Transcriptome Analysis Revealed a Highly Connected Gene Module Associated With Cirrhosis to Hepatocellular Carcinoma Development

Predictive Role of a Novel Ferroptosis-Related lncRNA Pairs Model in the Prognosis of Papillary Thyroid Carcinoma

This study aimed to evaluate the potential prognostic value of ferroptosis-related long non-coding RNAs (lncRNAs) in papillary thyroid carcinoma (PTC). Based on The TCGA database, lncRNAs and ferroptosis-related genes with differential expression levels in PTC tumors vs. normal tissues were screened. After the co-expression network construction, ferroptosis-related lncRNAs (FRLs) were screened. Kaplan-Meier analysis was conducted to compare the survival performance of patients with PTC in the high- and low-risk groups. Furthermore, a nomogram was created to enhance PTC prognosis. CIBERSORT was used to investigate the infiltration of various immune cells in high- and low-risk groups. In total, 10 lncRNA pairs with differential expression levels were obtained. There were significant differences in the histological subtype and pathological stage between the high- and low-risk groups, and age (P = 7.39E-13) and FRLM model status (P = 1.09E-04) were identified as independent prognostic factors. Subsequently, the nomogram survival model showed that the predicted one-, three-, and five-year survival rates were similar to the actual one- (c-index = 0.8475), three- (c-index = 0.7964), and five-year (c-index = 0.7555) survival rates. Subjects in the low-risk group had significantly more CD4 + memory T cells and resting myeloid dendritic cells, and subjects in the high-risk group had more plasma B cells and monocytes. The risk assessment model constructed using FRLs showed good predictive value for the prognosis of patients with PTC.

Immunotherapeutic value of NUSAP1 associated with bladder cancer through a comprehensive analysis of 33 human cancer cases

To investigate the correlation between nucleolar spindle-associated protein 1 (NUSAP1) and cancer immunotherapy across 33 different types of human cancers. We conducted an analysis of The Cancer Genome Atlas (TCGA) database to retrieve gene expression data and clinical characteristics for 33 different cancer types. The immunotherapy cohorts encompassed GSE67501, GSE78220, and IMvigor210. Relevant information was extracted from the gene expression repository. We assessed the prognostic significance of NUSAP1 by examining various clinical parameters. The single-sample gene-set enrichment analysis (ssGSEA) method was utilized to gauge NUSAP1 activity and to contrast NUSAP1 transcriptome and protein levels. We delved into the correlation between NUSAP1 and various immune processes and components to gain insights into NUSAP1's role. We also discussed coherent pathways associated with NUSAP1 signal transduction and its impact on immunotherapy biomarkers. To authenticate and validate the differential expression patterns of NUSAP1 in bladder tumor tissues versus normal bladder counterparts, we utilized Western blotting (WB), real-time quantitative polymerase chain reaction (RT-qPCR), and immunohistochemistry (IHC) techniques. NUSAP1 exhibits overexpression across a spectrum of malignancies, and its expression levels correlate with overall survival (OS), disease-specific survival, and tumor stage in specific cancer types. Furthermore, NUSAP1 expression is linked to mutations, methylation patterns, and immunotherapy responses in human cancers. Meanwhile, our experiments, involving WB, RT-qPCR, and IHC, consistently demonstrated significantly higher NUSAP1 expression in bladder tumor tissues compared to normal controls. Our study underscores the potential of NUSAP1 as a promising prognostic indicator and immunotherapeutic target for a range of malignant tumors.

Immune Cell Infiltration Types as Biomarkers for the Recurrence Diagnosis and Prognosis of Bladder Cancer

This study aimed to investigate the role of infiltrating immune cell types in diagnosing and predicting bladder cancer recurrence. This study mainly applied some algorithms, including Estimate the Proportion of Immune and Cancer Cells (EPIC), support vector machine-recursive feature elimination (SVM-RFE), random forest out-of-bag (RF-OOB) and least absolute shrinkage and selection operator (LASSO)-Cox regression analysis. We found six immune infiltrating cell types significantly associated with recurrence prognosis and two independent clinical prognostic factors. Infiltrating immune cell types (IICTs) based on the prognostic immune risk score (pIRS) models may provide significant biomarkers for the diagnosis and prognostic prediction of bladder cancer recurrence.

Nine-Gene Prognostic Signature Related to Gut Microflora for Predicting the Survival in Gastric Cancer Patients

BACKGROUND/AIMS

The purpose of this study is to screen the feature genes related to gut microflora and explore the role of the genes in predicting the prognosis of patients with gastric cancer.

MATERIALS AND METHODS

We downloaded the gene profile of gastric cancer from the University of California Santa Cruz, the gut microflora related to gastric cancer from The Cancer Microbiome Atlas. The GSE62254 dataset was downloaded from National Center for Biotechnology Information Gene Expression Omnibus as a validation dataset. A correlation network between differentially expressed genes and gut microflora was constructed using Cytoscape. The optimized prognostic differentially expressed genes were identified through least absolute shrinkage and selection operator (LASSO) algorithm and univariate Cox regression analysis. The risk score model was established and then measured via Kaplan-Meier and area under the curve. Finally, the nomogram model was constructed according to the independent clinical factors, which was evaluated using C-index.

