Screening and identification of key biomarkers in hepatocellular carcinoma: Evidence from bioinformatic analysis

Genetic alterations are related to clinicopathological features and risk of recurrence/metastasis of hepatocellular carcinoma

Lack of efficient biomarkers and clinical translation of molecular typing impedes the implementation of targeted therapy for hepatocellular carcinoma (HCC). High-throughput sequencing techniques represented by next-generation sequencing (NGS) are tools for detecting targetable genes. The objective of this study is to explore the genetic alterations associated with clinicopathological features and the risk of recurrence/metastasis in HCC. NGS analysis was conducted on formalin-fixed paraffin-embedded tissues from 164 resected liver samples obtained from Chinese patients. Morphologic subtypes were reviewed based on hematoxylin-eosin and immunohistochemistry staining, Correlation to the acquired molecular features were analyzed with clinicopathological information. We also retrieved follow-up information of the 123 transplanted cases from 2017 to 2019 to screen recurrence/metastasis-associated factors by univariate analysis. Generally, the most frequently mutated genes include TP53 and CTNNB1 which showed a trend of mutually exclusive mutation. Copy-number variant with the highest frequency was detected in TAF1 and CCND1 in 11q13.3 loci. Correlation analysis showed that various genetic alterations were associated with morphologic subtypes and other pathologic features. While gene signatures of proliferation/nonproliferation class were correlated with differentiation, satellite foci and other invasive morphological features. Macrotrabecular-massive subtype, TSC2 (tuberous sclerosis complex 2) mutation, Ki-67 expression, and other six factors were found to be associated with recurrence/metastasis after liver transplantation. Genetic alterations detected by NGS show correlation with not only pathological and clinical features, but also with recurrence/metastasis after liver transplantation. Further gene-level molecular typing will be practical for targeted therapy and individual recurrence risk assessment in HCC patients.

Upregulation of multiple key molecules is correlated with poor prognosis and immune infiltrates in hepatocellular carcinoma by bulk and single-cell RNA-seq

BACKGROUND

Recent discoveries in hepatocellular carcinoma (HCC) unveil key molecules. However, due to liver cancer's high heterogeneity, predicting patient prognosis is challenging. This study aims to construct a model for predicting HCC prognosis using multiple key genes.

METHODS

TCGA provided RNA expression and clinical data, differentially analyzed by DESeq2, edgeR, and Limma. The hub gene was pinpointed via CytoHubba's degree algorithm in Cytoscape. GO and KEGG analyses illuminated potential pathways. Single-cell sequencing detailed key gene expression in diverse cell types. The LASSO regression model predicted patient prognosis.

RESULT

In the RNA-seq analysis using three R packages, we identified 762 differentially expressed genes, with Cytoscape revealing ten key genes showing significant prognostic value (P < 0.05). GO and KEGG analyses highlighted key biological processes and pathways. IHC confirmed higher expression in cancer tissues. Reduced immune cell infiltration was observed in HCC tissues, and immune checkpoint analysis showed a strong correlation between PD1, CTLA4, and hub genes. Single-cell sequencing indicated higher expression of key genes in immune cells than hepatocytes. Cox analysis validated the riskScore as a reliable, independent prognostic marker (HR = 4.498, 95% CI: 2.526-8.007).

CONCLUSIONS

The results from differential analysis using three R packages are robust, revealing genes closely linked to immune cell infiltration in the tumor microenvironment. Additionally, a validated prognostic model for liver cancer was established based on key genes.

Decoding the possible mechanism of action of Paeoniflorigenone in combating Aflatoxin B1-induced liver cancer: an investigation using network pharmacology and bioinformatics analysis

Moutan cortex has demonstrated antitumor properties attributed to its bioactive compound Paeoniflorigenone (PA). Nevertheless, there is limited research on the efficacy of PA in the prevention and treatment of hepatocellular carcinoma (HCC). We aimed to investigate the potential pharmacological mechanisms of PA in the treatment of Aflatoxin B1 (AFB1)-induced hepatocarcinogenesis using network pharmacology and bioinformatics analysis approaches. Through various databases and bioinformatics analysis approaches, 34 shared targets were identified as potential candidate genes for PA in fighting liver cancer caused by AFB1. Pathway analysis revealed involvement in cell cycle, HIF-1, and Rap1 pathways. A risk assessment model was developed using LASSO regression, showing an association between the identified genes and the tumor immune microenvironment. The genes within the risk model were found to be linked to the immune response in liver cancer. Molecular docking studies indicated that PA interacts with its targets through hydrogen bonding and hydrophobic interactions. This study provides insights into the possible mechanisms of PA in liver cancer treatment and offers a predictive model for assessing the risk level of individuals with liver cancer. These findings have significant implications for the therapeutic strategies in managing liver cancer patients.

Mechanisms of Shufeng Jiedu Capsule in treating bacterial pneumonia based on network pharmacology and experimental verification

Objective

The aim of this study was to investigate the underlying mechanism of Shufeng Jiedu Capsule (SFJD) for treating bacterial pneumonia (BP) in vivo based on network pharmacology and experimental verification study.

Methods

Network pharmacology was used to screen the active compounds and target genes of SFJD. Then, the multi drug resistance-Pseudomonas aeruginosa (MDR-PA) mice lethal model and MDR-PA pneumonia model were established to evaluate the therapeutic effects and underlying mechanisms of SFJD. Western blot and ELISA were used to determinate the protein expression level of the IL-17 signaling pathway and JAK/STAT signaling pathway.

Results

After screening, 172 potential components of SFJD were generated, based on which we constructed an SFJD-component-target-BP interaction network. The Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment revealed that SFJD could regulate the IL-17 signaling pathway and Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling pathway. Molecular docking showed that the potential target proteins had good combinations with the main active components. SFJD significantly reduced the mortality and prolonged survival days in lethal models. The lung index and pathological changes in the lung were also significantly decreased. SFJD could significantly decrease the expression of interleukin-17A (IL-17A), TNF receptor associated factor 6 (TRAF6), phospho-inhibitor of nuclear factor-kappa B (p-IκB)/inhibitor of NF-κB (IκB), phospho-NF-κB p65 (p-NF-κB p65), phospho-protein kinase B (p-AKT)/AKT, phospho-signal transducer and activator of transcription 3 (p-STAT3)/STAT3, phospho-signal transducer and activator of transcription 1 (p-STAT1)/STAT1, and the protein level of interleukin-6 (IL-6), tumor necrosis factor α (TNF-α), and IL-1β.

Conclusion

Combined with network pharmacology and in vivo study, it was found that SFJD exerted its therapeutic effects on BP by inhibiting the IL-17 pathway and JAK/STAT signaling pathway. This study provides new evidence for SFJD in treatment of BP.

Exploration of N6-methyladenosine modification in ascorbic acid 2-glucoside constructed stem cell sheets

The aim of this study was to explore the mechanism of bone marrow stem cells (BMSCs) sheets constructed with different doses of Ascorbic acid 2-glucoside (AA-2G) in conjunction with N6-methyladenosine (m6A)-associated epigenetic genes analysing transcriptome sequencing data. Experimental groups of BMSCs induced by different AA-2G concentrations were set up, and the tissue structures were observed by histological staining of cell slices and scanning electron microscopy. Expression patterns of DEGs were analysed using short-time sequence expression mining software, and DEGs associated with m6A were selected for gene ontology analysis and pathway analysis. The protein-protein interaction (PPI) network of DEGs was analysed and gene functions were predicted using the search tool of the Retrieve Interacting Genes database. There were 464 up-regulated DEGs and 303 down-regulated DEGs between the control and high-dose AA-2G treatment groups, and 175 up-regulated DEGs and 37 down-regulated DEGs between the low and high-dose AA-2G treatment groups. The profile 7 exhibited a gradual increase in gene expression levels over AA-2G concentration. In contrast, profile 0 exhibited a gradual decrease in gene expression levels over AA-2G concentration. In the PPI network of m6A-related DEGs in profile 7, the cluster of metallopeptidase inhibitor 1 (Timp1), intercellular adhesion molecule 1 (Icam1), insulin-like growth factor 1 (Igf1), matrix metallopeptidase 2 (Mmp2), serpin family E member 1 (Serpine1), C-X-C motif chemokine ligand 2 (Cxcl2), galectin 3 (Lgals3) and angiopoietin-1 (Angpt1) was the top hub gene cluster. The expression of all hub genes was significantly increased after AA-2G intervention (P < 0.05), and the expression of Igf1 and Timp1 increased with increasing intervention concentration. The m6A epigenetic modifications were involved in the AA-2G-induced formation of BMSCs. Igf1, Serpine1 and Cxcl2 in DEGs were enriched for tissue repair, promotion of endothelial and epithelial proliferation and regulation of apoptosis.

Benchmark N-glycoproteomics study of common differential tissue and serum N-glycoproteins of patients with hepatocellular carcinoma

For hepatocellular carcinoma (HCC), N-glycosylation has been proved to be widely involved in various aspects of the disease, including development, metastasis, subtyping, diagnosis and prognosis. The common practice is to discover biomarkers in situ of cancer occurrence (i.e., cancer vs. adjacent tissues) yet to clinically monitor in sera because of non-invasiveness. This study benchmarks N-glycoproteomics characterization of common differential tissue and serum N-glycoproteins of patients with HCC. Differential N-glycosylation in matched tissue and serum samples from the same patients were quantitatively characterized at the intact N-glycopeptide molecular level, and 29 common N-glycoproteins were found. Subcellular localization analysis was carried out to confirm the tissue originality. Secreted N-glycoprotein APOH was up-regulated, and transmembrane and intracellular N-glycoproteins including OSMR, GAT2, CSF-1 and MAGI3 were down-regulated.

The consistently up-regulated expression of NLRP3 in severe asthma patients from mRNA microarray and ovalbumin-induced mouse model of asthma

Background

Severe asthma (SA) is a chronic lung disease, resistant to current treatments, symbolized by repeated symptoms of reversible airflow obstruction, airway hyper-responsiveness, and inflammation. The aim of this study was to identify genes exhibiting differential expression in individuals without asthma and SA patients. We aimed to pinpoint hub differentially expressed genes (DEGs) by utilizing a mouse model of asthma sensitized to ovalbumin (OVA).

