GSE Datasets Research Articles

Immune landscape of advanced gastric cancer tumor microenvironment identifies immunotherapeutic relevant gene signature

BackgroundAdvanced gastric cancer (AGC) is a disease with poor prognosis due to the current lack of effective therapeutic strategies. Immune checkpoint blockade treatments have shown effective responses in patient subgroups but biomarkers remain challenging. Traditional classification of gastric cancer (GC) is based on genomic profiling and molecular features. Therefore, it is critical to identify the immune-related subtypes and predictive markers by immuno-genomic profiling.MethodsSingle-sample gene-set enrichment analysis (ssGSEA) and ESTIMATE algorithm were used to identify the immue-related subtypes of AGC in two independent GEO datasets. Weighted gene co-expression network analysis (WGCNA) and Molecular Complex Detection (MCODE) algorithm were applied to identify hub-network of immune-related subtypes. Hub genes were confirmed by prognostic data of KMplotter and GEO datasets. The value of hub-gene in predicting immunotherapeutic response was analyzed by IMvigor210 datasets. MTT assay, Transwell migration assay and Western blotting were performed to confirm the cellular function of hub gene in vitro.ResultsThree immune-related subtypes (Immunity_H, Immunity_M and Immunity_L) of AGC were identified in two independent GEO datasets. Compared to Immunity_L, the Immuntiy_H subtype showed higher immune cell infiltration and immune activities with favorable prognosis. A weighted gene co-expression network was constructed based on GSE62254 dataset and identified one gene module which was significantly correlated with the Immunity_H subtype. A Hub-network which represented high immune activities was extracted based on topological features and Molecular Complex Detection (MCODE) algorithm. Furthermore, ADAM like decysin 1 (ADAMDEC1) was identified as a seed gene among hub-network genes which is highly associated with favorable prognosis in both GSE62254 and external validation datasets. In addition, high expression of ADAMDEC1 correlated with immunotherapeutic response in IMvigor210 datasets. In vitro, ADAMDEC1 was confirmed as a potential protein in regulating proliferation and migration of gastric cancer cell. Deficiency of ADAMDEC1 of gastric cancer cell also associated with high expression of PD-L1 and Jurkat T cell apoptosis.ConclusionsWe identified immune-related subtypes and key tumor microenvironment marker in AGC which might facilitate the development of novel immune therapeutic targets.

Identification of four hub genes in venous thromboembolism via weighted gene coexpression network analysis

BackgroundThe pathogenic mechanisms of venous thromboembolism (VT) remain to be defined. This study aimed to identify differentially expressed genes (DEGs) that could serve as potential therapeutic targets for VT.MethodsTwo human datasets (GSE19151 and GSE48000) were analyzed by the robust rank aggregation method. Gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analyses were conducted for the DEGs. To explore potential correlations between gene sets and clinical features and to identify hub genes, we utilized weighted gene coexpression network analysis (WGCNA) to build gene coexpression networks incorporating the DEGs. Then, the levels of the hub genes were analyzed in the GSE datasets. Based on the expression of the hub genes, the possible pathways were explored by gene set enrichment analysis and gene set variation analysis. Finally, the diagnostic value of the hub genes was assessed by receiver operating characteristic (ROC) analysis in the GEO database.ResultsIn this study, we identified 54 upregulated and 10 downregulated genes that overlapped between normal and VT samples. After performing WGCNA, the magenta module was the module with the strongest negative correlation with the clinical characteristics. From the key module, FECH, GYPA, RPIA and XK were chosen for further validation. We found that these genes were upregulated in VT samples, and high expression levels were related to recurrent VT. Additionally, the four hub genes might be highly correlated with ribosomal and metabolic pathways. The ROC curves suggested a diagnostic value of the four genes for VT.ConclusionsThese results indicated that FECH, GYPA, RPIA and XK could be used as promising biomarkers for the prognosis and prediction of VT.

Identification and verification of key cancer genes associated with prognosis of colorectal cancer based on bioinformatics analysis.

