Primer on the Gene Ontology

Sex-biased gene expression precedes sexual dimorphism in the agonadal annelid Platynereis dumerilii

Gametogenesis is the process by which germ cells differentiate into mature sperm and oocytes, cells essential for sexual reproduction. The sex-specific molecular programs that drive spermatogenesis and oogenesis can also serve as sex identification markers. Platynereis dumerilii is a research organism that has been studied in many areas of developmental biology. However investigations often disregard sex, as P. dumerilii juveniles lack sexual dimorphism. The molecular mechanisms of gametogenesis in the segmented worm P. dumerilii are also largely unknown. In this study, we used RNA sequencing to investigate the transcriptomic profiles of gametogenesis in P. dumerilii juveniles. Our analysis revealed that sex-biased gene expression becomes increasingly pronounced during the advanced developmental stages, particularly during the meiotic phases of gametogenesis. We identified conserved genes associated with spermatogenesis, such as dmrt1, and a novel gene psmt, that is associated with oogenesis. Additionally, putative long non-coding RNAs were upregulated in both male and female gametogenic programs. This study provides a foundational resource for germ cell research in P. dumerilii, markers for sex identification, and offers comparative data to enhance our understanding of the evolution of gametogenesis mechanisms across species.

Proteomic Insights into Osteoporosis: Unraveling Diagnostic Markers of and Therapeutic Targets for the Metabolic Bone Disease

Osteoporosis (OP), a prevalent skeletal disorder characterized by compromised bone strength and increased susceptibility to fractures, poses a significant public health concern. This review aims to provide a comprehensive analysis of the current state of research in the field, focusing on the application of proteomic techniques to elucidate diagnostic markers and therapeutic targets for OP. The integration of cutting-edge proteomic technologies has enabled the identification and quantification of proteins associated with bone metabolism, leading to a deeper understanding of the molecular mechanisms underlying OP. In this review, we systematically examine recent advancements in proteomic studies related to OP, emphasizing the identification of potential biomarkers for OP diagnosis and the discovery of novel therapeutic targets. Additionally, we discuss the challenges and future directions in the field, highlighting the potential impact of proteomic research in transforming the landscape of OP diagnosis and treatment.

A cell atlas of thoracic aortic perivascular adipose tissue: a focus on mechanotransducers

Perivascular adipose tissue (PVAT) is increasingly recognized for its function in mechanotransduction. However, major gaps remain in our understanding of the cells present in PVAT, as well as how different cells contribute to mechanotransduction. We hypothesized that snRNA-seq would reveal the expression of mechanotransducers, and test one (PIEZO1) to illustrate the expression and functional agreement between single-nuclei RNA sequencing (snRNA-seq) and physiological measurements. To contrast two brown tissues, subscapular brown adipose tissue (BAT) was also examined. We used snRNA-seq of the thoracic aorta PVAT (taPVAT) and BAT from male Dahl salt-sensitive (Dahl SS) rats to investigate cell-specific expression mechanotransducers. Localization and function of the mechanostransducer PIEZO1 were further examined using immunohistochemistry (IHC) and RNAscope, as well as pharmacological antagonism. Approximately 30,000 nuclei from taPVAT and BAT each were characterized by snRNA-seq, identifying eight major cell types expected and one unexpected (nuclei with oligodendrocyte marker genes). Cell-specific differential gene expression analysis between taPVAT and BAT identified up to 511 genes (adipocytes) with many (≥20%) being unique to individual cell types. Piezo1 was the most highly, widely expressed mechanotransducer. The presence of PIEZO1 in the PVAT but not the adventitia was confirmed by RNAscope and IHC in male and female rats. Importantly, antagonism of PIEZO1 by GsMTX4 impaired the PVAT's ability to hold tension. Collectively, the cell compositions of taPVAT and BAT are highly similar, and PIEZO1 is likely a mechanotransducer in taPVAT.NEW & NOTEWORTHY This study describes the atlas of cells in the thoracic aorta perivascular adipose tissue (taPVAT) of the Dahl-SS rat, an important hypertension model. We show that mechanotransducers are widely expressed in these cells. Moreover, PIEZO1 expression is shown to be restricted to the taPVAT and is functionally implicated in stress relaxation. These data will serve as the foundation for future studies investigating the role of taPVAT in this model of hypertensive disease.

Predicting Protein Functions Based on Heterogeneous Graph Attention Technique

In bioinformatics, protein function prediction stands as a fundamental area of research and plays a crucial role in addressing various biological challenges, such as the identification of potential targets for drug discovery and the elucidation of disease mechanisms. However, known functional annotation databases usually provide positive experimental annotations that proteins carry out a given function, and rarely record negative experimental annotations that proteins do not carry out a given function. Therefore, existing computational methods based on deep learning models focus on these positive annotations for prediction and ignore these scarce but informative negative annotations, leading to an underestimation of precision. To address this issue, we introduce a deep learning method that utilizes a heterogeneous graph attention technique. The method first constructs a heterogeneous graph that covers the protein-protein interaction network, ontology structure, and positive and negative annotation information. Then, it learns embedding representations of proteins and ontology terms by using the heterogeneous graph attention technique. Finally, it leverages these learned representations to reconstruct the positive protein-term associations and score unobserved functional annotations. It can enhance the predictive performance by incorporating these known limited negative annotations into the constructed heterogeneous graph. Experimental results on three species (i.e., Human, Mouse, and Arabidopsis) demonstrate that our method can achieve better performance in predicting new protein annotations than state-of-the-art methods.

Insight into telomere regulation: road to discovery and intervention in plasma drug-protein targets

BACKGROUND

Telomere length is a critical metric linked to aging, health, and disease. Currently, the exploration of target proteins related to telomere length is usually limited to the context of aging and specific diseases, which limits the discovery of more relevant drug targets. This study integrated large-scale plasma cis-pQTLs data and telomere length GWAS datasets. We used Mendelian randomization(MR) to identify drug target proteins for telomere length, providing essential clues for future precision therapy and targeted drug development.

METHODS

Using plasma cis-pQTLs data from a previous GWAS study (3,606 Pqtls associated with 2,656 proteins) and a GWAS dataset of telomere length (sample size: 472,174; GWAS ID: ieu-b-4879) from UK Biobank, using MR, external validation, and reverse causality testing, we identified essential drug target proteins for telomere length. We also performed co-localization, Phenome-wide association studies and enrichment analysis, protein-protein interaction network construction, search for existing intervening drugs, and potential drug/compound prediction for these critical targets to strengthen and expand our findings.

RESULTS

After Bonferron correction (p < 0.05/734), RPN1 (OR: 0.96; 95%CI: (0.95, 0.97)), GDI2 (OR: 0.94; 95%CI: (0.92, 0.96)), NT5C (OR: 0.97; 95%CI: (0.95, 0.98)) had a significant negative causal association with telomere length; TYRO3 (OR: 1.11; 95%CI: (1.09, 1.15)) had a significant positive causal association with telomere length. GDI2 shared the same genetic variants with telomere length (coloc.abf-PPH 4 > 0.8).

CONCLUSION

Genetically determined plasma RPN1, GDI2, NT5C, and TYRO3 have significant causal effects on telomere length and can potentially be drug targets. Further exploration of the role and mechanism of these proteins/genes in regulating telomere length is needed.

An Analysis Regarding the Association Between DAZ Interacting Zinc Finger Protein 1 (DZIP1) and Colorectal Cancer (CRC)

Colorectal cancer (CRC) is the third most common malignant disease worldwide, and its incidence is increasing, but the molecular mechanisms of this disease are highly heterogeneous and still far from being fully understood. Increasing evidence suggests that fibrosis mediated by abnormal activation of fibroblasts based in the microenvironment is associated with a poor prognosis. However, the function and pathogenic mechanisms of fibroblasts in CRC remain unclear. Here, combining scrna-seq and clinical specimen data, DAZ Interacting Protein 1 (DZIP1) was found to be expressed on fibroblasts and cancer cells and positively correlated with stromal deposition. Importantly, pseudotime-series analysis showed that DZIP1 levels were up-regulated in malignant transformation of fibroblasts and experimentally confirmed that DZIP1 modulates activation of fibroblasts and promotes epithelial-mesenchymal transition (EMT) in tumor cells. Further studies showed that DZIP1 expressed by tumor cells also has a driving effect on EMT and contributes to the recruitment of more fibroblasts. A similar phenomenon was observed in xenografted nude mice. And it was confirmed in xenograft mice that downregulation of DZIP1 expression significantly delayed tumor formation and reduced tumor size in CRC cells. Taken together, our findings suggested that DZIP1 was a regulator of the CRC mesenchymal phenotype. The revelation of targeting DZIP1 provides a new avenue for CRC therapy.

