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. 2024 Dec 13;19(12):e0314609. doi: 10.1371/journal.pone.0314609

Pan-cancer analysis of B3GNT5 with potential implications for cancer immunotherapy and cancer stem cell stemness

Feng Peng 1, Yechen Feng 1, Shuo Yu 1, Ruizhi He 1, Hebin Wang 1, Yu Xie 1,*, Renyi Qin 1,*
Editor: Zhiwen Luo2
PMCID: PMC11642946  PMID: 39671359

Abstract

B3GNT5, a critical member of the β-1,3-N-acetylglucosaminyl transferase gene family involved in lactose and glycosphingolipids biosynthesis, has been documented to promote tumor-infiltrating T-cell responses. Our research utilized the Pan-Cancer dataset from The Cancer Genome Atlas (TCGA) to explore the functional role of B3GNT5. Our study demonstrated that the antibody-driven inhibition of B3GNT5 diminished T cell-mediated anti-tumor responses in both in vitro and in vivo settings. By analyzing RNA-seq data from Genotype-Tissue Expression (GTEx) and TCGA databases, we observed differential expression levels of B3GNT5 across various tumor types accompanied by an unfavorable prognostic correlation. We further utilized integrated clinical survival data from TCGA and immune cell infiltration scoring patterns to identify significant associations between B3GNT5 expression and immune checkpoints, cancer stemness, chemokines, chemokine receptors, and immune-activating genes. B3GNT5’s expression was highly correlated with different immunoregulatory factors, including T cell infiltration, chemokine receptors, and activation genes. Subsequent experiments discovered that suppressing B3GNT5 expression in pancreatic adenocarcinoma cells significantly reduced their tumorigenicity by limiting sphere-forming ability and self-renewal capacity, thus underscoring B3GNT5’s vital role as a prognostic factor in immune regulation across pan-cancer. Our findings suggest that B3GNT5 presents a viable target for cancer immunotherapy by enabling effective communication between cancer stem cells and immune cells during tumor treatment.

Introduction

Cancer is a major contributor to mortality rates in developed and developing countries, and the accompanying clinical load is estimated to rise in tandem with population growth and aging demographics. This phenomenon is particularly pronounced in underdeveloped nations, where approximately 82% of the worldwide populace makes its home [1]. Despite the continued progression and evolution of medical technologies, encompassing surgical procedures, radiotherapy, chemotherapy, as well as immunotherapies, the clinical outcomes for patients in advanced stages of cancer remain unfulfilling. This is particularly true when examining the detrimental effects that some treatments may have on these individuals [2]. Therefore, the prompt detection of molecular targets is imperative to augment the therapeutic potency and specificity. This can be effectively achieved via Pan-cancer Analysis [3].

Glycosphingolipids (GSLs) are a vast group of glycoconjugates that occur in cellular membranes. GSLs carry out distinctive functions within the cell membrane, owing to the individual core structures they possess. Compared to alternative membrane lipids, GSLs exhibit significant molecular intricacy [4]. It plays a vital role in cellular adhesion, migration, modulation of signaling proteins, and engagement with pathogens and toxins [5, 6]. B3GNT5, a member of the β-1,3-N-acetylglucosaminyl transferase family, catalyzes the transfer of N-acetylglucosamine (GlcNAc) from UDP-GlcNAc to galactose positioned at the non-reducing end of the carbohydrate chain through β-1,3-linkage. This enzyme plays a vital role in lactose biosynthesis and generates new lactose series of glycosphingolipids (GSL). B3GNT5 initiates the synthesis of lactotriosylceramide by transferring N-acetylglucosamine to the C-3 position of galactose within lactose ceramide. Lactose ceramide synthase is an alternative name for B3GNT5 due to this function. B3GNT5-mediated glycolipid synthesis has been reported to play significant biological roles in B cell activation [7], preimplantation development, and nervous system development [8, 9] in multiple studies. Elevated levels of B3GNT5 appear to be significantly associated with the advancement of breast, lung, and ovarian carcinoma [10–12]. B3GNT5-mediated glycosphingolipids are essential for the differentiation of acute myeloid leukemia (AML) cells [13].

In this research, we explored the expression of B3GNT5 and its correlation with cancer prognosis by utilizing data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Cancer Cell Line Encyclopedia (CCLE) databases obtained through the UCSC XENA database. Furthermore, we examined how B3GNT5 expression correlates with immune cell infiltration score, cell cycle, immune checkpoints, immunosuppressive genes, immune-activating genes, chemokine receptors, chemokines, and drug resistance. Our findings offer novel perspectives on the function of B3GNT5 across various cancers, suggesting a potential mechanism by which B3GNT5 influences the tumor microenvironment (TME), cancer immunotherapy, and cancer stem cell (CSC) stemness.

Materials and methods

Data collection

We obtained RNA-seq and clinical information from the TCGA and GTEx databases to analyze 33 types of tumors alongside normal tissues. Data on tumor cell lines were sourced from the CCLE database, while information on DNA copy number and methylation was gathered via the cBioPortal database (https://www.cbioportal.org/).

Survival prognosis analysis and its relationship with clinical stage

We utilized Kaplan-Meier analysis to assess the overall survival (OS) among patients whthin the TCGA cohort. In addition, univariate Cox regression analysis was performed to ascertain the prognostic significance of B3GNT5 concerning OS, disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI) across various cancer cases. Statistical analysis and visualization were performed with the "survival" and "survminer" packages in R software version 4.1.1, Hypothesis testing was conducted through Cox regression, with a P-value below 0.05 considered statistically significant.

Gene set enrichment and gene set variation analyses

We conducted a Pearson correlation analysis to examine the association between B3GNT5 and all genes using TCGA data. Gene set enrichment analysis (GSEA) was conducted using the “clusterProfiler”R package, employing parameters: nPerm set to 1,000, minGSSize to 10, maxGSSize to 1,000, and a p-value threshold of 0.05. Genes associated with B3GNT5 and a P-value less than 0.05 were selected, and GSEA was carried out using gene sets from the Reactome pathway database. To identify pathways most closely related to B3GNT5, we utilized the ’GSVA’ R package to perform gene set variation analysis (GSVA). We categorized the 33 tumor types into two categories based on the median expression levels of B3GNT5 (high versus low expression). The reference genes was obtained from the Molecular Signature Database (MSigDB; http://software.broadinstitute.org/gsea/msigdb/index.jsp), with statistical significance defined as P < 0.05."

Immune cell infiltration

We acquired immune cell infiltration scores related to TCGA data from the TIMER2 and ImmuCellAI databases (http://timer.cistrome.org/ and http://bioinfo.life.hust.edu.cn/web/ImmuCellAI/). To evaluate the extent of immune cell infiltration, patients were categorized into two groups (B3GNT5 high-expression and low-expression) for each TCGA tumor type based on the median B3GNT5 expression level.

