BTG1 Mutation Correlates with Inferior Prognosis in Diffuse Large B-Cell Lymphoma

Chun-Yu Shang; Wei Hua; Tong-Yao Xing; Kai-Xin Du; Yi-fan Wu; Yue Li; Hua Yin; Hao-Rui Shen; Li Wang; Jian-Yong Li; Rui Gao; Wei Xu; Jin-Hua Liang

doi:10.4143/crt.2025.494

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Original Article Hematologic malignancy BTG1 Mutation Correlates with Inferior Prognosis in Diffuse Large B-Cell Lymphoma: Chun-Yu Shang¹, Wei Hua¹, Tong-Yao Xing¹, Kai-Xin Du¹, Yi-fan Wu¹, Yue Li¹, Hua Yin¹, Hao-Rui Shen¹, Li Wang¹, Jian-Yong Li¹, Rui Gao², Wei Xu¹, Jin-Hua Liang¹; Cancer Research and Treatment : Official Journal of Korean Cancer Association 2026;58(3):1002-1020.
DOI: https://doi.org/10.4143/crt.2025.494
Published online: July 30, 2025

¹Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China

²Department of Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China

Correspondence: Wei Xu, Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
Tel: 86-25-83781120 E-mail: xuwei10000@hotmail.com

Co-correspondence: Jin-Hua Liang, Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
Tel: 86-25-83781120 E-mail: liangjinhua1990@126.com

^*Chun-Yu Shang, Wei Hua, and Tong-Yao Xing contributed equally to this work.

• Received: May 8, 2025 • Accepted: July 30, 2025

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Abstract

Purpose
B-cell translocation gene 1 (BTG1) is a highly conserved gene and recurrently mutated in the MCD subtype of diffuse large B-cell lymphoma (DLBCL). The specific enrichment of BTG1 mutation (BTG1^mut) raises a potential hypothesis that they may actively contribute to DLBCL. However, the biological characteristics and prognostic signifi cance of BTG1 in DLBCL remain to be explored. Therefore, the objective of our study was to evaluate the value of BTG1 in DLBCL.
Materials and Methods
The available clinical information and corresponding mutation data of DLBCL were obtained from published articles. Tumor tissue samples of DLBCL patients diagnosed in Jiangsu Province Hospital (JSPH) from 2021 to 2023 were collected for next-generation sequencing, 195 samples were analyzed for gene expression levels using RNA sequencing, among them, 40 samples were analyzed by untargeted metabolomics.
Results
We enrolled 2,379 DLBCL patients from fi ve published studies and 243 DLBCL patients from JSPH cohort. In external cohort, 11.0% (262/2,379) of patients were BTG1^mut, compared with 25.1% (61/243) in the JSPH cohort. BTG1^mut was associated with adverse clinical features and was prone to involve testis. Patients with BTG1^mut exhibit inferior overall survival. Furthermore, pathway enrichment analysis of the untargeted metabolomics showed that several meaningful pathways have been found such as amino acid metabolism and lipid metabolism.
Conclusion
BTG1 mutation was promising prognostic predictor for DLBCL. The mechanism driving different survival outcomes may be attributed to the tumor metabolic reprogramming.
Key words: Diffuse large B-cell lymphoma, BTG1 mutation, Prognosis, Biological mechanism, Metabolomics

Introduction

Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of lymphoma and is a highly heterogeneous disease. Roughly two-thirds of patients diagnosed with DLBCL can achieve remission through intensive immunochemotherapy; however, this condition continues to pose an unmet medical need for patients who are not suitable for such aggressive treatment or for those with relapse or refractory disease [1]. The development of precision medicine approaches for DLBCL is confounded by its pronounced genetic, phenotypic, and clinical heterogeneity.

Gene expression profiling (GEP) has uncovered two major “cell-of-origin” subtypes arising from B cells in different phases of differentiation, namely germinal center B-cell-like DLBCL and activated B-cell-like DLBCL, which have distinct biology and survival rates [2]. Nevertheless, GEP is poorly available in routine clinical practice. With the development of next-generation sequencing (NGS) technology, a probabilistic classification tool for genetic subtypes was developed to divide DLBCL into seven molecular subtypes with varying prognoses and corresponding targeted therapies [3]. The high-throughput sequencing has facilitated our understanding of genetic aberrations in DLBCL. However, heterogeneity is observed within different molecular subtypes in clinical practice. Hence, more precise risk stratifications for each LymphGen genetic subtype are still urgently needed in clinical practice.

B-cell translocation gene 1 (BTG1), a prototype of the B cell translocation gene/transducer of ERBB2 (BTG/TOB) antiproliferative gene family, plays a pivotal role in a wide range of cellular activities including proliferation, apoptosis, and cell growth via modulating a variety of central biological steps [4]. Notably, BTG1 was highly expressed in lymph nodes and white blood cells among the BTG/TOB family, suggesting their specific role and characteristics. In hematopoietic malignancies, BTG1 is identified as a target of aberrant somatic hypermutation in DLBCL and involved in tumorigenesis. In the genetic classification era, BTG1 mutation was found to be frequently mutated in the MCD subtype but not all in MCD subtype [5], prompting the need to investigate potential roles in whole group of DLBCL patients.

The role of BTG1 in mature B cell lymphoma and contribution of BTG1 mutation (BTG^mut) to DLBCL need to be further explored. In this study, we intended to investigate the clinical significance associated with BTG1 mutations, preliminarily explore feature of BTG1 in DLBCL pathogenesis and progression, with the hope of supporting it as a potential therapeutic and prognostic molecular marker for DLBCL.

