Identification of genes related to ribosomal proteins in colorectal cancer: exploring their potential as biomarkers, prognostic indicators, and therapeutic targets.

BACKGROUND: Colorectal cancer (CRC) ranks as the third most commonly diagnosed cancer in both females and males, underscoring the need for the identification of effective biomarkers. METHODS AND RESULTS: We assessed the expression levels of ribosomal proteins (RPs) at both mRNA and protein levels. Subsequently, leveraging the STRING database, we constructed a protein-protein interaction network and identified hub genes. The co-expression network of differentially expressed genes associated with CRC and their target hub RPs was constructed using the weighted gene co-expression network analysis algorithm. Gene ontology and molecular signatures database were conducted to gain insights into the biological roles of genes associated with the identified module. To confirm the results, the expression level of the candidate genes in the CRC samples compared to the adjacent healthy was evaluated by the RT-qPCR method. Our findings indicated that the genes related to RPs were predominantly enriched in biological processes associated with Myc Targets, Oxidative Phosphorylation, and cell proliferation. Also, results demonstrated that elevated levels of GRWD1, MCM5, IMP4, and RABEPK that related to RPs were associated with poor prognostic outcomes for CRC patients. Notably, IMP4 and RABEPK exhibited higher diagnostic value. Moreover, the expression of IMP4 and RABEPK showed a significant association with drug resistance using cancer cell line encyclopedia and genomics of drug sensitivity in cancer databases. Also, the results showed that the expression level of IMP4 and RABEPK in cancerous samples was significantly higher compared to the adjacent healthy ones. CONCLUSION: The general results of this study have shown that many genes related to RPs are increased in cancer and could be associated with the death rate of patients. We also highlighted the therapeutic and prognostic potentials of RPs genes in CRC.

Essentiality, protein-protein interactions and evolutionary properties are key predictors for identifying cancer-associated genes using machine learning.

The distinctive nature of cancer as a disease prompts an exploration of the special characteristics the genes implicated in cancer exhibit. The identification of cancer-associated genes and their characteristics is crucial to further our understanding of this disease and enhanced likelihood of therapeutic drug targets success. However, the rate at which cancer genes are being identified experimentally is slow. Applying predictive analysis techniques, through the building of accurate machine learning models, is potentially a useful approach in enhancing the identification rate of these genes and their characteristics. Here, we investigated gene essentiality scores and found that they tend to be higher for cancer-associated genes compared to other protein-coding human genes. We built a dataset of extended gene properties linked to essentiality and used it to train a machine-learning model; this model reached 89% accuracy and > 0.85 for the Area Under Curve (AUC). The model showed that essentiality, evolutionary-related properties, and properties arising from protein-protein interaction networks are particularly effective in predicting cancer-associated genes. We were able to use the model to identify potential candidate genes that have not been previously linked to cancer. Prioritising genes that score highly by our methods could aid scientists in their cancer genes research.

An integrated analysis of the anticarcinogenic role of forkhead box protein 1 in oesophageal squamous cell carcinoma.

Forkhead box protein 1 (FOXP1) serves as a tumour promoter or suppressor depending on different cancers, but its effect in oesophageal squamous cell carcinoma has not been fully elucidated. This study investigated the role of FOXP1 in oesophageal squamous cell carcinoma through bioinformatics analysis and experimental verification. We determined through public databases that FOXP1 expresses low in oesophageal squamous cell carcinoma compared with normal tissues, while high expression of FOXP1 indicates a better prognosis. We identified potential target genes regulated by FOXP1, and explored the potential biological processes and signalling pathways involved in FOXP1 in oesophageal squamous cell carcinoma through GO and KEGG enrichment, gene co-expression analysis, and protein interaction network construction. We also analysed the correlation between FOXP1 and tumour immune infiltration levels. We further validated the inhibitory effect of FOXP1 on the proliferation of oesophageal squamous cell carcinoma cells through CCK-8, colony formation and subcutaneous tumour formation assays. This study revealed the anticarcinogenic effect of FOXP1 in oesophageal squamous cell carcinoma, which may serve as a novel biological target for the treatment of tumour.

Mendelian randomization and colocalization analysis reveal novel drug targets for myasthenia gravis.

