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

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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Identification of Hub Prognosis-Associated Oxidative Stress Genes in Pancreatic Cancer Using Integrated Bioinformatics Analysis

Frontiers in Genetics ◽

10.3389/fgene.2020.595361 ◽

2020 ◽

Vol 11 ◽

Author(s):

Xin Qiu ◽

Qin-Han Hou ◽

Qiu-Yue Shi ◽

Hai-Xing Jiang ◽

Shan-Yu Qin

Keyword(s):

Oxidative Stress ◽

Gene Expression ◽

Pancreatic Cancer ◽

Cancer Progression ◽

Risk Model ◽

Cox Regression ◽

Expression Profiles ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Cox Regression Analysis

BackgroundIntratumoral oxidative stress (OS) has been associated with the progression of various tumors. However, OS has not been considered a candidate therapeutic target for pancreatic cancer (PC) owing to the lack of validated biomarkers.MethodsWe compared gene expression profiles of PC samples and the transcriptome data of normal pancreas tissues from The Cancer Genome Atlas (TCGA) and Genome Tissue Expression (GTEx) databases to identify differentially expressed OS genes in PC. PC patients’ gene profile from the Gene Expression Omnibus (GEO) database was used as a validation cohort.ResultsA total of 148 differentially expressed OS-related genes in PC were used to construct a protein-protein interaction network. Univariate Cox regression analysis, least absolute shrinkage, selection operator analysis revealed seven hub prognosis-associated OS genes that served to construct a prognostic risk model. Based on integrated bioinformatics analyses, our prognostic model, whose diagnostic accuracy was validated in both cohorts, reliably predicted the overall survival of patients with PC and cancer progression. Further analysis revealed significant associations between seven hub gene expression levels and patient outcomes, which were validated at the protein level using the Human Protein Atlas database. A nomogram based on the expression of these seven hub genes exhibited prognostic value in PC.ConclusionOur study provides novel insights into PC pathogenesis and provides new genetic markers for prognosis prediction and clinical treatment personalization for PC patients.

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Construction of a Novel Prognostic Immune-Related LncRNA Risk Model for Gastric Cancer

10.21203/rs.3.rs-1010858/v1 ◽

2021 ◽

Author(s):

Gen-hua Yang

Keyword(s):

Gastric Cancer ◽

Risk Score ◽

Risk Model ◽

Cox Regression ◽

Vital Role ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis ◽

Immune Genes ◽

Cancer Genome Atlas ◽

Score Model

Abstract Background and AimStudies have recently shown that immune-related lncRNAs play a vital role in the occurrence and development of human malignancies. However, the study in gastric cancer (GC) remains unclear. Here, we aimed to identify immune-related lncRNAs and construct a risk score model to predict the prognosis of GC patients.Methods:RNA expression data and clinical characteristics of GC were download from The Cancer Genome Atlas (TCGA) database. Immune genes were obtained from the Molecular Signatures Database (MSigDB). Immune-related lncRNAs were acquired by correlation coefficient between the immune genes and lncRNAs using “limma R” package and Cytoscape 3.6.1. The risk score model was constructed by univariate and multivariate Cox regression, and its prognostic value was verified in TCGA cohort. Results:A total of 146 immune-related lncRNAs were obtained compared 375 GC samples with 32 normal samples. A five immune-related lncRNA (AP001528.2, LINC02542, LINC02526, PVT1 and LINC01094) risk score model was constructed to predict prognosis of GC patients by Cox regression analysis. Moreover, GC patients with higher risk score had a poorer overall survival than that with lower risk score (P<0.001). Furthermore, ROC analysis revealed that the risk score model had the best predictive effect compared with clinicopathological features during 5 years followed-up (AUC = 0.679). Indeed, PCA analysis showed that the patients in the low- and high- group were significantly distinguished in different directions based on the risk score model. Conclusion:This study indicated that a five immune-related lncRNA risk score model possessed a satisfactory predictive prognosis, which might be potential prognostic biomarkers and immunotherapy targets for GC patients in future.

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Predicting the Survival and Immune Landscape of Colorectal Cancer Patients Using an Immune-Related lncRNA Pair Model

Frontiers in Genetics ◽

10.3389/fgene.2021.690530 ◽

2021 ◽

Vol 12 ◽

Author(s):

Chao Ma ◽

Xin Zhang ◽

Xudong Zhao ◽

Nan Zhang ◽

Sixin Zhou ◽

...

