An easy-to-operate web-based calculator for predicting the progression of chronic kidney disease

CARARIME: Interactive web server for comprehensive analysis of renal allograft rejection in immune microenvironment

Background

Renal transplantation is a very effective treatment for renal failure patients following kidney transplant. However, the clinical benefit is restricted by the high incidence of organ rejection. Therefore, there exists a wealth of literature regarding the mechanism of renal transplant rejection, including a large library of expression data. In recent years, research has shown the immune microenvironment to play an important role in renal transplant rejection. Nephrology web analysis tools currently exist to address chronic nephropathy, renal tumors and children’s kidneys, but no such tool exists that analyses the impact of immune microenvironment in renal transplantation rejection.

Methods

To fill this gap, we have developed a web page analysis tool called Comprehensive Analysis of Renal Allograft Rerejction in Immune Microenvironment (CARARIME).

Results

CARARIME analyzes the gene expression and immune microenvironment of published renal transplant rejection cohorts, including differential analysis (gene expression and immune cells), prognosis analysis (logistics regression, Univariable Cox Regression and Kaplan Meier), correlation analysis, enrichment analysis (GSEA and ssGSEA), and ROC analysis.

Conclusions

Using this tool, researchers can easily analyze the immune microenvironment in the context of renal transplant rejection by clicking on the available options, helping to further the development of approaches to renal transplant rejection in the immune microenvironment field. CARARIME can be found in http://www.cararime.com.

Cuproptosis-related gene index: A predictor for pancreatic cancer prognosis, immunotherapy efficacy, and chemosensitivity

Aim

The term "Cuproptosis" was coined to describe a novel type of cell death triggered by intracellular copper buildup that is fundamentally distinct from other recognized types such as autophagy, ferroptosis, and pyroptosis in recent days. As the underlying mechanism was newly identified, its potential connection to pancreatic adenocarcinoma (PAAD) is still an open issue.

Methods

A set of machine learning algorithms was used to develop a Cuproptosis-related gene index (CRGI). Its immunological characteristics were studied by exploring its implications on the expression of the immunological checkpoints, prospective immunotherapy responses, etc. Moreover, the sensitivity to chemotherapeutic drugs was predicted. Unsupervised consensus clustering was performed to more precisely identify different CRGI-based molecular subtypes and investigate the immunotherapy and chemotherapy efficacy. The expression of DLAT, LIPT1 and LIAS were also investigated, through real-time quantitative polymerase chain reaction (RT-qPCR), western blot, and immunofluorescence staining (IFS).

Results

A novel CRGI was identified and validated. Additionally, correlation analysis revealed major changes in tumor immunology across the high- and low-CRGI groups. Through an in-depth study of each medication, it was determined that the predictive chemotherapeutic efficacy of 32 regularly used anticancer drugs differed between high- and low-CRGI groups. The results of the molecular subtyping provided more support for such theories. Expressional assays performed at transcriptomic and proteomic levels suggested that the aforementioned Cuproptosis-related genes might serve as reliable diagnostic biomarkers in PAAD.

Significance

This is, to the best of our knowledge, the first study to examine prognostic prediction in PAAD from the standpoint of Cuproptosis. These findings may benefit future immunotherapy and chemotherapeutic therapies.

A web-based calculator for predicting the prognosis of patients with sarcoma on the basis of antioxidant gene signatures

Background: Oxidative stress plays a critical role in tumorigenesis, tumor development, and resistance to therapy. A systematic analysis of the interactions between antioxidant gene expression and the prognosis of patients with sarcoma is lacking but urgently needed.

Methods: Gene expression and clinical data of patients with sarcoma were derived from The Cancer Genome Atlas Sarcoma (training cohort) and Gene Expression Omnibus (validation cohorts) databases. Least absolute shrinkage, selection operator regression, and Cox regression were used to develop prognostic signatures for overall survival (OS) and disease-free survival (DFS). Based on the signatures and clinical features, two nomograms for predicting 2-, 4-, and 6-year OS and DFS were established.

Results: On the basis of the training cohort, we identified five-gene (CHAC2, GPX5, GSTK1, PXDN, and S100A9) and six-gene (GGTLC2, GLO1, GPX7, GSTK1, GSTM5, and IPCEF1) signatures for predicting OS and DFS, respectively, in patients with sarcoma. Kaplan–Meier survival analysis of the training and validation cohorts revealed that patients in the high-risk group had a significantly poorer prognosis than those in the low-risk group. On the basis of the signatures and other independent risk factors, we established two models for predicting OS and DFS that showed excellent calibration and discrimination. For the convenience of clinical application, we built web-based calculators (OS: https://quankun.shinyapps.io/sarcOS/; DFS: https://quankun.shinyapps.io/sarcDFS/).

Conclusions: The antioxidant gene signature models established in this study can be novel prognostic predictors for sarcoma.