Bioinformatics Analysis Reveals an Association Between Cancer Cell Stemness, Gene Mutations, and the Immune Microenvironment in Stomach Adenocarcinoma

Cancer stem cells (CSCs), characterized by infinite proliferation and self-renewal, greatly challenge tumor therapy. Research into their plasticity, dynamic instability, and immune microenvironment interactions may help overcome this obstacle. Data on the stemness indices (mRNAsi), gene mutations, copy number variations (CNV), tumor mutation burden (TMB), and corresponding clinical characteristics were obtained from The Cancer Genome Atlas (TCGA) and UCSC Xena Browser. Tumor purity and infiltrating immune cells in stomach adenocarcinoma (STAD) tissues were predicted using the ESTIMATE R package and CIBERSORT method, respectively. Differentially expressed genes (DEGs) between the high and low mRNAsi groups were used to construct prognostic models with weighted gene co-expression network analysis (WGCNA) and Lasso regression. The association between cancer stemness, gene mutations, and immune responses was evaluated in STAD. A total of 6,739 DEGs were identified between the high and low mRNAsi groups. DEGs in the brown (containing 19 genes) and blue (containing 209 genes) co-expression modules were used to perform survival analysis based on Cox regression. A nine-gene signature prognostic model (ARHGEF38-IT1, CCDC15, CPZ, DNASE1L2, NUDT10, PASK, PLCL1, PRR5-ARHGAP8, and SYCE2) was constructed from 178 survival-related DEGs that were significantly related to overall survival, clinical characteristics, tumor microenvironment immune cells, TMB, and cancer-related pathways in STAD. Gene correlation was significant across the prognostic model, CNVs, and drug sensitivity. Our findings provide a prognostic model and highlight potential mechanisms and associated factors (immune microenvironment and mutation status) useful for targeting CSCs.

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Two predicted models based on ceRNAs and immune cells in lung adenocarcinoma

PeerJ ◽

10.7717/peerj.11029 ◽

2021 ◽

Vol 9 ◽

pp. e11029

Author(s):

Miaomiao Zhang ◽

Peiyan Zheng ◽

Yuan Wang ◽

Baoqing Sun

Keyword(s):

Lung Adenocarcinoma ◽

Immune Cells ◽

Cox Regression ◽

Expression Profiles ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prognostic Models ◽

Risk Scores ◽

Immune Microenvironment

Background It is well accepted that both competitive endogenous RNAs (ceRNAs) and immune microenvironment exert crucial roles in the tumor prognosis. The present study aimed to find prognostic ceRNAs and immune cells in lung adenocarcinoma (LUAD). Materials and Methods More specifically, we explored the associations of crucial ceRNAs with the immune microenvironment. The Cancer Genome Atlas (TCGA) database was employed to obtain expression profiles of ceRNAs and clinical data. CIBERSORT was utilized to quantify the proportion of 22 immune cells in LUAD. Results We constructed two cox regression models based on crucial ceRNAs and immune cells to predict prognosis in LUAD. Subsequently, seven ceRNAs and seven immune cells were involved in prognostic models. We validated both predicted models via an independent cohort GSE72094. Interestingly, both predicted models proved that the longer patients were smoking, the higher risk scores would be obtained. We further investigated the relationships between seven genes and immune/stromal scores via the ESTIMATE algorithm. The results indicated that CDC14A and H1F0 expression were significantly related to stromal scores/immune scores in LUAD. Moreover, based on the result of the ceRNA model, single-sample gene set enrichment analysis (ssGSEA) suggested that differences in immune status were evident between high- and low-risk groups.

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The establishment of a prognostic scoring model based on the new tumor immune microenvironment classification in acute myeloid leukemia

BMC Medicine ◽

10.1186/s12916-021-02047-9 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Tiansheng Zeng ◽

Longzhen Cui ◽

Wenhui Huang ◽

Yan Liu ◽

Chaozeng Si ◽

...