RESULTS

A total of 754 differentially expressed genes and 8 gut microflora were screened, based on which we successfully constructed the correlation network. We obtained 9 optimized prognostic differentially expressed genes, including HSD17B3, GNG7, CHAD, ARHGAP8, NOX1, YY2, GOLGA8A, DNASE1L3, and ABCA8. Moreover, Kaplan-Meier curves indicated the risk score model correctly predicted the prognosis of gastric cancer in both University of California Santa Cruz and GSE62254 dataset (area under the curve >0.8; area under the curve >0.7). Finally, we constructed the nomogram, in which the C index of 1, 3, and 5 years was 0.824, 0.772, and 0.735 representing that the nomogram was consistent with the actual situation.

CONCLUSIONS

These results indicate the 9 differentially expressed genes related to gut microflora might predict the survival time of patients with gastric cancer. Both risk signature and nomogram could effectively predict the prognosis for patients with gastric cancer.

A risk model based on lncRNA-miRNA-mRNA gene signature for predicting prognosis of patients with bladder cancer

OBJECTIVES

We aimed to analyze lncRNAs, miRNAs, and mRNA expression profiles of bladder cancer (BC) patients, thereby establishing a gene signature-based risk model for predicting prognosis of patients with BC.

METHODS

We downloaded the expression data of lncRNAs, miRNAs and mRNA from The Cancer Genome Atlas (TCGA) as training cohort including 19 healthy control samples and 401 BC samples. The differentially expressed RNAs (DERs) were screened using limma package, and the competing endogenous RNAs (ceRNA) regulatory network was constructed and visualized by the cytoscape. Candidate DERs were screened to construct the risk score model and nomogram for predicting the overall survival (OS) time and prognosis of BC patients. The prognostic value was verified using a validation cohort in GSE13507.

RESULTS

Based on 13 selected. lncRNAs, miRNAs and mRNA screened using L1-penalized algorithm, BC patients were classified into two groups: high-risk group (including 201 patients ) and low risk group (including 200 patients). The high-risk group's OS time ( hazard ratio [HR], 2.160; 95% CI, 1.586 to 2.942; P= 5.678e-07) was poorer than that of low-risk groups' (HR, 1.675; 95% CI, 1.037 to 2.713; P= 3.393 e-02) in the training cohort. The area under curve (AUC) for training and validation datasets were 0.852. Younger patients (age ⩽ 60 years) had an improved OS than the patients with advanced age (age > 60 years) (HR 1.033, 95% CI 1.017 to 1.049; p= 2.544E-05). We built a predictive model based on the TCGA cohort by using nomograms, including clinicopathological factors such as age, recurrence rate, and prognostic score.

CONCLUSIONS

The risk model based on 13 DERs patterns could well predict the prognosis for patients with BC.

Endoplasmic reticulum stress-related signature predicts prognosis and immune infiltration analysis in acute myeloid leukemia

OBJECTIVES

To construct an endoplasmic reticulum stress-related prognostic risk score (RS) model to predict prognosis and perform a preliminary analysis of immune infiltration in patients with acute myeloid leukemia (AML).

METHODS

The whole-genome expression data for AML and endoplasmic reticulum stress (ER stress)-related genes were downloaded from the GEO and GSEA databases, respectively. The samples were divided into death and survival groups, combined with clinical prognosis information. LASSO regression was used to construct a prognostic RS model. The Kaplan-Meier curve method was used to evaluate the association between different risk groups and actual survival prognosis information. A cox regression analysis was used to screen for independent survival prognostic clinical factors and construct a nomogram. CIBERSORT and ssGSEA was used for immune-related analysis.

RESULTS

Eighteen ER-stress related genes were identified and a comprehensive network was constructed. Further, 5 CC, 8 MF, 17 BP, and 2 KEGG pathways were enriched. Ten optimal DEGs were obtained and a prognostic risk model was constructed. Compared to the low RS group, the OS values of the high RS group were significantly lower. A significant correlation between the different risk groups and the actual prognosis was demonstrated. Ten immune cells with significantly different distributions in different risk groups were screened. KEGG enrichment analysis showed that there were 5 signaling pathways in the high-risk group.

CONCLUSIONS

The RS model can effectively predict the prognosis and has clinical implications for the prognosis of AML, combined with the correlation between different RS groups and the immune microenvironment.

