Construction and validation of a metabolic gene-associated prognostic model for cervical carcinoma and the role on tumor microenvironment and immunity

Abstract Background: Cholangiocarcinoma (CCA) is the most common malignancy of the biliary tract with a dismal prognosis. Increasing evidence suggests that tumor microenvironment (TME) is closely associated with cancer prognosis. However, the prognostic signature for CCA based on TME has not yet been reported. This study aimed to develop a TME-related prognostic signature for accurately predicting the prognosis of patients with CCA. Methods: Based on the TCGA database, we calculated the stromal and immune scores using the ESTIMATE algorithm to assess TME in stromal and immune cells derived from CCA. TME-related differentially expressed genes were identified, followed by functional enrichment analysis and PPI network analysis. Univariate Cox regression analysis, Lasso Cox regression model and multivariable Cox regression analysis were performed to identify and construct the TME-related prognostic gene signature. Gene Set Enrichment Analyses (GSEA) was performed to further investigate the potential molecular mechanisms. The correlations between the risk scores and tumor infiltration immune cells were analyzed using Tumor Immune Estimation Resource (TIMER) database. Results: A total of 784 TME-related differentially expressed genes (DEGs) were identified, which were mainly enriched in immune-related processes and pathways. Among these TME-related DEGs, A novel two‑gene signature (including GAD1 and KLRB1) was constructed for CCA prognosis prediction. The AUC of the prognostic model for predicting the survival of patients at 1-, 2-, and 3- years was 0.811, 0.772, and 0.844, respectively. Cox regression analysis showed that the two‑gene signature was an independent prognostic factor. Based on the risk scores of the prognostic model, CCA patients were divided into high- and low-risk groups, and patients with high-risk score had shorter survival time than those with low-risk score. Furthermore, we found that the risk scores were negatively correlated with TME-scores and the number of several tumor infiltration immune cells, including B cells and CD4+ T cells. Conclusion: Our study established a novel TME-related gene signature to predict the prognosis of patients with CCA. This might provide a new understanding of the potential relationship between TME and CCA prognosis, and serve as a prognosis stratification tool for guiding personalized treatment of CCA patients.

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Tumor microenvironment subtypes and immune-related signatures for the prognosis of breast cancer

10.21203/rs.3.rs-53701/v1 ◽

2020 ◽

Author(s):

Yiqun Han ◽

Jiayu Wang ◽

Binghe Xu

Keyword(s):

Breast Cancer ◽

Tumor Microenvironment ◽

T Lymphocytes ◽

Cancer Patients ◽

Prognostic Model ◽

Cox Regression ◽

Cell Types ◽

Well Performance ◽

Breast Cancer Patients ◽

Cox Regression Analysis

Abstract To better understand the heterogeneity of tumor microenvironment (TME) and establish a prognostic model for breast cancer in clinical practice, the leukocyte infiltrations of 22 cell types of interest from 2620 breast cancer patients were quantitatively estimated using deconvolution algorithms, and three TME subtypes with distinct molecular and clinical features were identified by unsupervised clustering approach. Then, we carried out systematic analyses to illustrate the contributing mechanisms for differential phenotypes, which suggested that the divergences were distinguished by cell cycle dysfunction, variation of cytotoxic T lymphocytes activity. Next, through dimensionally reduction and selection based on random-forest analysis, least absolute shrinkage and selection operator (LASSO) analysis, and uni- and multivariate COX regression analysis, a total of 15 significant genes were proposed to construct the prognostic immune-related score (pIRS) system and, in combinations with clinicopathological characteristics, a predictive model was ultimately built with well performance for survival of breast cancer patients. Comparative analyses demonstrated that proactivity of CD8 T lymphocytes and hyper-angiogenesis could be attributed to distinct prognostic outcomes. In conclusion, we retrieved three TME phenotypes and the curated prognostic model based on pIRS system for breast cancer. This model is justified for validation and optimized in the coming future.

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Tumor microenvironment related novel signature predict lung adenocarcinoma survival

PeerJ ◽

10.7717/peerj.10628 ◽

2021 ◽

Vol 9 ◽

pp. e10628

Author(s):

Juan Chen ◽

Rui Zhou

Keyword(s):

