Papillary renal cell carcinoma (pRCC) is one of the epithelial renal cell carcinoma (RCC) histological subtypes. Ferroptosis is a new iron-dependent form of cell death that has been seen in a variety of clinical situations. Using differentially expressed ferroptosis-related long non-coding RNAs (lncRNAs) from patients with pRCC in The Cancer Genome Atlas; we built a prognostic lncRNA-based signature. We discovered seven different lncRNAs that were strongly linked to the prognosis of patients with pRCC. High-risk scores were linked to a poor prognosis for pRCC, which was confirmed by the findings of Kaplan–Meier studies. In addition, the constructed lncRNA signature has a 1-year area under the curve (AUC) of 0.908, suggesting that it has a high predictive value in pRCC. In the high-risk group, Gene set enrichment analyses (GSEA) analysis identified immunological and tumor-related pathways. Furthermore, single-sample GSEA (ssGSEA) revealed significant differences in T cell functions checkpoint, antigen presenting cell (APC) co-stimulation, inflammation promoting, and para inflammation between the two groups with different risk scores. In addition, immune checkpoints like PDCD1LG2 (PD-L2), LAG3, and IDO1 were expressed differently in the two risk groups. In summary, a novel signature based on ferroptosis-related lncRNAs could be applied in predicting the prognosis of patients with pRCC.

Diabetes osteoporosis is a chronic complication of diabetes mellitus (DM) and is associated with osteoclast formation and enhanced bone resorption. Specnuezhenide (SPN) is an active compound with anti-inflammatory and immunomodulatory properties. However, the roles of SPN in diabetic osteoporosis remain unknown. In this study, primary bone marrow macrophages (BMMs) were pretreated with SPN and were stimulated with receptor activator of nuclear factor kappa B ligand (RANKL; 50 ng/mL) to induce osteoclastogenesis. The number of osteoclasts was detected by tartrate-resistant acid phosphatase (TRAP) staining. The protein levels of cellular oncogene fos/nuclear factor of activated T cells c1 (c-Fos/NFATc1), nuclear factor kappa-B (NF-κB), and mitogen-activated protein kinases (MAPKs) were evaluated by western blot analysis. NF-κB luciferase assays were used to examine the role of SPN in NF-κB activation. The DM model group received a high-glucose, high-fat diet and was then intraperitoneally injected with streptozotocin (STZ). Micro-CT scanning, serum biochemical analysis, histological analysis were used to assess bone loss. We found that SPN suppressed RANKL-induced osteoclast formation and that SPN inhibited the expression of osteoclast-related genes and c-Fos/NFATc1. SPN inhibited RANKL-induced activation of NF-κB and MAPKs. In vivo experiments revealed that SPN suppressed diabetes-induced bone loss and the number of osteoclasts. Furthermore, SPN decreased the levels of bone turnover markers and increased the levels of runt-related transcription factor 2 (RUNX2), osteoprotegerin (OPG), calcium (Ca) and phosphorus (P). SPN also regulated diabetes-related markers. This study suggests that SPN suppresses diabetes-induced bone loss by inhibiting RANKL-induced osteoclastogenesis, and provides an experimental basis for the treatment of diabetic osteoporosis.

Aberrant 5-methylcytidine (m5C) modification plays an essential role in the progression of different cancers. More and more researchers are focusing on developing a lncRNA-based risk model to assess the clinical prognosis of cancer patients. However, the impact of m5C-related lncRNAs on the prognosis of patients with uterine corpus endometrial carcinoma (UCEC), as well as the immune microenvironment of UCEC, remains unclear. Here, we comprehensively analyzed the predictive value of m5C-associated lncRNAs in UCEC and their association with the tumor immune microenvironment, according to the information extracted from the TCGA-UCEC dataset. We identified a total of 32 m5C-associated lncRNAs that were significantly correlated with the prognosis of UCEC patients. Two molecular subtypes were determined by consensus clustering analysis of these 32 m5C-associated prognostic lncRNAs. Further data showed that cluster 1 was associated with poor clinical prognosis, advanced tumor grade, higher PD-L1 expression levels, higher ESTIMATEScore, and higher immuneScore, as well as the immune cell infiltration. Then, 17 m5C-associated lncRNAs with prognostic values were obtained using LASSO regression analysis. And a risk model was constructed based on these 17 lncRNAs. It was revealed that the risk model could be used as an independent factor for UCEC prognosis. In addition, patients with UCEC in the high-risk group had higher tumor grades and immune scores. The risk model based on m5C-related lncRNAs was also closely associated with infiltrating immune cells. In conclusion, our study elucidated the crucial roles of the identified m5C-related lncRNAs in the UCEC patients prognoses, as well as in the immune microenvironment in UCEC. The results suggest that the components of risk models based on the m5C-related lncRNAs may serve as important mediators of the immune microenvironment in UCEC.