A Potential biomarker for the early diagnosis of OSCC: saliva and serum PrPC

Background: Oral squamous cell carcinoma (OSCC) is frequently diagnosed at an advanced stage, and the high mortality of patients is mainly due to the delay of diagnosis. Cellular prion protein (PrPC) contributes to the occurrence and development of many malignant tumors. However, little has been known about the clinical and diagnostic value of PrPC in OSCC. This study investigated the levels of PrPC in the saliva and serum of patients with OSCC, OPMD and control group and their diagnostic value. Methods: The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Clinical Proteome Tumor Analysis Consortium (CPTAC) databases were analyzed to evaluate the expression of human prion protein gene (PRNP) mRNA and PrPC in OSCC. Enzyme-linked Immunosorbent Assay (ELISA) was utilized to detect the expression of PrPC in saliva and serum samples of OSCC, OPMD and control groups. Furthermore, diagnostic value and clinical significance of PrPC in OSCC was identified. Protein-protein interaction (PPI) network was constructed by STRING. GO and KEGG analysis were performed by ClusterProfiler. Results: The levels of PRNP mRNA and PrPC in OSCC were significantly higher than those in the control group from databases (P<0.05). Besides, salivary and serum PrPC of OSCC patients showed increased levels compared with OPMD and control groups (P<0.05). The expression of salivary and serum PrPC of OSCC was correlated with the degree of differentiation (P<0.05), and the expression of PrPC from CPTAC was related to tumor stage of OSCC (P<0.05). The areas under the diagnostic curves (AUCs) of salivary and serum PrPC were 0.807 and 0.671, respectively. GO and KEGG analysis revealed that PrPC might be related to cell adhesion, cell differentiation, signal transduction and apoptosis, and participate in the pathways of focal adhesion, PI3K-Akt signaling pathway and ECM- receptor interaction in OSCC. Conclusion: PrPC in saliva and serum may be a potential biomarker for early diagnosis of OSCC.

A Novel Natural Killer Cell-related Gene Signature for Improving the Prediction of Prognosis and Immunotherapy Response in Bladder Cancer

BACKGROUND

Bladder cancer (BLCA) is a commonly diagnosed cancer worldwide that exhibits high rates of recurrence and metastasis. Immunotherapy is increasingly being recognised in the clinical management of bladder cancer. In addition, the prospect of developing Natural Killer (NK) cell-related immunotherapy is promising in BLCA.

METHODS

We established and verified a prognostic signature based on NK cell-related gene expression. We then calculated the NKscore of BLCA samples and correlated it with the clinical outcomes, molecular subtypes of BLCA, tumour microenvironment (TME), and predicted efficacy of immune checkpoint inhibitors (ICI) and chemotherapy drugs to thoroughly explore the implications of the NKscore. Finally, the role of the NK signature gene HECTD1 in BLCA was verified by Quantitative Real-time PCR, Cell Counting Kit-8 Assay (CCK-8), Transwell Assay and Colony Formation Experiment.

RESULTS

We analysed NK cell-associated genes and identified six genes with significant prognostic relevance. A high NK score significantly represents a worse prognosis. NKscore was significantly correlated with seven types of classical molecular subtype classifications of BLCA. In addition, NKscore positively correlates with NK-related immune checkpoints, suggesting that emerging NK cell immune checkpoint inhibitors, such as monalizumab, may have potential therapeutic promise for patients with high NKscore. The results of the T cell inflamed score (TIS) and tumour immune dysfunction exclusion (TIDE) score confirmed the suitability of immunotherapy for patients with a high NK score. Likewise, patients with a high NK score may be more suitable for several significant chemotherapeutic drugs. Functional experiments showed that the knockdown of HECTD1 significantly attenuated the proliferation, migration, and invasion ability of tumour cells.

CONCLUSION

To sum up, the capability of our signature to predict prognosis and immunotherapy response was robust. Hopefully, these results will provide new insights for BLCA research and patient immunotherapy.

Interpreting Gene Ontology Annotations Derived from Sequence Homology Methods

The Gene Ontology (GO) project describes the functions of the gene products of organisms from all kingdoms of life in a standardized way, enabling powerful analyses of experiments involving genome-wide analysis. The scientific literature is used to convert experimental results into GO annotations that systematically classify gene products' functions. However, to address the fact that only a minor fraction of all genes has been characterized experimentally, multiple predictive methods to assign GO annotations have been developed since the inception of GO. Sequence homologies between novel genes and genes with known functions help to approximate the roles of these non-characterized genes. Here we describe the main sequence homology methods to produce annotations: pairwise comparison (BLAST), protein profile models (InterPro), and phylogenetic-based annotation (PAINT). Some of these methods can be implemented with genome analysis pipelines (BLAST and InterPro2GO), while PAINT is curated by the GO consortium.

A model for predicting clinical prognosis based on brain metastasis-related genes in patients with breast cancer

Background

Brain metastasis (BM) is a clinically relevant cause of death in patients with breast cancer (BRCA). This study was designed to develop a clinical model capable of predicting BRCA patients' prognostic outcomes according to the expression of BM-related genes (BMRGs).

Methods

The public Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases served as data sources. BMRGs of BRCA were selected from previous literature. Differences among BRCA molecular subtypes were compared using R 'limma' package. The impact of BM-related differentially expressed genes (BM_DEGs) on BRCA patients' outcomes was explored with a risk score model, after which the relationship between these risk scores and immune cell infiltration was examined. Risk scores were also used to judge the predicted efficacy of immunotherapeutic interventions. The utility of risk scores in combination with clinicopathological characteristics was evaluated as a predictor of patient's survival through univariate and multivariate analyses.

Results

The R limma package was used to explore differential gene expression, after which 12 BM_DEGs were incorporated into a risk scoring model. The resultant risk scores were able to predict immunotherapeutic treatment efficacy. In addition, a nomogram incorporating risk scores, stage, and age was established. The nomogram was able to reliably predict the overall survival (OS) of BRCA patients, yielding predictive outcomes that aligned well with actual observations.

Conclusions

In summary, a predictive clinical model for BRCA patients was successfully established in this study, providing a valuable tool that may be particularly helpful for the assessment of patients facing a risk of BM development.

Adjustment of p-value expression to ontology using machine learning for genetic prediction, prioritization, interaction, and its validation in glomerular disease

The results of gene expression analysis based on p-value can be extracted and sorted by their absolute statistical significance and then applied to multiple similarity scores of their gene ontology (GO) terms to promote the combination and adjustment of these scores as essential predictive tasks for understanding biological/clinical pathways. The latter allows the possibility to assess whether certain aspects of gene function may be associated with other varieties of genes, to evaluate regulation, and to link them into networks that prioritize candidate genes for classification by applying machine learning techniques. We then detect significant genetic interactions based on our algorithm to validate the results. Finally, based on specifically selected tissues according to their normalized gene expression and frequencies of occurrence from their different biological and clinical inputs, a reported classification of genes under the subject category has validated the abstract (glomerular diseases) as a case study.

Network pharmacology and molecular docking-based investigation on traditional Chinese medicine Astragalus membranaceus in oral ulcer treatment

To analyze the mechanism of Astragalus membranaceus (AM) in molecular level in the oral ulcer (OU) treatment with reference to network pharmacology. Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform database was used in screening the AM active components and AM action targets; GeneCards database was used to screen OU targets; the common target were screened by Venny online tool; Cytoscape software was applied to construct the target gene regulation map of AM active components; STRING database was used to construct the protein-protein interaction network and the key targets were screened as per degree value; gene ontology enrichment and KEGG pathway enrichment of interactive genes were calculated through David database. There were 17 active ingredients and 429 target spots in Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform database. There are 606 target genes for OU in GeneCards database. There are 67 common targets, including 10 key targets: IL10, IL6, TNF, IL1B, CXCL8, CCL2, TLR4, IL4, ICAM1, and IFNG. It involves 30 gene ontology terms and 20 KEGG signal channels. The molecular docking results showed that quercetin and kaempferol had a good binding activity with IL6, IL1B, TNF, and CCL2. Network pharmacological analysis shows that AM can regulate multiple signal pathways through multiple targets to treat OU.

ProFAB-open protein functional annotation benchmark

As the number of protein sequences increases in biological databases, computational methods are required to provide accurate functional annotation with high coverage. Although several machine learning methods have been proposed for this purpose, there are still two main issues: (i) construction of reliable positive and negative training and validation datasets, and (ii) fair evaluation of their performances based on predefined experimental settings. To address these issues, we have developed ProFAB: Open Protein Functional Annotation Benchmark, which is a platform providing an infrastructure for a fair comparison of protein function prediction methods. ProFAB provides filtered and preprocessed protein annotation datasets and enables the training and evaluation of function prediction methods via several options. We believe that ProFAB will be useful for both computational and experimental researchers by enabling the utilization of ready-to-use datasets and machine learning algorithms for protein function prediction based on Gene Ontology terms and Enzyme Commission numbers. ProFAB is available at https://github.com/kansil/ProFAB and https://profab.kansil.org.

