Analyzing the impact of ATF3 in tumorigenesis and immune cell infiltration of ovarian tumor: a bioinformatics study

Identification of renal ischemia reperfusion injury-characteristic genes, pathways and immunological micro-environment features through bioinformatics approaches

BACKGROUND

Biomarkers and pathways associated with renal ischemia reperfusion injury (IRI) had not been well unveiled. This study was intended to investigate and summarize the regulatory networks for related hub genes. Besides, the immunological micro-environment features were evaluated and the correlations between immune cells and hub genes were also explored.

METHODS

GSE98622 containing mouse samples with multiple IRI stages and controls was collected from the GEO database. Differentially expressed genes (DEGs) were recognized by the R package limma, and the GO and KEGG analyses were conducted by DAVID. Gene set variation analysis (GSVA) and weighted gene coexpression network analysis (WGCNA) had been implemented to uncover changed pathways and gene modules related to IRI. Besides the known pathways such as apoptosis pathway, metabolic pathway, and cell cycle pathways, some novel pathways were also discovered to be critical in IRI. A series of novel genes associated with IRI was also dug out. An IRI mouse model was constructed to validate the results.

RESULTS

The well-known IRI marker genes (Kim1 and Lcn2) and novel hub genes (Hbegf, Serpine2, Apbb1ip, Trip13, Atf3, and Ncaph) had been proved by the quantitative real-time polymerase chain reaction (qRT-PCR). Thereafter, miRNAs targeted to the dysregulated genes were predicted and the miRNA-target network was constructed. Furthermore, the immune infiltration for these samples was predicted and the results showed that macrophages infiltrated to the injured kidney to affect the tissue repair or fibrosis. Hub genes were significantly positively or negatively correlated with the macrophage abundance indicating they played a crucial role in macrophage infiltration.

CONCLUSIONS

Consequently, the pathways, hub genes, miRNAs, and the immune microenvironment may explain the mechanism of IRI and might be the potential targets for IRI treatments.

To identify biomarkers associated with the transfer of diabetes combined with cancer in human genes using bioinformatics analysis

Currently, the incidence of diabetes mellitus is increasing rapidly, particularly in China, and its pathogenesis is still unclear. The goal of this study was to find meaningful biomarkers of metastasis in patients with diabetes and cancer using bioinformatic analysis in order to predict gene expression and prognostic importance for survival. We used the Differentially Expressed Gene, Database for Annotation Visualization and Integrated Discovery, and Gene Set Enrichment Analyses databases, as well as several bioinformatics tools, to explore the key genes in diabetes. Based on the above database, we ended up with 10 hub genes (FOS, ATF3, JUN, EGR1, FOSB, JUNB, BTG2, EGR2, ZFP36, and NR4A2). A discussion of the 10 critical genes, with extensive literature mentioned to validate the association between the 10 key genes and patients with diabetes and cancer, to demonstrate the importance of gene expression and survival prognosis. This study identifies several biomarkers associated with diabetes and cancer development and metastasis that may provide novel therapeutic targets for diabetes combined with cancer patients.

Identification of key genes for IgA nephropathy based on machine learning algorithm and correlation analysis of immune cells

INTRODUCTION

The pathogenesis and progression mechanism of Immunoglobulin A nephropathy (IgAN) is not fully understood. There is a lack of panoramic analysis of IgAN immune cell infiltration and algorithms that are more efficient and accurate for screening key pathogenic genes.

METHODS

RNA sequencing (RNA-seq) data sets on IgAN were downloaded from the Gene Expression Omnibus (GEO) database, including GSE93798, GSE35489, and GSE115857. The RNA-seq data set of kidney tissue as control samples were downloaded from the Genotype-Tissue Expression (GTEx) database. Three machine learning algorithms-weighted gene co-expression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO), and support vector machine-were used to identify the key pathogenic gene sets of the IgAN disease. The ssGSEA method was applied to calculate the immune cell infiltration (ICI) of IgAN samples, whereas the Spearman test was used for correlation analysis. The receiver operator characteristic curve (ROC) was used to evaluate the diagnostic efficacy of key genes. The correlation between the key genes and ICI was analyzed using the Spearman test.

