scholarly journals Discovery and Validation of a Metastasis-Related Prognostic and Diagnostic Biomarker for Melanoma Based on Single Cell and Gene Expression Datasets

2020 ◽  
Vol 10 ◽  
Author(s):  
Qi Wan ◽  
Chengxiu Liu ◽  
Chang Liu ◽  
Weiqin Liu ◽  
Xiaoran Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Malignant Melanoma ◽  
Single Cell ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Mesenchymal Transition ◽  
Immune Infiltration ◽  
Single Cell Sequencing ◽  
Melanoma Patients

BackgroundSingle cell sequencing can provide comprehensive information about gene expression in individual tumor cells, which can allow exploration of heterogeneity of malignant melanoma cells and identification of new anticancer therapeutic targets.MethodsSingle cell sequencing of 31 melanoma patients in GSE115978 was downloaded from the Gene Expression Omniniub (GEO) database. First, the limma package in R software was used to identify the differentially expressed metastasis related genes (MRGs). Next, we developed a prognostic MRGs biomarker in the cancer genome atlas (TCGA) by combining univariate cox analysis and the least absolute shrinkage and selection operator (LASSO) method and was further validated in another two independent datasets. The efficiency of MRGs biomarker in diagnosis of melanoma was also evaluated in multiple datasets. The pattern of somatic tumor mutation, immune infiltration, and underlying pathways were further explored. Furthermore, nomograms were constructed and decision curve analyses were also performed to evaluate the clinical usefulness of the nomograms.ResultsIn total, 41 MRGs were screened out from 1958 malignant melanoma cell samples in GSE115978. Next, a 5-MRGs prognostic marker was constructed and validated, which show more effective performance for the diagnosis and prognosis of melanoma patients. The nomogram showed good accuracies in predicting 3 and 5 years survival, and the decision curve of nomogram model manifested a higher net benefit than tumor stage and clark level. In addition, melanoma patients can be divided into high and low risk subgroups, which owned differential mutation, immune infiltration, and clinical features. The low risk subgroup suffered from a higher tumor mutation burden (TMB), and higher levels of T cells infiltrating have a significantly longer survival time than the high risk subgroup. Gene Set Enrichment Analysis (GSEA) revealed that the extracellular matrix (ECM) receptor interaction and epithelial mesenchymal transition (EMT) were the most significant upregulated pathways in the high risk group.ConclusionsWe identified a robust MRGs marker based on single cell sequencing and validated in multiple independent cohort studies. Our finding provides a new clinical application for prognostic and diagnostic prediction and finds some potential targets against metastasis of melanoma.

scholarly journals Identification of a Gene-Related Risk Signature in Melanoma Patients Using Bioinformatic Profiling

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jing Wang ◽  
Peng-Fei Kong ◽  
Hai-Yun Wang ◽  
Di Song ◽  
Wen-Qing Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Expression Patterns ◽  
Prognostic Significance ◽  
Gene Signature ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Impact ◽  
Related Risk ◽  
Melanoma Patients

Introduction. Gene signature has been used to predict prognosis in melanoma patients. Meanwhile, the efficacy of immunotherapy was correlated with particular genes expression or mutation. In this study, we systematically explored the gene expression pattern in the melanoma-immune microenvironment and its relationship with prognosis. Methods. A cohort of 122 melanoma cases with whole-genome microarray expression data were enrolled from the Gene Expression Omnibus (GEO) database. The findings were validated using The Cancer Genome Atlas (TCGA) database. A principal component analysis (PCA), gene set enrichment analysis (GSEA), and gene oncology (GO) analysis were performed to explore the bioinformatic implications. Results. Different gene expression patterns were identified according to the clinical stage. All eligible gene sets were analyzed, and the 8 genes (GPR87, KIT, SH3GL3, PVRL1, ATP1B1, CDAN1, FAU, and TNFSF14) with the greatest prognostic impact on melanoma. A gene-related risk signature was developed to distinguish patients with a high or low risk of an unfavorable outcome, and this signature was validated using the TCGA database. Furthermore, the prognostic significance of the signature between the classified subgroups was verified as an independent prognostic predictor of melanoma. Additionally, the low-risk melanoma patients presented an enhanced immune phenotype compared to that of the high-risk gene signature patients. Conclusions. The gene pattern differences in melanoma were profiled, and a gene signature that could independently predict melanoma patients with a high risk of poor survival was established, highlighting the relationship between prognosis and the local immune response.