A four-gene-based methylation signature associated with lymph node metastasis predicts overall survival in lung squamous cell carcinoma

We aimed to identify prognostic methylation genes associated with lymph node metastasis (LNM) in lung squamous cell carcinoma (LUSC). Bioinformatics methods were used to obtain optimal prognostic genes for risk model construction using data from the Cancer Genome Atlas database. ROC curves were adopted to predict the prognostic value of the risk model. Multivariate regression was carried out to identify independent prognostic factors and construct a prognostic nomogram. The differences in overall survival, gene mutation and pathways between high- and low-risk groups were analyzed. Finally, the expression and methylation level of the optimal prognostic genes among different LNM stages were analyzed. FGA, GPR39, RRAD and TINAGL1 were identified as the optimal prognostic genes and were applied to establish a prognostic risk model. Significant differences were found among the different LNM stages. The risk model could predict overall survival, showing a moderate performance with AUC of 0.64-0.68. The model possessed independent prognostic value, and could accurately predict 1-, 3- and 5-year survival. Patients with a high risk score showed poorer survival. Lower gene mutation frequencies and enrichment of leukocyte transendothelial migration and the VEGF signaling pathway in the high-risk group may lead to the poor prognosis. This study identified several specific methylation markers associated with LNM in LUSC and generated a prognostic model to predict overall survival for LUSC patients.

A risk score model based on endoplasmic reticulum stress related genes for predicting prognostic value of osteosarcoma

BACKGROUND

We aimed to establish an osteosarcoma prognosis prediction model based on a signature of endoplasmic reticulum stress-related genes.

METHODS

Differentially expressed genes (DEGs) between osteosarcoma with and without metastasis from The Cancer Genome Atlas (TCGA) database were mapped to ERS genes retrieved from Gene Set Enrichment Analysis to select endoplasmic reticulum stress-related DEGs. Subsequently, we constructed a risk score model based on survival-related endoplasmic reticulum stress DEGs and a nomogram of independent survival prognostic factors. Based on the median risk score, we stratified the samples into high- and low-risk groups. The ability of the model was assessed by Kaplan-Meier, receiver operating characteristic curve, and functional analyses. Additionally, the expression of the identified prognostic endoplasmic reticulum stress-related DEGs was verified using real-time quantitative PCR (RT-qPCR).

RESULTS

In total, 41 endoplasmic reticulum stress-related DEGs were identified in patients with osteosarcoma with metastasis. A risk score model consisting of six prognostic endoplasmic reticulum stress-related DEGs (ATP2A3, ERMP1, FBXO6, ITPR1, NFE2L2, and USP13) was established, and the Kaplan-Meier and receiver operating characteristic curves validated their performance in the training and validation datasets. Age, tumor metastasis, and the risk score model were demonstrated to be independent prognostic clinical factors for osteosarcoma and were used to establish a nomogram survival model. The nomogram model showed similar performance of one, three, and five year-survival rate to the actual survival rates. Nine immune cell types in the high-risk group were found to be significantly different from those in the low-risk group. These survival-related genes were significantly enriched in nine Kyoto Encyclopedia of Genes and Genomes pathways, including cell adhesion molecule cascades, and chemokine signaling pathways. Further, RT-qPCR results demonstrated that the consistency rate of bioinformatics analysis was approximately 83.33%, suggesting the relatively high reliability of the bioinformatics analysis.

CONCLUSION

We established an osteosarcoma prediction model based on six prognostic endoplasmic reticulum stress-related DEGs that could be helpful in directing personalized treatment.

Development of a prognostic metabolic signature in stomach adenocarcinoma

PURPOSE

The growth and aggressiveness of Stomach adenocarcinoma (STAD) is significantly affected by basic metabolic changes. This study aimed to identify metabolic gene prognostic signatures in STAD.

METHODS

An integrative analysis of datasets from the Cancer Genome Atlas and Gene Expression Omnibus was performed. A metabolic gene prognostic signature was developed using univariable Cox regression and Kaplan-Meier survival analysis. A nomogram model was developed to predict the prognosis of STAD patients. Finally, Gene Set Enrichment Analysis (GESA) was used to explore the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways significantly associated with the risk grouping.

RESULTS

A total of 327 metabolism-related differentially expressed genes were identified. Three subtypes of STAD were identified and nine immune cell types, including memory B cell, resting and activated CD4+ memory T cells, were significantly different among the three subgroups. A risk score model including nine survival-related genes which could separate high-risk patients from low-risk patients was developed. The prognosis of STAD patients likely benefited from lower expression levels of genes, including ABCG4, ABCA6, GPX8, KYNU, ST8SIA5, and CYP19A1. Age, radiation therapy, tumor recurrence, and risk score model status were found to be independent risk factors for STAD and were used for developing a nomogram. Nine KEGG pathways, including spliceosome, pentose phosphate pathway, and citrate TCA cycle were significantly enriched in GESA.

CONCLUSION

We propose a metabolic gene signature and a nomogram for STAD which might be used for predicting the survival of STAD patients and exploring prognostic markers.

Developing metabolic gene signatures to predict intrahepatic cholangiocarcinoma prognosis and mining a miRNA regulatory network

Background

Metabolic disturbance is closely correlated with intrahepatic cholangiocarcinoma (IHCC), and we aimed to identify metabolic gene marker for the prognosis of IHCC.

