Progresses in biomarkers for cancer immunotherapy.

Currently, checkpoint inhibitor-based immunotherapy has emerged as prevailing treatment modality for diverse cancers. However, immunotherapy as a first-line therapy has not consistently yielded durable responses. Moreover, the risk of immune-related adverse events increases with combination regimens. Thus, the development of predictive biomarkers is needed to optimize individuals benefit, minimize risk of toxicities, and guide combination approaches. The greatest focus has been on tumor programmed cell death-ligand 1 (PD-L1), microsatellite instability (MSI), and tumor mutational burden (TMB). However, there remains a subject of debate due to thresholds variability and significant heterogeneity. Major unmet challenges in immunotherapy are the discovery and validation of predictive biomarkers. Here, we show the status of tumor PD-L1, MSI, TMB, and emerging data on novel biomarker strategies with oncogenic signaling and epigenetic regulation. Considering the exploration of peripheral and intestinal immunity has served as noninvasive alternative in predicting immunotherapy, this review also summarizes current data in systemic immunity, encompassing solute PD-L1 and TMB, circulating tumor DNA and infiltrating lymphocytes, routine emerging inflammatory markers and cytokines, as well as gut microbiota. This review provides up-to-date information on the evolving field of currently available biomarkers in predicting immunotherapy. Future exploration of novel biomarkers is warranted.

Bronchoalveolar Lavage as Potential Diagnostic Specimens to Genetic Testing in Advanced Nonsmall Cell Lung Cancer.

Background: There is limited knowledge on the yield of performing capture-based targeted ultradeep sequencing on bronchoalveolar lavage (BAL) specimens from advanced nonsmall cell lung cancer (NSCLC) patients. This study aimed to evaluate gene variations and performance characteristics in BAL and tissue specimens using targeted sequencing. Methods: This cohort study retrospectively enrolled 20 patients with advanced NSCLC. The variant detection percentage, correlation of tumor mutation burden (TMB), and allele frequency heterogeneity (AFH) were compared between paired BAL and tissue samples. A three-tiered system was also applied for the interpretation of gene variants according to the guidelines. Results: No statistical difference was observed in variant detection between BAL and tissue samples (P = .591 for variant tier and P = .409 for variant type). In general, BAL achieved higher detection rates in tier I variants (96.2% vs 84.6%) and gene fusions (75% vs 50%) compared with tissue samples; tissue samples had better variants detection rates for other variants, such as tier II (89.6% vs 76.0%), tier III (87.1% vs 72.6%), single nucleotide variant (SNV, 89.6% vs 76.5%), insertion/deletion/duplication (InDel, 74.6% vs 69.8%) and copy number variation (CNV, 93.8% vs 43.8%). Besides, there were significant correlations of TMB (R2 = 0.96, P < .001) and AFH (R2 = 0.87, P < .001) between BALs and paired tissues. Conclusions: The findings demonstrate that BAL may serve as a supplement in liquid biopsy for mutation detection and for routine utilization in clinical settings.

Improvement in the healing of bone fractures using a cyclodextrin/Ni-MOF nanofibers network: the development of a novel substrate to increase the surface area with desirable functional properties.

In this study, a ß-cyclodextrins (ß-CDs)/Ni-based MOF (ß-CDs/Ni-based MOF) fibrous network with focus on biocompatible and biodegradable properties was used as a new material for orthopedic applications. The final products were synthesized by an efficient, rapid, and controllable electrospinning route under optimal conditions, including a flow rate of 0.3 mL g-1, applied voltage of 18 kV, and spinning distance of 20 cm. Efficient characterization by various analyzes showed that the ß-CDs/Ni-based MOF fibrous nanostructures had a thermal stability at about 320 °C and homogeneous particles with a narrow size distribution. The BET analysis results showed a specific surface area of 2140 m2 g-1 for these compounds, which facilized potential conditions needed for the application of these compounds as a new substrate to improve the healing of bone fractures. The results showed the better porosity of the ß-CDs/Ni-based MOF scaffolds as an essential property, leading to higher proliferation and nutrition and oxygen delivery, resulting in more tissue regeneration. This study proposes a novel strategy for a fibrous network substrate with distinct properties for orthopedic purposes.

