Integrated analysis of transcriptome and proteome to explore the genes related to steroid-induced femoral head necrosis.

PURPOSE: Femoral head necrosis (FHN) is a common disease of hip. However, the pathogenesis of FHN is not well understood. This study attempted to explore the potentially important genes and proteins involved in FHN. METHODS: We integrated the transcriptomic and proteomic methods to quantitatively screen the differentially expressed genes (DEGs) and proteins (DEPs) between Control and FHN groups. Gene ontology (GO) terms and KEGG pathway enrichment analysis were used to assess the roles of DEGs and DEPs. qRT-PCR and western blot were performed to verify the key genes/proteins in FHN. CCK-8 assay was performed to measure cell viability. The protein expression of Bax and Bcl-2 were used to evaluate cell apoptosis. RESULTS: Transcriptome and proteome studies indicated 758 DEGs and 1097 DEPs between Control and FHN groups, respectively. Cell division, extracellular exosome, and serine-type endopeptidase activity were the most common terms in biological process (BP), cellular component (CC), and molecular function (MF) enrichment, respectively. DEPs were mainly enriched in cellular process, cell, and binding for BP, CC, and MF categories, respectively. DEGs were mainly involved in PI3K-Akt pathway and DEPs were mainly focused in glycolysis/gluconeogenesis pathway. Notably, 14 down-regulated and 22 up-regulated genes/proteins were detected at both the transcript and protein level. LRG1, SERPINE2, STMN1, COL14A1, SLC37A2, and MMP2 were determined as the key genes/proteins in FHN. SERPINE2/STMN1 overexpression increased viability and decreased apoptosis of dexamethasone-treated MC3T3-E1 cells. CONCLUSIONS: Our study investigated some pivotal regulatory genes/proteins in the pathogenesis of FHN, providing novel insight into the genes/proteins involved in FHN.

COVID-2019: The role of the nsp2 and nsp3 in its pathogenesis.

Last December 2019, a new virus, named novel Coronavirus (COVID-2019) causing many cases of severe pneumonia was reported in Wuhan, China. The virus knowledge is limited and especially about COVID-2019 pathogenesis. The Open Reading Frame 1ab (ORF1ab) of COVID-2019 has been analyzed to evidence the presence of mutation caused by selective pressure on the virus. For selective pressure analysis fast-unconstrained Bayesian approximation (FUBAR) was used. Homology modelling has been performed by SwissModel and HHPred servers. The presence of transmembrane helical segments in Coronavirus ORF1ab non structural protein 2 (nsp2) and nsp3 was tested by TMHMM, MEMSAT, and MEMPACK tools. Three-dimensional structures have been analyzed and displayed using PyMOL. FUBAR analysis revealed the presence of potential sites under positive selective pressure (P < .05). Position 723 in the COVID-2019 has a serine instead a glycine residue, while at aminoacidic position 1010 a proline instead an isoleucine. Significant (P < .05) pervasive negative selection in 2416 sites (55%) was found. The positive selective pressure could account for some clinical features of this virus compared with severe acute respiratory syndrome (SARS) and Bat SARS-like CoV. The stabilizing mutation falling in the endosome-associated-protein-like domain of the nsp2 protein could account for COVID-2019 high ability of contagious, while the destabilizing mutation in nsp3 proteins could suggest a potential mechanism differentiating COVID-2019 from SARS. These data could be helpful for further investigation aimed to identify potential therapeutic targets or vaccine strategy, especially in the actual moment when the epidemic is ongoing and the scientific community is trying to enrich knowledge about this new viral pathogen.

Comparative transcriptomic analysis reveals mechanisms of divergence in osmotic regulation of the turbot Scophthalmus maximus.

The turbot Scophthalmus maximus has evolved extensive physiological ability to adapt to multiple environmental salinities. The morphological changes of the kidney indicated the adaptability difference and similarity of turbot to salinity stress. Identify transcriptome-wide differences between low-salinity seawater (LSW, salinity 5)- and high-salinity seawater (HSW, salinity 50)-acclimated kidneys of turbot to decipher the osmotic regulation mechanism. We identified 688 differentially expressed genes (DEGs) in the LSW-acclimated kidneys and 2441 DEGs in the HSW-acclimated kidneys of turbot compared with seawater-acclimated kidneys, respectively. We investigated three patterns of gene regulation to salinity stress that involved in ion channels and transporters, functions of calcium regulation, organic osmolytes, energy demand, cell cycle regulation, and cell protection. Additionally, protein-protein interaction (PPI) analysis of DEGs suggested the presence of a frequent functional interaction pattern and that crucial genes in the PPI network are involved in hyper-osmotic regulation. Based on the analysis of comparative transcriptome data and related literature reports, we conclude that the mechanisms responsible for osmotic regulation and its divergence in turbot are related to various genes that are involved in canonical physiological functions. These findings provide insight into the divergence in osmoregulation of turbot and valuable information about osmoregulation mechanisms that will benefit other studies in this field.

