Identification of a 15 DNA Damage Repair–Related Gene Signature as a Prognostic Predictor for Lung Adenocarcinoma

Background: Lung Adenocarcinoma (LUAD) is a common malignancy with a poor prognosis due to the lack of predictive markers. DNA Damage Repair (DDR)-related genes are closely related to cancer progression and treatment. Introduction: To identify a reliable DDR-related gene signature as an independent predictor of LUAD. Methods: DDR-related genes were obtained using combined analysis of TCGA-LUAD data and literature information, followed by the identification of DDR-related prognostic genes. The DDR-related molecular subtypes were then screened, followed by Kaplan–Meier analysis, feature gene identification, and pathway enrichment analysis of each subtype. Moreover, Cox and LASSO regression analyses were performed for the feature genes of each subtype to construct a prognostic model. The clinical utility of the prognostic model was confirmed using the validation dataset GSE72094 and nomogram analysis. Results: Eight DDR-related prognostic genes were identified from 31 DDR-related genes. Using consensus cluster analysis, three molecular subtypes were screened. Cluster 2 had the best prognosis, while cluster 3 had the worst. Compared to cluster 2, clusters 1 and 3 consisted of more stage 3 – 4, T2–T4, male, and older samples. The feature genes of clusters 1, 2, and 3 were mainly enriched in the cell cycle, arachidonic acid metabolism, and ribosomes. Furthermore, a 15-feature gene signature was identified for improving the prognosis of LUAD patients. Conclusion: The 15 DDR-related feature gene signature is an independent and powerful prognostic biomarker for LUAD that may improve risk classification and provide supplementary information for a more accurate evaluation and personalized treatment. Conclusion: The 15 DDR-related feature gene signature is an independent and powerful prognostic biomarker for LUAD that may improve risk classification and provide supplementary information for a more accurate evaluation and personalized treatment.

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Identification of a Liver Progenitor Cell-Related Genes Signature Predicting Overall Survival for Hepatocellular Carcinoma

Technology in Cancer Research & Treatment ◽

10.1177/15330338211041425 ◽

2021 ◽

Vol 20 ◽

pp. 153303382110414

Author(s):

Xiaoyong Li ◽

Jiaqong Lin ◽

Yuguo pan ◽

Peng Cui ◽

Jintang Xia

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Regression Analysis ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

Regression Analyses ◽

Related Gene ◽

Cox Regression Analysis

Background: Liver progenitor cells (LPCs) play significant roles in the development and progression of hepatocellular carcinoma (HCC). However, no studies on the value of LPC-related genes for evaluating HCC prognosis exist. We developed a gene signature of LPC-related genes for prognostication in HCC. Methods: To identify LPC-related genes, we analyzed mRNA expression arrays from a dataset (GSE57812 & GSE 37071) containing LPCs, mature hepatocytes, and embryonic stem cell samples. HCC RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to explore the differentially expressed genes (DEGs) related to prognosis through DEG analysis and univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed to construct the LPC-related gene prognostic model in the TCGA training dataset. This model was validated in the TCGA testing set and an external dataset (International Cancer Genome Consortium [ICGC] dataset). Finally, we investigated the relationship between this prognostic model with tumor-node-metastasis stage, tumor grade, and vascular invasion of HCC. Results: Overall, 1770 genes were identified as LPC-related genes, of which 92 genes were identified as DEGs in HCC tissues compared with normal tissues. Furthermore, we randomly assigned patients from the TCGA dataset to the training and testing cohorts. Twenty-six DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed in the TCGA training set, and a 3-gene signature was constructed to stratify patients into 2 risk groups: high-risk and low-risk. Patients in the high-risk group had significantly lower OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the signature's predictive capacity. Moreover, the risk score was confirmed to be an independent predictor for patients with HCC. Conclusion: We demonstrated that the LPC-related gene signature can be used for prognostication in HCC. Thus, targeting LPCs may serve as a therapeutic alternative for HCC.

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A Liquid-Liquid Phase Separation-Related Gene Signature as Prognostic Biomarker for Epithelial Ovarian Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.671892 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yan Qiu ◽

Min Pan ◽

Xuemei Chen

Keyword(s):

