Comprehensive Analysis of Prognostic Value and Immune Infiltration of the NT5DC Family in Hepatocellular Carcinoma

[An artificial neural network diagnostic model for scleroderma and immune cell infiltration analysis based on mitochondria-associated genes]

OBJECTIVE

To establish a diagnostic model for scleroderma by combining machine learning and artificial neural network based on mitochondria-related genes.

METHODS

The GSE95065 and GSE59785 datasets of scleroderma from GEO database were used for analyzing expressions of mitochondria-related genes, and the differential genes were identified by Random forest, LASSO regression and SVM algorithms. Based on these differential genes, an artificial neural network model was constructed, and its diagnostic accuracy was evaluated by 10-fold crossover verification and ROC curve analysis using the verification dataset GSE76807. The mRNA expressions of the key genes were verified by RT-qPCR in a mouse model of scleroderma. The CIBERSORT algorithm was used to estimate the bioinformatic association between scleroderma and the screened biomarkers.

RESULTS

A total of 24 differential genes were obtained, including 11 up-regulated and 13 down-regulated genes. Seven most relevant mitochondria-related genes (POLB, GSR, KRAS, NT5DC2, NOX4, IGF1, and TGM2) were screened using 3 machine learning algorithms, and the artificial neural network diagnostic model was constructed. The model showed an area under the ROC curves of 0.984 for scleroderma diagnosis (0.740 for the verification dataset and 0.980 for cross-over validation). RT-qPCR detected significant up-regulation of POLB, GSR, KRAS, NOX4, IGF1 and TGM2 mRNAs and significant down-regulation of NT5DC2 in the mouse models of scleroderma. Immune cell infiltration analysis showed that the differential genes in scleroderma were associated with follicular helper T cells, immature B cells, resting dendritic cells, memory activated CD4+T cells, M0 macrophages, monocytes, resting memory CD4+T cells and mast cell activation.

CONCLUSION

The artificial neural network diagnostic model for scleroderma established in this study provides a new perspective for exploring the pathogenesis of scleroderma.

Establishment and Evaluation of Exosomes-Related Gene Risk Model in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a challenging disease to evaluate in terms of prognosis, requiring close attention to the prognosis of HCC patients. Exosomes have been shown to play an important role in HCC development and have significant potential in managing HCC patient prognosis, as they are detectable in patients' blood. By using small extracellular vesicular RNA, liquid biopsies can reflect the underlying physiological and pathological status of the originating cells, providing a valuable assessment of human health. No study has explored the diagnostic value of mRNA expression changes in exosomes for liver cancer. The present study investigated establishing a risk prognosis model based on mRNA expression levels in exosomes from blood samples of liver cancer patients and evaluated its diagnostic and prognostic value, providing new targets for liver cancer detection. We obtained mRNA data from HCC patients and normal controls from the TCGA and exoRBase 2.0 databases and established a risk prognostic assessment model using exosomes-related risk genes selected through prognostic analysis and Lasso Cox analysis. The patients were divided into high-risk and low-risk groups based on median risk score values to validate the independence and evaluability of the risk score. The clinical value of the model was further analyzed using a nomograph model, and the efficacy of immunotherapy and cell-origin types of prognostic risk genes were further assessed in the high- and low-risk groups by immune checkpoint and single-cell sequencing. A total of 44 genes were found to be significantly associated with the prognosis of HCC patients. From this group, we selected six genes (CLEC3B, CYP2C9, GNA14, NQO1, NT5DC2, and S100A9) as exosomal risk genes and used them as a basis for the risk prognosis model. The clinical information of HCC patients from the TCGA and ICGC databases demonstrated that the risk prognostic score of the model established in this study was an independent prognostic factor with good robustness. When pathological stage and risk prognostic score were incorporated into the model to predict clinical outcomes, the nomograph model had the best clinical benefit. Furthermore, immune checkpoint assays and single-cell sequencing analysis suggested that exosomal risk genes were derived from different cell types and that immunotherapy in the high-risk groups could be beneficial. Our study demonstrated that the prognostic scoring model based on exosomal mRNA was highly effective. The six genes selected using the scoring model have been previously reported to be associated with the occurrence and development of liver cancer. However, this study is the first to confirm that these related genes existed in the blood exosomes, which could be used for liquid biopsy of patients with liver cancer, thereby avoiding the need for puncture diagnosis. This approach has a high value in clinical application. Through single-cell sequencing, we found that the six genes in the risk model originate from multiple cell types. This finding suggests that the exosomal characteristic molecules secreted by different types of cells in the microenvironment of liver cancer may serve as diagnostic markers.

The NT5DC family: expression profile and prognostic value in pancreatic adenocarcinoma

Pancreatic adenocarcinoma (PAAD) is a malignant tumor with high morbidity and mortality rates. The NT5DC family is an evolutionarily-conserved family of 5'-nucleosidases that catalyze the intracellular hydrolysis of nucleotides. Although the NT5DC family has been linked to the initiation and growth of several cancers, its function in PAAD remains unclear. A series of bioinformatic analyses was used to ascertain the expression, prognosis, gene changes, functional enrichment, and immune regulatory functions of the NT5DC family in PAAD. NT5C2 and NT5DC1/2 mRNA and protein levels are increased in PAAD. Furthermore, the high mRNA expressions of NT5C2, NT5DC2, and NT5DC4 indicate a poor prognosis in patients with PAAD. The enrichment of biological processes and gene expression in the NT5DC family in PAAD were investigated using Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses. Further investigations into immune infiltration revealed a close relationship between NT5DC gene expression and immune cell infiltration. These findings provide new insights into the biological function and prognostic value of the NT5DC gene family in PAAD.

Transcriptome and proteome profiling of activated cardiac fibroblasts supports target prioritization in cardiac fibrosis

BACKGROUND

Activated cardiac fibroblasts (CF) play a central role in cardiac fibrosis, a condition associated with most cardiovascular diseases. Conversion of quiescent into activated CF sustains heart integrity upon injury. However, permanence of CF in active state inflicts deleterious heart function effects. Mechanisms underlying this cell state conversion are still not fully disclosed, contributing to a limited target space and lack of effective anti-fibrotic therapies.

MATERIALS AND METHODS

To prioritize targets for drug development, we studied CF remodeling upon activation at transcriptomic and proteomic levels, using three different cell sources: primary adult CF (aHCF), primary fetal CF (fHCF), and induced pluripotent stem cells derived CF (hiPSC-CF).

RESULTS

All cell sources showed a convergent response upon activation, with clear morphological and molecular remodeling associated with cell-cell and cell-matrix interactions. Quantitative proteomic analysis identified known cardiac fibrosis markers, such as FN1, CCN2, and Serpine1, but also revealed targets not previously associated with this condition, including MRC2, IGFBP7, and NT5DC2.

CONCLUSION

Exploring such targets to modulate CF phenotype represents a valuable opportunity for development of anti-fibrotic therapies. Also, we demonstrate that hiPSC-CF is a suitable cell source for preclinical research, displaying significantly lower basal activation level relative to primary cells, while being able to elicit a convergent response upon stimuli.