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Prognostic Implications of Novel Gene Signatures in Gastric Cancer Microenvironment

Mengyu Sun, Jieping Qiu, Huazheng Zhai, Yaoqun Wang, Panpan Ma, Mengyin Li, Bo Chen

Department of Clinical Medicine, Anhui Medical University, Hefei, Anhui, China (mainland)

Med Sci Monit 2020; 26:e924604

DOI: 10.12659/MSM.924604

Available online: 2020-06-05

Published: 2020-08-02

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 AND 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.

Keywords: Biological Markers, Computational Biology, Prognosis, Stomach Neoplasms