Identification of Prognostic Biomarker Signatures and Candidate Drugs in Colorectal Cancer: Insights from Systems Biology Analysis

Pan-cancer analysis and single-cell analysis identifies the CENPN as a biomarker for survival prognosis and immunotherapy

BACKGROUND

Centromere protein N (CENPN), located on chromosome 16q23.2, encodes vital nucleosome-associated complexes that are essential for dynamic assembly processes. CENPN plays a pivotal role in regulating cell proliferation and cell cycle progression by influencing mitotic events. Despite its potential importance, the precise functional role and regulatory mechanisms of CENPN in diverse malignancies remain largely unexplored. This study aimed to elucidate the role of CENPN in human cancers and evaluate its prognostic significance.

METHODS

Investigate the role of CENPN in various malignancies, we leveraged data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. We employed a comprehensive suite of web platforms and software tools for data analysis, including R, Cytoscape, an integrated repository portal for tumor-immune system interactions (TISIDB), CBio Cancer Genomics Portal (cBioPortal), Search Tool for the Retrieval of Interaction Gene/Proteins (STRING), Gene Set Cancer Analysis (GSCALite), and a cancer single-cell state atlas (CancerSEA).

RESULTS

The findings demonstrated that CENPN expression was elevated in the majority of cancer types and differentially expressed across molecular and immune subtypes. Functional enrichment analysis in multiple tumors also identified possible pathways of CENPN involvement in tumorigenesis. Its expression positively correlated with Th2 and Tcm cells in most cancers. It is also correlated with genetic markers of immunomodulators in various cancers.

CONCLUSIONS

Overall, CENPN expression is closely related to cancers and has the potential to act as a cancer biomarker.

Identification of Novel Prognostic Biomarkers for Colorectal Cancer by Bioinformatics Analysis

BACKGROUND/AIMS

Colorectal cancer (CRC) ranks third among malignancies in terms of global incidence and has a poor prognosis. The identification of effective diagnostic and prognostic biomarkers is critical for CRC treatment. This study intends to explore novel genes associated with CRC progression via bioinformatics analysis.

MATERIALS AND METHODS

Dataset GSE184093 was selected from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) between CRC and noncancerous specimens. Functional enrichment analyses were implemented for probing the biological functions of DEGs. Gene Expression Profiling Interactive Analysis and Kaplan-Meier plotter databases were employed for gene expression detection and survival analysis, respectively. Western blotting and real-time quantitative polymerase chain reaction were employed for detecting molecular protein and messenger RNA levels, respectively. Flow cytometry, Transwell, and CCK-8 assays were utilized for examining the effects of GBA2 and ST3GAL5 on CRC cell behaviors.

RESULTS

There were 6464 DEGs identified, comprising 3005 downregulated DEGs (dDEGs) and 3459 upregulated DEGs (uDEGs). Six dDEGs were significantly associated with the prognoses of CRC patients, including PLCE1, PTGS1, AMT, ST8SIA1, ST3GAL5, and GBA2. Upregulating ST3GAL5 or GBA2 repressed the malignant behaviors of CRC cells.

CONCLUSION

We identified 6 genes related to CRC progression, which could improve the disease prognosis and treatment.

mRNA expression profile reveals differentially expressed genes in splenocytes of experimental autoimmune encephalomyelitis model

Experimental autoimmune encephalomyelitis (EAE) is a mouse model that can be used to investigate aetiology, pathogenesis, and treatment approaches for multiple sclerosis (MS). A novel integrated bioinformatics approach was used to understand the involvement of differentially expressed genes (DEGs) in the spleen of EAE mice through data mining of existing microarray and RNA-seq datasets. We screened differentially expressed mRNAs using mRNA expression profile data of EAE spleens taken from Gene Expression Omnibus (GEO). Functional and pathway enrichment analyses of DEGs were performed by Database for Annotation, Visualization, and Integrated Discovery (DAVID). Subsequently, the DEGs-encoded protein-protein interaction (PPI) network was constructed. The 784 DEGs in GSE99300 A.SW PP-EAE mice spleen mRNA profiles, 859 DEGs in GSE151701 EAE mice spleen mRNA profiles, and 646 DEGs in GSE99300 SJL/J PP-EAE mice spleen mRNA profiles were explored. Functional enrichment of 55 common DEGs among 3 sub-datasets revealed several immune-related terms, such as neutrophil extravasation, leucocyte migration, antimicrobial humoral immune response mediated by an antimicrobial peptide, toll-like receptor 4 bindings, IL-17 signalling pathway, and TGF-beta signalling pathway. In the screening of 10 hub genes, including MPO, ELANE, CTSG, LTF, LCN2, SELP, CAMP, S100A9, ITGA2B, and PRTN3, and in choosing and validating the 5 DEGs, including ANK1, MBOAT2, SLC25A21, SLC43A1, and SOX6, the results showed that SLC43A1 and SOX6 were significantly decreased in EAE mice spleen. Thus this study offers a list of genes expressed in the spleen that might play a key role in the pathogenesis of EAE.

Constructing a cancer stem cell related prognostic model for predicting immune landscape and drug sensitivity in colorectal cancer

Background: Colorectal cancer (CRC) ranks the second malignancy with high incidence and mortality worldwide. Cancer stem cells (CSCs) function critically in cancer progression and metastasis via the interplay with immune cells in tumor microenvironment. This study aimed to identify important CSC marker genes and parsed the role of these marker genes in CRC. Materials and methods: CRC samples' single-cell RNA sequencing data and bulk transcriptome data were utilized. Seurat R package annotated CSCs and identified CSC marker genes. Consensus clustering subtyped CRC samples based on CSC marker genes. Immune microenvironment, pathway and oxidative stress analysis was performed using ESTIMATE, MCP-counter analysis and ssGSEA analysis. A prognostic model was established by Lasso and stepAIC. Sensitivity to chemotherapeutic drugs was determined by the biochemical half maximal inhibitory concentration with pRRophetic R package. Results: We identified a total of 29 CSC marker genes related to disease-specific survival (DSS). Two clusters (CSC1 and CSC2) were determined, and CSC2 showed shorter DSS, a larger proportion of late-stage samples, and higher oxidative stress response. Two clusters exhibited differential activation of biological pathways associated with immune response and oncogenic signaling. Drug sensitivity analysis showed that 44 chemotherapy drugs were more sensitive to CSC2 that those in CSC1. We constructed a seven-gene prognostic model (DRD4, DPP7, UCN, INHBA, SFTA2, SYNPO2, and NXPH4) that was effectively to distinguish high-risk and low-risk patients. 14 chemotherapy drugs were more sensitive to high-risk group and 13 chemotherapy drugs were more sensitive to low-risk group. Combination of higher oxidative stress and risk score indicated dismal prognosis. Conclusion: The CSC marker genes we identified may help to further decipher the role of CSCs in CRC development and progression. The seven-gene prognostic model could serve as an indicator for predicting the response to immunotherapy and chemotherapy as well as prognosis of CRC patients.

Bioinformatics screening of colorectal-cancer causing molecular signatures through gene expression profiles to discover therapeutic targets and candidate agents

BACKGROUND

Detection of appropriate receptor proteins and drug agents are equally important in the case of drug discovery and development for any disease. In this study, an attempt was made to explore colorectal cancer (CRC) causing molecular signatures as receptors and drug agents as inhibitors by using integrated statistics and bioinformatics approaches.

METHODS

To identify the important genes that are involved in the initiation and progression of CRC, four microarray datasets (GSE9348, GSE110224, GSE23878, and GSE35279) and an RNA_Seq profiles (GSE50760) were downloaded from the Gene Expression Omnibus database. The datasets were analyzed by a statistical r-package of LIMMA to identify common differentially expressed genes (cDEGs). The key genes (KGs) of cDEGs were detected by using the five topological measures in the protein-protein interaction network analysis. Then we performed in-silico validation for CRC-causing KGs by using different web-tools and independent databases. We also disclosed the transcriptional and post-transcriptional regulatory factors of KGs by interaction network analysis of KGs with transcription factors (TFs) and micro-RNAs. Finally, we suggested our proposed KGs-guided computationally more effective candidate drug molecules compared to other published drugs by cross-validation with the state-of-the-art alternatives of top-ranked independent receptor proteins.

RESULTS

We identified 50 common differentially expressed genes (cDEGs) from five gene expression profile datasets, where 31 cDEGs were downregulated, and the rest 19 were up-regulated. Then we identified 11 cDEGs (CXCL8, CEMIP, MMP7, CA4, ADH1C, GUCA2A, GUCA2B, ZG16, CLCA4, MS4A12 and CLDN1) as the KGs. Different pertinent bioinformatic analyses (box plot, survival probability curves, DNA methylation, correlation with immune infiltration levels, diseases-KGs interaction, GO and KEGG pathways) based on independent databases directly or indirectly showed that these KGs are significantly associated with CRC progression. We also detected four TFs proteins (FOXC1, YY1, GATA2 and NFKB) and eight microRNAs (hsa-mir-16-5p, hsa-mir-195-5p, hsa-mir-203a-3p, hsa-mir-34a-5p, hsa-mir-107, hsa-mir-27a-3p, hsa-mir-429, and hsa-mir-335-5p) as the key transcriptional and post-transcriptional regulators of KGs. Finally, our proposed 15 molecular signatures including 11 KGs and 4 key TFs-proteins guided 9 small molecules (Cyclosporin A, Manzamine A, Cardidigin, Staurosporine, Benzo[A]Pyrene, Sitosterol, Nocardiopsis Sp, Troglitazone, and Riccardin D) were recommended as the top-ranked candidate therapeutic agents for the treatment against CRC.

