Comprehensive Analysis of Gene Expression Changes and Validation in Hepatocellular Carcinoma

Comprehensive Profiling and Therapeutic Insights into Differentially Expressed Genes in Hepatocellular Carcinoma

Background: Drug repurposing is a strategy that complements the conventional approach of developing new drugs. Hepatocellular carcinoma (HCC) is a highly prevalent type of liver cancer, necessitating an in-depth understanding of the underlying molecular alterations for improved treatment. Methods: We searched for a vast array of microarray experiments in addition to RNA-seq data. Through rigorous filtering processes, we have identified highly representative differentially expressed genes (DEGs) between tumor and non-tumor liver tissues and identified a distinct class of possible new candidate drugs. Results: Functional enrichment analysis revealed distinct biological processes associated with metal ions, including zinc, cadmium, and copper, potentially implicating chronic metal ion exposure in tumorigenesis. Conversely, up-regulated genes are associated with mitotic events and kinase activities, aligning with the relevance of kinases in HCC. To unravel the regulatory networks governing these DEGs, we employed topological analysis methods, identifying 25 hub genes and their regulatory transcription factors. In the pursuit of potential therapeutic options, we explored drug repurposing strategies based on computational approaches, analyzing their potential to reverse the expression patterns of key genes, including AURKA, CCNB1, CDK1, RRM2, and TOP2A. Potential therapeutic chemicals are alvocidib, AT-7519, kenpaullone, PHA-793887, JNJ-7706621, danusertibe, doxorubicin and analogues, mitoxantrone, podofilox, teniposide, and amonafide. Conclusion: This multi-omic study offers a comprehensive view of DEGs in HCC, shedding light on potential therapeutic targets and drug repurposing opportunities.

Identification of autophagy-related genes in diabetic foot ulcer based on bioinformatic analysis

Diabetic foot ulcer (DFU) complications involve autophagy dysregulation. This study aimed to identify autophagy-related bioindicators in DFU. Differentially expressed genes (DEGs) between DFU and healthy samples were analysed from the Gene Expression Omnibus (GEO) datasets, GSE7014 and GSE29221. The roles of autophagy-related DEGs were investigated using protein-protein interaction (PPI) networks, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, Gene Ontology (GO) enrichment, and Gene Set Enrichment Analysis (GSEA). Immune cell infiltration's correlation with these DEGs was also assessed. From the Human Autophagy Database (HADB), 232 autophagy-related genes (ARGs) were identified, with an intersection of 17 key DEGs between GSE7014 and GSE29221. These genes are involved in pathways like autophagy-animal, NOD-like receptor signalling, and apoptosis. In the protein network, epidermal growth factor receptor (EGFR) and phosphatase and tensin homologue (PTEN) showed significant interactions with ARGs. Survival analysis indicated the prognostic importance of calpain 2 (CAPN2), integrin subunit beta 1 (ITGB1), and vesicle-associated membrane protein 3 (VAMP3). Lower immune scores were observed in the type 2 diabetes mellitus (DM2) group than in controls. Autophagy and ARGs significantly influence DFU pathophysiology.

Identification of immune infiltration-related biomarkers in carotid atherosclerotic plaques

Atherosclerosis is a chronic lipid-driven inflammatory response of the innate and adaptive immune systems, and it is responsible for several cardiovascular ischemic events. The present study aimed to determine immune infiltration-related biomarkers in carotid atherosclerotic plaques (CAPs). Gene expression profiles of CAPs were extracted from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between the CAPs and control groups were screened by the "limma" package in R software. Immune cell infiltration between the CAPs and control groups was evaluated by the single sample gene set enrichment analysis. Key infiltrating immune cells in the CAPs group were screened by the Wilcoxon test and least absolute shrinkage and selection operator regression. The weighted gene co-expression network analysis was used to identify immune cell-related genes. Hub genes were identified by the protein-protein interaction (PPI) network. Receiver operating characteristic curve analysis was performed to assess the gene's ability to differentiate between the CAPs and control groups. Finally, we constructed a miRNA-gene-transcription factor network of hub genes by using the ENCODE database. Eleven different types of immune infiltration-related cells were identified between the CAPs and control groups. A total of 1,586 differentially expressed immunity-related genes were obtained through intersection between DEGs and immune-related genes. Twenty hub genes were screened through the PPI network. Eventually, 7 genes (BTK, LYN, PTPN11, CD163, CD4, ITGAL, and ITGB7) were identified as the hub genes of CAPs, and these genes may serve as the estimable drug targets for patients with CAPs.

