scholarly journals Molecular signatures of tumor progression in pancreatic adenocarcinoma identified by energy metabolism characteristics

2021 ◽  
Author(s):  
Xin Wang ◽  
Cong Tan ◽  
Weiwei Weng ◽  
Shujuan Ni ◽  
Meng Zhang ◽  
...  
Keyword(s):  
Energy Metabolism ◽  
Pancreatic Adenocarcinoma ◽  
Clustering Algorithm ◽  
Molecular Subtypes ◽  
Expression Profiles ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Algorithm Analysis ◽  
Receptor Interaction ◽  
Prognostic Gene

Abstract Background: In this study, we aimed to describe a molecular evaluation of primary pancreatic adenocarcinoma (PAAD) based on comprehensive analysis of energy-metabolism-related gene (EMRG) expression profiles.Methods: Molecular subtypes were identified by non-negative matrix clustering algorithm clustering on 565 EMRGs. The overall survival (OS) predictive gene signature was developed, internally and externally validated based on three online PAAD datasets. Hub genes were identified in molecular subtypes by weighted gene correlation network analysis (WGCNA) co-expression algorithm analysis, and then enrolled for determination of prognostic genes. Univariate, LASSO and multivariate Cox regression analyses were performed to assess prognostic genes and construct the prognostic gene signature. Time-dependent receiver operating characteristic (ROC) curve, Kaplan-Meier curve and nomogram were used to assess the performance of the gene signature.Results: On the basis of EMRGs expression profile, we propose a molecular classification dividing PAAD into two subtypes: Cluster 1, which display more immune and stromal cell components in tumor microenvironment and higher tumor purity; and Cluster 2, which display worse OS. Moreover, by using a three-phase training, test and validation process, we construct a 4-gene signature that can constantly classify the prognostic risk of patients in all three datasets, and which present higher robustness and clinical usability compared with four previous reported prognostic gene signatures. In addition, a novel nomogram constructed by combining clinical features and the 4-gene signature showed confident clinical utility in PAAD. According to gene set enrichment analysis (GSEA), gene sets related to the high-risk group were participated in the neuroactive ligand receptor interaction pathway. Conclusions: In summary, the EMRG-based molecular subtypes and prognostic gene model provides a roadmap for patient stratification and trials of targeted therapies.

scholarly journals Comprehensive Molecular Characterization and Identification of Prognostic Signature in Stomach Adenocarcinoma on The Basis of Energy-Metabolism-Related Genes

2021 ◽  
Author(s):  
Jinjia Chang ◽  
Xiaoyu Wang ◽  
Wenhua Li ◽  
Cong Tan ◽  
Weiqi Sheng ◽  
...  
Keyword(s):  
Energy Metabolism ◽  
High Risk ◽  
Risk Score ◽  
Expression Profiles ◽  
Young Patients ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Receptor Interaction ◽  
Prognostic Gene ◽  
Stomach Adenocarcinoma

Abstract Background: Stomach adenocarcinoma (STAD) is a leading cause of cancer deaths, but its molecular and prognostic characteristics has never been fully illustrated. Methods: We describe a comprehensive molecular evaluation of primary STAD based on comprehensive analysis of energy-metabolism-related gene (EMRG) expression profiles. Results: On the basis of 86 EMRGs that were significantly associated to patients’ progression-free survival (PFS), we propose a molecular classification dividing gastric cancer into two subtypes: Cluster 1, most of which are young patients and display more immune and stromal cell components in tumor microenvironment (TME) and lower tumor priority; and Cluster 2, which show early stages and better PFS. Moreover, we construct a 6-gene signature that can classify the prognostic risk of patients after a three-phase training, test and validation process. Compared with patients with low-risk score, patients with high risk score had shorter overall survival. Furthermore, calibration and DCA analysis plots indicate the excellent predictive performance of the 6-gene signature, and which present higher robustness and clinical usability compared with three previous reported prognostic gene signatures. According to gene set enrichment analysis (GSEA), gene sets related to the high-risk group were participated in the ECM receptor interaction and hedgehog signaling pathway.Conclusions: Identification of the EMRG-based molecular subtypes and prognostic gene model provides a roadmap for patient stratification and trials of targeted therapies.

scholarly journals An energy metabolism-based eight-gene signature correlates with the clinical outcome of esophagus carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Weifeng Zheng ◽  
Chaoying Chen ◽  
Jianghao Yu ◽  
Chengfeng Jin ◽  
Tiemei Han
Keyword(s):  
Energy Metabolism ◽  
Cox Regression ◽  
Expression Profiles ◽  
Predictive Performance ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Prognostic Gene ◽  
Kaplan Meier ◽  
Esophagus Carcinoma

