scholarly journals Identification of HCC Subtypes With Different Prognosis and Metabolic Patterns Based on Mitophagy

2021 ◽  
Vol 9 ◽  
Author(s):  
Yao Wang ◽  
Zhen Wang ◽  
Jingjing Sun ◽  
Yeben Qian
Keyword(s):  
Roc Curve ◽  
Cox Regression ◽  
Cholesterol Biosynthesis ◽  
Principal Component ◽  
Risk Groups ◽  
Prognostic Indicator ◽  
R Package ◽  
Low Risk ◽  
Rank Test ◽  
Risk Patients

Background: Mitophagy is correlated with tumor initiation and development of malignancy. However, HCC heterogeneity with reference to mitophagy has yet not been systematically explored.Materials and Methods: Mitophagy-related, glycolysis-related, and cholesterol biosynthesis-related gene sets were obtained from the Reactome database. Mitophagy-related and metabolism-related subtypes were identified using the ConsensusClusterPlus algorithm. Univariate Cox regression was analysis was performed to identify prognosis-related mitophagy regulators. Principal component analysis (PCA) was used to create composite measures of the prognosis-related mitophagy regulators (mitophagyscore). Individuals with a mitophagyscore higher or lower than the median value were classified in high- or low-risk groups. Kaplan-Meier survival and ROC curve analyses were utilized to evaluate the prognostic value of the mitophagyscore. The nomogram and calibration curves were plotted using the“rms” R package. The package “limma” was used for differential gene expression analysis. Differentially expressed genes (DEGs) between high- and low-risk groups were used as queries in the CMap database. R package “pRRophetic” and Genomics of Drug Sensitivity in Cancer (GDSC) database were used to determine the sensitivity of 21 previously reported anti-HCC drugs.Results: Three distinct HCC subtypes with different mitophagic accumulation (low, high, and intermediate mitophagy subtypes) were identified. High mitophagy subtype had the worst outcome and highest glycolysis level. The lowest degree of hypoxia and highest cholesterol biosynthesis was observed in the low mitophagy subtype; oncogenic dedifferentiation level in the intermediate mitophagy subtype was the lowest. Mitophagyscore could serve as a novel prognostic indicator for HCC.High-risk patients had a poorer prognosis (log-rank test, p < 0.001). The area under the ROC curve for mitophagyscore in 1-year survival was 0.77 in the TCGA cohort and 0.75 in the ICGC cohort. Nine candidate small molecules which were potential drugs for HCC treatment were identified from the CMap database. A decline in the sensitivity towards 21 anti-HCC drugs was observed in low-risk patients by GDSC database. We also identified a novel key gene, SPP1, which was highly associated with different mitophagic subtypes.Conclusion: Based on bioinformatic analyses, we systematically examined the HCC heterogeneity with reference to mitophagy and observed three distinct HCC subtypes having different prognoses and metabolic patterns.

scholarly journals The Landscape of Glycosyltransferase Gene Signature for Overall Survival Prediction in Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Qiang Cai ◽  
Shizhe Yu ◽  
Jian Zhao ◽  
Duo Ma ◽  
Long Jiang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Risk Score ◽  
Prognostic Value ◽  
Cox Regression ◽  
Risk Groups ◽  
Gene Signature ◽  
Low Risk ◽  
Regression Analyses ◽  
Risk Patients