Prediction of Transcription Factor Regulators and Gene Regulatory Networks in Tomato Using Binding Site Information

Gene regulatory networks (GRNs) represent the regulatory links between transcription factors (TF) and their target genes. In plants, they are essential to understand transcriptional programs that control important agricultural traits such as yield or (a)biotic stress response. Although several high- and low-throughput experimental methods have been developed to map GRNs in plants, these are sometimes expensive, come with laborious protocols, and are not always optimized for tomato, one of the most important horticultural crops worldwide. In this chapter, we present a computational method that covers two protocols: one protocol to map gene identifiers between two different tomato genome assemblies, and another protocol to predict putative regulators and delineate GRNs given a set of functionally related or coregulated genes by exploiting publicly available TF-binding information. As an example, we applied the motif enrichment protocol on tomato using upregulated genes in response to jasmonate, as well as upregulated and downregulated genes in plants with genotypes OENAM1 and nam1, respectively. We found that our protocol accurately infers the expected TFs as top enriched regulators and identifies GRNs functionally enriched in biological processes related with the experimental context under study.

UBE2S promotes the development of ovarian cancer by promoting PI3K/AKT/mTOR signaling pathway to regulate cell cycle and apoptosis

Background

Ovarian cancer is one of the important factors that seriously threaten women's health and its morbidity and mortality ranks eighth among female cancers in the world. It is critical to identify potential and promising biomarkers for prognostic evaluation and molecular therapy of OV. Ubiquitin-conjugating enzyme E2S (UBE2S), a potential oncogene, regulates the malignant progression of various tumors; however, its role in OV is still unclear.

Methods

The expression and prognostic significance of UBE2S at the pan-cancer level were investigated through high-throughput gene expression analysis and clinical prognostic data from TCGA, GEPIA, and GEO databases. 181 patients with OV were included in this study. Cell culture and cell transfection were performed on OV cell lines (SKOV3 and A2780) and a normal ovarian cell line (IOSE80). The expression level and prognostic significance of UBE2S in OV were verified by western blot, immunohistochemistry, and Kaplan–Meier survival analysis. Through cell transfection, CCK-8, Ki-67 immunofluorescence, wound healing, Transwell, clonogenic, and flow cytometry assays, the effect and detailed mechanism of UBE2S knockdown on the malignant biological behavior of OV cells were explored.

Results

UBE2S exhibited abnormally high expression at the pan-cancer level. The results of RT-qPCR and Western blotting indicated that UBE2S was significantly overexpressed in ovarian cancer cell lines compared with normal cell lines (P < 0.05). Kaplan–Meier survival analysis and Immunohistochemistry indicated that overexpression of UBE2S was related to poor prognosis of OV (HR > 1, P < 0.05). Results of in vitro experiments indicated that UBE2S gene knockdown might inhibit the proliferation, invasion, and prognosis of OV cells by inhibiting the PI3K/AKT/mTOR signaling pathway, thereby blocking the cell cycle and promoting apoptosis (P < 0.05).

Conclusion

UBE2S is a potential oncogene strongly associated with a poor prognosis of OV patients. Knockdown of UBE2S could block the cell cycle and promote apoptosis by inhibiting the PI3K/AKT/mTOR pathway and ultimately inhibit the proliferation, migration and prognosis of ovarian cancer, which suggested that UBE2S might be used for molecular therapy and prognostic evaluation of ovarian cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s10020-022-00489-2.

Network based systems biology approach to identify diseasome and comorbidity associations of Systemic Sclerosis with cancers

Systemic Sclerosis (SSc) is an autoimmune disease associated with changes in the skin's structure in which the immune system attacks the body. A recent meta-analysis has reported a high incidence of cancer prognosis including lung cancer (LC), leukemia (LK), and lymphoma (LP) in patients with SSc as comorbidity but its underlying mechanistic details are yet to be revealed. To address this research gap, bioinformatics methodologies were developed to explore the comorbidity interactions between a pair of diseases. Firstly, appropriate gene expression datasets from different repositories on SSc and its comorbidities were collected. Then the interconnection between SSc and its cancer comorbidities was identified by applying the developed pipelines. The pipeline was designed as a generic workflow to demonstrate a premise comorbid condition that integrate regarding gene expression data, tissue/organ meta-data, Gene Ontology (GO), Molecular pathways, and other online resources, and analyze them with Gene Set Enrichment Analysis (GSEA), Pathway enrichment and Semantic Similarity (SS). The pipeline was implemented in R and can be accessed through our Github repository: https://github.com/hiddenntreasure/comorbidity. Our result suggests that SSc and its cancer comorbidities share differentially expressed genes, functional terms (gene ontology), and pathways. The findings have led to a better understanding of disease pathways and our developed methodologies may be applied to any set of diseases for finding any association between them. This research may be used by physicians, researchers, biologists, and others.

Prognostic Value of a Ferroptosis-Related Gene Signature in Patients With Head and Neck Squamous Cell Carcinoma

Background: Ferroptosis is an iron-dependent programmed cell death (PCD) form that plays a crucial role in tumorigenesis and might affect the antitumor effect of radiotherapy and immunotherapy. This study aimed to investigate distinct ferroptosis-related genes, their prognostic value and their relationship with immunotherapy in patients with head and neck squamous cell carcinoma (HNSCC). Methods: The differentially expressed ferroptosis-related genes in HNSCC were filtered based on multiple public databases. To avoid overfitting and improve clinical practicability, univariable, least absolute shrinkage and selection operator (LASSO) and multivariable Cox algorithms were performed to construct a prognostic risk model. Moreover, a nomogram was constructed to forecast individual prognosis. The differences in tumor mutational burden (TMB), immune infiltration and immune checkpoint genes in HNSCC patients with different prognoses were investigated. The correlation between drug sensitivity and the model was firstly analyzed by the Pearson method. Results: Ten genes related to ferroptosis were screened to construct the prognostic risk model. Kaplan-Meier (K-M) analysis showed that the prognosis of HNSCC patients in the high-risk group was significantly lower than that in the low-risk group (P < 0.001), and the area under the curve (AUC) of the 1-, 3- and 5-year receiver operating characteristic (ROC) curve increased year by year (0.665, 0.743, and 0.755). The internal and external validation further verified the accuracy of the model. Then, a nomogram was build based on the reliable model. The C-index of the nomogram was superior to a previous study (0.752 vs. 0.640), and the AUC (0.729 vs. 0.597 at 1 year, 0.828 vs. 0.706 at 3 years and 0.853 vs. 0.645 at 5 years), calibration plot and decision curve analysis (DCA) also shown the satisfactory predictive capacity. Furthermore, the TMB was revealed to be positively correlated with the risk score in HNSCC patients (R = 0.14; P < 0.01). The differences in immune infiltration and immune checkpoint genes were significant (P < 0.05). Pearson analysis showed that the relationship between the model and the sensitivity to antitumor drugs was significant (P < 0.05). Conclusion: Our findings identified potential novel therapeutic targets, providing further potential improvement in the individualized treatment of patients with HNSCC.

Identification and Validation of Potential Candidate Genes of Colorectal Cancer in Response to Fusobacterium nucleatum Infection

Objective: Recent investigations revealed the relationship between Fusobacterium nucleatum (Fn) infection and colorectal cancer (CRC). However, how the host genes changes contribute to CRC in response to Fn infection remains largely unknown.

Materials and methods: In the present study, we aimed to comprehensively analyze microarray data obtained from a Caco-2 infection cell model using integrated bioinformatics analysis and further identify and validate potential candidate genes in Fn-infected Caco-2 cells and CRC specimens.

Results: We identified 10 hub genes potentially involved in Fn induced tumor initiation and progression. Furthermore, we demonstrated that the expression of centrosomal protein of 55 kDa (CEP55) is significantly higher in Fn-infected Caco-2 cells. Knocking down of CEP55 could arrest the cell cycle progression and induce apoptosis in Fn-infected Caco-2 cells. The expression of CEP55 was positively correlated with the Fn amount in Fn-infected CRC patients, and these patients with high CEP55expression had an obviously poorer differentiation, worse metastasis and decreased cumulative survival rate.

Conclusion: CEP55 plays an important role in Fn-infected colon cancer cell growth and cell cycle progression and could be used as a new diagnostic and prognostic biomarker for Fn-infected CRC.