Investigation of Titanium Mesh and Nano-Hydroxyapatite/Polyamide 66 Bone Graft in Cervical Reconstruction of Spondylosis After Sub-Total Vertebral Body Resection.

The nano-hydroxyapatite/polyamide 66 (n-HA/PA66) bionic bone column, as a high-performance tissue repair and replacement material, introduced as a high osteo-induction ability agent. Nanomaterial has significantly taken a place in orthopedic surgery, however, the efficacy of using n-HA/PA66 is yet to be established. In this regard, this study evaluated various sagittal parameters (such as imaging measurement) and clinical efficacy in postoperative patients, whom underwent cervical reconstruction surgery due to cervical spondylosis myelopathy (CSM). In this study, total 62 CSM cases were enrolled between October 2016 to March 2020, and were hospitalized for cervical reconstruction surgery. 31 cases were grafted with titanium mesh and 31 cases were grafted with n-HA/P66. The sagittal parameters such as cervical spine lateral radiographs (C0-2Coob, C2-7Coob, T1S, CSVA, and TIA) were taken before operation, after operation (within 1 week), 3, 6, and 9 months after operation. In order to evaluate the clinical efficacy, we used JOA scores before, after, 3 months, 6 months and 9 months after operation. Results showed that JOA scores after the re-examination in the two groups (titanium and n-HA/P66) were significantly higher than before the operation, suggesting a well postoperative functional recovery after surgery in both groups; however, there was no significant difference in JOA score and JOA improvement index between the two groups. In regard of angles measurement (C0-2Cobb, C2-7Cobb, T1S, CSVA, and TIA), we observed no significant difference between these two groups before and after the operation. In addition, we showed that C0-2Cobb and C2-7Cobb angle had a significant positive correlation; and C0-2Cobb angle is positively correlated with T1S, and negatively correlated with CSVA. Both titanium mesh and n-HA/PA66 can be well improved and maintained within 9 months after surgery with clinical efficacy, however, using n-HA/PA66 might have more benefits.

An analysis of lncRNA-miRNA-mRNA networks to investigate the effects of HDAC4 inhibition on skeletal muscle atrophy caused by peripheral nerve injury.

Background: Muscle atrophy caused by peripheral nerve injury is a common clinical disease, with no effective treatments currently available. Our previous studies have found that denervation-induced muscle atrophy can be alleviated by inhibiting histone deacetylase 4 (HDAC4). An increasing amount of evidence shows that microRNA (miRNA) and long noncoding RNA (lncRNA) are involved in the occurrence of muscle atrophy. This study aimed to find the mechanism by which HDAC4 regulates denervation-induced muscle atrophy based on lncRNA-associated competing endogenous RNA (ceRNA) networks. Methods: We analyzed the influence of short hairpin RNA (shRNA) knockdown of HDAC4 on lncRNAs and miRNAs after denervated muscle atrophy using RNA sequencing. A Pearson's correlation heat map and principal component analysis were employed to analyze differentially expressed miRNAs and lncRNAs. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses of target genes were conducted. The ceRNA network of lncRNA-miRNA-mRNA was constructed, and the core regulatory molecules in the ceRNA network were analyzed. Results: We found 32 miRNAs and 111 lncRNAs related to denervated muscle atrophy regulated by HDAC4. Moreover, 15 downregulated lncRNAs, 14 upregulated miRNAs, and 61 downregulated mRNAs constituted a ceRNA regulatory network, participating in the biological processes including response to denervation involved in regulation of muscle adaptation, along with the signaling pathways including autophagy, FoxO signaling pathways, and Jak-STAT signaling pathways. Additionally, 6 upregulated lncRNAs, 8 downregulated miRNAs, and 66 upregulated mRNAs constituted another ceRNA regulatory network, which was mainly involved in cell cycle-related biological processes and pathways. Finally, 3 lncRNAs, 4 miRNAs, and 12 mRNAs constituted a ceRNA sub-network, and XR_377582.2 and ENSMUST00000143649 were considered to be the key lncRNAs. Conclusions: In the ceRNA network, all nodes are directly or indirectly involved in the process by which HDAC4 regulates skeletal muscle atrophy caused by peripheral nerve injury. XR_377582.2 and ENSMUST00000143649 may be the key lncRNAs related to HDAC4 involved in the regulation of muscle atrophy.