The biomarkers targeting colorectal cancer (CRC) prognosis are short of high accuracy and sensitivity in clinic. Through bioinformatics analysis, we aim to identify and confirm a series of key genes referred to the diagnosis and prognosis of CRC. GSE31905, GSE35279, and GSE41657 were selected as complete RNA sequencing data sets of CRC and colorectal mucosa (CRM) tissues from the NCBI-GEO database, and the differentially expressed genes (DEGs) were analyzed. The common DEGs in these 3 data sets were obtained by Venn map, and enriched by STRING network system and Cytoscape software. The Kaplan-Meier plotter website was used to verify the correlation between the enriched genes and the prognosis of CRC. For the whole RNA sequencing data sets of CRC and normal intestinal mucosa samples, the DEGs of CRC and CRM in the 3 data sets (|log2FC|>2 and P<0.05) were screened by GEO2R tool in NCBI-GEO database. By using Venn graph analysis software, the intersection of up-regulated/down-regulated genes in 3 GSE datasets was obtained, and a total 105 up-regulated genes and 140 down-regulated genes were found in the 3 samples. The up-regulated/down-regulated genes were introduced into the STRING network system to obtain the interacting genes. The interacting gene sets were introduced into Cytoscape software, and 61 up-regulated genes were found by Molecular Complex Detection (MCODE) plug-in. Through the Kaplan-Meier plotter website, we found that EPHB2, KLK8, DIAPH3, STC2, OXTR, MMP7, MET, KRT85, KRT6B, KRT23, and KLK10 genes were highly expressed in CRC, and were related to the prognosis. The above 11 genes verified by bioinformatics retrieval and analysis can predict the poor prognosis of CRC to a certain extent, and they provide a possible target for the diagnosis and treatment of CRC.

Integrative analysis-based identification and validation of a prognostic immune cell infiltration-based model for patients with advanced gastric cancer

BackgroundsAdvanced gastric cancer (GC) remains difficult to conduct individualized prognostic evaluations owing to the highly heterogeneous nature and the low level of immune cell infiltration (ICI) within GC tumors. This study thus sought to develop a model capable of classifying GC patients according to the degree of tumor ICI and gauging prognosis. MethodsThe degree of ICI in GC patients from the GSE15459, GSE57303, and GSE62254 datasets were estimated, and these values were used to group patients via an unsupervised clustering approach, after which ICI cluster-related genes were identified the association with prognosis through Cox and LASSO regression analyses. The primary risk genes were then verified by immunohistochemical staining of GC tumor tissue samples. Results570 patients were clustered into three clusters and 289 ICI cluster-related genes were identified. A prognostic model based on the expression of six crucial ICI risk genes (CXCL11, RBPMS2, LOC400043, JCHAIN, CT83, and ORM1) wa constructed. Patients identified as being high risk based upon the model have poorer clinical features and survival outcomes compared to the other patients. Adjuvant intervention was found to be more beneficial for patients expressing high levels of RBPMS2, JCHAIN, or ORM1. Furthermore, patients expressing low levels of JCHAIN or CT83 in GC tumor tissues were verified to exhibit a significantly better prognosis in a CMU cohort. ConclusionAdvanced GC patients were successfully grouped into clusters based on the degree of intratumoral ICI, and a prognostic evaluation model based on 6 ICI risk genes was developed and validated.

MiRNA-382-5p Suppresses the Expression of Farnesoid X Receptor to Promote Progression of Liver Cancer.

BackgroundThe dysregulation of microRNAs (miRNAs) and hepatotoxicity due to the aberrant accumulation of bile acids (BAs) are notorious causes that predispose an individual to the development of hepatocellular carcinoma (HCC). Farnesoid X receptor (FXR), encoded by NR1H4 gene, has been identified as a crucial BA receptor to maintain the homeostasis of BA pool and its expression is decreased in HCC. miR-382-5p plays an important role in the pathogenesis of many human malignancies and was reported to promote the proliferation and differentiation of normal liver cells and liver regeneration. However, there is still some controversy about its role in HCC microenvironment. This study aims to explore the expression pattern of miR-382-5p in HCC and its role in regulating FXR during the development of HCC.MethodsTissues collected from 30 HCC patients were subjected to extraction of total RNA and quantitative real-time PCR (qRT-PCR) for the analyses of miR-382-5p expression and NR1H4 mRNA levels, and their expressions were verified by analyzing the online HCC-related GSE datasets. The role of miR-382-5p in regulating cellular proliferation and expression of FXR in different HCC cell lines was analyzed by qRT-PCR, Western Blot, real-time cellular analysis (RTCA) and luciferase reporter assays. The role of miR-382-5p in regulating downstream genes of FXR in HCC cells was also analyzed.ResultsmiR-382-5p was upregulated in HCC tissues and inversely associated with the downregulation of NR1H4 mRNA levels. The luciferase reporter assay proved that miR-382-5p directly targeted the 3ʹ-untranslated region (3ʹ-UTR) of human NR1H4 mRNA. Overexpression of miR-382-5p led to a malignant proliferation of HCC cells by suppressing the expression of FXR. In contrast, blocking the endogenous miR-382-5p was sufficient to suppress the cellular proliferation rate of HCC through increasing FXR expression. Additionally, miR-382-5p inhibited the expression of some target genes of FXR, including SHP, FGF19 and SLC51A, and this inhibitory effect was FXR-dependent.ConclusionTherefore, miR-382-5p promotes the progression of HCC in vitro by suppressing FXR and could serve as a valuable therapeutic target for HCC treatment.