Materials and Methods

1. Patients and cohorts

1) Jiangsu Province Hospital cohort

Our cohort consists of 243 DLBCL patients diagnosed between 2021 and 2023 at the First Affiliated Hospital of Nanjing Medical University (Jiangsu Province Hospital, JSPH). These diagnoses were made according to the classification criteria of the World Health Organization. All newly diagnosed DLBCL patients received standard R-CHOP–like regimens as first-line treatment. All tumor tissue samples were collected to perform NGS, while a total of 195 DLBCL samples were analyzed the gene expression levels using RNA sequencing (RNA-seq). Response was assessed by positron emission tomography/computed tomography and graded according to the Lugano 2014 criteria.

2) External integrated cohort

The available clinical information and corresponding mutation data of B-cell lymphoma patients were retrieved from published articles. A total of 2,379 DLBCL patients from five published studies were enrolled [6-10].

2. NGS and mutation analysis

As the capture probes, our panels covered 475 leukemia- and lymphoma-related genes (S1 Table). NGS and mutation analysis were performed with genomic DNA extracted from plasma and tumor tissue samples in DLBCL patients of JSPH cohort as previously described [11].

3. LymphGen subtypes

According to the LymphGen algorithm [3], the hallmark genetic mutations of the MCD subtype include MYD88^L265P, CD79B, PIM1, BTG1, and so on. Additionally, BN2 was defined by BCL6 translocations and NOTCH2 mutations, ST2 featured SGK1 and TET2 mutations, EZB was characterized by EZH2 mutations and BCL2 translocations, N1 included NOTCH1 mutations. Given the dependence of A53 subtyping on TP53 mutations and copy number variation (CNV), as well as the unavailability of some CNV data, both A53 and unassigned cases were classified as other subtypes in this study.

4. RNA-seq and gene expression analysis

RNA-seq and gene expression analysis were performed as previously described [11]. We selected differentially expressed genes (DEGs) with p-values < 0.05 and log2 (fold-change) > 0.8 or < ‒0.8 as significant. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were performed with the R package ‘clusterProfiler’ to decipher the meaningful signaling pathways and biological processes in the enrichment analysis of DEGs. Gene Set Enrichment Analysis (GSEA) was conducted to further elucidate the biological role of DEGs.

5. Liquid chromatography–mass spectrometry

Peripheral blood was collected using anticoagulated tubes from DLBCL patients. The blood samples were centrifuged and the upper layer was transferred to a fresh tube and stored at –80°C. Liquid chromatography–mass spectrometry (LC-MS) was performed by Novogene as previously described [12].

6. Statistical analysis

Progression-free survival (PFS) was defined as the time from diagnosis to disease progression or relapse. The overall survival (OS) time was defined as the date of diagnosis to the date of death or the last follow-up date. Unpaired Students’ t-tests were used for comparing normally distributed variables. The correlation analysis was actualized by Spearman’s correlation test. The Kaplan-Meier method and Log-Rank or Breslow tests were used for survival analysis. p < 0.05 was considered statistically significant. All the statistical analyses were performed using the SPSS software ver. 26.0 (IBM Corp.), R software ver. 4.3.1 (R Foundation for Statistical Computing), and Graphpad Prism 9.0 (GraphPad Software Inc.).

Results

1. The detailed distribution and characteristic of BTG1 mutation in DLBCL

We included 243 DLBCL patients from JSPH cohort and 2,379 DLBCL patients from five public databases as external cohort for analysis. A total of 262 patients (11.0%, 262/2,379) in the external cohort had BTG1^mut, while the ratio was slightly higher at 25.1% (61/243) in JSPH cohort. Patients with mutations were further analyzed (Fig. 1A), missense mutations (85.6%) dominated the mutation types in the external cohort, the remaining were nonsense mutation (5.7%), splice site (4.5%), frameshift (3.0%), and inframe (1.2%). These were also similar in the cohort of JSPH, including the dominant missense mutation (68.9%), nonsense mutations (12.6%), frameshift (8.8%), splice site (6.3%), and inframe (3.4%). BTG1 mutations were predominated by C>T (66/238, external cohort; 27/152, JSPH cohort) and G>A (47/238, external cohort; 48/152, JSPH cohort) transitions (Fig. 1B). According to LymphGen algorithm, 262 BTG1^mut patients in external cohort were classified into other subtype (unclassified, n=85, 32.4%), MCD subtype (n=77, 29.4%), EZB subtype (n=45, 17.2%), BN2 subtype (n=29, 11.0%), ST2 subtype (n=23, 8.8%), and N1 subtype (n=3, 1.2%), while 61 BTG1^mut patients in JSPH cohort were distributed in MCD subtype (n=35, 57.4%), other (n=10, 16.4%), BN2 subtype (n=10, 16.4%), ST2 subtype (n=4, 6.6%), and EZB subtype (n=2, 3.2%) (Fig. 1C). Upon analyzing the distribution patterns, 191 mutations (73.7%, external cohort) and 134 mutations (66.3%, JSPH cohort) occurred in exon 1 (Fig. 1D).