Keyword(s):

Acute Myeloid Leukemia ◽

Myeloid Leukemia ◽

Prognostic Model ◽

Prognostic Significance ◽

The Cancer Genome Atlas ◽

Prognostic Models ◽

Clustering Methods ◽

Immune Microenvironment ◽

Tumor Immune Microenvironment ◽

Acute Myeloid

Abstract Background The high degree of heterogeneity brought great challenges to the diagnosis and treatment of acute myeloid leukemia (AML). Although several different AML prognostic scoring models have been proposed to assess the prognosis of patients, the accuracy still needs to be improved. As important components of the tumor microenvironment, immune cells played important roles in the physiological functions of tumors and had certain research value. Therefore, whether the tumor immune microenvironment (TIME) can be used to assess the prognosis of AML aroused our great interest. Methods The patients’ gene expression profile from 7 GEO databases was normalized after removing the batch effect. TIME cell components were explored through Xcell tools and then hierarchically clustered to establish TIME classification. Subsequently, a prognostic model was established by Lasso-Cox. Multiple GEO databases and the Cancer Genome Atlas dataset were employed to validate the prognostic performance of the model. Receiver operating characteristic (ROC) and the concordance index (C-index) were utilized to assess the prognostic efficacy. Results After analyzing the composition of TIME cells in AML, we found infiltration of ten types of cells with prognostic significance. Then using hierarchical clustering methods, we established a TIME classification system, which clustered all patients into three groups with distinct prognostic characteristics. Using the differential genes between the first and third groups in the TIME classification, we constructed a 121-gene prognostic model. The model successfully divided 1229 patients into the low and high groups which had obvious differences in prognosis. The high group with shorter overall survival had more patients older than 60 years and more poor-risk patients (both P< 0.001). Besides, the model can perform well in multiple datasets and could further stratify the cytogenetically normal AML patients and intermediate-risk AML population. Compared with the European Leukemia Net Risk Stratification System and other AML prognostic models, our model had the highest C-index and the largest AUC of the ROC curve, which demonstrated that our model had the best prognostic efficacy. Conclusion A prognostic model for AML based on the TIME classification was constructed in our study, which may provide a new strategy for precision treatment in AML.

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

Bioscience Reports ◽

10.1042/bsr20201012 ◽

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.

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A Ferroptosis-Related Gene Model Predicts Prognosis and Immune Microenvironment for Cutaneous Melanoma

Frontiers in Genetics ◽

10.3389/fgene.2021.697043 ◽

2021 ◽

Vol 12 ◽

Author(s):

Congcong Xu ◽

Hao Chen

Keyword(s):

Cutaneous Melanoma ◽

Prognostic Model ◽

Cox Regression ◽

Tumor Biology ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Related Gene ◽

Immune Microenvironment ◽

Cancer Genome Atlas ◽

Prognostic Prediction

BackgroundCutaneous melanoma is a common but aggressive tumor. Ferroptosis is a recently discovered cell death with important roles in tumor biology. Nevertheless, the prognostic power of ferroptosis-linked genes remained unclear in cutaneous melanoma.MethodsCutaneous melanoma patients of TCGA (The Cancer Genome Atlas) were taken as the training cohort while GSE65904 and GSE22153 as the validation cohorts. Multifactor Cox regression model was used to build a prognostic model, and the performance of the model was assessed. Functional enrichment and immune infiltration analysis were used to clarify the mechanisms.ResultsA five ferroptosis-linked gene predictive model was developed. ALOX5 and GCH1 were illustrated as independent predictive factors. Functional assessment showed enriched immune-linked cascades. Immune infiltrating analysis exhibited the distinct immune microenvironment.ConclusionHerein, a novel ferroptosis-related gene prognostic model was built in cutaneous melanoma. This model could be used for prognostic prediction, and maybe helpful for the targeted and immunotherapies.