Abstract Background The essence of energy metabolism has spread to the field of esophageal cancer (ESC) cells. Herein, we tried to develop a prognostic prediction model for patients with ESC based on the expression profiles of energy metabolism associated genes. Materials and methods The overall survival (OS) predictive gene signature was developed, internally and externally validated based on ESC datasets including The Cancer Genome Atlas (TCGA), GSE54993 and GSE19417 datasets. Hub genes were identified in each energy metabolism related molecular subtypes by weighted gene correlation network analysis, and then enrolled for determination of prognostic genes. Univariate, LASSO and multivariate Cox regression analysis were applied to assess prognostic genes and build the prognostic gene signature. Kaplan-Meier curve, time-dependent receiver operating characteristic (ROC) curve, nomogram, decision curve analysis (DCA), and restricted mean survival time (EMST) were used to assess the performance of the gene signature. Results A novel energy metabolism based eight-gene signature (including UBE2Z, AMTN, AK1, CDCA4, TLE1, FXN, ZBTB6 and APLN) was established, which could dichotomize patients with significantly different OS in ESC. The eight-gene signature demonstrated independent prognostication potential in patient with ESC. The prognostic nomogram constructed based on the gene signature showed excellent predictive performance, whose robustness and clinical usability were higher than three previous reported prognostic gene signatures. Conclusions Our study established a novel energy metabolism based eight-gene signature and nomogram to predict the OS of ESC, which may help in precise clinical management.

scholarly journals Systematic Profiling of Invasion-related Genes Signature Predicts Prognostic Features and Identificates Molecular Subtypes of Lung Adenocarcinoma

2021 ◽  
Author(s):  
Ping Yu ◽  
Linlin Tong ◽  
Yujia Song ◽  
Hui Qu ◽  
Ying Chen
Keyword(s):  
Gene Expression ◽  
Lung Adenocarcinoma ◽  
Molecular Subtypes ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Normal Tissues ◽  
Tumor Tissues ◽  
Cox Analysis

Abstract Background: Due to the high heterogeneity of lung adenocarcinoma (LUAD), molecular subtype based on gene expression profiles is of great significance for diagnosis, and prognosis prediction in patients with LUAD.Methods: Invasion-related genes were obtained from the CancerSEA database, and LUAD expression profiles were downloaded from The Cancer Genome Atlas. The ConsensusClusterPlus was used to obtain molecular subtypes based on invasion-related genes. The limma software package was used to identify differentially expressed genes (DEGs). A multi-gene risk model was constructed by Lasso-Cox analysis. A nomogram was also constructed based on risk scores and meaningful clinical features.Results: 3 subtypes (C1, C2, C3) based on the expression of invasion-related genes were obtained. C3 had the worst prognosis. A total of 669 DEGs were identified among the subtypes. Pathway enrichment analysis results showed that the DEGs were mainly enriched in the cell cycle, DNA replication, the p53 signaling pathway, and other tumor-related pathways. A 5-gene signature (KRT6A, MELTF, IRX5, MS4A1, CRTAC1) was identified by using Lasso-Cox analysis. The training, validation, and external independent cohorts proved that the model was robust and had better prediction ability than other lung cancer models. The gene expression results showed that the expression levels of MS4A1 and KRT6A in tumor tissues were higher than in normal tissues, while CRTAC1 expression in tumor tissues was lower than in normal tissues. At the same time, the 5 genes were significantly expressed in pan-cancer immune subtypes. Gene set enrichment analysis showed that MS4A1, KRT6A, and CRAT1 genes were both enriched in the HALLMARK_IL2_STAT5_SIGNALING pathway, and IRX5 and MELTF gene were both enriched in the HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION pathway. Conclusion: The 5-gene signature prognostic stratification system based on invasion-related genes could be used to assess prognostic risk in patients with LUAD.

scholarly journals A novel pyroptosis-associated gene signature for immune status and prognosis of cutaneous melanoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12304
Author(s):  
Zhengyuan Wu ◽  
Leilei Chen ◽  
Chaojie Jin ◽  
Jing Xu ◽  
Xingqun Zhang ◽  
...  
Keyword(s):  
Cutaneous Melanoma ◽  
Immune Status ◽  
Immune Cell ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Tumor Cell Death ◽  
Prognostic Gene ◽  
Prognostic Gene Signature

