Prognostic Value of a Stemness Index-Associated Signature in Primary Lower-Grade Glioma

Zhang, Mingwei; Wang, Xuezhen; Chen, Xiaoping; Guo, Feibao; Hong, Jinsheng

doi:10.3389/fgene.2020.00441

Cited by 45 publications

(36 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result, even if a gene is associated with the pathophysiological behavior of PDAC, the association of its gene expression level may not tightly reflect OS in some cases. Third, we defined signature_high and signature_low groups based on the median value, which is a common method that has been widely applied in published studies (55)(56)(57)(58).…”

Section: Discussionmentioning

confidence: 99%

Deciphering the Prognostic Implications of the Components and Signatures in the Immune Microenvironment of Pancreatic Ductal Adenocarcinoma

et al. 2021

View full text Add to dashboard Cite

Background: The treatment modalities for pancreatic ductal adenocarcinoma (PDAC) are limited and unsatisfactory. Although many novel drugs targeting the tumor microenvironment, such as immune checkpoint inhibitors, have shown promising efficacy for some tumors, few of them significantly prolong the survival of patients with PDAC due to insufficient knowledge on the tumor microenvironment.Methods: A single-cell RNA sequencing (scRNA-seq) dataset and seven PDAC cohorts with complete clinical and bulk sequencing data were collected for bioinformatics analysis. The relative proportions of each cell type were estimated using the gene set variation analysis (GSVA) algorithm based on the signatures identified by scRNA-seq or previous literature.Results: A meta-analysis of 883 PDAC patients showed that neutrophils are associated with worse overall survival (OS) for PDAC, while CD8+ T cells, CD4+ T cells, and B cells are related to prolonged OS for PDAC, with marginal statistical significance. Seventeen cell categories were identified by clustering analysis based on single-cell sequencing. Among them, CD8+ T cells and NKT cells were universally exhausted by expressing exhaustion-associated molecular markers. Interestingly, signatures of CD8+ T cells and NKT cells predicted prolonged OS for PDAC only in the presence of “targets” for pyroptosis and ferroptosis induction. Moreover, a specific state of T cells with overexpression of ribosome-related proteins was associated with a good prognosis. In addition, the hematopoietic stem cell (HSC)-like signature predicted prolonged OS in PDAC. Weighted gene co-expression network analysis identified 5 hub genes whose downregulation may mediate the observed survival benefits of the HSC-like signature. Moreover, trajectory analysis revealed that myeloid cells evolutionarily consisted of 7 states, and antigen-presenting molecules and complement-associated genes were lost along the pseudotime flow. Consensus clustering based on the differentially expressed genes between two states harboring the longest pseudotime span identified two PDAC groups with prognostic differences, and more infiltrated immune cells and activated immune signatures may account for the survival benefits.Conclusion: This study systematically investigated the prognostic implications of the components of the PDAC tumor microenvironment by integrating single-cell sequencing and bulk sequencing, and future studies are expected to develop novel targeted agents for PDAC treatment.

show abstract

Section: Discussionmentioning

confidence: 99%

Deciphering the Prognostic Implications of the Components and Signatures in the Immune Microenvironment of Pancreatic Ductal Adenocarcinoma

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Studies have explored the effect of the stemness index on patient prognosis in glioma [ 9 ] and lung cancer [ 10 ], and a risk signature based on mRNAsi-related genes has been established. Through Kaplan–Meier analysis, we found that endometrial cancer patients with a high mRNAsi had significantly poorer overall survival ( P =0.046) than patients with a low mRNAsi.…”

Section: Discussionmentioning

confidence: 99%

“…Malta et al used a new one-class logistic regression machine learning algorithm (OCLR) to extract indicators describing the stemness characteristics of tumor cells, including the mRNA expression-based stemness index (mRNAsi) [ 8 ]. Studies have explored genes related to mRNAsi in a variety of cancers and have analyzed the effects of these genes on cancer patient prognosis [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Risk Signature with Nine Stemness Index-Associated Genes for Predicting Survival of Patients with Uterine Corpus Endometrial Carcinoma

