scholarly journals PDK2: a novel diagnostic and prognostic biomarker for liver cancer

2020 ◽  
Author(s):  
Zhi-Cheng Liu ◽  
Yan-Qing Li ◽  
Yan Jiao ◽  
Yue-Chen Zhao
Keyword(s):  
Liver Cancer ◽  
Pyruvate Dehydrogenase ◽  
Cox Regression ◽  
Area Under The Curve ◽  
Normal Liver ◽  
The Cancer Genome Atlas ◽  
High Morbidity ◽  
Cox Regression Analysis ◽  
Diagnosis And Prognosis ◽  
Potential Biomarker

Abstract Background: Liver cancer (LC) is a common malignancy with very high morbidity. Pyruvate dehydrogenase kinases (PDKs) are regulators of mitochondrial pyruvate dehydrogenase complexes (PDCs) and play an important role in regulating cellular energy metabolism. In this study, The Cancer Genome Atlas (TCGA) database was used to analyze the expression of PDK2 mRNA in LC, and to explore the value of PDK2 in the diagnosis and prognosis of LC.Methods: The TCGA database, containing the clinical data of 373 LC patients, includes information on PDK2 expression values. The receiver operating characteristic (ROC) curve of PDK2 was drawn to evaluate its diagnostic ability. Patients were divided into PDK2 high- and low-expressing groups by threshold levels. The Chi-square test was used to evaluate the correlation between PDK2 levels and clinicopathological characteristics. The Kaplan-Meier estimator and Cox regression analysis were performed to assess the effect of PDK2 levels on survival outcomes.Results: PDK2 expression in LC tissue was lower than that in normal liver tissues. According to the area under the curve (AUC) value calculated by ROC, PDK2 has a considerable diagnostic value for LC prognosis. The decreased expression of PDK2 is associated with clinical parameters, such as histologic grade ( P =0.0001), radiation therapy ( P =0.0490), vital status ( P =0.0240), and overall survival (OS) ( P =0.0222). Multivariate analysis shows that decreased PDK2 level is an independent risk factor for predicting poor prognosis in LC.Conclusions: PDK2 has a significant impact on the prognosis of LC and is a potential biomarker for the diagnosis and prognosis of LC.

scholarly journals Identification of prognostic alternative splicing events in sarcoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hongshuai Li ◽  
Jie Yang ◽  
Guohui Yang ◽  
Jia Ren ◽  
Yu Meng ◽  
...  
Keyword(s):  
Alternative Splicing ◽  
Cox Regression ◽  
Univariate Analysis ◽  
Area Under The Curve ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Functional Roles ◽  
Cancer Pathogenesis ◽  
Rare Malignancy

AbstractSarcoma is a rare malignancy with unfavorable prognoses. Accumulating evidence indicates that aberrant alternative splicing (AS) events are generally involved in cancer pathogenesis. The aim of this study was to identify the prognostic value of AS-related survival genes as potential biomarkers, and highlight the functional roles of AS events in sarcoma. RNA-sequencing and AS-event datasets were downloaded from The Cancer Genome Atlas (TCGA) sarcoma cohort and TCGA SpliceSeq, respectively. Survival-related AS events were further assessed using a univariate analysis. A multivariate Cox regression analysis was also performed to establish a survival-gene signature to predict patient survival, and the area-under-the-curve method was used to evaluate prognostic reliability. KOBAS 3.0 and Cytoscape were used to functionally annotate AS-related genes and to assess their network interactions. We detected 9674 AS events in 40,184 genes from 236 sarcoma samples, and the 15 most significant genes were then used to construct a survival regression model. We further validated the involvement of ten potential survival-related genes (TUBB3, TRIM69, ZNFX1, VAV1, KCNN2, VGLL3, AK7, ARMC4, LRRC1, and CRIP1) in the occurrence and development of sarcoma. Multivariate survival model analyses were also performed, and validated that a model using these ten genes provided good classifications for predicting patient outcomes. The present study has increased our understanding of AS events in sarcoma, and the gene-based model using AS-related events may serve as a potential predictor to determine the survival of sarcoma patients.

scholarly journals Clinical Value of lncRNA LUCAT1 Expression in Liver Cancer and its Potential Pathways

