scholarly journals Expression signatures based on pyroptosis-related genes can robustly diagnose skin cutaneous melanoma and predict the prognosis

2021 ◽  
Author(s):  
Anji Ju
Keyword(s):  
T Cells ◽  
Cutaneous Melanoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Γδ T Cells ◽  
Machine Learning Algorithms ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Cox Regression Analysis ◽  
Diagnosis And Prognosis

AbstractSkin cutaneous melanoma (SKCM) is a chronically malignant tumor with a high mortality rate. Pyroptosis, a kind of pro-inflammatory programmed cell death has been linked to cancer in recent studies. However, the value of pyroptosis in the diagnosis and prognosis of SKCM is not clear. In this study, it was discovered that 20 pyroptosis-related genes (PRGs) differed in expression between SKCM and normal tissues, which were related to diagnosis and prognosis. On one hand, based on these genes, nine commonly used machine-learning algorithms were shown to perform well in constructing diagnostic classifiers, including KNN, logistic regression, SVM, ANN, decision tree, random forest, XGBoost, LightGBM, and CatBoost. On the other hand, the least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied and the prognostic model was constructed based on 9 PRGs. Subgroups with low and high risks determined by the prognostic model were shown to have different survival. Then, functional enrichment analyses were performed by applying the gene set enrichment analysis (GSEA) and results suggested that the risk was related to immune response. Finally, immune infiltration analysis was carried out and showed that fractions of activated CD4+ memory T cells, γδ T cells, M1 macrophages, and M2 macrophages were significantly different between subgroups. In conclusion, the expression signatures of pyroptosis-related genes are valuable in the diagnosis and prognosis of SKCM, which is related to the immunity.

scholarly journals Pyroptosis-Related Gene Signatures Can Robustly Diagnose Skin Cutaneous Melanoma and Predict the Prognosis

2021 ◽  
Vol 11 ◽  
Author(s):  
Anji Ju ◽  
Jiaze Tang ◽  
Shuohua Chen ◽  
Yan Fu ◽  
Yongzhang Luo
Keyword(s):  
Cutaneous Melanoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Machine Learning Algorithms ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Cox Regression Analysis ◽  
Diagnosis And Prognosis

Skin cutaneous melanoma (SKCM) is a chronically malignant tumor with a high mortality rate. Pyroptosis, a kind of pro-inflammatory programmed cell death, has been linked to cancer in recent studies. However, the value of pyroptosis in the diagnosis and prognosis of SKCM is not clear. In this study, it was discovered that 20 pyroptosis-related genes (PRGs) differed in expression between SKCM and normal tissues, which were related to diagnosis and prognosis. Firstly, based on these genes, nine machine-learning algorithms were shown to perform well in constructing diagnostic classifiers, including K-Nearest Neighbor (KNN), logistic regression, Support Vector Machine (SVM), Artificial Neural Network (ANN), decision tree, random forest, XGBoost, LightGBM, and CatBoost. Secondly, the least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied and the prognostic model was constructed based on 9 PRGs. Subgroups in low and high risks determined by the prognostic model were shown to have different survival. Thirdly, functional enrichment analyses were performed by applying the gene set enrichment analysis (GSEA), and results suggested that the risk was related to immune response. In conclusion, the expression signatures of pyroptosis-related genes are effective and robust in the diagnosis and prognosis of SKCM, which is related to immunity.

scholarly journals Construction and validation of an RNA-binding protein-associated prognostic model for colorectal cancer

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11219
Author(s):  
Yandong Miao ◽  
Hongling Zhang ◽  
Bin Su ◽  
Jiangtao Wang ◽  
Wuxia Quan ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Prognostic Model ◽  
Rna Binding ◽  
Cox Regression ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Cox Regression Analysis ◽  
Diagnosis And Prognosis