scholarly journals Construction and Validation of a Ferroptosis-Related Prognostic Model for Gastric Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Xiaotao Jiang ◽  
Qiaofeng Yan ◽  
Linling Xie ◽  
Shijie Xu ◽  
Kailin Jiang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
High Risk ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment

Background. Gastric cancer (GC), an extremely aggressive tumor with a very different prognosis, is the third leading cause of cancer-related mortality. We aimed to construct a ferroptosis-related prognostic model that can be distinguished prognostically. Methods. The gene expression and the clinical data of GC patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database (GEO). The ferroptosis-related genes were obtained from the FerrDb. Using the “limma” R package and univariate Cox analysis, ferroptosis-related genes with differential expression and prognostic value were identified in the TCGA cohort. Last absolute shrinkage and selection operator (LASSO) Cox regression was applied to shrink ferroptosis-related predictors and construct a prognostic model. Functional enrichment, ESTIMATE algorithm, and single-sample gene set enrichment analysis (ssGSEA) were applied for exploring the potential mechanism. GC patients from the GEO cohort were used for validation. Results. An 8-gene prognostic model was constructed and stratified GC patients from TCGA and meta-GEO cohort into high-risk groups or low-risk groups. GC patients in high-risk groups have significantly poorer OS compared with those in low-risk groups. The risk score was identified as an independent predictor for OS. Functional analysis revealed that the risk score was mainly associated with the biological function of extracellular matrix (ECM) organization and tumor immunity. Conclusion. In conclusion, the ferroptosis-related model can be utilized for the clinical prognostic prediction in GC.

scholarly journals Development of a Novel Autophagy-Related Prognostic Signature and Nomogram for Hepatocellular Carcinoma

2020 ◽  
Vol 10 ◽  
Author(s):  
Qiongxuan Fang ◽  
Hongsong Chen
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Regression Analyses ◽  
Gene Model

BackgroundHepatocellular carcinoma (HCC) is the seventh most common malignancy and the second most common cause of cancer-related deaths. Autophagy plays a crucial role in the development and progression of HCC.MethodsUnivariate and Lasso Cox regression analyses were performed to determine a gene model that was optimal for overall survival (OS) prediction. Patients in the GSE14520 and GSE54236 datasets of the Cancer Genome Atlas (TCGA) were divided into the high-risk and low-risk groups according to established ATG models. Univariate and multivariate Cox regression analyses were used to identify risk factors for OS for the purpose of constructing nomograms. Calibration and receiver operating characteristic (ROC) curves were used to evaluate model performance. Real-time PCR was used to validate the effects of the presence or absence of an autophagy inhibitor on gene expression in HepG2 and Huh7 cell lines.ResultsOS in the high-risk group was significantly shorter than that in the low-risk group. Gene set enrichment analysis (GSEA) indicated that the association between the low-risk group and autophagy- as well as immune-related pathways was significant. ULK2, PPP3CC, and NAFTC1 may play vital roles in preventing HCC progression. Furthermore, tumor environment analysis via ESTIMATION indicated that the low-risk group was associated with high immune and stromal scores. Based on EPIC prediction, CD8+ T and B cell fractions in the TCGA and GSE54236 datasets were significantly higher in the low-risk group than those in the high-risk group. Finally, based on the results of univariate and multivariate analyses three variables were selected for nomogram development. The calibration plots showed good agreement between nomogram prediction and actual observations. Inhibition of autophagy resulted in the overexpression of genes constituting the gene model in HepG2 and Huh7 cells.ConclusionsThe current study determined the role played by autophagy-related genes (ATGs) in the progression of HCC and constructed a novel nomogram that predicts OS in HCC patients, through a combined analysis of TCGA and gene expression omnibus (GEO) databases.

Using a clinicopathologic and gene expression model to identify melanoma patients at high risk for disease relapse.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 10068-10068
Author(s):  
Alexander M. Eggermont ◽  
Domenico Bellomo ◽  
Félicia Tjien-Fooh ◽  
Renske Wever ◽  
Enrica Quattrocchi ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Early Stage ◽  
Low Risk ◽  
Stage I ◽  
Diagnostic Tools ◽  
Disease Relapse ◽  
High Risk Patients ◽  
Risk Patients ◽  
Melanoma Patients

