Gene co-expression network analysis reveals immune cell infiltration as a favorable prognostic marker in non-uterine leiomyosarcoma

Integrated Comparative Transcriptome and circRNA-lncRNA-miRNA-mRNA ceRNA Regulatory Network Analyses Identify Molecular Mechanisms Associated with Intramuscular Fat Content in Beef Cattle

Intramuscular fat content (IMF), one of the most important carcass traits in beef cattle, is controlled by complex regulatory factors. At present, molecular mechanisms involved in regulating IMF and fat metabolism in beef cattle are not well understood. Our objective was to integrate comparative transcriptomic and competing endogenous RNA (ceRNA) network analyses to identify candidate messenger RNAs (mRNAs) and regulatory RNAs involved in molecular regulation of longissimus dorsi muscle (LDM) tissue for IMF and fat metabolism of 5 beef cattle breeds (Angus, Chinese Simmental, Luxi, Nanyang, and Shandong Black). In total, 34 circRNAs, 57 lncRNAs, 15 miRNAs, and 374 mRNAs were identified by integrating gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Furthermore, 7 key subnets with 16 circRNAs, 43 lncRNAs, 7 miRNAs, and 237 mRNAs were detected through clustering analyses, whereas GO enrichment analysis of identified RNAs revealed 48, 13, and 28 significantly enriched GO terms related to IMF in biological process, molecular function, and cellular component categories, respectively. The main metabolic-signaling pathways associated with IMF and fat metabolism that were enriched included metabolic, calcium, cGMP-PKG, thyroid hormone, and oxytocin signaling pathways. Moreover, MCU, CYB5R1, and BAG3 genes were common among the 10 comparative groups defined as important candidate marker genes for fat metabolism in beef cattle. Contributions of transcriptome profiles from various beef breeds and a competing endogenous RNA (ceRNA) regulatory network underlying phenotypic differences in IMF provided novel insights into molecular mechanisms associated with meat quality.

Integrating differential expression, co-expression and gene network analysis for the identification of common genes associated with tumor angiogenesis deregulation

Angiogenesis is essential for tumor growth and cancer metastasis. Identifying the molecular pathways involved in this process is the first step in the rational design of new therapeutic strategies to improve cancer treatment. In recent years, RNA-seq data analysis has helped to determine the genetic and molecular factors associated with different types of cancer. In this work we performed integrative analysis using RNA-seq data from human umbilical vein endothelial cells (HUVEC) and patients with angiogenesis-dependent diseases to find genes that serve as potential candidates to improve the prognosis of tumor angiogenesis deregulation and understand how this process is orchestrated at the genetic and molecular level. We downloaded four RNA-seq datasets (including cellular models of tumor angiogenesis and ischaemic heart disease) from the Sequence Read Archive. Our integrative analysis includes a first step to determine differentially and co-expressed genes. For this, we used the ExpHunter Suite, an R package that performs differential expression, co-expression and functional analysis of RNA-seq data. We used both differentially and co-expressed genes to explore the human gene interaction network and determine which genes were found in the different datasets that may be key for the angiogenesis deregulation. Finally, we performed drug repositioning analysis to find potential targets related to angiogenesis inhibition. We found that that among the transcriptional alterations identified, SEMA3D and IL33 genes are deregulated in all datasets. Microenvironment remodeling, cell cycle, lipid metabolism and vesicular transport are the main molecular pathways affected. In addition to this, interacting genes are involved in intracellular signaling pathways, especially in immune system and semaphorins, respiratory electron transport and fatty acid metabolism. The methodology presented here can be used for finding common transcriptional alterations in other genetically-based diseases.

Inflammatory and glycolytic programs underpin a primed blood neutrophil state in patients with pneumonia

Neutrophils are potent immune cells with key antimicrobial functions. Previous in vitro work has shown that neutrophil effector functions are mainly fueled by intracellular glycolysis. Little is known about the state of neutrophils still in the circulation in patients during infection. Here, we combined flow cytometry, stimulation assays, transcriptomics, and metabolomics to investigate the link between inflammatory and metabolic pathways in blood neutrophils of patients with community-acquired pneumonia. Patients' neutrophils, relative to neutrophils from age- and sex- matched controls, showed increased degranulation upon ex vivo stimulation, and portrayed distinct upregulation of inflammatory transcriptional programs. This neutrophil phenotype was accompanied by a high-energy state with increased intracellular ATP content, and transcriptomic and metabolic upregulation of glycolysis and glycogenolysis. One month after hospital admission, these metabolic and transcriptomic changes were largely normalized. These data elucidate the molecular programs that underpin a balanced, yet primed state of blood neutrophils during pneumonia.

