Pan-Cancer Analysis of the Antigen-processing Gene PSMB8 as an Immunologic and Prognostic Biomarker

Recently some evidences have demonstrated the signicance of PSMB8 in various malignancies. Nevertheless, PSMB8 (proteasome subunit beta 8), more familiar in the eld of immunology contributing to the process of antigen presentation, is indeterminate in the role as a survival predictor of human pan-cancer. Besides, how PSMB8 interacts with immune cell inltration in the tumor microenvironment requires further research. We then penetrated into the analysis of PSMB8 expression prole among 33 types of cancer in TCGA database. The results show that overexpression of PSMB8 was associated with the poor clinical outcomes in overall survival (OS), disease-specic survival (DSS), disease-free interval (DFI) and progression-free interval (PFI) in most cancer varieties. In addition, there existed distinctly positive correlations between PSMB8 and immunity, reected straightfowardly in the form of immune scores, tumour-inltrating immune cells (TIICs) abundance, microsatellite instability, tumour mutation burden, and neoantigen level. Notably, specic markers of dendrite cells exhibited the tightest association with PSMB8 expression in terms of tumor-related immune inltration patterns. Moreover, gene enrichment analysis showed that elevated PSMB8 expression was related to multiple immune-related pathways. We nally validated the PSMB8 expression in our local breast samples via quantitative pCR assays and concluded that PSMB8 appeared to perform well in predicting the survival outcome of BRCA patients. These ndings elucidate the pivotal role of the antigen presentation-related gene PSMB8, which could potentially serve as a robust biomarker for the prognosis determination in multiple cancers. The scatter plots displayed purity-corrected partial Spearman’s rho values and statistical signicances evaluated by the Wilcoxon test. In addition, the ESTIMATE algorithm was exploited to infer the ratio of immune and stromal components from PSMB8 expression data in pan-cancer, whose results were presented in the form of and immune score, stromal score, and ESTIMATE score with the relative correlation coecients. Following the application of these immune algorithms, immunostimulatory and immunoinhibitory gene markers were gathered for additional correlation analysis in pan-cancer, which comprised BLTA, CD200, TNFRSF14, NRP1, LAIR1, TNFSF4, CD244, LAG3, ICOS, CD40LG, CTLA4, CD48, CD28, CD200R1, HAVCR2, ADORA2A, CD276, KIR3DL1, CD80, PDCD1, LGALS9, CD160, TNFSF14, IDO2, ICOSLG, TMIGD2, VTCN1, IDO1, PDCD1LG2, HHLA2, TNFSF18, BTNL2, CD70, TNFSF9, TNFRSF8, CD27, TNFRSF25, VSIR, TNFRSF4, CD40, TNFRSF18, TNFSF15, TIGIT, CD274, CD86, CD44, and TNFRSF9. To further evaluate the immune-predictor value of PSMB8 in pan-cancer, we evaluated two emerging parameters—tumor mutation burden (TMB) and microsatellite instability (MSI)—in the context of immunotherapy. We proposed that TMB and MSI-associated PSMB8 expression would provide evidence that a higher degree of genomic instability, as dened by the TMB and MSI, could extrapolate to more immunosurveillance opportunities. Furthermore, data of 33 cancer types as well as their survival-related follow-up to investigate the impact of PSMB8 expression on OS, DSS, DFI, and PFI. Cox regression analysis revealed that PSMB8 denoted favorable OS and DSS in bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), ovarian serous cyst adenocarcinoma(OV), mesothelioma (MESO), and skin cutaneous melanoma (SKCM). Our results regarding OS, DSS, PFI showed a robust carcinogenic-predictive role of PSMB8 in brain lower grade glioma (LGG), uveal melanoma (UVM), and PAAD. Furthermore, high expression of PSMB8 was associated with poor survival in LAML and lung adenocarcinoma (LUAD). With regard to DFI, increased PSMB8 expression showed a satisfactory prognosis in BRCA patients, and a poor outcome in PAAD, instead. All