Background Cutaneous melanoma (CM) is a life-threatening destructive malignancy. Pyroptosis significantly correlates with programmed tumor cell death and its microenvironment through active host-tumor crosstalk. However, the prognostic value of pyroptosis-associated gene signatures in CM remains unclear. Methods Gene profiles and clinical data of patients with CM were downloaded from The Cancer Genome Atlas (TCGA) to identify differentially expressed genes associated with pyroptosis and overall survival (OS). We constructed a prognostic gene signature using LASSO analysis, then applied immune cell infiltration scores and Kaplan-Meier, Cox, and pathway enrichment analyses to determine the roles of the gene signature in CM. A validation cohort was collected from the Gene Expression Omnibus (GEO) database. Results Four pyroptosis-associated genes were identified and incorporated into a prognostic gene signature. Integrated bioinformatics findings showed that the signature correlated with patient survival and was associated with tumor growth and metastasis. The results of Gene Set Enrichment Analysis of a risk signature indicated that several enriched pathways are associated with cancer and immunity. The risk signature for immune status significantly correlated with tumor stem cells, the immune microenvironment, immune cell infiltration and immune subtypes. The expression of four pyroptosis genes significantly correlated with the OS of patients with CM and was related to the sensitivity of cancer cells to several antitumor drugs. A signature comprising four genes associated with pyroptosis offers a novel approach to the prognosis and survival of patients with CM and will facilitate the development of individualized therapy.

scholarly journals Characterization of an Aging-Based Diagnostic Gene Signature and Molecular Subtypes With Diverse Immune Infiltrations in Atherosclerosis

2022 ◽  
Vol 8 ◽  
Author(s):  
Lei Zhao ◽  
Fengfeng Lv ◽  
Ye Zheng ◽  
Liqiu Yan ◽  
Xufen Cao
Keyword(s):  
Western Blotting ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Expression Profiles ◽  
Macrophage Polarization ◽  
Differential Expression Analysis ◽  
Gene Signature ◽  
Immune Checkpoints ◽  
Foam Cell Formation ◽  
Diagnostic Model

Objective: Advancing age is a major risk factor of atherosclerosis (AS). Nevertheless, the mechanism underlying this phenomenon remains indistinct. Herein, this study conducted a comprehensive analysis of the biological implications of aging-related genes in AS.Methods: Gene expression profiles of AS and non-AS samples were curated from the GEO project. Differential expression analysis was adopted for screening AS-specific aging-related genes. LASSO regression analysis was presented for constructing a diagnostic model, and the discriminatory capacity was evaluated with ROC curves. Through consensus clustering analysis, aging-based molecular subtypes were conducted. Immune levels were estimated based on the expression of HLAs, immune checkpoints, and immune cell infiltrations. Key genes were then identified via WGCNA. The effects of CEBPB knockdown on macrophage polarization were examined with western blotting and ELISA. Furthermore, macrophages were exposed to 100 mg/L ox-LDL for 48 h to induce macrophage foam cells. After silencing CEBPB, markers of cholesterol uptake, esterification and hydrolysis, and efflux were detected with western blotting.Results: This study identified 28 AS-specific aging-related genes. The aging-related gene signature was developed, which could accurately diagnose AS in both the GSE20129 (AUC = 0.898) and GSE43292 (AUC = 0.685) datasets. Based on the expression profiling of AS-specific aging-related genes, two molecular subtypes were clustered, and with diverse immune infiltration features. The molecular subtype–relevant genes were obtained with WGCNA, which were markedly associated with immune activation. Silencing CEBPB triggered anti-inflammatory M2-like polarization and suppressed foam cell formation.Conclusion: Our findings suggest the critical implications of aging-related genes in diagnosing AS and modulating immune infiltrations.

scholarly journals Identification of a Prognostic Gene Signature Associated with Microenvironment in Acute Myeloid Leukemia

2020 ◽  
Author(s):  
Zhixiang Chen ◽  
Luya Ye ◽  
Xuechun Wang ◽  
Fuquan Tu ◽  
Xuezhen Li ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Myeloid Leukemia ◽  
Expression Profiles ◽  
Critical Role ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Risk Scores ◽  
Acute Myeloid