2019 ◽  
Vol 28 (4) ◽  
pp. 439-447 ◽  
Author(s):  
Yan Jiao ◽  
Yanqing Li ◽  
Bai Ji ◽  
Hongqiao Cai ◽  
Yahui Liu
Keyword(s):  
Liver Cancer ◽  
Roc Curve ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Rank Test ◽  
P Value ◽  
Rna Seq ◽  
Cox Regression Analysis

Background and Aims: Emerging studies indicate that long noncoding RNAs (lncRNAs) play a role as prognostic markers in many cancers, including liver cancer. Here, we focused on the lncRNA lung cancer-associated transcript 1 (LUCAT1) for liver cancer prognosis. Methods: RNA-seq and phenotype data were downloaded from the Cancer Genome Atlas (TCGA). Chisquare tests were used to evaluate the correlations between LUCAT1 expression and clinical features. Survival analysis and Cox regression analysis were used to compare different LUCAT1 expression groups (optimal cutoff value determined by ROC). The log-rank test was used to calculate the p-value of the Kaplan-Meier curves. A ROC curve was used to evaluate the diagnostic value. Gene Set Enrichment Analysis (GSEA) was performed, and competing endogenous RNA (ceRNA) networks were constructed to explore the potential mechanism. Results: Data mining of the TCGA -Liver Hepatocellular Carcinoma (LIHC) RNA-seq data of 371 patients showed the overexpression of LUCAT1 in cancerous tissue. High LUCAT1 expression was associated with age (p=0.007), histologic grade (p=0.009), T classification (p=0.022), and survival status (p=0.002). High LUCAT1 patients had a poorer overall survival and relapse-free survival than low LUCAT1 patients. Multivariate analysis identified LUCAT1 as an independent risk factor for poor survival. The ROC curve indicated modest diagnostic performance. GSEA revealed the related signaling pathways, and the ceRNA network uncovered the underlying mechanism. Conclusion: High LUCAT1 expression is an independent prognostic factor for liver cancer.

scholarly journals A Novel Signature Constructed by Immune-Related LncRNA Predicts the Immune Landscape of Colorectal Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Mengyu Sun ◽  
Tongyue Zhang ◽  
Yijun Wang ◽  
Wenjie Huang ◽  
Limin Xia
Keyword(s):  
Colorectal Cancer ◽  
High Risk ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Univariate Analysis ◽  
The Cancer Genome Atlas ◽  
High Morbidity ◽  
Cox Regression Analysis ◽  
Chemotherapy Drugs

Colorectal cancer (CRC) has the characteristics of high morbidity and mortality. LncRNA not only participates in the progression of CRC through genes and transcription levels, but also regulates the tumor microenvironment and leads to the malignant phenotype of tumors. Therefore, we identified immune-related LncRNAs for the construction of clinical prognostic model. We searched The Cancer Genome Atlas (TCGA) database for original data. Then we identified differentially expressed irlncRNA (DEirlncRNA), which was paired and verified subsequently. Next, univariate analysis, Lasso and Cox regression analysis were performed on the DEirlncRNA pair. The ROC curve of the signature was drawn, and the optimal cut-off value was found. Then the cohort was divided into a high-risk and a low-risk group. Finally, we re-evaluated the signature from different perspectives. A total of 16 pairs of DEirlncRNA were included in the construction of the model. After regrouping according to the cut-off value of 1.275, the high-risk group showed adverse survival outcomes, progressive clinicopathological features, specific immune cell infiltration status, and high sensitivity to some chemotherapy drugs. In conclusion, we constructed a signature composed of immune-related LncRNA pair with no requirement of the specific expression level of genes, which shows promising clinical predictive value in CRC patients.

scholarly journals Prognostic Signatures of Alternative Splicing Events in Esophageal Carcinoma Based on TCGA Splice-Seq Data

2021 ◽  
Vol 11 ◽  
Author(s):  
Ping Ye ◽  
Yan Yang ◽  
Liqiang Zhang ◽  
Guixi Zheng
Keyword(s):  
Alternative Splicing ◽  
Esophageal Carcinoma ◽  
Cox Regression ◽  
Donor Site ◽  
Area Under The Curve ◽  
Clinical Information ◽  
Roc Curves ◽  
The Cancer Genome Atlas ◽  
Acceptor Site ◽  
Cox Regression Analysis

