scholarly journals Development and Validation of a Novel Metabolic-Related Signature Predicting Overall Survival for Pancreatic Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Junyu Huo ◽  
Liqun Wu ◽  
Yunjin Zang
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Lymph Node ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
External Validation ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
Cox Regression Analysis

Recently, growing evidence has revealed the significant effect of reprogrammed metabolism on pancreatic cancer in relation to carcinogenesis, progression, and treatment. However, the prognostic value of metabolism-related genes in pancreatic cancer has not been fully revealed. We identified 379 differentially expressed metabolic-related genes (DEMRGs) by comparing 178 pancreatic cancer tissues with 171 normal pancreatic tissues in The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression project (GTEx) databases. Then, we used univariate Cox regression analysis together with Lasso regression for constructing a prognostic model consisting of 15 metabolic genes. The unified risk score formula and cutoff value were taken into account to divide patients into two groups: high risk and low risk, with the former exhibiting a worse prognosis compared with the latter. The external validation results of the International Cancer Genome Consortium (IGCC) cohort and the Gene Expression Omnibus (GEO) cohort further confirm the effectiveness of this prognostic model. As shown in the receiver operating characteristic (ROC) curve, the area under curve (AUC) values of the risk score for overall survival (OS), disease-specific survival (DSS), and progression-free survival (PFS) were 0.871, 0.885, and 0.886, respectively. Based on the Gene Set Enrichment Analysis (GSEA), the 15-gene signature can affect some important biological processes and pathways of pancreatic cancer. In addition, the prognostic model was significantly correlated with the tumor immune microenvironment (immune cell infiltration, and immune checkpoint expression, etc.) and clinicopathological features (pathological stage, lymph node, and metastasis, etc.). We also built a nomogram based on three independent prognostic predictors (including individual neoplasm status, lymph node metastasis, and risk score) for the prediction of 1-, 3-, and 5-year OS of pancreatic cancer, which may help to further improve the treatment strategy of pancreatic cancer.

scholarly journals Five gene signatures were identified in the prediction of overall survival in resectable pancreatic cancer

BMC Surgery ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Chao Wu ◽  
Zuowei Wu ◽  
Bole Tian
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Regression Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Resectable Pancreatic Cancer ◽  
Cox Regression Analysis ◽  
Validation Set

Abstract Background Although genes have been previously detected in pancreatic cancer (PC), aberrant genes that play roles in resectable pancreatic cancer should be further assessed. Methods Messenger RNA samples and clinicopathological data corrected with PC were downloaded from The Cancer Genome Atlas (TCGA). Resectable PC patients were randomly divided into a primary set and a validation set. Univariable Cox regression analysis, lasso-penalized Cox regression analysis, and multivariable Cox analysis were implemented to distinguish survival-related genes (SRGs). A risk score based on the SRGs was calculated by univariable Cox regression analysis. A genomic-clinical nomogram was established by integrating the risk score and clinicopathological data to predict overall survival (OS) in resectable PC. Results Five survival-related genes (AADAC, DEF8, HIST1H1C, MET, and CHFR) were significantly correlated with OS in resectable PC. The resectable PC patients, based on risk score, were sorted into a high-risk group that showed considerably unfavorable OS (p < 0.001) than the low-risk group, in both the primary set and the validation set. The concordance index (C-index) was calculated to evaluate the predictive performance of the nomogram were respectively in the primary set [0.696 (0.608–0.784)] and the validation set [0.682 (0.606–0.758)]. Additionally, gene set enrichment Analysis discovered several meaningful enriched pathways. Conclusion Our study identified five prognostic gene biomarkers for OS prediction and which facilitate postoperative molecular target therapy for the resectable PC, especially the nomic-clinical nomogram which may be used as an effective model for the postoperative OS evaluation and also an optimal therapeutic tool for the resectable PC.

