A nuclear NKRF interacting long noncoding RNA controls EBV eradication and suppresses tumor progression in natural killer/T-cell lymphoma.

Long intergenic noncoding RNAs (lincRNAs) are differentially expressed in EBV-infected cells and play an essential role in tumor progression. Molecular pathogenesis of lincRNAs in EBV-driven natural killer T cell lymphoma (NKTCL) remains unclear. Here we investigated the ncRNA profile using high-throughput RNA sequencing data of 439 lymphoma samples and screened out LINC00486, whose downregulation was further validated by quantitative real-time polymerase chain reaction in EBV-encoded RNA (EBER)-positive lymphoma, particularly NKTCL. Both in vitro and in vivo studies revealed the tumor suppressive function of LINC00486 through inhibiting tumor cell growth and inducing G0/G1 cell cycle arrest. As mechanism of action, LINC00486 specifically interacted with NKRF to abrogate its binding with phosphorylated p65, activated NF-κB/TNF-α signaling and subsequently enhanced EBV eradication. Solute carrier family 1 member 1 (SLC1A1), upregulated and mediated the glutamine-addiction and tumor progression in NKTCL, was negatively correlated with the expression of NKRF. NKRF specifically bound to the promoter and transcriptionally downregulated the expression of SLC1A1, as evidenced by Chromatin Immunoprecipitation (ChIP) and luciferase assay. Collectively, LINC00486 functioned as a tumor suppressor and counteracted EBV infection in NKTCL. Our study improved the knowledge of EBV-driven oncogenesis in NKTCL and provided the clinical rationale of EBV eradication in anti-cancer treatment.

Screening and identifying a novel M-MDSCs-related gene signature for predicting prognostic risk and immunotherapeutic responses in patients with lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) shows intratumoral heterogeneity, a highly complex phenomenon that known to be a challenge during cancer therapy. Considering the key role of monocytic myeloid-derived suppressor cells (M-MDSCs) in the tumor microenvironment (TME), we aimed to build a prognostic risk model using M-MDSCs-related genes. Methods: M-MDSCs-related genes were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Utilized univariate survival analysis and random forest algorithm to screen candidate genes. A least absolute shrinkage and selection operator (LASSO) Cox regression analysis was selected to build the risk model. Patients were scored and classified into high- and low-risk groups based on the median risk scores. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis along with R packages "estimate" and "ssGSEA" were performed to reveal the mechanism of risk difference. Prognostic biomarkers and tumor mutation burden (TMB) were combined to predict the prognosis. Nomogram was carried out to predict the survival probability of patients in 1, 3, and 5 years. Results: 8 genes (VPREB3, TPBG, LRFN4, CD83, GIMAP6, PRMT8, WASF1, and F12) were identified as prognostic biomarkers. The GEO validation dataset demonstrated the risk model had good generalization effect. Significantly enrichment level of cell cycle-related pathway and lower content of CD8+ T cells infiltration in the high-risk group when compared to low-risk group. Morever, the patients were from the intersection of high-TMB and low-risk groups showed the best prognosis. The nomogram demonstrated good consistency with practical outcomes in predicting the survival rate over 1, 3, and 5 years. Conclusion: The risk model demonstrate good prognostic predictive ability. The patients from the intersection of low-risk and high-TMB groups are not only more sensitive response to but also more likely to benefit from immune-checkpoint-inhibitors (ICIs) treatment.