Identification of HPV16 positive cervical cancer subsets characterized by divergent immune and oncogenic phenotypes with potential implications for immunotherapy

BACKGROUND

Cervical cancers (CaCx), like many other cancer types, portray high molecular heterogeneity that affects response to therapy, including immunotherapy. In India and other developing countries, CaCx mortality rates are very high because women report to the clinics with advanced cancers in absence of organized screening programs. This calls for implementation of newer therapeutic regimens for CaCx, like immunotherapy, which is again not used commonly in such countries.

OBJECTIVE

Therefore, we focused on dissecting tumour immune heterogeneity, if any, identify immune gene-based biomarkers of heterogeneity and subsets of such cancers with the potential for immunotherapy. We also attempted to characterize the cancer-associated phenotypes of such subsets, including viral load, to decipher the relationship of tumour immunogenicity with oncogenicity.

METHODS

Employing RNA-seq analysis of 44 HPV16 positive CaCx patients, immune subtypes were identified by unsupervised hierarchical clustering of global immune-gene expression profiles. Proportions of tumor infiltrating immune cells in the tumor milieu were estimated, employing Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), using gene expression data from RNA-seq. The oncogenic phenotypes of the immune subtypes of CaCx were deciphered through differential gene expression (DEGs) and pathway enrichment analysis. Viral load was estimated through TaqMan-based qRT-PCR analysis.

RESULTS

Analysis revealed the presence of two immune subtypes of CaCx, A (26/44; 59.09%) and B (18/44; 40.90%). Compared to Subtype-A, Subtype-B portrayed overexpression of immune genes and high infiltration of immune cells, specifically CD8+ T cells (p < 0.0001). Besides, a significant correlation between PD-1 and PD-L1 co-expression among Subtype-B, as opposed to Subtype-A, confirmed the interactive roles of these immune checkpoint molecules in Subtype B. Stepwise discriminant analysis pin-pointed ten immune-genes that could classify 100% of the patients significantly (p < 0.0001) into the two immune subtypes and serve as potential biomarkers of CaCx immunity. Differential gene expression analysis between the subtypes unveiled that Subtype-B was more biologically aggressive than Subtype-A, reflecting loss of structural integrity and promotion of cancer progression. The viral load was significantly lower in Subtype-B (average viral load = 10.74/100 ng of genomic DNA) compared to Subtype-A (average viral load = 14.29/100 ng of genomic DNA). Thus viral load and the ten-gene panel underscore their association with immunogenicity and oncogenicity.

CONCLUSION

Our study provides strong evidence that only a subset, about 41% of HPV16 positive CaCx patients in India, portray immune enrichment of the tumor milieu coupled with aggressive phenotypes. Such subtypes are therefore likely to benefit through checkpoint molecule-based or tumor infiltrating lymphocyte-based immunotherapy, which could be a leap forward in tackling aggressive forms of such CaCx in India and other developing countries.

Drug Repurposing Using Gene Co-Expression and Module Preservation Analysis in Acute Respiratory Distress Syndrome (ARDS), Systemic Inflammatory Response Syndrome (SIRS), Sepsis, and COVID-19

SARS-CoV-2 infections are highly correlated with the overexpression of pro-inflammatory cytokines in what is known as a cytokine storm, leading to high fatality rates. Such infections are accompanied by SIRS, ARDS, and sepsis, suggesting a potential link between the three phenotypes. Currently, little is known about the transcriptional similarity between these conditions. Herein, weighted gene co-expression network analysis (WGCNA) clustering was applied to RNA-seq datasets (GSE147902, GSE66890, GSE74224, GSE177477) to identify modules of highly co-expressed and correlated genes, cross referenced with dataset GSE160163, across the samples. To assess the transcriptome similarities between the conditions, module preservation analysis was performed and functional enrichment was analyzed in DAVID webserver. The hub genes of significantly preserved modules were identified, classified into upregulated or downregulated, and used to screen candidate drugs using Connectivity Map (CMap) to identify repurposed drugs. Results show that several immune pathways (chemokine signaling, NOD-like signaling, and Th1 and Th2 cell differentiation) are conserved across the four diseases. Hub genes screened using intramodular connectivity show significant relevance with the pathogenesis of cytokine storms. Transcriptomic-driven drug repurposing identified seven candidate drugs (SB-202190, eicosatetraenoic-acid, loratadine, TPCA-1, pinocembrin, mepacrine, and CAY-10470) that targeted several immune-related processes. These identified drugs warrant further study into their efficacy for treating cytokine storms, and in vitro and in vivo experiments are recommended to confirm the findings of this study.