Gene expression profile after knockdown of USP18 in Hepg2.2.15 cells.

In our previous work, we found that the expression of ubiquitin-specific protease 18 (USP18), also known as UBP43, is associated with the efficiency of interferon alpha (IFN-α) treatment in patients with chronic hepatitis B (CHB). To elucidate the influence of USP18 on hepatitis B virus (HBV) replication and the mechanism of this activity, we silenced USP18 by introducing short hairpin RNA (shRNA) into Hepg2.2.15 cells. To identify the changed genes and pathways in Hepg2.2.15-shRNA-USP18 cells, we performed a microarray gene expression analysis to compare the Hepg2.2.15 stably expressing USP18-shRNA cells versus control cells using the Affymetrix Human Transcriptome Array (HTA) 2.0 microarrays. Microarray analysis indicated that genes involved in regulation of thyroid hormone signaling pathway, complement, and coagulation cascades, PERK-mediated unfolded protein response, and insulin-like growth factor-activated receptor activity were significantly altered after USP18 knockdown for 72 h. Furthermore, genes involved in hepatocyte proliferation, liver fibrosis, such as cell cycle regulatory gene CCND1, were also altered after USP18 knockdown in Hepg2.2.15 cells. In conclusion, USP18 is critical for regulating the replication of HBV in Hepg2.2.15 cells, which suggest that USP18 may be a candidate target for HBV treatment.

Identification of key lncRNAs in colorectal cancer progression based on associated protein-protein interaction analysis.

BACKGROUND: Colorectal cancer (CRC) was one of the most commonly diagnosed malignancies. The molecular mechanisms involved in the progression of CRC remain unclear. Accumulating evidences showed that long noncoding RNAs (lncRNAs) played key roles in tumorigenesis, cancer progression, and metastasis. Therefore, we aimed to explore the roles of lncRNAs in the progression of CRC. METHODS: In this study, we aimed to identify differentially expressed lncRNAs and messenger RNAs (mRNAs) in CRC by analyzing a cohort of previously published datasets: GSE64857. GO and KEGG pathway analyses were applied to give us insight in the functions of those lncRNAs and mRNAs in CRC. RESULTS: Totally, 46 lncRNAs were identified as differentially expressed between stage II and stage III CRC for the first time screening by microarray. GO and KEGG pathway analyses showed that differentially expressed lncRNAs were involved in regulating signal transduction, cell adhesion, cell differentiation, focal adhesion, and cell adhesion molecules. CONCLUSIONS: We found three lncRNAs (LOC100129973, PGM5-AS1, and TTTY10) widely co-expressed with differentially expressed mRNAs. We also constructed lncRNA-associated PPI in CRC and found that these lncRNAs may be associated with CRC progression. Moreover, we found that high PGM5-AS1 expression levels were associated with worse overall survival in CRC cancer. We believe that this study would provide novel potential therapeutic and prognostic targets for CRC.

Identification of CCR2 as a hub in septic myocardial injury and cardioprotection of silibinin.

Myocardial injury is a serious complication of sepsis associated with high morbidity and mortality. Our previous work has confirmed that silibinin (SIL) alleviates septic myocardial injury, but the specific molecular mechanism has not been fully elucidated. This study aimed to identify its potential targets through network pharmacology combined with experimental verification. Firstly, a total of 29 overlapping genes between sepsis and SIL targets were obtained from RNA-seq analysis and the known databases. Subsequently, KEGG and GO analysis showed that these genes were enriched in immune response and cytokine-cytokine receptor interaction pathways. Notably, CCR2 was identified as an important candidate hub by protein-protein interaction analysis and molecular docking approach. In vivo experiments showed that SIL treatment significantly improved survival rate and cardiac function in septic mice, accompanied by decreased CCR2 expression. Moreover, in vitro experiments obtained the similar results. Especially, CCR2 siRNA attenuated inflammation response. In conclusion, this study systematically elucidated the key target of SIL in the treatment of septic myocardial injury. These findings provide valuable insights into the targets of sepsis and offer new avenues for exploring drug effect systematically.