CONCLUSION

The findings of this study recommended that our proposed target proteins and agents might be considered as the potential diagnostic, prognostic and therapeutic signatures for CRC.

Chromatin remodeler Activity-Dependent Neuroprotective Protein (ADNP) contributes to syndromic autism

BACKGROUND

Individuals affected with autism often suffer additional co-morbidities such as intellectual disability. The genes contributing to autism cluster on a relatively limited number of cellular pathways, including chromatin remodeling. However, limited information is available on how mutations in single genes can result in such pleiotropic clinical features in affected individuals. In this review, we summarize available information on one of the most frequently mutated genes in syndromic autism the Activity-Dependent Neuroprotective Protein (ADNP).

RESULTS

Heterozygous and predicted loss-of-function ADNP mutations in individuals inevitably result in the clinical presentation with the Helsmoortel-Van der Aa syndrome, a frequent form of syndromic autism. ADNP, a zinc finger DNA-binding protein has a role in chromatin remodeling: The protein is associated with the pericentromeric protein HP1, the SWI/SNF core complex protein BRG1, and other members of this chromatin remodeling complex and, in murine stem cells, with the chromodomain helicase CHD4 in a ChAHP complex. ADNP has recently been shown to possess R-loop processing activity. In addition, many additional functions, for instance, in association with cytoskeletal proteins have been linked to ADNP.

CONCLUSIONS

We here present an integrated evaluation of all current aspects of gene function and evaluate how abnormalities in chromatin remodeling might relate to the pleiotropic clinical presentation in individual"s" with Helsmoortel-Van der Aa syndrome.

Drug Repurposing and Systems Biology approaches of Enzastaurin can target potential biomarkers and critical pathways in Colorectal Cancer

Colorectal cancer (CRC) is a severe health concern that results from a cocktail of genetic, epigenetic, and environmental abnormalities. Because it is the second most lethal malignancy in the world and the third-most common malignant tumor, but the treatment is unavailable. The goal of the current study was to use bioinformatics and systems biology techniques to determine the pharmacological mechanism underlying putative important genes and linked pathways in early-onset CRC. Computer-aided methods were used to uncover similar biological targets and signaling pathways associated with CRC, along with bioinformatics and network pharmacology techniques to assess the effects of enzastaurin on CRC. The KEGG and gene ontology (GO) pathway analysis revealed several significant pathways including in positive regulation of protein phosphorylation, negative regulation of the apoptotic process, nucleus, nucleoplasm, protein tyrosine kinase activity, PI3K-Akt signaling pathway, pathways in cancer, focal adhesion, HIF-1 signaling pathway, and Rap1 signaling pathway. Later, the hub protein module identified from the protein-protein interactions (PPIs) network, molecular docking and molecular dynamics simulation represented that enzastaurin showed strong binding interaction with two hub proteins including CASP3 (-8.6 kcal/mol), and MCL1 (-8.6 kcal/mol), which were strongly implicated in CRC management than other the five hub proteins. Moreover, the pharmacokinetic features of enzastaurin revealed that it is an effective therapeutic agent with minimal adverse effects. Enzastaurin may inhibit the potential biological targets that are thought to be responsible for the advancement of CRC and this study suggests a potential novel therapeutic target for CRC.

Exploring Core Genes by Comparative Transcriptomics Analysis for Early Diagnosis, Prognosis, and Therapies of Colorectal Cancer

Colorectal cancer (CRC) is one of the most common cancers with a high mortality rate. Early diagnosis and therapies for CRC may reduce the mortality rate. However, so far, no researchers have yet investigated core genes (CGs) rigorously for early diagnosis, prognosis, and therapies of CRC. Therefore, an attempt was made in this study to explore CRC-related CGs for early diagnosis, prognosis, and therapies. At first, we identified 252 common differentially expressed genes (cDEGs) between CRC and control samples based on three gene-expression datasets. Then, we identified ten cDEGs (AURKA, TOP2A, CDK1, PTTG1, CDKN3, CDC20, MAD2L1, CKS2, MELK, and TPX2) as the CGs, highlighting their mechanisms in CRC progression. The enrichment analysis of CGs with GO terms and KEGG pathways revealed some crucial biological processes, molecular functions, and signaling pathways that are associated with CRC progression. The survival probability curves and box-plot analyses with the expressions of CGs in different stages of CRC indicated their strong prognostic performance from the earlier stage of the disease. Then, we detected CGs-guided seven candidate drugs (Manzamine A, Cardidigin, Staurosporine, Sitosterol, Benzo[a]pyrene, Nocardiopsis sp., and Riccardin D) by molecular docking. Finally, the binding stability of four top-ranked complexes (TPX2 vs. Manzamine A, CDC20 vs. Cardidigin, MELK vs. Staurosporine, and CDK1 vs. Riccardin D) was investigated by using 100 ns molecular dynamics simulation studies, and their stable performance was observed. Therefore, the output of this study may play a vital role in developing a proper treatment plan at the earlier stages of CRC.

Transcriptomic Signatures in Colorectal Cancer Progression

AIMS

Due to a large number of identified hub-genes encoding key molecular regulators, which are involved in signal transduction and metabolic pathways in cancers, it is relevant to systemize and update these findings.

BACKGROUND

Colorectal cancer (CRC) is the third leading cause of cancer death in the world, with high metastatic potential. Elucidating the pathogenic mechanisms and selection of novel biomarkers in CRC is of great clinical significance.

OBJECTIVE

This analytical review aims at the systematization of bioinformatics and experimental identification of hub-genes associated with CRC for a more consolidated understanding of common features in networks and pathways in CRC progression as well as hub-genes selection.

RESULTS

In total, 301 hub-genes were derived from 40 articles. The "core" consisted of 28 hub-genes (CCNB1, LPAR1, BGN, CXCL3, COL1A2, UBE2C, NMU, COL1A1, CXCL2, CXCL11, CDK1, TOP2A, AURKA, SST, CXCL5, MMP3, CCND1, TIMP1, CXCL8, CXCL1, CXCL12, MYC, CCNA2, GCG, GUCA2A, PAICS, PYY and THBS2) mentioned in not less than three articles and having clinical significance in cancerassociated pathways. Of them, there were two discrete clusters enriched in chemokine signaling and cell cycle regulatory genes. High expression levels of BGN and TIMP1 and low expression levels of CCNB1, CXCL3, CXCL2, CXCL2 and PAICS were associated with unfavorable overall survival of patients with CRC. Differently expressed genes such as LPAR1, SST, CXCL12, GUCA2A, and PYY were shown as down regulated, whereas BGN, CXCL3, UBE2C, NMU, CXCL11, CDK1, TOP2A, AURKA, MMP3, CCND1, CXCL1, MYC, CCNA2, PAICS were up regulated genes in CRC. It was also found that MMP3, THBS2, TIMP1 and CXCL12 genes were associated with metastatic CRC. Network analysis in ONCO.IO showed that upstream master regulators RELA, STAT3, SOX2, FOXM1, SMAD3 and NF-kB were connected with "core" hub-genes. Conclusión: Results obtained are of useful fundamental information on revealing the mechanism of pathogenicity, cellular target selection for optimization of therapeutic interventions, as well as transcriptomics prognostic and predictive biomarkers development.

Clinical stratification improves the diagnostic accuracy of small omics datasets within machine learning and genome-scale metabolic modelling methods

BACKGROUND

Recently, multi-omic machine learning architectures have been proposed for the early detection of cancer. However, for rare cancers and their associated small datasets, it is still unclear how to use the available multi-omics data to achieve a mechanistic prediction of cancer onset and progression, due to the limited data available. Hepatoblastoma is the most frequent liver cancer in infancy and childhood, and whose incidence has been lately increasing in several developed countries. Even though some studies have been conducted to understand the causes of its onset and discover potential biomarkers, the role of metabolic rewiring has not been investigated in depth so far.

METHODS

Here, we propose and implement an interpretable multi-omics pipeline that combines mechanistic knowledge from genome-scale metabolic models with machine learning algorithms, and we use it to characterise the underlying mechanisms controlling hepatoblastoma.

RESULTS AND CONCLUSIONS

While the obtained machine learning models generally present a high diagnostic classification accuracy, our results show that the type of omics combinations used as input to the machine learning models strongly affects the detection of important genes, reactions and metabolic pathways linked to hepatoblastoma. Our method also suggests that, in the context of computer-aided diagnosis of cancer, optimal diagnostic accuracy can be achieved by adopting a combination of omics that depends on the patient's clinical characteristics.