Abstract Background: Acute myeloid leukemia (AML) is a common hematologic malignancy with poor prognosis. Accumulating reports have indicated that the tumor microenvironment (TME) performs a critical role in the progress of the disease and the clinical outcomes of patients. To date, the role of TME in AML remains clouded due to the complex regulatory mechanisms in it. In this study, We identified key prognostic genes relate to TME in AML and developed a novel gene signature for individualized prognosis assessment. Methods: The expression profiles of AML samples with clinical information were obtained from the Cancer Genome Atlas (TCGA). The ESTIMATE algorithm was applied to calculate the TME relevant immune and stromal scores. The differentially expressed genes (DEGs) were selected based on the immune and stromal scores. Then, the survival analysis was applied to select prognostic DEGs, and these genes were annotated by functional enrichment analysis. A TME relevant gene signature with predictive capability was constructed by a series of regression analyses and performed well in another cohort from the Gene Expression Omnibus (GEO) database. Moreover, we also developed a nomogram with the integration of the gene signature and clinical indicators to establish an individually quantified risk-scoring system. Results: In the AML microenvironment, a total of 181 DEGs with prognostic value were clarified. Then a seven-gene ( IL1R2, MX1, S100A4, GNGT2, ZSCAN23, PLXNB1 and DPY19L2 ) signature with robust prediction was identified, and was validated by an independent cohort of AML samples from the GSE71014. Gene set enrichment analysis (GSEA) of genes in the gene signature revealed these genes mainly enriched in the immune and inflammatory related processes. The correlation between the signature-calculated risk scores and the clinical features indicated that patients with high risk scores were accompanied by adverse survival. Finally, a nomogram with clinical utility was constructed. Conclusion: Our study explored and identified a novel TME relevant seven-gene signature, which could serve as a prognostic indicator for AML. Meanwhile, we also establish a nomogram with clinical significance. These findings might provide new insights into the diagnosis, treatment and prognosis of AML.

scholarly journals Comprehensive analysis based on DNA methylation and RNA-seq reveals hypermethylation of the up-regulated WT1 gene with potential mechanisms in PAM50 subtypes of breast cancer

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11377
Author(s):  
Chongyang Ren ◽  
Xiaojiang Tang ◽  
Haitao Lan
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Molecular Mechanisms ◽  
Molecular Subtypes ◽  
Wilms Tumor ◽  
Expression Profiles ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Body Region ◽  
Rna Seq

Background Breast cancer (BC), one of the most widespread cancers worldwide, caused the deaths of more than 600,000 women in 2018, accounting for about 15% of all cancer-associated deaths in women that year. In this study, we aimed to discover potential prognostic biomarkers and explore their molecular mechanisms in different BC subtypes using DNA methylation and RNA-seq. Methods We downloaded the DNA methylation datasets and the RNA expression profiles of primary tissues of the four BC molecular subtypes (luminal A, luminal B, basal-like, and HER2-enriched), as well as the survival information from The Cancer Genome Atlas (TCGA). The highly expressed and hypermethylated genes across all the four subtypes were screened. We examined the methylation sites and the downstream co-expressed genes of the selected genes and validated their prognostic value using a different dataset (GSE20685). For selected transcription factors, the downstream genes were predicted based on the Gene Transcription Regulation Database (GTRD). The tumor microenvironment was also evaluated based on the TCGA dataset. Results We found that Wilms tumor gene 1 (WT1), a transcription factor, was highly expressed and hypermethylated in all the four BC subtypes. All the WT1 methylation sites exhibited hypermethylation. The methylation levels of the TSS200 and 1stExon regions were negatively correlated with WT1 expression in two BC subtypes, while that of the gene body region was positively associated with WT1 expression in three BC subtypes. Patients with low WT1 expression had better overall survival (OS). Five genes including COL11A1, GFAP, FGF5, CD300LG, and IGFL2 were predicted as the downstream genes of WT1. Those five genes were dysregulated in the four BC subtypes. Patients with a favorable 6-gene signature (low expression of WT1 and its five predicted downstream genes) exhibited better OS than that with an unfavorable 6-gene signature. We also found a correlation between WT1 and tamoxifen using STITCH. Higher infiltration rates of CD8 T cells, plasma cells, and monocytes were found in the lower quartile WT1 group and the favorable 6-gene signature group. In conclusion, we demonstrated that WT1 is hypermethylated and up-regulated in the four BC molecular subtypes and a 6-gene signature may predict BC prognosis.

scholarly journals Gene Expression Meta-Analysis identifies Cytokine Pathways and 5q Aberrations involved in Metastasis of ERBB2 Amplified and Basal Breast Cancer