Abstract Background: Hepatocellular carcinoma (HCC) is heterogeneous disease occurring in the background of chronic liver diseases. The role of glycosyltransferase (GT) genes have recently been the focus of research associating with the development of tumors. However, the prognostic value of GT genes in HCC remains not elucidated. This study aimed to demonstrate the GT genes related to the prognosis of HCC through bioinformatics analysis.Methods: The GT genes signatures were identified from the training set of The Cancer Genome Atlas (TCGA) dataset using univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression analyses. Then, we analyzed the prognostic value of GT genes signatures related to the overall survival (OS) of HCC patients. A prognostic model was constructed, and the risk score of each patient was calculated as formula, which divided HCC patients into high- and low-risk groups. Kaplan-Meier (K-M) and Receiver operating characteristic (ROC) curves were used to assess the OS of HCC patients. The prognostic value of GT genes signatures was further investigated in the validation set of TCGA database. Univariate and multivariate Cox regression analyses were performed to demonstrate the independent factors on OS. Finally, we utilized the gene set enrichment analysis (GSEA) to annotate the function of these genes between the two risk categories. Results: In this study, we identified and validated 4 GT genes as the prognostic signatures. The K-M analysis showed that the survival rate of the high-risk patients was significantly lower than that of the low-risk patients. The risk score calculated with 4 gene signatures could predict OS for 3-, 5-, and 7-year in patients with HCC, revealing the prognostic ability of these gene signature. In addition, Multivariate Cox regression analyses indicated that the risk score was an independent prognostic factor for HCC. Functional analysis further revealed that immune-related pathways were enriched, and immune status in HCC were different between the two risk groups.Conclusion: In conclusion, a novel GT genes signature can be used for prognostic prediction in HCC. Thus, targeting GT genes may be a therapeutic alternative for HCC.

A new look at the IDEA classification: Defining novel predictive and prognostic markers in stage III colon cancer.

2018 ◽  
Vol 36 (4_suppl) ◽  
pp. 845-845
Author(s):  
Ofer Margalit ◽  
Ronac Mamtani ◽  
Yu-Xiao Yang ◽  
Kim Anna Reiss ◽  
Talia Golan ◽  
...  
Keyword(s):  
Colon Cancer ◽  
High Risk ◽  
Cox Regression ◽  
Secondary Analysis ◽  
Risk Groups ◽  
Low Risk ◽  
Stage Iii ◽  
Stage Iii Colon Cancer ◽  
Risk Patients ◽  
Doublet Chemotherapy

845 Background: The IDEA pooled analysis compared 3 to 6 months of adjuvant chemotherapy for newly defined low- and high-risk stage III colon cancer patients, suggesting low-risk patients may be offered only 3 months of treatment. We aimed to evaluate the benefit of monotherapy vs doublet chemotherapy in low and high IDEA risk groups. Methods: Using the NCDB (2004-2014) we identified 56,728 and 47,557 individuals as low and high IDEA risk groups, respectively. We used multivariate COX regression to evaluate the magnitude of survival differences between IDEA risk groups, according to treatment intensity (doublet vs monotherapy). In a secondary analysis, we examined the predictive value of subgroups of age. Results: Low and high IDEA risk groups derived similar benefit from doublet chemotherapy compared to monotherapy, with hazard ratios of 0.83 (95%CI 0.79-0.86) and 0.80 (95%CI 0.78-0.83), respectively. The only subpopulations that did not benefit from doublet chemotherapy were low-risk patients above the age of 72 (HR = 0.95, 95%CI 0.90-1.01) and high-risk patients above the age of 85 (HR = 0.90, 95%CI 0.77-1.05). Conclusions: IDEA risk classification does not predict benefit from doublet chemotherapy in stage III colon cancer. However, omission of oxaliplatin can be considered in IDEA low-risk patients above the age of 72.

scholarly journals Long-term colorectal cancer incidence after adenoma removal and the effects of surveillance on incidence: a multicentre, retrospective, cohort study

Gut ◽  
2020 ◽  
Vol 69 (9) ◽  
pp. 1645-1658 ◽  
Author(s):  
Amanda J Cross ◽  
Emma C Robbins ◽  
Kevin Pack ◽  
Iain Stenson ◽  
Paula L Kirby ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
High Risk ◽  
General Population ◽  
Cox Regression ◽  
Risk Groups ◽  
Low Risk ◽  
Intermediate Risk ◽  
Hazard Ratios ◽  
Risk Patients ◽  
Time Varying Covariates