SOCS3 is Related to Cell Proliferation in Neuronal Tissue: An Integrated Analysis of Bioinformatics and Experiments.

Glioma is the most common primary malignant tumor that occurs in the central nervous system. Gliomas are subdivided according to a combination of microscopic morphological, molecular, and genetic factors. Glioblastoma (GBM) is the most aggressive malignant tumor; however, efficient therapies or specific target molecules for GBM have not been developed. We accessed RNA-seq and clinical data from The Cancer Genome Atlas, the Chinese Glioma Genome Atlas, and the GSE16011 dataset, and identified differentially expressed genes (DEGs) that were common to both GBM and lower-grade glioma (LGG) in three independent cohorts. The biological functions of common DEGs were examined using NetworkAnalyst. To evaluate the prognostic performance of common DEGs, we performed Kaplan-Meier and Cox regression analyses. We investigated the function of SOCS3 in the central nervous system using three GBM cell lines as well as zebrafish embryos. There were 168 upregulated genes and 50 downregulated genes that were commom to both GBM and LGG. Through survival analyses, we found that SOCS3 was the only prognostic gene in all cohorts. Inhibition of SOCS3 using siRNA decreased the proliferation of GBM cell lines. We also found that the zebrafish ortholog, socs3b, was associated with brain development through the regulation of cell proliferation in neuronal tissue. While additional mechanistic studies are necessary, our results suggest that SOCS3 is an important biomarker for glioma and that SOCS3 is related to the proliferation of neuronal tissue.

Transcriptional analysis of the expression, prognostic value and immune infiltration activities of the COMMD protein family in hepatocellular carcinoma

BackgroundThe copper metabolism MURR1 domain (COMMD) protein family involved in tumor development and progression in several types of human cancer, but little is known about the function of COMMD proteins in hepatocellular carcinoma (HCC).MethodsThe ONCOMINE and the UALCAN databases were used to evaluate the expression of COMMD1–10 in HCC and the association of this family with individual cancer stage and tumor grade. Kaplan-Meier (K-M) Plotter and Cox analysis hint the prognostic value of COMMDs. A network comprising 50 most similar genes and COMMD1–10 was constructed with the STRING database. Gene set enrichment analysis (GSEA) was performed using LinkedOmics database. The correlations between COMMD expression and the presence of immune infiltrating cells were also analyzed by the tumor immune estimation resource (TIMER) database. GSE14520 dataset and 80 HCC patients were used to validated the expression and survival value of COMMD3. Human HCC cell lines were also used for validating the function of COMMD3.ResultsThe expression of all COMMD family members showed higher expression in HCC tissues than that in normal tissues, and is associated with clinical cancer stage and pathological tumor grade. In HCC patients, the transcriptional levels of COMMD1/4 are positively correlated with overall survival (OS), while those of COMMD2/3/7/8/9 are negatively correlated with OS. Multivariate analysis indicated that a high level of COMMD3 mRNA is an independent prognostic factor for shorter OS in HCC patients. However, the subset of patients with grade 3 HCC, K-M survival curves revealed that high COMMD3/5/7/8/9 expression and low COMMD4/10 expression were associated with shorter OS. In addition, the expression of COMMD2/3/10 was associated with tumor-induced immune response activation and immune infiltration in HCC. The expression of COMMD3 from GSE14520 dataset and 80 patients are both higher in tumor than that in normal tissue, and a higher level of COMMD3 mRNA is associated with shorter OS. Knockdown of COMMD3 inhibits human HCC cell lines proliferation in vitro.ConclusionsOur study indicates that COMMD3 is an independent prognostic biomarker for the survival of HCC patients. COMMD3 supports the proliferation of HCC cells and contributes to the poor OS in HCC patients.

Identification of Key Candidate Genes and Chemical Perturbagens in Diabetic Kidney Disease Using Integrated Bioinformatics Analysis.

