β2-Microglobulin is a potential pan-cancer biomarker and immunotherapy target

β2-Microglobulin is a potential pan-cancer biomarker and immunotherapy target | 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 β2-Microglobulin is a potential pan-cancer biomarker and immunotherapy target Xia Zhang, Shijie Shen, Yufei Sun, Yu Xie, Zilu Zhang, Saeed El-Ashram, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7445429/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : A important component of MHC-I-mediated antigen presentation, β2-Microglobulin (B2M), has a rising importance in carcinogenesis and immunological control. Although implicated in many malignancies, its pan-cancer prognostic value and therapeutic prospects are unknown. Methods : Multiple-omics analysis was performed on TCGA, CPTAC, and GEO datasets. Tools included TIMER2 (immune infiltration), GEPIA2 (survival/pathological staging), cBioPortal (genomic changes), UALCAN (methylation), and STRING (protein interactions). Drug sensitivity correlations were evaluated using GSCALite and CTRP. Key Results Expression characteristics: B2M is highly expressed in CHOL, GBM, and KIRC, while it is lowly expressed in COAD and LUAD (P<0.001). Prognostic value: High B2M expression is significantly associated with poor prognosis in LGG and UVM (OS: P20%) in DLBC, mainly consisting of L15Ffs * 41 truncated mutations. Immune regulation: B2M expression is positively correlated with CD8+T cell and Tregs infiltration, and significantly correlated with TMB and MSI . Functional pathway: B2M regulates tumor immunity through JAK-STAT and NOD like receptor signaling pathways, and its interacting genes (HLA-A/B/C, etc.) are significantly enriched in MHC-I complex function (P<0.05). Conclusion : As a pan cancer biomarker, the expression level of B2M is closely related to prognosis, immune microenvironment and treatment response, and may become a new target of immunotherapy. This study provides a theoretical basis for the individualized treatment of cancer, but further experiments are needed to verify its mechanism. β2-microglobulin pan-cancer immune infiltration prognostic biomarker tumor microenvironment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 1. Background Cancer is one of the major threats to global health. The reduction in smoking, early detection of certain cancers, and advancements in treatment (including recent developments in targeted and immunotherapy) have improved cancer mortality rates. However, these gains are still threatened by the rising incidence of six out of the top ten cancers [ 1 ]. Therefore, there is an urgent need to discover new biomarkers for cancer diagnosis and prognosis. The clinical application of bioinformatics has the potential to improve the diagnosis, treatment, and prediction of cancer outcomes[ 2 ]. By utilizing the Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database, it is easier to analyze the correlation and impact of individual genes on cancer prognosis and immune infiltration. The β2-microglobulin ( B2M ) gene is located on chromosome 15. B2M is a non-glycosylated protein with a molecular weight of 11,800 Da, synthesized by all nucleated cells[ 3 ]. B2M is composed of the light chains of Major Histocompatibility Complex I (MHC-I) molecules and assists in the effective transport of newly synthesized MHC-I chains to the surface of all nucleated cells[ 4 ]. B2M has been proposed to play a significant role in various diseases. Initially identified as a component present in the urine of patients with renal tubular proteinuria, B2M was considered an alternative marker for presumed intermediate molecular uremic toxins. Plasma B2M levels are elevated in conditions such as lymphoproliferative disorders, chronic renal failure (due to reduced filtration), inflammation, infection, and other conditions characterized by high cell turnover rates[ 5 ]. Smith's study found that the expression of B2M is associated with cognitive impairment in the adult brain, and has therefore been identified as a potential pro-aging factor[ 6 ]. B2M plays a crucial role in immune surveillance under both physiological and pathological conditions in both tumor and non-tumor cells. It has been reported that B2M is upregulated in gliomas[ 7 ]. Additionally, elevated serum B2M levels have been linked to human immunodeficiency virus infections, acquired immunodeficiency syndrome, rheumatoid arthritis, multiple myeloma, and hematological malignancies[ 8 ]. B2M mutations have been found in a considerable number of cancer patients. There is growing evidence that B2M gene alterations in tumor cells may reduce the efficacy of T-cell-based immunotherapies by blocking MHC I class-mediated tumor antigen presentation and avoiding T-cell recognition. However, the mechanisms behind tumor cell immune escape remain unclear. Therefore, we conducted a comprehensive analysis and evaluated the potential value of B2M in cancer diagnosis and prognosis. Our results show that B2M can function as a cancer gene and immune infiltration related biomarker in pan-cancer. 2. Materials and methods 2.1 Gene mapping analysis We obtained the genomic location information of B2M through the University of California, Santa Cruz (UCSC) Genome Browser ( http://genome.ucsc.edu/ )[ 9 ]. 2.2 Protein structure analysis We obtained the phylogenetic tree of B2M in multiple species using an online tool at the National Center for Biotechnology Information (NCBI). 2.3 Gene expression analysis The Human Proteome Atlas (HPA)( https://www.proteinatlas.org/ ) is a database that integrates various omics methods to map proteins in human organs, tissues, and cells[ 10 ]. We first logged into the HPA website by entering "B2M," obtaining data on expression across different cells, tissues, and cancers, including all locations of the B2M gene in cancer cells. For more details, please refer to the link https://www.proteinatlas.org/search/B2M . Immunohistochemical images of three tumor tissues and corresponding normal tissues were obtained from the HPA database, including head and neck squamous cell carcinoma (HNSC), pancreatic ductal adenocarcinoma (PAAD) and renal cell carcinoma. Using the "Gene_DE" module of the tumor immune estimation resource (TIMER,version 2) web ( http://timer.cistrome.org/ ), we input "B2M" to observe the expression differences of B2M in specific tumor subtypes across tumors and adjacent normal tissues or TCGA projects. Additionally, we use the "Expression Analysis-Box Plot" module of the Interactive Analysis of Gene Expression (GEPIA, Version 2) [ 11 ]web server to analyze certain tumors that lack normal tissue or have highly restricted normal tissue, setting p-value Cutoff = 1, |Log2FC| Cutoff = 1, and "Match TCGA normal and GTEx data." To perform protein expression analysis on the Clinical Proteomics Tumor Analysis Consortium (CPTAC) dataset, UALCAN was used to study the expression of B2M protein in tumors and normal tissues[ 12 ]. Here, we explored the total protein levels of B2M in primary tumors and normal tissues by entering "B2M". The "Pathologic Staging" module of GEPIA2 was used to analyze the association between B2M and different pathological stages of cancer (stages I, II, III, and IV). The expression data, converted using log2(TPM + 1) for log-scale, were applied to box plots or violin plots. 2.4 Survival prognosis analysis We also obtained the survival plots utilizing GEPIA2. Using the "Survival Map" module, set Cutoff-high (50%) and and cutoff-low (50%) to distinguish high and low expression cohorts. 2.5 Genetic alteration analysis CBioPortal [ 13 ]( http://www.cbioportal.org ) provides a platform where we selected "TCGA Pan-Cancer Atlas Study" in the "Quick Selection" section and entered "B2M" to query B2M's genetic alteration characteristics. In the "Cancer Type Overview" module, we observed the frequency of changes, mutation types, and copy number variations (CNAs) for all TCGA tumors. 2.6 Immune infiltration analysis We use the "immune gene" module of TIMER2 to analyze the correlation between the immune infiltration level and the B2M gene expression level. We used the "immune gene" module of TIMER2 to analyze the correlation between immune infiltration levels and B2M gene expression levels. By selecting immune cells such as CD8 T cells, T regulatory cells (Tregs), and cancer-associated fibroblasts, we obtained a visual heat map. Immune infiltration was analyzed using TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, XCELL, MCPCOUNTER and EPIC algorithms, with P-values and partial correlation (cor) values derived from the purity-adjusted Spearman rank correlation test. Clicking on cells in the heat map generates scatter plots to show the relationship between estimated infiltration volume and gene expression. 2.7 Relationship between B2M and DNA methylation in pan-cancer DNA methylation represents the predominant epigenetic modification and exerts a critical influence on tumor progression. In this study, we utilized the UALCAN database to investigate and compare the methylation profiles of B2M in both tumor and normal tissues. 2.8 Genomic heterogeneity and gene expression of B2M Sangerbox 3.0 ( http://vip.sangerbox.com/home.html ) was used to analyze genomic heterogeneity and gene expression of selected genes. "B2M", log2(x + 0.001) transformation and Pearson correlation were selected, and Tumor Mutation Burden (TMB), Microsatellite Instability (MSI), Neoantigen Expression (NEO) and Mutant-allele Tumor Heterogeneity (MATH) were selected for correlation analysis. 2.9 Drug sensitivity analysis GSCALite ( http://bioinfo.life.hust.edu.cn/web/GSCALite/ ) is an online platform for analyzing the correlation between gene expression and drug sensitivity[ 14 ]. Enter "B2M" to get the analysis data.Blue bubbles represent negative correlations, red bubbles represent positive correlations, and the deeper color, the higher, the correlation. Bubble size is positively correlated with the FDR significance. The black outline border indicates FDR ≤ 0.05. 2.10 B2M-related gene enrichment analysis We first searched for the gene of interest "B2M" and organism ("Homo sapiens") on the STRING tool ( https://string-db.org/ ) [ 15 ]to screen 50 experimentally validated B2M-binding proteins. Set the following parameters: meaning of network edges as "evidence", minimum required interaction score as "low confidence (0.150)," active interaction sources as "experiments" and max number of interactors to show as “no more than 50 interactors.” Finally, download 50 proteins and visualize them with Cytoscape (version 3.10.2). The first 100 B2M-related genes were obtained from a dataset based on all TCGA tumors and normal tissues using the "Lookalike Gene Detection" module of GEPIA2. The "Correlation Analysis" module of SHIYONG GEPIA2 performs paired gene Pearson correlation coefficient analysis for B2M and the first 5 related genes. The log2 TPM, correlation coefficient (R), and P-value were adopted and labeled. In addition, the "exploration-Gene-Corr" module of TIMER2 was applied to estimate the correlation heat map between B2M and the top 5 genes. Then, the Venn diagram ( http://bioinformatics.psb.ugent.be/Webtools/Venn/ )[ 16 ] was used by us to plot the intersection analysis of the B2M-interacting genes and related genes to obtain 8 intersecting genes. In addition, we combined two sets of data on B2M interacting genes and related genes, and conducted KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis. The clusterProfiler ( http://www.bioconductor.org/packages/release/bioc/html/clusterProfiler.html ) R package for gene ontology (GO) enrichment analysis.The results were visualized using the 'tidyr' and 'ggplot2' R packages. P < 0.05 was considered statistically significant. The R language software version used in this study is [R-4.0.3, 64-bit] ( https://www.r-project.org/ ). 3. Results 3.1 Gene ontology analysis The genome of human B2M (NM_000015.10 ) is on chromosome 15 (q21.1) (Fig. 1 A). The evolution of the B2M protein is shown in Fig. 1 B. 3.2 Gene expression analysis Based on the HPA dataset, we first examined the distribution of B2M in cells. B2M is primarily found in the Golgi apparatus and plasma membrane, and also in the cytoplasm (Fig. 2 A-B). We then analyzed the expression levels of B2M in various cells and tissues, as shown in the figure: B2M is expressed in all detected normal cells (Fig. 2 C). B2M is also expressed in normal tissues, with the highest expression in the spleen, followed by the lung and small intestine (Fig. 2 D), indicating a lower RNA cell type specificity. However, B2M is expressed in all cancer tissues, with relatively higher expression in the kidney, which contrasts with the brain (Fig. 3 A). In cancer cells, B2M is negative in testicular cancer but shows varying degrees of cytoplasmic or membrane positivity in other cancers, with lymphoma showing the highest positivity (Fig. 3 B). Additionally, the immunohistochemical results of B2M in HNSC, PAAD, and renal cell carcinoma compared to normal tissue (Fig. 3 C) show that the B2M protein levels are significantly higher in these three types of cancer tissues compared to normal tissue (P < 0.001). We utilized the TIMER2 method to analyze the expression status of B2M across various cancer types in The Cancer Genome Atlas (TCGA). As illustrated in Fig. 4 a, the expression levels of B2M were significantly elevated in tumor tissues compared to corresponding control tissues for cholangiocarcinoma (CHOL), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), HNSC, kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), and kidney renal papillary cell carcinoma (KIRP), (P < 0.001). In addition, B2M mRNA expression was low in colon adenocarcinoma (COAD), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), rectum adenocarcinoma (READ), prostate adenocarcinoma (PRAD) (P < 0.01) and uterine corpus endometrial carcinoma (UCEC) (P < 0.05). Using the normal tissues from the GTEx dataset as a control, we examined the expression differences of B2M in tumor tissues and normal tissues across various types of cancer, including Cervical squamous cell carcinoma and endocervical adenocarcinoma(CESC), CHOL, lymphoid neoplasm diffuse large B-cell lymphoma(DLBC), ESCA, GBM, HNSC, KICH, KIRC, acute myeloid leukemia(LAML), brain lower grade glioma(LGG), PAAD, stomach adenocarcinoma(STAD), and testicular germ cell tumors(TGCT) (Fig. 4 B, P > 0.05). Results from the CPTAC dataset showed that B2M total protein was expressed higher in primary tissues of hepatocellular carcinoma, GBM, pancreatic adenocarcinoma(PAAD), UCEC, HNSC, Clear cell RCC(P < .001) and ovarian cancer(OV) than in normal tissues, whereas B2M expression was lower in, LUAD, LUSC and breast cancer than in normal tissues (P < .001). (Fig. 4 C) Finally, we used the "Pathological Staging Map" module of GEPIA2 to observe the correlation between B2M expression and cancer pathological staging, including HNSC, Thyroid carcinoma(THCA) and SKCN (Fig. 5 , all P < .05), but not other. 