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 Identification and Validation of a Prognostic Model Based on Three Autophagy-Related Genes in Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Fanbo Qin ◽  
Junyong Zhang ◽  
Jianping Gong ◽  
Wenfeng Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Risk Score ◽  
Prognostic Value ◽  
Prognostic Model ◽  
Cox Regression ◽  
Cancer Genome ◽  
Clinical Parameters ◽  
Cox Regression Analysis ◽  
Model Based

Background. Accumulating studies have demonstrated that autophagy plays an important role in hepatocellular carcinoma (HCC). We aimed to construct a prognostic model based on autophagy-related genes (ARGs) to predict the survival of HCC patients. Methods. Differentially expressed ARGs were identified based on the expression data from The Cancer Genome Atlas and ARGs of the Human Autophagy Database. Univariate Cox regression analysis was used to identify the prognosis-related ARGs. Multivariate Cox regression analysis was performed to construct the prognostic model. Receiver operating characteristic (ROC), Kaplan-Meier curve, and multivariate Cox regression analyses were performed to test the prognostic value of the model. The prognostic value of the model was further confirmed by an independent data cohort obtained from the International Cancer Genome Consortium (ICGC) database. Results. A total of 34 prognosis-related ARGs were selected from 62 differentially expressed ARGs identified in HCC compared with noncancer tissues. After analysis, a novel prognostic model based on ARGs (PRKCD, BIRC5, and ATIC) was constructed. The risk score divided patients into high- or low-risk groups, which had significantly different survival rates. Multivariate Cox analysis indicated that the risk score was an independent risk factor for survival of HCC after adjusting for other conventional clinical parameters. ROC analysis showed that the predictive value of this model was better than that of other conventional clinical parameters. Moreover, the prognostic value of the model was further confirmed in an independent cohort from ICGC patients. Conclusion. The prognosis-related ARGs could provide new perspectives on HCC, and the model should be helpful for predicting the prognosis of HCC patients.

scholarly journals Identification of DNA methylation-driven genes and construction of a nomogram to predict overall survival in pancreatic cancer

2021 ◽  
Author(s):  
Guochao Deng ◽  
De-cong Sun ◽  
Qian Zhou ◽  
Yao Lv ◽  
Huan Yan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Dna Methylation ◽  
Overall Survival ◽  
Risk Score ◽  
Cox Regression ◽  
Clinical Samples ◽  
Cox Regression Analysis ◽  
Incidence And Mortality ◽  
Score Model

Abstract Background: The incidence and mortality of pancreatic cancer (PC) has gradually increased. The aim of this study was to identify survival-related DNA methylation (DNAm)-driven genes and establish a nomogram to predict outcomes in patients with PC.Methods: The gene expression, DNA methylation database, and PC clinical samples were downloaded from TCGA. DNAm-driven genes were identified by integrating analyses of gene expression and DNA methylation data. Survival-related DNAm-driven genes were screened via univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses to develop a risk score model for prognosis. Based on analyses of clinical parameters and risk score, a nomogram was built and validated. The independent cohort (GSE21501) was used for external validation.Results: A total of 16 differentially expressed methylation-driven genes were identified. Based on LASSO Cox regression and multivariate Cox regression analysis, six genes (FERMT1, LIPH, LAMA3, PPP1R14D, NQO1, VSIG2) were chosen to develop the risk score model. In the Kaplan–Meier analysis, age, T, N, stage, radiation therapy history, tumor size, surgery type performed, pathological type, chemotherapy history, and risk score were potential prognostic factors in PC (P < 0.1). In the multivariate analysis, stage, chemotherapy, and risk score were significantly correlated to overall survival (P < 0.05). The nomogram was constructed with the three variables (stage, chemotherapy, and risk score) for predicting the 1-, 2-, and 3-year survival rates of PC patients. The C-index of the nomogram model was 0.768. Nomogram performance was assessed by receiver operating characteristic (ROC) curves and calibration curves.Conclusion: In our study, we successfully identified the six DNAm-driven genes (FERMT1, LIPH, LAMA3, PPP1R14D, NQO1, VSIG2) with a relationship to the outcomes of PC patients. The nomogram including stage, chemotherapy, and risk score could be used to predict survival in PC patients.

scholarly journals ­ Characterization of the Immune Infiltration Landscape and Identification of Prognostic Biomarkers for Esophageal Cancer

