Development and validation of a cuproptosis-related lncRNA model correlated to the cancer-associated fibroblasts enable the prediction prognosis of patients with osteosarcoma

Roles and mechanisms of copper homeostasis and cuproptosis in osteoarticular diseases

Copper is an essential trace element in the human body that is extensively distributed throughout various tissues. The appropriate level of copper is crucial to maintaining the life activities of the human body, and the excess and deficiency of copper can lead to various diseases. The copper levels in the human body are regulated by copper homeostasis, which maintains appropriate levels of copper in tissues and cells by controlling its absorption, transport, and storage. Cuproptosis is a distinct form of cell death induced by the excessive accumulation of intracellular copper. Copper homeostasis and cuproptosis has recently elicited increased attention in the realm of human health. Cuproptosis has emerged as a promising avenue for cancer therapy. Studies concerning osteoarticular diseases have elucidated the intricate interplay among copper homeostasis, cuproptosis, and the onset of osteoarticular diseases. Copper dysregulation and cuproptosis cause abnormal bone and cartilage metabolism, affecting related cells. This phenomenon assumes a critical role in the pathophysiological processes underpinning various osteoarticular diseases, with implications for inflammatory and immune responses. While early Cu-modulating agents have shown promise in clinical settings, additional research and advancements are warranted to enhance their efficacy. In this review, we summarize the effects and potential mechanisms of copper homeostasis and cuproptosis on bone and cartilage, as well as their regulatory roles in the pathological mechanism of osteoarticular diseases (e.g., osteosarcoma (OS), osteoarthritis (OA), and rheumatoid arthritis (RA)). We also discuss the clinical-application prospects of copper-targeting strategy, which may provide new ideas for the diagnosis and treatment of osteoarticular diseases.

From clinical management to personalized medicine: novel therapeutic approaches for ovarian clear cell cancer

Ovarian clear-cell cancer is a rare subtype of epithelial ovarian cancer with unique clinical and biological features. Despite optimal cytoreductive surgery and platinum-based chemotherapy being the standard of care, most patients experience drug resistance and a poor prognosis. Therefore, novel therapeutic approaches have been developed, including immune checkpoint blockade, angiogenesis-targeted therapy, ARID1A synthetic lethal interactions, targeting hepatocyte nuclear factor 1β, and ferroptosis. Refining predictive biomarkers can lead to more personalized medicine, identifying patients who would benefit from chemotherapy, targeted therapy, or immunotherapy. Collaboration between academic research groups is crucial for developing prognostic outcomes and conducting clinical trials to advance treatment for ovarian clear-cell cancer. Immediate progress is essential, and research efforts should prioritize the development of more effective therapeutic strategies to benefit all patients.

Predicting colorectal cancer prognosis based on long noncoding RNAs of disulfidptosis genes

BACKGROUND

A recently hypothesized cause of cell death called disulfidptosis has been linked to the expansion, emigration, and vascular rebuilding of cancer cells. Cancer can be treated by targeting the pathways that trigger cell death.

AIM

To discover the long non-coding RNA of the disulfidaptosis-related lncRNAs (DRLs), prognosis clinical survival, and treat patients with colorectal cancer with medications.

METHODS

Initially, we queried the Cancer Genome Atlas database to collect transcriptome, clinical, and genetic mutation data for colorectal cancer (CRC). Training and testing sets for CRC patient transcriptome data were generated randomly. Key long non-coding RNAs (lncRNAs) related to DRLs were then identified and evaluated using a least absolute shrinkage and selection operator procedure, as well as univariate and multivariate Cox regression models. A prognostic model was then created after risk scoring. Also, Immune infiltration analysis, immune checkpoint analysis, and medication susceptibility analysis were used to investigate the causes of the different prognoses between high and low risk groups. Finally, we validated the differential expression and biomarker potential of risk-predictive lncRNAs through induction using both NCM460 and HT-29 cell lines, as well as a disulfidptosis model.

RESULTS

In this work, eight significant lncRNAs linked to disulfidptosis were found. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of differentially expressed genes between high- and low-risk groups from the prognostic model showed a close relationship with the immune response as well as significant enrichment in neutrophil extracellular trap formation and the IL-17 signaling pathway. Furthermore, significant immune cell variations between the high-risk and low-risk groups were seen, as well as a higher incidence of immunological escape risk in the high-risk group. Finally, Epirubicin, bortezomib, teniposide, and BMS-754807 were shown to have the lowest sensitivity among the four immunotherapy drugs.

