A Novel Immune-Gene Pair Signature Revealing the Tumor Microenvironment Features and Immunotherapy Prognosis of Muscle-Invasive Bladder Cancer

Identification of diagnostic immune-related gene biomarkers for predicting heart failure after acute myocardial infarction

Post-myocardial infarction heart failure (HF) is a major public health concern. Previous studies have reported the critical role of immune response in HF pathogenesis. However, limited studies have reported predictive immune-associated biomarkers for HF. So we attempted to identify potential immune-related indicators for HF early diagnosis and therapy guidance. This study identified two potential immune-related hub genes (IRHGs), namely CXCR5 and FOS, using bioinformatic approaches. The expression levels of CXCR5 and FOS and their ability to predict long-term HF were analyzed. Functional enrichment analysis revealed that the hub genes were enriched in immune system processes, including the interleukin-17 and nuclear factor-kappa B signaling pathways, which are involved in the pathogenesis of HF. Quantitative real-time polymerase chain reaction revealed that the Fos mRNA levels, but not the Cxcr5 mRNA levels, were downregulated in the mice of the HF group. This study successfully identified two IRHGs that were significantly and differentially expressed in the HF group and could predict long-term HF, providing novel insights for future studies on HF and developing novel therapeutic targets for HF.

Individualized prognosis stratification in muscle invasive bladder cancer: A pairwise TP53-derived transcriptome signature

TP53 is the most frequently mutated gene in muscle invasive bladder cancer (MIBC) and there are two gene signatures regarding TP53 developed for MIBC prognosis. However, they are limited to immune genes only and unable to be used individually across platforms due to their quantitative manners. We used 827 gene expression profiles from seven MIBC cohorts with varied platforms to build a pairwise TP53-derived transcriptome signature, 13 gene pairs (13-GPs). Since the 13-GPs model is a single sample prognostic predictor, it can be applied individually in practice and is applicable to any gene-expression platforms without specific normalization requirements. Survival difference between high-risk and low-risk patients stratified by the 13-GPs test was statistically significant (HR range: 2.26-2.76, all P < .0001). Discovery and validation sets showed that the 13-GPs was an independent prognostic factor after adjusting other clinical features (HR range: 2.21-2.82, all P < .05). Moreover, it was a potential supplement to the consensus molecular classification of MIBC to further stratify the LumP subtype (patients with better prognoses). High- and low-risk patients by the 13-GPs model presented distinct immune microenvironment and DDR mutation rates, suggesting that it might have the potential for immunotherapy. Being a general approach to other cancer types, this study demonstrated how we integrated gene variants with pairwise gene panels to build a single sample prognostic test in translational oncology.

Predicting potential biomarkers and immune infiltration characteristics in heart failure

BACKGROUND

Studies have demonstrated that immune cell activation and their infiltration in the myocardium can have adverse effects on the heart, contributing to the pathogenesis of heart failure (HF). The purpose of this study is used by bioinformatics analysis to determine the potential diagnostic markers of heart failure and establish an applicable model to predict the association between heart failure and immune cell infiltration.

METHODS

Firstly, gene expression profiles of dilated heart disease GSE3585 and GSE120895 were obtained in Gene Expression Omnibus (GEO) database. This study then selected differentially expressed genes (DEGs) in 54 patients with HF and 13 healthy controls. In this study, biomarkers were identified using Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE). Additionally, we evaluated the prognostic discrimination performance by the receiver operating characteristic (ROC) curve. Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) was used for analyzing immune cell infiltration in HF tissues. Lastly, immune biomarkers were correlated with each other.

RESULT

After 24 DEGs were analyzed using a combinatorial model of LASSO regression and SVM-RFE analysis, four key genes were obtained, namely NSG1, NPPB, PHLDA1, and SERPINE2.The area under the curve (AUC) of these four genes were greater than 0.8. Subsequently, using CIBERPORT, we also found that compared with normal people, the proportion of M1 macrophages and activated mast cells in heart failure tissues decreased. In addition, correlation analysis showed that NPPB, PHLDA1 and SERPINE2 were associated with immune cell infiltration.

CONCLUSION

NSG1, NPPB, PHLDA1 and SERPINE2 were identified as potential biomarkers of heart failure. It reveals the comprehensive role of relevant central genes in immune infiltration, which provides a new research idea for the treatment and early detection in heart failure.

Identification and validation of an immune-related gene pairs signature for three urologic cancers

Reliable biomarkers are needed to recognize urologic cancer patients at high risk for recurrence. In this study, we built a novel immune-related gene pairs signature to simultaneously predict recurrence for three urologic cancers. We gathered 14 publicly available gene expression profiles including bladder, prostate and kidney cancer. A total of 2,700 samples were classified into the training set (n = 1,622) and validation set (n = 1,078). The 25 immune-related gene pairs signature consisting of 41 unique genes was developed by the least absolute shrinkage and selection operator regression analysis and Cox regression model. The signature stratified patients into high- and low-risk groups with significantly different relapse-free survival in the meta-training set and its subpopulations, and was an independent prognostic factor of urologic cancers. This signature showed a robust ability in the meta-validation and multiple independent validation cohorts. Immune and inflammatory response, chemotaxis and cytokine activity were enriched with genes relevant to the signature. A significantly higher infiltration level of M1 macrophages was found in the high-risk group versus the low-risk group. In conclusion, our signature is a promising prognostic biomarker for predicting relapse-free survival in patients with urologic cancer.