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Identification of Prognostic Genes Relevant With the Nuclear Factors of Activated T Cells Based on Transcriptomics in
Julaiti Tuerxun1, Ailimaierdan Ainiwaer2, Tairan Ding1
1Department of Oral and Maxillofacial Trauma and Orthognathic Surgery, The First Affiliated Hospital of Xinjiang Medical University, Research Institute of Stomatology of Xinjiang Uygur Autonomous Region, Urumqi, China.
Background:
It has previously been demonstrated that the nuclear factor of activated T cells (NFAT) is crucial for the development of tumors. Given OSCC's drug resistance and poor outcomes, identifying NFAT-associated prognostic genes is urgent for better treatment.
Material And Methods:
The TCGA-OSCC and GSE41613 datasets were utilized to identify differentially expressed genes (DEGs) between oral squamous cell carcinoma (OSCC) and controls. Specifically, differentially expressed genes related to NFAT (DEG-NFATs) were further screened for NFAT scores in OSCC versus control. The intersection of DEGs and DEG-NFATs was taken to identify differentially expressed NFAT-related genes (DE-NFATRGs). Univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were employed to identify prognostic genes. A risk score was developed based solely on the expression levels of the seven-gene signature. Subsequently, independent prognostic analyses incorporating clinicopathological variables were performed using univariate and multivariate Cox regression to evaluate whether the risk score served as an independent predictor of survival. Lastly, targeted therapeutic agents for OSCC were predicted. In addition, prognostic gene expression was validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR).
Results:
Totally 4463 DEGs obtained were intersected with 310 DEG-NFATs to obtain 263 DE-NFATRGs. A risk model can be built using the seven prognostic genes associated with NFAT (ALB, PDK4, SERPINA5, SPINK6, SERPINA9, CGNL1 and IL17F). The risk score accurately predicts survival in OSCC patients. Finally, a total of 31 drugs with significant differences were predicted between risk groups. The most significant of these were AG.014699, Midostaurin, Gefitinib, and LFM.A13. Besides, the expression levels of ALB, PDK4, and SERPINA5 were significantly higher in tumors, while the expression levels of CGNL1 and IL17F were significantly lower in tumors.
Conclusion:
The identification of new prognostic genes (ALB, PDK4, SERPINA5, SPINK6, SERPINA9, CGNL1, and IL17F) was provided for the prognosis and treatment of OSCC.
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