Prognostic Implications and Immune Infiltration Analysis of ALDOA in Lung Adenocarcinoma

Background: aldolase A (ALDOA) has been reported to be involved in kinds of cancers. However, the role of ALDOA in lung adenocarcinoma has not been fully elucidated. In this study, we explored the prognostic value and correlation with immune infiltration of ALDOA in lung adenocarcinoma.Methods: The expression of ALDOA was analyzed with the Oncomine database, the Cancer Genome Atlas (TCGA), and the Human Protein Atlas (HPA). Mann-Whitney U test was performed to examine the relationship between clinicopathological characteristics and ALDOA expression. The receiver operating characteristic (ROC) curve and Kaplan-Meier method were conducted to describe the diagnostic and prognostic importance of ALDOA. The Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape were used to construct PPI networks and identify hub genes. Functional annotations and immune infiltration were conducted.Results: The mRNA and protein expression of ALDOA were higher in lung adenocarcinoma than those in normal tissues. The overexpression of ALDOA was significantly correlated with the high T stage, N stage, M stage, and TNM stage. Kaplan-Meier showed that high expression of ALDOA was correlated with short overall survival (38.9 vs 72.5 months, p < 0.001). Multivariate analysis revealed that ALDOA (HR 1.435, 95%CI, 1.013–2.032, p = 0.042) was an independent poor prognostic factor for overall survival. Functional enrichment analysis showed that positively co-expressed genes of ALDOA were involved in the biological progress of mitochondrial translation, mitochondrial translational elongation, and negative regulation of cell cycle progression. KEGG pathway analysis showed enrichment function in carbon metabolism, the HIF-1 signaling pathway, and glycolysis/gluconeogenesis. The “SCNA” module analysis indicated that the copy number alterations of ALDOA were correlated with three immune cell infiltration levels, including B cells, CD8+ T cells, and CD4+ T cells. The “Gene” module analysis indicated that ALDOA gene expression was negatively correlated with infiltrating levels of B cells, CD8+ T cells, CD4+ T cells, and macrophages.Conclusion: Our study suggested that upregulated ALDOA was significantly correlated with tumor progression, poor survival, and immune infiltrations in lung adenocarcinoma. These results suggest that ALDOA is a potential prognostic biomarker and therapeutic target in lung adenocarcinoma.

Minimalist module analysis for fault detection and localization

Traditional multivariate statistical-based process monitoring (MSPM) methods are effective data-driven approaches for monitoring large-scale industrial processes, but have a shortcoming in handling the redundant correlations between process variables. To address this shortcoming, this study proposes a new MSPM method called minimalist module analysis (MMA). MMA divides process data into several different minimalist modules and one more independent module. All variables in the minimalist module are strongly correlated, and no redundant variables exist; therefore, the extracted feature components in one minimalist module will not be disturbed by noise from the other modules. This study also proposes new monitoring indices and a fault localization strategy for MMA, and simulation tests demonstrate that MMA achieves superior performance in fault detection and localization.

IRIS-FGM: an integrative single-cell RNA-Seq interpretation system for functional gene module analysis

Motivation Single-cell RNA-Seq (scRNA-Seq) data is useful in discovering cell heterogeneity and signature genes in specific cell populations in cancer and other complex diseases. Specifically, the investigation of condition-specific functional gene modules (FGM) can help to understand interactive gene networks and complex biological processes in different cell clusters. QUBIC2 is recognized as one of the most efficient and effective biclustering tools for condition-specific FGM identification from scRNA-Seq data. However, its limited availability to a C implementation restricted its application to only a few downstream analysis functionalities. We developed an R package named IRIS-FGM (Integrative scRNA-Seq Interpretation System for Functional Gene Module analysis) to support the investigation of FGMs and cell clustering using scRNA-Seq data. Empowered by QUBIC2, IRIS-FGM can effectively identify condition-specific FGMs, predict cell types/clusters, uncover differentially expressed genes, and perform pathway enrichment analysis. It is noteworthy that IRIS-FGM can also take Seurat objects as input, facilitating easy integration with the existing analysis pipeline. Availability and Implementation IRIS-FGM is implemented in the R environment (as of version 3.6) with the source code freely available at https://github.com/BMEngineeR/IRISFGM. Supplementary information Supplementary data are available at Bioinformatics online.