3.3 Survival analysis data In addition, to investigate the correlation between B2M expression and prognosis in different tumor patients, we divided them into high-expressing and low-expressing groups based on their B2M expression levels. As shown in Fig. 6 , the overall survival (OS) analysis data indicate that poor prognosis in KIRC (P = 0.043), Sarcoma(SARC) (P = 0.009), and Skin Cutaneous Melanoma(SKCM) (P < 0.001) is associated with low B2M expression. The disease-free survival (DFS) data show that low B2M expression is linked to poor prognosis in Mesothelioma(MESO) (P = 0.019) and PRAD (P = 0.035) cases. In addition, high expression of the B2M gene was highly correlated with poor prognosis of overall survival in LGG (P < 0.001) and Uveal Melanoma(UVM) (P < 0.001), and poor prognosis of disease-free survival in LGG (P = 0.032) and UVM (P < 0.013). 3.4 Genetic alteration analysis data The cBioportal database can display the gene change status of B2M in different tumor samples in the TCGA queue, mainly in the form of mutations, structural variations, amplification, deep deletions, and multiple changes in different tumors. As shown in Fig. 7 A, the maximum frequency of B2M changes (> 20%) in DLBC patients occurs when "mutations" (green) are the main type. Copy number deletions of the B2M gene were observed in all cases of CHOL with genetic alterations. In addition, in LAML、LGG、KICH、PAAD、Pheochromocytoma and Paraganglioma、TGCT、Thymoma、THCA and UVM. No genetic changes were found in B2M. The types, locations, quantities, and mutation frequencies of B2M gene changes are shown in Fig. 7 B.The primary genetic change in the B2M gene is truncating mutations, with the highest frequency being the L15Ffs*41 truncating mutation. In addition, we analyzed the correlation between B2M expression and TMB /MSI across all tumors in TCGA. We calculated their Pearson correlation coefficients in each tumor type and observed significant correlations in 5 tumor types. Specifically, there were significant positive correlations in 3 tumor types: COAD (P = 0.012), READ (P = 0.008), and Breast invasive carcinoma(BRCA) (P = 0.01). There were also significant negative correlations in 2 tumor types: CESC (P = 0.038) and LUAD (P = 0.0003)(Fig. 8 A and supplemental tabl e1 ). For MSI, we observed significant correlations in 12 tumor types. Specifically, there were significant positive correlations in 2 tumor types: COAD (P = 0.004) and READ (P = 0.002). There were also significant negative correlations in 10 tumor types, including GBM, LGG (P < 0.001), LUAD (P < 0.001), Pan-kidney cohort (KICH + KIRC + KIRP)(KIPAN) (P = 0.003), PRAD (P < 0.001), HNSC (P = 0.042), LUSC (P < 0.001), MESO (P = 0.033), OV (P = 0.009), TGCT (P = 0.002), and Adrenocortical carcinoma(ACC) (P = 0.036)(Fig. 8 B and supplemental table2). In summary, B2M expression positively correlates with TMB and MSI in COAD and READ, indicating that these two tumor types may benefit from immunotherapy. Similarly, we analyzed the correlation between MATH and NEO with B2M expression. In MATH, a significant correlation was observed in 12 tumors, notably exhibiting a significant negative correlation in 12 types of tumors including GBM (P < 0.001), LGG (P = 0.004), LUAD (P < 0.001), Stomach and Esophageal carcinoma(STES) (P < 0.001), STAD (PP < 0.001), HNSC (P = 0.017), KIRC (P = 0.032), Liver hepatocellular carcinoma(LIHC) (P = 0.015), PAAD (P = 0.010), TGCT (P < 0.001), SKCM (P = 0.040), and Bladder Urothelial Carcinoma(BLCA) (P < 0.001)(Fig. 8 C and supplemental table3). In NEO, a significant correlation was observed in 4 tumors, specifically exhibiting a significant positive correlation in COAD (P < 0.001), READ (P < 0.001), SARC (P = 0.003), and a significant negative correlation in GBM (P = 0.022)(Fig. 8 D and supplemental table4). 3.5 DNA methylation analysis data We assessed the DNA methylation levels of the B2M promoter in normal tissues and eleven types of cancer tissues. According to the UALCAN database, the B2M promoter exhibited significantly higher methylation levels in PRAD, LUSC, and BRCA compared to normal tissues. Conversely, the B2M promoter was found to be hypomethylated in LUAD, KIRP, KIRC, HNSC, COAD, LIHC, UCEC, and TGCT. (Fig. 9 ) 3.6 Immune infiltration analysis data In this study, we used all or most of the algorithms to investigate the potential relationship between the estimated immune infiltration levels and B2M expression levels in different cancer types from the TCGA dataset, presenting these relationships in the form of heatmaps and scatter plots. After a series of analyses, we observed a statistically positive correlation between B2M expression and immune infiltration of CD8 + T cells in BRCA, KIRC, SKCM, SKCM-Metastasis, and STAD tumors. We also noted that B2M expression was positively correlated with regulatory T cell infiltration in BRCA, BRCA-LumA, SARC, and THCA tumors. Additionally, we found that B2M expression was positively correlated with the estimated infiltration values of cancer-associated fibroblasts in LGG, LUSC, and TGCT tumors but observed a negative correlation in OV. The scatter plot data for the aforementioned tumors generated using one algorithm is shown in Fig. 10 . These data indicate that B2M is an oncogene in TCGA tumors, and its overexpression may help inhibit tumor progression. 3.7 Drug sensitivity analysis The CTPR data set showed that the three drugs with positive correlation with B2M expression were austocystin D, niclosamide and SB − 525334. The top three drugs negatively associated with B2M expression were AZD7762, clofarabine, and chlorambucil (Fig. 11 A and supplemental table5, P < 0.0001). According to GDSC drug sensitivity results, the top three drugs negatively correlated with B2M expression were Z-LLNle-CHO, SNX-2112, and PIK-93 (Fig. 11 B and supplemental table 6, P < 0.0001). These results provide an important reference for drug screening and the formulation of personalized treatment. 3.8 Enrichment analysis of B2M-related partners To further investigate the molecular mechanisms of B2M target genes in tumor development, we attempted to screen for proteins that bind to B2M and conducted pathway enrichment analysis. First, using the STRING tool, we obtained 50 B2M-binding proteins supported by experimental evidence, and the protein interaction network illustrated these proteins (Fig. 12 A). Then, through the GEPIA2 tool, we integrated all tumor expression data from TCGA, ultimately identifying 100 genes most strongly associated with B2M expression. The expression levels of B2M were positively correlated with the expression levels of HLA-B (R = 0.72), HCP5 (R = 0.65), HLA-DRA (R = 0.64), HLA-A (R = 0.63), and PSMB8 (R = 0.61) (all P < 0.001) (Fig. 12 B). Correlation heat map data also showed a positive correlation between B2M and the above five genes (Fig. 12 C). However, the mechanisms and patterns of B2M gene function in tumors are not yet clear, thus further research on B2M-targeted binding proteins and B2M-related genes is needed. By conducting intersection analysis between the two daftasets, we identified eight genes: HLA-F, HLA-C, HLA-B, HLA-A, MR1, TAPBP, TAPBPL, and HLA-E (Fig. 13 A). Finally, the data of B2M targeted binding protein and related genes were integrated, and KEGG pathway and GO enrichment analysis were performed. The KEGG analysis revealed that JAK-STAT signaling pathway and NOD-like receptor signaling pathway may be associated with the impact of B2M on tumor pathogenesis(Fig. 13 B). Additionally, GO data indicated that B2M expression plays a role in tumors by regulating immune receptor activity and MHC protein complex binding (Fig. 13 C-D). 4. Discussion B2M, a small molecule globulin, serves as the β-chain (light chain) of human lymphocyte antigen (HLA) on cell surfaces and plays multiple critical roles in the body. To elucidate the mechanisms of B2M's role in cancer from clinical data, we conducted a pan-cancer analysis using the TCGA and CPTAC databases for the first time. Our phylogenetic tree reveals the structural and functional conservation of B2M, along with its abnormal expression in various disease states, making it a crucial target for disease diagnosis, prognosis assessment, and drug therapy. B2M has been extensively studied in autoimmune diseases, cancer, infections, kidney diseases, peripheral artery diseases, and cardiovascular diseases [ 7 , 16 ]. In our work, we conducted a comprehensive study of B2M genes in various tumors, focusing on aspects such as gene expression, survival analysis, genetic changes, DNA methylation, protein phosphorylation, and the correlation with B2M target genes. By downloading the pan-cancer expression profile data from the TCGA database, TCGA results show that B2M is overexpressed in various cancer tissues, including CHOL, ESCA, GBM, HNSC, KICH, KIRC, and KIRP, compared to normal tissues[ 17 – 20 ]. Conversely, its expression is reduced in COAD, LUAD, LUSC, READ, PRAD, and UCEC cancer tissues. Previous experimental studies have also confirmed these findings[ 21 – 24 ]. However, Tsoneva DK et al.'s study did not find any changes in B2M in CHOL[ 25 ]. Additionally, there is a lack of experimental data for KICH and KIRP, and the expression of READ shows a trend opposite to the experimental data, indicating the need for further clinical data collection and analysis. Furthermore, we found that B2M is closely associated with the staging of HNSC, THCA, and SKCN[ 7 , 24 , 26 ]. The survival prognosis analysis revealed a correlation between high B2M expression and poorer outcomes in LGG and UVM. This finding aligns with previous experimental studies. These observations suggest that B2M plays different roles in the development of various cancers. For instance, promoting B2M overexpression could serve as a means to inhibit tumor progression, potentially enabling personalized treatment for patients in clinical settings. Additionally, for tumors with varying B2M gene expression at different pathological stages, gene-targeted therapy can be initiated early in the disease, or personalized treatment can be tailored based on the disease's pathological stage. Overall, our findings provide valuable insights for clinical gene therapy. In the process of biological evolution, gene mutations are one of the most significant factors to consider[ 27 ]. The development and progression of tumors are driven by accumulated mutations and epigenetic changes[ 28 ]. Among these, B2M gene mutations play a crucial role in various types of cancer, including colon cancer, DLBC, melanoma, and glioma[ 7 , 29 – 32 ]. This study found that B2M mutations occur mainly in diffuse large B-cell lymphoma (DLBCL), which aligns with previous experimental and clinical data on DLBC[ 31 , 33 ]. Among the various B2M mutations, the single most frequent mutation is the truncated L15Ffs*41 mutation, which provides valuable insights for the study of B2M mutations. Numerous studies have confirmed the close link between DNA methylation and tumor development. Some drugs that inhibit DNA methylation have been approved by the FDA for treating certain human cancers[ 28 ]. The expression of B2M is also influenced by epigenetic mechanisms, including [ 34 ]DNA methylation. In this study, we observed a potential correlation between reduced DNA methylation in non-promoter regions and low B2M expression, suggesting that more evidence is needed to explore the potential role of B2M DNA methylation in the development of TGCT tumors. Immunotherapy has emerged as a powerful force in cancer treatment, achieving significant breakthroughs[ 34 ]. Immune cells form the cellular foundation of immunotherapy. In highly immunologically infiltrated tumors, tumor-infiltrating leukocytes (TIL) can account for over 40%[ 35 ]. B2M is the light chain (β-chain) of the MHC-I complex, which plays a crucial role in the restricted presentation of tumor antigens by MHC-I[ 34 ]. The MHC-I molecule complex can be recognized by CD8 + T cells that express specific TCRs. The signal from TCRs recognizing antigens is transmitted to CD8 + T cells, triggering cytotoxic[ 36 ]. Regulatory T cells (Tregs), tumor-associated macrophages, and tumor-associated fibroblasts contribute to the tolerance and functional inhibition of tumor-related antigens, which are also part of the mechanisms of tumor immune escape[ 37 ]. Our research confirms this, showing that B2M plays a specific role in regulating the tumor immune microenvironment. Abnormal expression of B2M can alter the tumor immune microenvironment. B2M protein expression is associated with immune infiltration and tumor regulation, but it exhibits differential regulatory effects across different types of tumors. Previous studies have shown that