Methods

Microarray data for SA were acquired from the Gene Expression Omnibus (GEO) databases. DEGs were identified, and functional enrichment analyses were carried out. STRING and Cytoscape were utilized to design a protein-protein interaction (PPI) network and conduct module analysis. An OVA-induced asthma mice model was established. Lung tissue from the mice was collected for quantitative reverse transcription polymerase chain reaction (qRT-PCR), Western blot, and immunohistochemistry (IHC) to assess the expression of DEGs.

Results

A total of 545 DEGs were identified, among which 172 genes were upregulated in SA patients compared to healthy controls. The nucleotide-binding oligomerization domain-like receptor family pyrin domain containing 3 (NLRP3) was significantly up-regulated in SA patients [adjusted P value (Padj) =0.001]. Analysis of lung tissue using qRT-PCR, western blot, and IHC revealed higher expression of NLRP3 in OVA-induced asthma mice compared to the control group. Enrichment analysis suggests the involvement of NLRP3 in pathways related to pyroptosis, c-type lectin receptor signaling, and NOD-like receptor signaling.

Conclusions

Through bioinformatics analysis, we identified a multitude of DEGs that could potentially contribute significantly to the development of SA. Notably, our findings highlight NLRP3 as a potential pivotal player in asthma pathogenesis, underscoring its prospective utility as a biomarker for SA.

Screening and molecular docking verification of feature genes related to phospholipid metabolism in hepatocarcinoma caused by hepatitis B

BACKGROUND

The progression of tumours is related to abnormal phospholipid metabolism. This study is anticipated to present a fresh perspective for disease therapy targets of hepatocarcinoma caused by hepatitis B virus in the future by screening feature genes related to phospholipid metabolism.

METHODS

This study analysed GSE121248 to pinpoint differentially expressed genes (DEGs). By examining the overlap between the metabolism-related genes and DEGs, the research focused on the genes involved in phospholipid metabolism. To find feature genes, functional enrichment studies were carried out and a network diagram was proposed. These findings were validated via data base of The Cancer Genome Atlas (TCGA). Further analyses included immune infiltration studies and metabolomics. Finally, the relationships between differentially abundant metabolites and feature genes were confirmed by molecular docking, providing a thorough comprehension of the molecular mechanisms.

RESULTS

The seven genes with the highest degree of connection (PTGS2, IGF1, SPP1, BCHE, NR1I2, NAMPT, and FABP1) were identified as feature genes. In the TCGA database, the seven feature genes also had certain diagnostic efficiency. Immune infiltration analysis revealed that feature genes regulate the infiltration of various immune cells. Metabolomics successfully identified the different metabolites of the phospholipid metabolism pathway between patients and normal individuals. The docking study indicated that different metabolites may play essential roles in causing disease by targeting feature genes.

CONCLUSIONS

In this study, for the first time, it reveals the possible involvement of genes linked to phospholipid metabolism-related genes using bioinformatics analysis. Identifying genes and probable therapeutic targets could provide clues for the further treatment of disease.

Analysis and validation of hub genes in neutrophil extracellular traps for the long-term prognosis of myocardial infarction

INTRODUCTION

The study focuses on the long-term prognosis of myocardial infarction (MI) influenced by neutrophil extracellular traps (NETs). It also aims to analyze and validate relative hub genes in this process, in order to further explore new therapeutic targets that can improve the prognosis of MI.

MATERIALS AND METHODS

We established a MI model in mice by ligating the left anterior descending branch (LAD) and conducted an 8-week continuous observation to study the dynamic changes in the structure and function of the heart in these mice. Meanwhile, we administered Apocynin, an inhibitor of NADPH Oxidase, which has also been shown to inhibit the formation of NETs, to mice undergoing MI surgery in order to compare. This study employed hematoxylin-eosin (HE) staining, echocardiography, immunofluorescence, and real-time quantitative PCR (RT-qPCR) to examine the impact of NETs on the long-term prognosis of MI. Next, datasets related to MI and NETs were downloaded from the GEO database, respectively. The Limma package of R software was used to identify differentially expressed genes (DEGs). After analyzing the "Robust Rank Aggregation (RRA)" package, we conducted a screening for robust differentially expressed genes (DEGs) and performed pathway enrichment analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to determine the functional roles of these robust DEGs. The protein-protein interaction (PPI) network was visualized and hub genes were filtered using Cytoscape.

RESULTS

Immunofluorescence and qPCR results showed an increase in the expression of Myeloperoxidase (MPO) at week 1 and week 8 in the hearts of mice after MI. HE staining reveals a series of pathological manifestations in the heart of the MI group during 8 weeks, including enlarged size, disordered arrangement of cardiomyocytes, infiltration of inflammatory cells, and excessive deposition of collagen fibers, among others. The utilization of Apocynin could significantly improve these poor performances. The echocardiography displayed the cardiac function of the heart in mice. The MI group has a reduced range of heart movement and decreased ejection ability. Moreover, the ventricular systolic movement was found to be abnormal, and its wall thickening rate decreased over time, indicating a progressive worsening of myocardial ischemia. The Apocynin group, on the contrary, showed fewer abnormal changes in the aforementioned aspects. A total of 81 DEGs and 4 hub genes (FOS, EGR1, PTGS2, and HIST1H4H) were obtained. The results of RT-qPCR demonstrated abnormal expression of these four genes in the MI group, which could be reversed by treatment of Apocynin.

CONCLUSION

The NETs formation could be highly related to MI and the long-term prognosis of MI can be significantly influenced by the NETs formation. Four hub genes, namely FOS, EGR1, PTGS2, and HIST1H4H, have the potential to be key genes related to this process. They could also serve as biomarkers for predicting MI prognosis and as targets for gene therapy.

Construction of m7G RNA modification-related prognostic model and prediction of immune therapy response in hepatocellular carcinoma

Background

RNA plays an important role in tumorigenesis. Changes in RNA may cause changes in the biological function. The N7-methylguanosine (m7G) methylation modification performs an integral function in tumor progression as the most widely existed RNA modification. Hepatocellular carcinoma (HCC) is among the greatest threats to human health worldwide. Low detection rates remain the main cause of advanced disease progression. Therefore, finding significant biomarkers for prognosis prediction and immune therapy response in HCC is valuable and urgently needed.

Methods

RNA expression and clinical data were acquired from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. Different subtypes screening was finished by consensus cluster. Different expression was performed by R software. The results were validated by western blot (WB) methods. Genes with HCC prognostic potential were identified utilizing least absolute shrinkage and selection operator (LASSO) analyses. A prognosis model was established with the help of the risk score that we calculated. Related genes screening and protein-protein interactions (PPI) network construction were performed using the GeneMANIA database. Functional annotation was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) databases. In addition, gene set enrichment analysis (GSEA) of key genes and immune infiltration status were both done by R software. Finally, the immune infiltration was performed by cibersort method and single sample GSEA (ssGSEA) method. The response of immune therapy was validated by Tumor Immune Dysfunction and Exclusion database (TIDE) and the immune therapy cohort in GEO database.

Results

We found that two different subtypes related with m7G RNA modification and four genes associated with m7G RNA modification were differentially expressed in the TCGA-Liver Hepatocellular Carcinoma (TCGA-LIHC) database. Additionally, to examine the value of these four genes in the HCC patients' prognoses according to the LASSO, we selected three genes, including WDR4, AGO2, and NCBP2, as prognostic related genes. Premised on the expression of these three genes, a risk score model and nomogram were constructed to provide a prediction of the HCC patients' prognoses. We performed functional annotation and created a PPI network based on the three genes (WDR4, NCBP2, and AGO2). Using R software, we performed the GSEA and immune regulation analyses. Finally, we predicted the relationship between the gene expression and the response of immune therapy.

Conclusions

Our study suggests that high expression of m7G RNA modification subtype is related with poor prognosis and immune response. WDR4, AGO2, and NCBP2 are key regulators of m7G RNA modification which can be clinically promising biomarkers that can be used to treat HCC. In addition, our risk score model was shown to have a strong link to OS in patients with HCC.

Regulation of TMEM100 expression by epigenetic modification, effects on proliferation and invasion of esophageal squamous carcinoma

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is a prevalent malignancy with a high morbidity and mortality rate. TMEM100 has been shown to be suppressor gene in a variety of tumors, but there are no reports on the role of TMEM100 in esophageal cancer (EC).

AIM

To investigate epigenetic regulation of TMEM100 expression in ESCC and the effect of TMEM100 on ESCC proliferation and invasion.

METHODS

Firstly, we found the expression of TMEM100 in EC through The Cancer Genome Atlas database. The correlation between TMEM100 gene expression and the survival of patients with EC was further confirmed through Kaplan-Meier analysis. We then added the demethylating agent 5-AZA to ESCC cell lines to explore the regulation of TMEM100 expression by epigenetic modification. To observe the effect of TMEM100 expression on tumor proliferation and invasion by overexpressing TMEM100. Finally, we performed gene set enrichment analysis using the Kyoto Encyclopaedia of Genes and Genomes Orthology-Based Annotation System database to look for pathways that might be affected by TMEM100 and verified the effect of TMEM100 expression on the mitogen-activated protein kinases (MAPK) pathway.

RESULTS

In the present study, by bioinformatic analysis we found that TMEM100 was lowly expressed in EC patients compared to normal subjects. Kaplan-meier survival analysis showed that low expression of TMEM100 was associated with poor prognosis in patients with EC. Then, we found that the demethylating agent 5-AZA resulted in increased expression of TMEM100 in ESCC cells [quantitative real-time PCR (qRT-PCR) and western blotting]. Subsequently, we confirmed that overexpression of TMEM100 leads to its increased expression in ESCC cells (qRT-PCR and western blotting). Overexpression of TMEM100 also inhibited proliferation, invasion and migration of ESCC cells (cell counting kit-8 and clone formation assays). Next, by enrichment analysis, we found that the gene set was significantly enriched in the MAPK signaling pathway. The involvement of TMEM100 in the regulation of MAPK signaling pathway in ESCC cell was subsequently verified by western blotting.

CONCLUSION

TMEM100 is a suppressor gene in ESCC, and its low expression may lead to aberrant activation of the MAPK pathway. Promoter methylation may play a key role in regulating TMEM100 expression.

