High expression of TMEM33 predicts poor prognosis and promotes cell proliferation in cervical cancer

Background As one of the most common cancer among women worldwide, the prognosis of patients with advanced cervical cancer remains unsatisfactory. A study indicated that transmembrane protein 33 (TMEM33) was implicated in tumor recurrence, while its role in cervical cancer has not been elucidated. Methods TMEM33 expression in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) was primarily screened in The Cancer Genome Altas (TCGA), and further validated in Gene Expression Omnibus (GEO) database. The Kaplan–Meier plotter analysis and Cox regression were constructed to evaluate the prognostic value of TMEM33 in CESC. Functional enrichment analysis was performed with GO, KEGG and GSEA tools. Protein-protein interaction analysis and correlated gene networks were conducted using STING and GEPIA2 websites, respectively. The expression of TMEM33 in cervical cancer cells were examined by immunoblotting and RT-qPCR. Finally, CCK-8 assay and colony formation assay were performed to investigate the role of TMEM33 in cervical cancer cell proliferation. Results TMEM33 expression was significantly elevated in CESC compared with normal tissues. High expression of TMEM33 was associated with poor prognostic clinical characteristics in CESC patients. KM-plotter analysis revealed that patients with increased TMEM33 had shorter overall survival (OS), progress free interval (PFI), and disease specific survival (DSS). Moreover, Multivariate Cox analysis further confirmed that high TMEM33 expression was an independent risk factor for OS in patients with CESC. TMEM33 was associated with immune cell infiltration, and its expression was correlated with tumorigenesis-related genes RNF4, OCIAD1, TMED5, DHX15, MED28 and LETM1. More importantly, knockdown of TMEM33 in cervical cancer cells decreased the expression of those genes and inhibited cell proliferation. Conclusions Increased TMEM33 in cervical cancer can serve as an independent prognostic marker and might play a role in tumorigenesis by promoting cell proliferation.

Network-based integrative analysis of lithium response in bipolar disorder using transcriptomic and GWAS data

Lithium (Li) is one of the most effective drugs for treating bipolar disorder (BD), however, there is presently no way to predict response to guide treatment. The aim of this study is to identify functional genes and pathways that distinguish BD Li responders (LR) from BD Li non-responders (NR). An initial Pharmacogenomics of Bipolar Disorder study (PGBD) GWAS of lithium response did not provide any significant results. As a result, we then employed network-based integrative analysis of transcriptomic and genomic data. In transcriptomic study of iPSC-derived neurons, 41 significantly differentially expressed (DE) genes were identified in LR vs NR regardless of lithium exposure. In the PGBD, post-GWAS gene prioritization using the GWA-boosting (GWAB) approach identified 1119 candidate genes. Following DE-derived network propagation, there was a highly significant overlap of genes between the top 500- and top 2000-proximal gene networks and the GWAB gene list (Phypergeometric=1.28E-09 and 4.10E-18, respectively). Functional enrichment analyses of the top 500 proximal network genes identified focal adhesion and the extracellular matrix (ECM) as the most significant functions. Our findings suggest that the difference between LR and NR was a much greater effect than that of lithium. The direct impact of dysregulation of focal adhesion on axon guidance and neuronal circuits could underpin mechanisms of response to lithium, as well as underlying BD. It also highlights the power of integrative multi-omics analysis of transcriptomic and genomic profiling to gain molecular insights into lithium response in BD.

MicroRNA-92a-3p Enhances Cisplatin Resistance by Regulating Krüppel-Like Factor 4-Mediated Cell Apoptosis and Epithelial-to-Mesenchymal Transition in Cervical Cancer

Recent studies have confirmed the existence and key roles of microRNA (miRNAs) in cancer drug resistance, including cervical cancer (CC). The present study aims to establish a novel role for miR-92a-3p and its associated gene networks in cisplatin (DDP) resistance of CC. First, the disparities in miRNA expression between CC tissues and adjacent normal tissues were screened based on GSE19611 microarray data that retrieved from Gene Expression Omnibus (GEO), and we identified several miRs that were significantly downregulated or upregulated in CC tissues including miR-92a-3p. Moreover, miR-92a-3p was significantly up-regulated in DDP-resistant cells and was the most differently expressed miRNA. Functionally, knockdown of miR-92a-3p increased the sensitivity of DDP-resistant cells to DDP via inhibiting cell proliferation, migration and invasion, and promoting apoptosis. Conversely, overexpression of miR-92a-3p significantly induced DDP resistance in CC parental cells including HeLa and SiHa cells. Moreover, Krüppel-like factor 4 (KLF4) was identified as a direct target of miR-92a-3p, and an obvious inverse correlation was observed between the expression of miR-92a-3p and KLF4 in 40 pairs of cancer tissues. Furthermore, KLF4 knockdown reversed the promoting effect of miR-92a-3p inhibition on DDP sensitivity in DDP-resistant CC cells. Besides, high expression of miR-92a-3p was associated with DDP resistance, as well as a short overall survival in clinic. Taken together, these findings provide important evidence that miR-92a-3p targets KLF4 and is significant in DDP resistance in CC, indicating that miR-92a-3p may be an attractive target to increase DDP sensitivity in clinical CC treatment.