The clinical characteristics between the BTG1^mut and BTG1^wt groups in the external cohort are summarized in Table 1, BTG1^mut was noticeably associated with older age (p < 0.001) and MCD subtype (p < 0.001) in the external cohort. In JSPH cohort, BTG1^mut was related to age (p=0.042), extranodal involvement (p=0.025), elevated serum lactate dehydrogenase (LDH) level (p=0.049), high International Prognostic Index scores (p=0.025), and MCD subtype (p < 0.001). Intriguingly, BTG1 alterations demonstrated a previously unrecognized association with double-expressor both in external (p=0.019) and JSPH cohort (p=0.006). Note that a comparison of the different sites of extranodal involvement showed that patients with BTG1^mut were more likely to have testis involvement (p=0.026) (Fig. 1E). Pretreatment plasma circulating tumor DNA (ctDNA) burden was similar between BTG1^mut and BTG1^wt in JSPH cohort (Fig. 1F). However, in the BTG1^mut subgroup, we found that high ctDNA burden was associated with unfavorable characteristics, such as advanced stage (p < 0.001), elevated serum LDH levels (p < 0.001), poor Eastern Cooperative Oncology Group (p < 0.001), and multiple extranodal involvement (p < 0.001) (Fig. 1G).

2. The prognostic impact of BTG1 mutation

We compared the prognostic significance of different BTG1 mutation status in JSPH DLBCL cohort and external cohort, patients with BTG1^mut showed significantly adverse impact on OS (p=0.005, external cohort; p=0.030, JSPH cohort) (Fig. 2A and B) in DLBCL. Although no significance was observed in PFS, the survival analysis curve showed a trend of poorer prognosis in patients with BTG1^mut (Fig. 2C and D). To address the difference, we compared the responses of the BTG1^wt and BTG1^mut group to first-line and post-progression subsequent treatments in JSPH cohort, respectively (S2 Fig). We found that the overall response rate (ORR) was 76.9% (95% confidence interval [CI], 70.7 to 83.1) in BTG1^wt group and 73.8% (95% CI, 62.4 to 85.1) in BTG1^mut group, and no significant difference was observed (p=0.617). This indicated that the BTG1 mutation status would not impact the response to the initial treatment. However, in second-line treatment with available data from 48 patients, the ORR of the BTG1^wt group could reach 70.3% (95% CI, 54.8 to 85.7), while that of the mutant group was only 36.4% (95% CI, 2.5 to 70.3), with a statistically significant difference (p=0.044). In terms of PFS of second-line treatment (S2J Fig.), a significant p-value was generated by the Breslow test (p=0.020), whereas the log rank test yielded a borderline p-value (p=0.088). Such a result would indicate that patients harboring BTG1^mut exhibited progressively diminished therapeutic efficacy with successive lines of therapy and progression of disease. It may also imply the cumulative drug resistance mechanism activated during the disease evolution process, thereby leading to survival differences. Further details disclose that the presence of missense mutations in BTG1 was tied to poorer OS (p=0.007, external cohort; p=0.041, JSPH cohort) compared to those with BTG1^wt. The C>T and G>A transitions in BTG1^mut were remarkably associated with poor OS (p=0.047, external cohort; p < 0.001, JSPH cohort). BTG1^mut occurring in exon 1 results in poorer OS similarly as well (p < 0.001, external cohort; p=0.015, JSPH cohort). Besides, OS were shorter in the multi-local mutation group than in the wild-type group (p=0.090, external cohort; p=0.0177, JSPH cohort) (Fig. 2E-L). Nevertheless, these significant differences were not observed in the PFS (S2A-S2H Fig.). Associations between BTG1 mutation status and OS were examined in different subgroups (Fig. 2M and N). In age subgroups, elders had a significantly worse prognosis among patients with BTG1^mut (p for interaction < 0.001, external cohort; p for interaction=0.001, JSPH cohort). Similar interactive relationships prevailed between BTG1^mut and other clinical factors including double-expressor (all p for interaction < 0.05 except sex and the limited sample double-hit).

3. Different mutation profile between BTG1^mut and BTG1^wt in JSPH cohort

The onco-plot was generated to contrast between BTG1^mut patients and BTG1^wt patients (Fig. 3A). In the BTG1^mut DLBCL patients of the JSPH cohort, the most frequently mutated genes in DLBCL plasma were PIM1, BCL6, KMT2D, BTG2, TP53, MYD88, and CD79B. The gene alteration frequencies of PIM1, KMT2D, BTG2, MYD88, CD79B, ETV6, KLF2, CD58, MYC, CD70, and TBL1XR1 were significantly higher in BTG1^mut than those in BTG1^wt group in JSPH cohort. Co-mutation and mutual exclusivity analysis identified interaction pair genes, BTG1 mutations co-occur with PIM1, MYD88, CD79B, BTG2, KLF2, CD58, and IRF4. the gene mutations of DTX1, SOCS1, ITPKB, TBL1XR1, and HIST1H1E were significantly correlated with BTG1 co-mutation (Fig. 3B).

