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• W2962280147 abstract "Immune infiltration may predict survival and have clinical significance in lung cancer. However, immune signatures derived from immune profiling based on bulk tumor transcriptomes have not been systematically established in lung adenocarcinoma. We aimed to construct an immune cell infiltrating score, using a new algorithm for evaluating immune infiltration, to improve the prognostic model of lung adenocarcinoma. Public datasets of lung adenocarcinoma from the Gene Expression Omnibus and The Cancer Genome Atlas were adopted as the training and validation cohorts. Fractions of different immune cell subtypes in each sample were estimated using the CIBERSORT algorithm. The immune infiltrating score was further developed by a least absolute shrinkage and selection operator regression model. The prognostic value and clinical relationship of the model was then further explored. An immune infiltrating score model was established on the basis of the immune cells in the training cohort. A high score was associated with significantly worse survival in patients with lung adenocarcinoma (P < 0.001). The prognostic value of the score was confirmed in the validation cohort. The immune infiltrating score could improve the accuracy of predictions of survival when combined with the staging system. Furthermore, the score was potentially associated with patient smoking status and histologic subtype of lung adenocarcinoma. Its possible association with the efficacy of adjuvant chemotherapy was not statistically significant. The immune cell infiltrating score has prognostic significance in predicting overall survival in patients with lung adenocarcinoma." @default.
• W2962280147 created "2019-07-23" @default.
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• W2962280147 date "2019-07-08" @default.
• W2962280147 modified "2023-10-17" @default.
• W2962280147 title "Identification and validation of an immune cell infiltrating score predicting survival in patients with lung adenocarcinoma" @default.
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• W2962280147 doi "https://doi.org/10.1186/s12967-019-1964-6" @default.
• W2962280147 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6615164" @default.
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