scholarly journals Development and validation of an immune-related gene prognostic model for stomach adenocarcinoma

2020 ◽  
Vol 40 (10) ◽  
Author(s):  
Ming Wu ◽  
Yu Xia ◽  
Yadong Wang ◽  
Fei Fan ◽  
Xian Li ◽  
...  
Keyword(s):  
Prognostic Model ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
Risk Group ◽  
Prognostic Models ◽  
Related Gene ◽  
Cox Regression Analysis ◽  
Model Based ◽  
Immune Related Gene ◽  
Stomach Adenocarcinoma

Abstract Purpose: Stomach adenocarcinoma (STAD) is one of the most common malignant tumors, and its occurrence and prognosis are closely related to inflammation. The aim of the present study was to identify gene signatures and construct an immune-related gene (IRG) prognostic model in STAD using bioinformatics analysis. Methods: RNA sequencing data from healthy samples and samples with STAD, IRGs, and transcription factors were analyzed. The hub IRGs were identified using univariate and multivariate Cox regression analyses. Using the hub IRGs, we constructed an IRG prognostic model. The relationships between IRG prognostic models and clinical data were tested. Results: A total of 289 differentially expressed IRGs and 20 prognostic IRGs were screened with a threshold of P<0.05. Through multivariate stepwise Cox regression analysis, we developed a prognostic model based on seven IRGs. The prognostic model was validated using a GEO dataset (GSE 84437). The IRGs were significantly correlated with the clinical outcomes (age, histological grade, N, and M stage) of STAD patients. The infiltration abundances of dendritic cells and macrophages were higher in the high-risk group than in the low-risk group. Conclusions: Our results provide novel insights into the pathogenesis of STAD. An IRG prognostic model based on seven IRGs exhibited the predictive value, and have potential application value in clinical decision-making and individualized treatment.

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

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.

scholarly journals Construction of a Prognostic Model for Predicting Overall Survival of Patients with Colorectal Cancer

2020 ◽  
Author(s):  
Shuwen Han ◽  
Kefeng Ding
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
Regression Analysis ◽  
Levulinic Acid ◽  
Prognostic Model ◽  
Cox Regression ◽  
Aminolevulinic Acid ◽  
Risk Group ◽  
Prognostic Models ◽  
Cox Regression Analysis

Abstract Background: Colorectal cancer (CRC) is one of the most common malignancies. The purpose of this study is to construct a prognostic model for predicting the overall survival (OS) in patients with CRC. Methods: The mRNA-seq and miRNA-seq data of colon adenocarcinoma (COAD) and rectal adenocarcinoma (READ) were downloaded from The Cancer Genome Atlas (TCGA) database. The differentially expressed RNAs (DE-RNAs) between tumor and normal tissues were screened. The Kaplan-Meier and univariate Cox regression analysis were used to screen the survival-related genes. Functional enrichment analysis of survival-related genes was conducted, followed by protein-protein interaction (PPI) analysis. Subsequently, the potential drugs targeting differentially expressed mRNAs (DE-mRNAs) were investigated. Multivariate Cox regression analysis was then conducted to screen the independent prognostic factors, and these genes were used to establish a prognostic model. A receiver operator characteristic (ROC) curve was constructed, and the area under the curve (AUC) value of ROC was calculated to evaluate the specificity and sensitivity of the model. Results: A total of 855 survival-related genes were screened. These genes were mainly enriched in Gene Ontology (GO) terms, such as methylation, synapse organization, and methyltransferase activity; and pathway analysis showed that these genes were significantly involved in N-Glycan biosynthesis and the calcium signaling pathway. PPI analysis showed that aminolevulinate dehydratase (ALAD) and cholinergic receptor muscarinic 2 (CHRM2) served vital roles in the development of CRC. Aminolevulinic acid, levulinic acid, and loxapine might be potential drugs for CRC treatment. The prognostic models were built and the patients were divided into high-risk and low-risk groups based on the median of risk score (RS) as screening threshold. The OS for patients in the high-risk group was markedly shorter than that for patients in the low-risk group. Meanwhile, kazal type serine peptidase inhibitor domain 1 (KAZALD1), hippocalcin like 4 (HPCAL4), cadherin 8 (CDH8), synaptopodin 2 (SYNPO2), cyclin D3 (CCND3), and hsa_mir_26b may be independent prognostic factors that could be considered as therapeutic targets for CRC.Conclusion: We established prognostic models that could predict the OS for CRC patients and may assist clinicians in providing personalized and precision treatment in this patient population.Highlights:1. ALAD served a vital role in the development of CRC.2. CHRM2 played a role in CRC development by affecting the calcium signaling pathway.3. Aminolevulinic acid, levulinic acid, and loxapine might be potential drugs for treating CRC.4. KAZALD1 and HPCAL4 were associated with the OS of CRC.5. CDH8, SYNPO2, CCND3, and hsa-mir-26b were closely related to the prognostic of CRC staging.