Methods

We obtained expression and clinical data from 141 patients with IHCC from public databases. Prognostic metabolic genes were selected using univariate Cox regression analysis. Unsupervised cluster analysis was applied to identify IHCC subtypes, and CIBERSORT was used for immune infiltration analysis of different subtypes. Then, the metabolic gene signature was screened using multivariate Cox regression analysis and the LASSO algorithm. The prognostic potential and regulatory network of the metabolic gene signature were further investigated.

Results

We screened 228 prognosis‐related metabolic genes. Based on their expression levels, IHCC samples were divided into two subtypes, which showed significant differences in survival and immune cell infiltration. After LASSO analysis, eight metabolic genes including CYP19A1, SCD5, ACOT8, SRD5A3, MOGAT2, PFKFB3, PPARGC1B, and RPL17 were identified as the optimal genes for the prognosis signature. The prognostic model had excellent predictive abilities, with areas under the receiver‐operating characteristic curves over 0.8. A nomogram model was also established based on two independent prognostic clinical factors (pathologic stage and prognostic model), and the generated calibration curves and c‐indexes determined its excellent accuracy and discriminative ability to predict 1‐ and 5‐year survival status (c‐indexes>0.7). Finally, we found that miR‐26a‐5p, miR‐27a‐3p, and miR‐27b‐3p were the upstream regulators that mediate the involvement of gene signatures in metabolic pathways.

Conclusion

We developed eight metabolic gene signatures to predict IHCC prognosis and proposed potential upstream regulatory axes of gene signatures.

The effect of key DNA methylation in different regions on gene expression in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a common cancer with high morbidity and mortality. As we all know, the alteration of DNA methylation has a crucial impact on the occurrence of HCC. However, the mechanism of the effect of DNA methylation in different regions on gene expression is still unclear. Here, by computing and analyzing the distribution of differential methylation in 12 different regions in HCC tissues and adjacent normal tissues, not only the hypermethylation of CpG islands and global hypomethylation were found, but also a stable distribution pattern of differential methylation in HCC was found. Then the correlations between DNA methylations in different regions and gene expressions were calculated, and the diversity of correlations in different regions was determined. The key genes of differential methylation and differential expression related to the survival of HCC patients were obtained by using Cox regression analysis, a four-gene prognostic risk scoring model was constructed, and the prognostic performance was well verified. The regions of the differentially methylated CpG sites corresponding to the four key genes were located and their influences on the expression were analyzed. The results indicate that the promoter, first exon, 5'UTR, sixth exon, N_Shore, and S_Shore hypomethylation promotes the expression of key oncogenes, which together lead to the occurrence of HCC. These results might help to study the role of DNA methylation in HCC and provide potential biomarkers for the diagnosis of HCC.

Immune Characteristics Analysis and Transcriptional Regulation Prediction Based on Gene Signatures of Chronic Obstructive Pulmonary Disease

Purpose

The variation in inflammation in chronic obstructive pulmonary disease (COPD) between individuals is genetically determined. This study aimed to identify gene signatures of COPD through bioinformatics analysis based on multiple gene sets and explore their immune characteristics and transcriptional regulation mechanisms.

Methods

Data from four microarrays were downloaded from the Gene Expression Omnibus database to screen differentially expressed genes (DEGs) between COPD patients and controls. Weighted gene co-expression network analysis was applied to identify trait-related modules and then select key module-related DEGs. The optimized gene set of signatures was obtained using the least absolute shrinkage and selection operator (LASSO) regression analysis. The CIBERSORT algorithm and Pearson correlation test were used to analyze the relationship between gene signatures and immune cells. Finally, public databases were used to predict the transcription factors (TFs) and upstream miRNAs.

Results

A total of 127 DEGs in COPD were identified from the combined dataset. By considering the intersection of DEGs and genes in two trait-related modules, 83 key module-related DEGs were identified, which were mainly enriched in interleukin-related pathways. Seven-gene signatures, including MTHFD2, KANK3, GFPT2, PHLDA1, HS3ST2, FGG, and RPS4Y1, were further selected using the LASSO algorithm. These gene signatures showed the predictive potential for COPD risks and were significantly correlated with 18 types of immune cells. Finally, nine miRNAs and three TFs were predicted to target MTHFD2, GFPT2, PHLDA1, and FGG.

Conclusion

We proposed the seven-gene-signature to predict COPD risk and explored its potential immune characteristics and regulatory mechanisms.