Integrative analysis reveals key mRNA and long non-coding RNA interaction in idiopathic pulmonary arterial hypertension.

Introduction: Idiopathic pulmonary arterial hypertension (IPAH) is a rare and sporadic form of pulmonary arterial hypertension (PAH), characterized by elevated pulmonary arterial resistance leading to right heart failure. However, molecular mechanisms of PAH development are still not completely understood. Material and methods: In this study, we aimed to uncover key mRNAs and long non-coding RNA (lncRNAs), functional modules and pathways. Moreover, to detect the dysregulated pathway or biological function, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. PPI and co-expression networks were constructed to reveal the potential roles of PAH-related mRNAs and lncRNAs. Results: A total of 3,134 genes, including 945 up-regulated and 2,189 down-regulated genes, were identified to be differentially expressed in IPAH by differential expression analysis. We identified T cell differentiation and the T cell receptor signaling pathway as up-regulated in IPAH by using GO and KEGG analysis. Based on the PPI module analysis, we identified that the pro-inflammatory genes, such as OAS1, CXCL10, STAT1 and TLR4, were the hub genes in the PPI modules. To link the lncRNAs to the PPI modules, we calculated the Spearman correlation coefficient for lncRNA-DE-mRNA pairs to identify the modules with high correlation with each lncRNA. Conclusions: Notably, 6 of these lncRNAs were associated with modules characterized by the NOD-like receptor signaling pathway and chemokine signaling pathway, suggesting that these lncRNAs may promote the occurrence of IPAH via participating in the pro-inflammatory pathways. In conclusion, our systematic analysis not only improved our understanding of the molecular mechanism, but also provided potential lncRNA biomarkers for further research.

Molecular characterization of circadian gene expression and its correlation with survival percentage in colorectal cancer patients.

Colorectal cancer (CRC) is a form of cancer characterized by many symptoms and readily metastasizes to different organs in the body. Circadian rhythm is one of the many processes that is observed to be dysregulated in CRC-affected patients. In this study, we aim to identify the dysregulated physiological processes in CRC-affected patients and correlate the expression profiles of the circadian clock genes with CRC-patients' survival rates. We performed an extensive microarray gene expression pipeline, whereby 471 differentially expressed genes (DEGs) were identified, following which, we streamlined our search to 43 circadian clock affecting DEGs. The Circadian Gene Database was accessed to retrieve the circadian rhythm-specific genes. The DEGs were then subjected to multi-level functional annotation, i.e., preliminary analysis using ClueGO/CluePedia and pathway enrichment using DAVID. The findings of our study were interesting, wherein we observed that the survival percentage of CRC-affected patients dropped significantly around the 100th-month mark. Furthermore, we identified hormonal activity, xenobiotic metabolism, and PI3K-Akt signaling pathway to be frequently dysregulated cellular functions. Additionally, we detected that the ZFYVE family of genes and the two genes, namely MYC and CDK4 were the significant DEGs that are linked to the pathogenesis and progression of CRC. This study sheds light on the importance of bioinformatics to simplify our understanding of the interactions of different genes that control different phenotypes.

Genome-Wide Analysis of Kidney Renal Cell Carcinoma: Exploring Differentially Expressed Genes for Diagnostic and Therapeutic Targets.

Kidney renal cell carcinoma (KIRC) is the most common type of renal cancer. Kidney malignancies have been ranked in the top 10 most frequently occurring cancers. KIRC is a prevalent malignancy with a poor prognosis. The disease has risen for the last 40 years, and robust biomarkers for KIRC are needed for precision/personalized medicine. In this bioinformatics study, we utilized genomic data of KIRC patients from The Cancer Genome Atlas for biomarker discovery. A total of 314 samples were used in this study. We identified many differentially expressed genes (DEGs) categorized as upregulated or downregulated. A protein-protein interaction network for the DEGs was then generated and analyzed using the Search Tool for the Retrieval of Interacting Genes plugin of Cytoscape. A set of 10 hub genes was selected based on the Maximum Clique Centrality score defined by the CytoHubba plugin. The elucidated set of genes, that is, CALCA, CRH, TH, CHAT, SLC18A3, FSHB, MYH6, CAV3, KCNA4, and GBX2, were then categorized as potential candidates to be explored as KIRC biomarkers. The survival analysis plots for each gene suggested that alterations in CHAT, CAV3, CRH, MYH6, SLC18A3, and FSHB resulted in decreased survival of KIRC patients. In all, the results suggest that genomic alterations in selected genes can be explored to inform biomarker discovery and for therapeutic predictions in KIRC.