mutations and deletions in B2M are linked to immune therapy resistance[ 38 , 39 ], while upregulation of B2M could offer a new therapeutic strategy to overcome resistance [ 40 , 41 ]. Natasja et al.'s research indicates that the expression of PD-1, activation, proliferation, and cytotoxicity genes in γδ1 and γδ3 T cells is significantly elevated in tumors lacking B2M[ 42 ]. Furthermore, in the absence of B2M, the activation of CD4 + T cells and NK cells can overcome resistance and exert anti-tumor effects. Recent studies have shown that inhibiting autophagy can restore B2M/MHC-I expression and improve antigen presentation, thereby enhancing the anti-tumor T cell response[ 43 ]. In summary, our findings offer new insights into tumor immunotherapy, suggesting that regulating B2M expression and immune infiltration could lead to synergistic therapeutic outcomes. Considering these results, we believe that B2M may be a potential target for immunotherapy, although this conclusion requires further experimental validation. Research indicates that B2M can influence patients' sensitivity to immunotherapy, potentially making it a promising therapeutic target for these tumors. In various cancers, the expression levels of B2M are associated with the tumor burden (TMB), microsatellite instability (MSI), mutant-allele tumor heterogeneity (MATH), and neoantigen load (NEO). Therefore, B2M could serve as a potential biomarker for the immune therapy response in patients across different cancer types. We also investigated the sensitivity of B2M-related drugs. The drug sensitivity analysis revealed that B2M is positively correlated with austocystin D, niclosamide, and SB − 525334, all of which have been confirmed to exhibit potential anti-tumor effects[ 44 – 46 ]. This finding has clinical implications, but further research is needed to elucidate the underlying molecular mechanisms. Furthermore, we identified proteins interacting with B2M and associated genes, and conducted GO and KEGG enrichment analyses. The results revealed the potential functions of the 'JAK-STAT signaling pathway' and the 'NOD-like receptor signaling pathway' in cancer development. Current evidence indicates that genes highly associated with B2M are positively correlated with the occurrence of various tumors. In our study, eight genes highly associated with B2M were found to promote the development of multiple tumors[ 47 – 51 ]. This finding aligns with previous research findings. 5. Limitations Although our research has extensively explored the potential of B2M as a prognostic biomarker in cancer, we must acknowledge that there are still some limitations. These limitations also point to directions for future research. First, the various analyses we conducted using retrospective data from public databases (like TCGA, CPTAC, GTEx) have resulted in our limitations. For example, studies have found significant correlations between B2M expression and various clinical features, but this does not directly prove that B2M is the direct cause of these phenotypes. Secondly, our study lacks relevant in vivo models or in vitro cell experiments to further confirm the function of B2M, which is also one of our core limitations. For example, it needs to be clarified through functional experiments whether the high expression of B2M in certain cancers plays a pro-cancer or anti-cancer role. Moreover, the research focus has been on the correlation between B2M and immune cell infiltration, but we did not validate this using data from patient cohorts receiving immune checkpoint inhibitor therapy. The tumor microenvironment is a heterogeneous and dynamic system that requires deeper exploration into how B2M specifically affects the functional status of different immune cell subsets, which depends on extensive clinical experimental evidence to follow. Finally, enrichment analysis suggests that B2M may play a role through pathways such as JAK-STAT, but the specific upstream regulatory mechanisms and downstream signaling pathways have not been elucidated through experiments. 6. conclusion In conclusion, our study shows that B2M expression is statistically correlated with clinical prognosis, DNA methylation, immune cell infiltration and gene enrichment in a variety of tumors, which helps to understand the role of B2M in tumor development from the perspective of clinical tumor samples. However, its conclusions still need to be further confirmed and expanded through prospective clinical studies and in-depth experimental biological function verification, to ultimately assess its true potential as a cancer biomarker or therapeutic target. Abbreviations ACC Adrenocortical carcinoma BLCA Bladder Urothelial Carcinoma BRCA Breast invasive carcinoma CESC Cervical squamous cell carcinoma and endocervical adenocarcinoma CHOL Cholangiocarcinoma COAD Colon adenocarcinoma DLBC Lymphoid Neoplasm Diffuse Large B-cell Lymphoma ESCA Esophageal carcinoma GBM Glioblastoma multiforme HNSC Head and Neck squamous cell carcinoma KICH Kidney Chromophobe KIRC Kidney renal clear cell carcinoma KIRP Kidney renal papillary cell carcinoma LAML Acute Myeloid Leukemia LGG Brain Lower Grade Glioma LIHC Liver hepatocellular carcinoma LUAD Lung adenocarcinoma LUSC Lung squamous cell carcinoma MESO Mesothelioma OV Ovarian serous cystadenocarcinoma PAAD Pancreatic adenocarcinoma PCPG Pheochromocytoma and Paraganglioma PRAD Prostate adenocarcinoma READ Rectum adenocarcinoma SARC Sarcoma STAD Stomach adenocarcinoma SKCM Skin Cutaneous Melanoma TGCT Testicular Germ Cell Tumors THCA Thyroid carcinoma THYM Thymoma UCEC Uterine Corpus Endometrial Carcinoma UCS Uterine Carcinosarcoma UVM Uveal Melanoma Declarations Author contributions SSJ contributed equally to this work. ZX and SSJ designed the search; ZX wrote the main manuscript text. Saeed, LTT, LKY, DSQ and CCZ gave me comprehensive writing advice. ZX, SSJ, SYF, XY and ZZL performed statistical analysis; WJD and WGL primary responsibility for final content.All authors read and approved the final manuscript. Ethics approval and consent to participate: All data used in this study are from publicly available databases, and no additional ethical approval and informed consent are required. Consent to publish: Not applicable. Conflict of interest: The authors declare that the research was conducted without any commercial or financial relationships that could potentially create a conflict of interest. Competing interests: The authors declare no competing interests. Funding: No funding was received. Author Contribution SSJ contributed equally to this work. ZX and SSJ designed the search; ZX wrote the main manuscript text. Saeed, LTT , LKY , DSQ and CCZ gave me comprehensive writing advice. ZX, SSJ, SYF, XY and ZZL performed statistical analysis; WJD and WGL primary responsibility for final content.All authors read and approved the final manuscript. Acknowledgement The authors would like to thank all members of our laboratory for their work. Saeed EI-Ashram , supported by Project number (RSP2024R99), King Saud University, Riyadh, Saudi Arabia, for their support for this project. Data Availability Data is provided within the supplementary information files: Supplementary Table 1 (Correlation between B2M expression and tumor mutational burden), Supplementary Table 2 (Correlation between B2M expression and microsatellite instability), Supplementary Table 3 (Correlation between B2M expression and Mutant-allele Tumor Heterogeneity), Supplementary Table 4 (Correlation between B2M expression and Neoantigen Expression), Supplementary Table 5 (Correlation between B2M and CTPR Drug sensitivity analysis) and Supplementary Table 6 (Correlation between B2M and GDSC Drug sensitivity analysis). References Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024;74(1):12–49. Dai F, et al. Identification of candidate biomarkers correlated with the diagnosis and prognosis of cervical cancer via integrated bioinformatics analysis. Onco Targets Ther. 2019;12:4517–32. Ploegh HL, Orr HT, Strominger JL. Major histocompatibility antigens: the human (HLA-A, -B, -C) and murine (H-2K, H-2D) class I molecules. Cell. 1981;24(2):287–99. Eichner T, Radford SE. Understanding the complex mechanisms of beta2-microglobulin amyloid assembly. FEBS J. 2011;278(20):3868–83. Corlin DB, Heegaard NH. beta(2)-microglobulin amyloidosis. Subcell Biochem. 2012;65:517–40. Smith LK, et al. beta2-microglobulin is a systemic pro-aging factor that impairs cognitive function and neurogenesis. Nat Med. 2015;21(8):932–7. Zhang H, et al. B2M overexpression correlates with malignancy and immune signatures in human gliomas. Sci Rep. 2021;11(1):5045. Baumann T, et al. Lymphocyte doubling time in chronic lymphocytic leukemia modern era: a real-life study in 848 unselected patients. Leukemia. 2021;35(8):2325–31. Kent WJ, et al. The human genome browser at UCSC. Genome Res. 2002;12(6):996–1006. Uhlen M, et al. Proteomics. Tissue-based map of the human proteome. Science. 2015;347(6220):1260419. Tang Z, et al. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 2019;47(W1):W556–60. Chandrashekar DS, et al. An update to the integrated cancer data analysis platform. Neoplasia. 2022;25:18–27. Cerami E, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401–4. Liu CJ, et al. GSCALite: a web server for gene set cancer analysis. Bioinformatics. 2018;34(21):3771–2. Szklarczyk D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47(D1):D607–13. Bardou P, et al. jvenn: an interactive Venn diagram viewer. BMC Bioinformatics. 2014;15(1):293. Wang J, et al. Mesenchymal Stromal Cells-Derived beta2-Microglobulin Promotes Epithelial-Mesenchymal Transition of Esophageal Squamous Cell Carcinoma Cells. Sci Rep. 2018;8(1):5422. Li D, et al. beta2-Microglobulin Maintains Glioblastoma Stem Cells and Induces M2-like Polarization of Tumor-Associated Macrophages. Cancer Res. 2022;82(18):3321–34. Gavrielatou N, et al. Digital Spatial Profiling Links Beta-2-microglobulin Expression with Immune Checkpoint Blockade Outcomes in Head and Neck Squamous Cell Carcinoma. Cancer Res Commun. 2023;3(4):558–63. Adachi Y, et al. Inhibition of FGFR Reactivates IFNgamma Signaling in Tumor Cells to Enhance the Combined Antitumor Activity of Lenvatinib with Anti-PD-1 Antibodies. Cancer Res. 2022;82(2):292–306. Lin H et al. Analysis of the B2M Expression in Colon Adenocarcinoma and Its Correlation with Patient Prognosis. Evid Based Complement Alternat Med, 2022. 2022: p. 7264503. Zhao Y, et al. B2M gene expression shapes the immune landscape of lung adenocarcinoma and determines the response to immunotherapy. Immunology. 2021;164(3):507–23. Lee SH, et al. Beta-2-microglobulin positive tumor cells and CD8 positive lymphocytes are associated with outcome in post-neoadjuvant colorectal cancer resections. Hum Pathol. 2025;155:105737. Liu AY. The opposing action of stromal cell proenkephalin and stem cell transcription factors in prostate cancer differentiation. BMC Cancer. 2021;21(1):1335. Tsoneva DK et al. Downregulation of Aging-Associated Gene SUCLG1 Marks the Aggressiveness of Liver Disease. Cancers (Basel), 2025. 17(3). Furdova A, Olah Z. [Use of oncologic markers in ophthalmic oncology]. Cesk Slov Oftalmol. 1998;54(5):294–8. Svensson EI, Berger D. The Role of Mutation Bias in Adaptive Evolution. Trends Ecol Evol. 2019;34(5):422–34. Zhang MW, et al. DNA methylation in the tumor microenvironment. J Zhejiang Univ Sci B. 2017;18(5):365–72. Yeon YS, et al. Immune checkpoint blockade resistance-related B2M hotspot mutations in microsatellite-unstable colorectal carcinoma. Pathol Res Pract. 2019;215(1):209–14. Zhang C, et al. B2M and JAK1/2-mutated MSI-H Colorectal Carcinomas Can Benefit From Anti-PD-1 Therapy. J Immunother. 2022;45(4):187–93. He M, et al. B2M mutation paves the way for immune tolerance in pathogenesis of Epstein-Barr virus positive diffuse large B-cell lymphomas. J Cancer. 2022;13(15):3615–22. Zaretsky JM, et al. Mutations Associated with Acquired Resistance to PD-1 Blockade in Melanoma. N Engl J Med. 2016;375(9):819–29. de Miranda NF, et al. Exome sequencing reveals novel mutation targets in diffuse large B-cell lymphomas derived from Chinese patients. Blood. 2014;124(16):2544–53. Han X, et al. The role of B2M in cancer immunotherapy resistance: function, resistance mechanism, and reversal strategies. Front Immunol. 2025;16:1512509. Duan Q, et al. Turning Cold into Hot: Firing up the Tumor Microenvironment. Trends Cancer. 2020;6(7):605–18. Muntjewerff EM, Meesters LD, van den Bogaart G. Antigen Cross-Presentation Macrophages Front Immunol. 2020;11:1276. Rui R, Zhou L, He S. Cancer immunotherapies: advances and bottlenecks. Front Immunol. 2023;14:1212476. Kypraios A et al. Identifying Candidate Gene Drivers Associated with Relapse in Pediatric T-Cell Acute Lymphoblastic Leukemia Using a Gene Co-Expression Network Approach. Cancers (Basel), 2024. 16(9). Zhang J, et al. Single-Cell Transcriptome Identifies Drug-Resistance Signature and Immunosuppressive Microenvironment in Metastatic Small Cell Lung Cancer. Adv Genet (Hoboken). 2022;3(2):2100060. Martinez-Morilla S, et al. Biomarker Discovery in Patients with Immunotherapy-Treated Melanoma with Imaging Mass Cytometry. Clin Cancer Res. 2021;27(7):1987–96. Bortolomeazzi M, et al. Immunogenomics of Colorectal Cancer Response to Checkpoint Blockade: Analysis of the KEYNOTE 177 Trial and Validation Cohorts. Gastroenterology. 2021;161(4):1179–93. de Vries NL, et al. gammadelta T cells are effectors of immunotherapy in cancers with HLA class I defects. Nature. 2023;613(7945):743–50. Yamamoto K, et al. Selective autophagy of MHC-I promotes immune evasion of pancreatic cancer. Autophagy. 2020;16(8):1524–5. Kojima Y, et al. Cytochrome P450 2J2 is required for the natural compound austocystin D to elicit cancer cell toxicity. Cancer Sci. 2024;115(9):3054–66. Jiang H, Li AM, Ye J. The magic bullet: Niclosamide. Front Oncol. 