Aberrant expression of KDM1A inhibits ferroptosis of lung cancer cells through up-regulating c-Myc

Ferroptosis is a cell death process caused by metabolic dysfunction with the feature of aberrant iron accumulation. Emerging studies have identified that ferroptosis is an important biological function involving in the tumorigenesis, and targeting ferroptosis could provide promising therapeutic targets for lung cancer. However, such therapeutic strategies show limited therapeutic effect owing to drug resistance and other unknown underlying mechanisms. In this study, lysine-specific demethylase 1 (LSD1/KDM1A) was found to be significantly upregulated in lung cancer cells and tissues. The patients with KDM1A downregulation displayed the good prognosis. Using gene set enrichment analysis (GSEA), we demonstrated that KDM1A-associated genes might participate in the regulation of cell ferroptosis and Myc signaling in lung cancer. Knockdown of KDM1A inhibited the level of c-Myc and increased the concentration of malondialdehyde (MDA) and irons in human lung cancer cells H1299 and A549. Downregulation of c-Myc could facilitate KDM1A knockdown-mediated ferroptosis. Our study has elucidated the effect of KDM1A/c-Myc regulatory axis in the ferroptosis resistance of lung cancer cells.

Prognostic and Immune Implications of a Novel Pyroptosis-Related Five-Gene Signature in Breast Cancer

Background

Breast cancer (BC) is the most common cancer among women worldwide, with enormous heterogeneity. Pyroptosis has a significant impact on the development and progression of tumors. Nonetheless, the possible correlation between pyroptosis-related genes (PRGs) and the BC immune microenvironment has yet to be investigated.

Materials and methods

In The Cancer Genome Atlas Breast Cancer cohort, 38 PRGs were shown to be significantly different between malignant and non-malignant breast tissues. The 38 PRGs’ consensus clustering grouped 1,089 individuals into two pyroptosis-related (PR) patterns. Using univariate and LASSO-Cox analyses, a PR five-gene predictive signature was constructed based on the differentially expressed genes between two clusters. The tools estimation of stromal and immune cells in malignant tumours using expression data (ESTIMATE), cell type identification by estimating relative subsets Of RNA transcripts (CIBERSORT), and single-sample gene set enrichment analysis (ssGSEA) were used to investigate the BC tumor microenvironment (TME).

Results

In TME, the two PR clusters displayed distinct clinicopathological characteristics, survival outcomes, and immunocyte infiltration features. The developed five-signature model (SEMA3B, IGKC, KLRB1, BIRC3, and PSME2) classified BC patients into two risk groups based on the estimated median risk score. Patients in the low-scoring category had a higher chance of survival and more extensive immunocyte infiltration. An external validation set can yield similar results.

Conclusion

Our data suggest that PRGs have a significant impact on the BC immunological microenvironment. The PR clusters and associated predictive signature stimulate additional research into pyroptosis in order to optimize therapeutic strategies for BC patients and their responses to immune therapy.

Structure, biochemistry, and gene expression patterns of the proline biosynthetic enzyme pyrroline-5-carboxylate reductase (PYCR), an emerging cancer therapy target

Proline metabolism features prominently in the unique metabolism of cancer cells. Proline biosynthetic genes are consistently upregulated in multiple cancers, while the proline catabolic enzyme proline dehydrogenase has dual, context-dependent pro-cancer and pro-apoptotic functions. Furthermore, the cycling of proline and Δ1-pyrroline-5-carboxylate through the proline cycle impacts cellular growth and death pathways by maintaining redox homeostasis between the cytosol and mitochondria. Here we focus on the last enzyme of proline biosynthesis, Δ1-pyrroline-5-carboxylate reductase, known as PYCR in humans. PYCR catalyzes the NAD(P)H-dependent reduction of Δ1-pyrroline-5-carboxylate to proline and forms the reductive half of the proline metabolic cycle. We review the research on the three-dimensional structure, biochemistry, inhibition, and cancer biology of PYCR. To provide a global view of PYCR gene upregulation in cancer, we mined RNA transcript databases to analyze differential gene expression in 28 cancer types. This analysis revealed strong, widespread upregulation of PYCR genes, especially PYCR1. Altogether, the research over the past 20 years makes a compelling case for PYCR as a cancer therapy target. We conclude with a discussion of some of the major challenges for the field, including developing isoform-specific inhibitors, elucidating the function of the long C-terminus of PYCR1/2, and characterizing the interactome of PYCR.