Bioinformatics Prediction and Machine Learning on Gene Expression Data Identifies Novel Gene Candidates in Gastric Cancer

Gastric cancer (GC) is one of the five most common cancers in the world and unfortunately has a high mortality rate. To date, the pathogenesis and disease genes of GC are unclear, so the need for new diagnostic and prognostic strategies for GC is undeniable. Despite particular findings in this regard, a holistic approach encompassing molecular data from different biological levels for GC has been lacking. To translate Big Data into system-level biomarkers, in this study, we integrated three different GC gene expression data with three different biological networks for the first time and captured biologically significant (i.e., reporter) transcripts, hub proteins, transcription factors, and receptor molecules of GC. We analyzed the revealed biomolecules with independent RNA-seq data for their diagnostic and prognostic capabilities. While this holistic approach uncovered biomolecules already associated with GC, it also revealed novel system biomarker candidates for GC. Classification performances of novel candidate biomarkers with machine learning approaches were investigated. With this study, AES, CEBPZ, GRK6, HPGDS, SKIL, and SP3 were identified for the first time as diagnostic and/or prognostic biomarker candidates for GC. Consequently, we have provided valuable data for further experimental and clinical efforts that may be useful for the diagnosis and/or prognosis of GC.

Network-Based Data Analysis Reveals Ion Channel-Related Gene Features in COVID-19: A Bioinformatic Approach

Coronavirus disease 2019 (COVID-19) seriously threatens human health and has been disseminated worldwide. Although there are several treatments for COVID-19, its control is currently suboptimal. Therefore, the development of novel strategies to treat COVID-19 is necessary. Ion channels are located on the membranes of all excitable cells and many intracellular organelles and are key components involved in various biological processes. They are a target of interest when searching for drug targets. This study aimed to reveal the relevant molecular features of ion channel genes in COVID-19 based on bioinformatic analyses. The RNA-sequencing data of patients with COVID-19 and healthy subjects (GSE152418 and GSE171110 datasets) were obtained from the Gene Expression Omnibus (GEO) database. Ion channel genes were selected from the Hugo Gene Nomenclature Committee (HGNC) database. The RStudio software was used to process the data based on the corresponding R language package to identify ion channel-associated differentially expressed genes (DEGs). Based on the DEGs, Gene Ontology (GO) functional and pathway enrichment analyses were performed using the Enrichr web tool. The STRING database was used to generate a protein-protein interaction (PPI) network, and the Cytoscape software was used to screen for hub genes in the PPI network based on the cytoHubba plug-in. Transcription factors (TF)-DEG, DEG-microRNA (miRNA) and DEG-disease association networks were constructed using the NetworkAnalyst web tool. Finally, the screened hub genes as drug targets were subjected to enrichment analysis based on the DSigDB using the Enrichr web tool to identify potential therapeutic agents for COVID-19. A total of 29 ion channel-associated DEGs were identified. GO functional analysis showed that the DEGs were integral components of the plasma membrane and were mainly involved in inorganic cation transmembrane transport and ion channel activity functions. Pathway analysis showed that the DEGs were mainly involved in nicotine addiction, calcium regulation in the cardiac cell and neuronal system pathways. The top 10 hub genes screened based on the PPI network included KCNA2, KCNJ4, CACNA1A, CACNA1E, NALCN, KCNA5, CACNA2D1, TRPC1, TRPM3 and KCNN3. The TF-DEG and DEG-miRNA networks revealed significant TFs (FOXC1, GATA2, HINFP, USF2, JUN and NFKB1) and miRNAs (hsa-mir-146a-5p, hsa-mir-27a-3p, hsa-mir-335-5p, hsa-let-7b-5p and hsa-mir-129-2-3p). Gene-disease association network analysis revealed that the DEGs were closely associated with intellectual disability and cerebellar ataxia. Drug-target enrichment analysis showed that the relevant drugs targeting the hub genes CACNA2D1, CACNA1A, CACNA1E, KCNA2 and KCNA5 were gabapentin, gabapentin enacarbil, pregabalin, guanidine hydrochloride and 4-aminopyridine. The results of this study provide a valuable basis for exploring the mechanisms of ion channel genes in COVID-19 and clues for developing therapeutic strategies for COVID-19.

Vineland Adaptive Behavior Scale in a Cohort of Four ADNP Syndrome Patients Implicates Age-Dependent Developmental Delays with Increased Impact of Activities of Daily Living

Activity-dependent neuroprotective protein (ADNP) is one of the lead genes in autism spectrum disorder/intellectual disability. Heterozygous, de novo ADNP mutations cause the ADNP syndrome. Here, to evaluate natural history of the syndrome, mothers of two ADNP syndrome boys aged 6 and a half and two adults aged 27 years (man and woman) were subjected to Vineland III questionnaire assessing adaptive behavior. The boys were assessed again about 2 years after the first measurements. The skill measures, presented as standard scores (SS) included domains of communication, daily living, socialization, motor skills and a sum of adaptive behavior composite. The age equivalent (AE) and growth scale values (GSV) encompassing 11 subdomains assess the age level at which the subject's raw score is found at a norm sample median and the individual temporal progression, respectively. The norm referenced standard scores age-matched, mean 100 ± 15 of the two children showed the lowest outcome in communication (SS: 20-30). Daily living skills presented SS of 50-60, with a possible potential loss of some activities as the child ages, especially in interpersonal relationships with people outside of the immediate family (boy A). In contrast, in socialization, both children were at the SS of 38, with some positive increase to SS of ~ 45 (interpersonal relations with family members and coping skills, depending on the particular individual), 2 years later. Interestingly, there was an apparent large difference in motor skills (gross and fine) at the young age, with subject B showing a relatively higher level of skills (SS: 70), decreasing to subject A level (SS: 40) 2 years later. Together, the adaptive behavior composite suggested a level of SS: 39-48 with B showing a potential increase (SS: 41-44) and A, a substantial decrease (SS: 48-39), suggesting a strong impact of daily living skills. Adults were at SS: 20, which is the lowest possible score. AE showed minor improvements for subject A and B, with all AE values being below 3 years. GSVs for subject A showed some improvement with age, especially in interpersonal, play and leisure, and gross motor subdomains. GSV for subject B showed minor improvements in the various subdomains. Notably, all subjects showed a percentile rank < 1 compared with age-matched norms except for subject B as to motor domain (2^nd percentile) at the age of 6 years. In summary, the results, especially comparing SS and AEs between childhood and adulthood, implied a continuous deterioration of activities compared to the general population, encompassing a slower developmental process coupled to possible neurodegeneration, strongly supporting a great need for disease modifying medicinal procedures.

Meta-Data Analysis to Explore the Hub of the Hub-Genes That Influence SARS-CoV-2 Infections Highlighting Their Pathogenetic Processes and Drugs Repurposing

The pandemic of SARS-CoV-2 infections is a severe threat to human life and the world economic condition. Although vaccination has reduced the outspread, but still the situation is not under control because of the instability of RNA sequence patterns of SARS-CoV-2, which requires effective drugs. Several studies have suggested that the SARS-CoV-2 infection causing hub differentially expressed genes (Hub-DEGs). However, we observed that there was not any common hub gene (Hub-DEGs) in our analyses. Therefore, it may be difficult to take a common treatment plan against SARS-CoV-2 infections globally. The goal of this study was to examine if more representative Hub-DEGs from published studies by means of hub of Hub-DEGs (hHub-DEGs) and associated potential candidate drugs. In this study, we reviewed 41 articles on transcriptomic data analysis of SARS-CoV-2 and found 370 unique hub genes or studied genes in total. Then, we selected 14 more representative Hub-DEGs (AKT1, APP, CXCL8, EGFR, IL6, INS, JUN, MAPK1, STAT3, TNF, TP53, UBA52, UBC, VEGFA) as hHub-DEGs by their protein-protein interaction analysis. Their associated biological functional processes, transcriptional, and post-transcriptional regulatory factors. Then we detected hHub-DEGs guided top-ranked nine candidate drug agents (Digoxin, Avermectin, Simeprevir, Nelfinavir Mesylate, Proscillaridin, Linifanib, Withaferin, Amuvatinib, Atazanavir) by molecular docking and cross-validation for treatment of SARS-CoV-2 infections. Therefore, the findings of this study could be useful in formulating a common treatment plan against SARS-CoV-2 infections globally.

Genetic variants in RNA m5 C modification genes associated with survival and chemotherapy efficacy of colorectal cancer

BACKGROUND

Colorectal cancer is one of the most common malignant digestive tract tumors with a poor prognosis. RNA 5-methylcytosine (m⁵ C) is an important posttranscriptional widespread modification involved in many biological processes. However, the association between genetic variations of m⁵ C modification genes and the prognostic value of colorectal cancer remains unclear.

METHODS

We investigated the association between candidate single nucleotide polymorphisms (SNPs) in 13 m⁵ C modification genes and colorectal cancer overall survival (OS) after chemotherapy by the Cox regression model. The combined effect of selected SNPs on OS, progression-free survival (PFS), and disease control rate (DCR) was assessed by the number of risk alleles (NRA). The GTEx and TCGA database were used to perform expression qualitative trait locus (eQTL) analysis.

RESULTS

We identified that two SNPs in YBX1 were associated with OS after chemotherapy (HR = 1.43, p = 0.001 for rs10890208; HR = 1.36, p = 0.025 for rs3862218). A striking dose-response effect between NRA and OS after chemotherapy was found (p_trend  = 0.002). The DCR of patients receiving oxaliplatin chemotherapy in the 3-4 NRA group was markedly reduced in comparison to that in the 0-2 NRA group (OR = 1.49, p = 0.036). Moreover, YBX1 mRNA expression was significantly overexpressed in tumor tissues (p < 0.05) in the TCGA database, and eQTL analysis demonstrated that the two SNPs were associated with YBX1 (p = 0.003 for rs10890208 and p = 0.024 for rs3862218).

