ADPRH is a prognosis-related biomarker and correlates with immune infiltrates in low grade glioma

The oxidative aging model integrated various risk factors in type 2 diabetes mellitus at system level

Background

Type 2 diabetes mellitus (T2DM) is a chronic endocrine metabolic disease caused by insulin dysregulation. Studies have shown that aging-related oxidative stress (as "oxidative aging") play a critical role in the onset and progression of T2DM, by leading to an energy metabolism imbalance. However, the precise mechanisms through which oxidative aging lead to T2DM are yet to be fully comprehended. Thus, it is urgent to integrate the underlying mechanisms between oxidative aging and T2DM, where meaningful prediction models based on relative profiles are needed.

Methods

First, machine learning was used to build the aging model and disease model. Next, an integrated oxidative aging model was employed to identify crucial oxidative aging risk factors. Finally, a series of bioinformatic analyses (including network, enrichment, sensitivity, and pan-cancer analyses) were used to explore potential mechanisms underlying oxidative aging and T2DM.

Results

The study revealed a close relationship between oxidative aging and T2DM. Our results indicate that nutritional metabolism, inflammation response, mitochondrial function, and protein homeostasis are key factors involved in the interplay between oxidative aging and T2DM, even indicating key indices across different cancer types. Therefore, various risk factors in T2DM were integrated, and the theories of oxi-inflamm-aging and cellular senescence were also confirmed.

Conclusion

In sum, our study successfully integrated the underlying mechanisms linking oxidative aging and T2DM through a series of computational methodologies.

Exploring the Characteristics of Monkeypox-Related Genes in Pan-Cancer

Monkeypox, an infectious virus that is a member of the Poxviridae family, has raised great threats to humans. Compared to the known oncoviruses, the relationship between monkeypox and cancer still remains obscure. Hence, in this study, we analyzed the multi-omics data from the Cancer Genome Atlas (TCGA) database by using genomic and transcriptomic approaches to comprehensively assess the monkeypox-related genes (MRGs) in tumor samples from 33 types of cancers. Based on the results, the expression of MRGs was highly correlated with the immune infiltration and could be further utilized to predict survival in cancer patients. Furthermore, it was shown that tumorigenesis and patient survival were frequently associated with the genomic alterations of MRGs. Moreover, pathway analysis showed that MRGs participated in the regulation of apoptosis, cell cycle, Epithelial to Mesenchymal Transition (EMT), DNA damage, and hormone androgen receptor (AR), as well as RAS/MAPK and RTK signaling pathways. Besides, we also developed the prognostic features and consensus clustering clusters of MRGs in cancers. Lastly, by mining the cancer drug sensitivity genomics database, we further identified a series of candidate drugs that may target MRGs. Collectively, this study revealed genomic alterations and clinical features of MRGs, which may provide new hints to explore the potential molecular mechanisms between viruses and cancers as well as to provide new clinical guidance of cancer patients who also face the threats during the monkeypox epidemic.

Identification of two robust subclasses of sepsis with both prognostic and therapeutic values based on machine learning analysis

Background

Sepsis is a heterogeneous syndrome with high morbidity and mortality. Optimal and effective classifications are in urgent need and to be developed.

Methods and results

A total of 1,936 patients (sepsis samples, n=1,692; normal samples, n=244) in 7 discovery datasets were included to conduct weighted gene co-expression network analysis (WGCNA) to filter out candidate genes related to sepsis. Then, two subtypes of sepsis were classified in the training sepsis set (n=1,692), the Adaptive and Inflammatory, using K-means clustering analysis on 90 sepsis-related features. We validated these subtypes using 617 samples in 5 independent datasets and the merged 5 sets. Cibersort method revealed the Adaptive subtype was related to high infiltration levels of T cells and natural killer (NK) cells and a better clinical outcome. Immune features were validated by single-cell RNA sequencing (scRNA-seq) analysis. The Inflammatory subtype was associated with high infiltration of macrophages and a disadvantageous prognosis. Based on functional analysis, upregulation of the Toll-like receptor signaling pathway was obtained in Inflammatory subtype and NK cell-mediated cytotoxicity and T cell receptor signaling pathway were upregulated in Adaptive group. To quantify the cluster findings, a scoring system, called, risk score, was established using four datasets (n=980) in the discovery cohorts based on least absolute shrinkage and selection operator (LASSO) and logistic regression and validated in external sets (n=760). Multivariate logistic regression analysis revealed the risk score was an independent predictor of outcomes of sepsis patients (OR [odds ratio], 2.752, 95% confidence interval [CI], 2.234-3.389, P<0.001), when adjusted by age and gender. In addition, the validation sets confirmed the performance (OR, 1.638, 95% CI, 1.309-2.048, P<0.001). Finally, nomograms demonstrated great discriminatory potential than that of risk score, age and gender (training set: AUC=0.682, 95% CI, 0.643-0.719; validation set: AUC=0.624, 95% CI, 0.576-0.664). Decision curve analysis (DCA) demonstrated that the nomograms were clinically useful and had better discriminative performance to recognize patients at high risk than the age, gender and risk score, respectively.