Platelet-rich plasma attenuates the severity of joint capsule fibrosis following post-traumatic joint contracture in rats.

Background: Post-traumatic joint contracture (PTJC) mainly manifests as excessive inflammation leading to joint capsule fibrosis. Transforming growth factor (TGF)-ß1, a key regulator of inflammation and fibrosis, can promote fibroblast activation, proliferation, migration, and differentiation into myofibroblasts. Platelet-rich plasma (PRP) is considered to have strong potential for improving tissue healing and regeneration, the ability to treat joint capsule fibrosis remains largely unknown. Methods: In this study, we aimed to determine the antifibrotic potential of PRP in vivo or in vitro and its possible molecular mechanisms. The TGF-ß1-induced primary joint capsule fibroblast model and rat PTJC model were used to observe several fibrotic markers (TGF-ß1, α-SMA, COL-Ⅰ, MMP-9) and signaling transduction pathway (Smad2/3) using histological staining, qRT-PCR and western blot. Results: Fibroblasts transformed to myofibroblasts after TGF-ß1 stimulation with an increase of TGF-ß1, α-SMA, COL-Ⅰ, MMP-9 and the activation of Smad2/3 in vitro. However, TGF-ß1-induced upregulation or activation of these fibrotic markers or signaling could be effectively suppressed by the introduction of PRP. Fibrotic markers' similar changes were observed in the rat PTJC model and PRP effectively reduced inflammatory cell infiltration and collagen fiber deposition in the posterior joint capsule. Interestingly, HE staining showed that articular cartilage was degraded after rat PTJC, and PRP injection also have the potential to protect articular cartilage. Conclusion: PRP can attenuate pathological changes of joint capsule fibrosis during PTJC, which may be implemented by inhibiting TGF-ß1/Smad2/3 signaling and downstream fibrotic marker expression in joint capsule fibroblasts.

Bioinformatics analysis of lncRNAs in the occurrence and development of osteosarcoma.

Background: Osteosarcoma (OS) is a disease with high mortality in children and adolescents, and metastasis is one of its important clinical features. However, the molecular mechanism of OS occurrence is not completely clear. Thus, we screened potential biomarkers of OS and analyze their prognostic value. Methods: The Cancer Genome Atlas (TCGA) datasets were used to analyze the differential lncRNAs in patients with OS of different immune score and the lncRNAs expressed by immune cells. Cox regression was used to develop the prognosis prediction model and specify the prognosis outcomes. Risk-proportional regression model was constructed, and the samples were divided into high and low groups based on the risk scores for the survival analysis. The areas under the receiver operating characteristic (ROC) curve were calculated and the risk-score model was verified. Finally, using 4 gene sets (comprising chemokines, immune checkpoint blockades, immune activity-related genes, and immune cells), and 4 analysis tools (CIBERSORT, TIMER, XCELL and MCP) to evaluated tumor immune infiltration. Results: Twenty-nine long non-coding ribonucleic acids (lncRNAs) were obtained from the intersection of the screened lncRNAs. Caspase recruitment domain-containing protein 8-antisense RNA 1 (CARD8-AS1), lncRNA five prime to Xist (FTX), KAT8 regulatory NSL complex unit 1-antisense RNA 1 (KANSL1-AS1), Neuroplastin Intronic Transcript 1 (NPTN-IT1), oligodendrocyte maturation-associated long intervening non-coding RNA (OLMALINC) and RPARP Antisense RNA 1 (RPARP-AS1) were found to be correlated with survival. Univariate and multivariate regression analysis showed risk score [HR (hazard ratio) 3.5, P value 0.0043; HR 3.7, P value 0.0033] and metastasis (HR 4.7, P value 6.60E-05; HR 4.8, P value 8.36E-05) were the key factors of patients with OS. The areas under curves (AUCs) of the 1-, 3-, and 5-year ROC curves of the prognostic model were 0.715, 0.729, and 0.771. The low-risk patients tended to have a high abundance of immune cells. Conclusions: This study showed that a risk score based on 6 lncRNAs has potential value in the prognosis of OS, and patients with low-risk scores have high immune cell infiltration and good prognosis. This study may enrich understandings of underlying mechanisms related to the occurrence and development of OS.