OBJECTIVE: Myasthenia gravis (MG) is a complex autoimmune disease affecting the neuromuscular junction with limited drug options, but the field of MG treatment recently benefits from novel biological agents. We performed a drug-targeted Mendelian randomization (MR) study to identify novel therapeutic targets of MG. METHODS: Cis-expression quantitative loci (cis-eQTL), which proxy expression levels for 2176 druggable genes, were used for MR analysis. Causal relationships between genes and disease, identified by eQTL MR analysis, were verified by comprehensive sensitivity, colocalization, and protein quantitative loci (pQTL) MR analyses. The protein-protein interaction (PPI) analysis was also performed to extend targets, followed by enzyme-linked immunosorbent assay (ELISA) to explore the serum level of drug targets in MG patients. A phenome-wide MR analysis was then performed to assess side effects with a clinical trial review assessing druggability. RESULTS: The eQTL MR analysis has identified eight potential targets for MG, one for early-onset MG and seven for late-onset MG. Further colocalization analyses indicated that CD226, CDC42BPB, PRSS36, and TNFSF12 possess evidence for colocalization with MG or late-onset MG. pQTL MR analyses identified the causal relations of TNFSF12 and CD226 with MG and late-onset MG. Furthermore, PPI analysis has revealed the protein interaction between TNFSF12-TNFSF13(APRIL) and TNFSF12-TNFSF13B(BLyS). Elevated TNFSF13 serum level of MG patients was also identified by ELISA experiments. This study has ultimately proposed three promising therapeutic targets (TNFSF12, TNFSF13, TNFSF13B) of MG. CONCLUSIONS: Three drug targets associated with the BLyS/APRIL pathway have been identified. Multiple biological agents, including telitacicept and belimumab, are promising for MG therapy.

[Screening of differentially expressed genes in gastric cancer based on GEO database and function and pathway enrichment analysis].

OBJECTIVE: To explore the core genes related to the diagnosis and prognosis of gastric cancer (GC) based on Gene Expression Omnibus (GEO) database and screen the molecular targets involved in the occurrence and development of GC. METHODS: GC microarray data GSE118916, GSE54129 and GSE79973 were downloaded from GEO database, and the differentially expressed genes (DEGs) were screened. Enrichment analysis of the signaling pathways and molecular functions were preformed and protein-protein interaction networks (PPI) were constructed to identify the hub genes, whose expression levels and diagnostic and prognostic values were verifies based on gastric adenocarcinoma data from TCGA. The expression levels of these core genes were also detected in different GC cell lines using qRT- PCR. RESULTS: Seventy-seven DEGs were identified, which encodes proteins located mainly in the extracellular matrix and basement membrane with activities of oxidoreductase and extracellular matrix receptor and ligand, involving the biological processes of digestion and hormone metabolism and the signaling pathways in retinol metabolism and gastric acid secretion. Nine hub genes were obtained, among which SPARC, TIMP1, THBS2, COL6A3 and THY1 were significantly up- regulated and TFF1, GKN1, TFF2 and PGC were significantly down-regulated in GC. The abnormal expressions of SPARC, TIMP1, THBS2, COL6A3, TFF2 and THY1 were significantly correlated with the survival time of GC patients. ROC curve analysis showed that aberrant expression of TIMP1 SPARC, THY1 and THBS2 had high diagnostic value for GC. High expressions of SPARC, TIMP1, THBS2 and COL6A3 were detected in GC tissues. In the GC cell lines, qRT- PCR revealed different expression patterns of these hub genes, but their expressions were largely consistent with those found in bioinformatics analyses. CONCLUSION: SPARC, TIMP1, THBS2 and other DEGs are probably involved in GC occurrence and progression and may serve as potential candidate molecular markers for early diagnosis and prognostic evaluation of GC.

Identification of hub genes and key pathways in spinal cord injury via bioinformatics analysis.