Zou

Liu

et al. 2021

Journal of Oncology

View full text Add to dashboard Cite

Purpose. To identify mRNA expression-based stemness index- (mRNAsi-) related genes and build an mRNAsi-related risk signature for endometrial cancer. Methods. We collected mRNAsi data of endometrial cancer samples from The Cancer Genome Atlas (TCGA) and analyzed their relationship with the main clinicopathological characteristics and prognosis of endometrial cancer patients. We screened the top 50% of the genes in TCGA for weighted gene correlation network analysis (WGCNA) to explore mRNAsi-related gene sets. Among these mRNAsi-related genes, we further screened for those related to the prognosis of endometrial cancer patients via univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Using stepwise multivariate Cox regression analysis, a stemness index-related risk signature was constructed. Finally, we identified potential prognostic biomarkers for endometrial cancer by combining the GEO database and immunohistochemical staining. Results. The mRNAsi of endometrial cancer samples was significantly higher than that of normal samples and was related to the International Federation of Gynecology and Obstetrics (FIGO) stage, pathological grade, postoperative tumor status, and overall survival of endometrial cancer patients. We identified 21 mRNAsi-related gene modules, and 1,324 genes were obtained from the most relevant module. TCGA samples were divided into training and validation cohorts, and the training cohort was used to construct a nine-mRNAsi-related gene signature (B3GAT2, CD3EAP, DMC1, FRMPD3, LINC01224, LINC02068, LY6H, NR6A1, and TLE2). High-risk and low-risk patients had significant prognostic differences, and the risk signature could accurately predict their 1-, 3-, and 5-year survival. The nomogram composed of risk score and multiple clinicopathological features could accurately predict 1-, 3-, and 5-year survival. Finally, CD3EAP was found to be a novel prognostic biomarker for endometrial cancer. Conclusion. Endometrial cancer cell stemness is related to patient prognosis. The nine-gene risk signature is an independent prognostic factor and can accurately predict endometrial cancer patient prognosis.

show abstract

“…To identify the prognosis-associated OS genes, hub genes identified in the PPI network were subjected to univariate Cox regression analysis using the “survival” package in R to identify genes that are highly crucial for patients’ survival ( Zhang et al, 2020a ), with a cut-off criterion of P < 0.05. After that, genes identified to be significantly associated with the overall survival of PC patients through the univariate Cox regression analysis were integrated for analysis using LASSO, a widely used machine-learning algorithm, which can preserve valuable variables and avoid overfitting ( Jiang et al, 2018 ), to complete the shrinkage of prognostic OS genes and categorizes patients into high- or low-risk subgroups.…”

Section: Methodsmentioning

confidence: 99%

Identification of Hub Prognosis-Associated Oxidative Stress Genes in Pancreatic Cancer Using Integrated Bioinformatics Analysis

et al. 2020

View full text Add to dashboard Cite

BackgroundIntratumoral oxidative stress (OS) has been associated with the progression of various tumors. However, OS has not been considered a candidate therapeutic target for pancreatic cancer (PC) owing to the lack of validated biomarkers.MethodsWe compared gene expression profiles of PC samples and the transcriptome data of normal pancreas tissues from The Cancer Genome Atlas (TCGA) and Genome Tissue Expression (GTEx) databases to identify differentially expressed OS genes in PC. PC patients’ gene profile from the Gene Expression Omnibus (GEO) database was used as a validation cohort.ResultsA total of 148 differentially expressed OS-related genes in PC were used to construct a protein-protein interaction network. Univariate Cox regression analysis, least absolute shrinkage, selection operator analysis revealed seven hub prognosis-associated OS genes that served to construct a prognostic risk model. Based on integrated bioinformatics analyses, our prognostic model, whose diagnostic accuracy was validated in both cohorts, reliably predicted the overall survival of patients with PC and cancer progression. Further analysis revealed significant associations between seven hub gene expression levels and patient outcomes, which were validated at the protein level using the Human Protein Atlas database. A nomogram based on the expression of these seven hub genes exhibited prognostic value in PC.ConclusionOur study provides novel insights into PC pathogenesis and provides new genetic markers for prognosis prediction and clinical treatment personalization for PC patients.

show abstract

Prognostic Value of a Stemness Index-Associated Signature in Primary Lower-Grade Glioma

Cited by 45 publications

References 61 publications

Deciphering the Prognostic Implications of the Components and Signatures in the Immune Microenvironment of Pancreatic Ductal Adenocarcinoma

Deciphering the Prognostic Implications of the Components and Signatures in the Immune Microenvironment of Pancreatic Ductal Adenocarcinoma

A Risk Signature with Nine Stemness Index-Associated Genes for Predicting Survival of Patients with Uterine Corpus Endometrial Carcinoma

Identification of Hub Prognosis-Associated Oxidative Stress Genes in Pancreatic Cancer Using Integrated Bioinformatics Analysis

Contact Info

Product

Resources

About