scholarly journals Ferroptosis-Related Long Non-coding RNA Predicts Prognosis Model of Hepatocellular Carcinoma

2022 ◽  
Author(s):  
Congyue Zhang ◽  
Chaoqun Zhang ◽  
Huifang Zhou ◽  
Zhankai Hu ◽  
Dianxing Sun
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Groups ◽  
Low Risk ◽  
Assessment Model ◽  
Differentially Expressed ◽  
Death Process ◽  
Type I ◽  
Aberrant Expression ◽  
Cox Regression Analysis ◽  
Cell Functions

Abstract Background: Hepatocellular carcinoma (HCC) is the most common malignancy globally, and ferroptosis is an iron-dependent cell death process. Furthermore, aberrant expression of long non-coding RNAs (lncRNAs) driving HCC development and progression has increased attention. Materials and Methods: We collected lncRNA expression profiles associated with ferroptosis from The Cancer Genome Atlas (TCGA) and FerrDb databases and clinicopathological and overall survival (OS) information to determine the association between ferroptosis-related lncRNAs(FRlncRNAs) and survival of HCC patients by co-expression analysis. A prognostic lncRNA model of 22 differentially expressed lncRNAs was constructed using Cox regression analysis and the LASSO algorithm. Kaplan-Meier analysis revealed that a high-risk lncRNAs profile was associated with poor prognosis in HCC. Our risk assessment model outperformed conventional clinical data in predicting the prognosis of HCC. Result: GSEA revealed immune and tumor-related pathways in individuals in the high- and low-risk groups. In addition, TCGA showed that T cell functions, including B cells, Cytolytic, macrophages, MHC-class-I, mast cells, neutrophils, NK cells, helper T cells, Type-I-IFN, and Type-II-IFN, were significantly different between high and low-risk groups. Immune checkpoints such as TNFSF18, IDO2, CD276, NRP1, and TNFSF4 were also differentially expressed between the two risk groups. Conclusions: Our findings provide a robust prognostic and immune response prediction model for HCC patients based on lncRNAs associated with ferroptosis.

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.

Development and validation of a robust immune-related risk signature for hepatocellular carcinoma

2020 ◽  
Author(s):  
Zaoqu Liu ◽  
Dechao Jiao ◽  
Xueliang Zhou ◽  
Yuan Yao ◽  
Zhaonan Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Risk Genes ◽  
Cox Regression Analysis ◽  
Related Risk

Abstract Background: A growing amount of evidence has suggested immune-related genes (IRGs) play a key role in the development of hepatocellular carcinoma (HCC). However, there have been no investigations proposing a reliable prognostic signature in terms of tumor immunology. This study aimed to develop a robust signature based on IRGs in HCC.Methods: A total of 597 HCC patients were enrolled. The TCGA database was utilized for discovery, and the ICGC database was utilized for validation. Multiple algorithms (including univariate Cox, LASSO, and multivariate Cox regression) were performed to identify key prognostic IRGs and establish an immune-related risk signature. Bioinformatics analysis and R soft tools were utilized to annotate underlying biological functions. Results: A total of 1416 differentially expressed mRNAs (DEMs) were screened in the TCGA cohort, of which 90 were differentially expressed IRGs (DEIRGs). Using univariate Cox regression analysis, we identified 33 prognostically relevant DEIRGs. Using LASSO regression and multivariate Cox regression analysis, we extracted 8 optimal DEIRGs (APLN, CDK4, CXCL2, ESR1, IL1RN, PSMD2, SEMA3F, and SPP1) to construct a risk signature with the ability to distinguish cases as having a high or low risk of unfavorable prognosis in the TCGA cohort, and the signature was verified in the ICGC cohort. The signature was prognostically significant in all stratified cohorts and was deemed an independent prognostic factor for HCC. We also built a nomogram with good performance by combining the signature with clinicopathological factors to increase the accuracy of predicting HCC prognosis. By investigating the relationship of the risk score and 8 risk genes from our signature with clinical traits, we found that the aberrant expression of the immune-related risk genes is correlated with the development of HCC. Moreover, the high-risk group was higher than the low-risk group in terms of tumor mutation burden (TMB), immune cell infiltration, and the expression of immune checkpoints (PD-1, PD-L1, and CTLA-4), and functional enrichment analysis indicated the signature enriched an intensive immune phenotype.Conclusions: This study developed a robust immune-related risk signature and built a predictive nomogram that reliably predict overall survival in HCC, which may be helpful for clinical management and personalized immunotherapy decisions.

