Background: The role of autophagy-related long-stranded non-coding RNA (lncRNA) in breast cancer (BRCA) is unclear. We proposed to screen autophagy-related lncRNAs in BRCA and construct a prognostic risk assessment model to explore prognostic correlates. Methods:We extracted BRCA lncRNAs from The Cancer Genome Atlas (TCGA) database and autophagy-related genes from the Human Autophagy Database (HADb), to screen for autophagy-related lncRNA pairs (ARLP) in BRCA. Single-factor Cox regression analysis and multi-factor Cox regression analysis were used to screen lncRNAs associated with BRCA prognosis, and risk models were established. We divided BRCA patients into high-risk and low-risk groups based on median risk scores. The single-sample gene set enrichment analysis (ssGSEA) algorithm was used to calculate the abundance of 28 immune cells in the TCGA-BRCA cohort and to analyze the relationship between the risk score and the level of immune cell infiltration by ARLP characteristics.Results: Univariate Cox regression results showed that 42 ARLPs were significantly associated with overall survival (OS) in BRCA patients. Further multifactorial analysis showed that a total of 11 lncRNAs, including

Background Neural tube defects (NTDs) are one of the most common types of birth defects. Oral folic acid (FA) prophylaxis is currently available, but the pathogenesis of NTDs is not fully understood. We conducted this study to examine the role of the immune landscape of NTDs and identify novel diagnostic and therapeutic biomarkers. Methods We downloaded the GSE33111 data set of 12 NTD embryos and 12 healthy embryos in the same period of fetal development from the Gene Expression Omnibus (GEO) database. We compared the healthy embryos and NTD embryos to identify the differentially expressed genes (DEGs). We also performed a functional enrichment analysis of the DEGs using the clusterProfiler package. We extracted the top 10 ranked genes as hub immune-related biomarkers. We then used receiver operating characteristic (ROC) curves to determine the expression levels of the hub immune-related genes and the accuracy of the diagnosis of NTDs. Finally, we analyzed the immune landscape of the NTD embryos and healthy embryos via a single-sample gene set enrichment analysis (ssGSEA). Results A total of 611 DEGs were identified by the differential analysis, including 95 immune genes. The functional enrichment analysis indicated that Epstein-Barr virus infection, antigen processing and presentation, inflammatory bowel disease, and type I diabetes mellitus were associated with NTDs. The results of the expression level analysis showed that the hub immune-related genes were more highly expressed in the NTD embryos than the healthy embryos. Additionally, the ROC curve analysis also indicated that the expression levels of the 10 hub immune-related genes were highly accurate in the diagnosis of NTDs [area under the curve (AUC) range, 0.708–0.812]. The immune infiltration analysis demonstrated that 20 of the 28 immune cell types were more highly infiltrated in the NTD embryos than the healthy embryos. Conclusions Immune-related genes are important regulators of the occurrence and development of NTDs.

Background: Immunotherapy has made great strides in cancer treatment. Endometrial carcinoma (EC) has been 1 of the most common tumors among women. This study aimed to screen immune-related prognosis biomarkers for EC. Methods:The transcriptome profiling and clinical data of EC were downloaded from The Carcinoma Genome Atlas (TCGA) public database, and differentially expressed genes (DEGs) were obtained through the limma package in R software. An immune-related genes (IRGs) list was collected from the ImmPort database. We constructed a free-scale gene co-expression network via weighted gene co-expression network analysis (WGCNA). Then, the intersection genes of the module genes which significantly related to EC, along with IRGs and DEGs were screened as the candidate genes for further analysis. We identified the hub gene via Venn analysis of the protein-protein interaction (PPI) network genes and the prognostic genes, and verified expression of the hub gene through Human Protein Atlas (HPA) and Gene Expression Omnibus (GEO) databases which provided the GSE17025 dataset. Furthermore, we used the CIBERSORT deconvolution algorithm to explore tumor immune cells infiltration in EC, and investigated correlations between the hub gene and immune cells. Results:The differential expression analysis demonstrated that there were 900 up-regulated genes and 1,008 down-regulated genes in TCGA-UCEC (Uterine Corpus Endometrial Carcinoma) cohort. There were 74 candidate intersection genes in blue module genes, IRGs, and DEGs. Finally, angiopoietin 1 (ANGPT1) was identified as the hub gene in EC. Low expression of ANGPT1 was associated with better overall survival (OS) in EC patients. The expression of ANGPT1 was negatively correlated with regulatory T cells (Tregs), but positively correlated with resting memory cluster of differentiation 4 (CD4) T cells, activated dendritic cells (DCs), activated natural killer (NK) cells, and activated memory CD4 T cells (P<0.05, Spearman). A highinfiltrating regulatory T cell would improve the prognosis for EC patients. Conclusions:The gene ANGPT1 can increase the infiltration of T cells and improve the prognosis of EC patients.

Background: Recurrent pregnancy loss (RPL) and unexplained infertility (UI) are common pregnancy disorders that affect women's physical and mental health and lack effective treatment. Endometrial factors are one factor that leads to RPL. The latest research indicates that ferroptosis and immunity are closely related to the normal physiological function of the endometrium and may play a role in the pathogenesis of RPL and UI. Therefore, the present study analyzed the relationship between ferroptosis genes and immune infiltration in RPL and UI. Methods:We downloaded the GSE165004 dataset and analyzed differences in ferroptosis-related genes (FRGs) between RPL and UI patients and healthy controls. Hub differentially expressed ferroptosis-related genes (DE-FRGs) were screened using the LASSO algorithm, the SVM-RFE algorithm and the proteinprotein interaction (PPI) network. Differences in immune infiltration between healthy endometrium and RPL and UI endometrium was analyzed, and the relationship between hub DE-FRGs and immune cell infiltration was examined.Results: We extracted 409 FRGs and identified 36 up-regulated and 32 down-regulated DE-FRGs in RPL and UI. Twenty-one genes were screened using the LASSO regression algorithm, and 17 genes were screened using the SVM-RFE algorithm. We intersected the LASSO genes, SVM-RFE genes and PPI network proteins to obtain 5 hub DE-FRGs. Gene Set Enrichment Analysis (GSEA) functional enrichment analysis results indicated that the cytokine-cytokine receptor interaction signaling pathway was the common pathway for hub DE-FRGs. T follicular helper cells were highly infiltrated in RPL and UI, and M1 and M2 macrophages were highly infiltrated. The expression levels of MAPK1 and RELA positively correlated with T follicular helper cells.Conclusions: Ferroptosis-related genes may disrupt endometrial functions and signaling pathways and lead to the occurrence of RPL and UI.