Dysfunction of CCT3-associated network signals for the critical state during progression of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors and is a serious threat to human health; thus, early diagnosis and adequate treatment are essential. However, there are still great challenges in identifying the tipping point and detecting early warning signals of early HCC. In this study, we aimed to identify the tipping point (critical state) of and key molecules involved in hepatocarcinogenesis based on time series transcriptome expression data of HCC patients. The phase from veHCC (very early HCC) to eHCC (early HCC) was identified as the critical state in HCC progression, with 143 genes identified as key candidate molecules by combining the DDRTree (dimensionality reduction via graph structure learning) and DNB (dynamic network biomarker) methods. Then, we ranked the candidate genes to verify their mRNA levels using the diethylnitrosamine (DEN)-induced HCC mouse model and identified five early warning signals, namely, CCT3, DSTYK, EIF3E, IARS2 and TXNRD1; these signals can be regarded as the potential early warning signals for the critical state of HCC. We identified CCT3 as an independent prognostic factor for HCC, and functions of CCT3 involving in the "MYCtargets_V1" and "E2F-Targets" are closely related to the progression of HCC. The predictive method combining the DDRTree and DNB methods can not only identify the key critical state before cancer but also determine candidate molecules of critical state, thus providing new insight into the early diagnosis and preemptive treatment of HCC.

Identification of allograft inflammatory factor-1 suppressing the progression and indicating good prognosis of osteosarcoma

BACKGROUND

Osteosarcoma is one of the most common cancers worldwide. Intense efforts have been made to elucidate the pathogeny, but the mechanisms of osteosarcoma are still not well understood. We aimed to investigate the potential biomarker, allograft inflammatory factor-1 (AIF1), affecting the progression and prognosis of osteosarcoma.

METHODS

Three microarray datasets were downloaded from GEO datasets and one was obtained from the TCGA dataset. The differentially expressed genes (DEGs) were identified. GO and KEGG functional enrichment analyses of overlapped DEGs were performed. The PPI network of overlapped DEGs was constructed by STRING and visualized with Cytoscape. Overall survival (OS) and Metastasis free survival (MFS) were analyzed from GSE21257. Finally, the effect of the most relevant core gene affecting the progression of osteosarcoma was examined in vitro.

RESULTS

One hundred twenty six DEGs were identified, consisting of 65 upregulated and 61 downregulated genes. Only AIF1 was significantly associated with OS and MFS. It was found that AIF1 could be enriched into the NF-κB signaling pathway. GSEA and ssGSEA analyses showed that AIF1 was associated with the immune invasion of tumors. Cell experiments showed that AIF1 was underexpressed in osteosarcoma cell lines, while the malignant propriety was attenuated after overexpressing the expression of AIF1. Moreover, AIF1 also affects the expression of the NF-κB pathway.

CONCLUSION

In conclusion, DEGs and hub genes identified in the present study help us understand the molecular mechanisms underlying the carcinogenesis and progression of osteosarcoma, and provide candidate targets for diagnosis and treatment of osteosarcoma.

Centrosome amplification-related signature correlated with immune microenvironment and treatment response predicts prognosis and improves diagnosis of hepatocellular carcinoma by integrating machine learning and single-cell analyses

BACKGROUND

Centrosome amplification is a well-recognized oncogenic driver of tumor initiation and progression across a variety of malignancies and has been linked with tumor aggressiveness, metastasis, and adverse prognosis. Nevertheless, the significance of centrosome amplification in HCC is not well understood.

METHODS

The TCGA dataset was downloaded for centrosome amplification-related signature construction using the LASSO-penalized Cox regression algorithm, while the ICGC dataset was obtained for signature validation. Single-cell RNA sequencing from GSE149614 was analyzed to profile gene expression and the liver tumor niche.

RESULTS

A total of 134 centrosome amplification-related prognostic genes in HCC were detected and 6 key prognostic genes (SSX2IP, SPAG4, SAC3D1, NPM1, CSNK1D, and CEP55) among them were screened out to construct a signature with both high sensitivity and specificity in diagnosis and prognosis of HCC patients. The signature, as an independent factor, was associated with frequent recurrences, high mortality rates, advanced clinicopathologic features, and high vascular invasions. Moreover, the signature was intimately associated with cell cycle-related pathways and TP53 mutation profile, suggesting its underlying role in accelerating cell cycle progression and leading to liver cancer development. Meanwhile, the signature was also closely correlated with immunosuppressive cell infiltration and immune checkpoint expression, making it a vital immunosuppressive factor in the tumor microenvironment. Upon single-cell RNA sequencing, SSX2IP and SAC3D1 were found to be specially expressed in liver cancer stem-like cells, where they promoted cell cycle progression and hypoxia.

CONCLUSIONS

This study provided a direct molecular link of centrosome amplification with clinical characteristics, tumor microenvironment, and clinical drug-response, highlighting the critical role of centrosome amplification in liver cancer development and therapy resistance, thereby providing valuable insights into prognostic prediction and therapeutic response of HCC.

Gene expression profiling of pancreatic neuroendocrine carcinoma and mixed neuroendocrine-non-neuroendocrine neoplasm

Pancreatic neuroendocrine carcinoma (NEC) and mixed neuroendocrine-non-neuroendocrine neoplasm (MiNEN) are rare pancreatic malignant tumors, and comprehensive gene analyses are scarce. In this study, six NECs and six MiNENs were collected, immunohistochemistry for synaptophysin, chromogranin A, INSM1, Ki-67, and Rb was conducted, and KRAS mutational status was examined. Among these cases, comprehensive gene expression analysis of oncogene pathways using nCounter® were performed with six NECs and four MiNENs, and those data were compared with that of three pancreatic ductal adenocarcinomas (PDACs), with that of three normal pancreatic ducts, and with each other. By dividing NEC and MiNEN cases into KRAS-mutated group and KRAS-wild group, the difference of clinicopathological data and gene expression profiling data were examined between the two groups. Compared to the data of normal pancreatic epithelium, all 13 cancer-related pathways were upregulated in PDAC, MiNEN, and NEC group with more upregulation in this order. Compared to the data of PDAC, genes of DNA Damage repair pathway was most upregulated both in NECs and MiNENs. Regarding the difference between KRAS-mutated and KRAS-wild groups, several genes were differentially expressed between the two, where MMP7 was the upregulated gene with highest p-value and NKD1 was the downregulated gene with highest p-value in KRAS-mutated group. From the extent of upregulation of 13 pathways, MiNEN was considered more progressed stage than PDAC, and NEC was considered more progressed than MiNEN. From the comparison of KRAS-mutated and KRAS-wild NECs and MiNENs, several differentially expressed genes were identified in this study.

WGCNA combined with machine learning to find potential biomarkers of liver cancer

The incidence of hepatocellular carcinoma (HCC) has been increasing in recent years. With the development of various detection technologies, machine learning is an effective method to screen disease characteristic genes. In this study, weighted gene co-expression network analysis (WGCNA) and machine learning are combined to find potential biomarkers of liver cancer, which provides a new idea for future prediction, prevention, and personalized treatment. In this study, the "limma" software package was used. P < .05 and log2 |fold-change| > 1 is the standard screening differential genes, and then the module genes obtained by WGCNA analysis are crossed to obtain the key module genes. Gene Ontology and Kyoto Gene and Genome Encyclopedia analysis was performed on key module genes, and 3 machine learning methods including lasso, support vector machine-recursive feature elimination, and RandomForest were used to screen feature genes. Finally, the validation set was used to verify the feature genes, the GeneMANIA (http://www.genemania.org) database was used to perform protein-protein interaction networks analysis on the feature genes, and the SPIED3 database was used to find potential small molecule drugs. In this study, 187 genes associated with HCC were screened by using the "limma" software package and WGCNA. After that, 6 feature genes (AADAT, APOF, GPC3, LPA, MASP1, and NAT2) were selected by RandomForest, Absolute Shrinkage and Selection Operator, and support vector machine-recursive feature elimination machine learning algorithms. These genes are also significantly different on the external dataset and follow the same trend as the training set. Finally, our findings may provide new insights into targets for diagnosis, prevention, and treatment of HCC. AADAT, APOF, GPC3, LPA, MASP1, and NAT2 may be potential genes for the prediction, prevention, and treatment of liver cancer in the future.

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.

Clinical importance and PI3K/Akt pathway-dependent anti-proliferative role of PALMD and DPT in breast cancer

This study aimed to identify novel differentially expressed genes in breast cancer and to explore the clinical value and the anti-tumor or oncogenic effects of the identified genes using bioinformatics analysis and in vitro experiments. The differentially expressed genes in breast cancer patients were identified using Gene Expression Omnibus (GEO) database with the cut-off criteria p < 0.05 and |logFC| > 1. The expression levels of palmdelphin (PALMD) and dermatopontin (DPT) in normal tissues and breast cancer tissues were evaluated based on GEPIA and UALCAN databases. PALMD and DPT expression levels in clinical subgroups of patients with breast cancer were analyzed to assess the association of PALMD and DPT expression with clinical characteristics. The prognostic and diagnostic values of PALMD and DPT in breast cancer were evaluated from Kaplan-Meier (K-M) survival curves and receiver operating characteristic (ROC) curves. Pearson's correlation coefficient was performed using LinkedOmics. KEGG pathway enrichment analysis was performed using DAVID. The protein levels were evaluated using western blot analysis. Cell proliferation was assessed using MTT and EdU assays. Two important genes, PALMD and DPT, were identified in breast cancer. The expression levels of PALMD and DPT were significantly lower in breast cancer tissues. The expression levels of PALMD were closely related to age, histological type, and T stage of breast cancer patients. The expression levels of DPT were closely related to age, histological type, T stage, N stage, estrogen receptor status, and progesterone receptor status of breast cancer patients. The K-M survival curves showed that PALMD or DPT was not an independent prognostic factor for breast cancer. The ROC curves showed that both PALMD and DPT had good diagnostic potential for breast cancer. KEGG pathway enrichment results showed that PI3K/Akt pathway was an important overlapping signaling for PALMD and DPT. Further studies proved that overexpression of PALMD and DPT inhibited proliferation in MCF-7 and MDA-MB-231 cells by suppressing the PI3K/Akt pathway. PALMD and DPT knockdown promoted proliferation in MCF-7 and MDA-MB-231 cells by activating the PI3K/Akt pathway. These results collectively suggested that PALMD and DPT might serve as potential diagnostic biomarkers and therapeutic targets for breast cancer.

