Background Osteosarcoma (OS) is the most prevalent orthopedic malignancy with a dismal prognosis. The high iron absorption rate in OS cells of patients suggests that ferroptosis may be related to the progression of OS, but its potential molecular regulatory role is still unclear. Based on the ability to couple with exosomes for targeted delivery of signals, exosome-derived micro ribonucleic acids (miRNAs) can potentially serve as diagnostic biomarkers for OS. Methods We identified ferroptosis-related miRNAs and messenger ribonucleic acids(mRNAs) in OS using bioinformatics analysis and performed survival analysis. Then we measured miRNA expression levels through exosome microarray sequencing, and used RT-qPCR and IHC to verify the expression level of miR-144-3p and ZEB1. Stable gene expression cell lines were fabricated for in vitro experiments. Cell viability, migration and invasion were determined by CCK-8 and transwell experiment. Use the corresponding reagent kit to detect GSH/GSSG ratio, Fe2+ level, MDA level and ROS level, and measure the expression levels of GPX4, ACSL4 and xCT through RT-qPCR and WB. We also constructed nude mice model for in vivo experiments. Finally, the stability of the miRNA/mRNA axis was verified through functional rescue experiments. Results Low expression of miR-144-3p and high expression of ZEB1 in OS cell lines and tissues was observed. Overexpression of miR-144-3p can promote ferroptosis, reduce the survival ability of OS cells, and prevent the progression of OS. In addition, overexpression of miR-144-3p can downregulate the expression of ZEB1 in cell lines and nude mice. Knockdown of miR-144-3p has the opposite effect. The functional rescue experiment validated that miR-144-3p can regulate downstream ZEB1, and participates in the occurrence and development of OS by interfering with redox homeostasis and iron metabolism. Conclusions MiR-144-3p can induce the occurrence of ferroptosis by negatively regulating the expression of ZEB1, thereby inhibiting the proliferation, migration, and invasion of OS cells. Graphical Abstract

Background. Gastric cancer (GC) is one of the gastrointestinal tumors with the highest mortality rate. The number of GC patients is still high. As a way of iron-dependent programmed cell death, ferroptosis activates lipid peroxidation and accumulates large reactive oxygen species. The role of ferroptosis in GC prognosis was underrepresented. The objective was to investigate the role of ferroptosis-related genes (FRGs) in the prognosis and development of GC. Methods. Datasets of GC patients were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database that include clinical information and RNA seq data. Through nonnegative matrix factorization (NMF) clustering, we identified and unsupervised cluster analysis of the expression matrix of FRGs. And we constructed the co-expression network between genes and clinical characteristics by consensus weighted gene co-expression network analysis (WGCNA). The prognostic model was constructed by univariate and multivariate regression analysis. The potential mechanisms of development and prognosis in GC were explored by Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, gene ontology (GO), tumor immune microenvironment (TIME), and tumor mutation burden (TMB). Results. Two molecular subclusters with different expression patterns of FRGs were identified, which have significantly different survival states. Ferroptosis subcluster-related modular genes were identified by WGCNA. Based on 8 ferroptosis subcluster-related modular genes (collagen triple helix repeat containing 1 (CTHRC1), podoplanin (PDPN), procollagen-lysine,2-oxoglutarate 5-dioxygenase 2 (PLOD2), glutamine-fructose-6-phosphate transaminase 2 (GFPT2), ATP-binding cassette subfamily A member 1 (ABCA1), G protein-coupled receptor 176 (GPR176), serpin family E member 1 (SERPINE1), dual specificity phosphatase 1 (DUSP1)) and clinicopathological features, a nomogram was constructed and validated for their predictive efficiency on GC prognosis. Through receiver operating characteristic (ROC) analysis, the results showed that the area under the curve (AUC) of 1-, 3-, and 5-year survival were 0.721, 0.747, and 0.803, respectively, indicating that the risk-scoring model we constructed had good prognosis efficacy in GC. The degree of immune infiltration in high-risk group was largely higher than low-risk group. It indicated that the immune cells have a good response in high-risk group of GC. The TMB of high-risk group was higher, which could generate more mutations and was more conducive to the body’s resistance to the development of cancer. Conclusion. The risk-scoring model based on 8 ferroptosis subcluster-related modular genes has shown outstanding advantages in predicting patient prognosis. The interaction of ferroptosis in GC development may provide new insights into exploring molecular mechanisms and targeted therapies for GC patients.