Analyzing Roles of NUSAP1 From Clinical, Molecular Mechanism and Immune Perspectives in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most common carcinomas worldwide. Our study aims to analyze how NUSAP1 affects progression of HCC from clinical, molecular mechanism and immune perspectives. Firstly, we downloaded GSE62232, GSE102079, GSE112790, and GSE121248 gene expression profile datasets from GEO database. R studio was used to screen DEGs of each dataset, and 86 overlapping DEGs of the four datasets were screened at last. Then, CytoHubba plug-in in Cytoscape software was used to screen out NUSAP1 from the 86 DEGs. Subsequently, survival analysis, clinical correlation analysis, independent prognostic analysis, and GSEA enrichment analysis of NUSAP1 were analyzed using HCC patients from GSE76427 dataset, ICGC database, and TCGA database. The results revealed that HCC patients with higher expression level of NUSAP1 had a worse prognosis. NUSAP1 was an independent prognostic factor of HCC, and it may promote HCC progress by regulating cell cycle. To further elucidate its underlying molecular mechanism, we used cBioProtal online data analysis tool to screen all co-expression genes of NUSAP1 and used top 300 co-expression genes to accomplish KEGG and GO enrichment analysis; the results confirmed that NUSAP1 accelerated progression of HCC by regulating cell cycle. We continued to draw KEGG pathway map of cell cycle using co-expression genes enriched in cell cycle pathway by KEGG online tool. The map depicted that most of co-expression genes of NUSAP1 were located in S phase and G2/M phase of the cell cycle, and they could regulate the genes in G1 phase. To further understand the mechanism of cell cycle, we also did qRT-PCR, Western blot, and flow cytometry; the results showed that NUSAP1 was closely associated with CDK4, CDK6, and cyclinD1, which could regulate G1 to S phase transition. Besides, we also analyzed correlation between NUSAP1 and immune cells using HCC patients from GSE76427 dataset, ICGC database, and TCGA database. NUSAP1 was associated with some immune cells, and we speculated that NUSAP1 could also promote HCC progression by influencing T cell CD4 memory resting and macrophage M0 through some underlying mechanism.

Prognostic Score Model Based on Ten Differentially Methylated Genes for Predicting Clinical Outcomes in Patients with Adenocarcinoma of the Colon

Purpose

We aimed to screen novel genetic biomarkers for use in a prognostic score (PS) model for the accurate prediction of survival outcomes for patients with colon adenocarcinoma (COAD).

Methods

Gene expression and methylation data were downloaded from The Cancer Genome Atlas database, and the samples were randomly divided into training and validation sets for the screening of differentially methylated genes (DMGs) and differentially expressed genes (DEGs). Co-methylated genes were screened using weighted gene co-expression network analysis. Functional enrichment analysis was performed using the Database for Annotation, Visualization, and Integrated Discovery. Univariate and multivariate Cox regression analyses were performed to identify prognosis-related genes and clinical factors. Receiver operating characteristic curve analysis was carried out to evaluate the predictive performance of the PS model.

Results

In total, 1434 DEGs and 1038 DMGs were screened in the training set, among which 284 were found to be overlapping genes. For 127 of these overlapping genes, the methylation and expression levels were significantly negatively correlated. An optimal signature from 10 DMGs was identified to construct the PS model. Patients with a high PS seemed to have worse outcomes than those with a low PS. Moreover, cancer recurrence and the PS model status were independent prognostic factors.

Conclusion

This PS model based on an optimal 10-gene signature would help in the stratification of patients with COAD and improve the assessment of their clinical outcomes.

A four-lncRNA signature for predicting prognosis of recurrence patients with gastric cancer

Purpose

This study aimed to develop a multi-long noncoding RNA (lncRNA) signature for the prediction of gastric cancer (GC) based on differential gene expression between recurrence and nonrecurrence patients.

Methods

By repurposing microarray expression profiles of RNAs from The Cancer Genome Atlas (TCGA), we performed differential expression analysis between recurrence and nonrecurrence patients. A prognostic risk prediction model was constructed based on data from TCGA database, and its reliability was validated using data from Gene Expression Omnibus database. Furthermore, the lncRNA-associated competing endogenous RNA (ceRNA) network was constructed, namely, DIANA-LncBasev2 and starBase database.

Results

We identified 363 differentially expressed RNAs (317 mRNAs, 18 lncRNAs, and 28 microRNAs [miRNAs]). Principal component analysis showed that the seven-feature lncRNAs screened by support vector machine-recursive feature elimination algorithm was more informative for predicting recurrence of GC in comparison with the eight-feature lncRNAs screened by random forest-out-of-bag algorithm. Four of the seven-feature lncRNAs including LINC00843, SNHG3, C21orf62-AS1, and MIR99AHG were chosen to develop a four-lncRNA risk score model. This risk score model was able to distinguish patients with high and low risk of recurrence, and was tested in two independent validation sets. The ceRNA network of this four-lncRNA signature included 10 miRNAs and 178 mRNAs. The mRNAs significantly related to the Wnt-signaling pathway and relevant biological processes.

Conclusion

A useful four-lncRNA signature recurrence was established to distinguish GC patients with high and low risk of recurrence. Regulating the relevant miRNAs and Wnt pathway might partly affect GC metastasisby.