Ovarian Cancer ◽

Phase Separation ◽

Prognostic Biomarker ◽

Risk Group ◽

Risk Index ◽

Gene Signature ◽

Related Gene ◽

Data Set ◽

Test Set ◽

Liquid Liquid Phase Separation

ObjectiveThe aim of the present study was to construct and test a liquid-liquid phase separation (LLPS)-related gene signature as a prognostic tool for epithelial ovarian cancer (EOC).Materials and MethodsThe data set GSE26712 was used to screen the differentially expressed LLPS-related genes. Functional enrichment analysis was performed to reveal the potential biological functions. GSE17260 and GSE32062 were combined as the discovery to construct an LLPS-related gene signature through a three-step analysis (univariate Cox, least absolute shrinkage and selection operator, and multivariate Cox analyses). The EOC data set from The Cancer Genome Atlas as the test set was used to test the LLPS-related gene signature.ResultsThe differentially expressed LLPS-related genes involved in several cancer-related pathways, such as MAPK signaling pathway, cell cycle, and DNA replication. Eleven genes were selected to construct the LLPS-related gene signature risk index as prognostic biomarker for EOC. The risk index could successfully divide patients with EOC into high- and low-risk groups. The patients in high-risk group had significantly shorter overall survival than those with in low-risk group. The LLPS-related gene signature was validated in the test set and may be an independent prognostic factor compared to routine clinical features.ConclusionWe constructed and validated an LLPS-related gene signature as a prognosis tool in EOC through integrated analysis of multiple data sets.

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Development and Validation of a Novel 11-Gene Prognostic Model for Serous Ovarian Carcinomas Based on Lipid Metabolism Expression Profile

International Journal of Molecular Sciences ◽

10.3390/ijms21239169 ◽

2020 ◽

Vol 21 (23) ◽

pp. 9169

Author(s):

Mingjun Zheng ◽

Heather Mullikin ◽

Anna Hester ◽

Bastian Czogalla ◽

Helene Heidegger ◽

...

Keyword(s):

Ovarian Cancer ◽

Lipid Metabolism ◽

Prognostic Model ◽

Molecular Subtypes ◽

Gene Signature ◽

Functional Enrichment ◽

Serous Ovarian Cancer ◽

Ovarian Carcinomas ◽

Comparable Amount ◽

Lipid Metabolism Genes

(1) Background: Biomarkers might play a significant role in predicting the clinical outcomes of patients with ovarian cancer. By analyzing lipid metabolism genes, future perspectives may be uncovered; (2) Methods: RNA-seq data for serous ovarian cancer were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. The non-negative matrix factorization package in programming language R was used to classify molecular subtypes of lipid metabolism genes and the limma package in R was performed for functional enrichment analysis. Through lasso regression, we constructed a multi-gene prognosis model; (3) Results: Two molecular subtypes were obtained and an 11-gene signature was constructed (PI3, RGS, ADORA3, CH25H, CCDC80, PTGER3, MATK, KLRB1, CCL19, CXCL9 and CXCL10). Our prognostic model shows a good independent prognostic ability in ovarian cancer. In a nomogram, the predictive efficiency was notably superior to that of traditional clinical features. Related to known models in ovarian cancer with a comparable amount of genes, ours has the highest concordance index; (4) Conclusions: We propose an 11-gene signature prognosis prediction model based on lipid metabolism genes in serous ovarian cancer.

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Identification of Immune-Related Gene Signatures in Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

Frontiers in Immunology ◽

10.3389/fimmu.2021.752643 ◽

2021 ◽

Vol 12 ◽

Author(s):

Na Li ◽

Jiahong Wang ◽

Xianquan Zhan

Keyword(s):

Prognostic Model ◽

Immune Cell ◽

Lung Squamous Cell Carcinoma ◽

Risk Groups ◽

Gene Signature ◽

Low Risk ◽

Prognostic Models ◽

Related Gene ◽

Immune Related Gene ◽

Immune Related Genes

Accumulating evidence indicates that immunotherapy helped to improve the survival and quality-of-life of patients with lung adenocarcinoma (LUAD) or lung squamous cell carcinoma (LUSC) besides chemotherapy and gene targeting treatment. This study aimed to develop immune-related gene signatures in LUAD and LUSC subtypes, respectively. LUAD and LUSC samples were divided into high- and low-abundance groups of immune cell infiltration (Immunity_H and Immunity_L) based on the abundance of immune cell infiltrations. The distribution of immune cells was significantly different between the high- and low-immunity subtypes in LUAD and LUSC samples. The differentially expressed genes (DEGs) between those two groups in LUAD and LUSC contain some key immune-related genes, such as PDL1, PD1, CTLA-4, and HLA families. The DEGs were enriched in multiple immune-related pathways. Furthermore, the seven-immune-related-gene-signature (CD1B, CHRNA6, CLEC12B, CLEC17A, CLNK, INHA, and SLC14A2) prognostic model-based high- and low-risk groups were significantly associated with LUAD overall survival and clinical characteristics. The eight-immune-related-gene-signature (C4BPB, FCAMR, GRAPL, MAP1LC3C, MGC2889, TRIM55, UGT1A1, and VIPR2) prognostic model-based high- and low-risk groups were significantly associated with LUSC overall survival and clinical characteristics. The prognostic models were tested as good ones by receiver operating characteristic, principal component analysis, univariate and multivariate analysis, and nomogram. The verifications of these two immune-related-gene-signature prognostic models showed consistency in the train and test cohorts of LUAD and LUSC. In addition, patients with LUAD in the low-risk group responded better to immunotherapy than those in the high-risk group. This study revealed two reliable immune-related-gene-signature models that were significantly associated with prognosis and tumor microenvironment cell infiltration in LUAD and LUSC, respectively. Evaluation of the integrated characterization of multiple immune-related genes and pathways could help to predict the response to immunotherapy and monitor immunotherapy strategies.