Vorinostat targets UBE2C to reverse epithelial-mesenchymal transition and control cervical cancer growth through the ubiquitination pathway

Vorinostat is a histone deacetylase inhibitor (HDACi) that was demonstrated in our previous study to inhibit the proliferation, migration, and invasion of cervical cancer cells by regulating the PI3K/Akt signaling pathway. However, the molecular mechanism of vorinostat in cervical cancer treatment remains to be further elucidated. A nude mouse xenograft model was established to analyze the antitumor effect of vorinostat in vivo. The combination of iTRAQ-based proteomics and parallel reaction monitoring (PRM) technology has proven to be an efficient and reliable method to identify potential targets for cancer chemotherapy. In this study, 254 differentially expressed proteins in vorinostat-treated cervical cancer cells, among which 180 were upregulated and 74 were downregulated, were identified by using an iTRAQ-based proteomic strategy. Subsequent bioinformatic and PRM analysis of these differentially expressed proteins indicated that UBE2C is a promising target of vorinostat in the inhibition of cervical cancer cell proliferation. We confirmed that the expression of endogenous UBE2C in cervical cancer cell lines was significantly higher than that in normal cervical epithelial cell lines. Additionally, we found that vorinostat downregulated the expression of UBE2C, SQSTM1/p62, N-cadherin, vimentin and upregulated E-cadherin in SiHa and HeLa cells. Our results also showed that vorinostat can downregulate the expression of SQSTM1/p62, N-cadherin, and vimentin during the treatment of cervical cancer cells by regulating UBE2C, while upregulating the expression of E-cadherin. In conclusion, vorinostat reverses epithelial-mesenchymal transition by targeting UBE2C and controls the proliferation of cervical cancer cells through the ubiquitination pathway. UBE2C can be used as a promising target for the development of vorinostat treatment strategies.

Gene Ontology representation for transcription factor functions

Transcription plays a central role in defining the identity and functionalities of cells, as well as in their responses to changes in the cellular environment. The Gene Ontology (GO) provides a rigorously defined set of concepts that describe the functions of gene products. A GO annotation is a statement about the function of a particular gene product, represented as an association between a gene product and the biological concept a GO term defines. Critically, each GO annotation is based on traceable scientific evidence. Here, we describe the different GO terms that are associated with proteins involved in transcription and its regulation, focusing on the standard of evidence required to support these associations. This article is intended to help users of GO annotations understand how to interpret the annotations and can contribute to the consistency of GO annotations. We distinguish between three classes of activities involved in transcription or directly regulating it - general transcription factors, DNA-binding transcription factors, and transcription co-regulators.

Identification of potential biomarkers in dengue via integrated bioinformatic analysis

Dengue fever virus (DENV) is a global health threat that is becoming increasingly critical. However, the pathogenesis of dengue has not yet been fully elucidated. In this study, we employed bioinformatics analysis to identify potential biomarkers related to dengue fever and clarify their underlying mechanisms. The results showed that there were 668, 1901, and 8283 differentially expressed genes between the dengue-infected samples and normal samples in the GSE28405, GSE38246, and GSE51808 datasets, respectively. Through overlapping, a total of 69 differentially expressed genes (DEGs) were identified, of which 51 were upregulated and 18 were downregulated. We identified twelve hub genes, including MX1, IFI44L, IFI44, IFI27, ISG15, STAT1, IFI35, OAS3, OAS2, OAS1, IFI6, and USP18. Except for IFI44 and STAT1, the others were statistically significant after validation. We predicted the related microRNAs (miRNAs) of these 12 target genes through the database miRTarBase, and finally obtained one important miRNA: has-mir-146a-5p. In addition, gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were carried out, and a protein–protein interaction (PPI) network was constructed to gain insight into the actions of DEGs. In conclusion, our study displayed the effectiveness of bioinformatics analysis methods in screening potential pathogenic genes in dengue fever and their underlying mechanisms. Further, we successfully predicted IFI44L and IFI6, as potential biomarkers with DENV infection, providing promising targets for the treatment of dengue fever to a certain extent.

Dengue fever is a mosquito borne viral disease caused by a single stranded RNA virus with four serotypes. DENV infection can cause various diseases, such as breakbone fever, haemorrhagic fever, and shock syndrome. As one of the most viral diseases leading to incidence rate and mortality in animal arthropods, Dengue fever has become an increasingly serious global health threat. However, the pathogenesis of dengue fever has not been fully elucidated. In this study, we used bioinformatics analysis to identify potential biomarkers associated with dengue fever and elucidate their underlying mechanisms. Finally, we predicted that IFI44L and IFI6 might be potential biomarkers of DENV infection. This finding provides a promising target for the treatment of dengue fever to a certain extent. In addition, the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, protein–protein interaction (PPI) network were implemented to analyze the key differentially expressed genes after DENV infection, and the related mechanisms were illuminated by this study.

Prognostic value of fatty acid metabolism-related genes in patients with hepatocellular carcinoma

The deregulation of fatty acid metabolism plays a crucial role in cancer. However, the prognostic value of genes involved in the metabolism in hepatocellular carcinoma (HCC) remains largely unknown. We first constructed a multi-fatty acid metabolic gene prognostic model of HCC based on The Cancer Genome Atlas (TCGA) and further validated it using the International Cancer Genome Consortium (ICGC) database. The model was integrated with the clinical parameters, and a nomogram was built and weighted. Moreover, immune cell infiltration of the tumor microenvironment was investigated. A prognostic model was constructed using 6 selected fatty acid metabolism-related genes, and HCC patients were divided into high- and low-risk groups. Receiver operating characteristic curve (ROC) analysis, principal component analysis (PCA), and t-distributed stochastic neighbor embedding (t-SNE) analysis showed the optimal performance of the model. The concordance index (C-index), ROC curve, calibration plot and decision curve analysis (DCA) all confirmed the satisfactory predictive capacity of the nomogram. The analysis of immune cell infiltration in HCC patients revealed a correlation with different risk levels. Our findings indicate that a prognostic model based on fatty acid metabolism-related genes has superior predictive capacities, which provides the possibility for further improving the individualized treatment of patients with HCC.

VWCE as a potential biomarker associated with immune infiltrates in breast cancer

PURPOSE

Von Willebrand Factor C and EGF Domains (VWCE) is an important gene that regulates cell adhesion, migration, and interaction. However, the correlation between VWCE expression and immune infiltrating in breast cancer remain unclear. In this study, we investigated the correlation between VWCE expression and immune infiltration levels in breast cancer.

METHODS

The expression of VWCE was analyzed by the tumor immune estimation resource (TIMER) and DriverDB databases. Furthermore, genes co-expressed with VWCE and gene ontology (GO) enrichment analysis were investigated by the STRING and Enrichr web servers. Also, we performed the single nucleotide variation (SNV), copy number variation (CNV), and pathway activity analysis through GSCALite. Subsequently, the relationship between VWCE expression and tumor immunity was analyzed by TIMER and TISIDB databases, and further verified the results using Quantitative Real-Time PCR (RT-PCR), Western blotting, and immunohistochemistry.

RESULTS

The results showed that the expression of VWCE mRNA in breast cancer tissue was significantly lower than that in normal tissues. We found that the expression level of VWCE was associated with subtypes, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2) status of breast cancer patients, but there was no significant difference in the expression of VWCE was found in age and nodal status. Further analyses indicated that VWCE was correlated with the activation or inhibition of multiple oncogenic pathways. Additionally, VWCE expression was negatively correlated with the expression of STAT1 (Th1 marker, r = - 0.12, p = 6e-05), but positively correlated with the expression of MS4A4A (r = 0.28, p = 0). These results suggested that the expression of VWCE was correlated with immune infiltration levels of Th1 and M2 macrophage in breast cancer.

CONCLUSIONS

In our study, VWCE expression was associated with a better prognosis and was immune infiltration in breast cancer. These findings demonstrate that VWCE is a potential prognostic biomarker and correlated with tumor immune cell infiltration, and maybe a promising therapeutic target in breast cancer.

SCUBE3 serves as an independent poor prognostic factor in breast cancer

BACKGROUND

Accumulating evidences indicate that the signal peptide-CUB-EGF-like domain-containing protein 3 (SCUBE3) plays a key role in the development and progression of many human cancers. However, the underlying mechanism and prognosis value of SCUBE3 in breast cancer are still unclear.

METHODS

The clinical data of 137 patients with breast cancer who underwent surgical resection in Taizhou Hospital of Zhejiang Province were retrospectively analyzed. We first conducted a comprehensive study on the expression pattern of SCUBE3 using the Tumor Immune Estimation Resource (TIMER) and UALCAN databases. In addition, the expression of SCUBE3 in breast tumor tissues was confirmed by immunohistochemistry. The protein-protein interaction analysis and functional enrichment analysis of SCUBE3 were analyzed using the STRING and Enrichr databases. Moreover, tissue microarray (TMA) was used to analyze the relationship between SCUBE3 expression levels and clinical-pathological parameters, such as histological type, grade, the status of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor (HER2). We further supplemented and identified the above results using the UALCAN and bc-GenExMiner v4.4 databases from TCGA data. The correlation between the expression of SCUBE3 and survival was calculated by multivariate Cox regression analysis to investigate whether SCUBE3 expression may be an independent prognostic factor of breast cancer.