Colorectal cancer (CRC) is one of the most prevalent and fatal malignancies, and novel biomarkers for the diagnosis and prognosis of CRC must be identified. RNA-binding proteins (RBPs) are essential modulators of transcription and translation. They are frequently dysregulated in various cancers and are related to tumorigenesis and development. The mechanisms by which RBPs regulate CRC progression are poorly understood and no clinical prognostic model using RBPs has been reported in CRC. We sought to identify the hub prognosis-related RBPs and to construct a prognostic model for clinical use. mRNA sequencing and clinical data for CRC were obtained from The Cancer Genome Atlas database (TCGA). Gene expression profiles were analyzed to identify differentially expressed RBPs using R and Perl software. Hub RBPs were filtered out using univariate Cox and multivariate Cox regression analysis. We used functional enrichment analysis, including Gene Ontology and Gene Set Enrichment Analysis, to perform the function and mechanisms of the identified RBPs. The nomogram predicted overall survival (OS). Calibration curves were used to evaluate the consistency between the predicted and actual survival rate, the consistency index (c-index) was calculated, and the prognostic effect of the model was evaluated. Finally, we identified 178 differently expressed RBPs, including 121 up-regulated and 57 down-regulated proteins. Our prognostic model was based on nine RBPs (PNLDC1, RRS1, HEXIM1, PPARGC1A, PPARGC1B, BRCA1, CELF4, AEN and NOVA1). Survival analysis showed that patients in the high-risk subgroup had a worse OS than those in the low-risk subgroup. The area under the curve value of the receiver operating characteristic curve of the prognostic model is 0.712 in the TCGA cohort and 0.638 in the GEO cohort. These results show that the model has a moderate diagnostic ability. The c-index of the nomogram is 0.77 in the TCGA cohort and 0.73 in the GEO cohort. We showed that the risk score is an independent prognostic biomarker and that some RBPs may be potential biomarkers for the diagnosis and prognosis of CRC.

scholarly journals BACH1: A Potential Predictor of Survival in Early-Stage Lung Adenocarcinoma

2022 ◽  
Vol 2022 ◽  
pp. 1-16
Author(s):  
Jin Zhou ◽  
Zheming Liu ◽  
Huibo Zhang ◽  
Tianyu Lei ◽  
Jiahui Liu ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Early Stage ◽  
External Validation ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Cox Regression Analysis

Purpose. Recent researches showed the vital role of BACH1 in promoting the metastasis of lung cancer. We aimed to explore the value of BACH1 in predicting the overall survival (OS) of early-stage (stages I-II) lung adenocarcinoma. Patients and Methods. Lung adenocarcinoma cases were screened from the Cancer Genome Atlas (TCGA) database. Functional enrichment analysis was performed to obtain the biological mechanisms of BACH1. Gene set enrichment analysis (GSEA) was performed to identify the difference of biological pathways between high- and low-BACH1 groups. Univariate and multivariate COX regression analysis had been used to screen prognostic factors, which were used to establish the BACH1 expression-based prognostic model in the TCGA dataset. The C-index and time-dependent AUC curve were used to evaluate predictive power of the model. External validation of prognostic value was performed in two independent datasets from Gene Expression Omnibus (GEO). Decision analysis curve was finally used to evaluate clinical usefulness of the BACH1-based model beyond pathologic stage alone. Results. BACH1 was an independent prognostic factor for lung adenocarcinoma. High-expression BACH1 cases had worse OS. BACH1-based prognostic model showed an ideal C-index and t -AUC and validated by two GEO datasets, independently. More importantly, the BACH1-based model indicated positive clinical applicability by DCA curves. Conclusion. Our research confirmed that BACH1 was an important predictor of prognosis in early-stage lung adenocarcinoma. The higher the expression of BACH1, the worse OS of the patients.

scholarly journals Prediction of Prognostic Biomarkers and Construction of an Autophagy Prognostic Model for Colorectal Cancer Using Bioinformatics

2020 ◽  
Vol 19 ◽  
pp. 153303382098417
Author(s):  
Ting-ting Liu ◽  
Shu-min Liu
Keyword(s):  
Colorectal Cancer ◽  
Regression Analysis ◽  
Prognostic Model ◽  
Cox Regression ◽  
Small Gtpase ◽  
Functional Enrichment ◽  
Clinical Correlation ◽  
Cox Regression Analysis ◽  
Prognostic Analysis ◽  
Key Genes