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Development and Validation of a Novel Mitophagy-Related Gene Prognostic Signature for Hepatocellular Carcinoma Based on Immunoscore Classification of Tumor

Journal of Oncology ◽

10.1155/2021/5070099 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Hao Chen ◽

Jinghua Wang ◽

Ruijie Zeng ◽

Yujun Luo ◽

Kehang Guo ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Prognostic Model ◽

Univariate Analysis ◽

Risk Groups ◽

Gene Signature ◽

Related Gene ◽

Prognosis Model ◽

Prognostic Prediction ◽

Selection Operator ◽

Functional Analyses

Emerging evidence suggested that mitophagy may play an important role in the progression of hepatocellular carcinoma (HCC), whereas the association between mitophagy-related genes and HCC patients’ prognosis remains unknown. In this study, we aimed to investigate the potential prognostic values of mitophagy-related genes (MRGs) on HCC patients at the genetic level. According to median immunoscore, we categorized HCC patients from TCGA cohort into two immune score groups, while 39 differential expression MRGs were identified. By using univariate analysis, we screened out 18 survival-associated MRGs, and then, the least absolute shrinkage and selection operator (LASSO) analysis was applied to construct a prognosis model that consisted of 9 MRGs (ATG7, ATG9A, BNIP3L, GABARAPL1, HTRA2, MAP1LC3B2, TFE3, TIGAR, and TOMM70). In our prognostic model, overall survival in the high and low-risk groups was significantly different P < 0.001 , and the respective areas under the curve (AUC) of our prognostic model were 0.686 for 3-year survival in the TCGA cohort and 0.776 for 3-year survival in the ICGC cohort. Moreover, we identified the risk score as the independent factor for predicting the HCC patients’ prognosis by using single and multifactor analyses, and a nomogram was also constructed for future clinical application. Further functional analyses showed that the immune status between two risk groups was significantly different. Our findings may provide a novel mitophagy-related gene signature, and these will be better used for prognostic prediction in HCC, thus improving patient outcome.

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Development of a novel miR-3648-related gene signature as a prognostic biomarker in esophageal adenocarcinoma

Annals of Translational Medicine ◽

10.21037/atm-21-6237 ◽

2021 ◽

Vol 9 (22) ◽

pp. 1702-1702

Author(s):

Donglei Zhang ◽

Hang Yin ◽

Thomas L. Bauer ◽

Michael P. Rogers ◽

Jeffrey B. Velotta ◽

...

Keyword(s):

Esophageal Adenocarcinoma ◽

Prognostic Biomarker ◽

Gene Signature ◽

Related Gene

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Comprehensive Analysis of Tumor Microenvironment Identified Prognostic Immune-Related Gene Signature in Ovarian Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.616073 ◽

2021 ◽

Vol 12 ◽

Author(s):

Na Li ◽

Biao Li ◽

Xianquan Zhan

Keyword(s):

Ovarian Cancer ◽

Immune Cells ◽

Prognostic Model ◽

Immune Cell ◽

Gene Signature ◽

Differentially Expressed ◽

Immune Cell Infiltration ◽

Related Gene ◽

Immune Related Gene ◽

Immune Related Genes

BackgroundAccumulating evidence demonstrated that tumor microenvironmental cells played important roles in predicting clinical outcomes and therapeutic efficacy. We aimed to develop a reliable immune-related gene signature for predicting the prognosis of ovarian cancer (OC).MethodsSingle sample gene-set enrichment analysis (ssGSEA) of immune gene-sets was used to quantify the relative abundance of immune cell infiltration and develop high- and low-abundance immune subtypes of 308 OC samples. The presence of infiltrating stromal/immune cells in OC tissues was calculated as an estimate score. We estimated the correlation coefficients among the immune subtype, clinicopathological feature, immune score, distribution of immune cells, and tumor mutation burden (TMB). The differentially expressed immune-related genes between high- and low-abundance immune subtypes were further used to construct a gene signature of a prognostic model in OC with lasso regression analysis.ResultsThe ssGSEA analysis divided OC samples into high- and low-abundance immune subtypes based on the abundance of immune cell infiltration, which was significantly related to the estimate score and clinical characteristics. The distribution of immune cells was also significantly different between high- and low-abundance immune subtypes. The correlation analysis showed the close relationship between TMB and the estimate score. The differentially expressed immune-related genes between high- and low-abundance immune subtypes were enriched in multiple immune-related pathways. Some immune checkpoints (PDL1, PD1, and CTLA-4) were overexpressed in the high-abundance immune subtype. Furthermore, the five-immune-related-gene-signature prognostic model (CCL18, CXCL13, HLA-DOB, HLA-DPB2, and TNFRSF17)-based high-risk and low-risk groups were significantly related to OC overall survival.ConclusionImmune-related genes were the promising predictors of prognosis and survival, and the comprehensive landscape of tumor microenvironmental cells of OC has potential for therapeutic schedule monitoring.