4. Difference of metabolic profiling between BTG1^mut and BTG1^wt

Due to the marked heterogeneity of DLBCL, the prognosis of patients tends to vary significantly. The conversion of cancer cells are accompanied by various biological characteristics with metabolic reprogramming being particularly notable. We speculated whether the variance in prognosis attributed to BTG1 was also a result of metabolic reprogramming. Therefore, we utilized LC-MS to conduct untargeted metabolomic analysis of serum samples from 40 DLBCL patients (20 with BTG1^mut vs. 20 with BTG1^wt). A total of 1,561 metabolites were detected in total ion mode, 64 of which were significantly different, including 32 downregulated metabolites and 32 upregulated metabolites (Fig. 4A). Sixty-four different metabolites were searched in human metabolome database (HMDB) (https://hmdb.ca/), and 51 of them could be classified into lipids and lipid-like molecules (43.1%), organoheterocyclic compounds (17.6%), benzenoids (13.7%), organic acids and derivatives (9.8%), phenylpropanoids and polyketides (5.9%), nucleosides, nucleotides, and analogues (5.9%), organic nitrogen compounds (2.0%), organic oxygen compounds (2.0%) (Fig. 4B). Hierarchical cluster analysis suggested that the distributions of different types of metabolites had certain heterogeneity among groups (Fig. 4C). The correlation analysis showed that differential metabolites had a synergistic or incompatible relationship, suggesting mutual regulation of differential metabolites (Fig. 4D). The metabolic pathways related to BTG1 and reliable results were analyzed by KEGG and HMDB, and the results of pathway analysis are shown in Fig. 4E, and several meaningful pathways have been found, including amino acid metabolism, lipid metabolism, metabolism of cofactors and vitamins, carbohydrate metabolism, nucleotide metabolism, and metabolism of other amino acids. The interaction diagram between different metabolic pathways establishes an essential network of metabolic reprogramming, as shown in Fig. 4F. Considering the correlation between high-frequency BTG1^mut and MCD subtype, we paid more attention to the differential metabolism of MCD subtype and non-MCD subtype in BTG1^mut. KEGG results showed significant enrichment of biosynthesis of unsaturated fatty acids and central carbon metabolism in cancer (Fig. 4G).

5. Potential biological mechanisms between BTG1^mut and BTG1^wt

Given the complex pathophysiological mechanisms involved in DLBCL, we wondered if there were underlying biological mechanisms responsible for the differences and outcomes between the two groups. RNA-seq analysis was performed on 195 DLBCL patients from JSPH cohort based on BTG1 mutation status, however significant difference of BTG1 expression was not observed (p=0.221) (Fig. 5A). In general, 543 DEGs including 353 downregulated and 190 upregulated genes were identified in BTG1^mut patients (Fig. 5B). The differential genes for BTG1 mutations and wild types were shown in the heat map (Fig. 5C). KEGG analysis further elucidated the abnormalities of crucial pathways such as the protein digestion and absorption, interleukin 17 (IL-17) signaling pathway, cell adhesion molecules, phosphoinositide 3-kinase (PI3K)–Akt signaling pathway, nuclear factor кB (NF-кB) signaling pathway, cytokine-cytokine receptor interaction in BTG1^mut DLBCL patients (Fig. 5D). Extracellular matrix organization, extracellular structure organization, external encapsulating structure organization, cell-cell adhesion via plasma-membrane adhesion molecules were detected in GO analysis (Fig. 5E). GSEA analysis indicates, the upregulated biological processes were mainly associated with ribosome, nucleotide excision repair, nucleocytoplasmic transport, while the downregulated genes were enriched in tumor necrosis factor (TNF) signaling pathway, Rap1 signaling pathway, IL-17 signaling pathway, AGE-RAGE (advanced glycation end products–receptor for advanced glycation end products) signaling pathway in diabetic complications (Fig. 5F).

Discussion

DLBCL is aggressive malignancy, wherein a subset of patients frequently experience relapse, mechanisms that mediate these poor outcomes are unknown. The extreme genetic and phenotypic heterogeneity of DLBCL presents a challenge to the development of precision therapies.

In this study, we performed a comprehensive analysis of the genomic characteristics of BTG1 through high-throughput sequencing in 243 DLBCL patients from JSPH cohort and 2,379 DLBCL patients from five public databases as external cohort. BTG1^mut have been observed in DLBCL with a frequency of 11.0% in external cohort and 25.1% in JSPH cohort, while Ren et al. [13] reported a mutation rate of 15% and 20% was reported in another Chinese cohort study [14]. Beyond differences in regional and sequencing techniques, enrichment of BTG1^mut in MCD DLBCL and the larger proportion of MCD subtype patients in our center may partly explain the slightly higher mutation rate of BTG1 in our cohort than in the external cohort. BTG1 mutations are missense with no mutational hotspot. Clinical relevance between BTG1^mut and clinicopathological characteristics, such as age, extranodal involvement, and elevated LDH level. In particular, patients harboring BTG1^mut more frequently had testis involvement. Pretreatment plasma ctDNA of BTG1^mut was remarkably higher in JSPH cohort, suggesting unfavorable outcomes as well. Among patients from the JSPH cohort, OS of BTG1^mut were significantly shorter than that of BTG1^wt group (p=0.030), similar results were confirmed in the external validation cohort (p=0.005). In short, BTG1^mut identify a molecular subgroup of DLBCL with inferior prognosis.

BTG1 was originally identified as a translocation partner of the c-Myc gene in a case of B-cell chronic lymphocytic leukemia and belongs to a family of anti-proliferative genes. BTG1 is constitutively expressed in quiescent cells [15]. They have been implicated in cell differentiation, development, and apoptosis [16]. It is implicated in regulating cell cycle progression, differentiation, and cell death by interacting with diverse transcriptional modulators, such as activating transcription factor 4 (ATF4) [17], nuclear receptors [18], protein arginine methyltransferase 1 (PRMT1) [19], and CCR4 transcriptional complex [20]. In B-cell neoplasms, BTG1 harboring a strong signature of the activity of activation-induced cytidine deaminase has mostly been considered as passengers of lymphomagenesis, its inactivation accelerates the development of fatal lymphoproliferative, and promotes lymphoma dissemination [21].