A transcriptomic evaluation of the mechanism of programmed cell death of the replaceable bud in Chinese chestnut

Previous studies suggest that the senescence and death of the replaceable bud of the Chinese chestnut cultivar (cv.) "Tima Zhenzhu" involves programmed cell death (PCD). However, the molecular network regulating replaceable bud PCD is poorly characterized. Here, we performed transcriptomic profiling on the chestnut cv. "Tima Zhenzhu" replaceable bud before (S20), during (S25), and after (S30) PCD to unravel the molecular mechanism underlying the PCD process. A total of 5,779, 9,867, and 2,674 differentially expressed genes (DEGs) were discovered upon comparison of S20 vs S25, S20 vs S30, and S25 vs S30, respectively. Approximately 6,137 DEGs common to at least two comparisons were selected for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to interrogate the main corresponding biological functions and pathways. GO analysis showed that these common DEGs could be divided into three functional categories, including 15 cellular components, 14 molecular functions, and 19 biological processes. KEGG analysis found that "plant hormone signal transduction" included 93 DEGs. Overall, 441 DEGs were identified as related to the process of PCD. Most of these were found to be genes associated with ethylene signaling, as well as the initiation and execution of various PCD processes.

ZNF765 is a prognostic biomarker of hepatocellular carcinoma associated with cell cycle, immune infiltration, m6A modification, and drug susceptibility

Hepatocellular carcinoma (HCC) is an ongoing challenge worldwide. Zinc finger protein 765 (ZNF765) is an important zinc finger protein that is related to the permeability of the blood-tumor barrier. However, the role of ZNF765 in HCC is unclear. This study evaluated the expression of ZNF765 in hepatocellular carcinoma and the impact of its expression on patient prognosis based on The Cancer Genome Atlas (TCGA). Immunohistochemical assays (IHC) were used to examine protein expression. Besides, a colony formation assay was used to examine cell viability. We also explored the relationship between ZNF765 and chemokines in the HCCLM3 cells by qRT-PCR. Moreover, we examined the effect of ZNF765 on cell resistance by measurement of the maximum half-inhibitory concentration. Our research revealed that ZNF765 expression in HCC samples was higher than that in normal samples, whose upregulation was not conducive to the prognosis. The results of GO, KEGG, and GSEA showed that ZNF765 was associated with the cell cycle and immune infiltration. Furthermore, we confirmed that the expression of ZNF765 had a strong connection with the infiltration level of various immune cells, such as B cells, CD4+ T cells, macrophages, and neutrophils. In addition, we found that ZNF765 was associated with m6A modification, which may affect the progression of HCC. Finally, drug sensitivity testing found that patients with HCC were sensitive to 20 drugs when they expressed high levels of ZNF765. In conclusion, ZNF765 may be a prognostic biomarker related to cell cycle, immune infiltration, m6A modification, and drug sensitivity for hepatocellular carcinoma.

KIF20A as a potential biomarker of renal and bladder cancers based on bioinformatics and experimental verification

BACKGROUND

Bladder cancer (BC) is a malignant tumor that occurs in the bladder wall and often appears in elderly individuals. Renal cancer (RC) arises from the renal tubular epithelium, but its molecular mechanism remains unclear.

METHODS

We downloaded RC datasets (GSE14762 and GSE53757) and a BC dataset (GSE121711) to screen differentially expressed genes (DEGs). We also performed weighted gene coexpression network analysis (WGCNA). We created a protein-protein interaction (PPI) network and performed functional enrichment analysis, such as gene set enrichment analysis (GSEA). Heatmaps were made for gene expression. Survival analysis and immunoinfiltration analysis were performed. Comparative toxicogenomics database (CTD) analysis was performed to find the relationship between disease and hub genes. Western blotting was performed to verify the role of KIF20A in apoptosis.

RESULTS

A total of 764 DEGs were identified. The GSEA showed that the DEGs were mainly enriched in organic acid metabolism, drug metabolism, mitochondria, and metabolism of cysteine and methionine. The PPI network in GSE121711 showed that KIF20A was a hub gene of renal clear cell carcinoma. Where the expression level of KIF20A was higher, the prognosis of patients was worse. CTD analysis showed that KIF20A was associated with inflammation, proliferation, and apoptosis. KIF20A expression in the RC group was upregulated, as shown by western blotting. The core proteins (including pRB Ser 780, CyclinA, E2F1, CCNE1, and CCNE2) in the pRB Ser 780/CyclinA signaling pathway were also upregulated in the RC group.

CONCLUSIONS

KIF20A might be a novel biomarker for researching renal and bladder cancers.

Transcriptomic profiling in hypoxia-induced trophoblast cells for preeclampsia

This study aimed to identify the expression profile of mRNAs and analyze the associated pathways in hypoxia-induced trophoblast cells to understand the effect of hypoxia on the pathophysiology of preeclampsia (PE). We downloaded two gene expression datasets (GSE47187 and GSE60432) from the Gene Expression Omnibus (GEO) datasets to identify altered transcriptomes. GEO2R, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction (PPI) networks were used to reveal the functional roles and regulatory networks of the differentially expressed genes (DEGs). In total, 224 DEGs (91 upregulated and 133 downregulated) were identified, and the "HIF-1 signaling pathway" was activated in placentas from patients with PE. We validated the expression levels of five proteins in the plasma of NP and PE patients during early or late pregnancy using western blotting. In primary trophoblast cells cultured under hypoxic conditions, 754 DEGs were identified, including 362 upregulated and 392 downregulated genes. These DEGs were associated with the "HIF-1signaling pathway," "response to hypoxia," and several glucose metabolism pathways. In addition, a PPI network was constructed, and an important module, including 18 hub genes, was identified. Finally, we validated 18 hub genes using qRT-PCR. Furthermore, we performed microarray profiling of hypoxia-treated HTR8/SVneo cells (immortalized human first-trimester extravillous trophoblast cells) to validate the DEGs and pathways identified in hypoxia-induced primary trophoblast cells. Our results stress the differential expression profiles of mRNAs in hypoxia-induced trophoblast cells, which provide potential pathophysiological mechanisms for preeclampsia.

Combined transcriptomics and proteomics forecast analysis for potential biomarker in the acute phase of temporal lobe epilepsy

Background

Temporal lobe epilepsy (TLE) is a common chronic episodic illness of the nervous system. However, the precise mechanisms of dysfunction and diagnostic biomarkers in the acute phase of TLE are uncertain and hard to diagnose. Thus, we intended to qualify potential biomarkers in the acute phase of TLE for clinical diagnostics and therapeutic purposes.

Methods

An intra-hippocampal injection of kainic acid was used to induce an epileptic model in mice. First, with a TMT/iTRAQ quantitative labeling proteomics approach, we screened for differentially expressed proteins (DEPs) in the acute phase of TLE. Then, differentially expressed genes (DEGs) in the acute phase of TLE were identified by linear modeling on microarray data (limma) and weighted gene co-expression network analysis (WGCNA) using the publicly available microarray dataset GSE88992. Co-expressed genes (proteins) in the acute phase of TLE were identified by overlap analysis of DEPs and DEGs. The least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) algorithms were used to screen Hub genes in the acute phase of TLE, and logistic regression algorithms were applied to develop a novel diagnostic model for the acute phase of TLE, and the sensitivity of the diagnostic model was validated using receiver operating characteristic (ROC) curves.

Results

We screened a total of 10 co-expressed genes (proteins) from TLE-associated DEGs and DEPs utilizing proteomic and transcriptome analysis. LASSO and SVM-RFE algorithms for machine learning were applied to identify three Hub genes: Ctla2a, Hapln2, and Pecam1. A logistic regression algorithm was applied to establish and validate a novel diagnostic model for the acute phase of TLE based on three Hub genes in the publicly accessible datasets GSE88992, GSE49030, and GSE79129.

Conclusion

Our study establishes a reliable model for screening and diagnosing the acute phase of TLE that provides a theoretical basis for adding diagnostic biomarkers for TLE acute phase genes.

Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning

Hepatocellular carcinoma (HCC) is the most common lethal malignancy of the liver worldwide. Thus, it is important to dig the key genes for uncovering the molecular mechanisms and to improve diagnostic and therapeutic options for HCC. This study aimed to encompass a set of statistical and machine learning computational approaches for identifying the key candidate genes for HCC. Three microarray datasets were used in this work, which were downloaded from the Gene Expression Omnibus Database. At first, normalization and differentially expressed genes (DEGs) identification were performed using limma for each dataset. Then, support vector machine (SVM) was implemented to determine the differentially expressed discriminative genes (DEDGs) from DEGs of each dataset and select overlapping DEDGs genes among identified three sets of DEDGs. Enrichment analysis was performed on common DEDGs using DAVID. A protein-protein interaction (PPI) network was constructed using STRING and the central hub genes were identified depending on the degree, maximum neighborhood component (MNC), maximal clique centrality (MCC), centralities of closeness, and betweenness criteria using CytoHubba. Simultaneously, significant modules were selected using MCODE scores and identified their associated genes from the PPI networks. Moreover, metadata were created by listing all hub genes from previous studies and identified significant meta-hub genes whose occurrence frequency was greater than 3 among previous studies. Finally, six key candidate genes (TOP2A, CDC20, ASPM, PRC1, NUSAP1, and UBE2C) were determined by intersecting shared genes among central hub genes, hub module genes, and significant meta-hub genes. Two independent test datasets (GSE76427 and TCGA-LIHC) were utilized to validate these key candidate genes using the area under the curve. Moreover, the prognostic potential of these six key candidate genes was also evaluated on the TCGA-LIHC cohort using survival analysis.

Elevated NDC1 expression predicts poor prognosis and correlates with immunity in hepatocellular carcinoma

Background

NDC1 was identified to be a tumor-promoting factor in non-small cell lung cancer and cervical cancer. However, no report had clarified the relationship between NDC1 and hepatocellular carcinoma (HCC). In this paper, we explored the expression and potential functions of NDC1 in HCC for the first time through the rational application of bioinformatics and relevant basic experiments.

Methods

NDC1-related expression profiles and clinical data of HCC patients were collected from The Cancer Genome Atlas (TCGA) database, which were verified via quantitative real-time polymerase chain reaction (qRT-PCR), immunohistochemistry and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. Univariate and multivariate Cox regression analyses were used to identify NDC1 as an independent factor for HCC prognosis, and NDC1-related signaling pathways were determined by gene set enrichment analysis (GSEA). Furthermore, we deeply probed the potential links of NDC1 to immunity and immune response. Finally, the bioeffects and underlying mechanisms of ectopic NDC1 overexpression and depletion were determined in HepG2 cells by immunoblotting, flow cytometry, Cell-Counting-Kit-8 (CCK-8), and EDU (5-Ethynyl-2'-deoxyuridine).