Association of B3GNT5 with IC50 values of anti-tumor drugs

Using over 1,000 cancer cell lines, we assessed response data for 192 anti-tumor drugs. The association between B3GNT5 expression levels and the IC50 of these drugs was illustrated with the R package "ggplot2".

Cell lines

The Panc-1 cell line, initially obtained from ATCC USA, was provided by Renyi Qin from the Affiliated Tongji Hospital in China. It was cultured in RPMI-1640 medium, enriched with 10% fetal bovine serum (FBS), L-glutamine, and 1% penicillin/streptomycin, and incubated at 37°C in a 5% CO2 atmosphere.

Transfections

Panc-1 cells were seeded in six-well plates and transfected with sh-B3GNT5 and an empty vector (Negative Control). Lentiviruses encoding sh-B3GNT5 were obtained from Genechem (Shanghai, China) and the transfections were performed based on the manufacturer’s guidelines. Lipofectamine was selected as transfection reagent which we purchased from Thermo Fisher Scientific (USA).

Suspension sphere culture and differentiation

As previously described [14], Lentivirus-transduced PANC-1 cells (1000 cells/mL) were cultured in suspension using serum-free DMED-12 medium (Hyclone, Logan, UT, USA) supplemented with B27 (1:50; Invitrogen, Carlsbad, CA, USA), 20 ng/mL epidermal growth factor (PeproTech EC, London, UK), and 100 ng/mL basic fibroblast growth factor. (PeproTech) [13].

Statistical analysis

The data were presented as mean ± standard error of the mean and differences between groups were evaluated using a two-tailed Student’s t-test. Statistical analysis was conducted with R program version 4.1.1, considering a significance level of P < 0.05. (two-tailed).

Results

Analysis of B3GNT5 expression variability and correlations across pan-cancer

We analyzed the expression levels of B3GNT5 among various cancer types using GTEx database data as controls. Our findings demonstrated that upregulation of B3GNT5 in eighteen different cancers, including cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), acute myeloid leukemia (LAML), lower-grade glioma (LGG), liver hepatocellular carcinoma (LIHC), lung squamous cell carcinoma (LUSC), pancreatic adenocarcinoma (PAAD), rectum adenocarcinoma (READ), stomach adenocarcinoma (STAD), uterine corpus endometrial carcinoma (UCEC), and uterine carcinosarcoma (UCS). On the other hand, we observed downregulation of B3GNT5 in five tumors which include breast invasive carcinoma (BRCA), prostate adenocarcinoma (PRAD), skin cutaneous melanoma (SKCM), testicular germ cell tumor (TGCT), and thyroid carcinoma (THCA) (Fig 1A). B3GNT5 expressional abundance in multiple cancer forms establishes its oncogenic significance, with ESCA, LUSC and HNSC demonstrating the highest expression levels (Fig 1B). The gene expression analysis was conducted on normal human tissues obtained from the GTEx database, and a comparative was conducted to assess the relative expression levels of B3GNT5. The findings revealed that this gene was most highly expressed in lung, muscle, and bone marrow tissues (Fig 1C). In various cancer cell lines, the expression of B3GNT5 was evaluated, revealing that the HNSC, ESCA, small cell lung cancer (SCLC), and PAAD cell lines exhibited the highest levels of B3GNT5 expression (Fig 1D).

Fig 1.

Fig 1

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The expression levels of B3GNT5 across pan-cancer were detailed as follows: (A) B3GNT5 expression in tumor tissues from The Cancer Genome Atlas (TCGA) and normal tissues from TCGA and Genotype-Tissue Expression (GTEx) databases. (B) B3GNT5 expression in tumor tissues from the TCGA database; (C) B3GNT5 expression in normal tissues from the GTEx database. (D) B3GNT5 expression in tumor cell lines from the Cancer Cell Line Encyclopedia (CCLE) database, with mean values represented by datapoints. Statistical significance was denoted by *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001, with "ns" indicated not significant.

We conducted an investigation into the correlation between B3GNT5 expression levels and the clinical significance of cancer therapies. Through our analysis of various stages of cancer defined by the World Health Organization (WHO) and B3GNT5, we discovered that the higher the stage in KICH, LIHC, LUAD, PAAD, THCA, and UCEC, the greater the expression of B3GNT5 (Fig 2A–2F). These findings indicate a notable reduction in B3GNT5 expression as stages advance in BRCA, COAD, MESO, and SKCM (Fig 2G–2J). Hence, the aberrant B3GNT5 expression observed in cancer cells may be closely linked to cancer progression and prognosis.

Fig 2.

Fig 2

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For pan-cancer B3GNT5 expression across various World Health Organization (WHO)-defined cancer stages, the differential expression for specified tumor types from the TCGA database is illustrated in Fig A–J. Statistical significance is marked by *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, with ’ns’ denoting not significant.

Comprehensive analysis of B3GNT5 genetic alterations and their correlations in pan-cancer

We conducted a thorough analysis on alterations in pan-cancers, encompassing a total number of 10,953 patients, to identify potential genetic modifications of B3GNT5 that could be linked to tumorigenesis. Our analysis detected genetic alterations (including missense mutations, amplification, deep deletion, truncating mutations, and structural variants) in around 6.0% of the cases (S1 Fig). The most common changes across various cancer types were amplifications of B3GNT5 gene, followed by mutations and deep deletions (Fig 3A). In LUSC, ESCA, UCS, CHOL, HNSC, LUAD, CESC, KICH, PAAD, READ, bladder urothelial carcinoma (BLCA), STAD, BRCA, TGCT, SARC, COAD, KIRP, OV, PCPG, LGG, GBM, PRAD, and SKCM, there was a positive correlation was observed between the copy number and B3GNT5 expression levels. However, in the case of UVM, the relationship between copy number and B3GNT5 expression was negative (Fig 3B). In THCA, LAML, CESC, COAD, BLCA, KICH, MESO, UVM, UCEC, HNSC, ESCA, ACC, KIRP, PCPG, STAD, PAAD, LIHC UCS, SKCM, LUSC, Thymoma (THYM), LGG, and GBM, it was found that the degree of methylation in the promoter region of B3GNT5 is adversely linked with the its expression. Conversely, a positive correlation between methylation levels and B3GNT5 expression was identified exclusively in OV (Fig 3C).

Fig 3. Analysis of B3GNT5 genetic alterations.

Fig 3

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(A) Mutation status across various tumors; (B) the association between B3GNT5 expression and gene copy number; and (C) the association between B3GNT5 expression and methylation levels.