RESULTS

We found that the expression level of SCUBE3 was significantly upregulated in breast cancer tissue compared with adjacent normal tissues. The results showed that the distribution of breast cancer patients in the high expression group and the low expression group was significantly different in ER, PR, HER2, E-cadherin, and survival state (p < 0.05), but there was no significant difference in histologic grade, histologic type, tumor size, lymph node metastasis, TMN stage, subtypes, or recurrence (p > 0.05). In addition, the high expression of SCUBE3 was associated with relatively poor prognosis of ER- (p = 0.012), PR- (p = 0.029), HER2 + (p = 0.007). The multivariate Cox regression analysis showed that the hazard ratio (HR) was 2.80 (95% CI 1.20-6.51, p = 0.0168) in individuals with high SCUBE3 expression, and HR was increased by 1.86 (95% CI 1.06-3.25, p = 0.0300) for per 1-point increase of SCUBE3 expression.

CONCLUSIONS

These findings demonstrate that the high expression of SCUBE3 indicates poor prognosis in breast cancer. SCUBE3 expression may serve as a potential diagnostic indicator of breast cancer.

Altered transcriptome and disease-related phenotype emerge only after fibroblasts harvested from patients with age-related macular degeneration are differentiated into retinal pigment epithelium

We have reported previously that retinal pigment epithelium (RPE) differentiated from induced pluripotent stem cells (iPSC) generated from fibroblasts of patients with age-related macular degeneration (AMD) exhibit a retinal degenerative disease phenotype and a distinct transcriptome compared to age-matched controls. Since the genetic composition of the iPSC and RPE are inherited from fibroblasts, we investigated whether differential behavior was present in the parental fibroblasts and iPSC prior to differentiation of the cell lines into RPE. Principal component analyses revealed significant overlap (essentially no differences) in the transcriptome of fibroblasts between AMD and controls. After reprogramming, there was no significant difference in the transcriptome of iPSC generated from AMD versus normal donors. In contrast, the transcriptome of RPE derived from iPSC segregated into two distinct clusters of AMD-derived cells versus controls. Interestingly, mitochondrial dysfunction in AMD-derived RPE was evident after approximately two months in culture. Moreover, these differences in mitochondrial dysfunction were not evident in the parental fibroblasts and iPSC. This study demonstrates an altered transcriptome and impaired mitochondrial function in RPE derived from AMD patients versus controls, and demonstrates these differences are not present in the original fibroblasts or iPSC. These results suggest that pathology in AMD is triggered upon differentiation of parent cells into RPE. More study of this phenomenon could advance the current understandings of the etiology of AMD and the development of novel therapeutic targets.

Dual RNA Sequencing Meta-analysis in Plasmodium Infection Identifies Host-Parasite Interactions

Dual RNA sequencing (RNA-Seq) is the simultaneous transcriptomic analysis of interacting symbionts, for example, in malaria. Potential cross-species interactions identified by correlated gene expression might highlight interlinked signaling, metabolic, or gene regulatory pathways in addition to physically interacting proteins. Often, malaria studies address one of the interacting organisms-host or parasite-rendering the other "contamination." Here we perform a meta-analysis using such studies for cross-species expression analysis. We screened experiments for gene expression from host and Plasmodium. Out of 171 studies in Homo sapiens, Macaca mulatta, and Mus musculus, we identified 63 potential studies containing host and parasite data. While 16 studies (1,950 samples) explicitly performed dual RNA-Seq, 47 (1,398 samples) originally focused on one organism. We found 915 experimental replicates from 20 blood studies to be suitable for coexpression analysis and used orthologs for meta-analysis across different host-parasite systems. Centrality metrics from the derived gene expression networks correlated with gene essentiality in the parasites. We found indications of host immune response to elements of the Plasmodium protein degradation system, an antimalarial drug target. We identified well-studied immune responses in the host with our coexpression networks, as our approach recovers known broad processes interlinked between hosts and parasites in addition to individual host and parasite protein associations. The set of core interactions represents commonalities between human malaria and its model systems for prioritization in laboratory experiments. Our approach might also allow insights into the transferability of model systems for different pathways in malaria studies.IMPORTANCE Malaria still causes about 400,000 deaths a year and is one of the most studied infectious diseases. The disease is studied in mice and monkeys as lab models to derive potential therapeutic intervention in human malaria. Interactions between Plasmodium spp. and its hosts are either conserved across different host-parasite systems or idiosyncratic to those systems. Here we use correlation of gene expression from different RNA-Seq studies to infer common host-parasite interactions across human, mouse, and monkey studies. First, we find a set of very conserved interactors, worth further scrutiny in focused laboratory experiments. Second, this work might help assess to which extent experiments and knowledge on different pathways can be transferred from models to humans for potential therapy.

Knowledge Representation for Multi-Scale Physiology Route Modeling

We present a framework for the topological and semantic assembly of multiscale physiology route maps. The framework, called ApiNATOMY, consists of a knowledge representation (KR) model and a set of knowledge management (KM) tools. Using examples of ApiNATOMY route maps, we present a KR format that is suitable for the analysis and visualization by KM tools. The conceptual KR model provides a simple method for physiology experts to capture process interactions among anatomical entities. In this paper, we outline the KR model, modeling format, and the KM procedures to translate concise abstraction-based specifications into fully instantiated models of physiology processes.

The Gene Ontology resource: enriching a GOld mine

The Gene Ontology Consortium (GOC) provides the most comprehensive resource currently available for computable knowledge regarding the functions of genes and gene products. Here, we report the advances of the consortium over the past two years. The new GO-CAM annotation framework was notably improved, and we formalized the model with a computational schema to check and validate the rapidly increasing repository of 2838 GO-CAMs. In addition, we describe the impacts of several collaborations to refine GO and report a 10% increase in the number of GO annotations, a 25% increase in annotated gene products, and over 9,400 new scientific articles annotated. As the project matures, we continue our efforts to review older annotations in light of newer findings, and, to maintain consistency with other ontologies. As a result, 20 000 annotations derived from experimental data were reviewed, corresponding to 2.5% of experimental GO annotations. The website (http://geneontology.org) was redesigned for quick access to documentation, downloads and tools. To maintain an accurate resource and support traceability and reproducibility, we have made available a historical archive covering the past 15 years of GO data with a consistent format and file structure for both the ontology and annotations.

Help, there are ‘omics’ in my comparative physiology!

‘Omics’ methods, such as transcriptomics, proteomics, lipidomics or metabolomics, yield simultaneous measurements of many related molecules in a sample. These approaches have opened new opportunities to generate and test hypotheses about the mechanisms underlying biochemical and physiological phenotypes. In this Commentary, we discuss general approaches and considerations for successfully integrating omics into comparative physiology. The choice of omics approach will be guided by the availability of existing resources and the time scale of the process being studied. We discuss the use of whole-organism extracts (common in omics experiments on small invertebrates) because such an approach may mask underlying physiological mechanisms, and we consider the advantages and disadvantages of pooling samples within biological replicates. These methods can bring analytical challenges, so we describe the most easily analyzed omics experimental designs. We address the propensity of omics studies to digress into ‘fishing expeditions’ and show how omics can be used within the hypothetico-deductive framework. With this Commentary, we hope to provide a roadmap that will help newcomers approach omics in comparative physiology while avoiding some of the potential pitfalls, which include ambiguous experiments, long lists of candidate molecules and vague conclusions.

Network pharmacology-based strategy for predicting therapy targets of Tripterygium wilfordii on acute myeloid leukemia

This is a study on the potential therapeutic targets and pharmacological mechanism of Tripterygium wilfordii (TW) in acute myeloid leukemia (AML) based on network pharmacology.Active components of TW were obtained by network pharmacology through oral bioavailability, drug-likeness filtration. Comparative analysis was used to investigate the overlapping genes between active ingredient's targets and AML treatment-related targets. Using STRING database to analyze interactions among overlapping genes. Both KEGG pathway analysis and Gene Ontology enrichment analysis were conducted in DAVID. These genes were analyzed for survival in OncoLnc database.We screened 53 active ingredients; the results of comparative analysis showed that 8 active ingredients had an effect on AML treatment. On the basis of the active ingredients and overlapping genes, we constructed the Drug-Compounds-Genes-Disease Network. Survival analysis of overlapping genes indicated that some targets possessed a significant influence on patients' survival and prognosis. The enrichment analysis showed that the main pathways of targets were Toll-like receptor signaling pathway, NF-kappa B signaling pathway, and HIF-1 signaling pathway.This study, using a network pharmacologic approach, provides another strategy that can help us to understand the mechanisms by which TW treats AML comprehensively.