This study aimed to explore the hub genes and related key pathways in Spinal Cord Injury (SCI) based on the bioinformatics analysis. Two microarray datasets (GSE45006, GSE45550) were obtained from the GEO database and were merged and batch-corrected. The differentially expressed genes (DEGs) in SCI were explored with the Limma, and the weighted gene co-expression network analysis (WGCNA) was conducted to explore the module genes. Functional enrichment analysis and Gene set variation analysis (GSVA) were used to investigate the biological functions and key pathways of the key genes related to SCI. Then the protein-protein interaction (PPI) network was generated using the STING online tool, and the hub genes in SCI were identified. Receiver operating characteristic (ROC) curves were applied to assess the diagnostic value of the selected hub genes. We identified 554 DEGs in SCI, and 1236 key genes in SCI were selected via WGCNA. Totally 111 key genes related to SCI were discovered. Furthermore, the functional enrichment analysis showed that these key mRNAs were primarily enriched in the extracellular matrix (ECM)-related pathways and processes associated with wound healing and cell growth. The PPI network further filtered six hub genes (Cd44, Timp1, Loxl1, Col6a1, Col3a1, Col5a1) ranked by the degree, and the diagnostic value of the six hub genes was confirmed by the ROC curves. Six hub genes including Cd44, Timp1, Loxl1, Col6a1, Col3a1, and Col5a1 were identified in SCI, with differential expression and excellent diagnostic value, which might provide insight into the targeted therapy of SCI.

Molecular subtype identification of cerebral ischemic stroke based on ferroptosis-related genes.

Cerebral ischemic stroke (CIS) has the characteristics of a high incidence, disability, and mortality rate. Here, we aimed to explore the potential pathogenic mechanisms of ferroptosis-related genes (FRGs) in CIS. Three microarray datasets from the Gene Expression Omnibus (GEO) database were utilized to analyze differentially expressed genes (DEGs) between CIS and normal controls. FRGs were obtained from a literature report and the FerrDb database. Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network were used to screen hub genes. The receiver operating characteristic (ROC) curve was adopted to evaluate the diagnostic value of key genes in CIS, followed by analysis of immune microenvironment, transcription factor (TF) regulatory network, drug prediction, and molecular docking. In total, 128 CIS samples were divided into 2 subgroups after clustering analysis. Compared with cluster A, 1560 DEGs were identified in cluster B. After the construction of the WGCNA and PPI network, 5 hub genes, including MAPK3, WAS, DNAJC5, PRKCD, and GRB2, were identified for CIS. Interestingly, MAPK3 was a FRG that differentially expressed between cluster A and cluster B. The expression levels of 5 hub genes were all specifically highly in cluster A subtype. It is noted that neutrophils were the most positively correlated with all 5 real hub genes. PRKCD was one of the target genes of FASUDIL. In conclusion, five real hub genes were identified as potential diagnostic markers, which can distinguish the two subtypes well.

UBE2C: A pan-cancer diagnostic and prognostic biomarker revealed through bioinformatics analysis.

BACKGROUND: The diverse and complex attributes of cancer have made it a daunting challenge to overcome globally and remains to endanger human life. Detection of critical cancer-related gene alterations in solid tumor samples better defines patient diagnosis and prognosis, and indicates what targeted therapies must be administered to improve cancer patients' outcome. MATERIALS AND METHODS: To identify genes that have aberrant expression across different cancer types, differential expressed genes were detected within the TCGA datasets. Subsequently, the DEGs common to all pan cancers were determined. Furthermore, various methods were employed to gain genetic alterations, co-expression genes network and protein-protein interaction (PPI) network, pathway enrichment analysis of common genes. Finally, the gene regulatory network was constructed. RESULTS: Intersectional analysis identified UBE2C as a common DEG between all 28 types of studied cancers. Upregulated UBE2C expression was significantly correlated with OS and DFS of 10 and 9 types of cancer patients. Also, UBE2C can be a diagnostic factor in CESC, CHOL, GBM, and UCS with AUC = 100% and diagnose 19 cancer types with AUC ≥90%. A ceRNA network constructed including UBE2C, 41 TFs, 10 shared miRNAs, and 21 circRNAs and 128 lncRNAs. CONCLUSION: In summary, UBE2C can be a theranostic gene, which may serve as a reliable biomarker in diagnosing cancers, improving treatment responses and increasing the overall survival of cancer patients and can be a promising gene to be target by cancer drugs in the future.