Impact of B3GNT5 expression on prognosis and survival across pan-cancer

We investigated the possible impacts of B3GNT5 expression on prognosis by scrutinizing its correlation with patient survival. By performing Kaplan-Meier OS analysis, we found that B3GNT5 was a determinative element for the prognosis of HNSC, KIRP, LGG, LIHC, LUAD, MESO, PAAD, SARC, and UVM patients, signifying its potential involvement in these diseases (S2 Fig). B3GNT5 was identified as an element influencing the risk of several cancers, including ACC, HNSC, KICH, KIRP, LGG, LIHC, LUAD, MESO, PAAD, SARC, THCA, THYM, and UVM, as demonstrated by univariate Cox regression analysis (Fig 4A). The DSS analysis determined that B3GNT5 played a protective role for patients afflicted with PCPG, but acted as a risk element for individuals diagnosed with HNSC, KICH, KIRP, LGG, LIHC, LUAD, MESO, PAAD, SARC, and UVM (Fig 4B). According to the DFI analysis, the involvement of B3GNT5 proved risky in cases of KIRP, LUAD, and PAAD (Fig 4C). In accordance with the PFI analysis, B3GNT5 stood as a perilous element in instances of ACC, KICH, KIRP, LGG, LUAD, PAAD, SARC, and UVM (Fig 4D).

Fig 4. Univariate Cox regression analysis of B3GNT5 expression in TCGA pan-cancer.

Fig 4

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(A) Forest maps illustrating the association linking B3GNT5 expression with overall survival (OS), (B) disease-specific survival (DSS), (C) disease-free interval (DFI), (D) and progression-free interval (PFI). Red indicates significant results.

B3GNT5-related gene pathways: Insights into cell cycle and immune regulation in pan-cancer

We performed a screening of B3GNT5-related genes and subjected them to subsequent enrichment analyses to elucidate the mechanism underlying cancer carcinogenesis involving B3GNT5. Using GSEA in 33 tumor types sourced from TCGA, we identified specific pathways associated with B3GNT5. Our findings revealed that B3GNT5 is significantly linked to pathways regulating the cell cycle and immune response in several malignancies such as CESC, ESCA, KIRP, LUAD, OV, PAAD, and STAD. Additionally, B3GNT5 was also strongly associated with the pathways involved in immunoregulatory interactions between lymphocytes and non-lymphocytes, cytokine communication, natural immune response, and acquired immune response in LGG, PCPG, KICH, and BRCA tumors. Therefore, our findings revealed that B3GNT5 is pivotal in modulating the cell cycle and tumor immune microenvironment in malignant tumor cells (Fig 5A–5F). The GSVA investigation findings evince that B3GNT5 expression has a correlation with the foremost 50 pathways of the Molecular Signatures Database (MsigDB). We observed that DNA repair, Oxidative phosphorylation, MYC targets, Bile acid metabolism, and K-Ras signaling had an adverse correlation with the GSVA score of B3GNT5 among the 33 cancer classifications (Fig 5G).

Fig 5. Gene set enrichment analysis (GSEA) of B3GNT5 across pan-cancer.

Fig 5

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(A–F) Top 20 GSEA terms for the specified tumor types: A: LUAD; B: OV; C: PAAD; D: BRCA; E: LGG; F: PCPG. (G) Gene set variation analysis (GSVA) of B3GNT5 across pan-cancer with the Top 50 GSEA terms for the indicated tumor types.

B3GNT5 expression and its impact on tumor microenvironment and immune cell infiltration across pan-cancer

We stratified the TCGA samples derived from 33 distinct types of tumors into two cohorts according to the median expression of B3GNT5. We then performed a comparative analysis of the correlated signature score for each tumor across the elevated and reduced expression levels of B3GNT5 to explore the plausible roles of B3GNT5 within the tumor microenvironment (TME). Our findings indicated significant associations between B3GNT5 and various critical pathways, namely nucleotide excision repair, DNA damage response, mismatch repair, DNA replication, base excision repair, epithelial-to-mesenchymal transition (EMT), immune checkpoints, and CD8-T effector (Fig 6A). We utilized the ESTIMATE algorithm to evaluate stromal and immune cell infiltration across the RNA sequencing profiles of 33 cancer types derived from TCGA database. Our findings indicate that B3GNT5 expression exhibits significant positive correlations with stromal, ESTIMATE, and immune scores; meanwhile, it displays negative associations with tumor purity scores in LGG, KICH, PCPG, ACC, BRCA, GBM, THCA, and PRAD. Conversely, B3GNT5 expression is negatively related to stromal, ESTIMATE, and immune scores while positively associated with tumor purity score in ESCA, STAD, and LUSC (Fig 6B).

Fig 6.

Fig 6

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(A) The heatmap illustrates the correlation linking B3GNT5 expression with tumor microenvironment (TME) characterization; (B) ESTIMATE analysis examines B3GNT5 expression across pan-cancer.

To gain deeper insights into how B3GNT5 expression affects immune cell infiltration, we performed correlation analyses using two independent sources of immune cell infiltration datasets. According to our findings from the TIMER2 database, B3GNT5 expression showed a positive correlation with levels of effective and resting memory CD4+ T cells, neutrophils, and macrophages (Fig 7A), conversely, it shows a negative correlation with B cells, central memory CD4+ T cells, Th1 CD4+ T cells, NK T cells, and regulatory T cells (Tregs) in the TCGA pan-cancer cohort (Fig 7B). Our analysis using the ImmuCellAI database demonstrated that B3GNT5 expression had an inverse association with CD8+ T cell infiltration levels in THYM, TGCT, LUSC, HNSC, CESC, STAD, SKCM, SARC, and ESCA, while showing a positive correlation in UVM and ACC (Fig 7C). Furthermore, B3GNT5 expression was positively linked to infiltration levels of Tregs, macrophages, and neutrophils while negatively related to those of B cells and CD8+ T cells. These findings align with the results from the TIMER2 database and suggested that B3GNT5 may contribute to decreased infiltration of B lymphocytes and CD8+ T lymphocytes, while promoting the accumulation of MDSCs, Tregs, and tumor-associated macrophages (TAMs), thus potentially explaining its role as a risk factor in various tumor types.

Fig 7. Immune cell infiltration analysis.

Fig 7

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(A) The association linking B3GNT5 with infiltration levels of macrophage lymphocytes, neutrophil cells, and regulatory T lymphocytes (Tregs) using TIMER2 data. (B) The association linking B3GNT5 with infiltration levels of B cells, CD4+ T lymphocytes, and CD8+ T lymphocytes using TIMER2 data. (C) Association between B3GNT5 and infiltration level of the indicated immune cells through ImmuCellAI data. (D) The heatmap illustrates the relationship linking B3GNT5 expression with immunosuppressive status-related genes. *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001.

