Deciphering the Intricate Roles of Radiation Therapy and Complement Activation in Cancer

Identification of a glycolysis-related miRNA Signature for Predicting Breast cancer Survival

Breast cancer (BC) is a common type of cancer and has a poor prognosis. In this study, we collected the mRNA and miRNA expression profiles of BC patients were obtained from The Cancer Genome Atlas (TCGA) to explore a novel prognostic strategy for BC patients using bioinformatics tools. We found that six glycolysis-related miRNAs (GRmiRs, including hsa-mir-1247, hsa-mir148b, hsa-mir-133a-2, has-mir-1307, hsa-mir-195 and hsa-mir-1258) were correlated with prognosis of BC samples. The risk score model was established based on 6 prognosis-associated GRmiRs. The outcome of high risk group was significantly poorer. Cox regression analysis showed that risk score was an independent prognostic factor. Differentially expressed genes identified between high and low risk groups were mainly enriched in inflammation and immune-related signaling pathways. The proportion of infiltration of 12 kinds of immune cells in high and low risk groups were significantly different. Risk score was closely associated with many immune indexes. Multiple DEGRGs and miRNAs were associated with drugs. In conclusion, glycolysis-related miRNA signature effectively predicts BC prognosis.

Matrix-assisted laser desorption ionization mass spectrometry profiling of plasma exosomes evaluates osteosarcoma metastasis

Osteosarcoma is the most common primary sarcoma of bone among adolescents, often characterized by early lung metastasis resulting in high mortality. Recently, exosomes have been used in liquid biopsy to monitor tumors. Herein, we used matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to profile human plasma exosomes for the evaluation of osteosarcoma lung metastasis. Forty patients with osteosarcoma with (n = 20) or without (n = 20) lung metastasis as well as 12 heathy controls were recruited. Exosomes were isolated from human plasma for MALDI-TOF MS analysis. Multivariate statistical analyses were performed based on the MALDI-TOF mass spectra. The strategy can efficiently differentiate osteosarcomas from healthy controls and further discriminate osteosarcoma lung metastasis from non-lung metastasis. We identified seven exosomal proteins as potential biomarkers of osteosarcoma lung metastasis. The proposed method holds great promise to clinically diagnose osteosarcoma and monitor osteosarcoma lung metastasis.

Identifying M1 Macrophage-Related Genes Through a Co-expression Network to Construct a Four-Gene Risk-Scoring Model for Predicting Thyroid Cancer Prognosis

Macrophages are key innate immune cells in the tumor microenvironment that regulate primary tumor growth, vascularization, metastatic spread and response to therapies. Macrophages can polarize into two different states (M1 and M2) with distinct phenotypes and functions. To investigate the known tumoricidal effects of M1 macrophages, we obtained RNA expression profiles and clinical data from The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA). The proportions of immune cells in tumor samples were assessed using CIBERSORT, and weighted gene co-expression network analysis (WGCNA) was used to identify M1 macrophage-related modules. Univariate Cox analysis and LASSO-Cox regression analysis were performed, and four genes (SPP1, DHRS3, SLC11A1, and CFB) with significant differential expression were selected through GEPIA. These four genes can be considered hub genes. The four-gene risk-scoring model may be an independent prognostic factor for THCA patients. The validation cohort and the entire cohort confirmed the results. Univariate and multivariate Cox analysis was performed to identify independent prognostic factors for THCA. Finally, a prognostic nomogram was built based on the entire cohort, and the nomogram combining the risk score and clinical prognostic factors was superior to the nomogram with individual clinical prognostic factors in predicting overall survival. Time-dependent ROC curves and DCA confirmed that the combined nomogram is useful. Gene set enrichment analysis (GSEA) was used to elucidate the potential molecular functions of the high-risk group. Our study identified four genes associated with M1 macrophages and established a prognostic nomogram that predicts overall survival for patients with THCA, which may help determine clinical treatment options for different patients.

Recognition of urinary N-linked glycopeptides in kidney cancer patients by hydrophilic carbohydrate functionalized magnetic metal organic framework combined with LC-MS/MS

A hydrophilic carbohydrate functionalized magnetic metal organic framework (Mag Zr-MOF@G6P) was synthesized via a facile one-step modification strategy for selective glycopeptide capture in virtue of hydrophilic interaction chromatography technique. The inherently hydrophilic Zr-MOF layer not only provides selective size-sieving pore structures but also offers large specific surface area to afford abundant affinity sites. Hydroxyl-rich glucose-6-phosphate was immobilized onto the Zr-MOF via a straightforward coordination manner to regulate its surface property, for the purpose of enhancing its hydrophilicity. Benefitting from the merits of Zr-MOF and glucose-6-phosphate, the as-designed composite exhibits good selectivity (the mass ratio of HRP digests to BSA digests was up to1:200) and low limit of detection (0.1 fmol μL^-1) towards the recognition of glycopeptides from standard samples. More excitingly, glycopeptides in urine of healthy people and patients with kidney cancer were successfully enriched and identified by the combined liquid chromatography-mass spectrometry/mass spectrometry technology (LC-MS/MS). Further gene ontology analysis of molecular function and biological process revealed that 13 original glycoproteins of the identified glycopeptides from urine of patients significantly participate in diverse cancer-associated events, including collagen binding, immunoglobulin receptor binding, antigen binding, and complement activation process. Graphical abstract.