Screening the Biomarkers and Related Medicines for Gastric Adenocarcinoma.

OBJECTIVE: To search for potential biomarkers and available medicines for gastric adenocarcinoma. STUDY DESIGN: Experimental study. Place and Duration of the Study: Scientific Research Section, Shenzhen Longhua District Central Hospital, Shenzhen, China, from January to April 2023. METHODOLOGY: Datasets were retrieved from the Gene Expression Omnibus (GEO). Differential gene expression analysis between gastric adenocarcinoma and normal samples was conducted using GEO2R. Subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed via the Enrichr website. Protein-protein interaction (PPI) networks were established using the STRING website. The central hub genes were identified using the cytoHubba plugin integrated within Cytoscape. Finally, the GEPIA2 and QuartataWeb websites were employed to validate the expression levels of the hub genes and to identify potential medicines for gastric adenocarcinoma. RESULTS: In total, 133 DEGs were identified. GO analysis revealed that these DEGs predominantly participate in processes such as cell adhesion, positive regulation of cell proliferation, and extracellular matrix organisation. In the KEGG pathways, DEGs were significantly enriched in gastric acid secretion, protein digestion and absorption, and ECM-receptor interaction. Following the construction of the PPI network, 10 central hub genes were identified and validated using GEPIA2. Notably, among these hub genes, SERPINE1 demonstrated a significant association with the prognosis of gastric adenocarcinoma, and potential therapeutic agents were subsequently predicted. CONCLUSION: SERPINE1 and potential therapeutic agents hold promise to enhance personalised diagnosis and treatment for gastric adenocarcinoma patients in the future. KEY WORDS: Biomarkers, Gastric adenocarcinoma, Bioinformatics, Differentially Expressed Genes (DEGs).

Exploring effective biomarkers and potential immune related gene in small cell lung cancer.

Small cell lung cancer (SCLC) is well known as a highly malignant neuroendocrine tumor. Immunotherapy combined with chemotherapy has become a standard treatment for extensive SCLC. However, since most patients quickly develop resistance and relapse, finding new therapeutic targets for SCLC is important. We obtained four microarray datasets from the Gene Expression Omnibus database and screened differentially expressed genes by two methods: batch correction and "RobustRankAggregation". After the establishment of a protein-protein interaction network through Cytoscape, seven hub genes (AURKB, BIRC5, TOP2A, TYMS, PCNA, UBE2C, and AURKA) with high expression in SCLC samples were obtained by eight CytoHubba algorithms. The Least Absolute Shrinkage and Selection Operator regression and the Wilcoxon test were used to analyze the differences in the immune cells' infiltration between normal and SCLC samples. The contents of seven kinds of immune cells were considered to differ significantly between SCLC samples and normal samples. A negative association was found between BIRC5 and monocytes in the correlation analysis between immune cells and the seven hub genes. The subsequent in vitro validation of experimental results showed that downregulating the expression of BIRC5 by siRNA can promote apoptotic activity of SCLC cells and inhibit their vitality, migration, and invasion. The use of BIRC5 inhibitor inhibited the vitality of SCLC cells and increased their apoptotic activity. BIRC5 may be a novel therapeutic target option for SCLC.

Bioinformatics integration reveals key genes associated with mitophagy in myocardial ischemia-reperfusion injury.