CONCLUSION

Our study indicates that genetic variants in m⁵ C modification genes may mediate changes in YBX1 mRNA levels and affect the chemotherapeutic efficacy of colorectal cancer patients.

Disclosing Potential Key Genes, Therapeutic Targets and Agents for Non-Small Cell Lung Cancer: Evidence from Integrative Bioinformatics Analysis

Non-small-cell lung cancer (NSCLC) is considered as one of the malignant cancers that causes premature death. The present study aimed to identify a few potential novel genes highlighting their functions, pathways, and regulators for diagnosis, prognosis, and therapies of NSCLC by using the integrated bioinformatics approaches. At first, we picked out 1943 DEGs between NSCLC and control samples by using the statistical LIMMA approach. Then we selected 11 DEGs (CDK1, EGFR, FYN, UBC, MYC, CCNB1, FOS, RHOB, CDC6, CDC20, and CHEK1) as the hub-DEGs (potential key genes) by the protein–protein interaction network analysis of DEGs. The DEGs and hub-DEGs regulatory network analysis commonly revealed four transcription factors (FOXC1, GATA2, YY1, and NFIC) and five miRNAs (miR-335-5p, miR-26b-5p, miR-92a-3p, miR-155-5p, and miR-16-5p) as the key transcriptional and post-transcriptional regulators of DEGs as well as hub-DEGs. We also disclosed the pathogenetic processes of NSCLC by investigating the biological processes, molecular function, cellular components, and KEGG pathways of DEGs. The multivariate survival probability curves based on the expression of hub-DEGs in the SurvExpress web-tool and database showed the significant differences between the low- and high-risk groups, which indicates strong prognostic power of hub-DEGs. Then, we explored top-ranked 5-hub-DEGs-guided repurposable drugs based on the Connectivity Map (CMap) database. Out of the selected drugs, we validated six FDA-approved launched drugs (Dinaciclib, Afatinib, Icotinib, Bosutinib, Dasatinib, and TWS-119) by molecular docking interaction analysis with the respective target proteins for the treatment against NSCLC. The detected therapeutic targets and repurposable drugs require further attention by experimental studies to establish them as potential biomarkers for precision medicine in NSCLC treatment.

The Genetic Association Identified Between Intervertebral Disc Degeneration and Associated Risk Factors Based on a Systems Biology Approach

STUDY DESIGN

Genetic cross-over study of intervertebral disc degeneration (IDD) and its associated risk factors.

OBJECTIVE

The purpose is to identify molecular biomarkers that may be involved in the IDD process and to provide effective recommendations in combination with drug analysis via systems biology methods.

SUMMARY OF BACKGROUND DATA

The pathogenesis and genetic links of IDD are still unclear. Related research is mainly based on a single data set or gene, and the impact of related risk factors on IDD is often ignored.

METHODS

Identifying disease-associated biomarkers and therapeutic targets through a systems biology approach that Integrative network-based gene and multi-omics analysis. In this study, the differential expression of genes was analyzed through NCBI datasets, followed by enrichment analysis. The central protein was identified through the establishment of protein-protein interaction (PPI) network. Key genes were screened out by VENN diagram and reasonable suggestions were put forward based on gene-chemical drug analysis.

RESULTS

The IDD database analysis revealed 669 differentially expressed genes (DEGs) which were 22, 26, 168, 5, 38, 36, and 16 common DEGs with AG, SM, DEP, NAD, CED, OB, and HFD, respectively. GO and KEGG enrichment analysis may reveal the pathway by which these DEGs were involved. PPI network identified 10 central proteins including CCNB1, RETN, HMMR, BUB1, MPO, OIP5, HP, KIF11, BUB1B, and CDC25A. Three key genes BUB1, BUB1B, and CCNB1 were screened out and their expression might be related to the pathogenesis of IDD. According to the three chemical Dexamethasone, Nicotine, and Resvera-trol obtained from the analysis of genes-chemical drugs, reasonable treatment suggestions were put forward.

CONCLUSION

Genetic association between IDD and risk factors in the general population was revealed by association network. Important gene-related molecular pathways and chemical drugs closely related to IDD have been found. Further study can provide guidance for the treatment and prognosis of IDD.Level of Evidence: N/A.

Computational identification of host genomic biomarkers highlighting their functions, pathways and regulators that influence SARS-CoV-2 infections and drug repurposing

The pandemic threat of COVID-19 has severely destroyed human life as well as the economy around the world. Although, the vaccination has reduced the outspread, but people are still suffering due to the unstable RNA sequence patterns of SARS-CoV-2 which demands supplementary drugs. To explore novel drug target proteins, in this study, a transcriptomics RNA-Seq data generated from SARS-CoV-2 infection and control samples were analyzed. We identified 109 differentially expressed genes (DEGs) that were utilized to identify 10 hub-genes/proteins (TLR2, USP53, GUCY1A2, SNRPD2, NEDD9, IGF2, CXCL2, KLF6, PAG1 and ZFP36) by the protein–protein interaction (PPI) network analysis. The GO functional and KEGG pathway enrichment analyses of hub-DEGs revealed some important functions and signaling pathways that are significantly associated with SARS-CoV-2 infections. The interaction network analysis identified 5 TFs proteins and 6 miRNAs as the key regulators of hub-DEGs. Considering 10 hub-proteins and 5 key TFs-proteins as drug target receptors, we performed their docking analysis with the SARS-CoV-2 3CL protease-guided top listed 90 FDA approved drugs. We found Torin-2, Rapamycin, Radotinib, Ivermectin, Thiostrepton, Tacrolimus and Daclatasvir as the top ranked seven candidate drugs. We investigated their resistance performance against the already published COVID-19 causing top-ranked 11 independent and 8 protonated receptor proteins by molecular docking analysis and found their strong binding affinities, which indicates that the proposed drugs are effective against the state-of-the-arts alternatives independent receptor proteins also. Finally, we investigated the stability of top three drugs (Torin-2, Rapamycin and Radotinib) by using 100 ns MD-based MM-PBSA simulations with the two top-ranked proposed receptors (TLR2, USP53) and independent receptors (IRF7, STAT1), and observed their stable performance. Therefore, the proposed drugs might play a vital role for the treatment against different variants of SARS-CoV-2 infections.

CENPN Acts as a Novel Biomarker that Correlates With the Malignant Phenotypes of Glioma Cells

Background: Gliomas are the most common intracranial malignant neoplasms and have high recurrence and mortality rates. Recent literatures have reported that centromere protein N (CENPN) participates in tumor development. However, the clinicopathologic significance and biological functions of CENPN in glioma are still unclear.

Methods: Clinicopathologic data and gene expression profiles of glioma cases downloaded from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases were utilized to determine the associations between the expression of CENPN and clinical features of glioma. Kaplan-Meier and ROC curves were plotted for prognostic analysis. Gene set enrichment analysis (GSEA) and single sample gene set enrichment analysis (ssGSEA) were applied to identify immune-related functions and pathways associated with CENPN’ differential expression. In vitro experiments were conducted to investigate the impacts of CENPN on human glioma cells.

Results: Elevated CENPN expression was associated with unfavorable clinical variables of glioma patients, which was validated in clinical specimens obtained from our institution by immunohistochemical staining (IHC). The GSEA and ssGSEA results revealed that CENPN expression was strongly correlated with inflammatory activities, immune-related signaling pathways and the infiltration of immune cells. Cell experiments showed that CENPN deficiency impaired cell proliferation, migration and invasion ability and increased glioma apoptosis.

Conclusion: CENPN could be a promising therapeutic target for glioma.

Novel diagnostic and prognostic biomarkers of colorectal cancer: Capable to overcome the heterogeneity-specific barrier and valid for global applications

INTRODUCTION

The heterogeneity-specific nature of the available colorectal cancer (CRC) biomarkers is significantly contributing to the cancer-associated high mortality rate worldwide. Hence, this study was initiated to investigate a system of novel CRC biomarkers that could commonly be employed to the CRC patients and helpful to overcome the heterogenetic-specific barrier.

METHODS

Initially, CRC-related hub genes were extracted through PubMed based literature mining. A protein-protein interaction (PPI) network of the extracted hub genes was constructed and analyzed to identify few more closely CRC-related hub genes (real hub genes). Later, a comprehensive bioinformatics approach was applied to uncover the diagnostic and prognostic role of the identified real hub genes in CRC patients of various clinicopathological features.

RESULTS

Out of 210 collected hub genes, in total 6 genes (CXCL12, CXCL8, AGT, GNB1, GNG4, and CXCL1) were identified as the real hub genes. We further revealed that all the six real hub genes were significantly dysregulated in colon adenocarcinoma (COAD) patients of various clinicopathological features including different races, cancer stages, genders, age groups, and body weights. Additionally, the dysregulation of real hub genes has shown different abnormal correlations with many other parameters including promoter methylation, overall survival (OS), genetic alterations and copy number variations (CNVs), and CD8+T immune cells level. Finally, we identified a potential miRNA and various chemotherapeutic drugs via miRNA, and real hub genes drug interaction network that could be used in the treatment of CRC by regulating the expression of real hub genes.