ObjectivePostpolypectomy colonoscopy surveillance aims to prevent colorectal cancer (CRC). The 2002 UK surveillance guidelines define low-risk, intermediate-risk and high-risk groups, recommending different strategies for each. Evidence supporting the guidelines is limited. We examined CRC incidence and effects of surveillance on incidence among each risk group.DesignRetrospective study of 33 011 patients who underwent colonoscopy with adenoma removal at 17 UK hospitals, mostly (87%) from 2000 to 2010. Patients were followed up through 2016. Cox regression with time-varying covariates was used to estimate effects of surveillance on CRC incidence adjusted for patient, procedural and polyp characteristics. Standardised incidence ratios (SIRs) compared incidence with that in the general population.ResultsAfter exclusions, 28 972 patients were available for analysis; 14 401 (50%) were classed as low-risk, 11 852 (41%) as intermediate-risk and 2719 (9%) as high-risk. Median follow-up was 9.3 years. In the low-risk, intermediate-risk and high-risk groups, CRC incidence per 100 000 person-years was 140 (95% CI 122 to 162), 221 (195 to 251) and 366 (295 to 453), respectively. CRC incidence was 40%–50% lower with a single surveillance visit than with none: hazard ratios (HRs) were 0.56 (95% CI 0.39 to 0.80), 0.59 (0.43 to 0.81) and 0.49 (0.29 to 0.82) in the low-risk, intermediate-risk and high-risk groups, respectively. Compared with the general population, CRC incidence without surveillance was similar among low-risk (SIR 0.86, 95% CI 0.73 to 1.02) and intermediate-risk (1.16, 0.97 to 1.37) patients, but higher among high-risk patients (1.91, 1.39 to 2.56).ConclusionPostpolypectomy surveillance reduces CRC risk. However, even without surveillance, CRC risk in some low-risk and intermediate-risk patients is no higher than in the general population. These patients could be managed by screening rather than surveillance.

scholarly journals Radiomic Score as a Potential Imaging Biomarker for Predicting Survival in Patients With Cervical Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Handong Li ◽  
Miaochen Zhu ◽  
Lian Jian ◽  
Feng Bi ◽  
Xiaoye Zhang ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cox Regression ◽  
Pearson Correlation ◽  
Risk Groups ◽  
Figo Stage ◽  
Low Risk ◽  
Youden Index ◽  
Gray Level ◽  
Treatment Standard ◽  
Risk Patients

ObjectivesAccurate prediction of prognosis will help adjust or optimize the treatment of cervical cancer and benefit the patients. We aimed to investigate the incremental value of radiomics when added to the FIGO stage in predicting overall survival (OS) in patients with cervical cancer.MethodsThis retrospective study included 106 patients with cervical cancer (FIGO stage IB1–IVa) between October 2017 and May 2019. Patients were randomly divided into a training cohort (n = 74) and validation cohort (n = 32). All patients underwent contrast-enhanced computed tomography (CT) prior to treatment. The ITK-SNAP software was used to delineate the region of interest on pre-treatment standard-of-care CT scans. We extracted 792 two-dimensional radiomic features by the Analysis Kit (AK) software. Pearson correlation coefficient analysis and Relief were used to detect the most discriminatory features. The radiomic signature (i.e., Radscore) was constructed via Adaboost with Leave-one-out cross-validation. Prognostic models were built by Cox regression model using Akaike information criterion (AIC) as the stopping rule. A nomogram was established to individually predict the OS of patients. Patients were then stratified into high- and low-risk groups according to the Youden index. Kaplan–Meier curves were used to compare the survival difference between the high- and low-risk groups.ResultsSix textural features were identified, including one gray-level co-occurrence matrix feature and five gray-level run-length matrix features. Only the FIGO stage and Radscore were independent risk factors associated with OS (p < 0.05). The C-index of the FIGO stage in the training and validation cohorts was 0.703 (95% CI: 0.572–0.834) and 0.700 (95% CI: 0.526–0.874), respectively. Correspondingly, the C-index of Radscore was 0.794 (95% CI: 0.707–0.880) and 0.754 (95% CI: 0.623–0.885). The incorporation of the FIGO stage and Radscore achieved better performance, with a C-index of 0.830 (95% CI: 0.738–0.922) and 0.772 (95% CI: 0.615–0.929), respectively. The nomogram based on the FIGO stage and Radscore could individually predict the OS probability with good discrimination and calibration. The high-risk patients had shorter OS compared with the low-risk patients (p < 0.05).ConclusionRadiomics has the potential for noninvasive risk stratification and may improve the prediction of OS in patients with cervical cancer when added to the FIGO stage.

