【佳學(xué)基因檢測】mRNAsi 相關(guān)代謝風(fēng)險評分模型通過機器學(xué)習(xí)識別結(jié)直腸癌患者的不良預(yù)后、免疫逃避背景和低化療反應(yīng)

腫瘤基因檢測公司排名解碼

探索看到《Front Immunol》在.?2022 Aug 23;13:950782.發(fā)表了一篇題目為《mRNAsi 相關(guān)代謝風(fēng)險評分模型通過機器學(xué)習(xí)識別結(jié)直腸癌患者的不良預(yù)后、免疫逃避背景和低化療反應(yīng)》腫瘤靶向藥物治療基因檢測臨床研究文章。該研究由Meilin Weng,?Ting Li,?Jing Zhao,?Miaomiao Guo,?Wenling Zhao,?Wenchao Gu,?Caihong Sun,?Ying Yue,?Ziwen Zhong,?Ke Nan,?Qingwu Liao,?Minli Sun,?Di Zhou,?Changhong Miao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：

機器學(xué)習(xí),結(jié)直腸癌,免疫逃避,免疫療法, mRNAsi,代謝,風(fēng)險評分模型,干性

腫瘤靶向治療基因檢測臨床應(yīng)用結(jié)果

結(jié)直腸癌 (CRC) 是消化系統(tǒng)中賊致命的癌癥之一。盡管癌癥干細胞和代謝重編程對腫瘤進展和耐藥性有重要影響，但它們對CRC預(yù)后的綜合影響仍不清楚。因此，我們生成了一個 21 基因 mRNA 干性指數(shù)相關(guān)的代謝風(fēng)險評分模型，該模型在癌癥基因組圖譜和基因表達綜合數(shù)據(jù)庫（1323 名患者）中進行了檢查，并使用中山醫(yī)院隊列（200 名患者）進行了驗證。高風(fēng)險組表現(xiàn)出更多的免疫浸潤；更高水平的免疫抑制檢查點，例如 CD274、腫瘤突變負荷和對化療藥物的耐藥性；對免疫治療可能有更好的反應(yīng)；預(yù)后較差；且腫瘤淋巴結(jié)轉(zhuǎn)移的晚期階段高于低危組。風(fēng)險評分和臨床特征相結(jié)合可有效預(yù)測總生存期。中山隊列驗證了高危評分組與CRC的惡性進展、較差的預(yù)后、較差的輔助化療反應(yīng)性相關(guān)，并形成了免疫逃避環(huán)境。該工具可以在 CRC 和篩查對免疫治療有反應(yīng)的 CRC 患者中提供更正確的風(fēng)險分層。結(jié)直腸癌;免疫逃避；免疫療法； mRNAsi;代謝;風(fēng)險評分模型；干性。

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

Colorectal cancer (CRC) is one of the most fatal cancers of the digestive system. Although cancer stem cells and metabolic reprogramming have an important effect on tumor progression and drug resistance, their combined effect on CRC prognosis remains unclear. Therefore, we generated a 21-gene mRNA stemness index-related metabolic risk score model, which was examined in The Cancer Genome Atlas and Gene Expression Omnibus databases (1323 patients) and validated using the Zhongshan Hospital cohort (200 patients). The high-risk group showed more immune infiltrations; higher levels of immunosuppressive checkpoints, such as CD274, tumor mutation burden, and resistance to chemotherapeutics; potentially better response to immune therapy; worse prognosis; and advanced stage of tumor node metastasis than the low-risk group. The combination of risk score and clinical characteristics was effective in predicting overall survival. Zhongshan cohort validated that high-risk score group correlated with malignant progression, worse prognosis, inferior adjuvant chemotherapy responsiveness of CRC, and shaped an immunoevasive contexture. This tool may provide a more accurate risk stratification in CRC and screening of patients with CRC responsive to immunotherapy.Keywords:?Machine learning; colorectal cancer; immune evasion; immunotherapy; mRNAsi; metabolism; risk score model; stemness.