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代谢重编程特征预测小细胞肺癌的预后和免疫格局:MOCS2验证及其对个性化治疗的意义

Metabolic reprogramming signature predicts prognosis and immune landscape in small cell lung cancer: MOCS2 validation and implications for personalized therapy.

作者信息

Wang Junyan, Sun Panpan, Zhang Fan, Xu Yu, Guo Shenghu

机构信息

Medical Oncology Department, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.

Department of Psychiatry, Hebei Province Rong-Jun Hospital, Baoding, Hebei, China.

出版信息

Front Mol Biosci. 2025 May 16;12:1592888. doi: 10.3389/fmolb.2025.1592888. eCollection 2025.

Abstract

INTRODUCTION

Small cell lung cancer (SCLC) remains a leading cause of cancer mortality worldwide, characterized by rapid progression and poor clinical outcomes, and the function of metabolic reprogramming remains unclear in SCLC.

METHODS

We performed multi-omics analysis using public SCLC datasets, analyzing single-cell RNA sequencing to identify metabolic reprogramming patterns between chemotherapy-resistant and sensitive samples. Bulk RNA sequencing from GSE60052 and cBioportal cohorts was used to identify metabolism-related gene modules through WGCNA and develop a Gradient Boosting Machine prognostic model. Functional validation of MOCS2, the top-ranked gene in our model, was conducted through siRNA knockdown experiments in SCLC cell lines.

RESULTS

Single-cell analysis revealed distinct metabolic reprogramming patterns between chemotherapy-resistant and sensitive samples. WGCNA identified a turquoise module strongly correlated with metabolic reprogramming (cor = 0.56, P < 0.005). The GBM-based prognostic model demonstrated excellent performance (C-index = 0.915) with MOCS2, USP39, SMYD2, GFPT1, and PRKRIR identified as the most important variables. Kaplan-Meier analysis confirmed significant survival differences between high-risk and low-risk groups in both validation cohorts (P < 0.001). experiments showed that MOCS2 knockdown significantly reduced SCLC cell proliferation, colony formation, and migration capabilities (all P < 0.01), confirming its crucial role in regulating SCLC cell biology. Immunological characterization revealed distinct immune landscapes between risk groups, and drug sensitivity analysis identified five compounds with significantly different response profiles between risk groups.

CONCLUSION

Our study established a robust metabolism-based prognostic model for SCLC that effectively stratifies patients into risk groups with distinct survival outcomes, immune profiles, and drug sensitivity patterns. Functional validation experiments confirmed MOCS2 as an important regulator of SCLC cell proliferation and migration, providing valuable insights for treatment selection and prognosis prediction in SCLC.

摘要

引言

小细胞肺癌(SCLC)仍是全球癌症死亡的主要原因,其特点是进展迅速且临床预后较差,而代谢重编程在SCLC中的作用仍不清楚。

方法

我们使用公开的SCLC数据集进行多组学分析,分析单细胞RNA测序以确定化疗耐药和敏感样本之间的代谢重编程模式。来自GSE60052和cBioportal队列的批量RNA测序用于通过加权基因共表达网络分析(WGCNA)识别代谢相关基因模块,并开发梯度提升机预后模型。通过在SCLC细胞系中进行小干扰RNA(siRNA)敲低实验,对我们模型中排名最高的基因MOCS2进行功能验证。

结果

单细胞分析揭示了化疗耐药和敏感样本之间不同的代谢重编程模式。WGCNA识别出一个与代谢重编程密切相关的蓝绿色模块(相关性系数=0.56,P<0.005)。基于梯度提升机的预后模型表现出色(C指数=0.915),其中MOCS2、USP39、SMYD2、GFPT1和PRKRIR被确定为最重要的变量。Kaplan-Meier分析证实了两个验证队列中高危组和低危组之间存在显著的生存差异(P<0.001)。实验表明,MOCS2敲低显著降低了SCLC细胞的增殖、集落形成和迁移能力(均P<0.01),证实了其在调节SCLC细胞生物学中的关键作用。免疫特征分析揭示了风险组之间不同的免疫格局,药物敏感性分析确定了五种在风险组之间具有显著不同反应谱的化合物。

结论

我们的研究为SCLC建立了一个强大的基于代谢的预后模型,该模型有效地将患者分为具有不同生存结果、免疫特征和药物敏感性模式的风险组。功能验证实验证实MOCS2是SCLC细胞增殖和迁移的重要调节因子,为SCLC的治疗选择和预后预测提供了有价值的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc00/12122325/8fabb17b0af1/fmolb-12-1592888-g001.jpg

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