An alternative splicing (AS) event is a highly complex process that plays an essential role in post-transcriptional gene expression. Several studies have suggested that abnormal AS events were the primary element in the pathological process of cancer. However, few works are dedicated to the study of AS events in esophageal carcinoma (EC). In the present study, clinical information and RNA-seq data of EC patients were downloaded from The Cancer Genome Atlas (TCGA) database. The percent spliced in (PSI) values of AS events were acquired from the TCGA Splice-seq. A total of 183 EC patients were enrolled in this study, and 2,212 AS events were found significantly associated with the overall survival of these patients by univariate Cox regression analysis. The prognostic signatures based on AS events were built by multivariate Cox analysis. Receiver operating characteristic (ROC) curves displayed that the area under the curve (AUC) of the following prognostic signatures, including exon skip (ES), alternate terminator (AT), alternate acceptor site (AA), alternate promoter (AP), alternate donor site (AD), retained intron (RI), and total events, was greater than 0.8, suggesting that these seven signatures had valuable prognosis prediction capacity. Finally, the risk score of prognostic signatures was indicated as an independent risk factor of survival. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to explore the function of splicing factors (SFs) that were associated with AS events. Also, the interactive network between AS events and SFs identified several hub genes and AS events which need further study. This was a comprehensive study that explored prognosis-related AS events and established valuable prognosis signatures in EC patients. The network of interactions between AS events and SFs might offer novel insights into the fundamental mechanisms of tumorigenesis and progression of EC.

A novel signature based on m6A related genes as the potential prognostic factor in hepatocellular carcinoma.

2020 ◽  
Author(s):  
Zhuomao Mo ◽  
Shaoju Luo ◽  
Hao Hu ◽  
Ling Yu ◽  
Zhirui Cao ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Roc Curve ◽  
Cox Regression ◽  
Risk Group ◽  
Immune Therapy ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Potential Biomarker ◽  
Significant Difference

Abstract Background Many different signatures and models have been established for patients with hepatocellular carcinoma (HCC), but no signature based on m6A related genes was developed. The objective of this research was to establish the signature with m6A related genes in HCC. Methods Data from 377 HCC patients from The Cancer Genome Atlas (TCGA) database was downloaded. The included m6A related genes were selected by Cox regression analysis and the signature was verified by survival analysis and multiple receiver operating characteristic (ROC) curve. Furthermore, the nomogram was constructed and evaluated by C-index, calibration plot and ROC curve. Results The signature was established with the four m6A related genes (YTHDF2, YTHDF1, METTL3 and KIAA1429). Under the grouping from signature, patients in high risk group of showed the poor prognosis than those in low risk group. And significant difference was found in two kinds of immune cells (T cell gamma delta and NK cells activated) between two groups. The univariate and multivariate Cox regression analysis indicated that m6A related signature can be the potential independent prognosis factor in HCC. Finally, we developed a clinical risk model predicting the HCC prognosis and successfully verified it in C-index, calibration and ROC curve. Conclusion Our study identified the m6A related signature for predicting prognosis of HCC and provided the potential biomarker between m6A and immune therapy.

scholarly journals The increasing expression of GPX7 related to the malignant clinical features leading to poor prognosis of glioma patients

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Jiawei Yao ◽  
Xin Chen ◽  
Zhendong Liu ◽  
Ruotian Zhang ◽  
Cheng Zhang ◽  
...  
Keyword(s):  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Multidimensional Data ◽  
Normal Brain ◽  
Cox Regression Analysis ◽  
Diagnosis And Prognosis ◽  
Kaplan Meier ◽  
Potential Biomarker ◽  
Therapy Target

