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Prognostic nomogram of hypoxia-related genes predicting overall survival of colorectal cancer-Analysis of TCGA database.

ABSTRACT: Hypoxia-related gene (HRG) expression is associated with survival outcomes of colorectal cancer (CRC). Our aim was developing a nomogram predicting CRC overall survival (OS) with HRGs and clinicopathological factors. The Cancer Genome Atlas (TCGA) database was used as discovery cohort and two Gene Expression Omnibus databases (GSE39582 and GSE41258) served as validation cohorts. A genetic risk score model prognosticating OS was developed using mRNA expression level of HRGs. Nomogram predicting OS was developed using genetic risk score model and clinicopathological variables. The genetic risk score model included four HRGs (HSPA1L, PUM1, UBE2D2, and HSP27) and successfully prognosticated OS of discovery and two validation cohorts (p < 0.001 for TCGA discovery set, p < 0.003 for the GSE39582 and p = 0.042 for the GSE41258 datasets). Nomogram included genetic risk score, age, and TNM stage. Harrell's concordance indexes of the nomogram were higher than those of TNM stage alone in the discovery set (0.77 vs. 0.69, p < 0.001), GSE39582 (0.65 vs. 0.63, p < 0.001), and GSE41258 datasets (0.78 vs. 0.77, p < 0.001). Our nomogram successfully predicted OS of CRC patients. The mRNA expression level of the HRGs might be useful as an ancillary marker for prognosticating CRC outcome.

SUBMITTER: Lee JH

PROVIDER: S-EPMC6372658 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Prognostic nomogram of hypoxia-related genes predicting overall survival of colorectal cancer-Analysis of TCGA database.

Lee Joon-Hyop JH Jung Sohee S Park Won Seo WS Choe Eun Kyung EK Kim Eunyoung E Shin Rumi R Heo Seung Chul SC Lee Jae Hyun JH Kim Kwangsoo K Chai Young Jun YJ

Scientific reports 20190212 1

Hypoxia-related gene (HRG) expression is associated with survival outcomes of colorectal cancer (CRC). Our aim was developing a nomogram predicting CRC overall survival (OS) with HRGs and clinicopathological factors. The Cancer Genome Atlas (TCGA) database was used as discovery cohort and two Gene Expression Omnibus databases (GSE39582 and GSE41258) served as validation cohorts. A genetic risk score model prognosticating OS was developed using mRNA expression level of HRGs. Nomogram predicting O ...[more]

PMID: 30755640

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Project description:BackgroundAlthough the incidence of late-onset colorectal cancer (LOCRC) has decreased, the incidence of early-onset colorectal cancer (EOCRC) is still rising dramatically. Heterogeneity in the genomic, biological, and clinicopathological characteristics between EOCRC and LOCRC has been revealed. Therefore, the previous prognostic models based on the total CRC patient population might not be suitable for EOCRC patients. Here, we constructed a prognostic classifier to enhance the precision of individualized treatment and management of EOCRC patients.MethodsEOCRC expression data were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The regulatory pathways were explored by gene set enrichment analysis (GSEA). The prognostic model was developed by univariate Cox-LASSO-multivariate Cox regression analyses of GEO samples. TCGA samples were used to verify the model. The expression and mutation profiles and immune landscape of the high-risk and low-risk cohorts were analyzed and compared. Finally, the expression and prognostic value of the model genes were verified by immunohistochemistry and qRT‒PCR analysis.ResultsThe cell cycle was identified as the most significantly enriched oncological signature of EOCRC. Then, a 4-gene prognostic signature comprising MCM2, INHBA, CGREF1, and KLF9 was constructed. The risk score was an independent predictor of overall survival. The area under the curve values of the classifier for 1-, 3-, and 5-year survival were 0.856, 0.893, and 0.826, respectively, in the training set and 0.749, 0.858, and 0.865, respectively, in the validation set. Impaired DNA damage repair capability (p < 0.05) and frequent PIK3CA mutations (p < 0.05) were found in the high-risk cohort. CD8 T cells (p < 0.05), activated memory CD4 T cells (p < 0.01), and activated dendritic cells (p < 0.05) were clustered in the low-risk group. Finally, we verified the expression of MCM2, INHBA, CGREF1, and KLF9. Their prognostic value was closely related to age.ConclusionIn this study, a robust prognostic classifier for EOCRC was established and validated. The findings may provide a reference for individualized treatment and medical decision-making for patients with EOCRC.

Project description:BackgroundLung adenocarcinoma (LUAD) is the major cause of cancer mortality. Traditional prognostic factors have limited importance after including other parameters. Thus, developing a more credible prognostic model combined with genes and clinical parameters is necessary.MethodsThe messenger RNA (mRNA) expression and clinical information from The Cancer Genome Atlas (TCGA)-LUAD datasets and microarray data from three Gene Expression Omnibus (GEO) databases were obtained. We identified differentially-expressed genes (DEGs) between lung tumor and normal tissues through integrated analysis of the three GEO datasets. Univariate and multivariate Cox regression analyses were conducted to select survival-associated DEGs and to establish a prognostic gene signature which was associated with overall survival (OS). The expression of gene proteins was assessed in 180 LUAD tissue microarrays (TMAs) by immunohistochemistry (IHC). We verified its predictive performance with a Kaplan-Meier (KM) curve, receiver operating characteristic (ROC) curve, and Harrell's concordance index (C-index) and validated it in external GEO databases. Multivariate Cox regression analysis was performed to identify the significant prognostic indicators in LUAD. Furthermore, we established a prognostic nomogram based on TCGA-LUAD dataset.ResultsA three-gene signature was constructed to predict the OS of LUAD patients. The KM analysis, ROC curve, and C-index present a good predictive ability of the gene signature in TCGA dataset [P<0.0001; C-index 0.6375; 95% confidence interval (CI): 0.5632-0.7118; area under the ROC curve (AUC) 0.674] and the external GEO datasets (P=0.05, 0.004, and 0.04, respectively). Univariate and multivariate Cox regression analyses also verified that LUAD patients with low-risk scores had a decreased risk of death compared to those with a high-risk score in TCGA database [hazard ratio (HR) =0.3898; 95% CI: 0.1938-0.7842; P<0.05]. Finally, we constructed a nomogram integrating the gene signature and clinicopathological parameters (P<0.0001; C-index 0.762; 95% CI: 0.714-0.845; AUC 0.8136). Compared with conventional staging, a nomogram can effectively improve prognosis prediction.ConclusionsThe nomogram is closely associated to the OS of LUAD patients. This consequence may be beneficial to individualized treatment and clinical decision-making.