scholarly journals A prognostic model for hepatocellular carcinoma based on apoptosis-related genes

2021 ◽  
Author(s):  
Renjie Liu ◽  
Guifu Wang ◽  
Chi Zhang ◽  
Dousheng Bai
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Model ◽  
Molecular Mechanisms ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Groups ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Risk Scores

Abstract Background: Dysregulation of the balance between proliferation and apoptosis is the basis for human hepatocarcinogenesis. In many malignant tumors, such as hepatocellular carcinoma (HCC), there is a correlation between apoptotic dysregulation and poor prognosis. However, the prognostic values of apoptosis-related genes (ARGs) in HCC have not been elucidated. Methods: To screen for differentially expressed ARGs, the expression levels of 161 ARGs from The Cancer Genome Atlas (TCGA) database(https://cancergenome.nih.gov/) were analyzed. Gene Ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to evaluate the underlying molecular mechanisms of differentially expressed ARGs in HCC. The prognostic values of ARGs were established using Cox regression, and subsequently, a prognostic risk model for scoring patients was developed. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curves were plotted to determine the prognostic value of the model. Results: Compared to normal tissues, 43 highly up-regulated and 8 down-regulated ARGs in HCC tissues were screened. GO analysis results revealed that these 51 genes are indeed related to the apoptosis function. KEGG analysis revealed that these 51 genes were correlated with MAPK, P53, TNF, and PI3K-AKT signaling pathways, while Cox regression revealed that 5 ARGs (PPP2R5B, SQSTM1, TOP2A, BMF, and LGALS3) were associated with prognosis and were, therefore, obtained to develop the prognostic model. Based on the median risk scores, patients were categorized into high-risk and low-risk groups. Patients in the low-risk groups exhibited significantly elevated two-year or five-year survival probabilities (p < 0.0001). The risk model had a better clinical potency than the other clinical characteristics, with the area under the ROC curve (AUC = 0.741). The prognosis of HCC patients was established from a plotted nomogram. Conclusion: Based on the differential expression of ARGs, we established a novel risk model for predicting HCC prognosis. This model can also be used to inform the individualized treatment of HCC patients.

scholarly journals A Novel Pyroptosis-related Prognostic Model for Hepatocellular Carcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Qianqian Wu ◽  
Sutian Jiang ◽  
Tong Cheng ◽  
Manyu Xu ◽  
Bing Lu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Groups ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Prognostic Biomarkers ◽  
Cox Regression Analysis ◽  
Public Data