B3GNT5 expression correlates with immune markers and pathways, highlighting its role across various cancers and immunotherapy

We explored how B3GNT5 levels correlate with a range of immune-related markers, including genes that activate the immune system, those that suppress it, as well as various chemokines and their receptors. Our analysis indicated a positive correlation between B3GNT5 and immune activation markers, including CD276, PVR, NT5E, STING1, and TNF-SF18 (Fig 9A), as well as immunosuppressive genes TGF-ΒR1, IL-10PR, KDR, CD274, PDCD1LG2, IL-10, and IDO1 in the pan-cancer cohort (Fig 7D). Furthermore, our results indicated a close association between B3GNT5 expression and immune checkpoints across various cancer types in the TCGA database (Figs 8 and S3). Moreover, our analysis demonstrated a positive association between B3GNT5 expression and chemokines including CXCL8, CXCL5, and CXCL16, as well as chemokine receptors such as CXCR2, CCR1, and CCR8 (Fig 9B and 9C). Additionally, we generated heatmaps to visualize the association between B3GNT5 expression and genes related to pyroptosis (Fig 9D), major histocompatibility complex (MHC) (Fig 9E), autophagy (S4A Fig), ferroptosis (S4B Fig), M6A (S4C Fig), EMT upregulation (S5A Fig), EMT downregulation (S5B Fig), TGF-β1 signaling (S6A Fig), and Wnt-β1-catenin signaling (S6B Fig). These results offer significant evidence regarding the potential mechanisms by which B3GNT5 influences cancer progression and immune-based therapies.

Fig 9. Heatmaps presenting the association between B3GNT5 expression and immunoregulation correlated genes.

Fig 9

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(A) Genes of immune activation, (B) chemokine genes, (C) chemokine receptor genes, (D) pyroptosis genes, and (E) major histocompatibility complex (MHC) genes. *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001.

Fig 8. Immune cell infiltration analysis.

Fig 8

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The associationg linking B3GNT5 expression with the immune checkpoints in ACC, BLCA, BRCA, CESC, CHOL, COAD, Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (DLBC), ESCA, GBM, HNSC, KICH, KIRC, KIRP, LAML, LGG, LIHC, LUAD, LUSC, MESO, OV, PAAD, PCPG, PRAD, and READ (A–X).

B3GNT5 expression correlates with sensitivity to key anticancer drugs: A comprehensive analysis of IC50 associations

In our study, we evaluated a total of 192 anticancer drugs and identified the IC50 values of 159 drugs showed a significant correlation with B3GNT5 levels. Based on significant positive or negative correlations, we selected the top 20 drugs, including Nutlin-3a (r = 0.341, P = 1.915 × 10−22), PRIMA-1MET (r = 0.331, P = 5.08 × 10−20), Elephantin (r = 0.325, P = 2.35 × 10−19), Sabutoclax (r = 0.314, P = 5.46 × 10−18), PCI-34051 (r = 0.307, P = 2.32 × 10−17), Nilotinib (r = 0.295, P = 1.26 × 10−16), AMG-319 (r = 0.287, P = 1.34 × 10−15), MIRA-1 (r = 0.2846, P = 2.08 × 10−15), Oxaliplatin (r = 0.1948, P = 1.66 × 10−14), Fulvestrant (r = 0.1969, P = 1.79 × 10−14), PD173074 (r = 0.2739, P = 1.84 × 10−14), AZD4547 (r = 0.2714, P = 3.50 × 10−14), Sorafenib (r = 0.2704, P = 4.18 × 10−14), EPZ004777 (r = 0.2614, P = 6.19 × 10−14), I-BRD9 (r = 0.2692, P = 6.65 × 10−14), BIBR-1532 (r = 0.2689, P = 7.94 × 10−14), Vorinostat (r = 0.2656, P = 1.16 × 10−13), MIM1 (r = 0.2607, P = 4.19 × 10−13), MK-8776 (r = 0.2526, P = 2.52 × 10−12), and Zoledronate (r = 0.2512, P = 6.51 × 10−12, Fig 10A–10U).

Fig 10. Drug resistance analysis.

Fig 10

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The association between B3GNT5 expression and IC50 for different anticancer drugs (A–U).

Impact of B3GNT5 downregulation on self-renewal in pancreatic cancer cells: A sphere formation study

In order to research the effects of B3GNT5 heterotopic expression on PAAD cells, we cultured a stable pancreatic cancer cell line (PANC-1) with sh-RNA inhibition of B3GNT5. To assess self-renewal ability of cells generated in serum-free conditions, the number and size of spheres were evaluated. The results showed that PANC-1 cells with downregulated B3GNT5 expression displayed smaller spheres than those in the negative control (NC) group, as shown in Fig 11A. Additionally, the number of cells per sphere exhibited a marked reduction in the Sh-B3GNT5 group relative to the NC group (Fig 11B, P<0.01). Cells with downregulated B3GNT5 expression had fewer spheres formed over three passages than those in the NC group (Fig 11C, P<0.01).

Fig 11. Modulating B3GNT5 expression influences the self-renewal capability of PAAD cells in vitro.

Fig 11

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(A) Sphere formation was observed in Panc-1 cells with B3GNT5 suppression at various time intervals. (B) Quantification of cell numbers within each sphere was conducted. (C) The number of spheres formed per 1000 cells reflects sphere-forming ability, relative to negative control group. **P < 0.01. NC denotes the negative control group; sh-B3GNT5 refers to small hairpin B3GNT5.

Discussion

Over the past few years, the use of inhibitors targeting immune checkpoints in immunotherapy has become a vital treatment strategy for multiple cancer types, leading to significant advances in cancer therapy [15]. The discovery and development of specific inhibitors for immune checkpoints, including CTLA-4 and programmed cell death protein 1 (PD-1), have revolutionized cancer immunotherapy. However, the efficacy of these therapies has varied significantly among different types of cancers and individuals. While certain malignancies such as lung cancer, breast cancer, and melanoma have exhibited promising outcomes with immunotherapeutic strategies, the potential of immunologically "cold" tumors like PAAD remains unclear. Therefore, it is important to investigate the distinct characteristics and fundamental mechanisms underlying the heterogeneous outcomes of immunotherapy among different cancer types. Cancer stem cells (CSCs) possess unique properties that enable them to evade immune recognition and elimination [16]. Multiple studies have recently established that CSCs are capable of shaping the immunosuppressive and tumor-promoting environment of TME via modulation of various immune cells, contributing to resistance towards immunotherapeutic approaches. Identifying the critical binding target between CSCs and cancer immunotherapy would be a significant advancement in this field. Given the potential for enhanced tumor cell immunogenicity and T cell activation, inhibition of GSL synthesis through suppression of B3GNT5 expression may be implemented as a complementary approach along with current immunotherapeutic strategies, including PD-1 blockade [17]. Study has indicated a noteworthy decrease in B3GNT5 expression during differentiation of glioblastoma stem cells [18], suggesting that B3GNT5 could potentially serve as a connecting link.