Project description:BackgroundMost colorectal cancer (CRC) patients are at an advanced stage when they are first diagnosed. Risk factors for predicting overall survival (OS) in advanced stage CRC patients are crucial, and constructing a prognostic nomogram model is a scientific method for survival analysis.MethodsA total of 2956 advanced stage CRC patients were randomised into training and validation groups at a 7:3 ratio. Univariate and multivariate Cox proportional hazards regression analyses were used to screen risk factors for OS and subsequently construct a prognostic nomogram model for predicting 1-, 3-, 5-, 8- and 10-year OS of advanced stage CRC patients. The performance of the model was demonstrated by the area under the curve (AUC) values, calibration curves and decision curve analysis (DCA). Kaplan-Meier curves were used to plot the survival probabilities for different strata of each risk factor.ResultsThere was no statistically significant difference (p > 0.05) in the 32 clinical variables between patients in the training and validation groups. Univariate and multivariate Cox proportional hazards regression analyses demonstrated that age, location, TNM, chemotherapy, liver metastasis, lung metastasis, MSH6, CEA, CA199, CA125 and CA724 were risk factors for OS. We estimated the AUC values for the nomogram model to predict 1-, 3-, 5-, 8- and 10-year OS, which in the training group were 0.826 (95% CI: 0.807-0.845), 0.836 (0.819-0.853), 0.839 (0.820-0.859), 0.835 (0.809-0.862) and 0.825 (0.779-0.870) respectively; in the validation group, the corresponding AUC values were 0.819 (0.786-0.852), 0.831 (0.804-0.858), 0.830 (0.799-0.861), 0.815 (0.774-0.857) and 0.802 (0.723-0.882) respectively. Finally, the 1-, 3-, 5-, 8- and 10-year OS rates for advanced stage CRC patients were 73.4 (71.8-75.0), 49.5 (47.8-51.4), 43.3 (41.5-45.2), 40.1 (38.1-41.9) and 38.6 (36.6-40.8) respectively.ConclusionWe constructed and validated an original nomogram for predicting the postoperative OS of advanced stage CRC patients, which can help facilitates physicians to accurately assess the individual survival of postoperative patients and identify high-risk patients.

Project description:BackgroundAn underweight individual is defined as one whose Body Mass Index (BMI) is < 18.5 kg/m2. Currently, the prognosis in patients with colorectal cancer (CRC) who are also underweight is unclear.MethodsInformation on South Korean patients who underwent curative resection for CRC without distant metastasis was collected from health insurance registry data between January 2014 and December 2016. We compared the overall survival (OS) of underweight and non-underweight (BMI ≥ 18.5 kg/m2) patients after adjusting for confounders using propensity score matching. A nomogram to predict OS in the underweight group was constructed using the significant risk factors identified in multivariate analysis. The predictive and discriminative capabilities of the nomogram for predicting 3- and 5-year OS in the underweight group were validated and compared with those of the tumor, node, and metastasis (TNM) staging system in the training and validation sets.ResultsA total of 23,803 (93.6%) and 1,644 (6.4%) patients were assigned to the non-underweight and underweight groups, respectively. OS was significantly worse in the underweight group than in the non-underweight group for each pathological stage (non-underweight vs. underweight: stage I, 90.1% vs. 77.1%; stage IIA, 85.3% vs. 67.3%; stage IIB/C, 74.9% vs. 52.1%; and stage III, 73.2% vs. 59.4%, P < 0.001). The calibration plots demonstrated that the nomogram exhibited satisfactory consistency with the actual results. The concordance index (C-index) and area under the receiver operating characteristic curve (AUC) of the nomogram exhibited better discriminatory capability than those of the TNM staging system (C-index, nomogram versus TNM staging system: training set, 0.713 versus 0.564, P < 0.001; validation set, 0.691 versus 0.548, P < 0.001; AUC for 3- and 5- year OS, nomogram versus TNM staging system: training set, 0.748 and 0.741 versus 0.610 and 0.601; validation set, 0.715 and 0.753 versus 0.586 and 0.579, respectively).ConclusionsUnderweight patients had worse OS than non-underweight patients for all stages of CRC. Our nomogram can guide prognostic predictions and the treatment plan for underweight patients with CRC.

Dataset Information

Prognostic nomogram of hypoxia-related genes predicting overall survival of colorectal cancer-Analysis of TCGA database.

Publications

Prognostic nomogram of hypoxia-related genes predicting overall survival of colorectal cancer-Analysis of TCGA database.

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