Integrated Network Analysis to Identify Key Modules and Potential Hub Genes Involved in Bovine Respiratory Disease: A Systems Biology Approach

Background: Bovine respiratory disease (BRD) is the most common disease in the beef and dairy cattle industry. BRD is a multifactorial disease resulting from the interaction between environmental stressors and infectious agents. However, the molecular mechanisms underlying BRD are not fully understood yet. Therefore, this study aimed to use a systems biology approach to systematically evaluate this disorder to better understand the molecular mechanisms responsible for BRD. Methods: Previously published RNA-seq data from whole blood of 18 healthy and 25 BRD samples were downloaded from the Gene Expression Omnibus (GEO) and then analyzed. Next, two distinct methods of weighted gene coexpression network analysis (WGCNA), i.e., module-trait relationships (MTRs) and module preservation (MP) analysis were used to identify significant highly correlated modules with clinical traits of BRD and non-preserved modules between healthy and BRD samples, respectively. After identifying respective modules by the two mentioned methods of WGCNA, functional enrichment analysis was performed to extract the modules that are biologically related to BRD. Gene coexpression networks based on the hub genes from the candidate modules were then integrated with protein-protein interaction (PPI) networks to identify hub-hub genes and potential transcription factors (TFs). Results: Four significant highly correlated modules with clinical traits of BRD as well as 29 non-preserved modules were identified by MTRs and MP methods, respectively. Among them, two significant highly correlated modules (identified by MTRs) and six nonpreserved modules (identified by MP) were biologically associated with immune response, pulmonary inflammation, and pathogenesis of BRD. After aggregation of gene coexpression networks based on the hub genes with PPI networks, a total of 307 hub-hub genes were identified in the eight candidate modules. Interestingly, most of these hub-hub genes were reported to play an important role in the immune response and BRD pathogenesis. Among the eight candidate modules, the turquoise (identified by MTRs) and purple (identified by MP) modules were highly biologically enriched in BRD. Moreover, STAT1, STAT2, STAT3, IRF7, and IRF9 TFs were suggested to play an important role in the immune system during BRD by regulating the coexpressed genes of these modules. Additionally, a gene set containing several hub-hub genes was identified in the eight candidate modules, such as TLR2, TLR4, IL10, SOCS3, GZMB, ANXA1, ANXA5, PTEN, SGK1, IFI6, ISG15, MX1, MX2, OAS2, IFIH1, DDX58, DHX58, RSAD2, IFI44, IFI44L, EIF2AK2, ISG20, IFIT5, IFITM3, OAS1Y, HERC5, and PRF1, which are potentially critical during infection with agents of bovine respiratory disease complex (BRDC). Conclusion: This study not only helps us to better understand the molecular mechanisms responsible for BRD but also suggested eight candidate modules along with several promising hub-hub genes as diagnosis biomarkers and therapeutic targets for BRD.

Estimation of Gene Regulatory Networks from Cancer Transcriptomics Data

Cancer is a genetic disease in which multiple genes are perturbed. Thus, information about the regulatory relationships between genes is necessary for the identification of biomarkers and therapeutic targets. In this review, methods for inference of gene regulatory networks (GRNs) from transcriptomics data that are used in cancer research are introduced. The methods are classified into three categories according to the analysis model. The first category includes methods that use pair-wise measures between genes, including correlation coefficient and mutual information. The second category includes methods that determine the genetic regulatory relationship using multivariate measures, which consider the expression profiles of all genes concurrently. The third category includes methods using supervised and integrative approaches. The supervised approach estimates the regulatory relationship using a supervised learning method that constructs a regression or classification model for predicting whether there is a regulatory relationship between genes with input data of gene expression profiles and class labels of prior biological knowledge. The integrative method is an expansion of the supervised method and uses more data and biological knowledge for predicting the regulatory relationship. Furthermore, simulation and experimental validation of the estimated GRNs are also discussed in this review. This review identified that most GRN inference methods are not specific for cancer transcriptome data, and such methods are required for better understanding of cancer pathophysiology. In addition, more systematic methods for validation of the estimated GRNs need to be developed in the context of cancer biology.

Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence

The rapid improvement of next-generation sequencing (NGS) technologies and their application in large-scale cohorts in cancer research led to common challenges of big data. It opened a new research area incorporating systems biology and machine learning. As large-scale NGS data accumulated, sophisticated data analysis methods became indispensable. In addition, NGS data have been integrated with systems biology to build better predictive models to determine the characteristics of tumors and tumor subtypes. Therefore, various machine learning algorithms were introduced to identify underlying biological mechanisms. In this work, we review novel technologies developed for NGS data analysis, and we describe how these computational methodologies integrate systems biology and omics data. Subsequently, we discuss how deep neural networks outperform other approaches, the potential of graph neural networks (GNN) in systems biology, and the limitations in NGS biomedical research. To reflect on the various challenges and corresponding computational solutions, we will discuss the following three topics: (i) molecular characteristics, (ii) tumor heterogeneity, and (iii) drug discovery. We conclude that machine learning and network-based approaches can add valuable insights and build highly accurate models. However, a well-informed choice of learning algorithm and biological network information is crucial for the success of each specific research question.