2021 ◽  
Author(s):  
Yuanmei Chen ◽  
Xinyi Huang ◽  
Haiyan Peng ◽  
Guibin Weng ◽  
Zhengrong Huang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Esophageal Cancer ◽  
Prognostic Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Cancer Genome ◽  
Prognostic Models ◽  
Tumor Mutation Burden ◽  
Cox Regression Analysis ◽  
Mutation Burden

Abstract Background. Esophageal cancer (EC) is the 7th most common neoplasm and the 6th most common cause of cancer-related death worldwide. Immunotherapy is an effective treatment for EC patients. However, there are no dependable markers for predicting prognosis and immunotherapy responses in EC. Our study aims to explore prognostic models and markers in EC as well as predictors for immunotherapy. Methods. The expression profiles of EC were obtained from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and International Cancer Genome Consortium (ICGC) databases. Cox regression analysis was performed to construct a prognostic model. Overall survival and receiver operating characteristic curve analyses were applied to verify the accuracy of the model. The CIBERSORT algorithm was conducted to quantify the infiltration of different immune cells, and EC was grouped into three immune cell infiltration (ICI) clusters. PD-1 and PD-L1 expressions were compared between the ICI clusters. Overall survival analysis between ICI score and tumor mutation burden was conducted. The immunotherapy response of patients in different ICI score clusters was also compared. The copy number variations, somatic mutations, and single nucleotide polymorphisms were analyzed. Enrichment analyses were also performed. Results. A prognostic model was successfully constructed. Three ICI clusters were identified, and the clusters with high immune and stromal scores tended to have more immune-activated phenotypes and higher expressions of PD-1 and PDL1. The ICI score may be used as a predictor independent of tumor mutation burden. Patients with higher ICI score tended to have better immunotherapeutic responses than those with lower scores. Enrichment analyses showed that the differentially expressed genes were mostly enriched in microvillus and the KRAS and IL6/JAK/STAT3 pathways. The top eight genes with the highest mutation frequencies in EC were identified and all related to the prognosis of EC patients. Conclusions. Our study established an effective prognostic model and identified markers for predicting the prognosis and immunotherapy response of EC patients.

scholarly journals Identification and Validation of Angiogenesis-Related Gene Expression for Predicting Prognosis in Patients With Ovarian Cancer

2022 ◽  
Vol 11 ◽  
Author(s):  
Yue Wang ◽  
Bao Xuan Li ◽  
Xiang Li
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
Regression Analysis ◽  
Risk Score ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
Risk Group ◽  
Cancer Genome ◽  
Risk Scores ◽  
Cox Regression Analysis

Ovarian cancer (OC) is a highly heterogeneous disease with different cellular origins reported; thus, precise prognostic strategies and effective new therapies are urgently needed for patients with OC. A growing number of studies have shown that most malignancies have intensive angiogenesis and rapid growth. Therefore, angiogenesis plays an important role in the development of tumor metastasis. However, the prognostic value of angiogenesis-related genes (ARGs) in OC remains to be further elucidated. In this study, the expression data and corresponding clinical data from patients with OC and normal control samples were downloaded with UCSC XENA. A total of 1,960 differentially expressed ARGs were screened and functionally annotated through Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Univariate Cox regression analysis was performed to identify ARGs associated with prognosis. New ARGs signatures (including ESM1, CXCL13, TPCN2, PTPRD, FOXO1, and ELK3) were constructed for the prediction of overall survival (OS) in OC based on the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. Patients were divided based on their median risk score. In the The Cancer Genome Atlas (TCGA) training dataset, the survival analysis showed that overall survival was lower in the high-risk group than that in the low-risk group (p &lt; 0.0001). The International Cancer Genome Consortium (ICGC) database was used for validation, and the receiver operating characteristic (ROC) curves showed good performance. Univariate and multivariate Cox analyses were conducted to identify independent predictors of OS. The nomogram, including the risk score, age, stage, grade, and position, can not only show good predictive ability but also can explore the correlation analysis based on ARGs for immunogenicity, immune components, and immune phenotypes with risk score. Risk scores were correlated strongly with the type of immune infiltration. Furthermore, homologous recombination defect (HRD), NtAIscore, LOH score, LSTm score, stemness index (mRNAsi), and stromal cells were significantly correlated with risk score. The present study suggests that the novel signature constructed from six ARGs may serve as effective prognostic biomarkers for OC and contribute to clinical decision making and personalized prognostic monitoring of OC.