Abstract Background Glioma is the most common malignant brain tumor in adults. The standard treatment scheme of glioma is surgical resection combined alternative radio- and chemotherapy. However, the outcome of glioma patients was unsatisfied. Here, we aimed to explore the molecular and biological function characteristics of GPX7 in glioma. Methods The multidimensional data of glioma samples were downloaded from Chinese Glioma Genome Atlas (CGGA). RT-qPCR method was used to identify the expression status of GPX7. Kaplan–Meier curves and Cox regression analysis were used to explore the prognostic value of GPX7. Gene Set Enrichment Analysis (GSEA) was applied to investigate the GPX7-related functions in glioma. Results The results indicated that the expression of GPX7 in glioma was higher compared to that in normal brain tissue. Univariate and multivariate Cox regression analyses confirmed that the expression value of GPX7 was an independent prognostic factor in glioma. The GSEA analysis showed that GPX7 was significantly enriched in the cell cycle pathway, ECM pathway, focal adhesion pathway, and toll-like receptor pathway. Conclusions The GPX7 was recommended as an independent risk factor for patients diagnosed with glioma for the first time and GPX7 could be potentially used as the therapy target in future. Furthermore, we attempted to explore a potential biomarker for improving the diagnosis and prognosis of patients with glioma.

scholarly journals Features of alternative splicing in stomach adenocarcinoma and their clinical implication: A research based on massive sequencing data

2020 ◽  
Author(s):  
Yuanyuan Zhang ◽  
Qian Niu ◽  
Yun Han ◽  
Xingyu Liu ◽  
Jie Jiang ◽  
...  
Keyword(s):  
Alternative Splicing ◽  
High Efficiency ◽  
Cox Regression ◽  
Area Under The Curve ◽  
The Cancer Genome Atlas ◽  
Regulation Mechanism ◽  
Sequencing Data ◽  
Cox Regression Analysis ◽  
Molecular Events ◽  
Stomach Adenocarcinoma

Abstract Background: Alternative splicing (AS) offers a main mechanism to form protein polymorphism. A growing body of evidence indicates the correlation between splicing disorders and carcinoma. Nevertheless, an overall analysis of AS signatures in stomach adenocarcinoma (STAD) is absent and urgently needed.Methods: Within this work, genetic expression and clinical data of STAD were queried from The Cancer Genome Atlas (TCGA), and profiles of AS events were searched from the SpliceSeq database. Cox regression analysis found survival associated AS events. Finally, the splicing network was constructed to reflect the correlation between survival associated AS events and splicing factors (SF).Results: 2042 splicing events were confirmed as prognostic molecular events. Furthermore, the final prognostic signature constructed by 10 AS events gave good result with an area under the curve (AUC) of receiver operating characteristic (ROC) curve up to 0.902 for 5 years, showing high potency in predicting patient outcome. We built the splicing regulatory network to show the internal regulation mechanism of splicing events in STAD. QKI may play a significant part in the prognosis induced by splicing events.Conclusions: In our study, a high-efficiency prognostic prediction model was built for STAD patients, and the results showed that AS events could become potential prognostic biomarkers for STAD. Meanwhile, QKI may become an important target for drug design in the future.

scholarly journals PGM5: a novel diagnostic and prognostic biomarker for liver cancer

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7070 ◽  
Author(s):  
Yan Jiao ◽  
Yanqing Li ◽  
Peiqiang Jiang ◽  
Wei Han ◽  
Yahui Liu
Keyword(s):  
Liver Cancer ◽  
Prognostic Biomarker ◽  
Area Under The Curve ◽  
Public Health Problem ◽  
Roc Curves ◽  
Normal Liver ◽  
Diagnostic Value ◽  
Metabolic Reprogramming ◽  
The Cancer Genome Atlas ◽  
Metabolic Enzyme

Background Liver cancer is a common malignancy and a significant public health problem worldwide, but diagnosis and prognostic evaluation remain challenging for clinicians. Metabolic reprogramming is a hallmark of cancer, and we therefore examined the diagnostic and prognostic value of a metabolic enzyme, phosphoglucomutase-like protein 5 (PGM5), in liver cancer. Methods All data were from The Cancer Genome Atlas database. R and related statistical packages were used for data analysis. Hepatic PGM5 expression was determined in different groups, and the chi-squared test and Fisher’s exact test were used to determine the significance of differences. The pROC package was used to determine receiver operating characteristic (ROC) curves, the survival package was used to for survival analysis and development of a Cox multivariable model, and the ggplot2 package was used for data visualization. Results PGM5 expression was significantly lower in cancerous than adjacent normal liver tissues, and had modest diagnostic value based on ROC analysis and calculations of area under the curve (AUC). Hepatic PGM5 expression had positive associations with male sex and survival, but negative associations with advanced histologic type, advanced histologic grade, advanced stage, and advanced T classification. Patents with low PGM5 levels had poorer overall survival and relapse-free survival. PGM5 was independently associated with patient prognosis. Conclusion PGM5 has potential use as a diagnostic and prognostic biomarker for liver cancer.