Gene Expression ◽

Overall Survival ◽

Regression Analysis ◽

Tumor Microenvironment ◽

Prognostic Model ◽

Cox Regression ◽

Survival Prediction ◽

Clinical Parameters ◽

Ppi Network ◽

Cox Regression Analysis

Background Lung adenocarcinoma (LUAD) is the most common histological type of lung cancers, which is the primary cause of cancer‐related mortality worldwide. Growing evidence has suggested that tumor microenvironment (TME) plays a pivotal role in tumorigenesis and progression. Hence, we investigate the correlation of TME related genes with LUAD prognosis. Method The information of LUAD gene expression data was obtained from The Cancer Genome Atlas (TCGA). According to their immune/stromal scores calculated by the ESTIMATE algorithm, differentially expressed genes (DEGs) were identified. Then, we performed univariate Cox regression analysis on DEGs to obtain genes that are apparently bound up with LUAD survival (SurGenes). Functional annotation and protein-protein interaction (PPI) was also conducted on SurGenes. By validating the SurGenes with data sets of lung cancer from the Gene Expression Omnibus (GEO), 106 TME related SurGenes were generated. Further, intersection analysis was executed between the 106 TME related SurGenes and hub genes from PPI network, PTPRC and CD19 were obtained. Gene Set Enrichment Analysis and CIBERSORT analysis were performed on PTPRC and CD19. Based on the TCGA LUAD dataset, we conducted factor analysis and Step-wise multivariate Cox regression analysis for 106 TME related SurGenes to construct the prognostic model for LUAD survival prediction. The LUAD dataset in GEO (GSE68465) was used as the testing dataset to confirm the prognostic model. Multivariate Cox regression analysis was used between risk score from the prognostic model and clinical parameters. Result A total of 106 TME related genes were collected in our research totally, which were markedly correlated with the overall survival (OS) of LUAD patient. Bioinformatics analysis suggest them mainly concentrated on immune response, cell adhesion, and extracellular matrix. More importantly, among 106 TME related SurGenes, PTPRC and CD19 were highly interconnected nodes among PPI network and correlated with immune activity, exhibiting significant prognostic potential. The prognostic model was a weighted linear combination of the 106 genes, by which the low-OS LUAD samples could be separated from the high-OS samples with success. This model was also able to rebustly predict the situation of survival (training set: p-value < 0.0001, area under the curve (AUC) = 0.649; testing set: p-value = 0.0009, AUC = 0.617). By combining with clinical parameters, the prognostic model was optimized. The AUC achieved 0.716 for 3 year and 0.699 for 5 year. Conclusion A series of TME-related prognostic genes were acquired in this research, which could reflect immune disorders within TME, and PTPRC and CD19 show the potential to be an indicator for LUAD prognosis and tumor microenvironment modulation. The prognostic model constructed base on those prognostic genes presented a high predictive ability, and may have clinical implications in the overall survival prediction of LUAD.

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Weighted Gene Coexpression Network Analysis Reveals Cancer Stem Cell-Associated Metabolic Gene Signature in Glioma

10.21203/rs.3.rs-205111/v1 ◽

2021 ◽

Author(s):

Yan Tang ◽

Yao Jiang ◽

Dan Zhang ◽

Jia Fan ◽

Juan Yao ◽

...

Keyword(s):

Stem Cell ◽

Network Analysis ◽

Risk Stratification ◽

Cancer Stem Cell ◽

Prognostic Model ◽

Cox Regression ◽

Roc Curves ◽

Gene Signature ◽

Metabolic Gene ◽

Genome Atlas

Abstract Background: Isocitrate dehydrogenase (IDH) mutant glioma patients have a favorable prognosis, accompanying with metabolic alterations and glioma cell dedifferentiation. Recently, mRNA expression-based stemness index (mRNAsi) characteristic relation to IDH status of gliomas has yet illuminated. Thus, we aimed to establish a cancer stem cell-associated metabolic gene signature for risk stratification of gliomas. Methods: The glioma samples came from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases. Next, we performed the differential expression analysis between IDH mutant and IDH wild-type gliomas and also conducted weighted gene correlation network analysis (WGCNA) for determining the modules associated with cancer stem cell trait. Subsequently, multivariate Cox regression analysis with the Akaike information criterion (AIC) algorithm was employed to establish a stemness-related metabolic gene signature, which was validated using time-dependent receiver operating characteristic (ROC) curves and concordance index (C-index). Also, we developed a nomogram based on clinical traits and prognostic model. Additionally, according to the results of immunohistochemistry (IHC) staining, the protein levels of gene signature were consistent with the genes expression’s direction.Results: Low expression of mRNAsi was capable of predicting the unfavourable OS of gliomas with a 5-year survival rate of 14.08%. The blue module and its 1466 genes were pertinent to mRNAsi characteristic. Next, Kaplan-Meier (KM) survival curves revealed that cancer stem cell-associated metabolic genes exerted impact on gliomas’ prognosis. Subsequently, univariate and multivariate Cox regression analyses were implemented, and gene signature (LCAT, UST, GALNT13, and SMPD3) was constructed, with C-index of 0.798 (95%CI: 0.769-0.827). Notably, the prognostic model presented a superior predictive value for gliomas’ survival, with the area under the curve (AUC) of ROC curves at 1-year, 3-year as well as 5-year time-point of 0.845, 0.85 and 0.811, respectively. And forest plot uncovered its role as a potential independent predictor for gliomas (HR=2.840, 95%CI: 1.961-4.113, P <0.001). Nomogram also presented superior predictive performance for gliomas’ OS. Conclusion: The gene signature (LCAT, UST, GALNT13, and SMPD3) can be used for risk stratification and also can serve as an independent prognostic factor of glioma patients.