Introduction
Currently, several checkpoint-blocking drugs such as anti-CTLA4 and anti-PD-L1 have provided superior performance compared to conventional cytotoxic drugs, which has fueled the eld of immune-related therapeutic targets in oncology. Considering the recent advances in immunotherapy, it is impossible to ignore the substantial in uence of the tumor microenvironment (TME), which is comprised of resident stromal cells and recruited immune cells. The antigen-presenting system, in concert with immune effector cells, orchestrate sustained input to the TME-tumor interaction via the enhancement of tumor immunogenicity and the modulation of an anti-tumor microenvironment.
The PSMB8 gene encodes an essential subunit of a specialized immunoproteasome complex [1]. And the generated peptides has higher a nity with major histocompatibility complex (MHC) I molecules and in turn enhanced antigenicity to CD8 + T cells [2][3][4]. Meanwhile, PSMB8 mutations have been observed to contribute to auto-in ammation and lipodystrophy in humans [1], which show the pleiotropic functions involved in the maintenance of dynamic equilibrium. Previous studies have revealed the context-speci c role of PSMB8, which varies in diverse cancers. Recent studies have recognized a neovascularizationsuppressive role exerted by PSMB8 in glioma, via modulation of the ERK1/2 and PI3K/AKT signaling pathways [5,6]. In the orthotopic mouse model, inducible knock-down of PSMB8 dampened the expression of vascular endothelial growth factor (VEGF) and CD31; and thus, favored invasive capacity in glioblastoma. In another functional analysis, the role of PSMB8 was recapitulated in mucinous ovarian carcinoma pathogenesis, which identi ed PSMB8 as a mediator between antigen presentation of exogenous antigen via MHC class I molecules and the noncanonical nuclear factor kappa-light-chainenhancer of activated B cells (NIK/NF-kB) pathway [7]. Furthermore, PSMB8 meditated PI3K/AKT pathway activation in acute myeloid leukemia (LAML) was established and was suggested as a promotor of tumorigenesis [8]. In addition to the previously mentioned tumors, a large-scale public database analysis and laboratory investigations have successively con rmed the role of PSMB8 in the evolution of cutaneous squamous cell carcinoma, papillary thyroid carcinoma, and prostate adenocarcinoma [9][10][11].
The association of aberrant expression of PSMB8 have been asynchronous with regard to tumor prognosis. In contrast, there is also a growing body of research lending support to a shielding role for PSMB8 via the promotion of immune cell in ltration. In T cell-mediated anti-tumor immunity [12], the overexpression of PSMB8 was reported to reduce colony formation after radiation with a signi cant increase in expression of apoptosis-inducing molecules, such as cleaved PARP and cleaved caspase-3 [13]. To date, the mechanisms that underline the tumorigenetic capacity of PSMB8 are not fully understood, and the immunological and prognostic roles of PSMB8 in the pan-cancer background remain to be elucidated.
Previously published studies conducting the cancer-associated analysis involving PSMB8 have been limited to speci c cancer type. Herein, we present a comprehensive evaluation of the immune-related prognostic landscape of PSMB8 in the pan-cancer eld pooling information from publicly available databases. Our study was exploratory and interpretative in nature, and required a longitudinal analysis. In detail, we rst compared the expression of PSMB8 in normal tissues, tumor cell lines, and pan-cancer.
Transcriptome-sequencing patterns were conventionally followed by survival analysis. Subsequently we evaluated the prognostic value of PSMB8 in pan-cancer using datasets from The Cancer Genome Atlas (TCGA) database. Next, we analyzed the association between PSMB8 expression and the degree of immune cell in ltration, immune checkpoint expression, and mutational burden. To this end, we have shifted the research focus of PSMB8 toward a multi-dimensional analysis of clinical relevance, with an unique immuno-correlation study based on pan-cancer analysis. This study sheds light on the potential role of PSMB8 as a prognostic-indicator in different cancers.