Results

Up-regulated expression of NDC1 was detected by means of the TCGA database, which was consistent with the results obtained from further qRT-PCR, immunohistochemistry and the CPTAC database. Kaplan-Meier (K-M) survival analysis revealed a worse prognosis in HCC patients with high NDC1 expression. Besides, NDC1 was certified to be closely linked to tumor histologic grade, clinical stage and T stage. Moreover, univariate and multivariate Cox regression analyses defined NDC1 as an independent element for HCC prognosis. NDC1-related signaling pathways, utilizing GSEA analysis, were subsequently found out. What's more, NDC1 expression was detected to be enormously associated with microsatellite instability (MSI), immune cell infiltration, immune checkpoint molecules and immune cell pathways. As for immunotherapy, we discovered that different risk groups tended to have different immune checkpoint inhibitor responses, which indicated crucial implication value of NDC1 for HCC immunotherapy. More interestingly, we observed that the overexpression of NDC1 could promote the migration and invasion of HCC cells.

Conclusions

Our article demonstrated that NDC1 might serve as a valuable predictor in the prognosis and immunotherapy of HCC. NDC1 played an oncogenic role in HCC.

The essential roles of FXR in diet and age influenced metabolic changes and liver disease development: a multi-omics study

BACKGROUND

Aging and diet are risks for metabolic diseases. Bile acid receptor farnesoid X receptor (FXR) knockout (KO) mice develop metabolic liver diseases that progress into cancer as they age, which is accelerated by Western diet (WD) intake. The current study uncovers the molecular signatures for diet and age-linked metabolic liver disease development in an FXR-dependent manner.

METHODS

Wild-type (WT) and FXR KO male mice, either on a healthy control diet (CD) or a WD, were euthanized at the ages of 5, 10, or 15 months. Hepatic transcriptomics, liver, serum, and urine metabolomics as well as microbiota were profiled.

RESULTS

WD intake facilitated hepatic aging in WT mice. In an FXR-dependent manner, increased inflammation and reduced oxidative phosphorylation were the primary pathways affected by WD and aging. FXR has a role in modulating inflammation and B cell-mediated humoral immunity which was enhanced by aging. Moreover, FXR dictated neuron differentiation, muscle contraction, and cytoskeleton organization in addition to metabolism. There were 654 transcripts commonly altered by diets, ages, and FXR KO, and 76 of them were differentially expressed in human hepatocellular carcinoma (HCC) and healthy livers. Urine metabolites differentiated dietary effects in both genotypes, and serum metabolites clearly separated ages irrespective of diets. Aging and FXR KO commonly affected amino acid metabolism and TCA cycle. Moreover, FXR is essential for colonization of age-related gut microbes. Integrated analyses uncovered metabolites and bacteria linked with hepatic transcripts affected by WD intake, aging, and FXR KO as well as related to HCC patient survival.

CONCLUSION

FXR is a target to prevent diet or age-associated metabolic disease. The uncovered metabolites and microbes can be diagnostic markers for metabolic disease.

Identification and Validation of Immune-Related Genes Diagnostic for Progression of Atherosclerosis and Diabetes

Background

Atherosclerosis and type 2 diabetes mellitus contribute to a large part of cardiovascular events, but the underlying mechanism remains unclear. In this study, we focused on identifying the linking genes of the diagnostic biomarkers and effective therapeutic targets associated with these two diseases.

Methods

The transcriptomic datasets of atherosclerosis and type 2 diabetes mellitus were obtained from the GEO database. Differentially expressed genes analysis was performed by R studio software, and differential analysis including functional enrichment, therapeutic small molecular agents prediction, and protein-protein interaction analysis were applied to the common shared differentially expressed genes. Hub genes were identified and further validated using an independent dataset and clinical samples. Furthermore, we measured the expression correlations, immune cell infiltration, and diagnostic capability of the three key genes.

Results

We screened out 28 up-regulated and six down-regulated common shared differentially expressed genes. Functional enrichment analysis showed that cytokines and immune activation were involved in the development of these two diseases. Six small molecules with the highest absolute enrichment value were identified. Three critical genes (CD4, PLEK, and THY1) were further validated both in validation sets and clinical samples. The gene correlation analysis showed that CD4 was strongly positively correlated with PLEK, and ROC curves confirmed the good discriminatory capacity of CD4 and PLEK in two diseases.

We have established the co-expression network between atherosclerosis lesions progressions and type 2 diabetes mellitus, and identified CD4 and PLEK as key genes in the two diseases, which may facilitate both development of diagnosis and therapeutic strategies.

Identification of potential biomarkers for colorectal cancer by clinical database analysis and Kaplan-Meier curves analysis

This study aimed to explore critical genes as potential biomarkers for the diagnosis and prognosis of colorectal cancer (CRC) for clinical utility. To identify and screen candidate genes involved in CRC carcinogenesis and disease progression, we downloaded microarray datasets GSE89076, GSE73360, and GSE32323 from the GEO database identified differentially expressed genes (DEGs), and performed a functional enrichment analysis. A protein-protein interaction network was constructed, and correlated module analysis was performed using STRING and Cytoscape. The Kaplan-Meier survival curve shows the survival of the hub genes. The expression of cyclin-dependent kinase (CDK1), cyclin B1 (CCNB1), and PCNA in tissues and changes in tumor grade were analyzed. A total of 329 DEGs were identified, including 264 upregulated and 65 downregulated genes. The functions and pathways of DEGs include the mitotic cell cycle, poly(A) RNA binding replication, ATP binding, DNA replication, ribosome biogenesis in eukaryotes, and RNA transport. Forty-seven Hub genes were identified, and biological process analysis showed that these genes were mainly enriched in cell cycle and DNA replication. Patients with mutations in CDK1, PCNA, and CCNB1 had poorer survival rates. CDK1, PCNA, and CCNB1 were significantly overexpressed in the tumor tissues. The expression of CDK1 and CCNB1 gradually decreased with increasing tumor grade. CDK1, CCNB1, and PCNA can be used as potential markers for the diagnosis and prognosis of CRC. These genes are overexpressed in colon cancer tissues and are associated with low survival rates in CRC patients.

High expression of KIF20A in bladder cancer as a potential prognostic target for poor survival of renal cell carcinoma

Urinary system tumors are malignant tumors, including renal cancer and bladder cancer. however, molecular target of them remains unclear. GSE14762 and GSE53757 were downloaded from GEO database to screen differentially expressed genes (DEGs). Weighted gene co-expression network analysis was performed. Gene Ontology (GO) and Kyoto encyclopedia of genes and genomes were used for enrichment analysis. Gene ontology and Kyoto encyclopedia of genes and genomes analyses were performed on whole genome, as formulated by gene set enrichment analysis. Survival analysis was also performed. Comparative toxicogenomics database was used to identify diseases most associated with hub genes. A total of 1517 DEGs were identified. DEGs were mainly enriched in cancer pathway, HIF-1 signaling pathway, organic acid metabolism, glyoxylate and dicarboxylate metabolism, and protein homodimerization activity. Ten hub genes (TPX2, ASPM, NUSAP1, RAD51AP1, CCNA2, TTK, PBK, MELK, DTL, kinesin family member 20A [KIF20A]) were obtained, which were up-regulated in tumor tissue. The expression of KIF20A was related with the overall survival of renal and bladder cancer. KIF20A was up-regulated in the tumor tissue, and might worsen the overall survival of bladder and kidney cancer. KIF20A could be a novel biomarker of bladder and kidney cancer.

Exploration and verification of COVID-19-related hub genes in liver physiological and pathological regeneration

Objectives An acute injury is often accompanied by tissue regeneration. In this process, epithelial cells show a tendency of cell proliferation under the induction of injury stress, inflammatory factors, and other factors, accompanied by a temporary decline of cellular function. Regulating this regenerative process and avoiding chronic injury is a concern of regenerative medicine. The severe coronavirus disease 2019 (COVID-19) has posed a significant threat to people's health caused by the coronavirus. Acute liver failure (ALF) is a clinical syndrome resulting from rapid liver dysfunction with a fatal outcome. We hope to analyze the two diseases together to find a way for acute failure treatment. Methods COVID-19 dataset (GSE180226) and ALF dataset (GSE38941) were downloaded from the Gene Expression Omnibus (GEO) database, and the "Deseq2" package and "limma" package were used to identify differentially expressed genes (DEGs). Common DEGs were used for hub genes exploration, Protein-Protein Interaction (PPI) network construction, Gene Ontology (GO) functional enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) was used to verify the role of hub genes in liver regeneration during in vitro expansion of liver cells and a CCl4-induced ALF mice model. Results: The common gene analysis of the COVID-19 and ALF databases revealed 15 hub genes from 418 common DEGs. These hub genes, including CDC20, were related to cell proliferation and mitosis regulation, reflecting the consistent tissue regeneration change after the injury. Furthermore, hub genes were verified in vitro expansion of liver cells and in vivo ALF model. On this basis, the potential therapeutic small molecule of ALF was found by targeting the hub gene CDC20. Conclusion We have identified hub genes for epithelial cell regeneration under acute injury conditions and explored a new small molecule Apcin for liver function maintenance and ALF treatment. These findings may provide new approaches and ideas for treating COVID-19 patients with ALF.

Integrated analysis of transcriptomics and metabolomics in human hepatocellular carcinoma HepG2215 cells after YAP1 knockdown

Yes-associated protein 1 (YAP1) plays a critical role in hepatocellular carcinoma (HCC). Inhibition of YAP1 expression suppresses HCC progression, but the underlying mechanism is still unclear. In this study, we studied the effects and molecular mechanisms of YAP1 knockdown on the growth and metabolism in human HCC HepG2215 cells. Inhibition of YAP1 expression inhibits the proliferation and metastasis in HepG2215 cells, and differentially expressed genes (DEGs) and metabolites were identified in shYAP1-HepG2215 cells. Further, 805 DEGs, mainly associated with metabolism and particularly lipid metabolism, were identified by transcriptome sequencing analyses in shYAP1-HepG2215 cells. YAP1 knockdown increased albumin (ALB) levels by Protein-protein interaction (PPI) network analyses in HepG2215 cells. Metabolomic profiling identified 37 metabolites with significant differences in the shYAP1 group, and amino acid metabolism generally decreased in the shYAP1 group. Comprehensive analysis of transcriptomics and metabolomics revealed that the ATP-binding cassette (ABC) transporters play a central role after YAP1 knockdown in HepG2215 cells. Therefore, YAP1 knockdown inhibited HCC growth, which affected the metabolism of lipids and amino acids by regulating the expression of ALB and ABC transporters in HepG2215 cells.

