【佳學(xué)基因檢測】三陰性乳腺癌壞死性凋亡相關(guān)亞型的鑒定及預(yù)后模型

腫瘤基因檢測需要多長時間香港

根據(jù)認(rèn)識到《Front Immunol》在.?2022 Aug 19;13:964118.發(fā)表了一篇題目為《三陰性乳腺癌壞死性凋亡相關(guān)亞型的鑒定及預(yù)后模型》腫瘤靶向藥物治療基因檢測臨床研究文章。該研究由Shengyu Pu,?Yudong Zhou,?Peiling Xie,?Xiaoqian Gao,?Yang Liu,?Yu Ren,?Jianjun He,?Na Hao?等完成。促進了腫瘤的正確治療與個性化用藥的發(fā)展，進一步強調(diào)了基因信息檢測與分析的重要性。

腫瘤靶向藥物及正確治療臨床研究內(nèi)容關(guān)鍵詞：

TCGA,免疫,壞死性凋亡,預(yù)后,三陰性乳腺癌

腫瘤靶向治療基因檢測臨床應(yīng)用結(jié)果

背景：壞死性凋亡被認(rèn)為是一種新的程序性壞死細胞死亡形式，與各類腫瘤的轉(zhuǎn)移、進展和預(yù)后有關(guān)。然而，壞死性凋亡相關(guān)基因（NRGs）在三陰性乳腺癌（TNBC）中的潛在作用尚不清楚。方法：我們從癌癥基因組圖譜（TCGA）數(shù)據(jù)庫和Gene表達式綜合 (GEO) 數(shù)據(jù)庫。我們分析了 TNBC 中 67 個 NRGs 的表達、體細胞突變和拷貝數(shù)變異 (CNV)，然后觀察它們的相互作用、生物學(xué)功能和預(yù)后價值。通過 Lasso 和 COX 回歸分析，構(gòu)建了預(yù)測總生存期（總生存期）的 NRGs 相關(guān)風(fēng)險模型，并驗證了其預(yù)測能力。賊后，分析了risk_score與免疫細胞浸潤、腫瘤微環(huán)境（TME）、免疫檢查點和腫瘤突變負荷（TMB）、癌癥干細胞（CSC）指數(shù)、藥物敏感性的關(guān)系。 結(jié)果：共鑒定出67個NRGs在我們的分析中。少數(shù)基因（23.81%）檢測到體細胞突變，大部分基因出現(xiàn)CNV的高頻率，它們之間存在密切的相互作用。這些基因在免疫相關(guān)過程中顯著富集。生成了一個七基因風(fēng)險評分，包含 TPSG1、KRT6A、GPR19、EIF4EBP1、TLE1、SLC4A7、ESPN。低危組在 TNBC 中 總生存期 較好，免疫評分較高，TMB 和 CSC 指數(shù)較高，常用治療藥物的 IC50 值較低。為了提高臨床實用性，我們在 risk_score 中添加了年齡、stage_T 和 stage_N，并構(gòu)建了一個更全面的列線圖來預(yù)測 總生存期。經(jīng)驗證，列線圖具有良好的預(yù)測能力，1年、3年和5年總生存期的AUC值分別為0.847、0.908和0.942。結(jié)論：我們的研究確定了NRGs對TNBC免疫和預(yù)后的顯著影響。這些發(fā)現(xiàn)有望為 TNBC 的個性化治療提供新的策略并提高其臨床獲益。免疫;壞死性凋亡；預(yù)后；三陰性乳腺癌。

腫瘤發(fā)生與反復(fù)轉(zhuǎn)移國際數(shù)據(jù)庫描述：

Background:?Necroptosis is considered to be a new form of programmed necrotic cell death, which is associated with metastasis, progression and prognosis of various types of tumors. However, the potential role of necroptosis-related genes (NRGs) in the triple negative breast cancer (TNBC) is unclear.Methods:?We extracted the gene expression and relevant clinicopathological data of TNBC from The Cancer Genome Atlas (TCGA) databases and the Gene Expression Omnibus (GEO) databases. We analyzed the expression, somatic mutation, and copy number variation (CNV) of 67 NRGs in TNBC, and then observed their interaction, biological functions, and prognosis value. By performing Lasso and COX regression analysis, a NRGs-related risk model for predicting overall survival (OS) was constructed and its predictive capabilities were verified. Finally, the relationship between risk_score and immune cell infiltration, tumor microenvironment (TME), immune checkpoint, and tumor mutation burden (TMB), cancer stem cell (CSC) index, and drug sensitivity were analyzed.Results:?A total 67 NRGs were identified in our analysis. A small number of genes (23.81%) detected somatic mutation, most genes appeared to have a high frequency of CNV, and there was a close interaction between them. These genes were remarkably enriched in immune-related process. A seven-gene risk_score was generated, containing?TPSG1, KRT6A, GPR19, EIF4EBP1, TLE1, SLC4A7, ESPN. The low-risk group has a better OS, higher immune score, TMB and CSC index, and lower IC50 value of common therapeutic agents in TNBC. To improve clinical practicability, we added age, stage_T and stage_N to the risk_score and construct a more comprehensive nomogram for predicting OS. It was verified that nomogram had good predictive capability, the AUC values for 1-, 3-, and 5-year OS were 0.847, 0.908, and 0.942.Conclusion:?Our research identified the significant impact of NRGs on immunity and prognosis in TNBC. These findings were expected to provide a new strategy for personalize the treatment of TNBC and improve its clinical benefit.Keywords:?TCGA; immune; necroptosis; prognosis; triple negative breast cancer.