Scholarlink

Background <p>Diffuse large B-cell lymphoma (DLBCL) is a clinically and molecularly heterogeneous malignancy with variable outcomes. Identification of reliable biomarkers is critical for risk assessment and targeted therapy.</p> Methods <p>We conducted a two-sample Mendelian randomization (MR) analysis to systematically evaluate the causal effects of 4907 plasma proteins on DLBCL. Transcriptomic data were integrated to screen proteins with transcriptional relevance. Bayesian co-localization and reverse MR analyses were applied to assess shared genetic variants and causal direction. Survival analyses were used to evaluate the prognostic significance of the identified biomarkers across independent cohorts.</p> Results <p>Fifty-two plasma proteins were identified as risk factors for DLBCL, of which six (GOLM1, ISOC1, MTHFD1, PPIL1, RACGAP1, and WDR5) demonstrated robust associations at both protein and transcriptional levels. No evidence of reverse causality was observed, and co-localization analyses did not support shared causal variants. Among the six biomarkers, MTHFD1, PPIL1, and WDR5 showed consistent associations with poor prognosis across cohorts.</p> Conclusion <p>This study identified novel biomarkers at both protein and transcriptional levels, offering valuable insights for risk assessment and early prognosis prediction in DLBCL.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Exploring potential biomarkers of diffuse large B-cell lymphoma through multi-dimensional data

  • 期刊论文

  • 发表日期：

    2026年2月25日

查看全文
  • Chi Li,
  • Xinli Han,
  • Xiaoshuang Hu,
  • Shurui Du,
  • Peihan An,
  • Dao Wang

摘要

Background

Diffuse large B-cell lymphoma (DLBCL) is a clinically and molecularly heterogeneous malignancy with variable outcomes. Identification of reliable biomarkers is critical for risk assessment and targeted therapy.

Methods

We conducted a two-sample Mendelian randomization (MR) analysis to systematically evaluate the causal effects of 4907 plasma proteins on DLBCL. Transcriptomic data were integrated to screen proteins with transcriptional relevance. Bayesian co-localization and reverse MR analyses were applied to assess shared genetic variants and causal direction. Survival analyses were used to evaluate the prognostic significance of the identified biomarkers across independent cohorts.

Results

Fifty-two plasma proteins were identified as risk factors for DLBCL, of which six (GOLM1, ISOC1, MTHFD1, PPIL1, RACGAP1, and WDR5) demonstrated robust associations at both protein and transcriptional levels. No evidence of reverse causality was observed, and co-localization analyses did not support shared causal variants. Among the six biomarkers, MTHFD1, PPIL1, and WDR5 showed consistent associations with poor prognosis across cohorts.

Conclusion

This study identified novel biomarkers at both protein and transcriptional levels, offering valuable insights for risk assessment and early prognosis prediction in DLBCL.