scholarly journals Development of an immune-related gene pairs signature for predicting clinical outcome in lung adenocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chunlei Wu ◽  
Quanteng Hu ◽  
Dehua Ma
Keyword(s):  
Overall Survival ◽  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Related Gene ◽  
Cox Regression Analysis ◽  
Gene Pairs ◽  
Immune Related Gene ◽  
Pathological Subtype

AbstractLung adenocarcinoma (LUAD) is the main pathological subtype of Non-small cell lung cancer. We downloaded the gene expression profile and immune-related gene set from the TCGA and ImmPort database, respectively, to establish immune-related gene pairs (IRGPs). Then, IRGPs were subjected to univariate Cox regression analysis, LASSO regression analysis, and multivariable Cox regression analysis to screen and develop an IRGPs signature. The receiver operating characteristic curve (ROC) was applied for evaluating the predicting accuracy of this signature by calculating the area under ROC (AUC) and data from the GEO set was used to validate this signature. The relationship of 22 tumor-infiltrating immune cells (TIICs) to the immune risk score was also investigated. An IRGPs signature with 8 IRGPs was constructed. The AUC for 1- and 3-year overall survival in the TCGA set was 0.867 and 0.870, respectively. Similar results were observed in the AUCs of GEO set 1, 2 and 3 (GEO set 1 [1-year: 0.819; 3-year: 0.803]; GEO set 2 [1-year: 0.834; 3-year: 0.870]; GEO set 3 [1-year: 0.955; 3-year: 0.827]). Survival analysis demonstrated high-risk LUAD patients exhibited poorer prognosis. The multivariable Cox regression indicated that the risk score was an independent prognostic factor. The immune risk score was highly associated with several TIICs (Plasma cells, memory B cells, resting memory CD4 T cells, and activated NK cells). We developed a novel IRGPs signature for predicting 1- and 3- year overall survival in LUAD, which would be helpful for prognosis assessment of LUAD.

scholarly journals Prognostic model of head and neck squamous cell carcinoma based on 12 ferroptosis-related genes

2021 ◽  
Author(s):  
Sijia Li ◽  
Hongyang Zhang ◽  
Wei Li
Keyword(s):  
Regression Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Risk Scores ◽  
Cox Regression Analysis ◽  
Normal Tissues ◽  
Model Based ◽  
Cox Analysis