Globally, nearly 40 percent of all diabetic patients develop serious diabetic kidney disease (DKD). The identification of the potential early-stage biomarkers and elucidation of their underlying molecular mechanisms in DKD are required. In this study, we performed integrated bioinformatics analysis on the expression profiles GSE111154, GSE30528 and GSE30529 associated with early diabetic nephropathy (EDN), glomerular DKD (GDKD) and tubular DKD (TDKD), respectively. A total of 1,241, 318 and 280 differentially expressed genes (DEGs) were identified for GSE30258, GSE30529, and GSE111154 respectively. Subsequently, 280 upregulated and 27 downregulated DEGs shared between the three GSE datasets were identified. Further analysis of the gene expression levels conducted on the hub genes revealed SPARC (Secreted Protein Acidic And Cysteine Rich), POSTN (periostin), LUM (Lumican), KNG1 (Kininogen 1), FN1 (Fibronectin 1), VCAN (Versican) and PTPRO (Protein Tyrosine Phosphatase Receptor Type O) having potential roles in DKD progression. FN1, LUM and VCAN were identified as upregulated genes for GDKD whereas the downregulation of PTPRO was associated with all three diseases. Both POSTN and SPARC were identified as the overexpressed putative biomarkers whereas KNG1 was found as downregulated in TDKD. Additionally, we also identified two drugs, namely pidorubicine, a topoisomerase inhibitor (LINCS ID- BRD-K04548931) and Polo-like kinase inhibitor (LINCS ID- BRD-K41652870) having the validated role in reversing the differential gene expression patterns observed in the three GSE datasets used. Collectively, this study aids in the understanding of the molecular drivers, critical genes and pathways that underlie DKD initiation and progression.

Identification and Validation of a Nine-Gene Amino Acid Metabolism-Related Risk Signature in HCC.

Background: Hepatocellular carcinoma (HCC) is the world’s second most deadly cancer, and metabolic reprogramming is its distinguishing feature. Among metabolite profiling, variation in amino acid metabolism supports tumor proliferation and metastasis to the most extent, yet a systematic study on the role of amino acid metabolism-related genes in HCC is still lacking. An effective amino acid metabolism-related prediction signature is urgently needed to assess the prognosis of HCC patients for individualized treatment.Materials and Methods: RNA-seq data of HCC from the TCGA-LIHC and GSE14520 (GPL3921) datasets were defined as the training set and validation set, respectively. Amino acid metabolic genes were extracted from the Molecular Signature Database. Univariate Cox and LASSO regression analyses were performed to build a predictive risk signature. K-M curves, ROC curves, and univariate and multivariate Cox regression were conducted to evaluate the predictive value of this risk signature. Functional enrichment was analyzed by GSEA and CIBERSORTx software.Results: A nine-gene amino acid metabolism-related risk signature including B3GAT3, B4GALT2, CYB5R3, GNPDA1, GOT2, HEXB, HMGCS2, PLOD2, and SEPHS1 was constructed to predict the overall survival (OS) of HCC patients. Patients were separated into high-risk and low-risk groups based on risk scores and low-risk patients had lower risk scores and longer survival time. Univariate and multivariate Cox regression verified that this signature was an independent risk factor for HCC. ROC curves showed that this risk signature can effectively predict the 1-, 2-, 3- and 5-year survival times of patients with HCC. Additionally, prognostic nomograms were established based on the training set and validation set. These genes were closely correlated with the immune regulation.Conclusion: Our study identified a nine-gene amino acid metabolism-related risk signature and built predictive nomograms for OS in HCC. These findings will help us to personalize the treatment of liver cancer patients.

Global analysis of DNA methylation in hepatocellular carcinoma via a whole-genome bisulfite sequencing approach

Alterations in DNA methylation patterns are considered early events in hepatocellular carcinoma (HCC). However, their mechanism and significance remain to be elucidated. We studied the genome-wide DNA methylation landscape of HCC by applying whole-genome bisulfite sequencing (WGBS) techonlogy. Overall, HCC exhibits a genome-wide hypomethylation pattern. After further annotation, we obtained 590 differentially hypermethylated genes (hyper-DMGs) and 977 differentially hypomethylated genes (hypo-DMGs) from three groups. Hyper-DMGs were mainly involved in ascorbate and alternate metabolism pathways, while hypo-DMGs were mainly involved in focal adhesion. By integrating the DMGs with HCC-related differentially expressed genes (DEGs) and DMGs from the TCGA database, we constructed prognostic model based on thirteen aberrantly methylated DEGs, and verified our prognostic model in GSE14520 dataset. This study compares the patterns of global epigenomic DNA methylation during the development of HCC, focusing on the role of DNA methylation in the early occurrence and development of HCC, providing a direction for future research on its epigenetic mechanism.

Systematic Analysis Identifies a Specific RNA-Binding Protein-Related Gene Model for Prognostication and Risk-Adjustment in HBV-Related Hepatocellular Carcinoma.

