Identification of gene biomarkers for brain diseases via multi-network topological semantics extraction and graph convolutional network

BACKGROUND

Brain diseases pose a significant threat to human health, and various network-based methods have been proposed for identifying gene biomarkers associated with these diseases. However, the brain is a complex system, and extracting topological semantics from different brain networks is necessary yet challenging to identify pathogenic genes for brain diseases.

RESULTS

In this study, we present a multi-network representation learning framework called M-GBBD for the identification of gene biomarker in brain diseases. Specifically, we collected multi-omics data to construct eleven networks from different perspectives. M-GBBD extracts the spatial distributions of features from these networks and iteratively optimizes them using Kullback-Leibler divergence to fuse the networks into a common semantic space that represents the gene network for the brain. Subsequently, a graph consisting of both gene and large-scale disease proximity networks learns representations through graph convolution techniques and predicts whether a gene is associated which brain diseases while providing associated scores. Experimental results demonstrate that M-GBBD outperforms several baseline methods. Furthermore, our analysis supported by bioinformatics revealed CAMP as a significantly associated gene with Alzheimer's disease identified by M-GBBD.

CONCLUSION

Collectively, M-GBBD provides valuable insights into identifying gene biomarkers for brain diseases and serves as a promising framework for brain networks representation learning.

Single-cell RNA-seq analysis to identify potential biomarkers for diagnosis, and prognosis of non-small cell lung cancer by using comprehensive bioinformatics approaches

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer and the leading cause of cancer-related deaths worldwide. Identification of gene biomarkers and their regulatory factors and signaling pathways is very essential to reveal the molecular mechanisms of NSCLC initiation and progression. Thus, the goal of this study is to identify gene biomarkers for NSCLC diagnosis and prognosis by using scRNA-seq data through bioinformatics techniques. scRNA-seq data were obtained from the GEO database to identify DEGs. A total of 158 DEGs (including 48 upregulated and 110 downregulated) were detected after gene integration. Gene Ontology enrichment and KEGG pathway analysis of DEGs were performed by FunRich software. A PPI network of DEGs was then constructed using the STRING database and visualized by Cytoscape software. We identified 12 key genes (KGs) including MS4A1, CCL5, and GZMB, by using two topological methods based on the PPI networking results. The diagnostic, expression, and prognostic potentials of the identified 12 key genes were assessed using the receiver operating characteristics (ROC) curve and a web-based tool, SurvExpress. From the regulatory network analysis, we extracted the 7 key transcription factors (TFs) (FOXC1, YY1, CEBPB, TFAP2A, SREBF2, RELA, and GATA2), and 8 key miRNAs (hsa-miR-124-3p, hsa-miR-34a-5p, hsa-miR-21-5p, hsa-miR-155-5p, hsa-miR-449a, hsa-miR-24-3p, hsa-let-7b-5p, and hsa-miR-7-5p) associated with the KGs were evaluated. Functional enrichment and pathway analysis, survival analysis, ROC analysis, and regulatory network analysis highlighted crucial roles of the key genes. Our findings might play a significant role as candidate biomarkers in NSCLC diagnosis and prognosis.

Explore association of genes in PDL1/PD1 pathway to radiotherapy survival benefit based on interaction model strategy

Purpose

To explore the association of genes in “PD-L1 expression and PD-1 check point pathway in cancer” to radiotherapy survival benefit.

Methods and materials

Gene expression data and clinical information of cancers were downloaded from TCGA. Radiotherapy survival benefit was defined based on interaction model. Fast backward multivariate Cox regression was performed using stacking multiple interpolation data to identify radio-sensitive (RS) genes.

Results

Among the 73 genes in PD-L1/PD-1 pathway, we identified 24 RS genes in BRCA data set, 25 RS genes in STAD data set and 20 RS genes in HNSC data set, with some crossover genes. Theoretically, there are two types of RS genes. The expression level of Type I RS genes did not affect patients' overall survival (OS), but when receiving radiotherapy, patients with different expression level of Type I RS genes had varied survival benefit. Oppositely, Type II RS genes affected patients' OS. And when receiving radiotherapy, those with lower OS could benefit a lot. Type II RS genes in BRCA had strong positive correlation and closely biological interactions. When performing cluster analysis using these related Type II RS genes, patients could be divided into RS group and non-RS group in BRCA and METABRIC data sets.

Conclusions

Our study explored potential radio-sensitive biomarkers of several main cancer types in an important tumor immune checkpoint pathway and revealed a strong association between this pathway and radiotherapy survival benefit. New types of RS genes could be identified based on expanded definition to RS genes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-021-01951-x.

Comparison of hormone-induced mRNA and protein biomarker expression changes in breast cancer cells

PURPOSE

Protein biomarkers estrogen receptor (ER), progesterone receptor (PR), and marker of proliferation (Ki67) are routinely assessed by immunohistochemistry to guide treatment decisions for breast cancer. Now, quantification of mRNA encoding these proteins is being adopted in the clinic. However, mRNA and protein biomarkers may be differentially regulated by fluctuations in estrogen and progesterone that occur across the menstrual cycle in premenopausal breast cancer patients. This study aimed to compare how estrogen and progesterone affect mRNA and protein biomarker expression in hormone-responsive breast cancer cells.

