value and HR were assessed, and the log-ranked P value with 95%CI were calculated in K-M method. To investigate the correlation between TAP1 expression and immune cell infiltration, immune regulators expression, TMB and MSI, Spearman’s correlation test was employed to calculate the significance. In immunotherapy cohort validation, the difference in the portion of responders and non-responders between low- and high- TAP1 expression groups was tested by chi-square test. Statistical significance was at P value < 0.05. Results Briefing of TAP1 in genetic, mRNA and protein level To have a basic landscape of TAP1 expression, multi-omics cancer data were used to represent the information of TAP1. Available transcriptional profiles from TCGA and GTEx database was combined as the sample number of several cancer types was limited. TAP1 mRNA expression in the normal and tumorous tissues from 27 cancer types was exhibited in Fig. 1 A. Except for the adrenocortical carcinoma (ACC), Kidney Chromophobe (KICH) and uterine carcinosarcoma (UCS), TAP1 was dominantly overexpressed in all the tumor tissues with strong significance. Among, TAP1 showed an overwhelming expression in cervical and endocervical cancers (CESC) and HNSC than ever tissues, and the greatest difference between normal and corresponding malignant tissues was discovered in Cholangiocarcinoma (CHOL), GBM and pancreatic adenocarcinoma (PAAD). GBM, the most malignant intracranial tumor type, showed significant advantages in the amount of TAP1 expression compared normal brain tissues (Fig. 1 B). In a word, aberrant expression levels of TAP1 mRNA were discovered among various cancer types. We suspected whether protein level of TAP1 was also aberrant expressed in cancer samples, then we performed a Western blot assay to verify the computerized results from RNA-seq. Just shown in Fig. 1 C and ID , our GBM samples had exactly higher TAP1 expression than corresponding adjacent normal tissues, keeping consistent with TAP1 RNA expression pattern. Then, we investigated the genetic alteration status of TAP1 in TCGA pan-cancer cohorts, including the genetic alteration types and frequency (Fig. 1 E). Among all alteration types, “Amplification” accounts for the most frequency, followed by “Mutations” and “Deep Deletion”. Of note, some of the cancer types were observed with only one type of genetic alteration, such as the esophageal adenocarcinoma (EAC) and uveal melanoma (UVM) being totally caused by amplification, or the thymoma (THYM), completely resulted from mutation. Generally, the frequency of TAP1 alternation in pan-cancer was fluctuated between 2% and 4%, but it was worth mentioning that the diffuse large B-cell lymphoma (DLBC) exhibited the highest value of more than 8%. When focusing on copy number variation in these pan-cancer types, TAP1 expression were demonstrated a significant correlation in KICH and kidney renal papillary cell carcinoma (KIRP) ( Fig. 1 F ) . The scatter plot shown in Fig. 1 G helps confirm the results and fit the suitable regression curve. In the subcellular level, immunofluorescence images of TAP1 in tumor cell line and non-tumorous derived cell line were recruited to show the protein distribution (Fig. 1 H). Both in the Para-cancerous normal HaCaT cell line and melanoma cell line SKMEL30, TAP1 protein was clearly located at ER, without change before and after tumorigenesis. Then, a PPI network uncovering the potential biological reactions is presented in Fig. 1 I. All description above is the briefing of TAP1, in gene, RNA and protein level. TAP1 expression results across multiple cell types in single-cell analysis To figure out the TAP1 expression pattern in tumor mass, further we explored the individual expression of TAP1 in immune and malignant cell population via TISCH tool. TAP1 expression was evaluated in all the separated cells and then presented as a mean value (Fig. 2 A). In the plotted heatmap, it was observed that TAP1 was mainly expressed in immune cells, especially the T lymphocytes (CD4 Tconv, T reg, T prolif, CD8 T, CD8 Tex cells), followed by non-specific immune cells including NK cell, DC and monocyte/macrophage. Of note, TAP1 expression in malignant cells was far from dominant, revealing an overexpression in non-malignant origin population. To focus on the TAP1 expression value, non-small cell lung cancer (NSCLC) cell line GSE99254, liver hepatocellular carcinoma (LIHC) GSE98638 and colorectal cancer (CRC) GSE108989 were more predominant than other cell lines. Specifically, we performed visualization of TAP1 expression in breast invasive carcinoma (BRCA) GSE11068 and skin cutaneous melanoma (SKCM) GSE12057 datasets, and highlighted the cell types with greatest expression in Fig. 2 B-E. Our results suggested a preferential expression of TAP1 in T lymphocytes and monocyte/macrophage in TME. Risk prediction based on correlation between prognosis and TAP1 expression To further explore the potential prognosis predictive value of TAP1, we subsequently analyzed the prognostic role of TAP1 across cancers. In terms of prognostic analysis, survival indexes OS, DSS, DFI and PFI were served as reliable indicators. Scanning and summarizing the clinical outcomes of tested pan-cancer cohort, the prognostic pattern was plotted as Fig. 3 A. Kaplan-Meier method and Cox regression analysis were performed to validate each other. The results suggested TAP1 was a risky factor for ACC, DLBC, KIRP, LGG, lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), PAAD and UVM, as the higher expression of TAP1 mRNA was correlated with poor prognosis, but also a potential protective factor in bladder urothelial carcinoma (BLCA), BRCA, KIRC, OV, rectum adenocarcinoma (READ), SKCM, STAD and UCS. In terms of the Cox regression analysis using OS data, our forest plot in Fig. 3 B revealed the association achieved significance in SARC, STAD, ovarian serous cystadenocarcinoma (OV), LUAD, UVM, KIRP, PAAD, LGG and THYM. Specifically, we emphasized on the several cancer types, Kaplan-Meier survival curves were depicted. In Fig. 3 C, OS probability in high-TAP1 expression group decreased rapidly against time, but low expression group has relative better outcomes in the same time points. While the clinical outcomes were reversed in BLCA, HNSC and SKCM patients (Fig. 3 D-F). There were also cancer types showing irrelevance to TAP1 expression level, such as CHOL, Esophageal carcinoma (ESCA), paraganglioma (PCPG), sarcoma (SARC) and UCS. It can be speculated that cancer types showing significant correlation could be taken as the potential objectives for TAP1-associated treatment, which brings advantages to the population sensitive to the regimen we will discuss later. TAP1 enriched hallmarks across pan-cancer cohort Given the significant prognostic implications of TAP1 in cancers, we further investigate the underlying biological processes or pathways associated with TAP1 to understand the potential mechanisms. In present study, hallmarks gene set that composed of the marker genes defining biological condition and progression was recruited. DEGs between high- and low-TAP1 subgroups was screened out and tested for enrichment analysis in hallmarks gene sets. In Fig. 4 , the enrichment status of TAP1 in each pathway was clearly plotted. Our results revealed a highly concentrated distribution of enrichment across 33 pan-cancer types that the immune-related pathway was strongly favored in TAP1 high expression cancers: tumor necrosis factor-α (TNF-α) signaling via NF-κB pathway, interferon-γ ((IFN-γ) response, IFN-α response, inflammatory response, IL6-JAK-STAT3 signaling pathway, IL2-STAT2 signaling pathway and allograft rejection. TAP1 was also enriched in apoptosis, complement and KRAS signaling pathway, to a less degree. To focus on cancer types, ACC, LGG, LUAD, PAAD and UCS showed more relevance to the mentioned pathways. Among the bubble plot, the majority of enrichment showed a positive correlation, with negative NES dots scattered plotted. Different correlation between TAP1 expression and enriched gene set in pan-cancer was displayed in bubble plot, with NES and log-ranked FDR presented. NES, normalized enrichment score; FDR, false discovery rate. Gene sets were considered significant only when Nom P < 0.05, FDR < 0.25. Correlation between immune cell infiltration and TAP1 expression in pan-cancer On the basis of the close correlation between TAP1 expression and immune pathways, we planned to further explore its possible correlation with immune cell infiltration. Using Correlation regression analysis, TAP1 expression was tested for the relationship with degree of infiltration of multiple immune cell lineages in pan-cancer cohort. Results in Fig. 5 indicated a positive relationship between TAP1 expression and several cell types, especially the macrophage, DC and CD8 + T cell. Besides, positive correlation was mostly concentrated in the same cell lineage, that is, certain infiltrated cell types was positively correlated with many cancer types. In a whole, TAP1 expression was positively correlated with the infiltration of most of the tested immune cells, except for some specific subtype like HSC, MDSC and macrophage. Specifically, most of the pan-cancer were dominantly infiltrated by CD8 + T cells, with few exceptions of ACC, CHOL, GBM, KICH, LGG, READ and UCS. Correlation between TAP1 expression and infiltration of 19 immune cell types were analyzed in TIMER 2.0. Spearman’s correlation method was employed to test the significance. Red and blue of the block indicate a positive or negative correlation, respectively. P < 0.05 was considered significant. Correlation between TAP1 expression and TME Normally, tumor cells evade immune attack by silencing the immune responses. One of the adopted strategies is to target and mute the immune regulators so that functional processing of immune signals is blocked, then cancer cells survive. Here, 47 common ICP genes were selected and combined with the information of TAP1 expression. We cited the analysis methods from previous study [ 28 ]. A Spearman’s correlation analysis was conducted to evaluate the correlation between the expression of TAP1 and individual ICP across TCGA pan-cancer types (Fig. 6 A). The general outline of our results strongly suggested a positive correlation, with evident significance supported. From the perspective of pan-cancer, the majority of cancer types had a positive association with immune regulators, especially the BRCA, KIRC, prostate adenocarcinoma (PRAD), testicular cancer (TGCT), thyroid carcinoma (THCA) and UVM. With respect to individual immune regulator, correlation with each cancer was highly significant, positively or negatively associated. Among, the LAG3, ICOS, HAVCR2, CD80, PDCD1, IDO1, PDCD1LG2, TIGIT, CD274, CD86 and TNFRSF9 exhibited overwhelming correlation compared with other ICPs. Tumor mutation burden (TMB), is the quantity of acquired somatic mutation after an exclusion of innate mutation, which encodes neoantigens as materials of antigenic presentation. Microsatellite instability, or MSI, represents an abnormal condition that the number of repeated sequence changes for the reason of random insertion or deletion, suggesting an impaired mismatch mechanism. The sorted diagram showed correlation between TAP1 expression and TMB (Fig. 6 B), and MSI (Fig. 6 C). The correlation worth mentioning is the ones showing great significance. TMB of cancer types BLCA, BRCA, CESC, colon adenocarcinoma (COAD), KIRC, LGG, LUAD, PAAD, SARC, stomach adenocarcinoma (STAD) and UCS was positively associated with TAP1 expression. As for MSI study, it was in the COAD, DLBC, KIRC, LUAD, LUSC, mesothelioma (MESO), OV and TGCT that showed significant correlation to different degree. ICI Cohort validation analysis Laboratory assumption always requires clinical practice and validation. Corresponding transcriptomic profiles and clinical information, including OS or PFI information and immunotherapy response data, of four cohorts in which cancer patients received different regimen of immunotherapy was