Identification and validation of immune-related hub genes based on machine learning in prostate cancer and AOX1 is an oxidative stress-related biomarker

To investigate potential diagnostic and prognostic biomarkers associated with prostate cancer (PCa), we obtained gene expression data from six datasets in the Gene Expression Omnibus (GEO) database. The datasets included 127 PCa cases and 52 normal controls. We filtered for differentially expressed genes (DEGs) and identified candidate PCa biomarkers using a least absolute shrinkage and selector operation (LASSO) regression model and support vector machine recursive feature elimination (SVM-RFE) analyses. A difference analysis was conducted on these genes in the test group. The discriminating ability of the train group was determined using the area under the receiver operating characteristic curve (AUC) value, with hub genes defined as those having an AUC greater than 85%. The expression levels and diagnostic utility of the biomarkers in PCa were further confirmed in the GSE69223 and GSE71016 datasets. Finally, the invasion of cells per sample was assessed using the CIBERSORT algorithm and the ESTIMATE technique. The possible prostate cancer (PCa) diagnostic biomarkers AOX1, APOC1, ARMCX1, FLRT3, GSTM2, and HPN were identified and validated using the GSE69223 and GSE71016 datasets. Among these biomarkers, AOX1 was found to be associated with oxidative stress and could potentially serve as a prognostic biomarker. Experimental validations showed that AOX1 expression was low in PCa cell lines. Overexpression of AOX1 significantly reduced the proliferation and migration of PCa cells, suggesting that the anti-tumor effect of AOX1 may be attributed to its impact on oxidative stress. Our study employed a comprehensive approach to identify PCa biomarkers and investigate the role of cell infiltration in PCa.

Investigating unique genes of five molecular subtypes of breast cancer using penalized logistic regression

Background

Breast cancer (BC) is the most common cancer and the fifth cause of death in women worldwide. Exploring unique genes for cancers has been interesting.

Patients and Methods

This study aimed to explore unique genes of five molecular subtypes of BC in women using penalized logistic regression models. For this purpose, microarray data of five independent GEO data sets were combined. This combination includes genetic information of 324 women with BC and 12 healthy women. Least absolute shrinkage and selection operator (LASSO) logistic regression and adaptive LASSO logistic regression were used to extract unique genes. The biological process of extracted genes was evaluated in an open-source GOnet web application. R software version 3.6.0 with the glmnet package was used for fitting the models.

Results

Totally, 119 genes were extracted among 15 pairwise comparisons. Seventeen genes (14%) showed overlap between comparative groups. According to GO enrichment analysis, the biological process of extracted genes was enriched in negative and positive regulation biological processes, and molecular function tracking revealed that most genes are involved in kinase and transferring activities. On the other hand, we identified unique genes for each comparative group and the subsequent pathways for them. However, a significant pathway was not identified for genes in normal-like versus ERBB2 and luminal A, basal versus control, and lumina B versus luminal A groups.

Conclusion

Most genes selected by LASSO logistic regression and adaptive LASSO logistic regression identified unique genes and related pathways for comparative subgroups of BC, which would be useful to comprehend the molecular differences between subgroups that would be considered for further research and therapeutic approaches in the future.

Development and validation of a genomic nomogram based on a ceRNA network for comprehensive analysis of obstructive sleep apnea

Objectives: Some ceRNA associated with lncRNA have been considered as possible diagnostic and therapeutic biomarkers for obstructive sleep apnea (OSA). We intend to identify the potential hub genes for the development of OSA, which will provide a foundation for the study of the molecular mechanism underlying OSA and for the diagnosis and treatment of OSA.

Methods: We collected plasma samples from OSA patients and healthy controls for the detection of ceRNA using a chip. Based on the differential expression of lncRNA, we identified the target genes of miRNA that bind to lncRNAs. We then constructed lncRNA-related ceRNA networks, performed functional enrichment analysis and protein-protein interaction analysis, and performed internal and external validation of the expression levels of stable hub genes. Then, we conducted LASSO regression analysis on the stable hub genes, selected relatively significant genes to construct a simple and easy-to-use nomogram, validated the nomogram, and constructed the core ceRNA sub-network of key genes.