CONCLUSION

In conclusion, we have identified six real hub genes as potential biomarkers of CRC patients that could help to overcome the heterogenetic-specific barrier across different clinicopathological features.

An Integrated Bioinformatics Analysis of the Potential Regulatory Effects of miR-21 on T-cell Related Target Genes in Multiple Sclerosis

BACKGROUND

Overexpression of miR-21 is a characteristic feature of patients with Multiple Sclerosis (MS) and is involved in gene regulation and the expression enhancement of pro-inflammatory factors including IFNγ and TNF-α following stimulation of T-cells via the T Cell Receptor (TCR). In this study, a novel integrated bioinformatics analysis was used to obtain a better understanding of the involvement of miR-21 in the development of MS, its protein biomarker signatures, RNA levels, and drug interactions through existing microarray and RNA-seq datasets of MS.

METHODS

In order to obtain data on the Differentially Expressed Genes (DEGs) in patients with MS and normal controls, the GEO2R web tool was used to analyze the Gene Expression Omnibus (GEO) datasets, and then Protein-Protein Interaction (PPI) networks of co-expressed DEGs were designed using STRING. A molecular network of miRNA-genes and drugs based on differentially expressed genes was created for T-cells of MS patients to identify the targets of miR-21, that may act as important regulators and potential biomarkers for early diagnosis, prognosis and, potential therapeutic targets for MS.

RESULTS

It found that seven genes (NRIP1, ARNT, KDM7A, S100A10, AK2, TGFβR2, and IL-6R) are regulated by drugs used in MS and miR-21. Finally, three overlapping genes (S100A10, NRIP1, KDM7A) were identified between miRNA-gene-drug network and nineteen genes as hub genes which can reflect the pathophysiology of MS.

CONCLUSION

Our findings suggest that miR-21 and MS-related drugs can act synergistically to regulate several genes in the existing datasets, and miR-21 inhibitors have the potential to be used in MS treatment.

Cancer Stem Cell Transcriptome Profiling Reveals Seed Genes of Tumorigenesis: New Avenues for Cancer Precision Medicine

Cancer stem-like cells (CSCs) possess the ability to self-renew and differentiate, and they are among the major factors driving tumorigenesis, metastasis, and resistance to chemotherapy. Therefore, it is critical to understand the molecular substrates of CSC biology so as to discover novel molecular biosignatures that distinguish CSCs and tumor cells. Here, we report new findings and insights by employing four transcriptome datasets associated with CSCs, with CSC and tumor samples from breast, lung, oral, and ovarian tissues. The CSC samples were analyzed to identify differentially expressed genes between CSC and tumor phenotypes. Through comparative profiling of expression levels in different cancer types, we identified 17 "seed genes" that showed a mutual differential expression pattern. We showed that these seed genes were strongly associated with cancer-associated signaling pathways and biological processes, the immune system, and the key cancer hallmarks. Further, the seed genes presented significant changes in their expression profiles in different cancer types and diverse mutation rates, and they also demonstrated high potential as diagnostic and prognostic biomarkers in various cancers. We report a number of seed genes that represent significant potential as "systems biomarkers" for understanding the pathobiology of tumorigenesis. Seed genes offer a new innovation avenue for potential applications toward cancer precision medicine in a broad range of cancers in oncology in the future.

Prognostic and clinicopathological insights of phosphodiesterase 9A gene as novel biomarker in human colorectal cancer

BACKGROUND

PDE9A (Phosphodiesterase 9A) plays an important role in proliferation of cells, their differentiation and apoptosis via intracellular cGMP (cyclic guanosine monophosphate) signaling. The expression pattern of PDE9A is associated with diverse tumors and carcinomas. Therefore, PDE9A could be a prospective candidate as a therapeutic target in different types of carcinoma. The study presented here was designed to carry out the prognostic value as a biomarker of PDE9A in Colorectal cancer (CRC). The present study integrated several cancer databases with in-silico techniques to evaluate the cancer prognosis of CRC.

RESULTS

The analyses suggested that the expression of PDE9A was significantly down-regulated in CRC tissues than in normal tissues. Moreover, methylation in the DNA promoter region might also manipulate PDE9A gene expression. The Kaplan-Meier curves indicated that high level of expression of PDE9A gene was associated to higher survival in OS, RFS, and DSS in CRC patients. PDE9A demonstrated the highest positive correlation for rectal cancer recurrence with a marker gene CEACAM7. Furtheremore, PDE9A shared consolidated pathways with MAPK14 to induce survival autophagy in CRC cells and showed interaction with GUCY1A2 to drive CRPC.

CONCLUSIONS

Overall, the prognostic value of PDE9A gene could be used as a potential tumor biomarker for CRC.

hTERT Promotes CRC Proliferation and Migration by Recruiting YBX1 to Increase NRF2 Expression

High human telomerase reverse transcriptase (hTERT) expression is related to severe Colorectal Cancer (CRC) progression and negatively related to CRC patient survival. Previous studies have revealed that hTERT can reduce cancer cellular reactive oxygen species (ROS) levels and accelerate cancer progression; however, the mechanism remains poorly understood. NFE2-related factor 2 (NRF2) is a molecule that plays a significant role in regulating cellular ROS homeostasis, but whether there is a correlation between hTERT and NRF2 remains unclear. Here, we showed that hTERT increases CRC proliferation and migration by inducing NRF2 upregulation. We further found that hTERT increases NRF2 expression at both the mRNA and protein levels. Our data also revealed that hTERT primarily upregulates NRF2 by increasing NRF2 promoter activity rather than by regulating NRF2 mRNA or protein stability. Using DNA pull-down/MS analysis, we found that hTERT can recruit YBX1 to upregulate NRF2 promoter activity. We also found that hTERT/YBX1 may localize to the P2 region of the NRF2 promoter. Taken together, our results demonstrate that hTERT facilitates CRC proliferation and migration by upregulating NRF2 expression through the recruitment of the transcription factor YBX1 to activate the NRF2 promoter. These results provide a new theoretical basis for CRC treatment.

Centromere protein N may be a novel malignant prognostic biomarker for hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the deadliest tumors. The majority of HCC is detected in the late stage, and the clinical results for HCC patients are poor. There is an urgent need to discover early diagnostic biomarkers and potential therapeutic targets for HCC.

Methods

The GSE87630 and GSE112790 datasets from the Gene Expression Omnibus (GEO) database were downloaded to analyze the differentially expressed genes (DEGs) between HCC and normal tissues. R packages were used for Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses of the DEGs. A Search Tool for Retrieval of Interacting Genes (STRING) database was used to develop a protein-protein interaction (PPI) network, and also cytoHubba, Molecular Complex Detection (MCODE), EMBL-EBI, CCLE, Gene Expression Profiling Interactive Analysis (GEPIA), and Oncomine analyses were performed to identify hub genes. Gene expression was verified with a third GEO dataset, GSE25097. The Cancer Genome Atlas (TCGA) database was used to explore the correlations between the hub genes and clinical indexes of HCC patients. The functions of the hub genes were enriched by gene set enrichment analysis (GSEA), and the biological significance of the hub genes was explored by real-time polymerase chain reaction (qRT-PCR), western blot, immunofluorescence, CCK-8, colony formation, Transwell and flow cytometry assays with loss-of-function experiments in vitro.

Results

Centromere protein N (CENPN) was screened as a hub gene affecting HCC tumorigenesis. Evaluation by Cox regression showed that a high level of CENPN expression was an independent danger variable for poor prognosis of HCC. GSEA showed that high CENPN expression was linked to the following pathways: liver cancer subclass proliferation, cell cycle, p53 signaling pathway, Rb1 pathway, positive regulation of cell cycle G1/S phase transition, and DNA damage response signal transduction by p53 class moderators. Further cell experiments showed that knocking down CENPN expression decreased the proliferation and colony-forming abilities of HepG2 and Huh7 cells as well as Ki67 expression in these cell lines. The cell cycle was arrested in G1 phase, which is consistent with previous experiments on CENPN downregulation., but neither migration nor invasion were significantly affected. Western blot results revealed that the expression of p53, p27, p21, CDK4, cyclin D1, CDK2, cyclin E, pRb, E2F1 and c-myc decreased after CENPN knockdown, but there was no significant change in total Rb levels. In addition, CENPN-knockdown cells subjected to irradiation showed significantly enhanced of γ-H2AX expression and reduced colony formation.

Conclusion

CENPN functions as an oncogene in HCC and may be a therapeutic target and promising prognostic marker for HCC.

Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19

Current coronavirus disease-2019 (COVID-19) pandemic has caused massive loss of lives. Clinical trials of vaccines and drugs are currently being conducted around the world; however, till now no effective drug is available for COVID-19. Identification of key genes and perturbed pathways in COVID-19 may uncover potential drug targets and biomarkers. We aimed to identify key gene modules and hub targets involved in COVID-19. We have analyzed SARS-CoV-2 infected peripheral blood mononuclear cell (PBMC) transcriptomic data through gene coexpression analysis. We identified 1520 and 1733 differentially expressed genes (DEGs) from the GSE152418 and CRA002390 PBMC datasets, respectively (FDR < 0.05). We found four key gene modules and hub gene signature based on module membership (MMhub) statistics and protein-protein interaction (PPI) networks (PPIhub). Functional annotation by enrichment analysis of the genes of these modules demonstrated immune and inflammatory response biological processes enriched by the DEGs. The pathway analysis revealed the hub genes were enriched with the IL-17 signaling pathway, cytokine-cytokine receptor interaction pathways. Then, we demonstrated the classification performance of hub genes (PLK1, AURKB, AURKA, CDK1, CDC20, KIF11, CCNB1, KIF2C, DTL and CDC6) with accuracy >0.90 suggesting the biomarker potential of the hub genes. The regulatory network analysis showed transcription factors and microRNAs that target these hub genes. Finally, drug-gene interactions analysis suggests amsacrine, BRD-K68548958, naproxol, palbociclib and teniposide as the top-scored repurposed drugs. The identified biomarkers and pathways might be therapeutic targets to the COVID-19.

miR-944 Suppresses EGF-Induced EMT in Colorectal Cancer Cells by Directly Targeting GATA6

Background

miR-944 belongs to the MicroRNAs family, as shown in our previous study, and is essential in the colorectal cancer (CRC) progression. It is negatively associated with invasion depth and lymph node status. Epithelial-mesenchymal transition (EMT) is essential in tumor invasion and metastasis. However, the relationship between miR-944 and EMT in CRC is unknown and should be further investigated.

Methods

Epithelial–mesenchymal transition (EMT) progression in CRC cell lines was detected with Cell morphology and Western blotting. CRC cell migration and invasion were examined using Transwell assays. Transcriptome and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. The potential pathway of miR-944 and GATA6 were predicted using KEGG analysis. Colocalization was validated using immunofluorescence and Immunohistochemistry. Nuclear and Cytoplasmic Protein Extraction assays were conducted to determine the effects of miR-944 on Wnt/β-catenin signaling.

Results

We found that miR‑944 influences EGF-induced EMT malignant phenotype in vitro. KEGG analyses showed that miR-944 and GATA6 are associated with EMT related pathways, wnt signaling pathways. On the other hand, Western Blot analyses showed that miR-944 can regulate EMT and wnt-β-catenin pathway-related protein, including β-catenin, ZEB1, snail1 via GATA6 regulation. miR-944 also abrogates E-ca after EGF induction. Immunohistochemistry (IHC) and Immunofluorescence (IF) co-expression showed that GATA6 expression is positively associated with β-catenin and ZEB1. GATA6 silencing can reverse EMT malignant phenotype and alterations of related protein induced by miR-944. Quantitative polymerase chain reaction analysis results showed that miR-944 is negatively associated with the UICC stage (P= 0.02), lymph nodes (p=0.04), and liver metastasis (p=0.03). Moreover, patients with high miR-944 expression have better survival (p=0.045). We finally combined miR-944 and GATA6 and found that miR-944/GATA6 ratio could be a novel prognostic biomarker in the TCGA dataset and it is an independent risk prognosis factor (p=0.045).

Conclusion

Our results suggest that miR-944 suppresses the aggressive biological processes by directly repressing GATA6 expression and could be a potential candidate for therapeutic applications in CRC.

Transcriptional dysregulation of TRIM29 promotes colorectal cancer carcinogenesis via pyruvate kinase-mediated glucose metabolism

Targeted molecular therapy is the most effective treatment for cancer. An effective therapeutic target for colorectal cancer (CRC) is urgently needed. However, the mechanisms of CRC remain poorly understood, which has hampered research and development of CRC-targeted therapy. TRIM29 is a ubiquitin E3 ligase that has been reported as an oncogene in several human tumors. In this study, we show that increased levels of TRIM29 were detected in CRC compared with normal tissues and were associated with poor clinical outcome, advanced stage and lymph node metastasis, particularly those with right-sided colorectal cancer (RSCC). Notably, GATA2 (GATA Binding Protein 2) transcriptionally repressed TRIM29 expression. The loss of GATA2 and high expression of TRIM29 occur more frequently in RSCC than in left-sided colorectal cancer (LSCC). Functional assays revealed that TRIM29 promotes the malignant CRC phenotype in vitro and in vivo. Mechanistic analyses indicate that TRIM29 promotes pyruvate kinase (mainly PKM1) degradation via the ubiquitin-proteasome pathway. TRIM29 directly targets PKM1 to reduce PKM1/PKM2 ratio, which results in PKM2-mediated aerobic glycolysis (Warburg effect) acting as the dominant energy source in CRC. Our findings suggest that TRIM29 acts as a tumor promoter in CRC, especially in RSCC, and is a potential therapeutic target for CRC treatment.

High Centromere Protein-A (CENP-A) Expression Correlates with Progression and Prognosis in Gastric Cancer

Purpose

Recent studies have established the ability of centromere protein-A (CENP-A) to perform as an oncogene, regulating tumor progression. The aim of this research was to explore the relationship between CENP-A expression and clinical significance in gastric cancer (GC) patients.

Materials and Methods

Experiments with a microarray were conducted using the Affymetrix U133 plus 2.0 GeneChip Array. Upregulated differentially expressed genes (DEGs) were identified via the GEO2R and intersected using a Venn diagram. Bioinformatic databases Omcomine, GEPIA, and Ualcan were applied to investigate the expression level of CENP-A in GC. The real-time quantitative RT-PCR (qRT-PCR) was used to validate the level of CENP-A mRNA in GC. Immunohistochemistry (IHC) was employed to verify the protein levels of CENP-A, while the relationship between CENP-A expression and patients’ clinical parameters in GC was explored through the use of IHC. Kaplan-Meier analysis was conducted to evaluate the prognostic significance of CENP-A. Additionally, the Kaplan-Meier plotter database (KM plotter) was used to verify the prognostic function of CENP-A in GC patients.

Results

The results indicated that CENP-A was significantly overexpressed, both in protein and mRNA levels of GC tissues, compared to adjacent noncancerous tissues (P<0.05). Furthermore, we observed that CENP-A expression was positively associated with TNM stage, tumor classification, lymph node metastasis, distant metastasis, and Lauren type (P<0.05). Kaplan-Meier analysis showed that patients with an overexpression of CENP-A had significantly poorer overall survival (OS) times (P<0.05). Multivariate analysis suggested CENP-A may serve as an independent predicting factor for the poor outcome of GC patients.

Conclusion

Our results show that CENP-A upregulation is significantly correlated with advanced tumor progression and poor prognosis. CENP-A may function as a novel potential biomarker for predicting the clinical outcomes of GC patients.

Identification of hub genes and small molecule therapeutic drugs related to breast cancer with comprehensive bioinformatics analysis

Breast cancer is one of the most common malignant tumors among women worldwide and has a high morbidity and mortality. This research aimed to identify hub genes and small molecule drugs for breast cancer by integrated bioinformatics analysis. After downloading multiple gene expression datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, 283 overlapping differentially expressed genes (DEGs) significantly enriched in different cancer-related functions and pathways were obtained using LIMMA, VennDiagram and ClusterProfiler packages of R. We then analyzed the topology of protein–protein interaction (PPI) network with overlapping DEGs and further obtained six hub genes (RRM2, CDC20, CCNB2, BUB1B, CDK1, and CCNA2) from the network via STRING and Cytoscape. Subsequently, we conducted genes expression verification, genetic alterations evaluation, immune infiltration prediction, clinicopathological parameters analysis, identification of transcriptional and post-transcriptional regulatory molecules, and survival analysis for these hub genes. Meanwhile, 29 possible drug candidates (e.g., Cladribine, Gallium nitrate, Alvocidib, 1β-hydroxyalantolactone, Berberine hydrochloride, Nitidine chloride) were identified from the DGIdb database and the GSE85871 dataset. In addition, some transcription factors and miRNAs (e.g., E2F1, PTTG1, TP53, ZBTB16, hsa-miR-130a-3p, hsa-miR-204-5p) targeting hub genes were identified as key regulators in the progression of breast cancer. In conclusion, our study identified six hub genes and 29 potential drug candidates for breast cancer. These findings may advance understanding regarding the diagnosis, prognosis and treatment of breast cancer.

Signatures of co-deregulated genes and their transcriptional regulators in colorectal cancer

The deregulated genes in colorectal cancer (CRC) vary significantly across different studies. Thus, a systems biology approach is needed to identify the co-deregulated genes (co-DEGs), explore their molecular networks, and spot the major hub proteins within these networks. We reanalyzed 19 GEO gene expression profiles to identify and annotate CRC versus normal signatures, single-gene perturbation, and single-drug perturbation signatures. We identified the co-DEGs across different studies, their upstream regulating kinases and transcription factors (TFs). Connectivity Map was used to identify likely repurposing drugs against CRC within each group. The functional changes of the co-upregulated genes in the first category were mainly associated with negative regulation of transforming growth factor β production and glomerular epithelial cell differentiation; whereas the co-downregulated genes were enriched in cotranslational protein targeting to the membrane. We identified 17 hub proteins across the co-upregulated genes and 18 hub proteins across the co-downregulated genes, composed of well-known TFs (MYC, TCF3, PML) and kinases (CSNK2A1, CDK1/4, MAPK14), and validated most of them using GEPIA2 and HPA, but also through two signature gene lists composed of the co-up and co-downregulated genes. We further identified a list of repurposing drugs that can potentially target the co-DEGs in CRC, including camptothecin, neostigmine bromide, emetine, remoxipride, cephaeline, thioridazine, and omeprazole. Similar analyses were performed in the co-DEG signatures in single-gene or drug perturbation experiments in CRC. MYC, PML, CDKs, CSNK2A1, and MAPKs were common hub proteins among all studies. Overall, we identified the critical genes in CRC and we propose repurposing drugs that could be used against them.

