Mining the tissue-tissue gene co-expression network for tumor microenvironment study and biomarker prediction

Identify potential driver genes for PAX-FOXO1 fusion-negative rhabdomyosarcoma through frequent gene co-expression network mining

Background

Rhabdomyosarcoma (RMS) is a soft tissue sarcoma usually originated from skeletal muscle. Currently, RMS classification based on PAX-FOXO1 fusion is widely adopted. However, compared to relatively clear understanding of the tumorigenesis in the fusion-positive RMS, little is known for that in fusion-negative RMS (FN-RMS).

Methods

We explored the molecular mechanisms and the driver genes of FN-RMS through frequent gene co-expression network mining (fGCN), differential copy number (CN) and differential expression analyses on multiple RMS transcriptomic datasets.

Results

We obtained 50 fGCN modules, among which five are differentially expressed between different fusion status. A closer look showed 23% of Module 2 genes are concentrated on several cytobands of chromosome 8. Upstream regulators such as MYC, YAP1, TWIST1 were identified for the fGCN modules. Using in a separate dataset we confirmed that, comparing to FP-RMS, 59 Module 2 genes show consistent CN amplification and mRNA overexpression, among which 28 are on the identified chr8 cytobands. Such CN amplification and nearby MYC (also resides on one of the above cytobands) and other upstream regulators (YAP1, TWIST1) may work together to drive FN-RMS tumorigenesis and progression. Up to 43.1% downstream targets of Yap1 and 45.8% of the targets of Myc are differentially expressed in FN-RMS vs. normal comparisons, which also confirmed the driving force of these regulators.

Discussion

We discovered that copy number amplification of specific cytobands on chr8 and the upstream regulators MYC, YAP1 and TWIST1 work together to affect the downstream gene co-expression and promote FN-RMS tumorigenesis and progression. Our findings provide new insights for FN-RMS tumorigenesis and offer promising targets for precision therapy. Experimental investigation about the functions of identified potential drivers in FN-RMS are in progress.

Extracellular Vesicles and Cellular Ageing

Ageing is a complex process characterized by deteriorated performance at multiple levels, starting from cellular dysfunction to organ degeneration. Stem cell-based therapies aim to administrate stem cells that eventually migrate to the injured site to replenish the damaged tissue and recover tissue functionality. Stem cells can be easily obtained and cultured in vitro, and display several qualities such as self-renewal, differentiation, and immunomodulation that make them suitable candidates for stem cell-based therapies. Current animal studies and clinical trials are being performed to assess the safety and beneficial effects of stem cell engraftments for regenerative medicine in ageing and age-related diseases.Since alterations in cell-cell communication have been associated with the development of pathophysiological processes, new research is focusing on the modulation of the microenvironment. Recent research has highlighted the important role of some microenvironment components that modulate cell-cell communication, thus spreading signals from damaged ageing cells to neighbor healthy cells, thereby promoting systemic ageing. Extracellular vesicles (EVs) are small-rounded vesicles released by almost every cell type. EVs cargo includes several bioactive molecules, such as lipids, proteins, and genetic material. Once internalized by target cells, their specific cargo can induce epigenetic modifications and alter the fate of the recipient cells. Also, EV's content is dependent on the releasing cells, thus, EVs can be used as biomarkers for several diseases. Moreover, EVs have been proposed to be used as cell-free therapies that focus on their administration to slow or even reverse some hallmarks of physiological ageing. It is not surprising that EVs are also under study as next-generation therapies for age-related diseases.

Extracellular RNAs from immune cells under obesity-a narrative review

Background and Objective

Obesity affects hundreds of millions of people worldwide and is characterized by chronic inflammation and insulin resistance, leading to Type II diabetes and atherosclerotic cardiovascular disease. Extracellular RNAs (exRNAs) are among the components which effect immune actions under obese conditions, and technological advances in recent years have rapidly increased our understanding of their roles and functions. Here we review essential background information on exRNAs and vesicles as well as the impact of immune-derived exRNAs in obesity-related disease. We also offer perspectives on clinical applications of exRNAs and future research directions.

Methods

We searched PubMed for articles relevant to immune-derived exRNAs in obesity. Articles written in English and published prior to May 25, 2022 were included.

Key Content and Findings

We report findings on the roles of immune-derived exRNAs which are important in obesity-related disease. We also highlight several exRNAs derived from other cell lineages which act on immune cells in metabolic disease.

Conclusions

ExRNAs produced by immune cells have profound local and systemic effects under obese conditions and can impact metabolic disease phenotypes. Immune-derived exRNAs represent an important target for future research and therapy.

Construction of a microenvironment immune gene model for predicting the prognosis of endometrial cancer

Background

Infiltrating immune and stromal cells are important components of the endometrial cancer (EC) microenvironment, which has a significant effect on the biological behavior of EC, suggesting that unique immune-related genes may be associated with the prognosis of EC. However, the association of immune-related genes with the prognosis of EC has not been elucidated. We attempted to identify immune-related genes with potentially prognostic value in EC using The Cancer Genome Atlas database and the relationship between immune microenvironment and EC.