Hepatocellular carcinoma (HCC) is the second most lethal malignant tumor because of its significant heterogeneity and complicated molecular pathogenesis. Novel prognostic biomarkers are urgently needed because no effective and reliable prognostic biomarkers currently exist for HCC patients. Increasing evidence has revealed that pyroptosis plays a role in the occurrence and progression of malignant tumors. However, the relationship between pyroptosis-related genes (PRGs) and HCC patient prognosis remains unclear. In this study, 57 PRGs were obtained from previous studies and GeneCards. The gene expression profiles and clinical data of HCC patients were acquired from public data portals. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to establish a risk model using TCGA data. Additionally, the risk model was further validated in an independent ICGC dataset. Our results showed that 39 PRGs were significantly differentially expressed between tumor and normal liver tissues in the TCGA cohort. Functional analysis confirmed that these PRGs were enriched in pyroptosis-related pathways. According to univariate Cox regression analysis, 14 differentially expressed PRGs were correlated with the prognosis of HCC patients in the TCGA cohort. A risk model integrating two PRGs was constructed to classify the patients into different risk groups. Poor overall survival was observed in the high-risk group of both TCGA (p &lt; 0.001) and ICGC (p &lt; 0.001) patients. Receiver operating characteristic curves demonstrated the accuracy of the model. Furthermore, the risk score was confirmed as an independent prognostic indicator via multivariate Cox regression analysis (TCGA cohort: HR = 3.346, p &lt; 0.001; ICGC cohort: HR = 3.699, p &lt; 0.001). Moreover, the single-sample gene set enrichment analysis revealed different immune statuses between high- and low-risk groups. In conclusion, our new pyroptosis-related risk model has potential application in predicting the prognosis of HCC patients.

scholarly journals Construction of a Prognostic Immune-Related LncRNA Risk Model for Lung Adenocarcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Yue Li ◽  
Ruoyi Shen ◽  
Anqi Wang ◽  
Jian Zhao ◽  
Jieqi Zhou ◽  
...  
Keyword(s):  
High Risk ◽  
Differentially Expressed Genes ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
Low Risk ◽  
Differentially Expressed ◽  
Cox Regression Analysis

BackgroundLung adenocarcinoma (LUAD) originates mainly from the mucous epithelium and glandular epithelium of the bronchi. It is the most common pathologic subtype of non-small cell lung cancer (NSCLC). At present, there is still a lack of clear criteria to predict the efficacy of immunotherapy. The 5-year survival rate for LUAD patients remains low.MethodsAll data were downloaded from The Cancer Genome Atlas (TCGA) database. We used Gene Set Enrichment Analysis (GSEA) database to obtain immune-related mRNAs. Immune-related lncRNAs were acquired by using the correlation test of the immune-related genes with R version 3.6.3 (Pearson correlation coefficient cor = 0.5, P &lt; 0.05). The TCGA-LUAD dataset was divided into the testing set and the training set randomly. Based on the training set to perform univariate and multivariate Cox regression analyses, we screened prognostic immune-related lncRNAs and given a risk score to each sample. Samples were divided into the high-risk group and the low-risk group according to the median risk score. By the combination of Kaplan–Meier (KM) survival curve, the receiver operating characteristic (ROC) (AUC) curve, the independent risk factor analysis, and the clinical data of the samples, we assessed the accuracy of the risk model. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the differentially expressed mRNAs between the high-risk group and the low-risk group. The differentially expressed genes related to immune response between two risk groups were analyzed to evaluate the role of the model in predicting the efficacy and effects of immunotherapy. In order to explain the internal mechanism of the risk model in predicting the efficacy of immunotherapy, we analyzed the differentially expressed genes related to epithelial-mesenchymal transition (EMT) between two risk groups. We extracted RNA from normal bronchial epithelial cell and LUAD cells and verified the expression level of lncRNAs in the risk model by a quantitative real-time polymerase chain reaction (qRT-PCR) test. We compared our risk model with other published prognostic signatures with data from an independent cohort. We transfected LUAD cell with siRNA-LINC0253. Western blot analysis was performed to observed change of EMT-related marker in protein level.ResultsThrough univariate Cox regression analysis, 24 immune-related lncRNAs were found to be strongly associated with the survival of the TCGA-LUAD dataset. Utilizing multivariate Cox regression analysis, 10 lncRNAs were selected to establish the risk model. The K-M survival curves and the ROC (AUC) curves proved that the risk model has a fine predictive effect. The GO enrichment analysis indicated that the effect of the differentially expressed genes between high-risk and low-risk groups is mainly involved in immune response and intercellular interaction. The KEGG enrichment analysis indicated that the differentially expressed genes between high-risk and low-risk groups are mainly involved in endocytosis and the MAPK signaling pathway. The expression of genes related to the efficacy of immunotherapy was significantly different between the two groups. A qRT-PCR test verified the expression level of lncRNAs in LUAD cells in the risk model. The AUC of ROC of 5 years in the independent validation dataset showed that this model had superior accuracy. Western blot analysis verified the change of EMT-related marker in protein level.ConclusionThe immune lncRNA risk model established by us could better predict the prognosis of patients with LUAD.

