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BackgroundSoft tissue sarcoma (STS) is a highly heterogeneous musculoskeletal tumor with a significant impact on human health due to its high incidence and malignancy. Long non-coding RNA (lncRNA) and Neutrophil Extracellular Traps (NETs) have crucial roles in tumors. Herein, we aimed to develop a novel NETsLnc-related signature using machine learning algorithms for clinical decision-making in STS.MethodsWe applied 96 combined frameworks based on 10 different machine learning algorithms to develop a consensus signature for prognosis and therapy response prediction. Clinical characteristics, univariate and multivariate analysis, and receiver operating characteristic curve (ROC) analysis were used to evaluate the predictive performance of our models. Additionally, we explored the biological behavior, genomic patterns, and immune landscape of distinct NETsLnc groups. For patients with different NETsLnc scores, we provided information on immunotherapy responses, chemotherapy, and potential therapeutic agents to enhance the precision medicine of STS. Finally, the gene expression was validated through real-time quantitative PCR (RT-qPCR).ResultsUsing the weighted gene co-expression network analysis (WGCNA) algorithm, we identified NETsLncs. Subsequently, we constructed a prognostic NETsLnc signature with the highest mean c-index by combining machine learning algorithms. The NETsLnc-related features showed excellent and stable performance for survival prediction in STS. Patients in the low NETsLnc group, associated with improved prognosis, exhibited enhanced immune activity, immune infiltration, and tended toward an immunothermal phenotype with a potential immunotherapy response. Conversely, patients with a high NETsLnc score showed more frequent genomic alterations and demonstrated a better response to vincristine treatment. Furthermore, RT-qPCR confirmed abnormal expression of several signature lncRNAs in STS.ConclusionIn conclusion, the NETsLnc signature shows promise as a powerful approach for predicting the prognosis of STS. which not only deepens our understanding of STS but also opens avenues for more targeted and effective treatment strategies.

Low-grade myofibroblastic sarcoma (LGMS) characterized by the increased proliferation of myofibroblasts is a rare type of malignant myofibroblastic tumor that frequently occurs in the head and neck region. Presently, there is no consensus regarding the treatment of LGMS. Here, we report a rare case of LGMS of the pharynx in a 40-year-old male admitted to our hospital. The patient underwent resection for a right metastatic lesion and parapharyngeal mass. However, he had recurrence and multiple metastases without a surgical indication. Then the patient received the treatment of anlotinib plus pembrolizumab for 4 cycles, and there was a partial response (PR) to the treatment. Due to the adverse reaction of anlotinib, the patient subsequently received monotherapy of pembrolizumab for 22 cycles and achieved a complete response (CR). As the first case report of the immunotherapy for LGMS, our study highlights that this strategy may be of great significance to the treatment of LGMS.

BackgroundSoft tissue sarcoma (STS) is a class of malignant tumors originating from mesenchymal stroma with a poor prognosis. Accumulating evidence has proved that angiogenesis is an essential hallmark of tumors. Nevertheless, there is a paucity of comprehensive research exploring the association of angiogenesis-related genes (ARGs) with STS.MethodsThe ARGs were extracted from previous literature, and the differentially expressed ARGs were screened for subsequent analysis. Next, the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were conducted to establish the angiogenesis-related signature (ARSig). The predictive performance of the novel ARSig was confirmed using internal and external validation, subgroup survival, and independent analysis. Additionally, the association of the ARSig with the tumor immune microenvironment, tumor mutational burden (TMB), and therapeutic response in STS were further investigated. Notably, we finally conducted in vitro experiments to verify the findings from the bioinformatics analysis.ResultsA novel ARSig is successfully constructed and validated. The STS with a lower ARSig risk score in the training cohort has an improved prognosis. Also, consistent results were observed in the internal and external cohorts. The receiver operating characteristic (ROC) curve, subgroup survival, and independent analysis further indicate that the novel ARSig is a promising independent prognostic predictor for STS. Furthermore, it is proved that the novel ARSig is relevant to the immune landscape, TMB, immunotherapy, and chemotherapy sensitivity in STS. Encouragingly, we also validate that the signature ARGs are significantly dysregulated in STS, and ARDB2 and SRPK1 are closely connected with the malignant progress of STS cells.ConclusionIn sum, we construct a novel ARSig for STS, which could act as a promising prognostic factor for STS and give a strategy for future clinical decisions, immune landscape, and personalized treatment of STS.

IntroductionOsteosarcoma (OS) is a highly aggressive bone malignancy with a poor prognosis, mainly in children and adolescents. Immunogenic cell death (ICD) is classified as a type of programmed cell death associated with the tumor immune microenvironment, prognosis, and immunotherapy. However, the feature of the ICD molecular subtype and the related tumor microenvironment (TME) and immune cell infiltration has not been carefully investigated in OS.MethodsThe ICD-related genes were extracted from previous studies, and the RNA expression profiles and corresponding data of OS were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus database. The ICD-related molecular subtypes were classed by the "ConsensusclusterPlus" package and the construction of ICD-related signatures through univariate regression analysis. ROC curves, independent analysis, and internal validation were used to evaluate signature performance. Moreover, a series of bioinformatic analyses were used for Immunotherapy efficacy, tumor immune microenvironments, and chemotherapeutic drug sensitivity between the high- and low-risk groups.ResultsHerein, we identified two ICD-related subtypes and found significant heterogeneity in clinical prognosis, TME, and immune response signaling among distinct ICD subtypes. Subsequently, a novel ICD-related prognostic signature was developed to determine its predictive performance in OS. Also, a highly accurate nomogram was then constructed to improve the clinical applicability of the novel ICD-related signature. Furthermore, we observed significant correlations between ICD risk score and TME, immunotherapy response, and chemotherapeutic drug sensitivity. Notably, the in vitro experiments further verified that high GALNT14 expression is closely associated with poor prognosis and malignant progress of OS.DiscussionHence, we identified and validated that the novel ICD-related signature could serve as a promising biomarker for the OS's prognosis, chemotherapy, and immunotherapy response prediction, providing guidance for personalized and accurate immunotherapy strategies for OS.

Sepsis is a life-threatening condition that often occurs in the intensive care unit. The excessive activation of the host's immune system at early stages contributes to multiple organ damage. Mitogen-activated protein kinase phosphatase-1 (MKP1) exerts an important effect on the inflammatory process. In our recent bioinformatic analysis, we confirmed that the inhibition of protein tyrosine phosphatase-1B (PTP1B) significantly promoted the expression of MKP1 in murine macrophages. However, the underlying mechanism and its effect on macrophage polarization remain unclear. In this study, we show that the suppression of PTP1B induced upregulation of MKP1 in M1 macrophages. A RayBiotech mouse inflammation antibody assay further revealed that MKP1-knockdown promoted pro-inflammatory cytokine (IL-1β, IL12p70, IL-17, IL-21, IL-23, and TNF-α) secretion but suppressed anti-proinflammatory cytokine (IL-10) production in M2 macrophages. Phospho-proteomics analysis further identified ERK1/2 and p38 as downstream molecules of MKP1. Moreover, we found that the inhibition of PTP1B lowered the expression of miR-26a, showing a negative correlation with MKP1 protein expression. Thus, we concluded that the inhibition of PTP1B contributes to M2 macrophage polarization via reducing mir-26a and afterwards enhancing MKP1 expression in murine macrophages.