As the most common malignancy from lymphoma, the metabolic reprogramming of DLBCL is important in its development. Nevertheless, the connection between the metabolic map and BTG1 mutation status is yet to be discovered. We performed the first untargeted metabolomic analysis of differential metabolites between BTG1^mut and BTG1^wt. The results highlight the critical role of metabolic pathways in the BTGmut, especially amino acid metabolism and lipid metabolism. Cho et al. [17] demonstrated that cystine and methionine deficiency induced ferroptosis by ATF4-dependent BTG1 induction. Moreover, we successfully pinpointed the crucial involvement of lipid metabolism in DLBCL patients with BTG1^mut. Organelles contain lipids that serve as essential nutrients for normal cell growth [22]. Research [23] showed that the alteration of lipid metabolism is vital in various activities such as signal transduction during cancer cell progression. Xiao et al. [24] have identified a role of BTG1 in regulating hepatic lipid metabolism. They observed that the abundance of BTG1 mRNA and protein was decreased in the liver of mice with a genetic model of obesity, whereas knockdown of BTG1 induced liver steatosis in wild-type mice. Consistent with these changes, BTG1 decreased triglyceride accumulation in cultured hepatocytes. Zhou et al. [25] demonstrated that overexpression of miR-183-5p led to hepatic triglyceride accumulation and upregulation of lipogenic genes by targeting BTG1. These findings could offer fresh insights for clinicians to make more accurate clinical assessments of BTG1^mut and formulate personalized metabolic treatment strategies.

By merging RNA-seq, we have revealed previously undiscovered pathways in BTG1^mut pathogenesis, such as cell interaction and adhesion, several of which are amenable to therapeutic attack [26]. The cell adhesion of BTG1 may partially contribute to extranodal involvement in MCD subtype. A subsequent study [21] demonstrated BTG1 inactivation promotes lymphoma dissemination through the BCAR1-RAC1 pathway, the interaction provokes major changes of the actin cytoskeleton, associated with increased adhesion and diffusion of fibronectin and increased migration. These modifications are targetable with the SRC inhibitor dasatinib, which opens novel therapeutic opportunities in BTG1-mutated DLBCL.

Furthermore, this study found that the BTG1 mutation status was different in OS but not in PFS. In addition to the minor impact of BTG1 status on response to first-line treatment, more importantly, BTG1 alterations significantly reduced the response rate and PFS of second-line treatment. Concerning potential biological mechanisms, in combination with the findings we have presented. First, abnormal activation of critical pathways such as nucleotide excision repair, PI3K-Akt signaling pathway, and NF-κB signaling pathway in BTG1^mut DLBCL patients might contribute to the failure of subsequent treatments. Furthermore, enhanced lipid metabolism in BTG1^mut also provided the necessary materials and energy for the resurgence of tumor cells. Finally, the activation of pathways, including TNF signaling pathway and IL-17 signaling pathway, in cases of BTG1^mut assisted in the remodeling of the tumor microenvironment, which played an indispensable role in second-line treatments and drug-resistant.

However, the disadvantage is that our study could not definitively confirm a direct association between BTG1 mutation and expression. One of the possible reasons for this is that in addition to the real gene expression status of lymphoma cells, the microenvironment cannot be ignored. From the reports, consistent with their growth inhibitory properties, BTG1 expression is reduced in several cancer tissues, pointing to a role for the BTG/TOB family in tumor suppression [27]. The expression of BTG1 is downregulated in non–small cell lung cancer and other tumors [28,29], which possibly related to tumor metastasis and poor prognosis.

This study has several limitations. Firstly, there was little investigation into the expression of proteins and RNA in this work, which was primarily concerned with the analysis of BTG1 mutations. We will examine the function at the protein level concurrently in further detail. In addition, we conducted mutation frequency analysis and survival analysis based on cross-gene panels from different published studies, which might have missed some significant gene changes. There were also a few details about treatment and other confounding factors which would require to be controlled to substantiate the conclusions. Finally, we need to investigate mechanisms in vivo and in vitro to make up for the lack in this study.

Our study uncovered that the BTG1 mutation contributes to the inferior prognosis of DLBCL through internal real data and external integration data, indicating that incorporating genetic detection of BTG1 mutations may aid in identifying high-risk DLBCL patients and could offer predictive information for tailoring personalized treatment strategies.

Electronic Supplementary Material

Supplementary materials are available at Cancer Research and Treatment website (https://www.e-crt.org).

crt-2025-494_S1_Table.pdf

crt-2025-494_S2_Fig.pdf

NOTES

Ethical Statement

This study was approved by the Ethics Committee of the Institutional Review Board of Jiangsu Province Hospital (No. 2023-SR-190). Informed consents were provided according to the Declaration of Helsinki.

Author Contributions

Conceived and designed the analysis: Li JY, Gao R, Xu W, Liang JH.

Collected the data: Du KX, Wu YF, Li Y.

Contributed data or analysis tools: Yin H, Shen HR, Wang L.

Performed the analysis: Shang CY, Hua W, Xing TY.

Wrote the paper: Shang CY, Hua W, Xing TY, Gao R, Liang JH.

Conflicts of Interest

Conflict of interest relevant to this article was not reported.