Bioinformatics analysis of key biomarkers for bladder cancer

Bladder cancer (BC) is one of the most prevalent genitourinary cancers. Despite the growing research interest in BC, the molecular mechanisms underlying its carcinogenesis remain poorly understood. The microarray datasets GSE38264 and GSE61615 obtained from the Gene Expression Omnibus (GEO) database were analyzed and differentially expressed genes (DEGs) were identified, which were then verified using a dataset from The Cancer Genome Atlas (TCGA). By taking the intersection of the two microarray datasets, the common DEGs were identified and these were selected as candidate genes associated with BC. The DEGs were further subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and the protein-protein interaction network was constructed. Further module analysis was performed using STRING and Cytoscape. A total of 362 DEGs were identified, including 13 hub genes, and the GO analysis revealed that these genes were mainly enriched in extracellular matrix organization, positive regulation of cell proliferation, angiogenesis and peptidyl-tyrosine phosphorylation. The expression changes of PTPRC, PDGFRA, CASQ2, TGFBI, KLRD1 and MT1X in the different datasets indicated that these genes were involved in the development of BC. Next, the differential expression of these genes was verified in the TCGA dataset, and ultimately, these 13 genes were determined to be related to the occurrence and development of BC. Finally, the cancer tissues and adjacent tissues of patients with BC were collected and subjected to reverse transcription-quantitative PCR, the results of which were consistent with the bioinformatics prediction. The present findings provide several vital genes for the clinical diagnosis and treatment of BC.

Prognosis-Related Molecular Subtypes and Immune Features Associated with Hepatocellular Carcinoma

Bioinformatics tools were used to identify prognosis-related molecular subtypes and biomarkers of hepatocellular carcinoma (HCC). Differential expression analysis of four datasets identified 3330 overlapping differentially expressed genes (DEGs) in the same direction in all four datasets. Those genes were involved in the cell cycle, FOXO signaling pathway, as well as complement and coagulation cascades. Based on non-negative matrix decomposition, two molecular subtypes of HCC with different prognoses were identified, with subtype C2 showing better overall survival than subtype C1. Cox regression and Kaplan-Meier analysis showed that 217 of the overlapping DEGs were closely associated with HCC prognosis. The subset of those genes showing an area under the curve >0.80 was used to construct random survival forest and least absolute shrinkage and selection operator models, which identified seven feature genes (SORBS2, DHRS1, SLC16A2, RCL1, IGFALS, GNA14, and FANCI) that may be involved in HCC occurrence and prognosis. Based on the feature genes, risk score and recurrence models were constructed, while a univariate Cox model identified FANCI as a key gene involved mainly in the cell cycle, DNA replication, and mismatch repair. Further analysis showed that FANCI had two mutation sites and that its gene may undergo methylation. Single-sample gene set enrichment analysis showed that Th2 and T helper cells are significantly upregulated in HCC patients compared to controls. Our results identify FANCI as a potential prognostic biomarker for HCC.

Custom gene expression panel for evaluation of potential molecular markers in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related mortality worldwide. It is a highly heterogeneous disease with poor prognosis and limited treatment options, which highlights the need for reliable biomarkers. This study aims to explore molecular markers that allow stratification of HCC and may lead to better prognosis and treatment prediction.

MATERIALS AND METHODS

We studied 20 candidate genes (HCC hub genes, potential drug target genes, predominant somatic mutant genes) retrieved from literature and public databases with potential to be used as the molecular markers. We analysed expression of the genes by RT-qPCR in 30 HCC tumour and adjacent non-tumour paired samples from Vietnamese patients. Fold changes in expression were then determined using the 2-∆∆CT method, and unsupervised hierarchical clustering was generated using Cluster v3.0 software.

RESULTS

Clustering of expression data revealed two subtypes of tumours (proliferative and normal-like) and four clusters for genes. The expression profiles of the genes TOP2A, CDK1, BIRC5, GPC3, IGF2, and AFP were strongly correlated. Proliferative tumours were characterized by high expression of the c-MET, ARID1A, CTNNB1, RAF1, LGR5, and GLUL1 genes. TOP2A, CDK1, and BIRC5 HCC hub genes were highly expressed (> twofold) in 90% (27/30), 83% (25/30), and 83% (24/30) in the tissue samples, respectively. Among the drug target genes, high expression was observed in the GPC3, IGF2 and c-MET genes in 77% (23/30), 63% (19/30), and 37% (11/30), respectively. The somatic mutant Wnt/ß-catenin genes (CTNNB1, GLUL and LGR5) and TERT were highly expressed in 40% and 33% of HCCs, respectively. Among the HCC marker genes, a higher percentage of tumours showed GPC3 expression compared to AFP expression [73% (23/30) vs. 43% (13/30)].

CONCLUSION

The custom panel and molecular markers from this study may be useful for diagnosis, prognosis, biomarker-guided clinical trial design, and prediction of treatment outcomes.

Bioinformatics analysis of prognostic value and immunological role of MeCP2 in pan-cancer

Methyl-CpG-binding protein 2(MeCP2) is an important epigenetic regulatory factor that promotes many tumor developments, such as liver cancer, breast cancer, and colorectal cancer. So far, no pan-cancer analysis has been reported. Therefore, this study aims to explore pan-cancer's prognostic value, immune infiltration pattern, and biological function. We used bioinformatics methods to analyze the expression and prognostic significance of MeCP2, and the relationship between MeCP2 and clinicopathological parameters, genetic variation, methylation, phosphorylation, immune cell infiltration, and biological function in pan-cancer from using a public database. The results showed that expression of MeCP2 was up-regulated in 8 cancers and down-regulated in 2 cancers, which was remarkably correlated with the prognosis, pathological stage, grade and subtype of cancers. The promoter methylation level of MeCP2 DNA was decreased in bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), liver hepatocellular carcinoma (LIHC), prostate adenocarcinoma (PRAD), uterine corpus endometrial carcinoma (UCEC), testicular germ cell tumors (TGCT), and stomach adenocarcinoma (STAD);decreased phosphorylation of S25, S90, S92, S241, S286, S325 and S435 was found in MeCP2, such as UCEC, lung adenocarcinoma (LUAD), ovarian serous cystadenocarcinoma (OV), colon adenocarcinoma (COAD), and kidney renal clear cell carcinoma (KIRC). Furthermore, MeCP2 expression was significantly associated with multiple immunomodulators and immune cell infiltration levels across most tumors. Therefore, our pan-cancer explored the prognostic markers and immunotherapeutic value of MeCP2 in different cancers.

Identification of potential hub genes of gastric cancer

BACKGROUND

Gastric cancer (GC) is a malignant tumor originated from gastric mucosa epithelium. It is the third leading cause of cancer mortality in China. The early symptoms are not obvious. When it is discovered, it has developed to the advanced stage, and the prognosis is poor. In order to screen for potential genes for GC development, this study obtained GSE118916 and GSE109476 from the gene expression omnibus (GEO) database for bioinformatics analysis.

METHODS

First, GEO2R was used to identify differentially expressed genes (DEG) and the functional annotation of DEGs was performed by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The Search Tool for the Retrieval of Interacting Genes (STRING) tool was used to construct protein-protein interaction (PPI) network and the most important modules and hub genes were mined. Real time quantitative polymerase chain reaction assay was performed to verify the expression level of hub genes.

RESULTS

A total of 139 DEGs were identified. The functional changes of DEGs are mainly concentrated in the cytoskeleton, extracellular matrix and collagen synthesis. Eleven genes were identified as core genes. Bioinformatics analysis shows that the core genes are mainly enriched in many processes related to cell adhesion and collagen.

CONCLUSION

In summary, the DEGs and hub genes found in this study may be potential diagnostic and therapeutic targets.

Bioinformatic identification of differentially expressed genes associated with hepatocellular carcinoma prognosis

Hepatocellular carcinoma (HCC) is still a significant global health problem. The development of bioinformatics may provide the opportunities to identify novel therapeutic targets. This study bioinformatically identified the differentially expressed genes (DEGs) in HCC and associated them with HCC prognosis using data from published databases. The DEGs downloaded from the Gene Expression Omnibus (GEO) website were visualized using the Venn diagram software, and then subjected to the GO and KEGG analyses, while the protein-protein interaction network was analyzed using Cytoscape software with the Search Tool for the search tool for the retrieval of interacting genes and the molecular complex detection plug-in. Kaplan-Meier curves and the log rank test were used to associate the core PPI network genes with the prognosis. There were 57 upregulated and 143 downregulated genes in HCC samples. The GO and pathway analyses revealed that these DEGs are involved in the biological processes (BPs), molecular functions (MFs), and cell components (CCs). The PPI network covered 50 upregulated and 108 downregulated genes, and the core modules of this PPI network contained 34 upregulated genes. A total of 28 of these upregulated genes were associated with a poor HCC prognosis, 27 of which were highly expressed in HCC tissues. This study identified 28 DEGs to be associated with a poor HCC prognosis. Future studies will investigate their possible applications as prognostic biomarkers and potential therapeutic targets for HCC.

Identifying hub genes and miRNAs in Crohn’s disease by bioinformatics analysis

Introduction: Crohn’s disease (CD) is a disease that manifests mainly as chronic inflammation of the gastrointestinal tract, which is still not well understood in terms of its pathogenesis. The aim of this study was to use bioinformatics analysis to identify differentially expressed genes (DEGs) and miRNAs with diagnostic and therapeutic potential in CD.

Materials and methods: Three CD datasets (GSE179285, GSE102133, GSE75214) were downloaded from the Gene Expression Omnibus (GEO) database. DEGs between normal and CD tissues were identified using the GEO2R online tool. The Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were conducted using the clusterProfiler function in the R package. Protein-protein interaction network (PPI) analysis and visualization were performed with STRING and Cytoscape. Ten hub genes were identified using cytoHubba’s MCC algorithm and validated with datasets GSE6731 and GSE52746. Finally, the miRNA gene regulatory network was constructed by Cytoscape and NetworkAnalyst to predict potential microRNAs (miRNAs) associated with DEGs.

