The landscape of gene co-expression modules correlating with prognostic genetic abnormalities in AML

Abstract Background The heterogenous cytogenetic and molecular variations were harbored by AML patients, some of which are related with AML pathogenesis and clinical outcomes. We aimed to uncover the intrinsic expression profiles correlating with prognostic genetic abnormalities by WGCNA. Methods We downloaded the clinical and expression dataset from BeatAML, TCGA and GEO database. Using R (version 4.0.2) and ‘WGCNA’ package, the co-expression modules correlating with the ELN2017 prognostic markers were identified (R2 ≥ 0.4, p < 0.01). ORA detected the enriched pathways for the key co-expression modules. The patients in TCGA cohort were randomly assigned into the training set (50%) and testing set (50%). The LASSO penalized regression analysis was employed to build the prediction model, fitting OS to the expression level of hub genes by ‘glmnet’ package. Then the testing and 2 independent validation sets (GSE12417 and GSE37642) were used to validate the diagnostic utility and accuracy of the model. Results A total of 37 gene co-expression modules and 973 hub genes were identified for the BeatAML cohort. We found that 3 modules were significantly correlated with genetic markers (the ‘lightyellow’ module for NPM1 mutation, the ‘saddlebrown’ module for RUNX1 mutation, the ‘lightgreen’ module for TP53 mutation). ORA revealed that the ‘lightyellow’ module was mainly enriched in DNA-binding transcription factor activity and activation of HOX genes. The ‘saddlebrown’ module was enriched in immune response process. And the ‘lightgreen’ module was predominantly enriched in mitosis cell cycle process. The LASSO- regression analysis identified 6 genes (NFKB2, NEK9, HOXA7, APRC5L, FAM30A and LOC105371592) with non-zero coefficients. The risk score generated from the 6-gene model, was associated with ELN2017 risk stratification, relapsed disease, and prior MDS history. The 5-year AUC for the model was 0.822 and 0.824 in the training and testing sets, respectively. Moreover, the diagnostic utility of the model was robust when it was employed in 2 validation sets (5-year AUC 0.743–0.79). Conclusions We established the co-expression network signature correlated with the ELN2017 recommended prognostic genetic abnormalities in AML. The 6-gene prediction model for AML survival was developed and validated by multiple datasets.

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The Landscape of Gene Co-Expression Modules Correlating with Prognostic Genetic Abnormalities in AML

10.21203/rs.3.rs-52517/v1 ◽

2020 ◽

Author(s):

Chao Guo ◽

Ya-yue Gao ◽

Qian-qian Ju ◽

Chun-xia Zhang ◽

Ming Gong ◽

...

Keyword(s):

Regression Analysis ◽

Prediction Model ◽

Hox Genes ◽

Expression Profiles ◽

Penalized Regression ◽

Diagnostic Utility ◽

Lasso Regression ◽

Hub Genes ◽

Npm1 Mutation ◽

Genetic Abnormalities

Abstract Background: The heterogenous cytogenetic and molecular variations were harbored by AML patients, some of which are related with AML pathogenesis and clinical outcomes. We aimed to uncover the intrinsic expression profiles correlating with prognostic genetic abnormalities by WGCNA.Methods: We downloaded the clinical and expression dataset from BeatAML, TCGA and GEO database. Using R (version 4.0.2) and ‘WGCNA’ package, the co-expression modules correlating with the ELN2017 prognostic markers were identified (R2 ≥ 0.4, p < 0.01). ORA detected the enriched pathways for the key co-expression modules. The patients in TCGA cohort were randomly assigned into the training set (50%) and testing set (50%). The LASSO penalized regression analysis was employed to build the prediction model, fitting OS to the expression level of hub genes by ‘glmnet’ package. Then the testing and 2 independent validation sets (GSE12417 and GSE37642) were used to validate the diagnostic utility and accuracy of the model. Results: A total of 37 gene co-expression modules and 973 hub genes were identified for the BeatAML cohort. We found that 3 modules were significantly correlated with genetic markers (the ‘lightyellow’ module for NPM1 mutation, the ‘saddlebrown’ module for RUNX1 mutation, the ‘lightgreen’ module for TP53 mutation). ORA revealed that the ‘lightyellow’ module was mainly enriched in DNA-binding transcription factor activity and activation of HOX genes. The ‘saddlebrown’ module was enriched in immune response process. And the ‘lightgreen’ module was predominantly enriched in mitosis cell cycle process. The LASSO- regression analysis identified 6 genes (NFKB2, NEK9, HOXA7, APRC5L, FAM30A and LOC105371592) with non-zero coefficients. The risk score generated from the 6-gene model, was associated with ELN2017 risk stratification, relapsed disease, and prior MDS history. The 5-year AUC for the model was 0.822 and 0.824 in the training and testing sets, respectively. Moreover, the diagnostic utility of the model was robust when it was employed in 2 validation sets (5-year AUC 0.743-0.79).Conclusions: We established the co-expression network signature correlated with the ELN2017 recommended prognostic genetic abnormalities in AML. The 6-gene prediction model for AML survival was developed and validated by multiple datasets.