obtained from published papers [ 24 – 27 ]. The carried immunotherapy was as followed: anti-PD-1 (programmed cell death protein1), anti-PDL1 (programmed cell death protein 1 ligand), anti-CTLA4 (cytotoxic T lymphocyte antigen 4) treatment using monoclonal antibodies. In Fig. 6 D-G, group with high TAP1 expression had overall higher OS/PFI probability and longer OS/PFI time than the low-expression group. Besides, the data of cancer therapeutic responses towards immune therapy indicated melanoma or bladder cancer cohorts with high TAP1 expression had more responders. The results indicated that melanoma and bladder cancer patients with high-TAP1 more than just had worse clinical prognosis, they may be more sensitive to ICIs therapy. Discussion As for tumorigenesis, genetic alteration endows neoantigens to be expressed and allows recognition under immune surveillance. The important intermediate step requires normal function of TAP1 to get antigens well presented for CD8 + CTL [ 29 ]. Within the elaborate immune regulation, TAP1 acts as an important factor vulnerable to be hijacked by tumor cells as strategies to evade immune response for survival and progression. The altered TAP1 expression in tumor tissues, as well as the pivotal function in immune responses, probably indicate its potential role in immune therapy. In this study, we performed a systemic bioinformatic analysis on TAP1, excavated its potential in predicting the clinical prognosis and effect of immunotherapy. Based on data mining, the plotted transcriptomic data of TAP1 was evident enough that almost all pan-cancer types revealed an elevated RNA level of TAP1 in tumor tissues (except for ACC, KICH and UCS). Our Western blot in GBM samples kept consistent with the RNA-seq results in protein level. However, previous studies had demonstrated a down-regulation of TAP1 in both mRNA and protein level, which is controversial to our results [ 10 – 15 ]. Thus, it is reasonable to speculate the alteration in genomics that counteracts the increased amount of TAP1. In the genetic alteration analysis, a maximum frequency of 8% in TAP1 gene alteration occurred in the tested pan-cancer cohort, and the mutation types were non-specific, accounting little for the cancer development. In addition, an alteration in transcriptomics may result in changes in protein. Sometimes, disability of a protein is reflected in the distribution that determines a different function. In present study, immunofluorescence images of melanoma cells and normal epithelial cells revealed a strict distribution of TAP1 on ER. Thus, the elevated expression of TAP1 was neither interpreted by specific types of genetic alteration or alteration in protein distribution. The reason why TAP1 was aberrantly expressed in tumor tissue remains to be explored. Generally, tumor tissue is composed of parenchyma and mesenchyme, more than just malignant cells but also resident stromal cells and infiltrated immune cells. The derived TAP1 expression level in tumor tissue is actually a summation of individual cells. Thus, a single-cell expression analysis was performed across samples from pan-cancer cohorts. In these tumor tissues, TAP1 expression was highly concentrated in various immune cells, especially the adaptive immune members like CD4 + and CD8 + T lymphocytes, followed by innate immune cells like monocyte/macrophage and DC. There was also scattered TAP1 expression among all the candidate tumor cell lineages, though, with less amount. The results, to some degree, may account for the contradictory opinion between the Big Data and individual studies. As the experiment conducted before were based on the cellular level, while our results were derived from a tissue-based analysis, without a separation of tumor cells from adjacent mesenchyme. Thus, the expression value of all non-malignant cells was counted and might cause an excess. Anyway, these detailed information of TAP1 expression atlas helps enrich the briefing of TAP1, which is set as the basic for our further investigation. In another side, we also focused on clinical significance of TAP1, and hopefully it would enlighten a practical application. TAP1 expression data was allocated to bivariate and continuous variable and analyzed for correlation with cancer prognosis, using Kaplan-Meier and univariate Cox regression methods, respectively. Here, clinical prognostic outcomes were presented as four indexes: OS, DFI, DSS and PFI, each of which is characterized by a specific endpoint and available to reflect prognosis in different conditions. Risky and protective indicators were primarily confirmed, suggesting a distinct effect of TAP1 in each cancer. In the forest plot of univariate Cox regression using OS data, the prognostic role of TAP1 in association with survival probability was varied in all the 32 cancer types. Results obtained in terms of OS showed TAP1 was a risk factor for 11 cancer types and protective for 9 types. For BLCA, BRCA, KICH, KIRC, LIHC, SKCM, STAD and THCA, they showed positive correlation with TAP1 expression and admitted it a protective role. While results of most cancer types (15 in 32) like HNSC, LGG and UVM demonstrated TAP1 as a net risk factor. Specifically, Kaplan-Meier survival curve in LGG suggested a protective effect of high-TAP1 expression in terms of OS, while the clinical outcomes were opposite in BLCA, HNSC and SKCM. Thus, TAP1 could be a promising and powerful prognostic biomarker for various cancers. With such a significant result, we wondered what functionating processes TAP1 may involve in. By taking the advantage of GSEA, we set emphasis on the enrichment of TAP1 in hallmarks gene sets, and our results showed a prominent enrichment in immune-related pathways. Here, based on TAP1 expression, the enriched pathways exhibited a consistent correlation across pan-cancer cohorts. TNF-α signaling pathway, IFN-γ response, IFN-α response, inflammatory response, IL6-JAK-STAT3 signaling, IL2-STAT5 signaling and allograft rejection with positive NES and little FDR were of great significance. According to previous study, IFN as well as TNF molecules promoted in-vivo MHC-I expression by inducing the transcription activity [ 30 , 31 ]. Besides, the inductive role of IFN-γ, IFN-α/β was more evident in TAP1, and IFN-γ is capable to facilitate TAP-dependent peptide transport [ 32 , 33 ]. Although MHC-I molecule and TAP as components ubiquitously expressed in all nucleated cells of distinct levels, they are mainly expressed in the site of inflammation in a short time after recognition and warning by immune system [ 34 ]. Our GSEA results just conformed to the proposed opinion in published papers. A strong correlation was observed between TAP1 expression and pathways of interest. Allograft rejection, an immune rejection response against grafts from the same species, is a typical inflammatory response of different severity [ 35 ]. The most common form is acute rejection that is mainly triggered by T-cell mediated immune responses [ 35 ]. For interleukin-mediated signaling pathway, IL6 and IL2 as well-known inflammatory factors also involve regulation of tumor immunity by facilitating the growth and function of lymphocytes [ 36 , 37 ]. In all, the surprising results point at immune-related mechanism, which encourages us to further explore the potential of TAP1 to predict the responses to immunotherapy. Actually, development and progression of tumor rely on adjacent environment, TME, which comprises of a complexity of non-malignant cell types (immune cells, fibroblasts, endothelia) and extracellular components (cytokines, hormones) [ 38 ]. Although the composition of TME for each cancer is diverse, some common features applied to all types. For instance, vascular network in most tumor is relative leaky and disorganized, allowing infiltration of multiple immune cells for tumor immunity [ 39 ]. Considering the results that TAP1 was enriched in immune-related pathways, an immune cell infiltration analysis was conducted to ascertain the association between TAP1 expression and infiltrated immune cells in TME. Scanning our results, an elaborate infiltration pattern was portrayed. It can be indicated that TAP1 expression was positively correlated with multiple immune cells, especially the CD8 + T cells, DC and macrophages. Interestingly, the results are in conformity with single-cell analysis, thus verifying mutually. The cell types highlighted in both analyses were CD8 + T cells and monocyte/macrophages, the killer cells in immune system. Within so many cell types from TIMER 2.0, M2 macrophage showed an opposite association. Probably, the distinct manifestation may result from its stimulative property in anti-inflammation, T helper 2 cell activation (assisting humoral immunity) and immunoregulation, which are contradictory to our proposal about TAP1-associated cell-mediated immunity [ 38 ]. Additionally, the IL2-STAT5 signaling pathway, inflammatory response and complement activity highlighted in GSEA are realized by macrophages and CD8 + CTL. Combining the currently available results, it is concluded that TAP1 expression is highly correlated with immune regulation, and it is corresponding to distinct immune signature for each pan-cancer type. Even though there are abundant immune cells infiltrated for tumor immunity, the relationship between TME and immune cells is quite complicated. T cells mediated tumor immunity is either pro-tumorous or anti-tumorous depending on the cells and regulators they encountered in the process of immune responses [ 39 ]. In our study, 47 ICPs were recruited to be tested for their correlation with TAP1 mRNA expression across pan-cancer. Tumor cells adopt strategies to activate the suppressive ICP pathways, thus silencing the effector lymphocytes and evading immune surveillance [ 40 ]. Through the heatmap, TAP1 expression was positively correlated with most ICPs in majority of pan-cancer types, especially the BRCA, KIRC, PRAD, TGCT, THCA and UVM. TMB and MSI are reported biomarker to predict TME condition and anti-tumor efficacy of ICI therapy [ 41 ]. A Spearman’ method was also conducted to test the correlation between TMB, MSI and TAP1 expression. The analyzed results highlighted specific cancer with significance association. For instance, COAD, KIRC and LUAD were correlated with TAP1 expression both in TMB and MSI analysis. Hence, our results may support the availability of TAP1 to predict the responses of immunotherapy that targets immune regulatory process. Based on distinct TAP1 expression level, precise therapy targeting tumor immunity shows a promising future for cancer patients. The anti-tumor immunity is regulated by a complex of factors in TME, including the ICP, TMB, MSI we discussed already, and then responds with different immune outcomes [ 40 ]. PD-1 and PD-L1 as well as CTLA-4 are the well-known immunosuppressive ICPs that determine the suppression of immune responses, usually recruited by tumor cells for immune evasion [ 42 , 43 ]. Up to now, monoclonal antibodies with high selectivity against PD-1 and CTLA-4 are approved and widely used in the clinical market. However, expected responses are only observed in a portion of patients. As the novel ICI therapy becomes popular, whether it will trigger a favorable response for certain individual remains a problem. In the case that TAP1 expression is highly correlated with immunotherapeutic biomarkers, it is reasonable to expect the feasibility of immunotherapy for patients who bore significant correlation with TAP1 expression. Cohort information of clinical outcomes and transcriptomic profiles from the patients receiving immune therapy was collected and analyzed. The obtained results may guide the therapeutic scheme for the patients waiting for therapy decision. In our study, we cited previous studies where cohorts of patients with primary or metastatic urothelial cancer, breast cancer and melanoma were treated with single or combined monoclonal antibody against PD-L1, PD-1 and CTLA-4, and all the clinical outcomes suggested a protective role