Materials And Methods
Data Acquisition and Processing TCGA, a landmark cancer genomic database containing vast information on cancer samples spanning 33 cancer types, was exploited to extract expression pro le data from matched tumor and adjacent normal samples, as well as information detailing the corresponding clinicopathological traits. Another comprehensive public resource, GTEx, was applied to enrich tissue-speci c normal samples alongside those obtained from TCGA. The broad institute CCLE was interrogated for PSMB8 mRNA expression in human cancer cell lines for a multi-dimensional inspection of PSMB8 expression.
Prior to commencing the study, the transcript data were checked for their robustness and normalization.
Subsequently, the RNA sequencing data were adjusted to eliminate missing and duplicated results, and were transformed by a log2(TPM + 1) normalization using the R package of "rma" in an R environment (R version: 3.6.1). Cases in pan-cancer were acquired having a thorough follow-up for survival analysis and their corresponding outcomes in terms of overall survival (OS), disease-speci c survival (DSS), and in the disease-free interval (DFI) and progression-free interval (PFI).

PSMB8 Gene Expression Analysis and Correlation with Malignancy Prognosis
The recruited samples from TCGA in the section of the survival analysis were preliminarily screened for their data integrity in terms of both PSMB8 expression and follow-up information. Consequently, a prognosis-correlated analysis of the respective outcome events of OS, DSS, DFI, PFI was conducted. The optimal PSMB8 cut-off values for Kaplan-Meier curves were calculated using the "survmine" package in R (R version: 3.6.1). Kaplan-Meier curves and log-rank tests were used to estimate the prognosis-predictive value of PSMB8. Hazard ratios (HRs) with 95% con dence intervals (CI) and log-rank Ps were calculated. HRs with a value less than 1 were detrimental to survival, while a value greater than 1 were bene cial to prognosis.

TIMER Analysis and Immune Microenvironment Correlation Analysis
To investigate the interplay between tumor and the TME, an immune correlation analysis was carried out.
First, the TIMER (Tumor Immune Estimation Resource) web server (https: //cistrome.shinyapps.io /timer/) was used to visualize the immune in ltration-associated expression pattern of PSMB8 in diverse cancer types. In the gene module of the TIMER database, we investigated the levels of PSMB8 expression and the abundance of tumor-in ltrating immune cells (TIICs), including CD4 + T cells, CD8 + T cells, B cells, neutrophils, dendritic cells (DCs), and macrophages. The scatter plots displayed purity-corrected partial Spearman's rho values and statistical signi cances evaluated by the Wilcoxon test. In addition, the ESTIMATE algorithm was exploited to infer the ratio of immune and stromal components from PSMB8 expression data in pan-cancer, whose results were presented in the form of and immune score, stromal score, and ESTIMATE score with the relative correlation coe cients. Following the application of these immune algorithms, immunostimulatory and immunoinhibitory gene markers were gathered for additional correlation analysis in pan-cancer, which comprised BLTA, CD200, TNFRSF14, NRP1, LAIR1, TNFSF4, CD244, LAG3, ICOS, CD40LG, CTLA4, CD48, CD28, CD200R1, HAVCR2, ADORA2A, CD276, KIR3DL1, CD80, PDCD1, LGALS9, CD160, TNFSF14, IDO2, ICOSLG, TMIGD2, VTCN1, IDO1, PDCD1LG2, HHLA2, TNFSF18, BTNL2, CD70, TNFSF9, TNFRSF8, CD27, TNFRSF25, VSIR, TNFRSF4, CD40, TNFRSF18, TNFSF15, TIGIT, CD274, CD86, CD44, and TNFRSF9. To further evaluate the immunepredictor value of PSMB8 in pan-cancer, we evaluated two emerging parameters-tumor mutation burden (TMB) and microsatellite instability (MSI)-in the context of immunotherapy. We proposed that TMB and MSI-associated PSMB8 expression would provide evidence that a higher degree of genomic instability, as de ned by the TMB and MSI, could extrapolate to more immunosurveillance opportunities. Furthermore, the relationship between different immune checkpoint genes and PSMB8 expression were analyzed via correlation coe cients.

