Integrated analysis identifies oxidative stress genes associated with progression and prognosis in gastric cancer

AbstractOxidative stress (OS) reactions are reported to be associated with oncogenesis and tumor progression. However, little is known about the potential diagnostic value of OS in gastric cancer (GC). This study identified hub OS genes associated with the prognosis and progression of GC and illustrated the underlying mechanisms. The transcriptome data and corresponding GC clinical information were collected from The Cancer Genome Atlas (TCGA) database. Aberrantly expressed OS genes between tumors and adjacent normal tissues were screened, and 11 prognosis-associated genes were identified with a series of bioinformatic analyses and used to construct a prognostic model. These genes were validated in the Gene Expression Omnibus (GEO) database. Furthermore, weighted gene co-expression network analysis (WGCNA) was subsequently conducted to identify the most significant hub genes for the prediction of GC progression. Analysis revealed that a good prognostic model was constructed with a better diagnostic accuracy than other clinicopathological characteristics in both TCGA and GEO cohorts. The model was also significantly associated with the overall survival of patients with GC. Meanwhile, a nomogram based on the risk score was established, which displayed a favorable discriminating ability for GC. In the WGCNA analysis, 13 progression-associated hub OS genes were identified that were also significantly associated with the progression of GC. Furthermore, functional and gene ontology (GO) analyses were performed to reveal potential pathways enriched with these genes. These results provide novel insights into the potential applications of OS-associated genes in patients with GC.

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Novel Oxidative Stress-related Prognostic Biomarkers for Melanoma Associated With Tumor Metastasis

10.21203/rs.3.rs-54156/v1 ◽

2020 ◽

Author(s):

Xianpei Wu ◽

Zhengyuan Wu ◽

Jinmin Zhao

Keyword(s):

Oxidative Stress ◽

Prognostic Model ◽

Risk Model ◽

Cox Regression ◽

Gene Expression Omnibus ◽

Vital Role ◽

The Cancer Genome Atlas ◽

High Morbidity ◽

Cox Regression Analysis ◽

Potential Applications

Abstract Background Skin cutaneous melanoma (SKCM) is a prevalent skin cancer whose metastatic form is dangerous due to its high morbidity and mortality. Previous studies have systematically established the vital role of oxidative stress (OS) in melanoma progression. This study aimed to identify prognostic OS genes closely associated with SKCM and illustrate their potential mechanisms. Methods Transcriptome data and corresponding clinical traits of patients with SKCM were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A weighted gene co-expression network analysis was conducted to identify relationships between clinical features and OS genes in specific modules. Subsequently, Cox regression analysis was performed on candidate OS genes; four hub prognosis-associated OS genes (AKAP9, VPS13C, ACSL4, and HMOX2) were identified to construct a prognostic model. Results After a series of bioinformatics analysis, our prognostic model was identified significantly associated with the overall survival of patients with SKCM and metastatic ability of the cancer. Furthermore, our risk model demonstrated improved diagnostic accuracy in TCGA and GEO cohorts. In addition, we established two nomograms based on either risk score or hub genes, which displayed favorable discriminating ability for SKCM. Conclusions Together, our results provide novel insight into the potential applications of OS-associated genes in SKCM.

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Immune infiltration and a ferroptosis-related gene signature for predicting the prognosis of patients with cholangiocarcinoma

10.21203/rs.3.rs-1175730/v1 ◽

2022 ◽

Author(s):

Zhijian Wang ◽

Xuenuo Chen ◽

Zheng Jiang

Keyword(s):

Cell Death ◽

Prognostic Model ◽

Roc Analysis ◽

Cox Regression ◽

Tumor Therapy ◽

Diagnostic Value ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Great Promise ◽