Results: We successfully identified 282 DElncRNAs and 380 DEmRNAs through differential analysis, and we constructed an OSA-related ceRNA network consisting of 292 miRNA-lncRNAs and 41 miRNA-mRNAs. Through PPI and hub gene selection, we obtained 7 additional robust hub genes, CCND2, WT1, E2F2, IRF1, BAZ2A, LAMC1, and DAB2. Using LASSO regression analysis, we created a nomogram with four predictors (CCND2, WT1, E2F2, and IRF1), and its area under the curve (AUC) is 1. Finally, we constructed a core ceRNA sub-network composed of 74 miRNA-lncRNA and 7 miRNA-mRNA nodes.

Conclusion: Our study provides a new foundation for elucidating the molecular mechanism of lncRNA in OSA and for diagnosing and treating OSA.

Identification of ferroptosis, necroptosis, and pyroptosis-associated genes in periodontitis-affected human periodontal tissue using integrated bioinformatic analysis

Introduction: Periodontitis is a chronic inflammatory oral disease that destroys soft and hard periodontal support tissues. Multiple cell death modes including apoptosis, necroptosis, pyroptosis, and ferroptosis play a crucial role in the pathogenicity of inflammatory diseases. This study aimed to identify genes associated with ferroptosis, necroptosis, and pyroptosis in different cells present in the periodontium of periodontitis patients. Methods: Gingival tissues' mRNA sequencing dataset GSE173078 of 12 healthy control and 12 periodontitis patients' and the microarray dataset GSE10334 of 63 healthy controls and 64 periodontitis patients' were obtained from Gene Expression Omnibus (GEO) database. A total of 910 differentially expressed genes (DEGs) obtained in GSE173078 were intersected with necroptosis, pyroptosis, and ferroptosis-related genes to obtain the differential genes associated with cell death (DCDEGs), and the expression levels of 21 differential genes associated with cell death were verified with dataset GSE10334. Results: Bioinformatic analysis revealed 21 differential genes associated with cell death attributed to ferroptosis, pyroptosis, and necroptosis in periodontitis patients compared with healthy controls. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed that 21 differential genes associated with cell death were related to various cellular and immunological pathways including inflammatory responses, necroptosis, and osteoclast differentiation. Additionally, the single-cell RNA (scRNA) sequencing data GSE171213 of 4 healthy controls and 5 periodontitis patients' periodontal tissue was analyzed to obtain cell clustering and cell types attributed to differential genes associated with cell death. We found that among 21 DCDEGs, SLC2A3, FPR2, TREM1, and IL1B were mainly upregulated in neutrophils present in the periodontium of periodontitis patients. Gene overlapping analysis revealed that IL-1B is related to necroptosis and pyroptosis, TREM1 and FPR2 are related to pyroptosis, and SLC2A3 is related to ferroptosis. Finally, we utilized the CIBERSORT algorithm to assess the association between DCDEGs and immune infiltration phenotypes, based on the gene expression profile of GSE10334. The results revealed that the upregulated SLC2A3, FPR2, TREM1, and IL1B were positively correlated with neutrophil infiltration in the periodontium. Discussion: The findings provide upregulated SLC2A3, FPR2, TREM1, and IL1B in neutrophils as a future research direction on the mode and mechanism of cell death in periodontitis and their role in disease pathogenicity.

Identification of Oxidative Stress-Related Biomarkers in Diabetic Kidney Disease

Background

Diabetic kidney disease (DKD) is a leading cause of end-stage renal disease throughout the world. In kidney disease, oxidative stress has been linked to both antioxidant depletions and increased reactive oxygen species (ROS) production. Thus, the objective of this study was to identify biomarkers related to oxidative stress in DKD.