Abstract Background: The purpose of our study is establishing a model based on ferroptosis-related genes predicting the prognosis of patients with head and neck squamous cell carcinoma (HNSCC).Methods: In our study, transcriptome and clinical data of HNSCC patients were from The Cancer Genome Atlas, ferroptosis-related genes and pathways were from Ferroptosis Signatures Database. Differentially expressed genes (DEGs) were screened by comparing tumor and adjacent normal tissues. Functional enrichment analysis of DEGs, protein-protein interaction network and gene mutation examination were applied. Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression were used to identified DEGs. The model was constructed by multivariate Cox regression analysis and verified by Kaplan-Meier analysis. The relationship between risk scores and other clinical features was also analyzed. Univariate and multivariate Cox analysis was used to verified the independence of our model. The model was evaluated by receiver operating characteristic analysis and calculation of the area under the curve (AUC). A nomogram model based on risk score, age, gender and TNM stages was constructed.Results: We analyzed data including 500 tumor tissues and 44 adjacent normal tissues and 259 ferroptosis-related genes, then obtained 73 DEGs. Univariate Cox regression analysis screened out 16 genes related to overall survival, and LASSO analysis fingered out 12 of them with prognostic value. A risk score model based on these 12 genes was constructed by multivariate Cox regression analysis. According to the median risk score, patients were divided into high-risk group and low-risk group. The survival rate of high-risk group was significantly lower than that of low-risk group in Kaplan-Meier curve. Risk scores were related to T and grade. Univariate and multivariate Cox analysis showed our model was an independent prognostic factor. The AUC was 0.669. The nomogram showed high accuracy predicting the prognosis of HNSCC patients.Conclusion: Our model based on 12 ferroptosis-related genes performed excellently in predicting the prognosis of HNSCC patients. Ferroptosis-related genes may be promising biomarkers for HNSCC treatment and prognosis.

scholarly journals A risk stratification model based on four novel biomarkers predicts prognosis for patients with renal cell carcinoma

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Shigehisa Kubota ◽  
Tetsuya Yoshida ◽  
Susumu Kageyama ◽  
Takahiro Isono ◽  
Takeshi Yuasa ◽  
...  
Keyword(s):  
Prognostic Model ◽  
Cox Regression ◽  
Treatment Strategies ◽  
Immunohistochemical Analysis ◽  
Cancer Specific Survival ◽  
Clinical Prognosis ◽  
Cox Regression Analysis ◽  
Model Based ◽  
Novel Biomarkers ◽  
Metastatic Rcc

Abstract Background Accurate prediction of the prognosis of RCC using a single biomarker is challenging due to the genetic heterogeneity of the disease. However, it is essential to develop an accurate system to allow better patient selection for optimal treatment strategies. ARL4C, ECT2, SOD2, and STEAP3 are novel molecular biomarkers identified in earlier studies as survival-related genes by comprehensive analyses of 43 primary RCC tissues and RCC cell lines. Methods To develop a prognostic model based on these multiple biomarkers, the expression of four biomarkers ARL4C, ECT2, SOD2, and STEAP3 in primary RCC tissue were semi-quantitatively investigated by immunohistochemical analysis in an independent cohort of 97 patients who underwent nephrectomy, and the clinical significance of these biomarkers were analyzed by survival analysis using Kaplan-Meier curves. The prognostic model was constructed by calculation of the contribution score to prognosis of each biomarker on Cox regression analysis, and its prognostic performance was validated. Results Patients whose tumors had high expression of the individual biomarkers had shorter cancer-specific survival (CSS) from the time of primary nephrectomy. The prognostic model based on four biomarkers segregated the patients into a high- and low-risk scored group according to defined cut-off value. This approach was more robust in predicting CSS compared to each single biomarker alone in the total of 97 patients with RCC. Especially in the 36 metastatic RCC patients, our prognostic model could more accurately predict early events within 2 years of diagnosis of metastasis. In addition, high risk-scored patients with particular strong SOD2 expression had a much worse prognosis in 25 patients with metastatic RCC who were treated with molecular targeting agents. Conclusions Our findings indicate that a prognostic model based on four novel biomarkers provides valuable data for prediction of clinical prognosis and useful information for considering the follow-up conditions and therapeutic strategies for patients with primary and metastatic RCC.

scholarly journals A Novel Signature of 23 Immunity-related Gene Pairs is Prognostic of Cutaneous Melanoma

2020 ◽  
Author(s):  
Yanan Xue ◽  
Yinan Xue ◽  
Zhengcai Wang ◽  
Yongzhen Mo ◽  
Pinyan Wang ◽  
...  
Keyword(s):  
Cutaneous Melanoma ◽  
Prognostic Model ◽  
Risk Group ◽  
Risk Groups ◽  
Related Gene ◽  
Prognostic Signature ◽  
Gene Pairs ◽  
Immune Related Gene ◽  
Technical Bias ◽  
Tcga Dataset