CONCLUSION

Our findings emphasizes the role of disulfidptosis in regulating tumor development, therapeutic response, and patient survival in CRC patients. For the clinical treatment of CRC, these important LncRNAs could serve as viable therapeutic targets.

Circadian rhythm-associated lncRNA RP11-414H17.5 as a key therapeutic target in osteosarcoma affects the tumor immune microenvironment and enhances malignancy

BACKGROUND

It has previously been proven that circadian rhythm disruption is associated with the incidence and deterioration of several tumors, which potentially leads to increased tumor susceptibility and a worse prognosis for tumor-bearing patients. However, their potential role in osteosarcoma has yet to be sufficiently investigated.

METHODS

Transcriptomic and clinical data of 84 osteosarcoma samples and 70 normal bone tissue samples were obtained from the TARGET and GTEx databases, circadian rhythm-related genes were obtained from Genecards, and circadian rhythm-related lncRNAs (CRLs) were obtained by Pearson correlation analysis, differential expression analysis, and protein-protein interaction (PPI) analysis. COX regression and LASSO regression were performed on the CRLs in order to construct a circadian rhythm-related prognostic prediction signature (CRPS). CRPS reliability was verified by Kaplan-Meier (KM), principal component analysis (PCA), nomogram, and receiver operating characteristic (ROC) curve. CRPS effects on the immune microenvironment of osteosarcoma were explored by enrichment analysis and immune infiltration analysis, and the effect of critical gene RP11-414H17.5 on osteosarcoma was experimentally verified.

RESULT

CRPS consisting of three CRLs was constructed and its area under the curve (AUC) values predicted that osteosarcoma prognosis reached 0.892 in the training group and 0.843 in the test group, with a p value of < 0.05 for the KM curve and stable performance across different clinical subgroups. PCA analysis found that CRPS could significantly distinguish between different risk subgroups, and exhibited excellent performance in the prediction of the immune microenvironment. The experiment verified that RP11-414H17.5 can promote metastasis and inhibit apoptosis of osteosarcoma cells.

CONCLUSION

The study revealed that circadian rhythm plays a crucial role in osteosarcoma progression and identified the impact of the key gene RP11-414H17.5 on osteosarcoma, which provides novel insights into osteosarcoma diagnosis and therapy.

The role of mitochondria-related lncRNAs in characterizing the immune landscape and supervising the prognosis of osteosarcoma

Mitochondrial damage is related to the functional properties of immune cells as well as to tumorigenesis and progression. Nevertheless, there is an absence concerning the systematic evaluation of mitochondria-associated lncRNAs (MALs) in the immune profile and tumor microenvironment of osteosarcoma patients. Based on transcriptomic and clinicopathological data from the TARGET database, MAL-related patterns were ascertained by consistent clustering, and gene set variation analysis of the different patterns was completed. Next, a MAL-derived scoring system was created using Cox and LASSO regression analyses and validated by Kaplan-Meier and ROC curves. The GSEA, ESTIMATE, and CIBERSORT algorithms were utilized to characterize the immune status and underlying biological functions in the different MAL score groups. MAL-derived risk scores were well stabilized and outperformed traditional clinicopathological features to reliably predict 5-year survival in osteosarcoma cohorts. Moreover, patients with increased MAL scores were observed to suffer from poorer prognosis, higher tumor purity, and an immunosuppressive microenvironment. Based on estimated half-maximal inhibitory concentrations, the low-MAL score group benefited more from gemcitabine and docetaxel, and less from thapsigargin and sunitinib compared to the high-MAL score group. Pan-cancer analysis demonstrated that six hub MALs were strongly correlated with clinical outcomes, immune subtypes, and tumor stemness indices in various common cancers. Finally, we verified the expression patterns of hub MALs in osteosarcoma with qRT-PCR. In summary, we identified the crosstalk between prognostic MALs and tumor-infiltrating immune cells in osteosarcoma, providing a potential strategy to ameliorate clinical stratification management.

Immune micro-environment analysis and establishment of response prediction model for PD-1 blockade immunotherapy in glioblastoma based on transcriptome deconvolution

PURPOSE

Only a small proportion of patients obtain survival benefit from PD-1 blockade immunotherapy due to the highly heterogeneous and suppressed immune micro-environment of GBM. We aimed at revealing the characteristics of tumor micro-environment (TME) of GBM related to response to PD-1 inhibitors and constructing a response prediction model for screening patients possibly benefit from PD-1 inhibitors.