Identification of Neurotransmission and Synaptic Biological Processes Disrupted in Autism Spectrum Disorder Using Interaction Networks and Community Detection Analysis.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by communication deficits and repetitive behavioral patterns. Hundreds of candidate genes have been implicated in ASD, including neurotransmission and synaptic (NS) genes; however, the genetic architecture of this disease is far from clear. In this study, we seek to clarify the biological processes affected by NS gene variants identified in individuals with ASD and the global networks that link those processes together. For a curated list of 1216 NS candidate genes, identified in multiple databases and the literature, we searched for ultra-rare (UR) loss-of-function (LoF) variants in the whole-exome sequencing dataset from the Autism Sequencing Consortium (N = 3938 cases). Filtering for population frequency was carried out using gnomAD (N = 60,146 controls). NS genes with UR LoF variants were used to construct a network of protein-protein interactions, and the network's biological communities were identified by applying the Leiden algorithm. We further explored the expression enrichment of network genes in specific brain regions. We identified 356 variants in 208 genes, with a preponderance of UR LoF variants in the PDE11A and SYTL3 genes. Expression enrichment analysis highlighted several subcortical structures, particularly the basal ganglia. The interaction network defined seven network communities, clustering synaptic and neurotransmitter pathways with several ubiquitous processes that occur in multiple organs and systems. This approach also uncovered biological pathways that are not usually associated with ASD, such as brain cytochromes P450 and brain mitochondrial metabolism. Overall, the community analysis suggests that ASD involves the disruption of synaptic and neurotransmitter pathways but also ubiquitous, but less frequently implicated, biological processes.

Identification of candidate biomarkers associated with gastric cancer prognosis based on an integrated bioinformatics analysis.

Background: This study sought to identify candidate biomarkers associated with gastric cancer (GC) prognosis based on an integrated bioinformatics analysis. Methods: First, the GSE54129 and GSE79973 data sets were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) identified between the 2 data sets were screened using the limma software package in R, and the intersection DEGs were obtained by a Venn analysis. Subsequently, gene clustering and a functional analysis were performed to explore the roles of the DEGs. The protein-protein interaction (PPI) network of the genes in clusters was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins. A survival analysis evaluated the associations between the candidate genes and the overall survival of GC patients. A drug-gene interaction analysis and an external data set analysis were conducted using The Cancer Genome Atlas-Stomach Adenocarcinoma (TCGA-STAD) data set to validate the prognostic genes. Results: We extracted 421 intersection DEGs from the 2 GEO data sets. There were 5 gene clusters, and the functional analysis revealed that they were mainly associated with the extracellular matrix-receptor interaction pathway. The PPI interaction analysis identified the top 36 hub genes. The survival analysis revealed that 7 upregulated genes [i.e., platelet-derived growth factor receptor beta (PDGFRB), angiopoietin 2 (ANGPT2), vascular endothelial growth factor C (VEGFC), collagen type IV alpha 2 chain (COL4A2), collagen type IV alpha 1 chain (COL4A1), thrombospondin 1 (THBS1), and fibronectin 1 (FN1)] were associated with the survival prognosis of GC patients. The 20 drug-gene interaction pairs among the 4 genes and 18 drugs were obtained. Finally, TCGA-STAD data set was used to validate the expression levels of COL4A1, PDGFRB, and FN1. Conclusions: We found that 7 upregulated genes (i.e., PDGFRB, ANGPT2, VEGFC, COL4A2, COL4A1, THBS1, and FN1) were promising markers of prognosis in GC patients.

Bioinformatics Analysis of the MicroRNA-Metabolic Gene Regulatory Network in Neuropathic Pain and Prediction of Corresponding Potential Therapeutics.