Funding

This research was funded by the National Natural Science Foundation of China (grant number 82200887 and 82370194), Jiangsu Science and Technology Department (grant number BK20220716 and BE2023780), China Postdoctoral Science Foundation (grant number 2022M7114034 and 2023M741463), Nanjing Municipal Returned Overseas Scholars Foundation (grant number BSHNJ2023011), Jiangsu Province Hospital Talent Specialized Foundation (grant number MXJL202107), Jiangsu Province “333” Project (grant number BRA2018085), Jiangsu Province Hospital Clinical Diagnosis and Treatment Technologies Innovation Project Foundation (grant number JBGS202405).

Fig. 1.

The detailed distribution and characteristic of B-cell translocation gene 1 (BTG1) mutation in diffuse large B-cell lymphoma. (A) The proportion of BTG1 mutation types in the external cohort and the Jiangsu Province Hospital (JSPH) cohort. (B) The proportion of BTG1 mutation according to different base substitution. (C) Column charts illustrating the percentage of BTG1^mut in different genetic subtypes in external cohort and JSPH cohort. (D) The lollipop plot illustrating specific localization of BTG1 variants. (E) Extranodal involvements according to BTG1 mutation status in JSPH cohort. (F) Pretreatment circulating tumor DNA (ctDNA) burden according to BTG1 mutation status in JSPH cohort. (G) The association of pretreatment ctDNA burden with different clinical characteristics within BTG1^mut group in JSPH cohort. BTG1^mut, BTG1 mutation; BTG1^wt, BTG1 wild-type; ECOG, Eastern Cooperative Oncology Group; LDH, lactate dehydrogenase. *p < 0.05, ****p < 0.0001; ns, not significant.

Fig. 2.

The prognostic impact of B-cell translocation gene 1 (BTG1) mutation. Kaplan-Meier curves of overall survival (OS) (A) and progression-free survival (PFS) (C) in external cohort. Kaplan-Meier curves of OS (B) and PFS (D) in Jiangsu Province Hospital (JSPH) cohort. (E) Kaplan-Meier curves of OS between BTG1^mut with missense and BTG1^wt in external cohort. (F) Kaplan-Meier curves of OS between BTG1^mut with C>T or G>T transitions and BTG1^wt in external cohort. (G) Kaplan-Meier curves of OS between BTG1^mut in exon1 and BTG1^wt in external cohort. (H) Kaplan-Meier curves of OS between BTG1^mut with multi-locus mutation and BTG1^wt in external cohort. (I) Kaplan-Meier curves of OS between BTG1^mut with missense and BTG1^wt in JSPH cohort. (J) Kaplan-Meier curves of OS between BTG1^mut with C>T transitions or G>T transitions and BTG1^wt in JSPH cohort. (K) Kaplan-Meier curves of OS between BTG1^mut in exon1 and BTG1^wt in JSPH cohort. (L) Kaplan-Meier curves of OS between BTG1^mut with multi-locus mutation and BTG1^wt in JSPH cohort. (M) Subgroup survival analyses for OS of clinical characteristics in external cohort. (N) Subgroup survival analyses for OS of clinical characteristics in JSPH cohort. The analysis of double-hit lymphoma was restricted by limited sample size. BTG1^mut, BTG1 mutation; BTG1^wt, BTG1 wild-type; CI, confidence interval; ECOG, Eastern Cooperative Oncology Group; HR, hazard ratio; LDH, lactate dehydrogenase; NA, not available.

Fig. 3.

Different mutation profile between BTG1^mut and BTG1^wt in Jiangsu Province Hospital (JSPH) cohort. (A) Overview of the distinct profiles of BTG1^mut and BTG1^wt in JSPH cohort. (B) Co-mutational and mutually exclusive patterns in gene pairs based on the top 30 most frequently mutated genes. The purple squares represent co-occurring mutations, and pink represent mutually exclusive mutations. BTG1^mut, B-cell translocation gene 1 (BTG1) mutation; BTG1^wt, BTG1 wild-type; CNV, copy number variation; SV, structural variant.

Fig. 4.

Difference of metabolic profiling between BTG1^mut and BTG1^wt. (A) Volcano diagram of differential metabolites. (B) Human metabolome database (HMDB) functional annotation of differential metabolites. (C) Heat map of serum metabolites. (D) Correlation analysis of differential metabolites. (E) Kyoto Encyclopedia of Genes and Genomes (KEGG) function annotation of differential metabolites. (F) Interaction diagrams between different metabolic pathway. (G) KEGG pathway enrichment of differential metabolites between MCD subtype and non-MCD subtype in BTG1^mut group. BTG1^mut, B-cell translocation gene 1 (BTG1) mutation; BTG1^wt, BTG1 wild-type.

Fig. 5.

Potential biological mechanisms between BTG1^mut and BTG1^wt. (A) BTG1 gene expression in tumor samples of diffuse large B-cell lymphoma patients revealed by RNA-seq in Jiangsu Province Hospital (JSPH) cohort. (B) Volcano plot showing differentially expressed genes between BTG1^mut and BTG1^wt in JSPH cohort. (C) Heat map revealing different gene expression patterns between BTG1^mut and BTG1^wt. (D) Bubble chart illustrating the enriched pathways in BTG1^mut and BTG1^wt by Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations. (E) Bubble chart reflecting the enriched pathways in BTG1^mut and BTG1^wt by Gene Ontology (GO) annotations. (F) Ridgeline plot depicting biological processes by gene set enrichment analysis enrichment analysis. AGE-RAGE, advanced glycation end products–receptor for advanced glycation end products; BP, biological process; BTG1^mut, B-cell translocation gene 1 (BTG1) mutation; BTG1^wt, BTG1 wild-type; CC, cellular component; ECM, extracellular matrix; IL-17, interleukin 17; MF, molecular function; ns, not significant; PI3K, phosphoinositide 3-kinase; TNF, tumor necrosis factor.