scholarly journals Predicting the difference in treatment response and survival time of lung adenocarcinoma patients based on a prognostic risk model of glycolysis-related genes

2021 ◽  
Author(s):  
Rongchang Zhao ◽  
Dan Ding ◽  
Yan Ding ◽  
Rongbo Han ◽  
Xiujuan Wang ◽  
...  
Keyword(s):  
High Risk ◽  
Lung Adenocarcinoma ◽  
Survival Time ◽  
Risk Score ◽  
Roc Curve ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Significant Difference

Abstract Background Multiple factors affect the survival time of patients with lung adenocarcinoma (LUAD). Specifically, the therapeutic effect of medicines and the disease recurrence probability differs among patients with the same stage of LUAD. Thus, effective prognostic predictors need to be identified. Methods Based on the tumor mutation burden (TMB) data obtained by TCGA, LUAD was divided into high and low groups, and the differentially expressed glycolysis-related genes between the two groups were screened out. Cox regression was used to obtain a prognostic model. A receiver operating characteristic (ROC) curve and calibration curve were generated to evaluate the nomogram that was constructed based on clinicopathological characteristics and the risk score. Two datasets (GSE68465 and GSE11969) from Gene Expression Omnibus (GEO) were used to verify the prognostic performance of the gene. Furthermore, differences in immune cell distribution, immune-related molecules and drug susceptibility were assessed for their relationship with the risk score. Results We confirmed a 5-gene signature (FKBP4, HMMR, B4GALT1, ERO1L, ENO1) capable of dividing patients into two risk groups. There was a significant difference in overall survival (OS) times between the high-risk group and the low-risk group (P = 1.085e-4), with the low-risk group having a better survival outcome. Through multivariate Cox analysis, the risk score was confirmed to be an independent prognostic factor (HR = 1.289, 95% CI = 1.202-1.383, P < 0.001), and the ROC curve and nomogram exhibited accurate prediction performance. Validation of the data obtained in the GEO database yielded similar results. Additionally, there were significant differences in cisplatin, paclitaxel, gemcitabine, docetaxel, gefitiniband erlotinib sensitivity between the low-risk and high-risk groups. Conclusions Our results reveal that glycolysis-related gene are feasible predictors of LUAD patient survival and response to therapeutics.

scholarly journals A prognostic model based on immune-related long noncoding RNAs for patients with epithelial ovarian cancer

2022 ◽  
Vol 15 (1) ◽  
Author(s):  
Yao Peng ◽  
Hui Wang ◽  
Qi Huang ◽  
Jingjing Wu ◽  
Mingjun Zhang
Keyword(s):  
High Risk ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Principal Component ◽  
Risk Groups ◽  
Low Risk ◽  
Training Set ◽  
Cox Regression Analysis ◽  
Validation Set