scholarly journals Development of a prognostic scoring model for predicting the survival of elderly patients with hepatocellular carcinoma

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8497 ◽  
Author(s):  
Sizhe Wan ◽  
Yuan Nie ◽  
Xuan Zhu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Factors ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Large Population ◽  
Risk Groups ◽  
Low Risk ◽  
Cox Regression Analysis ◽  
Scoring Model

Background The number of elderly hepatocellular carcinoma (HCC) patients is increasing, and precisely assessing of the prognosis of these patients is necessary. We developed a prognostic scoring model to predict survival in elderly HCC patients. Methods We extracted data from 4,076 patients ≥65 years old from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided them into training and validation groups. Cox regression analysis was used to screen for meaningful independent prognostic factors. The receiver operating characteristic curve reflected the model’s discrimination power. Results Age, race, American Joint Committee on Cancer stage, degree of tumour differentiation, tumour size, alpha-fetoprotein and tumour therapy were independent prognostic factors for survival in elderly HCC patients. We developed a prognostic scoring model based on the seven meaningful variables to predict survival in elderly HCC patients. The AUCs of the model were 0.805 (95% CI [0.788–0.821]) and 0.788 (95% CI [0.759–0.816]) in the training and validation groups, respectively. We divided the patients into low-risk groups and high-risk groups according to the optimal cut-off value. The Kaplan–Meier survival curve showed that in the training and validation groups, the survival rate of the low-risk group was significantly higher than that of the high-risk group (P < 0.001). Conclusion Based on a large population, we constructed a prognostic scoring model for predicting survival in elderly HCC patients. The model may provide a reference for clinicians for preoperative and postoperative evaluations of elderly HCC patients.

scholarly journals A signature related to lung adenocarcinoma prognosis: A study based on TCGA database

2020 ◽  
Author(s):  
Jia Wang ◽  
Xiaolu Zhang ◽  
Xiaoming Zhang ◽  
Yan Yao ◽  
Xiaoran Ma ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Survival Analysis ◽  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Risk Groups ◽  
Clinical Treatment ◽  
Low Risk ◽  
Differentially Expressed ◽  
Cox Regression Analysis ◽  
Cerna Network

Abstract Background: The intrinsic molecular subtypes of lung adenocarcinoma (LUAD) impact clinical treatment decision-making, but the molecular mechanisms are still unclear. Therefore, we aimed to identify sensitive biomarkers to evaluate LUAD patient prognosis. Methods: Differentially expressed RNAs from LUAD patients were obtained from The Cancer Genome Atlas (TCGA) database and they were used to construct a competitive endogenous RNA (ceRNA) network. Based on the examination of clinical data, long noncoding RNAs (lncRNAs) and mRNAs in the network were selected by univariate and multivariate Cox regression analysis. Finally, functional enrichment analysis was used to reveal prognostic signatures based on the classification into high and low-risk groups, survival analysis, and an independence test. Results: The ceRNA network consisted of 21 mRNAs, 53 lncRNAs, and 8 miRNAs that were selected from the differentially expressed RNAs identified. Next, based on univariate and multivariate Cox regression analysis, a prognostic signature, including two mRNAs (HOXA10 and CBX2) and four lncRNAs (LINC00460, LINC00330, DGCR5, and C14orf132) was constructed. Eventually, survival analysis showed that significant differences in survival rates between high and low-risk groups and the area under the curve (AUC) for three‐year survival was 0.714. Compared with clinical risk factors, including age, pathological stage, and TNM stage, our risk score had a higher prognostic value. Conclusion: By screening from a ceRNA network, we constructed a signature, including two mRNAs (HOXA10 and CBX2) and four lncRNAs (LINC00460, LINC00330, DGCR5, and C14orf132), that can be utilized as a prognostic biomarker in LUAD. This signature may provide options for clinical treatment.