Table 1.

Clinical characteristics of DLBCL patients according to BTG1 mutational status in external cohort and JSPH cohort

Characteristic	External cohort			JSPH cohort
Characteristic	BTG1^wt (n=2,117)	BTG1^mut (n=262)	p-value	BTG1^wt (n=182)	BTG1^mut (n=61)	p-value
Sex
Female	593	58	0.758	77	25	0.856
Male	803	83		105	36
Unknown	721	121		0	0
Age (yr)
≤ 60	778	63	< 0.001	102	25	0.042
> 60	1,281	198		80	36
Unknown	58	1		0	0
Stage
I/II	502	61	0.065	77	20	0.189
III/IV	862	75		105	41
Unknown	753	126		0	0
No. of extranodal sites
< 2	976	103	0.109	119	30	0.025
≥ 2	260	18		63	31
Unknown	881	141		0	0
Serum LDH level
Normal	575	62	0.571	104	26	0.049
Elevated	690	67		78	35
Unknown	852	133		0	0
ECOG
0-1	958	98	0.815	149	51	0.758
2-4	349	34		33	10
Unknown	810	130		0	0
IPI risk group
Low	545	74	0.726	77	19	0.025
Intermediate	844	102		70	20
High	353	42		35	22
Unknown	375	44		0	0
aaIPI risk group
Low	91	8	0.376	45	12	0.768
Low-Intermediate	105	3		21	3
High-Intermediate	115	7		24	6
High	67	3		12	4
Unknown	400	42		0	0
LymphGen subtype
Non-MCD	2,008	185	< 0.001	155	26	< 0.001
MCD	108	77		27	35
Unknown	1	0		0	0
Double-expressor
Non-double-expressor	444	36	0.019	143	37	0.006
Double-expressor	163	25		39	24
Unknown	1,510	201		0	0
Double-hit
Non-double-hit	577	57	0.540	91	33	1.000
Double-hit	30	4		2	1
Unknown	1,510	201		89	27

aaIPI, age-adjusted International Prognostic Index; BTG1^mut, BTG1 mutation; BTG1^wt, BTG1 wild-type; DLBCL, diffuse large B-cell lymphoma; ECOG, Eastern Cooperative Oncology Group; IPI, International Prognostic Index; JSPH, Jiangsu Province Hospital; LDH, lactate dehydrogenase.

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BTG1 Mutation Correlates with Inferior Prognosis in Diffuse Large B-Cell Lymphoma

Cancer Res Treat. 2026;58(3):1002-1020. Published online July 30, 2025

DOI: https://doi.org/10.4143/crt.2025.494

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Abstract
Introduction
Materials and Methods
Results
Discussion
Electronic Supplementary Material
NOTES
REFERENCES

A Novel CD58 Mutation–Related Signature Predicts Prognosis Risk in Diffuse Large B-Cell Lymphoma Patients

BTG1 Mutation Correlates with Inferior Prognosis in Diffuse Large B-Cell Lymphoma

Fig. 1. The detailed distribution and characteristic of B-cell translocation gene 1 (BTG1) mutation in diffuse large B-cell lymphoma. (A) The proportion of BTG1 mutation types in the external cohort and the Jiangsu Province Hospital (JSPH) cohort. (B) The proportion of BTG1 mutation according to different base substitution. (C) Column charts illustrating the percentage of BTG1mut in different genetic subtypes in external cohort and JSPH cohort. (D) The lollipop plot illustrating specific localization of BTG1 variants. (E) Extranodal involvements according to BTG1 mutation status in JSPH cohort. (F) Pretreatment circulating tumor DNA (ctDNA) burden according to BTG1 mutation status in JSPH cohort. (G) The association of pretreatment ctDNA burden with different clinical characteristics within BTG1mut group in JSPH cohort. BTG1mut, BTG1 mutation; BTG1wt, BTG1 wild-type; ECOG, Eastern Cooperative Oncology Group; LDH, lactate dehydrogenase. *p < 0.05, ****p < 0.0001; ns, not significant.

Fig. 2. The prognostic impact of B-cell translocation gene 1 (BTG1) mutation. Kaplan-Meier curves of overall survival (OS) (A) and progression-free survival (PFS) (C) in external cohort. Kaplan-Meier curves of OS (B) and PFS (D) in Jiangsu Province Hospital (JSPH) cohort. (E) Kaplan-Meier curves of OS between BTG1mut with missense and BTG1wt in external cohort. (F) Kaplan-Meier curves of OS between BTG1mut with C>T or G>T transitions and BTG1wt in external cohort. (G) Kaplan-Meier curves of OS between BTG1mut in exon1 and BTG1wt in external cohort. (H) Kaplan-Meier curves of OS between BTG1mut with multi-locus mutation and BTG1wt in external cohort. (I) Kaplan-Meier curves of OS between BTG1mut with missense and BTG1wt in JSPH cohort. (J) Kaplan-Meier curves of OS between BTG1mut with C>T transitions or G>T transitions and BTG1wt in JSPH cohort. (K) Kaplan-Meier curves of OS between BTG1mut in exon1 and BTG1wt in JSPH cohort. (L) Kaplan-Meier curves of OS between BTG1mut with multi-locus mutation and BTG1wt in JSPH cohort. (M) Subgroup survival analyses for OS of clinical characteristics in external cohort. (N) Subgroup survival analyses for OS of clinical characteristics in JSPH cohort. The analysis of double-hit lymphoma was restricted by limited sample size. BTG1mut, BTG1 mutation; BTG1wt, BTG1 wild-type; CI, confidence interval; ECOG, Eastern Cooperative Oncology Group; HR, hazard ratio; LDH, lactate dehydrogenase; NA, not available.