Typing characteristics of metabolism-related genes in osteoporosis.

Objective: Osteoporosis is a common musculoskeletal disease. Fractures caused by osteoporosis place a huge burden on global healthcare. At present, the mechanism of metabolic-related etiological heterogeneity of osteoporosis has not been explored, and no research has been conducted to analyze the metabolic-related phenotype of osteoporosis. This study aimed to identify different types of osteoporosis metabolic correlates associated with underlying pathogenesis by machine learning. Methods: In this study, the gene expression profiles GSE56814 and GSE56815 of osteoporosis patients were downloaded from the GEO database, and unsupervised clustering analysis was used to identify osteoporosis metabolic gene subtypes and machine learning to screen osteoporosis metabolism-related characteristic genes. Meanwhile, multi-omics enrichment was performed using the online Proteomaps tool, and the results were validated using external datasets GSE35959 and GSE7429. Finally, the immune and stromal cell types of the signature genes were inferred by the xCell method. Results: Based on unsupervised cluster analysis, osteoporosis metabolic genotyping can be divided into three distinct subtypes: lipid and steroid metabolism subtypes, glycolysis-related subtypes, and polysaccharide subtypes. In addition, machine learning SVM identified 10 potentially metabolically related genes, GPR31, GATM, DDB2, ARMCX1, RPS6, BTBD3, ADAMTSL4, COQ6, B3GNT2, and CD9. Conclusion: Based on the clustering analysis of gene expression in patients with osteoporosis and machine learning, we identified different metabolism-related subtypes and characteristic genes of osteoporosis, which will help to provide new ideas for the metabolism-related pathogenesis of osteoporosis and provide a new direction for follow-up research.

RNA sequencing (RNA-seq) analysis of gene expression provides new insights into hindlimb unloading-induced skeletal muscle atrophy.

BACKGROUND: Weightlessness-induced skeletal muscle atrophy, accompanied by complex biochemical and physiological changes, has potentially damaged consequences. However, there is still an insufficient effective strategy to treat skeletal muscle atrophy. Therefore, exploring the molecular mechanisms regulating skeletal muscle atrophy and effective protection is necessary. METHODS: RNA sequencing (RNA-seq) analysis was used to detect differentially expressed genes (DEGs) in the soleus muscle at 12, 24, 36 hours, three days, and seven days after hindlimb unloading in rats. Pearson correlation heatmaps and principal component analysis (PCA) were applied to analyze DEGs' expression profiles. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for cluster analysis of DEGs. Ingenuity pathway analysis (IPA) was used to analyze specific biological processes further. RESULTS: At different time points (12, 24, 36 hours, three days, seven days) after hindlimb unloading, the expression levels of 712, 1,109, 1,433, 1,162, and 1,182 genes in rat soleus muscle were upregulated, respectively, whereas the expression levels of 1,186, 1,324, 1,632, 1,446, and 1,596 genes were downregulated, respectively. PCA revealed that rat soleus muscle showed three different transcriptional phases within seven days after hindlimb unloading. KEGG and GO annotation indicated that the first transcriptional phase primarily involved the activation of stress responses, including oxidative stress, and the inhibition of cell proliferation and angiogenesis; the second transcriptional phase primarily involved the activation of proteolytic systems and, to a certain degree, inflammatory responses; and the third transcriptional phase primarily involved extensive activation of the proteolytic system, significant inhibition of energy metabolism, and activation of the aging process and slow-to-fast muscle conversion. CONCLUSIONS: Different physiological processes in rat skeletal muscles were activated sequentially after unloading. From these activated biological processes, the three transcriptional phases after skeletal muscle unloading can be successively defined as the stress response phase, the atrophic initiation phase, and the atrophic phase. Our study not only helps in the understanding of the molecular mechanisms underlying weightlessness-induced muscle atrophy but may also provide an important time window for the treatment and prevention of weightlessness-induced muscle atrophy.