Bioinformatics Analysis of Candidate Genes and Pathways Related to Hepatocellular Carcinoma in China: A Study Based on Public Databases

Background and Objective: Hepatocellular carcinoma (HCC) is a highly aggressive malignant tumor of the digestive system worldwide. Chronic hepatitis B virus (HBV) infection and aflatoxin exposure are predominant causes of HCC in China, whereas hepatitis C virus (HCV) infection and alcohol intake are likely the main risk factors in other countries. It is an unmet need to recognize the underlying molecular mechanisms of HCC in China.

Methods: In this study, microarray datasets (GSE84005, GSE84402, GSE101685, and GSE115018) derived from Gene Expression Omnibus (GEO) database were analyzed to obtain the common differentially expressed genes (DEGs) by R software. Moreover, the gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by using Database for Annotation, Visualization and Integrated Discovery (DAVID). Furthermore, the protein-protein interaction (PPI) network was constructed, and hub genes were identified by the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape, respectively. The hub genes were verified using Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, and Kaplan-Meier Plotter online databases were performed on the TCGA HCC dataset. Moreover, the Human Protein Atlas (HPA) database was used to verify candidate genes’ protein expression levels.

Results: A total of 293 common DEGs were screened, including 103 up-regulated genes and 190 down-regulated genes. Moreover, GO analysis implied that common DEGs were mainly involved in the oxidation-reduction process, cytosol, and protein binding. KEGG pathway enrichment analysis presented that common DEGs were mainly enriched in metabolic pathways, complement and coagulation cascades, cell cycle, p53 signaling pathway, and tryptophan metabolism. In the PPI network, three subnetworks with high scores were detected using the Molecular Complex Detection (MCODE) plugin. The top 10 hub genes identified were CDK1, CCNB1, AURKA, CCNA2, KIF11, BUB1B, TOP2A, TPX2, HMMR and CDC45. The other public databases confirmed that high expression of the aforementioned genes related to poor overall survival among patients with HCC.

Conclusion: This study primarily identified candidate genes and pathways involved in the underlying mechanisms of Chinese HCC, which is supposed to provide new targets for the diagnosis and treatment of HCC in China.

Identifying biomolecules and constructing a prognostic risk prediction model for recurrence in osteosarcoma

INTRODUCTION

Osteosarcoma is a high-morbidity bone cancer with an unsatisfactory prognosis. The aim of this study is to develop novel potential prognostic biomarkers and construct a prognostic risk prediction model for recurrence in osteosarcoma.

METHODS

By analyzing microarray data, univariate and multivariate Cox regression analyses were performed to screen prognostic RNA signatures and to build a prognostic model. The RNA signatures were validated using Kaplan-Meier curves. Then, we developed and validated a nomogram combining age, recurrence, metastatic, and Prognostic score (PS) models to predict the individual's overall survival at the 3- and 5-year points. Pathway enrichment of RNA was conducted based on the significant co-expressed RNAs.

RESULTS

A total of 319 mRNAs and 14 lncRNAs were identified in the microarray data. One lncRNA (LINC00957) and six mRNAs (METL1, CA9, B3GALT4, ALDH1A1, LAMB3, and ITGB4) were identified as RNA signatures and showed good performances in survival prediction for both the training and validation cohorts. Cox regression analysis showed that the seven RNA signatures could independently predict overall survival. Furthermore, age, recurrence, metastatic, and PS models were identified as independent prognostic factors via univariate and multivariate Cox analyses (P < 0.05) and included in the prognostic nomogram. The C-index values for the 3- and 5-year overall survival predictions of the nomogram were 0.809 and 0.740, respectively.

CONCLUSIONS

The current study provides the novel potential of seven RNA candidates as prognostic biomarkers. Nomograms were constructed to provide accurate and individualized survival prediction for recurrence in osteosarcoma patients.

Multiple microarray analyses identify key genes associated with the development of Non-Small Cell Lung Cancer from Chronic Obstructive Pulmonary Disease

Introduction: Chronic obstructive pulmonary disease (COPD) is an independent risk factor of non-small cell lung cancer (NSCLC). This study aimed to analyze the key genes and potential molecular mechanisms that are involved in the development from COPD to NSCLC.

Methods: Expression profiles of COPD and NSCLC in GSE106899, GSE12472, and GSE12428 were downloaded from the Gene Expression Omnibus (GEO) database, followed by identification of the differentially expressed genes (DEGs) between COPD and NSCLC. Based on the identified DEGs, functional pathway enrichment and lung carcinogenesis-related networks analyses were performed and further visualized with Cytoscape software. Then, principal component analysis (PCA), cluster analysis, and support vector machines (SVM) verified the ability of the top modular genes to distinguish COPD from NSCLC. Additionally, the corrections between these key genes and clinical staging of NSCLC were studied using the UALCAN and HPA websites. Finally, a prognostic risk model was constructed based on multivariate Cox regression analysis. Kaplan-Meier survival curves of the top modular genes on the training and verification sets were generated.