Results: A total of 97 DEGs were identified, consisting of 88 downregulated genes and 9 upregulated genes. The enriched functions and pathways of the DEGs include immune system process, response to stress, response to cytokine and extracellular region. KEGG pathway analysis indicates that the genes were significantly enriched in Cytokine-cytokine receptor interaction, IL-17 signaling pathway, Rheumatoid arthritis and TNF signaling pathway. In combination with the results of the protein-protein interaction (PPI) network and CytoHubba, 10 hub genes including IL1B, CXCL8, CXCL10, CXCL1, CXCL2, CXCL5, ICAM1, IL1RN, TIMP1 and MMP3 were selected. Based on the DEG-miRNAs network construction, 5 miRNAs including hsa-mir-21-5p, hsa-mir-93-5p, hsa-mir-98-5p, hsa-mir-1-3p and hsa-mir-335-5p were identified as potential critical miRNAs.

Conclusion: In conclusion, a total of 97 DEGs, 10 hub genes and 5 miRNAs that may be involved in the progression or occurrence of CD were identified in this study, which could be regarded as biomarkers of CD.

Identification of G6PC as a potential prognostic biomarker in hepatocellular carcinoma based on bioinformatics analysis

Hepatocellular carcinoma (HCC) has high mortality and incidence rates around the world with limited therapeutic options. There is an urgent need for identification of novel therapeutic targets and biomarkers for early diagnosis and predicting patient survival with HCC. Several studies (GSE102083, GSE29722, GSE101685, and GSE112790) from the GEO database in HCC were screened and analyzed by GEO2R, gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were conducted with the Database for Annotation, Visualization and Integrated Discovery. The protein-protein interaction network was plotted and the module analysis was performed using Search Tool for the Retrieval of Inter-acting Genes/Proteins database and Cytoscape. The expression and survival of key genes were identified using UALCAN, Kaplan-Meier Plotter and ONCOMINE online databases, and the immune infiltration level of key genes was analyzed via the Tumor Immune Estimation Resource (TIMER) database. Through database analysis, eight key genes were finally screened out, and the expressions of cyclin-dependent kinase regulatory subunit 2 and glucose-6-phosphatase catalytic (G6PC), which were closely related to the survival of HCC patients, was detected by using UALCAN. Further analysis on the differential expression of G6PC in multiple cancerous tumors and normal tissues revealed low expression in many solid tumors by Oncomine and TIMER. In addition, Kaplan-Meier plotter and UALCAN database analysis to access diseases prognosis suggested that low expression of G6PC was significantly associated with poor overall survival in HCC patients. Finally, TIMER database analysis showed a significant negative correlation between G6PC and infiltration levels of six kinds of immune cells. The somatic copy number alterations of G6PC were associated with B cells, CD8+ T cells, CD4+ T cells, macrophages, dentritic cells and neutrophils. These bioinformatic data identified G6PC as a potential key gene in the diagnosis and prognosis of HCC.

Integrated multi-omics analysis identifies ENY2 as a predictor of recurrence and a regulator of telomere maintenance in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and has a high recurrence rate. Accurate prediction of recurrence risk is urgently required for tailoring personalized treatment programs for individual HCC patients in advance. In this study, we analyzed a gene expression dataset from an HCC cohort with 247 samples and identified five genes including ENY2, GPAA1, NDUFA4L2, NEDD9, and NRP1 as the variables for the prediction of HCC recurrence, especially the early recurrence. The Cox model and risks score were validated in two public HCC cohorts (GSE76427 and The Cancer Genome Atlas (TCGA)) and one cohort from Huashan Hospital, which included a total of 641 samples. Moreover, the multivariate Cox regression analysis revealed that the risk score could serve as an independent prognostic factor in the prediction of HCC recurrence. In addition, we found that ENY2, GPAA1, and NDUFA4L2 were significantly upregulated in HCC of the two validation cohorts, and ENY2 had significantly higher expression levels than another four genes in malignant cells, suggesting that ENY2 might play key roles in malignant cells. The cell line analysis revealed that ENY2 could promote cell cycle progression, cell proliferation, migration, and invasion. The functional analysis of the genes correlated with ENY2 revealed that ENY2 might be involved in telomere maintenance, one of the fundamental hallmarks of cancer. In conclusion, our data indicate that ENY2 may regulate the malignant phenotypes of HCC via activating telomere maintenance.

Identifying potential prognosis markers in hepatocellular carcinoma via integrated bioinformatics analysis and biological experiments

Background: Hepatocellular carcinoma is one kind of clinical common malignant tumor with a poor prognosis, and its pathogenesis remains to be clarified urgently. This study was performed to elucidate key genes involving HCC by bioinformatics analysis and experimental evaluation.

Methods: We identified common differentially expressed genes (DEGs) based on gene expression profile data of GSE60502 and GSE84402 from the Gene Expression Omnibus (GEO) database. Gene Ontology enrichment analysis (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, REACTOME pathway enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were used to analyze functions of these genes. The protein-protein interaction (PPI) network was constructed using Cytoscape software based on the STRING database, and Molecular Complex Detection (MCODE) was used to pick out two significant modules. Hub genes, screened by the CytoHubba plug-in, were validated by Gene Expression Profiling Interactive Analysis (GEPIA) and the Human Protein Atlas (HPA) database. Then, the correlation between hub genes expression and immune cell infiltration was evaluated by Tumor IMmune Estimation Resource (TIMER) database, and the prognostic values were analyzed by Kaplan-Meier plotter. Finally, biological experiments were performed to illustrate the functions of RRM2.

Results: Through integrated bioinformatics analysis, we found that the upregulated DEGs were related to cell cycle and cell division, while the downregulated DEGs were associated with various metabolic processes and complement cascade. RRM2, MAD2L1, MELK, NCAPG, and ASPM, selected as hub genes, were all correlated with poor overall prognosis in HCC. The novel RRM2 inhibitor osalmid had anti-tumor activity, including inhibiting proliferation and migration, promoting cell apoptosis, blocking cell cycle, and inducing DNA damage of HCC cells.

Conclusion: The critical pathways and hub genes in HCC progression were screened out, and targeting RRM2 contributed to developing new therapeutic strategies for HCC.

Esophageal squamous cell carcinoma: Integrated bioinformatics analysis for differential gene expression with identification of hub genes and lncRNA

Background

Esophageal squamous cell carcinoma (ESCC) is a typical Gastro-Intestinal (GI) tract neoplasm. This study was conducted to know the Differential Expressed Genes (DEGs) profile of ESCC along with hub gene screening, lncRNA identification, and drug-genes interactions.

Methods

GSE161533, GSE20347, GSE45670 microarray datasets were retrieved from the NCBI Gene Expression Omnibus (GEO) database. GEO2R was used for the DEGs identification, whereas GO (Gene Ontology) and KEGG enrichment analysis were performed in DAVID. PPI network constructed using STRING and visualized with Cytoscape app with the help of MCODE. The top ten connectivity genes were selected as hub genes—further survival analysis was performed in the Kaplan-Meier plotter. Moreover, Boxplot, pathological stage plots were constructed using GEPIA (Gene Expression Profiling Interactive Analysis). The methylation heatmap assembled in the DiseaseMeth version 2.0. lncRNA (Long non-coding RNA) was identified comparing the list of genes in HUGO, and Gene-drug interactions were accumulated from the DgiDB platform.

Results

This experiment showed 16 upregulated, and 59 downregulated DEGs shared among the three datasets. Biological process analysis showed significant terms such as extracellular matrix disassembly and collagen catabolism. The extracellular region was detected as the most crucial cellular compartment. Notably, metalloen dopeptidease and serine-type endopeptidase activity showed significant molecular functions term. In contrast, transcriptional misregulation was a highly substantial KEGG pathway. Kaplan-Meier plotter showed higher expression of CXCL8, SPP1, MMP13, CXCL1, and TOP2A have a significant impact on the overall survival of the patients. Nine out of ten hub genes have significantly different expression levels than normal and cancer tissues. HYMAI was the only lncRNA commonly expressed upregulated among the three datasets. Drug-gene interaction showed multiple genes have no drug options exist till now.

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GSE161533, GSE20347, and GSE45670 microarray datasets were analyzed.

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16 upregulated and 59 downregulated DEGs shared among the three datasets.

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CXCL8, SPP1, MMP13, CXCL1, and TOP2A have a significant impact on survival.

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HYMAI was the only lncRNA commonly expressed.

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Multiple genes have no drug options that exist.

ARG1 as a promising biomarker for sepsis diagnosis and prognosis: evidence from WGCNA and PPI network

Background

Sepsis is a life-threatening multi-organ dysfunction caused by the dysregulated host response to infection. Sepsis remains a major global concern with high mortality and morbidity, while management of sepsis patients relies heavily on early recognition and rapid stratification. This study aims to identify the crucial genes and biomarkers for sepsis which could guide clinicians to make rapid diagnosis and prognostication.

Methods

Preliminary analysis of multiple global datasets, including 170 samples from patients with sepsis and 110 healthy control samples, revealed common differentially expressed genes (DEGs) in peripheral blood of patients with sepsis. After Gene Oncology (GO) and pathway analysis, the Weighted Gene Correlation Network Analysis (WGCNA) was used to screen for genes most related with clinical diagnosis. Also, the Protein-Protein Interaction Network (PPI Network) was constructed based on the DEGs and the hub genes were found. The results of WGCNA and PPI network were compared and one shared gene was discovered. Then more datasets of 728 experimental samples and 355 control samples were used to prove the diagnostic and prognostic value of this gene. Last, we used real-time PCR to confirm the bioinformatic results.

Results

Four hundred forty-four common differentially expressed genes in the blood of sepsis patients from different ethnicities were identified. Fifteen genes most related with clinical diagnosis were found by WGCNA, and 24 hub genes with most node degrees were identified by PPI network. ARG1 turned out to be the unique overlapped gene. Further analysis using more datasets showed that ARG1 was not only sharply up-regulated in sepsis than in healthy controls, but also significantly high-expressed in septic shock than in non-septic shock, significantly high-expressed in severe or lethal sepsis than in uncomplicated sepsis, and significantly high-expressed in non-responders than in responders upon early treatment. These all demonstrate the performance of ARG1 as a key biomarker. Last, the up-regulation of ARG1 in the blood was confirmed experimentally.

Conclusions

We identified crucial genes that may play significant roles in sepsis by WGCNA and PPI network. ARG1 was the only overlapped gene in both results and could be used to make an accurate diagnosis, discriminate the severity and predict the treatment response of sepsis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41065-022-00240-1.

Identification of Differentially Expressed Genes and miRNAs for Ulcerative Colitis Using Bioinformatics Analysis

Introduction: Ulcerative colitis (UC) is a chronic inflammatory disease of the intestine whose cause and underlying mechanisms are not fully understood. The aim of this study was to use bioinformatics analysis to identify differentially expressed genes (DEGs) with diagnostic and therapeutic potential in UC.

