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Background <p>Currently, there is a lack of precise instruments for predicting the prognosis and treatment efficacy of kidney renal clear cell carcinoma (KIRC), often resulting in delayed diagnosis and suboptimal treatment outcomes. De novo lipogenesis (DNL) has been documented to significantly impact the prognosis and treatment of several cancers and may serve as a biomarker for KIRC treatment and outcomes.</p> Methods <p>We utilized TCGA-KIRC as the training cohort and GSE22541, ematb1980, and emtab3267 as validation cohorts to construct prognostic genetic features (DPGS) for DNL using StepCox[backward] + RSF. We confirmed the independent prognostic value of DPGS through multivariate Cox analysis and integrated DPGS with clinical characteristics to construct comprehensive prognostic nomograms. Furthermore, we analyzed the correlations between DPGS and genomic instability, tumor immune microenvironment (TIME), and immunotherapy sensitivity at both the single-cell and RNA-seq levels. These findings were further validated in the GSE78220, IMvigor210, and Checkmate immunotherapy cohorts. Finally, we used immunohistochemistry (IHC), Western blotting (WB), and cell function assays to elucidate SLC19A1’s critical role in KIRC.</p> Results <p>The composite C-index for DPGS was 0.796, with hazard ratio (HR) values in multivariate Cox regression analyses of 1.06 and 1.01 in the TCGA-KIRC and EMTAB1980 datasets, respectively. These findings suggest that DPGS can function as an independent prognostic risk factor. Furthermore, the area under the curve (AUC) values for DPGS at 2, 3, and 5 years exceeded 0.67 across four cohorts, indicating robust prognostic predictive performance. In addition, DPGS exhibited a significant association with copy number variation (CNV) (<i>R</i> = 0.33, <i>P</i> &lt; 0.001) and tumor mutational burden (TMB) (<i>R</i> = 0.19, <i>P</i> &lt; 0.001). DPGS was found to accumulate in tumor epithelial cells, cancer-associated fibroblasts (CAFs), and macrophages, and acted on effector CD8 T cells via the HLA-E – KLRC1/NKG2A pathway, thereby diminishing immunotherapy sensitivity in the high-risk DPGS group. Furthermore, knockdown of the key gene SLC19A1 further reduced lipid storage in KIRC and mitigated the malignant phenotype.</p> Conclusions <p>Our work underscores the critical role of DPGS in modulating TIME, influencing immunotherapy response, and predicting prognosis in KIRC. The integration of DPGS and nomograms based on DNL provides innovated insights into survival prediction and therapy, contributing to the development of DNL-targeted therapeutic strategies.</p>

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In-silico study of machine learning discovers de novo lipogenesis for predicting prognosis and immunotherapy responses in kidney renal clear cell carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月25日

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  • Dongze Liu,
  • Weiyang Liu,
  • Bo Han,
  • Tiankai Ding,
  • Yunfeng Nan,
  • Yantong Zhang,
  • Hang Xu,
  • Haoran Pan,
  • Zhuolun Li,
  • Wei Zhang,
  • Enyang Zhao,
  • Bo Geng,
  • Chengluo Jin,
  • Xuedong Li,
  • Jinpeng Wang,
  • Bosen You

摘要

Background

Currently, there is a lack of precise instruments for predicting the prognosis and treatment efficacy of kidney renal clear cell carcinoma (KIRC), often resulting in delayed diagnosis and suboptimal treatment outcomes. De novo lipogenesis (DNL) has been documented to significantly impact the prognosis and treatment of several cancers and may serve as a biomarker for KIRC treatment and outcomes.

Methods

We utilized TCGA-KIRC as the training cohort and GSE22541, ematb1980, and emtab3267 as validation cohorts to construct prognostic genetic features (DPGS) for DNL using StepCox[backward] + RSF. We confirmed the independent prognostic value of DPGS through multivariate Cox analysis and integrated DPGS with clinical characteristics to construct comprehensive prognostic nomograms. Furthermore, we analyzed the correlations between DPGS and genomic instability, tumor immune microenvironment (TIME), and immunotherapy sensitivity at both the single-cell and RNA-seq levels. These findings were further validated in the GSE78220, IMvigor210, and Checkmate immunotherapy cohorts. Finally, we used immunohistochemistry (IHC), Western blotting (WB), and cell function assays to elucidate SLC19A1’s critical role in KIRC.

Results

The composite C-index for DPGS was 0.796, with hazard ratio (HR) values in multivariate Cox regression analyses of 1.06 and 1.01 in the TCGA-KIRC and EMTAB1980 datasets, respectively. These findings suggest that DPGS can function as an independent prognostic risk factor. Furthermore, the area under the curve (AUC) values for DPGS at 2, 3, and 5 years exceeded 0.67 across four cohorts, indicating robust prognostic predictive performance. In addition, DPGS exhibited a significant association with copy number variation (CNV) (R = 0.33, P < 0.001) and tumor mutational burden (TMB) (R = 0.19, P < 0.001). DPGS was found to accumulate in tumor epithelial cells, cancer-associated fibroblasts (CAFs), and macrophages, and acted on effector CD8 T cells via the HLA-E – KLRC1/NKG2A pathway, thereby diminishing immunotherapy sensitivity in the high-risk DPGS group. Furthermore, knockdown of the key gene SLC19A1 further reduced lipid storage in KIRC and mitigated the malignant phenotype.

Conclusions

Our work underscores the critical role of DPGS in modulating TIME, influencing immunotherapy response, and predicting prognosis in KIRC. The integration of DPGS and nomograms based on DNL provides innovated insights into survival prediction and therapy, contributing to the development of DNL-targeted therapeutic strategies.