scholarly journals A prognostic model for hepatocellular carcinoma based on apoptosis-related genes

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Renjie Liu ◽  
Guifu Wang ◽  
Chi Zhang ◽  
Dousheng Bai
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Model ◽  
Molecular Mechanisms ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Groups ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Risk Scores

Abstract Background Dysregulation of the balance between proliferation and apoptosis is the basis for human hepatocarcinogenesis. In many malignant tumors, such as hepatocellular carcinoma (HCC), there is a correlation between apoptotic dysregulation and poor prognosis. However, the prognostic values of apoptosis-related genes (ARGs) in HCC have not been elucidated. Methods To screen for differentially expressed ARGs, the expression levels of 161 ARGs from The Cancer Genome Atlas (TCGA) database (https://cancergenome.nih.gov/) were analyzed. Gene Ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to evaluate the underlying molecular mechanisms of differentially expressed ARGs in HCC. The prognostic values of ARGs were established using Cox regression, and subsequently, a prognostic risk model for scoring patients was developed. Kaplan–Meier (K-M) and receiver operating characteristic (ROC) curves were plotted to determine the prognostic value of the model. Results Compared with normal tissues, 43 highly upregulated and 8 downregulated ARGs in HCC tissues were screened. GO analysis results revealed that these 51 genes are indeed related to the apoptosis function. KEGG analysis revealed that these 51 genes were correlated with MAPK, P53, TNF, and PI3K-AKT signaling pathways, while Cox regression revealed that 5 ARGs (PPP2R5B, SQSTM1, TOP2A, BMF, and LGALS3) were associated with prognosis and were, therefore, obtained to develop the prognostic model. Based on the median risk scores, patients were categorized into high-risk and low-risk groups. Patients in the low-risk groups exhibited significantly elevated 2-year or 5-year survival probabilities (p < 0.0001). The risk model had a better clinical potency than the other clinical characteristics, with the area under the ROC curve (AUC = 0.741). The prognosis of HCC patients was established from a plotted nomogram. Conclusion Based on the differential expression of ARGs, we established a novel risk model for predicting HCC prognosis. This model can also be used to inform the individualized treatment of HCC patients.

scholarly journals A prognostic model based on seven immune-related genes predicts the overall survival of patients with hepatocellular carcinoma

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Qian Yan ◽  
Wenjiang Zheng ◽  
Boqing Wang ◽  
Baoqian Ye ◽  
Huiyan Luo ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Risk Score ◽  
Immune Cells ◽  
Prognostic Model ◽  
Risk Groups ◽  
Low Risk ◽  
Prognostic Performance ◽  
Immune Related Genes

