Single-cell RNA-seq identification of four differentially expressed survival-related genes by a TARGET: Osteosarcoma database analysis

Multi-omics analysis of renal vein serum with Ischemia-Reperfusion injury

BACKGROUND

Acute kidney injury (AKI) is frequently caused by renal ischemia-reperfusion injury (IRI). Identifying potential renal IRI disease biomarkers would be useful for evaluating AKI severity.

OBJECTIVE

We used proteomics and metabolomics to investigate the differences in renal venous blood between ischemic and healthy kidneys in an animal model by identifying differentially expressed proteins (DEPs) and differentially expressed protein metabolites (DEMs).

METHODS

Nine pairs of renal venous blood samples were collected before and at 20, 40, and 60 min post ischemia. The ischemia time of Group A, B and C was 20,40 and 60 min. The proteome and metabolome of renal venous blood were evaluated to establish the differences between renal venous blood before and after ischemia.

RESULTS

We identified 79 common DEPs in all samples of Group A, 80 in Group B, and 131 in Group C. Further common DEPs among all three groups were Tyrosineprotein kinase, GPR15LG, KAZALD1, ADH1B. We also identified 81, 64, and 83 common DEMs in each group respectively, in which 30 DEMs were further common to all groups. Bioinformatic analysis of the DEPs and DEMs was conducted.

CONCLUSION

This study demonstrated that different pathological processes occur during short- and long-term renal IRI. Tyrosine protein kinase, GPR15LG, Kazal-type serine peptidase inhibitor domain 1, and all-trans-retinol dehydrogenase are potential biomarkers of renal IRI.

Advances in prognostic models for osteosarcoma risk

The risk prognosis model is a statistical model that uses a set of features to predict whether an individual will develop a specific disease or clinical outcome. It can be used in clinical practice to stratify disease severity and assess risk or prognosis. With the advancement of large-scale second-generation sequencing technology, along Prognosis models for osteosarcoma are increasingly being developed as large-scale second-generation sequencing technology advances and clinical and biological data becomes more abundant. This expansion greatly increases the number of prognostic models and candidate genes suitable for clinical use. This article will present the predictive effects and reliability of various prognosis models, serving as a reference for their evaluation and application.

Carboxypeptidase E is a prognostic biomarker co-expressed with osteoblastic genes in osteosarcoma

Osteosarcoma (OS) is a rare primary malignant bone tumor in adolescents and children with a poor prognosis. The identification of prognostic genes lags far behind advancements in treatment. In this study, we identified differential genes using mRNA microarray analysis of five paired OS tissues. Hub genes, gene set enrichment analysis, and pathway analysis were performed to gain insight into the pathway alterations of OS. Prognostic genes were screened using the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) dataset, then overlapped with the differential gene dataset. The carboxypeptidase E (CPE) gene, found to be an independent risk factor, was further validated using RT-PCR and Gene Expression Omnibus (GEO) datasets. Additionally, we explored the specific expression of CPE in OS tissues by reanalyzing single-cell genomics. Interestingly, CPE was found to be co-expressed with osteoblast lineage cell clusters that expressed RUNX2, SP7, SPP1, and IBSP marker genes in OS. These results suggest that CPE could serve as a prognostic factor in osteoblastic OS and should be further investigated as a potential therapeutic target.

RNA-seq and Single-Cell Transcriptome Analyses of TRAIL Receptors Gene Expression in Human Osteosarcoma Cells and Tissues

Osteosarcoma (OS) is the most common primary cancer in the skeletal system, characterized by a high incidence of lung metastasis, local recurrence and death. Systemic treatment of this aggressive cancer has not improved significantly since the introduction of chemotherapy regimens, underscoring a critical need for new treatment strategies. TRAIL receptors have long been proposed to be therapeutic targets for cancer treatment, but their role in osteosarcoma remains unclear. In this study, we investigated the expression profile of four TRAIL receptors in human OS cells using total RNA-seq and single-cell RNA-seq (scRNA-seq). The results revealed that TNFRSF10B and TNFRSF10D but not TNFRSF10A and TNFRSF10C are differentially expressed in human OS cells as compared to normal cells. At the single cell level by scRNA-seq analyses, TNFRSF10B, TNFRSF10D, TNFRSF10A and TNFRSF10C are most abundantly expressed in endothelial cells of OS tissues among nine distinct cell clusters. Notably, in osteoblastic OS cells, TNFRSF10B is most abundantly expressed, followed by TNFRSF10D, TNFRSF10A and TNFRSF10C. Similarly, in an OS cell line U2-OS using RNA-seq, TNFRSF10B is most abundantly expressed, followed by TNFRSF10D, TNFRSF10A and TNFRSF10C. According to the TARGET online database, poor patient outcomes were associated with low expression of TNFRSF10C. These results could provide a new perspective to design novel therapeutic targets of TRAIL receptors for the diagnosis, prognosis and treatment of OS and other cancers.

Exploration of the underlying biological differences and targets in ovarian cancer patients with diverse immunotherapy response

Background

Preclinical trials of immunotherapy in ovarian cancer (OC) have shown promising results. This makes it meaningful to prospectively examine the biological mechanisms explaining the differences in response performances to immunotherapy among OC patients.

Methods

Open-accessed data was obtained from the Cancer Genome Atlas and Gene Expression Omnibus database. All the analysis was conducted using the R software.

Results

We firstly performed the TIDE analysis to evaluate the immunotherapy response rate of OC patients. The machine learning algorithm LASSO logistic regression and SVM-RFE were used to identify the characteristic genes. The genes DPT, RUNX1T1, PTPRN, LSAMP, FDCSP and COL6A6 were selected for molecular typing. Our result showed that the patients in Cluster1 might have a better prognosis and might be more sensitive to immunotherapy, including PD-1 and CTLA4 therapy options. Pathway enrichment analysis showed that in Cluster2, the pathway of EMT, TNFα/NF-kB signaling, IL2/STAT5 signaling, inflammatory response, KRAS signaling, apical junction, complement, interferon-gamma response and allograft rejection were significantly activated. Also, genomic instability analysis was performed to identify the underlying genomic difference between the different Cluster patients. Single-cell analysis showed that the DPT, COL6A6, LSAMP and RUNX1T1 were mainly expressed in the fibroblasts. We then quantified the CAFs infiltration in the OC samples. The result showed that patients with low CAFs infiltration might have a lower TIDE score and a higher proportion of immunotherapy responders. Also, we found all the characteristic genes DPT, RUNX1T1, PTPRN, LSAMP, FDCSP and COL6A6 were upregulated in the patients with high CAFs infiltration. Immune infiltration analysis showed that the patients in Cluster2 might have a higher infiltration of naive B cells, activated NK cells and resting Dendritic cells.

Conclusions

In summary, our study provides new insights into ovarian cancer immunotherapy. Meanwhile, specific targets DPT, RUNX1T1, PTPRN, LSAMP, FDCSP, COL6A6 and CAFs were identified for OC immunotherapy.