Zhang Pengcheng, Wang Lexin, Lin Haonan, Han Yihui, Zhou Jingfang, Song Hang, Wang Peng, Tan Huanhuan, Fu Yajuan
The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, 310002, China.
General Hospital of Ningxia Medical University, Yinchuan, Ningxia, China.
J Transl Med. 2025 Aug 7;23(1):877. doi: 10.1186/s12967-025-06725-7.
Gastric cancer (GC) is characterized by significant intertumoral heterogeneity, which often leads to the development of resistance to platinum-based chemotherapy. Combining platinum drugs with other therapeutic strategies may improve treatment efficacy; however, the mechanisms underlying platinum resistance in GC remain unclear.
Key genes related to platinum resistance in GC were selected from the platinum resistance gene database and GC resistance datasets. The Similarity Network Fusion (SNF) algorithm was employed, along with prognosis-related methylation data and somatic mutation data, to classify the molecular subtypes of GC based on GC platinum resistance genes. Gene expression profiles, prognosis, immune cell infiltration, chemotherapy sensitivity, and immunotherapy responsiveness were comprehensively evaluated for each subtype. Localization and functional evaluation were conducted at the single-cell and spatial transcriptomics levels, and predictive models were developed using machine learning techniques. These functional differences in platinum resistance gene models were further explored in GC. Moreover, experimental validation was conducted to elucidate the mechanisms of key genes involved in platinum resistance in GC.
Stomach adenocarcinoma (STAD) patients were classified into three subtypes using the SNF algorithm and multiomics data. Patients with subtype CS2 exhibited a significantly poorer prognosis than those with subtypes CS1 and CS3 (p < 0.05). Subtype CS1 was characterized as immune-deprived, CS2 as stroma-enriched, and CS3 as immune-enriched. Patients with subtype CS2 also exhibited the most adverse therapeutic responses to docetaxel, cisplatin, and gemcitabine. Single-cell analysis revealed high enrichment of M1 module cells with elevated expression of resistance genes, including the transcription factor KLF9. Spatial transcriptomic analysis further confirmed the independent spatial distribution of malignant cells with high expression of drug resistance genes (DRGs). Predictive models based on machine learning demonstrated excellent prognostic performance. Patients in the high DRG group also exhibited poorer responses to immunotherapy. Cellular experiments revealed that KLF9 overexpression significantly inhibited the proliferation of AGS cells (p < 0.05), reduced their resistance to platinum-based drugs, and markedly decreased the levels of inflammatory cytokines in them.
KLF9 was identified as a promising therapeutic target for overcoming platinum resistance in GC, warranting further investigation into its role and potential clinical applications.
胃癌（GC）具有显著的肿瘤间异质性，这常常导致对铂类化疗产生耐药性。将铂类药物与其他治疗策略相结合可能会提高治疗效果；然而，GC中铂耐药的潜在机制仍不清楚。
从铂耐药基因数据库和GC耐药数据集筛选出与GC铂耐药相关的关键基因。采用相似网络融合（SNF）算法，结合预后相关的甲基化数据和体细胞突变数据，基于GC铂耐药基因对GC的分子亚型进行分类。对每个亚型的基因表达谱、预后、免疫细胞浸润、化疗敏感性和免疫治疗反应性进行综合评估。在单细胞和空间转录组学水平上进行定位和功能评估，并使用机器学习技术建立预测模型。在GC中进一步探索铂耐药基因模型中的这些功能差异。此外，进行实验验证以阐明GC中参与铂耐药的关键基因的机制。
使用SNF算法和多组学数据将胃腺癌（STAD）患者分为三个亚型。CS2亚型患者的预后明显比CS1和CS3亚型患者差（p<0.05）。CS1亚型的特征是免疫缺陷，CS2亚型是基质丰富，CS3亚型是免疫丰富。CS2亚型患者对多西他赛、顺铂和吉西他滨的治疗反应也最不利。单细胞分析显示M1模块细胞高度富集，耐药基因表达升高，包括转录因子KLF9。空间转录组分析进一步证实了耐药基因（DRGs）高表达的恶性细胞的独立空间分布。基于机器学习的预测模型显示出优异的预后性能。高DRG组患者对免疫治疗的反应也较差。细胞实验表明，KLF9过表达显著抑制AGS细胞的增殖（p<0.05），降低其对铂类药物的耐药性，并显著降低其中炎症细胞因子的水平。
KLF9被确定为克服GC铂耐药的一个有前景的治疗靶点，值得进一步研究其作用和潜在的临床应用。