Expression Levels

Abstract Background Cholangiocarcinoma (CHOL) is a digestive tract tumor with high malignancy and poor prognosis and is extremely challenging to treat. At present, induced cell death holds great promise in tumor therapy. Ferroptosis is a recently proposed pattern of programmed cell death, and numerous studies have shown that it is intimately involved in tumors. However, the roles of differentially expressed ferroptosis-related genes (DEFRGs) in CHOL have not been investigated. Methods Our study was based on the The Cancer Genome Atlas (TCGA) database, DEFRGs were obtained to construct a prognostic riskScore model of CHOL by univariate and multivariate Cox regression analyses. Subsequently, the model was evaluated by nomogram construction, survival analysis, receiver operating characteristic (ROC) analysis and exploration of the immune microenvironment, and the mRNA and protein expression levels of each gene in the model were validated by Gene Expression Omnibus (GEO) database and quantitative real-time PCR (qRT-PCR). Results We screened four DEFRGs from the TCGA database to construct a prognostic model. The construction of a nomogram confirmed the predictive value of the model for overall survival (OS), and it was confirmed to have high diagnostic value by ROC analysis. The GSEA results suggested that these genes were mainly enriched in ferroptosis- and metabolism-related pathways. Finally, our experimental results validated the expression levels of the four DEFRGs, which were almost consistent with our bioinformatics results. Conclusion Our study found that the prognostic model showed extremely high diagnostic and prognostic value and could predict the possibility of immunotherapy, thus providing a new direction for individualized treatment of patients with CHOL.

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Development of Multiscale Transcriptional Regulatory Network in Esophageal Cancer Based on Integrated Analysis

BioMed Research International ◽

10.1155/2020/5603958 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17

Author(s):

Zihao Xu ◽

Zilong Wu ◽

Jingtao Zhang ◽

Ruihao Zhou ◽

Jiane Wu ◽

...

Keyword(s):

Esophageal Cancer ◽

Transcriptional Regulatory Network ◽

Enrichment Analysis ◽

Diagnostic Value ◽

Gene Expression Omnibus ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Integrated Analysis ◽

Rna Seq

Objective. To explore multiscale integrated analysis methods in identifying key regulators of esophageal cancer (ESCA). Methods. We downloaded the ESCA dataset from The Cancer Genome Atlas (TCGA) database, which contained RNA-seq data, miRNA-seq data, methylation data, and clinical phenotype information. Then, we combined ESCA-related genes from the NCBI-GENE and OMIM databases and RNA-seq dataset from TCGA to analyze differentially expressed genes (DEGs). Meanwhile, differentially expressed miRNAs (DEmiRNAs) and genes with differential methylation levels were identified. The pivot–module pairs were established using the RAID v2.0 database and TRRUST v2 database. Next, the multifactor-regulated functional network was constructed based on the above information. Additionally, gene corresponding targeted drug information was obtained from the DrugBank database. Moreover, we further screened regulators by assessing their diagnostic value and prognostic value, especially the value of distinguishing patients at TNM I stage from normal patients. In addition, the external database from the Gene Expression Omnibus (GEO) database was used for validation. Lastly, gene set enrichment analysis (GSEA) was performed to explore the potential biological functions of key regulators. Results. Our study indicated that CXCL8, CYP2C8, and E2F1 had excellent diagnostic and prognostic values, which may be potential regulators of ESCA. At the same time, the good early diagnosis ability of the three regulators also provided new insights for the diagnosis and early treatment of ESCA patients. Conclusion. We develop a multiscale integrated analysis and suggest that CXCL8, CYP2C8, and E2F1 are promising regulators with good diagnostic and prognostic values in ESCA.

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Comprehensive analyses of competing endogenous RNA networks reveal potential biomarkers for predicting hepatocellular carcinoma recurrence

BMC Cancer ◽

10.1186/s12885-021-08173-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Ping Yan ◽

Zuotian Huang ◽

Tong Mou ◽

Yunhai Luo ◽

Yanyao Liu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Malignant Tumors ◽

Expression Profiles ◽

Clinical Information ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

High Rate ◽

Competing Endogenous Rna ◽

Tissue Samples ◽

Potential Biomarkers

Abstract Background Hepatocellular carcinoma (HCC) is one of the most common and deadly malignant tumors, with a high rate of recurrence worldwide. This study aimed to investigate the mechanism underlying the progression of HCC and to identify recurrence-related biomarkers. Methods We first analyzed 132 HCC patients with paired tumor and adjacent normal tissue samples from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). The expression profiles and clinical information of 372 HCC patients from The Cancer Genome Atlas (TCGA) database were next analyzed to further validate the DEGs, construct competing endogenous RNA (ceRNA) networks and discover the prognostic genes associated with recurrence. Finally, several recurrence-related genes were evaluated in two external cohorts, consisting of fifty-two and forty-nine HCC patients, respectively. Results With the comprehensive strategies of data mining, two potential interactive ceRNA networks were constructed based on the competitive relationships of the ceRNA hypothesis. The ‘upregulated’ ceRNA network consists of 6 upregulated lncRNAs, 3 downregulated miRNAs and 5 upregulated mRNAs, and the ‘downregulated’ network includes 4 downregulated lncRNAs, 12 upregulated miRNAs and 67 downregulated mRNAs. Survival analysis of the genes in the ceRNA networks demonstrated that 20 mRNAs were significantly associated with recurrence-free survival (RFS). Based on the prognostic mRNAs, a four-gene signature (ADH4, DNASE1L3, HGFAC and MELK) was established with the least absolute shrinkage and selection operator (LASSO) algorithm to predict the RFS of HCC patients, the performance of which was evaluated by receiver operating characteristic curves. The signature was also validated in two external cohort and displayed effective discrimination and prediction for the RFS of HCC patients. Conclusions In conclusion, the present study elucidated the underlying mechanisms of tumorigenesis and progression, provided two visualized ceRNA networks and successfully identified several potential biomarkers for HCC recurrence prediction and targeted therapies.