Abstract Background Hepatocellular carcinoma (HCC) is a disease with a high incidence and a poor prognosis. Growing amounts of evidence have shown that the immune system plays a critical role in the biological processes of HCC such as progression, recurrence, and metastasis, and some have discussed using it as a weapon against a variety of cancers. However, the impact of immune-related genes (IRGs) on the prognosis of HCC remains unclear. Methods Based on The Cancer Gene Atlas (TCGA) and Immunology Database and Analysis Portal (ImmPort) datasets, we integrated the ribonucleic acid (RNA) sequencing profiles of 424 HCC patients with IRGs to calculate immune-related differentially expressed genes (DEGs). Survival analysis was used to establish a prognostic model of survival- and immune-related DEGs. Based on genomic and clinicopathological data, we constructed a nomogram to predict the prognosis of HCC patients. Gene set enrichment analysis further clarified the signalling pathways of the high-risk and low-risk groups constructed based on the IRGs in HCC. Next, we evaluated the correlation between the risk score and the infiltration of immune cells, and finally, we validated the prognostic performance of this model in the GSE14520 dataset. Results A total of 100 immune-related DEGs were significantly associated with the clinical outcomes of patients with HCC. We performed univariate and multivariate least absolute shrinkage and selection operator (Lasso) regression analyses on these genes to construct a prognostic model of seven IRGs (Fatty Acid Binding Protein 6 (FABP6), Microtubule-Associated Protein Tau (MAPT), Baculoviral IAP Repeat Containing 5 (BIRC5), Plexin-A1 (PLXNA1), Secreted Phosphoprotein 1 (SPP1), Stanniocalcin 2 (STC2) and Chondroitin Sulfate Proteoglycan 5 (CSPG5)), which showed better prognostic performance than the tumour/node/metastasis (TNM) staging system. Moreover, we constructed a regulatory network related to transcription factors (TFs) that further unravelled the regulatory mechanisms of these genes. According to the median value of the risk score, the entire TCGA cohort was divided into high-risk and low-risk groups, and the low-risk group had a better overall survival (OS) rate. To predict the OS rate of HCC, we established a gene- and clinical factor-related nomogram. The receiver operating characteristic (ROC) curve, concordance index (C-index) and calibration curve showed that this model had moderate accuracy. The correlation analysis between the risk score and the infiltration of six common types of immune cells showed that the model could reflect the state of the immune microenvironment in HCC tumours. Conclusion Our IRG prognostic model was shown to have value in the monitoring, treatment, and prognostic assessment of HCC patients and could be used as a survival prediction tool in the near future.

scholarly journals Prognostic Signature for Lung Adenocarcinoma Patients Based on Cell-Cycle-Related Genes

2021 ◽  
Vol 9 ◽  
Author(s):  
Wei Jiang ◽  
Jiameng Xu ◽  
Zirui Liao ◽  
Guangbin Li ◽  
Chengpeng Zhang ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Training Group ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
External Test

ObjectiveTo screen lung adenocarcinoma (LUAC)-specific cell-cycle-related genes (CCRGs) and develop a prognostic signature for patients with LUAC.MethodsThe GSE68465, GSE42127, and GSE30219 data sets were downloaded from the GEO database. Single-sample gene set enrichment analysis was used to calculate the cell cycle enrichment of each sample in GSE68465 to identify CCRGs in LUAC. The differential CCRGs compared with LUAC data from The Cancer Genome Atlas were determined. The genetic data from GSE68465 were divided into an internal training group and a test group at a ratio of 1:1, and GSE42127 and GSE30219 were defined as external test groups. In addition, we combined LASSO (least absolute shrinkage and selection operator) and Cox regression analysis with the clinical information of the internal training group to construct a CCRG risk scoring model. Samples were divided into high- and low-risk groups according to the resulting risk values, and internal and external test sets were used to prove the validity of the signature. A nomogram evaluation model was used to predict prognosis. The CPTAC and HPA databases were chosen to verify the protein expression of CCRGs.ResultsWe identified 10 LUAC-specific CCRGs (PKMYT1, ETF1, ECT2, BUB1B, RECQL4, TFRC, COCH, TUBB2B, PITX1, and CDC6) and constructed a model using the internal training group. Based on this model, LUAC patients were divided into high- and low-risk groups for further validation. Time-dependent receiver operating characteristic and Cox regression analyses suggested that the signature could precisely predict the prognosis of LUAC patients. Results obtained with CPTAC, HPA, and IHC supported significant dysregulation of these CCRGs in LUAC tissues.ConclusionThis prognostic prediction signature based on CCRGs could help to evaluate the prognosis of LUAC patients. The 10 LUAC-specific CCRGs could be used as prognostic markers of LUAC.

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