Results: A total of 2350, 1914, and 1850 DEGs were obtained from GSE106899, GSE12472, and GSE12428 datasets, respectively. Following analysis of protein-protein interaction networks, the identified modular gene signatures containing H2AFX, MCM2, MCM3, MCM7, POLD1, and RPA1 were identified as markers for discrimination between COPD and NSCLC. The modular gene signatures were mainly enriched in the processes of DNA replication, cell cycle, mismatch repair, and others. Besides, the expression levels of these genes were significantly higher in NSCLC than in COPD, which was further verified by the immunohistochemistry. In addition, the high expression levels of H2AFX, MCM2, MCM7, and POLD1 correlate with poor prognosis of lung adenocarcinoma (LUAD). The Cox regression prognostic risk model showed the similar results and the predictive ability of this model is independent of other clinical variables.

Conclusions: This study revealed several key modules that closely relate to NSCLC with underlying disease COPD, which provide a deeper understanding of the potential mechanisms underlying the malignant development from COPD to NSCLC. This study provides valuable prognostic factors in high-risk lung cancer patients with COPD.

Prognosis prediction model based on competing endogenous RNAs for recurrence of colon adenocarcinoma

BACKGROUND

Colon adenocarcinoma (COAD) patients who develop recurrence have poor prognosis. Our study aimed to establish effective prognosis prediction model based on competing endogenous RNAs (ceRNAs) for recurrence of COAD.

METHODS

COAD expression profilings downloaded from The Cancer Genome Atlas (TCGA) were used as training dataset, and expression profilings of GSE29623 retrieved from Gene Expression Omnibus (GEO) were set as validation dataset. Differentially expressed RNAs (DERs) between non-recurrent and recurrent specimens in training dataset were screened, and optimum prognostic signature DERs were revealed to establish prognostic score (PS) model. Kaplan-Meier survival analysis was conducted for PS model, and GEO dataset was used for validation. Prognosis prediction efficiencies were evaluated by area under curve (AUC) and C-index. Meanwhile, ceRNA regulatory network was constructed by using signature mRNAs, lncRNAs and miRNAs.

RESULTS

We identified 562 DERs including 42 lncRNAs, 36 miRNAs, and 484 mRNAs. PS prediction model, consisting of 17 optimum prognostic signature DERs, showed that high risk group had significantly poorer prognosis (5-year AUC = 0.951, C-index = 0.788), which also validated in GSE29623. Prognosis prediction model incorporating multi-RNAs with pathologic distant metastasis (M) and pathologic primary tumor (T) (5-year AUC = 0.969, C-index = 0.812) had better efficiency than clinical prognosis prediction model (5-year AUC = 0.712, C-index = 0.680). In the constructed ceRNA regulatory network, lncRNA NCBP2-AS1 could interact with hsa-miR-34c and hsa-miR-363, and lncRNA LINC00115 could interact with hsa-miR-363 and hsa-miR-4709. SIX4, GRAP, NKAIN4, MMAA, and ERVMER34-1 are regulated by hsa-miR-4709.

CONCLUSION

Prognosis prediction model incorporating multi-RNAs with pathologic M and pathologic T may have great value in COAD prognosis prediction.

Transcriptome analysis revealed key prognostic genes and microRNAs in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide. However, the molecular mechanisms involved in HCC remain unclear and are in urgent need of elucidation. Therefore, we sought to identify biomarkers in the prognosis of HCC through an integrated bioinformatics analysis.

Methods

Messenger RNA (mRNA) expression profiles were obtained from the Gene Expression Omnibus database and The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) for the screening of common differentially expressed genes (DEGs). Function and pathway enrichment analysis, protein-protein interaction network construction and key gene identification were performed. The significance of key genes in HCC was validated by overall survival analysis and immunohistochemistry. Meanwhile, based on TCGA data, prognostic microRNAs (miRNAs) were decoded using univariable and multivariable Cox regression analysis, and their target genes were predicted by miRWalk.

Results

Eleven hub genes (upregulated ASPM, AURKA, CCNB2, CDC20, PRC1 and TOP2A and downregulated AOX1, CAT, CYP2E1, CYP3A4 and HP) with the most interactions were considered as potential biomarkers in HCC and confirmed by overall survival analysis. Moreover, AURKA, PRC1, TOP2A, AOX1, CYP2E1, and CYP3A4 were considered candidate liver-biopsy markers for high risk of developing HCC and poor prognosis in HCC. Upregulation of hsa-mir-1269b, hsa-mir-518d, hsa-mir-548aq, hsa-mir-548f-1, and hsa-mir-6728, and downregulation of hsa-mir-139 and hsa-mir-4800 were determined to be risk factors of poor prognosis, and most of these miRNAs have strong potential to help regulate the expression of key genes.