Using Drosophila melanogaster To Discover Human Disease Genes: An Educational Primer for Use with "Amyotrophic Lateral Sclerosis Modifiers in Drosophila Reveal the Phospholipase D Pathway as a Potential Therapeutic Target"

Since the dawn of the 20th century, the fruit fly Drosophila melanogaster has been used as a model organism to understand the nature of genes and how they control development, behavior, and physiology. One of the most powerful experimental approaches employed in Drosophila is the forward genetic screen. In the 21st century, genome-wide screens have become popular tools for identifying evolutionarily conserved genes involved in complex human diseases. In the accompanying article "Amyotrophic Lateral Sclerosis Modifiers in Drosophila Reveal thePhospholipase DPathway as a Potential Therapeutic Target," Kankel and colleagues describe a forward genetic modifier screen to discover factors that contribute to the severe neurodegenerative disease amyotrophic lateral sclerosis (ALS). This primer briefly traces the history of genetic screens in Drosophila and introduces students to ALS. We then provide a set of guided reading questions to help students work through the data presented in the research article. Finally, several ideas for literature-based research projects are offered as opportunities for students to expand their appreciation of the potential scope of genetic screens. The primer is intended to help students and instructors thoroughly examine a current study that uses forward genetics in Drosophila to identify human disease genes.

Benchmarking gene ontology function predictions using negative annotations

MOTIVATION

With the ever-increasing number and diversity of sequenced species, the challenge to characterize genes with functional information is even more important. In most species, this characterization almost entirely relies on automated electronic methods. As such, it is critical to benchmark the various methods. The Critical Assessment of protein Function Annotation algorithms (CAFA) series of community experiments provide the most comprehensive benchmark, with a time-delayed analysis leveraging newly curated experimentally supported annotations. However, the definition of a false positive in CAFA has not fully accounted for the open world assumption (OWA), leading to a systematic underestimation of precision. The main reason for this limitation is the relative paucity of negative experimental annotations.

RESULTS

This article introduces a new, OWA-compliant, benchmark based on a balanced test set of positive and negative annotations. The negative annotations are derived from expert-curated annotations of protein families on phylogenetic trees. This approach results in a large increase in the average information content of negative annotations. The benchmark has been tested using the naïve and BLAST baseline methods, as well as two orthology-based methods. This new benchmark could complement existing ones in future CAFA experiments.

AVAILABILITY AND IMPLEMENTATION

All data, as well as code used for analysis, is available from https://lab.dessimoz.org/20_not.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Bioinformatics analysis reveals meaningful markers and outcome predictors in HBV-associated hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common type of malignant neoplasm of the liver with high morbidity and mortality. Extensive research into the pathology of HCC has been performed; however, the molecular mechanisms underlying the development of hepatitis B virus-associated HCC have remained elusive. Thus, the present study aimed to identify critical genes and pathways associated with the development and progression of HCC. The expression profiles of the GSE121248 dataset were downloaded from the Gene Expression Omnibus database and the differentially expressed genes (DEGs) were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) analyses were performed by using the Database for Annotation, Visualization and Integrated Discovery. Subsequently, protein-protein interaction (PPI) networks were constructed for detecting hub genes. In the present study, 1,153 DEGs (777 upregulated and 376 downregulated genes) were identified and the PPI network yielded 15 hub genes. GO analysis revealed that the DEGs were primarily enriched in ‘protein binding’, ‘cytoplasm’ and ‘extracellular exosome’. KEGG analysis indicated that DEGs were accumulated in ‘metabolic pathways’, ‘chemical carcinogenesis’ and ‘fatty acid degradation’. After constructing the PPI network, cyclin-dependent kinase 1, cyclin B1, cyclin A2, mitotic arrest deficient 2 like 1, cyclin B2, DNA topoisomerase IIα, budding uninhibited by benzimidazoles (BUB)1, TTK protein kinase, non-SMC condensin I complex subunit G, NDC80 kinetochore complex component, aurora kinase A, kinesin family member 11, cell division cycle 20, BUB1B and abnormal spindle microtubule assembly were identified as hub genes based on the high degree of connectivity by using Cytoscape software. In addition, overall survival (OS) and disease-free survival (DFS) analyses were performed using the Gene Expression Profiling Interactive Analysis online database, which revealed that the increased expression of all hub genes were associated with poorer OS and DFS outcomes. Receiver operating characteristic curves were constructed using GraphPad prism 7.0 software. The results confirmed that 15 hub genes were able to distinguish HCC form normal tissues. Furthermore, the expression levels of three key genes were analyzed in tumor and normal samples of the Human Protein Atlas database. The present results may provide further insight into the underlying mechanisms of HCC and potential therapeutic targets for the treatment of this disease.

CDK1-PLK1/SGOL2/ANLN pathway mediating abnormal cell division in cell cycle may be a critical process in hepatocellular carcinoma

This study aims to investigate the potential mechanisms and identify core biomarkers of Hepatocellular carcinoma (HCC). The profile GSE113850 was downloaded to analyze the differentially expressed genes. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction network analysis were used to reveal the main signal pathways of the differentially expressed genes (DEGs) and hub genes. The correlation between core gene expression and pathological stages, the disease-free survival analysis, the overall survival analysis were analyzed by Gene Expression Profiling Interactive Analysis. Furthermore, we reidentified the expression level of core genes of carcinoma tissues and para-carcinoma tissues from 14 HCC patients with real-time reverse transcription-polymerase chain reaction analysis (RT-PCR) and western blotting. After SK-Hep1 cell was treated with cyclin-dependent kinase 1 (CDK1) siRNA for 72 h, we detected the expression of the core genes and fluorescence-activated cell sorting analysis. A total of 378 DEGs were found. GO and KEGG analysis revealed that the DEGs were mainly enriched in the cell cycle. There were positive correlations among CDK1, polo-like kinase 1, shugoshin2 and anillin actin-binding protein. Moreover, the expression levels of four core genes were related to the HCC occurrence, pathological stages, and survivorship curve. The clinical HCC specimens verified the higher expression level of core genes by real-time RT-PCR. The transfection of siCDK1 in SK-Hep1 resulted in a disordered cell cycle. Furthermore, CDK1 knockdown suppressed the expression of PLK1, ANLN, and SGOL2. The CDK1-PLK1/SGOL2/ANLN pathway mediating abnormal cell division in the cell cycle might be a critical process in HCC.

Identification of Core Gene Expression Signature and Key Pathways in Colorectal Cancer

Objective

Colorectal cancer (CRC) is considered the most prevalent malignant tumor that contributes to high cancer-related mortality. However, the signaling pathways involved in CRC and CRC-driven genes are largely unknown. We sought to discover a novel biomarker in CRC.

Materials and Methods

All clinical CRC samples (n = 20) were from Renmin Hospital of Wuhan University. We first selected MAD2L1 by integrated bioinformatics analysis of a GSE dataset. Next, the expression of MAD2L1 in tissues and cell lines was verified by quantitative real-time PCR. The effects of MAD2L1 on cell growth, proliferation, the cell cycle, and apoptosis were examined by in vitro assays.

Results

We identified 683 shared DEGs (420 upregulated and 263 downregulated), and the top twenty genes (CDK1, CCNA2, TOP2A, PLK1, MAD2L1, AURKA, BUB1B, UBE2C, TPX2, RRM2, KIF11, NCAPG, MELK, NUSAP1, MCM4, RFC4, PTTG1, CHEK1, CEP55, DTL) were selected by integrated analysis. These hub genes were significantly overexpressed in CRC samples and were positively correlated. Our data revealed that the expression of MAD2L1 in CRC tissues is higher than that in normal tissues. MAD2L1 knockdown significantly suppressed CRC cell growth by impairing cell cycle progression and inducing cell apoptosis.

Conclusion

MAD2L1, as a novel oncogenic gene, plays a role in regulating cancer cell growth and apoptosis and could be used as a new biomarker for diagnosis and therapy in CRC.

The Identification of Key Gene Expression Signature and Biological Pathways in Metastatic Renal Cell Carcinoma

Purpose: To investigate the potential mechanisms contributing to metastasis of clear cell renal cell carcinoma (ccRCC), screen the hub genes, associated pathways of metastatic ccRCC and identify potential biomarkers.

Methods: The ccRCC metastasis gene expression profile GSE47352 was employed to analyze the differentially expressed genes (DEGs). DAVID was performed to assess Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The protein-protein interaction (PPI) network and modules were constructed. The function pathway, prognostic and diagnostic analysis of these hub genes was picked out to estimate their potential effects on metastasis of ccRCC.

Results: A total of 873 DEGs were identified (503 upregulated genes and 370 downregulated genes). Meanwhile, top 20 hub genes were displayed. GO analysis showed that the top 20 hub genes were enriched in regulation of phosphatidylinositol 3-kinase signaling, positive regulation of DNA replication, protein autophosphorylation, protein tyrosine kinase activity, etc. KEGG analysis indicated these hub genes were enriched in the Ras signaling pathway, PI3K-Akt signaling pathway, HIF-1 signaling pathway, Pathways in cancer, etc. The GO and KEGG enrichment analyses for the hub genes disclosed important biological features of metastatic ccRCC. PPI network showed the interaction of top 20 hub genes. Gene Set Enrichment Analysis (GSEA) revealed that some of the hub genes was associated with metastasis, epithelial mesenchymal transition (EMT), hypoxia cancer and adipogenesis of ccRCC. Some top hub genes were distinctive and new discoveries compared with that of the existing associated researches.