2022;12:1004978. Li Y, et al. Simultaneous blockage of contextual TGF-beta by cyto-pharmaceuticals to suppress breast cancer metastasis. J Control Release. 2021;336:40–53. Michelakos T et al. Differential role of HLA-A and HLA-B, C expression levels as prognostic markers in colon and rectal cancer. J Immunother Cancer, 2022. 10(3). Yan J, et al. Cells Promote Tumor Initiation, Growth, and Metastases via Tumor MR1. Cancer Discov. 2020;10(1):124–41. Sun J, et al. Exploring the cross-cancer effect of circulating proteins and discovering potential intervention targets for 13 site-specific cancers. J Natl Cancer Inst. 2024;116(4):565–73. Lin Y, et al. Identification of TAPBPL as a novel negative regulator of T-cell function. EMBO Mol Med. 2021;13(5):e13404. Hrbac T, et al. Are Overexpressed in Glioblastoma and HLA-E Increased After Exposure to Ionizing Radiation. Cancer Genomics Proteom. 2022;19(2):151–62. Additional Declarations No competing interests reported. Supplementary Files supplementary.zip Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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19:55:59","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":558926,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-7445429/v1/5585b22a2d22cd476c4b02da.png"},{"id":94141330,"identity":"c216cf62-0726-4faf-80da-abb95754a917","added_by":"auto","created_at":"2025-10-22 19:56:00","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1874243,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-7445429/v1/d24c487f38642a15348a963e.png"},{"id":94141323,"identity":"45fa51fd-7f78-46b3-9990-218e569ee9d5","added_by":"auto","created_at":"2025-10-22 19:55:59","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":896454,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-7445429/v1/fb3382b4d590c7415c5a6027.png"},{"id":94141317,"identity":"b137770b-3b93-4d4d-98d7-9d0a15a1f3be","added_by":"auto","created_at":"2025-10-22 19:55:59","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3309384,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7445429/v1/3d581762f7ce4ed23b77024d.png"},{"id":94141312,"identity":"845c960a-7aae-4d78-a06c-95c5bf66315f","added_by":"auto","created_at":"2025-10-22 19:55:59","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4372638,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7445429/v1/41a45662e92cfa2f7f108b0d.png"},{"id":94141513,"identity":"fbe7e13a-2d07-4588-a232-5fd05495e3d5","added_by":"auto","created_at":"2025-10-22 20:03:59","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2106478,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7445429/v1/dda214b623eab640ef43a134.png"},{"id":94141303,"identity":"2f61d5a0-cfc6-4f05-b88c-158112023d25","added_by":"auto","created_at":"2025-10-22 19:55:59","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":332122,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7445429/v1/643e235e575cf5c849396601.png"},{"id":94141512,"identity":"ed66c9d4-98f1-4e73-8ca3-611222ab830a","added_by":"auto","created_at":"2025-10-22 20:03:59","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1171562,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7445429/v1/76831a9f23d92cf8a83d10a1.png"},{"id":94141506,"identity":"6620d024-f97a-4cc2-9b20-22145d0a0373","added_by":"auto","created_at":"2025-10-22 20:03:59","extension":"png","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":986427,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7445429/v1/92ed58580944e3852654dfe4.png"},{"id":94141324,"identity":"7f1e12c1-4557-4300-9b32-666407959372","added_by":"auto","created_at":"2025-10-22 19:55:59","extension":"png","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":941318,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7445429/v1/29ddfbb906750e1ec9f427ca.png"},{"id":94141326,"identity":"fa7e933d-0d45-4bfa-8b30-1d3e73b2dd04","added_by":"auto","created_at":"2025-10-22 19:56:00","extension":"png","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":261351,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7445429/v1/b69f78ed5e1c1c8db2529268.png"},{"id":94141322,"identity":"d16a4247-d86b-4542-99c8-c833a2e6a4a0","added_by":"auto","created_at":"2025-10-22 19:55:59","extension":"xml","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":112990,"visible":true,"origin":"","legend":"","description":"","filename":"91778d21655b4ccdb4a30b435ff097361structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7445429/v1/3314c07064d053d7c39c7dc2.xml"},{"id":94141325,"identity":"d67356a9-31c4-448b-9f5a-c8039a8abbb9","added_by":"auto","created_at":"2025-10-22 19:56:00","extension":"html","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":127901,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7445429/v1/6cdcda68a2c38eaaa77d6bc2.html"},{"id":94141290,"identity":"5bb05295-5af9-45fb-9512-4d019d53619d","added_by":"auto","created_at":"2025-10-22 19:55:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":621473,"visible":true,"origin":"","legend":"\u003cp\u003eGene ontology analysis. (A) The UCSC dataset provides genomic location information for human B2M genes. (B) Phylogenetic tree of B2M genes.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7445429/v1/6a1508a87cc9d14fda43abad.png"},{"id":94141944,"identity":"fd207d79-1bff-4ce0-a32c-a52c068e171c","added_by":"auto","created_at":"2025-10-22 20:11:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1885064,"visible":true,"origin":"","legend":"\u003cp\u003eThe gene expression of B2M. (A) Subcellular localization of B2M (green indicates detected expression, gray indicates undetected expression). (B) Subcellular localization of B2M in A-431 cells from HPA datasets, U-251MG cell line , and U-20S cell line. (C) The specific expression of B2M in RNA single cells. (D) Expression of the B2M gene in various tissues.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7445429/v1/d061a7baa937851639814ae7.png"},{"id":94141499,"identity":"057b6c1d-53c2-40c8-ab2a-57045e708e2f","added_by":"auto","created_at":"2025-10-22 20:03:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2275492,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Specificity expression of B2M in RNA cancers. (B) Distribution of B2M in cancer cells. (C) Representative immunohistochemical micrographs of HPA006361 in cancer samples and normal tissues from the HPA database.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7445429/v1/a137a214a4da899d6b8223df.png"},{"id":94141294,"identity":"0627d4bd-a400-4fb8-9f42-e3e0e8ccd33d","added_by":"auto","created_at":"2025-10-22 19:55:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2228817,"visible":true,"origin":"","legend":"\u003cp\u003eExpression level of the B2M gene in different tumors. (A) The expression status of the B2M gene in different cancers or specific cancer subtypes was analyzed through TIMER2. (B) For the type of CESC, CHOL, DLBC, ESCA, GBM, HNSC, KICH, KIRC, LAML, LGG, PAAD, STAD and TGCT in the TCGA project, the corresponding normal tissues of the GTEx database were included as controls. The box plot data were supplied. (C) Based on the CPTAC dataset, we also analyzed the expression level of B2M total protein between normal tissue and primary tissue of Hepatocellular carcinoma, GBM, PAAD, LUAD, UCEC, HNSC, clear cell RCC, LUSC, breast cancer and OV. ** P \u0026lt; .01;*** P \u0026lt; .001.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7445429/v1/6b702bcdc43f1ad83a458940.png"},{"id":94141946,"identity":"f003cea1-665c-4736-ae90-e20c46c3b85b","added_by":"auto","created_at":"2025-10-22 20:11:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":345125,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation between the expression level of the B2M gene and the pathological stages of tumors.Based on the TCGA data, the expression levels of the B2M gene were analyzed by the main pathological stages (stage I, stage II, stage III, and stage IV) of HNSC, THCA, and SKCN. Log2 (TPM + 1) was applied for logscale.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7445429/v1/df617030e0480729337ac21a.png"},{"id":94141291,"identity":"7d71350f-baac-49db-9a6f-0291b75aee73","added_by":"auto","created_at":"2025-10-22 19:55:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1302288,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between B2M gene expression and survival prognosis of cancers in TCGA. We used the \u003ca href=\"https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/gene-expression-profiling\" title=\"Learn more about GEPIA2 from ScienceDirect's AI-generated Topic Pages\"\u003eGEPIA2\u003c/a\u003e tool to perform \u003ca href=\"https://www.sciencedirect.com/topics/pharmacology-toxicology-and-pharmaceutical-science/overall-survival\" title=\"Learn more about overall survival from ScienceDirect's AI-generated Topic Pages\"\u003eoverall survival\u003c/a\u003e (A) and disease-free survival (B) analyses of different tumors in TCGA by B2M gene expression.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7445429/v1/372c15222343b6cf30218bcf.png"},{"id":94141301,"identity":"4e18b4df-5e91-44f6-95ab-efecd9cfc92d","added_by":"auto","created_at":"2025-10-22 19:55:59","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1062654,"visible":true,"origin":"","legend":"\u003cp\u003eMutation feature of B2M in different tumors of TCGA. We analyzed the mutation features of B2M for the TCGA tumors using the cBioPortal tool. The alteration frequency with mutation type (A) and mutation site (B) is displayed.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7445429/v1/525532c91a82ad576f8a0dd7.png"},{"id":94141505,"identity":"0b6a339f-b612-46ed-bd44-2e0cfeb21046","added_by":"auto","created_at":"2025-10-22 20:03:59","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2011241,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of B2M expression and tumor mutation burden (TMB, A) microsatellite instability (MSI, B), mutant-allele tumor heterogeneity(MATH, C) and neoantigen expression (NEO, D) in pan-cancer tissues.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7445429/v1/e8c2323ceae97f7a16fbd30e.png"},{"id":94141948,"identity":"d1e4ba71-9ef9-4bf3-b203-46d9fc534364","added_by":"auto","created_at":"2025-10-22 20:11:59","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":469858,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of DNA methylation in B2M. Promoter methylation level in B2M between normal and tumor tissues in 11 types of cancer from UALCAN.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7445429/v1/e50c466eb0b7c1ec545c643a.png"},{"id":94141300,"identity":"6fe08612-1c04-4fef-8be5-c9501b6f692f","added_by":"auto","created_at":"2025-10-22 19:55:59","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":3613168,"visible":true,"origin":"","legend":"\u003cp\u003eImmune infiltration analysis data between B2M expression and immune infiltration. The heat maps and scatter plots of immune infiltration analysis data between B2M expression and immune infiltration were displayed. The p-value and cor were supplied.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-7445429/v1/f6a5deadaa69d3f3b4603879.png"},{"id":94141296,"identity":"bf73cd07-bf02-4190-9a47-319eb370552a","added_by":"auto","created_at":"2025-10-22 19:55:59","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":1013926,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship of B2M expression with drug sensitivity. (A)Correlaton between CTRP drug sensitivity and mRNA expression. (B)Correlaton between GDSC drug sensitivity and mRNA expression.\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-7445429/v1/c5a5a34cd290844fbead2e7c.png"},{"id":94141297,"identity":"b693b272-55cc-4b3e-9a4f-3e5e21d1111f","added_by":"auto","created_at":"2025-10-22 19:55:59","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":2110472,"visible":true,"origin":"","legend":"\u003cp\u003eB2M-related gene enrichment analysis. (A) We first obtained the available experimentally determined B2M-binding proteins using the STRING tool. (B) Using the GEPIA2 approach, we also obtained the top 100 B2M-correlated genes in TCGA projects and analyzed the expression correlation between B2M and selected targeting genes, including HLA-B, PSMB8, HLA-A, HCP5, and HLA-DRA. (C) The corresponding heatmap data in the detailed cancer types are displayed.\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-7445429/v1/3cdaed05f9954c6c3e4d6db8.png"},{"id":94141314,"identity":"05a4915c-703b-438d-b549-7c7a26066b52","added_by":"auto","created_at":"2025-10-22 19:55:59","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":1326430,"visible":true,"origin":"","legend":"\u003cp\u003eB2M-related gene enrichment analysis.(A) An intersection analysis of the B2M-binding and correlated genes was conducted. (B) Based on the B2M-binding and interacted genes, KEGG pathway analysis was performed. (C-D) The cnetplot for the molecular function data in \u003ca href=\"https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/gene-ontology\" title=\"Learn more about GO from ScienceDirect's AI-generated Topic Pages\"\u003eGO\u003c/a\u003e analysis is also shown.\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-7445429/v1/4d678c28353d3d3d11eefafe.png"},{"id":94672291,"identity":"15a467a5-f512-4070-9758-67f13311fde1","added_by":"auto","created_at":"2025-10-29 13:40:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":21184253,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7445429/v1/2a483f2e-5eca-4ecd-969a-c0ef6a6998c1.pdf"},{"id":94141285,"identity":"ea696206-ce81-415c-a5db-9db9e46ad0c4","added_by":"auto","created_at":"2025-10-22 19:55:58","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1479326,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary.zip","url":"https://assets-eu.researchsquare.com/files/rs-7445429/v1/c6d9a3ec1a4f2245884a8873.