Methods

The gene expression profile of the DKD was extracted from the Gene Expression Omnibus (GEO) database. The identification of the differentially expressed genes (DEGs) was performed using the "limma" R package, and weighted gene coexpression network analysis (WGCNA) was used to find the gene modules that were most related to DKD. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed using "Org.Hs.eg.db" R package. The protein-protein interaction (PPI) network was constructed using the STRING database. The hub genes were identified by the Molecular Complex Detection (MCODE) plug-in of Cytoscape software. The diagnostic capacity of hub genes was verified using the receiver operating characteristic (ROC) curve. Correlations between diagnostic genes were analyzed using the "corrplot" package. In addition, the miRNA gene transcription factor (TF) network was used to explain the regulatory mechanism of hub genes in DKD.

Results

DEGs analysis and WGCNA-identified 160 key genes were identified in DKD patients. Among them, nine oxidative stress-related genes were identified as candidate hub genes for DKD. Using the PPI network, five hub genes, NR4A2, DUSP1, FOS, JUN, and PTGS2, were subsequently identified. All the hub genes were downregulated in DKD and had a high diagnostic value of DKD. The regulatory mechanism of hub genes was analyzed from the miRNA gene-TF network.

Conclusion

Our study identified NR4A2, DUSP1, FOS, JUN, and PTGS2 as hub genes of DKD. These genes may serve as potential therapeutic targets for DKD patients.

The Relationship between the Prognostic Marker LIMA1 in Head and Neck Squamous Cell Carcinoma and Immune Infiltration

Background. Head and neck squamous cell carcinoma (HNSC) is one of the most common malignancies, and identification of HNSC biomarkers is critical. LIM Domain And Actin Binding 1 (LIMA1) is involved in actin cytoskeleton regulation and dynamics. The role of LIMA1 in HNSC is unclear. This is the first study to investigate the expression of LIMA1 in HNSC patients and its prognostic value, potential biological functions, and impact on the immune system. Methods. Gene expression and clinicopathological analysis, enrichment analysis, and immune infiltration analysis were all based on data from The Cancer Genome Atlas (TCGA) with additional bioinformatics analysis. Statistical analysis was performed using TIMER and ssGSEA to analyze the immune response to LIMA1 expression in HNSCs. In addition, Gene Expression Omnibus (GEO), Kaplan–Meier(K-M) survival analysis, and data from the Human Protein Atlas (HPA) were used to validate the results. Results. LIMA1 played a key role as an independent prognostic factor in HNSC patients. GSEA found that LIMA1 is associated with promoting cell adhesion and suppressing immune function. LIMA1 expression was significantly correlated with infiltration of B cells, CD8+ T cells, CD4+ T cells, dendritic cells, and neutrophils and was coexpressed with immune-related genes and immune checkpoints. Conclusion. The expression of LIMA1 is increased in HNSC, and the high expression of LIMA1 is associated with poor prognosis. LIMA1 may affect tumor development by regulating tumor-infiltrating cells in the tumor microenvironment (TME). LIMA1 may be a potential target for immunotherapy.

Development and Validation of a Machine Learning-Based Nomogram for Prediction of Ankylosing Spondylitis

Introduction

Ankylosing spondylitis (AS) is a chronic progressive inflammatory disease of the spine and its affiliated tissues. AS mainly affects the axial bone, sacroiliac joint, hip joint, spinal facet, and adjacent ligaments. We used machine learning (ML) methods to construct diagnostic models based on blood routine examination, liver function test, and kidney function test of patients with AS. This method will help clinicians enhance diagnostic efficiency and allow patients to receive systematic treatment as soon as possible.

Methods

We consecutively screened 348 patients with AS through complete blood routine examination, liver function test, and kidney function test at the First Affiliated Hospital of Guangxi Medical University according to the modified New York criteria (diagnostic criteria for AS). By using random sampling, the patients were randomly divided into training and validation cohorts. The training cohort included 258 patients with AS and 247 patients without AS, and the validation cohort included 90 patients with AS and 113 patients without AS. We used three ML methods (LASSO, random forest, and support vector machine recursive feature elimination) to screen feature variables and then took the intersection to obtain the prediction model. In addition, we used the prediction model on the validation cohort.

Results

Seven factors—erythrocyte sedimentation rate (ESR), red blood cell count (RBC), mean platelet volume (MPV), albumin (ALB), aspartate aminotransferase (AST), and creatinine (Cr)—were selected to construct a nomogram diagnostic model through ML. In the training cohort, the C value and area under the curve (AUC) value of this nomogram was 0.878 and 0.8779462, respectively. The C value and AUC value of the nomogram in the validation cohort was 0.823 and 0.8232055, respectively. Calibration curves in the training and validation cohorts showed satisfactory agreement between nomogram predictions and actual probabilities. The decision curve analysis showed that the nonadherence nomogram was clinically useful when intervention was decided at the nonadherence possibility threshold of 1%.