In this research, we performed an initial assessment of B3GNT5 expression and its prognostic relevance across multiple cancer types, revealing high expression levels to be present in 18 of these tumors. Upon conducting Kaplan-Meier overall survival analysis, we identified B3GNT5 as a risk element for patients across nine tumor types. Furthermore, our univariate Cox regression analysis disclosed that this gene acted as a risk element in twelve different tumor types. Similarly, B3GNT5 was implicated as a protective element in patients with PCPG but as a risk factor across eleven other tumor types according to our DSS analysis. These consistent findings indicate that B3GNT5 may possess proto-oncogenic properties across a majority of tumor types. By means of gene set enrichment analysis (GSEA) involving B3GNT5, we successfully identified strong correlations between this gene and numerous pathways related to cell cycle control and immune signaling, particularly immunoregulatory interactions between lymphocytes and non-lymphocytes, which encompasses a total of 135 genes, inclusive of receptors and cellular adhesion molecules that regulate the response of lymphocyte-related cells (i.e., B-, T-, and NK lymphocytes) towards to self-antigens, tumor antigens, and also to pathogens [19–23]. The findings of this investigation suggest a strong link between B3GNT5 and the regulation of immune microenvironment in tumors, as well as ligand-receptor communication between malignant cells and lymphoid cells. Prior research has demonstrated that such immune cells can maintain the stemness and viability of cancer stem cells (CSCs) [24]. In our study, we observed that decreased B3GNT5 expression negatively impacts the self-renewal capacity of PAAD cells. Consequently, these results infer that the influence of B3GNT5 on immune cells within the TME plays a regulatory role in CSC stemness and malignant features. Further research is needed to explicate the underlying mechanisms. CD8+ T cells, which belong to the T lymphocyte population, are cytotoxic killer cells essential for cell-mediated immunity, particularly within tumor tissues [25, 26]. The activation and formation of memory in cytotoxic CD8+ T lymphocytes are dependent on CD4+ T lymphocytes [27]. Multiple studies have found strong correlations between CSC stemness and CD8+ T cells in multiple cancer types [28]. For instance, CSCs can produce TGF-β and CCL2, which inhibite CD8+ and CD4+ T cell activation and proliferation [29]. Furthermore, certain chemokine family members such as CCL1, CCL2, and CCL5, which are highly expressed by CSCs in different cancer types, stimulate the infiltration of T-reg cells into the tumor microenvironment [29–31]. Moreover, in prostate cancer, CSCs secrete tenascin-C to hinder the activation and proliferation of CD8+ and CD4+ T cells through interaction with α5β1 integrin located on T cells [32]. Interestingly, T cells also regulate CSC stemness. Low IFN-γ levels activate the PI3K/AKT/NOTCH1 pathway and promote CSC stemness. In NSCLC, CD8+ T cells mainly produce IFN-γ [33]. Our analysis using two distinct data sources indicates that B3GNT5 expression is inversely associated with CD8+ T lymphocytes, CD4+ T lymphocytes, and natural killer cells, which could help shed light on the connection between B3GNT5 and diverse tumor types. T-reg cells prevent dangerous tumor cells from attack by cytotoxic CD8+ T lymphocytes [34, 35]. Many research pieces demonstrate that T-reg cells secrete IL-10, thereby promoting leukemic stem cell stemness through activation of PI3K/AKT/OCT4/NANOG pathways in AML [36]. Additionally, our immune cell infiltration data suggest that Treg infiltration levels and B3GNT5 expression are positively correlated, implying that even a large group of cytotoxic CD8+ T cells’ functioning is limited, and B cells are responsible for antigen presentation. Activated B cells from a control donor’s peripheral-blood lymphocytes (PBL) present antigens to CD4+ and CD8+ T cells [37, 38]. This selective presentation of cognate antigens using surface Ig molecules leads to tumor-infiltrating B cells (TIL-Bs) delivering antigens more efficiently than tumor dendritic cells (DCs). DCs excite CD4+ and CD8+ TILs in the lymph nodes, followed by TIL-Bs initiating recall responses in the tumor. TIL-Bs act as local antigen-presenting cells (APC) that provide secondary stimulation to CD4+ TILs, allowing their survival and proliferation for an extended period [39, 40]. Interestingly, B3GNT5 expression negatively correlates with B cell and CD8+ T cell infiltration levels while positively correlating with immune-activating and immunosuppressive genes across pan-cancer, which supports the potential role of B3GNT5 as an immune checkpoint molecule and a focal point of the CSC-immune cell crosstalk. We surmise that B3GNT5 regulates signal pathways or cell death processes, such as EMT, Wnt-β1-catenin, TGF-β1 signaling, pyroptosis, autophagy, or ferroptosis, which have all been related to B3GNT5 in our analysis, to impact CSC-immune cell crosstalk and carcinogenic biological properties.

Our study has several limitations that must be acknowledged. Additional experiments are necessary to evaluate the mechanisms by which B3GNT5-mediated interactions of cancer stem cells (CSCs) with immune cells, as well as to validate the potential of B3GNT5 as an immune checkpoint target in clinical trials. To conclude, we conducted a comprehensive analysis of B3GNT5 across various cancer types and highlighted its potential significance in regulating the immune response and serving as a prognostic indicator for patients. B3GNT5 is capable of suppressing the activation and proliferation of T lymphocytes by promoting the secretion of TGF-β and CCL2 by CSCs (cancer stem cells). These factors not only inhibit the function of effector T cells, but also enhance the infiltration of regulatory T cells (Tregs) into TME, thus inhibiting the anti-tumor immune response. B3GNT5 may become one of the risk factors of most tumors by promoting the accumulation of cells that suppress the immune response, such as Tregs and tumor-associated macrophages (TAMs). Abnormal synthesis of GSLs mediated by B3GNT5 may alter the glycosylation pattern on tumor cell membranes, subsequently impacting immune cell recognition and function.For example, in pancreatic cancer cell line PANC-1, a decrease in cell self-renewal ability was observed after down-regulation of B3GNT5 expression, indicating the importance of B3GNT5 in maintaining the characteristics of tumor stem cells. In addition, B3GNT5 is also associated with key pathways such as DNA repair pathway, epithelial-mesenchymal transition (EMT) and immune checkpoints. Abnormal activation or inhibition of these pathways may further aggravate the phenomenon of tumor immune escape, so that tumor cells can better adapt to unfavorable environment and escape the attack of host immune system. To sum up, B3GNT5 affects tumor immune response through many mechanisms, and its role in tumor occurrence and development can not be ignored. In the future, more experimental data are needed to further explore the specific mechanism of B3GNT5 and its possibility as a potential therapeutic target.

Supporting information

S1 Fig. Genetic alteration analysis of B3GNT5: The mutation status of B3GNT5 in different tumors.

(TIF)

pone.0314609.s001.tif (397.7KB, tif)
S2 Fig. Link between Kaplan–Meier overall survival estimates and B3GNT5 expression levels.

(A–I) The connection between B3GNT5 expression and Kaplan–Meier overall survival across Pan-Cancer types from the TCGA database is shown. The median B3GNT5 expression value for each tumor type was used as the threshold value.

