The prediction of survival in Gastric Cancer based on a Robust 13-Gene Signature

PAX5/ITGAX Contributed to the Progression of Atherosclerosis by Regulation of B Differentiation via TNF-α Signaling Pathway

To investigate the effect of paired box protein 5 (PAX5)/integrin subunit alpha X (ITGAX) in atherosclerosis (AS). AS model was established using ApoE-/- mice (C57BL/6). Human vascular smooth muscle cells (HVSMCs) were stimulated with ox-LDL. Quantitative reverse transcription polymerase chain reaction and Western blotting were used to detect the expression levels of genes and proteins. Reporter constructs and luciferase assays were used to investigate the role of ITGAX and PAX5. Cells proliferation and inflammation factors were detected. The results presented that aortic plaque area, lipid content, serum triglyceride, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol levels were significantly increased in the high-fat diet group (p < 0.05). ITGAX was upregulated in atherosclerotic tissues. In addition, ox-LDL treatment induced HVSMCs proliferation, migration, and invasion. Reporter constructs and luciferase assays indicated ITGAX interaction with PAX5. Furthermore, siITGAX and siPAX5 cotransfection restored the rate of HVSMCs in G1 and S and G2/M phases, decreased the content of tumor necrosis factor-alpha (TNF-ɑ), interleukin (IL)-6, and IL-8 (p < 0.05). Interestingly, siITGAX and siPAX5 cotransfection also decreased the expression levels of TNF-α, TNF-R1, TNF-R2, CD19, and CD86 (p < 0.05). Our results suggest that ITGAX may be a potential therapeutic target for AS.

A 9‑gene expression signature to predict stage development in resectable stomach adenocarcinoma

BACKGROUND

Stomach adenocarcinoma (STAD) is a highly heterogeneous disease and is among the leading causes of cancer-related death worldwide. At present, TNM stage remains the most effective prognostic factor for STAD. Exploring the changes in gene expression levels associated with TNM stage development may help oncologists to better understand the commonalities in the progression of STAD and may provide a new way of identifying early-stage STAD so that optimal treatment approaches can be provided.

METHODS

The RNA profile retrieving strategy was utilized and RNA expression profiling was performed using two large STAD microarray databases (GSE62254, n = 300; GSE15459, n = 192) from the Gene Expression Omnibus (GEO) and the RNA-seq database within the Cancer Genome Atlas (TCGA, n = 375). All sample expression information was obtained from STAD tissues after radical resection. After excluding data with insufficient staging information and lymph node number, samples were grouped into earlier-stage and later-stage. Samples in GSE62254 were randomly divided into a training group (n = 172) and a validation group (n = 86). Differentially expressed genes (DEGs) were selected based on the expression of mRNAs in the training group and the TCGA group (n = 156), and hub genes were further screened by least absolute shrinkage and selection operator (LASSO) logistic regression. Receiver operating characteristic (ROC) curves were used to evaluate the performance of the hub genes in distinguishing STAD stage in the validation group and the GSE15459 dataset. Univariate and multivariate Cox regressions were performed sequentially.

RESULTS

22 DEGs were commonly upregulated (n = 19) or downregulated (n = 3) in the training and TCGA datasets. Nine genes, including MYOCD, GHRL, SCRG1, TYRP1, LYPD6B, THBS4, TNFRSF17, SERPINB2, and NEBL were identified as hub genes by LASSO-logistic regression. The model achieved discrimination in the validation group (AUC = 0.704), training-validation group (AUC = 0.743), and GSE15459 dataset (AUC = 0.658), respectively. Gene Set Enrichment Analysis (GSEA) was used to identify the potential stage-development pathways, including the PI3K-Akt and Calcium signaling pathways. Univariate Cox regression indicated that the nine-gene score was a significant risk factor for overall survival (HR = 1.28, 95% CI 1.08-1.50, P = 0.003). In the multivariate Cox regression, only SCRG1 was an independent prognostic predictor of overall survival after backward stepwise elimination (HR = 1.21, 95% CI 1.11-1.32, P < 0.001).

CONCLUSION

Through a series of bioinformatics and validation processes, a nine-gene signature that can distinguish STAD stage was identified. This gene signature has potential clinical application and may provide a novel approach to understanding the progression of STAD.

Identification of a Lipid Metabolism-Associated Gene Signature Predicting Survival in Breast Cancer

Background

Cancer metabolism and specifically lipid metabolism play an important role in breast cancer (BC) progression and metastasis. However, the role of lipid metabolism-associated genes (LMGs) in the prognosis of breast cancer remains unknown.

Methods

The expression profiles and clinical follow-up information of 1053 BC were downloaded from The Cancer Genome Atlas (TCGA), and metabolic genes were downloaded from the Gene Set Enrichment Analysis (GSEA) dataset. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed on the differentially expressed metabolism-related genes. Then, the formula of the metabolism-related risk model was composed, and the risk score of each patient was calculated. The breast cancer patients were divided into high-risk and low-risk groups with a cutoff of the median expression value of the risk score, and the prognostic analysis was also used to analyze the survival time between these two groups. Finally, we analyzed the expression, interaction and correlation among the lipid metabolism-associated genes risk model.

Results

The results from the prognostic analysis indicated that the survival was significantly poorer in the high-risk group than in the low-risk group in TCGA, and single-sample gene set enrichment analysis (ssGSEA) shows it is plausible that lipid metabolism is highly correlated with tumor immunity.

Conclusion

Lipid metabolism-associated genes may become a new prognostic indicator predicting the survival of BC patients. The prognostic genes (n = 16) may help provide new strategies for tumor therapy.