肿瘤细胞治疗研究
英文原题：Integrated transcriptomics, network pharmacology and clinical expression validation reveal the prognostic significance of PANoptosis-related genes in cordycepin-treated lung adenocarcinoma.
Integrated transcriptomics, network pharmacology and clinical expression validation reveal the prognostic significance of PANoptosis-related genes in cordycepin-treated lung adenocarcinoma.
我们的结果表明，包括SLC2A1、SMS、CCNA2、CDC25C、RNASE1、NR3C2、CAT和ADA在内的8个生物标志物与LUAD中的PANoptosis相关。
背景：研究表明，PAN凋亡日益参与癌症发生及癌症治疗；虫草素也被发现与多种癌症的发展有关。然而，关于二者在肺腺癌（LUAD）中的研究仍相对有限。本研究旨在鉴定LUAD潜在预后基因，并阐明其对患者预后的影响，以期为该疾病的治疗策略提供新见解。 方法：研究从公共数据库获取虫草素靶基因和LUAD转录组数据集，并从文献中收集PAN凋亡相关基因。研究采用差异表达分析、一致性聚类分析、加权基因共表达网络分析（WGCNA）和Cox回归分析，鉴定潜在生物标志物。随后基于这些标志物构建反向传播神经网络（BPNN），通过富集分析探究潜在生物学机制。研究还评估了不同风险人群的功能通路、免疫浸润和药物敏感性。最后收集临床样本，并通过RT-qPCR验证生物标志物表达。 结果：本研究共鉴定出8种生物标志物：SLC2A1、SMS、CCNA2、CDC25C、RNASE1、NR3C2、CAT和ADA。其中，SLC2A1、SMS、CCNA2、CDC25C和ADA在临床疾病样本中显著上调，而RNASE1、NR3C2和CAT显著下调。基因集富集分析（GSEA）显示，这些标志物主要富集于错配修复和蛋白酶体相关通路。CIBERSORT算法检测到两个亚组间有10种差异免疫细胞（包括M0巨噬细胞、单核细胞、活化树突状细胞等）；ssGSEA算法则鉴定出18种差异免疫细胞，包括活化CD4⁺ T细胞、记忆B细胞和自然杀伤T细胞等。药物敏感性分析显示，两个风险组之间有22种药物存在显著差异。 结论：结果提示，SLC2A1、SMS、CCNA2、CDC25C、RNASE1、NR3C2、CAT和ADA这8种生物标志物与LUAD的PAN凋亡相关。这些发现可能为LUAD预后预测和治疗提供支持性证据。不过，目前这些基因参与PAN凋亡的观点仅是假设，基于生物信息学分析得出，未来仍需进一步实验验证。
BACKGROUND: Studies have shown that PANoptosis is increasingly involved in cancer and cancer treatment. The Cordycepin has also been found to be involved in the development of various cancers. However, relevant research on both in lung adenocarcinoma (LUAD) remains relatively scarce. The present study aims to identify potential prognostic genes in LUAD and elucidate their impact on the prognosis of LUAD patients, with the goal of providing novel insights into the therapeutic strategies for this disease. METHODS: The target genes for Cordycepin and transcriptomic datasets for LUAD were retrieved from public databases, and PANoptosis-related genes were retrieved from literature. This study employed differential expression analysis, consensus clustering analysis, weighted gene co-expression network analysis (WGCNA), and Cox regression analysis. This study identified potential biomarkers. The backpropagation neural networks (BPNNs) were constructed using these biomarkers. Furthermore, the study delved into underlying biological mechanisms through enrichment analysis. Additional analyses were performed to evaluate functional pathways, immune infiltration, and drug sensitivity in different risk individuals. Finally, clinical samples were collected and biomarker expression was validated using RT-qPCR. RESULTS: A total of 8 biomarkers including SLC2A1, SMS, CCNA2, CDC25C, RNASE1, NR3C2, CAT, and ADA were identified in this study. Among these, SLC2A1, SMS, CCNA2, CDC25C, and ADA were significantly upregulated in clinical disease samples, while RNASE1, NR3C2, and CAT were significantly downregulated. The GSEA results indicated that the biomarkers were primarily enriched in mismatch repair and proteasome. There were 10 differential immune cells (Macrophages M0, Monocytes, activated dendritic cells, etc.) between two subgroups detected by the CIBERSORT algorithm. Meanwhile, 18 differential immune cells including Activated CD4+ T cells, Memory B cells, Natural killer T cells, etc. were identified using the ssGSEA algorithm. The analysis of drug sensitivity showed significant differences in 22 drugs between the two risk groups. CONCLUSION: Our results suggested that 8 biomarkers including SLC2A1, SMS, CCNA2, CDC25C, RNASE1, NR3C2, CAT, and ADA were associated with PANoptosis in LUAD. These findings may provide supportive evidence for the prognosis prediction and treatment of LUAD. However, the involvement of these genes in PANoptosis is currently only a hypothesis based on bioinformatics analysis and requires further experimental validation in the future.
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