Molecular Mechanisms of lncRNAs in the Dependent Regulation of Cancer and Their Potential Therapeutic Use

Deep whole genome and transcriptome sequencing have highlighted the importance of an emerging class of non-coding RNA longer than 200 nucleotides (i.e., long non-coding RNAs (lncRNAs)) that are involved in multiple cellular processes such as cell differentiation, embryonic development, and tissue homeostasis. Cancer is a prime example derived from a loss of homeostasis, primarily caused by genetic alterations both in the genomic and epigenetic landscape, which results in deregulation of the gene networks. Deregulation of the expression of many lncRNAs in samples, tissues or patients has been pointed out as a molecular regulator in carcinogenesis, with them acting as oncogenes or tumor suppressor genes. Herein, we summarize the distinct molecular regulatory mechanisms described in literature in which lncRNAs modulate carcinogenesis, emphasizing epigenetic and genetic alterations in particular. Furthermore, we also reviewed the current strategies used to block lncRNA oncogenic functions and their usefulness as potential therapeutic targets in several carcinomas.

Divergent evolution of developmental timing in the neocortex revealed by marsupial and eutherian transcriptomes

Only mammals evolved a neocortex, which integrates sensory-motor and cognitive functions. Significant diversifications in the cellular composition and connectivity of the neocortex occurred between the two main Therian groups: marsupials and eutherians. However, the developmental mechanisms underlying these diversifications are largely unknown. Here, we compared the neocortical transcriptomes of Sminthopsis crassicaudata, a mouse-sized marsupial, with those of eutherian mice at two developmentally equivalent timepoints corresponding to deeper and upper layer neuron generation. Enrichment analyses revealed more mature gene networks in marsupials at the early stage, which reverted at the later stage, suggesting a more precocious but protracted neuronal maturation program relative to birth timing of cortical layers. We ranked genes expressed in different species and identified important differences in gene expression rankings between species. For example, genes known to be enriched in upper-layer cortical projection neuron subtypes, such as Cux1, Lhx2 and Satb2, likely relating to corpus callosum emergence in eutherians. These results show molecular heterochronies of neocortical development in Theria, and highlight changes in gene expression and cell type composition that may underlie neocortical evolution and diversification.

Stage specific comparative transcriptomic analysis to reveal gene networks regulating iron and zinc content in pearl millet [Pennisetum glaucum (L.) R. Br.]

Pearl millet is an important staple food crop of poor people and excels all other cereals due to its unique features of resilience to adverse climatic conditions. It is rich in micronutrients like iron and zinc and amenable for focused breeding for these micronutrients along with high yield. Hence, this is a key to alleviate malnutrition and ensure nutritional security. This study was conducted to identify and validate candidate genes governing grain iron and zinc content enabling the desired modifications in the genotypes. Transcriptome sequencing using ION S5 Next Generation Sequencer generated 43.5 million sequence reads resulting in 83,721 transcripts with N50 of 597 bp and 84.35% of transcripts matched with the pearl millet genome assembly. The genotypes having high iron and zinc showed differential gene expression during different stages. Of which, 155 were up-regulated and 251 were down-regulated while during flowering stage and milking stage 349 and 378 transcripts were differentially expressed, respectively. Gene annotation and GO term showed the presence of transcripts involved in metabolic activities associated with uptake and transport of iron and zinc. Information generated will help in gaining insights into iron and zinc metabolism and develop genotypes with high yield, grain iron and zinc content.