Fig. 3. Different mutation profile between BTG1mut and BTG1wt in Jiangsu Province Hospital (JSPH) cohort. (A) Overview of the distinct profiles of BTG1mut and BTG1wt in JSPH cohort. (B) Co-mutational and mutually exclusive patterns in gene pairs based on the top 30 most frequently mutated genes. The purple squares represent co-occurring mutations, and pink represent mutually exclusive mutations. BTG1mut, B-cell translocation gene 1 (BTG1) mutation; BTG1wt, BTG1 wild-type; CNV, copy number variation; SV, structural variant.

Fig. 4. Difference of metabolic profiling between BTG1mut and BTG1wt. (A) Volcano diagram of differential metabolites. (B) Human metabolome database (HMDB) functional annotation of differential metabolites. (C) Heat map of serum metabolites. (D) Correlation analysis of differential metabolites. (E) Kyoto Encyclopedia of Genes and Genomes (KEGG) function annotation of differential metabolites. (F) Interaction diagrams between different metabolic pathway. (G) KEGG pathway enrichment of differential metabolites between MCD subtype and non-MCD subtype in BTG1mut group. BTG1mut, B-cell translocation gene 1 (BTG1) mutation; BTG1wt, BTG1 wild-type.

Fig. 5. Potential biological mechanisms between BTG1mut and BTG1wt. (A) BTG1 gene expression in tumor samples of diffuse large B-cell lymphoma patients revealed by RNA-seq in Jiangsu Province Hospital (JSPH) cohort. (B) Volcano plot showing differentially expressed genes between BTG1mut and BTG1wt in JSPH cohort. (C) Heat map revealing different gene expression patterns between BTG1mut and BTG1wt. (D) Bubble chart illustrating the enriched pathways in BTG1mut and BTG1wt by Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations. (E) Bubble chart reflecting the enriched pathways in BTG1mut and BTG1wt by Gene Ontology (GO) annotations. (F) Ridgeline plot depicting biological processes by gene set enrichment analysis enrichment analysis. AGE-RAGE, advanced glycation end products–receptor for advanced glycation end products; BP, biological process; BTG1mut, B-cell translocation gene 1 (BTG1) mutation; BTG1wt, BTG1 wild-type; CC, cellular component; ECM, extracellular matrix; IL-17, interleukin 17; MF, molecular function; ns, not significant; PI3K, phosphoinositide 3-kinase; TNF, tumor necrosis factor.

Fig. 1.

Fig. 2.

Fig. 3.

Fig. 4.

Fig. 5.

BTG1 Mutation Correlates with Inferior Prognosis in Diffuse Large B-Cell Lymphoma

Characteristic	External cohort			JSPH cohort
Characteristic	BTG1^wt (n=2,117)	BTG1^mut (n=262)	p-value	BTG1^wt (n=182)	BTG1^mut (n=61)	p-value
Sex
Female	593	58	0.758	77	25	0.856
Male	803	83		105	36
Unknown	721	121		0	0
Age (yr)
≤ 60	778	63	< 0.001	102	25	0.042
> 60	1,281	198		80	36
Unknown	58	1		0	0
Stage
I/II	502	61	0.065	77	20	0.189
III/IV	862	75		105	41
Unknown	753	126		0	0
No. of extranodal sites
< 2	976	103	0.109	119	30	0.025
≥ 2	260	18		63	31
Unknown	881	141		0	0
Serum LDH level
Normal	575	62	0.571	104	26	0.049
Elevated	690	67		78	35
Unknown	852	133		0	0
ECOG
0-1	958	98	0.815	149	51	0.758
2-4	349	34		33	10
Unknown	810	130		0	0
IPI risk group
Low	545	74	0.726	77	19	0.025
Intermediate	844	102		70	20
High	353	42		35	22
Unknown	375	44		0	0
aaIPI risk group
Low	91	8	0.376	45	12	0.768
Low-Intermediate	105	3		21	3
High-Intermediate	115	7		24	6
High	67	3		12	4
Unknown	400	42		0	0
LymphGen subtype
Non-MCD	2,008	185	< 0.001	155	26	< 0.001
MCD	108	77		27	35
Unknown	1	0		0	0
Double-expressor
Non-double-expressor	444	36	0.019	143	37	0.006
Double-expressor	163	25		39	24
Unknown	1,510	201		0	0
Double-hit
Non-double-hit	577	57	0.540	91	33	1.000
Double-hit	30	4		2	1
Unknown	1,510	201		89	27

Table 1. Clinical characteristics of DLBCL patients according to BTG1 mutational status in external cohort and JSPH cohort

aaIPI, age-adjusted International Prognostic Index; BTG1^mut, BTG1 mutation; BTG1^wt, BTG1 wild-type; DLBCL, diffuse large B-cell lymphoma; ECOG, Eastern Cooperative Oncology Group; IPI, International Prognostic Index; JSPH, Jiangsu Province Hospital; LDH, lactate dehydrogenase.