Neuropathic pain (NP) involves metabolic processes that are regulated by metabolic genes and their non-coding regulator genes such as microRNAs (miRNAs). Here, we aimed at exploring the key miRNA signatures regulating metabolic genes involved in NP pathogenesis. We downloaded NP-related data from public databases and identified differentially expressed microRNAs (miRNAs) and mRNAs through differential gene expression analysis. The miRNA target prediction was performed, and integration with the differentially expressed metabolic genes (DEMGs) was used for constructing the miRNA-DEMG network. Subsequently, functional enrichment analysis and protein-protein interaction (PPI) analysis were performed to explore the role of DEMGs in the regulatory network. The drug prediction was performed based on the DEMGs in the miRNA-DEMG network. A total of 8251 differentially expressed mRNAs (4193 upregulated and 4058 downregulated), and 959 differentially expressed miRNAs (455 upregulated and 504 downregulated) were identified. Moreover, after target gene prediction, a miRNA-DEMG network composed of 22 miRNAs and 113 mRNAs was constructed. The network was constituted of 135 nodes and 236 edges. We found that DEMGs in the network were mainly enriched in metabolic pathways and metabolic processes. A total of 1200 drugs were predicted as potential therapeutics for NP based on the differentially expressed genes, while 170 drugs were predicted for the DEMGs in the miRNA-DEMG network. Conclusively, our study predicted drugs that may be effective against the metabolic changes induced by miRNA dysregulation in NP. This information will help further reveal the pathological mechanism of NP and provide more treatment options for NP patients.

A Crosslinking Mass Spectrometry Protocol for the Structural Analysis of Microtubule-Associated Proteins.

Microtubule-associated proteins (MAPs) engage microtubules (MTs) to regulate both the MT state and wide variety of cytoskeletal functions. A comprehensive understanding of MAPs function requires the structural characterization of physical contacts MAPs make with other proteins, particularly when engaged with the microtubule (MT) lattice. Most of the interaction between MAPs and MTs evade classical structural determination techniques, as the interactions can be both heterogenous and sub-stoichiometric. Crosslinking mass spectrometry (XL-MS) can aid in MAP-MT structure analysis by providing a wealth of residue-based distance restraints. This protocol provides an XL-MS workflow for accurate and unbiased sampling of an equilibrated MAP-MT interaction, involving modifications to the preparation and validation of a MAP-MT construct suitable for crosslinking with fast-sampling heterobifunctional crosslinkers. The distance restrains obtained by this protocol can be used to generate accurate models assembled with an integrative structural modeling approach.

Comprehensive multi-omics analysis of the m7G in pan-cancer from the perspective of predictive, preventive, and personalized medicine.

Background: The N7-methylguanosine modification (m7G) of the 5' cap structure in the mRNA plays a crucial role in gene expression. However, the relation between m7G and tumor immune remains unclear. Hence, we intended to perform a pan-cancer analysis of m7G which can help explore the underlying mechanism and contribute to predictive, preventive, and personalized medicine (PPPM / 3PM). Methods: The gene expression, genetic variation, clinical information, methylation, and digital pathological section from 33 cancer types were downloaded from the TCGA database. Immunohistochemistry (IHC) was used to validate the expression of the m7G regulator genes (m7RGs) hub-gene. The m7G score was calculated by single-sample gene-set enrichment analysis. The association of m7RGs with copy number variation, clinical features, immune-related genes, TMB, MSI, and tumor immune dysfunction and exclusion (TIDE) was comprehensively assessed. CellProfiler was used to extract pathological section characteristics. XGBoost and random forest were used to construct the m7G score prediction model. Single-cell transcriptome sequencing (scRNA-seq) was used to assess the activation state of the m7G in the tumor microenvironment. Results: The m7RGs were highly expressed in tumors and most of the m7RGs are risk factors for prognosis. Moreover, the cellular pathway enrichment analysis suggested that m7G score was closely associated with invasion, cell cycle, DNA damage, and repair. In several cancers, m7G score was significantly negatively correlated with MSI and TMB and positively correlated with TIDE, suggesting an ICB marker potential. XGBoost-based pathomics model accurately predicts m7G scores with an area under the ROC curve (AUC) of 0.97. Analysis of scRNA-seq suggests that m7G differs significantly among cells of the tumor microenvironment. IHC confirmed high expression of EIF4E in breast cancer. The m7G prognostic model can accurately assess the prognosis of tumor patients with an AUC of 0.81, which was publicly hosted at https://pan-cancer-m7g.shinyapps.io/Panca-m7g/. Conclusion: The current study explored for the first time the m7G in pan-cancer and identified m7G as an innovative marker in predicting clinical outcomes and immunotherapeutic efficacy, with the potential for deeper integration with PPPM. Combining m7G within the framework of PPPM will provide a unique opportunity for clinical intelligence and new approaches. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-022-00305-1.