10068 Background: The identification of early stage melanoma patients at high risk for relapse is still difficult. Roughly 50% of melanoma deaths occur in patients who were initially diagnosed with nonmetastatic melanoma. Therefore, a strong clinical need has emerged for diagnostic tools that can identify melanoma patients at high risk for relapse. Here, we assessed the performance of a recently developed model (Bellomo et al., JCO Precis Oncol. 2020: in press), combining clinicopathologic and gene expression variables (CP-GEP), in identifying melanoma patients that have a high risk for disease relapse. Methods: We assessed the prognostic performance of the CP-GEP model in a cohort of 837 consecutive melanoma patients from Mayo Clinic who had a sentinel lymph node biopsy (SLNb) performed within 90 days of their diagnosis. The CP-GEP model combines Breslow thickness and patient age, with the expression of 8 genes in the primary tumor, to stratify patients according to their risk of relapse: CP-GEP High Risk or CP-GEP Low Risk. The main clinical endpoint of this study was five-year relapse free survival (RFS). Results: Patients were stratified based on SLNb status and CP-GEP classification. 76% of the patients were SLNb negative and had an RFS of 79% versus 52% for SLNb positive patients; HR, 3.21; P < 0.0001. 60% of the patients were identified as CP-GEP High Risk and had an RFS of 62% versus 87% for CP-GEP Low Risk patients; HR, 4.12; P < 0.0001. Within the SLNb negative group (637 patients of which 65% stage I), 51% of patients were classified as CP-GEP High Risk. Here, RFS was 70% for CP-GEP High Risk patients versus 89% for CP-GEP Low Risk patients; HR, 3.61; P < 0.0001. The prognosis of these CP-GEP High Risk patients is similar to stage IIC/IIIA patients with reported RFS ranging from 63% to 77%. This confirms the heterogeneity in prognosis among patients with stage I/II melanoma disease. Conclusions: The CP-GEP model can be successfully used to stratify patients based on their risk for relapse. In particular, it can be used to identify SLNb negative patients with a high risk for disease relapse who may benefit from therapeutic interventions. Independent validation studies are ongoing to validate the CP-GEP model in various patient populations. [Table: see text]

scholarly journals Prognostic Signatures Based on Thirteen Immune-Related Genes in Colorectal Cancer

2021 ◽  
Vol 10 ◽  
Author(s):  
Xiao-Bo Ma ◽  
Yuan-Yuan Xu ◽  
Meng-Xuan Zhu ◽  
Lu Wang
Keyword(s):  
Colorectal Cancer ◽  
High Risk ◽  
Tumor Microenvironment ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Cox Regression Analysis ◽  
Immune Infiltration ◽  
Immune Related Genes

BackgroundThe immunosuppressive microenvironment is closely related to tumorigenesis and cancer development, including colorectal cancer (CRC). The aim of the current study was to identify new immune biomarkers for the diagnosis and treatment of CRC.Materials and MethodsCRC data were downloaded from the Gene Expression Omnibus and The Cancer Genome Atlas databases. Sequences of immune-related genes (IRGs) were obtained from the ImmPort and InnateDB databases. Gene set enrichment analysis (GSEA) and transcription factor regulation analysis were used to explore potential mechanisms. An immune-related classifier for CRC prognosis was conducted using weighted gene co-expression network analysis (WGCNA), Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) analysis. ESTIMATE and CIBERSORT algorithms were used to explore the tumor microenvironment and immune infiltration in the high-risk CRC group and the low-risk CRC group.ResultsBy analyzing the IRGs that were significantly associated with CRC in the module, a set of 13 genes (CXCL1, F2RL1, LTB4R, GPR44, ANGPTL5, BMP5, RETNLB, MC1R, PPARGC1A, PRKDC, CEBPB, SYP, and GAB1) related to the prognosis of CRC were identified. An IRG-based prognostic signature that can be used as an independent potentially prognostic indicator was generated. The ROC curve analysis showed acceptable discrimination with AUCs of 0.68, 0.68, and 0.74 at 1-, 3-, and 5- year follow-up respectively. The predictive performance was validated in the train set. The potential mechanisms and functions of prognostic IRGs were analyzed, i.e., NOD-like receptor signaling, and transforming growth factor beta (TGFβ) signaling. Besides, the stromal score and immune score were significantly different in high-risk group and low-risk group (p=4.6982e-07, p=0.0107). Besides, the proportions of resting memory CD4+ T cells was significantly higher in the high-risk groups.ConclusionsThe IRG-based classifier exhibited strong predictive capacity with regard to CRC. The survival difference between the high-risk and low-risk groups was associated with tumor microenvironment and immune infiltration of CRC. Innovative biomarkers for the prediction of CRC prognosis and response to immunological therapy were identified in the present study.

