Comprehensive pan-cancer multi-omics analysis of PSMB7 reveals its prognostic significance and oncogenic role with experimental validation in lung and gastric cancers

Comprehensive pan-cancer multi-omics analysis of PSMB7 reveals its prognostic significance and oncogenic role with experimental validation in lung and gastric cancers | 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 Comprehensive pan-cancer multi-omics analysis of PSMB7 reveals its prognostic significance and oncogenic role with experimental validation in lung and gastric cancers Zhou Sijiang, Yang Xia, Ran Qingfu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7125983/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Objective PSMB7 is a key component of the ATP-dependent proteolytic complex and plays an essential role in cellular protein degradation. While emerging evidence suggests its involvement in cancer, its roles in tumor progression, prognosis, and diagnosis remain largely uncharacterized. This study aimed to investigate the expression profile of PSMB7 and its association with clinical outcomes and tumor biology across multiple cancer types. Methods We conducted a comprehensive bioinformatic analysis using pan-cancer data from The Cancer Genome Atlas (TCGA). Gene set enrichment analysis (GSEA) and protein–protein interaction (PPI) network construction were performed to explore the functional roles of PSMB7. The correlation between PSMB7 expression and tumor-infiltrating immune cells was assessed using CIBERSORT, IPS, and QUANTISEQ. Single-cell RNA sequencing data were analyzed to determine PSMB7 expression across distinct cell populations. Furthermore, siRNA-mediated knockdown of PSMB7 was carried out in A549 and AGS cells. Functional assays—including Western blotting, colony formation, and Transwell migration/invasion—were used to assess phenotypic effects. Subcutaneous xenograft models of lung and gastric cancer were established in nude mice to evaluate the in vivo impact of PSMB7 depletion on tumor growth. Results PSMB7 was significantly upregulated in multiple cancer types compared to adjacent normal tissues. Its expression was positively associated with clinical and molecular features such as stemness scores, stromal and immune scores, tumor mutational burden (TMB), microsatellite instability (MSI), and drug sensitivity. Notably, high PSMB7 expression correlated with altered sensitivity to several chemotherapeutic agents. Functionally, PSMB7 knockdown markedly inhibited cancer cell proliferation, migration, and invasion in vitro, and suppressed tumor growth in vivo. Conclusion PSMB7 is broadly overexpressed in human cancers and is significantly associated with key prognostic indicators, immune cell infiltration, and therapeutic responsiveness. These findings highlight the clinical relevance of PSMB7 as a potential biomarker and therapeutic target, paving the way for its application in personalized cancer treatment strategies. PSMB7 biomarker prognostic immune microenvironment cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1 Introduction Cancer remains one of the most formidable global health challenges, characterized by rising incidence, high mortality, and substantial socioeconomic burden. Despite significant advances in diagnostics and therapeutic interventions, many malignancies continue to exhibit marked biological heterogeneity, therapeutic resistance, and dismal clinical outcomes. This complexity underscores an urgent need for robust, pan-cancer biomarkers capable of predicting disease progression, guiding personalized treatment strategies, and improving patient stratification to ultimately enhance clinical benefit. The ubiquitin-proteasome system plays a fundamental role in maintaining protein homeostasis by selectively degrading misfolded, damaged, or regulatory proteins in an ATP-dependent manner[ 1 ]. Central to this system is the 20S proteasome core, composed of four stacked rings, including two outer α rings and two inner β rings. Among the seven β-type subunits, PSMB5 (β5), PSMB6 (β1), and PSMB7 (β2) serve as the primary catalytic components, responsible for chymotrypsin-like, caspase-like, and trypsin-like activities, respectively. PSMB7, encoding the β2 subunit, is essential for intracellular proteolysis and proteasome activity, but has also been implicated in broader cellular processes, including cell cycle regulation, apoptosis, immune response, and adaptation to cellular stress[ 2 ]. Although proteasome inhibitors such as bortezomib have demonstrated therapeutic efficacy, particularly in multiple myeloma through targeting PSMB5, the functional roles of PSMB7 in cancer biology remain comparatively understudied[ 3 ]. However, emerging evidence suggests that PSMB7 may act as an oncogenic driver. Aberrant overexpression of PSMB7 has been reported in several malignancies, including colorectal cancer, breast cancer, and multiple myeloma, where it is associated with poor prognosis and resistance to chemotherapeutic agents such as anthracyclines and proteasome inhibitors[ 4 , 5 , 6 ]. Furthermore, stimuli like 3H-1,2-dithiole-3-thione (D3T) have been shown to induce PSMB7 expression in a tissue-specific manner across organs such as the liver, lung, and brain, indicating that its regulation may intersect with oncogenic and stress response pathways[ 7 ]. Functional studies further expand the biological significance of PSMB7. Knockdown of PSMB7 in cardiomyocytes induces endoplasmic reticulum (ER) stress and autophagy, accompanied by upregulation of stress response markers (e.g., CHOP, GRP78) and key autophagy regulators (e.g., MTOR)[ 8 , 9 , 10 ]. These findings highlight a noncanonical role of PSMB7 in modulating proteostasis and stress adaptation, beyond its established proteolytic function. In cancer contexts, such mechanisms may contribute to survival under hostile microenvironmental conditions, thereby enhancing tumor progression and therapy resistance. Despite these observations, a comprehensive pan-cancer characterization of PSMB7—integrating its transcriptional patterns, genomic alterations, prognostic implications, and immunological correlations—remains lacking. Given its dual relevance as a proteasome component and potential oncogenic mediator, we hypothesized that PSMB7 may serve as a broadly applicable, tissue-specific biomarker across diverse cancer types. To address this, we conducted an integrative multi-omics analysis across 33 human malignancies using publicly available datasets from TCGA, GTEx, CCLE, and GEO, assessing expression patterns, prognostic value, mutation burden, immune landscape associations, and therapeutic relevance. We further performed in vitro and in vivo functional validation in gastric and lung cancers—two of the most lethal solid tumors worldwide—to clarify the tumorigenic and immune-regulatory roles of PSMB7. Collectively, our study positions PSMB7 as a clinically actionable biomarker with both prognostic and therapeutic value across multiple cancer types. Our integrated approach not only unveils the oncogenic and immunological implications of PSMB7 but also exemplifies how multi-omics-driven biomarker discovery can guide the development of precision oncology strategies. 2 Materials and methods 2.1 Data Sources and Software Tools Transcriptomic and clinical data were obtained from The Cancer Genome Atlas (TCGA, https://cancergenome.nih.gov/ ), UCSC Xena Pan-Cancer Atlas ( https://xena.ucsc.edu/ ), and Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/ ). The analytical platforms and software tools utilized in this study are described in the respective subsections below. 2.2 Differential Expression Analysis To assess PSMB7 expression across 33 cancer types, differential expression analysis between tumor and normal tissues was performed using TIMER2.0 ( http://timer.cistrome.org/ )[ 11 ]. For refined analysis, GTEx and TCGA data were integrated via the SangerBox platform ( http://vip.sangerbox.com ) with quantile normalization and variance-stabilizing transformation[ 12 ]. Immunohistochemical validation of PSMB7 expression in gastric tissues was obtained from the Human Protein Atlas ( https://www.proteinatlas.org/ ). 2.3 Diagnostic and Clinical Stage Expression Analysis Receiver operating characteristic (ROC) curves were generated using the pROC package (v1.18.0, R) to evaluate the diagnostic value of PSMB7. The area under the curve (AUC) was calculated based on 1,000 bootstrap replicates with 95% confidence intervals. Cutoff values were determined using the Youden index. Differences in PSMB7 expression across AJCC TNM stages were analyzed via the Kruskal-Wallis test with Dunn’s post hoc correction, using SangerBox v3.8. 2.4 Survival Analysis Survival associations were evaluated using TCGA pan-cancer data through SangerBox v3.8. Patients were stratified into high and low expression groups based on median PSMB7 levels. Kaplan–Meier curves were generated, and the log-rank test was applied. False discovery rate (FDR) adjustment was performed using the Benjamini-Hochberg method (FDR < 0.1). 2.5 Protein-Protein Interaction (PPI) Network Analysis To explore potential protein-level interactions of PSMB7, a PPI network was constructed using the STRING database ( https://string-db.org/ ). The minimum interaction score threshold was set to 0.4, and interaction sources included text mining, databases, co-expression, gene fusion, experimental validation, neighborhood, and co-occurrence[ 13 ]. 2.6 Analysis of PSMB7 Expression in Tumor Mutational Burden (TMB) and Microsatellite Instability (MSI) Somatic mutation data were obtained from UCSC Xena and processed using VarScan2. Spearman correlation analyses were conducted to assess the relationship between PSMB7 expression and both TMB and MSI across cancer types. 2.7 Correlation Analysis Between Immune Cell Infiltration and PSMB7 Expression Tumor microenvironment characteristics were evaluated using the ESTIMATE algorithm to compute immune and stromal scores. The relative abundance immune cell subsets were quantified via the CIBERSORT, IPS, and QUANTISEQ algorithm[ 14 , 15 , 16 ], with deconvolution of TCGA bulk RNA-seq data. 2.8 Drug Sensitivity Analysis The pRRophetic R package (v1.8.0) was used to predict drug sensitivity based on gene expression profiles. Samples were dichotomized into high- and low-PSMB7 groups by median expression. Wilcoxon rank-sum tests were performed to assess differential drug responses, and violin plots were used for visualization. 2.9 Tissue Collection Gastric and lung cancer tissue samples were collected from patients at the First Affiliated Hospital of Guangxi Medical University. All patients provided informed consent. Samples were snap-frozen in liquid nitrogen and stored at − 80°C. Ethics approval was obtained from the institutional ethics committee. 2.10 Cell Culture Human gastric (AGS) and lung (A549) cancer cell lines were purchased from Prosperity Life Sciences Ltd. (Wuhan, China). Cells were cultured in Ham’s F12 medium (Gibco, China) supplemented with 10% fetal bovine serum (BI, Israel) and 1% penicillin-streptomycin (Wisent, Canada) at 37°C in a humidified incubator with 5% CO₂. 2.11 Reagents and Cell Transfection Small interfering RNAs (siRNAs) targeting PSMB7 and negative control siRNAs were obtained from Hanbio (China). Transfections were performed using Lipofectamine 3000 (Invitrogen, USA). Cells were harvested 48 hours post-transfection for RNA and protein extraction. siRNA sequences were as follows: si-NC (forward): UGUUCAGCGAAAUAUAACCUU, si-NC (reverse): UUACAAGUCGCUUUAUAUUGG, si-PSMB7 (forward): GCUAUUGCAGCUGGCAUC, si-PSMB7 (reverse): AGAUGCCAGCUGCAAUAGCUU. 2.12 Western Blotting Protein lysates were prepared using RIPA buffer (Solarbio, China) with protease inhibitor PMSF. Samples were separated by 10% SDS-PAGE and transferred to PVDF membranes (Millipore, USA). Membranes were probed with anti-PSMB7 (68641-1-Ig), anti-α-tubulin (66031-1-Ig) antibodies (Proteintech), and anti-Beta Actin (660009-1-Ig), followed by HRP-conjugated secondary antibodies. Signals were detected via chemiluminescence[ 17 ]. 2.13 Cell Proliferation, Invasion, and Migration Assays For colony formation assays, 1,000 cells/well were seeded in 6-well plates and cultured for 14 days, then fixed in 4% paraformaldehyde and stained with crystal violet. Transwell migration and invasion assays were conducted using 8-µm pore inserts (Corning, USA). For invasion assays, Matrigel (Yeason, China) was diluted 1:9 and coated in upper chambers. Cells were seeded in serum-free medium in the upper chamber and 10% FBS-containing medium in the lower chamber. After incubation, migrated/invaded cells were fixed and stained with 0.1% crystal violet. 