of TAP1[ 24 – 27 ]. In our study, BLCA, BRCA and SKCM all exhibited a better prognosis in high TAP1 expression group, in line with the cohort search. However, TAP1 is not a favorable factor for immune therapy responses in all the cancer types. Just as what we concluded in prognostic and ICPs correlation analysis, the correlation of TAP1 expression was diverse among all the cancer types, which may determine diverse predictive role of TAP1. In LGG, high-TAP1 expression displayed a risk effect in tested cohort. Besides, cancer in different stages may bear varied TAP1 expression level and clinical outcomes. In stage 1 and 2 breast cancer, TAP1 expression is reduced, while the trend is reversed in stage 3 and 4, but TAP1 was considered a protective factor in our study [ 14 ]. Thus, we proposed TAP1 as a promising and powerful biomarker to predict the effect of immunotherapy for cancer patients. Besides the immunotherapy, previous study had reported a success in increasing tumor-specific immune responses by restoration of TAP1 expression via a TAP1 expressing adenovirus [ 10 ]. Surprisingly, novel treatment like this inspires us to foresee the clinical prognosis and make the best treatment option on the basis of specific cancer types as well as individual transcriptomic pattern of biomarkers like TAP1. Although the present study provides rigorous evidence to demonstrate the predictive role of TAP1 in clinical prognosis and potential responses of immunotherapy across pan-cancer, it still bears limitations. TAP1 is conventionally tumor associated gene, however, whose correlation with prognosis showed diversity in pan-cancer analysis. Although we have proposed the possible explanation, a series of elaborate experiments are still required. What’s more, we have just proposed an essential role of TAP1 as predictor without verifying the clinical use in practical, allowing inaccuracy to occur. Furthermore, our investigation focused on population, whereas the individual difference was neglected. However, clinical therapy protocol is specific to individual, which also determines limitations. In turn, the left issues will indicate research directions for future study, and hopefully bring advantages to those who needs novel treatment for survive. In conclusion, a systemic pan-cancer analysis with novel design and character is conducted. Our results revealed an aberrant expression of TAP1 in most pan-cancer types, and this expression is significantly correlated with clinical prognosis, immune cell infiltration, expression of ICPs, TME biomarkers and efficacy of immunotherapy. Hence, we propose the TAP1 as a novel biomarker to predict the prognosis and immunotherapeutic responses in different cancer types, opening a new chapter in the exploration of TAP1. Abbreviations ABC, ATP-binding cassette; ACC, adrenocortical carcinoma; BLCA, bladder urothelial carcinoma; BRCA, breast invasive carcinoma; CAF, cancer-associated fibroblast; CESC, cervical and endocervical cancers; CHOL, Cholangiocarcinoma; CI, confidence interval; ComPPI, Compartmentalized Protein Protein Interaction; COAD, colon adenocarcinoma; CRC, colorectal cancer; CTL, cytotoxic T lymphocytes; CTLA-4, cytotoxic T lymphocyte antigen 4; DC, dendritic cell; DEG, Differential expression gene; DFI, disease-free interval; DLBC, diffuse large B-cell lymphoma; DSS, disease-specific survival; EAC, esophageal adenocarcinoma; ER, endoplasmic reticulum; ESCA, Esophageal carcinoma; FDR, false discovery rate; GBM, glioblastoma; GSEA, gene set enrichment analysis; GTEx, Genotype-Tissue Expression; HPA, Human Protein Atlas; HNSC, head and neck squamous cell carcinoma; HR, hazard ratio; HSC, hematopoietic stem cell; ICI, immune check-point inhibitor ICP, immune checkpoints; IFN, interferon; IL, interleukin ; KICH, Kidney Chromophobe; KIRP, kidney renal papillary cell carcinoma; LGG, low-grade glioma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; MDSC, myeloid-derived suppressor cell; MESO, mesothelioma; MHC-I, major histocompatibility complex class I; MSI, microsatellite instability; NES, normalized enrichment score; NSCLC, non-small cell lung cancer; OS, overall survival; OV, ovarian serous cystadenocarcinoma; PAAD, pancreatic adenocarcinoma; PCPG, paraganglioma; PD-1,Programmed cell death protein 1; PD-L1, programmed cell death protein 1 ligand; PFI, progression-free interval; PPI, Protein-protein Interaction; PRAD, prostate adenocarcinoma; READ, rectum adenocarcinoma; SARC, sarcoma; SCLC, small cell lung cancer; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; TAP1, transporter associated antigen processing 1; TCGA, The Cancer Genome Atlas; Tfh, T cell follicular helper; TGCT, testicular cancer; THCA, thyroid carcinoma; TNF, Tumor necrosis factor; Treg, regulatory T cell; γδT, T cell gamma delta; TIMER, Tumor Immune Estimation Resource; TISCH, Tumor Immune Single-cell Hub; THYM, thymoma; TMB, tumor mutational burden; TME, tumor microenvironment; UCS, uterine carcinosarcoma; UVM, uveal melanoma; Declarations Acknowledgements The authors would like to appreciate the contributions of TCGA, GTEx projects. Funding The study was funded by the National Science Foundation (grant number: 81860448, 82002660 and 82172989), the Natural Science Foundation of Jiangxi Province (grant number 20192BAB205077 and 20202ACB216004), Jiangxi Provincial Science and Technology Innovation Base Plan-Provincial Key Laboratory (20212BCD42008), the Jiangxi Key research and development projects-Key project (20212BBG71012), Construction of Science and Technology Innovation Base-Clinical Medicine Research Center (2021ZDG02001), Jiangxi Key research and development projects (20212BBG73021), Province-Youth Talent Project (20212BCJ23023). Competing interests All authors declare there are no competing interests concerning the manuscript. Author contribution All authors made contribution to the works. ZT and KL contributed equally to the study. Study design and conception and data analysis were performed by ZT. KL wrote the original manuscript and improved the language. SL and JL provided technical supports. LW, YH and KL collected the clinical data and performed experiments. XZ, KH and LW commented, supervised and supported the projects. All authors involved discussion and approved the final version of the manuscript. Data availability This research recruited public databases and website tools. The data is available here: UCSC Xena: https://xenabrowser.net/datapages/ . The supplementary materials can be found online. The original data and R codes can be obtained from the authors for reasonable requests. Ethical approval The study involving human subjects obtained approval from the Ethics Committee of the Second Affiliated Hospital of Nanchang University. The employment and processing of materials were permitted and obtained informed consent from each patient. References Trowsdale, J., et al., Sequences encoded in the class II region of the MHC related to the 'ABC' superfamily of transporters . Nature, 1990. 348 (6303): p. 741–4. Abele, R. and R. 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Exp Mol Med, 2018. 50 (12): p. 1–11. Xu, Y., et al., Predictive Biomarkers of Immune Checkpoint Inhibitors-Related Toxicities. Front Immunol, 2020. 11 : p. 2023. Han, Y., D. Liu, and L. Li, PD-1/PD-L1 pathway: current researches in cancer . Am J Cancer Res, 2020. 10 (3): p. 727–742. Rowshanravan, B., N. Halliday, and D.M. Sansom, CTLA-4: a moving target in immunotherapy . Blood, 2018. 131 (1): p. 58–67. Additional Declarations No competing interests reported. Supplementary Files SupplementalTable1.xlsx SupplementalTable2.xlsx Cite Share Download PDF Status: Published Journal Publication published 09 Feb, 2023 Read the published version in BMC Cancer → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-1544440\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":97972333,\"identity\":\"3ce72aed-794a-47ae-923c-f15d2e9771a2\",\"order_by\":0,\"name\":\"Zewei Tu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Institute of Neuroscience, Nanchang University, 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\\u003c/strong\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003e(A\\u003c/strong\\u003e) RNA expression of TAP1 in tumor and normal tissues of 27 pan-cancer types. (\\u003cstrong\\u003eB\\u003c/strong\\u003e) Comparison of the differential RNA expression of TAP1 in GBM and NBT. (\\u003cstrong\\u003eC\\u003c/strong\\u003e) Comparison of TAP1 protein expression between cancerous and adjacent normal tissue from GBM samples. (\\u003cstrong\\u003eD\\u003c/strong\\u003e) Expression difference of TAP1 within paired clinical GBM samples. Pair number \\u003cem\\u003en\\u003c/em\\u003e=7 (\\u003cstrong\\u003eE\\u003c/strong\\u003e) Genomic alteration of TAP1 across pan-cancer presented as alteration types and frequency. (\\u003cstrong\\u003eF\\u003c/strong\\u003e) Correlation between TAP1 expression and copy number variation in pan-cancer scale. (\\u003cstrong\\u003eG\\u003c/strong\\u003e) Correlation analysis of copy number variation against TAP1 expression level in KICH. (\\u003cstrong\\u003eH\\u003c/strong\\u003e) Distribution of TAP1 protein in HaCaT and SKMEL30 cell lines. HaCaT is human non-malignant keratinocyte line, SKMEL30 is human melanoma cell line. (\\u003cstrong\\u003eI\\u003c/strong\\u003e) Protein-protein interaction of TAP1 in different cellular structure. GBM, glioblastoma; NBT, normal brain tissue; KICH, Kidney Chromophobe. *P\\u0026lt;0.05, **P\\u0026lt;0.01, ***P\\u0026lt;0.001.\\u003c/p\\u003e\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-1544440/v1/b6f9f98556ad47ff6ccc343a.png\"},{\"id\":20364630,\"identity\":\"e45c3468-e7a3-4f80-88b0-6982434ad13d\",\"added_by\":\"auto\",\"created_at\":\"2022-04-14 20:32:41\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1144344,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eTAP1 expression atlas of pan-cancer cohorts in single-cell scale. \\u003c/strong\\u003e\\u003c/p\\u003e\\u003cp\\u003e(\\u003cstrong\\u003eA\\u003c/strong\\u003e) TAP1 expression value in each cell types from each cancer cohorts. (\\u003cstrong\\u003eB, C\\u003c/strong\\u003e) Main cell types expressing TAP1 in SKCM-GSE120575 cohorts. (\\u003cstrong\\u003eD, E\\u003c/strong\\u003e) Main cell types expression TAP1 in BRCA-GSE110686 cohorts. \\u003c/p\\u003e\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-1544440/v1/072ec9faf7e7a0cc0887dc37.png\"},{\"id\":20364629,\"identity\":\"8b17f143-3e39-42c6-92e1-012e8455380f\",\"added_by\":\"auto\",\"created_at\":\"2022-04-14 20:32:41\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":447328,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eCorrelation between TAP1 expression and cancer prognosis in pan-cancer cohorts.\\u003c/strong\\u003e\\u003c/p\\u003e\\u003cp\\u003e(\\u003cstrong\\u003eA\\u003c/strong\\u003e) Summary of prognostic role of TAP1 in pan-cancer using Kaplan-Meier and Univariate Cox regression analysis. Clinical prognosis is expressed as DFI, DSS, OS and PFI. Prognostic roles of TAP1 are limited as risky and protective. (\\u003cstrong\\u003eB\\u003c/strong\\u003e) Forest plot showing cancer types correlated with TAP1 expression using OS data, HR and 95% CI were presented. Kaplan-Meier survival curve showing the change of OS probability against times between low- and high- TAP1 expression group in LGG (\\u003cstrong\\u003eC\\u003c/strong\\u003e), BLCA (\\u003cstrong\\u003eD\\u003c/strong\\u003e), HNSC (\\u003cstrong\\u003eE\\u003c/strong\\u003e) and SKCM (\\u003cstrong\\u003eF\\u003c/strong\\u003e). DFI, disease-free interval; DSS, disease-specific survival; OS, overall survival; PFI, progression-free interval; HR, hazard ratio; CI, confidence interval; LGG, low-grade glioma; BLCA, bladder urothelial carcinoma; HNSC, head and neck squamous cell carcinoma; SKCM, skin cutaneous melanoma. Significant threshold was set as P \\u0026lt;0.05. *P\\u0026lt;0.05, **P\\u0026lt;0.01, ***P\\u0026lt;0.001.