Overexpressed GATA3 enhances the sensitivity of colorectal cancer cells to oxaliplatin through regulating MiR-29b

Background

GATA binding protein 3 (GATA3) and miR-29b are related to colorectal cancer (CRC). The current study explored the regulatory relationship between GATA3 and miR-29b, and the mechanism of the two in the drug resistance of CRC cells to oxaliplatin.

Method

Apoptosis of CRC cells induced by oxaliplatin at various doses was detected by flow cytometry. CRC cells were separately transfected with overexpression and knockdown of GATA3, miR-29b agomir and antagomir, and treated by oxaliplatin to detect the cell viability and apoptosis by performing Cell Couting Kit-8 (CCK-8) and flow cytometry. The expression levels of GATA3, caspase3 and cleaved caspase3 were determined by Western blot, and the expression of miR-29b was detected by quantitative real-time polymerase chain reaction (qRT-PCR). Animal experiments were performed to examine the changes of transplanted tumors in nude mouse xenograft studies and observed by in vivo imaging. TUNEL staining was performed to detect tumor cell apoptosis.

Result

Both GATA3 and miR-29b agomir inhibited the activity of the CRC cells, promoted apoptosis and Cleaved caspase3 expression, and reduced the resistance of the cells to chemotherapy drug oxaliplatin. Although GATA3 could up-regulate miR-29b expression, the tumor-suppressive effect of GATA3 was partially reversed by miR-29b antagomir. In vivo experiments showed that down-regulating the expression of GATA3 promoted the growth rate and volume of transplanted tumors, while overexpressing GATA3 had no significant effect on tumor growth. TUNEL staining results showed that knocking down or overexpression of GATA3 did not cause significant changes to apoptotic bodies of CRC cells, while oxaliplatin treatment increased the number of apoptotic bodies.

Conclusion

GATA3 inhibits the cell viability of CRC cells, promotes apoptosis, and reduces oxaliplatin resistance of CRC cells through regulating miR-29b.

miR-615 Fine-Tunes Growth and Development and Has a Role in Cancer and in Neural Repair

MicroRNAs (miRNAs) are small noncoding RNAs that function as epigenetic modulators regulating almost any gene expression. Similarly, other noncoding RNAs, as well as epigenetic modifications, can regulate miRNAs. This reciprocal interaction forms a miRNA-epigenetic feedback loop, the deregulation of which affects physiological processes and contributes to a great diversity of diseases. In the present review, we focus on miR-615, a miRNA highly conserved across eutherian mammals. It is involved not only during embryogenesis in the regulation of growth and development, for instance during osteogenesis and angiogenesis, but also in the regulation of cell growth and the proliferation and migration of cells, acting as a tumor suppressor or tumor promoter. It therefore serves as a biomarker for several types of cancer, and recently has also been found to be involved in reparative processes and neural repair. In addition, we present the pleiad of functions in which miR-615 is involved, as well as their multiple target genes and the multiple regulatory molecules involved in its own expression. We do this by introducing in a comprehensible way the reported knowledge of their actions and interactions and proposing an integral view of its regulatory mechanisms.

Detection of molecular signatures and pathways shared in inflammatory bowel disease and colorectal cancer: A bioinformatics and systems biology approach

Emerging evidence indicates IBD is a risk factor for the increasing incidence of colorectal cancer (CRC) development. We used a system biology approach to identify common molecular signatures and pathways that interact between IBD and CRC and the indispensable pathological mechanisms. First, we identified 177 common differentially expressed genes (DEGs) between IBD and CRC. Gene set enrichment, protein-protein, DEGs-transcription factors, DEGs-microRNAs, protein-drug interaction, gene-disease association, Gene Ontology, pathway enrichment analyses were conducted to these common genes. The inclusion of common DEGs with bimolecular networks disclosed hub proteins (LYN, PLCB1, NPSR1, WNT5A, CDC25B, CD44, RIPK2, ASAP1), transcription factors (SCD, SLC7A5, IKZF3, SLC16A1, SLC7A11) and miRNAs (mir-335-5p, mir-26b-5p, mir-124-3p, mir-16-5p, mir-192-5p, mir-548c-3p, mir-29b-3p, mir-155-5p, mir-21-5p, mir-15a-5p). Analysis of the interaction between protein and drug discovered ASAP1 interacts with cysteine sulfonic acid and double oxidized cysteine drug compounds. Gene-disease association analysis retrieved ASAP1 also associated with pulmonary and bladder neoplasm diseases.

Novel molecular signatures and potential therapeutics in renal cell carcinomas: Insights from a comparative analysis of subtypes

Renal cell carcinomas (RCCs) are among the highest causes of cancer mortality. Although transcriptome profiling studies in the last decade have made significant molecular findings on RCCs, effective diagnosis and treatment strategies have yet to be achieved due to lack of adequate screening and comparative profiling of RCC subtypes. In this study, a comparative analysis was performed on RNA-seq based transcriptome data from each RCC subtype, namely clear cell RCC (KIRC), papillary RCC (KIRP) and kidney chromophobe (KICH), and mutual or subtype-specific reporter biomolecules were identified at RNA, protein, and metabolite levels by the integration of expression profiles with genome-scale biomolecular networks. This approach revealed already-known biomarkers in RCCs as well as novel biomarker candidates and potential therapeutic targets. Our findings also pointed out the incorporation of the molecular mechanisms of KIRC and KIRP, whereas KICH was shown to have distinct molecular signatures. Furthermore, considering the Dipeptidyl Peptidase 4 (DPP4) receptor as a potential therapeutic target specific to KICH, several drug candidates such as ZINC6745464 were identified through virtual screening of ZINC molecules. In this study, we reported valuable data for further experimental and clinical efforts, since the proposed molecules have significant potential for screening and therapeutic purposes in RCCs.

Network-based computational approach to identify genetic links between cardiomyopathy and its risk factors

Cardiomyopathy (CMP) is a group of myocardial diseases that progressively impair cardiac function. The mechanisms underlying CMP development are poorly understood, but lifestyle factors are clearly implicated as risk factors. This study aimed to identify molecular biomarkers involved in inflammatory CMP development and progression using a systems biology approach. The authors analysed microarray gene expression datasets from CMP and tissues affected by risk factors including smoking, ageing factors, high body fat, clinical depression status, insulin resistance, high dietary red meat intake, chronic alcohol consumption, obesity, high-calorie diet and high-fat diet. The authors identified differentially expressed genes (DEGs) from each dataset and compared those from CMP and risk factor datasets to identify common DEGs. Gene set enrichment analyses identified metabolic and signalling pathways, including MAPK, RAS signalling and cardiomyopathy pathways. Protein-protein interaction (PPI) network analysis identified protein subnetworks and ten hub proteins (CDK2, ATM, CDT1, NCOR2, HIST1H4A, HIST1H4B, HIST1H4C, HIST1H4D, HIST1H4E and HIST1H4L). Five transcription factors (FOXC1, GATA2, FOXL1, YY1, CREB1) and five miRNAs were also identified in CMP. Thus the authors' approach reveals candidate biomarkers that may enhance understanding of mechanisms underlying CMP and their link to risk factors. Such biomarkers may also be useful to develop new therapeutics for CMP.

Network-Based Genetic Profiling Reveals Cellular Pathway Differences Between Follicular Thyroid Carcinoma and Follicular Thyroid Adenoma

Molecular mechanisms underlying the pathogenesis and progression of malignant thyroid cancers, such as follicular thyroid carcinomas (FTCs), and how these differ from benign thyroid lesions, are poorly understood. In this study, we employed network-based integrative analyses of FTC and benign follicular thyroid adenoma (FTA) lesion transcriptomes to identify key genes and pathways that differ between them. We first analysed a microarray gene expression dataset (Gene Expression Omnibus GSE82208, n = 52) obtained from FTC and FTA tissues to identify differentially expressed genes (DEGs). Pathway analyses of these DEGs were then performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) resources to identify potentially important pathways, and protein-protein interactions (PPIs) were examined to identify pathway hub genes. Our data analysis identified 598 DEGs, 133 genes with higher and 465 genes with lower expression in FTCs. We identified four significant pathways (one carbon pool by folate, p53 signalling, progesterone-mediated oocyte maturation signalling, and cell cycle pathways) connected to DEGs with high FTC expression; eight pathways were connected to DEGs with lower relative FTC expression. Ten GO groups were significantly connected with FTC-high expression DEGs and 80 with low-FTC expression DEGs. PPI analysis then identified 12 potential hub genes based on degree and betweenness centrality; namely, TOP2A, JUN, EGFR, CDK1, FOS, CDKN3, EZH2, TYMS, PBK, CDH1, UBE2C, and CCNB2. Moreover, transcription factors (TFs) were identified that may underlie gene expression differences observed between FTC and FTA, including FOXC1, GATA2, YY1, FOXL1, E2F1, NFIC, SRF, TFAP2A, HINFP, and CREB1. We also identified microRNA (miRNAs) that may also affect transcript levels of DEGs; these included hsa-mir-335-5p, -26b-5p, -124-3p, -16-5p, -192-5p, -1-3p, -17-5p, -92a-3p, -215-5p, and -20a-5p. Thus, our study identified DEGs, molecular pathways, TFs, and miRNAs that reflect molecular mechanisms that differ between FTC and benign FTA. Given the general similarities of these lesions and common tissue origin, some of these differences may reflect malignant progression potential, and include useful candidate biomarkers for FTC and identifying factors important for FTC pathogenesis.

