Aa-Z2 triggers ROS-induced apoptosis of osteosarcoma by targeting PDK-1.

BACKGROUND: Osteosarcoma (OS) is the most frequent cancer derived from bone, and the prognosis of OS is poor. Metabolic alterations have been previously reported to contribute to the development of OS, and arsenic compounds have been suggested to exhibit strong anti-OS effects. However, few studies have described the therapeutic efficiency of arsenic compounds by targeting metabolism in OS. METHODS: Here, we presented a novel organo-arsenic compound, Aa-Z2, and its antitumour efficacy against OS both in vitro and in vivo. RESULTS: Aa-Z2 induced OS cell apoptosis, G2/M phase arrest, and autophagy through the accumulation of reactive oxygen species (ROS). Elevated ROS functioned by promoting the mitochondrial-dependent caspase cascade and attenuating the PI3K/Akt/mTOR signalling pathway. N-acetylcysteine (NAC), a kind of ROS scavenger, could reverse the effects of Aa-Z2 treatment on 143B and HOS cells. Specifically, by targeting pyruvate dehydrogenase kinase 1 (PDK-1), Aa-Z2 induced changes in mitochondrial membrane potential and alterations in glucose metabolism to accumulate ROS. Overexpression of PDK-1 could partially desensitize OS cells to Aa-Z2 treatment. Importantly, Aa-Z2 suppressed tumour growth in our xenograft osteosarcoma model. CONCLUSION: The study provides new insights into the mechanism of Aa-Z2-related metabolic alterations in OS inhibition, as well as pharmacologic evidence supporting the development of metabolism-targeting therapeutics.

Clinical and prognostic role of sarcopenia based on masticatory muscle index on MR images in patients with extranodal natural killer/T cell lymphoma, nasal type.

Sarcopenia is known to be associated with an increased risk of adverse outcomes in a variety of malignancies, but its impact in extranodal natural killer/T cell lymphoma, nasal type (ENKTL-NT) is unknown. The aim of this study was to explore the prognostic relevance of sarcopenia defined by MRI-based masticatory muscle index in ENKTL-NT patients. A total of 112 patients with newly diagnosed ENKTL-NT who underwent cranial magnetic resonance imaging (MRI) were enrolled. The masticatory skeletal muscle index (M-SMI) was measured based on T2-weighted MR images and sarcopenia was defined by M-SMI<5.5 cm2/ m2. The median M-SMI was 5.47 (4.91-5.96) cm2/m2; 58 were identified with sarcopenia in this cohort. On multivariate analyses, sarcopenia was the only independently risk factor predicting overall survival (HR, 4.590; 95% CI, 1.657-12.715; p = 0.003), progression-free survival (HR, 3.048; 95% CI, 1.515-6.130; p = 0.002), and treatment response (HR, 0.112; 95% CI, 0.042-0.301; p < 0.001). In addition, we found that integrating sarcopenia into prognostic indices could improve the discriminative power of the corresponding original model. Stratification analysis showed that sarcopenia was able to further identify survival differences in patients that could not be distinguished by prognostic models. In summary, our study suggests that sarcopenia defined by MRI-based M-SMI represents a new and routinely applicable prognostic indicator of clinical outcome or predictor of treatment response in ENKTL-NT patients, and may aid in risk stratification and treatment decisions.

A depression-related lncRNA signature predicts the clinical outcome and immune characteristics of gastric cancer.

Background: Depression and long non-coding RNA (lncRNA) have been reported to be associated with tumor progression and prognosis in gastric cancer (GC). This study aims to build a GC risk classification and prognosis model based on depression-related lncRNAs (DRLs). Methods: To develop a risk model, we performed univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses using RNA sequencing data of GC from The Cancer Genome Atlas (TCGA) and depression-related genes (DRGs) from previous studies. Kaplan-Meier analysis, receiver operating characteristic (ROC) curve analysis, nomogram construction, pathway enrichment analysis, assessment of immunological features, and drug sensitivity testing were conducted using a series of bioinformatics methods. Results: Seven DRLs were identified to build a prognostic model, whose robustness was verified in an internal cohort. Subsequent prognostic analyses found that high risk scores were associated with worse overall survival (OS). Univariate and multivariate analyses revealed that the risk score could be used as an independent prognostic factor. Furthermore, the ROC curve indicated that the risk score had higher diagnostic efficiency than traditional clinicopathological features. The calibration curve confirmed the accuracy and reliability of the nomogram. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that there were differences in digestive system and nervous system-related pathways between the high- and low-risk groups. Results of tumor mutational burden (TMB) and tumor immune dysfunction and exclusion (TIDE) analyses indicated that patients in the low-risk group had a better response rate to immunotherapy. Finally, the results of drug sensitivity analysis showed that risk score could influence sensitivity to EHT 1864 in GC. Conclusion: We have successfully developed and verified a 7-DRL risk model which can assess the prognosis and immunological features and guide individualized therapy of GC patients.