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-1544440/v1/8e538262f43f442e243c08c7.png\"},{\"id\":20364916,\"identity\":\"eebda518-7144-4899-88c1-714df6f3806f\",\"added_by\":\"auto\",\"created_at\":\"2022-04-14 20:42:41\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":848591,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eCorrelation analysis between TAP1 expression level and enriched gene set.\\u003c/strong\\u003e\\u003c/p\\u003e\\u003cp\\u003eDifferent correlation between TAP1 expression and enriched gene set in pan-cancer was displayed in bubble plot, with NES and log-ranked FDR presented. NES, normalized enrichment score; FDR, false discovery rate. Gene sets were considered significant only when Nom P\\u0026lt;0.05, FDR \\u0026lt; 0.25.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-1544440/v1/ebb5d50fb797360d13f577e1.png\"},{\"id\":20364875,\"identity\":\"dccb8d47-a45e-41a8-8343-8c25d42116b0\",\"added_by\":\"auto\",\"created_at\":\"2022-04-14 20:37:41\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1975837,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eCorrelation between TAP1 expression and immune cell infiltration in various pan-cancer types. \\u003c/strong\\u003e\\u003c/p\\u003e\\u003cp\\u003eCorrelation between TAP1 expression and infiltration of 19 immune cell types were analyzed in TIMER 2.0. Spearman’s correlation method was employed to test the significance. Red and blue of the block indicate a positive or negative correlation, respectively. P \\u0026lt; 0.05 was considered significant.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-1544440/v1/9737d54cd065a6613330c3e6.png\"},{\"id\":20364878,\"identity\":\"12150a2f-9081-4ca6-b9dd-edc70ebcfecd\",\"added_by\":\"auto\",\"created_at\":\"2022-04-14 20:37:41\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1609755,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eCorrelation between TAP1 expression and TME biomarkers and clinical responses of immunotherapy.\\u003c/strong\\u003e\\u003c/p\\u003e\\u003cp\\u003e(\\u003cstrong\\u003eA\\u003c/strong\\u003e) Heatmap exhibiting correlation between TAP1 expression and 47 immune regulators using Spearman’s correlation test. (\\u003cstrong\\u003eB, C\\u003c/strong\\u003e) Correlation between TAP1 expression and TMB and MSI. (\\u003cstrong\\u003eD\\u003c/strong\\u003e) Survival analysis of high- (\\u003cem\\u003en\\u003c/em\\u003e=66) and low-(\\u003cem\\u003en\\u003c/em\\u003e=232) TAP1 expression using OS information from urothelial cancer cohort receiving anti-PDL1 immunotherapy, and the proportion of patients with different therapeutic responses towards the therapy (\\u003cstrong\\u003eE\\u003c/strong\\u003e) Survival analysis of high- (\\u003cem\\u003en\\u003c/em\\u003e=29) and low-(\\u003cem\\u003en\\u003c/em\\u003e=3) TAP1 expression using PFS information from melanoma cohort receiving anti-CTLA-4\\u0026amp;PD-1 immunotherapy, and the proportion of patients with different therapeutic responses towards the therapy. (\\u003cstrong\\u003eF\\u003c/strong\\u003e) Survival analysis of high- (\\u003cem\\u003en\\u003c/em\\u003e=43) and low-(\\u003cem\\u003en\\u003c/em\\u003e=6) TAP1 expression using OS information from breast cancer cohort receiving anti-PD-1 immunotherapy, and the proportion of patients with different therapeutic responses towards the therapy. (\\u003cstrong\\u003eG\\u003c/strong\\u003e) Survival analysis of high- (\\u003cem\\u003en\\u003c/em\\u003e=23) and low-(\\u003cem\\u003en\\u003c/em\\u003e=18) TAP1 expression using OS information from metastatic melanoma cohort receiving anti-CTLA-4 immunotherapy, and the proportion of patients with different therapeutic responses towards the therapy. TMB, tumor mutation burden; MSI, microsatellite instability; PD, progressive disease; SD, stable disease; CR, complete response; PR, partial responses. Significant threshold was P \\u0026lt; 0.05. *P\\u0026lt;0.05, **P\\u0026lt;0.01, ***P\\u0026lt;0.001.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-1544440/v1/54ddf7cbd7a64e4214e603d6.png\"},{\"id\":60157184,\"identity\":\"0700517e-9b68-44d3-9a72-2afb114db62e\",\"added_by\":\"auto\",\"created_at\":\"2024-07-12 12:14:40\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":6912605,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-1544440/v1/9a9c0546-8b75-4da1-8730-46e65b07da8a.pdf\"},{\"id\":20364962,\"identity\":\"0955d037-1b93-4f25-b3cc-b06360df4a19\",\"added_by\":\"auto\",\"created_at\":\"2022-04-14 20:47:41\",\"extension\":\"xlsx\",\"order_by\":9,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":13047,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementalTable1.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-1544440/v1/0a33abc93acc8e225fbec3f9.xlsx\"},{\"id\":20364635,\"identity\":\"cc075b11-6527-43d1-a4ea-3cfc5bc65a99\",\"added_by\":\"auto\",\"created_at\":\"2022-04-14 20:32:41\",\"extension\":\"xlsx\",\"order_by\":10,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":13619404,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementalTable2.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-1544440/v1/4eae6fa82c1c8039ba4ac92e.xlsx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Pan-cancer analysis: Predictive role of TAP1 in cancer prognosis and responses of immunotherapy\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eTransporter associated with antigen processing 1 (TAP1) is a member of ATP-binding cassette (ABC) superfamily, which forms heterodimeric complex with its homology TAP2 for intracellular translocation of antigenic peptide across endoplasmic reticulum (ER) membrane [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. In the ER, TAP assists the loading of cytosolic peptides onto adjacent major histocompatibility complex class I (MHC-I) molecules, which then transport the peptide to cell surface for recognition by CD8\\u003csup\\u003e+\\u003c/sup\\u003e cytotoxic T lymphocytes (CTL). MHC-I presentation occurs in every nucleated cell (i.e., not mature erythrocyte), and the presented antigens are generally derived from endogenous molecules [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. Moreover, it is demonstrated that MHC-I also presents exogenous antigens derived from pathogens or dead cells in dendritic cells (DC) [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. Because of the precise regulation, viral-infected cells and malignantly transformed cells that express abnormal protein are under strict immune surveillance and eliminated in time. Consequently, the pivotal function of TAP1 is prone to be hijacked by malignancy for immune evasion.\\u003c/p\\u003e \\u003cp\\u003eTAP complex executes its role via an elaborate mechanism. The antigenic proteins, either endogenously expressed or internalized by antigen presenting cells, are marked by ubiquitin and get degraded into small fragments of 8\\u0026ndash;10 amino acid peptides [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. CD8\\u003csup\\u003e+\\u003c/sup\\u003e CTL recognize the antigenic peptides presented by MHC-I and initiate an immune attack. In the case when assembly or translocation of MHC-I complex goes wrong, CTL-mediated immune surveillance will be suppressed [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. Therefore, malignant cells evolve strategies to escape from immune system by targeting and interfering the normal process of MHC-I antigen presentation, especially the \\u0026ldquo;peptide pump\\u0026rdquo; --TAP [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. Within the TAP complex, TAP1 is reported to stabilize the assembly of TAP2 [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. Thus, we focus on TAP1 as it might dominate the function of TAP proteins. Over these years, studies on the field of TAP1 emerge continuously, providing novel findings in several cancers [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. Down-regulation or defects of TAP1 expression were observed in primary cancer or autologously metastatic lesions of different stages, such as bladder cancer [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e], small cell lung cancer (SCLC)[\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e], glioma [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e], prostatic cancer [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e], head and neck squamous cell carcinoma (HNSC) [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e], breast cancer [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e] and colorectal cancer [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. However, there are few studies proposing TAP1 associated therapeutic regimen against tumor. As a novel therapy, immune therapy delays or even completely blocks the development and progression of tumor, being honored as the gimmers of hope for many cancer patients. Nevertheless, expected responses towards immunotherapy are not observed in every patient. Besides, random application of immunotherapy brings disadvantages to patients who bear tolerance or toxic reaction to the drugs. Therefore, exploring a reliable biomarker that predicts the effect of immunotherapy for individual patient is of great urgence. As for TAP1 molecule, it regulates normal immune responses, and is also reported an abnormal expression in various cancer types. We proposed the TAP1 as a potential biomarker to predict the immunotherapeutic efficacy.\\u003c/p\\u003e \\u003cp\\u003eIn this study, we performed a comprehensive pan-cancer analysis and built up a landscape of TAP1 across various cancer types. Here, we reported the basic information of TAP1 in pan-cancer cohorts and explored the relationship between TAP1 expression and prognosis, enriched gene sets, immune cell infiltration, expression of immune regulators and immunotherapeutic effects in pan-cancer scale. Based on the information, TAP1 is proposed as a novel biomarker to predict the prognosis and effect of immunotherapy in diverse cancers. Hopefully, our study will lead a future direction for the research of TAP1.\\u003c/p\\u003e\"},{\"header\":\"Methods And Materials\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eClinical samples and ethical statement\\u003c/h2\\u003e \\u003cp\\u003eThe clinical samples of glioblastoma (GBM) were obtained from inpatients of the Second Affiliated Hospital of Nanchang University between 2021 and 2022. Tumor core and the para-tumorous normal tissue were excised and stored in -80˚C until use. This study was approved by Medical Ethics Committee of The Second Affiliated Hospital of Nanchang University. Sample acquisition and utilization for study were admitted by each patient and performed based on the approved guidelines.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eData sources and processing\\u003c/h2\\u003e \\u003cp\\u003eThe combinative mRNA expression profiles of TAP1 in tumor and corresponding normal tissues were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. The available data was downloaded from UCSC Xena database (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://xenabrowser.net/datapages/\\u003c/span\\u003e\\u003cspan address=\\\"https://xenabrowser.net/datapages/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) \\u003csup\\u003e[16]\\u003c/sup\\u003e, and the data format was transformed into TPM format (transcripts per kilobase million). On cBioPortal website (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.cbioportal.org/\\u003c/span\\u003e\\u003cspan address=\\\"https://www.cbioportal.org/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e), query of TPA1 in \\u0026ldquo;TCGA pan-cancer atlas\\u0026rdquo; was submitted. Gene alteration data (mutation, structural variant, amplification, deep deletion and multiple alterations) matched with 32 cancer types was obtained from \\u0026ldquo;Cancer Type Summary\\u0026rdquo; item. In subcellular level, immunofluorescence images of TPA1 were obtained from The Human Protein Atlas (HPA, \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.proteinatlas.org/\\u003c/span\\u003e\\u003cspan address=\\\"https://www.proteinatlas.org/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) database and present the subcellular distribution of TAP1 protein. Inside the Compartmentalized Protein-Protein Interaction Database (ComPPI, \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://comppi.linkgroup.hu/\\u003c/span\\u003e\\u003cspan address=\\\"https://comppi.linkgroup.hu/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e), information of protein-protein Interaction (PPI) was explored. Names of protein were mapped using the \\u0026ldquo;Retrieve/ID mapping\\u0026rdquo; tool in the Uniprot website (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.uniprot.org/\\u003c/span\\u003e\\u003cspan address=\\\"https://www.uniprot.org/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e), and visualized by R package \\u0026ldquo;ggplot2\\u0026rdquo; in R programming environment. The abbreviations of cancer types were summarized in the \\u003cb\\u003eSupplementary Table\\u0026nbsp;1\\u003c/b\\u003e.