Gene Set Enrichment Analysis
To re ect the underlying biological function of PSMB8, we subsequently anatomized the biological traits by applying GSEA. Genes enriched in the prede ned gene sets in GSEA (http://software.broadinstitute.org) served as the reference to assess the overall coupling of aberrantlyexpressed PSMB8 enriched in the KEGG and HALLMARK collections, respectively. The signi cantly enriched pathways were nally identi ed based on the calculated net enrichment score (NES) with a false discovery rate (FDR) < 0.05 as the cut-off criterion.
Association Analysis of PSMB8 With DNA Mismatch Repair (MMR) Genes and Methyltransferases DNA mismatch repair is an essential intracellular repair mechanism, which leads to the risk reduction of genomic instability. DNA methylation is a form of epigenetic modi cation that does not alter the DNA sequence. The correlation of ve MMRs genes (MLH1, MSH2, MSH6, PMS2, EPCAM) as well as four methyltransferases with PSMB8 expression was assessed in TCGA.
quantitative realtime PCR assays (qRT-PCR) qRT-PCR was used to test the relative expression of PSMB8 from BRAC cells. RNA was extracted by Trizol reagent and then exploited for the process of reverse transcription via a reverse transcription reagent kit (Toyobo, Osaka, Japan). Finally, qRT-PCR was conducted using THUNDERBIRD SYBR qPCR Mix (Toyobo, Osaka, Japan) with the machine of Applied Biosystems 7500. Each sample was conducted at least triplicate and the relative expression of PSMB8 was measured by the 2 -ΔΔCt calculation fomula compared to GAPDH expression. The sequences of the primers used for cDNA ampli cation were listed as followed: PSMB8 Forward: 5′-GCTGCCTTCAACATAACATCA-3', and Reverse: 5′-CTGCCACCACCACCATTA − 3'; GADPH Forward: 5'-GTCTCCTCTGACTTCAACAGCG-3' and Reverse: 5'-ACCACCCTGTTGCTGTAGCCAA-3'.

Statistical Analysis
Gene expression data from the TCGA, GTEx, and CCLE databases were analyzed using Student's t-test.
The Kruskal Wallis test was used to evaluate PSMB8 expression in pan-cancer, and the Wilcoxon test was used to evaluate gene expression differences between normal and tumor tissues. OS was calculated using the Kaplan-Meier method, and survival curves were compared using log-rank tests. Pearson analysis was performed to evaluate the correlation between PSMB8 expression levels with checkpointrelated genes. All statistical analysis was conducted using R software (version 3.6.1). A P-value < 0.05 was considered statistically signi cant.

Results
Pan-cancer expression pro le of PSMB8 To start with, our research procedure towards PSMB8 in the pan-cancer was shown in Fig. 1. Previous studies investigating PSMB8 have alluded to two contradictory notions, one of which considered that elevated PSMB8 expression was adversely associated with tumor progression, such as in PTC, LAML, and GBM, while the other regarded PSMB8 as an immunostimulatory factor with protective characteristics. Thus, we rst performed a comprehensive evaluation of PSMB8 mRNA expression in normal tissues in the GTEx database and in cell lines in CCLE database. The results revealed that PSMB8 was expressed in relatively lower levels in the bone marrow, muscle, and testis, while expression was much higher in the spleen, bladder, and lung (Kruskal-Wallis test P < 0.05) ( Fig. 2A). As shown in Fig. 2B, the PSMB8 expression in 22 normal cell lines extracted from the CCLE database was also observed with substantially signi cant differences (Kruskal-Wallis test: P = 3.9e-47).
Further, to distinguish between the two potential roles of PSMB8, we conducted a pan-cancer expression pro le analysis using datasets from TCGA and the GTEx databases to compare differences in expression between tumor and normal tissue. The results obtained from the preliminary expression analysis of PSMB8 are presented in Fig. 2C and 2D. The analysis of differentially expressed PSMB8 in TCGA (Fig.  2C) revealed signi cant differences except for kidney chromophobe (KICH), pancreatic adenocarcinoma (PAAD), and prostate adenocarcinoma (PRAD). Subsequently, we combined gene expression from normal samples in the GTEx with those from TCGA and generated a new plot. As shown in Fig. 2D, PSMB8 was overexpressed in tumor tissues compared to normal tissues on average, except for lung squamous cell carcinoma (LUSC) and adrenocortical carcinoma (ACC).