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Development of a Survival Model Based on Autophagy-Associated Genes for Predicting Prognosis of Gastric Cancer

10.21203/rs.3.rs-30248/v1 ◽

2020 ◽

Author(s):

Wanli Yang ◽

Lili Duan ◽

Xinhui Zhao ◽

Liaoran Niu ◽

Yiding Li ◽

...

Keyword(s):

Gastric Cancer ◽

Regression Analysis ◽

Prediction Model ◽

Roc Curve ◽

Survival Model ◽

Differentially Expressed ◽

Lasso Regression ◽

Prognosis Prediction ◽

Cellular Processes ◽

Model Based

Abstract Background: Gastric cancer (GC) is one of lethal diseases worldwide. Autophagy-associated genes play a crucial role in the cellular processes of GC. Our study aimed to investigate and identify the prognostic potential of autophagy-associated genes signature in GC. Methods: RNA-seq and clinical information of GC and normal controls were downloaded from The Cancer Genome Atlas (TCGA) database. Then, the Wilcoxon signed-rank test was used to pick out the differentially expressed autophagy-associated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to investigate the potential roles and mechanisms of autophagy-associated genes in GC. Cox proportional hazard regression analysis and Lasso regression analysis were carried out to identify the overall survival (OS) related autophagy-associated genes, which were then collected to construct a predictive model. Kaplan-Meier method and receiver operating characteristic (ROC) curve were utilized to validate the accuracy of this model. Finally, a clinical nomogram was established by combining the clinical factors and autophagy-associated genes signature. Results: A total of 28 differentially expressed autophagy-associated genes were identified. GO and KEGG analyses revealed that several important cellular processes and signaling pathways were correlated with these genes. Through Cox regression and Lasso regression analyses, we identified 4 OS-related autophagy-associated genes (GRID2, ATG4D, GABARAPL2, and CXCR4) and constructed a prognosis prediction model. GC Patients with high-risk had a worse OS than those in low-risk group (5-year OS, 27.7% vs 38.3%; P=9.524e-07). The area under the ROC curve (AUC) of the prediction model was 0.67. The nomogram was demonstrated to perform better for predicting 3-year and 5-year survival possibility for GC patients with a concordance index (C-index) of 0.70 (95% CI: 0.65-0.72). The calibration curves also presented good concordance between nomogram-predicted survival and actual survival. Conclusions: We constructed and evaluated a survival model based on the autophagy-associated genes for GC patients, which may improve the prognosis prediction in GC.

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Construction of a risk prediction model for Alzheimer’s disease in the elderly population

BMC Neurology ◽

10.1186/s12883-021-02276-8 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Lingling Wang ◽

Ping Li ◽

Ming Hou ◽

Xiumin Zhang ◽

Xiaolin Cao ◽

...