Methods

We analyzed 578 EC samples from TCGA database and used weighted gene co-expression network analysis to screen out immune-related genes. We constructed a protein–protein interaction network and analyzed it using STRING and Cytoscape. Immune-related genes were analyzed through conjoint Cox regression and random forest algorithm analysis were to identify a multi-gene prediction model and stratify low-risk and high-risk groups of EC patients. Based on these data, we constructed a nomogram prediction model to improve prognosis assessment. Evaluation of Immunological, gene mutations and gene enrichment analysis were applied on these groups to quantify additional differences.

Results

Using conjoint Cox regression and random forest algorithm, we found that TRBC2, TRAC, LPXN, and ARHGAP30 were associated with the prognosis of EC and constructed four gene risk models for overall survival and a consistent nomogram. The time-dependent receiver operating characteristic curve analysis revealed that the area under the curve for 1-, 3-, and 5-y overall survival was 0.687, 0.699, and 0.76, respectively. These results were validated using a validation cohort. Immune-related pathways were mostly enriched in the low-risk group, which had higher levels of immune infiltration and immune status.

Conclusion

Our study provides new insights for novel biomarkers and immunotherapy targets in EC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08935-w.

RNA delivery by extracellular vesicles in mammalian cells and its applications

The term 'extracellular vesicles' refers to a heterogeneous population of vesicular bodies of cellular origin that derive either from the endosomal compartment (exosomes) or as a result of shedding from the plasma membrane (microvesicles, oncosomes and apoptotic bodies). Extracellular vesicles carry a variety of cargo, including RNAs, proteins, lipids and DNA, which can be taken up by other cells, both in the direct vicinity of the source cell and at distant sites in the body via biofluids, and elicit a variety of phenotypic responses. Owing to their unique biology and roles in cell-cell communication, extracellular vesicles have attracted strong interest, which is further enhanced by their potential clinical utility. Because extracellular vesicles derive their cargo from the contents of the cells that produce them, they are attractive sources of biomarkers for a variety of diseases. Furthermore, studies demonstrating phenotypic effects of specific extracellular vesicle-associated cargo on target cells have stoked interest in extracellular vesicles as therapeutic vehicles. There is particularly strong evidence that the RNA cargo of extracellular vesicles can alter recipient cell gene expression and function. During the past decade, extracellular vesicles and their RNA cargo have become better defined, but many aspects of extracellular vesicle biology remain to be elucidated. These include selective cargo loading resulting in substantial differences between the composition of extracellular vesicles and source cells; heterogeneity in extracellular vesicle size and composition; and undefined mechanisms for the uptake of extracellular vesicles into recipient cells and the fates of their cargo. Further progress in unravelling the basic mechanisms of extracellular vesicle biogenesis, transport, and cargo delivery and function is needed for successful clinical implementation. This Review focuses on the current state of knowledge pertaining to packaging, transport and function of RNAs in extracellular vesicles and outlines the progress made thus far towards their clinical applications.

Gene Co-Expression Networks Restructured Gene Fusion in Rhabdomyosarcoma Cancers

Rhabdomyosarcoma is subclassified by the presence or absence of a recurrent chromosome translocation that fuses the FOXO1 and PAX3 or PAX7 genes. The fusion protein (FOXO1-PAX3/7) retains both binding domains and becomes a novel and potent transcriptional regulator in rhabdomyosarcoma subtypes. Many studies have characterized and integrated genomic, transcriptomic, and epigenomic differences among rhabdomyosarcoma subtypes that contain the FOXO1-PAX3/7 gene fusion and those that do not; however, few investigations have investigated how gene co-expression networks are altered by FOXO1-PAX3/7. Although transcriptional data offer insight into one level of functional regulation, gene co-expression networks have the potential to identify biological interactions and pathways that underpin oncogenesis and tumorigenicity. Thus, we examined gene co-expression networks for rhabdomyosarcoma that were FOXO1-PAX3 positive, FOXO1-PAX7 positive, or fusion negative. Gene co-expression networks were mined using local maximum Quasi-Clique Merger (lmQCM) and analyzed for co-expression differences among rhabdomyosarcoma subtypes. This analysis observed 41 co-expression modules that were shared between fusion negative and positive samples, of which 17/41 showed significant up- or down-regulation in respect to fusion status. Fusion positive and negative rhabdomyosarcoma showed differing modularity of co-expression networks with fusion negative (n = 109) having significantly more individual modules than fusion positive (n = 53). Subsequent analysis of gene co-expression networks for PAX3 and PAX7 type fusions observed 17/53 were differentially expressed between the two subtypes. Gene list enrichment analysis found that gene ontology terms were poorly matched with biological processes and molecular function for most co-expression modules identified in this study; however, co-expressed modules were frequently localized to cytobands on chromosomes 8 and 11. Overall, we observed substantial restructuring of co-expression networks relative to fusion status and fusion type in rhabdomyosarcoma and identified previously overlooked genes and pathways that may be targeted in this pernicious disease.