Abstract Background: We aimed to identify immune-related signature for predicting cutaneous melanoma (CM) prognosis. Methods: We used TCGA samples (n=471) to develop the best 23 Immune related gene pairs (23-IRGP) prognostic signature and divided patients into high- and low-immune risk group in TCGA dataset and validation datasets: GSE65904 (n=214), GSE59455 (n=141), and GSE22153 (n=79). Results: 23-IRGP presented precise ability in cutaneous melanoma (CM) which high-risk groups showed poor prognosis and indicated significant predict power in immune micro-environment and biological analysis as well. Conclusions: we established a novel promising prognostic model in CM and built the bridge between immune micro-environment and CM patient results. This approach can be applied to discover the signatures in other diseases without technical bias from different platforms.

scholarly journals Nonnegative Matrix Factorization Model-Based Construction For Molecular Clustering and Prognostic Assessment of Squamous Carcinoma of Head and Neck

2021 ◽  
Author(s):  
Xin-Yu Li ◽  
Xi-Tao Yang
Keyword(s):  
Head And Neck ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Cox Regression ◽  
Nonnegative Matrix ◽  
Squamous Carcinoma ◽  
Prognostic Models ◽  
Cox Regression Analysis ◽  
Model Based ◽  
Prognostic Assessment

Abstract Purpose: Exploring nonnegative matrix factorization (NMF) model-based clustering and prognostic modeling of head and neck squamous carcinoma (HNSCC). Methods: The transcriptome microarray data of HNSCC samples were downloaded from The Cancer Genome Atlas (TCGA) and Shanghai Ninth People’s Hospital, and NMF clustering was constructed using the R software package. Relevant prognostic models were developed based on clustering. Results: Based on NMF, all samples were divided into 2 subgroups. Predictive models were constructed by analysing the differential gene between the two subgroups. Results of survival analysis in the current study revealed that the high-risk group had a poor prognosis. Further, results of multi-factor Cox regression analysis revealed that the predictive model was an independent predictor of prognosis. Conclusion: It was evident that the NMF-based prognostic model is a useful guide to the prognostic assessment of HNSCC.

scholarly journals Identification of IFN-β Associated Genes Signature Predicting Overall Survival for Glioblastoma

2021 ◽  
Author(s):  
Cheng Lijing ◽  
Yuan Meiling ◽  
Li Shu ◽  
Chen Junjing ◽  
Zhong Shupeng ◽  
...  
Keyword(s):  
Overall Survival ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Individual Treatment ◽  
Normal Brain ◽  
Data Set ◽  
Cox Regression Analysis ◽  
Prognosis Prediction

Abstract Background: Brain glioblastoma (GBM) is the most common primary malignant tumor of intracranial tumors. The prognosis of this disease is extremely poor. While the introduction of IFN-β regimen in the treatment of gliomas has significantly improved the outcome of patients, the underlying mechanism remains to be elucidated. Materials and methods: mRNA expression profiles and clinicopathological data were downloaded from TCGA-GBM and GSE83300 data set from the GEO. Univariate Cox regression analysis and lasso Cox regression model established a novel four‐gene IFN-β signature (including PRDX1, SEC61B, XRCC5, and BCL2L2) for GBM prognosis prediction. Further, GBM samples (n=50) and normal brain tissues (n=50) were then used for real-time polymerase chain reaction (PCR) experiments. Gene Set Enrichment Analyses (GSEA) was performed to further understand the underlying molecular mechanisms. Pearson correlation was applied to calculate the correlation between the lncRNAs and IFN-β associated genes. A lncRNA with a correlation coefficient |R2| > 0.3 and P < 0.05 was considered to be an IFN-β associated lncRNA.Results: Patients in the high‐risk group shown significantly poorer survival than patients in the low‐risk group. The signature was found to be an independent prognostic factor for GBM survival. Furthermore, GSEA revealed several significantly enriched pathways, which might help explain the underlying mechanisms. Our study identified a novel robust four‐gene IFN-β signature for GBM prognosis prediction. The signature might contain potential biomarkers for metabolic therapy and treatment response prediction in GBM.Conclusions: Our study established a novel IFN-β associated genes signature to predict overall survival of GBM, which may help in clinical decision making for individual treatment.