METHODS

Hormone-responsive ZR-75-1 and T-47D human breast cancer cell lines were xenografted into the mammary fat pad of BALB/c nude mice supplemented with estrogen. Progesterone or vehicle was administered prior to dissection of tumors. Protein expression of ER, PR and Ki67 was quantified by immunohistochemistry, and mRNA encoding these proteins, ESR1, PGR and KI67, respectively, was quantified by real-time PCR. mRNA expression was also quantified in breast cancer cell lines treated with estrogen and progesterone in vitro.

RESULTS

In T-47D-xenografted tumors, estrogen and progesterone treatment reduced PGR and KI67 mRNA expression, and reduced PR and Ki67 protein positivity, compared to estrogen treatment alone. In ZR-75-1 xenografted tumors, no significant differences in protein or mRNA biomarker expression were observed. In vitro, estrogen and progesterone co-treatment significantly reduced ESR1 and PGR mRNA expression in both T-47D and ZR-75-1 cell lines.

CONCLUSIONS

Estrogen and progesterone similarly affect mRNA and protein biomarker expression in hormone-responsive breast cancer xenografts. Further research is needed to investigate concordance between protein and mRNA biomarkers in premenopausal breast cancer.

Sunscreen active ingredients in loggerhead turtles (Caretta caretta) and their relation to molecular markers of inflammation, oxidative stress and hormonal activity in wild populations

The present study sought to determine bioaccumulation and potential molecular effects of four of the most environmentally significant sunscreen agents in juvenile loggerhead sea turtles (Caretta caretta) from the Italian coasts of the central Adriatic Sea. Each of these sunscreen agents were found in most of the plasma samples analyzed, with benzophenone-3 as the dominant species. Total concentrations of the sunscreen agents ranged from not detected (<LOD) to 28.43 μg mL^-1. Almost all of the gene biomarkers of inflammation, oxidative stress and hormonal activity assessed by RT-qPCR in blood tissue showed significant positive correlations with total sunscreen agent concentrations. The present study is the first to provide data on contamination by sunscreen agents in loggerheads, and indicates that these chemicals tend to bioaccumulate and exert potential adverse effects in sea turtles.

Gene expression profiles of putative biomarkers in juvenile loggerhead sea turtles (Caretta caretta) exposed to polycyclic aromatic hydrocarbons

There is evidence that polycyclic aromatic hydrocarbons (PAHs) are consistently the predominant organic contaminants in concentration found in loggerhead sea turtles (Caretta caretta) from the North and Central Adriatic Sea. Hence this study investigates the PAH toxicity to loggerheads by using a particular set of genes [i.e. CYP1B, CAT, GPX, GSTT1, SOD3, DNMT1, Epoxide hydrolase 1 (EPHX1), Poly (ADP-ribose) polymerase 1 (PARP1), Lamin-A/C isoform 3 (LMNA), Talin 1 (TLN1), Annexin A1 (ANXA1)] whose altered expression is potentially dependent on and specific for the PAH-related mechanism of action. Twenty healthy juvenile loggerheads were thus divided into high and low exposure groups (mean of ΣPAHs: 80.34 ng mL^-1 vs. 8.84 ng mL^-1, P < 0.0001) according to the median split of ΣPAHs. Interestingly, we found that the whole blood mRNA levels of each gene biomarker tested were significantly increased in high PAH-exposed turtles thus proving to be useful for the biological monitoring of PAH toxicity and hematotoxicity in sea turtles.

Linking in vitro estrogenicity to adverse effects in the inland silverside (Menidia beryllina)

High-throughput cell assays that detect and integrate the response of multiple chemicals acting via a common mode of action have the potential to enhance current environmental monitoring practices. Establishing the linkage between in vitro and in vivo responses is key to demonstrating that in vitro cell assays can be predictive of ecologically relevant outcomes. The present study investigated the potency of 17β-estradiol (E2), estrone (E1), nonylphenol (NP), and treated wastewater effluent using the readily available GeneBLAzer^® estrogen receptor transactivation assay and 2 life stages of the inland silverside (Menidia beryllina). In vitro estrogenic potencies were ranked as follows: E2 > E1 >> NP. All 3 model estrogens induced vitellogenin and choriogenin expression in a dose-dependent manner in larvae and juveniles. However, apical effects were only found for E2 and E1 exposures of juveniles, which resulted in female-skewed sex ratios. Wastewater effluent samples exhibiting low in vitro estrogenicity (below the 10% effective concentration [EC10]), did not cause significant changes in M. beryllina. Significant induction of estrogen-responsive genes was observed at concentrations 6 to 26 times higher than in vitro responses. Gonadal feminization occurred at concentrations at least 19 to 26 times higher than the in vitro responses. These findings indicated that in vitro cell assays were more sensitive than the fish assays, making it possible to develop in vitro effect thresholds protective of aquatic organisms. Environ Toxicol Chem 2018;37:884-892. © 2017 SETAC.