Single cell sequencing to identify temporal and cellular changes in gene expression post ischemic injury

2018 ◽  
Vol 120 ◽  
pp. 46
Author(s):  
M. Droog ◽  
H. de Ruiter ◽  
M.M. Gladka ◽  
B. Molenaar ◽  
S. van der Elst ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Ischemic Injury ◽  
Single Cell Sequencing ◽  
Cellular Changes

scholarly journals P02.10 FocuSCOPE: a single cell, multi-omics solution to simultaneously analyze tumor variants and microenvironment

2021 ◽  
Vol 9 (Suppl 1) ◽  
pp. A12.1-A12
Author(s):  
Y Arjmand Abbassi ◽  
N Fang ◽  
W Zhu ◽  
Y Zhou ◽  
Y Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Single Cell ◽  
High Throughput ◽  
Immune Cells ◽  
Genetic Variants ◽  
Expression Profiles ◽  
Single Cells ◽  
Gene Expression Profiles ◽  
Single Cell Sequencing

Recent advances of high-throughput single cell sequencing technologies have greatly improved our understanding of the complex biological systems. Heterogeneous samples such as tumor tissues commonly harbor cancer cell-specific genetic variants and gene expression profiles, both of which have been shown to be related to the mechanisms of disease development, progression, and responses to treatment. Furthermore, stromal and immune cells within tumor microenvironment interact with cancer cells to play important roles in tumor responses to systematic therapy such as immunotherapy or cell therapy. However, most current high-throughput single cell sequencing methods detect only gene expression levels or epigenetics events such as chromatin conformation. The information on important genetic variants including mutation or fusion is not captured. To better understand the mechanisms of tumor responses to systematic therapy, it is essential to decipher the connection between genotype and gene expression patterns of both tumor cells and cells in the tumor microenvironment. We developed FocuSCOPE, a high-throughput multi-omics sequencing solution that can detect both genetic variants and transcriptome from same single cells. FocuSCOPE has been used to successfully perform single cell analysis of both gene expression profiles and point mutations, fusion genes, or intracellular viral sequences from thousands of cells simultaneously, delivering comprehensive insights of tumor and immune cells in tumor microenvironment at single cell resolution.Disclosure InformationY. Arjmand Abbassi: None. N. Fang: None. W. Zhu: None. Y. Zhou: None. Y. Chen: None. U. Deutsch: None.

scholarly journals Immune‐related Long Noncoding RNA Signature for Patients with Cervical Cancer

2020 ◽  
Author(s):  
Jianfeng Zheng ◽  
Jinyi Tong ◽  
Benben Cao ◽  
Xia Zhang ◽  
Zheng Niu
Keyword(s):  
Cervical Cancer ◽  
High Risk ◽  
Risk Score ◽  
Immune Cell ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Training Set ◽  
Score Model

Abstract Background: Cervical cancer (CC) is a common gynecological malignancy for which prognostic and therapeutic biomarkers are urgently needed. The signature based on immune‐related lncRNAs(IRLs) of CC has never been reported. This study aimed to establish an IRL signature for patients with CC.Methods: The RNA-seq dataset was obtained from the TCGA, GEO, and GTEx database. The immune scores（IS）based on single-sample gene set enrichment analysis (ssGSEA) were calculated to identify the IRLs, which were then analyzed using univariate Cox regression analysis to identify significant prognostic IRLs. A risk score model was established to divide patients into low-risk and high-risk groups based on the median risk score of these IRLs. This was then validated by splitting TCGA dataset(n=304) into a training-set(n=152) and a valid-set(n=152). The fraction of 22 immune cell subpopulations was evaluated in each sample to identify the differences between low-risk and high-risk groups. Additionally, a ceRNA network associated with the IRLs was constructed.Results: A cohort of 326 CC and 21 normal tissue samples with corresponding clinical information was included in this study. Twenty-eight IRLs were collected according to the Pearson’s correlation analysis between immune score and lncRNA expression (P < 0.01). Four IRLs (BZRAP1-AS1, EMX2OS, ZNF667-AS1, and CTC-429P9.1) with the most significant prognostic values (P < 0.05) were identified which demonstrated an ability to stratify patients into low-risk and high-risk groups by developing a risk score model. It was observed that patients in the low‐risk group showed longer overall survival (OS) than those in the high‐risk group in the training-set, valid-set, and total-set. The area under the curve (AUC) of the receiver operating characteristic curve (ROC curve) for the four IRLs signature in predicting the one-, two-, and three-year survival rates were larger than 0.65. In addition, the low-risk and high-risk groups displayed different immune statuses in GSEA. These IRLs were also significantly correlated with immune cell infiltration. Conclusions: Our results showed that the IRL signature had a prognostic value for CC. Meanwhile, the specific mechanisms of the four-IRLs in the development of CC were ascertained preliminarily.