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"β2-Microglobulin is a potential pan-cancer biomarker and immunotherapy target","fulltext":[{"header":"1. Background","content":"\u003cp\u003eCancer is one of the major threats to global health. The reduction in smoking, early detection of certain cancers, and advancements in treatment (including recent developments in targeted and immunotherapy) have improved cancer mortality rates. However, these gains are still threatened by the rising incidence of six out of the top ten cancers [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Therefore, there is an urgent need to discover new biomarkers for cancer diagnosis and prognosis. The clinical application of bioinformatics has the potential to improve the diagnosis, treatment, and prediction of cancer outcomes[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. By utilizing the Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database, it is easier to analyze the correlation and impact of individual genes on cancer prognosis and immune infiltration.\u003c/p\u003e\u003cp\u003eThe \u003cem\u003eβ2-microglobulin\u003c/em\u003e (\u003cem\u003eB2M\u003c/em\u003e) gene is located on chromosome 15. B2M is a non-glycosylated protein with a molecular weight of 11,800 Da, synthesized by all nucleated cells[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. B2M is composed of the light chains of Major Histocompatibility Complex I (MHC-I) molecules and assists in the effective transport of newly synthesized MHC-I chains to the surface of all nucleated cells[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. B2M has been proposed to play a significant role in various diseases. Initially identified as a component present in the urine of patients with renal tubular proteinuria, B2M was considered an alternative marker for presumed intermediate molecular uremic toxins. Plasma B2M levels are elevated in conditions such as lymphoproliferative disorders, chronic renal failure (due to reduced filtration), inflammation, infection, and other conditions characterized by high cell turnover rates[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Smith's study found that the expression of B2M is associated with cognitive impairment in the adult brain, and has therefore been identified as a potential pro-aging factor[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. B2M plays a crucial role in immune surveillance under both physiological and pathological conditions in both tumor and non-tumor cells. It has been reported that B2M is upregulated in gliomas[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Additionally, elevated serum B2M levels have been linked to human immunodeficiency virus infections, acquired immunodeficiency syndrome, rheumatoid arthritis, multiple myeloma, and hematological malignancies[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. B2M mutations have been found in a considerable number of cancer patients. There is growing evidence that B2M gene alterations in tumor cells may reduce the efficacy of T-cell-based immunotherapies by blocking MHC I class-mediated tumor antigen presentation and avoiding T-cell recognition. However, the mechanisms behind tumor cell immune escape remain unclear.\u003c/p\u003e\u003cp\u003eTherefore, we conducted a comprehensive analysis and evaluated the potential value of B2M in cancer diagnosis and prognosis. Our results show that B2M can function as a cancer gene and immune infiltration related biomarker in pan-cancer.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Gene mapping analysis\u003c/h2\u003e\u003cp\u003eWe obtained the genomic location information of B2M through the University of California, Santa Cruz (UCSC) Genome Browser (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://genome.ucsc.edu/\u003c/span\u003e\u003cspan address=\"http://genome.ucsc.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Protein structure analysis\u003c/h2\u003e\u003cp\u003eWe obtained the phylogenetic tree of B2M in multiple species using an online tool at the National Center for Biotechnology Information (NCBI).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Gene expression analysis\u003c/h2\u003e\u003cp\u003eThe Human Proteome Atlas (HPA)(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org/\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a database that integrates various omics methods to map proteins in human organs, tissues, and cells[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. We first logged into the HPA website by entering \"B2M,\" obtaining data on expression across different cells, tissues, and cancers, including all locations of the B2M gene in cancer cells. For more details, please refer to the link \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org/search/B2M\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org/search/B2M\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eImmunohistochemical images of three tumor tissues and corresponding normal tissues were obtained from the HPA database, including head and neck squamous cell carcinoma (HNSC), pancreatic ductal adenocarcinoma (PAAD) and renal cell carcinoma.\u003c/p\u003e\u003cp\u003eUsing the \"Gene_DE\" module of the tumor immune estimation resource (TIMER,version 2) web (\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), we input \"B2M\" to observe the expression differences of B2M in specific tumor subtypes across tumors and adjacent normal tissues or TCGA projects. Additionally, we use the \"Expression Analysis-Box Plot\" module of the Interactive Analysis of Gene Expression (GEPIA, Version 2) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]web server to analyze certain tumors that lack normal tissue or have highly restricted normal tissue, setting p-value Cutoff\u0026thinsp;=\u0026thinsp;1, |Log2FC| Cutoff\u0026thinsp;=\u0026thinsp;1, and \"Match TCGA normal and GTEx data.\"\u003c/p\u003e\u003cp\u003eTo perform protein expression analysis on the Clinical Proteomics Tumor Analysis Consortium (CPTAC) dataset, UALCAN was used to study the expression of B2M protein in tumors and normal tissues[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Here, we explored the total protein levels of B2M in primary tumors and normal tissues by entering \"B2M\".\u003c/p\u003e\u003cp\u003eThe \"Pathologic Staging\" module of GEPIA2 was used to analyze the association between B2M and different pathological stages of cancer (stages I, II, III, and IV). The expression data, converted using log2(TPM\u0026thinsp;+\u0026thinsp;1) for log-scale, were applied to box plots or violin plots.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Survival prognosis analysis\u003c/h2\u003e\u003cp\u003eWe also obtained the survival plots utilizing GEPIA2. Using the \"Survival Map\" module, set Cutoff-high (50%) and and cutoff-low (50%) to distinguish high and low expression cohorts.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Genetic alteration analysis\u003c/h2\u003e\u003cp\u003eCBioPortal [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e](\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cbioportal.org\u003c/span\u003e\u003cspan address=\"http://www.cbioportal.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) provides a platform where we selected \"TCGA Pan-Cancer Atlas Study\" in the \"Quick Selection\" section and entered \"B2M\" to query B2M's genetic alteration characteristics. In the \"Cancer Type Overview\" module, we observed the frequency of changes, mutation types, and copy number variations (CNAs) for all TCGA tumors.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Immune infiltration analysis\u003c/h2\u003e\u003cp\u003eWe use the \"immune gene\" module of TIMER2 to analyze the correlation between the immune infiltration level and the B2M gene expression level. We used the \"immune gene\" module of TIMER2 to analyze the correlation between immune infiltration levels and B2M gene expression levels. By selecting immune cells such as CD8 T cells, T regulatory cells (Tregs), and cancer-associated fibroblasts, we obtained a visual heat map. Immune infiltration was analyzed using TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, XCELL, MCPCOUNTER and EPIC algorithms, with P-values and partial correlation (cor) values derived from the purity-adjusted Spearman rank correlation test. Clicking on cells in the heat map generates scatter plots to show the relationship between estimated infiltration volume and gene expression.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Relationship between B2M and DNA methylation in pan-cancer\u003c/h2\u003e\u003cp\u003eDNA methylation represents the predominant epigenetic modification and exerts a critical influence on tumor progression. In this study, we utilized the UALCAN database to investigate and compare the methylation profiles of B2M in both tumor and normal tissues.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Genomic heterogeneity and gene expression of B2M\u003c/h2\u003e\u003cp\u003eSangerbox 3.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://vip.sangerbox.com/home.html\u003c/span\u003e\u003cspan address=\"http://vip.sangerbox.com/home.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to analyze genomic heterogeneity and gene expression of selected genes. \"B2M\", log2(x\u0026thinsp;+\u0026thinsp;0.001) transformation and Pearson correlation were selected, and Tumor Mutation Burden (TMB), Microsatellite Instability (MSI), Neoantigen Expression (NEO) and Mutant-allele Tumor Heterogeneity (MATH) were selected for correlation analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Drug sensitivity analysis\u003c/h2\u003e\u003cp\u003eGSCALite (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinfo.life.hust.edu.cn/web/GSCALite/\u003c/span\u003e\u003cspan address=\"http://bioinfo.life.hust.edu.cn/web/GSCALite/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is an online platform for analyzing the correlation between gene expression and drug sensitivity[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Enter \"B2M\" to get the analysis data.Blue bubbles represent negative correlations, red bubbles represent positive correlations, and the deeper color, the higher, the correlation. Bubble size is positively correlated with the FDR significance. The black outline border indicates FDR\u0026thinsp;\u0026le;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.10 B2M-related gene enrichment analysis\u003c/h2\u003e\u003cp\u003eWe first searched for the gene of interest \"B2M\" and organism (\"Homo sapiens\") on the STRING tool (\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) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]to screen 50 experimentally validated B2M-binding proteins. Set the following parameters: meaning of network edges as \"evidence\", minimum required interaction score as \"low confidence (0.150),\" active interaction sources as \"experiments\" and max number of interactors to show as \u0026ldquo;no more than 50 interactors.\u0026rdquo; Finally, download 50 proteins and visualize them with Cytoscape (version 3.10.2).\u003c/p\u003e\u003cp\u003eThe first 100 B2M-related genes were obtained from a dataset based on all TCGA tumors and normal tissues using the \"Lookalike Gene Detection\" module of GEPIA2. The \"Correlation Analysis\" module of SHIYONG GEPIA2 performs paired gene Pearson correlation coefficient analysis for B2M and the first 5 related genes. The log2 TPM, correlation coefficient (R), and P-value were adopted and labeled. In addition, the \"exploration-Gene-Corr\" module of TIMER2 was applied to estimate the correlation heat map between B2M and the top 5 genes. Then, the Venn diagram (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinformatics.psb.ugent.be/Webtools/Venn/\u003c/span\u003e\u003cspan address=\"http://bioinformatics.psb.ugent.be/Webtools/Venn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] was used by us to plot the intersection analysis of the B2M-interacting genes and related genes to obtain 8 intersecting genes.\u003c/p\u003e\u003cp\u003eIn addition, we combined two sets of data on B2M interacting genes and related genes, and conducted KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis. The clusterProfiler (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioconductor.org/packages/release/bioc/html/clusterProfiler.html\u003c/span\u003e\u003cspan address=\"http://www.bioconductor.org/packages/release/bioc/html/clusterProfiler.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) R package for gene ontology (GO) enrichment analysis.The results were visualized using the 'tidyr' and 'ggplot2' R packages. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. The R language software version used in this study is [R-4.0.3, 64-bit] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Gene ontology analysis\u003c/h2\u003e\u003cp\u003eThe genome of human B2M (NM_000015.10 ) is on chromosome 15 (q21.1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The evolution of the B2M protein is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Gene expression analysis\u003c/h2\u003e\u003cp\u003eBased on the HPA dataset, we first examined the distribution of B2M in cells. B2M is primarily found in the Golgi apparatus and plasma membrane, and also in the cytoplasm (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B). We then analyzed the expression levels of B2M in various cells and tissues, as shown in the figure: B2M is expressed in all detected normal cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). B2M is also expressed in normal tissues, with the highest expression in the spleen, followed by the lung and small intestine (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD), indicating a lower RNA cell type specificity. However, B2M is expressed in all cancer tissues, with relatively higher expression in the kidney, which contrasts with the brain (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In cancer cells, B2M is negative in testicular cancer but shows varying degrees of cytoplasmic or membrane positivity in other cancers, with lymphoma showing the highest positivity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Additionally, the immunohistochemical results of B2M in HNSC, PAAD, and renal cell carcinoma compared to normal tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) show that the B2M protein levels are significantly higher in these three types of cancer tissues compared to normal tissue (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe utilized the TIMER2 method to analyze the expression status of B2M across various cancer types in The Cancer Genome Atlas (TCGA). As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, the expression levels of B2M were significantly elevated in tumor tissues compared to corresponding control tissues for cholangiocarcinoma (CHOL), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), HNSC, kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), and kidney renal papillary cell carcinoma (KIRP), (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In addition, B2M mRNA expression was low in colon adenocarcinoma (COAD), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), rectum adenocarcinoma (READ), prostate adenocarcinoma (PRAD) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and uterine corpus endometrial carcinoma (UCEC) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eUsing the normal tissues from the GTEx dataset as a control, we examined the expression differences of B2M in tumor tissues and normal tissues across various types of cancer, including Cervical squamous cell carcinoma and endocervical adenocarcinoma(CESC), CHOL, lymphoid neoplasm diffuse large B-cell lymphoma(DLBC), ESCA, GBM, HNSC, KICH, KIRC, acute myeloid leukemia(LAML), brain lower grade glioma(LGG), PAAD, stomach adenocarcinoma(STAD), and testicular germ cell tumors(TGCT) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eResults from the CPTAC dataset showed that B2M total protein was expressed higher in primary tissues of hepatocellular carcinoma, GBM, pancreatic adenocarcinoma(PAAD), UCEC, HNSC, Clear cell RCC(P\u0026thinsp;\u0026lt;\u0026thinsp;.001) and ovarian cancer(OV) than in normal tissues, whereas B2M expression was lower in, LUAD, LUSC and breast cancer than in normal tissues (P\u0026thinsp;\u0026lt;\u0026thinsp;.001). (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC)\u003c/p\u003e\u003cp\u003eFinally, we used the \"Pathological Staging Map\" module of GEPIA2 to observe the correlation between B2M expression and cancer pathological staging, including HNSC, Thyroid carcinoma(THCA) and SKCN (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, all P\u0026thinsp;\u0026lt;\u0026thinsp;.05), but not other.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Survival analysis data\u003c/h2\u003e\u003cp\u003eIn addition, to investigate the correlation between B2M expression and prognosis in different tumor patients, we divided them into high-expressing and low-expressing groups based on their B2M expression levels. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the overall survival (OS) analysis data indicate that poor prognosis in KIRC (P\u0026thinsp;=\u0026thinsp;0.043), Sarcoma(SARC) (P\u0026thinsp;=\u0026thinsp;0.009), and Skin Cutaneous Melanoma(SKCM) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) is associated with low B2M expression. The disease-free survival (DFS) data show that low B2M expression is linked to poor prognosis in Mesothelioma(MESO) (P\u0026thinsp;=\u0026thinsp;0.019) and PRAD (P\u0026thinsp;=\u0026thinsp;0.035) cases. In addition, high expression of the B2M gene was highly correlated with poor prognosis of overall survival in LGG (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Uveal Melanoma(UVM) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and poor prognosis of disease-free survival in LGG (P\u0026thinsp;=\u0026thinsp;0.032) and UVM (P\u0026thinsp;\u0026lt;\u0026thinsp;0.013).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Genetic alteration analysis data\u003c/h2\u003e\u003cp\u003eThe cBioportal database can display the gene change status of B2M in different tumor samples in the TCGA queue, mainly in the form of mutations, structural variations, amplification, deep deletions, and multiple changes in different tumors. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, the maximum frequency of B2M changes (\u0026gt;\u0026thinsp;20%) in DLBC patients occurs when \"mutations\" (green) are the main type. Copy number deletions of the B2M gene were observed in all cases of CHOL with genetic alterations. In addition, in LAML、LGG、KICH、PAAD、Pheochromocytoma and Paraganglioma、TGCT、Thymoma、THCA and UVM. No genetic changes were found in B2M. The types, locations, quantities, and mutation frequencies of B2M gene changes are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB.The primary genetic change in the B2M gene is truncating mutations, with the highest frequency being the L15Ffs*41 truncating mutation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn addition, we analyzed the correlation between B2M expression and TMB /MSI across all tumors in TCGA. We calculated their Pearson correlation coefficients in each tumor type and observed significant correlations in 5 tumor types. Specifically, there were significant positive correlations in 3 tumor types: COAD (P\u0026thinsp;=\u0026thinsp;0.012), READ (P\u0026thinsp;=\u0026thinsp;0.008), and Breast invasive carcinoma(BRCA) (P\u0026thinsp;=\u0026thinsp;0.01). There were also significant negative correlations in 2 tumor types: CESC (P\u0026thinsp;=\u0026thinsp;0.038) and LUAD (P\u0026thinsp;=\u0026thinsp;0.0003)(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA and supplemental tabl\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003ee1\u003c/span\u003e). For MSI, we observed significant correlations in 12 tumor types. Specifically, there were significant positive correlations in 2 tumor types: COAD (P\u0026thinsp;=\u0026thinsp;0.004) and READ (P\u0026thinsp;=\u0026thinsp;0.002). There were also significant negative correlations in 10 tumor types, including GBM, LGG (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), LUAD (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Pan-kidney cohort (KICH\u0026thinsp;+\u0026thinsp;KIRC\u0026thinsp;+\u0026thinsp;KIRP)(KIPAN) (P\u0026thinsp;=\u0026thinsp;0.003), PRAD (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), HNSC (P\u0026thinsp;=\u0026thinsp;0.042), LUSC (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), MESO (P\u0026thinsp;=\u0026thinsp;0.033), OV (P\u0026thinsp;=\u0026thinsp;0.009), TGCT (P\u0026thinsp;=\u0026thinsp;0.002), and Adrenocortical carcinoma(ACC) (P\u0026thinsp;=\u0026thinsp;0.036)(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB and supplemental table2). In summary, B2M expression positively correlates with TMB and MSI in COAD and READ, indicating that these two tumor types may benefit from immunotherapy.\u003c/p\u003e\u003cp\u003eSimilarly, we analyzed the correlation between MATH and NEO with B2M expression. In MATH, a significant correlation was observed in 12 tumors, notably exhibiting a significant negative correlation in 12 types of tumors including GBM (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), LGG (P\u0026thinsp;=\u0026thinsp;0.004), LUAD (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Stomach and Esophageal carcinoma(STES) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), STAD (PP\u0026thinsp;\u0026lt;\u0026thinsp;0.001), HNSC (P\u0026thinsp;=\u0026thinsp;0.017), KIRC (P\u0026thinsp;=\u0026thinsp;0.032), Liver hepatocellular carcinoma(LIHC) (P\u0026thinsp;=\u0026thinsp;0.015), PAAD (P\u0026thinsp;=\u0026thinsp;0.010), TGCT (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), SKCM (P\u0026thinsp;=\u0026thinsp;0.040), and Bladder Urothelial Carcinoma(BLCA) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC and supplemental table3). In NEO, a significant correlation was observed in 4 tumors, specifically exhibiting a significant positive correlation in COAD (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), READ (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), SARC (P\u0026thinsp;=\u0026thinsp;0.003), and a significant negative correlation in GBM (P\u0026thinsp;=\u0026thinsp;0.022)(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD and supplemental table4).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e3.5 DNA methylation analysis data\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eWe assessed the DNA methylation levels of the B2M promoter in normal tissues and eleven types of cancer tissues. According to the UALCAN database, the B2M promoter exhibited significantly higher methylation levels in PRAD, LUSC, and BRCA compared to normal tissues. Conversely, the B2M promoter was found to be hypomethylated in LUAD, KIRP, KIRC, HNSC, COAD, LIHC, UCEC, and TGCT. (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Immune infiltration analysis data\u003c/h2\u003e\u003cp\u003eIn this study, we used all or most of the algorithms to investigate the potential relationship between the estimated immune infiltration levels and B2M expression levels in different cancer types from the TCGA dataset, presenting these relationships in the form of heatmaps and scatter plots. After a series of analyses, we observed a statistically positive correlation between B2M expression and immune infiltration of CD8\u0026thinsp;+\u0026thinsp;T cells in BRCA, KIRC, SKCM, SKCM-Metastasis, and STAD tumors. We also noted that B2M expression was positively correlated with regulatory T cell infiltration in BRCA, BRCA-LumA, SARC, and THCA tumors. Additionally, we found that B2M expression was positively correlated with the estimated infiltration values of cancer-associated fibroblasts in LGG, LUSC, and TGCT tumors but observed a negative correlation in OV. The scatter plot data for the aforementioned tumors generated using one algorithm is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. These data indicate that B2M is an oncogene in TCGA tumors, and its overexpression may help inhibit tumor progression.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Drug sensitivity analysis\u003c/h2\u003e\u003cp\u003eThe CTPR data set showed that the three drugs with positive correlation with B2M expression were austocystin D, niclosamide and SB\u0026thinsp;\u0026minus;\u0026thinsp;525334. The top three drugs negatively associated with B2M expression were AZD7762, clofarabine, and chlorambucil (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA and supplemental table5, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e\u003cp\u003eAccording to GDSC drug sensitivity results, the top three drugs negatively correlated with B2M expression were Z-LLNle-CHO, SNX-2112, and PIK-93 (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB and supplemental table 6, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). These results provide an important reference for drug screening and the formulation of personalized treatment.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.8 Enrichment analysis of B2M-related partners\u003c/h2\u003e\u003cp\u003eTo further investigate the molecular mechanisms of B2M target genes in tumor development, we attempted to screen for proteins that bind to B2M and conducted pathway enrichment analysis. First, using the STRING tool, we obtained 50 B2M-binding proteins supported by experimental evidence, and the protein interaction network illustrated these proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eA). Then, through the GEPIA2 tool, we integrated all tumor expression data from TCGA, ultimately identifying 100 genes most strongly associated with B2M expression. The expression levels of B2M were positively correlated with the expression levels of HLA-B (R\u0026thinsp;=\u0026thinsp;0.72), HCP5 (R\u0026thinsp;=\u0026thinsp;0.65), HLA-DRA (R\u0026thinsp;=\u0026thinsp;0.64), HLA-A (R\u0026thinsp;=\u0026thinsp;0.63), and PSMB8 (R\u0026thinsp;=\u0026thinsp;0.61) (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eB). Correlation heat map data also showed a positive correlation between B2M and the above five genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eHowever, the mechanisms and patterns of B2M gene function in tumors are not yet clear, thus further research on B2M-targeted binding proteins and B2M-related genes is needed. By conducting intersection analysis between the two daftasets, we identified eight genes: HLA-F, HLA-C, HLA-B, HLA-A, MR1, TAPBP, TAPBPL, and HLA-E (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eA). Finally, the data of B2M targeted binding protein and related genes were integrated, and KEGG pathway and GO enrichment analysis were performed. The KEGG analysis revealed that JAK-STAT signaling pathway and NOD-like receptor signaling pathway may be associated with the impact of B2M on tumor pathogenesis(Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eB). Additionally, GO data indicated that B2M expression plays a role in tumors by regulating immune receptor activity and MHC protein complex binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eC-D).