Conclusion

Our ML model can satisfactorily predict patients with AS. This nomogram can help orthopedic surgeons devise more personalized and rational clinical strategies.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40744-022-00481-6.

AS is a chronic progressive inflammatory disease of the spine and its affiliated tissues. AS starts gradually, and its early symptoms are mild. Some hospitals lack HLA-B27 and related imaging instruments to assist in the diagnosis of AS. There are relatively few studies on liver function and kidney function of patients with AS. We used ML methods to construct diagnostic models. Our model can satisfactorily predict patients with AS. This diagnostic model can help orthopedic surgeons devise more personalized and rational clinical strategies.

Integrated Analysis of miRNAs and Gene Expression Profiles Reveals Potential Biomarkers for Osteoarthritis

Purpose: Currently, the early diagnosis and treatment of osteoarthritis (OA) remain a challenge. In the present study, we attempted to explore potential biomarkers for the diagnosis and treatment of OA.

Methods: The differentially expressed genes (DEGs) were identified based on three mRNA datasets of synovial tissues for OA patients and normal controls downloaded from the Gene Expression Omnibus (GEO) database. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were used for evaluating gene function related categories. Then, miRNA sequencing was performed for differentially expressed miRNAs’ identification. Finally, weighted gene co-expression network analysis (WGCNA) was performed for genes detected by the three mRNA datasets and a competing endogenous RNA (ceRNA) network with DEGs and differentially expressed microRNAs (miRNAs) was constructed for central genes identification. In addition, the relationship between central gene expression and immune infiltration was analyzed, and the candidate agents for OA were predicted based on the Connectivity Map database. Quantitative RT-PCR (qRT-PCR), Western blotting analysis, and immunofluorescent staining were performed to validate the expression levels of differentially expressed miRNAs and differentially expressed target genes in normal and OA tissues and chondrocytes. MiRNA–mRNA network was also validated in chondrocytes in vitro.

Results: A total of 259 DEGs and 26 differentially expressed miRNAs were identified, among which 94 miRNA–mRNA interactions were predicted. The brown module in WGCNA was most closely correlated with the clinical traits of OA. After overlapping the brown module genes with miRNA–mRNA pairs, 27 miRNA–mRNA pairs were obtained. A ceRNA network was constructed with 5505 lncRNA–miRNA–mRNA interactions. B-cell translocation gene 2(BTG2), Abelson-related gene (ABL2), and vascular endothelial growth factor A (VEGFA) were identified to be the central genes with good predictive performance, which were significantly correlated with immune cell infiltration in OA, reflected by declined activated dendritic cells (aDCs), and elevated contents of B cells, macrophages, neutrophils, and T helper cells. Anisomycin, MG-132, thapsigargin, and lycorine were predicted to be the potential candidate agents for OA intervention. In vitro, the expression levels of differentially expressed miRNAs and biomarkers identified in the present study were consistent with the results obtained in normal or OA knee cartilage tissues and chondrocytes. Furthermore, BTG2 was identified to be negatively regulated by miR-125a-5p.

Conclusion: BTG2, ABL2, and VEGFA can be regarded as potential predictive and treatment biomarkers for OA, which might guide the clinical therapy of OA.