ObjectiveIncreasing evidence shows that dysregulated RNA binding proteins (RBPs) modulate the progression of several malignancies. Nevertheless, their clinical implications of RBPs in HBV-related hepatocellular carcinoma (HCC) remain largely undefined. Here, this study systematically analyzed the associations of RBPs with HBV-related HCC prognosis.MethodsBased on differentially expressed RBPs between HBV-related HCC and control specimens, prognosis-related RBPs were screened by univariate analyses. A LASSO model was then created. Kaplan-Meier curves, ROCs, multivariate analyses, subgroup analyses and external verification were separately applied to assess the efficacy of this model in predicting prognosis and recurrence of patients. A nomogram was created by incorporating the model and clinical indicators, which was verified by ROCs, calibration curves and decision curve analyses. By CIBERSORT algorithm, the association between the risk score and immune cell infiltrations was evaluated.ResultsTotally, 54 RBPs were distinctly correlated to prognosis of HBV-related HCC. An 11-RBP model was created, containing POLR2L, MRPS12, DYNLL1, ZFP36, PPIH, RARS, SRP14, DDX41, EIF2B4, and NOL12. This risk score sensitively and accurately predicted one-, three- and five-year overall survival, disease-free survival, and progression-free interval. Compared to other clinical parameters, this risk score had the best predictive efficacy. Also, the clinical generalizability of the model was externally verified in the GSE14520 dataset. The nomogram may predict patients’ survival probabilities. Also, the risk score was related to the components in the immune microenvironment.ConclusionCollectively, RBPs may act as critical elements in the malignant progression of HBV-related HCC and possess potential implications on prognostication and therapy decision.

Identification of an EMT-Related Gene Signature for Predicting Overall Survival in Gastric Cancer.

BackgroundIt has been widely reported that epithelial-mesenchymal transition (EMT) is associated with malignant progression in gastric cancer (GC). Integration of the molecules related to EMT for predicting overall survival (OS) is meaningful for understanding the role of EMT in GC. Here, we aimed to establish an EMT-related gene signature in GC.MethodsTranscriptional profiles and clinical data of GC were downloaded from The Cancer Genome Atlas (TCGA). We constructed EMT-related gene signature for predicting OS by using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses. Time-dependent receiver operating characteristic (ROC), Kaplan-Meier analysis were performed to assess its predictive value. A nomogram combining the prognostic signature with clinical characteristics for OS prediction was established. And its predictive power was estimated by concordance index (C-index), time-dependent ROC curve, calibration curve and decision curve analysis (DCA). GSE62254 dataset from Gene Expression Omnibus (GEO) was used for external validation. Quantitative real-time PCR (qRT-PCR) was used to detected the mRNA expression of the five EMT-related genes in human normal gastric mucosal and GC cell lines. To further understand the potential mechanisms of the signature, Gene Set Enrichment Analysis (GSEA), pathway enrichment analysis, predictions of transcription factors (TFs)/miRNAs were performed.ResultsA novel EMT-related gene signature (including ITGAV, DAB2, SERPINE1, MATN3, PLOD2) was constructed for OS prediction of GC. With external validation, ROC curves indicated the signature’s good performance. Patients stratified into high- and low-risk groups based on the signature yielded significantly different prognosis. Univariate and multivariate Cox regression suggested that the signature was an independent prognostic variable. Nomogram for prognostication including the signature presented better predictive accuracy and clinical usefulness than the similar model without risk score to some extent with external validation. The qRT-PCR assays suggested that high expression of the five EMT-related genes could be found in human GC cell lines compared with normal gastric mucosal cell line. GSEA and pathway enrichment analysis revealed that focal adhesion and ECM-receptor interaction might be the two important pathways to the signature.ConclusionOur EMT-related gene signature may have practical application as an independent prognostic factor in GC.

Role of the miR-133a-5p/FBXO6 axis in the regulation of intervertebral disc degeneration.