Conclusions: Our analysis uncovered that changes in signal pathways such as Ras signaling pathway, PI3K-Akt signaling pathway, etc. may be the main signatures of metastatic ccRCC. We identified several candidate biomarkers related with overall survival (OS) and disease-free survival (DFS) of ccRCC patients. Accordingly, they might be novel therapeutic targets and used as potential biomarkers for diagnosis, prognosis of ccRCC.

Identification of Key Pathways and Genes in the Orai2 Mediated Classical and Mesenchymal Subtype of Glioblastoma by Bioinformatic Analyses

Background

Ca²⁺ release-activated Ca²⁺ channels (CRAC) are the main Ca²⁺ entry pathway regulating intracellular Ca²⁺ concentration in a variety of cancer types. Orai2 is the main pore-forming subunit of CRAC channels in central neurons. To explore the role of Orai2 in glioblastoma (GBM), we investigated the key pathways and genes in Orai2-mediated GBM by bioinformatic analyses.

Methods

Via The Cancer Genome Atlas (TCGA), French, Sun, and Gene Expression Omnibus (GEO) (GDS3885) datasets, we collected 1231 cases with RNA-seq data and analyzed the functional annotation of Orai2 by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Univariate and multivariate survival analyses were applied to 823 patients with survival data.

Results

We discovered that Orai2 was markedly upregulated in GBM compared to normal brain samples and lower-grade gliomas (LGG). Survival analysis found that higher expression of Orai2 was independently associated with a worse prognosis of patients with the classical and mesenchymal subtypes of GBM. Simultaneously, Orai2 expression was higher in tumors of the classical and mesenchymal subtypes than other subtypes and was significantly correlated with classical- and mesenchymal-related genes. GO and KEGG pathway analysis revealed that genes significantly correlated with Orai2 were involved in the JNK pathway. Through screening transcriptomic data, we found a strong association between Orai2 and apoptosis, stemness, and an epithelial-mesenchymal transition- (EMT-) like phenotype.

Conclusion

As a prognostic factor, Orai2 is obviously activated in the classical and mesenchymal subtypes of GBM and promotes glioma cell self-renewal, apoptosis, and EMT-like by the JNK pathway. These findings indicate that Orai2 could be a candidate prognostic and therapeutic target, especially for the classical and mesenchymal subtypes of GBM.

Noninvasive Identification of Immune-Related Biomarkers in Hepatocellular Carcinoma

Primary liver carcinoma is one of the most common malignant tumors with a poor prognosis. This study aims to uncover the potential mechanisms and identify core biomarkers of hepatocellular carcinoma (HCC). The HCC gene expression profile GSE49515 was chosen to analyze the differentially expressed genes from purified RNA of peripheral blood mononuclear cells, including 10 HCC patients and 10 normal individuals. GO and KEGG pathway analysis and PPI network were used, and the enrichment of the core genes out of 15 hub genes was evaluated using GEPIA and GSEA, respectively. We employed flow cytometry to count mononuclear cells to verify our opinions. In this analysis, 344 DEGs were captured, including 188 upregulated genes and 156 downregulated genes; besides that, 15 hub genes were identified. GO analysis and KEGG analysis showed the DEGs were particularly involved in immune response, antigen processing and presentation, and infection and inflammation. The PPI network uncovered 2 modules were also mainly involved in immune response. In conclusion, our analysis disclosed the immune subversion was the major signature of HCC associated closely with JUN, VEGFA, TNFSF10, and TLR4, which could be novel noninvasive biomarkers in peripheral blood and targets for early diagnosis and therapy of HCC.

Research on the Potential Mechanism of Gypenosides on Treating Thyroid-Associated Ophthalmopathy Based on Network Pharmacology

Thyroid-associated ophthalmopathy is the commonest orbital disease in adults. However, shortcomings still exist in treatments. The aim of this study was to identify the efficacy and potential mechanism of gypenosides in the treatment of thyroid-associated ophthalmopathy. The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform was screened for active compounds of gypenosides, and targets were predicted using Swiss Target Prediction. The targets of thyroid-associated ophthalmopathy were obtained from Online Mendelian Inheritance in Man, Comparative Toxicogenomic Database and GeneCards Human gene database. Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome Pathways were determined based on the common targets. Protein-protein interaction (PPI) network was constructed to further understand of relationship among target genes, compounds and proteins. Molecular docking was performed to investigate the binding ability between gypenosides and hub genes. A total of 70 targets for gypenosides and 804 targets for thyroid-associated ophthalmopathy were obtained with 8 common targets identified. GO analysis and KEGG pathway analysis revealed that the hub genes were enriched in JAK-STAT, while Reactome pathways analysis indicated genes enriched in interleukin pathways. PPI network showed STAT1, STAT3, and STAT4 were at the center. Additionally, molecular docking indicated that STAT1 and STAT3 display good binding forces with gypenosides. This study indicates that target genes mainly enriched in JAK-STAT signaling pathway, particularly in STATs, which can be combined with gypenosides. This may suggest that gypenosides have curative effect on thyroid-associated ophthalmopathy via the JAK-STAT pathway.

Identification of the key pathways and genes involved in HER2-positive breast cancer with brain metastasis

BACKGROUND

The risk of brain metastasis (BM) in HER2-positive (+) breast cancer (BC) patients is significantly higher than that in HER2-negative (-) BC patients. The high incidence and mortality rate makes it urgent to elucidate the key pathways and genes involved and identify patients who are more at risk of developing BM.

MATERIALS AND METHODS

To identify the target genes in HER2+BC patients with BM, we analyzed the microarray datasets (GSE43837) derived from the Gene Expression Omnibus (GEO) database. The GEO2R tool was used to extract the differentially expressed genes (DEGs) involved in HER2+ primary BC and BC with BM. Bioinformatics methods including Gene Ontology (GO) functional annotation analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed with the screened DEGs. The protein-protein interactions of the DEGs were analyzed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized using Cytoscape software. Finally, GSEA analysis was performed to identify the hub genes and the important pathways.

RESULTS

A total of 751 upregulated and 285 downregulated DEGs were identified. The GO function and KEGG pathway enrichment analyses indicated that the DEGs were all enriched in the protein binding molecular function. The top five hub nodes were screened out, included PHLPP1, UBC, ACACB, TGFB1, and ACTB. The GSEA results demonstrated that the five hub genes are mainly enriched in the ribosomal pathway.

CONCLUSION

Our study suggests that the five hub genes (PHLPP1, UBC, ACACB, TGFB1, and ACTB) are associated with HER2+BC with BM. The GSEA analysis revealed that the ribosomal pathway seems to play a very important role in the pathogenesis of HER2+BC with BM.

Screening candidate microRNA-mRNA network for predicting the response to chemoresistance in osteosarcoma by bioinformatics analysis

The search for biomarkers is important for providing more targeted treatments for osteosarcoma patients with chemoresistance. In this study, differentially expressed microRNAs (miRNAs) were identified from miRNA expression profiles. And the target messenger RNAs (mRNAs) of miRNA were obtained from two websites in public domains. Analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway by these miRNA targets suggests that they may have potential links to osteosarcoma chemoresistance. In the protein-protein interaction (PPI) network, we screened three subnetworks and 10 hub RNAs, and analyzed through KEGG pathway and searched the PubMed database, indicating that they were significantly associated with drug resistance. Then we found 12 key mRNAs by analyzing the mRNA expression profile. Survival analyses showed that most of the 10 hub mRNAs and 12 key mRNAs had a significant influence on the prognosis of patients with chemoresistance osteosarcoma. A miRNA-mRNA network is constructed by integrating mRNAs and miRNAs information. The network biomarkers in this study have an advantage over traditional single-molecule biomarkers in terms of predictive power. And the mRNAs in this network biomarkers are supported by survival analysis or by existing theories. These results will contribute to the choice of chemotherapy before treatment and the prediction of patient prognosis.

Introducing high school students to the Gene Ontology classification system

We present a tutorial that introduces high school students to the Gene Ontology classification system which is widely used in genomics and systems biology studies to characterize large sets of genes based on functional and structural information. This classification system is a valuable and standardized method used to identify genes that act in similar processes and pathways and also provides insight into the overall architecture and distribution of genes and gene families associated with a particular tissue or disease. By means of this tutorial, students learn how the classification system works through analyzing a gene set using DAVID the Database for Annotation, Visualization and Integrated Discovery that incorporates the Gene Ontology system into its suite of analysis tools. This method of analyzing genes is used by our high school student interns to categorize gene expression data related to behavioral neuroscience. Students will get a feel for working with genes and gene sets, acquire vocabulary, obtain an understanding of how a database is structured and gain an awareness of the vast amount of information that is known about genes as well as the online analysis tools to manage this information that is nowadays available. Based on survey responses, students intellectually benefit from learning about the Gene Ontology System and using the DAVID tools, they are better prepared for future database use and they also find it enjoyable.