scholarly journals Immune prognostic implications of PSMD14 and its associated genes signatures in hepatocellular carcinoma

2020 ◽  
Author(s):  
Chuan Tian ◽  
Mubalake Abudoureyimu ◽  
Xinrong Lin ◽  
Hao Zhou ◽  
Xiaoyuan Chu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Clinicopathologic Characteristics ◽  
Data Set ◽  
Cox Regression Analysis ◽  
Prognostic Implications

Abstract Background PSMD14 played a vital roles initiation and progression of hepatocellular carcinoma (HCC). However, PSMD14 and its-related genes for the immune prognostic implications of HCC patients have rarely been analyzed. Therefore, we aimed to explore gene signatures and immune prognostic values of PSMD14 and its-related genes in HCC. Method Analyzed the expression of PSMD14 in multiple databases, and clinicopathologic characteristics associated with PSMD14 overall survival using Wilcoxon signed-ranktest, logistic and Cox regression, Kaplan-Meier method. An immune prognostic signature (including RBM45, PSMD1, OLA1, CCT6A, LCAT and IVD) was constructed and validated using the co-expression and cox regression analyses in TCGA, ICGC and TIMER datasets. Gene Set Enrichment Analysis (GSEA) was performed using TCGA data set. Results Increased PSMD14 expression in HCC was significantly associated with poor prognosis and clinicopathologic characteristics (grade, histologic stage, surgical approach and T stage, all p-values < 0.05). A total of six PSMD14-related genes were detected, which markedly related to overall survival and immune infiltrating levels in HCC patients. Using cox regression analysis, the PSMD14 and its-related genes were found to be an independent prognostic factor for HCC survival. Calibration curves confirmed good consistency between clinical nomogram prediction and actual observation. Immune prognostic model suggests that patients in the high‐risk group shown significantly poorer survival than patients in the low‐risk group. Conclusion We screened potential immune prognostic genes and constructed and verified a novel PSMD14-based prognostic model of HCC, which provides new potential prognostic biomarkers and therapeutic targets and lays a theoretical foundation for immunotherapy of HCC.

scholarly journals Integrated analysis of iron metabolic-related genes in hepatocellular carcinoma

2021 ◽  
Author(s):  
Li Wang ◽  
Jialin Qu ◽  
Man Jiang ◽  
Na Zhou ◽  
Zhixuan Ren ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Clinicopathological Parameters ◽  
Integrated Analysis ◽  
Consensus Clustering ◽  
Cox Regression Analysis

Abstract Background Iron is a nutrient essential for hemoglobin synthesis, DNA synthesis, and energy metabolism in all mammals. Iron metabolic involved in numerous types of cancers including hepatocellular cancer. In this study, we aim to identify prognostic model that based on iron metabolic-related genes that could effectively predict the prognosis for HCC patients. Methods The RNA microarray and clinical data of HCC patients that obtained from The Cancer Genome Atlas (TCGA) database. We identify the clusters of HCC patients with different clinical outcome performed by consensus clustering analysis. Four iron metabolic-related genes (FLVCR1, FTL, HIF1A, HMOX1) were screen for prognostic model by performed the Cox regression analysis. The efficacy of prognostic model was validated by the International Cancer Genome Consortium (ICGC) database. Meantime, the expressions value of FLVCR1, FTL, HIF1A, HMOX1 was performed using Oncomine database, the Human Protein Atlas and Kaplan Meier-plotter. Result The patients with low-risk score have better prognosis than high risk score both in TCGA cohort and ICGC cohort. The prognostic model showed well performance for predicting the prognosis of HCC patients than other clinicopathological parameters by OS-related ROC curves. Conclusion Our survival models that based on Iron metabolic can be independent risk factors for hepatocellular carcinoma patients.

scholarly journals Identification of DNA methylation-driven genes and construction of a nomogram to predict overall survival in pancreatic cancer

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
G.C Deng ◽  
D.C Sun ◽  
Q Zhou ◽  
Y Lv ◽  
H Yan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Dna Methylation ◽  
Overall Survival ◽  
Risk Score ◽  
Cox Regression ◽  
Clinical Samples ◽  
Cox Regression Analysis ◽  
Incidence And Mortality ◽  
Score Model