A novel graph-based k-partitioning approach improves the detection of gene-gene correlations by single-cell RNA sequencing

Background Gene expression is regulated by transcription factors, cofactors, and epigenetic mechanisms. Coexpressed genes indicate similar functional categories and gene networks. Detecting gene-gene coexpression is important for understanding the underlying mechanisms of cellular function and human diseases. A common practice of identifying coexpressed genes is to test the correlation of expression in a set of genes. In single-cell RNA-seq data, an important challenge is the abundance of zero values, so-called “dropout”, which results in biased estimation of gene-gene correlations for downstream analyses. In recent years, efforts have been made to recover coexpressed genes in scRNA-seq data. Here, our goal is to detect coexpressed gene pairs to reduce the “dropout” effect in scRNA-seq data using a novel graph-based k-partitioning method by merging transcriptomically similar cells. Results We observed that the number of zero values was reduced among the merged transcriptomically similar cell clusters. Motivated by this observation, we leveraged a graph-based algorithm and develop an R package, scCorr, to recover the missing gene-gene correlation in scRNA-seq data that enables the reliable acquisition of cluster-based gene-gene correlations in three independent scRNA-seq datasets. The graphically partitioned cell clusters did not change the local cell community. For example, in scRNA-seq data from peripheral blood mononuclear cells (PBMCs), the gene-gene correlation estimated by scCorr outperformed the correlation estimated by the nonclustering method. Among 85 correlated gene pairs in a set of 100 clusters, scCorr detected 71 gene pairs, while the nonclustering method detected only 4 pairs of a dataset from PBMCs. The performance of scCorr was comparable to those of three previously published methods. As an example of downstream analysis using scCorr, we show that scCorr accurately identified a known cell type (i.e., CD4+ T cells) in PBMCs with a receiver operating characteristic area under the curve of 0.96. Conclusions Our results demonstrate that scCorr is a robust and reliable graph-based method for identifying correlated gene pairs, which is fundamental to network construction, gene-gene interaction, and cellular omic analyses. scCorr can be quickly and easily implemented to minimize zero values in scRNA-seq analysis and is freely available at https://github.com/CBIIT-CGBB/scCorr.

Space-log: a novel approach to inferring gene-gene net-works using SPACE model with log penalty

Gene expression data have been used to infer gene-gene networks (GGN) where an edge between two genes implies the conditional dependence of these two genes given all the other genes. Such gene-gene networks are of-ten referred to as gene regulatory networks since it may reveal expression regulation. Most of existing methods for identifying GGN employ penalized regression with L1 (lasso), L2 (ridge), or elastic net penalty, which spans the range of L1 to L2 penalty. However, for high dimensional gene expression data, a penalty that spans the range of L0 and L1 penalty, such as the log penalty, is often needed for variable selection consistency. Thus, we develop a novel method that em-ploys log penalty within the framework of an earlier network identification method space (Sparse PArtial Correlation Estimation), and implement it into a R package space-log. We show that the space-log is computationally efficient (source code implemented in C), and has good performance comparing with other methods, particularly for networks with hubs.Space-log is open source and available at GitHub, https://github.com/wuqian77/SpaceLog

Differential action of pro-angiogenic and anti-angiogenic components of Danhong injection in ischemic vascular disease or tumor models

Objective We investigate the chemical basis and mechanism of angiogenesis regulation by a multicomponent Chinese medicine Danhong injection (DHI). Methods DHI was fractionated and screened for angiogenesis activities by in vitro tube formation and migration assays. The composition of DHI components was determined by UPLC. The effects of the main active monomers on angiogenesis-related gene and protein expression in endothelial cells were determined by qPCR and Western blotting analyses. Mouse hind limb ischemia and tumor implant models were used to verify the angiogenesis effects in vivo by Laser Doppler and bioluminescent imaging, respectively. Results Two distinct chemical components, one promoting (pro-angiogenic, PAC) and the other inhibiting (anti-angiogenic, AAC) angiogenesis, were identified in DHI. PAC enhanced angiogenesis and improved recovery of ischemic limb perfusion while AAC reduced Lewis lung carcinoma growth in vivo in VEGFR-2-Luc mice. Among the PAC or AAC monomers, caffeic acid and rosmarinic acid upregulated TSP1 expression and downregulated KDR and PECAM expression. Caffeic acid and rosmarinic acid significantly decreased while protocatechuic aldehyde increased CXCR4 expression, which are consistent with their differential effects on EC migration. Conclusions DHI is capable of bi-directional regulation of angiogenesis in disease-specific manner. The pro-angiogenesis activity of DHI promotes the repair of ischemic vascular injury, whereas the anti-angiogenesis activity inhibits tumor growth. The active pro- and anti-angiogenesis activities are composed of unique chemical combinations that differentially regulate angiogenesis-related gene networks.