METHODS

Based on the composition and expression profiles of cell subpopulations calculated by deconvoluting the GBM bulk RNA-seq, differentially expressed gene analysis and gene set enrichment analysis (GSEA) were performed to explore genes and pathways related to response to PD-1 inhibitors. Further by combining least absolute shrinkage and selection operator (LASSO) regression and expression correlation with PD-L1, the response prediction genes of PD-1 inhibitors were identified and the response prediction model was constructed through binary logistic regression.

RESULTS

The comparison of abundances of tumor infiltrating immune cells showed that the abundance of M0 macrophages of responders was lower, while the abundance of activated dendritic cells (DCs) was higher before PD-1 inhibitors treatment; the abundances of plasma cells and M0 macrophages of responders were lower after PD-1 inhibitors treatment. In addition, GSEA showed that the main up-regulation pathways in the tumor micro-environment of responders before PD-1 inhibitors treatment included the regulation of T-helper 1 type immune response, the positive regulation of natural killer cell-mediated cytotoxicity, p53 signaling pathway, homotypic cell-cell adhesion, etc., the main down-regulation pathways included the activation of microglia and myeloid leukocytes, Ras signaling pathway, etc. Afterward, ITGAX, LRRFIP1 and FMN1 were identified as the key response prediction genes of PD-1 inhibitors and the response prediction model based on them showed good predictive performance with potential value of clinical application in its validation and verification.

CONCLUSIONS

ITGAX, LRRFIP1 and FMN1 were identified as the response prediction genes of PD-1 inhibitors and the response prediction model based on them was proved to have potential clinical value.

Comprehensive landscape of TGFβ-related signature in osteosarcoma for predicting prognosis, immune characteristics, and therapeutic response

Osteosarcoma (OS) is a highly heterogeneous malignant bone tumor, and its tendency to metastasize leads to a poor prognosis. TGFβ is an important regulator in the tumor microenvironment and is closely associated with the progression of various types of cancer. However, the role of TGFβ-related genes in OS is still unclear. In this study, we identified 82 TGFβ DEGs based on RNA-seq data from the TARGET and GETx databases and classified OS patients into two TGFβ subtypes. The KM curve showed that the Cluster 2 patients had a substantially poorer prognosis than the Cluster 1 patients. Subsequently, a novel TGFβ prognostic signatures (MYC and BMP8B) were developed based on the results of univariate, LASSO, and multifactorial Cox analyses. These signatures showed robust and reliable predictive performance for the prognosis of OS in the training and validation cohorts. To predict the three-year and five-year survival rate of OS, a nomogram that integrated clinical features and risk scores was also developed. The GSEA analysis showed that the different subgroups analyzed had distinct functions, particularly, the low-risk group was associated with high immune activity and a high infiltration abundance of CD8 T cells. Moreover, our results indicated that low-risk cases had higher sensitivity to immunotherapy, while high-risk cases were more sensitive to sorafenib and axitinib. scRNA-Seq analysis further revealed that MYC and BMP8B were strongly expressed mainly in tumor stromal cells. Finally, in this study, we confirmed the expression of MYC and BMP8B by performing qPCR, WB, and IHC analyses. To conclude, we developed and validated a TGFβ-related signature to accurately predict the prognosis of OS. Our findings might contribute to personalized treatment and making better clinical decisions for OS patients.