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eWestern blot\\u003c/h2\\u003e \\u003cp\\u003eProtein was extracted from the GBM cores and adjacent normal tissue in the collected GBM samples. The prepared samples were allocated to perform the western blot using the method and reagents we used in previous study [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. The information of antibodies we used is as below: rabbit TAP1 polyclonal antibody (Proteintech number: 11114-1-AP), WB dilution concentration 1:1000).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSingle-cell analysis of TAP1\\u003c/h2\\u003e \\u003cp\\u003eThe single-cell analysis of TAP1 was conducted on Tumor Immune Single-cell Hub (TISCH, \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://tisch.comp-genomics.org/\\u003c/span\\u003e\\u003cspan address=\\\"http://tisch.comp-genomics.org/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) website \\u003csup\\u003e[18]\\u003c/sup\\u003e. Gene \\u0026ldquo;TAP1\\u0026rdquo; was input and cell-type annotation \\u0026ldquo;major-lineage\\u0026rdquo; was searched in \\u0026ldquo;all cancers\\u0026rdquo;. In present study, 33 cell types were investigated in 78 cancer lineages for TAP1 expression evaluation.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePrognosis analysis in Cox regression analysis and Kaplan-Meier methods\\u003c/h2\\u003e \\u003cp\\u003eBased on the matched TAP1 expression and prognostic information from TCGA database, the role of TAP1 in predicting the prognosis in pan-cancer was explored. Univariate Cox regression analysis and Kaplan-Meier analysis were performed to assess the effect of TAP1 expression on patients\\u0026rsquo; prognosis in diverse cancer types, the prognostic indicators covering overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI) and progression-free interval (PFI). TAP1 expression pattern as continuous variable was tested by univariate Cox regression, and the expression level as bivariate was tested by Kaplan-Meier method. Within the algorithm, \\u0026ldquo;surv-cutpoint\\u0026rdquo; function of \\u0026ldquo;surminer\\u0026rdquo; R package was utilized to determine the cut-off point with maximal rank statistics that divides the undefined expression data into high- or low-expression. Because of the abnormal distribution of survival data, non-parametric test was recruited and the log-ranked P value was computed in our K-M analysis. For Cox regression analysis, hazard ratio (HR) and 95% confidence interval (CI) were presented.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eDifferential expression gene (DEG) screening from low and high TAP1 expression subgroups\\u003c/h2\\u003e \\u003cp\\u003eCancer patients were ordered according to their TAP1 expression value. 30% of the patient population at the top and bottom of the array was defined as high and low expression subgroup. Using \\u0026ldquo;limma\\u0026rdquo; R package \\u003csup\\u003e[19]\\u003c/sup\\u003e, differential expression analysis was performed, with log2(fold change) and rectified P-value calculated. Genes showing P-value under 0.05 was considered as DEGs. The DEGs list derived from our analysis was displayed in \\u003cb\\u003eSupplementary Table\\u0026nbsp;2\\u003c/b\\u003e.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eGene set enrichment analysis (GSEA)\\u003c/h2\\u003e \\u003cp\\u003eTo further explore the possible mechanisms or biological processes TAP1 may involve in, GSEA was performed. Hallmarks gene set (containing 50 gene sets) file was downloaded from MSigDB as \\u0026ldquo;gmt\\u0026rdquo; format. Then, \\u0026ldquo;clusterProfiler\\u0026rdquo; R package was performed to run GSEA based on the available data obtained from differential expression analysis, with false discovery rate (FDR) and normalized enrichment score (NES) computed for every hallmark in each cancer type [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. The TAP1 enrichment data in multiple pathways matched with corresponding pan-cancer types was visualized using \\u0026ldquo;ggplot2\\u0026rdquo; R package.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eTumor microenvironment analysis in pan-cancer\\u003c/h2\\u003e \\u003cp\\u003eTumor mass is normally infiltrated by a variety of immune cells and other functional cells that affect the cancer progression and therapeutic effect. The infiltrating cells and molecules inside tumor matrix comprise a so-called tumor microenvironment (TME) [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. To explore the quantity of immune infiltration cells inside tumor tissue, Tumor Immune Estimation Resource 2.0 (TIMER 2.0) was employed to evaluate immune cell infiltration by using the advantage of transcriptome data from pan-cancer cohort. Correlations between TAP1 expression and infiltrating cells of interest were investigated using Spearman\\u0026rsquo;s rank correlation analysis. The candidate cells include: CD4\\u003csup\\u003e+\\u003c/sup\\u003e T cell, cancer-associated fibroblast (CAF), lymphoid progenitor, myeloid progenitor, granulocyte-monocyte progenitor, endothelial, hematopoietic stem cell (HSC), T cell follicular helper (Tfh), T cell gamma delta (γδT), NK T cell, regulatory T cell (Treg), myeloid-derived suppressor cell (MDSC), B cell, neutrophil, monocyte, macrophage, dendritic cell, NK cell, mast cell, CD8\\u003csup\\u003e+\\u003c/sup\\u003e T cell. The infiltration pattern was visualized by R package \\u0026ldquo;ggplot2\\u0026rdquo;. As biomarker to predict TME condition, the microsatellite instability (MSI) and tumor mutational burden (TMB) were evaluated \\u003csup\\u003e[21, 22]\\u003c/sup\\u003e. Correlations between TAP1 mRNA expression and MSI, TMB were investigated by Spearman\\u0026rsquo;s correlation test. According to previous study, 47 immune checkpoints (ICP) were recruited and also estimated for their correlation with TAP1 expression [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eImmune check-point inhibitor (ICI) Cohort Validation\\u003c/h2\\u003e \\u003cp\\u003eA comprehensive study that summarized the clinical effect of immune checkpoint blockade therapy was conducted. Clinical information including the prognosis of immunotherapy matched with TPA1 expression data were obtained from previous studies [\\u003cspan additionalcitationids=\\\"CR25 CR26\\\" citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. The high or low expression was defined by method applied in K-M survival analysis, and the prognosis of patients with different TAP1 levels was compared by log-rank test. To assess the response towards immune ICIs, chi-square test was utilized to compare the proportion of patients that respond to ICI therapy.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical analysis\\u003c/h2\\u003e \\u003cp\\u003eTo test the difference of TAP1 expression between cancer and para-cancerous normal tissues, a Wilcoxon sum test was conducted to calculate the statistical significance. In the comparison of protein expression, a paired t-test was performed. To investigate the correlation between cancer prognosis and TAP1 expression, univariate Cox regression analysis and Kaplan-Meier method were recruited. In Cox regression test, Cox P value and HR were assessed, and the log-ranked P value with 95%CI were calculated in K-M method. To investigate the correlation between TAP1 expression and immune cell infiltration, immune regulators expression, TMB and MSI, Spearman\\u0026rsquo;s correlation test was employed to calculate the significance. In immunotherapy cohort validation, the difference in the portion of responders and non-responders between low- and high- TAP1 expression groups was tested by chi-square test. Statistical significance was at P value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e\\n\\u003ch2\\u003eBriefing of TAP1 in genetic, mRNA and protein level\\u003c/h2\\u003e\\n\\u003cp\\u003eTo have a basic landscape of TAP1 expression, multi-omics cancer data were used to represent the information of TAP1. Available transcriptional profiles from TCGA and GTEx database was combined as the sample number of several cancer types was limited. TAP1 mRNA expression in the normal and tumorous tissues from 27 cancer types was exhibited in Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA. Except for the adrenocortical carcinoma (ACC), Kidney Chromophobe (KICH) and uterine carcinosarcoma (UCS), TAP1 was dominantly overexpressed in all the tumor tissues with strong significance. Among, TAP1 showed an overwhelming expression in cervical and endocervical cancers (CESC) and HNSC than ever tissues, and the greatest difference between normal and corresponding malignant tissues was discovered in Cholangiocarcinoma (CHOL), GBM and pancreatic adenocarcinoma (PAAD). GBM, the most malignant intracranial tumor type, showed significant advantages in the amount of TAP1 expression compared normal brain tissues (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eB). In a word, aberrant expression levels of TAP1 mRNA were discovered among various cancer types. We suspected whether protein level of TAP1 was also aberrant expressed in cancer samples, then we performed a Western blot assay to verify the computerized results from RNA-seq.\\u0026nbsp;Just shown in Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eC and \\u003cstrong\\u003eID\\u003c/strong\\u003e, our GBM samples had exactly higher TAP1 expression than corresponding adjacent normal tissues, keeping consistent with TAP1 RNA expression pattern. Then, we investigated the genetic alteration status of TAP1 in TCGA pan-cancer cohorts, including the genetic alteration types and frequency (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eE). Among all alteration types, \\u0026ldquo;Amplification\\u0026rdquo; accounts for the most frequency, followed by \\u0026ldquo;Mutations\\u0026rdquo; and \\u0026ldquo;Deep Deletion\\u0026rdquo;. Of note, some of the cancer types were observed with only one type of genetic alteration, such as the esophageal adenocarcinoma (EAC) and uveal melanoma (UVM) being totally caused by amplification, or the thymoma (THYM), completely resulted from mutation. Generally, the frequency of TAP1 alternation in pan-cancer was fluctuated between 2% and 4%, but it was worth mentioning that the diffuse large B-cell lymphoma (DLBC) exhibited the highest value of more than 8%. When focusing on copy number variation in these pan-cancer types, TAP1 expression were demonstrated a significant correlation in KICH and kidney renal papillary cell carcinoma (KIRP) \\u003cstrong\\u003e(\\u003c/strong\\u003eFig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eF\\u003cstrong\\u003e)\\u003c/strong\\u003e. The scatter plot shown in Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eG helps confirm the results and fit the suitable regression curve. In the subcellular level, immunofluorescence images of TAP1 in tumor cell line and non-tumorous derived cell line were recruited to show the protein distribution (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eH). Both in the Para-cancerous normal HaCaT cell line and melanoma cell line SKMEL30, TAP1 protein was clearly located at ER, without change before and after tumorigenesis. Then, a PPI network uncovering the potential biological reactions is presented in Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eI. All description above is the briefing of TAP1, in gene, RNA and protein level.