Identification of a lactate metabolism-related lncRNAs signature for predicting the prognosis in patients with kidney renal clear cell carcinoma.

Background: Lactate metabolism-related (LMR) long noncoding RNAs (lncRNAs) play significant roles in various cancers, but their impact on kidney renal clear cell carcinoma (KIRC) remains unclear. This study aimed to explore the value of LMR lncRNA and develop a risk model for KIRC. Methods: Data on KIRC patients were downloaded from The Cancer Genome Atlas (TCGA) database. LMR lncRNAs were identified by co-expression, univariate and multivariate analyses, and least absolute shrinkage selection operator (LASSO) regression analysis. Subsequently, a prognostic signature was constructed and its accuracy was verified. To predict the prognosis of KIRC effectively, we established a nomogram based on this information. Enrichment analysis, tumor mutational burden (TMB) analysis, immune status and the therapeutic sensitivities of KIRC patients were also investigated. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to detect the expression of lncRNAs. Results: We constructed and verified a predictive signature based on six LMR lncRNA (LINC00944, AC090772.3, Z83745.1, AP001267.3, AC092296.1, and AL162377.1) to assess the patient prognoses of KIRC. Survival analyses showed a more unfavorable outcome in high-risk patients (P<0.001). Enrichment analysis demonstrated that immune-related pathways were enriched in the high-risk group. Besides, patients classified by risk scores had distinguishable immune status, TMB, response to immunotherapy, and sensitivity to chemotherapy and targeted drugs. Conclusions: The LMR lncRNAs signature has significant implications for prognostic assessment and clinical treatment guidance in KIRC.

Autophagy and its role in osteosarcoma.

Osteosarcoma (OS) is the most common bone malignancy and preferably occurs in children and adolescents. Despite significant advances in surgery and chemotherapy for OS over the past few years, overall survival rates of OS have reached a bottleneck. Thus, extensive researches aimed at developing new therapeutic targets for OS are urgently needed. Autophagy, a conserved process which allows cells to recycle altered or unused organelles and cellular components, has been proven to play a critical role in multiple biological processes in OS. In this article, we summarized the association between autophagy and proliferation, metastasis, chemotherapy, radiotherapy, and immunotherapy of OS, revealing that autophagy-related genes and pathways could serve as potential targets for OS therapy.

The Predictive Value of Tumor Mutation Burden on Clinical Efficacy of Immune Checkpoint Inhibitors in Melanoma: A Systematic Review and Meta-Analysis.

Background: Tumor mutational burden (TMB) is a genomic biomarker that can predict favorable responses to immune checkpoint inhibitors (ICIs). Although we have better understanding of TMB in cancer immunity and cancer immunotherapy, the relationship between TMB and the clinical efficacy of ICIs remains unknown in the treatment of melanoma patients. Here, we conduct a systematic review and meta-analysis to evaluate the predictive value of TMB on the efficacy of ICIs in patients with melanoma. Methods: We systematically collected data from PubMed, Embase, Cochrane Library, CNKI, China Biomedical Database (CBM), and Wanfang Database. The end date was set to 26 June 2021. We included retrospective studies or clinical trials of ICIs that reported hazard ratios (HRs) for overall survival and/or progression-free survival according to TMB. Data for 1,493 patients from 15 studies were included. In addition, pooled effect size, heterogeneity analysis, sensitivity analysis, publication bias detection, and subgroup analysis were performed based on the included data. Results: Patients with high TMB showed significantly improved OS (HR = 0.49, 95% CI: 0.33, 0.73; p = 0.001) and PFS (HR = 0.47, 95% CI: 0.33, 0.68; p < 0.001) compared with patients with low TMB. This association was very good in patients treated with monotherapy, that is, anti-CTLA-4 or anti-PD-(L)-1 inhibitors, but not for the patients treated with a combination of the two drugs. The subgroup analysis results showed that heterogeneity was substantial in the targeted next-generation sequencing (NGS) group. Publication bias was detected, and the results were visualized using the funnel chart. And sensitivity analysis and trim-and-fill method analysis showed that our results were stable and reliable. Conclusion: High TMB is associated with improved OS and PFS in melanoma patients treated with mono-drug ICIs. TMB determined by NGS should be standardized to eliminate heterogeneity. Therefore, the role of TMB in identifying melanoma patients who may benefit from ICI should be further determined in more randomized controlled trials in the future.