A Novel Mitochondrial-Related Gene Signature for the Tumor Immune Microenvironment Evaluation and Prognosis Prediction in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) remains the most common deadly disease and has a poor prognosis. More and more studies have reported that mitochondrial-related genes (MTRGs) were associated with the clinical outcomes of multiple tumors solely. In this study, we aimed to develop a novel prognostic model based on MTRGs. Differentially expressed MTRGs were identified from TCGA-LUAD and GSE31210 cohorts. Univariate Cox regression analysis was utilized to screen differentially expressed MTRGs that were related to prognosis of LUAD. Then, LASSO Cox regression analysis was used to develop a prognostic signature. ESTIMATE was used for estimating the fractions of immune cell types. In this study, we identified 44 overlapping differentially expressed MTRGs in TCGA-LUAD and GSE31210 cohorts. Among 44 overlapping differentially expressed MTRGs, nine genes were associated with prognosis of LUAD. When the penalty parameter lambda was the minimum, there were six genes meeting the conditions of constructing the signature, including SERPINB5, CCNB1, FGR MAOB, SH3BP5, and CYP24A1. The survival analysis suggested that prognosis of patients in the high-risk group was significantly worse than that in the low-risk group. Cox regression analyses showed that the risk score was an independent predictor of LUAD prognosis. As with the results of ESTIMATE score, the degree of immune cell infiltration in the low-risk group was higher than that in the high-risk group, such as TIL, Treg, and B cells. In addition, TMB and cancer stem cell infiltration were higher in the low-risk group than the high-risk group. In conclusion, we developed a novel MTRG signature acting as a negative independent prognostic factor. In the future, individualized treatments and medical decision-making may benefit from using the predicted model.

The Predictive Competing Endogenous RNA Regulatory Networks and Potential Prognostic and Immunological Roles of Cyclin A2 in Pan-Cancer Analysis

Although accumulating evidence has verified the relationship between CCNA2 and cancers, no pan-cancer analysis about the function and the upstream molecular mechanism of CCNA2 is available. For the first time, we analyzed potential oncogenic roles of CCNA2 in 33 cancer types via The Cancer Genome Atlas (TCGA) database. Overexpression of CCNA2 is widespread in almost all cancer types, and it is related to poor prognosis and advanced pathological stages in most cases. Moreover, we conducted upstream miRNAs and lncRNAs of CCNA2 to establish upstream regulatory networks in kidney renal clear cell carcinoma (LINC00997/miR-27b-3p/CCNA2), liver hepatocellular carcinoma (SNHG16, GUSBP11, FGD5-AS1, LINC00630, CD27-AS1, LINC00997/miR-22-3p/CCNA2, miR-29b-3p/CCNA2, miR-29c-3p/CCNA2, and miR-204-5p/CCNA2), and lung adenocarcinoma (miRNA-218-5p/CCNA2 and miR-204-5p/CCNA2) by expression analysis, survival analysis, and correlation analysis. The CCNA2 expression is positively correlated with Th2 cell infiltration and negatively correlated with CD4+ central memory and effector memory T-cell infiltration in different cancer types. Furthermore, CCNA2 is positively associated with expressions of immune checkpoints (CD274, CTLA4, HAVCR2, LAG3, PDCD1, and TIGIT) in most cancer types. Our first CCNA2 pan-cancer study contributes to understanding the prognostic and immunological roles and potential upstream molecular mechanisms of CCNA2 in different cancers.

CCNB1 promotes the development of hepatocellular carcinoma by mediating DNA replication in the cell cycle

In our studies, cyclin B1 (CCNB1) mRNA and protein were overexpressed in hepatocellular carcinoma (HCC) tissues compared with non-HCC tissues. Moreover, CCNB1 was overexpressed in the serum of HCC patients. The expression of CCNB1 was associated with several crucial clinicopathologic characteristics, and the HCC patients with overexpressed CCNB1 had worse overall survival outcomes. In the screening of interactional genes, a total of 266 upregulated co-expression genes, which were positively associated with CCNB1, were selected from the datasets, and 67 downregulated co-expression genes, which were negatively associated with CCNB1, were identified. The key genes might be functionally enriched in DNA replication and the cell cycle pathways. CDC20, CCNA2, PLK1, and FTCD were selected for further research because they were highly connected in the protein-protein interaction networks. Upregulated CDC20, CCNA2, and PLK1 and downregulated FTCD might result in undesirable overall survival outcomes for HCC patients. The univariate Cox analysis results showed that CDC20 and PLK1 might be two independent risk factors, while FTCD might be protective in HCC. Therefore, CCNB1 may participate in the cell cycle of HCC by regulating DNA replication, and CCNB1 may provide a direction for the diagnosis of early-stage HCC and targeted HCC therapy.

