There were no systematic researches about autophagy-related long noncoding RNA (lncRNA) signatures to predict the survival of patients with colon adenocarcinoma. It was necessary to set up corresponding autophagy-related lncRNA signatures. The expression profiles of lncRNAs which contained 480 colon adenocarcinoma samples were obtained from The Cancer Genome Atlas (TCGA) database. The coexpression network of lncRNAs and autophagy-related genes was utilized to select autophagy-related lncRNAs. The lncRNAs were further screened using univariate Cox regression. In addition, Lasso regression and multivariate Cox regression were used to develop an autophagy-related lncRNA signature. A risk score based on the signature was established, and Cox regression was used to test whether it was an independent prognostic factor. The functional enrichment of autophagy-related lncRNAs was visualized using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Ten prognostic autophagy-related lncRNAs (AC027307.2, AC068580.3, AL138756.1, CD27-AS1, EIF3J-DT, LINC01011, LINC01063, LINC02381, AC073896.3, and SNHG16) were identified to be significantly different, which made up an autophagy-related lncRNA signature. The signature divided patients with colon adenocarcinoma into the low-risk group and the high-risk group. A risk score based on the signature was a significantly independent factor for the patients with colon adenocarcinoma (HR=1.088, 95%CI=1.057−1.120; P<0.001). Additionally, the ten lncRNAs were significantly enriched in autophagy process, metabolism, and tumor classical pathways. In conclusion, the ten autophagy-related lncRNAs and their signature might be molecular biomarkers and therapeutic targets for the patients with colon adenocarcinoma.

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Background. Gastric cancer (GC), an extremely aggressive tumor with a very different prognosis, is the third leading cause of cancer-related mortality. We aimed to construct a ferroptosis-related prognostic model that can be distinguished prognostically. Methods. The gene expression and the clinical data of GC patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database (GEO). The ferroptosis-related genes were obtained from the FerrDb. Using the “limma” R package and univariate Cox analysis, ferroptosis-related genes with differential expression and prognostic value were identified in the TCGA cohort. Last absolute shrinkage and selection operator (LASSO) Cox regression was applied to shrink ferroptosis-related predictors and construct a prognostic model. Functional enrichment, ESTIMATE algorithm, and single-sample gene set enrichment analysis (ssGSEA) were applied for exploring the potential mechanism. GC patients from the GEO cohort were used for validation. Results. An 8-gene prognostic model was constructed and stratified GC patients from TCGA and meta-GEO cohort into high-risk groups or low-risk groups. GC patients in high-risk groups have significantly poorer OS compared with those in low-risk groups. The risk score was identified as an independent predictor for OS. Functional analysis revealed that the risk score was mainly associated with the biological function of extracellular matrix (ECM) organization and tumor immunity. Conclusion. In conclusion, the ferroptosis-related model can be utilized for the clinical prognostic prediction in GC.

Th17 cells, a lymphocyte subpopulation that is characterized by the expression of the transcription factor "retinoic acid receptor-related orphan receptor gamma-t" (RORγt), plays an important role in the pathogenesis of autoimmune disease. The current study was set up to discover novel and non-steroidal small-molecule inverse agonists of RORγt and to determine their effects on autoimmune disease. Structure-based virtual screening (SBVS) was used to find compounds targeting RORγt. Flow cytometry was used to detect the Th17 cell differentiation. Inverse agonists were intraperitoneally administered to mice undergoing experimental autoimmune uveitis (EAU), experimental autoimmune encephalomyelitis (EAE) or type 1 diabetes. The effects of the inverse agonists were evaluated by clinical or histopathological scoring. Among 1.3 million compounds screened, CQMU151 and CQMU152 were found to inhibit Th17 cell differentiation without affecting the differentiation of Th1 and Treg lineages (both P = 0.001). These compounds also reduced the severity of EAU (P = 0.01 and 0.013) and functional studies showed that they reduced the number of Th17 cell and the expression of IL-17(Th17), but not IFN-γ(Th1) and TGF-β(Treg) in mouse retinas. Further studies showed that these compounds may reduce the expression of p-STAT3 by reducing the positive feedback loop of IL-17/IL-6/STAT3. These compounds also reduced the impaired blood-retinal barrier function by upregulating the expression of tight junction proteins. These compounds were also found to reduce the severity of EAE and type 1 diabetes. Our results showed that RORγt inverse agonists may inhibit the development of autoimmune diseases and may provide new clues for the treatment of Th17-mediated immune diseases.