Identification of co‑expression modules and hub genes of retinoblastoma via co‑expression analysis and protein‑protein interaction networks

Noncoding RNA-associated competing endogenous RNA networks in trastuzumab-induced cardiotoxicity

Trastuzumab-induced cardiotoxicity (TIC) is a common and serious disease with abnormal cardiac function. Accumulating evidence has indicated certain non-coding RNAs (ncRNAs), functioning as competing endogenous RNAs (ceRNAs), impacting the progression of cardiovascular diseases. Nonetheless, the specific involvement of ncRNA-mediated ceRNA regulatory mechanisms in TIC remains elusive. The present research aims to comprehensively investigate changes in the expressions of all ncRNA using whole-transcriptome RNA sequencing. The sequencing analysis unveiled significant dysregulation, identifying a total of 43 circular RNAs (circRNAs), 270 long noncoding RNAs (lncRNAs), 12 microRNAs (miRNAs), and 4131 mRNAs in trastuzumab-treated mouse hearts. Subsequently, circRNA-based ceRNA networks consisting of 82 nodes and 91 edges, as well as lncRNA-based ceRNA networks comprising 111 nodes and 112 edges, were constructed. Using the CytoNCA plugin, pivotal genes-miR-31-5p and miR-644-5p-were identified within these networks, exhibiting potential relevance in TIC treatment. Additionally, KEGG and GO analyses were conducted to explore the functional pathways associated with the genes within the ceRNA networks. The outcomes of the predicted ceRNAs and bioinformatics analyses elucidated the plausible involvement of ncRNAs in TIC pathogenesis. This insight contributes to a better understanding of underlying mechanisms and aids in identifying promising targets for effective prevention and treatment strategies.

scGIR: deciphering cellular heterogeneity via gene ranking in single-cell weighted gene correlation networks

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular heterogeneity through high-throughput analysis of individual cells. Nevertheless, challenges arise from prevalent sequencing dropout events and noise effects, impacting subsequent analyses. Here, we introduce a novel algorithm, Single-cell Gene Importance Ranking (scGIR), which utilizes a single-cell gene correlation network to evaluate gene importance. The algorithm transforms single-cell sequencing data into a robust gene correlation network through statistical independence, with correlation edges weighted by gene expression levels. We then constructed a random walk model on the resulting weighted gene correlation network to rank the importance of genes. Our analysis of gene importance using PageRank algorithm across nine authentic scRNA-seq datasets indicates that scGIR can effectively surmount technical noise, enabling the identification of cell types and inference of developmental trajectories. We demonstrated that the edges of gene correlation, weighted by expression, play a critical role in enhancing the algorithm's performance. Our findings emphasize that scGIR outperforms in enhancing the clustering of cell subtypes, reverse identifying differentially expressed marker genes, and uncovering genes with potential differential importance. Overall, we proposed a promising method capable of extracting more information from single-cell RNA sequencing datasets, potentially shedding new lights on cellular processes and disease mechanisms.

In silico Identification of Hypoxic Signature followed by reverse transcription-quantitative PCR Validation in Cancer Cell Lines

Background

Hypoxic tumor microenvironment is one of the important impediments for conventional cancer therapy. This study aimed to computationally identify hypoxia-related messenger RNA (mRNA) signatures in nine hypoxic-conditioned cancer cell lines and investigate their role during hypoxia.

Methods

Nine RNA sequencing (RNA-Seq) expression data sets were retrieved from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified in each cancer cell line. Then 23 common DEGs were selected by comparing the gene lists across the nine cancer cell lines. Reverse transcription-quantitative PCR (qRT-PCR) was performed to validate the identified DEGs.

Results

By comparing the data sets, GAPDH, LRP1, ALDOA, EFEMP2, PLOD2, CA9, EGLN3, HK, PDK1, KDM3A, UBC, and P4HA1 were identified as hub genes. In addition, miR-335-5p, miR-122-5p, miR-6807-5p, miR-1915-3p, miR-6764-5p, miR-92-3p, miR-23b-3p, miR-615-3p, miR-124-3p, miR-484, and miR-455-3p were determined as common micro RNAs. Four DEGs were selected for mRNA expression validation in cancer cells under normoxic and hypoxic conditions with qRT-PCR. The results also showed that the expression levels determined by qRT-PCR were consistent with RNA-Seq data.

Conclusion

The identified protein-protein interaction network of common DEGs could serve as potential hypoxia biomarkers and might be helpful for improving therapeutic strategies.