Objective: The incidence of colorectal cancer is increasing every year, and autophagy may be related closely to the pathogenesis of colorectal cancer. Autophagy is a natural catabolic mechanism that allows the degradation of cellular components in eukaryotic cells. However, autophagy plays a dual role in tumorigenesis. It not only promotes normal cell survival and tumor growth but also induces cell death and suppresses tumors survival. In addition, the pathogenesis of various conditions, including inflammation, neurodegenerative diseases, or tumors, is associated with abnormal autophagy. The present work aimed to examine the significance of autophagy-related genes (ARGs) in prognosis prediction, to construct an autophagy prognostic model, and to identify independent prognostic factors for colorectal cancer (CRC). Methods: This study discovered a total of 36 ARGs in CRC cases using The Cancer Genome Atlas (TCGA) and Human Autophagy-dedicated (HADd) databases along with functional enrichment analysis. Then, an autophagy prognostic model was constructed using univariate Cox regression analysis, and the key prognostic genes were screened. Finally, independent prognostic markers were determined through independent prognostic analysis and clinical correlation analysis of key genes. Results: Of the 36 differentially expressed ARGs, 13 were related to prognosis, as determined by univariate Cox regression analysis. A total of 6 key genes were obtained by a multivariate Cox regression analysis. Independent prognostic values were shown by 3 genes, namely, microtubule-associated protein 1 light chain 3 (MAP1LC3C), small GTPase superfamily and Rab family (RAB7A), and WD-repeat domain phosphoinositide-interacting protein 2 (WIPI2) by independent prognostic analysis and clinical correlation. Conclusions: In this study, molecular bioinformatics technology was employed to determine and construct a prognostic model of autophagy for colon cancer patients, which revealed 3 autophagy-related features, namely, MAP1LC3C, WIPI2, and RAB7A.

A Six CT83-Related Genes Based Prognostic Signature for Lung Adenocarcinoma

2021 ◽  
Vol 24 ◽  
Author(s):  
Yongmei Wang ◽  
Guimin Zhang ◽  
Ruixian Wang
Keyword(s):  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Differential Expression ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Differential Expression Analysis ◽  
Functional Enrichment ◽  
Risk Scores ◽  
Cox Regression Analysis

Background: This study aims to explore the prognostic values of CT83 and CT83-related genes in lung adenocarcinoma (LUAD). Methods: We downloaded the mRNA profiles of 513 LUAD patients (RNA sequencing data) and 246 NSCLC patients (Affymetrix Human Genome U133 Plus 2.0 Array) from TCGA and GEO databases. According to the median expression of CT83, the TCGA samples were divided into high and low expression groups, and differential expression analysis between them was performed. Functional enrichment analysis of differential expression genes (DEGs) was conducted. Univariate Cox regression analysis and LASSO Cox regression analysis were performed to screen the optimal prognostic DEGs. Then we established the prognostic model. A Nomogram model was constructed to predict the overall survival (OS) probability of LUAD patients. Results: CT83 expression was significantly correlated to the prognosis of LUAD patients. A total of 59 DEGs were identified, and a predictive model was constructed based on six optimal CT83-related DEGs, including CPS1, RHOV, TNNT1, FAM83A, IGF2BP1, and GRIN2A, could effectively predict the prognosis of LUAD patients. The nomogram could reliably predict the OS of LUAD patients. Moreover, the six important immune checkpoints (CTLA4, PD1, IDO1, TDO2, LAG3, and TIGIT) were closely correlated with the Risk Score, which was also differentially expressed between the LUAD samples with high and low-Risk Scores, suggesting that the poor prognosis of LUAD patients with high-Risk Score might be due to the immunosuppressive microenvironments. Conclusion: A prognostic model based on six optimal CT83 related genes could effectively predict the prognosis of LUAD patients.

scholarly journals Development and Validation of an Mesenchymal-Related Long Non-Coding RNA Prognostic Model in Glioma