Cuproptosis-a potential target for the treatment of osteoporosis

Osteoporosis is an age-related disease of bone metabolism marked by reduced bone mineral density and impaired bone strength. The disease causes the bones to weaken and break more easily. Osteoclasts participate in bone resorption more than osteoblasts participate in bone formation, disrupting bone homeostasis and leading to osteoporosis. Currently, drug therapy for osteoporosis includes calcium supplements, vitamin D, parathyroid hormone, estrogen, calcitonin, bisphosphates, and other medications. These medications are effective in treating osteoporosis but have side effects. Copper is a necessary trace element in the human body, and studies have shown that it links to the development of osteoporosis. Cuproptosis is a recently proposed new type of cell death. Copper-induced cell death regulates by lipoylated components mediated via mitochondrial ferredoxin 1; that is, copper binds directly to the lipoylated components of the tricarboxylic acid cycle, resulting in lipoylated protein accumulation and subsequent loss of iron-sulfur cluster proteins, leading to proteotoxic stress and eventually cell death. Therapeutic options for tumor disorders include targeting the intracellular toxicity of copper and cuproptosis. The hypoxic environment in bone and the metabolic pathway of glycolysis to provide energy in cells can inhibit cuproptosis, which may promote the survival and proliferation of various cells, including osteoblasts, osteoclasts, effector T cells, and macrophages, thereby mediating the osteoporosis process. As a result, our group tried to explain the relationship between the role of cuproptosis and its essential regulatory genes, as well as the pathological mechanism of osteoporosis and its effects on various cells. This study intends to investigate a new treatment approach for the clinical treatment of osteoporosis that is beneficial to the treatment of osteoporosis.

Probing the Potential of Defense Response-Associated Genes for Predicting the Progression, Prognosis, and Immune Microenvironment of Osteosarcoma

BACKGROUND

The defense response is a type of self-protective response of the body that protects it from damage by pathogenic factors. Although these reactions make important contributions to the occurrence and development of tumors, the role they play in osteosarcoma (OS), particularly in the immune microenvironment, remains unpredictable.

METHODS

This study included the clinical information and transcriptomic data of 84 osteosarcoma samples and the microarray data of 12 mesenchymal stem cell samples and 84 osteosarcoma samples. We obtained 129 differentially expressed genes related to the defense response (DRGs) by taking the intersection of differentially expressed genes with genes involved in the defense response pathway, and prognostic genes were screened using univariate Cox regression. Least absolute shrinkage and selection operator (LASSO) penalized Cox regression and multivariate Cox regression were then used to establish a DRG prognostic signature (DGPS) via the stepwise method. DGPS performance was examined using independent prognostic analysis, survival curves, and receiver operating characteristic (ROC) curves. In addition, the molecular and immune mechanisms of adverse prognosis in high-risk populations identified by DGPS were elucidated. The results were well verified by experiments.

RESULT

BNIP3, PTGIS, and ZYX were identified as the most important DRGs for OS progression (hazard ratios of 2.044, 1.485, and 0.189, respectively). DGPS demonstrated outstanding performance in the prediction of OS prognosis (area under the curve (AUC) values of 0.842 and 0.787 in the training and test sets, respectively, adj-p < 0.05 in the survival curve). DGPS also performed better than a recent clinical prognostic approach with an AUC value of only 0.674 [metastasis], which was certified in the subsequent experimental results. These three genes regulate several key biological processes, including immune receptor activity and T cell activation, and they also reduce the infiltration of some immune cells, such as B cells, CD8+ T cells, and macrophages. Encouragingly, we found that DGPS was associated with sensitivity to chemotherapeutic drugs including JNK Inhibitor VIII, TGX221, MP470, and SB52334. Finally, we verified the effect of BNIP3 on apoptosis, proliferation, and migration of osteosarcoma cells through experiments.

CONCLUSIONS

This study elucidated the role and mechanism of BNIP3, PTGIS, and ZYX in OS progression and was well verified by the experimental results, enabling reliable prognostic means and treatment strategies to be proposed for OS patients.

Non-coding RNAs in metabolic reprogramming of bone and soft tissue sarcoma: Fundamental mechanism and clinical implication

Sarcomas, comprising approximately 1% of human malignancies, show a poor response to treatment and easy recurrence. Metabolic reprogramming play an important role in tumor development in sarcomas. Accumulating evidence shows that non-coding RNAs (ncRNAs) participate in regulating the cellular metabolism of sarcomas, which improves the understanding of the development of therapy-resistant tumors. This review addresses the regulatory roles of metabolism-related ncRNAs and their implications for sarcoma initiation and progression. Dysregulation of metabolism-related ncRNAs is common in sarcomas and is associated with poor survival. Emerging studies show that abnormal expression of metabolism-related ncRNAs affects cellular metabolism, including glucose, lipid, and mitochondrial metabolism, and leads to the development of aggressive sarcomas. This review summarizes recent advances in the roles of dysregulated metabolism-related ncRNAs in sarcoma development and stemness and describes their potential to serve as biological biomarkers for disease diagnosis and prognosis prediction, as well as therapeutic targets for treating refractory sarcomas.