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Exploring the Pyroptosis-related Genes of Breast Cancer and their Effects on Tumor Immune Microenvironment

10.21203/rs.3.rs-1049643/v1 ◽

2021 ◽

Author(s):

Congli Jia ◽

Fu Yang ◽

Ruining Li

Keyword(s):

Breast Cancer ◽

High Risk ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Immune Microenvironment ◽

Tumor Immune Microenvironment

Abstract Background: Breast cancer （BC） is the most common cancer among women, with high rates of metastasis and recurrence. Some studies have confirmed that pyroptosis is an immune-related programmed cell death. However, the correlation between the expression of pyroptosis-related genes in BC and its prognosis remains unclear. Methods: In this study, we identified 38 pyroptosis-related genes that were differentially expressed between BC and normal tissues. The prognostic value of each pyroptosis-related gene was evaluated using patient data from The Cancer Genome Atlas (TCGA). The Cox regression method was performed to establish a prognostic model for 16-gene signature, classifying all BC patients in the TCGA database into a low-or high-risk group. Results: The survival rate of BC patients in the high-risk group was significantly lower than that in the low-risk group (P＜0.01). Prognostic model is independent prognostic factor for BC patients compared to clinical features. Single sample gene set enrichment analysis (ssGSEA) showed a decrease for immune cells and immune function in the high-risk group. Conclusions: Pyroptosis-related genes influence the tumor immune microenvironment and can predict the prognosis of BC.

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A Potential Prognostic Gene Signature for Predicting Survival for Glioblastoma Patients

BioMed Research International ◽

10.1155/2019/9506461 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 3

Author(s):

Ziming Hou ◽

Jun Yang ◽

Hao Wang ◽

Dongyuan Liu ◽

Hongbing Zhang

Keyword(s):

Prognostic Model ◽

Cox Regression ◽

Risk Groups ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Prognostic Models ◽

Training Set ◽

Prognostic Gene ◽

Prognostic Gene Signature ◽

Genome Atlas

Objective. This study aimed to screen prognostic gene signature of glioblastoma (GBM) to construct prognostic model.Methods. Based on the GBM information in the Cancer Genome Atlas (TCGA, training set), prognostic genes (Set X) were screened by Cox regression. Then, the optimized prognostic gene signature (Set Y) was further screened by the Cox-Proportional Hazards (Cox-PH). Next, two prognostic models were constructed: model A was based on the Set Y; model B was based on part of the Set X. The samples were divided into low- and high-risk groups according to the median prognosis index (PI). GBM datasets in Gene Expression Ominous (GEO, GSE13041) and Chinese Glioma Genome Atlas (CGGA) were used as the testing datasets to confirm the prognostic models constructed based on TCGA.Results. We identified that the prognostic 14-gene signature was significantly associated with the overall survival (OS) in the TCGA. In model A, patients in high- and low-risk groups showed the significantly different OS (P = 7.47 × 10−9, area under curve (AUC) 0.995) and the prognostic ability were also confirmed in testing sets (P=0.0098 and 0.037). The model B in training set was significant but failed in testing sets.Conclusion. The prognostic model which was constructed based on the prognostic 14-gene signature presented a high predictive ability for GBM. The 14-gene signature may have clinical implications in the subclassification of GBM.

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Immune infiltration and a ferroptosis-related gene signature for predicting the prognosis of patients with cholangiocarcinoma

10.21203/rs.3.rs-1175730/v1 ◽

2022 ◽

Author(s):

Zhijian Wang ◽

Xuenuo Chen ◽

Zheng Jiang

Keyword(s):

Cell Death ◽

Prognostic Model ◽

Roc Analysis ◽

Cox Regression ◽

Tumor Therapy ◽

Diagnostic Value ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Great Promise ◽