Keyword(s):

Regression Analysis ◽

Health Status ◽

Prediction Model ◽

Genetic Risk ◽

Elderly Population ◽

Economic Status ◽

The Elderly ◽

Predictor Variables ◽

Lasso Regression ◽

Validation Set

Abstract Background Dementia is one of the greatest global health and social care challenges of the twenty-first century. The etiology and pathogenesis of Alzheimer’s disease (AD) as the most common type of dementia remain unknown. In this study, a simple nomogram was drawn to predict the risk of AD in the elderly population. Methods Nine variables affecting the risk of AD were obtained from 1099 elderly people through clinical data and questionnaires. Least Absolute Shrinkage Selection Operator (LASSO) regression analysis was used to select the best predictor variables, and multivariate logistic regression analysis was used to construct the prediction model. In this study, a graphic tool including 9 predictor variables (nomogram-see precise definition in the text) was drawn to predict the risk of AD in the elderly population. In addition, calibration diagram, receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to verify the model. Results Six predictors namely sex, age, economic status, health status, lifestyle and genetic risk were identified by LASSO regression analysis of nine variables (body mass index, marital status and education level were excluded). The area under the ROC curve in the training set was 0.822, while that in the validation set was 0.801, suggesting that the model built with these 6 predictors showed moderate predictive ability. The DCA curve indicated that a nomogram could be applied clinically if the risk threshold was between 30 and 40% (30 to 42% in the validation set). Conclusion The inclusion of sex, age, economic status, health status, lifestyle and genetic risk into the risk prediction nomogram could improve the ability of the prediction model to predict AD risk in the elderly patients.

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Identification of Two Novel Genes SNX10 and PTGDS with Cervical Squamous Cell Carcinoma Prognosis

10.21203/rs.3.rs-50778/v1 ◽

2020 ◽

Author(s):

Pinping Jiang ◽

Ying Cao ◽

Feng Gao ◽

Wei Sun ◽

Jinhui Liu ◽

...

Keyword(s):

Functional Analysis ◽

Regression Analysis ◽

Cox Regression ◽

Trichostatin A ◽

Expression Profiles ◽

Ppi Network ◽

Methylation Analysis ◽

Hub Genes ◽

Cox Regression Analysis ◽

Tumor Tissues

Abstract Background: Cervical cancer (CC) is the primary cause of death in women. This study sought to investigate the therapeutic targets of CC. Methods: We downloaded four gene expression profiles from GEO. The RRA method was used to integrate and screen DEGs between CC and normal samples. Functional analysis was performed by clusterprofiler. We built PPI network by STRING and selected hub modules via MCODE. CMap was used to find molecules with therapeutic potential for CC. We also validated hub genes in GEO datasets, GEPIA, immunohistochemistry. Cox regression analysis, TCGA methylation analysis and ONCOMINE were carried out. ROC curve analysis and GSEA were also done to dig out the significance of hub genes. Results: Functional analysis revealed that DEGs were significantly enriched in binding, cell proliferation, transcriptional activity and cell cycle regulation. PPI network screened 30 prominent proteins, with CDK1 having the strongest association with CC. Cmap showed that apigenin, thioguanine and trichostatin A might be used to treat CC. Eight genes were screened out through GEPIA. Of them, only PTGDS and SNX10 have not been reported in CC related articles. The validation in GEO showed that PTGDS showed low expression in tumor tissues while SNX10 showed high expression in tumor tissues. Their expression profiles were consistent with the results in immunohistochemistry. They can distinguish CC and normal tissue and have good diagnostic efficiency. GSEA showed that the two genes were associated with the chemokine signaling pathway. TCGA methylation analysis showed that patients with low-expressed and hyper-methylated PTGDS had a bad prognosis than the patients with high-expressed and hypo-methylated PTGDS. Cox regression analysis showed that SNX10 and PTGDS were independent prognostic indicators for OS among CC patients. Conclusions: In conclusion, PTGDS and SNX10 showed abnormal expression and methylation in CC. Both genes could be used to develop new target treatments for CC.

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Prediction Model for COVID-19 Vaccination Intention among the Mobile Population in China: Validation and Stability

Vaccines ◽

10.3390/vaccines9111221 ◽

2021 ◽

Vol 9 (11) ◽

pp. 1221

Author(s):

Fan Hu ◽

Ruijie Gong ◽

Yexin Chen ◽

Jinxin Zhang ◽

Tian Hu ◽

...

