LASTR is a novel prognostic biomarker and predicts response to cancer immunotherapy in gastric cancer.

Autor:	Liu JY; Department of General Surgery, The First Medical Center, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China., Yao J; Department of General Surgery, The First Medical Center, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China., Liu JJ; Department of General Surgery and Center of Minimal Invasive Gastrointestinal Surgery, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China., He T; Department of General Surgery and Center of Minimal Invasive Gastrointestinal Surgery, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China., Wang FJ; State Key Laboratory of Trauma, Burns and Combined Injury, Army Medical Center (Daping Hospital), Army Medical University, Chongqing, China., Xie TY; Department of General Surgery, The First Medical Center, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China., Cui JX; Department of General Surgery, The First Medical Center, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China., Yang XD; Department of General Surgery, The First Medical Center, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China.
Jazyk:	angličtina
Zdroj:	Frontiers in oncology [Front Oncol] 2022 Sep 29; Vol. 12, pp. 1020255. Date of Electronic Publication: 2022 Sep 29 (Print Publication: 2022).
DOI:	10.3389/fonc.2022.1020255
Abstrakt:	Gastric cancer (GC), a malignant tumor of digestive tract, is characterized by a high death rate. Thus, it is of particular importance to clarify the mechanisms of GC and gain new molecular targets for the sake of preventing and treating GC. It was reported that long non-coding RNAs (IncRNAs) are prognostic factors to cancer. Ferroptosis refers to a process of programmed cell death dependent on iron. This study sets out to investigate the expression and function of ferroptosis-related lncRNA (FRlncRNA) in GC. TCGA datasets offered RNA-seq data for 375 GC patients and clinical data for 443 GC patients. Based on Pearson's correlation analysis, we studied their expression and identified the FRlncRNAs. Differentially expressed prognosis related to FRlncRNA were determined with the help of the Wilcoxon test and univariate Cox regression analysis. To evaluate the accuracy of the prognostic capacity, researchers used the Kaplan-Meier technique, as well as univariate and multivariate Cox regression and receiver operating characteristic (ROC) curve studies. We also carried out the real-time PCR and CCK8 assays to examine the expression and function of FRlncRNA. In this study, we identified 50 ferroptosis-related DEGs which were involved in tumor progression. In addition, we identified 33 survival-related FRlncRNAs. Among them, lncRNA associated with SART3 regulation of splicing(LASTR) was confirmed to be highly expressed in GC specimens compared to non-tumor specimens in this cohort. Survival assays illuminated that the high LASTR expression predicted a shorter overall survival and progression-free survival of GC patients. Based on multivariate Cox regression analyses, it was confirmed that the GC had a worse chance of surviving the disease overall if their tumors expressed LASTR, which was an independent prognostic indication. Then, Loss-of-function tests showed that knocking down LASTR had a significant effect on reducing the proliferation of GC cells. Finally, we found that the expression of LASTR was negatively associated with CD8 T cells, T cells, Th17 cells, and T helper cells. Overall, our findings identified a novel survival-related FRlncRNA, LASTR which possibly can serve as a novel prognostic biomarker predicting response to cancer immunotherapy and therapeutic target for GC patients. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2022 Liu, Yao, Liu, He, Wang, Xie, Cui and Yang.)
Databáze:	MEDLINE
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