Abstract Background Long noncoding RNAs (lncRNAs) are important regulators of gene expression and can affect a variety of physiological processes. Recent studies have shown that immune-related lncRNAs play an important role in the tumour immune microenvironment and may have potential application value in the treatment and prognosis prediction of tumour patients. Epithelial ovarian cancer (EOC) is characterized by a high incidence and poor prognosis. However, there are few studies on immune-related lncRNAs in EOC. In this study, we focused on immune-related lncRNAs associated with survival in EOC. Methods We downloaded mRNA data for EOC patients from The Cancer Genome Atlas (TCGA) database and mRNA data for normal ovarian tissue from the Genotype-Tissue Expression (GTEx) database and identified differentially expressed genes through differential expression analysis. Immune-related lncRNAs were obtained through intersection and coexpression analysis of differential genes and immune-related genes from the Immunology Database and Analysis Portal (ImmPort). Samples in the TCGA EOC cohort were randomly divided into a training set, validation set and combination set. In the training set, Cox regression analysis and LASSO regression were performed to construct an immune-related lncRNA signature. Kaplan–Meier survival analysis, time-dependent ROC curve analysis, Cox regression analysis and principal component analysis were performed for verification in the training set, validation set and combination set. Further studies of pathways and immune cell infiltration were conducted through Gene Set Enrichment Analysis (GSEA) and the Timer data portal. Results An immune-related lncRNA signature was identified in EOC, which was composed of six immune-related lncRNAs (KRT7-AS, USP30-AS1, AC011445.1, AP005205.2, DNM3OS and AC027348.1). The signature was used to divide patients into high-risk and low-risk groups. The overall survival of the high-risk group was lower than that of the low-risk group and was verified to be robust in both the validation set and the combination set. The signature was confirmed to be an independent prognostic biomarker. Principal component analysis showed the different distribution patterns of high-risk and low-risk groups. This signature may be related to immune cell infiltration (mainly macrophages) and differential expression of immune checkpoint-related molecules (PD-1, PDL1, etc.). Conclusions We identified and established a prognostic signature of immune-related lncRNAs in EOC, which will be of great value in predicting the prognosis of clinical patients and may provide a new perspective for immunological research and individualized treatment in EOC.

scholarly journals A Prognostic Model Based on Immune-Related Long Non-Coding RNAs for Patients With Epithelial Ovarian Cancer

2021 ◽  
Author(s):  
Yao Peng ◽  
Hui Wang ◽  
Qi Huang ◽  
Jingjing Wu ◽  
Mingjun Zhang
Keyword(s):  
High Risk ◽  
Immune Cell ◽  
Cox Regression ◽  
Principal Component ◽  
Risk Groups ◽  
Low Risk ◽  
Training Set ◽  
Cox Regression Analysis ◽  
Validation Set ◽  
Differential Genes

Abstract Background: Long non-coding RNA (lncRNA), as an important regulator of gene expression, can affect a variety of physiological processes. Recent studies have shown that immune-related lncRNA play an important role in the tumor immune microenvironment and may have potential application value in the treatment and prognosis prediction of tumor patients. Epithelial ovarian cancer (EOC) is characterized by high incidence and poor prognosis. However, there are few studies on immune-related lncRNAs in EOC. In this study, we focused on the immune-related lncRNAs associated with survival in EOC. Methods: We downloaded mRNA data for EOC patients from The Cancer Genome Atlas (TCGA) database, and mRNA data for normal ovarian tissue from the Genotype-Tissue Expression (GTEx) database, and identified differential genes through differential Expression analysis. Immune-related lncRNAs were obtained through taking intersection and co-expression analysis of differential genes and immune-related genes from the Immunology Database and Analysis Portal (ImmPort). Samples in the TCGA EOC cohort were randomly divided into training set, validation set and combination set. In the training set, Cox regression analysis and LASSO regression were used to construct an immune-related lncRNA signature. Kaplan-Meier survival analysis, time-dependent ROC curve analysis, Cox regression analysis and principal component analysis were applied to verification in the training set, training set, validation set and combination set. Further studies of pathways and immune cell infiltration were conducted through Gene Set Enrichment Analysis (GESA) and the Timer data portal.Results: An immune-related lncRNA signature was identified in EOC, which was composed of six immune-related lncRNAs (KRT7-AS, USP30-AS1, AC011445.1, AP005205.2, DNM3OS and AC027348.1). The signature divided patients into high-risk and low-risk groups. The overall survival of the high-risk group was lower than that of the low-risk group, and was verified to be robust in both the training set and the combination set. This signature was identified as an independent prognostic biomarker. The signature was confirmed to be an independent prognostic biomarker. Principal component analysis showed the different distribution patterns of high-risk and low-risk groups. This signature may be related to immune cell infiltration (mainly macrophages) and differential expression of immune checkpoint-related molecules (PD-1, PDL1, etc.). Conclusions: we identified and established a prognostic signature of immune-related lncRNA in EOC, which is of great value in predicting the prognosis of clinical patients and may provide a new perspective for the immunological research and individualized treatment in EOC.

