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Prognostic Implications of Novel Gene Signatures in Gastric Cancer Microenvironment

Authors
  • Sun, Mengyu1
  • Qiu, Jieping1
  • Zhai, Huazheng1
  • Wang, Yaoqun1
  • Ma, Panpan1
  • Li, Mengying1
  • Chen, Bo2
  • 1 Department of Clinical Medicine, Anhui Medical University, Hefei, Anhui, P.R. China
  • 2 Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, P.R. China
Type
Published Article
Journal
Medical Science Monitor
Publisher
"International Scientific Information, Inc."
Publication Date
Aug 02, 2020
Volume
26
Identifiers
DOI: 10.12659/MSM.924604
PMID: 32740646
PMCID: PMC7418782
Source
PubMed Central
Keywords
License
Green

Abstract

Background Increasing studies have shown the important clinical role of immune and stromal cells in gastric cancer microenvironment. Based on information of immune and stromal cells in The Cancer Genome Atlas, this study aimed to construct a prognostic risk assessment model for gastric cancer. Material/Methods Based on the immune/structural scores, differentially expressed genes (DEGs) were filtered and analyzed. Afterwards, DEGs associated with prognosis were screened and the risk assessment model was constructed in the training set. Moreover, the validity of the model was verified both in the testing set and the overall sample. Results In this study, patients were divided into high-score and low-score groups based on immune/stromal score, and 919 DEGs were identified. By applying least absolute shrinkage and selection operator (LASSO) and Cox analysis, 10 mRNAs were selected to form a prognostic risk assessment model, risk score=(0.294*SLC17A9) + (−0.477*FERMT3) + (0.866*NRP1) + (0.350*MMRN1) + (0.381*RNASE1) + (0.189*TRIB3) + (0.230*PGAP3) + (0.087*MAGEA3) + (0.182*TACR2) + (0.368*CYP51A1). In the training set, the low-risk group divided by the model was found to have better overall survival, and the prediction efficiency of the model was demonstrated to be good. Multivariate Cox analysis indicated that the model could work as a prognostic factor independently. Similar results were shown in the testing group and overall patients cohort group. Finally, the risk assessment model and other clinical variables were integrated to construct a nomogram. Conclusions In general, this study constructs a prognostic risk assessment model for gastric cancer, which could improve the prognosis stratification of patients combined with other clinical indicators.

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