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Integrated analysis of methylation-driven genes and pretreatment prognostic factors in patients with hepatocellular carcinoma

BMC Cancer ◽

10.1186/s12885-021-08314-5 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Dongsheng He ◽

Shengyin Liao ◽

Lifang Cai ◽

Weiming Huang ◽

Xuehua Xie ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Prognostic Factors ◽

Risk Score ◽

Prognostic Model ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Integrated Analysis ◽

Calibration Plot ◽

Clinical Parameters ◽

T Stage

Abstract Background The potential reversibility of aberrant DNA methylation indicates an opportunity for oncotherapy. This study aimed to integrate methylation-driven genes and pretreatment prognostic factors and then construct a new individual prognostic model in hepatocellular carcinoma (HCC) patients. Methods The gene methylation, gene expression dataset and clinical information of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Methylation-driven genes were screened with a Pearson’s correlation coefficient less than − 0.3 and a P value less than 0.05. Univariable and multivariable Cox regression analyses were performed to construct a risk score model and identify independent prognostic factors from the clinical parameters of HCC patients. The least absolute shrinkage and selection operator (LASSO) technique was used to construct a nomogram that might act to predict an individual’s OS, and then C-index, ROC curve and calibration plot were used to test the practicability. The correlation between clinical parameters and core methylation-driven genes of HCC patients was explored with Student’s t-test. Results In this study, 44 methylation-driven genes were discovered, and three prognostic signatures (LCAT, RPS6KA6, and C5orf58) were screened to construct a prognostic risk model of HCC patients. Five clinical factors, including T stage, risk score, cancer status, surgical method and new tumor events, were identified from 13 clinical parameters as pretreatment-independent prognostic factors. To avoid overfitting, LASSO analysis was used to construct a nomogram that could be used to calculate the OS in HCC patients. The C-index was superior to that from previous studies (0.75 vs 0.717, 0.676). Furthermore, LCAT was found to be correlated with T stage and new tumor events, and RPS6KA6 was found to be correlated with T stage. Conclusion We identified novel therapeutic targets and constructed an individual prognostic model that can be used to guide personalized treatment in HCC patients.

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The expression characteristics and prognostic roles of autophagy-related genes in gastric cancer

PeerJ ◽

10.7717/peerj.10814 ◽

2021 ◽

Vol 9 ◽

pp. e10814

Author(s):

Mengya Wang ◽

Jingjing Jing ◽

Hao Li ◽

Jingwei Liu ◽

Yuan Yuan ◽

...

Keyword(s):

Gastric Cancer ◽

Prognostic Factors ◽

Differential Expression Analysis ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Normal Tissues ◽

Genes Expression ◽

Expression Characteristics ◽

Cellular Components

Background Autophagy is an evolutionally highly conserved process, accompanied by the dynamic changes of various molecules, which is necessary for the orderly degradation and recycling of cellular components. The aim of the study was to identify the role of autophagy-related (ATG) genes in the occurrence and development of gastric cancer (GC). Methods Data from Oncomine dataset was used for the differential expression analysis between cancer and normal tissues. The association of ATG genes expression with clinicopathologic indicators was evaluated by The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. Moreover, using the TCGA datasets, the prognostic role of ATG genes was assessed. A nomogram was further built to assess the independent prognostic factors. Results The expression of autophagy-related genes AMBRA1, ATG4B, ATG7, ATG10, ATG12, ATG16L2, GABARAPL2, GABARAPL1, ULK4 and WIPI2 showed differences between cancer and normal tissues. After verification, ATG14 and ATG4D were significantly associated with TNM stage. ATG9A, ATG2A, and ATG4D were associated with T stage. VMP1 and ATG4A were low-expressed in patients without lymph node metastasis. No gene in autophagy pathway was associated with M stage. Further multivariate analysis suggested that ATG4D and MAP1LC3C were independent prognostic factors for GC. The C-index of nomogram was 0.676 and the 95% CI was 0.628 to 0.724. Conclusion Our study provided a comprehensive illustration of ATG genes expression characteristics in GC. Abnormal expressions of the ubiquitin-like conjugated system in ATG genes plays a key role in the occurrence of GC. ATG8/LC3 sub-system may play an important role in development and clinical outcome of GC. In the future, it is necessary to further elucidate the alterations of specific ATG8/LC3 forms in order to provide insights for the discovery, diagnosis, or targeting for GC.