(TIF)

pone.0314609.s002.tif (1.4MB, tif)
S3 Fig. Immune cell infiltration analysis.

The correlation linking B3GNT5 expression with immune checkpoints in SARC, SKCM, STAD, TGCT, THCA, THYM, UCEC, UCS, and UVM (A–I).

(TIF)

pone.0314609.s003.tif (1.3MB, tif)
S4 Fig. Heatmaps presenting the association linking B3GNT5 expression with immunoregulation-related genes.

(A) Autophagy genes, (B) ferroptosis, and (C) M6A. *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001.

(TIF)

pone.0314609.s004.tif (4.9MB, tif)
S5 Fig. Heatmaps presenting the association between B3GNT5 expression and immunoregulation-related genes.

(A) EMT upregulated genes and (B) EMT downregulated genes. *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001.

(TIF)

pone.0314609.s005.tif (3.9MB, tif)
S6 Fig. Heatmaps presenting the association between B3GNT5 expression and immunoregulation-related genes.

(A) TGF-β1-signaling genes and (B) Wnt-β1-catenin-signaling genes. *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001.

(TIF)

pone.0314609.s006.tif (3.6MB, tif)

Acknowledgments

The authors express their gratitude to the TCGA and GEO projects for providing access to their data.

Data Availability

All data used in this study are publicly available and described as follows: 1 TCGA data TCGA pan-cancer data was downloaded from UCSC Xena database https://toil-xena-hub.s3.us-east-1.amazonaws.com/download/tcga_RSEM_gene_tpm.gz clinical data https://xenabrowser.net/datapages/?dataset=Survival_SupplementalTable_S1_20171025_xena_sp&host=https%3A%2F%2Fpancanatlas.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443 2 GTEx data GTEx data was downloaded from UCSC Xena database https://toil-xena-hub.s3.us-east-1.amazonaws.com/download/gtex_RSEM_gene_tpm.gz GTEx_phenotype https://toil-xena-hub.s3.us-east-1.amazonaws.com/download/GTEX_phenotype.gz 3 immune cell infiltration data 3.1 data from published paper https://www.cell.com/cms/10.1016/j.immuni.2018.03.023/attachment/1b63d4bc-af31-4a23-99bb-7ca23c7b4e0a/mmc2.xlsx 3.2 data from ImmuCellAI database http://bioinfo.life.hust.edu.cn/ImmuCellAI#!/resource 3.2 data from TIMER2 database http://timer.cistrome.org/infiltration_estimation_for_tcga.csv.gz 4-5 Copy number and methylation data of 33 TCGA tumors https://www.cbioportal.org/datasets

Funding Statement

Feng Peng, the funders of National Natural Science Foundation of China(NO.81402443), is the first author of manuscript and has conceived and directed the study, performed data simulation and algorithm assessment and optimisation under the supervision of Renyi Qin (the funders of National Natural Science Foundation of China, NO.81772950).

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Decision Letter 0

Zhiwen Luo

11 Sep 2024

PONE-D-24-23687Pan-cancer Analysis of B3GNT5 with Potential Implications for Cancer Immunotherapy and Cancer Stem Cell stemnessPLOS ONE

Dear Dr. Qin,

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Reviewer #1: 1. The study utilized data from multiple databases, yet it is still necessary to clarify whether the sample selection is representative.

2. When conducting gene set enrichment analysis and gene set variation analysis, multiple comparisons might be involved, but the text does not mention whether multiple comparison corrections were applied.

3. In the part "Regulating the expression of B3GNT5 can affect self-renewal of PAAD cells", "NC" is mentioned, but the specific setup of the NC group and the type of control are not detailed. In addition, it is not specified whether the experiment was repeated, the number of repetitions, or whether a control group was set up.

Reviewer #2: The manuscript presents a comprehensive analysis of B3GNT5 across various cancer types, exploring its potential implications for cancer immunotherapy and stemness. The study is timely and relevant. The authors have utilized robust datasets from TCGA and GEO, which strengthens the validity of their findings.

1. Please ensure that page and line numbers are provided in the correct format.

2. Please note that the current lack of clarity of the figures makes them more difficult to read.The figures have to be prepared at high resolution in a clear and meaningful manner. Additionally, providing a more detailed legend would help readers understand the data presented.

3. The font formatting of the references needs to be adjusted. Font style and size should be uniform throughout the manuscript, including references.

4. The introduction provides a good background on B3GNT5; however, it could benefit from a more detailed discussion on the specific mechanisms by which B3GNT5 influences immune responses in tumors.

5. It would be helpful to include more details on the statistical analyses performed, including any software used and specific tests applied.

6. The discussion effectively contextualizes the findings within the broader field of cancer research. Are there specific experimental approaches or clinical trials that could be proposed to confirm the role of B3GNT5 in cancer immunotherapy? This would strengthen the manuscript by providing a pathway for future research.cancer stem cell stemness.

7. The authors need to improve their language and grammar to enhance the flow of the text. For example, the phrase "B3GNT5's expression was highly correlated with different immunoregulatory factors" could be rephrased to "The expression of B3GNT5 shows a high correlation with a range of immunoregulatory factors." This change would enhance the clarity of the statement. In addition, the whole manuscript needs to be checked by native English speakers.

Reviewer #3: - The article should undergo extensive English proofreading.

- Too many full stops. They need to be removed. For example: This phenomenon is particularly visible in underdeveloped countries, where about 82% of the world's population lives.(1).

- In the methodology section: transfection is not sufficiently described... please provide details.

- In the results section, each paragraph should include a sentence summarizing what the results actually mean.

- There are no results confirming that sh-RNA transfection was successful. Please analyze B3GNT5 expression after shRNA silencing. Without confirmation of inhibition, it is not possible to show these results and draw any conclusions.

- The resolution of the figures is too small and the results are blurry.

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Reviewer #2: No

Reviewer #3: No

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PLoS One. 2024 Dec 13;19(12):e0314609. doi: 10.1371/journal.pone.0314609.r002

Author response to Decision Letter 0

24 Oct 2024

Point-to-Point Responses to the Reviewers’ Critiques (PONE-D-24-23687)

Notes. For purposes of this response letter, we have created figures to help illustrate our points. These figures do not appear in the manuscript.

For Journal Requirements:

1. When submitting your revision, we need you to address these additional requirements. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

Response: We thank the editor for suggestting this very important requirements, we will vevise the style of our manuscript to meet PLOS ONE’s style.

2. We noticed you have some minor occurrence of overlapping text with previous publication(s), which needs to be addressed.

Response: We thank the editor for pointing out this mistake. We have already polished the language and reduced the repetition in the manuscript and ensure that in our resubmitted manuscript this problem will be addressed.

3. We note that the grant information you provided in the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match.

Response: We appreciate the editor for highlighting this mistake. The issue with the mismatched grant information has been resolved.

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Response: We appreciate the editor for comment. We have updated the author information and added ORCID of Prof. Qin.