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Identification of Mast Cell-Based Molecular Subtypes and a Predictive Signature in Clear Cell Renal Cell Carcinoma

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.719982 ◽

2021 ◽

Vol 8 ◽

Author(s):

Hanxiang Liu ◽

Yi Yang

Keyword(s):

Renal Cell Carcinoma ◽

Mast Cell ◽

Mast Cells ◽

Cell Carcinoma ◽

Renal Cell ◽

Prognostic Model ◽

Cox Regression ◽

Clear Cell ◽

Molecular Subtypes ◽

Cluster 2

Background: Kidney renal clear cell carcinoma (KIRC) is a common malignant tumor of the urinary system. Surgery is the preferred treatment option; however, the rate of distant metastasis is high. Mast cells in the tumor microenvironment promote or inhibit tumorigenesis depending on the cancer type; however, their role in KIRC is not well-established. Here, we used a bioinformatics approach to evaluate the roles of mast cells in KIRC.Methods: To quantify mast cell abundance based on gene sets, a single-sample gene set enrichment analysis (ssGSEA) was utilized to analyze three datasets. Weighted correlation network analysis (WGCNA) was used to identify the genes most closely related to mast cells. To identify new molecular subtypes, the nonnegative matrix factorization algorithm was used. GSEA and least absolute shrinkage and selection operator (LASSO) Cox regression were used to identify genes with high prognostic value. A multivariate Cox regression analysis was performed to establish a prognostic model based on mast cell-related genes. Promoter methylation levels of mast cell-related genes and relationships between gene expression and survival were evaluated using the UALCAN and GEPIA databases.Results: A prolonged survival in KIRC was associated with a high mast cell abundance. KIRC was divided into two molecular subtypes (cluster 1 and cluster 2) based on mast cell-related genes. Genes in Cluster 1 were enriched for various functions related to cancer development, such as the TGFβ signaling pathway, renal cell carcinoma, and mTOR signaling pathway. Based on drug sensitivity predictions, sensitivity to doxorubicin was higher for cluster 2 than for cluster 1. By a multivariate Cox analysis, we established a clinical prognostic model based on eight mast cell-related genes.Conclusion: We identified eight mast cell-related genes and constructed a clinical prognostic model. These results improve our understanding of the roles of mast cells in KIRC and may contribute to personalized medicine.

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A Pyroptosis-Related Gene Signature for Predicting Survival in Glioblastoma

Frontiers in Oncology ◽

10.3389/fonc.2021.697198 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xin-Yu Li ◽

Lu-Yu Zhang ◽

Xue-Yuan Li ◽

Xi-Tao Yang ◽

Li-Xin Su

Keyword(s):

Cell Adhesion ◽

T Cell ◽

T Cell Activation ◽

Prognostic Model ◽

Cell Activation ◽

Cox Regression ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Related Gene ◽

Model Based

BackgroundIn this study, a prognostic model based on pyroptosis-related genes was established to predict overall survival (OS) in patients with glioblastoma (GBM).MethodsThe gene expression data and clinical information of GBM patients were obtained from The Cancer Genome Atlas (TCGA), and bioinformatics analysis of differentially expressed genes was performed. LASSO Cox regression model was used to construct a three-pyroptosis-related gene signature, and validation was performed using an experimental cohort.ResultsA total of three pyroptosis-related genes (CASP4, CASP9, and NOD2) were used to construct a survival prognostic model, and experimental validation was performed using an experimental cohort. Receiver operating characteristic (ROC) analysis was performed, and the area under the ROC curves (AUC) was 0.921, 0.840, and 0.905 at 1, 3, and 5 years, respectively. Functional analysis revealed that T-cell activation, regulation of T-cell activation, leukocyte cell-cell adhesion, and positive regulation of cell adhesion among other immune-related functions were enriched, and immune-related processes were different between the two risk groups.ConclusionIn this study, a novel prognostic model based on three pyroptosis-related genes is constructed and used to predict the prognosis of GBM patients. The model can accurately and conveniently predict the 1-, 3-, and 5-year OS of GBM patients.

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