A Reliable Prognostic Model for HCC Using Histological Grades and the Expression Levels of Related Genes

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and is a leading cause of cancer-related death worldwide. This study aimed to establish a reliable prognostic model for HCC using histological grades and the expression levels of related genes. The histological grade of a tumor provides prognostic information. The expression data of HCC samples were downloaded from The Cancer Genome Atlas (TCGA) database. We employed the univariate and multivariate Cox regression analyses, as well as the least absolute shrinkage and selection operator (LASSO) regression to establish the prognostic model. After verification of the proposed model using data downloaded from the International Cancer Genome Consortium (ICGC) database, we found that the model was highly reliable, and it was revealed that the prognosis in the high-risk group was significantly worse than that in the low-risk group. Next, we explored the correlation of RiskScore with patients' clinicopathological characteristics, and we found that the RiskScore could be used as an independent prognostic factor, which further confirmed the reliability of our model. In summary, the proposed model could accurately predict the prognosis of HCC patients, assisting clinicians to study the roles of different histological grades of HCC.

Identification and validation of hub genes of synovial tissue for patients with osteoarthritis and rheumatoid arthritis

Background

Osteoarthritis (OA) and rheumatoid arthritis (RA) were two major joint diseases with similar clinical phenotypes. This study aimed to determine the mechanistic similarities and differences between OA and RA by integrated analysis of multiple gene expression data sets.

Methods

Microarray data sets of OA and RA were obtained from the Gene Expression Omnibus (GEO). By integrating multiple gene data sets, specific differentially expressed genes (DEGs) were identified. The Gene Ontology (GO) functional annotation, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and protein–protein interaction (PPI) network analysis of DEGs were conducted to determine hub genes and pathways. The “Cell Type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT)” algorithm was employed to evaluate the immune infiltration cells (IICs) profiles in OA and RA. Moreover, mouse models of RA and OA were established, and selected hub genes were verified in synovial tissues with quantitative polymerase chain reaction (qPCR).

Results

A total of 1116 DEGs were identified between OA and RA. GO functional enrichment analysis showed that DEGs were enriched in regulation of cell morphogenesis involved in differentiation, positive regulation of neuron differentiation, nuclear speck, RNA polymerase II transcription factor complex, protein serine/threonine kinase activity and proximal promoter sequence-specific DNA binding. KEGG pathway analysis showed that DEGs were enriched in EGFR tyrosine kinase inhibitor resistance, ubiquitin mediated proteolysis, FoxO signaling pathway and TGF-beta signaling pathway. Immune cell infiltration analysis identified 9 IICs with significantly different distributions between OA and RA samples. qPCR results showed that the expression levels of the hub genes (RPS6, RPS14, RPS25, RPL11, RPL27, SNRPE, EEF2 and RPL19) were significantly increased in OA samples compared to their counterparts in RA samples (P < 0.05).

Conclusion

This large-scale gene analyses provided new insights for disease-associated genes, molecular mechanisms as well as IICs profiles in OA and RA, which may offer a new direction for distinguishing diagnosis and treatment between OA and RA.

Identification of key genes and immune infiltration modulated by CPAP in obstructive sleep apnea by integrated bioinformatics analysis

Patients with obstructive sleep apnea (OSA) experience partial or complete upper airway collapses during sleep resulting in nocturnal hypoxia-normoxia cycling, and continuous positive airway pressure (CPAP) is the golden treatment for OSA. Nevertheless, the exact mechanisms of action, especially the transcriptome effect of CPAP on OSA patients, remain elusive. The goal of this study was to evaluate the longitudinal alterations in peripheral blood mononuclear cells transcriptome profiles of OSA patients in order to identify the hub gene and immune response. GSE133601 was downloaded from Gene Expression Omnibus (GEO). We identified black module via weighted gene co-expression network analysis (WGCNA), the genes in which were correlated significantly with the clinical trait of CPAP treatment. Finally, eleven hub genes (TRAV10, SNORA36A, RPL10, OBP2B, IGLV1-40, H2BC8, ESAM, DNASE1L3, CD22, ANK3, ACP3) were traced and used to construct a random forest model to predict therapeutic efficacy of CPAP in OSA with a good performance with AUC of 0.92. We further studied the immune cells infiltration in OSA patients with CIBERSORT, and monocytes were found to be related with the remission of OSA and partially correlated with the hub genes identified. In conclusion, these key genes and immune infiltration may be of great importance in the remission of OSA and related research of these genes may provide a new therapeutic target for OSA in the future.