Stress and cancer: The mechanisms of immune dysregulation and management.

Advances in the understanding of psychoneuroimmunology in the past decade have emphasized the notion that stress and cancer are interlinked closely. Durable chronic stress accelerated tumorigenesis and progression, which is unfavorable for clinical outcomes of cancer patients. Available evidence has provided unprecedented knowledge about the role and mechanisms of chronic stress in carcinogenesis, the most well-known one is dysfunction of the hypothalamus-pituitary-adrenal (HPA) axis and the sympathetic nervous system (SNS). With abnormal activation of neuroendocrine system, stress-related hormones contribute to increased oncogenes expression, exacerbated chronic inflammation and impaired immunologic function. In addition, accumulating studies have demonstrated that diverse stress interventions including pharmacological approaches, physical exercises and psychological relaxation have been administered to assist in mental disorders reduction and life quality improvement in cancer patients. In this review, we systematically summarize the connection and mechanisms in the stress-immune-cancer axis identified by animal and clinical studies, as well as conclude the effectiveness and deficiencies of existing stress management strategies.

Construction and validation of a prognostic model for osteosarcoma patients based on autophagy-related genes.

BACKGROUND: Osteosarcoma is the most frequent primary bone malignancy with a poor prognosis because of pulmonary metastasis. Autophagy is strongly associated with tumor metastasis, and it is valuable to construct an autophagy-related gene risk model for predicting the prognosis of osteosarcoma. METHODS: We obtained ARGs from the Human Autophagy Database and RNA-sequencing data of osteosarcoma patients from the Gene Expression Omnibus (GEO) database. Subsequently, univariate and multivariate cox regression analyses were performed to construct a three-gene prognostic model and its accuracy was further confirmed in the Therapeutic Applications Research to Generate Effective Treatments (TARGET) database. Afterward, we detected the expression levels and effects on osteosarcoma cells metastasis of MYC and MBTPS2, which were involved in the model. RESULTS: In both training and verification cohorts, patients with lower risk scores had longer OS, and the model was identified as an independent prognostic factor in osteosarcoma. Besides, the ROC curve demonstrated the reliability of the model. Furthermore, RT-qPCR, Western Blotting and IHC results indicated that MYC and MBTPS2 were differently expressed in osteosarcoma tissues and cell lines. MYC knockdown or MBTPS2 overexpression prevented the capacity of migration and invasion in osteosarcoma cell lines through inhibiting cellular autophagy. CONCLUSION: The risk model based on three ARGs had a strong ability to predict the prognosis of osteosarcoma patients. Our findings also suggested that MYC and MBTPS2 were two major factors regulating autophagy in osteosarcoma, and could serve as potential therapeutic targets for osteosarcoma.

Preparation of a novel light-sensitive copolymer and its application in recycling aqueous two-phase systems.

Aqueous two-phase systems (ATPSs) are potential bioseparation techniques in industry. However, a key problem is that aqueous two-phase systems could not be effectively recycled to result in high cost and environment pollution. Recently, how to prepare recycling copolymers forming aqueous two-phase systems is focused on in the area. In this study, a light-sensitive copolymer (P(NNC)) was synthesized by using N-isopropylacrylamide (NIPA), N-vinyl-2-pyrrolidone (NVP), chlorophyllin sodium copper salt (CHL) as monomers. The copolymer P(NNC) can form ATPSs with another novel pH-sensitive copolymer (P(ADB)) which was synthesized by co-worker in our laboratory. Over 98% of the P(NNC) copolymer could be recovered by using laser radiation at 488 nm. The copolymer P(ADB) could be recovered by adjusting the isoelectric point (pI) to 4.1, with a recovery of 97%. Bovine serum albumin (BSA) and Tyr were partitioned in the P(NNC)-P(ADB) aqueous two-phase systems to examine the systems. It was found that partition coefficient of BSA and L-Tyr could reach 4.1 and 0.12 in the systems, respectively.