Conclusions

In-depth analysis of a comprehensive landscape of the transcriptome characteristics of sepsis might contribute to personalized treatments and prediction of clinical outcomes.

Differences of macrophages in the tumor microenvironment as an underlying key factor in glioma patients

Background

Macrophages, the major immune cells in glioma microenvironment, are closely related to tumor prognosis. Further studies are needed to investigate macrophages, which will be helpful to fully understand the role of it and early achieve clinical translation.

Methods

A total of 1334 glioma cases were enrolled in this study from 3 databases. In our works, the single cell cohorts from GSE89567, GSE84465, and the Chinese Glioma Genome Atlas (CGGA) datasets were used to analyze the key genes of macrophage. The bulk sequencing data from the Cancer Genome Atlas (TCGA) and CGGA datasets were respectively divided into the training set and validation set to test prognostic value of the key genes from single cell analysis.

Results

Quantitative and functional differences significantly emerge in macrophage clusters between LGG and GBM. Firstly, we used the Seurat R package to identify 281 genes differentially expressed genes in macrophage clusters between LGG and GBM. Furthermore, based on these genes, we developed a predictive risk model to predict prognosis and reflect the immune microenvironment in glioma. The risk score calculation formula was yielded as follows: Risk score = (0.11 × EXPMACC1) + (−0.31 × EXPOTUD1) + (−0.09 × EXPTCHH) + (0.26 × EXPADPRH) + (-0.40× EXPABCG2) + (0.21 × EXPPLBD1) + (0.12 × EXPANG) + (0.29 × EXPQPCT). The risk score was independently related to prognosis. Further, significant differences existed in immunological characteristics between the low- and high-risk score groups. What is more, mutation analysis found different genomic patterns associated with the risk score.

Conclusion

This study further confirms that the proportion of macrophage infiltration is not only significantly different, but the function of them is also different. The signature, identified from the differentially expressed macrophage-related genes impacts poor prognosis and short overall survival and may act as therapeutic targets in the future.

MS4A6A is a new prognostic biomarker produced by macrophages in glioma patients

MS4A6A has been recognized as being associated with aging and the onset of neurodegenerative disease. However, the mechanisms of MS4A6A in glioma biology and prognosis are ill-defined. Here, we show that MS4A6A is upregulated in glioma tissues, resulting in unfavorable clinical outcomes and poor responses to adjuvant chemotherapy. Multivariate Cox regression analysis suggested that MS4A6A expression can act as a strong and independent predictor for glioma outcomes (CGGA1: HR: 1.765, p < 0.001; CGGA2: HR: 2.626, p < 0.001; TCGA: HR: 1.415, p < 0.001; Rembrandt: HR: 1.809, p < 0.001; Gravendeel: HR: 1.613, p < 0.001). A protein–protein interaction (PPI) network revealed that MS4A6A might be coexpressed with CD68, CD163, and macrophage-specific signatures. Enrichment analysis showed the innate immune response and inflammatory response to be markedly enriched in the high MS4A6A expression group. Additionally, single-cell RNA sequencing (scRNA-seq) analysis revealed distinctive expression features for MS4A6A in macrophages in the glioma immune microenvironment (GIME). Immunofluorescence staining confirmed colocalization of CD68/MS4A6A and CD163/MS4A6A in macrophages. Correlation analysis revealed that MS4A6A expression is positively related to the tumor mutation burden (TMB) of glioma, displaying the high potential of applying MS4A6A to evaluate responsiveness to immunotherapy. Altogether, our research indicates that MS4A6A upregulation may be used as a promising and effective indicator for adjuvant therapy and prognosis assessment.