Abstract Background The incidence and mortality of pancreatic cancer (PC) has gradually increased. The aim of this study was to identify survival-related DNA methylation (DNAm)-driven genes and establish a nomogram to predict outcomes in patients with PC. Methods The gene expression, DNA methylation database, and PC clinical samples were downloaded from TCGA. DNAm-driven genes were identified by integrating analyses of gene expression and DNA methylation data. Survival-related DNAm-driven genes were screened via univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses to develop a risk score model for prognosis. Based on analyses of clinical parameters and risk score, a nomogram was built and validated. The independent cohort from GEO database were used for external validation. Results A total of 16 differentially expressed methylation-driven genes were identified. Based on LASSO Cox regression and multivariate Cox regression analysis, six genes (FERMT1, LIPH, LAMA3, PPP1R14D, NQO1, VSIG2) were chosen to develop the risk score model. In the Kaplan–Meier analysis, age, T stage, N stage, AJCC stage, radiation therapy history, tumor size, surgery type performed, pathological type, chemotherapy history, and risk score were potential prognostic factors in PC (P < 0.1). In the multivariate analysis, stage, chemotherapy, and risk score were significantly correlated to overall survival (P < 0.05). The nomogram was constructed with the three variables (stage, chemotherapy, and risk score) for predicting the 1-year, 2-year, and 3-year survival rates of PC patients. Nomogram performance was assessed by receiver operating characteristic (ROC) curves and calibration curves. 1-year, 2-year and 3-year AUC of nomogram model was 0.899, 0.765 and 0.776, respectively. Conclusions In our study, we successfully identified the six DNAm-driven genes (FERMT1, LIPH, LAMA3, PPP1R14D, NQO1, VSIG2) with a relationship to the outcomes of PC patients. The nomogram including stage, chemotherapy, and risk score could be used to predict survival in PC patients.

scholarly journals A signature of seven immune-related genes predicts overall survival in male gastric cancer patients

2020 ◽  
Author(s):  
Xin Xu ◽  
Youliang Wu ◽  
Mingliang Wang ◽  
Yida Lu ◽  
Xiaodong Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Overall Survival ◽  
Risk Score ◽  
Cox Regression ◽  
External Validation ◽  
Differentially Expressed ◽  
Survival Prediction ◽  
Cox Regression Analysis ◽  
Immune Related Genes

Abstract Background: Gastric cancer (GC) is one of the most fatal malignant tumors with a high mortality rate. Male sex has been proven as an independent risk factor for GC. This study aimed to identify immune-related genes (IRGs) for the prognosis of male GC.Method: RNA sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed IRGs between male GC and normal tissues were identified by integrated bioinformatics analysis. Univariate and multivariate Cox regression analysis were applied to screen survival-associated IRGs. Then, GC patients were separated into high- and low-risk groups based on the median risk score. Furthermore, a nomogram was constructed based on the TCGA dataset. The prognostic value of the risk signature model was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index and calibration curves. In addition, the gene expression dataset from Gene Expression Omnibus (GEO) was also downloaded for external validation. The relative proportions of 22 types of infiltrating immune cells in each male GC sample were evaluated using CIBERSORT.Results: A total of 276 differentially expressed IRGs were screened, including 189 up-regulated and 87 down-regulated genes. Subsequently, a seven-IRGs signature (LCN12, CCL21, RNASE2, CGB5, NRG4, AGTR1 and NPR3) was identified and showed a significant association with the overall survival (OS) of male GC patients. Survival analysis indicated that patients with high-risk group exhibited a poor clinical outcome. The result of multivariate analysis revealed that the risk score was an independent prognostic factor. The established nomogram could be used to evaluate the prognosis of individual male GC patients. Further analysis showed that the prognostic model had an excellent predictive performance in both TCGA and validated cohorts. Besides, the result of tumor-infiltrating immune cell analysis indicated that the seven-IRGs signature could reflect the status of the tumor immune microenvironment.Conclusions: Our study developed a novel seven-IRGs risk signature for individualized survival prediction of male GC patients.

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