Gene co-expression network analysis revealed novel biomarkers for ovarian cancer

Ovarian cancer is the second most common gynecologic cancer and remains the leading cause of death of all gynecologic oncologic disease. Therefore, understanding the molecular mechanisms underlying the disease, and the identification of effective and predictive biomarkers are invaluable for the development of diagnostic and treatment strategies. In the present study, a differential co-expression network analysis was performed via meta-analysis of three transcriptome datasets of serous ovarian adenocarcinoma to identify novel candidate biomarker signatures, i.e. genes and miRNAs. We identified 439 common differentially expressed genes (DEGs), and reconstructed differential co-expression networks using common DEGs and considering two conditions, i.e. healthy ovarian surface epithelia samples and serous ovarian adenocarcinoma epithelia samples. The modular analyses of the constructed networks indicated a co-expressed gene module consisting of 17 genes. A total of 11 biomarker candidates were determined through receiver operating characteristic (ROC) curves of gene expression of module genes, and miRNAs targeting these genes were identified. As a result, six genes (CDT1, CNIH4, CRLS1, LIMCH1, POC1A, and SNX13), and two miRNAs (mir-147a, and mir-103a-3p) were suggested as novel candidate prognostic biomarkers for ovarian cancer. Further experimental and clinical validation of the proposed biomarkers could help future development of potential diagnostic and therapeutic innovations in ovarian cancer.

Linking co-expression modules with phenotypes

The method for quantifying the association between co-expression module and clinical trait of interest requires application of dimensionality reduction to summaries modules as one dimensional (1D) vector. However, these methods are often linked with information loss. The amount of information lost depends upon the percentage of variance captured by the reduced 1D vector. Therefore, it is of interest to describe a method using analysis of rank (AOR) to assess the association between module and clinical trait of interest. This method works with clinical traits represented as binary class labels and can be adopted for clinical traits measured in continuous scale by dividing samples in two groups around median value. Application of the AOR method on test data for muscle gene expression profiles identifies modules significantly associated with diabetes status.

Weighted genes associated with the progression of retinoblastoma: Evidence from bioinformatic analysis

Mechanisms underlying the development of malignant retinoblastoma (RB) remain largely unknown. The purpose of this study was to identify weighted genes that are associated with the progression of RB and to assess the usefulness of bioinformatic analysis in RB research. Bioinformatic analysis was performed to construct weighted gene co-expression and protein-protein interaction (PPI) networks and to predict long non-coding RNA (lncRNA)-microRNA (miRNA)-mRNA regulatory networks. RNA extracted from RB and adjacent retinal tissue was used to validate the results obtained from bioinformatic analysis, using a semi-quantitative PCR (qPCR) assay. Twenty-one modules were generated from 5000 most variably expressed genes. Both the light-yellow and red modules were significantly associated with the cellular anaplastic grade of RB. The genes clustered in the light-yellow module included protocadherin beta (PCDHBs) family members. The red module included 5 hub genes involved in cell division. According to the hypothesis that lncRNA may serve as a competing endogenous RNA (ceRNA) for miRNAs and modulates mRNA expression, a network was constructed between lncRNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) and cell division-related mRNAs. PCR analysis using 23 tumor tissues and 5 adjacent retinal tissue showed increased expression of PCDHB5 in tumor samples, and supported the predicted upregulation of mitotic checkpoint serine/threonine kinase (BUB1) by MALAT1 via miR-495-3p. Our study highlights the importance of bioinformatic analysis in identifying potential markers and mechanisms associated with the malignant transformation of RB, and provides evidence to suggest that PCDHB5 and the ceRNA regulatory network of MALAT1/miR-495-3p/BUB1 are involved in the progression of RB.

Towards a global investigation of transcriptomic signatures through co-expression networks and pathway knowledge for the identification of disease mechanisms

We attempt to address a key question in the joint analysis of transcriptomic data: can we correlate the patterns we observe in transcriptomic datasets to known interactions and pathway knowledge to broaden our understanding of disease pathophysiology? We present a systematic approach that sheds light on the patterns observed in hundreds of transcriptomic datasets from over sixty indications by using pathways and molecular interactions as a template. Our analysis employs transcriptomic datasets to construct dozens of disease specific co-expression networks, alongside a human protein-protein interactome network. Leveraging the interoperability between these two network templates, we explore patterns both common and particular to these diseases on three different levels. Firstly, at the node-level, we identify most and least common proteins across diseases and evaluate their consistency against the interactome as a proxy for their prevalence in the scientific literature. Secondly, we overlay both network templates to analyze common correlations and interactions across diseases at the edge-level. Thirdly, we explore the similarity between patterns observed at the disease-level and pathway knowledge to identify signatures associated with specific diseases and indication areas. Finally, we present a case scenario in schizophrenia, where we show how our approach can be used to investigate disease pathophysiology.