2.14 In Vivo Tumor Xgnograft Model PSMB7-knockout AGS and A549 cells (5×10⁶ cells/mouse) were mixed 1:1 with Ceturegel® matrix gel (Yeason, China) and injected subcutaneously into the flanks of 6–7-week-old male BALB/c nude mice (n = 5 per group). Mice were maintained under specific pathogen-free (SPF) conditions, and tumor volumes were monitored every 3 days using calipers. Mice were euthanized when tumor volumes reached ≥ 1,000 mm³ or showed signs of distress, in accordance with ethical guidelines. Euthanasia was performed by intraperitoneal injection of sodium pentobarbital at a dosage of 150 mg/kg, ensuring deep anesthesia and loss of consciousness prior to sacrifice. Death was confirmed by cessation of heartbeat and respiration. This method complies with the AVMA Guidelines for the Euthanasia of Animals (2020). Tumors were then excised, weighed, and photographed. All animal experiments were approved by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University and conducted in accordance with institutional and national guidelines for animal welfare. 2.15 Statistical Analysis Statistical analyses were performed using GraphPad Prism v8.3.0. Data are presented as mean ± standard deviation (SD). Differences between groups were analyzed using unpaired or paired two-tailed Student’s t-tests. Correlations were evaluated using Pearson’s correlation coefficient. Significance thresholds were set as follows: *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, or ns (indicating no significance). 3 Result 3.1 PSMB7 Is Differentially Expressed Between Tumor and Normal Tissues Across Multiple Cancer Types As shown in Fig. 1 A, the differential expression of PSMB7 mRNA between tumor and normal tissues was first analyzed using the TIMER database (based on TCGA RNA-seq data). The results revealed that PSMB7 was significantly upregulated in several tumor types, including bladder cancer (BLCA), breast cancer (BRCA), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), skin cutaneous melanoma (SKCM), stomach adenocarcinoma (STAD), and uterine corpus endometrial carcinoma (UCEC), when compared with normal tissues. In contrast, PSMB7 expression was significantly downregulated in kidney chromophobe (KICH) and thyroid carcinoma (THCA). Further comparative analysis using the GTEx database, which incorporates normal tissue data not available in TCGA, confirmed significant differences in PSMB7 expression in additional cancer types, such as glioblastoma multiforme (GBM), brain lower-grade glioma (LGG), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), pheochromocytoma and paraganglioma (PCPG), and KICH, among others (Fig. 2 B). Moreover, Fig. 2 C shows that PSMB7 is significantly overexpressed in tumor tissues relative to matched adjacent normal tissues in a broad range of cancers, including BLCA, BRCA, CHOL, COAD, ESCA, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, PRAD, READ, and STAD, while lower expression was again noted in KICH. Consistently, Fig. 2 D reveals that PSMB7 expression is markedly elevated in tumor tissues compared with normal tissues across multiple tumor types, including BRCA, CESC, COAD, ESAD, ESCA, LIHC, LUAD, LUSC, OSCC, and STAD ( p < 0.05). 3.2 PSMB7 Expression Is Significantly Associated with Clinical Stage in Multiple Cancer Types Analysis of TCGA datasets revealed that PSMB7 expression is positively correlated with advanced clinical stages in several cancer types. Specifically, significant differences in PSMB7 expression were observed across tumor (T) stages in seven cancers, including lung adenocarcinoma (LUAD), pan-kidney cohort (KIPAN), prostate adenocarcinoma (PRAD), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), thyroid carcinoma (THCA), and testicular germ cell tumors (TGCT) (Fig. 2 A). In addition, PSMB7 expression was significantly associated with nodal (N) stage in LUAD, PRAD, THCA, skin cutaneous melanoma (SKCM), and adrenocortical carcinoma (ACC) (Fig. 2 B). Furthermore, a significant correlation was observed between PSMB7 expression and overall clinical stage in LUAD, KIPAN, liver hepatocellular carcinoma (LIHC), SKCM, and diffuse large B-cell lymphoma (DLBC) (Fig. 2 C). Collectively, these findings indicate that PSMB7 expression is closely associated with tumor progression and may serve as a potential indicator of cancer stage across diverse tumor types. 3.3 Diagnostic Value of PSMB7 Across Multiple Human Cancers The diagnostic utility of PSMB7 in distinguishing tumor from normal tissues across various cancer types was assessed through receiver operating characteristic (ROC) curve analysis, with the area under the curve (AUC) serving as the metric of diagnostic performance. As shown in Fig. 2 D, PSMB7 exhibited strong diagnostic power in several cancers, including bladder cancer (BLCA, AUC = 0.755), breast cancer (BRCA, AUC = 0.748), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC, AUC = 0.867), cholangiocarcinoma (CHOL, AUC = 0.921), colon adenocarcinoma (COAD, AUC = 0.915), liver hepatocellular carcinoma (LIHC, AUC = 0.869), lung adenocarcinoma (LUAD, AUC = 0.689), lung squamous cell carcinoma (LUSC, AUC = 0.797), oral squamous cell carcinoma (OSCC, AUC = 0.916), pheochromocytoma and paraganglioma (PCPG, AUC = 0.895), stomach adenocarcinoma (STAD, AUC = 0.915), and uterine corpus endometrial carcinoma (UCEC, AUC = 0.778). These findings support the potential of PSMB7 as a reliable diagnostic biomarker across a broad spectrum of malignancies. 3.4 Prognostic Significance of PSMB7 Expression in Pan-Cancer Analysis To explore the prognostic relevance of PSMB7 expression, we performed survival analysis across multiple cancer types using the SangerBox platform, with statistical significance determined by the log-rank test. As illustrated in Figs. 3 A–B, high PSMB7 expression was significantly associated with poorer overall survival (OS) in nine tumor types: TARGET-LAML (N = 142, p = 7.0 × 10⁻⁴, HR = 1.79 [1.28–2.51]), TCGA-LUAD (N = 490, p = 7.2 × 10⁻⁶, HR = 1.96 [1.47–2.61]), TCGA-HNSC (N = 509, p = 0.03, HR = 1.31 [1.03–1.67]), TCGA-SKCM (N = 444, p = 2.5 × 10⁻³, HR = 1.44 [1.14–1.83]), TCGA-SKCM-M (N = 347, p = 3.9 × 10⁻³, HR = 1.46 [1.13–1.89]), TCGA-MESO (N = 84, p = 0.01, HR = 2.26 [1.19–4.30]), TCGA-LAML (N = 209, p = 0.02, HR = 1.53 [1.08–2.15]), TARGET-ALL (N = 86, p = 2.4 × 10⁻⁴, HR = 3.86 [1.91–7.80]), TCGA-ACC (N = 77, p = 4.9 × 10⁻⁵, HR = 3.01 [1.75–5.18]). In contrast, elevated PSMB7 expression was significantly associated with improved OS in thymoma (TCGA-THYM, N = 117, p = 0.04, HR = 0.20 [0.04–0.95]). These results suggest that PSMB7 may serve as a context-dependent prognostic biomarker, with oncogenic potential in most cancer types but possibly a protective role in THYM. 3.5 Functional Enrichment Analysis of PSMB7 Co-Expressed Genes and Protein–Protein Interaction (PPI) Network To explore the potential biological functions of PSMB7 in tumor progression, we analyzed its co-expressed genes in lung adenocarcinoma (LUAD) using the LinkedOmics database. Heatmaps of the top 50 genes positively and negatively correlated with PSMB7 are presented in Figs. 4 A–B, with detailed correlation statistics shown in Fig. 4 C. To further investigate protein-level associations, a protein–protein interaction (PPI) network was constructed via the STRING database (Fig. 4 D), revealing that PSMB7 interacts with proteins involved in sphingolipid and ceramide metabolism, including DESI1, OAZ2, KIAA2012, and other members of the proteasome (PSM) family. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of PSMB7-associated genes indicated significant enrichment in biological processes such as mRNA metabolic process, RNA catabolic process, and cellular components such as the proteasome core complex and methylosome (Figs. 4 E–G). In KEGG pathway analysis, PSMB7 was primarily enriched in actin cytoskeleton regulation, proteasome function, and the spliceosome pathway (Fig. 4 H). Furthermore, HALLMARK gene set analysis identified associations with pathways such as Parkinson's disease, cell cycle regulation, transcriptional control, ubiquitin–proteasome pathway, and DNA replication (Fig. 4 I). These findings suggest that PSMB7 may be involved in post-transcriptional regulation, protein degradation, and cytoskeletal remodeling, implicating it in key oncogenic processes. 3.6 PSMB7 Expression Is Associated with Immune Infiltration and Immunogenicity Across Cancers To investigate the relationship between PSMB7 expression and the tumor immune microenvironment, we first applied the ESTIMATE algorithm to calculate stromal scores, immune scores, and tumor purity. PSMB7 expression showed significant positive correlations with immune scores in multiple tumor types, including LGG, LUAD, LUSC, SKCM, STAD, and THCA (Figs. 5 A–F). Subsequently, we analyzed the association between PSMB7 expression and the abundance of 22 immune cell types using the CIBERSORT algorithm across pan-cancer datasets (Fig. 5 G). Among them, follicular helper T cells showed the strongest positive correlation with PSMB7. In LUAD, PSMB7 expression was significantly correlated with memory B cells, plasma cells, CD8⁺ T cells, resting CD4⁺ memory T cells, follicular helper T cells, activated NK cells, monocytes, M0 and M1 macrophages, and resting mast cells. In STAD, significant correlations were observed with naive and memory B cells, plasma cells, resting and activated CD4⁺ memory T cells, Tregs, follicular helper T cells, resting NK cells, M0 and M1 macrophages, as well as resting and activated mast cells. We further assessed correlations between PSMB7 expression and immune-related scores, including MHC, EC, SC, CP, AZ, and Immunophenoscore (IPS) (Fig. 5 H). Notably, PSMB7 expression was positively correlated with IPS in several tumor types, such as TCGA-CESC (r = 0.40, p < 0.0001), TCGA-STES (r = − 0.17, p < 0.0001), and TCGA-COADREAD (r = 0.13, p < 0.05). In TCGA-CESC, PSMB7 was also positively associated with SC (r = 0.44), CP (r = 0.40), and AZ (r = 0.44), indicating a potential role in enhancing tumor immunogenicity. In contrast, negative correlations between PSMB7 and IPS were identified in TCGA-THCA (r = − 0.19, p < 0.05) and TCGA-ACC (r = − 0.45, p < 0.01), suggesting an immunosuppressive role in these tumor types. Additionally, in TCGA-LUAD and TCGA-STAD, weak but significant negative correlations with IPS were observed (r = − 0.10 and − 0.12, respectively; p < 0.05), suggesting a modest influence of PSMB7 on immune responsiveness. Using the QUANTISEQ algorithm, we quantified the infiltration levels of 11 major immune cell populations, including B cells, M1 and M2 macrophages, monocytes, neutrophils, NK cells, CD4⁺ T cells, CD8⁺ T cells, regulatory T cells (Tregs), dendritic cells, and others across 10,180 tumor samples spanning 44 cancer types (Fig. 5 I). PSMB7 expression was significantly correlated with immune infiltration in 41 cancer types, most notably GBM, STAD, LGG, UCEC, BRCA, CESC, LUAD, ESCA, and LUSC, supporting its role in regulating immune cell infiltration across the cancer spectrum. Moreover, Fig. 6 A demonstrates a positive correlation between PSMB7 and multiple immunomodulatory genes across pan-cancer, further supporting the potential of PSMB7 as a modulator of the tumor immune microenvironment. Collectively, these results highlight the potential of PSMB7 to influence immune surveillance, immune escape, and tumor immunogenicity in a cancer type–specific manner. 3.7 Association Between PSMB7 Expression and Genomic Heterogeneity To assess the potential involvement of PSMB7 in genomic instability and its implications for immunotherapy responsiveness, we analyzed its association with several genomic heterogeneity indicators, including mutant allele tumor heterogeneity (MATH), microsatellite instability (MSI), tumor mutational burden (TMB), and tumor purity across 37 cancer types. As shown in Fig. 6 B, PSMB7 expression exhibited a strong negative correlation with MATH in several tumor types, including GBMLGG, CESC, KIRP, KIPAN, UCEC, HNSC, and THYM, suggesting an inverse relationship between PSMB7 expression and intratumoral heterogeneity. Furthermore, PSMB7 expression was significantly associated with MSI in nine tumor types (Fig. 6 C), supporting its potential relevance in mismatch repair deficiency contexts. In Fig. 6 D, PSMB7 expression positively correlated with TMB in cancers such as STES, SARC, KIRP, STAD, PRAD, UCEC, and HNSC, while a negative association was observed in GBMLGG, suggesting tumor-type-specific roles in mutational burden regulation. Additionally, analysis of tumor purity revealed a significant positive correlation between PSMB7 expression and tumor purity in 17 cancer types (e.g., GBM, LUAD, COADREAD, BRCA, STAD, PRAD, KIRC, LUSC, THCA), and a negative correlation in THYM, PCPG, and BLCA (Fig. 6 E). These results indicate that PSMB7 is closely associated with multiple genomic heterogeneity parameters, particularly TMB and MSI, which may influence both tumor evolution and response to immunotherapy. 