scholarly journals Exploration of Various Roles of Hypoxia Genes in Osteosarcoma

2021 ◽  
Author(s):  
Jimin Ma ◽  
Yakun Zhu ◽  
Ziming Guo ◽  
Xuefei Yang ◽  
Haitao Fan
Keyword(s):  
High Risk ◽  
Immune Cells ◽  
Prognostic Model ◽  
Cox Regression ◽  
High Expression ◽  
Cox Regression Analysis ◽  
Model Based ◽  
Survival Difference ◽  
High Expression Group ◽  
Low Expression

Abstract Background: Osteosarcoma is a primary malignant tumor that often metastasizes in orthopedic diseases. Although multi-drug chemotherapy and surgical treatment have significantly improved the survival and prognosis of patients with osteosarcoma, the survival rate is still very low due to frequent metastases in patients with osteosarcoma. In-depth exploration of the relationship between various influencing factors of osteosarcoma is very important for screening promising therapeutic targets. Methods: This study used multivariate COX regression analysis to select the hypoxia genes SLC2A1 and FBP1 in patients with osteosarcoma, and used the expression of these two genes to divide the patients with osteosarcoma into high-risk and low-risk groups. Then, we first constructed a prognostic model based on the patient's risk value, and compared the survival difference between the high expression group and the low expression group. Second, in the high expression group and the low expression group, compare the differences in tumor invasion and inflammatory gene expression between the two groups of immune cells. Finally, the ferroptosis-related genes with differences between the high expression group and the low expression group were screened, and the correlation between these genes was analyzed. Results: In the high-risk group, immune cells with higher tumor invasiveness, macrophages M0 and immune cells with lower invasiveness included: mast cell resting, regulatory T cells (Tregs) and monocytes. Finally, among genes related to ferroptosis, we found AKR1C2, AKR1C1 and ALOX15 that may be related to hypoxia. These ferroptosis-related genes were discovered for the first time in osteosarcoma. Among them, the hypoxia gene FBP1 is positively correlated with the ferroptosis genes AKR1C1 and ALOX15, and the hypoxia gene SLC2A1 is negatively correlated with the ferroptosis genes AKR1C2, AKR1C1 and ALOX15. Conclusion: This study constructed a prognostic model based on hypoxia-related genes SLC2A1 and FBP1 in patients with osteosarcoma, and explored their correlation with immune cells, inflammatory markers and ferroptosis-related genes. This indicates that SLC2A1 and FBP1 are promising targets for osteosarcoma research.

scholarly journals An Independent Prognostic Model Based on Ten Autophagy-Related Genes in Pancreatic Cancer

2021 ◽  
Author(s):  
Jiahui Tian ◽  
yi wu ◽  
Xuan Zeng ◽  
Xiaoxiao Fang ◽  
Chunyan Fu
Keyword(s):  
Pancreatic Cancer ◽  
Roc Curve ◽  
Noncoding Rna ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Target Therapy ◽  
Clinical Prognosis ◽  
Cox Regression Analysis ◽  
Gene Sets

Abstract Purpose Pancreatic cancer(PC) is a common cancer with high lethality and low survival rate. Autophagy is involved in the biological process of PC. Thus, we intended to explore the function of autophagy-related long noncoding RNA signature for survival assessment in PC. Methods Based on 10 autophagy-related lncRNAs, the prognostic model was attained through univariate and multivariate Cox regression analysis. Subsequently, the relationship network of 10 lncRNAs was crystallized in co-expression network and Sankey diagram. Survival analysis and ROC curve were used to evaluate the signature. GSEA was utilized to screen enriched gene sets. Result The OS has significant deference in low-risk group and high-risk group(P < 0.001). The ROC curve further proved the potential utility of the signature(AUC = 0.815). GSEA was significantly enriched in cancer-related gene sets. Conclusion The signature has potential to evaluate clinical prognosis in PC. The 10 autophagy-related lncRNAs may achieve great development for PC in target therapy field.

Sign in / Sign up

Export Citation Format

Share Document