Keyword(s):

Regression Analysis ◽

Prediction Model ◽

External Validation ◽

Multivariate Logistic Regression Analysis ◽

Stability Index ◽

Lasso Regression ◽

Mobile Population ◽

Leading Position ◽

The Government ◽

Selection Operator

Since China’s launch of the COVID-19 vaccination, the situation of the public, especially the mobile population, has not been optimistic. We investigated 782 factory workers for whether they would get a COVID-19 vaccine within the next 6 months. The participants were divided into a training set and a testing set for external validation conformed to a ratio of 3:1 with R software. The variables were screened by the Lead Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Then, the prediction model, including important variables, used a multivariate logistic regression analysis and presented as a nomogram. The Receiver Operating Characteristic (ROC) curve, Kolmogorov–Smirnov (K-S) test, Lift test and Population Stability Index (PSI) were performed to test the validity and stability of the model and summarize the validation results. Only 45.54% of the participants had vaccination intentions, while 339 (43.35%) were unsure. Four of the 16 screened variables—self-efficacy, risk perception, perceived support and capability—were included in the prediction model. The results indicated that the model has a high predictive power and is highly stable. The government should be in the leading position, and the whole society should be mobilized and also make full use of peer education during vaccination initiatives.

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Identification of Genes Related to Immune Infiltration in the Tumor Microenvironment of Cutaneous Melanoma

Frontiers in Oncology ◽

10.3389/fonc.2021.615963 ◽

2021 ◽

Vol 11 ◽

Author(s):

Rujia Qin ◽

Wen Peng ◽

Xuemin Wang ◽

Chunyan Li ◽

Yan Xi ◽

...

Keyword(s):

Regression Analysis ◽

Tumor Microenvironment ◽

Cutaneous Melanoma ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Training Dataset ◽

Lasso Regression ◽

Hub Genes ◽

Dynamic Regulation ◽

Cox Regression Analysis

Cutaneous melanoma (CM) is the leading cause of skin cancer deaths and is typically diagnosed at an advanced stage, resulting in a poor prognosis. The tumor microenvironment (TME) plays a significant role in tumorigenesis and CM progression, but the dynamic regulation of immune and stromal components is not yet fully understood. In the present study, we quantified the ratio between immune and stromal components and the proportion of tumor-infiltrating immune cells (TICs), based on the ESTIMATE and CIBERSORT computational methods, in 471 cases of skin CM (SKCM) obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were analyzed by univariate Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) regression analysis, and multivariate Cox regression analysis to identify prognosis-related genes. The developed prognosis model contains ten genes, which are all vital for patient prognosis. The areas under the curve (AUC) values for the developed prognostic model at 1, 3, 5, and 10 years were 0.832, 0.831, 0.880, and 0.857 in the training dataset, respectively. The GSE54467 dataset was used as a validation set to determine the predictive ability of the prognostic signature. Protein–protein interaction (PPI) analysis and weighted gene co-expression network analysis (WGCNA) were used to verify “real” hub genes closely related to the TME. These hub genes were verified for differential expression by immunohistochemistry (IHC) analyses. In conclusion, this study might provide potential diagnostic and prognostic biomarkers for CM.

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Candidate Genes of Allergic Dermatitis Are Associated with Immune Response

Journal of Healthcare Engineering ◽

10.1155/2022/8745722 ◽

2022 ◽

Vol 2022 ◽

pp. 1-11

Author(s):

Lei Jin ◽

Lin Deng ◽

Wanchun Wang

Keyword(s):

Immune Response ◽

Logistic Regression ◽

Regression Analysis ◽

Candidate Genes ◽

Th17 Cell ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Enrichment Analysis ◽