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Integrated Analysis of Methylation-driven Genes and Pretreatment Prognostic Factors in Patients with Hepatocellular Carcinoma

10.21203/rs.3.rs-101415/v1 ◽

2020 ◽

Author(s):

Dongsheng He ◽

Lifang Cai ◽

Weiming Huang ◽

Xuehua Xie ◽

Mengxing You ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Prognostic Factors ◽

Risk Score ◽

Prognostic Model ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Integrated Analysis ◽

Calibration Plot ◽

Clinical Parameters ◽

T Stage

Abstract Background: The potential reversibility of aberrant DNA methylation indicates an opportunity for oncotherapy. This study aimed to integrate methylation-driven genes and pretreatment prognostic factors and then construct a new individual prognostic model in hepatocellular carcinoma (HCC) patients.Methods: The gene methylation, gene expression dataset and clinical information of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Methylation-driven genes were screened with a Pearson’s correlation coefficient less than -0.3 and a P value less than 0.05. Univariable and multivariable Cox regression analyses were performed to construct a risk score model and identify independent prognostic factors from the clinical parameters of HCC patients. The least absolute shrinkage and selection operator (LASSO) technique was used to construct a nomogram that might act to predict an individual’s OS, and then C-index, ROC curve and calibration plot were used to test the practicability. The correlation between clinical parameters and core methylation-driven genes of HCC patients was explored with Student’s t-test.Results: In this study, 44 methylation-driven genes were discovered, and three prognostic signatures (LCAT, RPS6KA6, and C5orf58) were screened to construct a prognostic risk model of HCC patients. Five clinical factors, including T stage, risk score, cancer status, surgical method and new tumor events, were identified from 13 clinical parameters as pretreatment-independent prognostic factors. To avoid overfitting, LASSO analysis was used to construct a nomogram that could be used to calculate the OS in HCC patients. The C-index was superior to that from previous studies (0.75 vs 0.717, 0.676). Furthermore, LCAT was found to be correlated with T stage and new tumor events, and RPS6KA6 was found to be correlated with T stage.Conclusion: We identified novel therapeutic targets and constructed an individual prognostic model that can be used to guide personalized treatment in HCC patients.

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Krüppel-Like Factor 7 is a Marker of Aggressive Gastric Cancer and Poor Prognosis

Cellular Physiology and Biochemistry ◽

10.1159/000481748 ◽

2017 ◽

Vol 43 (3) ◽

pp. 1090-1099 ◽

Cited By ~ 11

Author(s):

Zhonghua Jiang ◽

Tingting Yu ◽

Zhining Fan ◽

Hongmei Yang ◽

Xin Lin

Keyword(s):

