Background: Metabolic remodeling and changes in tumor immune microenvironment (TIME) in osteosarcoma are important factors affecting prognosis and treatment. However, the relationship between metabolism and TIME needs to be further explored. Methods: RNA-Seq data and clinical information of 84 patients with osteosarcoma from the TARGET database and an independent cohort from the GEO database were included in this study. The activity of seven metabolic super-pathways and immune infiltration levels were inferred in osteosarcoma patients. Metabolism-related genes (MRGs) were identified and different metabolic clusters and MRG-related gene clusters were identified using unsupervised clustering. Then the TIME differences between the different clusters were compared. In addition, an MRGs-based risk model was constructed and the role of a key risk gene, ST3GAL4, in osteosarcoma cells was explored using molecular biological experiments. Results: This study revealed four key metabolic pathways in osteosarcoma, with vitamin and cofactor metabolism being the most relevant to prognosis and to TIME. Two metabolic pathway-related clusters (C1 and C2) were identified, with some differences in immune activating cell infiltration between the two clusters, and C2 was more likely to respond to two chemotherapeutic agents than C1. Three MRG-related gene clusters (GC1-3) were also identified, with significant differences in prognosis among the three clusters. GC2 and GC3 had higher immune cell infiltration than GC1. GC3 is most likely to respond to immune checkpoint blockade and to three commonly used clinical drugs. A metabolism-related risk model was developed and validated. The risk model has strong prognostic predictive power and the low-risk group has a higher level of immune infiltration than the high-risk group. Knockdown of ST3GAL4 significantly inhibited proliferation, migration, invasion and glycolysis of osteosarcoma cells and inhibited the M2 polarization of macrophages. Conclusion: The metabolism of vitamins and cofactors is an important prognostic regulator of TIME in osteosarcoma, MRG-related gene clusters can well reflect changes in osteosarcoma TIME and predict chemotherapy and immunotherapy response. The metabolism-related risk model may serve as a useful prognostic predictor. ST3GAL4 plays a critical role in the progression, glycolysis, and TIME of osteosarcoma cells.

The liquid biopsy includes the detection of circulating tumor cells (CTCs) and CTC clusters in blood, as well as the detection of, cell-free DNA (cfDNA)/circulating tumor DNA (ctDNA) and extracellular vesicles (EVs) in the patient's body fluid. Liquid biopsy has important roles in translational research. But its clinical utility is still under investigation. Newly emerged patient-derived xenograft (PDX) and CTC-derived xenograft (CDX) faithfully recapitulate the genetic and morphological features of the donor patients’ tumor and patient-derived organoid (PDO) can mostly mimic tumor growth, tumor microenvironment and its response to drugs. In this review, we describe how the development of these patient-derived models has assisted the studies of CTCs and CTC clusters in terms of tumor biological behavior exploration, genomic analysis, and drug testing, with the help of the latest technology. We then summarize the studies of EVs and cfDNA/ctDNA in PDX and PDO models in early cancer diagnosis, tumor burden monitoring, drug test and response monitoring, and molecular profiling. The challenges faced and future perspectives of research related to liquid biopsy using patient-derived models are also discussed.

Background: Nonalcoholic steatohepatitis (NASH)-driven hepatocellular carcinoma (HCC) is becoming a major health-related problem. The exploration of NASH-related prognostic biomarkers and therapeutic targets is necessary. Methods: Data were downloaded from the GEO database. The “glmnet” package was used to identify differentially expressed genes (DEGs). The prognostic model was constructed by the univariate Cox and LASSO regression analyses. Validation of the expression and prognosis by immunohistochemistry (IHC) in vitro. Drug sensitivity and immune cell infiltration were analyzed by CTR-DB and ImmuCellAI. Results: We constructed a prognostic model that identified the NASH-related gene set (DLAT, IDH3B, and MAP3K4), which was validated in a real-world cohort. Next, seven prognostic transcription factors (TFs) were identified. The prognostic ceRNA network included three mRNAs, four miRNAs, and seven lncRNAs. Finally, we found that the gene set was associated with drug response which was validated in six clinical trial cohorts. Moreover, the expression level of the gene set was inversely correlated with CD8 T cell infiltration in HCC. Conclusions: We established a NASH-related prognostic model. Upstream transcriptome analysis and the ceRNA network provided clues for mechanism exploration. The mutant profile, drug sensitivity, and immune infiltration analysis further guided precise diagnosis and treatment strategies.