BACKGROUND: Myocardial ischemia is a prevalent cardiovascular disorder associated with significant morbidity and mortality. While prompt restoration of blood flow is essential for improving patient outcomes, the subsequent reperfusion process can result in myocardial ischemia-reperfusion injury (MIRI). Mitophagy, a specialized autophagic mechanism, has consistently been implicated in various cardiovascular disorders. However, the specific connection between ischemia-reperfusion and mitophagy remains elusive. This study aims to elucidate and validate central mitophagy-related genes associated with MIRI through comprehensive bioinformatics analysis. METHODS: We acquired the microarray expression profile dataset (GSE108940) from the Gene Expression Omnibus (GEO) and identified differentially expressed genes (DEGs) using GEO2R. Subsequently, these DEGs were cross-referenced with the mitophagy database, and differential nucleotide sequence analysis was performed through enrichment analysis. Protein-protein interaction (PPI) network analysis was employed to identify hub genes, followed by clustering of these hub genes using cytoHubba and MCODE within Cytoscape software. Gene set enrichment analysis (GSEA) was conducted on central genes. Additionally, Western blotting, immunofluorescence, and quantitative polymerase chain reaction (qPCR) analyses were conducted to validate the expression patterns of pivotal genes in MIRI rat model and H9C2 cardiomyocytes. RESULTS: A total of 2719 DEGs and 61 mitophagy-DEGs were identified, followed by enrichment analyses and the construction of a PPI network. HSP90AA1, RPS27A, EEF2, EIF4A1, EIF2S1, HIF-1α, and BNIP3 emerged as the seven hub genes identified by cytoHubba and MCODE of Cytoscape software. Functional clustering analysis of HIF-1α and BNIP3 yielded a score of 9.647, as determined by Cytoscape (MCODE). In our MIRI rat model, Western blot and immunofluorescence analyses confirmed a significant elevation in the expression of HIF-1α and BNIP3, accompanied by a notable increase in the ratio of LC3II to LC3I. Subsequently, qPCR confirmed a significant upregulation of HIF-1α, BNIP3, and LC3 mRNA in the MIRI group. Activation of the HIF-1α/BNIP3 pathway mediates the regulation of the degree of Mitophagy, thereby effectively reducing apoptosis in rat H9C2 cardiomyocytes. CONCLUSIONS: This study has identified seven central genes among mitophagy-related DEGs that may play a pivotal role in MIRI, suggesting a correlation between the HIF-1α/BNIP3 pathway of mitophagy and the pathogenesis of MIRI. The findings highlight the potential importance of mitophagy in MIRI and provide valuable insights into underlying mechanisms and potential therapeutic targets for further exploration in future studies.

Key candidate genes and pathways in T lymphoblastic leukemia/lymphoma identified by bioinformatics and serological analyses.

T-cell acute lymphoblastic leukemia (T-ALL)/T-cell lymphoblastic lymphoma (T-LBL) is an uncommon but highly aggressive hematological malignancy. It has high recurrence and mortality rates and is challenging to treat. This study conducted bioinformatics analyses, compared genetic expression profiles of healthy controls with patients having T-ALL/T-LBL, and verified the results through serological indicators. Data were acquired from the GSE48558 dataset from Gene Expression Omnibus (GEO). T-ALL patients and normal T cells-related differentially expressed genes (DEGs) were investigated using the online analysis tool GEO2R in GEO, identifying 78 upregulated and 130 downregulated genes. Gene Ontology (GO) and protein-protein interaction (PPI) network analyses of the top 10 DEGs showed enrichment in pathways linked to abnormal mitotic cell cycles, chromosomal instability, dysfunction of inflammatory mediators, and functional defects in T-cells, natural killer (NK) cells, and immune checkpoints. The DEGs were then validated by examining blood indices in samples obtained from patients, comparing the T-ALL/T-LBL group with the control group. Significant differences were observed in the levels of various blood components between T-ALL and T-LBL patients. These components include neutrophils, lymphocyte percentage, hemoglobin (HGB), total protein, globulin, erythropoietin (EPO) levels, thrombin time (TT), D-dimer (DD), and C-reactive protein (CRP). Additionally, there were significant differences in peripheral blood leukocyte count, absolute lymphocyte count, creatinine, cholesterol, low-density lipoprotein, folate, and thrombin times. The genes and pathways associated with T-LBL/T-ALL were identified, and peripheral blood HGB, EPO, TT, DD, and CRP were key molecular markers. This will assist the diagnosis of T-ALL/T-LBL, with applications for differential diagnosis, treatment, and prognosis.

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.

Identification of key autophagy-related genes and pathways in spinal cord injury.