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Analysis of the prognostic significance and potential mechanisms of lncRNAs associated with m6A methylation in papillary thyroid carcinoma

10.21203/rs.3.rs-807935/v1 ◽

2021 ◽

Author(s):

Yinde Huang ◽

Xin Li ◽

Wenbin Chen ◽

Yuzhen He ◽

Song Wu ◽

...

Keyword(s):

Papillary Thyroid Carcinoma ◽

Tumor Microenvironment ◽

Thyroid Carcinoma ◽

Immune Checkpoint ◽

Prognostic Model ◽

Prognostic Markers ◽

Clinicopathological Characteristics ◽

Papillary Thyroid ◽

The Relationship ◽

Pan Cancer

Abstract Background : m6A methylation-related long non-coding RNAs (lncRNAs) play a significant role in the progression of various tumors and can be used as prognostic markers. However, whether m6A-related lncRNAs also play the same function as prognostic markers in papillary thyroid carcinoma (PTC) remains unclear. Methods : Consensus cluster analysis was performed to divide PTC samples obtained from The Cancer Genome Atlas database into two clusters according to the expression of m6A-related lncRNAs. Then, the least absolute shrinkage and selection operator (LASSO) regression analysis was performed to create and verify a prognostic model. Furthermore, the relationship among risk scores, clusters, programmed death-ligand 1 (PD-L1), tumor microenvironment (TME), clinicopathological characteristics, immune infiltration, immune checkpoint, and tumor mutation burden (TMB) was analyzed. In addition, a nomogram was created, and subsequently, the drug sensitivity of lncRNAs in the prognostic model was analyzed. Finally, the relationship between these lncRNAs and prognosis in pan-cancer was investigated. Results: The prognosis, RAS, BRAF, M, and TME were found to be different in two clusters. The prognostic model included three lncRNAs: PSMG3-AS1 , BHLHE40-AS1 , and AC016747.3 . The risk score was associated with clusters, PD-L1, tumor microenvironment, clinicopathological characteristics, immune cell infiltration, immune checkpoint, and TMB, and thus, risk score was confirmed as useful prognostic indicators. Differentially expressed lncRNAs are involved in many malignancies and can be identified as cancer prognostic makers. Conclusion : According to our research, we can regard m6A-related lncRNAs involved in the procession of PTC as a biomarker of PFS for PTC patients, and pan-cancer.

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Development of an Oncogenic Driver Alteration Associated Immune-Related Prognostic Model for Stage I-II Lung Adenocarcinoma

Frontiers in Oncology ◽

10.3389/fonc.2020.593022 ◽

2021 ◽

Vol 10 ◽

Author(s):

Jian-Zhao Xu ◽

Chen Gong ◽

Zheng-Fu Xie ◽

Hua Zhao

Keyword(s):

High Risk ◽

Mast Cells ◽

Tumor Microenvironment ◽

Lung Adenocarcinoma ◽

Prognostic Model ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Stage I ◽

Oncogenic Driver

Lung adenocarcinoma (LUAD) needs to be stratified for its heterogeneity. Oncogenic driver alterations such as EGFR mutation, ALK translocation, ROS1 translocation, and BRAF mutation predict response to treatment for LUAD. Since oncogenic driver alterations may modulate immune response in tumor microenvironment that may influence prognosis in LUAD, the effects of EGFR, ALK, ROS1, and BRAF alterations on tumor microenvironment remain unclear. Immune-related prognostic model associated with oncogenic driver alterations is needed. In this study, we performed the Cox-proportional Hazards Analysis based on the L1-penalized (LASSO) Analysis to establish an immune-related prognostic model (IPM) in stage I-II LUAD patients, which was based on 3 immune-related genes (PDE4B, RIPK2, and IFITM1) significantly enriched in patients without EGFR, ALK, ROS1, and BRAF alterations in The Cancer Genome Atlas (TCGA) database. Then, patients were categorized into high-risk and low-risk groups individually according to the IPM defined risk score. The predicting ability of the IPM was validated in GSE31210 and GSE26939 downloaded from the Gene Expression Omnibus (GEO) database. High-risk was significantly associated with lower overall survival (OS) rates in 3 independent stage I-II LUAD cohorts (all P < 0.05). Moreover, the IPM defined risk independently predicted OS for patients in TCGA stage I-II LUAD cohort (P = 0.011). High-risk group had significantly higher proportions of macrophages M1 and activated mast cells but lower proportions of memory B cells, resting CD4 memory T cells and resting mast cells than low-risk group (all P < 0.05). In addition, the high-risk group had a significantly lower expression of CTLA-4, PDCD1, HAVCR2, and TIGIT than the low-risk group (all P < 0.05). In summary, we established a novel IPM that could provide new biomarkers for risk stratification of stage I-II LUAD patients.

