4.6 Article

The TP53-Related Signature Predicts Immune Cell Infiltration, Therapeutic Response, and Prognosis in Patients With Esophageal Carcinoma

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FRONTIERS IN GENETICS
卷 12, 期 -, 页码 -

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FRONTIERS MEDIA SA
DOI: 10.3389/fgene.2021.607238

关键词

oesophageal carcinoma; TP53; signature; immune cell infiltration; therapeutic responses

资金

  1. National Natural Science Foundation of China [81773116, 81760436, 81560399, 81460362, 81860518]
  2. National Early Detection and Treatment Project for Upper Digestive Tract in Rural Area in China

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In this study, a TP53(MUT)-related signature was constructed to predict the prognosis of esophageal cancer patients, which was successfully verified in different groups of patients. The risk scores were identified as better independent prognostic factors, and a convincing nomogram was established to predict overall survival accurately.
TP53 mutation (TP53(MUT)) is one of the most common gene mutations and frequently occurs in many cancers, especially esophageal carcinoma (ESCA), and it correlates with clinical prognostic outcomes. Nevertheless, the mechanisms by which TP53(MUT) regulates the correlation between ESCA and prognosis have not been sufficiently studied. Here, in the current research, we constructed a TP53(MUT)-related signature to predict the prognosis of patients with esophageal cancer and successfully verified this model in patients in the TP53 mutant group, esophageal squamous cell carcinoma group, and adenocarcinoma group. The risk scores proved to be better independent prognostic factors than clinical features, and prognostic features were combined with other clinical features to establish a convincing nomogram to predict overall survival from 1 to 3 years. In addition, we further predicted the tumor immune cell infiltration, chemical drugs, and immunotherapy responses between the high-risk group and low risk group. Finally, the gene expression of the seven-gene signature (AP002478.1, BHLHA15, FFAR2, IGFBP1, KCTD8, PHYHD1, and SLC26A9) can provide personalized prognosis prediction and insights into new treatments.

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