Consensus module analysis of abdominal fat deposition across multiple broiler lines

Background Despite several RNA-Seq and microarray studies on differentially expressed genes (DEGs) between high- and low-abdominal fat deposition in different broiler lines, to our knowledge, gene coexpression analysis across multiple broiler lines has rarely been reported. Here, we constructed a consensus gene coexpression network focused on identifying consensus gene coexpression modules associated with abdominal fat deposition across multiple broiler lines using two public RNA-Seq datasets (GSE42980 and GSE49121). Results In the consensus gene coexpression network, we identified eight consensus modules significantly correlated with abdominal fat deposition across four broiler lines using the consensus module analysis function in the weighted gene coexpression network analysis (WGCNA) package. The eight consensus modules were moderately to strongly preserved in the abdominal fat RNA-Seq dataset of another broiler line (SRP058295). Furthermore, we identified 5462 DEGs between high- and low-abdominal fat lines (FL and LL) (GSE42980) and 6904 DEGs between high- and low-growth (HG and LG) (GSE49121), including 1828 overlapping DEGs with similar expression profiles in both datasets, which were clustered into eight consensus modules. Pyruvate metabolism, fatty acid metabolism, and steroid biosynthesis were significantly enriched in the green, yellow, and medium purple 3 consensus modules. The PPAR signaling pathway and adipocytokine signaling pathway were significantly enriched in the green and purple consensus modules. Autophagy, mitophagy, and lysosome were significantly enriched in the medium purple 3 and yellow consensus modules. Conclusion Based on lipid metabolism pathways enriched in eight consensus modules and the overexpression of numerous lipogenic genes in both FL vs. LL and HG vs. LG, we hypothesize that more fatty acids, triacylglycerols (TAGs), and cholesterol might be synthesized in broilers with high abdominal fat than in broilers with low abdominal fat. According to autophagy, mitophagy, and lysosome enrichment in eight consensus modules, we inferred that autophagy might participate in broiler abdominal fat deposition. Altogether, these studies suggest eight consensus modules associated with abdominal fat deposition in broilers. Our study also provides an idea for investigating the molecular mechanism of abdominal fat deposition across multiple broiler lines.

Meta-analysis of virus-induced host gene expression reveals unique signatures of immune dysregulation induced by SARS-CoV-2

The clinical outcome of COVID-19 has an extreme age, genetic and comorbidity bias that is thought to be driven by an impaired immune response to SARS-CoV-2, the causative agent of the disease. The unprecedented impact of COVID-19 on global health has resulted in multiple studies generating a variety of large gene expression datasets in a relatively short period of time. In order to better understand the immune dysregulation induced by SARS-CoV-2, we carried out a meta-analysis of these transcriptomics data available in the published literature. Datasets included both those available from SARS-CoV-2 infected cell lines in vitro and those from patient samples. We focused our analysis on the identification of viral perturbed host functions as captured by co-expressed gene module analysis. Transcriptomics data from lung biopsies and nasopharyngeal samples, as opposed to those available from other clinical samples and infected cell lines, provided key signatures on the role of the host’s immune response on COVID-19 pathogenesis. For example, severity of infection and patients’ age are linked to the absence of stimulation of the RIG-I-like receptor signaling pathway, a known critical immediate line of defense against RNA viral infections that triggers type-I interferon responses. In addition, co-expression analysis of age-stratified transcriptional data provided evidence that signatures of key immune response pathways are perturbed in older COVID-19 patients. In particular, dysregulation of antigen-presenting components, down-regulation of cell cycle mechanisms and signatures of hyper-enriched monocytes were strongly correlated with the age of older individuals infected with SARS-CoV-2. Collectively, our meta-analysis highlights the ability of transcriptomics and gene-module analysis of aggregated datasets to aid our improved understanding of the host-specific disease mechanisms underpinning COVID-19.