ObjectiveLow back pain is a leading cause of disabilities worldwide, and intervertebral disc degeneration (IVDD)-related disorders have been recognised as one of the main contributors. Nevertheless, the underlying mechanism has not yet been fully understood. The aim of this study was to investigate the role of the miR-133a-5p/FBXO6 axis in the regulation of IVDD.MethodsRT-qPCR, WB and IHC were performed to assess the expression of FBXO6 in human IVD tissues. Nucleus pulposus (NP) cells were treated with IL-1β to induce IVDD cellular model. Silence of FBXO6 was achieved using specific siRNAs. CCK-8 assay, flow cytometry, TUNEL assay, RT-qPCR and WB were used to evaluate the role and mechanism of FBXO6 in the process of IVDD. Online tools, GSE datasets and RT-qPCR were used to search the candidate miRNAs targeting FBXO6. The direct binding sites between FBXO6 and miR-133a-5p were further verified by a dual luciferase assay. RT-qPCR, WB and rescue experiments were conducted to identify the regulatory function of miR-133a-5p on the expression of aggrecan, collagen Ⅱ, MMP3, ADAMTS5, IL-6 and COX2. In addition, the role of the NF-κB pathway in regulating miR-133a-5p was studied using lentiviral shRNA, WB and RT-qPCR.ResultsResults showed that FBXO6 mainly expressed in the NP tissue of IVD and the expression of FBXO6 decreased with the process of IVDD as well as under IL-1β stimulation. The silence of FBXO6 led to the decreased expression of aggrecan and collagen Ⅱ and the increased expression of MMP3, ADAMTS5, IL-6 and COX2, which further induced the degeneration of NP cells. The bioinformatic analysis showed that miR-133a-5p was the candidate miRNA targeting FBXO6. miR-133a-5p was upregulated in IVDD tissues and significantly inhibited the expression of FBXO6. The inhibition of miR-133a-5p ameliorated the acceleration of IVDD induced by the silence of FBXO6 in vitro. Moreover, it was demonstrated that IL-1β regulated the expression of the miR-133a-5p/FBXO6 axis via the NF-κB pathway in NP cells.ConclusionmiR-133a-5p was upregulated by IL-1β to aggravate intervertebral disc degeneration via sponging FBXO6. Inhibiting miR-133a-5p expression or rescuing FBXO6 expression may be promising strategies for the treatment of IVDD.The translational potential of this articleThis study suggests that the miR-133a-5p/FBXO6 axis could regulate NP cells proliferation, apoptosis, synthesis and degradation of extracellular matrix, which provides a promising therapeutic target and strategy for the treatment of IVDD.

Novel Hypoxia-Related Gene Signature for Risk Stratification and Prognosis in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the most common form of liver cancer with limited therapeutic options and low survival rate. The hypoxic microenvironment plays a vital role in progression, metabolism, and prognosis of malignancies. Therefore, this study aims to develop and validate a hypoxia gene signature for risk stratification and prognosis prediction of HCC patients. The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases were used as a training cohort, and one Gene Expression Omnibus database (GSE14520) was served as an external validation cohort. Our results showed that eight hypoxia-related genes (HRGs) were identified by the least absolute shrinkage and selection operator analysis to develop the hypoxia gene signature and demarcated HCC patients into the high- and low-risk groups. In TCGA, ICGC, and GSE14520 datasets, patients in the high-risk group had worse overall survival outcomes than those in the low-risk group (all log-rank P < 0.001). Besides, the risk score derived from the hypoxia gene signature could serve as an independent prognostic factor for HCC patients in the three independent datasets. Finally, a nomogram including the gene signature and tumor-node-metastasis stage was constructed to serve clinical practice. In the present study, a novel hypoxia signature risk model could reflect individual risk classification and provide therapeutic targets for patients with HCC. The prognostic nomogram may help predict individualized survival.

Identifying Dendritic Cell-Related Genes Through a Co-Expression Network to Construct a 12-Gene Risk-Scoring Model for Predicting Hepatocellular Carcinoma Prognosis.

The prognostic prediction of hepatocellular carcinoma (HCC) is still challenging. Immune cells play a crucial role in tumor initiation, progression, and drug resistance. However, prognostic value of immune-related genes in HCC remains to be further clarified. In this study, the mRNA expression profiles and corresponding clinical information of HCC patients were downloaded from public databases. Then, we estimated the abundance of immune cells and identified the differentially infiltrated and prognostic immune cells. The weighted gene co-expression network analysis (WGCNA) was performed to identify immune-related genes in TCGA cohort and GEO cohort. The least absolute shrinkage and selection operator (LASSO) Cox regression model was applied to establish a risk-scoring model in the TCGA cohort. HCC patients from the GSE14520 datasets were utilized for risk model validation. Our results found that high level of dendritic cell (DC) infiltration was associated with poor prognosis. Over half of the DC-related genes (58.2%) were robustly differentially expressed between HCC and normal specimens in the TCGA cohort. 17 differentially expressed genes (DEGs) were found to be significantly associated with overall survival (OS) by univariate Cox regression analysis. A 12-gene risk-scoring model was established to evaluate the prognosis of HCC. The high-risk group exhibits significantly lower OS rate of HCC patients than the low-risk group. The risk-scoring model shows benign predictive capacity in both GEO dataset and TCGA dataset. The 12-gene risk-scoring model may independently perform prognostic value for HCC patients. Receiver operating characteristic (ROC) curve analysis of the risk-scoring model in GEO cohort and TCGA cohort performed well in predicting OS. Taken together, the 12-gene risk-scoring model could provide prognostic and potentially predictive information for HCC. SDC3, NCF2, BTN3A3, and WARS were noticed as a novel prognostic factor for HCC.

Clinical Significance and Systematic Expression Analysis of the Thyroid Receptor Interacting Protein 13 (TRIP13) as Human Gliomas Biomarker.