3.8 Single-Cell Transcriptomic Profiling of PSMB7 in Tumor Microenvironments We next investigated PSMB7 expression at single-cell resolution using TISCH2 across three datasets representing stomach adenocarcinoma (STAD), non–small cell lung cancer (NSCLC), and small cell lung cancer (SCLC) (Figs. 7 A–C). Uniform Manifold Approximation and Projection (UMAP) was applied to visualize the distribution of cell types, including CD8⁺ T cells, CD4⁺ T conventional (CD4 conv) cells, dendritic cells, fibroblasts, epithelial cells, endothelial cells, macrophages, mast cells, plasma cells, and malignant cells. Violin plots and pie charts revealed that PSMB7 is highly expressed in CD8⁺ T cells, CD4⁺ conventional T cells, and malignant cells, while expression levels were markedly lower in mast cells, endothelial cells, oligodendrocytes, and fibroblasts. These findings were further validated in multiple GEO single-cell datasets, confirming preferential PSMB7 expression in CD8⁺ T cells. In addition, GSEA enrichment analysis indicated that PSMB7 co-expression signatures are enriched in immune-related gene sets, suggesting its functional involvement in tumor immunity. 3.9 Correlation Between PSMB7 Expression and Drug Sensitivity To investigate the therapeutic relevance of PSMB7, we performed a drug sensitivity analysis using the pRRophetic R package, correlating PSMB7 expression levels with drug response predictions (Figs. 8 A–E). The analysis included responses to multiple chemotherapeutic agents, such as Chelerythrine, Cladribine, Fludarabine, Kahalide F, and Uracil mustard. Notably, the low PSMB7 expression group exhibited greater sensitivity to Kahalide F, whereas tumors with high PSMB7 expression showed enhanced sensitivity to Chelerythrine, Cladribine, Fludarabine, and Uracil mustard. These results suggest that PSMB7 expression may serve as a predictive marker for drug responsiveness and highlight PSMB7 as a potential therapeutic target in precision oncology. 3.10 Subcellular Localization of PSMB7 and Its Expression in Gastric and Lung Cancer Tissues Subcellular localization analysis using the Human Protein Atlas (HPA) database indicated that PSMB7 is predominantly localized in the nucleoplasm, nuclear bodies, primary cilium, and cytosol (Fig. 9 A). Immunofluorescence staining further confirmed that PSMB7 protein is primarily distributed within the nucleoplasm and nuclear bodies (Fig. 9 B). In addition, immunohistochemistry data from the HPA demonstrated elevated PSMB7 protein expression in tumor tissues of the liver, stomach, and lung, compared with corresponding normal tissues (Fig. 9 C). Consistently, western blot analysis of six paired lung cancer and six paired gastric cancer samples validated these observations, showing increased PSMB7 protein levels in tumor tissues relative to adjacent normal controls (Fig. 10 A). 3.11 PSMB7 Knockdown Suppresses Cell Proliferation, Migration, and Invasion In Vitro To explore the functional role of PSMB7 in tumor progression, A549 (lung cancer) and AGS (gastric cancer) cells were transfected with siRNA to achieve PSMB7 knockdown. Western blot analysis confirmed effective silencing of PSMB7 in both cell lines (Fig. 10 B). Colony formation assays revealed that PSMB7 silencing significantly reduced the number of colonies formed, indicating impaired proliferative capacity (Fig. 10 C). Furthermore, Transwell migration and invasion assays demonstrated that PSMB7 knockdown resulted in a significant decrease in both cell migration and invasion in A549 and AGS cells (Fig. 10 D). These findings suggest that PSMB7 promotes tumor cell proliferation and metastatic potential in both gastric and lung cancer cell lines. 3.12 PSMB7 Knockdown Inhibits Tumor Growth In Vivo To confirm the in vitro findings, we conducted in vivo xenograft experiments using BALB/c-nude mice. A total of 5 × 10⁶ PSMB7-knockout (KO) A549 and AGS cells were suspended in sterile PBS and mixed with Ceturegel® matrixgel, then subcutaneously injected into the flanks of 6–7-week-old male nude mice (n = 5 per group). Tumor growth was monitored every few days, and mice were euthanized once tumor volume reached or exceeded 1,000 mm³. As shown in Fig. 10 E, PSMB7 knockout significantly reduced tumor volume and weight compared with control groups, indicating a tumor-suppressive effect of PSMB7 silencing in vivo. These in vivo results, consistent with in vitro experiments, demonstrate that PSMB7 plays a critical role in promoting tumorigenesis in gastric and lung cancers. 4 Discussion Our study presents the first comprehensive pan-cancer analysis of PSMB7, revealing its multifaceted roles as a prognostic biomarker, therapeutic target, and potential modulator of the tumor immune microenvironment across 33 cancer types. By integrating large-scale bioinformatics analyses with in vitro and in vivo experimental validation in gastric and lung cancer models, we identified PSMB7 as a key oncogenic driver associated with unfavorable prognosis, drug resistance, and immune modulation. PSMB7 encodes the β2 subunit of the 20S core proteasome complex and exhibits trypsin-like proteolytic activity. Along with PSMB5 (β5) and PSMB6 (β1), it constitutes the catalytic core of the proteasome, which is essential for the degradation of ubiquitinated proteins via the ATP-dependent, ubiquitin-proteasome system (UPS)[ 18 ]. The UPS is fundamental to maintaining intracellular protein homeostasis, regulating diverse biological processes including cell cycle progression, apoptosis, antigen processing, and stress responses[ 19 , 20 ]. Dysregulation of this system has been extensively linked to cancer pathogenesis and therapeutic resistance[ 21 , 22 ]. While PSMB5 has been the primary target of proteasome inhibitors (PIs) such as bortezomib[ 23 ], our study highlights that PSMB7, though historically underexplored, exerts equally critical functions in tumor biology and may represent an alternative or complementary therapeutic target. Across TCGA cohorts, PSMB7 was found to be significantly upregulated in a broad spectrum of malignancies, including gastric adenocarcinoma (STAD), lung adenocarcinoma (LUAD), breast cancer (BRCA), and multiple myeloma (MM). In gastric cancer, for example, high PSMB7 expression correlated with advanced tumor stage, lymph node metastasis, and poor overall survival, consistent with previous findings in hematological and solid malignancies[ 6 , 24 , 25 ]. In line with these observations, functional assays demonstrated that knockdown of PSMB7 in gastric and lung cancer cell lines markedly inhibited cell proliferation, migration, and invasion, suggesting a direct oncogenic role. These effects were further validated in vivo using subcutaneous xenograft models, where PSMB7-silenced tumors exhibited significantly reduced growth compared to control tumors. These findings support the hypothesis that PSMB7 is not merely a passive component of proteolysis but an active regulator of tumor progression[ 26 , 27 ]. From a therapeutic resistance perspective, PSMB7 emerged as a critical mediator of chemoresistance[ 28 ]. Notably, we observed that elevated PSMB7 expression was significantly associated with reduced sensitivity to multiple chemotherapeutic agents, including doxorubicin and bortezomib. This finding is supported by previous studies in breast cancer models, where PSMB7 overexpression was identified as a top transcript associated with resistance to topoisomerase inhibitors such as doxorubicin and daunorubicin[ 5 ]. In that context, silencing PSMB7 sensitized tumor cells to chemotherapy, while clinical data from over 1500 patients demonstrated that high PSMB7 expression predicted poor prognosis[ 5 ]. Similarly, in multiple myeloma, increased PSMB7 levels were associated with bortezomib resistance and poor overall survival in treated patients, suggesting a drug-specific resistance mechanism mediated by this proteasome subunit[ 6 ]. These insights position PSMB7 as a novel chemoresistance gene with clinical relevance, distinct from traditional resistance markers such as ABCB1 or TOP2A. Mechanistically, PSMB7 may exert its effects through both proteasome-dependent and -independent pathways. A previous study in cardiomyocytes demonstrated that PSMB7 knockdown induced endoplasmic reticulum (ER) stress and autophagy, as evidenced by upregulation of ER stress markers (e.g., CHOP/DDIT3, GRP78/HSPA5) and increased autophagic vacuole formation (LC3-labeled structures)[ 29 ]. Interestingly, while PSMB7 silencing did not affect chymotrypsin-like or trypsin-like activities, it significantly reduced peptidyl-glutamyl peptide-hydrolyzing activity, suggesting that it may selectively modulate proteasomal substrate processing[ 29 , 30 , 31 ]. These findings point to a unique functional profile for PSMB7 and raise the possibility that its targeting may induce autophagy as a compensatory survival mechanism—an aspect that could be therapeutically exploited by combining PSMB7 inhibition with autophagy blockers. In addition to its role in tumorigenesis and chemoresistance, PSMB7 appears to shape the tumor immune microenvironment (TME). Our analysis revealed that PSMB7 expression positively correlates with tumor mutational burden (TMB) and microsatellite instability (MSI), two genomic markers predictive of immune checkpoint inhibitor (ICI) responsiveness. Moreover, PSMB7 expression was associated with infiltration of various immune cells, including CD8⁺ T cells, regulatory T cells (Tregs), macrophages, and T follicular helper cells, across multiple cancers. Its expression also showed strong correlations with immune scores (IPS, SC, AZ, CP), as well as immune checkpoint genes such as PDCD1 (PD-1), CTLA4, and LAG3, suggesting a role in modulating immune evasion. These findings echo reports from other proteasome subunits involved in antigen processing and MHC class I presentation. Aberrant PSMB7 expression may thus impair tumor immunogenicity, facilitating immune escape and treatment resistance—particularly relevant in tumors characterized by an immunosuppressive TME. The immunomodulatory potential of PSMB7 opens avenues for therapeutic innovation. It may serve as a biomarker for stratifying patients for immunotherapy or as a combinatorial target to enhance the efficacy of ICIs. For instance, combining PSMB7 inhibitors with immune checkpoint blockade might overcome immune resistance in tumors with high PSMB7 expression. However, the clinical translation of PSMB7-targeted therapies remains challenging. RNA interference (RNAi) approaches, although promising in vitro, face delivery barriers in vivo, particularly in solid tumors[ 32 ]. Advances in nanoparticle delivery systems, lipid-based formulations, and tumor-targeted siRNA carriers could enhance specificity and bioavailability, making clinical application more feasible. Additionally, PSMB7’s link to tissue-specific proteasome induction by agents such as 3H-1,2-dithiole-3-thione (D3T) in organs like liver, lung, colon, and brain raises the possibility that PSMB7 expression is dynamically regulated by environmental or therapeutic stimuli[ 4 , 7 ]. This could explain the observed heterogeneity of PSMB7 across tumor types and its variable associations with clinical outcomes. Our study also found that PSMB7 was co-expressed with genes involved in immune regulation, stress response, and metabolic reprogramming, suggesting that it lies at the intersection of multiple oncogenic networks. Altogether, our findings position PSMB7 as a functionally relevant, clinically actionable molecule with pan-cancer implications. It contributes to cancer development through multiple mechanisms—supporting tumor growth, mediating chemotherapy resistance, promoting ER stress adaptation, and shaping the immune landscape. Its consistent overexpression across malignancies and association with adverse clinical outcomes make it an attractive candidate for diagnostic, prognostic, and therapeutic exploration. Future work should aim to define the upstream regulators of PSMB7 (e.g., transcription factors, non-coding RNAs), dissect its protein-protein interaction network, and investigate its interplay with metabolic and immune checkpoints in the TME. Employing CRISPR-based gene editing, organoid models, and patient-derived xenografts (PDX) will further clarify PSMB7’s function and validate its utility in personalized oncology. In conclusion, this study provides a compelling framework for understanding PSMB7’s multifaceted contributions to cancer biology. Its integration of genomic, transcriptomic, immunologic, and pharmacologic features underscores the potential of PSMB7 as both a biomarker and a therapeutic target, advancing the frontiers of precision oncology. 