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Tumor Microenvironment Subtypes and Immune-Related Signatures for the Prognosis of Breast Cancer

BioMed Research International ◽

10.1155/2021/6650107 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Yiqun Han ◽

Jiayu Wang ◽

Binghe Xu

Keyword(s):

Breast Cancer ◽

Clinical Practice ◽

Tumor Microenvironment ◽

Prognostic Model ◽

Cox Regression ◽

Molecular Taxonomy ◽

The Cancer Genome Atlas ◽

Molecular Heterogeneity ◽

Consensus Clustering ◽

Breast Cancer Patients

Objective. To better understand the immune-related heterogeneity of tumor microenvironment (TME) and establish a prognostic model for breast cancer in clinical practice. Methods. For the 2620 breast cancer cases obtained from The Cancer Genome Atlas and the Molecular Taxonomy of Breast Cancer International Consortium, the CIBERSORT algorithm was performed to identify the immunological pattern, which underwent consensus clustering to curate TME subtypes, and biological profiles were explored by enrichment analysis. Random forest analysis, least absolute shrinkage, and selection operator analysis, in addition to uni- and multivariate COX regression analyses, were successively employed to precisely select the significant genes with prediction values for the introduction of the prognostic model. Results. Three TME subtypes with distinct molecular and clinical features were identified by an unsupervised clustering approach, of which the molecular heterogeneity could be the result of cell cycle dysfunction and the variation of cytotoxic T lymphocyte activity. A total of 15 significant genes were proposed to construct the prognostic immune-related score system, and a predictive model was established in combination with clinicopathological characteristics for the survival of breast cancer patients. For immunological signatures, proactivity of CD8 T lymphocytes and hyperangiogenesis could be attributed to heterogeneous survival profiles. Conclusions. We developed and validated a prognostic model based on immune-related signatures for breast cancer. This promising model is justified for validation and optimized in future clinical practice.

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Analysis of the prognostic significance and potential mechanisms of lncRNAs associated with m6A methylation in papillary thyroid carcinoma

10.21203/rs.3.rs-807935/v2 ◽

2021 ◽

Author(s):

Yinde Huang ◽

Xin Li ◽

Wenbin Chen ◽

Yuzhen He ◽

Song Wu ◽

...

Keyword(s):

Papillary Thyroid Carcinoma ◽

Tumor Microenvironment ◽

Thyroid Carcinoma ◽

Immune Checkpoint ◽

Prognostic Model ◽

Prognostic Markers ◽

Clinicopathological Characteristics ◽

Papillary Thyroid ◽

The Relationship ◽

Pan Cancer

Abstract Background : m6A methylation-related long non-coding RNAs (lncRNAs) play a significant role in the progression of various tumors and can be used as prognostic markers. However, whether m6A-related lncRNAs also play the same function as prognostic markers in papillary thyroid carcinoma (PTC) remains unclear. Methods : Consensus cluster analysis was performed to divide PTC samples obtained from The Cancer Genome Atlas database into two clusters according to the expression of m6A-related lncRNAs. Then, the least absolute shrinkage and selection operator (LASSO) regression analysis was performed to create and verify a prognostic model. Furthermore, the relationship among risk scores, clusters, programmed death-ligand 1 (PD-L1), tumor microenvironment (TME), clinicopathological characteristics, immune infiltration, immune checkpoint, and tumor mutation burden (TMB) was analyzed. In addition, a nomogram was created, and subsequently, the drug sensitivity of lncRNAs in the prognostic model was analyzed. Finally, the relationship between these lncRNAs and prognosis in pan-cancer was investigated. Results: The prognosis, RAS, BRAF, M, and TME were found to be different in two clusters. The prognostic model included three lncRNAs: PSMG3-AS1 , BHLHE40-AS1 , and AC016747.3 . The risk score was associated with clusters, PD-L1, tumor microenvironment, clinicopathological characteristics, immune cell infiltration, immune checkpoint, and TMB, and thus, risk score was confirmed as useful prognostic indicators. Differentially expressed lncRNAs are involved in many malignancies and can be identified as cancer prognostic makers. Conclusion : According to our research, we can regard m6A-related lncRNAs involved in the procession of PTC as a biomarker of PFS for PTC patients, and pan-cancer.

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