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e\\n\\u003ch2\\u003eTAP1 expression results across multiple cell types in single-cell analysis\\u003c/h2\\u003e\\n\\u003cp\\u003eTo figure out the TAP1 expression pattern in tumor mass, further we explored the individual expression of TAP1 in immune and malignant cell population via TISCH tool. TAP1 expression was evaluated in all the separated cells and then presented as a mean value (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA). In the plotted heatmap, it was observed that TAP1 was mainly expressed in immune cells, especially the T lymphocytes (CD4 Tconv, T reg, T prolif, CD8 T, CD8 Tex cells), followed by non-specific immune cells including NK cell, DC and monocyte/macrophage. Of note, TAP1 expression in malignant cells was far from dominant, revealing an overexpression in non-malignant origin population. To focus on the TAP1 expression value, non-small cell lung cancer (NSCLC) cell line GSE99254, liver hepatocellular carcinoma (LIHC) GSE98638 and colorectal cancer (CRC) GSE108989 were more predominant than other cell lines. Specifically, we performed visualization of TAP1 expression in breast invasive carcinoma (BRCA) GSE11068 and skin cutaneous melanoma (SKCM) GSE12057 datasets, and highlighted the cell types with greatest expression in Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB-E. Our results suggested a preferential expression of TAP1 in T lymphocytes and monocyte/macrophage in TME.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e\\n\\u003ch2\\u003eRisk prediction based on correlation between prognosis and TAP1 expression\\u003c/h2\\u003e\\n\\u003cp\\u003eTo further explore the potential prognosis predictive value of TAP1, we subsequently analyzed the prognostic role of TAP1 across cancers. In terms of prognostic analysis, survival indexes OS, DSS, DFI and PFI were served as reliable indicators. Scanning and summarizing the clinical outcomes of tested pan-cancer cohort, the prognostic pattern was plotted as Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA. Kaplan-Meier method and Cox regression analysis were performed to validate each other. The results suggested TAP1 was a risky factor for ACC, DLBC, KIRP, LGG, lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), PAAD and UVM, as the higher expression of TAP1 mRNA was correlated with poor prognosis, but also a potential protective factor in bladder urothelial carcinoma (BLCA), BRCA, KIRC, OV, rectum adenocarcinoma (READ), SKCM, STAD and UCS. In terms of the Cox regression analysis using OS data, our forest plot in Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB revealed the association achieved significance in SARC, STAD, ovarian serous cystadenocarcinoma (OV), LUAD, UVM, KIRP, PAAD, LGG and THYM. Specifically, we emphasized on the several cancer types, Kaplan-Meier survival curves were depicted. In Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC, OS probability in high-TAP1 expression group decreased rapidly against time, but low expression group has relative better outcomes in the same time points. While the clinical outcomes were reversed in BLCA, HNSC and SKCM patients (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eD-F). There were also cancer types showing irrelevance to TAP1 expression level, such as CHOL, Esophageal carcinoma (ESCA), paraganglioma (PCPG), sarcoma (SARC) and UCS. It can be speculated that cancer types showing significant correlation could be taken as the potential objectives for TAP1-associated treatment, which brings advantages to the population sensitive to the regimen we will discuss later.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e\\n\\u003ch2\\u003eTAP1 enriched hallmarks across pan-cancer cohort\\u003c/h2\\u003e\\n\\u003cp\\u003eGiven the significant prognostic implications of TAP1 in cancers, we further investigate the underlying biological processes or pathways associated with TAP1 to understand the potential mechanisms. In present study, hallmarks gene set that composed of the marker genes defining biological condition and progression was recruited. DEGs between high- and low-TAP1 subgroups was screened out and tested for enrichment analysis in hallmarks gene sets. In Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e, the enrichment status of TAP1 in each pathway was clearly plotted. Our results revealed a highly concentrated distribution of enrichment across 33 pan-cancer types that the immune-related pathway was strongly favored in TAP1 high expression cancers: tumor necrosis factor-\\u0026alpha; (TNF-\\u0026alpha;) signaling via NF-\\u0026kappa;B pathway, interferon-\\u0026gamma; ((IFN-\\u0026gamma;) response, IFN-\\u0026alpha; response, inflammatory response, IL6-JAK-STAT3 signaling pathway, IL2-STAT2 signaling pathway and allograft rejection. TAP1 was also enriched in apoptosis, complement and KRAS signaling pathway, to a less degree. To focus on cancer types, ACC, LGG, LUAD, PAAD and UCS showed more relevance to the mentioned pathways. Among the bubble plot, the majority of enrichment showed a positive correlation, with negative NES dots scattered plotted.\\u003c/p\\u003e\\n\\u003cp\\u003eDifferent correlation between TAP1 expression and enriched gene set in pan-cancer was displayed in bubble plot, with NES and log-ranked FDR presented. NES, normalized enrichment score; FDR, false discovery rate. Gene sets were considered significant only when Nom P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05, FDR\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.25.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e\\n\\u003ch2\\u003eCorrelation between immune cell infiltration and TAP1 expression in pan-cancer\\u003c/h2\\u003e\\n\\u003cp\\u003eOn the basis of the close correlation between TAP1 expression and immune pathways, we planned to further explore its possible correlation with immune cell infiltration. Using Correlation regression analysis, TAP1 expression was tested for the relationship with degree of infiltration of multiple immune cell lineages in pan-cancer cohort. Results in Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e indicated a positive relationship between TAP1 expression and several cell types, especially the macrophage, DC and CD8\\u003csup\\u003e+\\u003c/sup\\u003e T cell. Besides, positive correlation was mostly concentrated in the same cell lineage, that is, certain infiltrated cell types was positively correlated with many cancer types. In a whole, TAP1 expression was positively correlated with the infiltration of most of the tested immune cells, except for some specific subtype like HSC, MDSC and macrophage. Specifically, most of the pan-cancer were dominantly infiltrated by CD8\\u0026thinsp;+\\u0026thinsp;T cells, with few exceptions of ACC, CHOL, GBM, KICH, LGG, READ and UCS.\\u003c/p\\u003e\\n\\u003cp\\u003eCorrelation between TAP1 expression and infiltration of 19 immune cell types were analyzed in TIMER 2.0. Spearman\\u0026rsquo;s correlation method was employed to test the significance. Red and blue of the block indicate a positive or negative correlation, respectively. P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 was considered significant.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e\\n\\u003ch2\\u003eCorrelation between TAP1 expression and TME\\u003c/h2\\u003e\\n\\u003cp\\u003eNormally, tumor cells evade immune attack by silencing the immune responses. One of the adopted strategies is to target and mute the immune regulators so that functional processing of immune signals is blocked, then cancer cells survive. Here, 47 common ICP genes were selected and combined with the information of TAP1 expression. We cited the analysis methods from previous study [\\u003cspan class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. A Spearman\\u0026rsquo;s correlation analysis was conducted to evaluate the correlation between the expression of TAP1 and individual ICP across TCGA pan-cancer types (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eA). The general outline of our results strongly suggested a positive correlation, with evident significance supported. From the perspective of pan-cancer, the majority of cancer types had a positive association with immune regulators, especially the BRCA, KIRC, prostate adenocarcinoma (PRAD), testicular cancer (TGCT), thyroid carcinoma (THCA) and UVM. With respect to individual immune regulator, correlation with each cancer was highly significant, positively or negatively associated. Among, the LAG3, ICOS, HAVCR2, CD80, PDCD1, IDO1, PDCD1LG2, TIGIT, CD274, CD86 and TNFRSF9 exhibited overwhelming correlation compared with other ICPs.\\u003c/p\\u003e\\n\\u003cp\\u003eTumor mutation burden (TMB), is the quantity of acquired somatic mutation after an exclusion of innate mutation, which encodes neoantigens as materials of antigenic presentation. Microsatellite instability, or MSI, represents an abnormal condition that the number of repeated sequence changes for the reason of random insertion or deletion, suggesting an impaired mismatch mechanism. The sorted diagram showed correlation between TAP1 expression and TMB (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eB), and MSI (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eC). The correlation worth mentioning is the ones showing great significance. TMB of cancer types BLCA, BRCA, CESC, colon adenocarcinoma (COAD), KIRC, LGG, LUAD, PAAD, SARC, stomach adenocarcinoma (STAD) and UCS was positively associated with TAP1 expression. As for MSI study, it was in the COAD, DLBC, KIRC, LUAD, LUSC, mesothelioma (MESO), OV and TGCT that showed significant correlation to different degree.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec20\\\" class=\\\"Section2\\\"\\u003e\\n\\u003ch2\\u003eICI Cohort validation analysis\\u003c/h2\\u003e\\n\\u003cp\\u003eLaboratory assumption always requires clinical practice and validation. Corresponding transcriptomic profiles and clinical information, including OS or PFI information and immunotherapy response data, of four cohorts in which cancer patients received different regimen of immunotherapy was obtained from published papers [\\u003cspan class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e\\u0026ndash;\\u003cspan class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. The carried immunotherapy was as followed: anti-PD-1 (programmed cell death protein1), anti-PDL1 (programmed cell death protein 1 ligand), anti-CTLA4 (cytotoxic T lymphocyte antigen 4) treatment using monoclonal antibodies. In Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eD-G, group with high TAP1 expression had overall higher OS/PFI probability and longer OS/PFI time than the low-expression group. Besides, the data of cancer therapeutic responses towards immune therapy indicated melanoma or bladder cancer cohorts with high TAP1 expression had more responders. The results indicated that melanoma and bladder cancer patients with high-TAP1 more than just had worse clinical prognosis, they may be more sensitive to ICIs therapy.\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eAs for tumorigenesis, genetic alteration endows neoantigens to be expressed and allows recognition under immune surveillance. The important intermediate step requires normal function of TAP1 to get antigens well presented for CD8\\u003csup\\u003e+\\u003c/sup\\u003e CTL [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. Within the elaborate immune regulation, TAP1 acts as an important factor vulnerable to be hijacked by tumor cells as strategies to evade immune response for survival and progression. The altered TAP1 expression in tumor tissues, as well as the pivotal function in immune responses, probably indicate its potential role in immune therapy. In this study, we performed a systemic bioinformatic analysis on TAP1, excavated its potential in predicting the clinical prognosis and effect of immunotherapy.