5 Conclusion This study identifies PSMB7 as a clinically significant pan-cancer biomarker with oncogenic and immunomodulatory functions. Integrative analyses and experimental validation reveal that PSMB7 overexpression is associated with poor prognosis, tumor progression, immune cell infiltration, and chemoresistance. Functional assays confirm its role in promoting tumor proliferation, migration, and invasion, while PSMB7 silencing suppresses tumor growth in vitro and in vivo. These findings underscore the potential of PSMB7 as both a prognostic indicator and a therapeutic target. Further investigation is warranted to elucidate its molecular mechanisms and evaluate its clinical utility in precision oncology. Declarations Funding Statement The author(s) declare that financial support was received for the research and/or publication of this article. This research was funded by the Innovation Project of Guangxi Graduate Education (No. YCBZ2024124) and Open Project of Guangxi Key Laboratory of Enhanced Recovery after Surgery for Gastrointestinal Cancer (No. GXEKL202404). Data availability statement The original contributions presented in the study are publicly available. This data can be found here: TCGA (https://www.cancer.gov/tcga), cBioPortal (https://www.cbioportal.org), GEO (https://www.ncbi.nlm.nih.gov/geo), and TheHluman Protein Atlas (https://www.proteinatlas.org). Ethics statement The studies involving humans were approved by the FirstAffliated Hospital of Guangxi Medical University EthicsCommittee. The studies were conducted in accordance with thelocal legislation and institutional requirements. The participantsprovided their written informed consent to participate in this study. Author contributions Zhou Sijiang: Validation, Conceptualization, Datacuration, Writing-original draft. Yang Xia: FormalAnalysis, Methodology, Writing- original draft. Ran Qingfu: Project administration, Supervision, Resources, Writing- review and editing. Acknowledgments We express our gratitude to all contributors ofthe TCGA, GEO, TIMER2.0, MsigDB, HPA, LinkedOmics, TISCH2, and Sangerboxdatabases, whose valuable work supported this research. 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Supplementary Files ThefulluncroppedGelsandBlotsimages.zip SupplementaryFile1.pptx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 25 Aug, 2025 Editor assigned by journal 13 Aug, 2025 Editor invited by journal 23 Jul, 2025 Submission checks completed at journal 22 Jul, 2025 First submitted to journal 22 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7125983","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":508642673,"identity":"d5a87cfb-3140-4320-b297-e0b3f8bf998b","order_by":0,"name":"Zhou Sijiang","email":"","orcid":"","institution":"The First Afiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhou","middleName":"","lastName":"Sijiang","suffix":""},{"id":508642675,"identity":"278f5bc3-beed-4f5d-a191-dac25ef6fda8","order_by":1,"name":"Yang Xia","email":"","orcid":"","institution":"The First Afiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Xia","suffix":""},{"id":508642676,"identity":"663779a2-2074-45e1-a8cf-73322736db59","order_by":2,"name":"Ran Qingfu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYDAC5gNgSo6NvfkAkVrYEsCUMR/PsQTStCTOk8hRIE6HwTEe4w8/d9SltzHkMDD8qNhGlBYDw94zh3PbGM4eYOw5c5sILfd7DJIZ2w7ktjH2JTAzthGjBWjLYca2unQ2Zh4DorUYNjO2MSewsRGrRfIYWzEj0C+GbTxsCQeJ8gvfMebNoBCTl5//+OCDHxVEaFE4wGHAwNgA4RwgrB4I5BvYH8C1jIJRMApGwSjACgB8/jx1pbdEHwAAAABJRU5ErkJggg==","orcid":"","institution":"The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Ran","middleName":"","lastName":"Qingfu","suffix":""}],"badges":[],"createdAt":"2025-07-15 04:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7125983/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7125983/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90371970,"identity":"ce2039d8-d9d5-4c0c-9a67-29ff3330b636","added_by":"auto","created_at":"2025-09-02 04:43:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":104887,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression pattern of PSMB7 in pan-cancer. (A) \u003c/strong\u003ePSMB7 expression across various cancer types and specific cancer subtypes analyzed using TIMER 2.0.\u003cstrong\u003e(B)\u003c/strong\u003e Differential PSMB7 mRNA expression between tumor and normal tissues based on TCGA and GTEx datasets. \u003cstrong\u003e(C)\u003c/strong\u003e Comparison of PSMB7 expression in paired tumor and adjacent normal tissues across multiple cancer types from TCGA. \u003cstrong\u003e(D)\u003c/strong\u003e PSMB7 expression in BRCA, CESC, COAD, ESAD, ESCA, LIHC, LUAD, LUSC, OSCC, and STAD, with normal controls from GTEx. \u003cem\u003e*P\u003c/em\u003e\u0026lt;0.05, \u003cem\u003e**P\u003c/em\u003e\u0026lt;0.01, \u003cem\u003e***P\u003c/em\u003e\u0026lt;0.001, \u003cem\u003e****P\u003c/em\u003e\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Picture1.png","url":"https://assets-eu.researchsquare.com/files/rs-7125983/v1/2f6539cf7fcf42ef17258ebc.png"},{"id":90372088,"identity":"ea593614-5256-427d-87c8-fae3cffbce16","added_by":"auto","created_at":"2025-09-02 04:51:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":96657,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical and diagnostic relevance of PSMB7.\u003c/strong\u003e \u003cstrong\u003e(A) \u003c/strong\u003eCorrelation between PSMB7 expression and T stage across cancers. \u003cstrong\u003e(B) \u003c/strong\u003eAssociation with N stage. \u003cstrong\u003e(C) \u003c/strong\u003eRelationship with overall tumor stage. \u003cstrong\u003e(D–K) \u003c/strong\u003eROC curve analyses evaluating the diagnostic performance of PSMB7 across twelve cancer types in TCGA. \u003cem\u003e*P\u003c/em\u003e\u0026lt;0.05, \u003cem\u003e**P\u003c/em\u003e\u0026lt;0.01, \u003cem\u003e***P\u003c/em\u003e\u0026lt;0.001, \u003cem\u003e****P\u003c/em\u003e\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Picture2.png","url":"https://assets-eu.researchsquare.com/files/rs-7125983/v1/0c15b8857c6333dd9ef6a06b.png"},{"id":90371971,"identity":"60c59a89-a813-44b9-832a-2b7301750c61","added_by":"auto","created_at":"2025-09-02 04:43:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54550,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrognostic significance of PSMB7 in pan-canc\u003c/strong\u003eer.\u003cstrong\u003e (A) \u003c/strong\u003eUnivariate Cox analysis of overall survival (OS) across 44 cancer types. \u003cstrong\u003e(B) \u003c/strong\u003eKaplan–Meier survival curves comparing high vs. low PSMB7 expression in 10 selected cancers.\u003c/p\u003e","description":"","filename":"Picture3.png","url":"https://assets-eu.researchsquare.com/files/rs-7125983/v1/e3d3884fc2a7a5b17612e797.png"},{"id":90372089,"identity":"829bbeaf-6b4f-446f-b513-105bd3416daf","added_by":"auto","created_at":"2025-09-02 04:51:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":103068,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis of PSMB7. (A-B)\u003c/strong\u003eHeatmaps showing the top genes positively and negatively correlated with PSMB7. \u003cstrong\u003e(C) \u003c/strong\u003eScatter plot of representative gene associations.\u003cstrong\u003e (D) \u003c/strong\u003eProtein-protein interaction (PPI) network of PSMB7-related genes. \u003cstrong\u003e(E-G) \u003c/strong\u003eGO enrichment circle plots. (H) KEGG pathway enrichment presented in bubble plot. \u003cstrong\u003e(I) \u003c/strong\u003eGene Set Enrichment Analysis (GSEA) of PSMB7 expression.\u003c/p\u003e","description":"","filename":"Picture4.png","url":"https://assets-eu.researchsquare.com/files/rs-7125983/v1/90d8c04246757913c9c100b1.png"},{"id":90371975,"identity":"e9dd4128-f2ac-433f-82e4-5a960cb41fab","added_by":"auto","created_at":"2025-09-02 04:43:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":134267,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between PSMB7 expression and immune cell infiltration. (A-F) \u003c/strong\u003eAssociation between PSMB7 expression and immune scores in LGG, LUAD, LUSC, SKCM, STAD, and THCA.\u003cstrong\u003e (G) \u003c/strong\u003eCorrelation between PSMB7 and the proportions of 22 infiltrating immune cell subsets across cancers.\u003cstrong\u003e (H) \u003c/strong\u003eImmune infiltration estimation using the Immunophenoscore (IPS) algorithm.\u003cstrong\u003e(I) \u003c/strong\u003eCorrelation between PSMB7 and 11 infiltrating immune cell subsets. \u003cem\u003e*P\u003c/em\u003e\u0026lt;0.05, \u003cem\u003e**P\u003c/em\u003e\u0026lt;0.01, \u003cem\u003e***P\u003c/em\u003e\u0026lt;0.001, \u003cem\u003e****P\u003c/em\u003e\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Picture5.png","url":"https://assets-eu.researchsquare.com/files/rs-7125983/v1/0fc21103c6cb99da454ba149.png"},{"id":90371976,"identity":"20adaafc-fdee-43b3-b8f5-3e34607af512","added_by":"auto","created_at":"2025-09-02 04:43:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":157048,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation of PSMB7 with genomic heterogeneity indicators. (A) \u003c/strong\u003eCorrelation between PSMB7 and 60 immune checkpoint genes. \u003cstrong\u003e(B–E) \u003c/strong\u003eAssociation of PSMB7 expression with MATH, MSI, TMB, and tumor purity, respectively. \u003cem\u003e*P\u003c/em\u003e\u0026lt;0.05, \u003cem\u003e**P\u003c/em\u003e\u0026lt;0.01, \u003cem\u003e***P\u003c/em\u003e\u0026lt;0.001, \u003cem\u003e****P\u003c/em\u003e\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Picture6.png","url":"https://assets-eu.researchsquare.com/files/rs-7125983/v1/ba89a834ef0ef4249bfab6bc.png"},{"id":90372090,"identity":"68484a13-96a2-4d5d-a15c-fa7d9c4ae466","added_by":"auto","created_at":"2025-09-02 04:51:20","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":119695,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell resolution of PSMB7 expression. (A-C)\u003c/strong\u003eExpression pattern of PSMB7 in STAD, NSCLC, and SCLC at the single-cell level, derived from the TISCH2 database.\u003c/p\u003e","description":"","filename":"Picture7.png","url":"https://assets-eu.researchsquare.com/files/rs-7125983/v1/0013af06435c730f4e2b8fcb.png"},{"id":90371994,"identity":"e23206f1-8bc4-47be-b783-ba88b0808ac4","added_by":"auto","created_at":"2025-09-02 04:43:20","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":103677,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDrug sensitivity analysis related to PSMB7 expression. (A-E) \u003c/strong\u003ePrediction of drug response to five chemotherapeutic agents using the “pRRophetic” algorithm based on PSMB7 expression. \u003cem\u003e*P\u003c/em\u003e\u0026lt;0.05, \u003cem\u003e**P\u003c/em\u003e\u0026lt;0.01, \u003cem\u003e***P\u003c/em\u003e\u0026lt;0.001, \u003cem\u003e****P\u003c/em\u003e\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Picture8.png","url":"https://assets-eu.researchsquare.com/files/rs-7125983/v1/dc7ba05335885dd96473dc5b.png"},{"id":90372091,"identity":"bad74a73-d29d-4590-845d-bfe8688e9bec","added_by":"auto","created_at":"2025-09-02 04:51:20","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":385309,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubcellular localization and tissue expression of PSMB7. (A)\u003c/strong\u003e Predicted subcellular localization of PSMB7. \u003cstrong\u003e(B) \u003c/strong\u003eImmunofluorescence images of PSMB7 in cancer cell lines from the Human Protein Atlas (HPA).\u003cstrong\u003e (C) \u003c/strong\u003eImmunohistochemistry of PSMB7 in normal and tumor tissues from HPA. \u003cem\u003e*P\u003c/em\u003e\u0026lt;0.05, \u003cem\u003e**P\u003c/em\u003e\u0026lt;0.01, \u003cem\u003e***P\u003c/em\u003e\u0026lt;0.001, \u003cem\u003e****P\u003c/em\u003e\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"Picture9.png","url":"https://assets-eu.researchsquare.com/files/rs-7125983/v1/5bfdb8f574ad49cfd9b286e6.png"},{"id":90371999,"identity":"37862254-b919-43de-b3da-b9a0b80787ac","added_by":"auto","created_at":"2025-09-02 04:43:21","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":348165,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExperimental validation of PSMB7 function in vitro and in vivo. (A) \u003c/strong\u003eWestern blot analysis of PSMB7 expression in paired lung and gastric cancer tissues. \u003cstrong\u003e(B) \u003c/strong\u003eConfirmation of PSMB7 knockdown efficiency by Western blot.\u003cstrong\u003e (C) \u003c/strong\u003eColony formation assay in PSMB7 knockdown A549 and AGS cells. \u003cstrong\u003e(D) \u003c/strong\u003eTranswell migration and invasion assays.\u003cstrong\u003e (E) \u003c/strong\u003eTumor growth in control vs. PSMB7-knockout groups in xenograft models.