Reviewer #1:

1. The study utilized data from multiple databases, yet it is still necessary to clarify whether the sample selection is representative.

Response:

We appreciate the reviewer’s insightful comment and fully agree on the importance of representative sample selection for our study. To ensure this, we integrated data from multiple databases. We obtained extensive RNA-seq and clinical information from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEX) databases, covering 33 tumor types and their corresponding normal tissue samples. This comprehensive data source ensures a wide coverage of cancer types and includes sufficient normal tissue controls, thereby enhancing the universality and reliability of our findings.

Additionally, we utilized tumor cell line data from the Cancer Cell Line Encyclopedia (CCLE) database to further increase sample diversity. To investigate the role of B3GNT5 in tumor development and immunotherapy, we conducted genetic variation analyses, including mutation status, gene copy number, and methylation levels. This multi-dimensional analysis helps us fully understand the expression patterns and potential mechanisms of B3GNT5 in various cancers.

We also employed Kaplan-Meier survival analysis and the Cox regression model to assess the impact of B3GNT5 on patient overall survival. These methods consider time factors and clinical outcomes, lending stability to our conclusions. Moreover, similar high-impact pan-cancer studies have used the same databases (1-3) (the paper we listed below), indicating the completeness of our data, which includes genome sequencing and postoperative follow-up survival data. In cases of incomplete data, we opted for exclusion.

In summary, this study effectively ensures the representativeness and scientific validity of sample selection through cross-database data integration, multi-faceted verification, and robust statistical methods. This systematic research design provides a solid foundation for elucidating the role of B3GNT5 in cancer.

2. When conducting gene set enrichment analysis and gene set variation analysis, multiple comparisons might be involved, but the text does not mention whether multiple comparison corrections were applied.

Response: We thank the reviewer for this insightful comment. In the gene set enrichment analysis and gene set variation analysis, multiple comparisons are indeed made, and we need to adjust our P-value to control the error rate, and the adjusted P-value is the original P-value adjusted by Bonferroni or Benjamini-Hochberg method

3. In the part "Regulating the expression of B3GNT5 can affect self-renewal of PAAD cells", "NC" is mentioned, but the specific setup of the NC group and the type of control are not detailed. In addition, it is not specified whether the experiment was repeated, the number of repetitions, or whether a control group was set up.

Response: We thank the reviewer for this insightful comment. We completely agree with your opinion that setting up a control group and repeating the experiment is of utmost significance. In our study of regulating the expression of B3GNT5 to affect the self-renewal of pancreatic adenocarcinoma (PAAD) cells, NC means negative control group, it refers to the cell group transfected with empty plasmid. This setting helps to rule out the influence of non-specific effects, thus verifying that the changes observed in the experimental group are indeed caused by the changes in B3GNT5 expression levels.

Reviewer #2:

1. Please ensure that page and line numbers are provided in the correct format.

Response: Thank you very much for pointing out this detail. We have revised it.

2. Please note that the current lack of clarity of the figures makes them more difficult to read. The figures have to be prepared at high resolution in a clear and meaningful manner. Additionally, providing a more detailed legend would help readers understand the data presented.

Response: Thank you for pointing out this mistake. While we revise the manuscript, we’ll upload the high-resolution figures with a minimum resolution of 300dpi. In the meantime, we will provide more detail in figure legend that could help readers understand data presented.

3. The font formatting of the references needs to be adjusted. Font style and size should be uniform throughout the manuscript, including references.

Response: Thank you very much for your suggestion. We have improved the manuscript format according to the journal style. We hope the revised version is more in line with requirements.

4. The introduction provides a good background on B3GNT5; however, it could benefit from a more detailed discussion on the specific mechanisms by which B3GNT5 influences immune responses in tumors.

Response: Thank you very much for your suggestion. We have added a more detailed discussion on the specific mechanisms by which B3GNT5 influences immune responses in tumors. The B3GNT5 affects tumor immunity by regulating the synthesis of glycolipids (GSL) on the cell surface, participating in the process of epithelial-mesenchymal transition (EMT), and promoting the secretion of TGF-β and CCL2 factors by CSCs (cancer stem cells)(4, 5).

5. It would be helpful to include more details on the statistical analyses performed, including any software used and specific tests applied.

Response: Thank you very much for your suggestion. In the process of data analysis, we use a variety of bioinformatics tools and software, such as the survival package in R language for survival analysis, and use GraphPad Prism software to complete the chart making. At the same time, in order to evaluate the degree of immune cell infiltration, we also visited the data provided by TCGA and GEO projects and analyzed them using a specific online platform (http://cibersortx.standford.edu/)

6. The discussion effectively contextualizes the findings within the broader field of cancer research. Are there specific experimental approaches or clinical trials that could be proposed to confirm the role of B3GNT5 in cancer immunotherapy? This would strengthen the manuscript by providing a pathway for future research cancer stem cell stemness.

Response: We thank the reviewer for this insightful comment. Regarding the role of B3GNT5 in cancer immunotherapy, the existing research mainly focuses on the relationship between its expression and immune cell infiltration in the tumor microenvironment (TME). Through gene expression analysis, it was found that the expression level of B3GNT5 was negatively correlated with the number of CD8 + T cells, CD4 + T cells and NK cells. In addition, in pancreatic cancer cell line PANC-1, inhibiting the expression of B3GNT5 can significantly reduce the ability of cell sphere formation, suggesting that B3GNT5 may be one of the potential immunotherapy targets. In future studies, we will further verify the specific role of B3GNT5 in cancer immunotherapy. For example, CRISPR/Cas9 technology was used to accurately edit B3GNT5 gene in human tumor cell line, and then co-cultured with different types of immune cells to detect the changes of immune cell activity and cytokine secretion. The mouse tumor model with B3GNT5 knockout or overexpression was constructed, and the tumor growth and the response difference to immune checkpoint inhibitors (such as PD-1/PD-L1 antibody) were observed. This is helpful to evaluate the role of B3GNT5 in immune escape mechanism. Based on the above experimental results, a suitable animal model was selected for pre-clinical test to evaluate the safety and effectiveness of combined use of B3GNT5 inhibitor and existing immunotherapy methods. Through the above research, we can not only deeply understand the function of B3GNT5 in the process of tumor occurrence and development, but also provide scientific basis for it as a new immunotherapy target. More large-scale clinical trials are needed in the future to verify these findings and explore the best treatment strategies.

7. The authors need to improve their language and grammar to enhance the flow of the text. For example, the phrase "B3GNT5's expression was highly correlated with different immunoregulatory factors" could be rephrased to "The expression of B3GNT5 shows a high correlation with a range of immunoregulatory factors." This change would enhance the clarity of the statement. In addition, the whole manuscript needs to be checked by native English speakers.

Response: Thank you for your comments. We have found a professional editing agency, to help us modify the English language. We hope the revised version will be of much better reading quality.