\\u003c/p\\u003e \\u003cp\\u003eBased on data mining, the plotted transcriptomic data of TAP1 was evident enough that almost all pan-cancer types revealed an elevated RNA level of TAP1 in tumor tissues (except for ACC, KICH and UCS). Our Western blot in GBM samples kept consistent with the RNA-seq results in protein level. However, previous studies had demonstrated a down-regulation of TAP1 in both mRNA and protein level, which is controversial to our results [\\u003cspan additionalcitationids=\\\"CR11 CR12 CR13 CR14\\\" citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. Thus, it is reasonable to speculate the alteration in genomics that counteracts the increased amount of TAP1. In the genetic alteration analysis, a maximum frequency of 8% in TAP1 gene alteration occurred in the tested pan-cancer cohort, and the mutation types were non-specific, accounting little for the cancer development. In addition, an alteration in transcriptomics may result in changes in protein. Sometimes, disability of a protein is reflected in the distribution that determines a different function. In present study, immunofluorescence images of melanoma cells and normal epithelial cells revealed a strict distribution of TAP1 on ER. Thus, the elevated expression of TAP1 was neither interpreted by specific types of genetic alteration or alteration in protein distribution. The reason why TAP1 was aberrantly expressed in tumor tissue remains to be explored.\\u003c/p\\u003e \\u003cp\\u003eGenerally, tumor tissue is composed of parenchyma and mesenchyme, more than just malignant cells but also resident stromal cells and infiltrated immune cells. The derived TAP1 expression level in tumor tissue is actually a summation of individual cells. Thus, a single-cell expression analysis was performed across samples from pan-cancer cohorts. In these tumor tissues, TAP1 expression was highly concentrated in various immune cells, especially the adaptive immune members like CD4\\u003csup\\u003e+\\u003c/sup\\u003e and CD8\\u003csup\\u003e+\\u003c/sup\\u003e T lymphocytes, followed by innate immune cells like monocyte/macrophage and DC. There was also scattered TAP1 expression among all the candidate tumor cell lineages, though, with less amount. The results, to some degree, may account for the contradictory opinion between the Big Data and individual studies. As the experiment conducted before were based on the cellular level, while our results were derived from a tissue-based analysis, without a separation of tumor cells from adjacent mesenchyme. Thus, the expression value of all non-malignant cells was counted and might cause an excess. Anyway, these detailed information of TAP1 expression atlas helps enrich the briefing of TAP1, which is set as the basic for our further investigation.\\u003c/p\\u003e \\u003cp\\u003eIn another side, we also focused on clinical significance of TAP1, and hopefully it would enlighten a practical application. TAP1 expression data was allocated to bivariate and continuous variable and analyzed for correlation with cancer prognosis, using Kaplan-Meier and univariate Cox regression methods, respectively. Here, clinical prognostic outcomes were presented as four indexes: OS, DFI, DSS and PFI, each of which is characterized by a specific endpoint and available to reflect prognosis in different conditions. Risky and protective indicators were primarily confirmed, suggesting a distinct effect of TAP1 in each cancer. In the forest plot of univariate Cox regression using OS data, the prognostic role of TAP1 in association with survival probability was varied in all the 32 cancer types. Results obtained in terms of OS showed TAP1 was a risk factor for 11 cancer types and protective for 9 types. For BLCA, BRCA, KICH, KIRC, LIHC, SKCM, STAD and THCA, they showed positive correlation with TAP1 expression and admitted it a protective role. While results of most cancer types (15 in 32) like HNSC, LGG and UVM demonstrated TAP1 as a net risk factor. Specifically, Kaplan-Meier survival curve in LGG suggested a protective effect of high-TAP1 expression in terms of OS, while the clinical outcomes were opposite in BLCA, HNSC and SKCM. Thus, TAP1 could be a promising and powerful prognostic biomarker for various cancers.\\u003c/p\\u003e \\u003cp\\u003eWith such a significant result, we wondered what functionating processes TAP1 may involve in. By taking the advantage of GSEA, we set emphasis on the enrichment of TAP1 in hallmarks gene sets, and our results showed a prominent enrichment in immune-related pathways. Here, based on TAP1 expression, the enriched pathways exhibited a consistent correlation across pan-cancer cohorts. TNF-α signaling pathway, IFN-γ response, IFN-α response, inflammatory response, IL6-JAK-STAT3 signaling, IL2-STAT5 signaling and allograft rejection with positive NES and little FDR were of great significance. According to previous study, IFN as well as TNF molecules promoted in-vivo MHC-I expression by inducing the transcription activity [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]. Besides, the inductive role of IFN-γ, IFN-α/β was more evident in TAP1, and IFN-γ is capable to facilitate TAP-dependent peptide transport [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e]. Although MHC-I molecule and TAP as components ubiquitously expressed in all nucleated cells of distinct levels, they are mainly expressed in the site of inflammation in a short time after recognition and warning by immune system [\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]. Our GSEA results just conformed to the proposed opinion in published papers. A strong correlation was observed between TAP1 expression and pathways of interest. Allograft rejection, an immune rejection response against grafts from the same species, is a typical inflammatory response of different severity [\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]. The most common form is acute rejection that is mainly triggered by T-cell mediated immune responses [\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]. For interleukin-mediated signaling pathway, IL6 and IL2 as well-known inflammatory factors also involve regulation of tumor immunity by facilitating the growth and function of lymphocytes [\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e]. In all, the surprising results point at immune-related mechanism, which encourages us to further explore the potential of TAP1 to predict the responses to immunotherapy.\\u003c/p\\u003e \\u003cp\\u003eActually, development and progression of tumor rely on adjacent environment, TME, which comprises of a complexity of non-malignant cell types (immune cells, fibroblasts, endothelia) and extracellular components (cytokines, hormones) [\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]. Although the composition of TME for each cancer is diverse, some common features applied to all types. For instance, vascular network in most tumor is relative leaky and disorganized, allowing infiltration of multiple immune cells for tumor immunity [\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e]. Considering the results that TAP1 was enriched in immune-related pathways, an immune cell infiltration analysis was conducted to ascertain the association between TAP1 expression and infiltrated immune cells in TME. Scanning our results, an elaborate infiltration pattern was portrayed. It can be indicated that TAP1 expression was positively correlated with multiple immune cells, especially the CD8\\u003csup\\u003e+\\u003c/sup\\u003e T cells, DC and macrophages. Interestingly, the results are in conformity with single-cell analysis, thus verifying mutually. The cell types highlighted in both analyses were CD8\\u003csup\\u003e+\\u003c/sup\\u003e T cells and monocyte/macrophages, the killer cells in immune system. Within so many cell types from TIMER 2.0, M2 macrophage showed an opposite association. Probably, the distinct manifestation may result from its stimulative property in anti-inflammation, T helper 2 cell activation (assisting humoral immunity) and immunoregulation, which are contradictory to our proposal about TAP1-associated cell-mediated immunity [\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]. Additionally, the IL2-STAT5 signaling pathway, inflammatory response and complement activity highlighted in GSEA are realized by macrophages and CD8\\u003csup\\u003e+\\u003c/sup\\u003e CTL. Combining the currently available results, it is concluded that TAP1 expression is highly correlated with immune regulation, and it is corresponding to distinct immune signature for each pan-cancer type. Even though there are abundant immune cells infiltrated for tumor immunity, the relationship between TME and immune cells is quite complicated. T cells mediated tumor immunity is either pro-tumorous or anti-tumorous depending on the cells and regulators they encountered in the process of immune responses [\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e]. In our study, 47 ICPs were recruited to be tested for their correlation with TAP1 mRNA expression across pan-cancer. Tumor cells adopt strategies to activate the suppressive ICP pathways, thus silencing the effector lymphocytes and evading immune surveillance [\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]. Through the heatmap, TAP1 expression was positively correlated with most ICPs in majority of pan-cancer types, especially the BRCA, KIRC, PRAD, TGCT, THCA and UVM. TMB and MSI are reported biomarker to predict TME condition and anti-tumor efficacy of ICI therapy [\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e]. A Spearman\\u0026rsquo; method was also conducted to test the correlation between TMB, MSI and TAP1 expression. The analyzed results highlighted specific cancer with significance association. For instance, COAD, KIRC and LUAD were correlated with TAP1 expression both in TMB and MSI analysis. Hence, our results may support the availability of TAP1 to predict the responses of immunotherapy that targets immune regulatory process.\\u003c/p\\u003e \\u003cp\\u003eBased on distinct TAP1 expression level, precise therapy targeting tumor immunity shows a promising future for cancer patients. The anti-tumor immunity is regulated by a complex of factors in TME, including the ICP, TMB, MSI we discussed already, and then responds with different immune outcomes [\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]. PD-1 and PD-L1 as well as CTLA-4 are the well-known immunosuppressive ICPs that determine the suppression of immune responses, usually recruited by tumor cells for immune evasion [\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e]. Up to now, monoclonal antibodies with high selectivity against PD-1 and CTLA-4 are approved and widely used in the clinical market. However, expected responses are only observed in a portion of patients. As the novel ICI therapy becomes popular, whether it will trigger a favorable response for certain individual remains a problem. In the case that TAP1 expression is highly correlated with immunotherapeutic biomarkers, it is reasonable to expect the feasibility of immunotherapy for patients who bore significant correlation with TAP1 expression. Cohort information of clinical outcomes and transcriptomic profiles from the patients receiving immune therapy was collected and analyzed. The obtained results may guide the therapeutic scheme for the patients waiting for therapy decision. In our study, we cited previous studies where cohorts of patients with primary or metastatic urothelial cancer, breast cancer and melanoma were treated with single or combined monoclonal antibody against PD-L1, PD-1 and CTLA-4, and all the clinical outcomes suggested a protective role of TAP1[\\u003cspan additionalcitationids=\\\"CR25 CR26\\\" citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. In our study, BLCA, BRCA and SKCM all exhibited a better prognosis in high TAP1 expression group, in line with the cohort search. However, TAP1 is not a favorable factor for immune therapy responses in all the cancer types. Just as what we concluded in prognostic and ICPs correlation analysis, the correlation of TAP1 expression was diverse among all the cancer types, which may determine diverse predictive role of TAP1. In LGG, high-TAP1 expression displayed a risk effect in tested cohort. Besides, cancer in different stages may bear varied TAP1 expression level and clinical outcomes. In stage 1 and 2 breast cancer, TAP1 expression is reduced, while the trend is reversed in stage 3 and 4, but TAP1 was considered a protective factor in our study [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. Thus, we proposed TAP1 as a promising and powerful biomarker to predict the effect of immunotherapy for cancer patients. Besides the immunotherapy, previous study had reported a success in increasing tumor-specific immune responses by restoration of TAP1 expression via a TAP1 expressing adenovirus [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. Surprisingly, novel treatment like this inspires us to foresee the clinical prognosis and make the best treatment option on the basis of specific cancer types as well as individual transcriptomic pattern of biomarkers like TAP1.