\u003c/p\u003e","description":"","filename":"Picture10.png","url":"https://assets-eu.researchsquare.com/files/rs-7125983/v1/090d0b5ddb1aee2d1d2c6a0b.png"},{"id":90372558,"identity":"10f53c3f-bece-411f-9d89-362a73b5fcff","added_by":"auto","created_at":"2025-09-02 05:07:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3263327,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7125983/v1/6a5bd446-0b59-44dc-b239-18cda5449184.pdf"},{"id":90372460,"identity":"a8de7982-b96d-4cce-8694-533fd3683033","added_by":"auto","created_at":"2025-09-02 04:59:20","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":3733139,"visible":true,"origin":"","legend":"","description":"","filename":"ThefulluncroppedGelsandBlotsimages.zip","url":"https://assets-eu.researchsquare.com/files/rs-7125983/v1/f2e173dc2365b6bec2168aa9.zip"},{"id":90372008,"identity":"cf7d3340-f70d-455f-be04-76a6b26db6da","added_by":"auto","created_at":"2025-09-02 04:43:22","extension":"pptx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":62890987,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile1.pptx","url":"https://assets-eu.researchsquare.com/files/rs-7125983/v1/069dff152ee035e9e2719d41.pptx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comprehensive pan-cancer multi-omics analysis of PSMB7 reveals its prognostic significance and oncogenic role with experimental validation in lung and gastric cancers","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eCancer remains one of the most formidable global health challenges, characterized by rising incidence, high mortality, and substantial socioeconomic burden. Despite significant advances in diagnostics and therapeutic interventions, many malignancies continue to exhibit marked biological heterogeneity, therapeutic resistance, and dismal clinical outcomes. This complexity underscores an urgent need for robust, pan-cancer biomarkers capable of predicting disease progression, guiding personalized treatment strategies, and improving patient stratification to ultimately enhance clinical benefit.\u003c/p\u003e\u003cp\u003eThe ubiquitin-proteasome system plays a fundamental role in maintaining protein homeostasis by selectively degrading misfolded, damaged, or regulatory proteins in an ATP-dependent manner[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Central to this system is the 20S proteasome core, composed of four stacked rings, including two outer α rings and two inner β rings. Among the seven β-type subunits, PSMB5 (β5), PSMB6 (β1), and PSMB7 (β2) serve as the primary catalytic components, responsible for chymotrypsin-like, caspase-like, and trypsin-like activities, respectively. PSMB7, encoding the β2 subunit, is essential for intracellular proteolysis and proteasome activity, but has also been implicated in broader cellular processes, including cell cycle regulation, apoptosis, immune response, and adaptation to cellular stress[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough proteasome inhibitors such as bortezomib have demonstrated therapeutic efficacy, particularly in multiple myeloma through targeting PSMB5, the functional roles of PSMB7 in cancer biology remain comparatively understudied[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, emerging evidence suggests that PSMB7 may act as an oncogenic driver. Aberrant overexpression of PSMB7 has been reported in several malignancies, including colorectal cancer, breast cancer, and multiple myeloma, where it is associated with poor prognosis and resistance to chemotherapeutic agents such as anthracyclines and proteasome inhibitors[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, stimuli like 3H-1,2-dithiole-3-thione (D3T) have been shown to induce PSMB7 expression in a tissue-specific manner across organs such as the liver, lung, and brain, indicating that its regulation may intersect with oncogenic and stress response pathways[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFunctional studies further expand the biological significance of PSMB7. Knockdown of PSMB7 in cardiomyocytes induces endoplasmic reticulum (ER) stress and autophagy, accompanied by upregulation of stress response markers (e.g., CHOP, GRP78) and key autophagy regulators (e.g., MTOR)[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These findings highlight a noncanonical role of PSMB7 in modulating proteostasis and stress adaptation, beyond its established proteolytic function. In cancer contexts, such mechanisms may contribute to survival under hostile microenvironmental conditions, thereby enhancing tumor progression and therapy resistance.\u003c/p\u003e\u003cp\u003eDespite these observations, a comprehensive pan-cancer characterization of PSMB7\u0026mdash;integrating its transcriptional patterns, genomic alterations, prognostic implications, and immunological correlations\u0026mdash;remains lacking. Given its dual relevance as a proteasome component and potential oncogenic mediator, we hypothesized that PSMB7 may serve as a broadly applicable, tissue-specific biomarker across diverse cancer types.\u003c/p\u003e\u003cp\u003eTo address this, we conducted an integrative multi-omics analysis across 33 human malignancies using publicly available datasets from TCGA, GTEx, CCLE, and GEO, assessing expression patterns, prognostic value, mutation burden, immune landscape associations, and therapeutic relevance. We further performed in vitro and in vivo functional validation in gastric and lung cancers\u0026mdash;two of the most lethal solid tumors worldwide\u0026mdash;to clarify the tumorigenic and immune-regulatory roles of PSMB7.\u003c/p\u003e\u003cp\u003eCollectively, our study positions PSMB7 as a clinically actionable biomarker with both prognostic and therapeutic value across multiple cancer types. Our integrated approach not only unveils the oncogenic and immunological implications of PSMB7 but also exemplifies how multi-omics-driven biomarker discovery can guide the development of precision oncology strategies.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data Sources and Software Tools\u003c/h2\u003e\u003cp\u003eTranscriptomic and clinical data were obtained from The Cancer Genome Atlas (TCGA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cancergenome.nih.gov/\u003c/span\u003e\u003cspan address=\"https://cancergenome.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), UCSC Xena Pan-Cancer Atlas (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xena.ucsc.edu/\u003c/span\u003e\u003cspan address=\"https://xena.ucsc.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and Gene Expression Omnibus (GEO, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The analytical platforms and software tools utilized in this study are described in the respective subsections below.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Differential Expression Analysis\u003c/h2\u003e\u003cp\u003eTo assess PSMB7 expression across 33 cancer types, differential expression analysis between tumor and normal tissues was performed using TIMER2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.cistrome.org/\u003c/span\u003e\u003cspan address=\"http://timer.cistrome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. For refined analysis, GTEx and TCGA data were integrated via the SangerBox platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://vip.sangerbox.com\u003c/span\u003e\u003cspan address=\"http://vip.sangerbox.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with quantile normalization and variance-stabilizing transformation[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Immunohistochemical validation of PSMB7 expression in gastric tissues was obtained from the Human Protein Atlas (\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).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Diagnostic and Clinical Stage Expression Analysis\u003c/h2\u003e\u003cp\u003eReceiver operating characteristic (ROC) curves were generated using the pROC package (v1.18.0, R) to evaluate the diagnostic value of PSMB7. The area under the curve (AUC) was calculated based on 1,000 bootstrap replicates with 95% confidence intervals. Cutoff values were determined using the Youden index. Differences in PSMB7 expression across AJCC TNM stages were analyzed via the Kruskal-Wallis test with Dunn\u0026rsquo;s post hoc correction, using SangerBox v3.8.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Survival Analysis\u003c/h2\u003e\u003cp\u003eSurvival associations were evaluated using TCGA pan-cancer data through SangerBox v3.8. Patients were stratified into high and low expression groups based on median PSMB7 levels. Kaplan\u0026ndash;Meier curves were generated, and the log-rank test was applied. False discovery rate (FDR) adjustment was performed using the Benjamini-Hochberg method (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.1).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Protein-Protein Interaction (PPI) Network Analysis\u003c/h2\u003e\u003cp\u003eTo explore potential protein-level interactions of PSMB7, a PPI network was constructed using the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The minimum interaction score threshold was set to 0.4, and interaction sources included text mining, databases, co-expression, gene fusion, experimental validation, neighborhood, and co-occurrence[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Analysis of PSMB7 Expression in Tumor Mutational Burden (TMB) and Microsatellite Instability (MSI)\u003c/h2\u003e\u003cp\u003eSomatic mutation data were obtained from UCSC Xena and processed using VarScan2. Spearman correlation analyses were conducted to assess the relationship between PSMB7 expression and both TMB and MSI across cancer types.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Correlation Analysis Between Immune Cell Infiltration and PSMB7 Expression\u003c/h2\u003e\u003cp\u003eTumor microenvironment characteristics were evaluated using the ESTIMATE algorithm to compute immune and stromal scores. The relative abundance immune cell subsets were quantified via the CIBERSORT, IPS, and QUANTISEQ algorithm[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], with deconvolution of TCGA bulk RNA-seq data.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Drug Sensitivity Analysis\u003c/h2\u003e\u003cp\u003eThe pRRophetic R package (v1.8.0) was used to predict drug sensitivity based on gene expression profiles. Samples were dichotomized into high- and low-PSMB7 groups by median expression. Wilcoxon rank-sum tests were performed to assess differential drug responses, and violin plots were used for visualization.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Tissue Collection\u003c/h2\u003e\u003cp\u003eGastric and lung cancer tissue samples were collected from patients at the First Affiliated Hospital of Guangxi Medical University. All patients provided informed consent. Samples were snap-frozen in liquid nitrogen and stored at \u0026minus;\u0026thinsp;80\u0026deg;C. Ethics approval was obtained from the institutional ethics committee.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.10 Cell Culture\u003c/h2\u003e\u003cp\u003eHuman gastric (AGS) and lung (A549) cancer cell lines were purchased from Prosperity Life Sciences Ltd. (Wuhan, China). Cells were cultured in Ham\u0026rsquo;s F12 medium (Gibco, China) supplemented with 10% fetal bovine serum (BI, Israel) and 1% penicillin-streptomycin (Wisent, Canada) at 37\u0026deg;C in a humidified incubator with 5% CO₂.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.11 Reagents and Cell Transfection\u003c/h2\u003e\u003cp\u003eSmall interfering RNAs (siRNAs) targeting PSMB7 and negative control siRNAs were obtained from Hanbio (China). Transfections were performed using Lipofectamine 3000 (Invitrogen, USA). Cells were harvested 48 hours post-transfection for RNA and protein extraction. siRNA sequences were as follows: si-NC (forward): UGUUCAGCGAAAUAUAACCUU, si-NC (reverse): UUACAAGUCGCUUUAUAUUGG, si-PSMB7 (forward): GCUAUUGCAGCUGGCAUC, si-PSMB7 (reverse): AGAUGCCAGCUGCAAUAGCUU.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.12 Western Blotting\u003c/h2\u003e\u003cp\u003eProtein lysates were prepared using RIPA buffer (Solarbio, China) with protease inhibitor PMSF. Samples were separated by 10% SDS-PAGE and transferred to PVDF membranes (Millipore, USA). Membranes were probed with anti-PSMB7 (68641-1-Ig), anti-α-tubulin (66031-1-Ig) antibodies (Proteintech), and anti-Beta Actin (660009-1-Ig), followed by HRP-conjugated secondary antibodies. Signals were detected via chemiluminescence[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.13 Cell Proliferation, Invasion, and Migration Assays\u003c/h2\u003e\u003cp\u003eFor colony formation assays, 1,000 cells/well were seeded in 6-well plates and cultured for 14 days, then fixed in 4% paraformaldehyde and stained with crystal violet. Transwell migration and invasion assays were conducted using 8-\u0026micro;m pore inserts (Corning, USA). For invasion assays, Matrigel (Yeason, China) was diluted 1:9 and coated in upper chambers. Cells were seeded in serum-free medium in the upper chamber and 10% FBS-containing medium in the lower chamber. After incubation, migrated/invaded cells were fixed and stained with 0.1% crystal violet.