Keyword(s):

Colorectal Cancer ◽

Prognostic Model ◽

Risk Model ◽

Cox Regression ◽

Kinase Inhibitor ◽

Low Risk ◽

The Cancer Genome Atlas ◽

Prognostic Indicators ◽

Immune Checkpoints ◽

Cox Regression Analysis

BackgroundAccumulating evidence has demonstrated that immune-related long non-coding ribonucleic acids (irlncRNAs) can be used as prognostic indicators of overall survival (OS) in patients with colorectal cancer (CRC). Our aim in this research, therefore, was to construct a risk model using irlncRNA pairs with no requirement for a specific expression level, in hope of reliably predicting the prognosis and immune landscape of CRC patients.MethodsClinical and transcriptome profiling data of CRC patients downloaded from the Cancer Genome Atlas (TCGA) database were analyzed to identify differentially expressed (DE) irlncRNAs. The irlncRNA pairs significantly correlated with the prognosis of patients were screened out by univariable Cox regression analysis and a prognostic model was constructed by Lasso and multivariate Cox regression analyses. A receiver operating characteristic (ROC) curve was then plotted, with the area under the curve calculated to confirm the reliability of the model. Based on the optimal cutoff value, CRC patients in the high- or low-risk groups were distinguished, laying the ground for evaluating the risk model from the following perspectives: survival, clinicopathological traits, tumor-infiltrating immune cells (TIICs), antitumor drug efficacy, kinase inhibitor efficacy, and molecules related to immune checkpoints.ResultsA prognostic model consisting of 15 irlncRNA pairs was constructed, which was found to have a high correlation with patient prognosis in a cohort from the TCGA (p < 0.001, HR = 1.089, 95% CI [1.067–1.112]). According to both univariate and multivariate Cox analyses, this model could be used as an independent prognostic indicator in the TCGA cohort (p < 0.001). Effective differentiation between high- and low-risk patients was also accomplished, on the basis of aggressive clinicopathological characteristics, sensitivity to antitumor drugs, and kinase inhibitors, the tumor immune infiltration status, and the expression levels of specific molecules related to immune checkpoints.ConclusionThe prognostic model established with irlncRNA pairs is a promising indicator for prognosis prediction in CRC patients.

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Identification of the Pyroptosis-Related Gene Signature and Risk Score Model for Colon Adenocarcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.771847 ◽

2021 ◽

Vol 12 ◽

Author(s):

Bixian Luo ◽

Jianwei Lin ◽

Wei Cai ◽

Mingliang Wang

Keyword(s):

Risk Score ◽

Risk Model ◽

Cox Regression ◽

Colon Adenocarcinoma ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Related Gene ◽

Cox Regression Analysis ◽

Model Based ◽

Patient Prognosis

The prognosis of advanced colon adenocarcinoma (COAD) remains poor. However, existing methods are still difficult to assess patient prognosis. Pyroptosis, a lytic and inflammatory process of programmed cell death caused by the gasdermin protein, is involved in the development and progression of various tumors. Moreover, there are no related studies using pyroptosis-related genes to construct a model to predict the prognosis of COAD patients. Thus, in this study, bioinformatics methods were used to analyze the data of COAD patients downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to construct a risk model for the patient prognosis. TCGA database was used as the training set, and GSE39582 downloaded from GEO was used as the validation set. A total of 24 pyroptosis-related genes shown significantly different expression between normal and tumor tissues in COAD and seven genes (CASP4, CASP5, CASP9, IL6, NOD1, PJVK, and PRKACA) screened by univariate and LASSO cox regression analysis were used to construct the risk model. The receiver operating characteristic (ROC) and Kaplan–Meier (K–M curves) curves showed that the model based on pyroptosis-related genes can be used to predict the prognosis of COAD and can be validated by the external cohort well. Then, the clinicopathological factors were combined with the risk score to establish a nomogram with a C-index of 0.774. In addition, tissue validation results also showed that CASP4, CASP5, PRKACA, and NOD1 were differentially expressed between tumor and normal tissues from COAD patients. In conclusion, the risk model based on the pyroptosis-related gene can be used to assess the prognosis of COAD patients well, and the related genes may become the potential targets for treatment.

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Identification and validation of immunotypes for clear cell RCC prognosis

10.21203/rs.3.rs-847352/v1 ◽

2021 ◽

Author(s):

Zhaolin Yang ◽

Jiale Zhou ◽

Yizheng Xue ◽

Yu Zhang ◽

Kaijun Zhou ◽

...