Materials and methods: Three UC datasets (GSE179285, GSE75214, GSE48958) were downloaded from the Gene Expression Omnibus (GEO) database. DEGs between normal and UC tissues were identified using the GEO2R online tool. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were performed using Metascape. Protein-protein interaction network (PPI) analysis and visualization using STRING and Cytoscape. Finally, the miRNA gene regulatory network was constructed by Cytoscape to predict potential microRNAs (miRNAs) associated with DEGs.

Results: A total of 446 DEGs were identified, consisting of 309 upregulated genes and 137 downregulated genes. The enriched functions and pathways of the DEGs include extracellular matrix, regulation of cell adhesion, inflammatory response, response to cytokine, monocarboxylic acid metabolic process, response to toxic substance. The analysis of KEGG pathway indicates that the DEGs were significantly enriched in Complement and coagulation cascades, Amoebiasis, TNF signaling pathway, bile secretion, and Mineral absorption. Combining the results of the PPI network and CytoHubba, 9 hub genes including CXCL8, ICAM1, CXCR4, CD44, IL1B, MMP9, SPP1, TIMP1, and HIF1A were selected. Based on the DEG-miRNAs network construction, 7 miRNAs including miR-335-5p, mir-204-5p, miR-93-5p, miR106a-5p, miR-21-5p, miR-146a-5p, and miR-155-5p were identified as potential critical miRNAs.

Conclusion: In summary, we identified DEGs that may be involved in the progression or occurrence of UC. A total of 446 DEGs,9 hub genes and 7 miRNAs were identified, which may be considered as biomarkers of UC. Further studies, however, are needed to elucidate the biological functions of these genes in UC.

Human Plasma Transcriptome Implicates Dysregulated S100A12 Expression: A Strong, Early-Stage Prognostic Factor in ST-Segment Elevated Myocardial Infarction: Bioinformatics Analysis and Experimental Verification

The ability of blood transcriptome analysis to identify dysregulated pathways and outcome-related genes following myocardial infarction remains unknown. Two gene expression datasets (GSE60993 and GSE61144) were downloaded from Gene Expression Omnibus (GEO) Datasets to identify altered plasma transcriptomes in patients with ST-segment elevated myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention. GEO2R, Gene Ontology/Kyoto Encyclopedia of Genes and Genomes annotations, protein–protein interaction analysis, etc., were adopted to determine functional roles and regulatory networks of differentially expressed genes (DEGs). Dysregulated expressomes were verified at transcriptional and translational levels by analyzing the GSE49925 dataset and our own samples, respectively. A total of 91 DEGs were identified in the discovery phase, consisting of 15 downregulated genes and 76 upregulated genes. Two hub modules consisting of 12 hub genes were identified. In the verification phase, six of the 12 hub genes exhibited the same variation patterns at the transcriptional level in the GSE49925 dataset. Among them, S100A12 was shown to have the best discriminative performance for predicting in-hospital mortality and to be the only independent predictor of death during follow-up. Validation of 223 samples from our center showed that S100A12 protein level in plasma was significantly lower among patients who survived to discharge, but it was not an independent predictor of survival to discharge or recurrent major adverse cardiovascular events after discharge. In conclusion, the dysregulated expression of plasma S100A12 at the transcriptional level is a robust early prognostic factor in patients with STEMI, while the discrimination power of the protein level in plasma needs to be further verified by large-scale, prospective, international, multicenter studies.

Identification of Genes Related to DNA Repair Mechanism in Glioblastoma by Bioinformatics Methods

Objective: Aberrant expression of genes involved in DNA repair mechanisms (DRM) have been associated with radiation sensitivity of glioblastoma (GBM) cells. Identification of genes in DRM through bioinformatics methods may help identify potential novel therapeutic targets that can be used in GBM treatment. This study aims to identify genes that play a role in DRM in GBM using bioinformatics methods. Methods: Genes associated with DRM were identified using the “Reactome” and “KEGG” databases. The mRNA expression profiles of DRM related genes were analyzed in the GEO GDS1813 and GDS2853 datasets including GBM tumor samples using the "Orange Canvas" software. Genetic changes of genes were identified in GBM TCGA cases using the cBioPortal database. The GEPIA2 was used to show the effect of altered expression profiles of these genes on patient survival. Results: The mRNA expression profiles of ERCC6, FAN1, MBD4, PARP1 and UNG genes were found to be altered in GBM tumors. Mutations and copy number alterations for the identified genes were observed in TCGA GBM cases. The overall survival and disease-free survival of TCGA GBM patients were not significantly different between high and low expression groups. Conclusion: ERCC6, PARP1 and UNG genes identified in the current study may be potential therapeutic targets that can increase the efficacy of radiotherapy in GBM in case of their suppression.

β-elemene regulates M1-M2 macrophage balance through the ERK/JNK/P38 MAPK signaling pathway

Macrophages are classified into classically activated M1 macrophages and alternatively activated M2 macrophages, and the two phenotypes of macrophages are present during the development of various chronic diseases, including obesity-induced inflammation. In the present study, β-elemene, which is contained in various plant substances, is predicted to treat high-fat diet (HFD)-induced macrophage dysfunction based on the Gene Expression Omnibus (GEO) database and experimental validation. β-elemene impacts the imbalance of M1-M2 macrophages by regulating pro-inflammatory cytokines in mouse white adipose tissue both in vitro and in vivo. In addition, the RAW 264 cell line, which are macrophages from mouse ascites, is used to identify the effects of β-elemene on inhibiting bacterial endotoxin lipopolysaccharide (LPS)-induced phosphorylation of mitogen-activated protein kinase (MAPK) pathways. These pathways both induce and are activated by pro-inflammatory cytokines, and they also participate in the process of obesity-induced inflammation. The results highlight that β-elemene may represent a possible macrophage-mediated therapeutic medicine.

NUF2 Is a Potential Immunological and Prognostic Marker for Non-Small-Cell Lung Cancer

Background

Globally, non-small-cell lung cancer (NSCLC) is one of the most prevalent tumors. Various studies have investigated its etiology, but the molecular mechanism of NSCLC has not been elucidated.

Methods

The GSE19804, GSE118370, GSE19188, GSE27262, and GSE33532 microarray datasets were obtained from the Gene Expression Omnibus (GEO) database for the identification of genes involved in NSCLC development as well as progression. Then, the identified differentially expressed genes (DEGs) were subjected to functional enrichment analyses. The protein-protein interaction (PPI) network was built after which module analysis was conducted via the Search Tool for Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape. There were 562 DEGs: 98 downregulated genes and 464 upregulated. These DEGs were established to be enriched in p53 signaling pathway, transendothelial leukocyte migration, cell adhesion molecules, contractions of vascular smooth muscles, coagulation and complement cascades, and axon guidance. Assessment of tumor immunity was performed to determine the roles of hub genes.

Results

There were 562 dysregulated genes, while 12 genes were hub genes. NUF2 was established to be a candidate immunotherapeutic target with potential clinical implications. The 12 hub genes were highly enriched in the p53 signaling pathway, the cell cycle, progesterone-associated oocyte maturation, cellular senescence, and oocyte meiosis. Survival analysis showed that NUF2 is associated with NSCLC occurrence, invasion, and recurrence.

Conclusion

The NUF2 gene discovered in this study helps us clarify the pathomechanisms of NSCLC occurrence as well as progression and provides a potential diagnostic and therapeutic target for NSCLC.

Identification of novel potential biomarkers in infantile hemangioma via weighted gene co-expression network analysis

Background

Infantile hemangioma (IH) is the most common benign tumor in children and is characterized by endothelial cells proliferation and angiogenesis. Some hub genes may play a critical role in angiogenesis. This study aimed to identify the hub genes and analyze their biological functions in IH.

Methods

Differentially expressed genes (DEGs) in hemangioma tissues, regardless of different stages, were identified by microarray analysis. The hub genes were selected through integrated weighted gene co-expression network analysis (WGCNA) and protein–protein interaction (PPI) network. Subsequently, detailed bioinformatics analysis of the hub genes was performed by gene set enrichment analysis (GSEA). Finally, quantitative real-time polymerase chain reaction (qRT-PCR) analysis was conducted to validate the hub genes expression in hemangioma-derived endothelial cells (HemECs) and human umbilical vein endothelial cells (HUVECs).

Results

In total, 1115 DEGs were identified between the hemangiomas and normal samples, including 754 upregulated genes and 361 downregulated genes. Two co-expression modules were identified by WGCNA and green module eigengenes were highly correlated with hemangioma (correlation coefficient = 0.87). Using module membership (MM) > 0.8 and gene significance (GS) > 0.8 as the cut-off criteria, 108 candidate genes were selected and put into the PPI network, and three most correlated genes (APLN, APLNR, TMEM132A) were identified as the hub genes. GSEA predicted that the hub genes would regulate endothelial cell proliferation and angiogenesis. The differential expression of these genes was validated by qRT-PCR.

Conclusions

This research suggested that the identified hub genes may be associated with the angiogenesis of IH. These genes may improve our understanding of the mechanism of IH and represent potential anti-angiogenesis therapeutic targets for IH.

A prognostic model for oral squamous cell carcinoma using 7 genes related to tumor mutational burden

Background

Oral squamous cell carcinoma (OSCC) is a rising problem in global public health. The traditional physical and imageological examinations are invasive and radioactive. There is a need for less harmful new biomarkers. Tumor mutational burden (TMB) is a novel prognostic biomarker for various cancers. We intended to explore the relationship between TMB-related genes and the prognosis of OSCC and to construct a prognostic model.

Methods

TMB-related differential expressed genes (DEGs) were screened by differential analysis and optimized via the univariate Cox and LASSO Cox analyses. Risk Score model was constructed by expression values of screened genes multiplying coefficient of LASSO Cox.

Results

Seven TMB-related DEGs (CTSG, COL6A5, GRIA3, CCL21, ZNF662, TDRD5 and GSDMB) were screened. Patients in high-risk group (Risk Score >  − 0.684511507) had worse prognosis compared to the low-risk group (Risk Score <  − 0.684511507). Survival rates of patients in the high-risk group were lower in the gender, age and degrees of differentiation subgroups compared to the low-risk group.

Conclusions

The Risk Score model constructed by 7 TMB-related genes may be a reliable biomarker for predicting the prognosis of OSCC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12903-022-02193-3.