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e2.14 In Vivo Tumor Xgnograft Model\u003c/h2\u003e\u003cp\u003ePSMB7-knockout AGS and A549 cells (5\u0026times;10⁶ cells/mouse) were mixed 1:1 with Ceturegel\u0026reg; matrix gel (Yeason, China) and injected subcutaneously into the flanks of 6\u0026ndash;7-week-old male BALB/c nude mice (n\u0026thinsp;=\u0026thinsp;5 per group). Mice were maintained under specific pathogen-free (SPF) conditions, and tumor volumes were monitored every 3 days using calipers. Mice were euthanized when tumor volumes reached\u0026thinsp;\u0026ge;\u0026thinsp;1,000 mm\u0026sup3; or showed signs of distress, in accordance with ethical guidelines. Euthanasia was performed by intraperitoneal injection of sodium pentobarbital at a dosage of 150 mg/kg, ensuring deep anesthesia and loss of consciousness prior to sacrifice. Death was confirmed by cessation of heartbeat and respiration. This method complies with the AVMA Guidelines for the Euthanasia of Animals (2020). Tumors were then excised, weighed, and photographed. All animal experiments were approved by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University and conducted in accordance with institutional and national guidelines for animal welfare.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e2.15 Statistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using GraphPad Prism v8.3.0. Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Differences between groups were analyzed using unpaired or paired two-tailed Student\u0026rsquo;s t-tests. Correlations were evaluated using Pearson\u0026rsquo;s correlation coefficient. Significance thresholds were set as follows: *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ****P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, or ns (indicating no significance).\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Result","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.1 PSMB7 Is Differentially Expressed Between Tumor and Normal Tissues Across Multiple Cancer Types\u003c/h2\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, the differential expression of PSMB7 mRNA between tumor and normal tissues was first analyzed using the TIMER database (based on TCGA RNA-seq data). The results revealed that PSMB7 was significantly upregulated in several tumor types, including bladder cancer (BLCA), breast cancer (BRCA), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), skin cutaneous melanoma (SKCM), stomach adenocarcinoma (STAD), and uterine corpus endometrial carcinoma (UCEC), when compared with normal tissues. In contrast, PSMB7 expression was significantly downregulated in kidney chromophobe (KICH) and thyroid carcinoma (THCA). Further comparative analysis using the GTEx database, which incorporates normal tissue data not available in TCGA, confirmed significant differences in PSMB7 expression in additional cancer types, such as glioblastoma multiforme (GBM), brain lower-grade glioma (LGG), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), pheochromocytoma and paraganglioma (PCPG), and KICH, among others (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Moreover, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC shows that PSMB7 is significantly overexpressed in tumor tissues relative to matched adjacent normal tissues in a broad range of cancers, including BLCA, BRCA, CHOL, COAD, ESCA, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, PRAD, READ, and STAD, while lower expression was again noted in KICH. Consistently, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD reveals that PSMB7 expression is markedly elevated in tumor tissues compared with normal tissues across multiple tumor types, including BRCA, CESC, COAD, ESAD, ESCA, LIHC, LUAD, LUSC, OSCC, and STAD (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.2 PSMB7 Expression Is Significantly Associated with Clinical Stage in Multiple Cancer Types\u003c/h2\u003e\u003cp\u003eAnalysis of TCGA datasets revealed that PSMB7 expression is positively correlated with advanced clinical stages in several cancer types. Specifically, significant differences in PSMB7 expression were observed across tumor (T) stages in seven cancers, including lung adenocarcinoma (LUAD), pan-kidney cohort (KIPAN), prostate adenocarcinoma (PRAD), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), thyroid carcinoma (THCA), and testicular germ cell tumors (TGCT) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In addition, PSMB7 expression was significantly associated with nodal (N) stage in LUAD, PRAD, THCA, skin cutaneous melanoma (SKCM), and adrenocortical carcinoma (ACC) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Furthermore, a significant correlation was observed between PSMB7 expression and overall clinical stage in LUAD, KIPAN, liver hepatocellular carcinoma (LIHC), SKCM, and diffuse large B-cell lymphoma (DLBC) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Collectively, these findings indicate that PSMB7 expression is closely associated with tumor progression and may serve as a potential indicator of cancer stage across diverse tumor types.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Diagnostic Value of PSMB7 Across Multiple Human Cancers\u003c/h2\u003e\u003cp\u003eThe diagnostic utility of PSMB7 in distinguishing tumor from normal tissues across various cancer types was assessed through receiver operating characteristic (ROC) curve analysis, with the area under the curve (AUC) serving as the metric of diagnostic performance. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, PSMB7 exhibited strong diagnostic power in several cancers, including bladder cancer (BLCA, AUC\u0026thinsp;=\u0026thinsp;0.755), breast cancer (BRCA, AUC\u0026thinsp;=\u0026thinsp;0.748), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC, AUC\u0026thinsp;=\u0026thinsp;0.867), cholangiocarcinoma (CHOL, AUC\u0026thinsp;=\u0026thinsp;0.921), colon adenocarcinoma (COAD, AUC\u0026thinsp;=\u0026thinsp;0.915), liver hepatocellular carcinoma (LIHC, AUC\u0026thinsp;=\u0026thinsp;0.869), lung adenocarcinoma (LUAD, AUC\u0026thinsp;=\u0026thinsp;0.689), lung squamous cell carcinoma (LUSC, AUC\u0026thinsp;=\u0026thinsp;0.797), oral squamous cell carcinoma (OSCC, AUC\u0026thinsp;=\u0026thinsp;0.916), pheochromocytoma and paraganglioma (PCPG, AUC\u0026thinsp;=\u0026thinsp;0.895), stomach adenocarcinoma (STAD, AUC\u0026thinsp;=\u0026thinsp;0.915), and uterine corpus endometrial carcinoma (UCEC, AUC\u0026thinsp;=\u0026thinsp;0.778). These findings support the potential of PSMB7 as a reliable diagnostic biomarker across a broad spectrum of malignancies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Prognostic Significance of PSMB7 Expression in Pan-Cancer Analysis\u003c/h2\u003e\u003cp\u003eTo explore the prognostic relevance of PSMB7 expression, we performed survival analysis across multiple cancer types using the SangerBox platform, with statistical significance determined by the log-rank test. As illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u0026ndash;B, high PSMB7 expression was significantly associated with poorer overall survival (OS) in nine tumor types: TARGET-LAML (N\u0026thinsp;=\u0026thinsp;142, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.0 \u0026times; 10⁻⁴, HR\u0026thinsp;=\u0026thinsp;1.79 [1.28\u0026ndash;2.51]), TCGA-LUAD (N\u0026thinsp;=\u0026thinsp;490, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.2 \u0026times; 10⁻⁶, HR\u0026thinsp;=\u0026thinsp;1.96 [1.47\u0026ndash;2.61]), TCGA-HNSC (N\u0026thinsp;=\u0026thinsp;509, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03, HR\u0026thinsp;=\u0026thinsp;1.31 [1.03\u0026ndash;1.67]), TCGA-SKCM (N\u0026thinsp;=\u0026thinsp;444, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.5 \u0026times; 10⁻\u0026sup3;, HR\u0026thinsp;=\u0026thinsp;1.44 [1.14\u0026ndash;1.83]), TCGA-SKCM-M (N\u0026thinsp;=\u0026thinsp;347, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.9 \u0026times; 10⁻\u0026sup3;, HR\u0026thinsp;=\u0026thinsp;1.46 [1.13\u0026ndash;1.89]), TCGA-MESO (N\u0026thinsp;=\u0026thinsp;84, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01, HR\u0026thinsp;=\u0026thinsp;2.26 [1.19\u0026ndash;4.30]), TCGA-LAML (N\u0026thinsp;=\u0026thinsp;209, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02, HR\u0026thinsp;=\u0026thinsp;1.53 [1.08\u0026ndash;2.15]), TARGET-ALL (N\u0026thinsp;=\u0026thinsp;86, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.4 \u0026times; 10⁻⁴, HR\u0026thinsp;=\u0026thinsp;3.86 [1.91\u0026ndash;7.80]), TCGA-ACC (N\u0026thinsp;=\u0026thinsp;77, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.9 \u0026times; 10⁻⁵, HR\u0026thinsp;=\u0026thinsp;3.01 [1.75\u0026ndash;5.18]). In contrast, elevated PSMB7 expression was significantly associated with improved OS in thymoma (TCGA-THYM, N\u0026thinsp;=\u0026thinsp;117, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04, HR\u0026thinsp;=\u0026thinsp;0.20 [0.04\u0026ndash;0.95]). These results suggest that PSMB7 may serve as a context-dependent prognostic biomarker, with oncogenic potential in most cancer types but possibly a protective role in THYM.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Functional Enrichment Analysis of PSMB7 Co-Expressed Genes and Protein\u0026ndash;Protein Interaction (PPI) Network\u003c/h2\u003e\u003cp\u003eTo explore the potential biological functions of PSMB7 in tumor progression, we analyzed its co-expressed genes in lung adenocarcinoma (LUAD) using the LinkedOmics database. Heatmaps of the top 50 genes positively and negatively correlated with PSMB7 are presented in Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u0026ndash;B, with detailed correlation statistics shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC. To further investigate protein-level associations, a protein\u0026ndash;protein interaction (PPI) network was constructed via the STRING database (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD), revealing that PSMB7 interacts with proteins involved in sphingolipid and ceramide metabolism, including DESI1, OAZ2, KIAA2012, and other members of the proteasome (PSM) family. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of PSMB7-associated genes indicated significant enrichment in biological processes such as mRNA metabolic process, RNA catabolic process, and cellular components such as the proteasome core complex and methylosome (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE\u0026ndash;G). In KEGG pathway analysis, PSMB7 was primarily enriched in actin cytoskeleton regulation, proteasome function, and the spliceosome pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH). Furthermore, HALLMARK gene set analysis identified associations with pathways such as Parkinson's disease, cell cycle regulation, transcriptional control, ubiquitin\u0026ndash;proteasome pathway, and DNA replication (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI). These findings suggest that PSMB7 may be involved in post-transcriptional regulation, protein degradation, and cytoskeletal remodeling, implicating it in key oncogenic processes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e3.6 PSMB7 Expression Is Associated with Immune Infiltration and Immunogenicity Across Cancers\u003c/h2\u003e\u003cp\u003eTo investigate the relationship between PSMB7 expression and the tumor immune microenvironment, we first applied the ESTIMATE algorithm to calculate stromal scores, immune scores, and tumor purity. PSMB7 expression showed significant positive correlations with immune scores in multiple tumor types, including LGG, LUAD, LUSC, SKCM, STAD, and THCA (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA\u0026ndash;F). Subsequently, we analyzed the association between PSMB7 expression and the abundance of 22 immune cell types using the CIBERSORT algorithm across pan-cancer datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG). Among them, follicular helper T cells showed the strongest positive correlation with PSMB7. In LUAD, PSMB7 expression was significantly correlated with memory B cells, plasma cells, CD8⁺ T cells, resting CD4⁺ memory T cells, follicular helper T cells, activated NK cells, monocytes, M0 and M1 macrophages, and resting mast cells. In STAD, significant correlations were observed with naive and memory B cells, plasma cells, resting and activated