scholarly journals Identifying and Validating an Acidosis-Related Signature Associated with Prognosis and Tumor Immune Infiltration Characteristics in Pancreatic Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Pingfei Tang ◽  
Weiming Qu ◽  
Dajun Wu ◽  
Shihua Chen ◽  
Minji Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Pancreatic Carcinoma ◽  
Cox Regression ◽  
Risk Group ◽  
Immune Dysfunction ◽  
Low Risk ◽  
Excellent Performance ◽  
Kaplan Meier ◽  
Prognostic Prediction

Background. Acidosis in the tumor microenvironment (TME) is involved in tumor immune dysfunction and tumor progression. We attempted to develop an acidosis-related index (ARI) signature to improve the prognostic prediction of pancreatic carcinoma (PC). Methods. Differential gene expression analyses of two public datasets (GSE152345 and GSE62452) from the Gene Expression Omnibus database were performed to identify the acidosis-related genes. The Cancer Genome Atlas–pancreatic carcinoma (TCGA-PAAD) cohort in the TCGA database was set as the discovery dataset. Univariate Cox regression and the Kaplan–Meier method were applied to screen for prognostic genes. The least absolute shrinkage and selection operator (LASSO) Cox regression was used to establish the optimal model. The tumor immune infiltrating pattern was characterized by the single-sample gene set enrichment analysis (ssGSEA) method, and the prediction of immunotherapy responsiveness was conducted using the tumor immune dysfunction and exclusion (TIDE) algorithm. Results. We identified 133 acidosis-related genes, of which 37 were identified as prognostic genes by univariate Cox analysis in combination with the Kaplan–Meier method ( p values of both methods < 0.05). An acidosis-related signature involving seven genes (ARNTL2, DKK1, CEP55, CTSV, MYEOV, DSG2, and GBP2) was developed in TCGA-PAAD and further validated in GSE62452. Patients in the acidosis-related high-risk group consistently showed poorer survival outcomes than those in the low-risk group. The 5-year AUCs (areas under the curve) for survival prediction were 0.738 for TCGA-PAAD and 0.889 for GSE62452, suggesting excellent performance. The low-risk group in TCGA-PAAD showed a higher abundance of CD8+ T cells and activated natural killer cells and was predicted to possess an elevated proportion of immunotherapeutic responders compared with the high-risk counterpart. Conclusions. We developed a reliable acidosis-related signature that showed excellent performance in prognostic prediction and correlated with tumor immune infiltration, providing a new direction for prognostic evaluation and immunotherapy management in PC.

scholarly journals An immune-related lncRNA signature as a prognostic immunotherapy target for colon cancer

2020 ◽  
Author(s):  
Bin Wu ◽  
Yi Yao ◽  
Yi Dong ◽  
Si Qi Yang ◽  
Deng Jing Zhou ◽  
...  
Keyword(s):  
Colon Cancer ◽  
High Risk ◽  
Risk Score ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Risk Scores ◽  
Gene Set Enrichment ◽  
Immunotherapy Target

Abstract Background：We aimed to investigate an immune-related long non-coding RNA (lncRNA) signature that may be exploited as a potential immunotherapy target in colon cancer. Materials and methods: Colon cancer samples from The Cancer Genome Atlas (TCGA) containing available clinical information and complete genomic mRNA expression data were used in our study. We then constructed immune-related lncRNA co-expression networks to identify the most promising immune-related lncRNAs. According to the risk score developed from screened immune-related lncRNAs, the high-risk and low-risk groups were separated on the basis of the median risk score, which served as the cutoff value. An overall survival analysis was then performed to confirm that the risk score developed from screened immune-related lncRNAs could predict colon cancer prognosis. The prediction reliability was further evaluated in the independent prognostic analysis and receiver operating characteristic curve (ROC). A principal component analysis (PCA) and gene set enrichment analysis (GSEA) were performed for functional annotation. Results: Information for a total of 514 patients was included in our study. After multiplex analysis, 12 immune-related lncRNAs were confirmed as a signature to evaluate the risk scores for each patient with cancer. Patients in the low-risk group exhibited a longer overall survival (OS) than those in the high-risk group. Additionally, the risk scores were an independent factor, and the Area Under Curve (AUC) of ROC for accuracy prediction was 0.726. Moreover, the low-risk and high-risk groups displayed different immune statuses based on principal components and gene set enrichment analysis.Conclusions: Our study suggested that the signature consisting of 12 immune-related lncRNAs can provide an accessible approach to measuring the prognosis of colon cancer and may serve as a valuable antitumor immunotherapy.

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