Functional Virtual Flow Cytometry: A Visual Analytic Approach for Characterizing Single-Cell Gene Expression Patterns

We presented a novel workflow for detecting distribution patterns in cell populations based on single-cell transcriptome study. With the fast adoption of single-cell analysis, a challenge to researchers is how to effectively extract gene features to meaningfully separate the cell population. Considering that coexpressed genes are often functionally or structurally related and the number of coexpressed modules is much smaller than the number of genes, our workflow uses gene coexpression modules as features instead of individual genes. Thus, when the coexpressed modules are summarized into eigengenes, not only can we interactively explore the distribution of cells but also we can promptly interpret the gene features. The interactive visualization is aided by a novel application of spatial statistical analysis to the scatter plots using a clustering index parameter. This parameter helps to highlight interesting 2D patterns in the scatter plot matrix (SPLOM). We demonstrated the effectiveness of the workflow using two large single-cell studies. In the Allen Brain scRNA-seq dataset, the visual analytics suggested a new hypothesis such as the involvement of glutamate metabolism in the separation of the brain cells. In a large glioblastoma study, a sample with a unique cell migration related signature was identified.

Bioinformatics analysis of the proteins interacting with LASP-1 and their association with HBV-related hepatocellular carcinoma

LIM and SH3 domain protein (LASP-1) is responsible for the development of several types of human cancers via the interaction with other proteins; however, the precise biological functions of proteins interacting with LASP-1 are not fully clarified. Although the role of LASP-1 in hepatocarcinogenesis has been reported, the implication of LASP-1 interactors in HBV-related hepatocellular carcinoma (HCC) is not clearly evaluated. We obtained information regarding LASP-1 interactors from public databases and published studies. Via bioinformatics analysis, we found that LASP-1 interactors were related to distinct molecular functions and associated with various biological processes. Through an integrated network analysis of the interaction and pathways of LASP-1 interactors, cross-talk between different proteins and associated pathways was found. In addition, LASP-1 and several its interactors are significantly altered in HBV-related HCC through microarray analysis and could form a complex co-expression network. In the disease, LASP-1 and its interactors were further predicted to be regulated by a complex interaction network composed of different transcription factors. Besides, numerous LASP-1 interactors were associated with various clinical factors and related to the survival and recurrence of HBV-related HCC. Taken together, these results could help enrich our understanding of LASP-1 interactors and their relationships with HBV-related HCC.

Racial differences in genome-wide methylation profiling and gene expression in breast tissues from healthy women

Breast cancer is more common in European Americans (EAs) than in African Americans (AAs) but mortality from breast cancer is higher among AAs. While there are racial differences in DNA methylation and gene expression in breast tumors, little is known whether such racial differences exist in breast tissues of healthy women. Genome-wide DNA methylation and gene expression profiling was performed in histologically normal breast tissues of healthy women. Linear regression models were used to identify differentially-methylated CpG sites (CpGs) between EAs (n = 61) and AAs (n = 22). Correlations for methylation and expression were assessed. Biological functions of the differentially-methylated genes were assigned using the Ingenuity Pathway Analysis. Among 485 differentially-methylated CpGs by race, 203 were hypermethylated in EAs, and 282 were hypermethylated in AAs. Promoter-related differentially-methylated CpGs were more frequently hypermethylated in EAs (52%) than AAs (27%) while gene body and intergenic CpGs were more frequently hypermethylated in AAs. The differentially-methylated CpGs were enriched for cancer-associated genes with roles in cell death and survival, cellular development, and cell-to-cell signaling. In a separate analysis for correlation in EAs and AAs, different patterns of correlation were found between EAs and AAs. The correlated genes showed different biological networks between EAs and AAs; networks were connected by Ubiquitin C. To our knowledge, this is the first comprehensive genome-wide study to identify differences in methylation and gene expression between EAs and AAs in breast tissues from healthy women. These findings may provide further insights regarding the contribution of epigenetic differences to racial disparities in breast cancer.

Gene Co-Expression Analysis Predicts Genetic Variants Associated with Drug Responsiveness in Lung Cancer

Responsiveness to drugs is an important concern in designing personalized treatment for cancer patients. Currently genetic markers are often used to guide targeted therapy. However, deeper understanding of the molecular basis for drug responses and discovery of new predictive biomarkers for drug sensitivity are much needed. In this paper, we present a workflow for identifying condition-specific gene co-expression networks associated with responses to the tyrosine kinase inhibitor, Erlotinib, in lung adenocarcinoma cell lines using data from the Cancer Cell Line Encyclopedia by combining network mining and statistical analysis. Particularly, we have identified multiple gene modules specifically co-expressed in the drug responsive cell lines but not in the unresponsive group. Interestingly, most of these modules are enriched on specific cytobands, suggesting potential copy number variation events on these loci. Our results therefore imply that there are multiple genetic loci with copy number variations associated with the Erlotinib responses. The existence of CNVs in these loci is also confirmed in lung cancer tissue samples using the TCGA data. Since these structural variations are inferred from functional genomics data, these CNVs are functional variations. These results suggest the condition specific gene co- expression network mining approach is an effective approach in predicting candidate biomarkers for drug responses.