scholarly journals Identification of Methylation Driven Biomarkers for Diagnosis and Prognosis in Colon Cancer by Integrative Analysis of TCGA, GTEx, and GEO Data

2021 ◽  
Author(s):  
Lichao Cao ◽  
Erfei Chen ◽  
Jin Yang
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Mrna Expression ◽  
Cox Regression ◽  
Integrative Analysis ◽  
The Cancer Genome Atlas ◽  
Expression Data ◽  
Cox Regression Analysis ◽  
Diagnosis And Prognosis ◽  
Differentially Methylated Genes

Abstract Background: The intention of the present work was to investigate methylation driven biomarkers for diagnosis and prognosis in colorectal cancer (CRC) by integrative analysis of DNA methylation and gene expression data from The Cancer Genome Atlas (TCGA), The Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO). Methods and Results: The differentially expression genes (DEGs) and differentially methylated genes (DMGs) were screened using mRNA expression and DNA methylation data from TCGA, respectively. And the methylation driven genes (MDGs) of CRC were further identified using MethylMix R package. Subsequently, the MDGs were underwent Random Forest (RF) analyses to establish diagnosis prediction model using mRNA expression data from TCGA and GTEx, which were then validated by GSE39582 from GEO dataset. In addition, prognostic biomarkers that were used to establish the methylation related risk score model was generated by the univariate and multivariate Cox regression analysis. 9 out of 10 DMGs revealed excellent performance as independent diagnostic predictors, including CLDN1, EPHX4, TCN1, ARHGAP20, LY6G6D, FAM150A, KCNJ12, KRT7 and STK33. Furthermore, STK33 and EPHX4 were found to be associated with Overall survival (OS). Conclusions: Our findings suggest that the identified MDGs could be potential biomarkers for diagnosis and prognosis of CRC.

scholarly journals Features of alternative splicing in stomach adenocarcinoma and their clinical implication: A research based on massive sequencing data

2020 ◽  
Author(s):  
Yuanyuan Zhang ◽  
Qian Niu ◽  
Yun Han ◽  
Xingyu Liu ◽  
Jie Jiang ◽  
...  
Keyword(s):  
Alternative Splicing ◽  
High Efficiency ◽  
Cox Regression ◽  
Area Under The Curve ◽  
The Cancer Genome Atlas ◽  
Regulation Mechanism ◽  
Sequencing Data ◽  
Cox Regression Analysis ◽  
Molecular Events ◽  
Stomach Adenocarcinoma

Abstract Background: Alternative splicing (AS) offers a main mechanism to form protein polymorphism. A growing body of evidence indicates the correlation between splicing disorders and carcinoma. Nevertheless, an overall analysis of AS signatures in stomach adenocarcinoma (STAD) is absent and urgently needed.Methods: Within this work, genetic expression and clinical data of STAD were queried from The Cancer Genome Atlas (TCGA), and profiles of AS events were searched from the SpliceSeq database. Cox regression analysis found survival associated AS events. Finally, the splicing network was constructed to reflect the correlation between survival associated AS events and splicing factors (SF).Results: 2042 splicing events were confirmed as prognostic molecular events. Furthermore, the final prognostic signature constructed by 10 AS events gave good result with an area under the curve (AUC) of receiver operating characteristic (ROC) curve up to 0.902 for 5 years, showing high potency in predicting patient outcome. We built the splicing regulatory network to show the internal regulation mechanism of splicing events in STAD. QKI may play a significant part in the prognosis induced by splicing events.Conclusions: In our study, a high-efficiency prognostic prediction model was built for STAD patients, and the results showed that AS events could become potential prognostic biomarkers for STAD. Meanwhile, QKI may become an important target for drug design in the future.

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