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全面分析揭示氨基酸代谢相关基因作为胃癌的预后指标。

Comprehensive Analysis to Reveal Amino Acid Metabolism-Associated Genes as a Prognostic Index in Gastric Cancer.

机构信息

Affiliated Xiaoshan Hospital, Hangzhou Normal University, Hangzhou, China.

Ningbo Medical Center Lihuili Hospital, Ningbo, China.

出版信息

Mediators Inflamm. 2023 May 11;2023:3276319. doi: 10.1155/2023/3276319. eCollection 2023.

DOI:10.1155/2023/3276319
PMID:37214189
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10195167/
Abstract

BACKGROUND

Amino acid metabolism (AAM) is related to tumor growth, prognosis, and therapeutic response. Tumor cells use more amino acids with less synthetic energy than normal cells for rapid proliferation. However, the possible significance of AAM-related genes in the tumor microenvironment (TME) is poorly understood.

METHODS

Gastric cancer (GC) patients were classified into molecular subtypes by consensus clustering analysis using AAMs genes. AAM pattern, transcriptional patterns, prognosis, and TME in distinct molecular subtypes were systematically investigated. AAM gene score was built by least absolute shrinkage and selection operator (Lasso) regression.

RESULTS

The study revealed that copy number variation (CNV) changes were prevalent in selected AAM-related genes, and most of these genes exhibited a high frequency of CNV deletion. Three molecular subtypes (clusters A, B, and C) were developed based on 99 AAM genes, which cluster B had better prognosis outcome. We developed a scoring system (AAM score) based on 4 AAM gene expressions to measure the AAM patterns of each patient. Importantly, we constructed a survival probability prediction nomogram. The AAM score was substantially associated with the index of cancer stem cells and sensitivity to chemotherapy intervention.

CONCLUSION

Overall, we detected prognostic AAM features in GC patients, which may help define TME characteristics and explore more effective treatment approaches.

摘要

背景

氨基酸代谢（AAM）与肿瘤生长、预后和治疗反应有关。肿瘤细胞比正常细胞以更少的合成能量使用更多的氨基酸，以实现快速增殖。然而，肿瘤微环境（TME）中与 AAM 相关的基因的可能意义尚未被充分理解。

方法

使用共识聚类分析对胃癌（GC）患者进行基于 AAMs 基因的分子亚型分类。系统研究了不同分子亚型中的 AAM 模式、转录模式、预后和 TME。通过最小绝对收缩和选择算子（Lasso）回归构建 AAM 基因评分。

结果

本研究揭示了选择的 AAM 相关基因中存在拷贝数变异（CNV）变化，并且这些基因中的大多数表现出高频 CNV 缺失。基于 99 个 AAM 基因，我们开发了三种分子亚型（A、B 和 C 簇），其中簇 B 具有更好的预后结局。我们基于 4 个 AAM 基因表达开发了一个评分系统（AAM 评分），用于衡量每个患者的 AAM 模式。重要的是，我们构建了一个生存概率预测列线图。AAM 评分与癌症干细胞指数显著相关，并且与化疗干预的敏感性相关。

结论

总的来说，我们在 GC 患者中检测到了预后 AAM 特征，这可能有助于定义 TME 特征并探索更有效的治疗方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b19e/10195167/a8d84cd5dd14/MI2023-3276319.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b19e/10195167/2df12e0666fa/MI2023-3276319.001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b19e/10195167/b09f81a28283/MI2023-3276319.003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b19e/10195167/406f8593978d/MI2023-3276319.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b19e/10195167/5b5b6611cf08/MI2023-3276319.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b19e/10195167/6f977af5e9bf/MI2023-3276319.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b19e/10195167/a8d84cd5dd14/MI2023-3276319.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b19e/10195167/2df12e0666fa/MI2023-3276319.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b19e/10195167/089cf6d2faa9/MI2023-3276319.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b19e/10195167/b09f81a28283/MI2023-3276319.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b19e/10195167/0cd91b28a498/MI2023-3276319.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b19e/10195167/406f8593978d/MI2023-3276319.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b19e/10195167/5b5b6611cf08/MI2023-3276319.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b19e/10195167/6f977af5e9bf/MI2023-3276319.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b19e/10195167/a8d84cd5dd14/MI2023-3276319.008.jpg

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