Machine Learning and Bioinformatics Models to Identify Pathways that Mediate Influences of Welding Fumes on Cancer Progression

Welding generates and releases fumes that are hazardous to human health. Welding fumes (WFs) are a complex mix of metallic oxides, fluorides and silicates that can cause or exacerbate health problems in exposed individuals. In particular, WF inhalation over an extended period carries an increased risk of cancer, but how WFs may influence cancer behaviour or growth is unclear. To address this issue we employed a quantitative analytical framework to identify the gene expression effects of WFs that may affect the subsequent behaviour of the cancers. We examined datasets of transcript analyses made using microarray studies of WF-exposed tissues and of cancers, including datasets from colorectal cancer (CC), prostate cancer (PC), lung cancer (LC) and gastric cancer (GC). We constructed gene-disease association networks, identified signaling and ontological pathways, clustered protein-protein interaction network using multilayer network topology, and analyzed survival function of the significant genes using Cox proportional hazards (Cox PH) model and product-limit (PL) estimator. We observed that WF exposure causes altered expression of many genes (36, 13, 25 and 17 respectively) whose expression are also altered in CC, PC, LC and GC. Gene-disease association networks, signaling and ontological pathways, protein-protein interaction network, and survival functions of the significant genes suggest ways that WFs may influence the progression of CC, PC, LC and GC. This quantitative analytical framework has identified potentially novel mechanisms by which tissue WF exposure may lead to gene expression changes in tissue gene expression that affect cancer behaviour and, thus, cancer progression, growth or establishment.

Image-Based Network Analysis of DNp73 Expression by Immunohistochemistry in Rectal Cancer Patients

Background: Rectal cancer is a disease characterized with tumor heterogeneity. The combination of surgery, radiotherapy, and chemotherapy can reduce the risk of local recurrence. However, there is a significant difference in the response to radiotherapy among rectal cancer patients even they have the same tumor stage. Despite rapid advances in knowledge of cellular functions affecting radiosensitivity, there is still a lack of predictive factors for local recurrence and normal tissue damage. The tumor protein DNp73 is thought as a biomarker in colorectal cancer, but its clinical significance is still not sufficiently investigated, mainly due to the limitation of human-based pathology analysis. In this study, we investigated the predictive value of DNp73 in patients with rectal adenocarcinoma using image-based network analysis.

Methods: The fuzzy weighted recurrence network of time series was extended to handle multi-channel image data, and applied to the analysis of immunohistochemistry images of DNp73 expression obtained from a cohort of 25 rectal cancer patients who underwent radiotherapy before surgery. Two mathematical weighted network properties, which are the clustering coefficient and characteristic path length, were computed for the image-based networks of the primary tumor (obtained after operation) and biopsy (obtained before operation) of each cancer patient.

Results: The ratios of two weighted recurrence network properties of the primary tumors to biopsies reveal the correlation of DNp73 expression and long survival time, and discover the non-effective radiotherapy to a cohort of rectal cancer patients who had short survival time.

Conclusion: Our work contributes to the elucidation of the predictive value of DNp73 expression in rectal cancer patients who were given preoperative radiotherapy. Mathematical properties of fuzzy weighted recurrence networks of immunohistochemistry images are not only able to show the predictive factor of DNp73 expression in the patients, but also reveal the identification of non-effective application of radiotherapy to those who had poor overall survival outcome.

Identification of differentially expressed microRNAs and the potential of microRNA-455-3p as a novel prognostic biomarker in glioma

Glioma is an aggressive central nervous system malignancy. MicroRNAs (miRNAs/miRs) have been reported to be involved in the tumorigenesis of numerous types of cancer, including glioma. The present study aimed to identify the differentially expressed miRNAs in glioma, and further explore the clinical value of miR-455-3p in patients with glioma. GEO2R was used for the identification of the differentially expressed miRNAs according to the miRNA expression profiles obtained from the Gene Expression Omnibus database. OncomiR was used to analyze the relationship of miRNAs with the survival outcomes of the patients with glioma. A total of 108 patients with glioma were recruited to examine the expression levels of miR-455-3p and further explore its clinical value. The bioinformatics analysis results suggested that a total of 64 and 48 differentially expressed miRNAs were identified in the GSE90603 and GSE103229 datasets, respectively. There were 12 miRNAs in the overlap of the two datasets, of which three were able to accurately predict overall cancer survival, namely hsa-miR-7-5p, hsa-miR-21-3p and hsa-miR-455-3p. In patients with glioma, miR-455-3p was determined to be significantly upregulated (P<0.001). Additionally, patients with high miR-455-3p expression had significantly lower 5-year overall survival than those with low miR-455-3p expression (log-rank test, P=0.001). Cox regression analysis further determined that miR-455-3p was an independent prognostic indicator for overall survival in patients with glioma (hazard ratio=2.136; 95% CI=1.177–3.877; P=0.013). In conclusion, the present study revealed a series of miRNAs with potential functional roles in the pathogenesis of glioma, and provides findings that indicate miR-455-3p as a promising biomarker for the prognosis of glioma.

Identification of molecular signatures and pathways to identify novel therapeutic targets in Alzheimer's disease: Insights from a systems biomedicine perspective

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by the accumulation of amyloid plaques and neurofibrillary tangles in the brain. However, there are no peripheral biomarkers available that can detect AD onset. This study aimed to identify the molecular signatures in AD through an integrative analysis of blood gene expression data. We used two microarray datasets (GSE4226 and GSE4229) comparing peripheral blood transcriptomes of AD patients and controls to identify differentially expressed genes (DEGs). Gene set and protein overrepresentation analysis, protein-protein interaction (PPI), DEGs-Transcription Factors (TFs) interactions, DEGs-microRNAs (miRNAs) interactions, protein-drug interactions, and protein subcellular localizations analyses were performed on DEGs common to the datasets. We identified 25 common DEGs between the two datasets. Integration of genome scale transcriptome datasets with biomolecular networks revealed hub genes (NOL6, ATF3, TUBB, UQCRC1, CASP2, SND1, VCAM1, BTF3, VPS37B), common transcription factors (FOXC1, GATA2, NFIC, PPARG, USF2, YY1) and miRNAs (mir-20a-5p, mir-93-5p, mir-16-5p, let-7b-5p, mir-708-5p, mir-24-3p, mir-26b-5p, mir-17-5p, mir-193-3p, mir-186-5p). Evaluation of histone modifications revealed that hub genes possess several histone modification sites associated with AD. Protein-drug interactions revealed 10 compounds that affect the identified AD candidate biomolecules, including anti-neoplastic agents (Vinorelbine, Vincristine, Vinblastine, Epothilone D, Epothilone B, CYT997, and ZEN-012), a dermatological (Podofilox) and an immunosuppressive agent (Colchicine). The subcellular localization of molecular signatures varied, including nuclear, plasma membrane and cytosolic proteins. In the present study, it was identified blood-cell derived molecular signatures that might be useful as candidate peripheral biomarkers in AD. It was also identified potential drugs and epigenetic data associated with these molecules that may be useful in designing therapeutic approaches to ameliorate AD.

Discovering Biomarkers and Pathways Shared by Alzheimer's Disease and Ischemic Stroke to Identify Novel Therapeutic Targets

Background and objectives: Alzheimer's disease (AD) is a progressive neurodegenerative disease that results in severe dementia. Having ischemic strokes (IS) is one of the risk factors of the AD, but the molecular mechanisms that underlie IS and AD are not well understood. We thus aimed to identify common molecular biomarkers and pathways in IS and AD that can help predict the progression of these diseases and provide clues to important pathological mechanisms. Materials and Methods: We have analyzed the microarray gene expression datasets of IS and AD. To obtain robust results, combinatorial statistical methods were used to analyze the datasets and 26 transcripts (22 unique genes) were identified that were abnormally expressed in both IS and AD. Results: Gene Ontology (GO) and KEGG pathway analyses indicated that these 26 common dysregulated genes identified several altered molecular pathways: Alcoholism, MAPK signaling, glycine metabolism, serine metabolism, and threonine metabolism. Further protein-protein interactions (PPI) analysis revealed pathway hub proteins PDE9A, GNAO1, DUSP16, NTRK2, PGAM2, MAG, and TXLNA. Transcriptional and post-transcriptional components were then identified, and significant transcription factors (SPIB, SMAD3, and SOX2) found. Conclusions: Protein-drug interaction analysis revealed PDE9A has interaction with drugs caffeine, γ-glutamyl glycine, and 3-isobutyl-1-methyl-7H-xanthine. Thus, we identified novel putative links between pathological processes in IS and AD at transcripts levels, and identified possible mechanistic and gene expression links between IS and AD.