Prognostic Signi cance of PSMB8 across cancers
The rst part of our analysis explored the expression pro le of PSMB8, which raised the issue of its prognostic value. We utilized TCGA database comprising Affymetrix microarray data of 33 cancer types as well as their survival-related follow-up to investigate the impact of PSMB8 expression on OS, DSS, DFI, and PFI. Cox regression analysis revealed that PSMB8 denoted favorable OS and DSS in bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), ovarian serous cyst adenocarcinoma(OV), mesothelioma (MESO), and skin cutaneous melanoma (SKCM). Our results regarding OS, DSS, PFI showed a robust carcinogenic-predictive role of PSMB8 in brain lower grade glioma (LGG), uveal melanoma (UVM), and PAAD. Furthermore, high expression of PSMB8 was associated with poor survival in LAML and lung adenocarcinoma (LUAD). With regard to DFI, increased PSMB8 expression showed a satisfactory prognosis in BRCA patients, and a poor outcome in PAAD, instead. All results are presented in Fig. 3.

Correlation of PSMB8 Expression with Immune In ltration Level and Components of Immune Cells
Previously, a variety of studies have revealed that TIICs in uence the response rate of immunotherapy, the e cacy of chemotherapy, and the ultimate prognosis of malignancies. Chen et al. demonstrated the seven steps of the cancer-immunity cycle, which have become the basic framework of cancer immunotherapy research. Portrayed as one of the constitutive proteasome genes (CP), expression of PSMB8 has been associated with levels of MHC-I, the antigen-presenting cells, and TIILs, and consequently plays a crucial part in the above-mentioned immunity cycle. Albeit proteasome inhibitors, such as bortezomib, may induce proteotoxic stress and apoptotic activity as targeted therapy, there are currently investigative areas worthy of attention involving the suppression of immunoproteasome (iproteasome) activity to induce immune evasion and metastatic progression [15]. Therefore, it is necessary to evaluate the immune properties of PSMB8 in the context of pan-cancer.
The online TIMER database was exploited to calculate the expression of the immune cells in association with a target gene. We found that all six subtypes of immune in ltration cells were associated with elevated PSMB8 expression in the following tissues: kidney renal papillary cell carcinoma (KIRP), brain lower grade glioma (LGG), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), sarcoma (SARC), skin cutaneous melanoma (SKCM), stomach adenocarcinoma (STAD), and testicular Germ Cell Tumors (TGCT). In contrast, there seemed to be no immune in ltration-associated expression patterns with PSMB8 in cholangiocarcinoma (CHOL) and rectum adenocarcinoma (READ). Furthermore, the Spearman correlation coe cient determined for the immune in ltration levels of B cells, CD8 + T cells, CD4 + T cells, macrophages, neutrophils, and dendritic cells were notably signi cant in 23, 23, 25, 23, 19, and 26 cancer types, which comprised a large proportion of the 32 cancer types interrogated ( Figures S1-8).
An additional immune algorithm, called the ESTIMATE algorithm, was applied. Three of the calculated scores were the StromalScore, ImmuneScore, and ESTIMATEScore, which were determined to be directly proportional to the ratio of the corresponding stromal, immune components, and their aggregates. In most cancer types, PSMB8 emerged as a statistically signi cant marker of the TME status. Thus, correlations between PSMB8 expression and StromalScore, ImmuneScore, and ESTIMATEScore were determined in 20, 31, and 29 cancer types, respectively (Figs. 4-6). The top three tumors most signi cantly associated with overexpression of PSMB8 based on the ESTIMATEScore were listed the following: TGCT, THCA, and UVM. Nevertheless, CHOL and DLBC showed no statistically signi cant association across all three scores. The ESTIMATE outcome generated interesting insight into THCA, which ranked second in the ESTIMATEScore as well as the ImmuneScore, and ranked fourth in the StromalScore, and thus, hereinafter we switched our focus to the role of PSMB8 in THCA.
To determine the underlying mechanism of PSMB8 in the enhanced immunological response, we calculated the Spearman correlations of PSMB8 expression with immunostimulatory and immunoinhibitory factors. We set the statistical criteria as P < 0.05. As illustrated in the heatmap, the bulk of cancer types were positively associated with most immune elements, with the exception of CHOL, DLBC, ESCA, GBM, KICH, MESO, and READ. These seven cancer types identi ed a subset in which the TME was irrelevant to PSMB8 expression, and thus the predictive role of PSMB8 was far from optimal in this subgroup, which is frequently encountered in the search for tumor biomarkers.
Our results revealed that a positive relationship between PSMB8 expression and the levels of immune checkpoint genes in various tumors, such as TGCT, THCA and UVM (Fig. 7). Thus, these ndings indicated that PSMB8 could play a role in tumor immunity by regulating the expression of these immune checkpoint genes. As shown in Fig. 8, we determined that the expression of PSMB8 was positively correlated with the number of neoantigens only in SKCM tumor tissues (R = 0.241, P < 0.05). Of note only a P-value of < 0.05 and R > 0.20 may be considered as signi cant and positive, respectively.
The TMB and MSI have conceptually emerged as predictors of an effective immune response, which are promoted within the spectrum of tumor exacerbation. We further evaluated the strength of the relationship between PSMB8 expression and the TMB or MSI in pan-cancer. The most distinct correlation coe cients in the analysis of the TMB and MSI were ± 0.47 and ± 0.31, respectively. In detail, a signi cant correlation was identi ed between overexpression of PSMB8 and an increased TMB in UCS, STAD, SKCM, PAAD, LGG, KIRC, ESCA, COAD, and BRCA tissues, while an opposite association was observed in THYM and PRAD tumor tissues (Fig. 9A). Similarly, increased PSMB8 expression was signi cantly associated with an increased MSI in THCA, KIRC, DLBC, and COAD tissues and was inversely correlated with in UCEC, TGCT, PRAD, OV, MESO, LUSC, LUAD, and CESC (Fig. 9B).

