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探讨与二硫键相关基因相关的长链非编码 RNA 在透明细胞肾细胞癌中的预后作用。

Investigating the prognostic role of lncRNAs associated with disulfidptosis-related genes in clear cell renal cell carcinoma.

机构信息

Department of Urology, Xiaogan Hospital Affiliated to Wuhan University of Science and Technology, Xiaogan, China.

Department of Urology, China-Japan Union Hospital of Jilin University, Changchun, China.

出版信息

J Gene Med. 2024 Jan;26(1):e3608. doi: 10.1002/jgm.3608. Epub 2023 Oct 28.

Abstract

INTRODUCTION

Renal cell carcinoma (RCC) is a grave malignancy that poses a significant global health burden with over 400,000 new cases annually. Disulfidptosis, a newly discovered programmed cell death process, is linked to the actin cytoskeleton, which plays a vital role in maintaining cell shape and survival. The role of disulfidptosis is poorly depicted in the clear cell histologic variant of RCC (ccRCC).

METHODS

Three sets of ccRCC cohorts, ICGC_RECA-EU (n = 91), GSE76207 (n = 32) and TCGA-KIRC (n = 607), were included in our study, the batch effect of which was removed using the "combat" function. Correlation was calculated using the "rcorr" function of the "Hmisc" package for Pearson analysis, which was visualized using the "pheatmap" package. Principal component analysis was performed by the "vegan" package, visualized using the "scatterplot3d" package. Long non-coding RNAs (lncRNAs) associated with disulfidptosis were screened out using least absolute shrinkage and selection operator (LASSO) and COX analysis. Tumor mutation, immune landscaping and immunotherapy prediction were performed for further characterization of two risk groups.

RESULTS

A total of 1822 disulfidptosis-related lncRNAs was selected, among which 308 lncRNAs were found to be significantly associated with the clinical outcome of ccRCC patients. We retained 11 disulfidptosis-related lncRNAs, namely, AP000439.3, RP11-417E7.1, RP11-119D9.1, LINC01510, SNHG3, AC156455.1, RP11-291B21.2, EMX2OS, AC093850.2, HAGLR and RP11-389C8.2, through LASSO and COX analysis for prognosis model construction, which displayed satisfactory accuracy (area under the curve, AUC, values all above 0.6 in multiple cohorts) in stratification of ccRCC prognosis. A nomogram model was constructed by integrating clinical factors with risk score, which further enhanced the prediction efficacy (AUC values all above 0.7 in multiple cohorts). We found that patients of male gender, higher clinical stages and advanced pathological T stage were inclined to have higher risk score values. Dactinomycin_1911, Vinblastine_1004, Daporinad_1248 and Vinorelbine_2048 were identified as promising candidate drugs for treating ccRCC patients of higher risk score value. Moreover, patients of higher risk value were prone to be resistant to immunotherapy.

CONCLUSION

We developed a prognosis predicting model based on 11 selected disulfidptosis-related lncRNAs, the efficacy of which was verified in different cohorts. Furthermore, we delineated an intricate portrait of tumor mutation, immune topography and pharmacosensitivity evaluations within disparate risk stratifications.

摘要

简介

肾细胞癌(RCC)是一种严重的恶性肿瘤,每年全球有超过 40 万例新发病例,给全球健康带来了巨大负担。细胞程序性死亡过程中的二硫键分解,与肌动蛋白细胞骨架有关,肌动蛋白细胞骨架在维持细胞形态和生存中起着至关重要的作用。二硫键分解在肾透明细胞组织学变异型(ccRCC)中的作用还没有被充分描述。

方法

本研究纳入了三个 ccRCC 队列,分别是 ICGC_RECA-EU(n=91)、GSE76207(n=32)和 TCGA-KIRC(n=607)。我们使用“combat”函数消除了这些数据集的批次效应。使用“Hmisc”包中的“rcorr”函数进行 Pearson 分析的相关性计算,并使用“pheatmap”包进行可视化。使用“vegan”包进行主成分分析,并使用“scatterplot3d”包进行可视化。使用最小绝对收缩和选择算子(LASSO)和 COX 分析筛选与二硫键分解相关的长链非编码 RNA(lncRNA)。对两个风险组进行肿瘤突变、免疫景观和免疫治疗预测,以进一步对其进行特征描述。

结果

筛选出与二硫键分解相关的 1822 个 lncRNA,其中 308 个 lncRNA与 ccRCC 患者的临床结局显著相关。我们通过 LASSO 和 COX 分析保留了 11 个与二硫键分解相关的 lncRNA,即 AP000439.3、RP11-417E7.1、RP11-119D9.1、LINC01510、SNHG3、AC156455.1、RP11-291B21.2、EMX2OS、AC093850.2、HAGLR 和 RP11-389C8.2,用于预后模型构建,在多个队列中均具有满意的准确性(曲线下面积,AUC 值均高于 0.6),可对 ccRCC 的预后进行分层。通过整合临床因素和风险评分,构建了一个列线图模型,进一步提高了预测效果(多个队列中的 AUC 值均高于 0.7)。我们发现,男性、较高的临床分期和较晚的病理 T 分期的患者更倾向于具有较高的风险评分值。达泊西汀_1911、长春新碱_1004、达泊那嗪_1248 和长春瑞滨_2048 被鉴定为治疗高风险评分值 ccRCC 患者的潜在候选药物。此外,高风险值的患者更有可能对免疫治疗产生耐药性。

结论

我们基于 11 个筛选出的与二硫键分解相关的 lncRNA 开发了一个预后预测模型,并在不同的队列中验证了其有效性。此外,我们在不同的风险分层中描绘了肿瘤突变、免疫地形图和药物敏感性评估的复杂情况。

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