Spinal cord injury (SCI) can cause a range of functional impairments, and patients with SCI have limited potential for functional recovery. Previous studies have demonstrated that autophagy plays a role in the pathological process of SCI, but the specific mechanism of autophagy in this context remains unclear. Therefore, we explored the role of autophagy in SCI by identifying key autophagy-related genes and pathways. This study utilized the GSE132242 expression profile dataset, which consists of four control samples and four SCI samples; autophagy-related genes were sourced from GeneCards. R software was used to screen differentially expressed genes (DEGs) in the GSE132242 dataset, which were then intersected with autophagy-related genes to identify autophagy-related DEGs in SCI. Subsequently, the expression levels of these genes were confirmed and analyzed with gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). A protein-protein interaction (PPI) analysis was conducted to identify interaction genes, and the resulting network was visualized with Cytoscape. The MCODE plug-in was used to build gene cluster modules, and the cytoHubba plug-in was applied to screen for hub genes. Finally, the GSE5296 dataset was used to verify the reliability of the hub genes. We screened 129 autophagy-related DEGs, including 126 up-regulated and 3 down-regulated genes. GO and KEGG pathway enrichment analysis showed that these 129 genes were mainly involved in the process of cell apoptosis, angiogenesis, IL-1 production, and inflammatory reactions, the TNF signaling pathway and the p53 signaling pathway. PPI identified 10 hub genes, including CCL2, TGFB1, PTGS2, FN1, HGF, MYC, IGF1, CD44, CXCR4, and SERPINEL1. The GSE5296 dataset revealed that the control group exhibited lower expression levels than the SCI group, although only CD44 and TGFB1 showed significant differences. This study identified 129 autophagy-related genes that might play a role in SCI. CD44 and TGFB1 were identified as potentially important genes in the autophagy process after SCI. These findings provide new targets for future research and offer new perspectives on the pathogenesis of SCI.

The high expression of TOP2A and MELK induces the occurrence of psoriasis.

BACKGROUND: Psoriasis is a chronic inflammatory skin disease. However, the influence of the TOP2A and MELK genes on psoriasis remains unclear. METHODS: Psoriasis datasets GSE166388 and GSE181318 were downloaded from the Gene Expression Omnibus (GEO) database generated from GPL570 and GPL22120. Differential gene expression (DEGs) was identified. Functional enrichment analysis, gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), and immune infiltration analysis were conducted. The protein-protein interaction (PPI) network was constructed and analyzed. Gene expression heat map was generated. The most relevant diseases associated with core genes were determined through comparison with the Comparative Toxicogenomics Database (CTD) website. TargetScan was used to select miRNAs regulating central DEGs. RESULTS: A total of 773 DEGs were identified. According to Gene Ontology (GO) analysis, they were mainly enriched in mitochondrial gene expression, oxidative phosphorylation, mitochondrial envelope, mitochondria and ribosome. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that target cells were mainly enriched in metabolic pathways, proteasome, and oxidative phosphorylation. Seven core genes (TOP2A, NUF2, MELK, ASPM, DLGAP5, CCNA2, DEPDC1B) were obtained. The gene expression heatmap showed high expression of core genes (TOP2A, MELK) in psoriasis samples, while DEPDC1B, CCNA2, DLGAP5, NUF2, ASPM were lowly expressed in psoriasis samples. CTD analysis found that TOP2A and MELK were related to skin neoplasms, skin diseases, psoriasis, erythema, dermatitis, and infections. CONCLUSION: TOP2A and MELK genes are highly expressed in psoriasis, and higher expression of TOP2A and MELK genes is associated with poorer prognosis.

Comprehensive analysis identifies crucial genes associated with immune cells mediating progression of carotid atherosclerotic plaque.