The concept of the microbiota‐gut‐brain axis has witnessed significant advancements, and observational studies revealed dysbiosis in the gut microbiota of patients with brain tumors. The causal relationship between gut microbiota and brain tumors and the potential prognostic value of microbiota are still unclear. Based on multiple Mendelian randomization analyses, this study confirms the causal effects of four gut microbes on meningioma, seven gut microbes on pituitary tumor, and eight gut microbes on glioma. Based on the Sherlock framework, this study identifies 103 meningioma‐related microbe‐related genes (MRGs), 40 pituitary tumor‐related MRGs, and 63 glioma‐related MRGs expressed in brain tissues. Almost all glioma‐related MRGs are associated with tumor grade and prognosis. Lastly, the prognostic model based on machine learning and microbiota established in this study, namely microbe‐related signature (MRS), could robustly predict the prognosis of glioma and provide insights for immunotherapy benefits. This study presents evidence of the causal effects of gut microbes on brain tumors, which contributes to our understanding of the microbiota‐gut‐brain axis. The relationship between glioma‐related MRGs and glioma prognosis, along with the prognostic prediction capacity of MRS and its association with immunotherapy, provides support for the use of gut microbiota as biomarkers to evaluate the prognosis and treatment response of glioma.

Background: Osteoarthritis (OA) is a prevalent degenerative joint disease that causes disability and diminishes quality of life. The pathogenesis of OA remains poorly understood, creating an urgent need for biomarkers to aid research, diagnosis, and treatment. Methods: This study integrated transcriptome data from the GEO database with bioinformatics analyses to identify biomarkers associated with OA. The bioinformatics methods utilized include the Limma package, WGCNA, PPI network analysis, and machine learning algorithms. Genetic variants were used as instrumental variables to evaluate the potential causal impact of circulating white blood cell (WBC) counts on OA. Data sources encompassed the largest genome-wide analysis for OA and a comprehensive GWAS summary for circulating WBC counts. Four mendelian randomization (MR) methods were employed to investigate the genetic association, with a primary focus on findings from the inverse variance-weighted (IVW) method. Results: Total of 233 OA-related genes were identified, showing significant enrichment in pathways associated with WBC function. Key biomarkers, including CD4, CSF1R, and TYROBP, were upregulated in OA samples and exhibited strong diagnostic potential. MR analysis findings provided evidence of a genetic association between elevated neutrophil counts and a reduced risk of OA across sites (IVW: OR = 0.97, 95% CI 0.93–1.00, p = 0.047). Additionally, higher circulating WBC counts, particularly neutrophil counts, were associated with a suggestive decrease in hip OA (WBC IVW: OR = 0.94, 95% CI 0.89–0.99, p = 0.015; neutrophil IVW: OR = 0.93, 95% CI 0.88–0.99, p = 0.017). Conversely, reverse MR analysis found no evidence to support a genetic effect of OA on circulating WBC counts. Conclusion: Our findings suggest that elevated neutrophil counts may offer protective effects against OA, underscoring the interplay between the immune functions and OA pathogenesis. CD4, CSF1R, and TYROBP emerge as promising OA biomarkers, meriting further validation in prospective studies.

Detection of biomarkers of breast cancer incurs additional costs and tissue burden. We propose a deep learning-based algorithm (BBMIL) to predict classical biomarkers, immunotherapy-associated gene signatures, and prognosis-associated subtypes directly from hematoxylin and eosin stained histopathology images. BBMIL showed the best performance among comparative algorithms on the prediction of classical biomarkers, immunotherapy related gene signatures, and subtypes.