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eB2M, a small molecule globulin, serves as the β-chain (light chain) of human lymphocyte antigen (HLA) on cell surfaces and plays multiple critical roles in the body. To elucidate the mechanisms of B2M's role in cancer from clinical data, we conducted a pan-cancer analysis using the TCGA and CPTAC databases for the first time. Our phylogenetic tree reveals the structural and functional conservation of B2M, along with its abnormal expression in various disease states, making it a crucial target for disease diagnosis, prognosis assessment, and drug therapy. B2M has been extensively studied in autoimmune diseases, cancer, infections, kidney diseases, peripheral artery diseases, and cardiovascular diseases [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In our work, we conducted a comprehensive study of B2M genes in various tumors, focusing on aspects such as gene expression, survival analysis, genetic changes, DNA methylation, protein phosphorylation, and the correlation with B2M target genes.\u003c/p\u003e\u003cp\u003eBy downloading the pan-cancer expression profile data from the TCGA database, TCGA results show that B2M is overexpressed in various cancer tissues, including CHOL, ESCA, GBM, HNSC, KICH, KIRC, and KIRP, compared to normal tissues[\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Conversely, its expression is reduced in COAD, LUAD, LUSC, READ, PRAD, and UCEC cancer tissues. Previous experimental studies have also confirmed these findings[\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, Tsoneva DK et al.'s study did not find any changes in B2M in CHOL[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Additionally, there is a lack of experimental data for KICH and KIRP, and the expression of READ shows a trend opposite to the experimental data, indicating the need for further clinical data collection and analysis.\u003c/p\u003e\u003cp\u003eFurthermore, we found that B2M is closely associated with the staging of HNSC, THCA, and SKCN[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The survival prognosis analysis revealed a correlation between high B2M expression and poorer outcomes in LGG and UVM. This finding aligns with previous experimental studies. These observations suggest that B2M plays different roles in the development of various cancers. For instance, promoting B2M overexpression could serve as a means to inhibit tumor progression, potentially enabling personalized treatment for patients in clinical settings. Additionally, for tumors with varying B2M gene expression at different pathological stages, gene-targeted therapy can be initiated early in the disease, or personalized treatment can be tailored based on the disease's pathological stage. Overall, our findings provide valuable insights for clinical gene therapy.\u003c/p\u003e\u003cp\u003eIn the process of biological evolution, gene mutations are one of the most significant factors to consider[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The development and progression of tumors are driven by accumulated mutations and epigenetic changes[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Among these, B2M gene mutations play a crucial role in various types of cancer, including colon cancer, DLBC, melanoma, and glioma[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study found that B2M mutations occur mainly in diffuse large B-cell lymphoma (DLBCL), which aligns with previous experimental and clinical data on DLBC[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Among the various B2M mutations, the single most frequent mutation is the truncated L15Ffs*41 mutation, which provides valuable insights for the study of B2M mutations.\u003c/p\u003e\u003cp\u003eNumerous studies have confirmed the close link between DNA methylation and tumor development. Some drugs that inhibit DNA methylation have been approved by the FDA for treating certain human cancers[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The expression of B2M is also influenced by epigenetic mechanisms, including [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]DNA methylation. In this study, we observed a potential correlation between reduced DNA methylation in non-promoter regions and low B2M expression, suggesting that more evidence is needed to explore the potential role of B2M DNA methylation in the development of TGCT tumors.\u003c/p\u003e\u003cp\u003eImmunotherapy has emerged as a powerful force in cancer treatment, achieving significant breakthroughs[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Immune cells form the cellular foundation of immunotherapy. In highly immunologically infiltrated tumors, tumor-infiltrating leukocytes (TIL) can account for over 40%[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. B2M is the light chain (β-chain) of the MHC-I complex, which plays a crucial role in the restricted presentation of tumor antigens by MHC-I[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The MHC-I molecule complex can be recognized by CD8\u0026thinsp;+\u0026thinsp;T cells that express specific TCRs. The signal from TCRs recognizing antigens is transmitted to CD8\u0026thinsp;+\u0026thinsp;T cells, triggering cytotoxic[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Regulatory T cells (Tregs), tumor-associated macrophages, and tumor-associated fibroblasts contribute to the tolerance and functional inhibition of tumor-related antigens, which are also part of the mechanisms of tumor immune escape[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Our research confirms this, showing that B2M plays a specific role in regulating the tumor immune microenvironment. Abnormal expression of B2M can alter the tumor immune microenvironment. B2M protein expression is associated with immune infiltration and tumor regulation, but it exhibits differential regulatory effects across different types of tumors. Previous studies have shown that mutations and deletions in B2M are linked to immune therapy resistance[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], while upregulation of B2M could offer a new therapeutic strategy to overcome resistance [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Natasja et al.'s research indicates that the expression of PD-1, activation, proliferation, and cytotoxicity genes in γδ1 and γδ3 T cells is significantly elevated in tumors lacking B2M[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Furthermore, in the absence of B2M, the activation of CD4\u0026thinsp;+\u0026thinsp;T cells and NK cells can overcome resistance and exert anti-tumor effects. Recent studies have shown that inhibiting autophagy can restore B2M/MHC-I expression and improve antigen presentation, thereby enhancing the anti-tumor T cell response[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In summary, our findings offer new insights into tumor immunotherapy, suggesting that regulating B2M expression and immune infiltration could lead to synergistic therapeutic outcomes. Considering these results, we believe that B2M may be a potential target for immunotherapy, although this conclusion requires further experimental validation.\u003c/p\u003e\u003cp\u003eResearch indicates that B2M can influence patients' sensitivity to immunotherapy, potentially making it a promising therapeutic target for these tumors. In various cancers, the expression levels of B2M are associated with the tumor burden (TMB), microsatellite instability (MSI), mutant-allele tumor heterogeneity (MATH), and neoantigen load (NEO). Therefore, B2M could serve as a potential biomarker for the immune therapy response in patients across different cancer types. We also investigated the sensitivity of B2M-related drugs. The drug sensitivity analysis revealed that B2M is positively correlated with austocystin D, niclosamide, and SB\u0026thinsp;\u0026minus;\u0026thinsp;525334, all of which have been confirmed to exhibit potential anti-tumor effects[\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. This finding has clinical implications, but further research is needed to elucidate the underlying molecular mechanisms.\u003c/p\u003e\u003cp\u003eFurthermore, we identified proteins interacting with B2M and associated genes, and conducted GO and KEGG enrichment analyses. The results revealed the potential functions of the 'JAK-STAT signaling pathway' and the 'NOD-like receptor signaling pathway' in cancer development. Current evidence indicates that genes highly associated with B2M are positively correlated with the occurrence of various tumors. In our study, eight genes highly associated with B2M were found to promote the development of multiple tumors[\u003cspan additionalcitationids=\"CR48 CR49 CR50\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. This finding aligns with previous research findings.\u003c/p\u003e"},{"header":"5. Limitations","content":"\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eAlthough our research has extensively explored the potential of B2M as a prognostic biomarker in cancer, we must acknowledge that there are still some limitations. These limitations also point to directions for future research. First, the various analyses we conducted using retrospective data from public databases (like TCGA, CPTAC, GTEx) have resulted in our limitations. For example, studies have found significant correlations between B2M expression and various clinical features, but this does not directly prove that B2M is the direct cause of these phenotypes. Secondly, our study lacks relevant in vivo models or in vitro cell experiments to further confirm the function of B2M, which is also one of our core limitations. For example, it needs to be clarified through functional experiments whether the high expression of B2M in certain cancers plays a pro-cancer or anti-cancer role. Moreover, the research focus has been on the correlation between B2M and immune cell infiltration, but we did not validate this using data from patient cohorts receiving immune checkpoint inhibitor therapy. The tumor microenvironment is a heterogeneous and dynamic system that requires deeper exploration into how B2M specifically affects the functional status of different immune cell subsets, which depends on extensive clinical experimental evidence to follow. Finally, enrichment analysis suggests that B2M may play a role through pathways such as JAK-STAT, but the specific upstream regulatory mechanisms and downstream signaling pathways have not been elucidated through experiments.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e"},{"header":"6. conclusion","content":"\u003cp\u003eIn conclusion, our study shows that B2M expression is statistically correlated with clinical prognosis, DNA methylation, immune cell infiltration and gene enrichment in a variety of tumors, which helps to understand the role of B2M in tumor development from the perspective of clinical tumor samples. However, its conclusions still need to be further confirmed and expanded through prospective clinical studies and in-depth experimental biological function verification, to ultimately assess its true potential as a cancer biomarker or therapeutic target.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eACC \u0026nbsp;\u0026nbsp;Adrenocortical carcinoma\u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBLCA\u0026nbsp; Bladder Urothelial Carcinoma\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBRCA \u0026nbsp; Breast invasive carcinoma\u003c/p\u003e\n\u003cp\u003eCESC \u0026nbsp;Cervical squamous cell carcinoma and endocervical adenocarcinoma\u003c/p\u003e\n\u003cp\u003eCHOL \u0026nbsp;Cholangiocarcinoma\u003c/p\u003e\n\u003cp\u003eCOAD \u0026nbsp;Colon adenocarcinoma\u003c/p\u003e\n\u003cp\u003eDLBC \u0026nbsp;Lymphoid Neoplasm Diffuse Large B-cell Lymphoma\u003c/p\u003e\n\u003cp\u003eESCA \u0026nbsp;Esophageal carcinoma\u003c/p\u003e\n\u003cp\u003eGBM \u0026nbsp;Glioblastoma multiforme\u003c/p\u003e\n\u003cp\u003eHNSC \u0026nbsp;Head and Neck squamous cell carcinoma\u003c/p\u003e\n\u003cp\u003eKICH \u0026nbsp;Kidney Chromophobe\u003c/p\u003e\n\u003cp\u003eKIRC \u0026nbsp;Kidney renal clear cell carcinoma\u003c/p\u003e\n\u003cp\u003eKIRP \u0026nbsp;Kidney renal papillary cell carcinoma\u003c/p\u003e\n\u003cp\u003eLAML \u0026nbsp;Acute Myeloid Leukemia\u003c/p\u003e\n\u003cp\u003eLGG \u0026nbsp;Brain Lower Grade Glioma\u003c/p\u003e\n\u003cp\u003eLIHC \u0026nbsp;Liver hepatocellular carcinoma\u003c/p\u003e\n\u003cp\u003eLUAD \u0026nbsp;Lung adenocarcinoma\u003c/p\u003e\n\u003cp\u003eLUSC \u0026nbsp;Lung squamous cell carcinoma\u003c/p\u003e\n\u003cp\u003eMESO \u0026nbsp;Mesothelioma\u003c/p\u003e\n\u003cp\u003eOV \u0026nbsp;Ovarian serous cystadenocarcinoma\u003c/p\u003e\n\u003cp\u003ePAAD \u0026nbsp;Pancreatic adenocarcinoma\u003c/p\u003e\n\u003cp\u003ePCPG \u0026nbsp;Pheochromocytoma and Paraganglioma\u003c/p\u003e\n\u003cp\u003ePRAD \u0026nbsp;Prostate adenocarcinoma\u003c/p\u003e\n\u003cp\u003eREAD \u0026nbsp;Rectum adenocarcinoma\u003c/p\u003e\n\u003cp\u003eSARC \u0026nbsp; Sarcoma\u003c/p\u003e\n\u003cp\u003eSTAD \u0026nbsp;Stomach adenocarcinoma\u003c/p\u003e\n\u003cp\u003eSKCM \u0026nbsp;Skin Cutaneous Melanoma\u003c/p\u003e\n\u003cp\u003eTGCT \u0026nbsp; Testicular Germ Cell Tumors\u003c/p\u003e\n\u003cp\u003eTHCA \u0026nbsp;Thyroid carcinoma\u003c/p\u003e\n\u003cp\u003eTHYM \u0026nbsp;Thymoma\u003c/p\u003e\n\u003cp\u003eUCEC \u0026nbsp;Uterine Corpus Endometrial Carcinoma\u003c/p\u003e\n\u003cp\u003eUCS \u0026nbsp;Uterine Carcinosarcoma\u003c/p\u003e\n\u003cp\u003eUVM \u0026nbsp;Uveal Melanoma\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003e\u003cb\u003eAuthor contributions\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eSSJ contributed equally to this work. ZX and SSJ designed the search; ZX wrote the main manuscript text. Saeed, LTT, LKY, DSQ and CCZ gave me comprehensive writing advice. ZX, SSJ, SYF, XY and ZZL performed statistical analysis; WJD and WGL primary responsibility for final content.All authors read and approved the final manuscript.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003cp\u003eAll data used in this study are from publicly available databases, and no additional ethical approval and informed consent are required.