【佳學(xué)基因檢測(cè)】mRNAsi 相關(guān)代謝風(fēng)險(xiǎn)評(píng)分模型通過(guò)機(jī)器學(xué)習(xí)識(shí)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(cè)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(xiǎn)評(píng)分模型通過(guò)機(jī)器學(xué)習(xí)識(shí)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(cè)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(jìn)了腫瘤的正確治療與個(gè)性化用藥的發(fā)展，進(jìn)一步強(qiáng)調(diào)了基因信息檢測(cè)與分析的重要性。

腫瘤靶向藥物及正確治療臨床研究?jī)?nèi)容關(guān)鍵詞：

機(jī)器學(xué)習(xí),結(jié)直腸癌,免疫逃避,免疫療法, mRNAsi,代謝,風(fēng)險(xiǎn)評(píng)分模型,干性

腫瘤靶向治療基因檢測(cè)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(xì)胞和代謝重編程對(duì)腫瘤進(jìn)展和耐藥性有重要影響，但它們對(duì)CRC預(yù)后的綜合影響仍不清楚。因此，我們生成了一個(gè) 21 基因 mRNA 干性指數(shù)相關(guān)的代謝風(fēng)險(xiǎn)評(píng)分模型，該模型在癌癥基因組圖譜和基因表達(dá)綜合數(shù)據(jù)庫(kù)（1323 名患者）中進(jìn)行了檢查，并使用中山醫(yī)院隊(duì)列（200 名患者）進(jìn)行了驗(yàn)證。高風(fēng)險(xiǎn)組表現(xiàn)出更多的免疫浸潤(rùn)；更高水平的免疫抑制檢查點(diǎn)，例如 CD274、腫瘤突變負(fù)荷和對(duì)化療藥物的耐藥性；對(duì)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(xiǎn)評(píng)分和臨床特征相結(jié)合可有效預(yù)測(cè)總生存期。中山隊(duì)列驗(yàn)證了高危評(píng)分組與CRC的惡性進(jìn)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(duì)免疫治療有反應(yīng)的 CRC 患者中提供更正確的風(fēng)險(xiǎn)分層。結(jié)直腸癌;免疫逃避；免疫療法； mRNAsi;代謝;風(fēng)險(xiǎn)評(píng)分模型；干性。

腫瘤發(fā)生與反復(fù)轉(zhuǎn)移國(guó)際數(shù)據(jù)庫(kù)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.