Scholarlink

Background <p>Liposarcomas (LPS) is a highly heterogeneous malignant soft tissue tumor. Tumor microenvironment immune traits critically affect cancer progression and treatment efficacy. However, immune-related biomarkers for prognostic assessment, reflecting tumor immune microenvironment features and with diagnostic potential, remain insufficiently explored in LPS.</p> Methods <p>The RNA-seq data and clinical information of patients with liposarcoma were downloaded from the GEO and TCGA database. The “limma” package performed the differential expression genes (DEGs) analysis, and the weighted gene co-expression network analysis (WGCNA) method was used to identify the liposarcoma-related module. We performed the single sample gene set enrichment analysis (ssGSEA) to calculate the enrichment scores for 28 tumor-infiltrating lymphocyte (TIL) subpopulations based on previously established immune signatures. Then, the consensus clustering was conducted using the “ConsensusClusterPlus” package. After that, the lasso and multivariate Cox regression analysis was applied for the risk model construction. The ESTIMATE algorithm for immune infiltration, “clusterProfiler” for function enrichment, “survival” for prognostic difference and “timeROC” for receiver operator characteristic curve (ROC) analysis were performed. The wound healing and transwell assay were conducted.</p> Results <p>After differential expression analysis, 852 DEGs between liposarcoma and para-cancer samples were obtained, and the turquoise was the liposarcoma-related gene module through WGCNA analysis. Consensus clustering based on immune signatures stratified patients into immunity-high (H) and immunity-low (L) subtypes with significant survival differences. Integration of these findings led to a robust 2-gene prognostic signature (<i>KNTC1</i> and <i>PRC1</i>) via LASSO and Cox regression. Both model genes exhibited outstanding diagnostic performance (AUC &gt; 0.9). High RiskScore was significantly associated with aggressive pathological subtypes, metastasis, and an immunosuppressive microenvironment characterized by lower overall immune infiltration. Conversely, low-risk patients showed enhanced immune cell abundance and activity. In addition, functional validation confirmed that <i>KNTC1</i> silencing significantly impaired LPS cell migration and invasion, underscoring its role in tumor progression.</p> Conclusions <p>We constructed a robust two-gene prognostic model that effectively predicts survival, reflects tumor immune microenvironment heterogeneity, and distinguishes pathological subtypes in LPS. Our findings provided valuable insights for prognostic stratification and personalized treatment strategies.</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

KNTC1 and PRC1 define an immunosuppressive microenvironment and poor prognosis in liposarcoma

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月9日

查看全文
  • Lele Zhang,
  • Xiangjie Fang,
  • Zihan Yang,
  • Minghang Zhang,
  • Yanpin Ma,
  • Penghui Li

摘要

Background

Liposarcomas (LPS) is a highly heterogeneous malignant soft tissue tumor. Tumor microenvironment immune traits critically affect cancer progression and treatment efficacy. However, immune-related biomarkers for prognostic assessment, reflecting tumor immune microenvironment features and with diagnostic potential, remain insufficiently explored in LPS.

Methods

The RNA-seq data and clinical information of patients with liposarcoma were downloaded from the GEO and TCGA database. The “limma” package performed the differential expression genes (DEGs) analysis, and the weighted gene co-expression network analysis (WGCNA) method was used to identify the liposarcoma-related module. We performed the single sample gene set enrichment analysis (ssGSEA) to calculate the enrichment scores for 28 tumor-infiltrating lymphocyte (TIL) subpopulations based on previously established immune signatures. Then, the consensus clustering was conducted using the “ConsensusClusterPlus” package. After that, the lasso and multivariate Cox regression analysis was applied for the risk model construction. The ESTIMATE algorithm for immune infiltration, “clusterProfiler” for function enrichment, “survival” for prognostic difference and “timeROC” for receiver operator characteristic curve (ROC) analysis were performed. The wound healing and transwell assay were conducted.

Results

After differential expression analysis, 852 DEGs between liposarcoma and para-cancer samples were obtained, and the turquoise was the liposarcoma-related gene module through WGCNA analysis. Consensus clustering based on immune signatures stratified patients into immunity-high (H) and immunity-low (L) subtypes with significant survival differences. Integration of these findings led to a robust 2-gene prognostic signature (KNTC1 and PRC1) via LASSO and Cox regression. Both model genes exhibited outstanding diagnostic performance (AUC > 0.9). High RiskScore was significantly associated with aggressive pathological subtypes, metastasis, and an immunosuppressive microenvironment characterized by lower overall immune infiltration. Conversely, low-risk patients showed enhanced immune cell abundance and activity. In addition, functional validation confirmed that KNTC1 silencing significantly impaired LPS cell migration and invasion, underscoring its role in tumor progression.

Conclusions

We constructed a robust two-gene prognostic model that effectively predicts survival, reflects tumor immune microenvironment heterogeneity, and distinguishes pathological subtypes in LPS. Our findings provided valuable insights for prognostic stratification and personalized treatment strategies.