Background. Atherosclerotic plaque instability is a common cause of stroke and ischemic infarction, and identification of monocyte-associated genes has become a prominent feature in cardiovascular research as a contributing/predictive marker. Methods. Whole genome sequencing data were downloaded from GSE159677, GSE41571, GSE120521, and GSE118481. Single-cell sequencing data analysis was conducted to cluster molecular subtypes of atherosclerotic plaques and identify specific genes. Differentially expressed genes (DEGs) between normal subjects and patients with unstable atheromatous plaques were screened. Weighted gene coexpression network analysis (WGCNA) was performed to find key module genes. In addition, GO and KEGG enrichment analyses explored potential biological signaling pathways to generate protein interaction (PPI) networks. GSEA and GSVA demonstrated activations in plaque instability subtypes. Results. 239 monocyte-associated genes were identified based on bulk and single-cell RNA-sequencing, followed by the recognition of 1221 atherosclerotic plaque-associated DEGs from the pooled matrix. GO and KEGG analyses suggested that DEGs might be related to inflammation response and the PI3K-Akt signaling pathway. Eight no-grey modules were obtained through WGCNA analysis, and the turquoise module has the highest correlation with unstable plaque ( R 2 = 0.40 ), which contained 1323 module genes. After fetching the intersecting genes, CXCL3, FPR1, GK, and LST1 were obtained that were significantly associated with plaque instability, which had an intense specific interaction. Monocyte-associated genes associated with atherosclerotic plaque instability have certain diagnostic significance and are generally overexpressed in this patient population. In addition, 11 overlapping coexpressed genes (CEG) might also activated multiple pathways regulating inflammatory responses, platelet activation, and hypoxia-inducible factors. GSVA showed that the corresponding pathways were significantly activated in high expression samples. Conclusions. Overexpression of CXCL3, GK, FPR1, and LST1 was advanced recognition and intervention factors for unstable plaques, which might become targets for atherosclerosis rupture prevention. We also analyzed the potential mechanisms of CEG from inflammatory and oxidative stress pathways.

As a highly malignant tumor, the morbidity and mortality of cutaneous melanoma (CM) are increasing year by year. A novel type of cell death connected to mitochondrial metabolism is called cuproptosis. Cuproptosis regulates tumor biological behavior. Thus, genes controlling cuproptosis could be a promising candidate bioindicator for cancer therapy. Datasets of CM patients were obtained from the public database that includes clinical information and RNA-seq data. We divided CM patients into three different subgroups by unsupervised clustering method and explored the differences in functional pathways among the three subgroups by GSVA to prove the possible potential mechanism of copper death-related genes in the formation and development of CM. Secondly, we used differential analysis and Cox regression analysis to find the differential genes related to prognosis, constructed the CRG score, found the critical score for dividing high and low CRG score groups, and then analyzed the prognosis and immune infiltration of high and low CRG score groups. The results show a great correlation between OS and CRG scores. Compared with patients with high CRG scores, patients with low CRG scores have a significantly higher survival rate. In a word, copper sagging plays a certain role in the progress of CM.

Background. Despite tremendous advances in treating osteosarcoma (OS), the survival rates of patients have failed to improve dramatically over the past decades. Ferroptosis, a newly discovered iron-dependent type of regulated cell death, is implicated in tumors, and its features in OS remain unascertained. We designed to determine the involvement of ferroptosis subcluster-related modular genes in OS progression and prognosis. Methods. The OS-related datasets retrieved from GEO and TARGET database were clustered for identifying molecular subclusters with different ferroptosis-related genes (FRGs) expression patterns. Weighted gene coexpression network analysis (WGCNA) was applied to identify modular genes from FRG subclusters. The least absolute shrinkage and selection operator (LASSO) algorithm and multivariable Cox regression analysis were adopted to develop the prognostic model. Potential mechanisms of development and prognosis in OS were explored by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA). Then, a comprehensive analysis was conducted for immune checkpoint markers and assessment of predictive power to drug response. The protein expression levels of the three ferroptosis subcluster-related modular genes were verified by immunohistochemistry. Results. Two independent subclusters presenting diverse expression profiles of FRGs were obtained, with significantly different survival states. Ferroptosis subcluster-related modular genes were screened with WGCNA, and the GESA results showed that ferroptosis subcluster-related modular genes could affect the cellular energy metabolism, thus influencing the development and prognosis of osteosarcoma. A prognostic model was established by incorporating three ferroptosis subcluster-related modular genes (LRRC1, ACO2, and CTNNBIP1) and a nomogram by integrating clinical features, and they were evaluated for the predictive power on OS prognosis. The 20 immune checkpoint-related genes confirmed the insensitivity to tumor immunotherapy in high-risk patients. IC50s of Axitinib and Cytarabine suggested a higher sensitivity to the targeted drug. Finally, the quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and immunohistochemistry were consistent with bioinformatics analysis. Conclusion. Ferroptosis are closely associated with the OS prognosis. The risk-scoring model incorporating three ferroptosis subcluster-related modular genes has shown outstanding advantages in predicting patient prognosis.