Hidden in plain sight: what remains to be discovered in the eukaryotic proteome?

The first decade of genome sequencing stimulated an explosion in the characterization of unknown proteins. More recently, the pace of functional discovery has slowed, leaving around 20% of the proteins even in well-studied model organisms without informative descriptions of their biological roles. Remarkably, many uncharacterized proteins are conserved from yeasts to human, suggesting that they contribute to fundamental biological processes (BP). To fully understand biological systems in health and disease, we need to account for every part of the system. Unstudied proteins thus represent a collective blind spot that limits the progress of both basic and applied biosciences. We use a simple yet powerful metric based on Gene Ontology BP terms to define characterized and uncharacterized proteins for human, budding yeast and fission yeast. We then identify a set of conserved but unstudied proteins in S. pombe, and classify them based on a combination of orthogonal attributes determined by large-scale experimental and comparative methods. Finally, we explore possible reasons why these proteins remain neglected, and propose courses of action to raise their profile and thereby reap the benefits of completing the catalogue of proteins' biological roles.

The Identification of Potential Biomarkers and Biological Pathways in Prostate Cancer

Purpose: The present study aims to explore the potential mechanisms contributing to prostate cancer (PCa), screen the hub genes, and identify potential biomarkers and correlated pathways of PCa progression.

Methods: The PCa gene expression profile GSE3325 was operated to analyze the differentially expressed genes (DEGs). DAVID was used to evaluate Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. A protein-protein interaction (PPI) network was constructed to visualize interactions of the hub genes. The prognostic and diagnostic analysis of these hub genes was carried out to evaluate their potential effects on PCa.

Results: A total of 847 DEGs were identified (427 upregulated genes and 420 downregulated genes). Meanwhile, top15 hub genes were showed. GO analysis displayed that the DEGs were mainly enriched in cell cycle, DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest and proteinaceous extracellular matrix. KEGG analysis indicated the DEGs were enriched in the p53 signaling pathway and cell cycle pathway. The GO and KEGG enrichment analyses for the DEGs disclosed important biological features of PCa. PPI network showed the interaction of top 15 hub genes. Gene Set Enrichment Analysis (GSEA) revealed that some of the hub genes were associated with biochemical recurrence (BCR) and metastasis of PCa. Some top hub genes were distinctive and new discoveries compared with that of the existing associated researches.

Conclusions: Our analysis revealed that the changes of cell cycle and p53 signaling pathway are two major signatures of PCa. CENPA, KIF20A and CDCA8 might promote the tumorigenesis and progression of PCa, especially in BCR and metastasis, which could be novel therapeutic targets and biomarkers for diagnosis, prognosis of PCa.

The Gene Ontology Resource: 20 years and still GOing strong

The Gene Ontology resource (GO; http://geneontology.org) provides structured, computable knowledge regarding the functions of genes and gene products. Founded in 1998, GO has become widely adopted in the life sciences, and its contents are under continual improvement, both in quantity and in quality. Here, we report the major developments of the GO resource during the past two years. Each monthly release of the GO resource is now packaged and given a unique identifier (DOI), enabling GO-based analyses on a specific release to be reproduced in the future. The molecular function ontology has been refactored to better represent the overall activities of gene products, with a focus on transcription regulator activities. Quality assurance efforts have been ramped up to address potentially out-of-date or inaccurate annotations. New evidence codes for high-throughput experiments now enable users to filter out annotations obtained from these sources. GO-CAM, a new framework for representing gene function that is more expressive than standard GO annotations, has been released, and users can now explore the growing repository of these models. We also provide the 'GO ribbon' widget for visualizing GO annotations to a gene; the widget can be easily embedded in any web page.

Annotation of gene product function from high-throughput studies using the Gene Ontology

High-throughput studies constitute an essential and valued source of information for researchers. However, high-throughput experimental workflows are often complex, with multiple data sets that may contain large numbers of false positives. The representation of high-throughput data in the Gene Ontology (GO) therefore presents a challenging annotation problem, when the overarching goal of GO curation is to provide the most precise view of a gene's role in biology. To address this, representatives from annotation teams within the GO Consortium reviewed high-throughput data annotation practices. We present an annotation framework for high-throughput studies that will facilitate good standards in GO curation and, through the use of new high-throughput evidence codes, increase the visibility of these annotations to the research community.

Identification of Core Gene Biomarkers in Patients with Diabetic Cardiomyopathy

Diabetic cardiomyopathy (DCM) is a disorder of the myocardium in diabetic patients, which is one of the critical complications of diabetes giving rise to an increased mortality. However, the underlying mechanisms of DCM remain incompletely understood presently. This study was designed to screen the potential molecules and pathways implicated with DCM. GSE26887 involving 5 control individuals and 7 DCM patients was selected from the GEO database to identify the differentially expressed genes (DEGs). DAVID was applied to perform gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. A protein-protein interaction (PPI) network was also constructed to visualize the interactions among these DEGs. To further validate significant genes and pathways, quantitative real-time PCR (qPCR) and Western blot were performed. A total of 236 DEGs were captured, including 134 upregulated and 102 downregulated genes. GO, KEGG, and the PPI network disclosed that inflammation, immune disorders, metabolic disturbance, and mitochondrial dysfunction were significantly enriched in the development of DCM. Notably, IL6 was an upregulated hub gene with the highest connectivity degree, suggesting that it may interact with a great many molecules and pathways. Meanwhile, SOCS3 was also one of the top 15 hub genes in the PPI network. Herein, we detected the protein level of STAT3 and SOCS3 in a mouse model with DCM. Western blot results showed that the protein level of SOCS3 was significantly lower while phosphorylated-STAT3 (P-STAT3) was activated in mice with DCM. In vitro results also uncovered the similar alterations of SOCS3 and P-STAT3 in cardiomyocytes and cardiac fibroblasts induced by high glucose (HG). However, overexpression of SOCS3 could significantly reverse HG-induced cardiomyocyte hypertrophy and collagen synthesis of cardiac fibroblasts. Taken together, our analysis unveiled potential biomarkers and molecular mechanisms in DCM, which could be helpful to the diagnosis and treatment of DCM.

The Identification of Core Gene Expression Signature in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignancies, which causes serious financial burden worldwide. This study aims to investigate the potential mechanisms contributing to HCC and identify core biomarkers. The HCC gene expression profile GSE41804 was picked out to analyze the differentially expressed genes (DEGs). Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were carried out using DAVID. We constructed a protein-protein interaction (PPI) network to visualize interactions of the DEGs. The survival analysis of these hub genes was conducted to evaluate their potential effects on HCC. In this analysis, 503 DEGs were captured (360 downregulated genes and 143 upregulated genes). Meanwhile, 15 hub genes were identified. GO analysis showed that the DEGs were mainly enriched in oxidative stress, cell cycle, and extracellular structure. KEGG analysis suggested the DEGs were enriched in the absorption, metabolism, and cell cycle pathway. PPI network disclosed that the top3 modules were mainly enriched in cell cycle, oxidative stress, and liver detoxification. In conclusion, our analysis uncovered that the alterations of oxidative stress and cell cycle are two major signatures of HCC. TOP2A, CCNB1, and KIF4A might promote the development of HCC, especially in proliferation and differentiation, which could be novel biomarkers and targets for diagnosis and treatment of HCC.

Identification of Key Genes and miRNAs in Osteosarcoma Patients with Chemoresistance by Bioinformatics Analysis

Chemoresistance is a significant factor associated with poor outcomes of osteosarcoma patients. The present study aims to identify Chemoresistance-regulated gene signatures and microRNAs (miRNAs) in Gene Expression Omnibus (GEO) database. The results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) included positive regulation of transcription, DNA-templated, tryptophan metabolism, and the like. Then differentially expressed genes (DEGs) were uploaded to Search Tool for the Retrieval of Interacting Genes (STRING) to construct protein-protein interaction (PPI) networks, and 9 hub genes were screened, such as fucosyltransferase 3 (Lewis blood group) (FUT3) whose expression in chemoresistant samples was high, but with a better prognosis in osteosarcoma patients. Furthermore, the connection between DEGs and differentially expressed miRNAs (DEMs) was explored. GEO2R was utilized to screen out DEGs and DEMs. A total of 668 DEGs and 5 DEMs were extracted from GSE7437 and GSE30934 differentiating samples of poor and good chemotherapy reaction patients. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to perform GO and KEGG pathway enrichment analysis to identify potential pathways and functional annotations linked with osteosarcoma chemoresistance. The present study may provide a deeper understanding about regulatory genes of osteosarcoma chemoresistance and identify potential therapeutic targets for osteosarcoma.