Explore the Underlying Mechanism Between Atopic Dermatitis and Major Depressive Disorder.

Adult patients with atopic dermatitis (AD) present relatively higher rates of major depressive disorder (MDD). However, the underlying mechanism is largely unknown. Here, we first conducted a systematic literature-based data mining to identify entities linking AD and MDD, including proteins, cells, functional classes, and small molecules. Then we conducted an AD-RNA expression data-based mega-analysis to test the expression variance of the genes that were regulators of MDD. After that, a Fisher Exact test-based pathway enrichment analysis (PEA) was performed to explore the AD-driven MDD-genetic regulators' functionality. We identified 22 AD-driven entities that were up-stream MDD regulators, including 11 genes, seven small molecules, three functional classes, and one cell. AD could exert a promoting effect on the development of MDD. Four of the 11 genes demonstrated significant expression changes in AD patients in favor of the development of MDD. PEA results showed that AD mainly drives cytokine/chemokine regulation and neuroinflammatory response-related pathways to influence the pathological development of MDD. Our results supported the promotion role of AD in the pathological development of MDD, including the regulation of multiple genetic regulators of MDD involved in cytokine/chemokine regulation and inflammatory response.

Analysis of key genes reveal lysine demethylase 5B promotes prostate cancer progression.

Prostate cancer (PCa) is one of the most common types of cancer in males globally. However, the molecular mechanisms underlying PCa progression remain largely unclear. In the present study, Gene Expression Omnibus (GEO) datasets and datasets from The Cancer Genome Atlas (TCGA) were used to analyze the expression of lysine demethylase 5B (KDM5B) in PCa. Proliferation, cell cycle and migration assays were used to detect the functional roles of KDM5B. It was found KDM5B was upregulated in PCa tissues by analyzing GEO and TCGA datasets. KDM5B knockdown significantly suppressed proliferation and cell cycle progression in PCa cells. In additional, KDM5B knockdown inhibited PCa cell migration. By analyzing a TCGA dataset, KDM5B was found to be upregulated in patients at N1 stage compared with N0 stage PCa, in patients at T3+T4 stages compared with T2 stage and in patients with Gleason score ≥8 compared with those with score ≤7. Kaplan-Meier analysis revealed that higher expression of KDM5B was associated with shorter biochemical recurrence-free survival and overall survival time in patients with PCa. These results suggest that expression of KDM5B may serve as a biomarker to predict the outcome of PCa.

The novel lncRNA PTTG3P is downregulated and predicts poor prognosis in non-small cell lung cancer.

INTRODUCTION: Lung cancer is the leading cause of cancer-associated mortality worldwide. Recently, long non-coding RNAs (lncRNAs) have been studied as key regulators in some biological processes. Of note, the molecular mechanism and prognostic value of lncRNAs in non-small cell lung cancer (NSCLC) have largely remained unclear. MATERIAL AND METHODS: In this study, we compared the PTTG3P expression levels between lung cancer and normal lung samples by analyzing 5 public datasets (GSE18842, GSE19804, GSE27262, GSE30219, and GSE19188). Next, pentose phosphate pathway and co-expression networks were constructed to identify key targets of lncRNA PTTG3P. Furthermore, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to explore the potential roles of lncRNA PTTG3P. Moreover, we constructed PTTG3P-mediated ceRNA networks in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). RESULTS: In the present study, our analysis showed that PTTG3P expression was higher in high T stage LUAD and LUSC samples, as well as high N stage NSCLC tissues. Of note, we found that higher PTTG3P expression is correlated with shorter survival time in NSCLC patients by analyzing Kaplan-Meier plotter datasets. We found that PTTG3P was significantly associated with NSCLC cell proliferation regulation by affecting a series of cell cycle related biological processes. CONCLUSIONS: Bioinformatics analysis showed that PTTG3P was associated with NSCLC cell proliferation. These results suggested that PTTG3P could serve as a new therapeutic and prognostic target for NSCLC.

Exploration of Sorafenib Influences on Gene Expression of Hepatocellular Carcinoma.