Lasso Regression ◽

Allergic Dermatitis

Allergic dermatitis (AD) is a common and burdensome inflammatory skin disease, and diagnosis is challenging. This study was conducted to identify candidate genes for AD diagnosis and underlying molecular mechanisms. Gene expression profiles were obtained from datasets GSE121212, GSE130588, and GSE157194. Use differential analysis to identify differentially expressed genes (DEGs) between AD and control. Use enrichment analysis to identify potential molecular dysregulation mechanisms. Comprehensive least absolute shrinkage and selection operator (LASSO) logistic regression, receiver operator characteristic (ROC) curve, and logistic regression analysis are used to identify candidate genes. In addition, ssGSEA and ImmPort database were used to identify AD-related immune response abnormalities. In this study, a total of 60 common genes were identified. Enrichment analysis found that these genes are mainly involved in Th17 cell immune and complement and coagulation cascades. LASSO regression analysis identified 18 feature genes, and screened genes with AUC >0.75 were selected as candidate genes. Finally, PLA2G4D, IFI6, AGR3, IGFL1, SPRR3, ATP13A5, SERPINB13, KRT16, HAS3, and CH25H were recognized as candidate genes and may be able to diagnose AD. PLA2G4D, CH25H, and IFI6 may be risk factors for AD based on logistic analysis. Furthermore, we identified the abnormalities of immune response activation in AD patients. Interestingly, PLA2G4D, CH25H, and IFI6 had positive correlations with immune cells and signaling pathways. PLA2G4D, CH25H, and IFI6 may be candidate diagnostic genes for AD. This may be related to their promotion of abnormal immune activation, especially Th17 cell immune.

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Prognostic value of two novel genes SNX10 and PTGDS for cervical squamous cell carcinoma: an integrated bioinformatic analysis

10.21203/rs.3.rs-18046/v1 ◽

2020 ◽

Author(s):

Pinping Jiang ◽

Ying Cao ◽

Feng Gao ◽

Wei Sun ◽

Jinhui Liu ◽

...

Keyword(s):

Functional Analysis ◽

Regression Analysis ◽

Cox Regression ◽

Trichostatin A ◽

Expression Profiles ◽

Ppi Network ◽

Methylation Analysis ◽

Hub Genes ◽

Cox Regression Analysis ◽

Tumor Tissues

Abstract Background Cervical cancer (CC) is the primary cause of death in women. This study sought to investigate the therapeutic targets of CC. Methods We downloaded four gene expression profiles from GEO. The RRA method was used to integrate and screen DEGs between CC and normal samples. Functional analysis was performed by clusterprofiler. We built PPI network by STRING and selected hub modules via MCODE. CMap was used to find molecules with therapeutic potential for CC. We also validated hub genes in GEO datasets, GEPIA, immunohistochemistry. Cox regression analysis, TCGA methylation analysis and ONCOMINE were carried out. ROC curve analysis and GSEA were also done to dig out the significance of hub genes. Results Functional analysis revealed that DEGs were significantly enriched in binding, cell proliferation, transcriptional activity and cell cycle regulation. PPI network screened 30 prominent proteins, with CDK1 having the strongest association with CC. Cmap showed that apigenin, thioguanine and trichostatin A might be used to treat CC. Eight genes were screened out through GEPIA. Of them, only PTGDS and SNX10 have not been reported in CC related articles. The validation in GEO showed that PTGDS showed low expression in tumor tissues while SNX10 showed high expression in tumor tissues. Their expression profiles were consistent with the results in immunohistochemistry. They can distinguish CC and normal tissue and have good diagnostic efficiency. GSEA showed that the two genes were associated with the chemokine signaling pathway. TCGA methylation analysis showed that patients with low-expressed and hyper-methylated PTGDS had a bad prognosis than the patients with high-expressed and hypo-methylated PTGDS. Cox regression analysis showed that SNX10 and PTGDS were independent prognostic indicators for OS among CC patients. Conclusions PTGDS and SNX10 showed abnormal expression and methylation in CC. Both genes could be used to develop new target treatments for CC.