\\u003c/p\\u003e \\u003cp\\u003eAlthough the present study provides rigorous evidence to demonstrate the predictive role of TAP1 in clinical prognosis and potential responses of immunotherapy across pan-cancer, it still bears limitations. TAP1 is conventionally tumor associated gene, however, whose correlation with prognosis showed diversity in pan-cancer analysis. Although we have proposed the possible explanation, a series of elaborate experiments are still required. What\\u0026rsquo;s more, we have just proposed an essential role of TAP1 as predictor without verifying the clinical use in practical, allowing inaccuracy to occur. Furthermore, our investigation focused on population, whereas the individual difference was neglected. However, clinical therapy protocol is specific to individual, which also determines limitations. In turn, the left issues will indicate research directions for future study, and hopefully bring advantages to those who needs novel treatment for survive.\\u003c/p\\u003e \\u003cp\\u003eIn conclusion, a systemic pan-cancer analysis with novel design and character is conducted. Our results revealed an aberrant expression of TAP1 in most pan-cancer types, and this expression is significantly correlated with clinical prognosis, immune cell infiltration, expression of ICPs, TME biomarkers and efficacy of immunotherapy. Hence, we propose the TAP1 as a novel biomarker to predict the prognosis and immunotherapeutic responses in different cancer types, opening a new chapter in the exploration of TAP1.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003eABC, ATP-binding cassette; ACC, adrenocortical carcinoma; BLCA, bladder urothelial carcinoma; BRCA, breast invasive carcinoma; CAF, cancer-associated fibroblast; CESC, cervical and endocervical cancers; CHOL, Cholangiocarcinoma; CI, confidence interval; ComPPI, Compartmentalized Protein Protein Interaction; COAD, colon adenocarcinoma; CRC, colorectal cancer; CTL, cytotoxic T lymphocytes; CTLA-4, cytotoxic T lymphocyte antigen 4; DC, dendritic cell; DEG, Differential expression gene; DFI, disease-free interval; DLBC, diffuse large B-cell lymphoma; DSS, disease-specific survival; EAC, esophageal adenocarcinoma; ER, endoplasmic reticulum; ESCA, Esophageal carcinoma; FDR, false discovery rate; GBM, glioblastoma; GSEA, gene set enrichment analysis; GTEx, Genotype-Tissue Expression; HPA, Human Protein Atlas; HNSC, head and neck squamous cell carcinoma; HR, hazard ratio; HSC, hematopoietic stem cell; ICI, immune check-point inhibitor ICP, immune checkpoints; IFN, interferon; IL, interleukin ; KICH, Kidney Chromophobe; KIRP, kidney renal papillary cell carcinoma; LGG, low-grade glioma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; MDSC, myeloid-derived suppressor cell; MESO, mesothelioma; MHC-I, major histocompatibility complex class I; MSI, microsatellite instability; NES, normalized enrichment score; NSCLC, non-small cell lung cancer; OS, overall survival; OV, ovarian serous cystadenocarcinoma; PAAD, pancreatic adenocarcinoma; PCPG, paraganglioma; PD-1,Programmed cell death protein 1; PD-L1, programmed cell death protein 1 ligand; PFI, progression-free interval; PPI, Protein-protein Interaction; PRAD, prostate adenocarcinoma; READ, rectum adenocarcinoma; SARC, sarcoma; SCLC, small cell lung cancer; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; TAP1, transporter associated antigen processing 1; TCGA, The Cancer Genome Atlas; Tfh, T cell follicular helper; TGCT, testicular cancer; THCA, thyroid carcinoma; TNF, Tumor necrosis factor; Treg, regulatory T cell; \\u0026gamma;\\u0026delta;T, T cell gamma delta; TIMER, Tumor Immune Estimation Resource; TISCH, Tumor Immune Single-cell Hub; THYM, thymoma; TMB, tumor mutational burden; TME, tumor microenvironment; UCS, uterine carcinosarcoma; UVM, uveal melanoma;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cbr\\u003e\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors would like to appreciate the contributions of TCGA, GTEx projects.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe study was funded by the National Science Foundation (grant number: 81860448, 82002660 and 82172989), the Natural Science Foundation of Jiangxi Province (grant number 20192BAB205077 and 20202ACB216004), Jiangxi Provincial Science and Technology Innovation Base Plan-Provincial Key Laboratory (20212BCD42008), the Jiangxi Key research and development projects-Key project (20212BBG71012), Construction of Science and Technology Innovation Base-Clinical Medicine Research Center (2021ZDG02001), Jiangxi Key research and development projects (20212BBG73021), Province-Youth Talent Project (20212BCJ23023).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll authors declare there are no competing interests concerning the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor contribution\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eAll authors made contribution to the works. ZT and KL contributed equally to the study. Study design and conception and data analysis were performed by ZT. KL wrote the original manuscript and improved the language. SL and JL provided technical supports. LW, YH and KL collected the clinical data and performed experiments. XZ, KH and LW commented, supervised and supported the projects. All authors involved discussion and approved the final version of the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis research recruited public databases and website tools. The data is available here: UCSC Xena:\\u0026nbsp;\\u003ca href=\\\"https://xenabrowser.net/datapages/\\\"\\u003ehttps://xenabrowser.net/datapages/\\u003c/a\\u003e. The supplementary materials can be found online. The original data and R codes can be obtained from the authors for reasonable requests.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthical approval\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe study involving human subjects obtained approval from the Ethics Committee of the Second Affiliated Hospital of Nanchang University. The employment and processing of materials were permitted and obtained informed consent from each patient.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003c/div\\u003e \\u003cli\\u003e\\u003cspan\\u003eTrowsdale, J., et al., \\u003cem\\u003eSequences encoded in the class II region of the MHC related to the 'ABC' superfamily of transporters\\u003c/em\\u003e. Nature, 1990. \\u003cb\\u003e348\\u003c/b\\u003e(6303): p.\\u0026nbsp;741\\u0026ndash;4.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAbele, R. and R. 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Blood, 2018. \\u003cb\\u003e131\\u003c/b\\u003e(1): p.\\u0026nbsp;58\\u0026ndash;67.\\u003c/span\\u003e\\u003c/li\\u003e\\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\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"transporter associated with antigen processing 1 (TAP1), Pan-cancer, Prognostic biomarker, Cancer immunotherapy, Immune Check-point Inhibitor (ICI).\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-1544440/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-1544440/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackground\\u003c/strong\\u003e: Transporter associated with antigen processing 1 (TAP1) is a transporter that processes and presents the major histocompatibility complex class I (MHC-I) restricted antigens, including tumor-associated antigens. TAP1 is aberrantly expressed in multiple cancer types, and also involves in tumor immunity. Therefore, the predictive role of TAP1 in cancer development and treatment is expected. \\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eMethods\\u003c/strong\\u003e: Transcriptomic profiles were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) database. Genetic alteration, protein distribution and interaction information were downloaded from cbioPortal, Human Protein Atlas (HPA) and Compartmentalized Protein-Protein Interaction (ComPPI), respectively. Single-cell analysis was conducted on Tumor Immune Single-cell Hub (TISCH) website. Gene set enrichment analysis (GSEA) was employed to investigate the functioning mechanism of TAP1 by R package “clusterProfiler”. Immune cell infiltration of Pan-cancer was explored by Tumor Immune Estimation Resource (TIMER) 2.0 webtool and visualized by R programming language. Correlation between TAP1 expression and immunotherapy biomarkers was explored using Spearman’s correlation test. Association with immunotherapy responses of TAP1 was investigated using the information of the cancer cohorts with patients received immune checkpoint inhibitors (ICIs). \\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eResults\\u003c/strong\\u003e: TAP1 expression was elevated in most pan-cancer types and exhibited distinct prognostic value in diverse cancer types. Within tumor tissues, immune cells expressed more TAP1 than malignant cells. TAP1 expression was significantly correlated with immune-related pathways, infiltration of T lymphocytes and immunotherapeutic biomarkers. Cohort validation revealed a significant correlation with immune therapeutic effects and verified the prognostic role of TAP1 in immunotherapy. Western blot assay indicated that TAP1 is upregulated in GBMs compared with adjacent normal brain tissues (NBTs).\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eConclusion\\u003c/strong\\u003e: TAP1 was a robust tumor biomarker, and a novel predictor of clinical prognosis and immunotherapeutic responses in distinct cancer types.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Pan-cancer analysis: Predictive role of TAP1 in cancer prognosis and responses of immunotherapy\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2022-04-14 20:32:38\",\"doi\":\"10.21203/rs.3.rs-1544440/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"097a24c0-93be-44a9-9f92-24c7b4803495\",\"owner\":[],\"postedDate\":\"April 14th, 2022\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-07-12T12:14:30+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-1544440\",\"link\":\"https://doi.org/10.1186/s12885-022-10491-w\",\"journal\":{\"identity\":\"bmc-cancer\",\"isVorOnly\":false,\"title\":\"BMC Cancer\"},\"publishedOn\":\"2023-02-09 12:14:30\",\"publishedOnDateReadable\":\"February 9th, 2023\"},\"versionCreatedAt\":\"2022-04-14 20:32:38\",\"video\":\"\",\"vorDoi\":\"10.1186/s12885-022-10491-w\",\"vorDoiUrl\":\"https://doi.org/10.1186/s12885-022-10491-w\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-1544440\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-1544440\",\"identity\":\"rs-1544440\",\"version\":[\"v1\"]},\"buildId\":\"WrCJVZZCHTDjtuVLN7oU0\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}