Keyword(s):

Overall Survival ◽

Cox Regression ◽

Clear Cell ◽

Survival Rates ◽

Prognostic Index ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis ◽

Immune Genes ◽

Metastasis Model

Abstract Purpose To develop an immunotype-based prognostic model for predicting the overall survival (OS) of patients with clear cell renal carcinoma (ccRCC). We explored novel immunotypes of patients with ccRCC, particularly those associated with overall survival. A risk-metastasis model was constructed by integrating the immunotypes with immune genes and used to test the accuracy of the immunotype model. Patients and Methods Patient cohort data were obtained from The Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus (GEO) database, Renji database, and Surveillance, Epidemiology, and End Results (SEER) database. We employed the R software to select 3 immune cells and construct an immunotype-based prediction model. Immune genes selected using random Forest Algorithm were validated by immunohistochemistry (IHC). The H&L risk-metastasis model was constructed to assess the accuracy of the immunotype model through Multivariate COX regression analysis. Result Patients with ccRCC were categorized into immunotype H subgroup and immunotype L subgroup based on the overall survival rates. The immunotypes were found to be the independent prognostic index for ccRCC prognosis. As such, we constructed a new immunotypes-based SSIGN model. Three immune genes associated with difference between immunotype H and L were identified. An H&L risk-metastasis model was constructed to evaluate the accuracy of the immunotype model. Compared to the W-Risk-metastasis model which did not incorporate immunotypes, the H&L risk-metastasis model was more precise in predicting the survival of ccRCC patients. Conclusion The established immunotype model can effectively predict the survival of ccRCC patients. Except for mast cells, T cells and macrophages are positively associated with the overall survival of patients. The three immune genes identified, herein, can predict the survival rate of ccRCC patients, and expression of these immune genes is strongly linked to poor survival. The new SSIGN model provides an accurate tool for predicting the survival of ccRCC patients. H&L risk-metastasis model can effectively predict the risk of tumor metastasis.

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A Novel Six-mRNA Signature Predicts Survival of Patients With Glioblastoma Multiforme

Frontiers in Genetics ◽

10.3389/fgene.2021.634116 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zhentao Liu ◽

Hao Zhang ◽

Hongkang Hu ◽

Zheng Cai ◽

Chengyin Lu ◽

...

Keyword(s):

Glioblastoma Multiforme ◽

Cox Regression ◽

Methylation Status ◽

Survival Rates ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Survival Prediction ◽

Lasso Regression ◽

Cox Regression Analysis ◽

Score Model

Glioblastoma multiforme (GBM) is a devastating brain tumor and displays divergent clinical outcomes due to its high degree of heterogeneity. Reliable prognostic biomarkers are urgently needed for improving risk stratification and survival prediction. In this study, we analyzed genome-wide mRNA profiles in GBM patients derived from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify mRNA-based signatures for GBM prognosis with survival analysis. Univariate Cox regression model was used to evaluate the relationship between the expression of mRNA and the prognosis of patients with GBM. We established a risk score model that consisted of six mRNA (AACS, STEAP1, STEAP2, G6PC3, FKBP9, and LOXL1) by the LASSO regression method. The six-mRNA signature could divide patients into a high-risk and a low-risk group with significantly different survival rates in training and test sets. Multivariate Cox regression analysis confirmed that it was an independent prognostic factor in GBM patients, and it has a superior predictive power as compared with age, IDH mutation status, MGMT, and G-CIMP methylation status. By combining this signature and clinical risk factors, a nomogram can be established to predict 1-, 2-, and 3-year OS in GBM patients with relatively high accuracy.

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Construction and Validation of a Prognostic Risk Model Based on Autophagy-Related Genes in Hepatocellular Carcinoma Cells

10.21203/rs.3.rs-714025/v1 ◽

2021 ◽

Author(s):

Rui Feng ◽

Jian Li ◽

Weiling Xuan ◽

Hanbo Liu ◽

Dexin Cheng ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Differential Expression ◽

Prognostic Model ◽

Risk Model ◽

Cox Regression ◽

Univariate Analysis ◽

Differential Expression Analysis ◽

Cox Regression Analysis ◽

Dismal Prognosis

Abstract Background Hepatocellular carcinoma (HCC) is a prevalent primary liver cancer and the main cause of cancer mortality. Its high complexity and dismal prognosis bring dramatic difficulty to treatment. Due to the disclosed dual functions of autophagy in cancer development, understanding autophagy-related genes devotes into seeking novel biomarkers for HCC. Methods Differential expression of genes in normal and tumor groups was analyzed to acquire autophagy-related genes in HCC. GO and KEGG pathway analyses were conducted on these genes. Genes were then screened by univariate regression analysis. The screened genes were subjected to multivariate Cox regression analysis to build a prognostic model. The model was validated by ICGC validation set. Results Altogether, 42 autophagy-related differential genes were screened by differential expression analysis. Enrichment analysis showed that they were mainly enriched in pathways including regulation of autophagy and cell apoptosis. Genes were screened by univariate analysis and multivariate Cox regression analysis to build a prognostic model. The model was constituted by 6 feature genes: EIF2S1, BIRC5, SQSTM1, ATG7, HDAC1, FKBP1A. Validation confirmed the accuracy and independence of this model in predicting HCC patient’s prognosis. Conclusion A total of 6 feature genes were identified to build a prognostic risk model. This model is conducive to investigating interplay between autophagy-related genes and HCC prognosis.