CD4⁺ memory T cells, Tregs, follicular helper T cells, resting NK cells, M0 and M1 macrophages, as well as resting and activated mast cells. We further assessed correlations between PSMB7 expression and immune-related scores, including MHC, EC, SC, CP, AZ, and Immunophenoscore (IPS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH). Notably, PSMB7 expression was positively correlated with IPS in several tumor types, such as TCGA-CESC (r\u0026thinsp;=\u0026thinsp;0.40, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), TCGA-STES (r = \u0026minus;\u0026thinsp;0.17, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and TCGA-COADREAD (r\u0026thinsp;=\u0026thinsp;0.13, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In TCGA-CESC, PSMB7 was also positively associated with SC (r\u0026thinsp;=\u0026thinsp;0.44), CP (r\u0026thinsp;=\u0026thinsp;0.40), and AZ (r\u0026thinsp;=\u0026thinsp;0.44), indicating a potential role in enhancing tumor immunogenicity. In contrast, negative correlations between PSMB7 and IPS were identified in TCGA-THCA (r = \u0026minus;\u0026thinsp;0.19, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and TCGA-ACC (r = \u0026minus;\u0026thinsp;0.45, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting an immunosuppressive role in these tumor types. Additionally, in TCGA-LUAD and TCGA-STAD, weak but significant negative correlations with IPS were observed (r = \u0026minus;\u0026thinsp;0.10 and \u0026minus;\u0026thinsp;0.12, respectively; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting a modest influence of PSMB7 on immune responsiveness. Using the QUANTISEQ algorithm, we quantified the infiltration levels of 11 major immune cell populations, including B cells, M1 and M2 macrophages, monocytes, neutrophils, NK cells, CD4⁺ T cells, CD8⁺ T cells, regulatory T cells (Tregs), dendritic cells, and others across 10,180 tumor samples spanning 44 cancer types (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI). PSMB7 expression was significantly correlated with immune infiltration in 41 cancer types, most notably GBM, STAD, LGG, UCEC, BRCA, CESC, LUAD, ESCA, and LUSC, supporting its role in regulating immune cell infiltration across the cancer spectrum. Moreover, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA demonstrates a positive correlation between PSMB7 and multiple immunomodulatory genes across pan-cancer, further supporting the potential of PSMB7 as a modulator of the tumor immune microenvironment. Collectively, these results highlight the potential of PSMB7 to influence immune surveillance, immune escape, and tumor immunogenicity in a cancer type\u0026ndash;specific manner.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Association Between PSMB7 Expression and Genomic Heterogeneity\u003c/h2\u003e\u003cp\u003eTo assess the potential involvement of PSMB7 in genomic instability and its implications for immunotherapy responsiveness, we analyzed its association with several genomic heterogeneity indicators, including mutant allele tumor heterogeneity (MATH), microsatellite instability (MSI), tumor mutational burden (TMB), and tumor purity across 37 cancer types. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, PSMB7 expression exhibited a strong negative correlation with MATH in several tumor types, including GBMLGG, CESC, KIRP, KIPAN, UCEC, HNSC, and THYM, suggesting an inverse relationship between PSMB7 expression and intratumoral heterogeneity. Furthermore, PSMB7 expression was significantly associated with MSI in nine tumor types (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC), supporting its potential relevance in mismatch repair deficiency contexts. In Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD, PSMB7 expression positively correlated with TMB in cancers such as STES, SARC, KIRP, STAD, PRAD, UCEC, and HNSC, while a negative association was observed in GBMLGG, suggesting tumor-type-specific roles in mutational burden regulation. Additionally, analysis of tumor purity revealed a significant positive correlation between PSMB7 expression and tumor purity in 17 cancer types (e.g., GBM, LUAD, COADREAD, BRCA, STAD, PRAD, KIRC, LUSC, THCA), and a negative correlation in THYM, PCPG, and BLCA (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). These results indicate that PSMB7 is closely associated with multiple genomic heterogeneity parameters, particularly TMB and MSI, which may influence both tumor evolution and response to immunotherapy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e3.8 Single-Cell Transcriptomic Profiling of PSMB7 in Tumor Microenvironments\u003c/h2\u003e\u003cp\u003eWe next investigated PSMB7 expression at single-cell resolution using TISCH2 across three datasets representing stomach adenocarcinoma (STAD), non\u0026ndash;small cell lung cancer (NSCLC), and small cell lung cancer (SCLC) (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA\u0026ndash;C). Uniform Manifold Approximation and Projection (UMAP) was applied to visualize the distribution of cell types, including CD8⁺ T cells, CD4⁺ T conventional (CD4 conv) cells, dendritic cells, fibroblasts, epithelial cells, endothelial cells, macrophages, mast cells, plasma cells, and malignant cells. Violin plots and pie charts revealed that PSMB7 is highly expressed in CD8⁺ T cells, CD4⁺ conventional T cells, and malignant cells, while expression levels were markedly lower in mast cells, endothelial cells, oligodendrocytes, and fibroblasts. These findings were further validated in multiple GEO single-cell datasets, confirming preferential PSMB7 expression in CD8⁺ T cells. In addition, GSEA enrichment analysis indicated that PSMB7 co-expression signatures are enriched in immune-related gene sets, suggesting its functional involvement in tumor immunity.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e3.9 Correlation Between PSMB7 Expression and Drug Sensitivity\u003c/h2\u003e\u003cp\u003eTo investigate the therapeutic relevance of PSMB7, we performed a drug sensitivity analysis using the pRRophetic R package, correlating PSMB7 expression levels with drug response predictions (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA\u0026ndash;E). The analysis included responses to multiple chemotherapeutic agents, such as Chelerythrine, Cladribine, Fludarabine, Kahalide F, and Uracil mustard. Notably, the low PSMB7 expression group exhibited greater sensitivity to Kahalide F, whereas tumors with high PSMB7 expression showed enhanced sensitivity to Chelerythrine, Cladribine, Fludarabine, and Uracil mustard. These results suggest that PSMB7 expression may serve as a predictive marker for drug responsiveness and highlight PSMB7 as a potential therapeutic target in precision oncology.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003e3.10 Subcellular Localization of PSMB7 and Its Expression in Gastric and Lung Cancer Tissues\u003c/h2\u003e\u003cp\u003e Subcellular localization analysis using the Human Protein Atlas (HPA) database indicated that PSMB7 is predominantly localized in the nucleoplasm, nuclear bodies, primary cilium, and cytosol (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). Immunofluorescence staining further confirmed that PSMB7 protein is primarily distributed within the nucleoplasm and nuclear bodies (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). In addition, immunohistochemistry data from the HPA demonstrated elevated PSMB7 protein expression in tumor tissues of the liver, stomach, and lung, compared with corresponding normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC). Consistently, western blot analysis of six paired lung cancer and six paired gastric cancer samples validated these observations, showing increased PSMB7 protein levels in tumor tissues relative to adjacent normal controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003e3.11 PSMB7 Knockdown Suppresses Cell Proliferation, Migration, and Invasion In Vitro\u003c/h2\u003e\u003cp\u003eTo explore the functional role of PSMB7 in tumor progression, A549 (lung cancer) and AGS (gastric cancer) cells were transfected with siRNA to achieve PSMB7 knockdown. Western blot analysis confirmed effective silencing of PSMB7 in both cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB). Colony formation assays revealed that PSMB7 silencing significantly reduced the number of colonies formed, indicating impaired proliferative capacity (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eC). Furthermore, Transwell migration and invasion assays demonstrated that PSMB7 knockdown resulted in a significant decrease in both cell migration and invasion in A549 and AGS cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eD). These findings suggest that PSMB7 promotes tumor cell proliferation and metastatic potential in both gastric and lung cancer cell lines.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e\u003ch2\u003e3.12 PSMB7 Knockdown Inhibits Tumor Growth In Vivo\u003c/h2\u003e\u003cp\u003eTo confirm the in vitro findings, we conducted in vivo xenograft experiments using BALB/c-nude mice. A total of 5 \u0026times; 10⁶ PSMB7-knockout (KO) A549 and AGS cells were suspended in sterile PBS and mixed with Ceturegel\u0026reg; matrixgel, then subcutaneously injected into the flanks of 6\u0026ndash;7-week-old male nude mice (n\u0026thinsp;=\u0026thinsp;5 per group). Tumor growth was monitored every few days, and mice were euthanized once tumor volume reached or exceeded 1,000 mm\u0026sup3;. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eE, PSMB7 knockout significantly reduced tumor volume and weight compared with control groups, indicating a tumor-suppressive effect of PSMB7 silencing in vivo. These in vivo results, consistent with in vitro experiments, demonstrate that PSMB7 plays a critical role in promoting tumorigenesis in gastric and lung cancers.\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eOur study presents the first comprehensive pan-cancer analysis of PSMB7, revealing its multifaceted roles as a prognostic biomarker, therapeutic target, and potential modulator of the tumor immune microenvironment across 33 cancer types. By integrating large-scale bioinformatics analyses with in vitro and in vivo experimental validation in gastric and lung cancer models, we identified PSMB7 as a key oncogenic driver associated with unfavorable prognosis, drug resistance, and immune modulation.\u003c/p\u003e\u003cp\u003ePSMB7 encodes the β2 subunit of the 20S core proteasome complex and exhibits trypsin-like proteolytic activity. Along with PSMB5 (β5) and PSMB6 (β1), it constitutes the catalytic core of the proteasome, which is essential for the degradation of ubiquitinated proteins via the ATP-dependent, ubiquitin-proteasome system (UPS)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The UPS is fundamental to maintaining intracellular protein homeostasis, regulating diverse biological processes including cell cycle progression, apoptosis, antigen processing, and stress responses[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Dysregulation of this system has been extensively linked to cancer pathogenesis and therapeutic resistance[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. While PSMB5 has been the primary target of proteasome inhibitors (PIs) such as bortezomib[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], our study highlights that PSMB7, though historically underexplored, exerts equally critical functions in tumor biology and may represent an alternative or complementary therapeutic target.\u003c/p\u003e\u003cp\u003eAcross TCGA cohorts, PSMB7 was found to be significantly upregulated in a broad spectrum of malignancies, including gastric adenocarcinoma (STAD), lung adenocarcinoma (LUAD), breast cancer (BRCA), and multiple myeloma (MM). In gastric cancer, for example, high PSMB7 expression correlated with advanced tumor stage, lymph node metastasis, and poor overall survival, consistent with previous findings in hematological and solid malignancies[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In line with these observations, functional assays demonstrated that knockdown of PSMB7 in gastric and lung cancer cell lines markedly inhibited cell proliferation, migration, and invasion, suggesting a direct oncogenic role. These effects were further validated in vivo using subcutaneous xenograft models, where PSMB7-silenced tumors exhibited significantly reduced growth compared to control tumors. These findings support the hypothesis that PSMB7 is not merely a passive component of proteolysis but an active regulator of tumor progression[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFrom a therapeutic resistance perspective, PSMB7 emerged as a critical mediator of chemoresistance[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Notably, we observed that elevated PSMB7 expression was significantly associated with reduced sensitivity to multiple chemotherapeutic agents, including doxorubicin and bortezomib. This finding is supported by previous studies in breast cancer models, where PSMB7 overexpression was identified as a top transcript associated with resistance to topoisomerase inhibitors such as doxorubicin and daunorubicin[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In that context, silencing PSMB7 sensitized tumor cells to chemotherapy, while clinical data from over 1500 patients demonstrated that high PSMB7 expression predicted poor prognosis[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Similarly, in multiple myeloma, increased PSMB7 levels were associated with bortezomib resistance and poor overall survival in treated patients, suggesting a drug-specific resistance mechanism mediated by this proteasome subunit[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These insights position PSMB7 as a novel chemoresistance gene with clinical relevance, distinct from traditional resistance markers such as ABCB1 or TOP2A.