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Background: Changes in gene expression are associated with malignancy. Analysis of gene expression data could be used to reveal cancer subtypes, key molecular drivers, and prognostic characteristics and to predict cancer susceptibility, treatment response, and mortality. It has been reported that inflammation plays an important role in the occurrence and development of tumors. Our aim was to establish a risk signature model of breast cancer with inflammation-related genes (IRGs) to evaluate their survival prognosis. Methods: We downloaded 200 IRGs from the Molecular Signatures Database (MSigDB). The data of breast cancer were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Differential gene expression analysis, the least absolute shrinkage and selection operator (LASSO), Cox regression analysis, and overall survival (OS) analysis were used to construct a multiple-IRG risk signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were carried out to annotate functions of the differentially expressed IRGs (DEIRGs) The predictive accuracy of the prognostic model was evaluated by time-dependent receiver operating characteristic (ROC) curves. Subsequently, nomograms were constructed to guide clinical application according to the univariate and multivariate Cox proportional hazards regression analyses. Eventually, we applied gene set variation analysis (GSVA), mutation analysis, immune infiltration analysis, and drug response analysis to compare the differences between high- and low-risk patients. Results: Totally, 65 DEIRGs were obtained after comparing 1,092 breast cancer tissues with 113 paracancerous tissues in TCGA. Among them, 11 IRGs (IL18, IL12B, RASGRP1, HPN, CLEC5A, SCARF1, TACR3, VIP, CCL2, CALCRL, ABCA1) were screened with nonzero coefficient by LASSO regression analysis to construct the prognostic model, which was validated in GSE96058.The 11-gene IRGs risk signature model stratified patients into high- or low-risk groups, with those in the low-risk group having longer survival time and less deaths. Multivariate Cox analysis manifested that risk score, age, and stage were the three independent prognostic factors for breast cancer patients. There were 12 pathways with higher activities and 24 pathways with lower activities in the high-risk group compared with the low-risk group, yet no difference of gene mutation load was observed between the two groups. In immune infiltration analysis, we noted that the proportion of T cells showed a decreased trend according to the increase of risk score and most of the immune cells were enriched in the low-risk group. Inversely, macrophages M2 were more highly distributed in the high-risk group. We identified 67 approved drugs that showed a different effect between the high- and low-risk patients and the top 2 gene-drug pairs were IL12B-sunitinib and SCARF1-ruxolitinib. Conclusions: The 11-IRG risk signature model is a promising tool to predict the survival of breast cancer patients and the expressions of IL12B and SCARF1 may serve as potential targets for therapy of breast cancer.
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Circular RNAs (circRNAs) perform important functions in the regulation of diverse physiological and pathological processes. CircABHD2 exhibits down-regulation in both endometrial cancer (EC) cells and tissues, but the biological roles and mechanisms of action in EC are still unclear. This study aims to provide a theoretical basis for the role of circABHD2 in EC and potential targets for individualized precision therapy. Dysregulated circRNAs were identified using RNA sequencing (RNA-Seq) from EC tissues and validated using RT-qPCR. CCK-8, colony formation assay, wound healing assay, transwell assay, cell cycle, and apoptosis assay were used to evaluate the effects of circABHD2 on EC cells. Metabolomics assay and western blot analyses were used to investigate the potential mechanisms of circABHD2. From sequencing of RNA (RNA-Seq) analysis of EC tissues, we obtained 19 dysregulated circRNAs, including 8 upregulated ones and 11 downregulated ones. Using RT-qPCR on 32 EC tissues and 19 normal endometrial tissues, we confirmed that circABHD2 was downregulated in EC tissues. The expression levels of circABHD2 were closely relevant to the International Federation of Gynecology and Obstetrics (FIGO) stage and differentiation degree of EC. Functional experiments demonstrated that overexpression of circABHD2 decreased proliferation, migration, invasion, and promoted cell apoptosis. Un-targeted metabolomic assay revealed 31 differential metabolites in EC cells overexpressing circABHD2. KEGG analysis of differential metabolites indicated that NAD+ is the core metabolite regulated by circABHD2. NAMPT is one key enzyme involved in the synthetic pathway responsible for NAD+. Subsequent experiments confirmed that by inhibiting NAMPT protein expression in EC cells, cirABHD2 can inhibit NAD+ level, suggesting that circABHD2 may inhibit EC by regulating the metabolic axis of NAD+/NAMPT. CircABHD2, a downregulated circRNA in EC cells and tissues, inhibits the malignant progression of EC via the NAD+/NAMPT metabolic axis. This discovery presents a promising diagnostic biomarker and potential therapeutic target for EC.
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BACKGROUND: The mechanism promoting papillary thyroid carcinoma (PTC) metastasis remains unclear. We aimed to investigate the potential metastatic mechanisms at a single-cell resolution. METHODS: We performed single-cell RNA-seq (scRNA-seq) profiling of thyroid tumour (TT), adjacent normal thyroid (NT) and lymph node metastasized tumour (LN) from a young female with PTC. Validation of our results was conducted in 31 tumours with metastasis and 30 without metastasis. RESULTS: ScRNA-seq analysis generated data on 38,215 genes and 0.14 billion transcripts from 28,839 cells, classified into 18 clusters, each annotated to represent 10 cell types. PTC cells were found to originate from epithelial cells. Epithelial cells and macrophages emerged as the strongest signal emitters and receivers, respectively. After reclustering epithelial cells and macrophages, our analysis, incorporating gene set variation analysis (GSVA), SCENIC analysis, and pseudotime trajectory analysis, indicated that subcluster 0 of epithelial cells (EP_0) showed a more malignant phenotype, and subclusters 3 and 4 of macrophages (M_3 and M_4) demonstrated heightened activity. Further analysis suggested that EP_0 may suppress the activity of M_3 and M_4 via MIF - (CD74 + CXCR4) in the MIF pathway. After analysing the expression of the 4 genes in the MIF pathway in both the TCGA cohort and our cohort (n = 61), CD74 was identified as significantly overexpressed in PTC tumours particularly those with lymph node metastasis. CONCLUSION: Our study revealed that PTC may facilitate lymph node metastasis by inhibiting macrophages via MIF signalling. It is suggested that malignant PTC cells may suppress the immune activity of macrophages by consistently releasing signals to them via MIF-(CD74 + CXCR4).