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PDK2: a novel diagnostic and prognostic biomarker for liver cancer

10.21203/rs.2.24570/v1 ◽

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.

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Integrated analysis of the functions and prognostic values of RNA binding proteins in colon adenocarcinoma

10.21203/rs.3.rs-29330/v1 ◽

2020 ◽

Author(s):

Xinhong Liu ◽

Fang Tan ◽

Xingyao Long ◽

Ruokun Yi ◽

Dingyi Yang ◽

...

Keyword(s):

Prognostic Model ◽

Binding Proteins ◽

Rna Binding ◽

Prediction Models ◽

Cox Regression ◽

Rna Binding Proteins ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Integrated Analysis ◽

Cox Regression Analysis

Abstract Background RNA binding proteins (RBPs) play an important role in a variety of cancers. However, the role of RBPs in colorectal adenocarcinoma (COAD) has not been studied. Integrated analysis of RBPs will provide a better understanding of disease genesis and new insights into COAD treatment. Methods The gene expression data and corresponding clinical information for COAD were downloaded from The Cancer Genome Atlas (TCGA) database. Univariate Cox regression analysis was used to screen for RBPs associated with COAD recurrence, and multivariate Cox proportional hazards regression analyses were used to identify genes that were associated with COAD recurrence. A nomogram was constructed to predict the recurrence of COAD, and a receiver operating characteristic (ROC) curve analysis was performed to determine the accuracy of the prediction models. The Human Protein Atlas database was used in prediction models to confirm the expression of key genes in COAD patients. Result A total of 177 differentially expressed RBPs was obtained, comprising 123 upregulated and 54 downregulated. GO and KEGG enrichment analysis showed that the differentially expressed RBPs were mainly related to mRNA metabolism, RNA processing and translation regulation. Seven RBP genes (TDRD6, POP1, TDRD7, PPARGC1A, LIN28B, LRRFIP2 and PNLDC1) were identified as prognosis-associated genes and were used to construct the prognostic model. Conclusion We constructed a COAD prognostic model through bioinformatics analysis, which indicated that prognostic model RBPs have a potential role in the diagnosis and prognosis of COAD. Moreover, the nomogram can effectively predict the 1-year, 3-year, and 5-year survival rate for COAD patients.

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A Five Autophagy-Related Long Non-Coding RNA Prognostic Model for Patients with Lung Adenocarcinoma

10.21203/rs.3.rs-543033/v1 ◽

2021 ◽

Author(s):

Boxuan Liu ◽

Yun Zhao ◽

Shuanying Yang

Keyword(s):

Lung Adenocarcinoma ◽

Risk Score ◽

Prognostic Model ◽

Risk Model ◽

Cox Regression ◽

Predictive Ability ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Lasso Regression ◽

Cox Regression Analysis

Abstract Background: Lung adenocarcinoma is the most occurred pathological type among non-small cell lung cancer. Although huge progress has been made in terms of early diagnosis, precision treatment in recent years, the overall 5-year survival rate of a patient remains low. In our study, we try to construct an autophagy-related lncRNA prognostic signature that may guide clinical practice.Methods: The mRNA and lncRNA expression matrix of lung adenocarcinoma patients were retrieved from TCGA database. Next, we constructed a co-expression network of lncRNAs and autophagy-related genes. Lasso regression and multivariate Cox regression were then applied to establish a prognostic risk model. Subsequently, a risk score was generated to differentiate high and low risk group and a ROC curve and Nomogram to visualize the predictive ability of current signature. Finally, gene ontology and pathway enrichment analysis were executed via GSEA.Results: A total of 1,703 autophagy-related lncRNAs were screened and five autophagy-related lncRNAs (LINC01137, AL691432.2, LINC01116, AL606489.1 and HLA-DQB1-AS1) were finally included in our signature. Judging from univariate(HR=1.075, 95% CI: 1.046–1.104) and multivariate(HR =1.088, 95%CI = 1.057 − 1.120) Cox regression analysis, the risk score is an independent factor for LUAD patients. Further, the AUC value based on the risk score for 1-year, 3-year, 5-year, was 0.735, 0.672 and 0.662 respectively. Finally, the lncRNAs included in our signature were primarily enriched in autophagy process, metabolism, p53 pathway and JAK/STAT pathway. Conclusions: Overall, our study indicated that the prognostic model we generated had certain predictability for LUAD patients’ prognosis.

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