2021 ◽  
Vol 11 ◽  
Author(s):  
Kebing Huang ◽  
Xiaoyu Yue ◽  
Yinfei Zheng ◽  
Zhengwei Zhang ◽  
Meng Cheng ◽  
...  
Keyword(s):  
High Risk ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Migration And Invasion ◽  
Aberrant Expression ◽  
Cox Regression Analysis ◽  
Genome Atlas

Glioma is well known as the most aggressive and prevalent primary malignant tumor in the central nervous system. Molecular subtypes and prognosis biomarkers remain a promising research area of gliomas. Notably, the aberrant expression of mesenchymal (MES) subtype related long non-coding RNAs (lncRNAs) is significantly associated with the prognosis of glioma patients. In this study, MES-related genes were obtained from The Cancer Genome Atlas (TCGA) and the Ivy Glioblastoma Atlas Project (Ivy GAP) data sets of glioma, and MES-related lncRNAs were acquired by performing co-expression analysis of these genes. Next, Cox regression analysis was used to establish a prognostic model, that integrated ten MES-related lncRNAs. Glioma patients in TCGA were divided into high-risk and low-risk groups based on the median risk score; compared with the low-risk groups, patients in the high-risk group had shorter survival times. Additionally, we measured the specificity and sensitivity of our model with the ROC curve. Univariate and multivariate Cox analyses showed that the prognostic model was an independent prognostic factor for glioma. To verify the predictive power of these candidate lncRNAs, the corresponding RNA-seq data were downloaded from the Chinese Glioma Genome Atlas (CGGA), and similar results were obtained. Next, we performed the immune cell infiltration profile of patients between two risk groups, and gene set enrichment analysis (GSEA) was performed to detect functional annotation. Finally, the protective factors DGCR10 and HAR1B, and risk factor SNHG18 were selected for functional verification. Knockdown of DGCR10 and HAR1B promoted, whereas knockdown of SNHG18 inhibited the migration and invasion of gliomas. Collectively, we successfully constructed a prognostic model based on a ten MES-related lncRNAs signature, which provides a novel target for predicting the prognosis for glioma patients.

scholarly journals FABP4 Might be an Indicator of the Status of Tumor Microenvironment in Colorectal Cancer

2021 ◽  
Author(s):  
Lijun Ning ◽  
Yuqing Yan ◽  
Tianying Tong ◽  
Ziyun Gao ◽  
Zhe Cui ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
T Cells ◽  
Tumor Microenvironment ◽  
Cox Regression ◽  
Nk Cell ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Fatty Acid Binding ◽  
Cox Regression Analysis ◽  
The Status

Abstract Background: As tumor microenvironment (TME) play an indispensable role in tumorigenesis of colorectal cancer, this study performs a bunch of bioinformatics analysis to identify the indicator of the status of TME in Colorectal cancer (CRC). Results: In the presented study, we applied CIBERSORT and ESTIMATE computational methods to calculate the proportion of tumor-infiltrating immune cells (TICs) and the amount of immune and stromal components in 444 COAD-READ cases from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were analyzed by COX regression analysis and protein–protein interaction (PPI) network construction. Then, fatty acid-binding protein four ( FABP4 ) was determined as a predictive factor by the intersection analysis of univariate COX and PPI. Further analysis revealed that FABP4 expression was positively correlated with the clinical pathologic characteristics (clinical stage, distant metastasis) and negatively correlated with the survival of CRC patients. Gene Set Enrichment Analysis (GSEA) showed that the genes in the high-expression FABP4 group were mainly enriched in immune-related activities. In the low-expression FABP4 group, the genes were enriched in metabolic pathways. CIBERSORT analysis for the proportion of TICs revealed that NK cell, CD4 + T cells and CD8 + T cells were negatively correlated with FABP4 expression, suggesting that FABP4 might be a potential prognostic factor of CRC patients. Conclusion: Our study has developed a new biomarker (FABP4) that can predict the status of tumor microenvironment in Colorectal cancer. Keywords: FABP4, tumor microenvironment, ESTIMATE, CIBERSORT, colorectal cancer

scholarly journals The Construction and Exploration of the CeRNA Network and Patterns of Tumor-infiltrating Immune Cells in Kidney Renal Clear Cell Carcinoma