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Screening of immunosuppressive cells from colorectal adenocarcinoma and identification of prognostic markers

10.21203/rs.3.rs-23944/v1 ◽

2020 ◽

Author(s):

Fazhan Li ◽

Jun Zhou ◽

Zedong Li ◽

Leiyi Zhang

Keyword(s):

Colorectal Cancer ◽

Colorectal Adenocarcinoma ◽

Immune Cell ◽

Expression Profiles ◽

Human Life ◽

Common Type ◽

The Cancer Genome Atlas ◽

Lasso Regression ◽

Hub Genes ◽

Th 17

Abstract Background Colorectal cancer (CRC) is the most common type of gastrointestinal malignant tumor. Colorectal adenocarcinoma (COAD) is the most common type of colorectal cancer, and it is extremely harmful to human life and health. In recent years, the role of the immune system in the development of tumor-associated inflammation and cancer has received increasing attention. Results In this study, we compiled the expression profiles of 262 patients with complete follow-up data from the Cancer Genome Atlas (TCGA) database as an experimental group and selected 65 samples from the GEO dataset (of which M0 totaled 46 samples). Validated as a verification group. First, we screened the immune cell Th-17 cells related to the prognosis of COAD disease, and then identified the Th-17 cells-related hub genes by constructing co-expression network analysis (WGCNA) and lasso regression analysis (LASSO), which determined that it may be related to Th-17 cells. Six genes associated with prognosis in patients with COAD:”KRT23 ULBP2 ASRGL1 SERPINA1 SCIN SLC28A2”. Finally, we constructed a clinical prediction model and analyzed the predictive power of the model. These hub genes have been shown to be involved in the development of many diseases and are closely linked to digestive diseases. Our results suggest that the hub gene may influence the prognosis of COAD by regulating the immune infiltration of Th-17cells. Conclusions These newly discovered hub genes help to understand the mechanisms of COAD development and metastasis, thereby promoting the development of COAD and providing new therapeutic targets and biomarkers for COAD.

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Development and validation of a survival model based on autophagy-associated genes for predicting prognosis of hepatocellular carcinoma

10.21203/rs.3.rs-31172/v1 ◽

2020 ◽

Author(s):

Wanli Yang ◽

Liaoran Niu ◽

Xinhui Zhao ◽

Lili Duan ◽

Yiding Li ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Survival Rate ◽

Prediction Model ◽

Risk Score ◽

Cox Regression ◽

Survival Model ◽

Differentially Expressed ◽

Lasso Regression ◽

Prognostic Prediction

Abstract Background: Hepatocellular carcinoma (HCC) is one of the devastating tumors with increasing incidence. Autophagy-associated genes (ARGs) are widely participated in the cellular processes of HCC. This study proposed to identify the novel prognostic gene signature based on ARGs in HCC. Methods: We downloaded the RNA sequencing data and clinical information of HCC and normal tissues from The Cancer Genome Atlas (TCGA) database. The differentially expressed ARGs were screened by the Wilcoxon signed-rank test. Functional enrichment analyses were conducted to explore the biological implications and mechanisms of ARGs in HCC. Cox regression analysis and Lasso regression analysis were performed to screen the ARGs which related to overall survival (OS). The OS-related ARGs were then used to establish a prognostic prediction model. Kaplan-Meier curves and receiver operating characteristic (ROC) curves were both applied to evaluate the accuracy of the model. GSE14520 dataset was downloaded as the testing cohort to validate the prognostic risk model in TCGA. A nomogram based on the clinical features and risk signature was established to predict the 3-year and 5-year survival rate of HCC patients. Results: Totally 27 differentially expressed ARGs were screened in this study. Then, 3 OS-related ARGs (SQSTM1, HSPB8, and BIRC5) were identified via the Cox regression and Lasso regression analyses. Based on these 3 ARGs, a prognostic prediction model was constructed. HCC patients in high-risk group presented poorer prognosis than those with low risk score in TCGA cohort (3-year OS, 53.7% vs 70.2%; 5-year OS, 42.0 % vs 55.2%; P=4.478e-04) and in the testing group (3-year OS, 57.7% vs 73.5%; 5-year OS, 43.2% vs 63.0%; P=1.274e-03). The risk score curve showed a well feasibility in predicting the patients’ survival both in TCGA and GEO cohort with the area under the ROC curve (AUC) of 0.756 and 0.672, respectively. Besides, the calibration curves and C-index indicated that the clinical nomogram performs well to predict the 3-year and 5-year survival rate in HCC patients. Conclusions: The survival model based on the ARGs may be a promising tool to predict the prognosis in HCC patients.

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