BACKGROUNDS: Carotid atherosclerosis is prone to rupture and cause ischemic stroke in advanced stages of development. Our research aims to provide markers for the progression of atherosclerosis and potential targets for its treatment. METHODS: We performed a thorough analysis using various techniques including DEGs, GO/KEGG, xCell, WGCNA, GSEA, and other methods. The gene expression omnibus datasets GSE28829 and GSE43292 were utilized for this comprehensive analysis. The validation datasets employed in this study consisted of GSE41571 and GSE120521 datasets. Finally, we validated PLEK by immunohistochemistry staining in clinical samples. RESULTS: Using the WGCNA technique, we discovered 636 differentially expressed genes (DEGs) and obtained 12 co-expression modules. Additionally, we discovered two modules that were specifically associated with atherosclerotic plaque. A total of 330 genes that were both present in DEGs and WGCNA results were used to create a protein-protein network in Cytoscape. We used four different algorithms to get the top 10 genes and finally got 6 overlapped genes (TYROBP, ITGB2, ITGAM, PLEK, LCP2, CD86), which are identified by GSE41571 and GSE120521 datasets. Interestingly, the area under curves (AUC) of PLEK is 0.833. Besides, we found PLEK is strongly positively correlated with most lymphocytes and myeloid cells, especially monocytes and macrophages, and negatively correlated with most stromal cells (e.g, neurons, myocytes, and fibroblasts). The expression of PLEK were consistent with the immunohistochemistry results. CONCLUSIONS: Six genes (TYROBP, ITGB2, ITGAM, PLEK, LCP2, CD86) were found to be connected with carotid atherosclerotic plaques and PLEK may be an important biomarker and a potential therapeutic target.

ZC3H12A inhibits tumor growth and metastasis of breast cancer under hypoxic condition via the inactivation of IL-17 signaling pathway.

Hypoxia is a major contributor to tumor microenvironment (TME) and metastasis in most solid tumors. We seek to screen hypoxia-related genes affecting metastasis in breast cancer and to reveal relative potential regulatory pathway. Based on gene expression profiling of GSE17188 dataset, differential expressed genes (DEGs) were identified between highly metastatic breast cancer cells under hypoxia and samples under normoxia. The protein-protein interaction (PPI) network was utilized to determine hub genes. The gene expression profiling interactive analysis database (GEPIA2) and quantitative reverse-transcription polymerase chain reaction (qRT-PCR) were employed to quantify hub genes. Moreover, overexpression of zinc finger CCCH-type containing 12A (ZC3H12A) was performed both in breast cancer cells and xenograft mouse model to determine the role of ZC3H12A. We identified 134 DEGs between hypoxic and normoxic samples. Based on PPI analysis, 5 hub genes interleukin (IL)-6, GALN (GAL), CD22 molecule (CD22), ZC3H12A and TNF receptor associated factor 1 (TRAF1) were determined; the expression levels of TRAF1, IL-6, ZC3H12A and GAL were remarkably downregulated while CD22 was upregulated in breast cancer cells. Besides, patients with higher expression of ZC3H12A had favorable prognosis. Overexpression of ZC3H12A could inhibit metastasis and tumor growth of breast cancer; overexpression of ZC3H12A downregulated the expression of IL-17 signaling pathway-related proteins such as IL-17 receptor A (IL-17RA), IL-17A and nuclear factor κB activator 1 (Act1). This study reveals ZC3H12A and IL-17 signaling pathway as potential therapeutic targets for hypoxic breast cancer.

BNIPL is a promising biomarker of laryngeal cancer: novel insights from bioinformatics analysis and experimental validation.

BACKGROUND: Laryngeal cancer (LC) is a malignant tumor with high incidence and mortality. We aim to explore key genes as novel biomarkers to find potential target of LC in clinic diagnosis and treatment. METHODS: We retrieved GSE143224 and GSE84957 datasets from the Gene Expression Omnibus database to screen the differentially expressed genes (DEGs). Hub genes were identified from protein-protein interaction networks and further determined using receiver operating characteristic curves and principal component analysis. The expression of hub gene was verified by quantitative real time polymerase chain reaction. The transfection efficiency of BCL2 interacting protein like (BNIPL) was measured by western blot. Proliferation, migration, and invasion abilities were detected by Cell Counting Kit-8, wound-healing, and transwell assays, respectively. RESULTS: Total 96 overlapping DEGs were screened out from GSE143224 and GSE84957 datasets. Six hub genes (BNIPL, KRT4, IGFBP3, MMP10, MMP3, and TGFBI) were identified from PPI network. BNIPL was selected as the target gene. The receiver operating characteristic curves of BNIPL suggested that the false positive rate was 18.5% and the true positive rate was 81.5%, showing high predictive values for LC. The expression level of BNIPL was downregulated in TU212 and TU686 cells. Additionally, overexpression of BNIPL suppressed the proliferation, migration, and invasion of TU212 and TU686 cells. CONCLUSION: BNIPL is a novel gene signature involved in LC progression, which exerts an inhibitory effect on LC development. These findings provide a novel insight into the pathogenesis of LC.