2021 ◽  
Author(s):  
Weiwei Jia ◽  
Pengjia Li ◽  
Mingxia Ma ◽  
Xiaochen Niu ◽  
Lina Bai ◽  
...  
Keyword(s):  
T Cells ◽  
Regression Analysis ◽  
Mast Cells ◽  
Immune Cells ◽  
Prognostic Model ◽  
Cox Regression ◽  
Cox Regression Analysis ◽  
Cerna Network ◽  
Follicular Helper ◽  
Lncrna Malat1

Abstract KIRC is the malignant tumor with the highest incidence and poor prognosis in renal cell carcinoma. We want to explore the possible mechanisms of KIRC and effective prognostic-related biomarkers. The sequencing information of 3 types of RNA (mRNA, lncRNA and miRNA) in 539 cases of KIRC tissues and 72 cases of normal tissues is obtained from the TCGA database. Methods such as univariate Cox regression analysis, lasso regression screening, and multivariate Cox regression analysis were used to construct a prognostic model based on the CeRNA network. There are 3074 mRNAs, 359 lncRNAs and 132 miRNAs differentially expressed that have been identified through differential analysis. A complete mRNA-miRNA-lncRNA (SIX1-hsa-miR-200b-3p-MALAT1) network was obtained based on the CeRNA network. The CIBERSORT algorithm was used to analyze the degree of infiltration of 22 kinds of immune cells from each sample of KIRC. Construction of a prognostic model based on tumor-infiltrating immune cells, 2 immune cells (Mast cells resting, T cells follicular helper) were identified by constructing a prognostic model. There was a negative correlation between lncRNA MALAT1 and Mast cells resting (R= -0.27, P < 0.001); while there was a positive correlation between lncRNA MALAT1 and T cells follicular helper (R = 0.23, P < 0.001).

scholarly journals Identification of Prognostic Biomarkers of Cutaneous Melanoma Based on Analysis of Tumor Mutation Burden

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jiaqiong Lin ◽  
Yan Lin ◽  
Zena Huang ◽  
Xiaoyong Li
Keyword(s):  
Cutaneous Melanoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Predictive Biomarkers ◽  
Tumor Mutation Burden ◽  
Skin Development ◽  
Cox Regression Analysis ◽  
Immune Infiltration ◽  
Mutation Burden

Background. Immunotherapy offers a novel approach for the treatment of cutaneous melanoma, but the clinical efficiency varies for individual patients. In consideration of the high cost and adverse effects of immunotherapy, it is essential to explore the predictive biomarkers of outcomes. Recently, the tumor mutation burden (TMB) has been proposed as a predictive prognosticator of the immune response. Method. RNA-seq and somatic mutation datasets of 472 cutaneous melanoma patients were downloaded from The Cancer Genome Atlas (TCGA) database to analyze mutation type and TMB. Differently expressed genes (DEGs) were identified for functional analysis. TMB-related signatures were identified via LASSO and multivariate Cox regression analysis. The association between mutants of signatures and immune cells was evaluated from the TIMER database. Furthermore, the Wilcox test was applied to assess the difference in immune infiltration calculated by the CIBERSORT algorithm in risk groupings. Results. C>T substitutions and TTN were the most common SNV and mutated gene, respectively. Patients with low TMB presented poor prognosis. DEGs were mainly implicated in skin development, cell cycle, DNA replication, and immune-associated crosstalk pathways. Furthermore, a prognostic model consisting of eight TMB-related genes was developed, which was found to be an independent risk factor for treatment outcome. The mutational status of eight TMB-related genes was associated with a low level of immune infiltration. In addition, the immune infiltrates of CD4+ and CD8+ T cells, NK cells, and M1 macrophages were higher in the low-risk group, while those of M0 and M2 macrophages were higher in the high-risk group. Conclusion. Our study demonstrated that a higher TMB was associated with favorable survival outcome in cutaneous melanoma. Moreover, a close association between prognostic model and immune infiltration was identified, providing a new potential target for immunotherapy.

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.

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