2013 ◽  
Vol 12 ◽  
pp. CIN.S12840 ◽  
Author(s):  
Mads Thomassen ◽  
Qihua Tan ◽  
Mark Burton ◽  
Torben A. Kruse
Keyword(s):  
Gene Expression ◽  
Molecular Subtypes ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Breast Tumors ◽  
Outcome Data ◽  
Gene Set Enrichment Analysis ◽  
Interleukin 12 ◽  
Biological Mechanisms ◽  
Basal Breast Cancer

Background Breast tumors have been described by molecular subtypes characterized by pervasively different gene expression profiles. The subtypes are associated with different clinical parameters and origin of precursor cells. However, the biological pathways and chromosomal aberrations that differ between the subgroups are less well characterized. The molecular subtypes are associated with different risk of metastatic recurrence of the disease. Nevertheless, the performance of these overall patterns to predict outcome is far from optimal, suggesting that biological mechanisms that extend beyond the subgroups impact metastasis. Results We have scrutinized publicly available gene expression datasets and identified molecular subtypes in 1,394 breast tumors with outcome data. By analysis of chromosomal regions and pathways using “Gene set enrichment analysis” followed by a meta-analysis, we identified comprehensive mechanistic differences between the subgroups. Furthermore, the same approach was used to investigate mechanisms related to metastasis within the subgroups. A striking finding is that the molecular subtypes account for the majority of biological mechanisms associated with metastasis. However, some mechanisms, aside from the subtypes, were identified in a training set of 1,239 tumors and confirmed by survival analysis in two independent validation datasets from the same type of platform and consisting of very comparable node-negative patients that did not receive adjuvant medical therapy. The results show that high expression of 5q14 genes and low levels of TNFR2 pathway genes were associated with poor survival in basal-like cancers. Furthermore, low expression of 5q33 genes and interleukin-12 pathway genes were associated with poor outcome exclusively in ERBB2-like tumors. Conclusion The identified regions, genes, and pathways may be potential drug targets in future individualized treatment strategies.

scholarly journals PATH-63. TRANSCRIPTIONAL SIGNATURES IN HISTOLOGIC STRUCTURES WITHIN GLIOBLASTOMA TUMORS MAY PREDICT PERSONALIZED TREATMENT SENSITIVITY AND SURVIVAL

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi157-vi157
Author(s):  
Cymon Kersch ◽  
Cheryl Claunch ◽  
Prakash Ambady ◽  
Elmar Bucher ◽  
Daniel Schwartz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Personalized Treatment ◽  
Survival Prediction ◽  
Histological Structure ◽  
Patient Stratification ◽  
Microvascular Proliferation ◽  
The Impact

Abstract OBJECTIVE Personalized treatment strategies in Glioblastoma multiforme (GBM) has been hampered by intra-tumoral heterogeneity. The goals of this study were to (1) determine the impact of intra-tumoral heterogeneity on established predictive and prognostic transcriptional signatures in human GBM, and (2) develop methods to mitigate the impact of tissue heterogeneity on transcriptomic-based patient stratification. METHODS We analyzed transcriptional profiles of GBM histological structures from the open-source Ivy Glioblastoma Atlas Project. To generate these data, infiltrative tumor, leading edge, cellular tumor [CT], perinecrotic zones, pseudopalisading cells, hyperplastic blood vessels and microvascular proliferation were microdissected from 34 newly diagnosed GBM and underwent RNA sequencing. Data from The Cancer Genome Atlas were used for validation. Principle component analysis, network analysis and gene set enrichment analysis were used to probe gene expression patterns. RESULTS Distinct biological networks were enriched in each tumor histological structure. Classification of patients into GBM molecular subtypes varied based on the structure assessed, with many patients classified as every subtype depending on the structure analyzed. Using only CT to classify subtypes, we identified biologically unique patterns suggesting that proneural and mesenchymal tumors may be more sensitive to chemoradiotherapy and immunotherapy, respectively. Survival outcome predicted by an established multigene panel was confounded by histologic structure. Utilizing CT transcriptomics we developed a novel survival prediction gene signature that identified the highest-risk GBM patients in both CT and bulk tissue gene expression profiles. CONCLUSIONS Histologic structures contribute to intra-tumoral heterogeneity in GBM. Using mixed-structure biopsy samples could incorrectly subtype tumors and produce invalid patient stratification. Limiting transcriptomic analysis to the CT allowed us to develop a new survival prediction gene signature that appears accurate even in mixed tissue samples. The biological patterns uncovered in the subtypes and risk-stratified groups have important implications for guiding the development of precision medicine in GBM.

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