PURPOSE: The aim of this study was to develop a comprehensive differential gene profile for hepatocellular carcinoma (HCC) patients treated with sorafenib. METHODS: The RNA sequencing data and miRNA sequencing data of 114 HCC patients treated with sorafenib only and 326 HCC control patients treated without any chemotherapeutic drugs were studied using differential expression, functional enrichment, and protein-protein interaction analysis. RESULTS: Compared with HCC patients without any chemotherapy drugs, the sorafenib-treated patients develop 66 differentially expressed genes (DEGs), including 12 upregulated genes and 54 downregulated genes. Additionally, three differentially expressed miRNAs (DEMs) also show specific expression pattern. With further analysis, five primary genes including HTR2C, TRH, AGTR2, MCHR2, and SLC6A2 as well as three miRNAs (hsa-miR-4445, hsa-miR-466, and hsa-miR-2114) have been suggested as the potential targets for sorafenib. The specific gene expression of five genes has been validated in clinical HCC patients by ELISA method. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses indicate several significantly enriched biological processes (BPs), cellular components (CCs), and molecular functions (MFs). CONCLUSION: Since sorafenib is becoming increasingly important in HCC treatment clinically, this study will help us understand the potential targets and eliminate diverse existing side effects of it as well as explore several potential clinical biomarkers with comprehensive analysis of differential gene expression profile.

Integrated network analysis to explore the key mRNAs and lncRNAs in acute myocardial infarction.

Acute myocardial infarction (AMI) is the most severe cardiovascular event in the world. However, the molecular mechanisms underlying AMI remained largely unclear. Recently, long non-coding RNAs (lncRNAs) were reported to play important roles in human diseases. In the present work, we analyzed a public dataset GSE48060 to confirm key lncRNAs and mRNAs in AMI. We observed 4835 mRNAs and 442 lncRNAs were significantly differently expressed in AMI. Then, we for the first time constructed PPI networks and lncRNA co-expression networks in AMI. The protein-protein interaction (PPI) networks revealed several mRNAs such as RHOA, GNB1, GNG, RAC1, FBXO32, DET1, MEX3C and HECTD1 functioned as key regulators in AMI. LncRNA co-expression network analysis showed 8 lncRNAs (CA5BP1, LOC101927608, BZRAP1-AS1, EBLN3, FGD5-AS1, HNRNPU-AS1, LINC00342, and LOC101927204) played key roles in AMI. Gene ontology (GO) analysis demonstrated these differently expressed lncRNAs were associated with more signaling pathways, such as regulating transcription, protein amino acid phosphorylation, signal transduction, development. Taken together, our research unveiled a series of key lncRNAs and mRNAs in AMI. Several lncRNAs, including CA5BP1, LOC101927608, BZRAP1-AS1, EBLN3, FGD5-AS1, HNRNPU-AS1, LINC00342, and LOC101927204 were identified as key lncRNAs. PPI networks were constructed to reveal key mRNAs in AMI. These results provided useful information for exploring novel molecular target therapy for AMI.

Comprehensive analysis of differentially expressed lncRNAs as diagnostic and prognostic markers for colorectal cancer.

Colorectal cancer (CRC) is the third most common type of cancer worldwide. Recent studies had revealed the important roles of long non-coding RNAs (lncRNAs) in a variety of human cancers, including CRC. However, the molecular mechanisms associated with CRC remain largely undetermined. In the current study, the GSE21510 dataset was analyzed to identify differentially expressed mRNAs and lncRNAs in CRC samples. The Database for Annotation, Visualization and Integrated Discovery was used to perform Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway bioinformatics analysis. Furthermore, protein-protein interaction networks were constructed to reveal interactions among differentially expressed proteins. Kaplan-Meier analysis was subsequently performed to determine the association between key lncRNA expression and the overall survival of patients with CRC. A total of 107 upregulated lncRNAs and 43 downregulated lncRNAs were identified in CRC. A lncRNA mediated co-expression network was also constructed in CRC. Bioinformatics analysis indicated that lncRNAs were associated with a series of biological processes, including 'xenobiotic glucuronidation', 'rRNA processing', 'sister chromatid cohesion', 'cell proliferation', 'mitotic nuclear division' and 'cell cycle regulation'. Furthermore, a higher expression of small nucleolar RNA host gene 17, tetratricopeptide repeat domain 2B-antisense RNA (AS) 1, erythrocyte membrane protein band 4.1 like 4A-AS2, deleted in lymphocytic leukemia 2, and a lower expression of muscle blind like splicing regulator 1-AS1 and LOC389332 were associated with shorter overall survival time in CRC samples. The present study provides useful information that can be used in the identification of novel biomarkers for CRC.