Association Analysis of PSMB8 with DNA Mismatch Repair Genes and Methyltransferases
As shown in Fig. 10A, a signi cant correlation between PSMB8 and four methyltransferase expression levels could be observed in most cancer types. In addition, DNA mismatch repair gene expressions were almost inversely correlated with the PSMB8 expression in pan-cancer, whereas MLH1 was of positive correlation (Fig. 10B). Our analysis revealed that PSMB8 could regulate epigenetic status in pan-cancer.

Gene-Annotation and Pathway Enrichment Analysis
To uncover the potential signaling pathways and immunocompetences associated with the involvement of PSMB8 in tumorigenesis, we applied GSEA using KEGG and HALLMARK terms. Only enriched gene sets with the absolute value of normalized enrichment score (NES) > 1, nominal (NOM) P < 0.05, and an FDR q-value < 0.25 were considered statistically signi cant. As shown in Fig. 11A-C, KEGG and HALLMARK enriched terms showed that overexpression of PSMB8 was mainly associated with immunological processes, including antigen processing and presentation (NES = -2.6, NOM P < 0.05), natural killer cell-mediated cytotoxicity (NES = -2.3, NOM P < 0.05), allograft rejection (NES = -2.6, NOM P < 0.05), the interferon (IFN)-gamma response (NES = -2.7, NOM P < 0.05), and the IFN-alpha response (NES = -2.5, NOM P < 0.05). Furthermore, high expression of PSMB8 was associated with metabolic syndromes, such as type I diabetes mellitus (NES = -2.3, NOM P < 0.05). However, there was no signi cant enrichment in the analysis based on low expression of PSMB8 (Fig. 11B-D).