Protein-protein interaction network-based integration of GWAS and functional data for blood pressure regulation analysis.

BACKGROUND: It is valuable to analyze the genome-wide association studies (GWAS) data for a complex disease phenotype in the context of the protein-protein interaction (PPI) network, as the related pathophysiology results from the function of interacting polyprotein pathways. The analysis may include the design and curation of a phenotype-specific GWAS meta-database incorporating genotypic and eQTL data linking to PPI and other biological datasets, and the development of systematic workflows for PPI network-based data integration toward protein and pathway prioritization. Here, we pursued this analysis for blood pressure (BP) regulation. METHODS: The relational scheme of the implemented in Microsoft SQL Server BP-GWAS meta-database enabled the combined storage of: GWAS data and attributes mined from GWAS Catalog and the literature, Ensembl-defined SNP-transcript associations, and GTEx eQTL data. The BP-protein interactome was reconstructed from the PICKLE PPI meta-database, extending the GWAS-deduced network with the shortest paths connecting all GWAS-proteins into one component. The shortest-path intermediates were considered as BP-related. For protein prioritization, we combined a new integrated GWAS-based scoring scheme with two network-based criteria: one considering the protein role in the reconstructed by shortest-path (RbSP) interactome and one novel promoting the common neighbors of GWAS-prioritized proteins. Prioritized proteins were ranked by the number of satisfied criteria. RESULTS: The meta-database includes 6687 variants linked with 1167 BP-associated protein-coding genes. The GWAS-deduced PPI network includes 1065 proteins, with 672 forming a connected component. The RbSP interactome contains 1443 additional, network-deduced proteins and indicated that essentially all BP-GWAS proteins are at most second neighbors. The prioritized BP-protein set was derived from the union of the most BP-significant by any of the GWAS-based or the network-based criteria. It included 335 proteins, with ~ 2/3 deduced from the BP PPI network extension and 126 prioritized by at least two criteria. ESR1 was the only protein satisfying all three criteria, followed in the top-10 by INSR, PTN11, CDK6, CSK, NOS3, SH2B3, ATP2B1, FES and FINC, satisfying two. Pathway analysis of the RbSP interactome revealed numerous bioprocesses, which are indeed functionally supported as BP-associated, extending our understanding about BP regulation. CONCLUSIONS: The implemented workflow could be used for other multifactorial diseases.

Identification of metabolic pathways and key genes associated with atypical parkinsonism using a systems biology approach.

Atypical parkinsonism (AP) is a group of complex neurodegenerative disorders with marked clinical and pathophysiological heterogeneity. The use of systems biology tools may contribute to the characterization of hub-bottleneck genes, and the identification of its biological pathways to broaden the understanding of the bases of these disorders. A systematic search was performed on the DisGeNET database, which integrates data from expert curated repositories, GWAS catalogues, animal models and the scientific literature. The tools STRING 11.0 and Cytoscape 3.8.2 were used for analysis of protein-protein interaction (PPI) network. The PPI network topography analyses were performed using the CytoHubba 0.1 plugin for Cytoscape. The hub and bottleneck genes were inserted into 4 different sets on the InteractiveVenn. Additional functional enrichment analyses were performed to identify Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and gene ontology for a described set of genes. The systematic search in the DisGeNET database identified 485 genes involved with Atypical Parkinsonism. Superimposing these genes, we detected a total of 31 hub-bottleneck genes. Moreover, our functional enrichment analyses demonstrated the involvement of these hub-bottleneck genes in 3 major KEGG pathways. We identified 31 highly interconnected hub-bottleneck genes through a systems biology approach, which may play a key role in the pathogenesis of atypical parkinsonism. The functional enrichment analyses showed that these genes are involved in several biological processes and pathways, such as the glial cell development, glial cell activation and cognition, pathways were related to Alzheimer disease and Parkinson disease. As a hypothesis, we highlight as possible key genes for AP the MAPT (microtubule associated protein tau), APOE (apolipoprotein E), SNCA (synuclein alpha) and APP (amyloid beta precursor protein) genes.