Lupus nephritis (LN) constitutes a substantial contributor to morbidity and mortality in systemic lupus erythematosus (SLE). The monitoring of renal function in patients with LN is associated with improved prognostication. The objective of this study was to evaluate the clinical utility of serum galectin-3 (Gal-3) levels in differentiating LN from SLE. Moreover, we sought to ascertain whether serum galectin-3 levels can serve as a marker for the degree of renal impairment in patients with LN. In this cross-sectional study, 42 patients with LN and 12 patients with SLE without nephritis were enrolled. Furthermore, 110 healthy subjects were recruited as controls. Serum Gal-3 levels were quantified using a time-resolved fluoroimmunoassay. Furthermore, Gal-3 levels were analyzed in conjunction with other clinical variables. The results demonstrated that patients with LN exhibited a significantly elevated serum Gal-3 concentration (35.98 ± 20.68 ng/mL) in comparison to healthy controls (10.11 ± 2.75 ng/mL, P < .001) and patients with SLE (14.38 ± 2.26, P < .001). The area under the curve of Gal-3 in distinguishing patients with SLE from patients with LN was 0.9157. When the cutoff value was set to 18.91 ng/mL, the sensitivity was 83.33%, and the specificity was 100%. There was a tendency for serum Gal-3 levels to increase with worsening renal impairment in patients with LN. In conclusion, Gal-3 could be a valuable biomarker for distinguishing LN from SLE, providing a useful clinical reference. Elevated serum Gal-3 levels may be associated with the severity of renal impairment in patients with LN.

Cancer Dependency Map (CDM) genes comprise an extensive series of genome-scale RNAi-based loss-of-function tests; hence, it served as a method based on the CRISPR-Cas9 technique that could assist scientists in investigating potential gene functions. These CDM genes have a role in tumor cell survival and proliferation, suggesting that they may be used as new therapeutic targets for some malignant tumors. So far, there have been less research on the involvement of CDM genes in breast cancer, and only a tiny percentage of CDM genes have been studied. In this study, information of patients with breast cancer was extracted from The Cancer Genome Atlas (TCGA), from which differentially expressed CDM genes in breast cancer were determined. A variety of bioinformatics techniques were used to assess the functions and prognostic relevance of these confirmed CDM genes. In all, 290 CDM genes were found differentially expressed. Six CDM genes (SRF, RAD51, PMF1, EXOSC3, EXOC1, and TSEN54) were found to be associated with the prognosis of breast cancer samples. Based on the expression of the identified CDM genes and their coefficients, a prognosis model was constructed for prediction, according to which patients with breast cancer were separated into two risk groups. Those with high risk had substantially poorer overall survival (OS) than patients in the other risk group in the TCGA training set, TCGA testing set, and an external cohort from Gene Expression Omnibus (GEO) database. The area under the receiver operating characteristic (ROC) curve for this prognostic signature was, respectively, 0.717 and 0.635 for TCGA training and testing sets, demonstrating its reliability in predicting the prognosis of patients with breast cancer. We next created a nomogram using the six CDM genes discovered to create a therapeutically useful model. The Human Protein Atlas database was used to acquire all immunohistochemistry staining images of the discovered CDM genes. The proportions of tumor-infiltrating immune cells, as well as the expression levels of checkpoint genes, varied substantially between the two risk groups, according to the analyses of immune response. In conclusion, the findings of this research may aid in the understanding of the prognostic value and biological roles of CDM genes in breast cancer.