Native validation of PSMB8 expression in the BRCA subtypes
To further validation, we chose 60 pairs BRCA tissues and their corresponding adjacent normal tissues to perform qRT-PCR. Our local specimens were all sampled from the operation room in the First A liated Hospital of Wenzhou Medical University, which were made up of luminal(HR+, HER2-), TNBC(HR-,HER2-), and HER2+( HR-,HER2+) subgroups. And Fig. 12 directly show the disparity tendency of PSMB8 expression between tumor and normal tissues with signi cantly statistical difference(* P < 0.05, ** P < 0.01, and *** P < 0.001).

Discussion
The awareness of remodeling of an e cient TME evolved along with the widely-spread application of immune checkpoint blockade tumor therapy. With the exception of some cancer types such as melanoma and Hodgkin's disease, which have been veri ed to greatly bene t much from blocking immune checkpoints, the overall objective response rates of other tumors have ranged from 15 to 25%, which is far from satisfactory [16]. Previous studies have concluded that a TME with negative immunocompetence comprised six characteristics: the heterogeneity of the TME, low antigen burden, defects in the antigen-presenting cell, damaged T-cell in ltration, activation of immunosuppressive signaling, and improvement of immunosuppressive metabolism. Furthermore, an insu cient antigen presentation system contributed to the anergy status of T cells and nulli ed anti-tumor immunity mechanisms. The third generation of combined immunotherapy strategies that integrate an immunomodi er with immune checkpoint blockage has achieved synergistic effects in the maintenance of a dominant and enhanced immunological response [17].
PSMB8, a catalytic subunit of immunoproteasomes, plays a critical role in the process of proteolysis to generate the antigenic epitopes, which are in turn transmitted to MHC class I molecules for further antigen-presentation. The IFN-gamma inducible genes PSMB8 and its chaperone RTP4 have command of the tumor vulnerability to antigen-dependent killer cells [18]. Malignancies are usually subjected to proteotoxic stress, under which circumstances tumorigenic proteins induced by genomic aberrations are assembled at the expense of proteasome-promoted activities regulating proteostasis [19]. The increased release of IFN-gamma by the TIIL in the TME trigger the incorporation of immuno-subunits into the catalytic core of the proteasome and transform the excess proteasomes into immunoproteasomes. Accordingly, a range of tumor types addictively resort to immunoproteasome activities. An experimental study determined that isolated T cells, in which both β2i (PSMB10) and β5i (PSMB8) genes were knocked out, exhibited higher rates of cell division induced by the mitogenic stimulation compared to wild-type cells. Thus, essential subunits, or more explicitly PSMB units, were fundamentally engaged in the immunoproteasome transformation, acting against the construction of anti-tumor immunity. Nevertheless, antigen presentation by the catalytically converted immunoproteasomes, to some extent, signi cantly enhanced the pool of MHC-I compatible peptides. These contradictory mechanisms underlying the activity of PSMB8 result in greater antigen presentation to the immune system and enhanced immunotherapy bene ts. Previous studies investigating PSMB8 revealed a chaotic picture of the functional orientation in neoplasms. Proteasome-targeted treatment has already been a therapeutic approach in various types of human cancers for ago [20], although relevant studies on this immunebased activity have not been cohesive or sustainable. Hence, our study which investigated the general applicability of the antigen-presentation regulator PSMB8 as a prognostic biomarker in pan-cancer from an immuno-oncological perspective, could provide a rational and theoretical basis for future individuallytailored mechanism studies.
As there have not been any credible immuno-oncology trends emerging in studies from previous decades, we explored this antigen presentation-associated biomarker from an immunological standpoint. As justi ed by previous experimental or bioinformatic studies combined with our pan-cancer survey described herein, different tumor types and their corresponding microenvironments essentially did not in uence the functional role of PSMB8.