Expression Levels

Abstract Background Cholangiocarcinoma (CHOL) is a digestive tract tumor with high malignancy and poor prognosis and is extremely challenging to treat. At present, induced cell death holds great promise in tumor therapy. Ferroptosis is a recently proposed pattern of programmed cell death, and numerous studies have shown that it is intimately involved in tumors. However, the roles of differentially expressed ferroptosis-related genes (DEFRGs) in CHOL have not been investigated. Methods Our study was based on the The Cancer Genome Atlas (TCGA) database, DEFRGs were obtained to construct a prognostic riskScore model of CHOL by univariate and multivariate Cox regression analyses. Subsequently, the model was evaluated by nomogram construction, survival analysis, receiver operating characteristic (ROC) analysis and exploration of the immune microenvironment, and the mRNA and protein expression levels of each gene in the model were validated by Gene Expression Omnibus (GEO) database and quantitative real-time PCR (qRT-PCR). Results We screened four DEFRGs from the TCGA database to construct a prognostic model. The construction of a nomogram confirmed the predictive value of the model for overall survival (OS), and it was confirmed to have high diagnostic value by ROC analysis. The GSEA results suggested that these genes were mainly enriched in ferroptosis- and metabolism-related pathways. Finally, our experimental results validated the expression levels of the four DEFRGs, which were almost consistent with our bioinformatics results. Conclusion Our study found that the prognostic model showed extremely high diagnostic and prognostic value and could predict the possibility of immunotherapy, thus providing a new direction for individualized treatment of patients with CHOL.

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A prognostic model constructed by CTHRC1 and LRFN4 in Stomach adenocarcinoma by Bioinformatics Analysis

10.21203/rs.3.rs-52939/v1 ◽

2020 ◽

Author(s):

Songling Han ◽

Wei Zhu ◽

Qijie Guan ◽

Zhuoheng Zhong ◽

Ruoke Zhao ◽

...

Keyword(s):

Gene Expression ◽

Prognostic Model ◽

Cox Regression ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Clinical Information ◽

The Cancer Genome Atlas ◽

Risk Scores ◽

Cancer Genome Atlas ◽

Stomach Adenocarcinoma

Abstract Background Stomach adenocarcinoma (STAD) is the most common histological type of stomach cancer, which causes a considerable number of deaths worldwide. This study specifically aimed to identify potential biomarkers and reveal the underlying molecular mechanisms. Methods Gene expression profiles microarray data were downloaded from the Gene Expression Omnibus (GEO) database. The ‘limma’ R package was used to screen the differentially expressed genes (DEGs) between STAD and matched normal tissues. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used for function enrichment analyses of DEGs. The data of STAD cases with both RNA sequencing and clinical information of The Cancer Genome Atlas (TCGA) were obtained from Genomic Data Commons (GDC) data portal. Survival curves were analyzed by the Kaplan-Meier method, univariate Cox regression analysis and multivariate Cox regression were performed using ‘survival’ package. CIBERSORT algorithm used approach to characterize the 22 human immune cell composition. Gene expression profiles microarray data and clinical information were downloaded from GEO database to validate prognostic model. Results Three public datasets including 90 STAD patients and 43 healthy controls were used and 44 genes were differentially expressed in all three datasets. These genes were primarily implicated in biological processes including cell adhesion, wound healing and extracellular matrix organization. Seven out of 44 genes showed significant survival differences based on their expression differences. CTHRC1 and LRFN4 were eventually used to constructed risk score and prognostic model by univariate Cox regression and stepwise multivariate Cox regression in The Cancer Genome Atlas (TCGA)-STAD dataset. The group having high risk scores and the group having low risk scores had significant differences in the infiltration level of multiple immune cells including CD4 memory resting T cells, M2 macrophages, memory B cells, resting dendritic cells, eosinophils, and gamma delta T cells. Multivariate Cox regression analyses indicated that the risk score was an independent predictor after adjusting for age, sex, and tumor stage. At last, the model was verified and evaluated by another independent dataset and showed a good classification effect. Conclusions The present study constructed the prognostic model by expression of CTHRC1 and LRFN4 for the first time via comprehensive bioinformatics analysis, which may provide clinical guidance and potential therapeutic targets for STAD.

