肾细胞癌（RCC）是一种可能在肾脏中发展的恶性肿瘤。RCC是此类肿瘤最常见的一种，最常见的病理亚型是肾透明细胞癌（KIRC）。然而，RCC的病因和发病机制仍需要进一步阐明。探索RCC的内部机制有助于诊断和治疗该疾病。火凝死是与细胞死亡相关的重要过程。最近的研究表明，火凝死是肿瘤形成的启动和进展的关键因素。迄今为止，研究人员逐渐揭示了长非编码RNA（lncRNA）对火凝死的调控影响的证据。在本研究中，我们采用了综合生物信息学方法，根据与火凝死相关的lncRNA，生成了一个预测模型，用于预测KIRC患者的总体生存和分子免疫特性。该模型从多个角度进行验证。首先，我们使用TCGA数据库和桑基图发现了与火凝死相关的KIRC患者中的lncRNA。然后，我们基于与火凝死相关的lncRNA开发并验证了一个KIRC患者风险模型。我们通过主成分分析证明了PLnRM的分组能力，并使用PLnRM评估肿瘤免疫微环境和对免疫治疗的反应。评估了不同PLnRM亚组的免疫学和分子特征，以及临床KIRC患者的特征和预测风险模型。在此基础上，我们开发和分析了一个预测刻度图，并识别了新的PLnRM候选化合物。最后，我们研究了KIRC患者可能使用的药物。结果表明，所生成的模型在KIRC的临床实践中具有显著的价值。© 2023. BioMed Central Ltd., part of Springer Nature.

Renal cell carcinoma (RCC) is a malignant tumour that may develop in the kidney. RCC is one of the most common kinds of tumours of this sort, and its most common pathological subtype is kidney renal clear cell carcinoma (KIRC). However, the aetiology and pathogenesis of RCC still need to be clarified. Exploring the internal mechanism of RCC contributes to diagnosing and treating this disease. Pyroptosis is a critical process related to cell death. Recent research has shown that pyroptosis is a critical factor in the initiation and progression of tumour formation. Thus far, researchers have progressively uncovered evidence of the regulatory influence that long noncoding RNAs (lncRNAs) have on pyroptosis.In this work, a comprehensive bioinformatics approach was used to produce a predictive model according to pyroptosis-interrelated lncRNAs for the purpose of predicting the overall survival and molecular immune specialties of patients diagnosed with KIRC. This model was verified from multiple perspectives.First, we discovered pyroptosis-associated lncRNAs in KIRC patients using the TCGA database and a Sankey diagram. Then, we developed and validated a KIRC patient risk model based on pyroptosis-related lncRNAs. We demonstrated the grouping power of PLnRM through PCA and used PLnRM to assess the tumour immune microenvironment and response to immunotherapy. Immunological and molecular traits of diverse PLnRM subgroups were evaluated, as were clinical KIRC patient characteristics and predictive risk models. On this basis, a predictive nomogram was developed and analyzed, and novel PLnRM candidate compounds were identified. Finally, we investigated possible medications used by KIRC patients.The results demonstrate that the model generated has significant value for KIRC in clinical practice.© 2023. BioMed Central Ltd., part of Springer Nature.