Construction and Validation of a Macrophage-Associated Risk Model for Predicting the Prognosis of Osteosarcoma: Background. Osteosarcoma is one of the most common bone tumors among children. Tumor-associated macrophages have been found to interact with tumor cells, secreting a variety of cytokines about tumor growth, metastasis, and prognosis. This study aimed to identify macrophage-associated genes (MAGs) signatures to predict the prognosis of osteosarcoma. Methods. Totally 384 MAGs were collected from GSEA software C7: immunologic signature gene sets. Differential gene expression (DGE) analysis was performed between normal bone samples and osteosarcoma samples in GSE99671. Kaplan–Meier survival analysis was performed to identify prognostic MAGs in TARGET-OS. Decision curve analysis (DCA), nomogram, receiver operating characteristic (ROC), and survival curve analysis were further used to assess our risk model. All genes from TARGET-OS were used for gene set enrichment analysis (GSEA). Immune infiltration of osteosarcoma sample was calculated using CIBERSORT and ESTIMATE packages. The independent test data set GSE21257 from gene expression omnibus (GEO) was used to validate our risk model. Results. 5 MAGs (MAP3K5, PML, WDR1, BAMBI, and GNPDA2) were screened based on protein-protein interaction (PPI), DGE, and survival analysis. A novel macrophage-associated risk model was constructed to predict a risk score based on multivariate Cox regression analysis. The high-risk group showed a worse prognosis of osteosarcoma ( < 0.001) while the low-risk group had higher immune and stromal scores. The risk score was identified as an independent prognostic factor for osteosarcoma. MAGs model for diagnosis of osteosarcoma had a better net clinical benefit based on DCA. The nomogram and ROC curve also effectively predicted the prognosis of osteosarcoma. Besides, the validation result was consistent with the result of TARGET-OS. Conclusions. A novel macrophage-associated risk score to differentiate low- and high-risk groups of osteosarcoma was constructed based on integrative bioinformatics analysis. Macrophages might affect the prognosis of osteosarcoma through macrophage differentiation pathways and bring novel sights for the progression and prognosis of osteosarcoma.

Purpose. Complete resection and adjuvant chemotherapy are recommended as the standard strategy for patients with stage I-IIA small cell lung cancer (SCLC). However, the role of additional postoperative radiotherapy (PORT) in treatment remains controversial. Methods. Patients with stage I-IIA SCLC undergoing surgery and adjuvant chemotherapy were extracted from the Surveillance, Epidemiology, and End Results database. Stage I-IIA, defined as T1-2N0M0, was recalculated according to the 8th AJCC TNM staging system. Propensity score matching (PSM) was conducted to identify the therapeutic impact of PORT. Univariate Cox hazards regression and least absolute shrinkage and selection operator regression were utilized for primary screening of prognostic variables for I-IIA SCLC disease. A nomogram to predict overall survival (OS) was constructed based on the multivariate Cox proportional hazards model, evaluated with area under the curve, calibration curve, and decision curve analysis, and validated with bootstrap resampling. Results. Our results demonstrated that compared with no PORT, PORT significantly prolonged the median OS (8.58 vs. 5.17 years, HR = 0.61 [0.39–0.96], ) and median cancer-specific survival (11.33 vs. 8.08, HR = 0.47 [0.27–0.82], ) after PSM. The 5-year OS rate was 61.56% vs. 46.60%. Five variables including age at diagnosis, gender, T stage, surgical type, and PORT were elucidated to impact on prognosis and included in a nomogram to predict 3-/5-/10-year OS probability. The area under the curve values were 0.72, 0.71, and 0.81, respectively. The nomogram also exhibited satisfactory accuracy and clinical usefulness. Conclusion. PORT was verified to improve the OS of patients with T1-2N0M0 SCLC after surgery and chemotherapy. A prognostic nomogram was developed and validated for OS prediction for these patients.

Mature red blood cells (RBCs), the most abundant anucleate cells in humans, have long been overlooked as DNA carriers. Recent evidence implicates RBC‐derived DNA (rbcDNA) as a potential biomarker for cancer diagnostics, yet its origin and how RBCs acquire tumor DNA remain poorly understood. Here, we find that mature RBCs harbor short DNA fragments distinct from genomic DNA. Both in vivo and in vitro experiments confirm that RBCs can internalize extracellular DNA and reflect tumor burden. Oxford Nanopore sequencing of rbcDNA reveals that short rbcDNA fragments are homologous to extracellular cell‐free DNA (cfDNA). We identify apoptotic bodies (apoBDs) as key mediators of extracellular DNA uptake by RBCs, triggering RBC deformation, Heinz body formation, oxidative stress, and vesiculation. Tumor apoBD‐treated RBCs are rapidly cleared in vivo via a partly macrophage‐dependent effect, causing local immunosuppression in the spleen. Clinically, rbcDNA shows no advantage in detecting driver mutations compared with cfDNA, but its abundance significantly correlates with tumor burden and treatment response. Overall, our findings offer novel insights into RBC biology and support rbcDNA's clinical application in liquid biopsy.

Background: Tumor microenvironment has complex effects on tumorigenesis and metastasis in lung adenocarcinoma (LUAD). However, there is still a lack of comprehensive understanding of the relationship between immune and non-immune stromal characteristics in tumor microenvironment. Patients and methods: Eight cohort of 1681 lung caner patients were included in this study. The immune and non-immune stromal signatures of tumor microenvironment were identified by eigendecomposition and extraction algorithms. We developed a novel immune and stromal scoring system to quantify anti-tumor immune and promote-metastasis stromal activation, namely PMBT (prognostic model based on tumor microenvironment) as an R package. Tumors were classified into 4 subtypes according to PMBT system. Comprehensive analysis was performed in different subtypes. Results: The 4 subgroups had different mutation landscape, molecular, cellular characteristics and prognosis, which validated by 7 data sets containing 1175 patients. 19% patients was characterized by highly active anti-tumor reaction with high production of immunoactive mediators, immunocyte, low fibroblasts infiltration, low TGF-β, VEGFA, collagen and glucose catabolic, low immune checkpoint per T cell, tumor mutation burden, and favourable overall survival (all, P < 0.05) named high-immune and low-stromal activation subgroup (HL). Cellular paracrine network showed both high humoral and cellular immune interaction in HL group. The low-immune and high-stromal activation group (19%) had opposite characteristics with HL group. Moreover, the PMBT system showed the value to predict overall survival and immunotherapy responses (all, P < 0.05). Conclusions: Different molecular, cellular characteristics, mutation landscape and prognosis were discovered in the 4 subgroups. Our classification, PMBT system provided novel insight for clinical monitoring and treatment in LUAD.