Identification of ribosomal protein family in triple-negative breast cancer by bioinformatics analysis

Triple-negative breast cancer (TNBC) accounts for ~20% of all breast cancer cases. The management of TNBC represents a challenge due to its worse prognosis, heterogeneity and lack of targeted therapy. Moreover, its mechanisms are not fully clear. The aim of the study is to identify crucial genes between TNBC and non-TNBC for underlying targets for diagnostic and therapeutic methods of TNBC. The differentially expressed genes (DEGs) between TNBC and non-TNBC were selected from the Gene Expression Omnibus (GEO) database after the integrated analysis of two datasets (GSE65194 and GSE76124). Then Gene ontology (GO) and KEGG analysis were performed by DAVID database, protein-protein interaction (PPI) of DEGs was constructed by STRING database. Furthermore, centrality analysis and module analysis were carried out by Cytoscape to analysis the TNBC-related PPI. Subsequently, overall survival analysis was performed by GEPIA. Finally, the expressions of these key genes in TNBC and non-TNBC tissues were tested by qRT-PCR. The results showed that 955 DEGs were obtained, which were mainly enriched in ribosome, ribosomal subunit, and so on. Moreover, 19 candidate genes were focused on by centrality analysis and module analysis. Furthermore, we found the low expressions of RPS9, RPS14, RPS27, RPL11 and RPL14 were related to a poor overall survival (OS) in breast cancer patients. Additionally, qRT-PCR results suggested that these five genes were notably down-regulated in TNBC tissues. In summary, the present study suggests that ribosomal proteins are related to TNBC, and they may play an important role in the diagnosis, treatment and prognosis of TNBC.

Module Analysis Using Single-Patient Differential Expression Signatures Improves the Power of Association Studies for Alzheimer's Disease

The causal mechanism of Alzheimer's disease is extremely complex. Achieving great statistical power in association studies usually requires a large number of samples. In this work, we illustrated a different strategy to identify AD risk genes by clustering AD patients into modules based on their single-patient differential expression signatures. The evaluation suggested that our method could enrich AD patients with similar clinical manifestations. Applying this to a cohort of only 310 AD patients, we identified 174 AD risk loci at a strict threshold of empirical p < 0.05, while only two loci were identified using all the AD patients. As an evaluation, we collected 23 AD risk genes reported in a recent large-scale meta-analysis and found that 18 of them were rediscovered by association studies using clustered AD patients, while only three of them were rediscovered using all AD patients. Functional annotation suggested that AD-associated genetic variants mainly disturbed neuronal/synaptic function. Our results suggested module analysis helped to enrich AD patients affected by the common risk variants.

IRIS-FGM: an integrative single-cell RNA-Seq interpretation system for functional gene module analysis

SummarySingle-cell RNA-Seq (scRNA-Seq) data is useful in discovering cell heterogeneity and signature genes in specific cell populations in cancer and other complex diseases. Specifically, the investigation of functional gene modules (FGM) can help to understand gene interactive networks and complex biological processes. QUBIC2 is recognized as one of the most efficient and effective tools for FGM identification from scRNA-Seq data. However, its limited availability to a C implementation restricted its application to only a few downstream analyses functionalities. We developed an R package named IRIS-FGM (Integrative scRNA-Seq Interpretation System for Functional Gene Module analysis) to support the investigation of FGMs and cell clustering using scRNA-Seq data. Empowered by QUBIC2, IRIS-FGM can effectively identify co-expressed and co-regulated FGMs, predict cell types/clusters, uncover differentially expressed genes, and perform functional enrichment analysis. It is noteworthy that IRIS-FGM can also takes Seurat objects as input, which facilitate easy integration with existing analysis pipeline.Availability and ImplementationIRIS-FGM is implemented in R environment (as of version 3.6) with the source code freely available at https://github.com/OSU-BMBL/[email protected] informationSupplementary data are available at Bioinformatics online.