Simple SummaryHigh expression of TRIP13 is associated with IDH-wild type gliomas. Patients with elevated TRIP13 levels are indicative of the poor survival outcome. The study aimed to provide comprehensive information about the oncogenic potential of TRIP13 in clinical significance for gliomas. We found that TRIP13 co-expressed genes implicated in tumorigenesis and therapeutic resistance may regulated by TP53 and FOXM1. The aberrant expression of TRIP13 in gliomas was uncovered to be regulated by distinct underlying mechanisms, including the DNA methylation and dysfunction of miRNA targeting (such as miR-29 family). TRIP13-expressing tumors also showed high aneuploidy levels and reduced ratio of CD8+/Treg, which have unfavorable effects on patient outcome. These results demonstrate that TRIP13 is crucial for tumor development and reveal the emerging therapeutic potential of gliomas.The prognosis of malignant gliomas such as glioblastoma multiforme (GBM) has remained poor due to limited therapeutic strategies. Thus, it is pivotal to determine prognostic factors for gliomas. Thyroid Receptor Interacting Protein 13 (TRIP13) was found to be overexpressed in several solid tumors, but its role and clinical significance in gliomas is still unclear. Here, we conducted a comprehensive expression analysis of TRIP13 to determine the prognostic values. Gene expression profiles of the Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA) and GSE16011 dataset showed increased TRIP13 expression in advanced stage and worse prognosis in IDH-wild type lower-grade glioma. We performed RT-PCR and Western blot to validate TRIP13 mRNA expression and protein levels in GBM cell lines. TRIP13 co-expressed genes via database screening were regulated by essential cancer-related upstream regulators (such as TP53 and FOXM1). Then, TCGA analysis revealed that more TRIP13 promoter hypomethylation was observed in GBM than in low-grade glioma. We also inferred that the upregulated TRIP13 levels in gliomas could be regulated by dysfunction of miR-29 in gliomas patient cohorts. Moreover, TRIP13-expressing tumors not only had higher aneuploidy but also tended to reduce the ratio of CD8+/Treg, which led to a worse survival outcome. Overall, these findings demonstrate that TRIP13 has with multiple functions in gliomas, and they may be crucial for therapeutic potential.

Identification of a five-gene signature in association with overall survival for hepatocellular carcinoma.

BackgroundHepatocellular carcinoma (HCC) is considered to be a malignant tumor with a high incidence and a high mortality. Accurate prognostic models are urgently needed. The present study was aimed at screening the critical genes for prognosis of HCC.MethodsThe GSE25097, GSE14520, GSE36376 and GSE76427 datasets were obtained from Gene Expression Omnibus (GEO). We used GEO2R to screen differentially expressed genes (DEGs). A protein-protein interaction network of the DEGs was constructed by Cytoscape in order to find hub genes by module analysis. The Metascape was performed to discover biological functions and pathway enrichment of DEGs. MCODE components were calculated to construct a module complex of DEGs. Then, gene set enrichment analysis (GSEA) was used for gene enrichment analysis. ONCOMINE was employed to assess the mRNA expression levels of key genes in HCC, and the survival analysis was conducted using the array from The Cancer Genome Atlas (TCGA) of HCC. Then, the LASSO Cox regression model was performed to establish and identify the prognostic gene signature. We validated the prognostic value of the gene signature in the TCGA cohort.ResultsWe screened out 10 hub genes which were all up-regulated in HCC tissue. They mainly enrich in mitotic cell cycle process. The GSEA results showed that these data sets had good enrichment score and significance in the cell cycle pathway. Each candidate gene may be an indicator of prognostic factors in the development of HCC. However, hub genes expression was weekly associated with overall survival in HCC patients. LASSO Cox regression analysis validated a five-gene signature (including CDC20, CCNB2, NCAPG, ASPM and NUSAP1). These results suggest that five-gene signature model may provide clues for clinical prognostic biomarker of HCC.

Development and Validation of a Metabolic Gene-Based Prognostic Signature for Hepatocellular Carcinoma