RESULTS

A total of 177 genes were screened out as differentially expressed genes (DEGs) for IgAN, including 135 up-regulated genes and 42 down-regulated genes. The DEGs were significantly enriched in the inflammatory- or immune-related pathways (gene sets). Activating transcription factor 3 (AFT3), C-X-C Motif Chemokine Ligand 6 (CXCL6), and v-fos FBJ murine osteosarcoma viral oncogene homolog B (FOSB) were identified using WGCNA, support vector machine, and LASSO algorithms. These three genes revealed good diagnostic efficacy in the training and test cohorts. The CXCL6 expression positively correlated with activated B cells and memory B cells.

CONCLUSION

ATF3, FOSB, and CXCL6 genes were identified as potential biomarkers of IgAN. These three genes exhibited good diagnostic efficacy for IgAN. We described the landscape of immune cell infiltration for IgAN. Activated B cells and memory B cells were more highly expressed in the IgAN samples than in the control samples. CXCL6 seems crucial to the pathogenesis of IgAN and may induce IgAN by enriching immune cells. Our study may contribute to developing CXCL6 inhibitors that target B cells for IgAN therapy.

Adaptive transcriptomic and immune infiltrate responses in the tumor immune microenvironment following neoadjuvant chemotherapy in high grade serous ovarian cancer reveal novel prognostic associations and activation of pro-tumorigenic pathways

The high rate of ovarian cancer recurrence and chemoresistance necessitates further research into how chemotherapy affects the tumor immune microenvironment (TIME). While studies have shown that immune infiltrate increases following neoadjuvant (NACT) chemotherapy, there lacks a comprehensive understanding of chemotherapy-induced effects on immunotranscriptomics and cancer-related pathways and their relationship with immune infiltrate and patient responses. In this study, we performed NanoString nCounter^® PanCancer IO360 analysis of 31 high grade serous ovarian cancer (HGSOC) patients with matched pre-treatment biopsy and post-NACT tumor. We observed increases in pro-tumorigenic and immunoregulatory pathways and immune infiltrate following NACT, with striking increases in a cohort of genes centered on the transcription factors ATF3 and EGR1. Using quantitative PCR, we analyzed several of the top upregulated genes in HGSOC cell lines, noting that two of them, ATF3 and AREG, were consistently upregulated with chemotherapy exposure and significantly increased in platinum resistant cells compared to their sensitive counterparts. Furthermore, we observed that pre-NACT immune infiltrate and pathway scores were not strikingly related to platinum free interval (PFI), but post-NACT immune infiltrate, pathway scores, and gene expression were. Finally, we found that higher levels of a cohort of proliferative and DNA damage-related genes was related to shorter PFI. This study underscores the complex alterations in the ovarian TIME following chemotherapy exposure and begins to untangle how immunologic factors are involved in mediating chemotherapy response, which will allow for the future development of novel immunologic therapies to combat chemoresistance.

Induction of heparanase 2 (Hpa2) expression by stress is mediated by ATF3

Activity of heparanase, endoglycosidase that cleaves heparan sulfate side chains in heparan sulfate proteoglycans, is highly implicated in tumor progression and metastasis. Heparanase inhibitors are therefore being evaluated clinically as anti-cancer therapeutics. Heparanase 2 (Hpa2) is a close homolog of heparanase that lacks HS-degrading activity and functions as an endogenous inhibitor of heparanase. As a result, Hpa2 appears to attenuate tumor growth but mechanisms that regulate Hpa2 expression and determine the ratio between heparanase and Hpa2 are largely unknown. We have recently reported that the expression of Hpa2 is induced by endoplasmic reticulum (ER) and proteotoxic stresses, but the mechanism(s) underlying Hpa2 gene regulation was obscure. Here we expand the notion that Hpa2 is regulated by conditions of stress. We report that while ER and hypoxia, each alone, resulted in a 3-7 fold increase in Hpa2 expression, combining ER stress and hypoxia resulted in a noticeable, over 40-fold increase in Hpa2 expression. A prominent induction of Hpa2 expression was also quantified in cells exposed to heat shock, proteotoxic stress, lysosomal stress, and chemotherapy (cisplatin), strongly implying that Hpa2 is regulated by conditions of stress. Furthermore, analyses of the Hpa2 gene promoter led to the identification of activating-transcription-factor 3 (ATF3) as a transcription factor that mediates Hpa2 induction by stress, thus revealing, for the first time, a molecular mechanism that underlies Hpa2 gene regulation. Induction of Hpa2 and ATF3 by conditions of stress that often accompany the rapid expansion of tumors is likely translated to improved survival of cancer patients.