scholarly journals Biomarker potential of lncRNA GNAS-AS1 in osteosarcoma prognosis and effect on cellular function

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Zhanhu Mi ◽  
Yanyun Dong ◽  
Zhibiao Wang ◽  
Peng Ye
Keyword(s):  
Cell Proliferation ◽  
Cell Function ◽  
Cox Regression ◽  
Bone Cancer ◽  
Prognostic Indicator ◽  
Migration And Invasion ◽  
Rank Test ◽  
Chi Square ◽  
Invasion And Migration ◽  
Novel Therapeutic Strategies

Abstract Background Osteosarcoma (OS) is a type of bone cancer that occurs in children and adolescents at a rate of 5%. The purpose of this study is to explore the lncRNA GNAS-AS1 expression profile, prognosis significance in OS, and biological effect on OS cell function. Methods One hundred eight pairs of tissues were collected, and OS cell lines were purchased. lncRNA GNAS-AS1 expression in these tissues and cells were analyzed by qRT-PCR. Clinical data were analyzed using chi-square tests, Kaplan-Meier curves (log-rank test), and Cox regression. CCK-8 and transwell assay were conducted to analyze the effect of lncRNA GNAS-AS1 on cell proliferation, invasion, and migration. The downstream miRNA was presumed. Results The expression of lncRNA GNAS-AS1 was significantly increased in OS cells and tissues, and related to Enneking staging and distant metastasis. Patients with high lncRNA GNAS-AS1 expression represented shorter overall survival and was an independent prognostic predictor of OS. LncRNA GNAS-AS1 knockdown inhibited cell proliferation, migration, and invasion by regulated miR-490-3p partly at least. Conclusions LncRNA GNAS-AS1 can be used as a prognostic indicator and its inhibition suppress the development of OS, suggesting its value as novel therapeutic strategies in OS.

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.

scholarly journals Development and Verification of the Hypoxia- and Immune-Associated Prognostic Signature for Pancreatic Ductal Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Dongjie Chen ◽  
Hui Huang ◽  
Longjun Zang ◽  
Wenzhe Gao ◽  
Hongwei Zhu ◽  
...  
Keyword(s):  
High Risk ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Pearson Correlation ◽  
Risk Groups ◽  
Low Risk ◽  
Ductal Adenocarcinoma ◽  
Prognostic Signature ◽  
Score Model

We aim to construct a hypoxia- and immune-associated risk score model to predict the prognosis of patients with pancreatic ductal adenocarcinoma (PDAC). By unsupervised consensus clustering algorithms, we generate two different hypoxia clusters. Then, we screened out 682 hypoxia-associated and 528 immune-associated PDAC differentially expressed genes (DEGs) of PDAC using Pearson correlation analysis based on the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression project (GTEx) dataset. Seven hypoxia and immune-associated signature genes (S100A16, PPP3CA, SEMA3C, PLAU, IL18, GDF11, and NR0B1) were identified to construct a risk score model using the Univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, which stratified patients into high- and low-risk groups and were further validated in the GEO and ICGC cohort. Patients in the low-risk group showed superior overall survival (OS) to their high-risk counterparts (p &lt; 0.05). Moreover, it was suggested by multivariate Cox regression that our constructed hypoxia-associated and immune-associated prognosis signature might be used as the independent factor for prognosis prediction (p &lt; 0.001). By CIBERSORT and ESTIMATE algorithms, we discovered that patients in high-risk groups had lower immune score, stromal score, and immune checkpoint expression such as PD-L1, and different immunocyte infiltration states compared with those low-risk patients. The mutation spectrum also differs between high- and low-risk groups. To sum up, our hypoxia- and immune-associated prognostic signature can be used as an approach to stratify the risk of PDAC.

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