BackgroundHepatocellular carcinoma (HCC) is a malignant tumor with great variation in prognosis among individuals. Changes in metabolism influence disease progression and clinical outcomes. The objective of this study was to determine the overall survival (OS) risk of HCC patients from a metabolic perspective.Patients and MethodsThe model was constructed using the least absolute shrinkage and selection operator (LASSO) COX regression based on The Cancer Genome Atlas (TCGA, n=342) dataset. The International Cancer Genome Consortium (ICGC, n=232), GSE14520 (n=242) datasets, and a clinical cohort (n=64) were then used to assess the prognostic value of the signature.ResultsA 10 metabolic gene-based signature was constructed and verified as a robust and independent prognostic classifier in public and real-world validation cohorts. Meanwhile, the signature enabled the identification of HCC molecular subtypes, yielding an AUC value of 0.678 [95% CI: 0.592–0.763]. Besides, the signature was associated with metabolic processes like glycolysis, supported by a clear correlation between the risk score and expression of rate-limiting enzymes. Furthermore, high-risk tumor was likely to have a high tumor infiltration status of immunosuppressive cells, as well as elevated expression of some immune checkpoint molecules. For final clinical translation, a nomogram integrating the signature and tumor stage was established, and showed improved predictive accuracy of 3- and 5-year OS and brought more net benefit to patients.ConclusionWe developed a prognostic signature based on 10 metabolic genes, which has proven to be an independent and reliable prognostic predictor for HCC and reflects the metabolic and immune characteristics of tumors.

T cell immune regulator 1 is a prognostic marker associated with immune infiltration in glioblastoma multiforme.

Glioma is the most common primary brain tumor and glioblastoma multiforme (GBM) is the most malignant brain glioma with the worst prognosis. T cell immune regulator 1 (TCIRG1) constitutes the V0a3 subunit of vacuolar ATPase (V-ATPase), and the function of V-ATPase in malignant tumors, such as breast cancer, melanoma and hepatocellular carcinoma, has been reported. However, the effect of the TCIRG1 subunit on GBM remains to be fully elucidated. mRNA levels of TCIRG1 in different cancer types and the corresponding normal tissues were extracted from the Oncomine and Tumor Immune Estimation Resource (TIMER) databases. The Gene Expression Omnibus (access number: GSE16011), the Chinese Glioma Genome Atlas and The Cancer Genome Atlas were used to investigate the mRNA level of TCIRG1 in glioma. Protein level validation in glioma was performed using western blotting. The Database for Annotation, Visualization and Integrated Discovery was used to analyze Gene Ontology (GO) categories for genes correlated with TCIRG1 in GBM. Protein-protein interaction (PPI) networks and module analyses were performed using Cytoscape software and the MCODE plugin. The correlation between tumor immune cell infiltration and TCIRG1 expression was explored using the TIMER database. Additionally, the correlation between TCIRG1 and the gene signature of immune infiltration was explored through TIMER and Gene Expression Profiling Interactive Analysis. External validation of TCIRG1 expression according to immune signatures in GBM was performed using the GSE16011 dataset with the GlioVis online tool. It was found that TCIRG1 expression was increased in GBM and numerous malignant tumors and may serve as a biomarker of the mesenchymal subtype of GBM. GO category analysis of positively correlated genes revealed that TCIRG1 was correlated with the immune response in GBM. PPI network and module analyses also supported the potential function of TCIRG1 in the local immune response. The expression of TCIRG1 was associated with various immune markers. It was therefore speculated that TCIRG1 is associated with glioma malignancy and may be a marker of unfavorable prognosis in patients with GBM, and it could be regarded as a prognostic biomarker and an indicator of immune infiltration in GBM.

PolG Inhibits Gastric Cancer Glycolysis and Viability by Suppressing PKM2 Phosphorylation.

PurposeGastric cancer (GC) is the fifth most frequently diagnosed cancer and the third leading cause of cancer-related death. There is a critical need for the development of novel therapies in GC. DNA polymerase gamma (PolG) has been implicated in mitochondrial homeostasis and affects the development of numerous types of cancer, however, its effects on GC and molecular mechanisms remain to be fully determined. The aim of the present research was to clarify the effects of PolG on GC and its possible molecular mechanism of action.MethodsThe GSE62254 dataset was used to predict the effect of PolG on prognostic value in GC patients. Lentivirus-mediated transduction was used to silence PolG expression. Western blot analysis evinced the silencing effect. Co-immunoprecipitation (Co-IP) analysis was performed to explore the potential molecular mechanism of action. Analysis of the glycolysis process in GC cells was also undertaken. Cell proliferation was determined using a CCK-8 (Cell Counting Kit-8) proliferation assay. Cell migration was detected using the Transwell device. Animal experiments were used to measure in vivo xenograft tumor growth.ResultsGC patients with low PolG expression have worse overall survival (OS) and progression-free survival (PFS). PolG binds to PKM2 and affects the activation of Tyr105-site phosphorylation, thus interfering with the glycolysis of GC cells. In vitro tumor formation experiments in mice also confirmed that PolG silencing of GC has a stronger proliferation ability. PolG can suppress GC cell growth both in vivo and in vitro.ConclusionOur study reveals a potential molecular mechanism between PolG and the energy metabolic process of GC tumor cells for the first time, suggesting PolG as an independent novel potential therapeutic target for tumor therapy, and providing new ideas for clinical GC treatment.