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A signature of Ten Chromodomain Helicase DNA (CHD) Genes Related LncRNAs Predicts Overall Survival in Gastric Cancer Patients

10.21203/rs.3.rs-428006/v1 ◽

2021 ◽

Author(s):

xiaodong Wang ◽

yaxian Li ◽

yida Lu ◽

Yongxiang Li

Keyword(s):

Gastric Cancer ◽

Risk Score ◽

Prognostic Model ◽

Cox Regression ◽

Gene Mutations ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis ◽

Limma Package ◽

Prognosis Model ◽

Gastric Cancer Patients

Abstract Background: Gastric cancer (GC) is one of the most common tumors in the world and is often found at an advanced stage. Gene mutations and changes in lncRNA expression often led to the occurrence of GC. In this study, we explored the function of Chromodomain Helicase DNA (CHD) genes and a multi-gene prognostic model was established for its co-expressed lncRNA.Methods: We used the "Limma" package of R software to analyze the expression differences of CHDs genes. Use Kaplan-Meier curve to plot the relationship between risk score and patient survival time. Receiver operating characteristic (ROC) curve was used to describe the reliability of the model. The data used is obtained from The Cancer Genome Atlas (TCGA) database and CellMiner database. Results: We found that CHDs gene mutations had significant statistical differences with tumor mutation load and were involved in biological functions such as DNA winding and transcription. 10 CHDs gene-related lncRNAs were used to establish a prognosis model for GC patients. Univariate and multivariate Cox regression analysis proved that risk score could be an independent prognostic factor. Conclusion: In our study, we developed a novel prognostic model using 10 CHDs-related lncRNAs to predict individualized survival in patients with GC.

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A Novel Pyroptosis-Related Signature for Predicting Prognosis and Indicating Immune Microenvironment Features in Osteosarcoma

Frontiers in Genetics ◽

10.3389/fgene.2021.780780 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yiming Zhang ◽

Rong He ◽

Xuan Lei ◽

Lianghao Mao ◽

Pan Jiang ◽

...

Keyword(s):

Immune Cells ◽

Lower Risk ◽

Clinical Characteristics ◽

Prognostic Model ◽

Validation Cohort ◽

Lasso Regression ◽

Immune Microenvironment ◽

Risk Genes ◽

Tumor Purity

Osteosarcoma is a common malignant bone tumor with a propensity for drug resistance, recurrence, and metastasis. A growing number of studies have elucidated the dual role of pyroptosis in the development of cancer, which is a gasdermin-regulated novel inflammatory programmed cell death. However, the interaction between pyroptosis and the overall survival (OS) of osteosarcoma patients is poorly understood. This study aimed to construct a prognostic model based on pyroptosis-related genes to provide new insights into the prognosis of osteosarcoma patients. We identified 46 differentially expressed pyroptosis-associated genes between osteosarcoma tissues and normal control tissues. A total of six risk genes affecting the prognosis of osteosarcoma patients were screened to form a pyroptosis-related signature by univariate and LASSO regression analysis and verified using GSE21257 as a validation cohort. Combined with other clinical characteristics, including age, gender, and metastatic status, we found that the pyroptosis-related signature score, which we named “PRS-score,” was an independent prognostic factor for patients with osteosarcoma and that a low PRS-score indicated better OS and a lower risk of metastasis. The result of ssGSEA and ESTIMATE algorithms showed that a lower PRS-score indicated higher immune scores, higher levels of tumor infiltration by immune cells, more active immune function, and lower tumor purity. In summary, we developed and validated a pyroptosis-related signature for predicting the prognosis of osteosarcoma, which may contribute to early diagnosis and immunotherapy of osteosarcoma.

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