\u003c/p\u003e\u003cp\u003eMechanistically, PSMB7 may exert its effects through both proteasome-dependent and -independent pathways. A previous study in cardiomyocytes demonstrated that PSMB7 knockdown induced endoplasmic reticulum (ER) stress and autophagy, as evidenced by upregulation of ER stress markers (e.g., CHOP/DDIT3, GRP78/HSPA5) and increased autophagic vacuole formation (LC3-labeled structures)[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Interestingly, while PSMB7 silencing did not affect chymotrypsin-like or trypsin-like activities, it significantly reduced peptidyl-glutamyl peptide-hydrolyzing activity, suggesting that it may selectively modulate proteasomal substrate processing[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. These findings point to a unique functional profile for PSMB7 and raise the possibility that its targeting may induce autophagy as a compensatory survival mechanism\u0026mdash;an aspect that could be therapeutically exploited by combining PSMB7 inhibition with autophagy blockers.\u003c/p\u003e\u003cp\u003eIn addition to its role in tumorigenesis and chemoresistance, PSMB7 appears to shape the tumor immune microenvironment (TME). Our analysis revealed that PSMB7 expression positively correlates with tumor mutational burden (TMB) and microsatellite instability (MSI), two genomic markers predictive of immune checkpoint inhibitor (ICI) responsiveness. Moreover, PSMB7 expression was associated with infiltration of various immune cells, including CD8⁺ T cells, regulatory T cells (Tregs), macrophages, and T follicular helper cells, across multiple cancers. Its expression also showed strong correlations with immune scores (IPS, SC, AZ, CP), as well as immune checkpoint genes such as PDCD1 (PD-1), CTLA4, and LAG3, suggesting a role in modulating immune evasion. These findings echo reports from other proteasome subunits involved in antigen processing and MHC class I presentation. Aberrant PSMB7 expression may thus impair tumor immunogenicity, facilitating immune escape and treatment resistance\u0026mdash;particularly relevant in tumors characterized by an immunosuppressive TME.\u003c/p\u003e\u003cp\u003eThe immunomodulatory potential of PSMB7 opens avenues for therapeutic innovation. It may serve as a biomarker for stratifying patients for immunotherapy or as a combinatorial target to enhance the efficacy of ICIs. For instance, combining PSMB7 inhibitors with immune checkpoint blockade might overcome immune resistance in tumors with high PSMB7 expression. However, the clinical translation of PSMB7-targeted therapies remains challenging. RNA interference (RNAi) approaches, although promising in vitro, face delivery barriers in vivo, particularly in solid tumors[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Advances in nanoparticle delivery systems, lipid-based formulations, and tumor-targeted siRNA carriers could enhance specificity and bioavailability, making clinical application more feasible.\u003c/p\u003e\u003cp\u003eAdditionally, PSMB7\u0026rsquo;s link to tissue-specific proteasome induction by agents such as 3H-1,2-dithiole-3-thione (D3T) in organs like liver, lung, colon, and brain raises the possibility that PSMB7 expression is dynamically regulated by environmental or therapeutic stimuli[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This could explain the observed heterogeneity of PSMB7 across tumor types and its variable associations with clinical outcomes. Our study also found that PSMB7 was co-expressed with genes involved in immune regulation, stress response, and metabolic reprogramming, suggesting that it lies at the intersection of multiple oncogenic networks.\u003c/p\u003e\u003cp\u003eAltogether, our findings position PSMB7 as a functionally relevant, clinically actionable molecule with pan-cancer implications. It contributes to cancer development through multiple mechanisms\u0026mdash;supporting tumor growth, mediating chemotherapy resistance, promoting ER stress adaptation, and shaping the immune landscape. Its consistent overexpression across malignancies and association with adverse clinical outcomes make it an attractive candidate for diagnostic, prognostic, and therapeutic exploration. Future work should aim to define the upstream regulators of PSMB7 (e.g., transcription factors, non-coding RNAs), dissect its protein-protein interaction network, and investigate its interplay with metabolic and immune checkpoints in the TME. Employing CRISPR-based gene editing, organoid models, and patient-derived xenografts (PDX) will further clarify PSMB7\u0026rsquo;s function and validate its utility in personalized oncology.\u003c/p\u003e\u003cp\u003eIn conclusion, this study provides a compelling framework for understanding PSMB7\u0026rsquo;s multifaceted contributions to cancer biology. Its integration of genomic, transcriptomic, immunologic, and pharmacologic features underscores the potential of PSMB7 as both a biomarker and a therapeutic target, advancing the frontiers of precision oncology.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study identifies PSMB7 as a clinically significant pan-cancer biomarker with oncogenic and immunomodulatory functions. Integrative analyses and experimental validation reveal that PSMB7 overexpression is associated with poor prognosis, tumor progression, immune cell infiltration, and chemoresistance. Functional assays confirm its role in promoting tumor proliferation, migration, and invasion, while PSMB7 silencing suppresses tumor growth in vitro and in vivo. These findings underscore the potential of PSMB7 as both a prognostic indicator and a therapeutic target. Further investigation is warranted to elucidate its molecular mechanisms and evaluate its clinical utility in precision oncology.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare that financial support was received for the research and/or publication of this article. This research was funded by the Innovation Project of Guangxi Graduate Education (No. YCBZ2024124) and Open Project of Guangxi Key Laboratory of Enhanced Recovery after Surgery for Gastrointestinal Cancer (No. GXEKL202404).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are publicly available. This data can be found here: TCGA (https://www.cancer.gov/tcga), cBioPortal (https://www.cbioportal.org), GEO (https://www.ncbi.nlm.nih.gov/geo), and TheHluman Protein Atlas (https://www.proteinatlas.org).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving humans were approved by the FirstAffliated Hospital of Guangxi Medical University EthicsCommittee. The studies were conducted in accordance with thelocal legislation and institutional requirements. The participantsprovided their written informed consent to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZhou Sijiang: Validation, Conceptualization, Datacuration, Writing-original draft. Yang Xia: FormalAnalysis, Methodology, Writing- original draft. Ran Qingfu: Project administration, Supervision, Resources, Writing- review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe express our gratitude to all contributors ofthe TCGA, GEO, TIMER2.0, MsigDB, HPA, LinkedOmics, TISCH2, and Sangerboxdatabases, whose valuable work supported this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in theabsence of any commercial or financial relationships that could beconstrued as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenerative AI statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare that no Generative AI was used in thecreation of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdams J, Palombella VJ, Sausville EA, Johnson J, Destree A, Lazarus DD, et al. 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J Control Release. 2022;342:228\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jconrel.2022.01.012\u003c/span\u003e\u003cspan address=\"10.1016/j.jconrel.2022.01.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"PSMB7, biomarker, prognostic, immune microenvironment, cancer","lastPublishedDoi":"10.21203/rs.3.rs-7125983/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7125983/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003ePSMB7 is a key component of the ATP-dependent proteolytic complex and plays an essential role in cellular protein degradation. While emerging evidence suggests its involvement in cancer, its roles in tumor progression, prognosis, and diagnosis remain largely uncharacterized. This study aimed to investigate the expression profile of PSMB7 and its association with clinical outcomes and tumor biology across multiple cancer types.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a comprehensive bioinformatic analysis using pan-cancer data from The Cancer Genome Atlas (TCGA). Gene set enrichment analysis (GSEA) and protein\u0026ndash;protein interaction (PPI) network construction were performed to explore the functional roles of PSMB7. The correlation between PSMB7 expression and tumor-infiltrating immune cells was assessed using CIBERSORT, IPS, and QUANTISEQ. Single-cell RNA sequencing data were analyzed to determine PSMB7 expression across distinct cell populations. Furthermore, siRNA-mediated knockdown of PSMB7 was carried out in A549 and AGS cells. Functional assays\u0026mdash;including Western blotting, colony formation, and Transwell migration/invasion\u0026mdash;were used to assess phenotypic effects. Subcutaneous xenograft models of lung and gastric cancer were established in nude mice to evaluate the in vivo impact of PSMB7 depletion on tumor growth.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003ePSMB7 was significantly upregulated in multiple cancer types compared to adjacent normal tissues. Its expression was positively associated with clinical and molecular features such as stemness scores, stromal and immune scores, tumor mutational burden (TMB), microsatellite instability (MSI), and drug sensitivity. Notably, high PSMB7 expression correlated with altered sensitivity to several chemotherapeutic agents. Functionally, PSMB7 knockdown markedly inhibited cancer cell proliferation, migration, and invasion in vitro, and suppressed tumor growth in vivo.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003ePSMB7 is broadly overexpressed in human cancers and is significantly associated with key prognostic indicators, immune cell infiltration, and therapeutic responsiveness. These findings highlight the clinical relevance of PSMB7 as a potential biomarker and therapeutic target, paving the way for its application in personalized cancer treatment strategies.\u003c/p\u003e","manuscriptTitle":"Comprehensive pan-cancer multi-omics analysis of PSMB7 reveals its prognostic significance and oncogenic role with experimental validation in lung and gastric cancers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-02 04:43:15","doi":"10.21203/rs.3.rs-7125983/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-08-25T07:53:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-13T07:09:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-23T09:13:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-22T18:36:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-07-22T18:31:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"71ca9526-0de0-4565-8226-b38e848bfbb8","owner":[],"postedDate":"September 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-02T04:43:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-02 04:43:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7125983","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7125983","identity":"rs-7125983","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}