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to publish:\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e\u003cp\u003eThe authors declare that the research was conducted without any commercial or financial relationships that could potentially create a conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eNo funding was received.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSSJ contributed equally to this work. ZX and SSJ designed the search; ZX wrote the main manuscript text. Saeed, LTT , LKY , DSQ and CCZ gave me comprehensive writing advice. ZX, SSJ, SYF, XY and ZZL performed statistical analysis; WJD and WGL primary responsibility for final content.All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to thank all members of our laboratory for their work. Saeed EI-Ashram , supported by Project number (RSP2024R99), King Saud University, Riyadh, Saudi Arabia, for their support for this project.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the supplementary information files: Supplementary Table 1 (Correlation between B2M expression and tumor mutational burden), Supplementary Table 2 (Correlation between B2M expression and microsatellite instability), Supplementary Table 3 (Correlation between B2M expression and Mutant-allele Tumor Heterogeneity), Supplementary Table 4 (Correlation between B2M expression and Neoantigen Expression), Supplementary Table 5 (Correlation between B2M and CTPR Drug sensitivity analysis) and Supplementary Table 6 (Correlation between B2M and GDSC Drug sensitivity analysis).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024;74(1):12\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDai F, et al. Identification of candidate biomarkers correlated with the diagnosis and prognosis of cervical cancer via integrated bioinformatics analysis. Onco Targets Ther. 2019;12:4517\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePloegh HL, Orr HT, Strominger JL. Major histocompatibility antigens: the human (HLA-A, -B, -C) and murine (H-2K, H-2D) class I molecules. Cell. 1981;24(2):287\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEichner T, Radford SE. Understanding the complex mechanisms of beta2-microglobulin amyloid assembly. FEBS J. 2011;278(20):3868\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCorlin DB, Heegaard NH. beta(2)-microglobulin amyloidosis. Subcell Biochem. 2012;65:517\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmith LK, et al. beta2-microglobulin is a systemic pro-aging factor that impairs cognitive function and neurogenesis. Nat Med. 2015;21(8):932\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang H, et al. B2M overexpression correlates with malignancy and immune signatures in human gliomas. Sci Rep. 2021;11(1):5045.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaumann T, et al. Lymphocyte doubling time in chronic lymphocytic leukemia modern era: a real-life study in 848 unselected patients. Leukemia. 2021;35(8):2325\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKent WJ, et al. The human genome browser at UCSC. Genome Res. 2002;12(6):996\u0026ndash;1006.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUhlen M, et al. Proteomics. Tissue-based map of the human proteome. Science. 2015;347(6220):1260419.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTang Z, et al. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 2019;47(W1):W556\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChandrashekar DS, et al. An update to the integrated cancer data analysis platform. Neoplasia. 2022;25:18\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCerami E, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu CJ, et al. GSCALite: a web server for gene set cancer analysis. Bioinformatics. 2018;34(21):3771\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSzklarczyk D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47(D1):D607\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBardou P, et al. jvenn: an interactive Venn diagram viewer. BMC Bioinformatics. 2014;15(1):293.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang J, et al. Mesenchymal Stromal Cells-Derived beta2-Microglobulin Promotes Epithelial-Mesenchymal Transition of Esophageal Squamous Cell Carcinoma Cells. Sci Rep. 2018;8(1):5422.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi D, et al. beta2-Microglobulin Maintains Glioblastoma Stem Cells and Induces M2-like Polarization of Tumor-Associated Macrophages. Cancer Res. 2022;82(18):3321\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGavrielatou N, et al. Digital Spatial Profiling Links Beta-2-microglobulin Expression with Immune Checkpoint Blockade Outcomes in Head and Neck Squamous Cell Carcinoma. Cancer Res Commun. 2023;3(4):558\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdachi Y, et al. Inhibition of FGFR Reactivates IFNgamma Signaling in Tumor Cells to Enhance the Combined Antitumor Activity of Lenvatinib with Anti-PD-1 Antibodies. Cancer Res. 2022;82(2):292\u0026ndash;306.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin H et al. Analysis of the B2M Expression in Colon Adenocarcinoma and Its Correlation with Patient Prognosis. Evid Based Complement Alternat Med, 2022. 2022: p. 7264503.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao Y, et al. B2M gene expression shapes the immune landscape of lung adenocarcinoma and determines the response to immunotherapy. Immunology. 2021;164(3):507\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee SH, et al. Beta-2-microglobulin positive tumor cells and CD8 positive lymphocytes are associated with outcome in post-neoadjuvant colorectal cancer resections. Hum Pathol. 2025;155:105737.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu AY. The opposing action of stromal cell proenkephalin and stem cell transcription factors in prostate cancer differentiation. BMC Cancer. 2021;21(1):1335.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTsoneva DK et al. Downregulation of Aging-Associated Gene SUCLG1 Marks the Aggressiveness of Liver Disease. Cancers (Basel), 2025. 17(3).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFurdova A, Olah Z. [Use of oncologic markers in ophthalmic oncology]. Cesk Slov Oftalmol. 1998;54(5):294\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSvensson EI, Berger D. The Role of Mutation Bias in Adaptive Evolution. Trends Ecol Evol. 2019;34(5):422\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang MW, et al. DNA methylation in the tumor microenvironment. J Zhejiang Univ Sci B. 2017;18(5):365\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYeon YS, et al. Immune checkpoint blockade resistance-related B2M hotspot mutations in microsatellite-unstable colorectal carcinoma. Pathol Res Pract. 2019;215(1):209\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang C, et al. B2M and JAK1/2-mutated MSI-H Colorectal Carcinomas Can Benefit From Anti-PD-1 Therapy. J Immunother. 2022;45(4):187\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHe M, et al. B2M mutation paves the way for immune tolerance in pathogenesis of Epstein-Barr virus positive diffuse large B-cell lymphomas. J Cancer. 2022;13(15):3615\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZaretsky JM, et al. Mutations Associated with Acquired Resistance to PD-1 Blockade in Melanoma. N Engl J Med. 2016;375(9):819\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ede Miranda NF, et al. Exome sequencing reveals novel mutation targets in diffuse large B-cell lymphomas derived from Chinese patients. Blood. 2014;124(16):2544\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHan X, et al. The role of B2M in cancer immunotherapy resistance: function, resistance mechanism, and reversal strategies. Front Immunol. 2025;16:1512509.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDuan Q, et al. Turning Cold into Hot: Firing up the Tumor Microenvironment. Trends Cancer. 2020;6(7):605\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMuntjewerff EM, Meesters LD, van den Bogaart G. Antigen Cross-Presentation Macrophages Front Immunol. 2020;11:1276.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRui R, Zhou L, He S. Cancer immunotherapies: advances and bottlenecks. Front Immunol. 2023;14:1212476.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKypraios A et al. Identifying Candidate Gene Drivers Associated with Relapse in Pediatric T-Cell Acute Lymphoblastic Leukemia Using a Gene Co-Expression Network Approach. Cancers (Basel), 2024. 16(9).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang J, et al. Single-Cell Transcriptome Identifies Drug-Resistance Signature and Immunosuppressive Microenvironment in Metastatic Small Cell Lung Cancer. Adv Genet (Hoboken). 2022;3(2):2100060.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMartinez-Morilla S, et al. Biomarker Discovery in Patients with Immunotherapy-Treated Melanoma with Imaging Mass Cytometry. Clin Cancer Res. 2021;27(7):1987\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBortolomeazzi M, et al. Immunogenomics of Colorectal Cancer Response to Checkpoint Blockade: Analysis of the KEYNOTE 177 Trial and Validation Cohorts. Gastroenterology. 2021;161(4):1179\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ede Vries NL, et al. gammadelta T cells are effectors of immunotherapy in cancers with HLA class I defects. Nature. 2023;613(7945):743\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYamamoto K, et al. Selective autophagy of MHC-I promotes immune evasion of pancreatic cancer. Autophagy. 2020;16(8):1524\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKojima Y, et al. Cytochrome P450 2J2 is required for the natural compound austocystin D to elicit cancer cell toxicity. Cancer Sci. 2024;115(9):3054\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang H, Li AM, Ye J. The magic bullet: Niclosamide. Front Oncol. 2022;12:1004978.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Y, et al. Simultaneous blockage of contextual TGF-beta by cyto-pharmaceuticals to suppress breast cancer metastasis. J Control Release. 2021;336:40\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMichelakos T et al. Differential role of HLA-A and HLA-B, C expression levels as prognostic markers in colon and rectal cancer. J Immunother Cancer, 2022. 10(3).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYan J, et al. Cells Promote Tumor Initiation, Growth, and Metastases via Tumor MR1. Cancer Discov. 2020;10(1):124\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun J, et al. Exploring the cross-cancer effect of circulating proteins and discovering potential intervention targets for 13 site-specific cancers. J Natl Cancer Inst. 2024;116(4):565\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin Y, et al. Identification of TAPBPL as a novel negative regulator of T-cell function. EMBO Mol Med. 2021;13(5):e13404.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHrbac T, et al. Are Overexpressed in Glioblastoma and HLA-E Increased After Exposure to Ionizing Radiation. Cancer Genomics Proteom. 2022;19(2):151\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"β2-microglobulin, pan-cancer, immune infiltration, prognostic biomarker, tumor microenvironment","lastPublishedDoi":"10.21203/rs.3.rs-7445429/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7445429/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground : A important component of MHC-I-mediated antigen presentation, β2-Microglobulin (B2M), has a rising importance in carcinogenesis and immunological control. Although implicated in many malignancies, its pan-cancer prognostic value and therapeutic prospects are unknown. \u003cbr\u003e\nMethods : Multiple-omics analysis was performed on TCGA, CPTAC, and GEO datasets. Tools included TIMER2 (immune infiltration), GEPIA2 (survival/pathological staging), cBioPortal (genomic changes), UALCAN (methylation), and STRING (protein interactions). Drug sensitivity correlations were evaluated using GSCALite and CTRP.\u003cbr\u003e\nKey Results \u003cbr\u003e\nExpression characteristics: B2M is highly expressed in CHOL, GBM, and KIRC, while it is lowly expressed in COAD and LUAD (P\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003ePrognostic value: High B2M expression is significantly associated with poor prognosis in LGG and UVM (OS: P\u0026lt;0.001), but is associated with better survival in KIRC (P=0.043).\u003c/p\u003e\n\u003cp\u003eGenetic variation: B2M has the highest mutation frequency (\u0026gt;20%) in DLBC, mainly consisting of L15Ffs * 41 truncated mutations.\u003c/p\u003e\n\u003cp\u003eImmune regulation: B2M expression is positively correlated with CD8+T cell and Tregs infiltration, and significantly correlated with TMB and MSI .\u003c/p\u003e\n\u003cp\u003eFunctional pathway: B2M regulates tumor immunity through JAK-STAT and NOD like receptor signaling pathways, and its interacting genes (HLA-A/B/C, etc.) are significantly enriched in MHC-I complex function (P\u0026lt;0.05).\u003cbr\u003e\nConclusion : As a pan cancer biomarker, the expression level of B2M is closely related to prognosis, immune microenvironment and treatment response, and may become a new target of immunotherapy. This study provides a theoretical basis for the individualized treatment of cancer, but further experiments are needed to verify its mechanism.\u003c/p\u003e","manuscriptTitle":"β2-Microglobulin is a potential pan-cancer biomarker and immunotherapy target","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-22 19:55:54","doi":"10.21203/rs.3.rs-7445429/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fae040e1-c4dd-4d40-98fb-38e3934cdee2","owner":[],"postedDate":"October 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-24T09:23:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-22 19:55:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7445429","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7445429","identity":"rs-7445429","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}