As high-throughput sequencing techniques become relatively easier to perform, large-scale public databases have also ourished. Independent datasets with pan-cancer expression pro les are available from TCGA, GTEx, and CCLE, which include normal tissues, tumor tissues, and cancer cell lines. We extracted expression data of PSMB8 and visually rendered the presentation of the comparison in pancancer. Using GTEx and TCGA, we found that PSMB8 expression was generally lower in normal tissues, while tumor cell lines in CCLE exhibited varied expression. Next, we explored differences in expression between tumor and normal tissues using TCGA datasets and determined that PSMB8 was generally prone to overexpression in tumor tissues compared to normal tissues, except for LUSC, PAAD, PRAD, and KICH tumors. Although, PSMB8 expression was higher in PAAD, PRAD, and KICH than corresponding adjacent normal tissues when data from normal tissues in the GTEx was combined with the TCGA datasets. Thus, only PSMB8 expression in LUSC was inconsistent and indicated an overexpression of PSMB8 when compared to the normal tissue counterparts. We next attempted to identify a prognosispredictor value for PSMB8. Our ndings showed that high expression of PSMB8 in BLCA, BRCA, MESO, OV, and SKCM was successful in predicting better OS and DSS. Considering these survival outcomes, for BRCA the predictive ability of PSMB8 was signi cant given the protective role of PSMB8. Conversely, increased PSMB8 expression revealed poor overall survival for LAML, LGG, LUAD, PAAD, and UVM based on Kaplan-Meier plot analysis. Our results sustained prior studies and revealed the functional pleiotropy of PSMB8, albeit with unknown molecular mechanisms.
Given the overpowering energy of the immune microenvironment implicated in the development of tumorigenesis and tumor invasion, a signi cant common relationship across different cancers was corroborated by our analysis. More than two-thirds of cancer types were signi cantly associated with the presence of speci c immune in ltrating cells. Since the TME are constantly in a state of ux, tumor progression along a relatively unmanageable pathway may demand a metabolic reprogramming obtained from the TIICs, which leads to subsequent vicious circle of tumor differentiation. Unfortunately, CHOL was considered an exception on account of the irrelevance between PSMB8 and the constituents of its TME, including both immune and stromal elements, which could be partly in explanation of its very poor survival rates.
Proteasomes are associated with a immunocyte-speci c expression pattern, which promotes the degradation of endogenous and exogenous antigens presented by the MHC class I system. Thus, proteasomes, as well as their subunits, were linked to antigen presentation. The maturation of dendritic cells (DC), which are acknowledged as the most powerful APCs, orchestrate the changes in proteasomal composition. More immunoproteasomes populate DCs and inducible cytotoxic T lymphocytes (CTLs) are unable to properly identify tumor cells that constitutively express these proteasomes [21]. Inhibition of the catalytic subunit involved in immunoproteasome-transformed activities, herein referred to as PSMB8, promoted an alteration of antigenic peptide-repertoires expressed by antigen-loaded DCs. In turn, the bene cial changes are sustained by the capacity of mature DCs to trigger anti-tumor immune responses. In our Estimate algorithm-based analysis, all cancers types except for READ, CHOL, UVM, THYM, ESCA, GBM, and DLBC, presented a signi cant correlation between PSMB8 expression and DC. In brief, DCs were the most closely-associated with PSMB8 levels in the scope of pan-cancer, and both were observed to be involved with the antigen-presenting system.
Notably, one of the strongest positive associations was observed between PSMB8 expression in THCA and immune markers, which comprised the Estimate Score, various levels of TIIC, TMB, and MSI. Locally validated cohorts of THCA collected in our department supported immune-associations, which has already been validated by previous experimental studies [10].
The fact that prognosis-associations varied for PSMB8 across different tumor contexts literally repudiated frequently proposed labels suggesting there were limitations to generalizing data supporting a speci c biomarker.
Nevertheless, this study extending across multiple databases presented some limitations. First, we only conducted a bioinformatic analysis of PSMB8, from which it was di cult to assess the value of clinical transformation. In addition, since the resources in all the databases were tissue-derived, these ndings cannot be associated with cell-level approaches, while the global analysis of immune cell markers may give rise to systematic bias. Finally, the polytropic function of PSMB8 may represent an initial effect, and the underlying mechanisms engaged in tumor activities remain still elusive.
Our pan-cancer research merely embodied an immune-related pan-cancer analysis and awaits future investigation into the mechanism involved in tumorigenesis. WOC discussed the results and participated in the critical review of the manuscript. All authors contributed to the article and approved the submitted version.

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