Reviewer #3:

1. The article should undergo extensive English proofreading.

Response: Thank you for your comments. We have engaged a professional editing agency to assist with refining the English language. We hope the revised version will offer significantly improved readability.

2. Too many full stops. They need to be removed. For example: This phenomenon is particularly visible in underdeveloped countries, where about 82% of the world's population lives.

Response: Thank you very much for pointing out this mistake. We have revised it.

3. In the methodology section: transfection is not sufficiently described. please provide details.

Response: Thank you very much for your suggestion, we will provide more details about transfection in the methodology section.

4. In the results section, each paragraph should include a sentence summarizing what the results actually mean.

Response: Thank you for this insightful comment. We have added a summary sentence to each paragraph to ensure that readers can clearly understand the implications of the results.

5. There are no results confirming that sh-RNA transfection was successful. Please analyze B3GNT5 expression after shRNA silencing. Without confirmation of inhibition, it is not possible to show these results and draw any conclusions.

Response: Thank you for your valuable comment. We conducted an examination to analyze B3GNT5 expression following shRNA silencing. Using qRT-PCR, we assessed B3GNT5 expression in Panc-1 cells transfected with sh-B3GNT5 and a negative control. The figure below illustrates the relative expression of B3GNT5 in Panc-1 cells transfected with sh-B3GNT5 compared to those transfected with the negative control.

6. The resolution of the figures is too small and the results are blurry.

Response: Thank you for highlighting this mistake. As we revise the manuscript, we will upload the high-resolution figures with a minimum resolution of 300 dpi. Additionally, we will enhance the figure legends to provide more detail, aiding readers in understanding the presented data.

1. Wu Z, Uhl B, Gires O, Reichel CA. A transcriptomic pan-cancer signature for survival prognostication and prediction of immunotherapy response based on endothelial senescence. J Biomed Sci. 2023;30(1):21.

2. Zhuang K, Wang L, Lu C, Liu Z, Yang D, Zhong H, et al. Assessment of SWI/SNF chromatin remodeling complex related genes as potential biomarkers and therapeutic targets in pan-cancer. Mol Cancer. 2024;23(1):176.

3. Luo H, Xia X, Huang LB, An H, Cao M, Kim GD, et al. Pan-cancer single-cell analysis reveals the heterogeneity and plasticity of cancer-associated fibroblasts in the tumor microenvironment. Nat Commun. 2022;13(1):6619.

4. Miao Z, Cao Q, Liao R, Chen X, Li X, Bai L, et al. Elevated transcription and glycosylation of B3GNT5 promotes breast cancer aggressiveness. J Exp Clin Cancer Res. 2022;41(1):169.

5. Zhang X, Zeng B, Zhu H, Ma R, Yuan P, Chen Z, et al. Role of glycosphingolipid biosynthesis coregulators in malignant progression of thymoma. Int J Biol Sci. 2023;19(14):4442-56.

Attachment

Submitted filename: Response to Reviewers 2024.10.22 completed.docx

pone.0314609.s007.docx (134KB, docx)

Decision Letter 1

Zhiwen Luo

14 Nov 2024

Pan-cancer Analysis of B3GNT5 with Potential Implications for Cancer Immunotherapy and Cancer Stem Cell stemness

PONE-D-24-23687R1

Dear Dr. Qin,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Zhiwen Luo

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #3: All comments have been addressed

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The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #3: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #3: Yes

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Reviewer #3: Yes

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Reviewer #3: Yes

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Reviewer #3: No

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Acceptance letter

Zhiwen Luo

3 Dec 2024

PONE-D-24-23687R1

PLOS ONE

Dear Dr. Qin,

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Kind regards,

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on behalf of

Dr. Zhiwen Luo

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Fig. Genetic alteration analysis of B3GNT5: The mutation status of B3GNT5 in different tumors.

    (TIF)

    pone.0314609.s001.tif (397.7KB, tif)
    S2 Fig. Link between Kaplan–Meier overall survival estimates and B3GNT5 expression levels.

    (A–I) The connection between B3GNT5 expression and Kaplan–Meier overall survival across Pan-Cancer types from the TCGA database is shown. The median B3GNT5 expression value for each tumor type was used as the threshold value.

    (TIF)

    pone.0314609.s002.tif (1.4MB, tif)
    S3 Fig. Immune cell infiltration analysis.

    The correlation linking B3GNT5 expression with immune checkpoints in SARC, SKCM, STAD, TGCT, THCA, THYM, UCEC, UCS, and UVM (A–I).

    (TIF)

    pone.0314609.s003.tif (1.3MB, tif)
    S4 Fig. Heatmaps presenting the association linking B3GNT5 expression with immunoregulation-related genes.

    (A) Autophagy genes, (B) ferroptosis, and (C) M6A. *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001.

    (TIF)

    pone.0314609.s004.tif (4.9MB, tif)
    S5 Fig. Heatmaps presenting the association between B3GNT5 expression and immunoregulation-related genes.

    (A) EMT upregulated genes and (B) EMT downregulated genes. *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001.

    (TIF)

    pone.0314609.s005.tif (3.9MB, tif)
    S6 Fig. Heatmaps presenting the association between B3GNT5 expression and immunoregulation-related genes.

    (A) TGF-β1-signaling genes and (B) Wnt-β1-catenin-signaling genes. *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001.

    (TIF)

    pone.0314609.s006.tif (3.6MB, tif)
    Attachment

    Submitted filename: Response to Reviewers 2024.10.22 completed.docx

    pone.0314609.s007.docx (134KB, docx)

    Data Availability Statement

    All data used in this study are publicly available and described as follows: 1 TCGA data TCGA pan-cancer data was downloaded from UCSC Xena database https://toil-xena-hub.s3.us-east-1.amazonaws.com/download/tcga_RSEM_gene_tpm.gz clinical data https://xenabrowser.net/datapages/?dataset=Survival_SupplementalTable_S1_20171025_xena_sp&host=https%3A%2F%2Fpancanatlas.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443 2 GTEx data GTEx data was downloaded from UCSC Xena database https://toil-xena-hub.s3.us-east-1.amazonaws.com/download/gtex_RSEM_gene_tpm.gz GTEx_phenotype https://toil-xena-hub.s3.us-east-1.amazonaws.com/download/GTEX_phenotype.gz 3 immune cell infiltration data 3.1 data from published paper https://www.cell.com/cms/10.1016/j.immuni.2018.03.023/attachment/1b63d4bc-af31-4a23-99bb-7ca23c7b4e0a/mmc2.xlsx 3.2 data from ImmuCellAI database http://bioinfo.life.hust.edu.cn/ImmuCellAI#!/resource 3.2 data from TIMER2 database http://timer.cistrome.org/infiltration_estimation_for_tcga.csv.gz 4-5 Copy number and methylation data of 33 TCGA tumors https://www.cbioportal.org/datasets

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