Scholarlink

Objective <p>To identify shared molecular mechanisms and crosstalk genes (CGs) between diffuse large B-cell lymphoma (DLBCL) and primary Sjögren’s syndrome (PSS), and to explore the role of MICA in the immune microenvironment (IME).</p> Methods <p>Bioinformatics analysis of GEO datasets identified differentially expressed genes (DEGs). Enrichment, immune infiltration, and Mendelian randomization analyses were performed. Diagnostic biomarkers were screened using Lasso regression. Functional roles of MICA were validated in SUDHL-6 cells via overexpression/knockdown, assessing immune cell infiltration, cytokine secretion, proliferation, and apoptosis.</p> Results <p>We identified 50 shared DEGs. RPL31, HNMT, and IFI27 were defined as diagnostic biomarkers. MR analysis confirmed a causal effect of PSS on DLBCL risk. MICA interacted with RPL31 and HNMT. MICA overexpression enhanced CD8<sup>+</sup> T-cell infiltration and activation, elevated pro-inflammatory cytokine release, suppressed tumor cell proliferation, and promoted apoptosis. Conversely, MICA knockdown suppressed anti-tumor immune activity and promoted tumor growth.</p> Conclusion <p>MICA plays a key role in shaping the immune microenvironment of DLBCL and PSS. By reinforcing anti-tumor immune responses and limiting tumor growth, it emerges as a promising biomarker and therapeutic target in both diseases.</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

MICA gene modulates immune microenvironment and comorbid mechanisms in diffuse large B-cell lymphoma and primary Sjögren’s syndrome

  • 期刊论文

  • 发表日期：

    2026年7月31日

查看全文
  • Hancheng Qin,
  • Kuai Yu,
  • Xuefei Deng,
  • Tiantian Sun,
  • Yaqun Wang,
  • Mei Xie,
  • Xiaojun Xu,
  • Yunxin Zeng,
  • Wei Xiao

摘要

Objective

To identify shared molecular mechanisms and crosstalk genes (CGs) between diffuse large B-cell lymphoma (DLBCL) and primary Sjögren’s syndrome (PSS), and to explore the role of MICA in the immune microenvironment (IME).

Methods

Bioinformatics analysis of GEO datasets identified differentially expressed genes (DEGs). Enrichment, immune infiltration, and Mendelian randomization analyses were performed. Diagnostic biomarkers were screened using Lasso regression. Functional roles of MICA were validated in SUDHL-6 cells via overexpression/knockdown, assessing immune cell infiltration, cytokine secretion, proliferation, and apoptosis.

Results

We identified 50 shared DEGs. RPL31, HNMT, and IFI27 were defined as diagnostic biomarkers. MR analysis confirmed a causal effect of PSS on DLBCL risk. MICA interacted with RPL31 and HNMT. MICA overexpression enhanced CD8+ T-cell infiltration and activation, elevated pro-inflammatory cytokine release, suppressed tumor cell proliferation, and promoted apoptosis. Conversely, MICA knockdown suppressed anti-tumor immune activity and promoted tumor growth.

Conclusion

MICA plays a key role in shaping the immune microenvironment of DLBCL and PSS. By reinforcing anti-tumor immune responses and limiting tumor growth, it emerges as a promising biomarker and therapeutic target in both diseases.