Gastric Cancer ◽

Disease Free Survival ◽

Clinicopathological Characteristics ◽

The Cancer Genome Atlas ◽

Strong Negative Correlation ◽

Functional Studies ◽

Expression Of Genes ◽

Cancer Genome Atlas

Background/Aims: Krüppel-like factor (KLF) 7 protein is a member of the KLF transcription factor family, which plays important roles in regulating the expression of genes involved in cell growth, proliferation, differentiation and metabolism. However, the role of KLF7 in gastric cancer (GC) is unknown. The aim of this study is to explore the role of KLF7 in GC and its correlation with clinicopathological characteristics and prognosis of GC patients. Methods: We first systematically evaluated dysregulation of the KLF family in The Cancer Genome Atlas (TCGA) GC database. Then, 252 patients who underwent surgery for GC were enrolled to validate the results from the TCGA. Functional studies were also used to explore the role of KLF7 in GC. Results: In the TCGA database, we found that KLF7 was an independent predictor for survival by both univariate and multivariate analysis (P<0.05). In a validation cohort, KLF7 expression was significantly increased in GC tissues compared with adjacent normal controls (P=0.013). High KLF7 expression correlated with inferior prognostic factors, such as T stage (P=0.022), N stage (P =0.005) and lymphovascular invasion (P=0.009). Furthermore, we observed a strong negative correlation between KLF7 expression and 5-year overall survival and disease-free survival in GC patients (P<0.05). Moreover, our in vitro studies showed a notable decrease in migration in KLF7 knockdown cells. Conclusion: KLF7 has an important role in GC progression, as it inhibits GC cell migration and may serve as a prognostic marker.

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Epigenetic Variation Analysis Leads to Biomarker Discovery in Gastric Adenocarcinoma

Frontiers in Genetics ◽

10.3389/fgene.2020.551787 ◽

2020 ◽

Vol 11 ◽

Author(s):

Yan Zhang ◽

Dianjing Guo

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Dna Methylation ◽

Gastric Adenocarcinoma ◽

Histone Modifications ◽

Biomarker Discovery ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Integrated Analysis ◽

Epigenetic Variation

As one of the most common malignant tumors worldwide, gastric adenocarcinoma (GC) and its prognosis are still poorly understood. Various genetic and epigenetic factors have been indicated in GC carcinogenesis. However, a comprehensive and in-depth investigation of epigenetic alteration in gastric cancer is still missing. In this study, we systematically investigated some key epigenetic features in GC, including DNA methylation and five core histone modifications. Data from The Cancer Genome Atlas Program and other studies (Gene Expression Omnibus) were collected, analyzed, and validated with multivariate statistical analysis methods. The landscape of epi-modifications in gastric cancer was described. Chromatin state transition analysis showed a histone marker shift in gastric cancer genome by employing a Hidden-Markov-Model based approach, indicated that histone marks tend to label different sets of genes in GC compared to control. An additive effect of these epigenetic marks was observed by integrated analysis with gene expression data, suggesting epigenetic modifications may cooperatively regulate gene expression. However, the effect of DNA methylation was found more significant without the presence of the five histone modifications in our study. By constructing a PPI network, key genes to distinguish GC from normal samples were identified, and distinct patterns of oncogenic pathways in GC were revealed. Some of these genes can also serve as potential biomarkers to classify various GC molecular subtypes. Our results provide important insights into the epigenetic regulation in gastric cancer and other cancers in general. This study describes the aberrant epigenetic variation pattern in GC and provides potential direction for epigenetic biomarker discovery.

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Construction and analysis of the lncRNA-miRNA-mRNA network based on competitive endogenous RNA reveal functional lncRNAs in oral cancer

10.21203/rs.3.rs-18709/v2 ◽

2020 ◽

Author(s):

Junhao Yin ◽

Xiaoli Zeng ◽

Zexin Ai ◽

Miao Yu ◽

Yang’ou Wu ◽

...

Keyword(s):

Oral Cancer ◽

Expression Profiles ◽

Topological Analysis ◽

Enrichment Analysis ◽

Clinical Information ◽

R Package ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Cerna Network ◽

Competitive Endogenous Rna

Abstract Background: A growing evidence suggests that long non-coding RNAs (lncRNAs) can function as a microRNA (miRNA) sponge in various diseases including oral cancer. However, the pathophysiological function of lncRNAs remains unclear. Methods: Based on the competitive endogenous RNA (ceRNA) theory, we constructed a lncRNA-miRNA-mRNA network in oral cancer with the human expression profiles GSE74530 from the Gene Expression Omnibus (GEO) database. We used topological analysis to determine the hub lncRNAs in the regulatory ceRNA network. Then, function enrichment analysis was performed using the clusterProfiler R package. Clinical information was downloaded from The Cancer Genome Atlas (TCGA) database and survival analysis was performed with Kaplan-Meier analysis. Results: A total of 238 potential co-dysregulated competing triples were obtained in the lncRNA-associated ceRNA network in oral cancer, which consisted of 10 lncRNA nodes, 41 miRNA nodes and 122 mRNA nodes. Additionally, we found lncRNA HCG22 exhibiting superior potential as a diagnostic and prognostic marker of oral cancer. Conclusions: Our findings provide novel insights to understand the ceRNA regulation in oral cancer and identify a novel lncRNA as a potential molecular biomarker.

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