Conclusions

This study undertook the first large-scale integrated bioinformatics analysis of the data from Illumina BeadArray platforms and the TCGA database. With a comprehensive analysis of transcriptional alterations, including mRNAs and miRNAs, in HCC, our study presented candidate biomarkers for the surveillance and prognosis of the disease, and also identified novel therapeutic targets at the molecular and pathway levels.

Expression-based, consistent biomarkers for prognosis and diagnosis in lung cancer

OVERVIEW

Lung cancer is one of the deadliest cancers in the world. Its histological classification depends on early diagnosis and successful treatment. Therefore, having specific biomarkers for a quick sorting widens the successful output of lung cancer treatment.

MATERIAL AND METHODS

High-throughput sequencing (RNA-seq) was performed of small cohorts of BioBanco samples from healthy and tumour cells from lung adenocarcinoma (LUAD) and squamous cell carcinoma of the lung (lSCC). RNA-seq samples from small cell lung cancer (SCLC) were downloaded from databases. A bioinformatic workflow has been programmed for the identification of differentially expressed genes (DEGs).

RESULTS

A total of 4777 DEGs were differentially expressed in SCLC, 3676 DEGs were in lSCC, while the lowest number of DEGs, 2819, appeared in LUAD. Among them, 945 DEGs were common to the three histological types. Once validated their expression profile and their survival predictive capacity in large, public cohorts, three DEGs can be exclusively considered as diagnostic biomarkers, three as prognosis biomarkers, and other three exhibit both diagnosis and prognosis capabilities.

CONCLUSIONS

This prospective study presents evidences for the diagnostic and prognostic capabilities of expression changes in CAPN8-2, TMC5 and MUC1 in LUAD, while they are non-significant in SCLC and lSCC. Their translation to clinical practice is proposed.

Bioinformatics-based screening of key genes for transformation of liver cirrhosis to hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is the most common type of liver tumour, and is closely related to liver cirrhosis. Previous studies have focussed on the pathogenesis of liver cirrhosis developing into HCC, but the molecular mechanism remains unclear. The aims of the present study were to identify key genes related to the transformation of cirrhosis into HCC, and explore the associated molecular mechanisms.

Methods

GSE89377, GSE17548, GSE63898 and GSE54236 mRNA microarray datasets from Gene Expression Omnibus (GEO) were analysed to obtain differentially expressed genes (DEGs) between HCC and liver cirrhosis tissues, and network analysis of protein–protein interactions (PPIs) was carried out. String and Cytoscape were used to analyse modules and identify hub genes, Kaplan–Meier Plotter and Oncomine databases were used to explore relationships between hub genes and disease occurrence, development and prognosis of HCC, and the molecular mechanism of the main hub gene was probed using Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analysis.

Results

In total, 58 DEGs were obtained, of which 12 and 46 were up- and down-regulated, respectively. Three hub genes (CDKN3, CYP2C9 and LCAT) were identified and associated prognostic information was obtained. CDKN3 may be correlated with the occurrence, invasion, and recurrence of HCC. Genes closely related to changes in the CDKN3 hub gene were screened, and Kyoto Encyclopedia of Genes and Genomes (KEGGs) pathway analysis identified numerous cell cycle-related genes.

Conclusion

CDKN3 may affect the transformation of liver cirrhosis into HCC, and represents a new candidate molecular marker of the occurrence and progression of HCC.

MicroRNA-582-3p negatively regulates cell proliferation and cell cycle progression in acute myeloid leukemia by targeting cyclin B2

Background

MicroRNAs (miRNAs) function as post-transcriptional gene expression regulators. Some miRNAs, including the recently discovered miR-582–3p, have been implicated in leukemogenesis. This study aimed to reveal the biological function of miR-582–3p in acute myeloid leukemia (AML), which is one of the most frequently diagnosed hematological malignancies.

Methods

The expression of miR-582–3p was determined using quantitative real-time PCR in blood samples from leukemia patients and in cell lines. Cell proliferation and cell cycle distribution were analyzed using the CCK-8, colony formation and flow cytometry assays. The target gene of miR-582–3p was verified using a dual-luciferase reporter assay. The G2/M phase arrest-related molecule contents were measured using western blotting analysis.

Results

We found miR-582–3p was significantly downregulated in the blood samples from leukemia patients and in the cell lines. MiR-582–3p overexpression significantly impaired cell proliferation and induced G2/M cell cycle arrest in THP-1 cells. Furthermore, cyclin B2 (CCNB2) was confirmed as a target gene of miR-582–3p and found to be negatively regulated by miR-582–3p overexpression. More importantly, CCNB2 knockdown showed suppressive effects on cell proliferation and cell cycle progression similar to those caused by miR-582–3p overexpression. The inhibitory effects of miR-582–3p overexpression on cell proliferation and cell cycle progression were abrogated by CCNB2 transfection.

Conclusion

These findings indicate new functions and mechanisms for miR-582–3p in AML development. Further study could clarify if miR-582–3p and CCNB2 are potential therapeutic targets for the treatment of AML.